Characterization of dietary patterns and assessment of their relationships with metabolomic profiles: A community-based study. Issue 5 (May 2021)

• Record Type: Journal Article
• Authors: Ru, Yuan; Wang, Ninglin; Min, Yan; Wang, Xuemiao; McGurie, Valerie; Duan, Meng; Xu, Xiaochen; Zhao, Xueyin; Wu, Yi-Hsuan; Lu, Ying; Hsing, Ann W.; Zhu, Shankuan
• Is Part Of: Clinical nutrition. Volume 40:Issue 5(2021)
• Page Start: 3531
• Page End: 3541
• Publication Date: 2021-05
• Subjects: Dietary pattern -- Metabolomics -- Metabolic conditions; T2DM diabetes 2 mellitus -- CVD cardiovascular diseases -- TG triglycerides -- TC Total cholesterol -- LDL low-density lipoprotein cholesterol -- HDL high-density lipoprotein cholesterol -- IDF International Diabetes Federation -- AHA American Heart Association -- FFQ Food frequency questionnaire -- BMI body mass index -- OPLS-DA orthogonal partial least-squares discriminant analysis -- VIP variable importance in projection; Critically ill -- Nutrition -- Periodicals; Diet therapy -- Periodicals; Parenteral feeding -- Periodicals; Enteral feeding -- Periodicals; Enteral Nutrition -- Periodicals; Parenteral Nutrition -- Periodicals; Metabolism -- Periodicals; Diétothérapie -- Périodiques; Alimentation parentérale -- Périodiques; Alimentation entérale -- Périodiques; Nutrition -- Périodiques; Diet therapy; Enteral feeding; Nutrition; Parenteral feeding; Electronic journals; Periodicals; Electronic journals; 615.854
• DOI: 10.1016/j.clnu.2020.12.006
• Languages: English
• ISSNs: 0261-5614

Abstract

Summary: Background & aims: Determining dietary patterns in China is challenging due to lack of external validation and objective measurements. We aimed to characterize dietary patterns in a community-based population and to validate these patterns using external validation cohort and metabolomic profiles. Design: We studied 5145 participants, aged 18–80 years, from two districts of Hangzhou, China. We used one district as the discovery cohort (N = 2521) and the other as the external validation cohort (N = 2624). We identified dietary patterns using a k-means clustering. Associations between dietary patterns and metabolic conditions were analyzed using adjusted logistic models. We assessed relationships between metabolomic profile and dietary patterns in 214 participants with metabolomics data. Results: We identified three dietary patterns: the traditional (rice-based), the mixed (rich in dairy products, eggs, nuts, etc.), and the high-alcohol diets. Relative to the traditional diet, the mixed (ORadj = 1.7, CI 1.3–2.4) and the high-alcohol diets (ORadj = 1.9, CI 1.3–2.7) were associated with type 2 diabetes and hypertension, respectively. Similar results were confirmed in the external validation cohort. In addition, we also identified 18 and 22 metabolites that could distinguish the mixed (error rate = 12%; AUC = 96%) and traditional diets (error rate = 19%; AUC = 88%) from the high-alcohol diet. Conclusions: Despite the complexity of Chinese diet, identifying dietary patterns helps distinguish groups of individuals with high risk of metabolic diseases, which can also be validated by external population and metabolomic profiles.