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- Title
Postprandial Metabolite Profiles and Risk of Prediabetes in Young People: A Longitudinal Multicohort Study.
- Authors
Goodrich, Jesse A.; Wang, Hongxu; Walker, Douglas I.; Lin, Xiangping; Hu, Xin; Alderete, Tanya L.; Chen, Zhanghua; Valvi, Damaskini; Baumert, Brittney O.; Rock, Sarah; Berhane, Kiros; Gilliland, Frank D.; Goran, Michael I.; Jones, Dean P.; Conti, David V.; Chatzi, Leda
- Abstract
OBJECTIVE: Prediabetes in young people is an emerging epidemic that disproportionately impacts Hispanic populations. We aimed to develop a metabolite-based prediction model for prediabetes in young people with overweight/obesity at risk for type 2 diabetes. RESEARCH DESIGN AND METHODS: In independent, prospective cohorts of Hispanic youth (discovery; n = 143 without baseline prediabetes) and predominately Hispanic young adults (validation; n = 56 without baseline prediabetes), we assessed prediabetes via 2-h oral glucose tolerance tests. Baseline metabolite levels were measured in plasma from a 2-h postglucose challenge. In the discovery cohort, least absolute shrinkage and selection operator regression with a stability selection procedure was used to identify robust predictive metabolites for prediabetes. Predictive performance was evaluated in the discovery and validation cohorts using logistic regression. RESULTS: Two metabolites (allylphenol sulfate and caprylic acid) were found to predict prediabetes beyond known risk factors, including sex, BMI, age, ethnicity, fasting/2-h glucose, total cholesterol, and triglycerides. In the discovery cohort, the area under the receiver operator characteristic curve (AUC) of the model with metabolites and known risk factors was 0.80 (95% CI 0.72–0.87), which was higher than the risk factor-only model (AUC 0.63 [0.53–0.73]; P = 0.001). When the predictive models developed in the discovery cohort were applied to the replication cohort, the model with metabolites and risk factors predicted prediabetes more accurately (AUC 0.70 [95% CI 40.55–0.86]) than the same model without metabolites (AUC 0.62 [0.46–0.79]). CONCLUSIONS: Metabolite profiles may help improve prediabetes prediction compared with traditional risk factors. Findings suggest that medium-chain fatty acids and phytochemicals are early indicators of prediabetes in high-risk youth.
- Publication
Diabetes Care, 2024, Vol 47, Issue 1, p151
- ISSN
0149-5992
- Publication type
Article
- DOI
10.2337/dc23-0327