Nuclear magnetic resonance-based metabolomics with machine learning for predicting progression from prediabetes to diabetes.

ABSTRACT:

Background

Identification of individuals with prediabetes who are at high risk of developing diabetes allows for precise interventions. We aimed to determine the role of nuclear magnetic resonance (NMR)-based metabolomic signature in predicting the progression from prediabetes to diabetes.

Methods

This prospective study included 13,489 participants with prediabetes who had metabolomic data from the UK Biobank. Circulating metabolites were quantified via NMR spectroscopy. Cox proportional hazard (CPH) models were performed to estimate the associations between metabolites and diabetes risk. Supporting vector machine, random forest, and extreme gradient boosting were used to select the optimal metabolite panel for prediction. CPH and random survival forest (RSF) models were uti

SUBMITTER: Li J 

PROVIDER: S-EPMC11415073 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature