Multi-omic prediction of incident type 2 diabetes

• Published in: Diabetologia 2024-01, Vol.67 (1), p.102-112
• Main Authors: Carrasco-Zanini, Julia, Pietzner, Maik, Wheeler, Eleanor, Kerrison, Nicola D., Langenberg, Claudia, Wareham, Nicholas J.
• Format: Article
• Language: English
• Subjects: Biomarkers; Cohort Studies; Diabetes; Diabetes mellitus (non-insulin dependent); Diabetes Mellitus, Type 2 - diagnosis; Diabetes Mellitus, Type 2 - epidemiology; Diabetes Mellitus, Type 2 - genetics; Genetic screening; Genomes; Human Physiology; Humans; Internal Medicine; Medicine; Medicine & Public Health; Metabolic Diseases; Multiomics; Population genetics; Prediabetic State - complications; Prediction models; Prospective Studies; Proteome; Proteomes; Risk Factors
• ISSN: 0012-186X; 1432-0428
• DOI: 10.1007/s00125-023-06027-x

Aims/hypothesis The identification of people who are at high risk of developing type 2 diabetes is a key part of population-level prevention strategies. Previous studies have evaluated the predictive utility of omics measurements, such as metabolites, proteins or polygenic scores, but have considered these separately. The improvement that combined omics biomarkers can provide over and above current clinical standard models is unclear. The aim of this study was to test the predictive performance of genome, proteome, metabolome and clinical biomarkers when added to established clinical prediction models for type 2 diabetes. Methods We developed sparse interpretable prediction models in a prospective, nested type 2 diabetes case-cohort study ( N =1105, incident type 2 diabetes cases=375) with 10,792 person-years of follow-up, selecting from 5759 features across the genome, proteome, metabolome and clinical biomarkers using least absolute shrinkage and selection operator (LASSO) regression. We compared the predictive performance of omics-derived predictors with a clinical model including the variables from the Cambridge Diabetes Risk Score and HbA 1c . Results Among single omics prediction models that did not include clinical risk factors, the top ten proteins alone achieved the highest performance (concordance index [C index]=0.82 [95% CI 0.75, 0.88]), suggesting the proteome as the most informative single omic layer in the absence of clinical information. However, the largest improvement in prediction of type 2 diabetes incidence over and above the clinical model was achieved by the top ten features across several omic layers (C index=0.87 [95% CI 0.82, 0.92], Δ C index=0.05, p =0.045). This improvement by the top ten omic features was also evident in individuals with HbA 1c