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Cross-sectional data accurately model longitudinal growth in the craniofacial skeleton

Dense, longitudinal sampling represents the ideal for studying biological growth. However, longitudinal samples are not typically possible, due to limits of time, prohibitive cost, or health concerns of repeat radiologic imaging. In contrast, cross-sectional samples have few such drawbacks, but it i...

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Autores principales: Middleton, Kevin M., Duren, Dana L., McNulty, Kieran P., Oh, Heesoo, Valiathan, Manish, Sherwood, Richard J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10630296/
https://www.ncbi.nlm.nih.gov/pubmed/37935807
http://dx.doi.org/10.1038/s41598-023-46018-x
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author Middleton, Kevin M.
Duren, Dana L.
McNulty, Kieran P.
Oh, Heesoo
Valiathan, Manish
Sherwood, Richard J.
author_facet Middleton, Kevin M.
Duren, Dana L.
McNulty, Kieran P.
Oh, Heesoo
Valiathan, Manish
Sherwood, Richard J.
author_sort Middleton, Kevin M.
collection PubMed
description Dense, longitudinal sampling represents the ideal for studying biological growth. However, longitudinal samples are not typically possible, due to limits of time, prohibitive cost, or health concerns of repeat radiologic imaging. In contrast, cross-sectional samples have few such drawbacks, but it is not known how well estimates of growth milestones can be obtained from cross-sectional samples. The Craniofacial Growth Consortium Study (CGCS) contains longitudinal growth data for approximately 2000 individuals. Single samples from the CGCS for individuals representing cross-sectional data were used to test the ability to predict growth parameters in linear trait measurements separately by sex. Testing across a range of cross-sectional sample sizes from 5 to the full sample, we found that means from repeated samples were able to approximate growth rates determined from the full longitudinal CGCS sample, with mean absolute differences below 1 mm at cross-sectional sample sizes greater than ~ 200 individuals. Our results show that growth parameters and milestones can be accurately estimated from cross-sectional data compared to population-level estimates from complete longitudinal data, underscoring the utility of such datasets in growth modeling. This method can be applied to other forms of growth (e.g., stature) and to cases in which repeated radiographs are not feasible (e.g., cone-beam CT).
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spelling pubmed-106302962023-11-07 Cross-sectional data accurately model longitudinal growth in the craniofacial skeleton Middleton, Kevin M. Duren, Dana L. McNulty, Kieran P. Oh, Heesoo Valiathan, Manish Sherwood, Richard J. Sci Rep Article Dense, longitudinal sampling represents the ideal for studying biological growth. However, longitudinal samples are not typically possible, due to limits of time, prohibitive cost, or health concerns of repeat radiologic imaging. In contrast, cross-sectional samples have few such drawbacks, but it is not known how well estimates of growth milestones can be obtained from cross-sectional samples. The Craniofacial Growth Consortium Study (CGCS) contains longitudinal growth data for approximately 2000 individuals. Single samples from the CGCS for individuals representing cross-sectional data were used to test the ability to predict growth parameters in linear trait measurements separately by sex. Testing across a range of cross-sectional sample sizes from 5 to the full sample, we found that means from repeated samples were able to approximate growth rates determined from the full longitudinal CGCS sample, with mean absolute differences below 1 mm at cross-sectional sample sizes greater than ~ 200 individuals. Our results show that growth parameters and milestones can be accurately estimated from cross-sectional data compared to population-level estimates from complete longitudinal data, underscoring the utility of such datasets in growth modeling. This method can be applied to other forms of growth (e.g., stature) and to cases in which repeated radiographs are not feasible (e.g., cone-beam CT). Nature Publishing Group UK 2023-11-07 /pmc/articles/PMC10630296/ /pubmed/37935807 http://dx.doi.org/10.1038/s41598-023-46018-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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/) .
spellingShingle Article
Middleton, Kevin M.
Duren, Dana L.
McNulty, Kieran P.
Oh, Heesoo
Valiathan, Manish
Sherwood, Richard J.
Cross-sectional data accurately model longitudinal growth in the craniofacial skeleton
title Cross-sectional data accurately model longitudinal growth in the craniofacial skeleton
title_full Cross-sectional data accurately model longitudinal growth in the craniofacial skeleton
title_fullStr Cross-sectional data accurately model longitudinal growth in the craniofacial skeleton
title_full_unstemmed Cross-sectional data accurately model longitudinal growth in the craniofacial skeleton
title_short Cross-sectional data accurately model longitudinal growth in the craniofacial skeleton
title_sort cross-sectional data accurately model longitudinal growth in the craniofacial skeleton
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10630296/
https://www.ncbi.nlm.nih.gov/pubmed/37935807
http://dx.doi.org/10.1038/s41598-023-46018-x
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