TY  - JOUR
AU  - Hellmuth, Christian
AU  - Kirchberg, Franca F
AU  - Brandt, Stephanie
AU  - Moß, Anja
AU  - Walter, Viola
AU  - Rothenbacher, Dietrich
AU  - Brenner, Hermann
AU  - Grote, Veit
AU  - Gruszfeld, Dariusz
AU  - Socha, Piotr
AU  - Closa-Monasterolo, Ricardo
AU  - Escribano, Joaquin
AU  - Luque, Veronica
AU  - Verduci, Elvira
AU  - Mariani, Benedetta
AU  - Langhendries, Jean-Paul
AU  - Poncelet, Pascale
AU  - Heinrich, Joachim
AU  - Lehmann, Irina
AU  - Standl, Marie
AU  - Uhl, Olaf
AU  - Koletzko, Berthold
AU  - Thiering, Elisabeth
AU  - Wabitsch, Martin
TI  - An individual participant data meta-analysis on metabolomics profiles for obesity and insulin resistance in European children.
JO  - Scientific reports
VL  - 9
IS  - 1
SN  - 2045-2322
CY  - [London]
PB  - Macmillan Publishers Limited, part of Springer Nature
M1  - DKFZ-2019-00766
SP  - 5053
PY  - 2019
AB  - 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.
LB  - PUB:(DE-HGF)16
C6  - pmid:30911015
DO  - DOI:10.1038/s41598-019-41449-x
UR  - https://inrepo02.dkfz.de/record/143167
ER  -