Cardiovascular diseases have their origin in childhood. Early biomarkers identifying individuals with increased risk for disease are needed to support early detection and to optimize prevention strategies. By applying machine learning approach on high throughput NMR-based metabolomics data, we identified metabolic predictors of cardiovascular risk in circulation in a cohort of 396 females, followed from childhood (mean age 11.2 years) to early adulthood (mean age 18.1 years). The identified childhood metabolic signature included three circulating biomarkers robustly associating with increased cardiovascular risk in early adulthood (AUC = 0.641 to 0.802, all p<0.01). These associations were confirmed in two validation cohorts including middle-aged women, with similar effect estimates. We subsequently applied random intercept cross-lagged panel model analysis, which suggested causal relationship between metabolites and cardio-metabolic risk score from childhood to early adulthood. These results provide evidence for the utility of circulating metabolomics panel to identify children and adolescents at risk for cardiovascular disease, to whom preventive measures and follow-up could be indicated.

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