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Screening for novel risk factors related to high myopia using machine learning

BACKGROUND: High myopia-related complications have become a major cause of irreversible vision loss. Evaluating the association between potential factors and high myopia can provide insights into pathophysiologic mechanisms and further intervention targets for myopia progression. METHOD: Participant...

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Autores principales: Zhang, Ruiheng, Dong, Li, Yang, Qiong, Zhou, Wenda, Wu, Haotian, Li, Yifan, Li, Heyan, Wei, Wenbin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9558412/
https://www.ncbi.nlm.nih.gov/pubmed/36229775
http://dx.doi.org/10.1186/s12886-022-02627-0
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author Zhang, Ruiheng
Dong, Li
Yang, Qiong
Zhou, Wenda
Wu, Haotian
Li, Yifan
Li, Heyan
Wei, Wenbin
author_facet Zhang, Ruiheng
Dong, Li
Yang, Qiong
Zhou, Wenda
Wu, Haotian
Li, Yifan
Li, Heyan
Wei, Wenbin
author_sort Zhang, Ruiheng
collection PubMed
description BACKGROUND: High myopia-related complications have become a major cause of irreversible vision loss. Evaluating the association between potential factors and high myopia can provide insights into pathophysiologic mechanisms and further intervention targets for myopia progression. METHOD: Participants aged 12–25 years from National Health and Nutrition Examination Survey 2001–2006 were selected for the analysis. Myopia was defined as spherical equivalent (sum of spherical error and half of the cylindrical error) of any eyes ≤-0.5 diopters. High myopia was defined as the spherical equivalent of any eye ≤ − 5.00 diopters. Essential variables were selected by Random Forest algorithm and verified by multivariable logistic regression. RESULTS: A total of 7,033 participants and 74 potential factors, including demographic (4 factors), physical examination (6 factors), nutritional and serological (45 factors), immunological (9 variables), and past medical history factors (10 factors), were included into the analysis. Random Forest algorithm found that several anthropometric, nutritional, and serological factors were associated with high myopia. Combined with multivariable logistic regression, high levels of serum vitamin A was significantly associated with an increased prevalence of high myopia (adjusted odd ratio = 1.46 for 1 µmol/L increment, 95% confidence interval [CI] 1.01–2.10). Furthermore, we found that neither C-reactive protein nor asthma increased the risk and severity of myopia. CONCLUSION: High levels of serum vitamin A was seemingly associated with an increased prevalence of high myopia. This borderline significant association should be interpreted with caution because the potential increased type I error after the multiple testing. It still needs further investigation regarding the mechanism underlying this association. Neither C-reactive protein nor asthma increased the risk and severity of myopia. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12886-022-02627-0.
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spelling pubmed-95584122022-10-14 Screening for novel risk factors related to high myopia using machine learning Zhang, Ruiheng Dong, Li Yang, Qiong Zhou, Wenda Wu, Haotian Li, Yifan Li, Heyan Wei, Wenbin BMC Ophthalmol Research BACKGROUND: High myopia-related complications have become a major cause of irreversible vision loss. Evaluating the association between potential factors and high myopia can provide insights into pathophysiologic mechanisms and further intervention targets for myopia progression. METHOD: Participants aged 12–25 years from National Health and Nutrition Examination Survey 2001–2006 were selected for the analysis. Myopia was defined as spherical equivalent (sum of spherical error and half of the cylindrical error) of any eyes ≤-0.5 diopters. High myopia was defined as the spherical equivalent of any eye ≤ − 5.00 diopters. Essential variables were selected by Random Forest algorithm and verified by multivariable logistic regression. RESULTS: A total of 7,033 participants and 74 potential factors, including demographic (4 factors), physical examination (6 factors), nutritional and serological (45 factors), immunological (9 variables), and past medical history factors (10 factors), were included into the analysis. Random Forest algorithm found that several anthropometric, nutritional, and serological factors were associated with high myopia. Combined with multivariable logistic regression, high levels of serum vitamin A was significantly associated with an increased prevalence of high myopia (adjusted odd ratio = 1.46 for 1 µmol/L increment, 95% confidence interval [CI] 1.01–2.10). Furthermore, we found that neither C-reactive protein nor asthma increased the risk and severity of myopia. CONCLUSION: High levels of serum vitamin A was seemingly associated with an increased prevalence of high myopia. This borderline significant association should be interpreted with caution because the potential increased type I error after the multiple testing. It still needs further investigation regarding the mechanism underlying this association. Neither C-reactive protein nor asthma increased the risk and severity of myopia. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12886-022-02627-0. BioMed Central 2022-10-13 /pmc/articles/PMC9558412/ /pubmed/36229775 http://dx.doi.org/10.1186/s12886-022-02627-0 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
Zhang, Ruiheng
Dong, Li
Yang, Qiong
Zhou, Wenda
Wu, Haotian
Li, Yifan
Li, Heyan
Wei, Wenbin
Screening for novel risk factors related to high myopia using machine learning
title Screening for novel risk factors related to high myopia using machine learning
title_full Screening for novel risk factors related to high myopia using machine learning
title_fullStr Screening for novel risk factors related to high myopia using machine learning
title_full_unstemmed Screening for novel risk factors related to high myopia using machine learning
title_short Screening for novel risk factors related to high myopia using machine learning
title_sort screening for novel risk factors related to high myopia using machine learning
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9558412/
https://www.ncbi.nlm.nih.gov/pubmed/36229775
http://dx.doi.org/10.1186/s12886-022-02627-0
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