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

ABSTRACT: 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.

PROVIDER: EGAS00001007145 | EGA |

REPOSITORIES: EGA

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Project description:BACKGROUND & AIMS: The effectiveness of surveillance for hepatocellular carcinoma (HCC) in patients with cirrhosis is limited, due to inadequate risk stratification and suboptimal performance of current screening modalities.METHODS: We developed a multicenter prospective cohort of patients with cirrhosis undergoing surveillance with MRI and applied global untargeted metabolomics to 612 longitudinal serum samples from 203 patients. Among them, 37 developed HCC during follow-up.RESULTS: We identified 150 metabolites with significant abundance changes in samples collected prior to HCC (Cases) compared to samples from patients who did not develop HCC (Controls). Tauro-conjugated bile acids and gamma-glutamyl amino acids were increased, while acyl-cholines and deoxycholate derivatives were decreased. Seven amino acids including serine and alanine had strong associations with HCC risk, while strong protective effects were observed for N-acetylglycine and glycerophosphorylcholine. Machine learning using the 150 metabolites, age, gender, and PNPLA3 and TMS6SF2 single nucleotide polymorphisms, identified 15 variables giving optimal performance. Among them, N-acetylglycine had the highest AUC in discriminating Cases and Controls. When restricting Cases to samples collected within 1 year prior to HCC (Cases-12M), additional metabolites including microbiota-derived metabolites were identified. The combination of the top six variables identified by machine learning (alpha-fetoprotein, 6-bromotryptophan, N-acetylglycine, salicyluric glucuronide, testosterone sulfate and age) had good performance in discriminating Cases-12M from Controls (AUC 0.88, 95% CI 0.83-0.93). Finally, 23 metabolites distinguished Cases with LI-RADS-3 lesions from Controls with LI-RADS-3 lesions, with reduced abundance of acyl-cholines and glycerophosphorylcholine-related lysophospholipids in Cases.CONCLUSIONS: This study identified N-acetylglycine, amino acids, bile acids and choline-derived metabolites as biomarkers of HCC risk, and microbiota-derived metabolites as contributors to HCC development.

Dataset Information

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

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