Towards early risk biomarkers: serum metabolic signature in childhood predicts cardio-metabolic risk in adulthood

• Main Authors: Xiaowei Ojanen, Ph.D, Runtan Cheng, Msc, Timo Törmäkangas, Ph.D, Noa Rappaport, Ph.D, Tomasz Wilmanski, Ph.D, Na Wu, Msc, Erik Fung, M.B.Ch.B., Ph.D, Rozenn Nedelec, MSc, Sylvain Sebert, PhD, Dimitris Vlachopoulos, Ph.D, Wei Yan, Ph.D, Nathan D. Price, Ph.D, Sulin Cheng, Ph.D, Petri Wiklund, Ph.D
• Format: Article
• Language: English
• Published: Elsevier 2021-10-01
• Series: EBioMedicine
• Subjects: metabolomics; cardio-metabolic risk; children; longitudinal-study; ALSPAC
• Online Access: http://www.sciencedirect.com/science/article/pii/S2352396421004047

Summary: Background: Cardiovascular diseases may originate in childhood. Biomarkers identifying individuals with increased risk for disease are needed to support early detection and to optimise prevention strategies. Methods: In this prospective study, by applying a machine learning to high throughput NMR-based metabolomics data, we identified circulating childhood metabolic predictors of adult cardiovascular disease risk (MetS score) in a cohort of 396 females, followed from childhood (mean age 11·2 years) to early adulthood (mean age 18·1 years). The results obtained from the discovery cohort were validated in a large longitudinal birth cohort of females and males followed from puberty to adulthood (n = 2664) and in four cross-sectional data sets (n = 6341). Findings: The identified childhood metabolic signature included three circulating biomarkers, glycoprotein acetyls (GlycA), large high-density lipoprotein phospholipids (L-HDL-PL), and the ratio of apolipoprotein B to apolipoprotein A-1 (ApoB/ApoA) that were associated with increased cardio-metabolic risk in early adulthood (AUC = 0·641‒0·802, all p<0·01). These associations were confirmed in all validation cohorts with similar effect estimates both in females (AUC = 0·667‒0·905, all p<0·01) and males (AUC = 0·734‒0·889, all p<0·01) as well as in elderly patients with and without type 2 diabetes (AUC = 0·517‒0·700, all p<0·01). We subsequently applied random intercept cross-lagged panel model analysis, which suggested bidirectional causal relationship between metabolic biomarkers and cardio-metabolic risk score from childhood to early adulthood. Interpretation: These results provide evidence for the utility of a circulating metabolomics panel to identify children and adolescents at risk for future cardiovascular disease, to whom preventive measures and follow-up could be indicated.