Long-term Glucocorticoid Concentrations As a Risk Factor for Childhood Obesity and Adverse Body-fat Distribution

Overview

Journal Int J Obes (Lond)

Specialty Endocrinology

Date 2016 Jun 25

PMID 27339603

Citations 26

Authors

G Noppe

E L T van den Akker

Y B de Rijke

J W Koper

V W Jaddoe

E F C van Rossum

Affiliations

Soon will be listed here.

Abstract

Background: Childhood obesity is an important risk factor for premature development of the metabolic syndrome (MetS) at adulthood. There is need for understanding of the mechanisms underlying the MetS and obesity. Patients with Cushing's disease suffer from similar metabolic complications, leading to the hypothesis that inter-individual cortisol variation may contribute to the onset of obesity. In addition, glucocorticoid receptor (GR)-gene polymorphisms resulting in differential glucocorticoid (GC) sensitivity, have been associated with an adverse metabolic profile.

Aim: To study associations of GC levels in scalp hair, as a marker of long-term systemic GC concentrations, and genetically determined GC sensitivity with obesity and body-fat distribution in children.

Methods: We performed a cross-sectional study of cortisol and cortisone concentrations over a 3-month period, measured by LC-MS/MS (Liquid Chromatography Tandem Mass Spectrometry) in hair of 3019 6-year-old children participating in the Generation R study. Genotyping of GR-gene polymorphisms was performed.

Results: Of all children, 4.3% was obese and 13.4% overweight. Cortisol was significantly associated with risk of obesity (odd ratio (OR): 9.4 (3.3-26.9)) and overweight (OR: 1.4 (1.0-2.0)). Cortisone was associated with risk of obesity (OR: 1.9 (1.0-3.5)). Cortisol and cortisone were significantly positively associated with body mass index, fat mass (FM) index and android/gynecoid FM ratio. GR polymorphisms were not associated with adiposity parameters.

Conclusion: Long-term cortisol concentrations are strongly associated with an increased risk of childhood obesity and adverse body-fat distribution. Future research may reveal whether these are causal relations and may be a target for therapy.

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