| Title: | New Insights into Polygenic Score-Lifestyle Interactions for Cardiometabolic Risk Factors from Genome-Wide Interaction Analyses |
| Journal: | Nutrients |
| Published: | 17 Nov 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38004209/ |
| DOI: | https://doi.org/10.3390/nu15224815 |
| URL: | https://www.mdpi.com/2072-6643/15/22/4815/pdf?version=1700215509 |
Publication 8313
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
The relationship between lifestyles and cardiometabolic outcomes varies between individuals. In 382,275 UK Biobank Europeans, we investigate how lifestyles interact with polygenic scores (PGS) of cardiometabolic risk factors. We identify six interactions (PGS for body mass index with meat diet, physical activity, sedentary behaviour and insomnia; PGS for high-density lipoprotein cholesterol with sedentary behaviour; PGS for triglycerides with meat diet) in multivariable linear regression models including an interaction term and show stronger associations between lifestyles and cardiometabolic risk factors among individuals with high PGSs than those with low PGSs. Genome-wide interaction analyses pinpoint three genetic variants (FTO rs72805613 for BMI; CETP rs56228609 for high-density lipoprotein cholesterol; TRIB2 rs4336630 for triglycerides; PInteraction < 5 × 10-8). The associations between lifestyles and cardiometabolic risk factors differ between individuals grouped by the genotype of these variants, with the degree of differences being similar to that between individuals with high and low values for the corresponding PGSs. This study demonstrates that associations between lifestyles and cardiometabolic risk factors can differ between individuals based upon their genetic profiles. It further suggests that genetic variants with interaction effects contribute more to such differences compared to those without interaction effects, which has potential implications for developing PGSs for personalised intervention.</p>
2 Authors
- Shannon D'Urso
- Liang-Dar Hwang
1 Application
| Application ID | Title |
|---|---|
| 53641 | Determining the genetic and environmental aetiology of complex traits and diseases using existing and novel statistical methodologies |
Enabling scientific discoveries that improve human health