Risk Factors in Early Multiple Sclerosis (RISEMS)

• ClinicalTrials.gov Identifier: NCT03586986
• Recruitment Status: Recruiting
• First Posted: July 16, 2018
• Last Update Posted: August 12, 2021
• Sponsor: University of Colorado, Denver
• Collaborator: Colorado School of Public Health
• Information provided by (Responsible Party): University of Colorado, Denver

Study Description

Brief Summary:

The central hypothesis of this protocol is that it is possible, using First Degree Relatives (FDRs) of patients with Multiple Sclerosis (MS) and assessing a variety of both known and unknown risk factors for MS, to define a risk algorithm for earliest signs of development of MS. The plan will be to do an abbreviated brain Magnetic Resonance Imaging (MRI) scan in asymptomatic, young FDRs, analyze blood for a variety of immunological, genetic, neuroaxonal damage, metabolic, viral serology and other markers, and have FDRs fill out a detailed bioscreen questionnaire about lifestyle factors and perform a cognitive screening test. The investigators will then compare the results of the various blood/other studies in FDRs with and without an MRI showing signs signs concerning for MS, as well as age-and sex-matched NON-FDRs who will have blood drawn and fill out the questionnaire. With this preliminary cross-sectional study, the investigators hope to begin to identify a risk stratification model for those at highest risk of developing MS, ie FDRs, with a long-term goal of developing a longitudinal study to increase sensitivity and specificity of the risk model.

Condition or disease	Intervention/treatment
Multiple Sclerosis; Magnetic Resonance Imaging; Biomarkers; Clinically Isolated Syndrome; Radiologically Isolated Syndrome	Diagnostic Test: Brain MRI

Detailed Description:

Specific Aims, among asymptomatic first degree relatives (FDRs), aged 18-30, of multiple sclerosis (MS) patients:

1. Determine the prevalence of brain MRI lesions disseminated in space (DIS), consistent with MS
2. Gather data on potential risk factors or early signs related to MS development, including markers for: genes, immunological function, environmental factors, neuroaxonal damage, Vitamin D levels, lipid metabolism, activity levels, mood abnormalities, and cognitive function. From this, develop a risk factor score, incorporating all relevant potential markers of increased risk of DIS.
3. To use this pilot, cross-sectional study as a base for development of a long-term, longitudinal, multi-center study to determine genetic and environmental risks for pre-symptomatic MS
4. To create and maintain a biobank of specimens for future analysis as other potential biomarkers become available.

Sequences to include

1. Whole brain T1-weighted images acquired using 3-Dimensional fast spoiled-gradient recalled acquisitions (T1-3D-FSPGR) with 1 mm slice thickness and isotropic voxel dimensions.
2. Whole brain 3-Dimensional Double Inversion Recovery (DIR) acquisition with 1.5 mm slice thickness.
3. Whole brain T2-weighted Fluid Attenuated Inversion Recovery (FLAIR ) images with 1 mm slice thickness and isotropic voxel dimensions will be combined with
4. Whole brain T2*-weighted segmented echo-planar imaging (segEPI) images with <=1 mm slice thickness and isotropic voxel dimensions to obtain FLAIR* images.

Blood analysis for:

1. Single Nucleotide Polymorphism analysis of greater than 200 known mutations associated with risk of MS, and do an human leukocyte antigen (HLA) analysis
2. CD20 on CD4+ cell analysis
3. B Cell Anergy analysis Lipid levels
4. Viral serologies (HSV, EBV, CMV, VZV)
5. Neurofilament Light
6. Vitamin D levels

Bioscreen analysis based on that from the Diabetes Autoimmunity Study in the Young (DAISY); and to include the Godin Leisure-Time Exercise Questionnaire (GLTEQ), which has been validated in both adults and children and a validated dietary/food frequency questionnaire. In addition perform a cognitive screen with a Symbol Digit Modality test.

Blood will also be stored for future potential analysis, including peripheral blood mononuclear cells, serum, and Ribonucleic acid (RNA).

Study Design

• Study Type: Observational
• Estimated Enrollment: 330 participants
• Observational Model: Case-Control
• Time Perspective: Prospective
• Official Title: Risk Factors in Early Multiple Sclerosis
• Actual Study Start Date: July 26, 2018
• Estimated Primary Completion Date: May 30, 2022
• Estimated Study Completion Date: May 30, 2022

Groups and Cohorts

Group/Cohort	Intervention/treatment
FDR with abnormal brain MRI; First degree relatives fulfilling lesions disseminated in space on MRI	Diagnostic Test: Brain MRI; Perform brain MRI; draw blood; fill out bioscreen questionnaire; perform SDMT; Other Names: Blood analysis; Bioscreen; Symbol Digit Modality Test (SDMT)
FDR with normal brain MRI; First degree relatives not fulfilling lesions disseminated in space on MRI	Diagnostic Test: Brain MRI; Perform brain MRI; draw blood; fill out bioscreen questionnaire; perform SDMT; Other Names: Blood analysis; Bioscreen; Symbol Digit Modality Test (SDMT)
Non-FDR; Age and sex-matched controls to FDRs noted above

Outcome Measures

Primary Outcome Measures :

1. Discovery of Lesions disseminated in space on brain MRI [ Time Frame: At the subject's Initial Visit ]
   Lesions disseminated in space on brain MRI. The presence or absence of these lesions will only be measured once at the initial visit.

Secondary Outcome Measures :

1. Define risk stratification algorithm for prediction of MS [ Time Frame: Within 4 months of the Initial Visit ]
   The investigators will measure the prevalence of specific genetic markers including, single nucleotide polymorphism analysis of more than 200 known mutations and analysis of HLA-DRB1 variants, with the goal of defining an expanded risk stratification scheme.
2. Define risk stratification algorithm for prediction of MS [ Time Frame: Within 4 months of the Initial Visit ]
   The investigators will measure the prevalence of immunological markers including, CD4, CD8, CD40, CD19 and CD20 with the goal of defining an expanded risk stratification scheme.
3. Define risk stratification algorithm for prediction of MS [ Time Frame: Within 4 months of the Initial Visit ]
   The investigators will measure the prevalence of vitamin D deficiencies, with the goal of defining an expanded risk stratification scheme.
4. Define risk stratification algorithm for prediction of MS [ Time Frame: Within 4 months of the Initial Visit ]
   The investigators will measure the prevalence of lipid markers including, total cholesterol, high and low density lipoproteins, and triglycerides, with the goal of defining an expanded risk stratification scheme.
5. Define risk stratification algorithm for prediction of MS [ Time Frame: Within 4 months of the Initial Visit ]
   The investigators will measure the presence of the viral infection Epstein-Barr virus, with the goal of defining an expanded risk stratification scheme.
6. Define risk stratification algorithm for prediction of MS [ Time Frame: Within 4 months of the Initial Visit ]
   The investigators will measure the presence of the viral infection Cytomegalovirus, with the goal of defining an expanded risk stratification scheme.
7. Define risk stratification algorithm for prediction of MS [ Time Frame: Within 4 months of the Initial Visit ]
   The investigators will measure the presence of the viral infection Herpes Simplex I, with the goal of defining an expanded risk stratification scheme.
8. Define risk stratification algorithm for prediction of MS [ Time Frame: Within 4 months of the Initial Visit ]
   The investigators will measure the presence of the viral infection Herpes Simplex II, with the goal of defining an expanded risk stratification scheme.
9. Define risk stratification algorithm for prediction of MS [ Time Frame: Within 4 months of the Initial Visit ]
   The investigators will measure the presence of the viral infection Varicella zoster virus, with the goal of defining an expanded risk stratification scheme.
10. Define risk stratification algorithm for prediction of MS [ Time Frame: Within 4 months of the Initial Visit ]
    The investigators will measure the prevalence of CNS damage marker Neurofillament light, with the goal of defining an expanded risk stratification scheme.
11. Define risk stratification algorithm for prediction of MS [ Time Frame: Within 4 months of the Initial Visit ]
    The investigators will measure activity level, using the Godin Leisure-time Exercise questionnaire, with the goal of defining an expanded risk stratification scheme. Weekly frequencies of strenuous, moderate, and light activities (i.e. number of times per week an individual performs these activities) are multiplied by nine, five, and three, respectively. Total weekly leisure activity is calculated in arbitrary units by summing the products of the separate components, as shown in the following formula: Weekly leisure activity score = (9 × Strenuous) + (5 × Moderate) + (3 × Light). As there is no limit for the number of times a participant can perform an exercise per week, the maximum score is boundless. While, a sedentary individual may exhibit the minimum score of 0.
12. Define risk stratification algorithm for prediction of MS [ Time Frame: Within 4 months of the Initial Visit ]
    The investigators will evaluate cognitive functions using the Perceived Stress Scale, with the goal of defining an expanded risk stratification scheme. This will be used to evaluate stress domains. Individual scores on the Perceived Stress Scale can range from 0 to 40 with higher scores indicating higher perceived stress. Scores ranging from 0-13 would be considered low stress. Scores ranging from 14-26 would be considered moderate stress. Scores ranging from 27-40 would be considered high perceived stress.
13. Define risk stratification algorithm for prediction of MS [ Time Frame: Within 4 months of the Initial Visit ]
    The investigators will evaluate cognitive functions using University of Colorado's NeuroQol Depression Scale, with the goal of defining an expanded risk stratification scheme. This assessment will be used to evaluate depression domains. The minimum raw score is 8, which represents better (desirable) self-reported health, and the maximum score is 40, which represents worse (undesirable) self-reported health. These raw scores will be converted to T-scores, with 36.9 being the minimum and 75.0 being the maximum T-score.
14. Define risk stratification algorithm for prediction of MS [ Time Frame: Within 4 months of the Initial Visit ]
    The investigators will evaluate cognitive functions using University of Colorado's NeuroQol Anxiety Scale, with the goal of defining an expanded risk stratification scheme. This assessment will be used to measure anxiety domains, with the highest raw score of 40 representing worse (undesirable) self-reported anxiety, and a raw score of 8 representing better (desirable) self-reported anxiety. These raw scores will be converted to T-scores, with 36.4 being the minimum and 76.8 being the maximum T-score.

Biospecimen Retention:   Samples With DNA

Blood drawn for SNP, HLA typing, and possible RNA analysis

Eligibility Criteria

• Ages Eligible for Study: 18 Years to 30 Years (Adult)
• Sexes Eligible for Study: All
• Sampling Method: Non-Probability Sample

Study Population

The population will be a total of 300 women and men, of all races and ethnicities, between the ages of 18-30, inclusive, who have no symptoms of MS, but have at least one FDR with documented MS fulfilling McDonald 2017 criteria. FDRs with one or more siblings/parents with MS will be included, and more than one FDR of a proband MS patient will be allowed. A control group of age- and sex-matched controls who have no known family members with MS (out to first cousins and grandparents) will have blood drawn and fill out the environmental questionnaire, but will not undergo a brain/spine MRI. The controls will be matched 1:1:1 for those FDRs with positive MRI scans suggesting pre-symptomatic MS and those age-and sex-matched FDRs who do NOT have positive MRI scans suggesting pre-symptomatic MS.

Criteria

Inclusion Criteria:

• FDR Inclusion Criteria

  1. Male and female
  2. All races and ethnicities
  3. Ages 18-30
  4. Must have a parent, sibling or child with MS fulfilling McDonald 2017 criteria
  5. No symptoms suggestive of MS on formal screen
  6. Ability to undergo a non-contrast MRI and venipuncture, and perform an environmental screen, mood screen and cognitive test

• Non-FDR Inclusion Criteria

  1. Male and female
  2. All races and ethnicities
  3. Ages 18-30
  4. Must NOT have a FDR (parent, sibling, child) or second degree relative (grandparent, aunt or uncle) with MS fulfilling McDonald 2017 criteria
  5. No symptoms suggestive of MS on formal screen
  6. Ability to undergo venipuncture and perform an environmental screen.

Exclusion Criteria:

• FDR Exclusion Criteria

  1. Symptoms suggestive of MS on formal screen
  2. Prior diagnosis of autoimmune disease that may be associated with CNS dysfunction and MRI lesions, e.g. Sjogren's

• Non-FDR Exclusion Criteria

  1. Symptoms suggestive of MS on formal screen
  2. Prior diagnosis of autoimmune disease that may be associated with CNS dysfunction and MRI lesions, e.g. Sjogren's

Contacts and Locations

Contacts

Contact: John R Corboy, MD; 303-724-2187 ext 42196; john.corboy@ucdenver.edu

Contact: Tyler Borko, BA; 303-724-0864; tyler.borko@cuanschutz.edu

Locations

United States, Colorado

University of Colorado Anschutz Medical Campus; Recruiting

Aurora, Colorado, United States, 80045

Contact: John Corboy, MD 303-724-2187 john.corboy@ucdenver.edu

Principal Investigator: John Corboy, MD

Principal Investigator: Jill Norris, PhD

Principal Investigator: Enrique Alvarez, MD, PhD

Sponsors and Collaborators

University of Colorado, Denver

Colorado School of Public Health

Investigators

• Principal Investigator: John R Corboy, MD; University of Colorado - Anschutz Medical Campus

  Study Documents (Full-Text)

Documents provided by University of Colorado, Denver:

Study Protocol and Statistical Analysis Plan  [PDF] April 21, 2021

More Information

Publications:

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Okuda DT, Siva A, Kantarci O, Inglese M, Katz I, Tutuncu M, Keegan BM, Donlon S, Hua le H, Vidal-Jordana A, Montalban X, Rovira A, Tintoré M, Amato MP, Brochet B, de Seze J, Brassat D, Vermersch P, De Stefano N, Sormani MP, Pelletier D, Lebrun C; Radiologically Isolated Syndrome Consortium (RISC); Club Francophone de la Sclérose en Plaques (CFSEP). Radiologically isolated syndrome: 5-year risk for an initial clinical event. PLoS One. 2014 Mar 5;9(3):e90509. doi: 10.1371/journal.pone.0090509. eCollection 2014.

Rojas JI, Patrucco L, Míguez J, Besada C, Cristiano E. Brain atrophy in radiologically isolated syndromes. J Neuroimaging. 2015 Jan-Feb;25(1):68-71. doi: 10.1111/jon.12182. Epub 2014 Oct 13.

Disanto G, Barro C, Benkert P, Naegelin Y, Schädelin S, Giardiello A, Zecca C, Blennow K, Zetterberg H, Leppert D, Kappos L, Gobbi C, Kuhle J; Swiss Multiple Sclerosis Cohort Study Group. Serum Neurofilament light: A biomarker of neuronal damage in multiple sclerosis. Ann Neurol. 2017 Jun;81(6):857-870. doi: 10.1002/ana.24954.

Weinstock-Guttman B, Zivadinov R, Mahfooz N, Carl E, Drake A, Schneider J, Teter B, Hussein S, Mehta B, Weiskopf M, Durfee J, Bergsland N, Ramanathan M. Serum lipid profiles are associated with disability and MRI outcomes in multiple sclerosis. J Neuroinflammation. 2011 Oct 4;8:127. doi: 10.1186/1742-2094-8-127.

Munger KL, Levin LI, Hollis BW, Howard NS, Ascherio A. Serum 25-hydroxyvitamin D levels and risk of multiple sclerosis. JAMA. 2006 Dec 20;296(23):2832-8.

Salzer J, Hallmans G, Nyström M, Stenlund H, Wadell G, Sundström P. Smoking as a risk factor for multiple sclerosis. Mult Scler. 2013 Jul;19(8):1022-7. doi: 10.1177/1352458512470862. Epub 2012 Dec 20.

De Stefano N, Cocco E, Lai M, Battaglini M, Spissu A, Marchi P, Floris G, Mortilla M, Stromillo ML, Paolillo A, Federico A, Marrosu MG. Imaging brain damage in first-degree relatives of sporadic and familial multiple sclerosis. Ann Neurol. 2006 Apr;59(4):634-9.

Gabelic T, Ramasamy DP, Weinstock-Guttman B, Hagemeier J, Kennedy C, Melia R, Hojnacki D, Ramanathan M, Zivadinov R. Prevalence of radiologically isolated syndrome and white matter signal abnormalities in healthy relatives of patients with multiple sclerosis. AJNR Am J Neuroradiol. 2014 Jan;35(1):106-12. doi: 10.3174/ajnr.A3653. Epub 2013 Jul 25.

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• Responsible Party: University of Colorado, Denver
• ClinicalTrials.gov Identifier: NCT03586986
• Other Study ID Numbers: 17-1884
• First Posted: July 16, 2018
• Last Update Posted: August 12, 2021
• Last Verified: August 2021

Individual Participant Data (IPD) Sharing Statement:

• Plan to Share IPD: No

• Studies a U.S. FDA-regulated Drug Product: No
• Studies a U.S. FDA-regulated Device Product: No

Keywords provided by University of Colorado, Denver:

First degree relative

Additional relevant MeSH terms:

Multiple Sclerosis; Syndrome; Sclerosis; Disease; Pathologic Processes; Demyelinating Autoimmune Diseases, CNS; Autoimmune Diseases of the Nervous System; Nervous System Diseases; Demyelinating Diseases; Autoimmune Diseases; Immune System Diseases