Scientific Reports (Mar 2019)

An individual participant data meta-analysis on metabolomics profiles for obesity and insulin resistance in European children

  • Christian Hellmuth,
  • Franca F. Kirchberg,
  • Stephanie Brandt,
  • Anja Moß,
  • Viola Walter,
  • Dietrich Rothenbacher,
  • Hermann Brenner,
  • Veit Grote,
  • Dariusz Gruszfeld,
  • Piotr Socha,
  • Ricardo Closa-Monasterolo,
  • Joaquin Escribano,
  • Veronica Luque,
  • Elvira Verduci,
  • Benedetta Mariani,
  • Jean-Paul Langhendries,
  • Pascale Poncelet,
  • Joachim Heinrich,
  • Irina Lehmann,
  • Marie Standl,
  • Olaf Uhl,
  • Berthold Koletzko,
  • Elisabeth Thiering,
  • Martin Wabitsch

DOI
https://doi.org/10.1038/s41598-019-41449-x
Journal volume & issue
Vol. 9, no. 1
pp. 1 – 13

Abstract

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Abstract Childhood obesity prevalence is rising in countries worldwide. A variety of etiologic factors contribute to childhood obesity but little is known about underlying biochemical mechanisms. We performed an individual participant meta-analysis including 1,020 pre-pubertal children from three European studies and investigated the associations of 285 metabolites measured by LC/MS-MS with BMI z-score, height, weight, HOMA, and lipoprotein concentrations. Seventeen metabolites were significantly associated with BMI z-score. Sphingomyelin (SM) 32:2 showed the strongest association with BMI z-score (P = 4.68 × 10−23) and was also closely related to weight, and less strongly to height and LDL, but not to HOMA. Mass spectrometric analyses identified SM 32:2 as myristic acid containing SM d18:2/14:0. Thirty-five metabolites were significantly associated to HOMA index. Alanine showed the strongest positive association with HOMA (P = 9.77 × 10−16), while acylcarnitines and non-esterified fatty acids were negatively associated with HOMA. SM d18:2/14:0 is a powerful marker for molecular changes in childhood obesity. Tracing back the origin of SM 32:2 to dietary source in combination with genetic predisposition will path the way for early intervention programs. Metabolic profiling might facilitate risk prediction and personalized interventions in overweight children.