ESPE Yearbook of Paediatric Endocrinology

Brief summary: In this paper introduced a new energy trade-off score (ETOS) and index to characterize four extreme human body types regarding height and weight in young adulthood (e.g. tall-slender, short-stout, short-slender, tall-stout) for growth patterns and underlying genetic background. Growth data of 1889 subjects (996 girls) of the GrowUp 1974 Gothenburg study were investigated for the four body types showing that the two trade-off body types tall-slender<...

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...

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...