ISSN 1662-4009 (online)

ey0019.11-11 | Weight regulation and endocrine circuits (including interventions) | ESPEYB19

11.11. The pubertal growth spurt is diminished in children with severe obesity

A Holmgren , GA Martos-Moreno , A Niklasson , J Martinez-Villanueva , J Argente , K Albertsson-Wikland

anton.holmgren@regionhalland.se Pediatr Res. 2021 Jul;90(1):184–190. doi: 10.1038/s41390-020-01234-3.Brief Summary: This observational study compared the pubertal growth spurt of children in a Spanish study group with severe early onset obesity to children in a Swedish community-based study. The authors show that childhood obesity i...

ey0018.15-11 | (1) | ESPEYB18

15.11. Prediction of adult height by machine learning technique

Shmoish Michael , German Alina , Devir Nurit , Hecht Anna , Butler Gary , Niklasson Aimon , Albertsson-Wikland Kerstin , Hochberg Ze'ev

J Clin Endocrinol Metab. 2021; 16;106(7):e2700–e2710.PMID: 33606028 doi: 10.1210/clinem/dgab093This paper illustrates the power of machine learning to successfully predict adult height using growth measurements before age 6 years, without the need for bone age.Computers beat us in games of predictions, such as chess. They beat us also in the exercise of predictin...

ey0020.14-2 | Section | ESPEYB20

14.2. Prediction of adult height by machine learning technique

Shmoish Michael , German Alina , Devir Nurit , Hecht Anna , Butler Gary , Niklasson Aimon , Albertsson-Wikland Kerstin , Hochberg Ze'ev

Brief summary: Growth data from three independent longitudinal cohort studies (Gothenburg GrowUp 1974 (n 1596); Gothenburg GrowUp 1990 (n 1890); Edinburgh Growth Study (n 145)) were used to train machine learning (ML) to predict adult height (AH) based on growth measurements until the age of 6 years. Five ML algorithms were tested. A random forest model predicted best, with sex and height at age 3.4–6.0 years being the most influencing factors. The model was cross-validat...