Plasma Metabolomic Signatures of Sugar-Sweetened Beverage Consumption and Risk of Type 2 Diabetes Among US Adults. (7th June 2021)

• Record Type: Journal Article
• Authors: Haslam, Danielle; Li, Jun; Guasch-Ferre, Marta; Liang, Liming; Clish, Clary B; Manson, JoAnn; Tobias, Deirdre; Wittenbecher, Clemens; Willett, Walter; Stampfer, Meir; McKeown, Nicola; Malik, Vasanti; Meigs, James; Hu, Frank; Bhupathiraju, Shilpa
• Is Part Of: Current developments in nutrition. Volume 5(2021)Supplement 2
• Page Start: 1040
• Page End: 1040
• Publication Date: 2021-06-07
• Subjects: Nutrition -- Periodicals; Nutritional Physiological Phenomena; Nutrition; Periodicals; Periodicals; Fulltext; Internet Resources; Periodicals; 612.3
• DOI: 10.1093/cdn/nzab053_033
• Languages: English
• ISSNs: 2475-2991

Abstract

Abstract: Objectives: Sugar-sweetened beverage (SSB) consumption is associated with a higher risk of type 2 diabetes (T2D), but the metabolic changes linking SSB consumption to T2D are not fully understood. Thus, we aimed to identify a plasma metabolomic signature of SSB consumption and evaluate its association with incident T2D. Methods: We used liquid chromatography–mass spectrometry to measure plasma metabolites (>200) among 3, 434 participants from three US cohorts: Nurses' Health Study (NHS), NHS II, and Health Professionals Follow-up Study (HPFS). SSB consumption (servings/day; sodas, fruit punches, and other sugary drinks) was estimated from food frequency questionnaires. We used elastic net regression with 10-fold-cross-validation to identify metabolites associated with higher SSB consumption among a training set of participants ( n = 2068) and replicated the association in a testing set ( n = 1366). A metabolomic signature score was calculated as the weighted sum of SSB-associated metabolites. Pearson correlation ( r ) coefficients and 95% confidence intervals (CI) between the metabolomic signature and self-reported SSB consumption were calculated. We used multivariable Cox regression models to estimate hazard ratios (HR) and CI of the identified metabolomic signature with incident T2D among all participants. Results: We identified an SSB plasma metabolomic signature of 71 metabolites, primarily lipids and amino acids. Pearson correlation ( r ) coefficients between self-reported SSBs and the plasma metabolomic signature were 0.18 (95% CI: 0.14, 0.22; P < 0.0001) and 0.19 (95% CI: 0.14, 0.24; P < 0.0001) in the training and testing sets, respectively. After a median follow-up of 22 years, the metabolomic signature was significantly associated with higher T2D risk [HR for quartile (Q) 1 versus 4 (95% CI): 1.45 (1.02, 2.05); P = 0.04] in models adjusting for factors related to demographics, lifestyle, diet, and body mass index. The association persisted when further adjusting for self-reported SSB consumption [HR for Q1 versus Q4 (95% CI): 1.42 (1.00, 2.02); P = 0.05]. Conclusions: We identified a novel metabolomic signature of SSB consumption in US adults that associated with elevated incident T2D risk. This signature may reflect both SSB consumption and metabolic changes related to T2D risk, although residual confounding cannot be ruled out. Funding Sources: NIH.