Machine learning based metabolomic and genetic profiles for predicting multiple brain phenotypes.

ABSTRACT:

Background

It is unclear regarding the association between metabolomic state/genetic risk score(GRS) and brain volumes and how much of variance of brain volumes is attributable to metabolomic state or GRS.

Methods

Our analysis included 8635 participants (52.5% females) aged 40-70 years at baseline from the UK Biobank. Metabolomic profiles were assessed using nuclear magnetic resonance at baseline (between 2006 and 2010). Brain volumes were measured using magnetic resonance imaging between 2014 and 2019. Machine learning was used to generate metabolomic state and GRS for each of 21 brain phenotypes.

Results

Individuals in the top 20% of metabolomic state had 2.4-35.7% larger volumes of 21 individual brain phenotypes compared to those in the bottom 20% while the corresp

SUBMITTER: Zhang X 

PROVIDER: S-EPMC11613467 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature