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

Niousha Dehestani, Nandita Vijayakumar, Gareth Ball, Sina Mansour L, Sarah Whittle, Timothy J. Silk

Abstract:

Puberty 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 that was built upon multiple pubertal features and
their nonlinear changes over time (i.e., with age), and investigated its association with mental health problems. Using the
Adolescent Brain Cognitive Development (ABCD) cohort (N ~ 9900, aged 9–13 years), we employed three different models to assess
pubertal timing. These models aimed to predict chronological age based on: (i) observed physical development, (ii) hormone levels
(testosterone and dehydroepiandrosterone [DHEA]), and (iii) a combination of both physical development and hormones. To
achieve this, we utilized a supervised machine learning approach, which allowed us to train the models using the available data and
make age predictions based on the input pubertal features. The accuracy of these three models was evaluated, and their
associations with mental health problems were examined. The new pubertal timing model performed better at capturing age
variance compared to the more commonly used linear regression method. Further, the model based on physical features
accounted for the most variance in mental health, such that earlier pubertal timing was associated with higher symptoms. This
study demonstrates the utility of our new model of pubertal timing and suggests that, relative to hormonal measu