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New advances in type 1 diabetes

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• New advances in type 1 diabetes - June 03, 2024
• Savitha Subramanian , professor of medicine ,
• Farah Khan , clinical associate professor of medicine ,
• Irl B Hirsch , professor of medicine
• University of Washington Diabetes Institute, Division of Metabolism, Endocrinology and Nutrition, University of Washington, Seattle, WA, USA
• Correspondence to: I B Hirsch ihirsch{at}uw.edu

Type 1 diabetes is an autoimmune condition resulting in insulin deficiency and eventual loss of pancreatic β cell function requiring lifelong insulin therapy. Since the discovery of insulin more than 100 years ago, vast advances in treatments have improved care for many people with type 1 diabetes. Ongoing research on the genetics and immunology of type 1 diabetes and on interventions to modify disease course and preserve β cell function have expanded our broad understanding of this condition. Biomarkers of type 1 diabetes are detectable months to years before development of overt disease, and three stages of diabetes are now recognized. The advent of continuous glucose monitoring and the newer automated insulin delivery systems have changed the landscape of type 1 diabetes management and are associated with improved glycated hemoglobin and decreased hypoglycemia. Adjunctive therapies such as sodium glucose cotransporter-1 inhibitors and glucagon-like peptide 1 receptor agonists may find use in management in the future. Despite these rapid advances in the field, people living in under-resourced parts of the world struggle to obtain necessities such as insulin, syringes, and blood glucose monitoring essential for managing this condition. This review covers recent developments in diagnosis and treatment and future directions in the broad field of type 1 diabetes.

Introduction

Type 1 diabetes is an autoimmune condition that occurs as a result of destruction of the insulin producing β cells of the pancreatic islets, usually leading to severe endogenous insulin deficiency. 1 Without treatment, diabetic ketoacidosis will develop and eventually death will follow; thus, lifelong insulin therapy is needed for survival. Type 1 diabetes represents 5-10% of all diabetes, and diagnosis classically occurs in children but can also occur in adulthood. The burden of type 1 diabetes is expansive; it can result in long term complications, decreased life expectancy, and reduced quality of life and can add significant financial burden. Despite vast improvements in insulin, insulin delivery, and glucose monitoring technology, a large proportion of people with type 1 diabetes do not achieve glycemic goals. The massive burden of type 1 diabetes for patients and their families needs to be appreciated. The calculation and timing of prandial insulin dosing, often from food with unknown carbohydrate content, appropriate food and insulin dosing when exercising, and cost of therapy are all major challenges. The psychological realities of both acute management and the prospect of chronic complications add to the burden. Education programs and consistent surveillance for “diabetes burnout” are ideally available to everyone with type 1 diabetes.

In this review, we discuss recent developments in the rapidly changing landscape of type 1 diabetes and highlight aspects of current epidemiology and advances in diagnosis, technology, and management. We do not cover the breadth of complications of diabetes or certain unique scenarios including psychosocial aspects of type 1 diabetes management, management aspects specific to older adults, and β cell replacement therapies. Our review is intended for the clinical reader, including general internists, family practitioners, and endocrinologists, but we acknowledge the critical role that people living with type 1 diabetes and their families play in the ongoing efforts to understand this lifelong condition.

Sources and selection criteria

We did individual searches for studies on PubMed by using terms relevant to the specific topics covered in this review pertaining to type 1 diabetes. Search terms used included “type 1 diabetes” and each individual topic—diagnosis, autoantibodies, adjuvant therapies, continuous glucose monitoring, automated insulin delivery, immunotherapies, diabetic ketoacidosis, hypoglycemia, and under-resourced settings. We considered all studies published in the English language between 1 January 2001 and 31 January 2023. We selected publications outside of this timeline on the basis of relevance to each topic. We also supplemented our search strategy by a hand search of the references of key articles. We prioritized studies on each highlighted topic according to the level of evidence (randomized controlled trials (RCTs), systematic reviews and meta-analyses, consensus statements, and high quality observational studies), study size (we prioritized studies with at least 50 participants when available), and time of publication (we prioritized studies published since 2003 except for the landmark Diabetes Control and Complications Trial and a historical paper by Tuomi on diabetes autoantibodies, both from 1993). For topics on which evidence from RCTs was unavailable, we included other study types of the highest level of evidence available. To cover all important clinical aspects of the broad array of topics covered in this review, we included additional publications such as clinical reviews as appropriate on the basis of clinical relevance to both patients and clinicians in our opinion.

Epidemiology

The incidence of type 1 diabetes is rising worldwide, possibly owing to epigenetic and environmental factors. Globally in 2020 an estimated 8.7 million people were living with type 1 diabetes, of whom approximately 1.5 million were under 20 years of age. 2 This number is expected to rise to more than 17 million by 2040 ( https://www.t1dindex.org/#global ). The International Diabetes Federation estimates the global prevalence of type 1 diabetes at 0.1%, and this is likely an underestimation as diagnoses of type 1 diabetes in adults are often not accounted for. The incidence of adult onset type 1 diabetes is higher in Europe, especially in Nordic countries, and lowest in Asian countries. 3 Adult onset type 1 diabetes is also more prevalent in men than in women. An increase in prevalence in people under 20 years of age has been observed in several western cohorts including the US, 4 5 Netherlands, 6 Canada, 7 Hungary, 8 and Germany. 9

Classically, type 1 diabetes presents over the course of days or weeks in children and adolescents with polyuria, polydipsia, and weight loss due to glycosuria. The diagnosis is usually straightforward, with profound hyperglycemia (often >300 mg/dL) usually with ketonuria with or without ketoacidemia. Usually, more than one autoantibody is present at diagnosis ( table 1 ). 10 The number of islet autoantibodies combined with parameters of glucose tolerance now forms the basis of risk prediction for type 1 diabetes, with stage 3 being clinical disease ( fig 1 ). 11 The originally discovered autoantibody, islet cell antibody, is no longer used clinically owing to variability of the assay despite standardisation. 12

Autoantibody characteristics associated with increased risk of type 1 diabetes 10

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Natural history of type 1 diabetes. Adapted with permission from Insel RA, et al. Diabetes Care 2015;38:1964-74 11

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Half of all new cases of type 1 diabetes are now recognized as occurring in adults. 13 Misclassification due to misdiagnosis (commonly as type 2 diabetes) occurs in nearly 40% of people. 14 As opposed to typical childhood onset type 1 diabetes, progression to severe insulin deficiency, and therefore its clinical presentation in adults, is variable. The term latent autoimmune diabetes of adults (LADA) was introduced 30 years ago to identify adults who developed immune mediated diabetes. 15 An international consensus defined the diagnostic criteria for LADA as age >30 years, lack of need for insulin use for at least six months, and presence of islet cell autoantibodies. 16 However, debate as to whether the term LADA should even be used as a diagnostic term persists. The American Diabetes Association (ADA) Standards of Care note that for the purpose of classification, all forms of diabetes mediated by autoimmune β cell destruction are included in the classification of type 1 diabetes. 17 Nevertheless, they note that use of the term LADA is acceptable owing to the practical effect of heightening awareness of adults likely to have progressive autoimmune β cell destruction and thereby accelerating insulin initiation by clinicians to prevent diabetic ketoacidosis.

The investigation of adults with suspected type 1 diabetes is not always straightforward ( fig 2 ). 18 Islet cell autoantibodies such as glutamic acid decarboxylase antibody (GADA), tyrosine phosphatase IA2 antibody, and zinc transporter isoform 8 autoantibody act as markers of immune activity and can be detected in the blood with standardized assays ( table 1 ). The presence of one or more antibodies in adults with diabetes could mark the progression to severe insulin deficiency; these individuals should be considered to have type 1 diabetes. 1 Autoantibodies, especially GADA, should be measured only in people with clinically suspected type 1 diabetes, as low concentrations of GADA can be seen in type 2 diabetes and thus false positive measurements are a concern. 19 That 5-10% of cases of type 1 diabetes may occur without diabetes autoantibodies is also now clear, 20 and that the diabetes autoantibodies disappear over time is also well appreciated. 21

Flowchart for investigation of suspected type 1 diabetes in adults, based on data from white European populations. No single clinical feature in isolation confirms type 1 diabetes. The most discriminative feature is younger age at diagnosis (<35 years), with lower body mass index (<25), unintentional weight loss, ketoacidosis, and glucose >360 mg/dL at presentation. Adapted with permission from Holt RIG, et al. Diabetes Care 2021;44:2589-625 1

Genetic risk scoring (GRS) for type 1 diabetes has received attention to differentiate people whose classification is unclear. 22 23 24 Developed in 2019, the T1D-GRS2 uses 67 single nucleotide polymorphisms from known autoimmune loci and can predict type 1 diabetes in children of European and African ancestry. Although GRS is not available for routine clinical use, it may allow prediction of future cases of type 1 diabetes to allow prevention strategies with immune intervention (see below).

A major change in the type 1 diabetes phenotype has occurred over the past few decades, with an increase in obesity; the reasons for this are complex. In the general population, including people with type 1 diabetes, an epidemic of sedentary lifestyles and the “westernized diet” consisting of increased processed foods, refined sugars, and saturated fat is occurring. In people with type 1 diabetes, the overall improvement in glycemic control since the report of the Diabetes Control and Complications Trial (DCCT) in 1993 (when one or two insulin injections a day was standard therapy) has resulted in less glycosuria so that the typical patient with lower body weight is uncommon in high income countries. In the US T1D Exchange, more than two thirds of the adult population were overweight or obese. 25

Similarly, obesity in young people with type 1 diabetes has also increased over the decades. 26 The combination of autoimmune insulin deficiency with obesity and insulin resistance has received several descriptive names over the years, with this phenotype being described as double diabetes and hybrid diabetes, among others, 26 27 but no formal nomenclature in the diabetes classification exists. Many of these patients have family members with type 2 diabetes, and some patients probably do have both types of diabetes. Clinically, minimal research has been done into how this specific population responds to certain antihyperglycemic oral agents, such as glucagon-like peptide 1 (GLP-1) receptor agonists, given the glycemic, weight loss, and cardiovascular benefits seen with these agents. 28 These patients are common in most adult diabetes practices, and weight management in the presence of insulin resistance and insulin deficiency remains unclear.

Advances in monitoring

The introduction of home blood glucose monitoring (BGM) more than 45 years ago was met with much skepticism until the report of the DCCT. 29 Since then, home BGM has improved in accuracy, precision, and ease of use. 30 Today, in many parts of the world, home BGM, a static measurement of blood glucose, has been replaced by continuous glucose monitoring (CGM), a dynamic view of glycemia. CGM is superior to home BGM for glycemic control, as confirmed in a meta-analysis of 21 studies and 2149 participants with type 1 diabetes in which CGM use significantly decreased glycated hemoglobin (HbA 1c ) concentrations compared with BGM (mean difference −0.23%, 95% confidence interval −3.83 to −1.08; P<0.001), with a greater benefit if baseline HbA 1c was >8% (mean difference −0.43%, −6.04 to −3.30; P<0.001). 31 This newer technology has also evolved into a critical component of automated insulin delivery. 32

CGM is the standard for glucose monitoring for most adults with type 1 diabetes. 1 This technology uses interstitial fluid glucose concentrations to estimate blood glucose. Two types of CGM are available. The first type, called “real time CGM”, provides a continuous stream of glucose data to a receiver, mobile application, smartwatch, or pump. The second type, “intermittently scanned CGM,” needs to be scanned by a reader device or smartphone. Both of these technologies have shown improvements in HbA 1c and amount of time spent in the hypoglycemic range compared with home BGM when used in conjunction with multiple daily injections or “open loop” insulin pump therapy. 33 34 Real time CGM has also been shown to reduce hypoglycemic burden in older adults with type 1 diabetes ( table 2 ). 36 Alerts that predict or alarm with both hypoglycemia and hyperglycemia can be customized for the patient’s situation (for example, a person with unawareness of hypoglycemia would have an alert at a higher glucose concentration). Family members can also remotely monitor glycemia and be alerted when appropriate. The accuracy of these devices has improved since their introduction in 2006, so that currently available sensors can be used without a confirmation glucose concentration to make a treatment decision with insulin. However, some situations require home BGM, especially when concerns exist that the CGM does not match symptoms of hypoglycemia.

Summary of trials for each topic covered

Analysis of CGM reports retrospectively can assist therapeutic decision making both for the provider and the patient. Importantly, assessing the retrospective reports and watching the CGM in real time together offer insight to the patient with regard to insulin dosing, food choices, and exercise. Patients should be encouraged to assess their data on a regular basis to better understand their diabetes self-management. Table 3 shows standard metrics and targets for CGM data. 52 Figure 3 shows an ambulatory glucose profile.

Standardized continuous glucose monitoring metrics for adults with diabetes 52

Example of ambulatory glucose profile of 52 year old woman with type 1 diabetes and fear of hypoglycemia. CGM=continuous glucose monitoring; GMI=glucose management indicator

Improvements in technology and evidence for CGM resulting in international recommendations for its widespread use have resulted in greater uptake by people with type 1 diabetes across the globe where available and accessible. Despite this, not everyone wishes to use it; some people find wearing any device too intrusive, and for many the cost is prohibitive. These people need at the very least before meal and bedtime home BGM.

A next generation implantable CGM device (Sensionics), with an improved calibration algorithm that lasts 180 days after insertion by a healthcare professional, is available in both the EU and US. Although fingerstick glucose calibration is needed, the accuracy is comparable to that of other available devices. 53

Advances in treatments

The discovery of insulin in 1921, resulting in a Nobel Prize, was considered one of the greatest scientific achievements of the 20th century. The development of purified animal insulins in the late 1970s, followed by human insulin in the early 1980s, resulted in dramatic reductions in allergic reactions and lipoatrophy. Introduction of the first generation of insulin analogs, insulin lispro in the mid-1990s followed by insulin glargine in the early 2000s, was an important advance for the treatment of type 1 diabetes. 54 We review the next generation of insulin analogs here. Table 4 provides details on available insulins.

Pharmacokinetics of commonly used insulin preparations

Ultra-long acting basal insulins

Insulin degludec was developed with the intention of improving the duration of action and achieving a flatter profile compared with the original long acting insulin analogs, insulin glargine and insulin detemir. Its duration of action of 42 hours at steady state means that the profile is generally flat without significant day-to-day variability, resulting in less hypoglycemia compared with U-100 glargine. 39 55

When U-100 insulin glargine is concentrated threefold, its action is prolonged. 56 U-300 glargine has a different kinetic profile and is delivered in one third of the volume of U-100 glargine, with longer and flatter effects. The smaller volume of U-300 glargine results in slower and more gradual release of insulin monomers owing to reduced surface area in the subcutaneous space. 57 U-300 glargine also results in lesser hypoglycemia compared with U-100 glargine. 58

Ultra-rapid acting prandial insulins

Rapid acting insulin analogs include insulin lispro, aspart, and glulisine. With availability of insulin lispro, the hope was for a prandial insulin that better matched food absorption. However, these newer insulins are too slow to control the glucose spike seen with ingestion of a high carbohydrate load, leading to the development of insulins with even faster onset of action.

The first available ultra-rapid prandial insulin was fast acting insulin aspart. This insulin has an onset of appearance approximately twice as fast (~5 min earlier) as insulin aspart, whereas dose-concentration and dose-response relations are comparable between the two insulins ( table 4 ). 59 In adults with type 1 diabetes, mealtime and post-meal fast acting aspart led to non-inferior glycemic control compared with mealtime aspart, in combination with basal insulin. 60 Mean HbA 1c was 7.3%, 7.3%, and 7.4% in the mealtime faster aspart, mealtime aspart, and post‐meal faster aspart arms, respectively (P<0.001 for non-inferiority).

Insulin lispro-aabc is the second ultra-rapid prandial insulin. In early kinetic studies, insulin lispro-aabc appeared in the serum five minutes faster with 6.4-fold greater exposure in the first 15 minutes compared with insulin lispro. 61 The duration of exposure of the insulin concentrations in this study was 51 minutes faster with lispro-aabc. Overall insulin exposure was similar between the two groups. Clinically, lispro-aabc is non-inferior to insulin lispro, but postprandial hyperglycemia is lower with the faster acting analog. 62 Lispro-aabc given at mealtime resulted in greater improvement in post-prandial glucose (two hour post-prandial glucose −31.1 mg/dL, 95% confidence interval −41.0 to −21.2; P<0.001).

Both ultra-rapid acting insulins can be used in insulin pumps. Lispro-aabc tends to have more insertion site reactions than insulin lispro. 63 A meta-analysis including nine studies and 1156 participants reported increased infusion set changes on rapid acting insulin analogs (odds ratio 1.60, 95% confidence interval 1.26 to 2.03). 64

Pulmonary inhaled insulin

The quickest acting insulin is pulmonary inhaled insulin, with an onset of action of 12 minutes and a duration of 1.5-3 hours. 65 When used with postprandial supplemental dosing, glucose control is improved without an increase in hypoglycemia. 66

Insulin delivery systems

Approved automated insulin delivery systems.

CGM systems and insulin pumps have shown improvement in glycemic control and decreased risk of severe hypoglycemia compared with use of self-monitoring of blood glucose and multiple daily insulin injections in type 1 diabetes. 67 68 69 Using CGM and insulin pump together (referred to as sensor augmented pump therapy) only modestly improves HbA 1c in patients who have high sensor wear time, 70 71 but the management burden of diabetes does not decrease as frequent user input is necessary. Thus emerged the concept of glucose responsive automated insulin delivery (AID), in which data from CGM can inform and allow adjustment of insulin delivery.

In the past decade, exponential improvements in CGM technologies and refined insulin dosing pump algorithms have led to the development of AID systems that allow for minimization of insulin delivery burden. The early AID systems reduced hypoglycemia risk by automatically suspending insulin delivery when glucose concentrations dropped to below a pre-specified threshold but did not account for high glucose concentrations. More complex algorithms adjusting insulin delivery up and down automatically in response to real time sensor glucose concentrations now allow close replication of normal endocrine pancreatic physiology.

AID systems (also called closed loop or artificial pancreas systems) include three components—an insulin pump that continuously delivers rapid acting insulin, a continuous glucose sensor that measures interstitial fluid glucose at frequent intervals, and a control algorithm that continuously adjusts insulin delivery that resides in the insulin pump or a smartphone application or handheld device ( fig 4 ). All AID systems that are available today are referred to as “hybrid” closed loop (HCL) systems, as users are required to manually enter prandial insulin boluses and signal exercise, but insulin delivery is automated at night time and between meals. AID systems, regardless of the type used, have shown benefit in glycemic control and cost effectiveness, improve quality of life by improving sleep quality, and decrease anxiety and diabetes burden in adults and children. 72 73 74 Limitations to today’s HCL systems are primarily related to pharmacokinetics and pharmacodynamics of available analog insulins and accuracy of CGM in extremes of blood glucose values. The iLet bionic pancreas, cleared by the US Food and Drug Administration (FDA) in May 2023, is an AID system that determines all therapeutic insulin doses for an individual on the basis of body weight, eliminating the need for calculation of basal rates, insulin to carbohydrate ratios, blood glucose corrections, and bolus dose. The control algorithms adapt continuously and autonomously to the individual’s insulin needs. 38 Table 5 lists available AID systems.

Schematic of closed loop insulin pump technology. The continuous glucose monitor senses interstitial glucose concentrations and sends the information via Bluetooth to a control algorithm hosted on an insulin pump (or smartphone). The algorithm calculates the amount of insulin required, and the insulin pump delivers rapid acting insulin subcutaneously

Comparison of commercially available hybrid closed loop systems 75

Unapproved systems

Do-it-yourself (DIY) closed loop systems—DIY open artificial pancreas systems—have been developed by people with type 1 diabetes with the goal of self-adjusting insulin by modifying their individually owned devices. 76 These systems are built by the individual using an open source code widely available to anyone with compatible medical devices who is willing and able to build their own system. DIY systems are used by several thousand people across the globe but are not approved by regulatory bodies; they are patient-driven and considered “off-label” use of technology with the patient assuming full responsibility for their use. Clinicians caring for these patients should ensure basic diabetes skills, including pump site maintenance, a knowledge of how the chosen system works, and knowing when to switch to “manual mode” for patients using an artificial pancreas system of any kind. 76 The small body of studies on DIY looping suggests improvement in HbA 1c , increased time in range, decreased hypoglycemia and glucose variability, improvement in night time blood glucose concentrations, and reduced mental burden of diabetes management. 77 78 79 Although actively prescribing or initiating these options is not recommended, these patients should be supported by clinical teams; insulin prescription should not be withheld, and, if initiated by the patient, unregulated DIY options should be openly discussed to ensure open and transparent relationships. 78

In January 2023, the US FDA cleared the Tidepool Loop app, a DIY AID system. This software will connect the CGM, insulin pump, and Loop algorithm, but no RCTs using this method are available.

β cell replacement therapies

For patients with type 1 diabetes who meet specific clinical criteria, β cell replacement therapy using whole pancreas or pancreatic islet transplantation can be considered. Benefits of transplantation include immediate cessation of insulin therapy, attainment of euglycemia, and avoidance of hypoglycemia. Additional benefits include improved quality of life and stabilization of complications. 80 Chronic immunosuppression is needed to prevent graft rejection after transplantation.

Pancreas transplantation

Whole pancreas transplantation, first performed in 1966, involves complex abdominal surgery and lifelong immunosuppressive therapy and is limited by organ donor availability. Today, pancreas transplants are usually performed simultaneously using two organs from the same donor (simultaneous pancreas-kidney transplant (SPKT)), sequentially if the candidate has a living donor for renal transplantation (pancreas after kidney transplant (PAKT)) or on its own (pancreas transplantation alone). Most whole pancreas transplants are performed with kidney transplantation for end stage diabetic kidney disease. Pancreas graft survival at five years after SPKT is 80% and is superior to that with pancreas transplants alone (62%) or PAKT (67%). 81 Studies from large centers where SPKT is performed show that recipients can expect metabolic improvements including amelioration of problematic hypoglycemia for at least five years. 81 The number of pancreas transplantations has steadily decreased in the past two decades.

Islet transplantation

Islet transplantation can be pursued in selected patients with type 1 diabetes marked by unawareness of hypoglycemia and severe hypoglycemic episodes, to help restore the α cell response critical for responding to hypoglycemia. 82 83 Islet transplantation involves donor pancreas procurement with subsequent steps to isolate, purify, culture, and infuse the islets. Multiple donors are needed to provide enough islet cells to overcome islet cell loss during transplantation. Survival of the islet grafts, limited donor supply, and lifelong need for immunosuppressant therapy remain some of the biggest challenges. 84 Islet transplantation remains experimental in the US and is offered in a few specialized centers in North America, some parts of Europe, and Australia. 85

Disease modifying treatments for β cell preservation

Therapies targeting T cells, B cells, and cytokines that find use in a variety of autoimmune diseases have also been applied to type 1 diabetes. The overarching goal of immune therapies in type 1 diabetes is to prevent or delay the loss of functional β cell mass. Studies thus far in early type 1 diabetes have not yet successfully shown reversal of loss of C peptide or maintenance of concentrations after diagnosis, although some have shown preservation or slowing of loss of β cells. This suggests that a critical time window of opportunity exists for starting treatment depending on the stage of type 1 diabetes ( fig 1 ).

Teplizumab is a humanized monoclonal antibody against the CD3 molecule on T cells; it is thought to modify CD8 positive T lymphocytes, key effector cells that mediate β cell death and preserves regulatory T cells. 86 Teplizumab, when administered to patients with new onset of type 1 diabetes, was unable to restore glycemia despite C peptide preservation. 87 However, in its phase II prevention study of early intervention in susceptible individuals (at least two positive autoantibodies and an abnormal oral glucose tolerance test at trial entry), a single course of teplizumab delayed progression to clinical type 1 diabetes by about two years ( table 2 ). 43 On the basis of these results, teplizumab received approval in the US for people at high risk of type 1 diabetes in November 2022. 88 A phase III trial (PROTECT; NCT03875729 ) to evaluate the efficacy and safety of teplizumab versus placebo in children and adolescents with new diagnosis of type 1 diabetes (within six weeks) is ongoing. 89

Thus far, targeting various components of the immune response has been attempted in early type 1 diabetes without any long term beneficial effects on C peptide preservation. Co-stimulation blockade using CTLA4-Ig abatacept, a fusion protein that interferes with co-stimulation needed in the early phases of T cell activation that occurs in type 1 diabetes, is being tested for efficacy in prevention of type 1 diabetes ( NCT01773707 ). 90 Similarly, several cytokine directed anti-inflammatory targets (interleukin 6 receptor, interleukin 1β, tumor necrosis factor ɑ) have not shown any benefit.

Non-immunomodulatory adjunctive therapies

Adjunctive therapies for type 1 diabetes have been long entertained owing to problems surrounding insulin delivery, adequacy of glycemic management, and side effects associated with insulin, especially weight gain and hypoglycemia. At least 50% of adults with type 1 diabetes are overweight or obese, presenting an unmet need for weight management in these people. Increased cardiovascular risk in these people despite good glycemic management presents additional challenges. Thus, use of adjuvant therapies may tackle these problems.

Metformin, by decreasing hepatic glucose production, could potentially decrease fasting glucose concentrations. 91 It has shown benefit in reducing insulin doses and possibly improving metabolic control in obese/overweight people with type 1 diabetes. A meta-analysis of 19 RCTs suggests short term improvement in HbA 1c that is not sustained after three months and is associated with higher incidence of gastrointestinal side effects. 92 No evidence shows that metformin decreases cardiovascular morbidity in type 1 diabetes. Therefore, owing to lack of conclusive benefit, addition of metformin to treatment regimens is not recommended in consensus guidelines.

Glucagon-like peptide receptor agonists

Endogenous GLP-1 is an incretin hormone secreted from intestinal L cells in response to nutrient ingestion and enhances glucose induced insulin secretion, suppresses glucagon secretion, delays gastric emptying, and induces satiety. 93 GLP-1 promotes β cell proliferation and inhibits apoptosis, leading to expansion of β cell mass. GLP-1 secretion in patients with type 1 diabetes is similar to that seen in people without diabetes. Early RCTs of liraglutide in type 1 diabetes resulted in weight loss and modest lowering of HbA 1c ( table 2 ). 49 50 Liraglutide 1.8 mg in people with type 1 diabetes and higher body mass index decreased HbA 1c , weight, and insulin requirements with no increased hypoglycemia risk. 94 However, on the basis of results from a study of weekly exenatide that showed similar results, these effects may not be sustained. 51 A meta-analysis of 24 studies including 3377 participants showed that the average HbA 1c decrease from GLP-1 receptor agonists compared with placebo was highest for liraglutide 1.8 mg daily (−0.28%, 95% confidence interval −0.38% to−0.19%) and exenatide (−0.17%, −0.28% to 0.02%). The estimated weight loss from GLP-1 receptor agonists compared with placebo was −4.89 (−5.33 to−4.45)  kg for liraglutide 1.8 mg and −4.06  (−5.33 to−2.79) kg for exenatide. 95 No increase in severe hypoglycemia was seen (odds ratio 0.67, 0.43 to 1.04) but therapy was associated with higher levels of nausea. GLP-1 receptor agonist use may be beneficial for weight loss and reducing insulin doses in a subset of patients with type 1 diabetes. GLP-1 receptor agonists are not a recommended treatment option in type 1 diabetes. Semaglutide is being studied in type 1 diabetes in two clinical trials ( NCT05819138 ; NCT05822609 ).

Sodium-glucose cotransporter inhibitors

Sodium-glucose cotransporter 2 (SGLT-2), a protein expressed in the proximal convoluted tubule of the kidney, reabsorbs filtered glucose; its inhibition prevents glucose reabsorption in the tubule and increases glucose excretion by the kidney. Notably, the action of these agents is independent of insulin, so this class of drugs has potential as adjunctive therapy for type 1 diabetes. Clinical trials have shown significant benefit in cardiovascular and renal outcomes in type 2 diabetes; therefore, significant interest exists for use in type 1 diabetes. Several available SGLT-2 inhibitors have been studied in type 1 diabetes and have shown promising results with evidence of decreased total daily insulin dosage, improvement in HbA 1c , lower rates of hypoglycemia, and decrease in body weight; however, these effects do not seem to be sustained at one year in clinical trials and seem to wane with time. Despite beneficial effects, increased incidence of diabetic ketoacidosis has been observed in all trials, is a major concern, and is persistent despite educational efforts. 96 97 98 Low dose empagliflozin (2.5 mg) has shown lower rates of diabetic ketoacidosis in clinical trials ( table 2 ). 47 Favorable risk profiles have been noted in Japan, the only market where SGLT-2 inhibitors are approved for adjunctive use in type 1 diabetes. 99 In the US, SGLT-2 inhibitors are approved for use in type 2 diabetes only. In Europe, although dapagliflozin was approved for use as adjunct therapy to insulin in adults with type 1 diabetes, the manufacturer voluntarily withdrew the indication for the drug in 2021. 100 Sotagliflozin is a dual SGLT-1 and SGLT-2 inhibitor that decreases renal glucose reabsorption through systemic inhibition of SGLT-2 and decreases glucose absorption in the proximal intestine by SGLT-1 inhibition, blunting and delaying postprandial hyperglycemia. 101 Studies of sotagliflozin in type 1 diabetes have shown sustained HbA 1c reduction, weight loss, lower insulin requirements, lesser hypoglycemia, and more diabetic ketoacidosis relative to placebo. 102 103 104 The drug received authorization in the EU for use in type 1 diabetes, but it is not marketed there. Although SGLT inhibitors are efficacious in type 1 diabetes management, the risk of diabetic ketoacidosis is a major limitation to widespread use of these agents.

Updates in acute complications of type 1 diabetes

Diabetic ketoacidosis.

Diabetic ketoacidosis is a serious and potentially fatal hyperglycemic emergency accompanied by significant rates of mortality and morbidity as well as high financial burden for healthcare systems and societies. In the past decade, increasing rates of diabetic ketoacidosis in adults have been observed in the US and Europe. 105 106 This may be related to changes in the definition of diabetic ketoacidosis, use of medications associated with higher risk, and admission of patients at lower risk. 107 In a US report of hospital admissions with diabetic ketoacidosis, 53% of those admitted were between the ages of 18 and 44, with higher rates in men than in women. 108 Overall, although mortality from diabetic ketoacidosis in developed countries remains low, rates have risen in people aged >60 and in those with coexisting life threatening illnesses. 109 110 Recurrent diabetic ketoacidosis is associated with a substantial mortality rate. 111 Frequency of diabetic ketoacidosis increases with higher HbA 1c concentrations and with lower socioeconomic status. 112 Common precipitating factors include newly diagnosed type 1 diabetes, infection, poor adherence to insulin, and an acute cardiovascular event. 109

Euglycemic diabetic ketoacidosis refers to the clinical picture of an increased anion gap metabolic acidosis, ketonemia, or significant ketonuria in a person with diabetes without significant glucose elevation. This can be seen with concomitant use of SGLT-2 inhibitors (currently not indicated in type 1 diabetes), heavy alcohol use, cocaine use, pancreatitis, sepsis, and chronic liver disease and in pregnancy 113 Treatment is similar to that for hyperglycemic diabetic ketoacidosis but can require earlier use and greater concentrations of a dextrose containing fluid for the insulin infusion in addition to 0.9% normal saline resuscitation fluid. 114

The diagnosis of diabetic ketoacidosis has evolved from a gluco-centric diagnosis to one requiring hyperketonemia. By definition, independent of blood glucose, a β-hydroxybutyrate concentration >3 mmol/L is required for diagnosis. 115 However, the use of this ketone for assessment of the severity of the diabetic ketoacidosis is controversial. 116 Bedside β-hydroxybutyrate testing during treatment is standard of care in many parts of the world (such as the UK) but not others (such as the US). Concerns have been raised about accuracy of bedside β-hydroxybutyrate meters, but this is related to concentrations above the threshold for diabetic ketoacidosis. 116

Goals for management of diabetic ketoacidosis include restoration of circulatory volume, correction of electrolyte imbalances, and treatment of hyperglycemia. Intravenous regular insulin infusion is the standard of care for treatment worldwide owing to rapidity of onset of action and rapid resolution of ketonemia and hyperglycemia. As hypoglycemia and hypokalemia are more common during treatment, insulin doses are now recommended to be reduced from 0.1 u/kg/h to 0.05 u/kg/h when glucose concentrations drop below 250 mg/dL or 14 mM. 115 Subcutaneous rapid acting insulin protocols have emerged as alternative treatments for mild to moderate diabetic ketoacidosis. 117 Such regimens seem to be safe and have the advantages of not requiring admission to intensive care, having lower rates of complications related to intravenous therapy, and requiring fewer resources. 117 118 Ketonemia and acidosis resolve within 24 hours in most people. 115 To prevent rebound hyperglycemia, the transition off an intravenous insulin drip must overlap subcutaneous insulin by at least two to four hours. 115

Hypoglycemia

Hypoglycemia, a common occurrence in people with type 1 diabetes, is a well appreciated effect of insulin treatment and occurs when blood glucose falls below the normal range. Increased susceptibility to hypoglycemia from exogenous insulin use in people with type 1 diabetes results from multiple factors, including imperfect subcutaneous insulin delivery tools, loss of glucagon within a few years of diagnosis, progressive impairment of the sympatho-adrenal response with repeated hypoglycemic episodes, and eventual development of impaired awareness. In 2017 the International Hypoglycemia Study Group developed guidance for definitions of hypoglycemia; on the basis of this, a glucose concentration of 3.0-3.9 mmol/L (54-70 mg/dL) was designated as level 1 hypoglycemia, signifying impending development of level 2 hypoglycemia—a glucose concentration <3 mmol/L (54 mg/dL). 119 120 At approximately 54 mg/dL, neuroglycopenic hypoglycemia symptoms, including vision and behavior changes, seizures, and loss of consciousness, begin to occur as a result of glucose deprivation of neurons in the central nervous system. This can eventually lead to cerebral dysfunction at concentrations <50 mg/dL. 121 Severe hypoglycemia (level 3), denoting severe cognitive and/or physical impairment and needing external assistance for recovery, is a common reason for emergency department visits and is more likely to occur in people with lower socioeconomic status and with the longest duration of diabetes. 112 Prevalence of self-reported severe hypoglycemia is very high according to a global population study that included more than 8000 people with type 1 diabetes. 122 Severe hypoglycemia occurred commonly in younger people with suboptimal glycemia according to a large electronic health record database study in the US. 123 Self- reported severe hypoglycemia is associated with a 3.4-fold increase in mortality. 124 125

Acute consequences of hypoglycemia include impaired cognitive function, temporary focal deficits including stroke-like symptoms, and memory deficits. 126 Cardiovascular effects including tachycardia, arrhythmias, QT prolongation, and bradycardia can occur. 127 Hypoglycemia can impair many activities of daily living, including motor vehicle safety. 128 In a survey of adults with type 1 diabetes who drive a vehicle at least once a week, 72% of respondents reported having hypoglycemia while driving, with around 5% reporting a motor vehicle accident due to hypoglycemia in the previous two years. 129 This contributes to the stress and fear that many patients face while grappling with the difficulties of ongoing hypoglycemia. 130

Glucagon is highly efficacious for the primary treatment of severe hypoglycemia when a patient is unable to ingest carbohydrate safely, but it is unfortunately under-prescribed and underused. 131 132 Availability of nasal, ready to inject, and shelf-stable liquid glucagon formulations have superseded the need for reconstituting older injectable glucagon preparations before administration and are now preferred. 133 134 Real time CGM studies have shown a decreased hypoglycemic exposure in people with impaired awareness without a change in HbA 1c . 34 135 136 137 138 CGM has shown benefit in decreasing hypoglycemia across the lifespan, including in teens, young adults, and older people. 36 139 Although CGM reduces the burden of hypoglycemia including severe hypoglycemia, it does not eliminate it; overall, such severe level 3 hypoglycemia rates in clinical trials are very low and hard to decipher in the real world. HCL insulin delivery systems integrated with CGM have been shown to decrease hypoglycemia. Among available rapid acting insulins, ultra-rapid acting lispro (lispro-aabc) seems to be associated with less frequent hypoglycemia in type 1 diabetes. 140 141

As prevention of hypoglycemia is a crucial aspect of diabetes management, formal training programs to increase awareness and education on avoidance of hypoglycemia, such as the UK’s Dose Adjustment for Normal Eating (DAFNE), have been developed. 142 143 This program has shown fewer severe hypoglycemia (mean 1.7 (standard deviation 8.5) episodes per person per year before training to 0.6 (3.7) episodes one year after training) and restoration of recognition of hypoglycemia in 43% of people reporting unawareness. Clinically relevant anxiety and depression fell from 24.4% to 18.0% and from 20.9% to 15.5%, respectively. A structured education program with cognitive and psychotherapeutic aspects for changing hypoglycemia related behaviors, called the Hypoglycemia Awareness Restoration Program despite optimized self-care (HARPdoc), showed a positive effect on changing unhelpful beliefs around hypoglycemia and improved diabetes related and general distress and anxiety scores. 144

Management in under-resourced settings

According to a recent estimate from the International Diabetes Federation, 1.8 million people with type 1 diabetes live in low and middle income countries (LMICs). 2 In many LMICs, the actual burden of type 1 diabetes remains unknown and material resources needed to manage type 1 diabetes are lacking. 145 146 Health systems in these settings are underequipped to tackle the complex chronic disease that is type 1 diabetes. Few diabetes and endocrinology specialist physicians are available owing to lack of specific postgraduate training programs in many LMICs; general practitioners with little to no clinical experience in managing type 1 diabetes care for these patients. 146 This, along with poor availability and affordability of insulin and lack of access to technology, results in high mortality rates. 147 148 149 In developed nations, low socioeconomic status is associated with higher levels of mortality and morbidity for adults with type 1 diabetes despite access to a universal healthcare system. 150 Although global governments have committed to universal health coverage and therefore widespread availability of insulin, it remains very far from realization in most LMICs. 151

Access to technology is patchy and varies globally. In the UST1DX, CGM use was least in the lowest fifth of socioeconomic status. 152 Even where technology is available, successful engagement does not always occur. 153 In a US cohort, lower CGM use was seen in non-Hispanic Black children owing to lower rates of device initiation and higher rates of discontinuation. 154 In many LMICs, blood glucose testing strips are not readily available and cost more than insulin. 151 In resource limited settings, where even diagnosis, basic treatments including insulin, syringes, and diabetes education are limited, use of CGM adds additional burden to patients. Need for support services and the time/resources needed to download and interpret data are limiting factors from a clinician’s perspective. Current rates of CGM use in many LMICs are unknown.

Inequities in the availability of and access to certain insulin formulations continue to plague diabetes care. 155 In developed countries such as the US, rising costs have led to insulin rationing by around 25% of people with type 1 diabetes. 156 LMICs have similar trends while also remaining burdened by disproportionate mortality and complications from type 1 diabetes. 155 157 With the inclusion of long acting insulin analogs in the World Health Organization’s Model List of Essential Medicines in 2021, hope has arisen that these will be included as standard of care across the world. 158 In the past, the pricing of long acting analogs has limited their use in resource poor settings 159 ; however, their inclusion in WHO’s list was a major step in improving their affordability. 158 With the introduction of lower cost long acting insulin biosimilars, improved access to these worldwide in the future can be anticipated. 160

Making insulin available is not enough on its own to improve the prognosis for patients with diabetes in resource poor settings. 161 Improved healthcare infrastructure, better availability of diabetes supplies, and trained personnel are all critical to improving type 1 diabetes care in LMICs. 161 Despite awareness of limitations and barriers, a clear understanding of how to implement management strategies in these settings is still lacking. The Global Diabetes Compact was launched in 2021 with the goal of increasing access to treatment and improving outcomes for people with diabetes across the globe. 162

Emerging technologies and treatments

Monitoring systems.

The ability to measure urinary or more recently blood ketone concentrations is an integral part of self-management of type 1 diabetes, especially during acute illness, intermittent fasting, and religious fasts to prevent diabetic ketoacidosis. 163 Many people with type 1 diabetes do not adhere to urine or blood ketone testing, which likely results in unnecessary episodes of diabetic ketoacidosis. 164 Noting that blood and urine ketone testing is not widely available in all countries and settings is important. 1 Regular assessment of patients’ access to ketone testing (blood or urine) is critical for all clinicians. Euglycemic diabetic ketoacidosis in type 1 diabetes is a particular problem with concomitant use of SGLT-2 inhibitors; for this reason, these agents are not approved for use in these patients. For sick day management (and possibly for the future use of SGLT-2 inhibitors in people with type 1 diabetes), it is hoped that continuous ketone monitoring (CKM) can mitigate the risks of diabetic ketoacidosis. 165 Like CGM, the initial CKM device measures interstitial fluid β-hydroxybutyrate instead of glucose. CKM use becomes important in conjunction with a hybrid closed loop insulin pump system and added SGLT-2 inhibitor therapy, where insulin interruptions are common and hyperketonemia is frequent. 166

Perhaps the greatest technological challenge to date has been the development of non-invasive glucose monitoring. Numerous attempts have been made using strategies including optics, microwave, and electrochemistry. 167 Lack of success to date has resulted in healthy skepticism from the medical community. 168 However, active interest in the development of non-invasive technology with either interstitial or blood glucose remains.

Insulin and delivery systems

In the immediate future, two weekly basal insulins, insulin icodec and basal insulin Fc, may become available. 169 Studies of insulin icodec in type 1 diabetes are ongoing (ONWARDS 6; NCT04848480 ). How these insulins will be incorporated in management of type 1 diabetes is not yet clear.

Currently available AID systems use only a single hormone, insulin. Dual hormone AID systems incorporating glucagon are in development. 170 171 Barriers to the use of dual hormone systems include the need for a second chamber in the pump, a lack of stable glucagon formulations approved for long term subcutaneous delivery, lack of demonstrated long term safety, and gastrointestinal side effects from glucagon use. 74 Similarly, co-formulations of insulin and amylin (a hormone co-secreted with insulin and deficient in people with type 1 diabetes) are in development. 172

Immunotherapy for type 1 diabetes

As our understanding of the immunology of type 1 diabetes expands, development of the next generation of immunotherapies is under active pursuit. Antigen specific therapies, peptide immunotherapy, immune tolerance using DNA vaccination, and regulatory T cell based adoptive transfer targeting β cell senescence are all future opportunities for drug development. Combining immunotherapies with metabolic therapies such as GLP-1 receptor agonists to help to improve β cell mass is being actively investigated.

The quest for β cell replacement methods is ongoing. Transplantation of stem cell derived islets offers promise for personalized regenerative therapies as a potentially curative method that does away with the need for donor tissue. Since the first in vivo model of glucose responsive β cells derived from human embryonic stem cells, 173 different approaches have been attempted. Mesenchymal stromal cell treatment and autologous hematopoietic stem cells in newly diagnosed type 1 diabetes may preserve β cell function without any safety signals. 174 175 176 Stem cell transplantation for type 1 diabetes remains investigational. Encapsulation, in which β cells are protected using a physical barrier to prevent immune attack and avoid lifelong immunosuppression, and gene therapy techniques using CRISPR technology also remain in early stages of investigation.

Until recently, no specific guidelines for management of type 1 diabetes existed and management guidance was combined with consensus statements developed for type 2 diabetes. Table 6 summarizes available guidance and statements from various societies. A consensus report for management of type 1 diabetes in adults by the ADA and European Association for the Study of Diabetes became available in 2021; it covers several topics of diagnosis and management of type 1 diabetes, including glucose monitoring, insulin therapy, and acute complications. Similarly, the National Institute for Health and Care Excellence also offers guidance on management of various aspects of type 1 diabetes. Consensus statements for use of CGM, insulin pump, and AID systems are also available.

Guidelines in type 1 diabetes

Conclusions

Type 1 diabetes is a complex chronic condition with increasing worldwide prevalence affecting several million people. Several successes in management of type 1 diabetes have occurred over the years from the serendipitous discovery of insulin in 1921 to blood glucose monitoring, insulin pumps, transplantation, and immunomodulation. The past two decades have seen advancements in diagnosis, treatment, and technology including development of analog insulins, CGM, and advanced insulin delivery systems. Although we have gained a broad understanding on many important aspects of type 1 diabetes, gaps still exist. Pivotal research continues targeting immune targets to prevent or delay onset of type 1 diabetes. Although insulin is likely the oldest of existing modern drugs, no low priced generic supply of insulin exists anywhere in the world. Management of type 1 diabetes in under resourced areas continues to be a multifaceted problem with social, cultural, and political barriers.

Glossary of abbreviations

ADA—American Diabetes Association

AID—automated insulin delivery

BGM—blood glucose monitoring

CGM—continuous glucose monitoring

CKM—continuous ketone monitoring

DCCT—Diabetes Control and Complications Trial

DIY—do-it-yourself

FDA—Food and Drug Administration

GADA—glutamic acid decarboxylase antibody

GLP-1—glucagon-like peptide 1

GRS—genetic risk scoring

HbA1c—glycated hemoglobin

HCL—hybrid closed loop

LADA—latent autoimmune diabetes of adults

LMIC—low and middle income country

PAKT—pancreas after kidney transplant

RCT—randomized controlled trial

SGLT-2—sodium-glucose cotransporter 2

SPKT—simultaneous pancreas-kidney transplant

Questions for future research

What future new technologies can be helpful in management of type 1 diabetes?

How can newer insulin delivery methods benefit people with type 1 diabetes?

What is the role of disease modifying treatments in prevention and delay of type 1 diabetes?

Is there a role for sodium-glucose co-transporter inhibitors or glucagon-like peptide 1 receptor angonists in the management of type 1 diabetes?

As the population with type 1 diabetes ages, how should management of these people be tailored?

How can we better serve people with type 1 diabetes who live in under-resourced settings with limited access to medications and technology?

How patients were involved in the creation of this manuscript

A person with lived experience of type 1 diabetes reviewed a draft of the manuscript and offered input on important aspects of their experience that should be included. This person is involved in large scale education and activism around type 1 diabetes. They offered their views on various aspects of type 1 diabetes, especially the use of adjuvant therapies and the burden of living with diabetes. This person also raised the importance of education of general practitioners on the various stages of type 1 diabetes and the management aspects. On the basis of this feedback, we have highlighted the burden of living with diabetes on a daily basis.

Series explanation: State of the Art Reviews are commissioned on the basis of their relevance to academics and specialists in the US and internationally. For this reason they are written predominantly by US authors

Contributors: SS and IBH contributed to the planning, drafting, and critical review of this manuscript. FNK contributed to the drafting of portions of the manuscript. All three authors are responsible for the overall content as guarantors.

Competing interests: We have read and understood the BMJ policy on declaration of interests and declare the following interests: SS has received an honorarium from Abbott Diabetes Care; IBH has received honorariums from Abbott Diabetes Care, Lifescan, embecta, and Hagar and research support from Dexcom and Insulet.

Provenance and peer review: Commissioned; externally peer reviewed.

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• Open access
• Published: 01 April 2021

Diagnosis and treatment of type 1 diabetes at the dawn of the personalized medicine era

• Ammira Al-Shabeeb Akil   ORCID: orcid.org/0000-0001-5381-070X 1 ,
• Esraa Yassin 1 ,
• Aljazi Al-Maraghi 1 ,
• Elbay Aliyev 1 ,
• Khulod Al-Malki 1 &
• Khalid A. Fakhro 1 , 2 , 3  

Journal of Translational Medicine volume  19 , Article number:  137 ( 2021 ) Cite this article

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Type 1 diabetes affects millions of people globally and requires careful management to avoid serious long-term complications, including heart and kidney disease, stroke, and loss of sight. The type 1 diabetes patient cohort is highly heterogeneous, with individuals presenting with disease at different stages and severities, arising from distinct etiologies, and overlaying varied genetic backgrounds. At present, the “one-size-fits-all” treatment for type 1 diabetes is exogenic insulin substitution therapy, but this approach fails to achieve optimal blood glucose control in many individuals. With advances in our understanding of early-stage diabetes development, diabetes stratification, and the role of genetics, type 1 diabetes is a promising candidate for a personalized medicine approach, which aims to apply “the right therapy at the right time, to the right patient”. In the case of type 1 diabetes, great efforts are now being focused on risk stratification for diabetes development to enable pre-clinical detection, and the application of treatments such as gene therapy, to prevent pancreatic destruction in a sub-set of patients. Alongside this, breakthroughs in stem cell therapies hold great promise for the regeneration of pancreatic tissues in some individuals. Here we review the recent initiatives in the field of personalized medicine for type 1 diabetes, including the latest discoveries in stem cell and gene therapy for the disease, and current obstacles that must be overcome before the dream of personalized medicine for all type 1 diabetes patients can be realized.

Introduction

Type 1 Diabetes (T1D) is a potentially life-threatening multifactorial autoimmune disorder characterized by T-cell-mediated destruction of pancreatic β cells, resulting in a deficiency of insulin synthesis and secretion [ 1 ]. The incidence of T1D has been rising globally since the 1950s, with an average annual increase of 3–4% over the past three decades [ 2 ]. In particular, the incidence of childhood T1D is increasing, most rapidly in populations that previously had low incidence [ 3 , 4 , 5 ], and varying by ethnicity and race [ 4 ].

This worrying growth in T1D incidence has driven concerted research efforts to better understand the underlying risk factors, etiology, and pathology of the disease.

T1D has a largely heritable element, supported by a twin concordance rate of up to 70% [ 6 ] and of 8–10% sibling risk [ 7 ]. The bulk of risk is explained by difference at a several but strongly associated loci involving the HLA region “HLA class II, DQ and DR loci and HLA class I region” on chromosome 6p21 that account for ~ 50% of familial T1D [ 8 , 9 ]. Genome‐wide association (GWAS) and candidate gene association studies have produced an abundance body of evidence and provided convincing support about other genes and loci external to the HLA region that protect or confer the risk for T1D [ 8 , 10 ]. Single nucleotide polymorphisms (SNPs) comprising insulin gene ( INS ) presents ~ 10% of genetic predisposition of T1D [ 8 , 11 ], cytotoxic T-lymphocyte–associated antigen ( CTLA )-4 gene [ 12 ], protein tyrosine phosphatase non-receptor type 22 ( PTPN22) [ 8 , 13 ], nterferon induced with helicase C domain 1 ( IFIH1 ) genes [ 14 ] and Interleukin-2 receptor alpha chain ( IL2RA ) [ 11 ]. This great genetic heritability generates the capacity for effective diagnostic discrimination if the most of genetic risk for T1D can be allocated [ 15 , 16 ].

Prospective birth cohorts studies have facilitated the identification of potential triggers of islet autoimmunity (IA) and the natural history of progression to T1D [ 17 , 18 , 19 , 20 ]. Candidate triggers such as infections [ 21 ], early life diet [ 22 ], vitamin D levels [ 23 ], gut microbiota composition [ 24 ], vaccinations [ 25 ], pollutants and toxins [ 26 ], and geographic variation [ 27 ] when combine with genetic susceptibility [ 28 ] and specific epigenetic modifications [ 29 , 30 , 31 ], the perfect storm occurs and autoimmune destruction of pancreatic β cells is initiated (Fig.  1 ). These triggers required to be logged prospectively in well-designed studies instead of recollected retrospectively at the time of T1D diagnosis, couple of years later.

Environmental factors associated with initiation of, or protection from islet autoimmunity (IA) and progression to T1D. Adopted with permission from (Craig et al. 2019)

The plethora of factors that can lead to development and expression of T1D underpin the clinical heterogeneity of the disease. The gene polymorphisms and environmental triggers combinations that impact the risk of T1D and lead to the disease development are tremendously high [ 32 ]. Until now, this heterogeneity has not been taken into account and almost all T1D patients are treated with the standard approach of regular blood glucose monitoring combined with exogenous insulin replacement. However, the rising social and healthcare costs globally associated with T1D and its complications are providing the impetus for prioritizing more tailored approaches [ 33 , 34 , 35 ]. There is now increasing recognition of the opportunity to identify specific patient subgroups at different stages or with different driving factors of their early disease and prevent or even reverse their emerging T1D: this is the concept of personalized medicine. Personalized medicine is characterized by the mantra of "offering the right therapy at the right time for the right affected individual"; as an idea it is not new, but only recently has scientific and clinical research provided us with the necessary information and the means with which to apply it to novel treatment strategies for T1D.

In this review, we bring together the latest knowledge of the factors underpinning T1D heterogeneity in distinct patient groups and how these differences are being used to design personalized medicine approaches to diagnose, prevent, and hopefully treat the disease. We will discuss recent advances in gene therapy and stem cell-based treatments for specific groups of T1D patients, and will highlight key obstacles that must be overcome if further progress towards the goal of personalized medicine for all T1D patients is to be achieved.

Personalized diagnosis of T1D

Although all patients with overt T1D exhibit pancreatic destruction and consequent dysregulation of blood glucose levels, not all cases of the disease are driven by the same factors or along the same timeline. Many patients experience a sometimes prolonged clinically silent phase in which it might have been possible to intervene and prevent or even reverse the course of disease. This knowledge has led to development of a staging classification system for T1D. Even once T1D is clinically evident, we are now beginning to appreciate that not all cases are the same, and that particular sub-types of the disease would benefit from distinct treatment strategies. We discuss both of these important advances within the field below.

Staging classification system for T1D

By dissecting population- and individual-level risk factors for developing T1D, we now know that the disorder exists across developmental spectrum that can be categorized into distinct stages, and the likelihood of an individual developing clinically symptomatic status can be foreseen with considerable accuracy.

All cases are proposed to start with a period of "incubation" where exposure to defined and undefined driving factors creates the conditions for β-cell autoimmunity to emerge. When the process of ß-cell autoimmunity begins, the development towards clinical T1D can be classified into three distinct main stages: (I) asymptomatic ß-cell autoimmunity, defined by the presence of ≥ 2 types of autoantibodies such as GAD65 (GADA), zinc transporter 8 (ZnT8A), insulin (IAA), islet cell antibodies (ICA), insulinoma-associated proteins (IA-2A and IA-2β), with normoglycemia; (II) asymptomatic ß-cell autoimmunity, characterized by the presence of ≥ 2 types of autoantibodies but with dysglycemia, indicating functional damage to ß-cells; and (III) symptomatic T1D recognized by the symptoms of dysglycemia including polyuria or diabetic ketoacidosis (DKA) (Fig.  2 ). The sequence of events from emerging autoimmunity to dysglycemia and then to overt diabetes occurs along this predictable course, but the length of each stage may vary broadly between different individuals [ 36 , 37 , 38 ].

adapted from the same publication on addition to [ 36 ]© 2015 The American Diabetes Association

Development and staging of type 1 diabetes. T1D is characterized by a gradual loss of β-cell function (black dashed-dotted line) over time. As the disease progresses, beta cell function falls below the threshold required to maintain glucose control creating a requirement for insulin replacement therapy. Genetic and environmental risk are both included in the disease etiology. In stage 1, β-cell autoantibodies are persistent, but normoglycemia remains and there are no clinical symptoms. Throughout stage 2, the number of β-cell autoantibodies may induce dysglycemia but still without any diabetes symptoms. In stage 3, β-cell autoantibodies are predominant and clear symptoms of diabetes have emerged. In the white boxes are categories of biomarkers which could be leveraged to refine the staging paradigm, improve prognostic predictions, or subset individuals within a given stage of disease [ 38 ]. The specifics of these biomarkers are discussed in the text related to the relevant stage. The staging of T1D pathogenesis was proposed by Insel et al. [ 36 ] and the figure explanation was

There are several valuable clinical outcomes for children monitored across prospective longitudinal natural history studies such as. Notably, those children have better metabolic markers at and soon after the clinical diagnosis stage, making the disease management relatively easier, reduce hypoglycemic incidents and delay the progress of the associated long-term complications. Rigorous diabetes management commenced afterward the diagnosis of symptomatic T1D increases the chance of a honeymoon phase [ 39 ], assists patients to preserve greater C-peptide ranges [ 40 , 41 ], and reduce mortality rate [ 42 ], indicating that patients who are treated earlier will have improved long-term outcomes. In addition, genetically at risk children of DAISY (Diabetes Autoimmunity Study in the Young) cohort had lower HbA 1C levels maintained within the normal range, a figure much lower than the average HbA 1C levels of T1D children in the community [ 43 , 44 ]. Also, only 3% of the DAISY children were hospitalized at T1D diagnosis compared to 44% of matched children in the community [ 44 ]. The DKA levels was detected in around 30% of the participants of the SEARCH for diabetes in youth study [ 45 ], while the same marker observed in lower prevalence in children screened positive for islet autoantibodies followed by German BABYDIAB and Munich family study [ 46 ].

Children followed by Diabetes Prediction in Skåne (DiPiS) study experienced decreased HbA 1C up to 24 months after the diagnosis against similar daily insulin dose requirements [ 47 ].

The predictable progression of T1D from early stages of autoimmunity to dysglycemia ahead of the symptomatic clinical disease could ease the design of reliable clinical trials using intermediate endpoint that require ~ 50% smaller sample size that those using T1D as the endpoint. In TrialNet natural history study, diabetes- related autoantibodies were analyzed in relatives of T1D patients in respect to elevated HbA 1C, decreased C-peptide following oral glucose tolerance test (OGTT) value as intermediate markers of T1D progression [ 48 ]. Also, the TrialNet CTLA4-Ig (abatacept) ongoing trial designed to test whether intervention with Abatacept could prevent or delay the development of abnormal glucose tolerance (AGT) in at-risk relatives of T1D patients [ 49 ]. Combined predictive risk score for an improved prediction of disease progression by incorporating fixed and variable factors (genetic, immunologic and metabolic markers) in newborn screening to prevent DKA and to enhance personalized risk predication for better T1D prevention trial selection [ 50 , 51 ]. The crucial benefit of utilizing this staging system is to aid in development of innovative, stage-specific diagnostic and predictive biomarkers, support the design of clinical trials that utilizing the available data on risk profiles and individuals’ pre-symptomatic classification to design therapies specifically targeted to each phase of disease and ultimately, practice of personalized medicine approaches to avert symptomatic T1D. Future research will be needed to identify the main drivers of the transitions between stages in order to identify novel therapeutic targets to prevent the emergence of T1D in high-risk populations.

Diagnostic sub-groups within symptomatic T1D

Diagnosis of T1D has historically been made on the basis of detecting blood glucose dysregulation; however, this has led to patients with diverse underlying pathologies being grouped, and treated, together. Evidence of β-cell destruction via the presence of anti-islet-autoantibodies (which may recognize insulin, Glutamic Acid Decarboxylase 65(GAD65), zinc transporter isoform 8 (ZnT8), or islet cell antigen (ICA512) and the age at which initial autoantibodies were detected are important factors that characterize the “classical” etiological subtype of T1D. However, less frequently, hypoglycemia might be caused by loss of function or de novo mutation in a sporadic gene, giving rise to monogenic diabetes, which represents 3% of all diabetes cases in children and adults [ 52 ]. The heterozygous activation of genes encoding the ATP-sensitive potassium-channel subunit Kir6.2 reported to cause permanent neonatal diabetes in addition to some neurological abnormalities in some affected individuals. Distinguishing monogenic diabetes from T1D is crucial for accurate diagnosis, applying the correct treatment “such as sulfonylureas in Kir6.2 mutation”, and in the future, stratifying these patients into a group most likely to benefit from gene therapy targeting the mutation.

The aim of increasing correct diagnosis of classical versus monogenic T1D has been assisted by the introduction of the genomic risk score (GRS), which assesses an individual’s risk of T1D based on their possession of a collection of multiple (10–40) T1D risk variants [ 53 , 54 ]. The GRS also effectively identifies those individuals with early-onset or pre-clinical T1D who show more autoimmunity and fewer syndromic features in comparison with those of monogenic diabetes [ 55 ]. The sensitivity and specificity of the T1D-GRS exceeds 80% [ 55 ], but this figure might reasonably expect to be increased when the GRS is combined with the available clinical data and autoantibody results. Accordingly, incorporating the T1D-GRS into strategies aimed at intervening in the pre-symptomatic T1D stages noted above (Fig.  1 , [ 31 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 ]) is likely to prove productive in the development of personalized diabetes-preventative therapies targeting either mutational correction or prevention of overt autoimmunity.

Somewhat surprisingly, T1D and type 2 diabetes (T2D) are often distinguished based on whether the person exhibiting blood glucose dysregulation is young and a healthy weight (T1D-typical), or instead an older adult with obesity (T2D-typical). However, these two manifestations have different causes and medication requirements [ 80 ]. Research in 2017 found that approximately 40% of people who developed T1D after the age of 30 were initially diagnosed and treated for T2D [ 81 ]. Given the potentially life-threatening nature of insulin-deficiency status [ 81 , 82 ], these findings call for increased use of autoantibody testing to discriminate T1D and T2D, and widespread recognition of the fact that clinical features alone cannot reliably distinguish these two conditions.

Current advances in affordable high-throughput genomic and molecular deep phenotyping technologies have pushed the rise of “next-generation epidemiology” with a more systematic focus than before. In particular, deep phenotyping can be described as the precise and broad analysis of phenotypic data to aid in identifying disease biomarkers that assist the prediction, prevention and disease monitoring [ 83 ]. Recently, an integrative multi-omics approaches were used on the Environmental Determinants of Diabetes in the Young (TEDDY) children, a prospective longitudinal birth cohort created to study T1D by following children with high genetic risk [ 84 ]. The analysis identified a multi-omics signature that able to predict the IA before seroconversion in one year, in addition, defects in lipid metabolism, problems with nutrient absorption, reactive oxygen species (ROS) detected prior to the IA progression.

In conclusion, identification of high risk for T1D genetic groups in the pre-symptomatic stages, coupled with the use of autoantibody testing, GRS and molecular deep phenotyping through utilizing the advanced integrative data analysis, could support the development of approaches for early diagnosis and treatment of T1D in both symptomatic and pre-symptomatic patients. This strategy could form the mainstay of accurate “personalized diagnoses” moving forward. Understanding the genetic etiology and specific pathophysiology of these distinct patient groups within the T1D family will be necessary for the rationale design and application of personalized therapies in the future.

Personalized treatment of T1D

Progress in recognition of the need for personalized diagnosis in T1D has been accompanied by intense research efforts towards personalized therapies. Before the discovery of insulin in 1921, it was remarkable for T1D patients to live more than one or two years after disease onset: one of the twentieth century’s utmost medical breakthroughs, insulin replacement, is still the mainstay of treatment for the vast majority of T1D patients today. That said, innovative ways of achieving improved insulin-mediated glycemic control are becoming accessible to patients, while tissue transplants, genetic modification and stem-cell therapies are showing promise in pre-clinical models and human trials in specific sub-groups of patients. In this section we will discuss the “old and new” of T1D therapies and moves towards personalization to increase treatment efficacy.

Insulin and combination drug therapies

By far, the most common T1D treatment approach is manual testing of blood sugar levels followed by sub-cutaneous injections of insulin, repeated throughout the day. Insulin pumps may be used in place of traditional injections [ 85 ]; these have the advantage of being able to continuously infuse small amounts of insulin sub-cutaneously, helping those patients with difficult-to-control glucose levels to better treat their disease. This is especially the case when coupled with continuous glucose monitoring (CGM) technology, which has been shown to improve control of blood glucose, thereby reducing long-term risks of diabetic complications [ 86 , 87 ].

Taking the combination of CGM and continuous insulin infusion to the next level is the advent of the artificial pancreas. By utilizing a CGM coupled via a control algorithm to an implanted insulin pump, people with T1D can achieve improved glycemic outcomes while reducing the burden of self-management [ 88 , 89 , 90 ]. A closed-loop artificial pancreas approach removes the need for the patients to manage their dosages at all, and some models also incorporate the pancreatic hormone glucagon, enabling glucose-responsive hormone delivery guided by real-time glucose sensor readings. This approach has the potential to accommodate highly variable day-to-day insulin/glucagon requirements. There will be a shift toward systems that offer more personalization, and individualization of adjusting parameters, glucose set algorithm aggressiveness proposed to be individualized including the daily targets [ 91 ] that can ensure tight glycemic control in affected patients [ 92 , 93 ]. Despite these advantages, still relatively few T1D patients are using an artificial pancreas, with the main obstacles being cost of the equipment, the need for a training infrastructure for users and clinicians, and a lack of clarity around which patient groups would benefit most from this technology (reviewed in [ 92 ]). In this case the technology has preceded the clinical sub-group analysis required to identify the patient groups who are most suited to the approach, calling for urgent research in order to fully exploit this important advance in insulin-replacement therapy.

Alongside developments in insulin replacement therapy, there has been a focus on identifying other drugs that can be combined with insulin to reduce hyper/hypoglycemia and improve metabolic variables without increasing adverse events (reviewed in [ 94 ]). Obese/T1D patients who predisposed to hypoglycemia and others with residual β-cell function could benefit from non-insulin antidiabetic drugs for future clinical trials [ 94 , 95 ]. Of these, promising candidates include metformin [ 96 ] and pramlintide, which have a role in glycemic control in both T1D and T2D and can modestly reduce triglyceride levels in T1D patients, as well as lowering hemoglobin A1c (HbA1 c ) and supporting weight loss [ 97 ]. In addition, glucagon-like peptide-1 receptor agonists (GLP-RAs) combined with insulin can reduce the daily bolus insulin dose required and improve glucose control and weight loss [ 98 ]. The incretins glucagon-like peptide 1 (GLP-1) is gut-derived hormone secreted upon food ingestion. The key physiological actions of GLP-1 are to accelerate nutrient-induced insulin release and inhibit glucagon secretion, in that way contributing to regulate postprandial glucose excursions [ 99 ]. In addition, other functions represented by inhibition of gastrointestinal motility and therefore works as “enterogastrone”, a hormone released by the lower gastrointestinal tract in reaction to lipids intake that constrains the caudal motion of the guts of chyme [ 100 ]. GLP-RAs used peripherally or centrally reduce food intake and escalate glucose-stimulated insulin secretion. The enzyme dipeptidyl peptidase-4 inhibitors (DPP-4) prevents the inactivation of GLP-1 and an adjunct therapy in a closed loop-system that can reduce postprandial blood glucose levels [ 101 ] and can significantly reduce the daily insulin dose but not the HbA1c level or the risk of hypoglycemia [ 102 ]. The DPP-4 enzyme is widely released in multiple organs and acts by cleavage of the two NH 2 -terminal amino acids of bioactive peptides if the second amino acid is alanine or proline [ 103 ]. It functions through affixed transmembrane fragment and a soluble protein. Both transmembrane fragment and soluble DPP-4 apply catalytic cleavage which alternatively inactivates peptides or generates new bioactive moieties that may exert competing or unique functions. Finally, sodium-glucose co-transporter inhibitors (SGLTi) are associated with improved glycemic control and a reduced insulin dosage leading to lower rate of hypoglycemic episodes [ 104 ]. In non-diabetics, approximately, 180 g of glucose is filtered diurnal through the renal glomeruli and is then re reabsorbed in the proximal convoluted tubule (PCT). This mechanism attained by inactive transporters, specifically, facilitated glucose transporters (GLUTs), and by active co-transporters, precisely, sodium-glucose co-transporters (SGLTs). SGLT1 and SGLT2 are considered most important out of the six identified SGLTs [ 105 ]. SGLTi acts by inhibiting SGLT2 in the PCT to block glucose reabsorption and ease its secretion in urine. The plasma glucose levels drop resulting in an improvement in the entire glycemic parameters [ 106 ].

In summary, traditional and combined approaches to insulin therapy remain important tools in the treatment of T1D, but they do not represent a cure and may not be able to achieve the level of glucose control necessary to avoid long-term complications arising from diabetes. Automated full closed-loop systems that can be programed to automatically manage meals may substantially benefit from faster acting insulins with a shorter duration of action. Proposing automatic flexibility to the individual’s changes not only daily patterns of insulin sensitivity but also to mechanically adjust to changes developing from illness, workout practices, eating routines and menstrual cycles. With the applications of machine learning (artificial intelligence), (AI), the future devices with the AI technologies could achieve the above relationship and to provide treatment suggestions and decisions based on the available data input. A unique and individualized predictive and decision support models using complex machine learning software and algorithms developed for insulin pumps for easier use and much more spontaneous daily life. Recently, Tyler et al. (reviewed in [ 107 ]) reported an algorithm for early recognition of unsafe insulin regimens which could be useful for improvement the glycemic results and minimize the dangerous complications of T1D [ 107 ]. Briefly, the algorithm offers weekly insulin dosage recommendations for adult patients with T1D using multiple daily injections protocol of long-acting basal and short-acting bolus insulin [ 108 ]. The hyperglycemia or hypoglycemia causes identification performed through validated single and dual hormone mathematical models that demonstrate a virtual platform of T1D patients [ 109 ]. The novel “virtual platform” employed to generate glucose observations used to train “decision making system”, which appeared to be in agreement with the endocrinologists’ decision of 67.9% when confirmed on actual human data [ 107 , 110 ]. In conclusion, such data provides guidance to physicians and T1D patients in effective use of insulin pumps data including but not limited to insulin dosing adjustments and other treatment decisions. It’s worth to mention how crucial that both physicians and diabetic patients understand the usefulness and limitations of insulin pumps and related treatment technologies. Sustaining the relationship between both will remain a critical factor in safe, thriving T1D treatment technology use.

• Gene therapy

Given the strong genetic component of T1D development, gene therapy offers a promising alternative to insulin injection for T1D treatment. Gene therapy is the procedure of transporting or manipulating genetic substances inside the cell as a therapeutic technique to cure disease [ 111 ]; it aims to modify faulty genes that are accountable for disease progression and thereby prevent disease onset or reverse its development (Fig.  3 ). The three key methodologies in gene therapy are: (I) introducing a new gene into the body (II) substituting defective genes with functional genes, and (III) deactivating the faulty genes triggering the disease [ 112 ]. Pre-clinical trials of gene therapy have now been tested with the aims of preventing or delaying onset of T1D, correcting insulin deficiency, promoting β-cell proliferation and survival, modulating the immune/inflammatory response or inducing insulin secretion by non-β cells (reviewed in [ 113 ]).

How genes are delivered to the human body during gene therapy approaches. Gene therapy have utilized two major approaches for transferring therapeutic transgenes into recipients 'body. First approach, is by direct infusion of the therapeutic gene into human body through a vehicle. Altered viruses often used for delivering the gene into specific human cell types. This method is inexact as it is limited to specific cell types that the viral vehicle can infect. Nonviral vehicles for directly delivering genes into cells are also being explored, including the use of plain DNA and DNA wrapped in a coat of fatty molecules known as liposomes. Th second approach utilize a living cells to transfer the therapeutic transgenes into recipients 'body. The transferring cells often a type of stem cell that removed from the body, and the therapeutic transgene is presented to them through direct transfer method. The genetically altered cells then grow and multiply before infused back to the recipient

Over the last few decades, gene transfer trials for the treatment of inherited or acquired diseases have mainly been performed in mice models. Non-obese diabetic (NOD) mouse has been the main animal model for studying autoimmune T1D. A key element of NOD model is the presence of spontaneous autoimmunity and T1D. The incidence of T1D is higher in females in NOD mice, [ 114 , 115 ], and is stated to have a minor prevalence in males in humans [ 116 , 117 ]. Like human, NOD mice develop autoantibodies and show elevated levels of autoreactive T-Cells ahead of disease onset [ 118 , 119 , 120 ]. The targeted antigens of β cell are also similar of both species, however, in the NOD mouse, the insulin seems to be the initiating antigen, while in human T1D, several antigens thought to be involved in this stage [ 118 ]. Gradual β cell death or malfunction, and autoimmune phenotypes shadowed by the onset of hyperglycemia exist in both human and NOD mouse [ 121 ], however, the appearance of pathogenic T cells have been noticed at 5-week-old NOD mice followed by insulitis throughout the pancreas by 12 weeks, reflecting the very aggressive nature of disease onset hits in shortened timeline (weeks only), compared to slower onset in humans (years after the autoantibodies appearance) [ 122 , 123 ].

The paradoxical assumption is that preventing T1D in NOD mice does not certainly convey what triggered the disease nor how to converse it. The NOD mouse model could be suitable to understand the genetic and immunologic features and causes of T1D including reversing the hyperglycemia when occurs. The model could serve as an approach to identify causative gene variants that can be tailored to discover novel therapeutic approaches for reversing new-onset T1D.

One particularly interesting strategy is the induced over-expression of insulin-like growth factor 1 (IGF1), which regulates immune functions and enhances the survival and proliferation of β-cells. Non-obese diabetic (NOD) mice spontaneously develop diabetes from around 10 weeks-of-age; however, when 4-week-old NOD mice underwent intra-ductal injection of an adeno-associated virus (AAV) encoding IGF1 to specifically transduce pancreatic cells, normoglycemia remained in 80% of these mice at week 28 [ 124 ]. Importantly, the same study also showed that treating NOD mice with the IGF1-encoding virus at 11 weeks-of-age, by which time significant β-cell destruction was evident, was able to re-establish lasting normoglycemia in 75% of mice [ 124 ].

In other animal studies, induced expression of regenerating islet-derived protein 3 gamma (Reg3g) has been reported to be able to regenerate β cells and preserve the cells despite autoimmune attacks [ 125 , 126 ]. Alongside, another study demonstrated the dynamic regulation of blood glucose levels in a model of T1D by stimulating the expression of glucose 6-phosphatase (G6Pase) in the liver of diabetic rats [ 127 ]. Here, expression of the G6Pase gene was induced by rising glucose levels and inhibited by insulin expression; in addition to achieving normoglycemia within a few hours of eating, no hypoglycemia was observed in the tested animals [ 127 ].

Gene therapy can also be used to induce insulin production in non-β-cells. Initial studies conducted on genetically engineered intestinal K cells [ 128 ] and hepatocytes showed that these cells were sensitive to glucose and could be induced to produce insulin. More recently, Jaen et al. demonstrated that a single injection of an AAV encoding insulin and glucokinase genes into skeletal muscle of diabetic dogs was able to induce metabolic normalization and normoglycemia lasting 8 years [ 129 ]. This study represents an important safety and efficacy step forwards for diabetes gene therapy, as although AAV vectors have been trialed in humans, their therapeutic use for gene transduction has yet to be tested clinically. There are concerns that transduced cells might be susceptible to recurring autoimmune attack, so enduring autoimmune protection must be demonstrated [ 130 , 131 ]. It is also possible that the viral vectors themselves might trigger an immune response that could worsen the disease condition [ 132 ], though Jaen et al. did not report any evidence of this in their study [ 129 ]. Modifications to the AAV vectors might hold some of the answers: in response to concerns that constitutive over-expression of insulin might risk hypo-glycaemia, one group has developed a Tet-off regulatable AAV vector for insulin expression that was able to both induce the expression of human insulin in diabetic mice, and be reversibly switched off to reduce insulin levels [ 133 ]. Thus, fine tuning of viral vectors combined with more long-term studies will be required to move towards vector-mediated reinstatement of insulin production in human patients.

In addition to induced insulin expression, several studies have looked at other targets implicated in T1D pathogenesis. For example, Klotho is an anti-aging gene that is expressed in pancreatic islets in mice [ 134 ] and humans [ 135 ]; a Klotho deficiency is linked with β-cell apoptosis, and reinstating its expression in mice under the control of a β-cell-specific promoter led to protection of β-cell function [ 134 ]. In human islet cells, treatment with the T1D drug gamma-aminobutyric acid in vitro significantly increased Klotho expression [ 136 ], indicating the possible clinical potential for this approach. A study by Flotyńska et al. demonstrated the relationship between fibroblast growth factor 23 (FGF23)/ Klotho system as a player in the human body metabolism, in addition to promoting longevity [ 137 ]. Despite the improvements in diabetes treatment, the long-term complications remain a big problem. The interesting correlation between the FGF23/Klotho system concentration and T1D management, duration, insulin resistance, and complications development require further attention and could be a predictor of cardiovascular risk in diabetic patients [ 138 ]. Combining gene therapy with immune modulation may also be promising. When NOD mice were pre-treated with anti-T-cell receptor β chain monoclonal antibody followed by hepatic gene therapy with Neurogenin-3 (which determines islet lineage) and the islet growth factor betacellulin, the researchers observed sustained induction of insulin-producing cells in the liver that achieved enduring reversal of new-onset or overt diabetes [ 139 ].

The discovery of β-cell mitogenic effects of ANGPTL8 (Angiopoietin Like 8), which was renamed “Betatrophin” to underline its effect on β cell replication, initially, created large interest but consequently, have been subjected to substantial debate regarding its anticipated mitogenic effects [ 140 ]. The initial findings proposed that the over expression of ANGPTL8 in mice model stimulated a 17-fold increase in pancreatic β-cell proliferation [ 140 , 141 ]. Consequent research studies in mice disputed this statement as no substantial evidence could be observed to support the direct effects of ANGPTL8 on beta-cell proliferation [ 140 , 142 , 143 ], Therefore, ANGPTL8 is not considered as a potential agent for diabetes intervention although some reports supported the initial observations in rats [ 144 ]. In a study performed by Chen et al. (reviewed by [ 144 ]), targeted gene delivery approach has been used to deliver human ANGPTL8 gene plasmids to different organs of normal adult rats including the pancreas, liver and skeletal muscles and compared the efficiency of beta β cell replication induced by ANGPTL8 gene using the rat model of streptozotocin (STZ)-induced diabetes. The improvement in glucose tolerance plus the elevated fasting plasma insulin levels were directly associated with β cell proliferation. A novel gene therapy technique used here through targeting the transfer of non-viral DNA to the pancreatic islet by using ultrasound-targeted microbubble destruction (UTMD) beside an altered insulin promoter [ 140 , 145 ]. UTMD considered as promising method for target-specific gene delivery, and it has been successfully investigated for the treatment of many diseases in the past decade including cardiovascular disorders and cancer.

A novel approach to gene therapy for T1D involves targeting post-transcriptional modifications that give rise to pathogenic splice variants. Cytotoxic T-lymphocyte–associated antigen-4 (CTLA-4) is an immune-modulatory protein where expression of different forms has been linked to T1D susceptibility or resistance in T1D patients [ 146 ] and some other autoimmune diseases [ 147 ]. To modulate the immune response leading to T1D onset, Mourich et al. employed an antisense-targeted splice-switching approach to produce CTLA-4 splice forms in NOD mouse T-cells [ 148 ]. In this study, when the antisense approach was used to mask pre-mRNA splice recognition sites and redirect the splicing machinery to skip selected exons, induced over-expression of the protective ligand-independent form of CTLA-4 protected NOD mice from disease [ 148 ].

Lastly, while these studies clearly indicate the exciting potential of in vivo gene therapy, the process remains complex, in addition, the possible toxicity of the viral vectors and the improvements needed to the delivery systems to achieve the maximum levels of gene expression still under development [ 125 ]. That said, twenty gene and cell-based gene therapy products have now been licensed for the treatment of human cancers and monogenic disorders “e.g., Neovasculgen (Vascular endothelial growth factor, VEGF), Glybera (lipoprotein lipase, LPL S447X gene), Defitelio (single-stranded oligonucleotides-VOD), Rexin-G (Retroviral vector encoding cyclin G1 inhibitor), Onpattro (RNAi-transthyretin gene)” and clinical trials in these diseases are ongoing [ 149 ]. There is real hope that effective approaches to direct gene therapy for T1D patients, particularly those with monogenic T1D, will be developed in the near future, building on its success in other conditions.

Stem cell therapies

Perhaps the most promising innovation in T1D therapy has been the exploration of the potential of stem cells. This unique population is able to self-renew indefinitely, form single cell-derived clonal cell populations, and differentiate into various cell types [ 150 ]. Stem cells from diverse sources have now been investigated for their potential in β-cell regeneration, as discussed below.

Embryonic stem cells

Embryonic Stem Cells (ESCs) are derived from the undifferentiated inner cell mass of human embryos and have the advantage of being completely pluripotent. Several different approaches to generating insulin-producing cells (IPCs) from ESCs have been explored. Human Embryonic Stem Cells ESCs (hESCs) in feeder-free cultures avoid the risk of animal pathogen transfer and are readily scalable, making this approach best-suited to clinical use [ 151 ].

Kroon et al. instructed the differentiation of hESCs by directly overexpressing essential β-cell transcription factors (TFs) including Pancreatic and Duodenal Homeobox 1 (PDX1), SRY-Box Transcription Factor 9 (SOX9), Homeobox protein Nkx-6.1 (NKX6.1) and Neurogenin 3 (NGN3; following engraftment into diabetic mice, the resulting cells recapitulated key features of pancreatic β-cells and protected against hyperglycemia [ 152 ]. Subsequently, an important step forwards in the use of hESCs for T1D therapy occurred when scientists from the University of British Columbia developed a seven-stage protocol that efficiently converted hESCs into IPCs. This protocol generated endocrine cells with insulin content similar to that of human islet cells and that were capable of glucose-stimulated insulin secretion in vitro as well as rapid reversal of diabetes in vivo in mice [ 153 ]. Additional studies have highlighted the possible roles of other growth and extracellular matrix factors, including laminin, nicotinamide, insulin [ 154 ], and retinoic acid [ 155 ] in the generation of IPCs from ESCs, but these findings have yet to be integrated into a combined approach suitable for clinical use.

hESCs also have the potential to generate cells uniquely tailored for the recipient. Recently, Sui et al. showed that transferring the nucleus of skin fibroblasts from T1D patients into hESCs gave rise to differentiated β-cells with comparable performance to naturally occurring β-cells when transplanted into mice [ 156 ].

Despite the promise of hESCs, great concern around their potential to initiate teratomas has largely limited their clinical exploration in T1D. However, Qadir et al. recently demonstrated a means of overcoming this risk: the authors modified hESCs to include two suicide gene cassettes, whose expression results in cell death in the presence of specific pro-drugs [ 157 ]. Their method is designed to provide a double fail-safe control, such that I) only IPCs survive selection; and II) cells that may de-differentiate after transplantation can be eliminated. Furthermore, ensuring that undifferentiated cells are sensitive to two pro-drugs makes it less likely than any tumorigenic cells would survive or became resistant [ 158 ].

Human pluripotent stem cells

Naturally, Human Pluripotent Stem Cells (hPSCs) are immature cells that have the capacity to become nearly any cell type in the body. Accordingly, there has been much research interest in using them to regenerate a wide range of tissues, including the pancreas. Under the control of specific growth factors, signaling pathways and activating/inhibitory molecules [ 159 , 160 ] the steps of pancreatic cell differentiation have been successfully recreated in vitro.

The importance of this approach is its potential to generate a ready supply of in vitro-differentiated β-cells for transplantation into T1D patients. Recent studies have reported the successful differentiation of β-like cells with enhanced function from pancreatic progenitors through modulating Epidermal growth factor beta (EGF-β) signaling and cellular cluster size, giving rise to stem cell-derived β-cells with the ability to express key β-cell markers and insulin [ 161 , 162 ]. What remains unclear is how well these in vitro-derived cells will function in vivo , but this is nonetheless a promising first step.

Hematopoietic stem cells

Taking a different approach, myeloablation coupled with autologous Hematopoietic Stem Cells (HSCs) transplantation aims to halt the autoimmune destruction of the pancreas and reestablish tolerance. The first autologous HSCs transplantation in a T1D patient was executed by the Voltarelli’ group in 2007: 15 patients aged between 14 add 31 years, and with recent T1D onset (previous 6 weeks) diagnosed by clinical findings, hyperglycemia and GAD65 autoantibodies were involved in the study [ 163 ]. When these patients were treated with autologous HSCs, most achieved insulin independence with good glycemic control lasting until the final 29.8-month follow-up, together with a notable increase in β-cell function [ 164 ]. Autologous HSC transplantation has also been used successfully to treat diabetic sequelae, including vascular complications [ 165 ] and retinopathy [ 166 ]. Other studies have focused on understanding the mechanisms underlying successful HSCs transplantation in T1D: for example, Ye et al., found that autologous HSC treatment was associated with the inhibition of T-cell proliferation and pro-inflammatory cytokine production [ 167 ]; while Xiang et al. uncovered a critical role for the remaining functional β-cells on the autologous transplant of HSCs [ 168 ].

Despite the evident successes of autologous HSCs transplantation for T1D, various complications can occur, ranging from relatively mild symptoms such as febrile neutropenia, nausea, and alopecia to more severe complications such as de novo autoimmunity and systemic infections, which in one case resulted in death [ 169 , 170 ]. The development of new strategies involving autologous HSCs therapy for newly-diagnosed T1D patients coupled with appropriate and effective use of immunosuppressive drugs will be crucial to maximize the frequency and function of T and B regulatory cells, while minimizing the activity of autoreactive islet-specific T and B memory cells. In this way, we should be able to improve treatment outcomes in T1D patients undergoing transplantation.

Mesenchymal stem cells

Mesenchymal Stem Cells (MSCs) are multi-potent stromal cells able to differentiate in vitro into a range of cell types; characteristically adipocytes, chondrocytes, myocytes, and osteoblasts [ 171 ]. MSCs are relatively easy to isolate from different sources in the body and numerous studies have assessed their use in T1D therapy.

Historically, the bone marrow has been the main source of MSCs [ 172 ]. Xie et al. first trialed generating IPCs from T1D patients’ bone marrow MSCs (BM-MSCs) and showed the co-expression of insulin and C-peptide in cells injected into diabetic mice, leading to attenuated hyperglycemia [ 173 ]. Alongside, genetically-modified human BM-MSCs expressing VEGF and PDX1 reversed hyperglycemia in more than half of diabetic mice and enabled survival and weight maintenance in all animals [ 174 ]. These promising pre-clinical results led to human trials: when BM-MSCs were injected into the splenic artery of T1D patients, they induced an increase in C-peptide levels that was maintained for 3 years; unfortunately, this had no significant effects on glycemic control due to insufficient production of insulin by the grafted cells [ 175 ]. Since then, new methods have been developed aiming to improve in vivo outcomes. For example, Zhang et al. co-cultured BM-MSCs with pancreatic stem cells which led the MSCs to adopt a pancreatic islet morphology; when these cells were injected into diabetic rats they attenuated glycated albumin levels and significantly increased serum insulin and C-peptide [ 176 ].

The main disadvantage of BM-MSCs is the difficulty in isolating the cells and the morbidity associated with the procedure. These issues led to interest in the use of Muscle-Derived Stem/Progenitor Cells (MDSPCs), which exist in skeletal muscle and have the capacity for long-term proliferation, are resistant to oxidative and inflammatory stress, and show multi-lineage differentiation potential [ 177 ]. To investigate the therapeutic potential of autologous MDSPCs transplantation for T1D, Lan et al. applied a four-stage MDSPCs differentiation protocol to generate IPCs in vitro and injected them into diabetic mice: these β-cell-like-cells effectively improved hyperglycemia and glucose intolerance and increased the survival rate in diabetic mice without the use of immunosuppressants [ 178 ].

Building on the promise of BM-MSCs and MDSPCs, researchers sought an equally potent but more abundant and easily accessed source of stem cells. Adipose-Derived Stem Cells (ADSCs) have recently been explored for T1D treatment, and have the advantage over MDSPCs of being readily accessible and harvested, even in older patients [ 179 ]. IPCs differentiated from ADSCs show significant expression of β-cell markers, insulin and c-peptide following transfer into diabetic mice [ 180 ]. In 2019, IPCs derived from ADSCs using a novel three-dimensional (3D) xenoantigen-free protocol were shown to exhibit key features of pancreatic β cells in vitro and differentiated into IPCs in diabetic nude mice in vivo [ 181 ]. Another study showed the potential for combining ADSCs treatment with gene therapy by transducing ADSCs with a furin-cleavable insulin gene (INS-FUR), which led to enhanced insulin expression in the differentiated adipocytes, and alleviated hyperglycemia in diabetic mice [ 182 ].

Removing the need for adult stem cell donors completely, the umbilical cord is now used as a successful alternative stem cell source for regenerative medicine. Umbilical cord blood (UCB) is rich in HSCs, can be easily harvested without the need for interventions, and also contains a large number of naive functioning T-regulatory cells (Treg) with the potential to reduce autoimmunity [ 183 , 184 ]. Moreover, the MSCs within UCB (UCB-MSCs) have high proliferative capacity, are easily bankable and have low tumorigenicity [ 185 ]. Together these features are making UCB-MSCs the preferred option for potential T1D cell-based therapies. Studies in animal models have showed encouraging results: when Prabakar et al. adapted an ESC protocol for IPC culture and applied it to UCB-MSCs they generated expanded populations of undifferentiated IPCs expressing the key pancreatic TFs PDX1, NGN3, Neuronal Differentiation 1 (NEUROD1), NKX6.1, and Insulin Gene Enhancer Protein ISL-1 “ISL LIM Homeobox 1” (ISL1) [ 186 ]. Following transplantation into mice, these cells subsequently differentiated into glucose-responsive IPCs [ 186 ]. Zhao et al. took a different approach to exploiting stem cells for T1D treatment, instead focusing on their capacity to downregulate immune responses. The authors achieved reversal of the autoimmune response in NOD mice by transferring autologous Tregs that had been co-cultured with human UCB-MSCs; this led to increased insulin secretion, reduced hyperglycemia and preservation of islet architecture [ 187 , 188 , 189 ].

Despite promising signs in rodent studies, the potential of UCB-MSCs treatment for T1D in humans has yet to be fully realized. Haller et al. attempted the first autologous UCB-MSCs transplantation in recently-diagnosed T1D patients in 2008: early indications were encouraging, with transplanted patients showing slowed loss of endogenous insulin production and an increase in peripheral blood Treg cells after 6 months [ 190 ]. However, a subsequent study by the same group found no significant difference in C-peptide levels after autologous transfusion of UCB-MSCs combined with oral docosahexaenoic acid and vitamin D supplementation [ 191 ]. Similarly, in a non-randomized controlled trial in seven new-onset T1D children who underwent autologous UCB-MSCs infusion, there was no evidence of improvements in metabolic regulation or immune function at the one-year follow-up [ 192 ].

The possible reasons for the failure of UCB-MSCs to effectively halt the autoimmune progression in human subjects’ trials, could be the inadequate number of cells with immunomodulation capacity being transferred to T1D patients, or due to the ongoing autoimmune reactions especially in new-onset T1D patients that may comprise memory T-cells, refractive to regulation by Tregs, that enhance the autoimmune destruction of β-cells [ 193 ]. Merging transient immune depletion agents with consequent infusion of expanded UCB Tregs may effectively balance the environment of Tregs and effector T cells in T1D patients. Finally, more controlled and randomized clinical trials are crucial to further improve the transplantation process and to investigate the mechanism of UB-MSC survival and behavior in live bodies overtime. Further investigations with larger sample sizes will be important to understand how to translate the successful application of UCB-MSCs infusion from mouse to human.

Cord blood is not the only source of stem cells within the human umbilical cord; Wharton’s jelly is a mucoid connective tissue in the umbilical cord that can also serve as a source of clinically-relevant MSCs (Wharton’s jelly-derived mesenchymal stem cells, WJ-MSCs) for both IPC derivation and immunosuppression [ 194 ]. Briefly, WJ-MSCs collection occurs at the time of delivery and avoids the known adverse effects associated with adult stem cell collection from the bone marrow or adipose tissue. Furthermore, features including a high WJ-MSCs proliferation rate, an immune privileged status, minimal associated ethical concerns, and non-tumorigenic capacity render these cells an excellent option to be used in regenerative medicine applications [ 195 ].

One of the first studies to use β-cell-like cells derived from WJ-MSCs tested their effects following transplantation into patients with new-onset T1D [ 196 ]. Interestingly, a concurrent study suggested that the WJ-MSCs might restore the function of β-cell in T1D patients but it could be affected by the patient’s ketoacidosis history [ 197 ], though the underlying mechanism to support this has not yet been tested. A genetically and chemically combined approach for WJ-MSCs induction into IPCs has also been shown to improve the cells’ homing efficiency to the pancreatic gland of diabetic rats [ 198 ]; taken together with a growing body of clinical data, these findings may help optimize the use of differentiated WJ-MSCs in T1D.

Undifferentiated WJ-MSCs also have the capacity to induce a protective immune-suppressive state in animal models of T1D and in patients. A study in mice performed by Tsai et al. showed that undifferentiated WJ-MSCs implanted into NOD mice both differentiated into IPCs in vivo, leading to islet repair and maintaining levels of C-peptide and insulin production, and induced beneficial immunosuppression [ 199 ]. Such evidence in rodents has since led to the initiation of human trials. A safety and dose-escalation trial is ongoing: in the first stage, Carlsson et al. are carrying out WJ-MSCs allotransplantation into newly-diagnosed (< 2 years) T1D adult men with dose-escalation to establish safety parameters; in the second double-blinded, parallel, placebo-controlled stage, a cohort of T1D patients (men and women) will undergo WJ-MSCs allotransplantation aiming to achieve immunosuppression and preserve endogenous insulin production [ 200 ]. Altogether, comparing WJ-MSCs, UCB-MSCs [ 201 ] and BM-MSCs [ 202 ], it seems that WJ-MSCs are the better anti-diabetic agents, being more homogenous and having greater potential to initiate pancreatic regeneration.

Medical nutrition therapy in managing T1D

A healthy lifestyle including eating pattern beside pharmacotherapy are major components of managing T1D. For many diabetic patients, determining what to eat is the most challenging part of the treatment plan. Effectual nutrition therapy interventions may be an element of a comprehensive T1D education package or an individualized session [ 203 ]. Furthermore, T1D individuals on multiple daily insulin doses, the main focus for nutrition therapy must be on how to adjust insulin doses based on scheduled carbohydrate intake [ 204 , 205 ]. Reported HbA1 C from medical nutrition therapy (MNT) decreases are similar or greater than what would be expected with currently available pharmacologic therapies for T1D [ 206 ]. Rigorous insulin management education programs that include MNT have been shown to reduce HbA1 C up to 1.9% at 3–6 months, in addition to significant improvement in quality of life over the time [ 203 , 207 ]. There is no “one-size-fits-all” eating pattern that could work collectively for all T1D individuals, nutritional therapy should be individualized and supervised under the care of a dietitian based on the heath goals, personal favorites and access to healthy options should be considered [ 208 , 209 ].

Remaining obstacles and future directions

Marked progress has been made in the past decade towards both personalized diagnosis and treatment for T1D, but significant obstacles and research gaps remain between the current state of knowledge and its translation into widespread clinical benefit. As in many other diseases, the precision medicine for T1D is a new and growing field. Increases ethical, social and legal issues and the necessity to find precise ways to protect subjects’ privacy and confidentiality of their health data. In addition, patients need to know and understand the associated risks and expected benefits of being part of precision medicine research, which requires researchers to create a meticulous approach of obtaining informed consent to recruit participants to research studies. Furthermore, cost-effectiveness of precision medicine approaches comparing to the current standard of care is a gap that needs to be resolved. The impact of diabetes on healthcare systems has been evaluated as the largest contributor to entire healthcare costs. For example, in a study performed by Stedman et al. (reviewed in [ 34 ]), the differences between T1D/T2D and non-diabetes subjects in connection to hospital and associated costs in in England. In summary, T1D individuals demanded five times additional secondary care support than non-diabetes subjects. The analysis shows that extra cost of running of hospital services due to their diabetes comorbidities is £3 billion over that for non-diabetes, within this figure, T1D has three times as much cost impact as T2D, suggesting that supporting patients in diabetes management may considerably decrease hospital activity, in addition, the possibility and potential for precision treatment in diabetes is massive, yet profound understanding is missing. It will be vital to decide when and how the application of therapeutics in precision diabetes medicine improves outcomes in a cost-effective style.

Much of our current knowledge of personalized therapeutic approaches to treat T1D comes from experiments in animal models; but a recurring theme in the T1D therapy field is the lack of translation between promising results in mice and the same outcome in humans. Mice are most commonly used for these experiments but exhibit both macroscopic and microscopic differences in pancreatic physiology and T1D pathophysiology. For example, rodents islets have a distinct core structure comprising 60–80% β-cells, 15–20% α- cells, < 10% δ-cells and < 1% PP cells [ 210 , 211 , 212 ]; while human islets tend to have ~ 50% β-cells, ~ 40% α- cells, ~ 10% δ-cells and < 5% P-cells [ 213 , 214 ]. In addition, notable differences in the repertoire of receptors and long non-coding RNAs between mouse and human beta cells have been identified [ 215 ]. In terms of modeling T1D, the NOD mouse has long been the approach of choice for majority of pre-clinical and translational invasive studies [ 216 ]. The main strength of the NOD mouse is the presence of spontaneous autoimmunity leading to T1D [ 118 , 216 ] however, in the mice, this is triggered by the insulin antigen, while in humans this phenomenon is more complex, involving several inducing antigens followed by hyperglycemia [ 217 , 218 ]. Taken together, extreme caution must be exercised when attempting to draw conclusions from animal models and apply them to the human situation [ 219 ].

Despite advances in the various therapies discussed above, an ongoing challenge in T1D treatment is the extreme heterogeneity in patients’ disease triggers, prognosis, pathological pathways and thus the response to treatment [ 220 , 221 , 222 , 223 ]. Important research in human populations has revealed previously unappreciated heterogeneity within the T1D patient population. This has two major implications: firstly, that we are unlikely to discover a “one-size-fits-all” therapy able to cure every case; and secondly that personalized diagnosis is a necessary pre-requisite for personalized treatment. The first step towards this will be the routine assessment of T1D subtype in newly diagnosed patients, including screening for monogenic T1D as well as autoantibody testing to distinguish idiopathic T1D, and, in future, genetic profiling to inform potential gene therapy or stem cell approaches.

In diabetes, the precision medicine approach has been inspired by work including that of Zhao et al., who first developed stem cell educator therapy where T1D patients’ lymphocytes are briefly separated from the blood and co-cultured with UC-MSCs within a closed-loop-system, before being returned to the patient; this treatment dramatically improved metabolic control, reversed autoimmunity and promoted β-cell regeneration [ 143 ]. Al-Anazi et al. used a similar approach to try and treat multiple myeloma in 45 adults with T1D who had undergone autologous HSCs; surprisingly the patients were also cured of their diabetes and became insulin-independent [ 144 ].

In fact, the next step towards stem-cell-mediated precision medicine for T1D is likely to involve the incorporation of gene therapeutic approaches, synergizing existing stem cell knowledge with advances in cellular and genetic engineering techniques, such as nuclear transfer and genome editing. Moreover, an emerging understanding of the TFs and epigenetic processes that control pancreatic islet lineage-commitment [ 224 ], as well as the role of microRNAs in driving cell lineage differentiation [ 225 ] are beginning to unlock new knowledge on T1D pathogenesis [ 226 , 227 ], and are opening fresh possibilities in β-cell generation [ 228 , 229 , 230 ].

Together these factors can all be used towards designing a successful protocol for precision medicine in T1D. Alongside, the reframing of T1D as primarily a metabolic disorder (rather than an autoimmune condition) that reflects the combined genomic and environmental landscape of the patient, has facilitated the discovery of new therapeutic targets and diagnostic/prognostic biomarkers [ 231 , 232 ]. Finally, the ongoing discovery of new and important influences on diabetic pathology, such as the role of gut microbiota [ 233 ], and the latest perceptions into the mechanism of T1D and the accumulated recent data that being translated into prospects for tissue-specific prevention trials toward eliminating progressive β-cell loss [ 234 ], continues to add to our understanding of this important disease, and thereby our ability to rationally design and test novel interventions with the promise of the future eradication of T1D.

Availability of data and materials

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Acknowledgements

We wish to thank Lucy Robinson of Insight Editing London for assistance with editing support and critical reading of the manuscript prior to submission.

This research was funded by Sidra Medicine through its Precision Medicine Program Grant—SDR#400149, Doha, Qatar.

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Department of Human Genetics-Precision Medicine Program, Sidra Medicine, P.O. Box 26999, Doha, Qatar

Ammira Al-Shabeeb Akil, Esraa Yassin, Aljazi Al-Maraghi, Elbay Aliyev, Khulod Al-Malki & Khalid A. Fakhro

Department of Genetic Medicine, Weill Cornell Medicine, P.O. Box 24144, Doha, Qatar

Khalid A. Fakhro

College of Health and Life Sciences, Hamad Bin Khalifa University, P.O. Box 34110, Doha, Qatar

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Akil, A.AS., Yassin, E., Al-Maraghi, A. et al. Diagnosis and treatment of type 1 diabetes at the dawn of the personalized medicine era. J Transl Med 19 , 137 (2021). https://doi.org/10.1186/s12967-021-02778-6

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• Type 1 diabetes
• Autoimmunity
• Personalized medicine
• Personalized treatment
• Genomic Risk Score
• Insulin therapy
• Gene polymorphism
• Pancreatic β cells

Journal of Translational Medicine

ISSN: 1479-5876

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REVIEW article

Prediction and prevention of type 1 diabetes.

• Department of Pediatrics, University of Chieti, Chieti, Italy

Type 1 Diabetes (T1D) is one of the most common chronic autoimmune diseases in children. The disease is characterized by the destruction of beta cells, leading to hyperglycemia, and to a lifelong insulin-dependent state. Although several studies in the last decades have added relevant insights, the complex pathogenesis of the disease is not yet completely understood. Recent studies have been focused on several factors, including family history and genetic predisposition (HLA and non-HLA genes) as well as environmental and metabolic biomarkers, with the aim of predicting the development and progression of T1D. Once a child becomes symptomatic, beta cell mass has already reached a critical threshold (usually a residual of 20–30% of normal amounts), thus representing only the very late phase of the disease. In particular, this final stage follows two preceding asymptomatic stages, which have been precisely identified. In view of the long natural history and complex pathogenesis of the disease, many strategies may be proposed for primary, secondary, and tertiary prevention. Strategies of primary prevention aim to prevent the onset of autoimmunity against beta cells in asymptomatic individuals at high risk for T1D. In addition, the availability of novel humoral and metabolic biomarkers that are able to characterize subjects at high risk of progression, have stimulated several studies on secondary and tertiary prevention, aimed to preserve residual beta cell destruction and/or to prolong the remission phase after the onset of T1D. This review focuses on the major current knowledge on prediction and prevention of T1D in children.

Introduction

Type 1 diabetes (T1D) is a chronic autoimmune disease characterized by pancreatic beta cell destruction in which genetic susceptibility combined with environmental factors, mostly in early life, plays a crucial role. Several studies have been focusing on the identification of individuals at risk for T1D, early in the natural history of the disease, using prediction models in which the genetic factors are considered to be important for their time-independence in all subjects. These results have offered the possibility of identifying people at risk and to follow them during the years, in order to try to prevent or revert the progression of T1D. Nevertheless, genetic factors do not provide a sufficient explanation regarding the development of the disease. In the last decade, the Eisenbarth model has tried to explain the progression of T1D ( 1 ), suggesting three main stages in the natural history of T1D. The first stage is featured by the presence of autoantibodies (at least two islet autoantibodies) with normal blood glucose levels and no symptoms (stage 1, or the “asymptomatic phase”) ( 2 ). In genetically predisposed individuals, environmental factors could act as a trigger of T-cell and humoral autoimmune responses against beta cells ( 3 ). Stage 2 is defined by the positivity of two or more autoantibodies with alterations of glucose metabolism not diagnostic for diabetes still in absence of clinical symptoms (“early metabolic alterations with asymptomatic state”). “Clinical diabetes,” or stage 3, is characterized by the onset of clinical manifestations ( Table 1 ) ( 4 ). The duration of each phase and the risk of progression from one stage to the other are not completely known. At the moment, one relevant focus is to characterize each phase of this complex disease in order to predict and prevent T1D, which is the dream as well as the most challenging obstacle for clinicians and scientists. This review has the aim to describe the most recent knowledges on the main and recent strategies of prediction and prevention of T1D.

Table 1 . Staging of Type 1 Diabetes according to JDRF, the Endocrine Society, and the American Diabetes Association ( 4 ).

Predictors of Risk for T1D

Ongoing research on T1D has produced abundant data evaluating potential predictive factors associated with the risk of beta cell destruction. Although several factors have been proposed, the genetic, infective, dietary, and humoral factors are the most relevant. More importantly, due the multifactorial nature of the disease, these factors might be considered not individually but as being on a spectrum and interactive factors that if combined might strongly enhance the risk of developing the disease. Therefore, the complete characterization of each of these components might be of relevance in order to properly define the risk of T1D development.

Genetic Factors

In T1D, a clear pattern of inheritance is lacking; nevertheless, many studies have reported that genetic predisposition might explain up to 50% of the risk ( 5 ). Relatives of T1D patients have higher risk of developing T1D (about 15–20 times, since the risk is about 0.4% among the general population) ( 6 , 7 ). The concordance rate for T1D is, respectively, 25–50% in identical twins and 6–7% in dizygotic twins and siblings ( 7 , 8 ). The human leukocyte antigen (HLA) complex plays a critical role in the pathogenesis of T1D, representing a substantial component of the genetic risk (about 50%). The HLA region on chromosome 6p21 encodes class-I, class-II, and class-III genes. The telomeric boundary of the locus comprises the class-I genes, including HLA-A, HLA-B, and HLA-C, whereas the centromeric boundary comprises the class-II genes, including HLA-DP, HLA-DQ, and HLA-DR. Class III is located in the middle part of the HLA region ( 9 ). Combinations of specific alleles of HLA class II strongly influence the risk of T1D. For example, the combination of HLA-DRB1 * 04 with DQA1 * 03:01-DQB1 * 03:02 (known as DR4-DQ8) increases the risk of developing T1D, while HLA DRB1 * 04 combined with DQA1 * 03-DQB1 * 03:01 does not ( 10 , 11 ). The highest risk of T1D is linked not only to DR4-DQ8 haplotype, but also to another class-II haplotype known as DR3-DQ2 (DRB1 * 03:01-DQA1 * 05:01-DQB1 * 02:01) ( 2 ). HLA is involved in the immune process of antigen presentation; therefore, it is clear how this gene region can influence both etiology and pathogenesis of T1D, and this is confirmed by the sequence of appearance of islet autoantibodies. Insulin autoantibodies (IAA) appear in children up to 6 years of age with DR4-DQ8 haplotype, while GAD65 autoantibodies first appear in carriers of DR3-DQ2 ( 12 ). If the haplotype of HLA influences the appearance of the first autoantibody, no similar associations are reported for the appearance of subsequent autoantibodies ( 13 ). Some haplotypes could be protective factors for the development of T1D for example, DQB1 * 06:02-DRB1 * 15:01-DQA1 * 01:02 (also known as DR2) is detected in ~20% of the individuals, but in only 1% of patients with T1D ( 14 ). HLA class I is expressed in all nucleated cells, and it is also involved in the antigen-presenting process to lymphocytes. However, the risk for T1D in patients with HLA class-I haplotypes is relatively low compared to those with HLA-DR and HLA-DQ ( 15 ). In addition, it is important to underline that <10% of individuals with HLA-conferred susceptibility develop T1D ( 16 ). Therefore, new genes probably need to be characterized to better define the risk of the disease. In fact, to date our knowledge on HLA haplotypes does not completely define the genetic risk of the disease, suggesting the direct effects of other genes ( 17 , 18 ). Thus, non-HLA genes have been described as likewise playing a pivotal role in the pathogenesis of T1D as with other autoimmune diseases. Amon them, particularly the genes encoding, respectively, for pre-proinsulin (INS), or protein tyrosine phosphatase (PTPN22) or IL-2 receptor subunit alpha (IL2RA) are largely described ( 19 ). Other genes have been identified by genome wide association study (GWAS); among these, the 6q22.23 chromosomal region encoding protein tyrosine phosphatase receptor kappa (PTPRK) and thymocyte expressed molecule involved in selection (THEMIS) are well-studied for their critical role in thymic T cell development ( 20 ). In addition, genetic scores were proposed in recent years in order to evaluate the combined effects of different genes on the risk of T1D. Among them, Type 1 Diabetes Genetic Risk Score (T1D GRS) has been validated to predict progression of islet autoimmunity and development of T1D in at-risk individuals. Oram et al. ( 21 ) have validated a T1D GRS that incorporates HLA and non-HLA genes T1D-associated single nucleotide polymorphisms (SNPs) and that also discriminates T1D from Type 2 diabetes (T2D), monogenic diabetes, and controls ( 22 ). Redondo et al. have tested the prognostic utility of T1D GRS to differentiate rates of progression of autoimmunity against beta cells and development of clinical T1D in autoantibody-positive relatives of patients with T1D ( 23 ). GRS can predict more than 10% of risk for pre-symptomatic T1D in children without afflicted first-degree relatives ( 24 ).

Childhood infections are surely among the most widely studied factors. The role of viral infections in the pathogenesis of T1D is supported by epidemiological, serological, and histological studies. Two main hypotheses have been proposed: the hygiene hypothesis and the triggering hypothesis. It has been speculated that infections in early childhood may be a protection against T1D as described in explanations of childhood allergy. On the other hand, specific or combined infections might cause T1D by destroying pancreatic beta cells ( 25 ). Among viruses, enteroviruses are the most commonly studied. The Diabetes Prediction and Prevention (DIPP) study demonstrated a relationship between the enteroviruses infection and the appearance of first autoantibody ( 26 , 27 ); in particular, early serological studies suggested coxsackie B viruses (CBVs), especially the CBV4 serotype, may be linked to T1D ( 28 , 29 ). In contrast, the role of rubella infection is controversial, because an atypical form of T1D without islet autoimmunity is described in congenital rubella syndrome. It is interesting to observe the correlation in young children between respiratory infections and the increased risk of islet autoimmunity described in The Environmental Determinants of Diabetes in the Young (TEDDY) study. The incidence of islet autoimmunity has a peak between 6 and 9 months, followed by a decline; the same trend is described for respiratory infections episodes ( 30 ). Although these results add relevant information, further studies are needed in order to properly define the role of viruses and infections in the risk of T1D in children and adolescents.

The role of diet in T1D history is not fully understood, and the results are still conflicting. Cow's milk proteins have been proposed as triggers of an autoimmune response in hosts at genetic risk, leading to pancreatic beta cell destruction ( 31 – 35 ). Studies in animals have suggested that bovine serum albumin (BSA) is the milk protein responsible of the development of diabetes ( 31 ). Karjalainen et al. have studied the serum of 142 Finnish children with newly diagnosed insulin-dependent diabetes mellitus, 79 healthy children and 300 adult blood donors ( 32 ); all diabetic patients had increased serum concentrations of anti-bovine serum albumin (BSA) antibodies at the beginning of the disease. Anti-BSA antibodies were predominantly IgG and react against an albumin peptide containing 17 amino acids (ABBOS) ( 32 ). This epitope could cross-react with a beta cell surface protein 69 kd in size (p69) inducible by interferon gamma representing the target antigen for milk-induced beta cell-specific immunity. The Diabetes Autoimmunity Study in the Young (DAISY) has shown that only in low-/moderate-risk HLA-DR individuals, was the intake of cow's milk protein associated with a higher risk of developing beta cell autoimmunity, at variance of children at high risk ( 33 ). These results have been confirmed by the Trial to Reduce Insulin-Dependent Diabetes Mellitus in the Genetically at Risk (TRIGR), since no difference between the ingestion of cow's milk and the ingestion of hydrolyzed formula was found ( 34 , 35 ).

Conflicting results have been also described on the use of vitamin D. Several studies demonstrate the beneficial effect of vitamin D supplementation against some autoimmune diseases ( 36 ). It has been demonstrated that all cells of the immune system have vitamin D receptors, and thus they could be regulated by calcitriol ( 37 ). Vitamin D influences the innate immune system cells (dendritic cells and macropaghes) as well as the adaptive immune system cells (B and T lymphocytes). Calcitriol enhances the tolerogenic status which results in a suppression and increase of pro-inflammatory and anti-inflammatory cytokines, respectively. It also reduces the expression of MHC class I and II and costimulatory molecules ( 38 ). Regarding vitamin D and T1D, it would seem that calcitriol supplementation would reduce serum levels of antibodies and delay the progression of beta cell destruction but only in the early stages of the disease ( 39 ). This could explain the reported controversial results. A recent study shows that the integration of vitamin D with ω-3 co-supplementation and arachidonic acid reduction in the Mediterranean diet have benefits for T1D children at onset ( 40 ). On the other hand, in the Type 1 Diabetes Prediction and Prevention Study (DIPP), Mäkinen et al. compared the 25(OH)D umbilical cord serum concentration of 764 children born between 1994 and 2004 who participated in DIPP in Finland. Results reported in this study have shown that fetal vitamin D status, measured through the concentration of 25(OH)D in umbilical cord serum, is not linked to the islet autoimmunity ( 41 ). Although these results add relevant information on the risk of T1D, other components still need to be evaluated. In fact, it might be postulated that a complex combination of early-life and probably even fetal-life factors influence the development of pancreatic autoimmunity. Understanding the burden of each of these components is the way to strategically prevent one of the most demanding chronic illnesses in children.

Serological Biomarkers

The characterization of serological biomarkers that evaluate the pancreatic autoimmunity and the beta cell dysfunction or death represents an effective way to try to outline the progression of the disease. The positivity of autoantibodies against beta cells and the combination of them are considered the main relevant strategies to predict T1D progression. There are five primary types of islet autoantibodies: autoantibodies against insulin (IAA), autoantibodies against insulinoma-associated antigen-2 (IA-2), autoantibodies against glutamic acid decarboxylase (GAD), autoantibodies against zinc-transporter 8 (ZnT8), and islet cell antibodies (ICA) ( 42 ). Although these autoantibodies could appear at any age, they rarely appear before the age of 6 months ( 43 ). The peak incidence of appearance of a first islet autoantibody is before the age of 3 years ( 43 – 45 ). After this age the risk of developing islet autoimmunity declines. Both the young age of seroconversion and the positivity for multiple autoantibodies are considered the major risk factors for the development of the disease. Ziegler et al. have demonstrated that the progression to clinical T1D was faster in children who had the appearance of autoantibodies against beta cells before the age of 3 years than those who were 3 years old or older ( 46 ). In addition, progression to T1D at 10-year follow-up was about 14.5% in 474 children with a single islet autoantibody, in contrast to 69.7% in 585 children with multiple islet autoantibodies. By the age of 15 years the risk of diabetes was about 0.4% in children without islet autoantibodies ( 46 ). The titers of autoantibodies also influence the risk of progression; high titer of islet cell autoantibodies of IAA and IA-2 is associated with a high risk of progression in the 5 years following the appearance of the first autoantibody. In contrast, GADA concentrations did not differ between progressors and non-progressors ( 47 ). Nevertheless, it is important to underline that the role of islet autoantibodies positivity and titers have not a clear prognostic significance because a revert to seronegativity was found up to 60% of individuals with a single autoantibody and the antibody titers may actually change ( 48 , 49 ). To date, islet autoantibody remains as the gold standard for risk stratification for the development of clinically manifest T1D, although not even the positivity of multiple autoantibodies is specific for the disease.

Therefore, the better characterization of the main risk factors previously discussed (namely genetic factors, the role of infections, diet, and serological markers) combined with the definition of novel and still unknown factors will surely help in the future to predict the development of the disease ( Figure 1 ). Further, ongoing researches will likely offer new perspectives in this field.

Figure 1 . Main predictive factors associated to the risk of T1D.

Prediction strategies are important to avoid the development of autoimmunity processes in subjects at risk of T1D. More importantly, they are extremely relevant to stop the natural progression of the disease. To date there are three levels of prevention: primary prevention, intended for individuals at high risk of developing T1D and aimed at preventing the autoimmunity against islet autoantigens; secondary prevention, which relates to individuals with multiple islet autoantibodies with the aim of halting autoimmunity processes and possibly avoid the clinical onset of diabetes; and, once the disease is clinically manifested, the tertiary prevention of T1D that is focused on complications of the disease, attempting to reduce or minimize these with the main goal at least of delaying their onset ( 50 ).

Primary Prevention

Strategies for primary prevention must be started early in life, because when the earlier process of beta cell autoimmunity is initiated, the progression to T1D accelerates significantly ( 46 ). The POInT study, an investigator-initiated, randomized, placebo-controlled, double-blind, primary prevention trial has been started through a network of collaborating clinical study centers from European countries in Belgium, Germany, Poland, the United Kingdom, and Sweden. This study seeks to determine whether daily administration of oral insulin, from the age of 4–7 months until the age of 36 months to children with elevated genetic risk for T1D, reduces the incidence of beta cell autoantibodies and diabetes ( 51 ). The rationale of this study was that immunological tolerance can be achieved by the administration of antigens ( 52 , 53 ). However, although the rationale of this study is very promising, complete data are not yet available; we will probably have new data in the years to come. Nevertheless, a previous study conducted between March 2, 2007 and December 21, 2015 has demonstrated that oral insulin at a dose of 7.5 mg/d, compared with a placebo, did not delay or prevent the development of T1D over 2.7 years in autoantibody-positive relatives of T1D patients ( 54 ).

Due to the potential role of infections in the pathogenesis of T1D, the opportunity to administer a vaccine against viruses associated to T1D is being explored. In particular, the Juvenile Diabetes Research Foundation (JDRF) is now funding research in this field, likely offering promising perspectives in the near future ( 55 ). Under development are not only viral vaccines, but also vaccines inducing immune tolerance to beta cell antigens ( 56 , 57 ). Neoepitopes are very important because they could be an alternative antigenic target for T1D tolerogenic vaccines.

The role of gut microbiome is critical for the immune regulation, education, and maturation of the immune system in infants. Several cohorts have been studied in order to investigate the relationship between early microbiome or its perturbations with the development of islets autoantibodies. Studies are underway in order to clarify the role of intestinal bacterial diversity in inducing the risk of T1D development in children. In the TEDDY study, modest alterations of microbial composition have been found in patients with islet autoantibodies or T1D not revealing clear taxonomic differences ( 58 , 59 ). However, a relevant point is that the microbiomes of progressors to islet autoimmunity or T1D contained notably higher numbers of genes involved in fermentation pathways and production of Short Chain Fatty Acids (SCFA) by-products. This is relevant because some SCFA products, like butyrate, are involved in the mechanisms of gut epithelial integrity maintenance, promoting anti-inflammatory responses, and regulating the activity of regulatory T cells ( 58 , 59 ). Investigating the role of the microbiome may provide insights into developing safe strategies to modulate immune regulation in infants and children.

Secondary Prevention

Strategies for secondary prevention apply to individuals with multiple autoantibodies (at least two), with or without evidence of beta cell dysfunction. Islet autoantibodies currently represent a relevant approach in the prediction of clinical T1D. The number of autoantibodies, the age of onset, and the combination of these could be highly predictive of the progression to clinical T1D.

Recent evidence remarks how post translational modifications (PTM) of self-antigens as oxidation ( 60 , 61 ), glycosylation ( 60 ), citrullination ( 62 , 63 ), and deamination ( 64 ) supply neoepitopes that are able to breach immune tolerance in T1D. Strollo et al. demonstrated a new autoantibody in most of T1D individuals ( 61 ) or prediabetic children ( 65 ). They also demonstrated that the best sensitivity and specificity of the humoral biomarkers are defined by the positivity of oxPTM-INS-Ab and IA-2A, in contrast to GADA and IAA that show a lower sensitivity and specificity. In detail, the sensitivity of oxPTM-INS-Ab, IA-2A, GADA, and IAA was about 74, 71, 65, and 50%, respectively, while the specificity was 91, 91, 66, and 68%, respectively ( 66 ). They found that in GADA + individuals, the further positivity of IA-2A and oxPTM-INS-Ab was the better and the more accurate combination when compared to IA-2A + /IAA + or oxPTM-INS-Ab + /IAA + . In children oxPTM-INS-Ab + ,GADA + , and IA-2A + had twice the risk of progression to clinical diabetes within 5 years when compared with children with IAA + , GADA + , IA-2A + . At 10 years of follow-up, diabetes risk increased to 100% in the first group, compared to 84.37% in the second group ( 66 ). Although this study demonstrates the greater accuracy of oxPTM-INS-Ab in identifying progressors to T1D compared to IAA, additional studies are necessary to confirm the predictive value of oxPTM- INS-Ab in T1D.

In addition, metabolic markers have been proposed for secondary prevention. Continuous glucose monitoring (CGM) seems to have a role in predicting T1D onset in at-risk persons. Steck et al. enrolled 23 participants with positive autoantibodies who wore a CGM; they demonstrated that those children reporting a 18% or greater CGM time spent at >140 mg/dL are at increased risk to progress to clinical diabetes ( 67 ). However, to date, larger studies are needed to confirm the predictive value of CGM. Also, mild fasting or after glucose load dysglycemia increase the risk of T1D. Metabolic markers derived from oral glucose tolerance test (OGTT) accurately predict the progression to T1D in high-risk individuals ( 68 , 69 ). OGTT examines the response to an artificial sugar load, CGM does not—this is the relevant advantage of this method.

Several immune interventions have been reported to delay the decline in beta-cell function ( 70 ). A promising drug is teplizumab an Fc receptor-non-binding anti-CD3 monoclonal antibody. In a phase-2 trial, Herold et al. have demonstrated that teplizumab significantly delays (by 2 years) the clinical onset of T1D in high-risk, non-diabetic relatives of diabetic patients and with at least two autoantibodies and abnormal OGTT at trial entry ( 71 ). The presence of HLA-DR4 and the absence of HLA-DR3 and of anti-ZnT8 antibodies identified the persons most likely to have a response ( 71 ). Preclinical studies suggested that an anti-CD3 monoclonal antibody needs an active autoimmune response; thus, the administration of these drugs during stage 1 of diabetes could be ineffective ( 72 , 73 ).

Tertiary Prevention

Strategies to preserve beta cell mass and/or to prolong the remission phase after T1D onset are of relevant importance, because beta cell mass rapidly declines during the first 1–2 years or following the onset of T1D; these strategies could also allow us to avoid or delay the complications of T1D ( 74 , 75 ). In order to understand the immune mechanisms underlying the destruction of beta cell mass, it is key to try to halt autoimmunity and to preserve beta cell mass with the hope of eventually curing T1D. Previous pilot, randomized, placebo-controlled, single-masked clinical trial was performed with the aim to characterize the tertiary prevention strategies. Results from this study have shown that anti-thymocyte globulin ATG given at low dose (2.5 mg/kg) combined with the administration of 6 mg subcutaneously every 2 weeks for six doses of pegylated granulocyte colony-stimulating factor GCSF in individuals with T1D (duration 4–24 months) is able to preserve C-peptide ( 76 , 77 ), contrary to higher doses of ATG (6.5 mg/kg) in monotherapy ( 78 , 79 ). Flow cytometry analysis showed that the combination of low-dose ATG/GCSF increased the proportion of Tregs to conventional CD4 + T cells, while higher-dose ATG decreased Tregs proportionally ( 77 – 79 ). The National Institute of Health Type 1 Diabetes TrialNet Study Group (TrialNet) performed a three-arm randomized, double-masked, placebo-controlled trial (low-dose ATG/GCSF, low-dose ATG, and placebo) to compare the power of low-dose ATG/GCSF and low-dose ATG alone in preserving beta cell mass ( 80 ). This study showed that the addition of GCSF may decrease the benefits of low-dose ATG alone in the reduction of HbA1c, preservation of beta cell function, and favorable changes in immune cells subsets ( 80 ).

Many other immunotherapeutic approaches are being studied and proposed to prevent T1D. Jacobsen et al. reviewed and summarized recent interventional approaches ( 81 ), defining their proposed mechanism. Treatments include cyclosporine plus methotrexate ( 82 ), rituximab (anti-CD20) ( 83 , 84 ), teplizumab (anti-CD3) ( 85 , 86 ), otelixizumab (chimeric anti-CD-3) ( 87 – 89 ), ATG ( 78 , 79 ), ATG+G-CSF ( 76 , 77 , 90 ), abatacept (CTLA-4/Fc fusion protein) ( 91 , 92 ), ex-vivo -expanded autologous CD4 + CD127 lo/− CD25 + polyTregs ( 93 ), autologous hematopoietic stem cell transplant (AHSCT) ( 94 ), alefacept (LFA-3/Fc fusion protein) ( 95 ), alpha-1-antitrypsin (acute phase reactant) ( 96 , 97 ), canakinumab (anti-IL-1 mAb) and anakinra (IL-1-R antagonist) ( 98 , 99 ), proleukin (IL2) ( 100 , 101 ), etanercept (anti-TNF-α) ( 102 ), sitagliptin+lansoprazole (DPP-4 inhibitor + PPI) ( 103 ), and verapamil ( 104 ). Results of these studies are relevant in possibly offering new and promising approaches for the cure of the disease in the near future.

Vitamin D supplementation is another strategy proposed to slow the progression of the disease. In this regard, it is an ongoing randomized, placebo-controlled clinical trial to check vitamin D effectiveness in prolonging the duration of partial clinical remission (PCR), or “honeymoon phase,” increasing residual beta cell function. It began on October 19, 2017 and will conclude on July 31, 2020 ( 105 ).

In the field of tertiary prevention, it is crucial to note that about 50% of T1D patients fail to undergo partial clinical remission ( 106 ). These children, also called “non-remitters” have a prognostic disadvantage for the short- and long-term complications of T1D ( 107 – 110 ). A predictive model evaluating of bicarbonate <15 mg/dL, age <5 years, female sex, and >3 diabetes-associated autoantibodies has a 73% predictive power in identifying non-remission in children and adolescents with new-onset T1D ( 111 ). It is a challenge for scientists to identify this group of patients at high risk in order to properly treat them with other strategies to have a better glycemic control and to avoid or delay vascular complications.

Conclusions

T1D is a T cell-mediated autoimmune disease characterized by selective destruction of pancreatic beta cells. The pathogenesis of T1D is very complex, and the network of factors involved needs to be better described. To date, the genetic factors are surely relevant to estimate the risk of developing T1D. In fact, the familial aggregation of T1D certainly remarks an inheritable genetic predisposition for the development of this chronic disease. Risk of T1D progression is conferred by specific HLA DR/DQ alleles (i.e., DR3/DQ2 or DR4/DQ8), but it is important to note there are also alleles that would seem to be protective factors for the development of T1D (i.e., DQB1 * 0602).

In addition, non-HLA genes are also involved in the polygenic inheritance of T1D.

Although, the genetic factors certainly have an important role in the risk of T1D, the concordance rate not equal to 100% between monozygotic twins underlines the importance of possible environmental factors and the crucial aim to define them to truly predict and prevent T1D. Among the potential factors related to the risk of progression to T1D, the positivity of multiple autoantibodies is demonstrated to be a major risk factor of developing insulin-requiring diabetes. The role of infections, diet, and other still unknown factors potentially involved in the pathogenesis of T1D have to be better investigated to accurately predict the risk of T1D. These studies will pave the way to studies for primary and secondary prevention of the disease, with the final aim of avoiding or limiting insulin-dependence. Finally, strategies of tertiary prevention are necessary to delay or prevent diabetes-related complications.

Author Contributions

FC and CG reviewed the paper. MP wrote the manuscript.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords: type 1 diabetes, children, prediction, primary prevention, secondary prevention, tertiary prevention

Citation: Primavera M, Giannini C and Chiarelli F (2020) Prediction and Prevention of Type 1 Diabetes. Front. Endocrinol. 11:248. doi: 10.3389/fendo.2020.00248

Received: 31 January 2020; Accepted: 03 April 2020; Published: 02 June 2020.

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*Correspondence: Francesco Chiarelli, chiarelli@unich.it

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Understanding adult-onset type 1 diabetes, article information, adult-onset type 1 diabetes: current understanding and challenges.

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R. David Leslie , Carmella Evans-Molina , Jacquelyn Freund-Brown , Raffaella Buzzetti , Dana Dabelea , Kathleen M. Gillespie , Robin Goland , Angus G. Jones , Mark Kacher , Lawrence S. Phillips , Olov Rolandsson , Jana L. Wardian , Jessica L. Dunne; Adult-Onset Type 1 Diabetes: Current Understanding and Challenges. Diabetes Care 1 November 2021; 44 (11): 2449–2456. https://doi.org/10.2337/dc21-0770

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Recent epidemiological data have shown that more than half of all new cases of type 1 diabetes occur in adults. Key genetic, immune, and metabolic differences exist between adult- and childhood-onset type 1 diabetes, many of which are not well understood. A substantial risk of misclassification of diabetes type can result. Notably, some adults with type 1 diabetes may not require insulin at diagnosis, their clinical disease can masquerade as type 2 diabetes, and the consequent misclassification may result in inappropriate treatment. In response to this important issue, JDRF convened a workshop of international experts in November 2019. Here, we summarize the current understanding and unanswered questions in the field based on those discussions, highlighting epidemiology and immunogenetic and metabolic characteristics of adult-onset type 1 diabetes as well as disease-associated comorbidities and psychosocial challenges. In adult-onset, as compared with childhood-onset, type 1 diabetes, HLA-associated risk is lower, with more protective genotypes and lower genetic risk scores; multiple diabetes-associated autoantibodies are decreased, though GADA remains dominant. Before diagnosis, those with autoantibodies progress more slowly, and at diagnosis, serum C-peptide is higher in adults than children, with ketoacidosis being less frequent. Tools to distinguish types of diabetes are discussed, including body phenotype, clinical course, family history, autoantibodies, comorbidities, and C-peptide. By providing this perspective, we aim to improve the management of adults presenting with type 1 diabetes.

Clinically, it has been relatively easy to distinguish the acute, potentially lethal, childhood-onset diabetes from the less aggressive condition that affects adults. However, experience has taught us that not all children with diabetes are insulin dependent and not all adults are non–insulin dependent. Immune, genetic, and metabolic analysis of these two, apparently distinct, forms of diabetes revealed inconsistencies, such that insulin-dependent and immune-mediated diabetes was redefined as type 1 diabetes, while most other forms were relabeled as type 2 diabetes. Recent data suggest a further shift in our thinking, with the recognition that more than half of all new cases of type 1 diabetes occur in adults. However, many adults may not require insulin at diagnosis of type 1 diabetes and have a more gradual onset of hyperglycemia, often leading to misclassification and inappropriate care. Indeed, misdiagnosis occurs in nearly 40% of adults with new type 1 diabetes, with the risk of error increasing with age ( 1 , 2 ). To consider this important issue, JDRF convened a workshop of international experts in November 2019 in New York, NY. In this Perspective, based on that workshop, we outline the evidence for a new viewpoint, suggesting future directions of research and ways to alter disease management to help adults living with type 1 diabetes.

Incidence of Type 1 Diabetes Among Adults Worldwide

Adult-onset type 1 diabetes is more common than childhood-onset type 1 diabetes, as shown from epidemiological data from both high-risk areas such as Northern Europe and low-risk areas such as China ( 3 – 8 ). In southeastern Sweden, the disease incidence among individuals aged 0–19 years is similar to that among individuals 40–100 years of age (37.8 per 100,000 persons per year and 34.0/100,000/year, respectively) ( 3 ). Given that the comparable incidence spans only two decades in children, it follows that adult-onset type 1 diabetes is more prevalent. Similarly, analysis of U.S. data from commercially insured individuals demonstrated an overall lower incidence in individuals 20–64 years of age (18.6/100,000/year) than in youth aged 0–19 years (34.3/100,000/year), but the total number of new cases in adults over a 14-year period was 19,174 compared with 13,302 in youth ( 4 ). Despite the incidence of childhood-onset type 1 diabetes in China being among the lowest in the world, prevalence data show similar trends across the life span. From 2010–2013, the incidence was 1.93/100,000 among individuals aged 0–14 years and 1.28/100,000 among those 15–29 years of age versus 0.69/100,000 among older adults ( 5 ). In aggregate, adults comprised 65.3% of all clinically defined newly diagnosed type 1 diabetes cases in China, which is similar to estimates using genetically stratified data from the population-based UK Biobank using a childhood-onset polygenic genetic risk score (GRS) ( 6 ). It is important to note that the proportion would likely be higher if autoimmune cases not requiring insulin initially were classified as type 1 diabetes. For example, in a clinic-based European study, the proportion of adults with diabetes not initially requiring insulin yet with type 1 diabetes–associated autoantibodies was even higher than those started on insulin at diagnosis with a defined type 1 diabetes diagnosis ( 9 ). Moreover, in an adult population-based study in China, the fraction (8.6%) with diabetes not requiring insulin yet with type 1 diabetes–associated autoantibodies was similar to that in Europe, implying that there could be over 6 million Chinese with adult-onset type 1 diabetes ( 10 ). While there is a wide range in the incidence of type 1 diabetes across different ethnic groups, even using differing methods of case identification ( 7 ), these data support the notion that, worldwide, over half of all new-onset type 1 diabetes cases occur in adults.

Natural History Studies of Type 1 Diabetes

Our understanding of the natural history of type 1 diabetes has been informed by a number of longitudinal and cross-sectional studies. At one end of the spectrum are prospective birth cohort studies, such as the BABYDIAB study in Germany and The Environmental Determinants of Diabetes in the Young (TEDDY) study, which includes sites in Germany, Finland, Sweden, and the U.S. While these studies now have the potential to explore the pathogenesis of islet autoimmunity by being extended into adulthood, they have primarily focused on events occurring in childhood ( 11 ). Clinical centers in North America, Europe, and Australia collaborate within Type 1 Diabetes TrialNet, a study that identifies autoantibody-positive adults and children in a cross-sectional manner to examine the pathogenesis of type 1 diabetes and to perform clinical trials on those at high risk in order to preserve β-cell function ( 12 ). At the other end of the spectrum, the European Prospective Investigation into Cancer and Nutrition (EPIC)-InterAct study is a case-cohort study nested in the U.K. prospective adult population-based EPIC study ( 13 ), while the clinical, immunogenetic, and metabolic characteristics of autoimmune adult-onset type 1 diabetes have been extensively studied in large American, European, and Chinese studies, including UK Prospective Diabetes Study (UKPDS), Action LADA, Scandia, Non Insulin Requiring Autoimmune Diabetes (NIRAD), and LADA China ( 9 , 14 – 19 ). Based on these cross-sectional and prospective studies, considerable data have been generated to define differences within type 1 diabetes according to the age at onset. Here, we highlight key aspects of age-related genetic, immune, and metabolic heterogeneity in type 1 diabetes. Of note, the term latent autoimmune diabetes in adults (LADA) has been used to describe adults with slowly progressive autoimmunity, sometimes exhibiting features overlapping with those of type 2 diabetes ( 9 , 14 , 18 ). At the outset of the workshop and for the purposes of this Perspective, LADA was not considered a unique entity; rather, we considered the classification of type 1 diabetes to include all individuals with evidence of autoimmunity, regardless of the trajectory of disease development (i.e., rapid or slowly progressive) or other associated demographic and/or clinical features (e.g., obesity).

Age-Related Genetic Heterogeneity

Type 1 diabetes shows heterogeneity across a broad range of clinical, genetic, immune, histological, and metabolic features ( 20 ). Childhood-onset type 1 diabetes is most often attributed to susceptibility alleles in human leukocyte antigen (HLA), which contribute ∼50% of the disease heritability. Whereas ethnic differences exist, notably for specific HLA genotypes, several broad principles apply. Compared with childhood-onset disease, adult-onset type 1 diabetes cases show lower type 1 diabetes concordance rates in twins ( 21 ), less high-risk HLA heterozygosity ( 19 ), lower HLA class I ( 14 ), more protective genotypes ( 14 , 15 ), and lower GRS ( 6 , 22 ), which are calculated by summing the odds ratios (OR) for disease-risk alleles.

Diabetes-Associated Immune Changes

Adult-onset type 1 diabetes, like childhood-onset type 1 diabetes, is associated with the presence of serum autoantibodies against β-cell antigens. Serum glutamic acid decarboxylase (GADA) autoantibodies may be useful as a predictor of type 1 diabetes in adults, as adult-onset cases most often present with GADA positivity ( 9 , 10 , 15 , 17 , 18 , 20 , 22 ) and possess an HLA-DR3 genotype ( 9 , 14 , 15 , 20 , 21 , 23 ). In one prospective study of a general population, the hazard risk of incident diabetes in those with a high type 1 diabetes GRS and GADA positivity was 3.23 compared with all other individuals, suggesting that 1.8% of incident diabetes in adults was attributable to that combination of risk factors ( 13 ). In adult-onset type 1 diabetes, multiple diabetes-associated autoantibodies tend to be less prevalent with increasing age at diagnosis ( 1 , 8 ), yet GADA remains the dominant autoantibody irrespective of the need for insulin treatment at diagnosis and irrespective of ethnicity ( 9 , 17 , 18 , 24 , 25 ), even despite a paucity of HLA DR3, as in Japan and China ( 17 , 18 ). In contrast, childhood-onset type 1 diabetes cases often have insulin autoantibodies and an HLA-DR4 genotype, higher identical twin disease concordance, more HLA heterozygosity, and higher GRS ( 20 ). Taken together, these data indicate that type 1 diabetes is heterogeneous across the spectrum of diagnoses, suggesting that pathogenesis and optimal therapy are also diverse.

Data from the TrialNet Pathway to Prevention cohort demonstrated lower risk of progression to type 1 diabetes in adults than children, even when both show multiple autoantibodies on a single occasion and are monitored over 10 years ( 12 ). One recent analysis found that the 5-year rate of progression to diabetes in multiple autoantibody–positive adults was only ∼15%, with a number of them remaining diabetes-free for decades ( 26 ). A combined cohort study, known as the Slow or Nonprogressive Autoimmunity to the Islets of Langerhans (SNAIL) study, is following such “slow progressors” with multiple autoantibodies who have yet to progress to stage 3 type 1 diabetes (i.e., clinical diagnosis) over at least a 10-year period ( 27 ). Many of these slow progressors lose disease-associated autoantibodies over time, adding complexity to cross-sectional classification ( 28 ). Based on estimates from natural history studies, slow progressors, even if identified when young, cannot account for all autoimmune adult-onset diabetes, indicating that autoantibodies must develop at all ages ( 11 ). However, little is known about those who initially develop autoimmunity as adults, mostly due to the lack of longitudinal studies focusing on this population.

People with type 1 diabetes, in contrast to the majority of those with type 2 diabetes, have altered adaptive immunity (i.e., islet autoantibodies and T-cell activation), while innate immune changes, including cytokine changes, are common to both ( 29 ). Increased T-cell activation by islet proteins has also been found in a proportion of adults with initially non-insulin-requiring diabetes, even when they lack diabetes autoantibodies ( 30 ). However, there is a paucity of immune studies on adult-onset type 1 diabetes and few histologic studies. An analysis of tissues from the Network for Pancreatic Organ Donors with Diabetes (nPOD) showed no relationship between age at diabetes onset and the frequency of islet insulitis ( 31 ). The composition of islet insulitis differs in very young children compared with older individuals, with the former having an increased frequency of B cells in islet infiltrates ( 32 ). However, relating pancreatic histological changes to changes in peripheral blood remains a challenge.

Adults with new-onset type 1 diabetes are at increased risk of other autoimmune conditions. About 30% of individuals with adult-onset type 1 diabetes have thyroid autoimmunity ( 27 , 29 ). In addition, adults with type 1 diabetes who possess high-titer GADA and/or multiple islet autoantibodies are at increased risk of progression to hypothyroidism ( 24 , 33 ). In a large population-based Chinese study, the prevalence of adult-onset type 1 diabetes was 6% among initially non-insulin-requiring diabetes cases, and 16.3% of them had thyroid autoimmunity (OR 2.4) ( 10 ). Of note, those with islet antigen 2 autoantibodies had a high risk of tissue transglutaminase autoantibodies, a marker for celiac disease (OR 19.1) ( 10 ). Thus, in the clinical setting, there should be a high index of suspicion for other autoimmune conditions in individuals with adult-onset type diabetes, and associated autoimmunity should be screened where clinically indicated.

Metabolic Characteristics of Adult-Onset Type 1 Diabetes

Age-related differences in type 1 diabetes extend to metabolic parameters. C-peptide at diagnosis is higher in adults than children, driven in part by higher BMI ( 34 ). Analysis of U.K., TrialNet, and Chinese cohorts has identified two distinct phases of C-peptide decline in stage 3 disease: an initial exponential fall followed by a period of relative stability. Along with initial differences at the time of clinical diagnosis, the rate of decline over 2–4 years was inversely related to age at onset ( 10 , 34 – 36 ). Furthermore, the U.S. T1D Exchange Study found that glycemic control was better in adults with type 1 diabetes than in children and adolescents with type 1 diabetes ( 37 ). The American Diabetes Association (ADA) targets for glycemia are higher in children, so that in this same cohort, 17% of children, compared with 21% of adults, achieved the ADA hemoglobin A 1c (HbA 1c ) goal of <7.5% and <7.0%, respectively ( 37 ). Other factors confound this relationship between age at diagnosis and metabolic control. First, individuals with adult-onset type 1 diabetes are more likely to have residual insulin-producing β-cells and persistent measurable C-peptide in disease of long duration, the latter of which has been linked to improved glycemic control ( 38 , 39 ). Second, individuals with adult-onset type 1 diabetes, initially not on insulin therapy, tend to have worse metabolic control than people with type 2 diabetes, even when receiving insulin treatment ( 9 , 40 ). The sole exception is the LADA China study, where worse control was noted only among those with a high GAD titer ( 18 ). Metabolic differences between adults and children extend beyond C-peptide. Adults with autoantibody positivity who progressed to type 1 diabetes were less likely than very young children to exhibit elevated proinsulin/C-peptide ratios prior to stage 3 disease onset ( 41 ). In addition, in individuals with disease of long duration, those diagnosed at an older age had evidence of improved proinsulin processing and nutrient-induced proinsulin secretory capacity ( 42 ).

Diagnosis and Management of Adult-Onset Type 1 Diabetes

Correctly identifying diabetes etiology and type is difficult, and misclassification may occur in up to 40% of adults presenting with type 1 diabetes ( 1 , 2 ). Reasons underlying misclassification are multiple and include 1 ) lack of awareness that the onset of type 1 diabetes is not limited to children; 2 ) the overwhelming majority of people developing diabetes as older adults have type 2 diabetes, contributing to a confirmation bias ( 2 ); 3 ) typical clinical criteria, such as BMI and metabolic syndrome, can be poor discriminators, especially as rates of obesity in the overall population increase ( 9 , 43 ); 4 ) clinical characteristics of adult-onset type 1 diabetes can masquerade as type 2 diabetes, given their slow metabolic progression and risk of metabolic syndrome (which occurs in about 40%), so that the distinction between types of diabetes may be blurred ( 43 – 45 ); and 5 ) lack of awareness of and accessibility to biomarkers that may serve as tools to distinguish type 1 diabetes and type 2 diabetes.

Tools to distinguish type 1 and type 2 diabetes are under active development. For example, classification models integrating up to five prespecified predictor variables, including clinical features (age of diagnosis and BMI) and clinical biomarkers (autoantibodies and GRS) in a White European population, had high accuracy to identify adults with recently diagnosed diabetes with rapid insulin requirement despite using GRS derived from childhood-onset type 1 diabetes. While GRS have the potential to assist diagnosis of type 1 diabetes in uncertain cases, they are not yet widely available in clinical practice. Moreover, it is important to note that while the model was optimized with the inclusion of all five variables, the addition of GRS had only a modest effect on overall model performance ( 22 ).

Classification can be aided by the measurement of autoantibodies and C-peptide. Recommended autoantibodies to assay at the time of diagnosis include those to insulin (insulin autoantibody), glutamate decarboxylase isoform 65 (GAD65A), insulinoma antigen 2, and zinc transporter isoform 8 (Znt8A), with GAD65A being the most prevalent autoantibody among adults. High levels or the presence of more than one antibody increases the likelihood of type 1 diabetes. However, it is important to realize that islet autoantibodies are a continuous marker that can also occur in the population without diabetes. As with many other tests, an abnormal test is usually based on a threshold signal from control populations without diabetes, usually the 97.5th or the 99th centile. Therefore, false-positive results with these assays can occur and can be reduced by using higher-specificity assays or thresholds and targeting testing toward those with clinical features suggestive of type 1 diabetes ( 46 ). Finally, since antibody levels can wane over time in established type 1 diabetes, the absence of autoantibodies does not rule out the possibility of a diagnosis of type 1 diabetes.

Measurement of C-peptide, paired with a blood glucose in the same sample, provides an estimate of endogenous insulin production and has the most utility in disease of long duration when levels fall below 300 pmol/L ( 39 , 47 ). However, C-peptide levels are typically higher at presentation and may be difficult to distinguish from levels in type 2 diabetes, which are usually >600 pmol/L. Thus, thresholds of C-peptide that clearly delineate type 1 diabetes from type 2 diabetes at diagnosis cannot be categorically defined, and C-peptide must be interpreted within the context of other clinical and laboratory features. Measurement of a random nonfasting C-peptide is superior to fasting C-peptide in identifying type 1 diabetes ( 48 ) and is well correlated with stimulated C-peptide levels measured during a mixed-meal tolerance test, which is considered the gold standard assessment of insulin secretory function in established type 1 diabetes ( 49 ). A recent analysis found that concomitant blood glucose ≥144 mg/dL (8 mmol/L) increased the specificity of random C-peptide in predicting a stimulated C-peptide level <600pmol/L, suggesting this is a reasonable threshold of blood glucose to employ for C-peptide interpretation ( 49 ).

C-peptide also can be used to guide therapy ( 50 ). Individuals with a random C-peptide level ≤300 pmol/L should be managed mainly with insulin. For those with random C-peptide levels >300 pmol/L, insulin could be combined with other diabetes therapies, although evidence about safety and efficacy is limited. It is generally agreed that sulfonylureas should be avoided because of the potential to hasten β-cell failure ( 50 ). There is concern for increased risk of diabetic ketoacidosis (DKA) with sodium–glucose cotransporter 1 (SGLT1) and SGLT2 inhibitors when these agents are used in type 1 diabetes, especially in nonobese individuals who may need only low dosages of insulin ( 51 ). All other agents could be considered for therapy in those not requiring insulin initially. In individuals with random C-peptide levels exceeding 600 pmol/L, management can be much as recommended for type 2 diabetes, with the caveats outlined above ( 50 ). An important consideration is that loss of β-cell function may be rapid in autoimmune diabetes. As such, individuals treated without insulin should be closely monitored.

In the absence of prospectively validated decision support tools that have been tested in multiethnic populations, we suggest, as an approach to aid the practicing physician, assessment of age, autoimmunity, body habitus/BMI, background, control, and comorbidities, using the acronym AABBCC ( Table 2 ). This approach includes the clinical consideration of autoimmunity and other clinical features suggestive of type 1 diabetes, including age at diagnosis, low BMI, an unexplained or rapid worsening of clinical course manifesting as a lack of response or rising HbA 1c with type 2 diabetes medications, and a rapid requirement for insulin therapy, especially within 3 years of diagnosis. It should be emphasized that among these features, age at diagnosis (<40 years), low BMI (<25 kg/m 2 ), and rapid need for insulin therapy are the most discriminatory ( 43 ). We recommend measurement of islet antibodies and C-peptide be considered in all older people with clinical features that suggest type 1 diabetes, with islet autoantibodies being the initial test of choice in short-duration disease (<3 years) and C-peptide the test of choice at longer durations.

Diabetes-Associated Comorbidities and Complications

The U.S. SEARCH for Diabetes in Youth study reported that nearly 30% of youth with newly diagnosed type 1 diabetes age <20 years presented with DKA ( 52 ). The frequency of DKA among adults at diagnosis with type 1 diabetes is unknown but is believed to be lower given that they often have higher C-peptide levels at diagnosis and a slower decline in β-cell function over time, even in those requiring insulin initially ( 34 ). Among childhood-onset type 1 diabetes, most episodes of DKA beyond diagnosis are associated with insulin omission, pump failure, or treatment error ( 53 ). However, for adults with type 1 diabetes, the primary risk factors are noncompliance and infections ( 54 ), the former sometimes due to the cost of insulin ( 55 ). Thus, there is a need to further understand DKA in adults, not least because it is associated with long-term worsening glycemic control ( 56 ).

Hypoglycemia

Fear of hypoglycemia remains a major problem in the clinical management of adults with type 1 diabetes ( 57 ), influencing quality of life and glycemic control. The effect of diabetes duration or age at diagnosis on hypoglycemia risk is not consistent among different studies. However, α-cell responses to hypoglycemia and hypoglycemia risk are both lower in individuals with higher C-peptide levels ( 38 ). Because residual C-peptide is more likely to be observed in those with a later age of onset, hypoglycemia risk may be different between those with childhood- and adult-onset diabetes. While insulin pumps and continuous glucose monitors are associated with improved glycemic control and reduced hypoglycemia ( 37 ), adults may show reluctance or inertia in adopting newer technologies. In the T1D Exchange study population, 63% of adults used an insulin pump while only 30% used a continuous glucose monitor, and use of these technologies tended to be lower in adults than in children ( 37 ). Factors that dictate use of these technologies are multiple and may include reduced access to or acceptance of wearable technology, challenges with insurance coverage, especially in the context of past misclassification, and/or inadequate education about hypoglycemia risk ( 58 ). A better understanding of potential barriers to technology use in adult-onset type 1 diabetes is needed. Furthermore, little is known about changes in hypoglycemia risk across the life span of individuals with adult-onset disease, representing an important gap in knowledge.

Microvascular and Macrovascular Disease Complications

Despite the prevalence of adult-onset type 1 diabetes, there is a paucity of data on the burden of microvascular complications in this population. Current knowledge is largely based on small, cross-sectional studies. In aggregate, these studies suggest that the prevalence of nephropathy and retinopathy are lower in adult-onset type 1 than in type 2 diabetes, but this conclusion is potentially confounded by diabetes duration. For example, the prevalence of nephropathy and retinopathy was lower in Chinese individuals with adult-onset type 1 diabetes than in those with type 2, but only in those with a disease duration <5 years, while in the Botnia Study, retinopathy risk in adult-onset type 1 diabetes increased, as expected, with disease duration ( 59 ). Two substantial prospective studies recently reported that those adults with diabetes enrolled in the UKPDS who were also GADA positive (i.e., presumably with type 1 diabetes) compared with those who were GADA negative (with type 2 diabetes) showed a higher prevalence of retinopathy and lower prevalence of cardiovascular events ( 60 , 61 ). These results are consistent with people with adult-onset type 1 diabetes compared with those with type 2 diabetes, showing a general tendency to higher HbA 1c levels ( 40 , 44 , 60 , 61 ) as well as reduced traditional cardiovascular risk factors, including reduced adiposity (BMI and waist circumference), metabolic (lipid levels), and vascular (blood pressure) profiles ( 9 , 24 , 62 ). Nevertheless, all-cause mortality and cardiovascular mortality rates in such individuals with adult-onset type 1 diabetes ( 59 ) are still higher than those among individuals without diabetes. In addition, there are discrepancies across studies, likely related to differences in populations under study (i.e., age, race/ethnicity, and diabetes duration), lack of consistent case definitions (i.e., adult-onset type 1 diabetes or LADA cases), and different outcomes, as well as small sample sizes with insufficient events on which to base strong recommendations.

Psychosocial Challenges

Negative stressors, including pressure to achieve target HbA 1c levels, lifestyle considerations, and fear of complications, are factors leading to the increased frequency of mood disorders, attempted suicide, and psychiatric care in adults with diabetes ( 63 ). In individuals who have experienced misclassification, additional stress derives from conflicting messages about the nature of their diabetes. Among adults with type 1 diabetes, those with high psychological coping skills (e.g., self-efficacy, self-esteem, and optimism) and adaptive skills may buffer the negative effect of stress and should be cultivated ( 64 ). Relationship challenges, including sexual intimacy, starting a family, caring for children, and relational stress, are major stressors for adults with type 1 diabetes ( 65 ). In addition, there is the looming threat of complications, including blindness and amputations ( 65 ). Adults with type 1 diabetes describe a sense of powerlessness, fear of hypoglycemia, and the challenges of both self-management and appropriate food management ( 66 ). A common misunderstanding is that while they face the same life choices associated with type 2 diabetes (e.g., weight loss, exercise, and limiting intake of simple sugars), adults with type 1 diabetes may require different management skills ( 67 ). Moreover, there is a strong association in adults with type 1 diabetes between chronic, stressful life events and fluctuating HbA 1c , possibly due to indirect mechanisms, including adherence to diabetes management ( 68 ). Whether these risks differ between those diagnosed as children or as adults is unclear and requires additional study.

In this Perspective, we have summarized the current understanding of adult-onset type 1 diabetes while identifying many knowledge gaps ( Table 1 ). Epidemiological data from diverse ethnic groups show that adult-onset type 1 diabetes is often more prevalent than childhood-onset type 1 diabetes. However, our understanding of type 1 diabetes presenting in adults is limited. This striking shortfall in knowledge ( Table 1 ) results in frequent misclassification, which may negatively impact disease management. Here, we outline a roadmap for addressing these deficiencies ( Fig. 1 ). A cornerstone of this roadmap is a renewed emphasis on the careful consideration of the underlying etiology of diabetes in every adult presenting with diabetes.

Proposed roadmap to better understand, diagnose, and care for adults with type 1 diabetes (T1D). Created in BioRender ( BioRender.com ).

Knowledge gaps

In the absence of data-driven classification tools capable of estimating individual-level risk, we offer a simple set of questions, incorporating what we have termed the AABBCCs of diabetes classification and management ( Table 2 ). In parallel, we invite the research community to join together in addressing key gaps in knowledge through studies aimed at defining the genetic, immunologic, and metabolic phenotype of adult-onset type 1 diabetes with the goal of using this knowledge to develop improved approaches for disease management and prevention ( Fig. 1 ).

AABBCC approach to diabetes classification

Acknowledgments. Sharon Saydah, Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, attended the workshop and participated in subsequent discussions of the manuscript. Elizabeth Seaquist, Division of Diabetes, Endocrinology, and Metabolism at the University of Minnesota, participated in the workshop. The authors acknowledge Marilyn L. Wales for her assistance with formatting the manuscript.

Funding and Duality of Interest. This manuscript is the result of a one-day meeting held at JDRF headquarters in New York, NY. Financial support for the workshop was provided by JDRF and Janssen Research and Development, LLC. Financial support from Janssen Research and Development, LLC, for the workshop was in an unrestricted grant to JDRF. JDRF provided participants with transportation, lodging, and meals to attend the workshop. No additional support was provided for the writing of the manuscript. R.D.L. is supported by a grant from the European Union (contract no. QLGi-CT-2002-01886). C.E.-M. is supported by National Institute for Health Research grants R01 DK093954, R21DK11 9800, U01DK127786, R01DK127308, and P30DK 097512; VA Merit Award I01BX001733; JDRF grant 2-SRA-2019-834-S-B; and gifts from the Sigma Beta Sorority, the Ball Brothers Foundation, and the George and Frances Ball Foundation. R.B. is supported in part by the Italian Ministry of University and Research (project code 20175L9H7H). A.G.J. is funded by a National Institute for Health Research (NIHR) Clinician Scientist fellowship (CS-2015-15-018). L.S.P. is supported in part by U.S. Department of Veterans Affairs (VA) awards CSP #2008, I01 CX001899, I01 CX001737, and Health Services Research & Development IIR 07-138; National Institute for Health Research awards R21 DK099716, R18 DK066204, R03 AI133172, R21 AI156161, U01 DK091958, U01 DK098246, and UL1 TR002378; and Cystic Fibrosis Foundation award PHILLI12A0.

R.D.L. received unrestricted educational grants from Novo Nordisk, Sanofi, MSD, and AstraZeneca. C.E.-M. has participated in advisory boards for Dompé Pharmaceuticals, Provention Bio, MaiCell Technologies, and ISLA Technologies. C.E.M. is the recipient of in-kind research support from Nimbus Pharmaceuticals and Bristol Myers Squibb and an investigator-initiated research grant from Eli Lilly and Company. J.F.-B. and J.L.D. were employed by JDRF during the workshop and early stages of writing. J.F.-B. is currently an employee of Provention Bio, and J.L.D. is currently an employee of Janssen Research and Development, LLC. R.B. participated in advisory boards for Sanofi and Eli Lilly and received honoraria for speaker bureaus from Sanofi, Eli Lilly, AstraZeneca, Novo Nordisk, and Abbott. L.S.P. has served on scientific advisory boards for Janssen and has or had research support from Merck, Pfizer, Eli Lilly, Novo Nordisk, Sanofi, PhaseBio, Roche, AbbVie, Vascular Pharmaceuticals, Janssen, GlaxoSmithKline, and the Cystic Fibrosis Foundation. L.S.P. is also a cofounder and officer and board member and stockholder for a company, Diasyst, Inc., that markets software aimed to help improve diabetes management. No other potential conflicts of interest relevant to this article were reported.

The sponsors had no role in the design and conduct of the study, collection, management, analysis, and interpretation of the data, and preparation, review, or approval of the manuscript. This work is not intended to reflect the official opinion of the VA or the U.S. Government.

Author Contributions. R.D.L., C.E.M., J.F.-B., and J.L.D. conceived of the article and wrote and edited the manuscript. All other authors were involved in the writing and editing of the manuscript. R.D.L. and C.E.-M. are guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

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Review Series Free access | 10.1172/JCI142242

Type 1 diabetes mellitus: much progress, many opportunities

Alvin c. powers.

Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA. Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA. VA Tennessee Valley Healthcare System, Nashville, Tennessee, USA.

Address correspondence to: Alvin C. Powers, Vanderbilt University Medical Center, 8435 MRBIV, 2215 Garland Ave, Nashville, Tennessee 37232, USA. Phone: 615.936.7678; Email: [email protected] .

Published March 24, 2021 - More info

As part of the centennial celebration of insulin’s discovery, this review summarizes the current understanding of the genetics, pathogenesis, treatment, and outcomes in type 1 diabetes (T1D). T1D results from an autoimmune response that leads to destruction of the β cells in the pancreatic islet and requires lifelong insulin therapy. While much has been learned about T1D, it is now clear that there is considerable heterogeneity in T1D with regard to genetics, pathology, response to immune-based therapies, clinical course, and susceptibility to diabetes-related complications. This Review highlights knowledge gaps and opportunities to improve the understanding of T1D pathogenesis and outlines emerging therapies to treat or prevent T1D and reduce the burden of T1D.

The discovery of insulin and its rapid incorporation into clinical practice is one the greatest examples of scientific research saving lives and transforming clinical care. Prior to the discovery of insulin, type 1 diabetes mellitus (T1D) was a uniformly lethal disease that led to either rapid death from diabetic ketoacidosis or, with adherence to a strict starvation protocol, severe malnutrition and death within months or 1–2 years. As outlined by historians ( 1 ), the discovery of insulin was a reprieve from the death sentence of T1D, such that in 1922–1923, the timing of T1D onset and access to insulin determined whether some individuals died or lived for many years ( 2 ). The stories of people rescued from certain death by insulin’s discovery are nothing short of miraculous. Multiple millions of individuals with T1D and their descendants are alive today because of insulin’s discovery.

This Review provides a brief, comprehensive overview of human T1D, including what is known about its genetics, pathogenesis, and natural history, before concluding with a discussion of current and future therapeutic or preventive strategies. This Review describes exciting advances in the understanding and treatment of human T1D, but also highlights gaps in our knowledge that must be overcome to reverse and/or prevent T1D and its related complications. A recurring theme will be the growing recognition that T1D is not a single, monolithic disease but is instead heterogenous, and that the term T1D likely encompasses several different pathological processes within the clinical phenotype of T1D. While this Review is aimed at medical and scientific readers, it is essential to recognize the critical roles played by individuals with T1D and their families in advancing the understanding and treatment of T1D. These individuals have participated in paradigm-changing clinical trials, advocated for biomedical research and new technologies, and incorporated many new approaches into their daily lives.

Traditionally, the diagnosis of T1D is based on the clinical phenotype of insulin-dependent diabetes with onset in childhood or adolescence and possibly diabetic ketoacidosis. With the increasing emphasis on precision medicine, most discussion of diabetes has focused on heterogeneity in type 2 diabetes (T2D) and distinguishing T2D from monogenic diabetes or atypical forms of diabetes ( 3 – 5 ), with the assumption that one can accurately define T1D based on the clinical phenotype. This view has been substantially altered by two major changes in our thinking: (a) T1D begins with evidence of islet-directed autoimmunity prior to appearance of dysglycemia or hyperglycemia; (b) T1D likely results from different pathways to β cell destruction as reflected by differences in age of onset, genetics, pancreas pathology, metabolism, insulin secretion, and ultimately by differences in response to therapies and diabetes-related complications.

The model proposed by George Eisenbarth in 1986 has long been the standard schematic for how T1D develops, and even with many noted caveats and inconsistencies, it remains useful in framing today’s discussion about the development of T1D ( Figure 1 and Table 1 ). Components, revisions, and limitations of this model are discussed below and include new information about genetic susceptibility, pathogenic and molecular aspects of β cell–directed autoimmunity, and the interactions between immunology and islet biology ( 6 ). The current model suggests that the natural progression of T1D in genetically susceptible individuals consists of three stages ( 7 ). In stage 1, normoglycemia is accompanied by two or more islet-directed autoantibodies. The autoimmune process, as reflected by these autoantibodies, is presumably initiated by an unidentified triggering event or events. In stage 2, the autoantibodies are accompanied by dysglycemia reflecting inadequate insulin secretion after a glucose or nutrient challenge. In stage 3, the time that T1D is usually clinically diagnosed, symptoms are usually present and insulin therapy is initiated. Presentation may range from diabetic ketoacidosis to modest hyperglycemia. β cells are still present and functional (it is estimated that ~60%–90% of β cell mass has been lost at clinical presentation; ref. 8 ). β Cell loss continues over the ensuing months to years, but many individuals with longstanding T1D still secrete small amounts of insulin. Although these stages are a helpful framework, it should be noted that not all individuals in stage 1 or stage 2 progress to hyperglycemia requiring insulin treatment. A significant limitation of the model is that it is almost entirely based on measurements gathered from the peripheral blood (insulin secretion, immunological markers, etc.) and clinical parameters (age of onset, BMI, exogenous insulin requirements, etc.); only recently have there been studies of the “scene of the crime,” the human pancreas, in stages 1, 2, and 3 and recent-onset T1D (discussed below).

Model of stages of type 1 diabetes (T1D). Graph shows functional β cell mass through the stages of T1D. The blue shaded area shows number or insulin secretory capacity of β cells, with time on the x axis reflecting a broad range (could be months or years of T1D development). See text for definition of T1D stages. Roman numerals on the graph refer to questions about T1D pathogenesis shown in Table 1 .

Questions about current model of T1D development

There is a lack of accepted, specific criteria for diagnosis of T1D. Instead, the clinical diagnosis continues to rely on two main features: (a) insulin deficiency and need for exogenous insulin therapy (insulin requirements may be modest in early stage 3 and the first years after hyperglycemia onset) and (b) presence of islet-directed autoantibodies. These criteria are reasonably accurate in individuals who develop diabetes prior to 20 years of age, but are considerably less informative in those over the age of 20 years, highlighting the need for additional criteria (e.g., genetic risk score, other immunological makers, subtypes of T1D). Other autoimmune endocrinopathies (autoimmune thyroid disease, autoimmune adrenal insufficiency, celiac disease, or atrophic gastritis) may be present in up to 20% of individuals with T1D and should be considered ( 9 ).

The precise number of individuals with T1D is not known, but more than 1.6 million people in the United States are thought to have T1D ( 10 ). The incidence of T1D varies widely among countries, but this likely reflects differences in T1D-susceptibility genetic loci in a country’s population rather than environmental exposures. T1D incidence is highest in Finland, Sardinia, Sweden, Norway, and Portugal, with the rate being 60- to 300-fold greater than in China and Venezuela ( 11 – 13 ). The incidence in the United States and Europe is intermediate. These rates of T1D are likely an underestimate and do not include many young adults and adults who develop T1D because almost all incidence data comes from registries with individuals under 20 years of age. The SEARCH for Diabetes in Youth study in the United States found a 1.4% per year increase in T1D incidence from 2002 to 2012, with an unexpected increase in Hispanic youths ( 14 ). This increase in the United States is similar to the gradual worldwide annual increase in T1D incidence over the past 30 years. The reasons for this increase are not known, but may reflect changing diet and environmental exposures. There are some signs, however, that this increase is not a continuing trend ( 13 ). The number of individuals developing T1D who have the high-risk HLA alleles is declining ( 15 ), suggesting that gene-environment interactions such as diet or environmental exposure play a role.

A genetic susceptibility to T1D, although incompletely defined, is clear: identical twins have a concordance rate of 60%–70% with long-term follow-up ( 16 ) and first-degree relatives have a 5%–6% lifetime risk of T1D. Despite the clear genetic risk, most individuals with T1D have no family history of T1D. Approximately 50% of the genetic risk is related to class II HLA alleles (DR, DQ, and others). Loci related to the insulin gene and variable number tandem repeat convey the next highest genetic risk ( 15 , 17 ). At least 50 other genetic loci or SNPs, mostly in regions of the genome predicted to be involved in gene regulation, have been identified as providing a small risk for T1D. Until recently, these loci were thought to affect immune cell function and influence nearby genes, but new data suggest that some directly affect the β cell, perhaps altering β cell response to inflammation or cytokines, such as IFN-γ or IL-1β, or act by influencing distant regulatory regions ( 17 ).

The polygenic nature of T1D genetic susceptibility has stimulated efforts to combine SNPs into a T1D genetic risk score (T1D-GRS). Such a T1D-GRS has proven useful when combined with autoantibody measurements in enhancing prediction of future T1D development in individuals at high risk for T1D who are being followed in The Environmental Determinants of Diabetes in the Young (TEDDY) study ( 18 ). Similarly, application of such a T1D-GRS to the UK Biobank predicted that as many as 42% of individuals with T1D developed T1D over 30 years of age, highlighting the heterogeneity in T1D and the difficulty of diagnosing T1D in the adult population where T2D is much more frequent ( 19 ). For now, T1D-GRS is a research tool, but with further refinements it may prove useful in supporting or refuting the diagnosis of T1D in an individual or in population screening to identify individuals at high risk for developing T1D.

The pathological features of T1D, although often definitively stated (autoimmune, T cell–mediated β cell destruction accompanied by nonpathogenic, islet-directed autoantibodies), in reality has many unknowns, unproven assumptions, and knowledge gaps ( Figure 1 and Table 1 ). It is widely assumed that a triggering event or events initiate the β cell–directed autoimmunity, but these events or events remain unknown despite sustained efforts over many years. Many environmental agents have been postulated, including virus infection (Coxsackie, rubella, enterovirus, etc.), diet, intestinal microbiota, cleanliness of the environment, and gene-environmental interactions ( 20 – 24 ). The search continues in the TEDDY study, which is prospectively and systematically investigating potential environmental triggers.

Studies by a number of investigators have provided critical insight by collecting and studying the human pancreas from the few individuals who die at T1D onset, after a relatively short duration of T1D (<5 years), or who were found to have islet-related autoantibodies at the time of organ donation ( 25 – 35 ). Some of the pathological findings in the pancreas in recent-onset T1D include modest, patchy, variable insulitis (immune cell infiltration of islet); some pseudoatrophic, insulin-negative islets; and some islets with normal-appearing β cells ( Figure 2 ). The clinical features of T1D (age of diabetes diagnosis, BMI) and biomarkers (autoantibodies, C-peptide, T1D-GRS) mostly correlated with these pathological features of T1D ( 29 ), thus providing an important connection between peripheral measurements and pancreas pathology.

Pancreatic changes and immune abnormalities in T1D. The reduction of pancreas size or volume from normal to stage 2 to stage 3 T1D is shown at the top of the figure. The insets below shows a stylized section of the pancreas with islets and acinar cells in stage 2 and stage 3 T1D. Circulating autoantibodies directed at islet-enriched molecules are present in stage 2 and stage 3 but are not cytotoxic. In stage 3, immune cells are present within islets and exocrine pancreas, and there is a loss of both β cells and acinar cells. Other changes (not shown) in the stage 3 T1D islets include (a) insulin-negative, pseudoatrophic islets with rare islets appearing normal or having β cells; (b) alterations in proinsulin and insulin processing and expression of islet-enriched transcription factors such as PDX-1 and NKX6.1; (c) islet cell hyperexpression of HLA class I and class II molecules; (d) insulitis (immune cell infiltration in some islets) is variable, involving primarily CD8 + T cells, but also B lymphocytes, CD4 + T lymphocytes, and macrophages; CD20 + B lymphocytes are more common in recent-onset T1D in younger individuals; and (e) β cell mass is variable in stage 1, 2, and 3 (see Figure 1 ). Changes in the exocrine pancreas include (a) reduced pancreatic volume/mass at T1D onset and in autoantibody-positive individuals; progressive decline in pancreas volume in first 5 years of T1D; (b) acinar cell loss, some exocrine fibrosis in stage 3 pancreas; and (c) leukocyte infiltration of exocrine compartment. See text for additional details.

β Cell–directed autoimmunity involves humoral and cell-mediated autoimmunity ( Figure 2 ). Some combination of autoantibodies against glutamic acid decarboxylase (GAD), insulin (IAA), insulinoma-associated antigen-2 (IA-2), zinc transporter 8 (ZnT8), or tetraspanin-7 (Tspan7) are present at the onset of hyperglycemia in more than 90% of individuals with the typical T1D phenotype. These nonpathogenic autoantibodies are viewed as markers of the autoimmune process, with the presence of multiple autoantibodies during stage 2 in younger individuals predictive of transition to stage 3 over a 5- to 10-year timeline ( 36 ). In the TEDDY study that enrolled and followed from birth individuals genetically at high risk for T1D, insulin and GAD autoantibodies appeared in the first 5 years of life, sometimes within the first year of life, and the presence of 2 or more autoantibodies was associated with 60%–80% T1D development in follow-up over 10–15 years ( 37 , 38 ). The appearance of these autoantibodies is at a time of considerable change and plasticity in human islet morphology, cell composition, and maturation and immune system maturation and establishment, raising the possibility that developmental processes contribute to initiation of β cell–directed autoimmunity.

Despite the assumption that β cell destruction in T1D is mediated by T cells, it has been difficult to develop robust, standardized T cell–based assays ( 39 ). One reason is the low frequency of the diabetogenic T cells in the peripheral circulation of individuals with recent-onset T1D (stages 2 and 3) and the assumption that the pathogenic T cells of interest are within the islet or pancreas-draining lymph nodes. Recent work has validated this view with the isolation and cloning of CD4 + and CD8 + T cell lines from T1D islets and pancreatic lymph nodes that react to a range of islet antigens (including insulin, GAD, islet-specific glucose-6-phosphatase catalytic subunit–related protein, islet-associated amyloid polypeptide) and, interestingly, also to posttranslationally modified peptides or neoantigens (hybrid insulin peptides, etc.) ( 26 , 28 , 40 , 41 ). There is an increasing awareness that B lymphocytes are also involved in the autoimmune process ( 42 ).

β Cells exist within the human islet, a complex miniorgan ( 43 ), and according to current thinking, the β cell is not an innocent bystander simply targeted by a misdirected autoimmune process, but instead the β cell is an active participant either in the initiation or acceleration of the process leading to its own death ( 6 , 44 – 46 ). For example, the β cell’s extremely high rate of insulin biosynthesis and protein processing has been proposed to render it more susceptible to ER stress and the unfolded protein response, especially in the setting of cytokines released by infiltrating immune cells ( 47 ). Altered proinsulin processing in the pancreas and in the peripheral blood in T1D, including preclinical stages, has been noted and correlated with immune markers and markers of β cell stress ( 35 , 48 – 51 ). Multiplexed imaging mass cytometry studies of pancreatic samples collected by the Network for Pancreatic Organ donors with Diabetes ( 52 ) or the Human Pancreas Analysis Program ( 53 ) showed that at the time insulitis developed, some β cells had lost or changed expression of markers of β cell identity (C-peptide, PDX-1, NKX6.1), perhaps as a response or adaptation to immune attack ( 54 , 55 ). Perhaps portending the clinical heterogeneity of T1D, there was considerable variability in insulitis, β cell number, and gene expression within and between T1D pancreatic samples. Surprisingly, many islets in recent-onset T1D still had normal appearing β cells, consistent with observations that individuals with longstanding diabetes secrete small amounts of insulin and that the T1D pancreas continues to harbor insulin-positive cells that are glucose responsive ( 35 , 56 – 59 ). Although impaired glucagon and catecholamine secretion in response to hypoglycemia is a feature of T1D (especially longer duration T1D) and is thought to reflect autonomic neuropathy ( 60 ), recent information suggests that impaired α cell function with decreased expression of markers of cell identity (ARX) and ectopic expression of NKX6.1 in T1D islets also plays a role ( 55 , 59 ). It is not known whether the impaired α cell function is part of the T1D process or secondary to loss of β cell contact with α cells.

In T1D there is not only loss of β cells but the entire pancreas is smaller in individuals with longstanding T1D. More recently it has been found that pancreas size by MRI is reduced at the time of T1D onset and in stages 1, 2, and 3 ( 30 , 61 – 64 ). Since islets represent only 1%–2% of the pancreas, the exocrine compartment of the pancreas must also be affected in T1D ( Figure 2 ). Prior studies of pancreas size in T1D were conflicting, likely because they were cross-sectional autopsy studies or utilized a single noninvasive imaging session (ultrasound, computerized tomography, or MRI) that made it difficult to adequately account for the normal variation in pancreas size among individuals. Recent efforts, enabled by improvements in pancreas imaging and the availability of postmortem T1D human pancreata for study, have provided new information. For example, two MRI studies showed that pancreas volume is reduced at the time of clinical T1D onset, that it declines further in the first year after onset, and that it is reduced in autoantibody-positive individuals in stage 2 and in some first-degree T1D relatives ( 62 , 64 ). Pancreas size does not correlate with T1D duration, and it appears that the decline in pancreas size mostly occurs in the 5 years after clinical T1D onset ( 65 ).

The molecular mechanism responsible for the reduced volume is not clear, but there is reduced acinar cell number and evidence of fibrosis ( 61 , 65 ). Loss of an islet-derived trophic factor such as insulin has been proposed. T cell infiltration, including some T cells that recognize proinsulin, have been noted in the T1D exocrine pancreas ( 66 , 67 ). However, it is unclear how changes in the exocrine pancreas are temporally or mechanistically related to β cell loss or clinical T1D onset. One hypothesis is that the process affecting the exocrine compartment leads ultimately to β cell loss ( 68 , 69 ). How the reduced pancreas volume affects exocrine function in individuals with T1D is not well defined, but reduced pancreatic function (fecal elastase) and pancreatic exocrine insufficiency have been reported in some individuals with T1D ( 61 , 63 ).

There is emerging consensus that considerable heterogeneity exists on many levels within the clinical phenotype of T1D ( 3 , 70 ). T1D heterogeneity should not be confused with other causes of insulin deficiency such as certain forms of monogenic diabetes, immune checkpoint–related diabetes, or rare monogenic causes of immune-related diabetes ( 71 – 75 ) that can clinically mimic T1D, but instead refers to clinical, immunological, metabolic, and/or pathological heterogeneity in individuals when the clinical diagnosis of T1D seems likely ( Table 2 ). For example, there is heterogeneity in age of onset, rate of disease progression (decline in β cell function and mass), residual C-peptide production ( 27 , 36 , 37 , 50 , 76 – 79 ), a spectrum of immunological signatures (autoantibody, T cell signatures, innate immunity, etc.; refs. 80 – 84 ), and pathological abnormalities ( 29 , 31 , 49 , 85 , 86 ). Residual β cells, as reflected by low levels of C-peptide, persist for years after onset of hyperglycemia in some individuals with T1D ( 35 , 56 , 57 ). Since it is not currently possible to noninvasively assess β cell mass by imaging or biomarkers other than C-peptide, defining the natural history of β cell loss or the impact of interventions to sustain or improve β cell mass is problematic. For example, although the decline in β cell mass in Figure 1 is shown as a gradual, smooth decline, this is mostly speculative and it is possible that the decline may stop and restart, may stop and remain flat, etc. Using a nomenclature from other diseases with clinical heterogeneity such as asthma, some have proposed that T1D consists of multiple “endotypes,” meaning different underlying pathogenic processes that produce a similar phenotype ( Table 2 ) ( 70 ).

Examples of T1D heterogeneity

Two other examples of this heterogeneity are adult-onset T1D (onset after 20 years of age) and a form of diabetes sometimes termed latent autoimmune diabetes in adults (LADA). In the former, individuals have similar phenotypic features to T1D with onset in childhood or adolescence (non-obese, islet-directed autoantibodies, insulin deficiency, glycemic lability, etc.), but fewer CD20 + B lymphocytes in the few pancreata that have been studied, and a presumably slower loss of β cell mass. In contrast, LADA is often clinically confused with T2D, with onset in middle age, obesity, initial insulin independence then progressing to insulin-dependence, and presence of autoantibodies (often autoantibodies only to GAD in distinction to T1D of childhood or adolescent onset) ( 87 , 88 ). The genetics of LADA show similarities, but not complete overlap, with genetic loci associated with T1D and in some series, the T2D genetic risk loci TCF7L2 is linked ( 89 , 90 ). Whether this variability in genetic loci reflects imprecise phenotyping and inclusion of individuals with T2D is not clear. Critical unanswered questions are whether T1D with onset in the first or second decade of life is the same disease as T1D with onset in older adolescents or young adults, and how these relate to LADA with its onset in middle age.

Current T1D therapy focuses on matching exogenous insulin and food intake while incorporating daily activities such as exercise and sleep. Remarkable advances have been made in insulin formulation and diabetes technology, including methods for insulin delivery and glucose monitoring ( 91 – 97 ). Additionally, T1D clinical care is evolving to incorporate mobile technology (smart phones, wearable devices, telemedicine, etc.; ref. 91 ) and provide greater emphasis on behavioral and psychosocial aspects, the social determinants of health, and health care access/cost. The goal is near normoglycemia while avoiding hypoglycemia and allowing for normal daily activities. Traditionally, the glycemic goal in T1D has been an A1C of 7.0 or lower, with this target individualized for age, comorbidities, and lifestyle ( 98 ). Importantly, hypoglycemia, a major adverse effect of intensive glycemic control, is a substantial, lifelong burden of current therapy for T1D ( 99 ).

The foundation of insulin therapy for T1D is a combination of basal insulin and bolus insulin associated with nutrient or caloric consumption. Insulin can be given by syringe, pen (including “smart” pens), catheter connected to insulin pump or an insulin delivery device (continuous subcutaneous insulin injection), or more rarely by inhalation ( 92 , 94 , 100 ). By manipulating the insulin amino acid sequence or by incorporating attachments to the insulin protein (e.g., fatty acid, additional amino acids), the absorption profile of injected insulin can be greatly prolonged or accelerated compared with native insulin protein in a neutral buffer. In addition, insulin diluents can be used to delay absorption (such as protamine in neutral protamine Hagedorn plus native insulin) or accelerate absorption (L-arginine and niacinamide plus insulin aspart analog) ( 92 ). Because of the importance of basal insulin in regulating hepatic glucose output, basal insulin formulations with action duration of more than 40 hours or up to a week have recently become available ( 92 , 101 ). An advantage of longer-acting formulations is reduced frequency of severe hypoglycemia. Based on decades of research to understand insulin structure and function ( 102 – 104 ), future possibilities include glucose-responsive insulin ( 105 ). The price of insulin formulations in the United States has risen dramatically over the past decade, placing a tremendous financial burden on individuals with T1D ( 106 , 107 ).

These remarkable advances in insulin formulations and insulin delivery devices have greatly improved clinical care. However, many individuals with T1D state that continuous glucose monitoring (CGM) has had an even greater and more dramatic impact on their daily lives. Individuals with T1D for more than 40 years have lived through dramatic transitions from using urine glucose to infer blood glucose over past hours, to finger-stick capillary blood glucose measurements at multiple times a day, to CGM providing an essentially unlimited number of glucose values a day. Although A1C remains the standard for diabetes diagnosis and a predictor of diabetes-related complications, CGM and its dense glycemic datasets allow glycemic metrics, such as the ambulatory glucose profile using time in a defined glycemic range (TIR), the glucose management indicator (GMI) based on the mean glucose and correlative with A1C, glycemic variability or coefficient of variation, and the amount of time in the hypoglycemic range ( 98 , 108 ). These metrics correlate with A1C, are changing how the provider and patient adjust insulin, and are empowering individuals with T1D with information to better organize their daily living routines.

The rapidly evolving CGM technology is currently based on a sensor or electrode detecting the electrochemical product (e.g., hydrogen peroxide) of the reaction between interstitial glucose and a glucose oxidase. Importantly, all current CGM technologies measure interstitial glucose, which, while in equilibrium with blood glucose, may lag behind or differ from blood glucose, especially when the blood glucose is changing rapidly ( 98 , 108 – 110 ). Currently, the two broad categories of CGM are real-time CGM, in which interstitial glucose is monitored and sent to the recording device essentially continuously, and intermittent CGM, in which the sensor is in place continuously, but the glucose is only recorded when the detector is placed over the sensor. Both approaches use a subcutaneously placed sensor that must be replaced every 3–14 days; an implanted sensor that must be replaced every 6 months is also available. Although the TIR and GMI are critical CGM outputs, other features such as rate of glucose change, glucose trends, hypoglycemia alarms, and suspension of insulin delivery are extremely valuable, enabling the individual with T1D to respond preemptively and avoid anticipated hyper- or hypoglycemia and to adapt diabetes self-management in terms of diet or activities such as exercise. The pairing of CGM and an insulin delivery device or a closed loop system where the sensor-derived data regulate insulin delivery is discussed below.

Diabetes-related complications, both microvascular (retinopathy, nephropathy, and neuropathy) and macrovascular (cardiovascular disease, peripheral artery disease), are responsible for the considerable morbidity and mortality in T1D. Although hyperglycemia duration and severity are the drivers of diabetes-related complications in T1D, the molecular mechanisms of how excess glucose leads to a specific organ dysfunction are incompletely defined and likely distinctive for the affected organ system. Diabetes-related complications are likely multifactorial and involve genetic susceptibility to glucose exposure, epigenetic changes induced by hyperglycemia, associated dyslipidemia, and cellular pathways such as advanced glycation end products, sphingolipid metabolism (neuropathy), cytokines, multiple growth factors (VEGF in retinopathy), and/or oxidative stress ( 111 – 114 ). Prevention of diabetes-related complications by intensive glycemic control is a major focus of T1D clinical care but is challenging because the complications occur years or decades after T1D onset. Improved glycemic control does not reverse established diabetes-related complications.

Long-term outcomes in individuals with T1D have greatly improved with reduced frequency of retinopathy, nephropathy, and neuropathy and improved cardiovascular outcomes; it is becoming increasingly common for individuals to live for more than 50 years with T1D ( 115 ). This is partly due to improvements in glycemic control as demonstrated in the Diabetes Control and Complications Trial (DCCT) ( 116 ) and partly because of improvements in lipid and blood pressure management or treatments that reduce or delay nephropathy or retinopathy. The DCCT also demonstrated that intensive glycemic control (near normoglycemia) begun at T1D onset partially preserves β cell function (and presumably β cell mass) as reflected by C-peptide secretion, subsequently leading to improved glycemic control and less hypoglycemia. However, hypoglycemia is a major adverse effect of intensive glycemic control (increased 3-fold in DCCT) and a substantial, lifelong burden for individuals with T1D ( 99 ). Individuals with T1D using intensive glycemic therapy also gained more weight. In the years after the intensive glycemic control phase of the DCCT, study participants in the intense glycemic or control groups were followed as part of the Epidemiology of Diabetes Interventions and Complications (EDIC) study. Both groups in this next phase had similar glycemic control (A1C ~8%), but strikingly, the incidence of complications between the intense treatment groups further widened in the two groups, suggesting that the period of improved glycemic control during the intensive treatment phase translated into a continued and sustained reduction in microvascular complications ( 116 ). Termed “metabolic memory,” this also translated into improvement in cardiovascular outcomes ( 117 – 119 ). Although some have disputed the concept of metabolic memory ( 120 ), it seems likely that early glycemic control has long-term benefits, perhaps lasting more than a decade, maybe through epigenetic mechanisms ( 116 ). The clear clinical implication is that glycemic control as close to normoglycemia as safely possible should be the goal beginning immediately after the onset of hyperglycemia.

The management of T1D-related nephropathy, retinopathy, neuropathy, and cardiovascular disease continues to improve with clear benefits of ACE inhibitors and angiotensin receptor blockers in slowing the decline in glomerular filtration rate, anti-VEGF therapies affecting diabetic macular edema and retinopathy, and possibly sodium-glucose cotransporter-2 (SGLT-2) inhibitors or GLP-1 receptor agonists in T1D-related cardiovascular disease ( 111 , 121 , 122 ). Still, some individuals develop debilitating or life-threatening complications such as end-stage renal disease or neuropathy ( 122 – 125 ). Although intense glycemic control clearly reduces microvascular complications, other factors, such as genetic susceptibility, residual insulin secretion, activity of selected glycolytic enzymes, advanced glycation end-product production, and degree of activation of the renin–angiotensin–aldosterone system, may explain the difference in the rates of complications across the T1D population ( 115 , 125 ). A recent estimate predicts a lifespan in individuals with T1D and near-normal glycemic control similar to the general population, a remarkable change from past outcomes ( 126 , 127 ).

At this dawn of the next century after insulin’s discovery, anticipated new therapies fall into three broad categories ( Figure 3 ): (a) exogenous insulin replacement; (b) cell-based insulin delivery from new sources of insulin-producing cells; and (c) protection of endogenous β cells by immunomodulation.

Emerging or future T1D therapies. ( A ) Exogenous insulin replacement includes insulin analogs designed to optimize absorption, integrated closed-loop systems combining insulin delivery devices and glucose-sensing technology, and personalized algorithms (AI, artificial intelligence) to tailor insulin replacement. ( B ) Cell-based insulin delivery options include transplantation of islets or insulin-producing cells (derived from ES or iPS cells), strategies to stimulate β cell proliferation or regeneration, and approaches that encourage transdifferentiation of host cells into insulin-producing cells. ( C ) Protective strategies include immunomodulatory approaches to block inflammatory cytokines or pathogenic immune cells and prevent damage or loss of β cells. See text for additional information.

The combination of a glucose sensor and an automatically adjusting insulin delivery device in a closed loop system, often imprecisely termed an “artificial pancreas,” is very effective in T1D in children, adults, and pregnancy ( 93 , 128 – 131 ). This hardware/software is being further improved by emerging algorithms to “correct” interstitial glucose to the actual blood glucose and to predict insulin dosing based on artificial intelligence–based interpretations of personalized glucose excursions and activity. Future improvements in sensor technology may include measuring glucose at other body sites (e.g., eye, skin) or optical/vascular approaches to assess blood rather than interstitial glucose. Improvements in insulin administration are needed as subcutaneous insulin absorption into the vascular system is considerably slower than physiological insulin secretion even with modified insulin analogs ( 92 ). Plus, insulin, delivered peripherally and not into the portal vein, leads to reduced insulin action at the liver; it remains unclear how close to “normal” glucose homeostasis can be achieved by insulin delivery at a peripheral site, which also induces insulin resistance as an additional challenge ( 132 ). Devices that also deliver glucagon may be part of future therapies ( 133 ).

Cell-based insulin delivery may include transplantation of insulin-producing cells, transdifferentiation of other pancreatic cell types (exocrine, ductal, or α) into insulin-producing cells ( 134 , 135 ), or amplification/regeneration of endogenous β cells ( 136 , 137 ) ( Figure 3 ). The combination of islet allotransplantation, normal human islets from cadaveric donor(s) infused into the portal vein, and immunosuppression to prevent allo- and autoimmunity is effective in reducing hypoglycemia frequency in T1D, sometimes leading to independence from exogenous insulin ( 138 – 142 ). Because human islet supply is quite limited, insulin-producing cells from embryonic stem cells (ES cells), human induced pluripotent stem cells (iPS cells), or even xenografts (porcine) are also under investigation ( 139 , 143 – 148 ). Considerable progress in ES and iPS cell differentiation into insulin-producing cells will soon lead to clinical trials and offer the future possibility of transplanting insulin-producing cells modified to resist allo- and autoimmunity. The optimal site for transplantation of islets or insulin-producing cells is uncertain. Transdifferentiation and regeneration strategies, although attractive, need more research before testing in humans. Glucose- and nutrient-regulated insulin secretion from these new sources of insulin-producing cells is incompletely defined, but crucial. Particularly critical is the appropriate cessation of insulin secretion when the blood glucose is in the normal range to avoid hypoglycemia.

A number of studies, often part of TrialNet ( 149 ) or the Immune Tolerance Network ( 150 ), suggest that survival of endogenous β cells remaining in early stage 3 T1D can be enhanced and partially protected by immunomodulatory approaches directed at B lymphocytes, T lymphocytes, regulatory T cells, or inflammation by anti-CD3, anti-CD20, CTLA-4Ig, anti-CD2, anti–IL-1, anti–TNF-α, or thymoglobulin ( 39 , 151 – 153 ). However, thus far, such immunomodulatory interventions given soon after hyperglycemia onset have had the modest impact of preserving C-peptide secretion, which is not often sustained. Efforts are underway to understand why only some individuals respond to such therapies, if repeat dosing is needed, and if antigen-specific therapy or exposure (insulin, GAD) can reduce the autoimmune response ( 39 , 154 ). A single course of an Fc receptor–nonbinding anti-CD3 monoclonal antibody given to first-degree relatives at high risk for developing T1D (stage 2, two islet-directed autoantibodies) delayed the onset of hyperglycemia by 2 years, with twice as many individuals progressing to clinical diabetes in the control group (57% versus 28%) ( 155 ). This intervention is thought to target “pathogenic” T cells, raising the possibility of induction of immune tolerance or a reset of the immune system ( 156 ). This study also highlights how current efforts in the United States, Europe, and Australia to identify high-risk individuals by screening the general population for T1D-GRS and/or islet-directed autoantibodies may lead to new efforts to prevent T1D ( 154 ). Efforts to prevent T1D would also be greatly accelerated by identification of the triggering event(s) for β cell–directed autoimmunity.

Over the next decade, it truly will be a competition between these three clinically viable therapeutic options for T1D. For illustrative purposes, consider these scenarios in which glycemia is essentially normalized and diabetes-related complications are prevented by the following: (a) a mechanical insulin-delivery device/glucose sensor system, requiring little input from the patient and with little limitation of daily diet or activities; (b) transplantation of insulin-producing cells every 1–2 years, accompanied by continuous, “safe” immunomodulation/immunosuppression; and (c) intermittent, immunomodulation/immunosuppression beginning at the onset of hyperglycemia or prior to hyperglycemia (stage 2).

What will be the therapy for T1D in 5 years, 10 years, or 20 years? This will be determined by balancing the benefit, sustainability, adverse impact, safety, convenience, personal preference, and financial cost of these evolving options ( 39 , 157 – 159 ). Plus, as therapies in each category continue to improve, the “bar is raised” for other competing therapies and the optimal therapy will likely change. In addition, an individual’s age and T1D duration will be important considerations in choosing among competing therapies — the choice will likely be different in a 12-year-old with new-onset T1D and a 42-year-old with T1D for 30 years.

In summary, since insulin’s discovery, there has been much progress in understanding the pathogenesis of T1D and in using insulin to improve the lives of individuals with T1D. These efforts are incomplete in that T1D continues to be a substantial burden for individuals with T1D and their families. Hopefully, the opportunities discussed here will be realized, leading to the prevention of T1D and its associated burdens.

The author apologizes to the many investigators whose important work on T1D was not cited because of space limitations. Research by the author and his colleagues is or has been supported by the National Institute of Diabetes and Digestive and Kidney Diseases; the Human Islet Research Network (RRID: SCR_014393; https://hirnetwork.org; DK112232, DK123716, DK123743, DK104211, DK108120); and by DK106755, DK117147, DK20593 (Vanderbilt Diabetes Research and Training Center); the Leona M. and Harry B. Helmsley Charitable Trust; the Juvenile Diabetes Research Foundation; and the Department of Veterans Affairs (BX000666). The author thanks Vanderbilt colleagues, Daniel J. Moore, John T. Walker, and Jordan J. Wright, for reading the manuscript and providing insightful suggestions.

Conflict of interest: The author has declared that no conflict of interest exists.

Copyright: © 2021, American Society for Clinical Investigation.

Reference information: J Clin Invest . 2021;131(8):e142242.https://doi.org/10.1172/JCI142242.

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Type 1 Research Highlights

While the Association’s priority is to improve the lives of all people affected by diabetes, type 1 diabetes is a critical focus of the organization. In fact, in 2016, 37 percent of our research budget was dedicated to projects relevant to type 1 diabetes. Read more about the critical research made possible by the American Diabetes Association.

Smart Insulin Patch

American Diabetes Association Pathway to Stop Diabetes Scientist Zhen Gu, PhD, recently published a paper describing the development of an innovative "smart insulin" patch that imitates the body's beta cells by both sensing blood glucose levels and releasing insulin.

A Possible Trigger for Type 1 Diabetes

In order to prevent or reverse the development of type 1 diabetes, it is essential to understand why and how the immune system attacks the body’s own cells. Association-funded Researcher Thomas Delong, PhD, found a possible answer to these questions.

Enhancing Survival of Beta Cells for Successful Transplantation

Islet transplantation has long offered hope as a curative measure for type 1 diabetes. However, more than 80% of transplanted islets die within one week after transplantation. Research efforts are working to improve their survival and the promise of stem cells to reverse diabetes.

Explore: Type 1 Research Highlights

Investments in type 1 diabetes research

The CDC estimates that nearly 1.6 million Americans have it, including about 187,000 children and adolescents. The American Diabetes Association funds a productive research portfolio that offers significant progress and hope for improved outcomes for people with type 1 diabetes.

Identifying type 1 diabetes before beta cell loss

Dr. Hessner is investigating so-called “biomarkers,” which are components in blood or tissue samples that can be measured to predict which individuals are most likely to develop type 1 diabetes. 

Beta cell replacement

Both type 1 and type 2 diabetes result from a complete or partial loss of beta cell number and function. Finding a way to successfully replace functional beta cell is key to efforts to one day cure diabetes.

Enhancing survival of beta cells for successful transplantation

Islet transplantation has long offered hope as a curative measure for type 1 diabetes. However, more than 80% of transplanted islets die within one week after transplantation. Research efforts are working to improve their survival and the promise of stem cells to reverse diabetes.


New insight into how diabetes leads to blindness

New research is uncovering how diabetes changes the kinds of proteins that are made in the eye. These changes may lead to diabetic retinopathy, a leading cause of blindness. This information is allowing researchers to identify new targets for therapies that could delay or prevent the development of diabetic retinopathy.

Give Today and Change lives!

With your support, the American Diabetes Association® can continue our lifesaving work to make breakthroughs in research and provide people with the resources they need to fight diabetes.

What is type 1.5 diabetes? It’s a bit like type 1 and a bit like type 2 – but it’s often misdiagnosed

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While you’re likely familiar with type 1 and type 2 diabetes, you’ve probably heard less about type 1.5 diabetes.

Also known as latent autoimmune diabetes in adults (LADA), type 1.5 diabetes has features of both type 1 and type 2 diabetes .

More people became aware of this condition after Lance Bass , best known for his role in the iconic American pop band NSYNC, recently revealed he has it.

So, what is type 1.5 diabetes? And how is it diagnosed and treated?

There are several types of diabetes

Diabetes mellitus is a group of conditions that arise when the levels of glucose (sugar) in our blood are higher than normal. There are actually more than ten types of diabetes, but the most common are type 1 and type 2.

Type 1 diabetes is an autoimmune condition where the body’s immune system attacks and destroys the cells in the pancreas that make the hormone insulin. This leads to very little or no insulin production.

Insulin is important for moving glucose from the blood into our cells to be used for energy, which is why people with type 1 diabetes need insulin medication daily . Type 1 diabetes usually appears in children or young adults.

Type 2 diabetes is not an autoimmune condition. Rather, it happens when the body’s cells become resistant to insulin over time, and the pancreas is no longer able to make enough insulin to overcome this resistance . Unlike type 1 diabetes, people with type 2 diabetes still produce some insulin.

Type 2 is more common in adults but is increasingly seen in children and young people. Management can include behavioural changes such as nutrition and physical activity, as well as oral medications and insulin therapy.

How does type 1.5 diabetes differ from types 1 and 2?

Like type 1 diabetes, type 1.5 occurs when the immune system attacks the pancreas cells that make insulin. But people with type 1.5 often don’t need insulin immediately because their condition develops more slowly. Most people with type 1.5 diabetes will need to use insulin within five years of diagnosis, while those with type 1 typically require it from diagnosis.

Type 1.5 diabetes is usually diagnosed in people over 30 , likely due to the slow progressing nature of the condition. This is older than the typical age for type 1 diabetes but younger than the usual diagnosis age for type 2.

Type 1.5 diabetes shares genetic and autoimmune risk factors with type 1 diabetes such as specific gene variants. However, evidence has also shown it may be influenced by lifestyle factors such as obesity and physical inactivity which are more commonly associated with type 2 diabetes.

What are the symptoms, and how is it treated?

The symptoms of type 1.5 diabetes are highly variable between people. Some have no symptoms at all. But generally, people may experience the following symptoms :

• increased thirst
• frequent urination
• blurred vision
• unintentional weight loss.

Typically, type 1.5 diabetes is initially treated with oral medications to keep blood glucose levels in normal range. Depending on their glucose control and the medication they are using, people with type 1.5 diabetes may need to monitor their blood glucose levels regularly throughout the day.

When average blood glucose levels increase beyond normal range even with oral medications, treatment may progress to insulin. However, there are no universally accepted management or treatment strategies for type 1.5 diabetes.

Type 1.5 diabetes is often misdiagnosed

Lance Bass said he was initially diagnosed with type 2 diabetes , but later learned he actually has type 1.5 diabetes. This is not entirely uncommon . Estimates suggest type 1.5 diabetes is misdiagnosed as type 2 diabetes 5–10% of the time .

There are a few possible reasons for this.

First, accurately diagnosing type 1.5 diabetes, and distinguishing it from other types of diabetes, requires special antibody tests (a type of blood test) to detect autoimmune markers. Not all health-care professionals necessarily order these tests routinely, either due to cost concerns or because they may not consider them.

Second, type 1.5 diabetes is commonly found in adults, so doctors might wrongly assume a person has developed type 2 diabetes, which is more common in this age group (whereas type 1 diabetes usually affects children and young adults).

Third, people with type 1.5 diabetes often initially make enough insulin in the body to manage their blood glucose levels without needing to start insulin medication. This can make their condition appear like type 2 diabetes, where people also produce some insulin.

Finally, because type 1.5 diabetes has symptoms that are similar to type 2 diabetes, it may initially be treated as type 2.

We’re still learning about type 1.5

Compared with type 1 and type 2 diabetes, there has been much less research on how common type 1.5 diabetes is, especially in non-European populations . In 2023, it was estimated type 1.5 diabetes represented 8.9% of all diabetes cases, which is similar to type 1. However, we need more research to get accurate numbers.

Overall, there has been a limited awareness of type 1.5 diabetes and unclear diagnostic criteria which have slowed down our understanding of this condition.

A misdiagnosis can be stressful and confusing. For people with type 1.5 diabetes, being misdiagnosed with type 2 diabetes might mean they don’t get the insulin they need in a timely manner. This can lead to worsening health and a greater likelihood of complications down the road.

Getting the right diagnosis helps people receive the most appropriate treatment, save money, and reduce diabetes distress . If you’re experiencing symptoms you think may indicate diabetes, or feel unsure about a diagnosis you’ve already received, monitor your symptoms and chat with your doctor.

• Type 2 diabetes
• Type 1 diabetes
• Blood sugar
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• Published: 30 March 2017

Type 1 diabetes mellitus

• Anastasia Katsarou 1 ,
• Soffia Gudbjörnsdottir 2 , 3 ,
• Araz Rawshani 2 , 3 ,
• Dana Dabelea 4 ,
• Ezio Bonifacio 5 ,
• Barbara J. Anderson 6 ,
• Laura M. Jacobsen 7 ,
• Desmond A. Schatz 7 &
• Åke Lernmark 1  

Nature Reviews Disease Primers volume  3 , Article number:  17016 ( 2017 ) Cite this article

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• Autoimmune diseases
• Diabetic nephropathy
• Diagnostic markers
• Insulin signalling
• Type 1 diabetes

Type 1 diabetes mellitus (T1DM), also known as autoimmune diabetes, is a chronic disease characterized by insulin deficiency due to pancreatic β-cell loss and leads to hyperglycaemia. Although the age of symptomatic onset is usually during childhood or adolescence, symptoms can sometimes develop much later. Although the aetiology of T1DM is not completely understood, the pathogenesis of the disease is thought to involve T cell-mediated destruction of β-cells. Islet-targeting autoantibodies that target insulin, 65 kDa glutamic acid decarboxylase, insulinoma-associated protein 2 and zinc transporter 8 — all of which are proteins associated with secretory granules in β-cells — are biomarkers of T1DM-associated autoimmunity that are found months to years before symptom onset, and can be used to identify and study individuals who are at risk of developing T1DM. The type of autoantibody that appears first depends on the environmental trigger and on genetic factors. The pathogenesis of T1DM can be divided into three stages depending on the absence or presence of hyperglycaemia and hyperglycaemia-associated symptoms (such as polyuria and thirst). A cure is not available, and patients depend on lifelong insulin injections; novel approaches to insulin treatment, such as insulin pumps, continuous glucose monitoring and hybrid closed-loop systems, are in development. Although intensive glycaemic control has reduced the incidence of microvascular and macrovascular complications, the majority of patients with T1DM are still developing these complications. Major research efforts are needed to achieve early diagnosis, prevent β-cell loss and develop better treatment options to improve the quality of life and prognosis of those affected.

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The authors were supported by the US NIH (grants K12DK097696 and R21DK106505 to B.J.A.; DK60987, DK60987, DK104216 and UL1TR001427 to D.A.S. and L.M.J.; and DK063861 to Å.L.), The Leona M. and Harry B. Helmsley Charitable Trust (grants 2015PG-T1D084 and 2016PG-T1D011 to B.J.A.) and the Swedish Research Council (Å.L., S.G. and A.R.).

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Introduction (A.K. and Å.L.); Epidemiology (A.K., Å.L. and D.D.); Mechanisms/pathophysiology (E.B.); Diagnosis, screening and prevention (A.K., Å.L., E.B. and D.D.); Management (D.A.S., L.M.J., S.G. and A.R.); Quality of life (B.J.A.); Outlook (Å.L.); Overview of Primer (Å.L.).

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Katsarou, A., Gudbjörnsdottir, S., Rawshani, A. et al. Type 1 diabetes mellitus. Nat Rev Dis Primers 3 , 17016 (2017). https://doi.org/10.1038/nrdp.2017.16

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Effects of pendulum orientation and excitation type on the energy harvesting performance of a pendulum based wave energy converter

• Open access
• Published: 28 August 2024

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• Mollie Reid 1 ,
• Vladislav Sorokin 1 &
• Kean Aw 1  

With global electricity requirements due to increase in the coming years and growing pressure to reduce dependence on fossil fuels, universal demand for renewable energy is projected to grow. Marine energy, including wave energy, is an active research area, with potential to meet future energy demands, due to its high energy density. With a view to using a pendulum system in a floating object to extract energy from ocean waves, this paper analyses the effects of pendulum orientation and excitation type on the system’s dynamics. Three excitation scenarios, surge, heave and dynamic tilt of the floating object, with various pendulum orientations, were analysed and simulated. Both linearised and nonlinear systems were investigated with the former providing insight into the nonlinear system’s behaviour. Effects of pendulum orientation on power output potential differs significantly with excitation type and pendulum properties. While expected peak power output is observed at the resonant frequency and twice the resonant frequency under direct and parametric excitations respectively for both systems, the linearised system also exhibits regions of instability. These instability regions under parametric excitations were investigated with consideration for energy harvesting applications. Theoretical and experimental findings revealed that dynamic tilt excitations can be utilised for broadband energy harvesting at the expense of the peak power output. While peak average power output for these excitations for the considered system parameters is relatively low, 1 W versus 12.5 W for heave excitation, the bandwidth is very broad and starts from 0 rad/s frequency if tilt excitation amplitude is above 1.1 rad.

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1 Introduction

In recent years there has been particular interest in energy harvesting from low frequency sources naturally occurring in the environment [ 1 , 2 ]. Energy harvesters utilising naturally occurring low frequency vibrations from waves to human motion have been developed to reduce reliance on fossil fuels and meet sustainable goals [ 3 , 4 ]. With the growing use of electronic devices across various sectors, the global requirement for renewable electricity will only increase.

A common theme in many of the developed energy harvesters is the use of pendulums as a mechanical mechanism to capture the motion of the excitation before it is converted to electricity [ 5 ]. Pendulum systems can be tuned to a target excitation frequency range and can offer a sustained dynamic output with low frequency excitations [ 6 ]. The rotational motion of the pendulum is easily adaptable to many power take off mechanisms such as electromagnetic [ 7 , 8 , 9 ], piezoelectric [ 10 , 11 , 12 ] and triboelectric [ 13 , 14 , 15 ] generators, thus contributing to the pendulum’s versatility.

However, active tuning can increase the cost and complexity of the device, counteracting the increased power output afforded by resonance tuning [ 16 ]. Additionally, the pendulum’s high mechanical power output is relatively narrow banded, occurring close to the resonance frequency and at twice the resonance frequency, depending on the excitation type. These factors may restrain the wide use of pendulums in energy harvesting.

Various forms of pendulum energy harvesters have been established utilising the classic vertical pendulum [ 17 , 18 ], horizontal pendulum [ 19 , 20 ], double pendulum [ 21 , 22 ], inverted pendulum [ 23 , 24 ], and spring pendulum [ 25 , 26 ]have been considered with a scope to energy harvesting. Additionally, studies focusing on the effects of various excitation types, such as heave [ 27 , 28 ] and surge [ 29 , 30 ] on pendulum dynamics have been conducted. Each pendulum system has merits and shortcomings based on the intended excitation source.

Despite the vast amount of research into pendulum dynamics in numerous orientations for wave energy harvesting, there is no clear comparison of different pendulum orientations, excitation type and associated power output potential as a wave energy harvesting tool. This paper studies the effect of heave, surge and dynamic tilt motion on the dynamics of various pendulum orientations. Single frequency excitations are first considered; an equation of motion describing all pendulum orientations and excitation scenarios is derived and subsequently numerically integrated. A linearised equation is also considered to obtain insight into the pendulum’s response under direct and parametric excitations. The linear system’s stability is analysed to identify regions of dynamic instability that can lead to relatively large power output of the nonlinear system. The nonlinear model is used to predict the dynamic response of the pendulum under the various excitation types. Finally, experiments are conducted to validate the obtained theoretical results.

2 Equation of motion

The equation of motion for a simple pendulum, represented by a concentrated mass m at a distance l from the point of support, is considered. This pendulum can be excited in a heave and surge direction and tilted by an angle of φ by the incident waves. The pendulum system considered is demonstrated in Fig.  1 . In the neutral position, the position of the pendulum mass, m , can be described using spherical coordinates relating to the pendulum angular displacement, θ and fixed axes X, Y and Z. Effects of the tilt excitation are considered by assuming that the plane of the pendulum motion is tilting with respect to axis Y . Axes X’ and Z’ are introduced and are tilting with the plane by angle φ .

Heave, Surge & Tilting Pendulum Schematic

The angle of tilt, φ , similar to roll motion, is defined in Eq. ( 1 ), where the latter term describes a dynamic tilt due to wave excitation and the former the static plane of the pendulum. The dynamic term consists of the tilt amplitude, D , and µ , the tilt frequency. The static angle of tilt, α , remains constant and dictates the pendulum’s orientation.

According to the excitation direction, the effects of heave and surge are added to the pendulum coordinates. Heave, H , is included in the Z coordinate as it excites the pendulum vertically. The effect of surge is split into two terms, S 1 and S 2 , as it acts horizontally in both the X and Y directions.

Where \(\varphi =\alpha +D\text{cos}\mu t\) . Altering φ changes the pendulum's orientation from horizontal to vertical or vice versa. In the case of \(\alpha =\frac{\pi }{2}\) and neglecting dynamic tilt, \(\varphi =\frac{\pi }{2}\) , therefore the pendulum will operate vertically, and the output will be that of a classic vertical pendulum.

In order to utilise the Lagrange approach to obtain equations of motions for the pendulum, the displacement and velocity components of the pendulum with respect to the fixed coordinate system X-Y-Z are calculated:

The system's potential and kinetic energy are then calculated, giving:

Potential energy, with g relating to gravitational acceleration (m/s 2 ), is

Using the Lagrange Equation,

From further simplification and adding linear damping into the system, we get:

where the final term in Eq. ( 9 ), containing C (Ns/m), corresponds to the effects of linear damping in the system.

For the case of pure tilt excitation, with \(\ddot{{S}_{1} , }\ddot{{S}_{2} , }\ddot{H}= 0,\) Eq. ( 9 ) reduces to:

Corresponding to equation studied in [ 19 ]. For the case of a vertical pendulum with pure heave excitation we let \(\ddot{{S}_{1}}=0, \dot{\varphi }=0\) , \(\varphi =\frac{\pi }{2}\) , and Eq. ( 9 ) is reduced to:

which is similar to the classical equation, cf. e.g. [ 31 ]. Finally, considering the vertical pendulum with single surge excitation, \(\ddot{H}=0, \dot{ \varphi }=0\) , \(\varphi =\frac{\pi }{2}\) , we get

which is analogous to equation derived by [ 30 ].

3 Linearised system behaviour

In order to predict the pendulum response to a variety of excitation types and determine conditions under which a relatively large response can be achieved, Eq. ( 9 ) was linearised around the fixed equilibrium point \(\theta =0\) . Using small angle approximations, small angles of displacement, \(sin\theta \approx \theta \) and \(cos\theta \approx 1\) . Applying these assumptions gives:

Using the derived linearised Eq. ( 13 ), variations of the pendulum orientation and excitation conditions will be analysed below. Due to the small angle approximations used in Eq. ( 13 ) the linearised model is expected to accurately replicate the nonlinear model at low amplitudes of the pendulum motion.

Employing relations for S 1 , S 2 , and H , where \({S}_{1}={S}_{2}=A\text{sin}\rho t\) and \(H=B\text{cos}\omega t\) , Eq. ( 13 ) can be presented as:

As seen above, surge excitation S 2 excites the system directly, whereas heave excitation H and surge excitation S 1 excite the system parametrically; tilt excitation φ also excites the system parametrically.

3.1 Pure heave excitation

In the case of pure heave excitation, with \({\text{S}}_{1} = 0\) , \({\text{S}}_{2} = 0\) , and φ is constant, Eq. ( 13 ) reduces to:

Rearranging into the classical Mathieu Equation.

The linear damping coefficient in this system is denoted by β , equal to \(\frac{C}{ml}\) . The term \(P\text{cos}\Omega t\) is the heaving parametric excitation, P is equal \(\frac{B{\omega }^{2}}{g}\) and Ω is ω, heave excitation frequency, and the natural frequency of the system is \(\sigma = \sqrt {\frac{g}{l}\sin \varphi } .\) The instability criterion of the system response due to the primary parametric resonance is [ 32 ] [ 33 ].

Which, in terms of the original parameters of the system can be written as:

To obtain the frequency bandwidth of the instability, the below equation is considered:

The corresponding frequencies at the instability boundary for given values of σ, β and B are:

where \({\omega }^{2}=\Lambda \) . For the original nonlinear system Eq. ( 9 ), the instability of the linearised model implies relatively large power output.

The system’s instability frequency bandwidth for given values of the excitation amplitude B and frequency ω is plotted in Fig.  2 a, with σ = 0.5 Hz = 3.13 rad/s, and \(\upbeta =1\) . Figure  2 b demonstrates the system with a lower natural frequency, σ , is set to 0.1 Hz = 0.6 rad/s, a typical low frequency ocean wave frequency.

Linearised vertical pendulum system’s instability bandwidth while undergoing heave excitations

Figure  2 a and b provides an overview of the system performance for the heave excitation amplitude, B , equal to 0–5 m. For excitation amplitudes of less than 0.15 m in Fig.  2 a, at all frequencies, the system has a stable (zero) response; characterised by the complex values of the frequencies governed by Eq. ( 20 ). At higher amplitudes, between 0.15 and 0.5 m, values of the frequencies limiting the instability region \({\omega }_{1}\) and \({\omega }_{2}\) become real. Excitation conditions within the instability range highlighted in red result in an unstable system response. Finally, at amplitudes greater than 0.5 m, the system becomes unstable for excitation frequencies higher than \({\omega }_{1}\) . It is expected that excitation conditions within the instability region of the linear system will result in a larger power output of the corresponding nonlinear system.

Focusing on Fig.  2 b we see that the frequency bandwidth at which the system is unstable is much narrower for all values of B , as compared to the previously examined case, Fig.  2 a.

3.2 Pure parametric surge excitation

The case of pure surge excitation S 1 , with \(H=0\) , \({S}_{2}=0\) , and φ is constant is considered here, the direct surge excitation scenario is further analysed in Appendix A.

Considering only parametric surge excitations, Eq. ( 13 ) reduces to:

where \(\ddot{{S}_{1}}=\frac{A{\rho }^{2}}{l}cos\rho t\) .

It can be rearranged into the classical Mathieu Equation, Eq. ( 17 ). The excitation amplitude P is \(\frac{{A\rho^{2} }}{g\sin \varphi }\cos \varphi\) . The instability criterion then gives:

The associated frequency values bounding the instability region for given values of A, C and φ are:

Where \({\rho }^{2}= \Gamma \) . Figure  3 a, depicts the frequency bandwidth of the system where σ is 3.13 rad/s, \(\varphi = \frac{\pi }{4}\) and β is 1 . Figure  3 b shows the frequency bandwidth for σ = 0.6 rad/s and and \(\upbeta =1\) .

Linearised tilted pendulum system’s instability bandwidth while undergoing surge excitations

Figure  3 a depicts the system output under parametric surge excitation with amplitude A, ranging from 0 to 4.5 m. A stable response is exhibited at excitation amplitudes under 0.22 m, typified by the complex values of the bounding frequencies described by Eq. ( 23 ). The frequencies ρ 1 and ρ 2 constraining the instability region become real at excitation amplitudes between 0.22 and 0.5 m, resulting in an unstable system response denoted in red. For excitation amplitudes exceeding 0.5 m, the system has a generally unstable response restricted on the lower bound by ρ 2 .

Under the conditions shown in Fig.  3 b, the system’s stability frequency bandwidth is narrower and the amplitude at which instabilities occur is greater. The results shown in Fig.  3 b are comparable to the results exhibited in Fig.  2 b under parametric heave excitation, with the latter possessing a slightly lower amplitude at which the instabilities occur.

3.3 Pure dynamic tilt excitation

Finally, the effects of a varying angle of tilt on the linearised system response were considered, this parameter is characterised in Eq. ( 9 ) by the \(\dot{\varphi }\) term. The effects of heave and surge on the system were neglected resulting in:

The undamped natural frequency of the system is:

It is noted that Eq. ( 24 ) depends on the amplitude and frequency of the tilt excitation rather than physical pendulum properties. Equation ( 24 ) involves two parametric excitation terms, one of which contains the frequency 2 μ. Considering this excitation only, the equation can be reduced to Eq. ( 26 ) via:

The corresponding instability condition reduces to:

The values of the frequencies bounding the instability region are given by:

For \(\upbeta =0.1\) and 1 , the instability region is presented in the Fig.  4 a and b respectively.

Linearised tilted pendulum system’s first instability bandwidth while undergoing dynamic tilt excitations

Figure  4 a and b depicts the system performance under dynamic tilt excitation between excitation amplitudes of 0–5 rads. The system exhibits instabilities, highlighted in red, between excitation amplitudes of 1.1–2 rads. Real values of excitation frequency, µ 2 , form the boundaries of the instability region with excitation conditions within the instability region yielding unstable system responses. Unlike other tested excitation scenarios, such as parametric heave and surge, dynamic tilt can exhibit unstable regions at very low excitation frequencies ranging from 0 rad/s independent of the physical properties of the pendulum. We see that for different values of \(\upbeta \) , correlating to the system’s physical properties, the amplitude of excitation D , where instability regions occur does not change, however, the upper frequency limits where the system is unstable reduces as \(\upbeta \) reduces.

To further investigate the system’s instability region under parametric dynamic tilt excitation, the second instability region is examined. This instability region occurs when the excitation frequency is close to the systems natural frequency, as seen in Eq. ( 29 ) below.

The corresponding instability region is shown in Fig.  5 .

Linearised tilted pendulum system’s second instability bandwidth under dynamic tilt excitations

From Fig.  5 , we see that the instability region occurs at amplitudes above 2 rads. In this scenario, instabilities occur at low frequencies but at relatively high amplitudes, additionally, the instability frequency bandwidth is relatively narrow. Comparing the results for both instability regions of the dynamic tilt excitation, we would expect the linear system to be unstable at all amplitudes above \(\sqrt{2}\)  rads for low excitation frequencies.

Wide instability bandwidths occurring at low excitation frequencies are favoured for wave energy harvesting applications due to the low frequency broadband nature of ocean waves. Based on the linear systems analysed and their associated excitation types, surge and heave excitations generally have narrow instability bandwidths occurring at relatively high frequencies, making them suboptimal for this application. In contrast, the dynamic tilt excitation scenarios of the linear system possess wider instability regions occurring at lower excitation frequencies from 0 rad/s and above. Ultimately, the instability regions of the system under dynamic tilt excitation correlate well with the low frequency nature of ocean waves.

4 Power output of the nonlinear system

The pendulum’s output, defined in Eq. ( 31 ) where C is damping and \(\dot{\theta }\) is the mass velocity ( rad/s ),

and consistency across different excitation ranges should be examined to establish the most suitable pendulum and excitation type to harvest excitations from ocean waves. To evaluate the analytical results, the linear and nonlinear systems are compared. Frequency bandwidths in which instabilities are present in the linear system are used when studying the nonlinear system. Excitations at these frequency bandwidths are expected to yield higher average mechanical power output,

of the nonlinear system at steady state, where t 1 and t 2 are equal to time at steady state.

In simulations, parameters for the nonlinear system were selected based on the equivalent linear system’s instability regions. Physical parameters for each of the simulated systems are kept constant to facilitate a direct comparison between excitation types. It is noted that optimisation of the systems’ parameters for given excitation conditions has not been undertaken.

4.1 Heave excitation

First, focusing on pure heave excitation, from Fig.  2 a, it can be seen that for excitation amplitude, B = 0.3 m, the instability frequency bandwidth is between 5 and 9.5 rad/s. Therefore, it is expected that when the nonlinear system is excited within this frequency range, there will be an increase in power output. Taking \(\text{C }= 1\)  Ns/m, \(\text{m }= 1\)  kg and \(\text{l }= 1\)  m, the static angle of tilt to be \(\frac{\pi }{2}\) and gravity to be 9.8 m/s 2 , the nonlinear systems average power output versus excitation frequency, ω , is plotted in Fig.  6 a. Similarly, reducing the system’s natural frequency to 0.6 rad/s yields the results shown in Fig.  6 b.

Power output versus frequency for heave excitation

The nonlinear system’s peak average power output occurs within the instability frequency bandwidth identified in Fig.  6 a confirming the correlation between the linear system instabilities to the power output of the nonlinear system. Figure  6 b depicts an average steady state power output occurring within the instability bandwidth identified by the linear system as predicted.

4.2 Surge excitation

A similar analysis is undertaken for pure surge excitation, S 1 . Figure  3 a shows a frequency bandwidth at which instabilities occur of between 4 and 9.3 rad/s for an amplitude of \(\text{A}=0.4\) m . Taking the static angle of tilt to be \(\frac{\pi }{4}\) , \(\text{C }= 1\)  Ns/m, \(\text{m}=1\)  kg and \(\text{l}=1\)  m the power output of the nonlinear system can be determined Fig.  7 a . For Fig.  7 b the system’s natural frequency was decreased to 0.6 rad/s, a surge excitation amplitude of \(A=3\)  m was considered, where the static angle of tilt equals \(\frac{\pi }{4}\) and damping, \(C=1\)  Ns/m and mass, \(m =1\)  kg .

Power output versus frequency for surge excitation

Figure  7 a depicts the power output of the nonlinear system under pure surge excitation with varying excitation frequencies. As predicted, the peak average power output at steady state falls within the frequency range in which instabilities occur in the linear system. Considering Fig.  7 b, under the described excitation parameters, linear system again accurately predicts the power output bandwidth at which the nonlinear system produces a steady state power output. Comparing the response of pure heave and surge excitations, a narrower instability bandwidth is observed, where the natural frequency is lower. Again, both surge and heave excitation types exhibit narrow power output bandwidths governed by the linear system’s instability frequency bandwidth.

4.3 Dynamic tilt excitation

From Fig.  4 power output peaks are expected at any excitation frequency between \(\sqrt{2}\) and 2 rad. Compared to pure surge and pure heave excitations, dynamic tilt has the broadest frequency bandwidth at which instabilities in the linear system occur.

For varying amplitudes, D , and where the pendulum length, \(\text{l}=1\)  m, mass, \(\text{m}=1\)  kg and damping coefficient \(\text{C}=0.1\)  Ns/m and µ is 0.6 rad/s. The average peak power output at steady state, Fig.  8 a, is plotted for the nonlinear system and compared to the instability regions determined. Figure  8 b considers a constant excitation amplitude, where \(D = 1.16\) rad , damping, \(C=0.1\)  Ns/m, mass, \(m=1\)  kg and length, \(l=1\)  m, while the excitation frequency µ is varied.

Power output versus amplitude and frequency for dynamic tilt excitation

Comparing the results of the linearised and nonlinear models for Fig.  8 a, the average power output at steady state from the nonlinear system begins to increase around the instability regions identified by the linear models, with the average power output peaking within this range.

For the scenario depicted in Fig.  8 b the linear system predicted the frequencies in which the nonlinear system would elicit a dynamic response corresponding to higher power. For low frequencies of less than 2 rad/s the system still yields a power output as predicted by the linear system, further consolidating the suitability of dynamic tilt excitations for wave energy harvesting applications.

5 Experimental analysis

An experimental study was conducted to verify the analytical results. A pendulum arm was attached to an 8 mm ball bearing and support plate. The orientation of the support plate can be varied using brackets, giving the pendulum a static angle of tilt, α . A Spekra APS113 shaker controlled by a VR Revolution controller excites the experimental assembly. A Dytran 7500A1 accelerometer was used with an APS 125 power amplifier and a Dytran 4010DC signal conditioner to provide feedback for the controller. A schematic of the equipment used is shown below (Figs. 9 , 10 ).

Experimental Set up

Vertical pendulum orientation assembly with vertical low frequency long stroke shaker

The vertical pendulum employs a pendulum arm of length, \(l=0.1\)  m and a mass \(m =0.1\)  kg. The tilted pendulum experimental set up utilised an arm of length \(l=0.1\)  m and \(m=0.04\)  kg. A smaller mass was used for the tilted pendulum as the larger 0.1 kg mass resulted in slight deflection of the pendulum arm, which in turn caused the arm to collide with the supporting structure. A protractor was used behind the pendulum assembly to provide calibration for results processing.

The pendulum’s displacement was recorded using an HD camera operating at 30 frames per second and processed using Tracker software. Analysis of the experiment videos yields the pendulum’s angular displacement; from this, the damping of the experimental set up is calculated in addition to the angular velocity. A rig detailed in Fig.  11 was used to simulate dynamic tilt.

Dynamic tilt experimental schematic

The vertical shaker is used to excite the adjacent platform, which is attached to a shaft and bearing assembly, allowing the platform to tilt, thus creating the dynamic tilting motion which excites the attached pendulum assembly.

Free vibration of the vertical and tilted pendulums was recorded multiple times to obtain the average response in order to determine the damping. The logarithmic decay of the free vibration oscillations, δ , was calculated. Where \({\theta }_{i}\) is the initial displacement and \({\theta }_{i+n}\) is the displacement after n oscillations.

From this, the damping ratio, ζ , of the experimental set up was determined:

The damping coefficient of the pendulum can then be determined Eq. ( 35 ), where σ is the system’s natural frequency (rad/s), m is the mass (kg), l is the pendulum arm length (m) and C is linear damping (Ns/m).

Using this methodology, the linear damping component for the vertical and tilted pendulum, presented in Fig.  12 a and b, respectively, is determined and compared to simulated results to verify.

Experimental and simulated free vibration for a vertical and tilted pendulum system

Focusing on Fig.  12 a, despite some initial phase discrepancies, the experimental and simulation free vibration output for the vertical pendulum is reasonably accurate and will be utilised to determine the linear systems instability bandwidth. As a result, the linear damping of the system is taken to be \(C = 0.0043\)  Nm/s .

For the tilted pendulum (Fig.  12 b), the linear damping is determined as \(C= 0.002\)  Nm/s. However, friction force with coefficient, \({\mu }_{f},\) was added to the nonlinear simulation to increase the accuracy of the simulation.

The force acting in the normal direction F is equal to \(mg\text{cos}\varphi \) and \({\mu }_{f},\) is the friction coefficient of 3D printed PLA equal to 0.4 Nm/s [ 34 ]. These factors increased the accuracy of the simulated model; however, they were not considered in the linearised system. Additionally, it was determined that the experimental results were more conducive to the simulated results when the pendulum arm length, l , was equal to 0.09 m due to the higher positioning of the smaller mass on the pendulum arm.

Instabilities in the linear system have resulted in a larger response of the full nonlinear system in the simulated results. Achieving this steady state response experimentally can verify the validity of the analytical results. It is hypothesised that excitations within the instability bandwidth will yield high mechanical power output.

Mechanical power output is used in this paper to evaluate the system’s potential electrical power output, this allows for a simple comparison disregarding generator efficiencies between the simulated and experimental system. The rotational motion of pendulums makes the mechanism suitable for various power take off types such as electromagnetic, triboelectric and piezoelectric generators. However, alterations to the pendulum mechanism may be required to optimise the motion for the intended generator type.

5.1 Heave excitation

From the linear damping determined using the methodology described previously, and the experimental system geometries, the predicted frequency bandwidth at which the equivalent linearised system is unstable can be calculated. Based on the linear system, for \(\varphi =\frac{\pi }{2}\) , at an excitation amplitude of 5 mm, the instability region occurs between 18.5 and 21 rad/s.

The experimental assembly was excited over the range of frequencies identified by the linear system at an amplitude of 5 mm between 12.5 and 23.9 rad/s, both inside and outside the identified frequency bandwidth. Mechanical power output was obtained using the angular velocity of the pendulum at steady state determined from experiment recordings in addition to the calculated damping using Eq. ( 31 ).

Figure  13 compares the steady state power output obtained from the experiment to the Simulink model. The peak power output of the simulated results is echoed in the experimental results at an excitation frequency of approximately 18 rad/s; this is within the instability bandwidth identified by the linear system.

Vertical Pendulum Heave Mechanical Power Output Experimental versus Simulation

5.2 Dynamic tilt excitation

Using the aforementioned methodology, the static tilted pendulum was excited using dynamic tilt. Based on the equivalent linear systems for the excitation scenario, the predicted instability bandwidth can be determined. The pendulum assembly was excited inside and outside the instability region identified at amplitude \(D = 1.17\)  rad to validate the dynamic tilt analysis. Based on the excitation conditions a higher power output is expected at excitation frequencies between 0 and 5.6 rad/s. The angular velocity was calculated to determine the mechanical power output using the methodology previously discussed and compared to the equivalent simulated mechanical power output.

From Fig.  14 above, parallels between the nonlinear and experimental systems’ power outputs can be drawn. Additionally, these power output profiles agree with the instability regions of the linear system, with the power output decreasing at excitation frequencies greater than 5.6 rad/s .

Dynamic Tilt Mechanical Power Output Experimental versus Simulation

Comparing the heave and dynamic tilt excitation instability bandwidths, the former occurs at relatively high frequencies over a narrow bandwidth, characterised in the experimental and nonlinear results by a sharp peak in power output at a given frequency. These instability regions are not optimal when deployed in wave energy harvesting applications due to the highly variable properties of ocean waves.

Conversely, for dynamic tilt excitation, the instability region of the linear system occurs at low frequencies, starting from 0 rad/s; supported by the experimental and nonlinear simulation power output, this yields higher power output at low frequencies. Generally, the characteristics exhibited under dynamic tilt excitations are more apt for wave energy harvesting due to their low frequency nature.

6 Conclusion

This paper focuses on the dynamic response of a pendulum system under different geometric orientations and excitation types. Equations of motion for the system were derived and linearised for further analysis. The linear system features unstable frequency bandwidths where a large dynamic response of the nonlinear system is predicted to occur. It was demonstrated that instability frequency bandwidth of the linear system corresponded to relatively high power output of the nonlinear system. From simulations conducted, pure parametric surge and heave excitations yield similar results with instability bandwidths occurring at lower excitation amplitudes but at higher frequencies with a narrower bandwidth. Contrariwise, low frequency instability regions with wider bandwidths were identified when utilising dynamic tilt excitation; suggesting that dynamic tilt excitations are more suitable for the broadband and highly variable nature of wave energy harvesting. Analytical and simulated results were further validated through the conducted experiments. Experimental results confirmed that the tested pendulum system yielded a peak average power output when excited by dynamic tilt at frequencies similar to that of ocean waves. When compared to heave and surge, dynamic tilt excitations exhibited a power output profile more suitable for broadband and low frequency ocean wave energy harvesting.

Data availability

No datasets were generated or analysed during the current study.

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Mollie Reid, Vladislav Sorokin & Kean Aw

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Here the case of pure surge excitation S 2 is considered, with \({\text{S}}_{1} = 0\) , \(\text{H }= 0\) , and φ is constant. Equation ( 14 ) reduces to

with the steady-state solution amplitude:

where damping ratio ζ is defined as:

Maximum response is achieved at excitation frequency, ρ , close to natural frequency of the system:

The corresponding power output of the system is: \(P=C{\dot{{\theta }_{p}(t)}}^{2}\) , with the maximum value achieved at the resonance frequency \(\sigma =\rho \) .

The half-power bandwidth is [ 35 ].

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Reid, M., Sorokin, V. & Aw, K. Effects of pendulum orientation and excitation type on the energy harvesting performance of a pendulum based wave energy converter. Nonlinear Dyn (2024). https://doi.org/10.1007/s11071-024-10137-5

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DOI : https://doi.org/10.1007/s11071-024-10137-5

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Type 1 diabetes.

Jessica Lucier ; Scott C. Dulebohn .

Last Update: March 3, 2023 .

• Continuing Education Activity

Type 1 diabetes mellitus (T1D) is an autoimmune disease that leads to the destruction of insulin-producing pancreatic beta cells. Individuals with T1D require life-long insulin replacement with multiple daily insulin injections daily, insulin pump therapy, or the use of an automated insulin delivery system. Without insulin, diabetic ketoacidosis (DKA) develops and is life-threatening. In addition to insulin therapy, glucose monitoring with (preferably) a continuous glucose monitor (CGM) and a blood glucose monitor if CGM is unavailable is recommended. Self-management education and support should include training on monitoring, insulin administration, ketone testing when indicated, nutrition including carbohydrate estimates, physical activity, ways of avoiding and treating hypoglycemia, and use of sick day rules. Psychosocial issues also need to be recognized and addressed. This activity reviews the evaluation and management of T1D. It highlights the importance of a multidisciplinary approach to enhance outcomes.

• Describe the pathophysiology of type 1 diabetes mellitus.
• Explain the management of type 1 diabetes mellitus.
• Review other conditions for which patients with type 1 diabetes mellitus are at increased risk of developing.
• Explain the importance of improving coordination amongst the interprofessional team to enhance care for patients affected by type 1 diabetes mellitus.
• Introduction

Type 1 diabetes mellitus (T1D) is an autoimmune disease that leads to the destruction of insulin-producing pancreatic beta cells. There is heterogeneity in the metabolic, genetic, and immunogenetic characteristics of T1D and age-related differences, requiring a personalized approach for each individual. Loss of insulin secretion can occur quickly or gradually. Residual insulin production (detectable/higher c-peptide) is more common in adult-onset compared to youth-onset T1D, whereas diabetic ketoacidosis is more common in youth with T1D. [1]  Detectable c-peptide is associated with better glycemic control. [2]  The presence of other autoimmune conditions, obesity, comorbidities, and the development of diabetes-related complications is also variable. [3]

Successful management of T1D requires multiple daily insulin injections (MDI), insulin pump therapy, or the use of an automated insulin delivery system, as well as glucose monitoring, preferably with a continuous glucose monitor (CGM). All people with T1D should be able to perform capillary blood glucose monitoring (BGM) if CGM is unavailable. Self-management education, training, and support, as well as addressing psychosocial issues, help to optimize outcomes. A collaborative multidisciplinary approach, utilizing medical providers, nurse and dietitian educators, pharmacists, community resources, and specialists as needed (including podiatrists, mental health professionals, social workers, ophthalmologists, cardiologists, and others), is recommended. [4]

In T1D, there is autoimmune destruction of the beta cells in the pancreatic islets over months or years, causing an absolute deficiency of insulin. Although the exact etiology of T1D is still unknown, researchers believe there is a genetic predisposition with a strong link with specific HLA (DR and DQ) alleles. This association is more pronounced in youth-onset T1D compared to adult-onset T1D. [5]  Multiple other genes contribute to heritability as well. [6]

In those at risk, it is generally believed that viruses,  environmental including dietary factors, and/or other stressors can trigger autoimmune beta-cell destruction. Some studies have found an increased risk of development of T1D related to infection with Coxsackie virus, enteroviruses, cytomegalovirus, rubella virus, influenza B, mumps virus, and more recently, SARS-CoV-2 (COVID-19). [7] [8] [9]  In The Environmental Determinants of Diabetes in the Young (TEDDY) study, breastfeeding was not associated with the risk of islet autoimmunity in children genetically at increased risk. However, a systematic review and meta-analysis concluded that breastfeeding and the later introduction of gluten, fruit, and cow’s milk were associated with a lower risk of developing T1D. [10]  Research to better understand the etiology of T1D is ongoing.

The presence of circulating pancreatic islet autoantibodies suggests that the individual is at risk for or has developed T1D. These antibodies include islet cell cytoplasmic antibodies (ICA), antibodies to insulin (IAA), glutamic acid decarboxylase isoform 65 (GAD65), insulinoma antigen 2/islet tyrosine phosphatase 2 (IA-2) and zinc transporter isoform 8 (ZnT8). IAAs are primarily detected in children. [11]  GAD65 is the most common autoantibody detected in adults [3] . ICA is no longer routinely recommended, as it is an imprecise assay. The greater the number of detectable antibodies and the higher their titers, the greater the risk of developing T1D. 

• Epidemiology

T1D is one of the most frequent chronic diseases in children but can have its onset at any age. In adults, new-onset type 1 diabetes may be misdiagnosed as type 2 diabetes and is more common than youth-onset T1D. [3] [ [5]  There has been a steady increase in the incidence and prevalence of T1D, representing approximately 5% to 10% of people with diabetes. A systematic review and meta-analysis reported that the worldwide prevalence of T1D was 9.5%, with an incidence of 15 per 100,000 people. [12]  Worldwide, there is also a considerable geographic variation in incidence. The highest reported incidences are in Finland and other Northern European nations, with rates approximately 400 times greater than those seen in China and Venezuela, where there is the lowest reported incidence. 

• Pathophysiology

The development of T1D occurs in 3 stages. Stage 1 is asymptomatic and characterized by normal fasting glucose, normal glucose tolerance, and the presence of  ≥2 pancreatic autoantibodies. Stage 2 diagnostic criteria include the presence of pancreatic autoantibodies (usually multiple) and dysglycemia: impaired fasting glucose (fasting glucose 100 to 125 mg/dL) or impaired glucose tolerance (2-hour post-75 gm glucose load glucose 140 to 199 mg/dL) or an HbA1c  5.7% to 6.4%. Individuals remain asymptomatic. In stage 3, there is diabetes, defined by hyperglycemia (random glucose ≥200 mg/dL) with clinical symptoms, fasting glucose ≥126 mg/dL, glucose ≥200 mg/dL two hours after ingesting 75 g of glucose during an oral glucose tolerance test and/or HbA1c ≥6.5%. If the individual lacks classic symptoms of hyperglycemia or hyperglycemic crisis, it is recommended that two tests be performed (simultaneously or at different times) to confirm the diagnosis. If there is an acute onset of symptoms with hyperglycemia, as more often occurs in youth-onset T1D, HbA1c may be misleading at the time of diagnosis, and glucose criteria should be used. [4]

T1D, especially in children, classically presents with hyperglycemic symptoms, which can be sudden, and include polydipsia, polyuria, polyphagia, nocturnal enuresis, blurred vision, unintentional weight loss, fatigue, and weakness. If not evaluated and treated promptly, it can become a medical emergency. In addition to hyperglycemia, electrolyte abnormalities may be present. If these individuals are not treated,  DKA can develop, requiring hospitalization and treatment with intravenous fluids, insulin, potassium, and careful monitoring. Almost one-third of youth present with DKA. [13]  

In adult-onset diabetes, the onset of symptoms is more variable than in youth, and DKA is less common. It can be difficult to distinguish T1D and type 2 diabetes. GAD65 should be the initial antibody tested when diagnosing T1D in adults is suspected. If negative and/or if available, IA2 and/or ZNT8 should be measured as well. C-peptide levels can be used when there is a question about which type of diabetes is present. A random C-peptide should be drawn with concurrent serum glucose. If the duration of diabetes exceeds three years, c-peptide >600 pmol/L strongly suggests type 2 diabetes. A low (<200 pmol/L) or undetectable c-peptide confirms the diagnosis of T1D. [3]

• History and Physical

At the initial outpatient visit, obtaining a complete medical, surgical, psychosocial, and family history, including pregnancy and contraception history, is essential. History of prior diabetes education,  monitoring of BG and ketones, use of CGM, administration of insulin, recognition/treatment of hypoglycemia, use of glucagon, diet, physical activity, smoking and alcohol use, understanding of sick-day rules, ability to problem solve and immunization history, should also be obtained. Particular attention should be paid to the date of diagnosis, prior treatment, current medications, presence of hypoglycemia unawareness, and history of acute complications (hypoglycemia including severe episodes and episodes of DKA) and chronic complications (skin disorders, dental problems, retinopathy, macular edema, neuropathy, kidney disease, cardiovascular disease, peripheral arterial disease, stroke, foot ulcers, amputations, hearing loss, sleep disorders). Since people with type 1 diabetes are at increased risk of other autoimmune disorders, including autoimmune thyroid disease and celiac disease, the history should also focus on these conditions. [3]

Clinicians should measure height, weight, and blood pressure. The skin should be examined, especially at insulin injection or infusion sites. If lipodystrophy is evident, they should be educated on the importance of varying insulin injection/infusion sites. The thyroid, heart, chest, and abdomen should also be examined. A foot exam is performed to examine pedal pulses and detect foot deformities, pre-ulcerative lesions, ulcerations, calluses, and onychomycosis. It is also important to test vibratory and protective sensations; abnormal testing with a 10-g monofilament exam suggests an increased risk of ulceration.  

When screening for psychosocial issues, a number of measures are available such as the Patient Health Questionnaire (PHQ-2/PHQ-9) for Depression and Generalized Anxiety Disorder (GAD-7). Diabetes distress and social determinants of health should be assessed. Since eating disorders are more common in type 1 diabetes, particularly in young women, evaluation should be considered clinically indicated. Early cognitive decline is also common, so cognitive testing should be considered when impairment is suspected. [3]  

Data from CGMs, blood glucose meters, insulin pumps, and automated insulin delivery systems should be downloaded, examined, and discussed at each visit and between visits when needed to adjust treatment regimens to achieve glycemic goals. 

CGMs are devices that measure glucose in interstitial fluid and are extremely useful tools for people with T1D. Sensors are inserted into the subcutaneous tissue and transmit glucose readings every 5 minutes to a receiver where they can be displayed in real-time. One can examine trends and use low and high glucose alarms to prevent serious hypoglycemia and hyperglycemia episodes. Alarms can also alert to a rapid change in glucose value. Readings from certain CGM sensors can be transmitted to smartphones and can be shared with relatives, friends, or caregivers. A less expensive CGM option uses a “reader” (a device the user scans over the site of sensor placement) or a smartphone to visualize recent glucose readings and trends. All these devices make it easier to monitor glucose values throughout the day and night. Users examine trends and are provided with important information to guide insulin therapy and food intake to help avoid wide glycemic excursions and hypoglycemia.

Data from CGMs can be uploaded and stored in cloud-based systems. These data include percent: Time in range TIR, usually 70 to 180 mg/dL; TIR targets are lower during pregnancy and higher in those who are frail and/or with complex comorbidities or limited life expectancy), time below range (TBR; <70 mg/dL; level 1 hypoglycemia is 54-69 mg/dL and level 2 hypoglycemia is <54 mg/dL); time above range (TAR; usually >180 mg/dL; level 1 hyperglycemia is 181 to 250 mg/dL and level 2 hyperglycemia is  >250 mg/dL); and glycemic variability (% CV; coefficient of variation).  These data should be reviewed with the goal of understanding factors contributing to hypoglycemia and hyperglycemia and to help guide insulin dosing, diet, and physical activity to achieve goals. A primary goal should be minimizing hypoglycemia. A higher percent TIR is associated with decreased diabetes-related complications. [14] [15]  HbA1c, TIR, and TBR improve when MDI or pump therapy is augmented with CGM use. The glucose management indicator (GMI) is calculated using average sensor readings over a 14-day period and correlates with the estimated HbA1c. [16]

When CGM data are unavailable, examination of BG data fasting, pre-meal, 1 to 3 hours postprandial (when adjusting prandial dosing), bedtime, when hypoglycemia is suspected, and occasionally in the middle of the night, should be used to direct insulin dosing. Insulin dosing data from connected insulin pens and pumps should also be discussed. 

HbA1c is recommended every 3 to 6 months. The HbA1c reflects glycemic control over the previous 2 to 3 months. A typical goal HbA1c is <7.0%, with higher goals in people with frailty, cardiovascular disease/multiple comorbidities, history of severe hypoglycemia, and/or hypoglycemia unawareness. Lower goals are used when they can be achieved safely (without an increase in hypoglycemia).

Other laboratory tests include a yearly lipid profile, serum creatinine, eGFR, and urine albumin to creatinine ratio. Serum potassium should be monitored if taking an ACE-I, ARB, or diuretic, and AST, ALT,  TSH, celiac screen, vitamin B12, and vitamin D at least once and as indicated clinically. These tests could be repeated more frequently if the previous results were abnormal. Since people with T1D are at an increased risk of developing other autoimmune diseases, such as autoimmune thyroid disease, celiac disease, primary adrenal insufficiency, and rheumatoid arthritis, screening for autoimmune disorders should be considered when clinically appropriate. [17] [4]

• Treatment / Management

People with T1D require insulin therapy, glucose monitoring (preferably CGM), and diabetes self-management education and support. Multiple daily insulin injections (MDI) using basal (preferably a long-acting insulin) and bolus (preferably rapid-acting insulin for meals and correction)insulins, continuous subcutaneous insulin infusion (rapid-acting insulin) through an insulin pump, or use of automated insulin delivery (hybrid closed loop) systems with rapid-acting insulin, are available. Automated insulin delivery is associated with greater time in the target range and less hypoglycemia. When initiating a treatment plan, use shared decision-making, considering individualized realistic and attainable goals, risk of hypoglycemia, lifestyle, and the availability and affordability of different regimens. [4]

 Hypoglycemia is the most frequent adverse effect of insulin therapy. It is important to educate people with diabetes and their partners about the signs and symptoms of hypoglycemia, which include diaphoresis, tachycardia, lightheadedness, confusion, hunger, visual changes, and tremors. With a long duration of T1D, hypoglycemia unawareness becomes more common. Generally, 15 to 20 g of glucose should be given orally for blood glucose below 70 mg/dL. [17]  Glucose readings should be rechecked 15 minutes later, with additional carbohydrates given if needed.d Once the glucose reading has normalized, if glucose readings again begin to fall, a snack should be given to prevent a recurrence. Glucagon should be prescribed for emergency use for severe hypoglycemia (when there is an inability to consume carbohydrates by mouth). People with T1D should also receive sick day instructions, including how to manage hyperglycemia and ketone testing. When initiating insulin therapy in an adult, the person’s weight in kilograms is multiplied by 0.2 to 0.6 units to calculate the initial total daily insulin dose (TDD). Generally, basal requirements are 40% to 50% of the TDD, and the rest approximates the daily rapid-acting insulin that must be given before or with meals. Dosing is modified based on many factors, including diet, physical activity, and CGM and/or BGM results. 

When possible, people with T1D should meet with a dietitian, be taught carbohydrate counting, and be instructed to use an insulin-to-carbohydrate ratio (grams of carbohydrate covered by one unit of insulin) for mealtime dosing. If carbohydrate counting is not possible, a carbohydrate-consistent diet is helpful. Estimating the fall in glucose resulting from 1 unit of rapid-acting insulin, called a correction or insulin sensitivity factor, is also recommended when treating hyperglycemia. The correction factor can be initially estimated using the formula 1800 divided by the TDD. This number will need to be adjusted per subsequent glucose monitoring results. When using correction doses, the individual needs to be careful not to take injections too close together (“stacking”) to avoid overdosing (insulin administered when there is still active insulin from previous doses causing overlapping insulin doses) and hypoglycemia.  

It is important to note that insulin requirements vary across the lifespan and under specific circumstances. For example, larger insulin doses are typically required during puberty, pregnancy, when steroids are given, and with the development of obesity. Individuals need less insulin when they are engaged in aerobic exercise and during the “honeymoon period.” The honeymoon period occurs soon after diagnosis when there can be a temporary recovery of beta-cell function.

Multiple types of insulin can be used for insulin injection therapy. [18]  Rapid-acting insulin (lispro, aspart, glulisine) will generally have onset in 12 to 30 minutes, peak in 1 to 3 hours, and have a duration of action of 3-6 hours. Ultra-rapid-acting lispro or aspart have a slightly quicker onset of action and somewhat shorter duration of action. . Short-acting insulin (regular insulin) has an onset in 30 minutes to 1 hour and peak in 2 to 4 hours with a duration of 5 to 8 hours. 

For basal insulin injection therapy, long-acting insulin is preferred,  often given once a day (U-100 and U-300 glargine, degludec) or 1 to 2 times daily (detemir and U-100 glargine). Glargine does not have a pronounced peak and lasts approximately 20 to 24 hours. U-300 glargine lasts more than 24 hours, and degludec has a longer duration of action, up to 42 hours. Intermediate insulin (NPH, NPL) is the least expensive basal insulin, but it is associated with more hypoglycemia. It has onset in 1 to 2 hours, peak action at 2 to 8 hours, duration of 12 to 24 hours, and is usually given before breakfast and bedtime. When MDI is used, the individual will ideally use rapid-acting insulin with each meal for hyperglycemia correction and a daily long-acting basal insulin. 

Insulin pumps deliver insulin every 5 minutes to provide basal needs and deliver boluses of insulin to control mealtime excursions and correct hyperglycemia. Only rapid-acting insulin is used in insulin pumps/automated insulin delivery systems. Some pumps use external tubing to infuse insulin from the pump to the infusion site in the subcutaneous tissue, while another pump uses a “pod” that contains insulin, which is directly applied to the skin and is controlled via a wireless connection to a controller or smartphone. Insulin pumps are programmed with adjustable basal rates, insulin-to-carbohydrate ratios, correction factors, and target glucose ranges.

Some insulin pumps communicate with CGMs and have threshold/predictive low-glucose suspend features. With these devices, insulin delivery is suspended when hypoglycemia occurs or is predicted to occur. In the newer hybrid closed-loop automated insulin delivery systems, the CGM sends glucose data to an insulin pump with a control algorithm. Basal insulin delivery is automated based on the CGM readings received every 5 minutes and the target glucose. Advanced systems deliver automated correction boluses as well. Mealtime bolus insulin is still required to be delivered under the direction of the user. 

Several clinical trials are currently underway, testing “closed-loop” fully automated insulin delivery systems, as well as a closed-loop system that delivers insulin and glucagon. The hope is that these closed-loop automated insulin systems will lead to better glucose management, with minimal risk of hypoglycemia and reduced burden for people with T1D.

Physical activity is recommended for people with T1D. Exercise increases insulin sensitivity, improves cardiovascular health, improves lipid profiles, decreases microvascular complications, reduces the risk of osteoporosis, and decreases mortality. Glycemic control can be more difficult during times of activity related to the intensity and duration of the activity, amount of circulating insulin, glucose level before exercise, and dietary intake. Individuals should be taught the effect of different types of activity (aerobic vs. anaerobic) on glucose levels, how to balance carbohydrate intake and insulin doses when active, and how to avoid hypoglycemia and wide glycemic excursions with exercise. 

In addition to insulin therapy, diet, and physical activity, individuals with T1D should generally have an annual eye exam by an eye care specialist and an annual foot exam. Those with foot deformities, neuropathy, a history of foot ulcers, or peripheral arterial disease should have their feet examined at each visit, be educated in proper foot care/footwear, and, if available, see a podiatrist and be evaluated for orthotics if indicated. Other specialists, such as nephrologists, ophthalmologists, and cardiologists, as well as referrals to community resources, social workers, and mental health professionals, may be needed. The use of statins and other anti-hyperlipidemic therapy, smoking cessation, and anti-hypertension therapy to reduce cardiovascular risk and risk of nephropathy and retinopathy are important and discussed later in this article. Pancreatic and islet cell transplantation are two treatment options that can restore normoglycemia.

A pancreatic transplant is usually performed simultaneously with a kidney transplant (SPK transplant). These transplants are considered when end-stage renal disease is present, in relatively younger individuals (<50 years old without coronary artery disease), and when usual treatment options have been unsuccessful in preventing large variability and severe hypoglycemia. Individuals who receive a pancreatic transplant or an islet-cell transplantation require immunosuppressive therapy. Encapsulated islets could obviate the need for immunosuppressive therapy and are a promising future therapy. These and other research initiatives give hope to the increasing number of people with T1D that a cure is in their future. [4]

• Differential Diagnosis
• Diabetes mellitus type 2
•  Pancreatic diabetes
• Steroid-induced diabetes
• Diabetes insipidus
• Factitious illness
• Psychogenic polydipsia
• Renal glycosuria

With better glucose, blood pressure, lipid control, and better foot care, there has been a reduction in the morbidity and mortality associated with T1D. Rates of serious diabetes-related complications are lower; if present, their onset has been delayed for many. Although people with T1D have 2 to 5-fold higher mortality than those without diabetes, mortality rates have declined. This is discussed further in other sections. [19]

• Complications

The major acute complications of diabetes are hypoglycemia and serious hyperglycemia, including diabetic ketoacidosis. The major chronic complications are listed below:

• Nephropathy
• Neuropathy: peripheral and autonomic
• Retinopathy/macular edema
• Heart disease, including coronary artery disease, heart failure, cardiomyopathy
• Peripheral arterial disease
• Cerebrovascular disease, including stroke and TIA
• Hearing loss
• Diabetic foot diseases, including foot ulcers and amputations
• Deterrence and Patient Education

Patient medication compliance and follow-up with specialists and educators are critical factors in preventing complications. At every patient encounter, the pharmacist, nurse, and clinicians should emphasize the importance of blood glucose control, long-term complications, and management goals. The patient should be encouraged to modify their lifestyle to reduce the risk of complications. In addition, all patients with diabetes should be made aware of the signs and symptoms of hypoglycemia and ways of managing it. Patients should be educated about available resources and the benefits of joining support groups. A dietitian should educate the patient about foods that can be consumed, and the nurse should educate the patient on blood glucose monitoring at home.

• Enhancing Healthcare Team Outcomes

Self-management of T1D  includes administering insulin multiple times daily with glucose monitoring and attention to food intake and physical activity every day, which is a considerable burden. Whereas newer technologies have helped people improve their glycemic control, they are costly, complex, and require education and training. Many people with diabetes fear hypoglycemia, hyperglycemia, and the development of complications, and depression, anxiety, and eating disorders can develop. The medical, education, training, psychological, and social challenges faced by people with T1D daily are best addressed by an interprofessional team that includes clinicians (MDs, DOs, NPs, and PAs), nurses (including diabetes nurse educators), pharmacists, dieticians, mental health professionals, social workers, podiatrists, and the use of community resources. Individualized treatment approaches, which can reduce the burden and further improve outcomes, are needed, and the interprofessional care model will yield the best possible patient outcomes. [3]

It is imperative for all interprofessional team members to coordinate their activities and interventions with the rest of the team and utilize open communication channels to ensure everyone involved in patient care, as well as the patient themselves, has access to the same accurate, updated patient information. Nurses are often crucial in coordinating activities between various professionals on the case and play a role in patient evaluation, education, and monitoring. Pharmacists should work directly with diabetes educators to ensure proper insulin dosing and participate in patient medication education and reconciliation. These examples of interprofessional care will help drive improved patient outcomes. [Level 5]

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Disclosure: Jessica Lucier declares no relevant financial relationships with ineligible companies.

Disclosure: Scott Dulebohn declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

• Cite this Page Lucier J, Dulebohn SC. Type 1 Diabetes. [Updated 2023 Mar 3]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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