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

Christian Hellmuth1, Franca F Kirchberg1, Stephanie Brandt2

  • 1LMU - Ludwig-Maximilians-Universität München, Dr. von Hauner Children's Hospital, Div. Metabolic and Nutritional Medicine, 80336, Munich, Germany.

Scientific Reports
|March 27, 2019
PubMed

Insights

Childhood obesity involves complex biochemical factors. This study identified specific metabolites, like Sphingomyelin (SM) 32:2, strongly linked to BMI and weight in children, offering potential for early intervention.

Area of Science:

  • Metabolomics
  • Pediatric Endocrinology
  • Biochemistry

Background:

  • Childhood obesity is a growing global health concern.
  • Underlying biochemical mechanisms remain poorly understood.
  • Identifying molecular markers is crucial for understanding and managing the condition.

Purpose of the Study:

  • To investigate associations between metabolites and obesity indicators in pre-pubertal children.
  • To identify specific biochemical markers related to Body Mass Index (BMI) z-score and insulin resistance (HOMA).
  • To explore the potential of metabolic profiling for risk prediction and personalized interventions.

Main Methods:

  • Individual participant meta-analysis of 1,020 pre-pubertal children from three European cohorts.
  • Liquid chromatography-tandem mass spectrometry (LC/MS-MS) to measure 285 metabolites.
  • Statistical analysis of metabolite associations with BMI z-score, height, weight, HOMA, and lipoprotein concentrations.

Main Results:

  • Seventeen metabolites significantly associated with BMI z-score, with Sphingomyelin (SM) 32:2 showing the strongest link (P=4.68×10⁻²³).
  • SM 32:2 identified as SM d18:2/14:0, also related to weight, height, and LDL cholesterol.
  • Thirty-five metabolites associated with HOMA index, including alanine (positive association) and acylcarnitines/fatty acids (negative association).

Conclusions:

  • SM d18:2/14:0 is a potent molecular marker for childhood obesity.
  • Understanding the dietary and genetic origins of SM 32:2 can inform early intervention strategies.
  • Metabolic profiling shows promise for predicting risk and enabling personalized care for overweight children.

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