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Probabilistic prediction of segmental body composition in Iranian children and adolescents
BACKGROUND: Adolescents' body composition is considered an important measure to evaluate health status. An examination of any of the segmental compartments by anthropometric indices is a more usable method than direct methods. OBJECTIVES: To propose a method based on the network approach for pr...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9440596/ https://www.ncbi.nlm.nih.gov/pubmed/36057547 http://dx.doi.org/10.1186/s12887-022-03580-z |
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author | Rahmani, Mahsa Ardalan, Arash Ghaderi-Zefrehei, Mostafa Jeddi, Marjan Heydari, Seyed Taghi Dabbaghmanesh, Mohammad Hossein |
author_facet | Rahmani, Mahsa Ardalan, Arash Ghaderi-Zefrehei, Mostafa Jeddi, Marjan Heydari, Seyed Taghi Dabbaghmanesh, Mohammad Hossein |
author_sort | Rahmani, Mahsa |
collection | PubMed |
description | BACKGROUND: Adolescents' body composition is considered an important measure to evaluate health status. An examination of any of the segmental compartments by anthropometric indices is a more usable method than direct methods. OBJECTIVES: To propose a method based on the network approach for predicting segmental body composition components in adolescent boys and girls using anthropometric measurements. METHODS: A dual-energy X-ray absorptiometry (DXA) dataset in the south of Iran, including 476 adolescents (235 girls and 241 boys) with a range of 9–18 years, was obtained. Several anthropometric prediction models based on the network approach were fitted to the training dataset (TRD 80%) using bnlearn, an R add-in package. The best fitted models were applied to the validation dataset (VAD 20%) to assess the prediction accuracy. RESULTS: Present equations consisting of age, weight, height, body mass index (BMI), and hip circumference accounted for 0.85 (P < 0.001) of the variability of DXA values in the corresponding age groups of boys. Similarly, reasonable estimates of DXA values could be obtained from age, weight, height, and BMI in girls over 13 years, and from age, weight, height, BMI, and waist circumference in girls under 13 years, respectively, of 0.77 and 0.83 (P < 0.001). Correlations between robust Gaussian Bayesian network (RGBN) predictions and DXA measurements were highly significant, averaging 0.87 for boys and 0.82 for girls (P < 0.001). CONCLUSIONS: The results revealed that, based on the present study’s predictive models, adolescents' body composition might be estimated by input anthropometric information. Given the flexibility and modeling of the present method to test different motivated hypotheses, its application to body compositional data is highly appealing. |
format | Online Article Text |
id | pubmed-9440596 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-94405962022-09-04 Probabilistic prediction of segmental body composition in Iranian children and adolescents Rahmani, Mahsa Ardalan, Arash Ghaderi-Zefrehei, Mostafa Jeddi, Marjan Heydari, Seyed Taghi Dabbaghmanesh, Mohammad Hossein BMC Pediatr Research BACKGROUND: Adolescents' body composition is considered an important measure to evaluate health status. An examination of any of the segmental compartments by anthropometric indices is a more usable method than direct methods. OBJECTIVES: To propose a method based on the network approach for predicting segmental body composition components in adolescent boys and girls using anthropometric measurements. METHODS: A dual-energy X-ray absorptiometry (DXA) dataset in the south of Iran, including 476 adolescents (235 girls and 241 boys) with a range of 9–18 years, was obtained. Several anthropometric prediction models based on the network approach were fitted to the training dataset (TRD 80%) using bnlearn, an R add-in package. The best fitted models were applied to the validation dataset (VAD 20%) to assess the prediction accuracy. RESULTS: Present equations consisting of age, weight, height, body mass index (BMI), and hip circumference accounted for 0.85 (P < 0.001) of the variability of DXA values in the corresponding age groups of boys. Similarly, reasonable estimates of DXA values could be obtained from age, weight, height, and BMI in girls over 13 years, and from age, weight, height, BMI, and waist circumference in girls under 13 years, respectively, of 0.77 and 0.83 (P < 0.001). Correlations between robust Gaussian Bayesian network (RGBN) predictions and DXA measurements were highly significant, averaging 0.87 for boys and 0.82 for girls (P < 0.001). CONCLUSIONS: The results revealed that, based on the present study’s predictive models, adolescents' body composition might be estimated by input anthropometric information. Given the flexibility and modeling of the present method to test different motivated hypotheses, its application to body compositional data is highly appealing. BioMed Central 2022-09-03 /pmc/articles/PMC9440596/ /pubmed/36057547 http://dx.doi.org/10.1186/s12887-022-03580-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Rahmani, Mahsa Ardalan, Arash Ghaderi-Zefrehei, Mostafa Jeddi, Marjan Heydari, Seyed Taghi Dabbaghmanesh, Mohammad Hossein Probabilistic prediction of segmental body composition in Iranian children and adolescents |
title | Probabilistic prediction of segmental body composition in Iranian children and adolescents |
title_full | Probabilistic prediction of segmental body composition in Iranian children and adolescents |
title_fullStr | Probabilistic prediction of segmental body composition in Iranian children and adolescents |
title_full_unstemmed | Probabilistic prediction of segmental body composition in Iranian children and adolescents |
title_short | Probabilistic prediction of segmental body composition in Iranian children and adolescents |
title_sort | probabilistic prediction of segmental body composition in iranian children and adolescents |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9440596/ https://www.ncbi.nlm.nih.gov/pubmed/36057547 http://dx.doi.org/10.1186/s12887-022-03580-z |
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