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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Korean Association for the Study of the Liver 2022
Materias:
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)
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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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