“Puberty age gap”: New method of assessing pubertal timing and its association with mental health problems

Abstract

AbstractPuberty is linked to mental health problems during adolescence, and in particular, the timing of puberty is thought to be an important risk factor. This study developed a new measure of pubertal timing using multiple pubertal features and nonlinear associations with age and investigated its association with mental health problems. Using the Adolescent Brain Cognitive Development (ABCD) cohort, we implemented three models of pubertal timing by predicting chronological age from i) observed physical development, ii) hormones (testosterone and dehydroepiandrosterone [DHEA]), and iii) a combination of the two, using a supervised machine learning method (n ∼9,900). Accuracy of the new models, and their associations with mental health problems were evaluated. The new pubertal timing measure performed better in capturing age variance compared to a commonly used method, and the physical measure accounted for more variance in mental health, such that earlier pubertal timing was associated with higher symptoms. This study demonstrates the utility for a new model of pubertal timing and suggests that physical maturation may play a predominant role in predicting mental health problems in early adolescence.