<b>Postprandial metabolite profiles </b><b>and risk of </b><b>prediabetes in young persons</b><b>: a longitudinal multi-cohort study</b>
Authors: A. Goodrich Jesse , Wang Hongxu , I. Walker Douglas , Lin Xiangping , Hu Xin , L. Alderete Tanya , Chen Zhanghua , Valvi Damaskini , O. Baumert Brittney , Rock Sarah , Berhane Kiros , D. Gilliland Frank , I. Goran Michael , P. Jones Dean , V. Conti David , Chatzi Leda 
Published: November 2023

<p dir="ltr"><a href="" target="_blank"><b><u>Objective</u></b></a><b>: </b>Prediabetes in young persons is an emerging epidemic that disproportionately impacts Hispanic populations. We aimed to develop a metabolite-based prediction model for prediabetes in young persons with overweight/obesity at risk of type 2 diabetes.</p><p dir="ltr"><b><u>Research Design and Methods</u></b>: 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 two-hour oral glucose tolerance tests. Baseline metabolite levels were measured in plasma from two-hours post-glucose challenge. In the discovery cohort, LASSO 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.</p><p dir="ltr"><b><u>Results</u></b><b>:</b> Two metabolites (<a href="" target="_blank">allylphenol sulfate and caprylic acid</a>) were found to predict prediabetes beyond known risk factors, including sex, BMI, age, ethnicity, fasting/two-hour 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% confidence interval: [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 was applied to the replication cohort, the model with metabolites and risk factors predicted prediabetes more accurately (AUC: 0.70 [0.55-0.86]) than the same model without metabolites (AUC: 0.62 [0.46-0.79]).</p><p dir="ltr"><b><u>Conclusions</u></b><b>:</b> Metabolite profiles may help improve prediabetes prediction compared to traditional risk factors. Findings suggest that medium-chain fatty acids and phytochemicals are early indicators of prediabetes in high-risk youth.</p><p><br></p>Article Highlights<p dir="ltr"><a href="" target="_blank">a. Young-onset prediabetes is an emerging epidemic, especially in Hispanic adolescents, and early identification and treatment can potentially decrease the risk of type 2 diabetes later in life.</a></p><p dir="ltr"><a href="" target="_blank">b. We aimed to develop </a>a metabolite-based prediction model for prediabetes in two independent cohorts of youth and young adults with overweight/obesity.</p><p dir="ltr">c. We found that postprandial levels of two metabolites, a phytochemical and a medium-chain fatty acid, showed strong potential to enhance the prediction of prediabetes beyond established clinical risk factors. </p><p dir="ltr"><a href="" target="_blank">These metabolites may be early surrogate markers for prediabetes and could help identify youth who would benefit most from preventive lifestyle changes.</a></p>