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Adult height prediction using the growth curve comparison method

Understanding the growth pattern is important in view of child and adolescent development. Due to different tempo of growth and timing of adolescent growth spurt, individuals reach their adult height at different ages. Accurate models to assess the growth involve intrusive radiological methods where...

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Autores principales: Mlakar, Miha, Gradišek, Anton, Luštrek, Mitja, Jurak, Gregor, Sorić, Maroje, Leskošek, Bojan, Starc, Gregor
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9934345/
https://www.ncbi.nlm.nih.gov/pubmed/36795791
http://dx.doi.org/10.1371/journal.pone.0281960
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author Mlakar, Miha
Gradišek, Anton
Luštrek, Mitja
Jurak, Gregor
Sorić, Maroje
Leskošek, Bojan
Starc, Gregor
author_facet Mlakar, Miha
Gradišek, Anton
Luštrek, Mitja
Jurak, Gregor
Sorić, Maroje
Leskošek, Bojan
Starc, Gregor
author_sort Mlakar, Miha
collection PubMed
description Understanding the growth pattern is important in view of child and adolescent development. Due to different tempo of growth and timing of adolescent growth spurt, individuals reach their adult height at different ages. Accurate models to assess the growth involve intrusive radiological methods whereas the predictive models based solely on height data are typically limited to percentiles and therefore rather inaccurate, especially during the onset of puberty. There is a need for more accurate non-invasive methods for height prediction that are easily applicable in the fields of sports and physical education, as well as in endocrinology. We developed a novel method, called Growth Curve Comparison (GCC), for height prediction, based on a large cohort of > 16,000 Slovenian schoolchildren followed yearly from ages 8 to 18. We compared the GCC method to the percentile method, linear regressor, decision tree regressor, and extreme gradient boosting. The GCC method outperformed the predictions of other methods over the entire age span both in boys and girls. The method was incorporated into a publicly available web application. We anticipate our method to be applicable also to other models predicting developmental outcomes of children and adolescents, such as for comparison of any developmental curves of anthropometric as well as fitness data. It can serve as a useful tool for assessment, planning, implementation, and monitoring of somatic and motor development of children and youth.
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spelling pubmed-99343452023-02-17 Adult height prediction using the growth curve comparison method Mlakar, Miha Gradišek, Anton Luštrek, Mitja Jurak, Gregor Sorić, Maroje Leskošek, Bojan Starc, Gregor PLoS One Research Article Understanding the growth pattern is important in view of child and adolescent development. Due to different tempo of growth and timing of adolescent growth spurt, individuals reach their adult height at different ages. Accurate models to assess the growth involve intrusive radiological methods whereas the predictive models based solely on height data are typically limited to percentiles and therefore rather inaccurate, especially during the onset of puberty. There is a need for more accurate non-invasive methods for height prediction that are easily applicable in the fields of sports and physical education, as well as in endocrinology. We developed a novel method, called Growth Curve Comparison (GCC), for height prediction, based on a large cohort of > 16,000 Slovenian schoolchildren followed yearly from ages 8 to 18. We compared the GCC method to the percentile method, linear regressor, decision tree regressor, and extreme gradient boosting. The GCC method outperformed the predictions of other methods over the entire age span both in boys and girls. The method was incorporated into a publicly available web application. We anticipate our method to be applicable also to other models predicting developmental outcomes of children and adolescents, such as for comparison of any developmental curves of anthropometric as well as fitness data. It can serve as a useful tool for assessment, planning, implementation, and monitoring of somatic and motor development of children and youth. Public Library of Science 2023-02-16 /pmc/articles/PMC9934345/ /pubmed/36795791 http://dx.doi.org/10.1371/journal.pone.0281960 Text en © 2023 Mlakar et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Mlakar, Miha
Gradišek, Anton
Luštrek, Mitja
Jurak, Gregor
Sorić, Maroje
Leskošek, Bojan
Starc, Gregor
Adult height prediction using the growth curve comparison method
title Adult height prediction using the growth curve comparison method
title_full Adult height prediction using the growth curve comparison method
title_fullStr Adult height prediction using the growth curve comparison method
title_full_unstemmed Adult height prediction using the growth curve comparison method
title_short Adult height prediction using the growth curve comparison method
title_sort adult height prediction using the growth curve comparison method
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9934345/
https://www.ncbi.nlm.nih.gov/pubmed/36795791
http://dx.doi.org/10.1371/journal.pone.0281960
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