The Sys4MS cohort: a prospective cohort of patients with Multiple Sclerosis and omics

Click on a Dataset ID in the table below to learn more, and to find out who to contact about access to these data

Dataset ID	Description	Technology	Samples
EGAD00001010867	The Sys4MS cohort clinical data - 419 patients		419
EGAD00010002505	The sys4MS cohort comprises 350 patients with Multiple Sclerosis (MS) and 9 controls, with 2 years of follow-up. Baseline data includes demographics, clinical scales, disease duration and subtype and use of disease-modifying drugs, brain MRI (volumetry and lesion load), retinal thickness by OCT, genomics (GWAS), cytomics, and phosphoproteomics. Data at the end of follow-up includes clinical scales, brain MRI and OCT.	Illumina HumanOmniExpress-24 v1.2 array	400

Publications	Citations
Impact of treatment on cellular immunophenotype in MS: A cross-sectional study. Cellerino M, Ivaldi F, Pardini M, Rotta G, Vila G, Bäcker-Koduah P, Berge T, Laroni A, Lapucci C, Novi G, Boffa G, Sbragia E, Palmeri S, Asseyer S, Høgestøl E, Campi C, Piana M, Inglese M, Paul F, Harbo HF, Villoslada P, Kerlero de Rosbo N, Uccelli A. Neurol Neuroimmunol Neuroinflamm 7: 2020 e693	13
Serum neurofilament light chain concentration predicts disease worsening in multiple sclerosis. Brune S, Høgestøl EA, de Rodez Benavent SA, Berg-Hansen P, Beyer MK, Leikfoss IS, Bos SD, Sowa P, Brunborg C, Andorra M, Pulido Valdeolivas I, Asseyer S, Brandt A, Chien C, Scheel M, Blennow K, Zetterberg H, Kerlero de Rosbo N, Paul F, Uccelli A, Villoslada P, Berge T, Harbo HF. Mult Scler 28: 2022 1859-1870	11
Brain disconnectome mapping derived from white matter lesions and serum neurofilament light levels in multiple sclerosis: A longitudinal multicenter study. Rise HH, Brune S, Chien C, Berge T, Bos SD, Andorrà M, Valdeolivas IP, Beyer MK, Sowa P, Scheel M, Brandt AU, Asseyer S, Blennow K, Pedersen ML, Zetterberg H, de Schotten MT, Cellerino M, Uccelli A, Paul F, Villoslada P, Harbo HF, Westlye LT, Høgestøl EA. Neuroimage Clin 35: 2022 103099	6
Predicting disease severity in multiple sclerosis using multimodal data and machine learning. Andorra M, Freire A, Zubizarreta I, de Rosbo NK, Bos SD, Rinas M, Høgestøl EA, de Rodez Benavent SA, Berge T, Brune-Ingebretse S, Ivaldi F, Cellerino M, Pardini M, Vila G, Pulido-Valdeolivas I, Martinez-Lapiscina EH, Llufriu S, Saiz A, Blanco Y, Martinez-Heras E, Solana E, Bäcker-Koduah P, Behrens J, Kuchling J, Asseyer S, Scheel M, Chien C, Zimmermann H, Motamedi S, Kauer-Bonin J, Brandt A, Saez-Rodriguez J, Alexopoulos LG, Paul F, Harbo HF, Shams H, Oksenberg J, Uccelli A, Baeza-Yates R, Villoslada P. J Neurol 271: 2024 1133-1149	2
Multiscale networks in multiple sclerosis. Kennedy KE, Kerlero de Rosbo N, Uccelli A, Cellerino M, Ivaldi F, Contini P, De Palma R, Harbo HF, Berge T, Bos SD, Høgestøl EA, Brune-Ingebretsen S, de Rodez Benavent SA, Paul F, Brandt AU, Bäcker-Koduah P, Behrens J, Kuchling J, Asseyer S, Scheel M, Chien C, Zimmermann H, Motamedi S, Kauer-Bonin J, Saez-Rodriguez J, Rinas M, Alexopoulos LG, Andorra M, Llufriu S, Saiz A, Blanco Y, Martinez-Heras E, Solana E, Pulido-Valdeolivas I, Martinez-Lapiscina EH, Garcia-Ojalvo J, Villoslada P. PLoS Comput Biol 20: 2024 e1010980	1