# Personalised Nutrition in Obesity and Prediabetes: Do Genotypes Matter?

## TL;DR

This study reviews how genetic factors influence the effectiveness of dietary interventions for obesity and prediabetes, suggesting personalized nutrition strategies based on genotype could improve outcomes.

## Contribution

The paper highlights the importance of genotype-specific dietary strategies for high-risk populations and identifies key gene-diet interactions in cardiometabolic health.

## Key findings

- Healthy dietary patterns reduce risk in high polygenic-risk groups but not in low-risk groups.
- TCF7L2 variants affect macronutrient thresholds for visceral adiposity, while APOA2 variants influence inflammation.
- Genotype-informed nutrition yields larger risk reduction in high-risk populations compared to uniform recommendations.

## Abstract

Background/Objectives: Obesity and prediabetes are overlapping global epidemics. This systematic review synthesises evidence on gene-diet interactions in adults with obesity, prediabetes, or related cardiometabolic risks. It evaluates Mediterranean and DASH dietary patterns, macronutrient quality, and energy restriction across both single-variant and polygenic score approaches. Methods: PubMed was searched for English language papers published in the last 5 years (last run: 31 October 2025). Fewer than 200 studies were retained after excluding those lacking explicit statistical testing for gene-diet interactions or relevant endpoints. Results: Evidence supports restricting saturated fat and preserving carbohydrate quality as general baseline targets, with associations heterogeneous by genotype. Effect modification was observed: healthy dietary patterns were associated with lower risk in high polygenic-risk strata (OR~0.53) but little or no benefit in low-risk groups. TCF7L2 variants were associated with macronutrient thresholds (e.g., protein > 18%, carbohydrate < 48%) affecting visceral adiposity, while APOA2 variants showed genotype-dependent inflammation, including paradoxical increases in markers with higher dietary antioxidant capacity. Interpretation was limited by underpowered interaction tests, multiplicity, and uneven ancestry representation (e.g., unique SLC16A11 and CREBRF signals). Conclusions: While anti-inflammatory dietary substitutions improve biomarkers irrespective of some variants (e.g., TCF7L2), genotype-informed nutrition appears to yield the largest absolute risk reduction in high-risk populations. Clinical implementation should therefore combine baseline diet-quality guidance with targeted strategies for genotype-specific response patterns (e.g., APOA2 antioxidant heterogeneity and TCF7L2 carbohydrate thresholds), rather than rely on uniform recommendations alone. Future progress requires preregistered, genotype-stratified trials and locally trained polygenic scores to address ancestry-specific genetic architecture.