Gene-diet interactions in carbonated sugar-sweetened beverage consumption and metabolic syndrome risk: A machine learning analysis in a large hospital-based cohort

IF 2.9 Q3 NUTRITION & DIETETICS

Clinical nutrition ESPEN Pub Date : 2024-10-11 DOI:10.1016/j.clnesp.2024.10.004

Sunmin Park , Da Sol Kim , Suna Kang

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引用次数: 0

Abstract

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.

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碳酸含糖饮料消费与代谢综合征风险中的基因-饮食相互作用：基于大型医院队列的机器学习分析。

背景和目的：碳酸含糖饮料（CSSB）的摄入量与代谢性疾病的关系日益密切。研究碳酸饮料摄入量与代谢综合征（MetS）风险之间的关系，以及碳酸饮料摄入量的遗传易感性与饮食模式之间的相互作用：我们研究了一个以医院为基础的队列，该队列共有 57,940 名参与者，根据每日 50 毫升的摄入量临界值分为低 CSSB 组（n=52,848）和高 CSSB 组（n=5,092）。一项全基因组关联研究（GWAS）确定了与CSSB摄入量相关的单核苷酸多态性（SNPs），并探讨了SNP-SNP/SNP-环境之间的相互作用。利用XGBoost和深度神经网络（DNN）方法，我们建立了CSSB摄入量预测模型：结果：低CSSB组和高CSSB组平均每天从苏打水中摄入的糖分别为0.56克和8.91克。与预测模型的结果一致，高CSSB组的饮食习惯不健康，类胡萝卜素、叶酸、维生素C和D、钙、类黄酮和酚的摄入量低于低CSSB组。基于与肥胖、糖尿病和神经系统疾病相关基因的 6 个选定 SNPs 的多基因风险评分（PRS）显示，CSSB 摄入量与胰岛素抵抗的关联性最强。值得注意的是，碳水化合物、脂肪和西式饮食（WSD）摄入量与PRS之间存在相互作用，碳水化合物摄入量越低、脂肪和西式饮食摄入量越高，PRS与糖摄入量之间的关系就越密切。XGboost和DNN的预测模型主要包括饮食因素来解释CSSB摄入量：结论：遗传易感性和不良饮食习惯之间存在着明显的相互作用，尤其是与 WSD 相关的 CSSB 摄入量增加导致了 MetS 风险。这表明，基于基因图谱的个性化膳食干预可以降低 MetS 风险，尤其是在向西化膳食过渡的人群中。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Clinical nutrition ESPEN NUTRITION & DIETETICS-

CiteScore

4.90

自引率

3.30%

发文量

512

期刊介绍： Clinical Nutrition ESPEN is an electronic-only journal and is an official publication of the European Society for Clinical Nutrition and Metabolism (ESPEN). Nutrition and nutritional care have gained wide clinical and scientific interest during the past decades. The increasing knowledge of metabolic disturbances and nutritional assessment in chronic and acute diseases has stimulated rapid advances in design, development and clinical application of nutritional support. The aims of ESPEN are to encourage the rapid diffusion of knowledge and its application in the field of clinical nutrition and metabolism. Published bimonthly, Clinical Nutrition ESPEN focuses on publishing articles on the relationship between nutrition and disease in the setting of basic science and clinical practice. Clinical Nutrition ESPEN is available to all members of ESPEN and to all subscribers of Clinical Nutrition.