ESPE Abstracts

Background: Differences in hypothalamus–pituitary–adrenal (HPA-)axis functioning have been proposed to underlie gender-specific cardiovascular and neurocognitive disease susceptibility.

Objective and hypotheses: We conducted a systematic review and meta-analysis to test the hypothesis that gender-specific differences in HPA-axis activity are already present in childhood.

Method: We searched two electronic databases (PubMed and EMBASE) to identify potentially relevant studies. We included studies that assessed random, non-stimulated cortisol in serum or saliva, or cortisol in 24 h-urine in healthy males and females aged ≤18 years who did not use glucocorticoid-containing medications. Two researchers independently reviewed the data. Results were analyzed by age groups <8 years (prepubertal) and 8–18 years (peri-/postpubertal).

Results: A total of 5834 articles were identified with our search strategy. 351 (6%) publications were selected for full-text screening, of which 70 (20%) met our inclusion criteria. Our meta-analysis included the data of >13.000 subjects. In both serum and saliva, differences between males and females seemed to be age-dependent, with cortisol concentrations (in nmol/l) in boys being higher before age 8 years (mean difference (95% CI): 0.37 (0.25–0.48) for serum, and 0.20 (0.04–0.36) for saliva and lower after age 8 years (−0.52 (−0.59 to −0.42) for serum, and −0.46 (−0.50 to −0.41) for saliva, as compared to girls. In 24 h-urine, gender-differences were found to be stable throughout childhood, with urinary cortisol excretion being higher in boys.

Conclusion: Differences between males and females in cortisol production and/or metabolism are already present early in life, with cortisol being higher in boys. A gender-specific evolution of cortisol metabolism seems to be induced by puberty, resulting in higher random, non-stimulated cortisol levels in girls. Urinary cortisol excretion seems to be stable between genders with age. Although the differences found were small, these patterns might contribute to gender-specific differences in the origins of health and disease.