%0 Journal Article
%A Hellmuth, Christian
%A Kirchberg, Franca F
%A Brandt, Stephanie
%A Moß, Anja
%A Walter, Viola
%A Rothenbacher, Dietrich
%A Brenner, Hermann
%A Grote, Veit
%A Gruszfeld, Dariusz
%A Socha, Piotr
%A Closa-Monasterolo, Ricardo
%A Escribano, Joaquin
%A Luque, Veronica
%A Verduci, Elvira
%A Mariani, Benedetta
%A Langhendries, Jean-Paul
%A Poncelet, Pascale
%A Heinrich, Joachim
%A Lehmann, Irina
%A Standl, Marie
%A Uhl, Olaf
%A Koletzko, Berthold
%A Thiering, Elisabeth
%A Wabitsch, Martin
%T An individual participant data meta-analysis on metabolomics profiles for obesity and insulin resistance in European children.
%J Scientific reports
%V 9
%N 1
%@ 2045-2322
%C [London]
%I Macmillan Publishers Limited, part of Springer Nature
%M DKFZ-2019-00766
%P 5053
%D 2019
%X 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.
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:30911015
%R 10.1038/s41598-019-41449-x
%U https://inrepo02.dkfz.de/record/143167