# Assessing Genetic Risk for Physical Activity and Its Interaction with Diet in Predicting Activity Levels and Weight Loss in the iMPROVE Study

## TL;DR

This study shows how genetic risk scores for physical activity interact with diet to influence weight loss and activity levels.

## Contribution

The study identifies gene-diet interactions affecting physical activity and weight outcomes using polygenic risk scores.

## Key findings

- A genetic score for physical activity interacted with diet group to predict weight loss (R2 = 0.06).
- A sedentary behavior genetic score interacted with a dietary pattern to predict baseline activity levels (R2 = 0.091).
- Dietary patterns moderate the effects of genetic predisposition on activity and weight outcomes.

## Abstract

Background: Physical activity (PA) and weight regulation are influenced by both genetic and lifestyle factors. This study aimed to evaluate the predictive value of Polygenic Risk Scores (PRSs) for PA and weight outcomes, and their interaction with dietary habits. Methods: Baseline phenotypic data from 202 participants enrolled in the iMPROVE study were analyzed. The sample included 59 men and 143 women, aged 19–65 years. Based on baseline Body Mass Index (BMI), 75 participants were classified as having overweight and 126 as having obesity. Polygenic risk scores (PRSs) were calculated for 197 participants with available genetic data. PA was operationalized as metabolic equivalent of task minutes per week (MET-mins/week), derived from self-reported activity questionnaires. Weight-related outcomes included log-transformed weight loss from baseline to month 3 and change in BMI post-intervention. Interactions with diet were examined using both the randomized intervention dietary groups and previously extracted dietary patterns from the iMPROVE cohort. Correlation analyses and linear regression models were used to assess the main effects of PRSs and dietary patterns, as well as gene–diet interactions. Results: The measured PA PGS002254 presented a nominal significant interaction with diet group for weight loss post-intervention (B = 7.57, SE = 3.57 × 100, p = 0.04; R2 = 0.06). Similarly, the sedentary behavior PGS001923 presented a significant interaction with the “High in unsaturated fats and fruit juice consumption” pattern for baseline MET-mins/week (B = 1.51 × 103, SE = 4.135 × 102, p = 0.001; R2 = 0.091). Conclusions: Genetic predisposition influences short-term activity and weight outcomes, with dietary patterns moderating these effects. However, the multifactorial nature of lifestyle behaviors is being underscored by the modest variance explained.