case study about multiple sclerosis

Clinical Presentation: Case History # 1 Ms. C is a 35 year old white female. She came to Neurology Clinic for evaluation of her long-term neurologic complaints. The patient relates that for many years she had noticed some significant changes in neurologic functions, particularly heat intolerance precipitating a stumbling gait and a tendency to fall. Her visual acuity also seemed to change periodically during several years. Two months ago the patient was working very hard and was under a lot of stress. She got sick with a flu and her neurologic condition worsened. At that time, she could not hold objects in her hands, had significant tremors and severe exhaustion. She also had several bad falls. Since that time she had noticed arthralgia on the right and subsequently on the left side of her body. Then, the patient abruptly developed a right hemisensory deficit after several days of work. The MRI scan was performed at that time and revealed a multifocal white matter disease - areas of increased T2 signal in both cerebral hemispheres. Spinal tap was also done which revealed the presence of oligoclonal bands in CSF. Visual evoked response testing was abnormal with slowed conduction in optic nerves.    (Q.1)    (Q. 2)    (Q.3) Today, the patient has multiple problems related to her disease: she remains weak and numb on the right side; she has impaired urinary bladder function which requires multiple voids in the mornings, and nocturia times 3. She became incontinent and now has to wear a pad during the day.   (Q.4)   She also has persistent balance problems with some sensation of spinning, and she is extremely fatigued. REVIEW OF SYSTEMS is also significant for a number of problems related to her suspected MS. The patient has a tendency to aspirate liquids and also solids.    (Q.5)   (Q.6) She complains of tinnitus which is continuous and associated with hearing loss, more prominent on the left. She has decreased finger dexterity and weakness of the hands bilaterally. She also complains of impaired short-term memory and irritability. FAMILY HISTORY is significant for high blood pressure, cancer and heart disease in the immediate family. PERSONAL HISTORY is significant for mumps and chicken pox as a child, and anemia and allergies with hives later in life. She also had a tubal ligation. NEUROLOGIC EXAMINATION: Cranial Nerve II - disks are sharp and of normal color. Funduscopic examination is normal. Cranial Nerves III, IV, VI - no extraocular motor palsy or difficulties with smooth pursuit or saccades are seen. Remainder of the cranial nerve exam is normal except for decreased hearing on the left, and numbness in the right face, which extends down into the entire right side. The Weber test reveals greater conductance to the right. Rinne's test reveals air greater than bone bilaterally.   (Q.7) The palate elevates well. Swallow appears to be intact. Tongue movements are slowed, but tongue power appears to be intact. Motor examination reveals relatively normal strength in the upper extremities throughout. However, rapid alternating movements are decreased in both upper extremities and the patient has dysdiadochokinesia in the left hand.   (Q.8) Mild paraparesis is noted in both legs without severe spasticity. Deep tendon reflexes are +2 and symmetrical in the arms, +3 at the ankles and at the knees. Bilateral extensor toe sign are present. Sensory exam reveals paresthesia on the right to touch and decreased pin sensation on the right diffusely. The patient has mild vibratory sense loss in the distal lower extremities. Romberg's is negative.   (Q.9) Tandem gait is mildly unstable. Ambulation index is 7.0 seconds for 25 feet. (The patient takes 7.0 seconds to walk 25 feet.) Diagnosis: Multiple Sclerosis with laboratory support.   ©   John W.Rose, M.D.,   Maria Houtchens, MSIII,   Sharon G. Lynch, M.D.

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Multiple Sclerosis Case Study

Janet has experienced periodic episodes of tingling in her extremities, dizziness, and even episodes of blindness. After 12 years, doctors have finally given her a diagnosis. Follow Janet through her journey and find out why her disease is so difficult to diagnose.

Module 3: Multiple Sclerosis

Janet, age 22, was preparing for her 6-week postpartum checkup...

MS - Page 1

Three years later, at 34, Janet awoke to a prickly tingling feeling...

MS - Page 2

The neurologist made a diagnosis of multiple sclerosis based on the MRI...

MS - Page 3

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Diagnosis and management of multiple sclerosis: case studies

Affiliation.

• 1 Multiple Sclerosis Program, Department of Neurology, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75390-9036, USA.
• PMID: 16684629
• DOI: 10.1016/j.ncl.2006.01.002

Although substantial capabilities have emerged in the ability to globally manage patients who have MS, clinicians continue to be confronted with formidable challenges. Reduction in disease activity and its impact on dis-ability progression remains the central objective of disease-modifying therapy and most current MS research initiatives. Nevertheless, the principal factors that determine the day-to-day limitations on functional capabilities(activities of daily living, work performance, quality of life, and so forth)are a derivative of the pathophysiology of the disease process itself. The substrate for these limitations is inherent in the pathology of demyelination and axonal dysfunction. Identifying measures that can optimize the performance and fidelity of axonal conduction mechanisms may translate into a reduction in MS-related symptoms. Chronic neurologic disease management (with MS representing a signature example) can be optimized when all members of the care team (including patients and their families) collaborate in the co-ordination of interdisciplinary care models that address all aspects of suffering.

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• [Relapsing-remitting inflammatory disease of the central nervous system with normal MRI: multiple sclerosis or phenocopy in a series of 15 patients]. Lebrun C, Bourg V, Chanalet S, Chatel M. Lebrun C, et al. Rev Neurol (Paris). 2003 Apr;159(4):397-404. Rev Neurol (Paris). 2003. PMID: 12773868 Review. French.
• Long-term subcutaneous interferon beta-1a therapy in patients with relapsing-remitting MS. Kappos L, Traboulsee A, Constantinescu C, Erälinna JP, Forrestal F, Jongen P, Pollard J, Sandberg-Wollheim M, Sindic C, Stubinski B, Uitdehaag B, Li D. Kappos L, et al. Neurology. 2006 Sep 26;67(6):944-53. doi: 10.1212/01.wnl.0000237994.95410.ce. Neurology. 2006. PMID: 17000959 Clinical Trial.
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• [Therapy of multiple sclerosis]. Simó M. Simó M. Neuropsychopharmacol Hung. 2009 Mar;11(1):23-6. Neuropsychopharmacol Hung. 2009. PMID: 19731815 Review. Hungarian.
• Axonal damage in multiple sclerosis: a complex issue in a complex disease. Grigoriadis N, Ben-Hur T, Karussis D, Milonas I. Grigoriadis N, et al. Clin Neurol Neurosurg. 2004 Jun;106(3):211-7. doi: 10.1016/j.clineuro.2004.02.017. Clin Neurol Neurosurg. 2004. PMID: 15177770 Review.
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Multiple sclerosis.

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Multiple Sclerosis: Case Studies on the Importance of Early Diagnosis and Optimal Treatment

Internet Enduring Material sponsored by Stanford University School of Medicine. Presented by the Stanford University School of Medicine Department of Neurology and Center for Continuing Medical Education.

Have you encountered a patient with Multiple Sclerosis? This disease affects 2.8 million people worldwide, with a disproportionate number in the United States. In this interactive module about Multiple Sclerosis, explore four case studies that illustrate the importance of early diagnosis and optimal treatment of patients. Providers can learn how to diagnose efficiently and best provide early interventional treatment to Multiple Sclerosis patients.

     Release Date : December 20, 2022      Expiration Date : December 19, 2025      Estimated Time to Complete : 25 minutes       Registration Fee : FREE 

Click  Begin  to launch the module. View the entire  Multiple Sclerosis Series here.

• Apply diagnostic criteria and select appropriate tests used to confirm the diagnosis of MS.
• Discuss the role of disease-modifying therapy in the management of MS, including the expected benefit, mode of action, and selection of options available.
• Assess the conditions that should be considered in the differential diagnosis of MS.

In support of improving patient care, Stanford Medicine is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team. 

Credit Designation  American Medical Association (AMA)  Stanford Medicine designates this Enduring Material for a maximum of 0.50  AMA PRA Category 1 Credits TM .  Physicians should claim only the credit commensurate with the extent of their participation in the activity. 

American Board of Internal Medicine MOC Credit  Successful completion of this CME activity, which includes participation in the evaluation component, enables the participant to earn up to 0.5 MOC points in the American Board of Internal Medicine’s (ABIM) Maintenance of Certification (MOC) program. It is the CME activity provider’s responsibility to submit participant completion information to ACCME for the purpose of granting ABIM MOC credit.

MOC Participation Threshold

Learner completes the online workshop, credits will be awarded upon completion.

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Mowry. “Gut Microbiome and Multiple Sclerosis.” Current Neurology and Neuroscience Reports 14, no. 10 (September 10, 2014): 492. https://doi.org/10.1007/s11910-014-0492-2.  Chen, Jun, Nicholas Chia, Krishna R. Kalari, Janet Z. Yao, Martina Novotna, M. Mateo Paz Soldan, David H. Luckey, et al. “Multiple Sclerosis Patients Have a Distinct Gut Microbiota Compared to Healthy Controls.” Scientific Reports 6 (June 27, 2016): 28484. https://doi.org/10.1038/srep28484.  “Clinical Presentation, Course, and Prognosis of Multiple Sclerosis in Adults - UpToDate.” Accessed January 25, 2022. https://www-uptodate-com.laneproxy.stanford.edu/contents/clinical-presentation-course-and-prognosis-of-multiple-sclerosis-in-adults?search=multiple%20sclersosis&source=search_result&selectedTitle=2~150&usage_type=default&display_rank=2.  Dendrou, Calliope A., Lars Fugger, and Manuel A. Friese. “Immunopathology of Multiple Sclerosis.” Nature Reviews. 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Tuesday, December 20, 2022 Multiple Sclerosis: Case Studies on the Importance of Early Diagnosis and Optimal Treatment 12:00AM - 12:10AM Lucas B. Kipp, MD Wednesday, December 21, 2022 Thursday, December 22, 2022 Friday, December 23, 2022 Saturday, December 24, 2022 Sunday, December 25, 2022 Monday, December 26, 2022 Tuesday, December 27, 2022 Wednesday, December 28, 2022 Thursday, December 29, 2022 Friday, December 30, 2022 Saturday, December 31, 2022 Sunday, January 1, 2023 Monday, January 2, 2023 Tuesday, January 3, 2023 Wednesday, January 4, 2023 Thursday, January 5, 2023 Friday, January 6, 2023 Saturday, January 7, 2023 Sunday, January 8, 2023 Monday, January 9, 2023 Tuesday, January 10, 2023 Wednesday, January 11, 2023 Thursday, January 12, 2023 Friday, January 13, 2023 Saturday, January 14, 2023 Sunday, January 15, 2023 Monday, January 16, 2023 Tuesday, January 17, 2023 Wednesday, January 18, 2023 Thursday, January 19, 2023 Friday, January 20, 2023 Saturday, January 21, 2023 Sunday, January 22, 2023 Monday, January 23, 2023 Tuesday, January 24, 2023 Wednesday, January 25, 2023 Thursday, January 26, 2023 Friday, January 27, 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Saturday, July 15, 2023 Sunday, July 16, 2023 Monday, July 17, 2023 Tuesday, July 18, 2023 Wednesday, July 19, 2023 Thursday, July 20, 2023 Friday, July 21, 2023 Saturday, July 22, 2023 Sunday, July 23, 2023 Monday, July 24, 2023 Tuesday, July 25, 2023 Wednesday, July 26, 2023 Thursday, July 27, 2023 Friday, July 28, 2023 Saturday, July 29, 2023 Sunday, July 30, 2023 Monday, July 31, 2023 Tuesday, August 1, 2023 Wednesday, August 2, 2023 Thursday, August 3, 2023 Friday, August 4, 2023 Saturday, August 5, 2023 Sunday, August 6, 2023 Monday, August 7, 2023 Tuesday, August 8, 2023 Wednesday, August 9, 2023 Thursday, August 10, 2023 Friday, August 11, 2023 Saturday, August 12, 2023 Sunday, August 13, 2023 Monday, August 14, 2023 Tuesday, August 15, 2023 Wednesday, August 16, 2023 Thursday, August 17, 2023 Friday, August 18, 2023 Saturday, August 19, 2023 Sunday, August 20, 2023 Monday, August 21, 2023 Tuesday, August 22, 2023 Wednesday, August 23, 2023 Thursday, August 24, 2023 Friday, August 25, 2023 Saturday, August 26, 2023 Sunday, August 27, 2023 Monday, August 28, 2023 Tuesday, August 29, 2023 Wednesday, August 30, 2023 Thursday, August 31, 2023 Friday, September 1, 2023 Saturday, September 2, 2023 Sunday, September 3, 2023 Monday, September 4, 2023 Tuesday, September 5, 2023 Wednesday, September 6, 2023 Thursday, September 7, 2023 Friday, September 8, 2023 Saturday, September 9, 2023 Sunday, September 10, 2023 Monday, September 11, 2023 Tuesday, September 12, 2023 Wednesday, September 13, 2023 Thursday, September 14, 2023 Friday, September 15, 2023 Saturday, September 16, 2023 Sunday, September 17, 2023 Monday, September 18, 2023 Tuesday, September 19, 2023 Wednesday, September 20, 2023 Thursday, September 21, 2023 Friday, September 22, 2023 Saturday, September 23, 2023 Sunday, September 24, 2023 Monday, September 25, 2023 Tuesday, September 26, 2023 Wednesday, September 27, 2023 Thursday, September 28, 2023 Friday, September 29, 2023 Saturday, September 30, 2023 Sunday, October 1, 2023 Monday, October 2, 2023 Tuesday, October 3, 2023 Wednesday, October 4, 2023 Thursday, October 5, 2023 Friday, October 6, 2023 Saturday, October 7, 2023 Sunday, October 8, 2023 Monday, October 9, 2023 Tuesday, October 10, 2023 Wednesday, October 11, 2023 Thursday, October 12, 2023 Friday, October 13, 2023 Saturday, October 14, 2023 Sunday, October 15, 2023 Monday, October 16, 2023 Tuesday, October 17, 2023 Wednesday, October 18, 2023 Thursday, October 19, 2023 Friday, October 20, 2023 Saturday, October 21, 2023 Sunday, October 22, 2023 Monday, October 23, 2023 Tuesday, October 24, 2023 Wednesday, October 25, 2023 Thursday, October 26, 2023 Friday, October 27, 2023 Saturday, October 28, 2023 Sunday, October 29, 2023 Monday, October 30, 2023 Tuesday, October 31, 2023 Wednesday, November 1, 2023 Thursday, November 2, 2023 Friday, November 3, 2023 Saturday, November 4, 2023 Sunday, November 5, 2023 Monday, November 6, 2023 Tuesday, November 7, 2023 Wednesday, November 8, 2023 Thursday, November 9, 2023 Friday, November 10, 2023 Saturday, November 11, 2023 Sunday, November 12, 2023 Monday, November 13, 2023 Tuesday, November 14, 2023 Wednesday, November 15, 2023 Thursday, November 16, 2023 Friday, November 17, 2023 Saturday, November 18, 2023 Sunday, November 19, 2023 Monday, November 20, 2023 Tuesday, November 21, 2023 Wednesday, November 22, 2023 Thursday, November 23, 2023 Friday, November 24, 2023 Saturday, November 25, 2023 Sunday, November 26, 2023 Monday, November 27, 2023 Tuesday, November 28, 2023 Wednesday, November 29, 2023 Thursday, November 30, 2023 Friday, December 1, 2023 Saturday, December 2, 2023 Sunday, December 3, 2023 Monday, December 4, 2023 Tuesday, December 5, 2023 Wednesday, December 6, 2023 Thursday, December 7, 2023 Friday, December 8, 2023 Saturday, December 9, 2023 Sunday, December 10, 2023 Monday, December 11, 2023 Tuesday, December 12, 2023 Wednesday, December 13, 2023 Thursday, December 14, 2023 Friday, December 15, 2023 Saturday, December 16, 2023 Sunday, December 17, 2023 Monday, December 18, 2023 Tuesday, December 19, 2023 Wednesday, December 20, 2023 Thursday, December 21, 2023 Friday, December 22, 2023 Saturday, December 23, 2023 Sunday, December 24, 2023 Monday, December 25, 2023 Tuesday, December 26, 2023 Wednesday, December 27, 2023 Thursday, December 28, 2023 Friday, December 29, 2023 Saturday, December 30, 2023 Sunday, December 31, 2023 Monday, January 1, 2024 Tuesday, January 2, 2024 Wednesday, January 3, 2024 Thursday, January 4, 2024 Friday, January 5, 2024 Saturday, January 6, 2024 Sunday, January 7, 2024 Monday, January 8, 2024 Tuesday, January 9, 2024 Wednesday, January 10, 2024 Thursday, January 11, 2024 Friday, January 12, 2024 Saturday, January 13, 2024 Sunday, January 14, 2024 Monday, January 15, 2024 Tuesday, January 16, 2024 Wednesday, January 17, 2024 Thursday, January 18, 2024 Friday, January 19, 2024 Saturday, January 20, 2024 Sunday, January 21, 2024 Monday, January 22, 2024 Tuesday, January 23, 2024 Wednesday, January 24, 2024 Thursday, January 25, 2024 Friday, January 26, 2024 Saturday, January 27, 2024 Sunday, January 28, 2024 Monday, January 29, 2024 Tuesday, January 30, 2024 Wednesday, January 31, 2024 Thursday, February 1, 2024 Friday, February 2, 2024 Saturday, February 3, 2024 Sunday, February 4, 2024 Monday, February 5, 2024 Tuesday, February 6, 2024 Wednesday, February 7, 2024 Thursday, February 8, 2024 Friday, February 9, 2024 Saturday, February 10, 2024 Sunday, February 11, 2024 Monday, February 12, 2024 Tuesday, February 13, 2024 Wednesday, February 14, 2024 Thursday, February 15, 2024 Friday, February 16, 2024 Saturday, February 17, 2024 Sunday, February 18, 2024 Monday, February 19, 2024 Tuesday, February 20, 2024 Wednesday, February 21, 2024 Thursday, February 22, 2024 Friday, February 23, 2024 Saturday, February 24, 2024 Sunday, February 25, 2024 Monday, February 26, 2024 Tuesday, February 27, 2024 Wednesday, February 28, 2024 Thursday, February 29, 2024 Friday, March 1, 2024 Saturday, March 2, 2024 Sunday, March 3, 2024 Monday, March 4, 2024 Tuesday, March 5, 2024 Wednesday, March 6, 2024 Thursday, March 7, 2024 Friday, March 8, 2024 Saturday, March 9, 2024 Sunday, March 10, 2024 Monday, March 11, 2024 Tuesday, March 12, 2024 Wednesday, March 13, 2024 Thursday, March 14, 2024 Friday, March 15, 2024 Saturday, March 16, 2024 Sunday, March 17, 2024 Monday, March 18, 2024 Tuesday, March 19, 2024 Wednesday, March 20, 2024 Thursday, March 21, 2024 Friday, March 22, 2024 Saturday, March 23, 2024 Sunday, March 24, 2024 Monday, March 25, 2024 Tuesday, March 26, 2024 Wednesday, March 27, 2024 Thursday, March 28, 2024 Friday, March 29, 2024 Saturday, March 30, 2024 Sunday, March 31, 2024 Monday, April 1, 2024 Tuesday, April 2, 2024 Wednesday, April 3, 2024 Thursday, April 4, 2024 Friday, April 5, 2024 Saturday, April 6, 2024 Sunday, April 7, 2024 Monday, April 8, 2024 Tuesday, April 9, 2024 Wednesday, April 10, 2024 Thursday, April 11, 2024 Friday, April 12, 2024 Saturday, April 13, 2024 Sunday, April 14, 2024 Monday, April 15, 2024 Tuesday, April 16, 2024 Wednesday, April 17, 2024 Thursday, April 18, 2024 Friday, April 19, 2024 Saturday, April 20, 2024 Sunday, April 21, 2024 Monday, April 22, 2024 Tuesday, April 23, 2024 Wednesday, April 24, 2024 Thursday, April 25, 2024 Friday, April 26, 2024 Saturday, April 27, 2024 Sunday, April 28, 2024 Monday, April 29, 2024 Tuesday, April 30, 2024 Wednesday, May 1, 2024 Thursday, May 2, 2024 Friday, May 3, 2024 Saturday, May 4, 2024 Sunday, May 5, 2024 Monday, May 6, 2024 Tuesday, May 7, 2024 Wednesday, May 8, 2024 Thursday, May 9, 2024 Friday, May 10, 2024 Saturday, May 11, 2024 Sunday, May 12, 2024 Monday, May 13, 2024 Tuesday, May 14, 2024 Wednesday, May 15, 2024 Thursday, May 16, 2024 Friday, May 17, 2024 Saturday, May 18, 2024 Sunday, May 19, 2024 Monday, May 20, 2024 Tuesday, May 21, 2024 Wednesday, May 22, 2024 Thursday, May 23, 2024 Friday, May 24, 2024 Saturday, May 25, 2024 Sunday, May 26, 2024 Monday, May 27, 2024 Tuesday, May 28, 2024 Wednesday, May 29, 2024 Thursday, May 30, 2024 Friday, May 31, 2024 Saturday, June 1, 2024 Sunday, June 2, 2024 Monday, June 3, 2024 Tuesday, June 4, 2024 Wednesday, June 5, 2024 Thursday, June 6, 2024 Friday, June 7, 2024 Saturday, June 8, 2024 Sunday, June 9, 2024 Monday, June 10, 2024 Tuesday, June 11, 2024 Wednesday, June 12, 2024 Thursday, June 13, 2024 Friday, June 14, 2024 Saturday, June 15, 2024 Sunday, June 16, 2024 Monday, June 17, 2024 Tuesday, June 18, 2024 Wednesday, June 19, 2024 Thursday, June 20, 2024 Friday, June 21, 2024 Saturday, June 22, 2024 Sunday, June 23, 2024 Monday, June 24, 2024 Tuesday, June 25, 2024 Wednesday, June 26, 2024 Thursday, June 27, 2024 Friday, June 28, 2024 Saturday, June 29, 2024 Sunday, June 30, 2024 Monday, July 1, 2024 Tuesday, July 2, 2024 Wednesday, July 3, 2024 Thursday, July 4, 2024 Friday, July 5, 2024 Saturday, July 6, 2024 Sunday, July 7, 2024 Monday, July 8, 2024 Tuesday, July 9, 2024 Wednesday, July 10, 2024 Thursday, July 11, 2024 Friday, July 12, 2024 Saturday, July 13, 2024 Sunday, July 14, 2024 Monday, July 15, 2024 Tuesday, July 16, 2024 Wednesday, July 17, 2024 Thursday, July 18, 2024 Friday, July 19, 2024 Saturday, July 20, 2024 Sunday, July 21, 2024 Monday, July 22, 2024 Tuesday, July 23, 2024 Wednesday, July 24, 2024 Thursday, July 25, 2024 Friday, July 26, 2024 Saturday, July 27, 2024 Sunday, July 28, 2024 Monday, July 29, 2024 Tuesday, July 30, 2024 Wednesday, July 31, 2024 Thursday, August 1, 2024 Friday, August 2, 2024 Saturday, August 3, 2024 Sunday, August 4, 2024 Monday, August 5, 2024 Tuesday, August 6, 2024 Wednesday, August 7, 2024 Thursday, August 8, 2024 Friday, August 9, 2024 Saturday, August 10, 2024 Sunday, August 11, 2024 Monday, August 12, 2024 Tuesday, August 13, 2024 Wednesday, August 14, 2024 Thursday, August 15, 2024 Friday, August 16, 2024 Saturday, August 17, 2024 Sunday, August 18, 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This educational activity is supported in part by an educational grant from Novartis Pharmaceuticals Corporation.

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Chief complaint:  “When I woke up yesterday my legs felt so stiff, I could barely walk. My hands shake every time I try to paint. I feel uncoordinated, exhausted,  and just not like myself lately.”

History of present illness:  Ms. N.S. is a 29 yo white female, who was referred to the neurology clinic by her PCP after complaining of frequent episodes of weakness, fatigue, and a tingling sensation in different areas of her body. She states she first started noticing symptoms about 2 weeks ago and has been feeling progressively worse. Patient has also reported some urinary frequency, but she has been attributing it to her recent childbirth.

Past medical history

• Tonsillectomy, age 5 years
• Epstein-Barr virus, age 14 years
• Reports a “precancerous mole” that was removed at age 18 years with no complications
• Gave birth to first child 3 months ago with no complications

Pertinent family history:

• Mother alive and healthy at age 58 years
• Father alive, patient reports he takes medication for blood pressure but otherwise is healthy at age 59 years
• Patient has no siblings
• Maternal Aunt alive, living with multiple sclerosis, age 54 years
• Paternal grandfather died after suffering a stroke 2 years ago

Pertinent social history:

• Former smoker, patient quit at age 23
• Recently moved to Columbus, Ohio from Toronto, Canada
• Social drinker with approx. 1-2 alcoholic beverages per week
• Patient is an artist, enjoys going to the gym 2-3 times per week, and is currently working part time in an art gallery
• Patient likes being outdoors and states she recently returned from a several day hiking trip
• None known to patient

Medications:

• Pt states takes Tylenol “occasionally only for headaches”

Focused physical exam:

• Height 5′ 6″
• Weight 135 lb.
• Skin: clear with no lesions or rashes
• Heart Sounds: S1, S2 Regular
• Lungs: Clear in all fields, respirations regular and unlabored
• Abdomen: Soft, nontender, active bowel sounds. Pt reports last BM was this morning.
• Genitourinary: Patient reports urinary frequency, states urine is clear and yellow with no foul odor
• Patient is alert, oriented x 4 to person, place, time, situation
• Pupils are equal, round, and reactive
• Strength equal bilaterally in all extremities, patient has a slight intention tremor present in both hands
• Patient reports feeling “pins and needles” in her left and right lower legs
• Distal pulses are palpable in all extremities with no abnormalities

Vital Signs:

• Temperature: 37.0 degrees celsius
• Respiratory rate: 18
• Blood pressure: 108/76
• Oxygen saturation: 99% on room air
• Pain: 0/10 pt states: “No pain, I just feel tired and my legs are tingling”

Lab Values/Imaging:

• CBC: unremarkable
• Antinuclear antibodies (ANA) test: negative
• Enzyme-linked immunosorbent assay (ELISA) test: negative
• Urinary analysis: unremarkable
• Urine culture: pending
• CT Scan: unremarkable
• MRI with IV contrast: results are positive for inflammation in areas of the brain and spinal cord

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Palliative Care for Patients With Multiple Sclerosis

Recommendations emerging from a case study.

Wilder, Carolyn Ann BSN, RN

Carolyn Ann Wilder, BSN, RN , is registered nurse, Neurosciences Division, University of California San Diego; and PhD student, Loma Linda University School of Nursing, CA.

Address correspondence to Carolyn Ann Wilder, BSN, RN, 4231 Calle Del Vista, Oceanside, CA 92057 ( [email protected] ).

The author has no conflicts of interest to disclose.

Multiple sclerosis (MS) affects more than 2.8 million people worldwide and is an incurable, heterogeneous, chronic, degenerative, demyelinating, immune-mediated neurological disease of the central nervous system. It affects the physical, mental, psychosocial, financial, and spiritual dimensions of patients and their families. Given this illness trajectory and the multiple complex symptoms associated with MS, palliative care services would improve the quality of life for MS patients. Palliative care is a human right for all patients with a life-limiting, progressive disease. The goal of palliative care is the prevention and relief of suffering by means of assessment and treatment that holistically addresses symptoms and suffering. Thus, this article argues for the early integration of palliative care for persons given a diagnosis of MS. This argument is underscored by the analysis of a case study of a typical patient with MS who would have benefited from conjunctive palliative care.

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• Open access
• Published: 31 July 2024

Meta-analysis identifies common gut microbiota associated with multiple sclerosis

• Qingqi Lin 1 , 2 ,
• Yair Dorsett 2 ,
• Ali Mirza 3 ,
• Helen Tremlett 3 ,
• Laura Piccio 4 , 5 ,
• Erin E. Longbrake 6 ,
• Siobhan Ni Choileain 6 ,
• David A. Hafler 6 ,
• Laura M. Cox 7 ,
• Howard L. Weiner 7 ,
• Takashi Yamamura 8 ,
• Kun Chen 9 ,
• Yufeng Wu 1 &
• Yanjiao Zhou   ORCID: orcid.org/0000-0001-6528-7039 2  

Genome Medicine volume  16 , Article number:  94 ( 2024 ) Cite this article

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Previous studies have identified a diverse group of microbial taxa that differ between patients with multiple sclerosis (MS) and the healthy population. However, interpreting findings on MS-associated microbiota is challenging, as there is no true consensus. It is unclear whether there is gut microbiota commonly altered in MS across studies.

To answer this, we performed a meta-analysis based on the 16S rRNA gene sequencing data from seven geographically and technically diverse studies comprising a total of 524 adult subjects (257 MS and 267 healthy controls). Analysis was conducted for each individual study after reprocessing the data and also by combining all data together. The blocked Wilcoxon rank-sum test and linear mixed-effects regression were used to identify differences in microbial composition and diversity between MS and healthy controls. Network analysis was conducted to identify bacterial correlations. A leave-one-out sensitivity analysis was performed to ensure the robustness of the findings.

The microbiome community structure was significantly different between studies. Re-analysis of data from individual studies revealed a lower relative abundance of Prevotella in MS across studies, compared to controls. Meta-analysis found that although alpha and beta diversity did not differ between MS and controls, a higher abundance of Actinomyces and a lower abundance of Faecalibacterium were reproducibly associated with MS. Additionally, network analysis revealed that the recognized negative Bacteroides-Prevotella correlation in controls was disrupted in patients with MS.

Conclusions

Our meta-analysis identified common gut microbiota associated with MS across geographically and technically diverse studies.

Multiple sclerosis (MS) is a complex autoimmune disease of the central nervous system affecting 2.8 million people worldwide. Advances in microbiome research have identified the gut microbiome as a significant player in MS [ 1 ]. A number of case-control studies have demonstrated different degrees of gut microbiota alterations in patients with MS, regardless of ethnicity or disease duration [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ]. These include depletion or enrichment of specific bacteria at different taxonomical levels, differences of overall microbial community structure, and, less commonly, differences in alpha and beta diversity. In addition, patients treated with disease modifying therapies, such as glatiramer acetate (GA) and dimethyl fumarate (DMF), displayed distinct microbiota composition compared to non-treated patients [ 3 ].

The microbiome difference, either specific taxa or gut microbiota diversity between patients with MS and healthy controls, has been inconsistent across studies. This is not surprising, given that analyses can be affected by many technical and biological factors. DNA extraction, regions of 16S rRNA gene sequencing, downstream data processing pipeline, and sequencing platform can all contribute to the observed discordance [ 18 ]. In addition, a small sample size is likely to cause false positive/negative discoveries and poor reproducibility from lack of statistical power [ 19 ]. Moreover, findings of gut microbiota differences are challenging to interpret, as different taxonomical levels have been used to express results. Consequently, the fundamental question of whether there is common MS-associated gut microbiota that could be utilized as a biomarker for MS has not been resolved. To increase the robustness of the microbiota biomarker discovery in MS, a meta-analysis leveraging existing studies is highly desirable.

The goal of our study is to understand the degrees of microbiota variation across MS studies and determine whether there is any common MS-related microbiota using publicly available datasets. We were able to obtain raw 16S rRNA gene sequences of the gut microbiota from 524 participants (257 MS and 267 controls) and relevant clinical data in seven case-control microbiota studies conducted across different countries from 2008 to 2020. After consistent data reprocessing, we determined MS-related gut microbiota by analyzing each study separately and then jointly. We identified common MS-related microbiota across geographically (US and Asia) and technically diverse studies.

Study inclusion, exclusion, and data acquisition

We identified gut microbiota studies in patients with MS from 2008 to 2020 in PubMed with key words (multiple sclerosis) AND (human) AND ((microbiota) OR (microbiome)) AND ((gut) OR (intestine)). Pediatric-onset MS, animal studies, non-sequencing-based microbiota studies, and review papers were filtered out from our collection. We retrieved raw sequencing data through accession numbers provided in the publications and downloaded meta data directly from publications if available or indirectly through communication with the authors. This led to seven case-control microbiome studies using 16S rRNA gene sequencing for our data analysis (Additional file 1 : Fig. S1). Demographic categories (sex, age, Body mass index (BMI)), group (MS/control), PubMed unique identifier (PMID) and sample size of each study, country origins, DNA extraction kit, sequencing region of the 16S rRNA gene, and sequencing platforms were summarized in Additional file 2 : Table S1. For patients with relapsing-remitting MS, information on disease status (active or remission) and disease treatment, including disease modifying therapy (DMT), immunosuppressants (in Zeng’s study [ 11 ] from China), or any other MS drug treatments (Zeng’s study only) at the time of stool collection was obtained from published paper or directly provided by authors. In two studies (Chen and Zeng) [ 5 , 11 ], stool samples were considered from an active status if they were collected within a month of a relapse; otherwise, they were considered a remission status. No treatment was defined as no MS related treatment for at least 3 months prior to stool collections in four studies [ 2 , 4 , 6 , 7 ]. Details on treatment and disease activity in each study can be found in Additional file 2 : Table S2.

Data processing of 16S rRNA sequences

Raw sequences from each study were processed separately using the DADA2 pipeline to generate the taxonomic profile [ 20 ]. In brief, primers were trimmed by trimmomatic (V.38) [ 21 ], and paired reads were merged by fastq_mergepairs of VSEARCH (V2.4.3) [ 22 ] with default parameters. Bases with quality score lower than 20 were trimmed using Filter and Trim function with pre-determined parameters “trimLeft” and “truncLen.” Reads shorter than truncLen after trimming were filtered out. The resulting reads were subjected to chimera removal and then taxonomic classification. “Silva_v138” was used as the reference database for taxonomic classification. Samples with less than 1000 reads were removed from downstream analysis. All downstream statistical analyses were performed at the genus level. OTUs or ASVs level analyses are not feasible as the microbiota data was generated from different regions of the 16S rRNA gene.

Re-analysis of the microbiota data for individual studies

To determine the microbiota difference between MS and controls, we compared alpha diversity (richness and Shannon diversity), beta diversity (Bray-Curtis dissimilarity), and specific genera between the two groups for each reprocessed individual study. Wilcoxon rank-sum tests were used to compare the statistical difference of alpha and beta diversity between MS and controls. To visualize differences in overall microbial community structure, principal component analysis (PCA) was conducted using relative abundance data after centered log-ratio transformation using the “Compositions” package in R. To determine if the microbiota differed at the global level between MS and controls, we conducted permutational multivariate analysis of variance (PERMANOVA) using the “vegan” package, followed by dispersion test with “disper” function in “vegan” to assess homogeneity of dispersion in MS and the controls.

Differential taxa identification is sensitive to analytical approaches [ 23 ]. We applied both non-parametric Wilcoxon rank-sum test and DESeq2 differential abundance test based on the negative binomial distribution, to identify specific genera that differed statistically between MS and controls. The relative abundance was used for Wilcoxon rank-sum test and raw counts were used for DESeq2 analysis. Adjusted p -values with a false discovery rate (FDR) of < 0.05 were considered as statistically significant. Significant genera identified by DESeq2 can be driven by one or two outlier values, which can lead to potentially high false positive rate [ 24 ]. We manually inspected the results by plotting raw data and removed results that were driven by one or two outlier values, as we have done previously [ 25 ]. The final differential genera were reported by combining results (union) from both the Wilcoxon rank-sum test and DSEseq2 test.

Microbiota variation across all studies

To view microbiota variation across studies, PCA analysis was conducted using data from all studies in the same fashion as done for the single study analysis. Using the PERMANOVA model and Bray-Curtis dissimilarity, we calculated the microbiota variance introduced by several individual factors, including group (MS vs controls), study (seven studies treated as a categorical variable), geographical location, DNA extraction kits, sequencing platform, and sequencing region. The variance explained by each factor was calculated independently of other factors and should therefore be considered the variance explainable by that variable.

• Meta-analysis

A blocked Wilcoxon method was performed based on a previous microbiota meta-analysis given non-Gaussian distribution of the microbiota data while controlling for major confounding variables [ 26 ]. Two-group alpha and beta diversity comparisons were conducted using blocked Wilcoxon rank-sum test by controlling the “study” factor and using the “coin” package in R [ 26 ]. To identify statistically relevant differential genera between MS and controls, we performed blocked Wilcoxon rank-sum test by controlling the “study” factor for any genera that were present in more than 50% of participants in either MS group or control group. We also applied linear mixed-effects regression for the same genera after log transformation of the relative abundance [ 27 ], with group (MS/control) as fixed effect and study as random effect, using “nlme” package. We chose to control the “study” factor as our analysis showed it was the most predominant factor driving the microbiota variation across all combined studies. Residual analysis was conducted to validate the appropriateness of the linear mixed-effects regression model. The final differential genera were reported by combining results (union) from both blocked Wilcoxon rank-sum test and linear mixed-effects regression. To determine effects of disease status and treatment on the gut microbiota characteristics within MS, we performed blocked Wilcoxon test and linear mixed-effects regression tests in a similar fashion as the combined meta-analysis. Adjusted p -values with a FDR of < 0.1 were considered as statistically significant.

Random forest classification

We chose random forest (RF) classifier for MS and control classification as RF was reported to perform well for microbiota data [ 28 ]. We performed classification using three different strategies with “randomForest” package in R. First, we used the microbiota data from each study to train RF model and assess the performance of the classifier on the other 6 studies separately. Second, we used six combined data sets as a training set, and tested one left-out data set. Third, we randomly selected 3/4 of microbiota data from the seven combined datasets as the training set and the remaining 1/4 as the test set. For each type of classification, we tuned parameters “mtry” and “ntree” to achieve optimal model accuracy. The model with relatively highest accuracy (see Additional file 2 : Table S3 for details of each classifier) would be chosen as the classifier. The accuracy of model itself was evaluated by the confusion matrix. The prediction performance of the model was evaluated by areas of under the receiver operating characteristic (ROC) curve (AUC).

Network analysis

We computed correlations between bacterial genera within MS/control group separately using SparCC in “SpiecEasi” package. Bootstrap method was used to calculate the p -values of the correlations with 1000 bootstrap samples. Correlations with values larger than 0.2 and adjusted p -value less than 0.05 were used to construct the network.

Sensitivity analysis

We conducted a sensitivity analysis to evaluate the robustness of the results obtained from the combined analysis of the seven studies. The main approach involves systematically excluding one study at a time and re-analyzing the data with the remaining six studies (the leave-one-out approach). This process is repeated for each of the seven studies in turn. We maintained the same analysis approach in the sensitivity analysis as analyzing all seven studies. The results were summarized and compared with the current findings. In network analysis part, we compared network structures by using functions in “igraph” package in R.

Microbiota datasets overview

Seven studies included in our analysis have heterogeneous geographical locations, as five studies are from different states of the USA, one from China and one from Japan. Females accounted for a large proportion of the participants, reflecting MS epidemiology in which women are more affected than men. Most patients with MS were RRMS (253/257 = 98.44%). Among all RRMS patients whose disease status was available, 19.41% (40/206) had active disease (see Methods ). Among all RRMS whose treatment information was available, 72.22% (169/234) received no treatment at the time of stool sample collection. The rest were treated with disease modifying therapy or immune suppressant (see Additional file 2 : Table S1 for details).

The gut microbiota in the seven studies was characterized using stool specimens. DNA extractions were primarily performed using PowerSoil DNA extraction kits. Qiagen and home-kits were employed by Zeng’s et al. and Miyake’s et al., respectively. All sequences were generated from Illumina platforms, except the study by Miyake and colleagues that utilized Roche 454 sequencing. 16S rRNA gene sequencing was conducted by targeting different 16S rRNA gene regions such as V1-2, V1-3, V3-4, V3-5 and V4 regions (Table 1 ).

Identification of common MS-associated microbiota by re-analysis of each individual study

We first re-analyzed data from each individual study to evaluate differences in the microbial composition between MS and controls and identified common and unique MS-associated microbiota across all studies. PERMANOVA analysis showed that the global gut microbiota profile was significantly different between MS and controls in 5 out of 7 studies (Fig. 1 a). However, the variance explained by group (case/control) was low, ranging from 0.6 to 6%. Lower alpha diversity including richness and Shannon diversity in MS (versus controls) was identified in Ni Choileain’s study (Wilcoxon rank-sum test, p = 0.01 for Shannon diversity and p = 0.011 for richness). By contrast, richness was significantly higher in MS than controls in Chen’s study ( p = 0.03). There was no statistically significant difference in alpha diversity between MS and controls in the other five studies (Fig. 1 a). Beta-diversity measured by Bray-Curtis dissimilarity was significantly higher in MS compared to controls from four studies, lower in two studies, and not statistically different in one study. This data suggests there are no consistent alpha or beta diversity differences between MS and controls.

Common microbiota revealed by individual study analysis. a Summary of microbiota analysis at the microbial community level for each study. Significant differences are demonstrated by different colors. The sizes of the circles represent scale of -log10 p -value. b Genera that are significantly different in MS vs controls by Wilcoxon rank-sum test or DESeq2 test. Significant genera identified in at least two studies are shown in descending order from top to bottom by its shared frequency. For each individual study, the sizes of the circles represent mean relative abundance of the genera, and the colors of the circles represent statistically higher (blue) or lower (orange) of the relative abundance of the genera in MS, compared to controls. Taxonomy at the class level for each genus is indicated on the class column. Different shapes in the class column indicate the abundance of the genera are consistently (triangles) and inconsistently (circles) higher or lower in MS in at least two studies, compared to controls

We next tested individual genera differences between MS and controls within each project using Wilcoxon rank-sum tests and Deseq2 analysis. Genera with a relative abundance of > 0.1%, in either MS or controls, were tested. This analysis revealed that all seven studies had at least 1 genus that differed in relative abundance in MS vs controls after multiple testing adjustment with FDR controlled at 0.05. Twenty-five genera were significantly different between MS and controls in at least two separate studies (Fig. 1 b). Among the 25 genera, 17 of them (17/25 = 68.0%) were either consistently increased or decreased in MS patients in at least two studies. Of interest, the relative abundance of Prevotella in MS patients was decreased in all seven studies and was statistically significant in four (Cantoni, Chen, Miyake, and Zeng) which had participants from the USA, Japan, and China. This result suggests that the decreased relative abundance of Prevotella is a common feature in MS patients and it is independent of geographical locations.

The analysis also revealed that three genera Faecalibacterium , Lachnospira , and Megamonas from the Clostridia class were significantly decreased in MS patients from three studies. Six genera were significantly decreased in MS from two studies. Difference between MS and controls was also reflected by increases of seven genera in two studies. Interestingly, a closer examination at the taxonomy of seventeen genera that consistently differed between MS and controls revealed 64.7% (11/17) belonged to the Clostridia class. Furthermore, all seven studies had at least one genus from the Clostridia class that differed significantly between MS and controls.

Taken together, re-analyses of the seven studies individually indicated that MS and controls had distinct microbiota profiles, but differences in alpha and beta diversity were not consistently found across studies. Decreases of relative abundance of Prevotella and dysbiosis of genera from the Clostridia class were commonly associated with MS.

Major factors driving microbiota variation across studies

To identify microbiota differences between MS and controls in the seven studies combined, we first examined the microbial compositions and distribution patterns among studies. We showed the relative abundance and prevalence of all 652 genera identified in seven studies (Fig. 2 a). One hundred and twenty-two genera (19.0%) were detected in all 7 studies, and the relative abundance of these 122 genera accounted for 86.2% of the total abundance in all 7 studies. Strikingly, 38.1% of the genera were detected in one single study. The relative abundance of the dominant genera demonstrated high inter-study variation.

Microbiota variation across all studies. a Abundance and prevalence of 652 genera identified in 7 studies. Left Y -axis shows number of studies that detect a given genus (frequency); right Y -axis represents the relative abundance of each genus in a study. Several relatively high abundant genera are labeled in the plot. b Principal component analysis of samples from all seven studies based on Bray-Curtis distance; different studies are color-coded and group (MS vs controls) is indicated by different shapes. Boxplots at the bottom and at the right show PC1 and PC2 loadings for different studies or group (MS vs controls). Studies are significantly different in both PC1 and PC2 ( p < 0.001). Group (MS and controls) is significantly different in PC2 after controlling for study effect ( p = 0.0002). c Percent of variance that is significantly contributed by each factor

A PCA plot was used to visualize sample clustering patterns by studies. As shown in Fig. 2 b, PC1 and PC2 accounted for 13.0% variance of the microbiota. Chen, Jangi, and Miyake’s studies were distinct from each other and the rest of the studies along PC1 and PC2 (Fig. 2 b). Kruskal-Wallis test further supported statistical difference among studies at PC1 and PC2 ( p < 0.001 for both PC1 and PC2). Additional testing showed studies were all significantly different along PC3 to PC10 (all p \(<\) 0.001). Notably, Miyake’s study showed the least dispersion (inter-subject variation) compared to the rest of the studies.

Variance analysis using PERMANOVA showed that the study variable accounted for 19.17% of total microbiota variance (Fig. 2 c). Other technical variables, such as sequencing region, geographical location, DNA extraction kit, and sequencing platforms, also contributed to microbiota variations ranging from 4.9 to 14.5%, with statistical significance ( p = 0.001) (Fig. 2 c, Additional file 1 : Fig. S2-S4). However, these variables were likely to have overlapping contributions to the microbiota variation. For example, Miyake’s study was conducted in Japan using an in-house DNA extraction kit and V12 sequencing on a Roche 454 sequencing platform. We also tested the variance contributed by available biological variables such as group (MS vs control) and sex. We found disease group had a minimal influence (0.6%) on the overall microbiota variation and had no effect on PC1. However, disease group was significantly different in PC2 after controlling for the study effect (Fig. 2 b, blocked Wilcoxon test, p = 0.0002), as well as in PC5 ( p = 0.01) and PC10 ( p = 0.002), but the differences or effect sizes were small. Together, these data suggest that the “study” factor has a predominant effect in driving heterogeneity of the microbiota composition. Study effect should be controlled when identifying disease-associated microbiota by meta-analysis.

Identification of common MS-associated microbiota by meta -analysis

We next combined the data matrix from all 7 studies and performed a meta-analysis using a blocked Wilcoxon rank-sum test with “study” factor as a blocking factor. We also performed a linear mixed regression analysis with group as fix effect and study as random effect. Consistent with the individual studies, our meta-analysis found no significant differences in alpha diversity between MS and controls by blocked Wilcoxon test. Beta diversity, as measured by Bray-Curtis dissimilarity, was also not different between the two groups (Fig. 3 a). Sensitivity analysis using the leave-one-out method for affirmed these results, demonstrating robustness of our findings.

Common microbiota revealed by meta-analysis. a Alpha and beta diversity in MS and controls. Diversity is not statistically different between MS and controls ( p = 0.72 for Bray-Curtis dissimilarity; p = 0.57 for richness; p = 0.63 for Shannon diversity). b Significantly different genera between MS and controls identified by meta-analysis. Fifteen genera are significantly different between MS and controls after controlling for the “study” factor. Mean and standard error for each genus are illustrated. Class level taxonomy for each genus is indicated next to the genus. c Results of leave-one-out sensitivity analysis

Fifteen genera were significantly different between MS and controls as shown by either blocked Wilcoxon test or linear mixed regression analysis in the meta-analysis (Fig. 3 b). Seven of the fifteen genera were also identified from the individual studies as being consistently increased ( UBA1819 , unclassified Lachnospiraceae and Flavonifractor ) and decreased ( Prevotella , Faecalibacterium , Lachnospira , Megamonas ) in MS patients in at least two studies (Fig. 1 b). We also identified eight new genera associated with MS that were not identified by individual study analysis. These included Clostridium innocuum group , Eubacterium fissicatena group , Actinomyces , Agathobacter , Erysipelatoclostridium , Flavobacterium , Lachnospiraceae ND3007 group , and Streptococcus. Six of the eight newly identified genera were increased in MS compared to controls. Notably, more than half of the genera identified by meta-analysis belonged to the Clostridia class. This is consistent with the findings of our individual study.

Sensitivity analysis of differential taxa revealed significant variability attributed to the two Asian cohorts. Removing a US study could still replicate 73.33 to 100% of the differential taxa identified from the seven studies. However, when an Asian study was omitted, only 26.67% (Miyake) and 20% (Zeng) of the taxa in Fig. 3 b were maintained. Notably, despite variations driven by specific studies, the genera Actinomyces and Faecalibacterium were consistently identified in every iteration of the leave-one-out analysis, indicating a stable trend (Fig. 3 c). Together, our meta-analysis suggests there are reproducible MS-associated microbiota alterations across studies.

Disruption of Bacteroides -Prevotella correlative network in MS

Because the gut microbiota forms a complex interactive network through cooperation/competition which collectively affects host health and diseases [ 29 ], we tested the hypothesis that this microbiota interaction network is disrupted in MS patients. Using SparCC [ 30 ], we identified nine positive correlations that were shared between MS and controls (Fig. 4 a, b), suggesting these interactions may be fundamental structures of the microbiota network that are resilient to changes related to MS. For example, the most abundant genus, Bacteroides , was positively correlated with Alistipes and Parabacteroides , and Blautia was positively correlated with Bifidobacterium [ 31 ]. We identified 13 (Fig. 4 c) and 16 (Fig. 4 d) unique correlations in control and MS, respectively. Of the 13 unique correlations identified in controls, the negative correlation between Bacteroides and Prevotella that were highly abundant genera in our dataset (Fig. 2 a) was the strongest (Fig. 4 c) This strong negative correlation appears to be a fundamental characteristic of the microbiota in the gut of healthy adults [ 32 , 33 ]. However, this correlation was completely lost in patients with MS (Fig. 4 d). In MS, Bacteroides formed a new correlation network, as indicated by a positive correlation with Lachnoclostridium and negative correlations with five other genera.

Disruption of Bacteroides - Prevotella correlative network in MS. The network is constructed using correlations with p -value less than 0.05 and correlation coefficient larger than 0.2. Red lines represent positive correlations and blue lines represent negative correlations; The width of line varies by absolute value of correlation coefficient, and the nodes size represents relative abundance of genera. a , b Correlations shared between MS and controls. Nine positive correlations are shared in MS ( a ) and controls ( b ). c Correlations that are unique in controls. d Correlations that are unique in MS

To test the reliability of the network findings, we constructed networks within MS group or control group using leave-one-out sensitivity analysis. Rand index was used to assess the similarities of network structures between the network generated from any six studies with the network generated from the seven studies combined. The rand values ranged from 0.77 to 0.90, indicating that network structures are similar and supporting the robustness of our findings (Additional file 2 : Table S4). Notably, the relationship between Bacteroides and Prevotella are consistently maintained in the control and MS groups (Additional file 1 : Fig. S5-S11). Taken together, our findings suggest that the normal microbe-microbe correlation network is substantially disrupted and replaced by new correlations in patients with MS.

Classification of MS and control using the gut microbiota

To test the potential of using the gut microbiota to differentiate MS from controls, we trained RF models using each of the seven data sets and evaluated the accuracy of the classifiers (Additional file 1 : Fig. S12a blue diagonal from top left to bottom right). For each classifier built based on one study, we tested the prediction performance for the other six datasets (Additional file 1 : Fig. S12a, off-diagonal values). We found that the accuracy of the models (as measured based on confusion table) was generally low and varied widely, ranging from 0.48 to 0.72 (diagonal from top left to bottom right). Prediction performance (as measured by AUC) using these classifiers also varied widely, from 0.40 to 0.84. However, it is notable that the microbiota classifier from five studies (Cantoni, Chen, Cekanaviciute, Ni Choileain, and Zeng) provided a prediction performance for Miyake’s study, with AUCs more than 0.65. Interestingly, prediction using the microbiota data from Zeng’s study led to AUCs above 0.8 for Miyake’s study (AUC = 0.84) and vice versa (AUC = 0.83). To investigate whether the higher prediction performance observed between Miyake’s and Zeng’s studies (two Asian cohorts) was attributed to similar microbiome composition, we calculated pairwise beta-diversity across all studies. The microbiome similarity between Miyake’s and Zeng’s studies was not more pronounced compared to others (Additional file 1 : Fig. S13). However, taxa of importance that differentiate MS from controls in both Miyake’s and Zeng’s studies, identified through the RF analysis, exhibited a 40% overlap. Taxa of importance that differentiate MS from controls in other studies in the RF analysis showed only a 20–30% overlap with Miyake’s study. This suggests that distinguishing taxa, rather than similarity of overall microbiome composition, is crucial for achieving good predictability between the two Asian cohorts.

We next trained RF classifiers using data from a combination of six of the studies and tested their prediction performance on the remaining dataset. Training with a large sample size did not achieve higher prediction AUCs in the remaining dataset, with the exception of Miyake’s study (Additional file 1 : Fig. S12b). Lastly, we built a RF classifier using three quarters of all the data from the seven studies and then tested its prediction performance of the remaining data (Additional file 1 : Fig. S12c). This approach yielded an AUC of 0.67. Taken together, machine learning based on the gut microbiota profile has potential to differentiate MS from controls, but the prediction performance needs to be improved before any clinical application.

Association of the gut microbiota with disease status and treatment in MS patients

We next explored whether the composition of the microbiota is associated with clinical characteristics of MS. Among all 253 patients with RRMS, information on disease status and disease treatment was available for 207 patients and 234 patients, respectively. NO CIS and PPMS patients were included in this analysis. Alpha and beta diversity and specific taxa were not significantly different between 40 active cases and 166 remission cases after controlling for the study variable (Additional file 1 : Fig. S14a). Sixty-five patients received different therapies including DMT Copaxone ( n = 15), interferon beta ( n = 32), and immunosuppressive agents azathioprine ( n = 5), mycophenolate mofetil ( n = 2), methotrexate ( n = 2), Tysabri ( n = 5), and others ( n = 4). One hundred sixty-nine patients did not receive any treatment at the time of stool collection. There were no significant differences in alpha and beta diversity as well as relative abundance between non-treated patients and treated patients after FDR adjustment (Additional file 1 : Fig. S14b).

Over the past several years, around 100 different bacterial taxa have been reported to be associated with MS across different studies [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ]. Our work reconciles discordant findings in previous studies and establishes a generalized and common gut microbiome pattern in patients with MS across geographically and technically diverse studies.

A recent report from the International Multiple Sclerosis Microbiome Study (iMSMS) based on a large multi-center dataset of MS patients and household healthy control (HHC) subjects has provided the most comprehensive microbiome data analysis in MS to date [ 34 ]. Comparison of findings between iMSMS and our meta-analysis has revealed several common insights: (1) geography has a more dominant effect on microbial composition than disease diagnosis (Fig. 2 c). (2) No significant difference was detected in alpha diversity between MS and healthy controls (Fig. 3 a). (3) While directionality (negative vs positive) of correlation was not reported in the iMSMS study, it identified unique presence of Bacteroides and Prevotella species in the microbiome network in healthy controls. (4) Despite many differences in MS-associated taxa, the relative abundance of Faecalibacterium was found to be decreased in MS patients compared to healthy controls (Fig. 3 c) in both studies. The different MS-associated taxa between our study and iMSMS can be due to numerous known or unknown factors that influence microbiome variation, as we have demonstrated in Fig. 2 c. For example, MS-associated taxa identified by iMSMS study were based on shallow whole genome shotgun sequencing. While it avoids amplification bias from 16S rRNA gene sequencing, it may not capture important rare taxa that can be detected by 16S rRNA gene sequencing (i.e., Actinomyces , Flavonifractor , etc., in Fig. 3 ). In addition to sex bias, using household control may decrease sensitivity of detecting MS-associated microbiome because individuals from the same household tend to share gut microbes, and a shared microbe may still influence MS development in genetically predisposed individuals. Our meta-analysis of previous highly cited microbiome studies in MS and non-household healthy controls across diverse of locations from USA and Asia provides a complementary view of the microbiome in MS to the iMSMS study. The share findings from the two large MS microbiome analyses derived from different study designs and research approaches provided robust evidence on microbiome markers in MS patients.

One major discordance between our findings and those from the iMSMS study is the absence of treatment-associated taxa in our study. It is worth noting that many treatment-associated taxa identified in the iMSMS study were derived from a comparison between treated MS patients and their household controls, and only a few taxa were significantly different between treated and non-treated MS patients. Before p -value adjustment, we also identified several taxa that differentiated treated from non-treated MS patients, but they were no longer significant after FDR adjustment. Due to the relatively small sample size, we did not perform a comparison between specific DMT treatments and non-treated MS.

Our analysis identified several important MS-associated taxa . Prevotella , one of the most abundant genus in the healthy gut [ 35 ], was decreased in patients with MS in re-analysis of seven individual studies, suggesting that alteration in Prevotella plays a key role in the disease. Indeed, Prevotella histicola has been shown to suppress a Th17-mediated autoimmune response and improve disease course in a mouse model of MS, experimental autoimmune encephalomyelitis (EAE) [ 36 ]. However, increased relative abundance of Prevotella copri has also been associated with higher inflammation in patients with rheumatoid arthritis, indicating that the immunomodulatory role of Prevotella may be context dependent and/or specific to Prevotella species. Prevotella and Bacteroides are two common enterotypes identified in healthy human populations [ 35 , 37 ]. Our study revealed the loss of negative correlation between Prevotella and Bacteroides in MS patients, which is likely due to significant reduction of Prevotella in MS patients. This further led to a different microbiome interactive network in MS.

Faecalibacterium is one of the most common health-promoting bacteria identified from various studies [ 38 ]. In our study, Faecalibacterium was significantly lower in patients with MS than controls, and this finding was proved to be robust based on the sensitivity analysis . Like patients with MS, Faecalibacterium was found less abundant patients with IBD [ 39 ] and different neurodegenerative disorders [ 40 , 41 ], suggesting Faecalibacterium may modulate the immune responses in several different diseases.

In addition to Faecalibacterium , Actinomyces also showed a significant association with MS in the sensitivity analysis. Actinomyces is a genus commonly found in the oral cavity and gut. Although the current study could not determine its origin, we cannot rule out the possibility of an oral origin for Actinomyces . Similarly, Streptococcus is a dominant genus in the oral cavity but also part of the normal microbiota in the intestinal tract. Streptococcus was significantly increased in MS compared to controls, and it remained significant in most iterations of the sensitivity analysis. The presence of S. oralis and S. mitis , which are of oral origin, had been detected in the small intestine of RRMS patients [ 42 ]. Pathogenic Streptococcus species in the gut have been associated with IBD [ 43 ] and colon cancer [ 44 ], and childhood Streptococcus infections have been investigated as a putative risk factor for MS [ 45 ]. Streptococcus pneumoniae infection is thought to aggravate EAE in a TLR2 dependent manner [ 46 ]. Ectopic gut colonization by oral bacteria along with Th17 cells migration from oral mucosa to the gut promote gut inflammation and colitis in mice [ 47 ]. Identification of increased Streptococcus or Actinomyces in MS through our meta-analysis begs the questions whether there is an oral-gut microbiota connection in MS.

Our machine learning had only modest predictive power to differentiate MS vs healthy controls. However, the two Asian cohorts (Miyake and Zeng) exhibited greater predictability with each other. This observation was not due to similar microbiome compositions resulting from the proximity of geographical locations of the two studies. Instead, it is likely that the higher overlap of distinguishing taxa identified through RF analyses in both studies played an important role. In addition, because MS has a complex pathogenesis and etiology, besides increasing sample size to train the classifier, we believe that combining knowledge about the gut microbiota with clinical data and other OMICS data will add additional value to existing approaches to facilitate diagnosis, risk prediction, or prognosis of MS in future.

Our study has several limitations. Only seven cohort studies were included due to difficulty in obtaining sequencing data or complete clinical data from published works. Data sharing is crucial to validate findings and enables new discoveries, especially for studies related to rare diseases. Our analysis was also limited to genus level because different sequencing platforms and sequencing of different 16S rRNA gene regions preclude species level analysis across datasets. With more widespread use of whole genome shotgun sequencing and meta-transcriptomic techniques, future meta-analyses may be able to incorporate species and strain level taxonomies. Lastly, a comprehensive analysis of the effect of disease status (remission/active) on the gut microbiota could not be performed due to a lack of detailed information on patients in the MS group across studies. The iMSMS study has shown that different DMTs have different effects on the microbiota composition [ 34 ]. We did not perform comparison of the microbiota changes in different DMTs as this will not be accurate and robust due to even smaller sample size for each specific treatment group.

Future studies with standardized sample collection, sample processing, sequencing approach, and data analysis procedures, as well as well-organized data management and sharing plan, will maximize utilization of microbiome resource and strengthen microbiome research in MS. In addition, longitudinal studies are highly warranted to better understand the dynamics of the microbiota over the clinical course and treatment course of MS.

There are consistent microbial signatures associated with MS across studies. Prevotella is a significant biomarker in MS diagnosis in individual project analysis. Faecalibacterium and Actinomyces are associated with MS diagnosis in the meta-analysis. Furthermore, the correlation of Prevotella negatively related to Bacteroides is disrupted in MS in the network analysis.

Availability of data and materials

All the sequencing data can be accessed directly from the published paper. The related analysis code and table can be found in GitHub ( https://github.com/YZhouLabUConnHealth/meta_analysis_MS ) [ 48 ].

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Acknowledgements

We thank Professor Yongjun Lu from Sun Yat-sen University for providing the detailed data information of study “Zeng.” We are grateful to Cassandra Suther for the copyedit of our paper.

Drs. Yanjiao Zhou and Laura Piccio was supported by R01 NS102633, and this work was also supported by the start-up funding to Dr. Yanjiao Zhou from UConn Health.

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Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA

Qingqi Lin & Yufeng Wu

Department of Medicine, University of Connecticut Health Center, Farmington, CT, USA

Qingqi Lin, Yair Dorsett & Yanjiao Zhou

Department of Medicine, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada

Ali Mirza & Helen Tremlett

Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA

Laura Piccio

Brain and Mind Centre, School of Medical Sciences, University of Sydney, Sydney, New South Wales, Australia

Departments of Neurology and Immunobiology, Yale University School of Medicine, New Haven, CT, 06511, USA

Erin E. Longbrake, Siobhan Ni Choileain & David A. Hafler

Ann Romney Center for Neurologic Diseases, Harvard Medical School, Brigham & Women’s Hospital, Boston, MA, 02115, USA

Laura M. Cox & Howard L. Weiner

Department of Immunology, National Institute of Neuroscience, Tokyo, Japan

Takashi Yamamura

Department of Statistics, University of Connecticut, Storrs, CT, USA

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Contributions

QL contributed to the data analysis, drafting, and critical revision of the manuscript. YZ contributed to the study conception and design, data acquisition, analysis, drafting, and critical revision of the manuscript. AM contributed to the data acquisition and critical revision of the manuscript. HT contributed to the study conception and critical revision of the manuscript. YD contributed to the drafting and critical revision of the manuscript. LP, EL, SN, DH, LC, HW, and TY contributed to providing the data and critically revising the manuscript. YW and KC contributed to critically revising the manuscript. All authors read and approved the final manuscript.

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Correspondence to Yanjiao Zhou .

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Competing interests.

YZ holds shares at General Biomics Inc. HT has, in the last 5 years, received research support from the Canada Research Chair Program, the National Multiple Sclerosis Society, the Canadian Institutes of Health Research, the Multiple Sclerosis Society of Canada, the Multiple Sclerosis Scientific Research Foundation, and the EDMUS Foundation (“Fondation EDMUS contre la sclérose en plaques”) and, in addition, in the last 5 years, has had travel expenses or registration fees prepaid or reimbursed to present at CME conferences from the Consortium of MS Centres (2018), National MS Society (2018), ECTRIMS/ACTRIMS (2017–2022), and American Academy of Neurology (2019). Speaker honoraria are either declined or donated to an MS charity or to an unrestricted grant for use by HT’s research group. EEL has received honoraria over the last 5 years for consulting for Bristol Myers Squibb, Genentech, TG Therapeutics, NGM Bio, Janssen, Biogen, Genzyme, Alexion, EMD Serono, Celgene, and Teva. She has received research support from Genentech, the National Institutes of Health (NIH K23 NS107624 and KL2 TR001862), the Race to Erase MS, and the Robert E Leet and Clara Guthrie Patterson Trust. She is an assistant editor for Annals of Neurology . The remaining authors declare that they have no competing interests.

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Additional file 1. fig. s1-s14., 13073_2024_1364_moesm2_esm.xlsx.

Additional file 2: Table S1. Demographic table for each subject. Table S2. Summary of treatment regimens and disease activity across studies. Table S3. Details of random forest classifiers. Table S4. Rand index values for network comparisons.

Additional file 3: PRISMA files.

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Lin, Q., Dorsett, Y., Mirza, A. et al. Meta-analysis identifies common gut microbiota associated with multiple sclerosis. Genome Med 16 , 94 (2024). https://doi.org/10.1186/s13073-024-01364-x

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How to Manage Multiple Sclerosis (MS) Relapses

BY Lisa Fields July 29, 2024

When a person has multiple sclerosis (MS) , their immune system mistakenly attacks the protective myelin sheath that covers the nerves in the central nervous system. This damages nerves in the brain and spinal cord, causing symptoms like vision loss, stiff muscles, and/or fatigue. But not every case of MS is the same, as nerves are affected in different ways, resulting in a variety of symptoms .

Most people with MS—roughly 85% of patients—are initially diagnosed with relapsing-remitting MS, a form of the disease in which flare-ups, or relapses, of neurological symptoms, such as muscle weakness, balance problems, or vertigo, occur. Then, after a period of time, their symptoms partially or completely resolve, or go into remission. The frequency of relapses varies, ranging from less than one relapse per year to more than two relapses per year.

Other less common forms of MS include primary progressive MS and secondary progressive MS. In the former, patients experience ongoing neurological symptoms and the progression of disability from the time they’re diagnosed, without initial periods of relapses and remissions. In the latter, patients initially diagnosed with relapsing-remitting MS start to experience a constant worsening of symptoms over time, rather than having intermittent symptoms. The diagnosis changes from relapsing-remitting to secondary progressive MS.

The good news is that there are newer MS treatments that can help patients experience fewer relapses and less disability. Medicines that deplete circulating B cells (more on that below), such as ocrelizumab (brand name: Ocrevus®) and ofatumumab (brand name: Kesimpta®), can prevent relapses and are thought to slow the progression of the disease. (Ocrevus received Food and Drug Administration [FDA] approval in 2017; Kesimpta was FDA-approved in 2020.)

“We’ve reached a very interesting new stage in the treatment of MS,” says David A. Hafler, MD , an MS expert and chair of the Department of Neurology at Yale School of Medicine. “We have an incredibly effective treatment if it is used early in the course of the disease.”

We talk more with Dr. Hafler about how to recognize an MS relapse and what to do about it.

What happens during a relapsing-remitting MS relapse?

During MS relapses, self-reactive immune T cells wrongly attack the myelin sheath that surrounds nerves in the brain and spinal cord. This causes inflammation and harm to the myelin and the nerves. The damage interrupts messages the brain sends through the nervous system to different parts of the body. It also causes scars (lesions) in the brain and/or spinal cord.

MS relapses may cause a variety of symptoms, including:

• Numbness, weakness, or tingling in the arms or legs
• Difficulty walking or maintaining balance
• Stiffness or muscle spasticity
• Vision problems, including blurred vision, double vision, or temporary vision loss
• Cognitive problems
• Bladder or bowel dysfunction problems, such as urinary frequency or urgency, constipation, and loss of bladder or bowel control

How can you tell if you’ve had a multiple sclerosis relapse?

Patients with relapsing-remitting disease who experience common MS symptoms will likely know that they’re having a relapse based on how they’re feeling.

However, some relapses are silent, especially early in the course of the disease. These relapses may damage the myelin and cause new lesions without perceived symptoms or disability. Silent relapses can be detected by magnetic resonance imaging (MRI) , which may be recommended periodically for patients who have recently been diagnosed with the disease.

“If you do MRIs looking for new lesions at the beginning of the disease, lesions are occurring all the time,” Dr. Hafler says. “You could go years with this happening and not have an ‘attack’ or relapse. It’s only when you have a lesion in a part of the brain—like the optic nerve, the spinal cord, or the brainstem—where there’s a lot going on in a small area, that you have this so-called ‘clinical relapse’ with accompanying symptoms.”

What medications are available for people with relapsing-remitting multiple sclerosis?

When patients with relapsing-remitting MS experience flare-ups, doctors may prescribe intravenous corticosteroids. These powerful medications help reduce inflammation in the body and shorten the duration of exacerbations, but they do not have a long-term benefit for treating the disease.

When patients with relapsing-remitting MS are diagnosed, they are prescribed medications to take regularly to decrease the risk of relapses. For many years, doctors prescribed immunomodulatory drugs, such as beta interferons and Copaxone®. These medications modulated the immune system, but had only mild effects in terms of stopping disease flare-ups.

In recent years, neurologists have turned to more effective treatments that target different white blood cells in the immune system—known as B cells. B cells and T cells normally protect the body. When people have MS, it is now thought that B cells activate the T cells, which damage the myelin. Treatments, known as monoclonal antibodies, are used to deplete the B cells, limiting damage to the myelin. Doctors often prescribe Ocrevus, which is given by infusion twice a year, or Kesimpta, which is given under the skin each month.

“If you deplete the circulating B cells, it’s 98% effective in stopping relapses. Neurologists now typically prescribe B cell-depleting monoclonal antibodies to patients who are newly diagnosed with MS; we basically treat everyone with Ocrevus or its equivalent. The best thing to do is treat patients before they have any clinical symptoms," Dr. Hafler says. "For example, let’s say there is a patient who has a headache or minor head trauma and gets an MRI. If we see lesions on the MRI that look like MS, we would do a spinal tap, and if that shows inflammation, we could then diagnose them with MS and start treatment.”

For patients with pre-existing relapsing-remitting MS who have taken other drugs for years, doctors may choose to keep them on their existing medication regimen. “If you're doing well on the drug you’ve been on for a long time, then we typically continue that drug, but we still monitor you very closely,” Dr. Hafler says.

B cell-depleting drugs, such as ocrelizumab, have also been successful in slowing the accrual of disability in patients with primary progressive MS and secondary progressive MS, adds Dr. Hafler.

“We’ve been very effective in stopping the inflammatory part of MS and the attacks, but it will take decades to know how effective early treatment is in preventing the progression of the disease,” Dr. Hafler says.

Are there lifestyle treatments that can help manage multiple sclerosis relapses?

Certain habits may help patients with relapsing-remitting MS manage their health:

• Adopt a healthy diet. Doctors who treat patients with MS recommend a low-fat, low-salt diet. “A low-salt diet helps because research shows that salt is highly inflammatory,” Dr. Hafler says.
• Exercise. Getting 30 minutes of physical activity at least five times a week may help patients feel stronger, improve their sense of balance, increase flexibility, and minimize stiffness.
• Quit smoking. Cigarette smoking isn’t healthy for anyone, but it’s especially harmful for MS patients. Smoking may increase inflammation in the body and cause the disease to progress more quickly.
• Seek physical therapy as needed. Some patients with relapsing-remitting MS benefit from physical therapy, “particularly patients developing disabilities,” Dr. Hafler says.

Why is it important for someone with MS to be treated as soon as possible?

When patients with relapsing-remitting MS begin taking medication soon after their diagnosis, they’re more likely to have fewer relapses and less disability from the disease.

Often, patients with suspected MS must wait several months before they’re able to get an appointment with a neurologist for a definitive diagnosis. This significantly delays the start of their treatment.

“The Yale Multiple Sclerosis Center’s MS Access Program helps patients with suspected MS get diagnosed and treated as soon as possible,” says Dr. Hafler. “Physicians can contact the program to refer patients when MRI results suggest that MS is present. Anyone who contacts us with a positive MRI and new onset will be seen within 24 hours. We rush to get them in, work them up, and get them on the B-cell depletion therapy. It’s the only drug we use in our clinic. We’re changing the course of the disease.”

The message is that the earlier you treat, the better the outcome, he adds. “The key is early diagnosis, early treatment,” Dr. Hafler says.

More news from Yale Medicine

Increased level of GATA3-AS1 long non-coding RNA is correlated with the upregulation of GATA3 and IL-4 genes in multiple sclerosis patients

• Original Article
• Published: 30 July 2024
• Volume 51 , article number  874 , ( 2024 )

Cite this article

• Fatemeh Keshavarz 1 ,
• Mohammad Javad Mokhtari 1 &
• Maryam Poursadeghfard 2  

Long non-coding RNAs (lncRNAs) play various roles in gene regulation. GATA3 antisense RNA 1 ( GATA3-AS1 ) is an lncRNA gene neighboring GATA binding protein 3 ( GATA3 ). The current study aims to quantitatively compare the levels of the expression of GATA3-AS1 , GATA3 , and Interleukin-4 ( IL-4) in peripheral blood mononuclear cells (PBMC) samples of MS patients and healthy individuals under the hypothesis of regulation of GATA3 and IL-4 expression orchestrated by GATA3-AS1 .

Methods and results

In this case-control study, the GATA3-AS1 , GATA3 and IL-4 expression profiles were assessed using real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR). Also, we assessed the IL-4 levels in the serum. The median fold changes in MS patients vs. controls were (4.39 ± 0.38 vs. 2.44 ± 0.20) for GATA3-AS1 , (5.22 ± 0.51 vs. 2.86 ± 0.30) for GATA3 , and (6.16 ± 0.52 vs. 3.57 ± 0.38) for IL-4 , ( P  < 0.001). Furthermore, the mean serum levels of IL-4 were 30.85 ± 1.53 pg/ml in MS patients and 11.15 ± 4.23 pg/ml in healthy controls ( P  < 0.001). ROC curve analysis showed that the level of GATA3-AS1 might serve as a biomarker for diagnosing MS patients with the area under the curve (AUC = 0.918, P  < 0.0001). Based on our results, this GATA3-AS1/GATA3/IL-4 pathway may increase IL-4 expression in MS patients.

Conclusions

Our results indicate a probably regulatory function for GATA3-AS1 and the levels of GATA3-AS1 in blood could be important biomarkers for MS diagnosis. To confirm and be more certain of these results, it is necessary to study neuromyelitis optica (NMO) and asthma patients in future studies.

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Expression analysis of long non-coding RNAs and their target genes in multiple sclerosis patients

A Novel Regulatory Function of Long Non-coding RNAs at Different Levels of Gene Expression in Multiple Sclerosis

Dysregulation of long noncoding RNA MEG3 and NLRC5 expressions in patients with relapsing-remitting multiple sclerosis: is there any correlation?

Data availability.

No datasets were generated or analysed during the current study.

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Acknowledgements

The present study was derived from a thesis by Fatemeh Keshavarz. This work was supported by Islamic Azad University, Zarghan Branch, Zarghan, Iran.

No financial assistance was received in support of the study.

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Department of Biology, Zarghan Branch, Islamic Azad University, Zarghan, Iran

Fatemeh Keshavarz & Mohammad Javad Mokhtari

Clinical Neurology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran

Maryam Poursadeghfard

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Contributions

F.K. and M.P.; Methodology, Software. M.J.M. and M.P.; Data curation, and Supervision. M.J.M.; Conceptualization, Writing- Original draft preparation, and Writing- Reviewing and Editing. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Mohammad Javad Mokhtari .

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Competing interests.

The authors declare no competing interests.

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All methods were performed in accordance with the relevant guidelines and regulations. Ethics approval for this research was obtained by the local ethics committee of the Arsanjan Branch, Islamic Azad University, Iran (IR.IAU.A.REC.1399.031). The written informed consent form was obtained from all participants.

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Keshavarz, F., Mokhtari, M.J. & Poursadeghfard, M. Increased level of GATA3-AS1 long non-coding RNA is correlated with the upregulation of GATA3 and IL-4 genes in multiple sclerosis patients. Mol Biol Rep 51 , 874 (2024). https://doi.org/10.1007/s11033-024-09818-6

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COVID ‐19 vaccination and relapse activity: A nationwide cohort study of patients with multiple sclerosis in Denmark

Dominika stastna.

1 Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague Czechia

2 Danish Multiple Sclerosis Registry, Department of Neurology, Copenhagen University Hospital–Rigshospitalet, Glostrup Denmark

Frederik Elberling

Luigi pontieri, elisabeth framke, dana horakova, jiri drahota.

3 Endowment Fund IMPULS, Prague Czechia

Petra Nytrova

Melinda magyari.

4 Danish Multiple Sclerosis Centre, Department of Neurology, Copenhagen University Hospital–Rigshospitalet, Glostrup Denmark

5 Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen Denmark

Associated Data

All data relevant to the study are included in the article. The data underlying this article are stored in a protected server environment at the Danish Health Data Authority and cannot be shared publicly due to data protection regulations. Data are accessible to authorized researchers after application to the Danish Health Data Authority and the board of the DMSR.

Background and purpose

We evaluated whether there was a difference in the occurrence of relapses pre‐ and post‐COVID‐19 vaccination in a nationwide cohort of Danish patients with relapsing multiple sclerosis.

We conducted a population‐based, nationwide cohort study with a cutoff date of 1 October 2022. We used McNemar tests to assess changes in the proportion of patients with recorded relapses within 90 days and 180 days before and after first vaccine dose, and a negative binomial regression model to compare the 90 and 180 days postvaccination annualized relapse rate (ARR) to the 360 days prevaccination ARR. Multivariate Cox regression was used to estimate relapse risk factors.

We identified 8169 vaccinated (87.3% Comirnaty) patients without a recorded history of a positive COVID‐19 test. We did not find statistically significant changes in the proportion of patients with relapses in the 90 days (1.3% vs. 1.4% of patients, p  = 0.627) and 180 days (2.7% vs. 2.6% of patients, p  = 0.918) pre‐ and postvaccination. Also, a comparison of the ARR 360 days before (0.064, 95% confidence interval [CI] = 0.058–0.070) with the ARR 90 (0.057, 95% CI = 0.047–0.069, p  = 0.285) and 180 (0.055, 95% CI = 0.048–0.063, p  = 0.060) days after vaccination did not show statistically significant differences. Lower age, higher Expanded Disability Status Scale score, and relapse within 360 days before vaccination were associated with a higher risk of relapse.

Conclusions

We did not find evidence of increased relapse activity following the administration of the first dose of the COVID‐19 vaccine.

INTRODUCTION

The COVID‐19 infection has posed a global health challenge since 2020 and will likely stay with us for the foreseeable future [ 1 , 2 ]. Despite many other efforts being implemented worldwide, vaccination with regular booster administration remains a key strategy to reduce the severity and suppress the spread of infection.

Generally, vaccines work by stimulating the immune system. Their immunogenicity is key to achieving protection against specific pathogens. Specifically, still relatively new mRNA vaccines fighting against SARS‐CoV‐2 stimulate the immune system to produce high concentrations of neutralizing antibodies rapidly. They also activate T‐cell responses, which seem to play a key role in preventing severe COVID‐19 [ 3 ]. These advantages of mRNA vaccines in terms of efficacy were readily noticeable right at the beginning of their administration to the general population. In contrast, over time, we have become aware of some of their limitations, especially after the selection and propagation of SARS‐CoV‐2 variants. The most significant are the rare but serious adverse events specifically associated with these mRNA vaccines, short‐lived protection, reduced efficacy towards variants of concern, and activation of Th17 immune responses, which can exacerbate inflammatory reactions [ 4 ]. Therefore, compared to the populations without autoimmune diseases, there is one more potential risk of vaccination in patients with multiple sclerosis (MS), the risk of induction of disease exacerbation. In addition, the immunosuppressive or immunomodulatory activity of disease‐modifying treatments (DMTs) affect the immune response to the vaccine [ 5 , 6 ]. Given this, there is a degree of hesitancy among MS patients to be vaccinated against COVID‐19 [ 7 , 8 , 9 ].

To our knowledge the most extensive investigation so far (with 1661 MS patients) showed only a slight increase in the proportion of patients with relapse 90 days after vaccination compared to 90 days before vaccination [ 10 ]. However, these results come from a single‐centre study and contradict other smaller studies [ 11 , 12 , 13 ]. Thus, an effort is still needed to evaluate the risk of relapse after COVID‐19 vaccination. This study aimed to evaluate whether there is a difference in the occurrence of relapses before and after the first COVID‐19 vaccine dose in a large cohort of Danish patients with relapsing MS and to identify relapse risk factors.

Data source

The Danish Multiple Sclerosis Registry (DMSR) [ 14 ] served as primary data source. The target population of the DMSR are all Danish residents diagnosed with MS and related demyelinating disorders. Care for patients with these demyelinating diseases is centralized at 13 MS centres across Denmark. The recording of demographic, clinical, and paraclinical data during regular clinical visits has been mandatory since 1996.

Dates of death were retrieved from the Danish Register of Causes of Death [ 15 ]. Results of SARS‐CoV‐2 polymerase chain reaction (PCR) tests, antigen tests, and dates and types of COVID‐19 vaccines were retrieved from the nationwide Danish COVID‐19 Test and Vaccination Registry [ 16 ]. A pseudonymized unique personal identification number was used as key to identify individuals across Danish national registries [ 17 ].

Population of interest and variables assessed

We conducted a registry‐based nationwide cohort study using demographic, clinical, and COVID‐19 vaccination data, updated as of 1 October 2022, from the different national Danish registries. Based on the literature [ 10 , 18 ], analyses were performed under the assumption that a relapse due to vaccination would occur within 2 months after vaccination. The recommended time between the most common vaccine (Comirnaty) doses was at least 4 weeks. Thus, to maintain sufficient follow‐up time also after the second dose of the vaccine, we primarily chose a 90‐day interval after the first vaccine dose for the evaluation of the incidence of relapses. To examine the safety of the vaccine in the longer term, we also performed analyses using a 180‐day period. This is also in view of the fact that after vaccination by Comirnaty, a rapid decline in both antibody concentrations and T‐cell responses after 6 months has been described [ 19 ].

The selection flowchart towards the final study population is shown in Figure  1 . The inclusion criteria were (i) at least one COVID‐19 vaccine dose by 1 October 2022, (ii) at least 18 years old at the time of the first vaccine dose, (iii) a minimum of 180 days follow‐up after the first vaccine dose, (iv) a relapsing–remitting MS (RRMS) or clinically isolated syndrome (CIS) course throughout the observational period of 360 days before and 180 days after the first vaccine dose, and (v) no history of SARS‐CoV‐2 infection (positive PCR or antigen test) up to 180 days after the first vaccine dose (to exclude relapses caused by the infection itself [ 10 ]; Figure  1 ).

Flowchart towards the final study population. BL, baseline = the first vaccine dose administration; CIS, clinically isolated syndrome; RRMS, relapsing–remitting multiple sclerosis.

Given that DMTs are one of the most influential and varying monitored factors affecting relapse occurrence and, on the other hand, their switches are most often conditional on the incidence of relapses, subgroup analyses including only patients who did not initiate, switch, or discontinue DMT during the observational period were performed. Other potential relapse triggers (e.g., other infections) were not considered in the analyses.

Statistical analyses

First, we evaluated the difference in relapses preceding and following the first vaccine dose (baseline). We compared the proportion of patients with at least one MS relapse in the 90 and 180 days following baseline with the 90‐ and 180‐day intervals before baseline via McNemar paired test. Second, we estimated annualized relapse rates (ARRs) 90 and 180 days after baseline and 360 days before baseline by fitting negative binomial regression models to account for overdispersion. Third, we used multivariable Cox regression to estimate the hazard ratio (HR) and 95% confidence interval (CI) for the association between demographic (age, sex) and clinical (MS duration, Expanded Disability Status Scale [EDSS], MS relapse in the previous 360 days, no DMT) variables at baseline and time to first relapse during 180 days of follow‐up.

Fourth, we assessed the proportion of patients who received the third COVID‐19 vaccine dose (or the second dose in case the first was Jcovden) and were followed for a minimum of 90 days after the third dose until 1 October 2022. Then, we evaluated a number of patients who had relapsed in the 90‐day interval after the third dose.

As supplemental analyses, all analyses were repeated on the subgroup of patients who did not initiate, switch, or discontinue DMT during the observational period. All data analyses were performed in R version 4.2.2.

Standard protocol approvals

The Danish Data Protection Agency approved the study through the joint notification of the Capital Region of Denmark. In Denmark, studies based on registry data only do not require approval from the National Committee on Health Research Ethics.

Demographic and clinical characteristics

We identified 8169 vaccinated adult patients with CIS or RRMS without a recorded history of COVID‐19. The majority (87.3%) of patients were vaccinated with Comirnaty, and 99.7% also received a second dose (all Comirnaty). For more baseline characteristics, see Figure  1 and Table  1 .

Clinical and demographical characteristics measured at the time of the first vaccine dose.

Abbreviations: CIS, clinically isolated syndrome; DMT, disease‐modifying treatment; EDSS, Expanded Disability Status Scale; HET, high‐efficacy treatment; IQR, interquartile range; RRMS, relapsing–remitting multiple sclerosis; S1P, sphingosine‐1‐phosphate.

Relapse activity following COVID‐19 vaccination

The mean time from the first dose of the vaccine to the first relapse was 85.1 days (SD = 49.3). Using the McNemar test, we did not find statistically significant changes in the proportion of patients with relapses in the 90 days (1.3% vs. 1.4% of patients, p  = 0.627) and 180 days (2.7% vs. 2.6% of patients, p  = 0.918) pre‐ and postvaccination (Figure  2 ).

Proportion of patients with at least one relapse in the 90 days (left) and 180 days (right) pre‐ and postvaccination ( N  = 8169). 0: baseline = the first vaccine dose application.

A comparison of the ARR 360 days before (0.064, 95% CI = 0.058–0.070) with the ARR 90 (0.057, 95% CI = 0.047–0.069, p  = 0.285) and 180 days (0.055, 95% CI = 0.048–0.062, p  = 0.060) after vaccination did not show statistically significant differences (Figure  3 ).

Annualized relapse rates (ARRs) before and after first vaccine dose in all patients ( N  = 8169) and after the exclusion of patients with disease‐modifying treatment change ( n  = 6899). * p  < 0.05. BL, baseline = the first vaccine dose application; CI, confidence interval; DMT, disease‐modifying treatment.

Risk factors of MS relapse after COVID‐19 vaccination

In total, 214 patients experienced a relapse within 180 days after vaccination. Results from a multivariate Cox regression model for time to the first relapse after vaccination showed a lower risk for higher age. Higher EDSS score at baseline and relapse during the previous 360 days were associated with higher risk for relapse. Sex, disease duration, and absence of DMT treatment were not associated with relapse risk (Table  2 ).

Time to first relapse after the first vaccine dose up to 180 days of follow‐up.

Note : Bold emphasize significant results.

Abbreviations: BL, baseline = the first vaccine dose application; CI, confidence interval; DMT, disease‐modifying treatment; EDSS, Expanded Disability Status Scale; HR, hazard ratio; MS, multiple sclerosis.

Relapse following the third COVID‐19 vaccine dose

Of the 8169 vaccinated patients, 7674 (93.96%) received a third vaccine dose and were followed for a minimum of 90 days after the third dose until 1 October 2022. A total of 115 (1.5%) of them had a recorded relapse within the 90 days following the third dose.

Relapse activity and risk factors in patients without treatment change

After excluding patients with DMT initiation, switch, or discontinuation during the observational period, we also did not find statistically significant changes in the proportion of patients with relapses in the 90 days pre‐ and postvaccination ( p  = 0.576). The difference between 180 days before and after vaccination became accentuated (a total of 6899 patients; 1.49% of patients before vs. 1.91% after vaccination, p  = 0.053) but was still not statistically significant.

The difference between ARR 360 days before (0.031, 95% CI = 0.027–0.036) and 90 days (0.038, 95% CI = 0.029–0.048, p  = 0.200) after vaccination was also not statistically significant. However, the comparison between ARR 360 days before and 180 days (0.039, 95% CI = 0.033–0.047, p  = 0.040) after vaccination showed a statistically significant difference (Figure  3 ).

When repeating the multivariate Cox regression analysis for the time to the first relapse after vaccination in the subgroup of patients who did not initiate, switch, or discontinue DMT between 360 days before and 180 days after vaccination, risk estimates remained the same as in the primary analysis (Table  3 ).

Time to first relapse after the first vaccine dose up to 180 days of follow‐up after excluding patients with DMT switch.

In this large, population‐based, nationwide cohort study, we investigated the relapse activity levels 90 and 180 days after COVID‐19 vaccination in patients with relapsing MS. In the main analysis, we did not find a statistically significant difference between 90 or additionally 180 days before and after vaccination, nor a statistically significant increase in ARR between 360 days before and 90 or 180 days after vaccination. We also repeated the analysis in a subgroup of patients who did not change therapy during the observational period; although the difference between ARR 360 days before (0.031, 95% CI = 0.27–0.036) and 180 days (0.039, 95% CI = 0.033–0.047, p  = 0.040) after vaccination was accentuated and became statistically significant, the ARR difference of 0.008 in this subgroup of patients is too negligible to draw conclusions from. Moreover, it is questionable whether reported relapses can be attributed to the vaccination, especially given the average latency of 85 days following the first vaccine dose. Although it is true that the antibody and T‐cell responses, including Th17 activation, generally do not begin to decline considerably until approximately 6 months postvaccination [ 19 , 20 ], the initial weeks are usually considered the risk period concerning bystander activation, molecular mimicry, epitope diffusion, or polyclonal activation of B cells [ 18 , 21 , 22 ]. This is exemplified also by a review, where the time to the described autoimmune complications after COVID‐19 vaccination ranged up to 5 weeks after the second dose of the vaccine [ 23 ]. Lower age, higher EDSS, and relapse within 360 days before vaccination, but not sex, MS duration, and absence of DMT were associated with a higher risk of relapse, both in the main and subgroup analysis. These findings are probably due to a more vigorous immune response mounted by younger individuals, due to immunosenescence with age [ 24 , 25 , 26 , 27 ]. Given that only patients with CIS and RRMS were included, the association of higher EDSS with higher relapse risk (HR = 1.18) is most likely related to the strongest relapse predictor—the presence of relapse during the previous 360 days (HR 2.82)—and generally more active MS with residual neurological impairment.

Compared to other studies [ 10 , 11 , 12 , 13 ], our study evaluated the risk of relapses during a longer observational period after vaccination. Regardless, our results are consistent with an Italian multicentre study with 324 patients with MS, which did not identify a statistically significant increased relapse risk after Comirnaty vaccination (1.9% 2 months before vs. 2.2% 2 months after the first vaccine dose) [ 11 ]. Additionally, an Israeli study on 555 patients with MS compared the incidence of relapses after Comirnaty vaccination with the incidence in a period of the same length during the prepandemic era and did not find statistically significant differences. However, this study has the limitation of a heterogenous follow‐up period, as 6.5% and 13.1% of patients were followed <14 days after the first and second vaccine doses, respectively, which might have lowered the number of recorded relapses [ 12 ]. A statistically significant difference 2 months before and 2 months after vaccination (69% CoronaVac [Sinovac], 29% Comirnaty) was also not observed in 178 patients from Chile [ 13 ].

On the other hand, the Prague monocentric cohort study with 1661 vaccinated patients with MS reported a statistically significant increase of relapse risk within 90 days after the first (90.0% Comirnaty) vaccine dose compared to 90 days before vaccination. Thus, 5.30% of patients had a relapse after vaccination (compared to 3.79% before vaccination). That study also looked at risk factors for relapse, and in agreement with our results, lower age was associated with a higher risk of relapse [ 10 ]. The reasons for the difference between our study and the Prague study [ 10 ] may be (i) mainly the larger scope and nationwide nature of this work, but also (ii) different rates of reported relapses [ 10 ], (iii) different antiepidemic measures that may have had an influence (e.g., the incidence of other infections with a possible impact on the frequency of relapses), (iv) the different timing of the COVID‐19 waves and vaccination in both countries, (v) different national recommendations regarding timing of vaccination (e.g. in Denmark, patients were advised to be vaccinated immediately after the notification from the Danish health authority [ 28 ]; at the same time, in Czechia, rules were set to restrict vaccination, e.g., after administering glucocorticoids or initiating certain types of DMTs [ 29 ]), or (vi) the influence of unmeasured confounders that probably differ between the Czech and Danish populations.

The strengths of our study include the virtually complete unselected large nationwide cohort of patients with relapsing MS, a follow‐up at minimum of 180 days after the first vaccine, and the completeness of the data due to the mandatory recording of demographic, clinical, and paraclinical information. However, the study has some limitations. Due to the limited frequency of magnetic resonance imaging performed during the pandemic, we did not include radiological disease activity as an outcome measure. Although we used registry data on all SARS‐CoV‐2 tests performed at public test centres, some patients with COVID‐19 may not have been tested, particularly at the beginning of the pandemic. This is relevant given that a higher relapse incidence was previously described in association with COVID‐19 infection [ 10 , 30 ]. Similarly, due to the generally higher risk of relapse in association with infections [ 31 , 32 ], results may be affected by fluctuations and the absence of other infectious diseases, particularly given restrictions during a pandemic. Finally, despite the large size of our cohort, further multinational research with even larger numbers of patients will be needed to allow analysis of more patient subgroups.

CONCLUSIONS

This large, population‐based nationwide cohort study found no evidence showing a clinically relevant risk of relapse associated with COVID‐19 vaccination. Thus, our results can contribute to guidance in clinical practice and to reducing vaccine hesitancy in patients with MS.

AUTHOR CONTRIBUTIONS

Dominika Stastna: Conceptualization; methodology; writing – original draft; visualization; project administration. Frederik Elberling: Conceptualization; methodology; formal analysis; data curation; writing – review and editing; visualization. Luigi Pontieri: Conceptualization; methodology; data curation; formal analysis; writing – review and editing. Elisabeth Framke: Conceptualization; methodology; writing – review and editing. Dana Horakova: Conceptualization; methodology; writing – review and editing. Jiri Drahota: Methodology; writing – review and editing. Petra Nytrova: Methodology; conceptualization; writing – review and editing. Melinda Magyari: Conceptualization; methodology; writing – review and editing; supervision.

FUNDING INFORMATION

This work was supported by the Czech Ministry of Health, the institutional support of the hospital research (MH CZ‐DRO‐VFN64165), the Czech Health Research Council grant (AZV NU22‐A‐150), the Charles University Cooperation Program in Neuroscience, and the National Institute for Neurological Research project funded by the European Union—Next Generation EU (Programme EXCELES, ID Project No. LX22NPO5107).

CONFLICT OF INTEREST STATEMENT

D.S. has received financial support for conference travel and/or speaker honoraria from Novartis, Biogen, Merck, Teva, Janssen‐Cilag, and Roche. D.H. has received compensation for travel and/or speaker honoraria and/or consultant fees from Biogen, Novartis, Merck, Bayer, Sanofi, Roche, and Teva, as well as support for research activities from Biogen. P.N. has received speaker honoraria and consultant fees from Biogen, Novartis, Merck, and Roche, and financial support for research activities from Roche and Merck. M.M. has served on scientific advisory boards for Sanofi, Novartis, and Merck, and has received honoraria for lecturing from Biogen, Merck, Novartis, Roche, Genzyme, and Bristol Myers Squibb. None of the other authors has any conflict of interest to disclose.

ACKNOWLEDGEMENTS

Many thanks to all colleagues from the DMSR for their warm welcome, introduction to the issue of Danish registers, and their willingness and time for discussions and essential ideas and advice. Special acknowledgements are also due Eliza Varju for language editing.

Stastna D, Elberling F, Pontieri L, et al. COVID‐19 vaccination and relapse activity: A nationwide cohort study of patients with multiple sclerosis in Denmark . Eur J Neurol . 2024; 31 :e16163. doi: 10.1111/ene.16163 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

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