Publication Details |
| Category | Text Publication |
| Reference Category | Journals |
| DOI | 10.1038/s41598-019-41449-x |
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| Title (Primary) | An individual participant data meta-analysis on metabolomics profiles for obesity and insulin resistance in European children |
| Author | Hellmuth, C.; Kirchberg, F.F.; Brandt, S.; Moß, A.; Walter, V.; Rothenbacher, D.; Brenner, H.; Grote, V.; Gruszfeld, D.; Socha, P.; Closa-Monasterolo, R.; Escribano, J.; Luque, V.; Verduci, E.; Mariani, B.; Langhendries, J.-P.; Poncelet, P.; Heinrich, J.; Lehmann, I.; Standl, M.; Uhl, O.; Koletzko, B.; Thiering, E.; Wabitsch, M. |
| Source Titel | Scientific Reports |
| Year | 2019 |
| Department | IMMU |
| Volume | 9 |
| Page From | art. 5053 |
| Language | englisch |
| Supplements | https://static-content.springer.com/esm/art%3A10.1038%2Fs41598-019-41449-x/MediaObjects/41598_2019_41449_MOESM1_ESM.pdf |
| 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. |
| Persistent UFZ Identifier | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=21750 |
| Hellmuth, C., Kirchberg, F.F., Brandt, S., Moß, A., Walter, V., Rothenbacher, D., Brenner, H., Grote, V., Gruszfeld, D., Socha, P., Closa-Monasterolo, R., Escribano, J., Luque, V., Verduci, E., Mariani, B., Langhendries, J.-P., Poncelet, P., Heinrich, J., Lehmann, I., Standl, M., Uhl, O., Koletzko, B., Thiering, E., Wabitsch, M. (2019): An individual participant data meta-analysis on metabolomics profiles for obesity and insulin resistance in European children Sci. Rep. 9 , art. 5053 10.1038/s41598-019-41449-x |
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