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.