Understanding Puberty and Its Measurement: Ideas for Research in a New Generation

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/148344/1/jora12371.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148344/2/jora12371_am.pd

Morningness/eveningness and menstrual symptoms in adolescent females

Photograph of the exterior of the Cox Convention Center

The Relations of Pubertal Status to Intrapersonal Changes in Young Adolescents

The purpose of this study was to investigate the relations between pubertal development and adolescents’ perceptions of their physical and emotional states. Two research questions were posed: (a) What are the effects of pubertal status upon satisfaction with appearance and mood states of the adolescent; and, (b) What are the patterns of those pubertal effects in sixth, seventh, and eighth grades? A nonclinical group of adolescents (N = 253) was selected from two cohorts and was followed from sixth through eighth grade. Multiple measures were used to assess satisfaction with appearance and mood states. Significant multivariate findings were limited to seventh and eighth grades. For satisfaction with appearance in girls, there were significant multivariate effects for pubertal status in both grades with the salient variable being satisfaction with weight. Girls who were more physically mature reported being less satisfied with their weight. For moods in seventh grade boys, the multivariate effect was significant for pubertal status with the salient variables being Impulse Control and Emotional Tone. Pubertal boys rated themselves higher, that is better on Impulse Control and Emotional Tone. Other significant univariate effects and polynomial trends are also discussed

420 Computable Phenotyping with “Big Data” as a Foundation for Artificial Intelligence Algorithm Construction: Puberty as a Transdisciplinary Case Example

OBJECTIVES/GOALS: Artificial intelligence (AI) depends on quality machine learning (ML) algorithms constructed with high-quality training data. This TL1 trainee project develops a disease-agnostic computable phenotype framework for ML algorithm construction, modeling male puberty as a case example. METHODS/STUDY POPULATION: A computable phenotype of male puberty was constructed to answer the question: “Does early pubertal timing increase the risk of developing type II diabetes (T2D) in males?” A computable phenotype of males 85th percentile. Males diagnosed with precocious puberty (E30.1) were 6.89 times more likely to develop T2D when aged 14-18 years old than those without (OR 6.89, 95% CI: 5.17-9.19, p<0.0001). Next steps involve training a ML model on each computable phenotype groupings’ health data, with anticipated results identifying underlying salient pathophysiologic variables. A generalized computable phenotype approach is further developed to: 1) explore clinical questions in large databases like TriNetX©, and 2) model disease development with AI/ML algorithm construction. DISCUSSION/SIGNIFICANCE: Computed phenotypes reveal males with precocious puberty may have increased T2D risk. Next steps utilize subject data to train an AI/ML algorithm, model development to identify salient pathophysiologic variables, and synthesize a generalized AI/ML developmental research framework for dissemination