Signature of the Risk Profile of Mortality in a Hospital Cohort of Patients With Metabolic Diseases

Overview

Epidemiological studies are usually conducted in the general population in adults without complications or pathology at baseline. The results obtained are therefore often better designed for primary prevention use. The prediction of mortality risk in patients with complications and requiring hospital follow-up is less well known. The study purpose is to determine a mortality risk profile in a hospital cohort of patients with pathologies associated with metabolic diseases. Today the "multimaker" scores based on a panel of biomarkers - have significantly improved the discriminating power of prediction models existing in many pathologies. It is no longer a single biomarker that can improve risk prediction but a complete and cross-sectional profile that is sought after. We aim to establish a personalised mortality risk profile by combining clinical and biological parameters including metabolomics, genetics, transcriptomics and epigenomics by high throughput screening of biological samples.

The prediction of the onset of diabetes and metabolic complications, especially cardiovascular, renal, or hepatic, is a major challenge to optimize the management of this disease. Teams from the University Hospital of Lille have developed the Integra cohort study to identify the clinical and biological determinants of the occurrence of these complications and the mortality of patients with metabolic disorders. The aim of the study is to identify clinico-biological determinants that are able to predict the occurence of death, cardiovascular events as well as hepatic or nephrotic one. Follow-up data will be collected from National System of Health Data (SNDS) where data concerning hospitalisations, medical consultations and treatments are registered. Biological samples are collected at baseline for a large OMICs analysis (metabolomics, genetics, transcriptomics and epigenomics) that would feed our predictive scoring system. This project will allow us to describe new models of prediction of metabolic diseases and its complications, and to offer adapted and personalised methods of management, which can slow the progression of the disease and improve its prognosis.

Conditions