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Diet-Genetic Interactions in Type 2 Diabetes: Comparing Polygenic Risk in Rice- and Wheat-Consuming Populations
Shahla Naran Chirakkal1, Ananthakrishnan Anilkumar Indu1, Ranajit Das1
1Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya Research Centre, Yenepoya (Deemed to be University), Deralakatte, Mangalore, Karnataka, India.
Background:
Rice and wheat are the 2 dominant global cereal staples, differing substantially in starch composition and glycaemic impact. Polished rice is typically rich in amylopectin, promoting rapid postprandial glucose responses, whereas wheat-based foods contain relatively more amylose and fibre, resulting in slower digestion. While diet is a key determinant of type 2 diabetes (T2D), it remains unclear whether long-term exposure to contrasting staple diets is associated with differences in inherited genetic susceptibility at the population level.
Methods:
We applied a population genomics approach to compare polygenic risk scores (PRS) for T2D between historically rice- and wheat-dominant populations. GWAS summary statistics from the IEU OpenGWAS resource were used to derive SNP weights, and genotype data from GenomeAsia 100K and HumanOrigins datasets were analysed. PRS were computed using PRSice, and group differences were assessed using Welch's 2-sample t-test at global and South Asia-specific levels.
Results:
At the global scale, rice-based populations exhibited significantly higher and more dispersed PRS distributions than wheat-based populations, with strong statistical support (t = -32.758; P = 2.2 × 10⁻16). In contrast, South Asia-only analyses showed substantial overlap between dietary groups, with only a modest difference (t = -2.281; P = .02), likely reflecting dietary heterogeneity, admixture, and complex population structure.
Conclusions:
These findings support a gene-diet coevolutionary framework in which long-term staple carbohydrate environments may influence the distribution of genetic susceptibility to T2D. The results emphasise the importance of carbohydrate quality in metabolic health and highlight the need for culturally and regionally tailored dietary strategies. However, PRS should not be used for individual-level clinical prediction, particularly across diverse ancestries.
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