Using metabolite profiling to construct and validate a metabolite risk score for predicting future weight gain.

ABSTRACT:

Background

Excess weight gain throughout adulthood can lead to adverse clinical outcomes and are influenced by complex factors that are difficult to measure in free-living individuals. Metabolite profiling offers an opportunity to systematically discover new predictors for weight gain that are relatively easy to measure compared to traditional approaches.

Methods and results

Using baseline metabolite profiling data of middle-aged individuals from the Framingham Heart Study (FHS; n = 1,508), we identified 42 metabolites associated (p < 0.05) with longitudinal change in body mass index (BMI). We performed stepwise linear regression to select 8 of these metabolites to build a metabolite risk score (MRS) for predicting future weight gain. We replicated the MRS using data from th

SUBMITTER: Geidenstam N 

PROVIDER: S-EPMC6764659 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature