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Clinical Trial Details — Status: Recruiting

Administrative data

NCT number NCT03586986
Other study ID # 17-1884
Secondary ID
Status Recruiting
Phase
First received
Last updated
Start date July 26, 2018
Est. completion date May 2024

Study information

Verified date February 2023
Source University of Colorado, Denver
Contact John R Corboy, MD
Phone 303-724-2187
Email john.corboy@ucdenver.edu
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial 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.


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. 5. Evaluate for potential dissemination in time (DIT) and dissemination in space (DIS) in asymptomatic FDRs through longitudinal assessment. 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).


Recruitment information / eligibility

Status Recruiting
Enrollment 300
Est. completion date May 2024
Est. primary completion date May 2024
Accepts healthy volunteers
Gender All
Age group 18 Years to 30 Years
Eligibility 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

Study Design


Intervention

Diagnostic Test:
Brain MRI
Perform brain MRI; draw blood; fill out bioscreen questionnaire; perform SDMT

Locations

Country Name City State
United States University of Colorado Anschutz Medical Campus Aurora Colorado

Sponsors (2)

Lead Sponsor Collaborator
University of Colorado, Denver Colorado School of Public Health

Country where clinical trial is conducted

United States, 

References & Publications (19)

Bar-Or A. Multiple sclerosis and related disorders: evolving pathophysiologic insights. Lancet Neurol. 2016 Jan;15(1):9-11. doi: 10.1016/S1474-4422(15)00342-7. Epub 2015 Dec 8. No abstract available. — View Citation

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. doi: 10.1002/ana.20767. — View Citation

Disanto G, Barro C, Benkert P, Naegelin Y, Schadelin 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. — View Citation

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. — View Citation

Giovannoni G. The neurodegenerative prodrome in multiple sclerosis. Lancet Neurol. 2017 Jun;16(6):413-414. doi: 10.1016/S1474-4422(17)30127-8. No abstract available. — View Citation

International Multiple Sclerosis Genetics Consortium (IMSGC); Beecham AH, Patsopoulos NA, Xifara DK, Davis MF, Kemppinen A, Cotsapas C, Shah TS, Spencer C, Booth D, Goris A, Oturai A, Saarela J, Fontaine B, Hemmer B, Martin C, Zipp F, D'Alfonso S, Martinelli-Boneschi F, Taylor B, Harbo HF, Kockum I, Hillert J, Olsson T, Ban M, Oksenberg JR, Hintzen R, Barcellos LF; Wellcome Trust Case Control Consortium 2 (WTCCC2); International IBD Genetics Consortium (IIBDGC); Agliardi C, Alfredsson L, Alizadeh M, Anderson C, Andrews R, Sondergaard HB, Baker A, Band G, Baranzini SE, Barizzone N, Barrett J, Bellenguez C, Bergamaschi L, Bernardinelli L, Berthele A, Biberacher V, Binder TM, Blackburn H, Bomfim IL, Brambilla P, Broadley S, Brochet B, Brundin L, Buck D, Butzkueven H, Caillier SJ, Camu W, Carpentier W, Cavalla P, Celius EG, Coman I, Comi G, Corrado L, Cosemans L, Cournu-Rebeix I, Cree BA, Cusi D, Damotte V, Defer G, Delgado SR, Deloukas P, di Sapio A, Dilthey AT, Donnelly P, Dubois B, Duddy M, Edkins S, Elovaara I, Esposito F, Evangelou N, Fiddes B, Field J, Franke A, Freeman C, Frohlich IY, Galimberti D, Gieger C, Gourraud PA, Graetz C, Graham A, Grummel V, Guaschino C, Hadjixenofontos A, Hakonarson H, Halfpenny C, Hall G, Hall P, Hamsten A, Harley J, Harrower T, Hawkins C, Hellenthal G, Hillier C, Hobart J, Hoshi M, Hunt SE, Jagodic M, Jelcic I, Jochim A, Kendall B, Kermode A, Kilpatrick T, Koivisto K, Konidari I, Korn T, Kronsbein H, Langford C, Larsson M, Lathrop M, Lebrun-Frenay C, Lechner-Scott J, Lee MH, Leone MA, Leppa V, Liberatore G, Lie BA, Lill CM, Linden M, Link J, Luessi F, Lycke J, Macciardi F, Mannisto S, Manrique CP, Martin R, Martinelli V, Mason D, Mazibrada G, McCabe C, Mero IL, Mescheriakova J, Moutsianas L, Myhr KM, Nagels G, Nicholas R, Nilsson P, Piehl F, Pirinen M, Price SE, Quach H, Reunanen M, Robberecht W, Robertson NP, Rodegher M, Rog D, Salvetti M, Schnetz-Boutaud NC, Sellebjerg F, Selter RC, Schaefer C, Shaunak S, Shen L, Shields S, Siffrin V, Slee M, Sorensen PS, Sorosina M, Sospedra M, Spurkland A, Strange A, Sundqvist E, Thijs V, Thorpe J, Ticca A, Tienari P, van Duijn C, Visser EM, Vucic S, Westerlind H, Wiley JS, Wilkins A, Wilson JF, Winkelmann J, Zajicek J, Zindler E, Haines JL, Pericak-Vance MA, Ivinson AJ, Stewart G, Hafler D, Hauser SL, Compston A, McVean G, De Jager P, Sawcer SJ, McCauley JL. Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis. Nat Genet. 2013 Nov;45(11):1353-60. doi: 10.1038/ng.2770. Epub 2013 Sep 29. — View Citation

Kappos L, Freedman MS, Polman CH, Edan G, Hartung HP, Miller DH, Montalban X, Barkhof F, Radu EW, Metzig C, Bauer L, Lanius V, Sandbrink R, Pohl C; BENEFIT Study Group. Long-term effect of early treatment with interferon beta-1b after a first clinical event suggestive of multiple sclerosis: 5-year active treatment extension of the phase 3 BENEFIT trial. Lancet Neurol. 2009 Nov;8(11):987-97. doi: 10.1016/S1474-4422(09)70237-6. Epub 2009 Sep 10. — View Citation

Kavaliunas A, Manouchehrinia A, Stawiarz L, Ramanujam R, Agholme J, Hedstrom AK, Beiki O, Glaser A, Hillert J. Importance of early treatment initiation in the clinical course of multiple sclerosis. Mult Scler. 2017 Aug;23(9):1233-1240. doi: 10.1177/1352458516675039. Epub 2016 Oct 17. — View Citation

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. doi: 10.1001/jama.296.23.2832. — View Citation

Okuda DT, Mowry EM, Beheshtian A, Waubant E, Baranzini SE, Goodin DS, Hauser SL, Pelletier D. Incidental MRI anomalies suggestive of multiple sclerosis: the radiologically isolated syndrome. Neurology. 2009 Mar 3;72(9):800-5. doi: 10.1212/01.wnl.0000335764.14513.1a. Epub 2008 Dec 10. Erratum In: Neurology. 2009 Apr 7;72(14):1284. — View Citation

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, Tintore 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 Sclerose 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. — View Citation

Rojas JI, Patrucco L, Miguez 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. — View Citation

Salzer J, Hallmans G, Nystrom M, Stenlund H, Wadell G, Sundstrom 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. — View Citation

Sati P, Oh J, Constable RT, Evangelou N, Guttmann CR, Henry RG, Klawiter EC, Mainero C, Massacesi L, McFarland H, Nelson F, Ontaneda D, Rauscher A, Rooney WD, Samaraweera AP, Shinohara RT, Sobel RA, Solomon AJ, Treaba CA, Wuerfel J, Zivadinov R, Sicotte NL, Pelletier D, Reich DS; NAIMS Cooperative. The central vein sign and its clinical evaluation for the diagnosis of multiple sclerosis: a consensus statement from the North American Imaging in Multiple Sclerosis Cooperative. Nat Rev Neurol. 2016 Dec;12(12):714-722. doi: 10.1038/nrneurol.2016.166. Epub 2016 Nov 11. — View Citation

Signori A, Schiavetti I, Gallo F, Sormani MP. Subgroups of multiple sclerosis patients with larger treatment benefits: a meta-analysis of randomized trials. Eur J Neurol. 2015 Jun;22(6):960-6. doi: 10.1111/ene.12690. Epub 2015 Mar 19. — View Citation

Solomon AJ, Corboy JR. The tension between early diagnosis and misdiagnosis of multiple sclerosis. Nat Rev Neurol. 2017 Sep;13(9):567-572. doi: 10.1038/nrneurol.2017.106. Epub 2017 Aug 11. No abstract available. — View Citation

Solomon AJ, Schindler MK, Howard DB, Watts R, Sati P, Nickerson JP, Reich DS. "Central vessel sign" on 3T FLAIR* MRI for the differentiation of multiple sclerosis from migraine. Ann Clin Transl Neurol. 2015 Dec 16;3(2):82-7. doi: 10.1002/acn3.273. eCollection 2016 Feb. — View Citation

Swanton JK, Fernando K, Dalton CM, Miszkiel KA, Thompson AJ, Plant GT, Miller DH. Modification of MRI criteria for multiple sclerosis in patients with clinically isolated syndromes. J Neurol Neurosurg Psychiatry. 2006 Jul;77(7):830-3. doi: 10.1136/jnnp.2005.073247. Epub 2005 Jul 25. — View Citation

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. — View Citation

* Note: There are 19 references in allClick here to view all references

Outcome

Type Measure Description Time frame Safety issue
Primary Discovery of Lesions disseminated in space on brain MRI Lesions disseminated in space on brain MRI. The presence or absence of these lesions will only be measured once at the initial visit. At the subject's Initial Visit
Secondary Define risk stratification algorithm for prediction of MS 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. Within 4 months of the Initial Visit
Secondary Define risk stratification algorithm for prediction of MS 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. Within 4 months of the Initial Visit
Secondary Define risk stratification algorithm for prediction of MS The investigators will measure the prevalence of vitamin D deficiencies, with the goal of defining an expanded risk stratification scheme. Within 4 months of the Initial Visit
Secondary Define risk stratification algorithm for prediction of MS 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. Within 4 months of the Initial Visit
Secondary Define risk stratification algorithm for prediction of MS The investigators will measure the presence of the viral infection Epstein-Barr virus, with the goal of defining an expanded risk stratification scheme. Within 4 months of the Initial Visit
Secondary Define risk stratification algorithm for prediction of MS The investigators will measure the presence of the viral infection Cytomegalovirus, with the goal of defining an expanded risk stratification scheme. Within 4 months of the Initial Visit
Secondary Define risk stratification algorithm for prediction of MS The investigators will measure the presence of the viral infection Herpes Simplex I, with the goal of defining an expanded risk stratification scheme. Within 4 months of the Initial Visit
Secondary Define risk stratification algorithm for prediction of MS The investigators will measure the presence of the viral infection Herpes Simplex II, with the goal of defining an expanded risk stratification scheme. Within 4 months of the Initial Visit
Secondary Define risk stratification algorithm for prediction of MS The investigators will measure the presence of the viral infection Varicella zoster virus, with the goal of defining an expanded risk stratification scheme. Within 4 months of the Initial Visit
Secondary Define risk stratification algorithm for prediction of MS The investigators will measure the prevalence of CNS damage marker Neurofillament light, with the goal of defining an expanded risk stratification scheme. Within 4 months of the Initial Visit
Secondary Define risk stratification algorithm for prediction of MS 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. Within 4 months of the Initial Visit
Secondary Define risk stratification algorithm for prediction of MS 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. Within 4 months of the Initial Visit
Secondary Define risk stratification algorithm for prediction of MS 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. Within 4 months of the Initial Visit
Secondary Define risk stratification algorithm for prediction of MS 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. Within 4 months of the Initial Visit
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