Nonalcoholic fatty liver disease and early prediction of gestational diabetes mellitus using machine learning methods
BACKGROUND/AIMS: To develop an early prediction model for gestational diabetes mellitus (GDM) using machine learning and to evaluate whether the inclusion of nonalcoholic fatty liver disease (NAFLD)-associated variables increases the performance of model. METHODS: This prospective cohort study evalu...
Autores principales: | , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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The Korean Association for the Study of the Liver
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8755469/ https://www.ncbi.nlm.nih.gov/pubmed/34649307 http://dx.doi.org/10.3350/cmh.2021.0174 |
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author | Lee, Seung Mi Hwangbo, Suhyun Norwitz, Errol R. Koo, Ja Nam Oh, Ig Hwan Choi, Eun Saem Jung, Young Mi Kim, Sun Min Kim, Byoung Jae Kim, Sang Youn Kim, Gyoung Min Kim, Won Joo, Sae Kyung Shin, Sue Park, Chan-Wook Park, Taesung Park, Joong Shin |
author_facet | Lee, Seung Mi Hwangbo, Suhyun Norwitz, Errol R. Koo, Ja Nam Oh, Ig Hwan Choi, Eun Saem Jung, Young Mi Kim, Sun Min Kim, Byoung Jae Kim, Sang Youn Kim, Gyoung Min Kim, Won Joo, Sae Kyung Shin, Sue Park, Chan-Wook Park, Taesung Park, Joong Shin |
author_sort | Lee, Seung Mi |
collection | PubMed |
description | BACKGROUND/AIMS: To develop an early prediction model for gestational diabetes mellitus (GDM) using machine learning and to evaluate whether the inclusion of nonalcoholic fatty liver disease (NAFLD)-associated variables increases the performance of model. METHODS: This prospective cohort study evaluated pregnant women for NAFLD using ultrasound at 10–14 weeks and screened them for GDM at 24–28 weeks of gestation. The clinical variables before 14 weeks were used to develop prediction models for GDM (setting 1, conventional risk factors; setting 2, addition of new risk factors in recent guidelines; setting 3, addition of routine clinical variables; setting 4, addition of NALFD-associated variables, including the presence of NAFLD and laboratory results; and setting 5, top 11 variables identified from a stepwise variable selection method). The predictive models were constructed using machine learning methods, including logistic regression, random forest, support vector machine, and deep neural networks. RESULTS: Among 1,443 women, 86 (6.0%) were diagnosed with GDM. The highest performing prediction model among settings 1–4 was setting 4, which included both clinical and NAFLD-associated variables (area under the receiver operating characteristic curve [AUC] 0.563–0.697 in settings 1–3 vs. 0.740–0.781 in setting 4). Setting 5, with top 11 variables (which included NAFLD and hepatic steatosis index), showed similar predictive power to setting 4 (AUC 0.719–0.819 in setting 5, P=not significant between settings 4 and 5). CONCLUSIONS: We developed an early prediction model for GDM using machine learning. The inclusion of NAFLD-associated variables significantly improved the performance of GDM prediction. (ClinicalTrials.gov Identifier: NCT02276144) |
format | Online Article Text |
id | pubmed-8755469 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | The Korean Association for the Study of the Liver |
record_format | MEDLINE/PubMed |
spelling | pubmed-87554692022-01-20 Nonalcoholic fatty liver disease and early prediction of gestational diabetes mellitus using machine learning methods Lee, Seung Mi Hwangbo, Suhyun Norwitz, Errol R. Koo, Ja Nam Oh, Ig Hwan Choi, Eun Saem Jung, Young Mi Kim, Sun Min Kim, Byoung Jae Kim, Sang Youn Kim, Gyoung Min Kim, Won Joo, Sae Kyung Shin, Sue Park, Chan-Wook Park, Taesung Park, Joong Shin Clin Mol Hepatol Original Article BACKGROUND/AIMS: To develop an early prediction model for gestational diabetes mellitus (GDM) using machine learning and to evaluate whether the inclusion of nonalcoholic fatty liver disease (NAFLD)-associated variables increases the performance of model. METHODS: This prospective cohort study evaluated pregnant women for NAFLD using ultrasound at 10–14 weeks and screened them for GDM at 24–28 weeks of gestation. The clinical variables before 14 weeks were used to develop prediction models for GDM (setting 1, conventional risk factors; setting 2, addition of new risk factors in recent guidelines; setting 3, addition of routine clinical variables; setting 4, addition of NALFD-associated variables, including the presence of NAFLD and laboratory results; and setting 5, top 11 variables identified from a stepwise variable selection method). The predictive models were constructed using machine learning methods, including logistic regression, random forest, support vector machine, and deep neural networks. RESULTS: Among 1,443 women, 86 (6.0%) were diagnosed with GDM. The highest performing prediction model among settings 1–4 was setting 4, which included both clinical and NAFLD-associated variables (area under the receiver operating characteristic curve [AUC] 0.563–0.697 in settings 1–3 vs. 0.740–0.781 in setting 4). Setting 5, with top 11 variables (which included NAFLD and hepatic steatosis index), showed similar predictive power to setting 4 (AUC 0.719–0.819 in setting 5, P=not significant between settings 4 and 5). CONCLUSIONS: We developed an early prediction model for GDM using machine learning. The inclusion of NAFLD-associated variables significantly improved the performance of GDM prediction. (ClinicalTrials.gov Identifier: NCT02276144) The Korean Association for the Study of the Liver 2022-01 2021-10-15 /pmc/articles/PMC8755469/ /pubmed/34649307 http://dx.doi.org/10.3350/cmh.2021.0174 Text en Copyright © 2022 by The Korean Association for the Study of the Liver https://creativecommons.org/licenses/by-nc/3.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article Lee, Seung Mi Hwangbo, Suhyun Norwitz, Errol R. Koo, Ja Nam Oh, Ig Hwan Choi, Eun Saem Jung, Young Mi Kim, Sun Min Kim, Byoung Jae Kim, Sang Youn Kim, Gyoung Min Kim, Won Joo, Sae Kyung Shin, Sue Park, Chan-Wook Park, Taesung Park, Joong Shin Nonalcoholic fatty liver disease and early prediction of gestational diabetes mellitus using machine learning methods |
title | Nonalcoholic fatty liver disease and early prediction of gestational diabetes mellitus using machine learning methods |
title_full | Nonalcoholic fatty liver disease and early prediction of gestational diabetes mellitus using machine learning methods |
title_fullStr | Nonalcoholic fatty liver disease and early prediction of gestational diabetes mellitus using machine learning methods |
title_full_unstemmed | Nonalcoholic fatty liver disease and early prediction of gestational diabetes mellitus using machine learning methods |
title_short | Nonalcoholic fatty liver disease and early prediction of gestational diabetes mellitus using machine learning methods |
title_sort | nonalcoholic fatty liver disease and early prediction of gestational diabetes mellitus using machine learning methods |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8755469/ https://www.ncbi.nlm.nih.gov/pubmed/34649307 http://dx.doi.org/10.3350/cmh.2021.0174 |
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