Background and aim: Carbonated sugar-sweetened beverages (CSSB) intake has been increasingly linked to metabolic diseases. To investigate the association between CSSB intake and metabolic syndrome (MetS) risk, and the interaction between genetic predisposition to CSSB intake and dietary patterns.
Methods: We examined a hospital-based cohort of 57,940 participants, categorized into low-CSSB (n = 52,848) and high-CSSB (n = 5092) groups based on a 50 ml daily consumption cutoff. A genome-wide association study (GWAS) identified single-nucleotide polymorphisms (SNPs) associated with CSSB intake, and SNP-SNP/SNP-environment interactions were explored. Using XGBoost and deep neural network (DNN) approaches, we developed prediction models for CSSB intake.
Results: The low- and high-CSSB groups daily consumed an average of 0.56 and 8.91 g sugar from the soda, respectively. The high-CSSB group had unhealthy dietary habits and a lower intake of carotenoids, folate, vitamins C and D, calcium, flavonoids, and phenols than the low-CSSB group, consistent with the results of the prediction models. A polygenic risk score (PRS) based on 6 selected SNPs, linked to genes involved in obesity, diabetes, and nervous system disorders, showed the strongest association with CSSB intake and insulin resistance. Notably, carbohydrate, fat, and Western-style diet (WSD) intake interacted with the PRS, with lower carbohydrate and higher fat and WSD intakes associated with a stronger PRS-sugar intake relationship. The prediction models by XGboost and DNN mainly included dietary factors to explain CSSB intake.
Conclusions: A significant interplay between genetic predisposition and poor dietary habits, particularly increased CSSB intake associated with WSD, contributed to MetS risk. It suggested that personalized dietary interventions based on genetic profiles could mitigate MetS risk, especially in populations transitioning to Westernized diets.