Nonalcoholic fatty liver disease and early prediction of gestational diabetes mellitus using machine learning methods

Seung Mi Lee, Suhyun Hwangbo, Errol R Norwitz, Ja Nam Koo, Ig Hwan Oh, Eun Saem Choi, Young Mi Jung, Sun Min Kim, Byoung Jae Kim, Sang Youn Kim, Gyoung Min Kim, Won Kim, Sae Kyung Joo, Sue Shin, Chan-Wook Park, Taesung Park, Joong Shin Park, Seung Mi Lee, Suhyun Hwangbo, Errol R Norwitz, Ja Nam Koo, Ig Hwan Oh, Eun Saem Choi, Young Mi Jung, Sun Min Kim, Byoung Jae Kim, Sang Youn Kim, Gyoung Min Kim, Won Kim, Sae Kyung Joo, Sue Shin, Chan-Wook Park, Taesung Park, Joong Shin Park

Abstract

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).

Conclusion: We developed an early prediction model for GDM using machine learning. The inclusion of NAFLDassociated variables significantly improved the performance of GDM prediction. (ClinicalTrials.gov Identifier: NCT02276144).

Keywords: Diabetes, Gestational; Machine learning; Nonalcoholic fatty liver disease; Prediction; Pregnancy, High-risk.

Conflict of interest statement

Conflicts of Interest

The authors have no conflicts to disclose.

Figures

Figure 1.
Figure 1.
Workflow of the study.
Figure 2.
Figure 2.
Receiver operating characteristic curves of the best prediction model for gestational diabetes in settings 1–5. Setting 1, conventional risk factors using older ACOG criteria. Setting 2, addition of new ACOG risk factors to setting 1. Setting 3, addition of routine clinical variables to setting 2. Setting 4, addition of variables associated with NAFLD to setting 3. Setting 5, top 11 variables. High risk 1, old criteria (from the 4th international workshop) had a sensitivity of 59.3% and specificity of 71.5% for GDM. High risk 2, new criteria (from the ADA) had a sensitivity of 41.9% and specificity of 85.9% for GDM. ACOG, American College of Obstetricians and Gynecologists; NAFLD, nonalcoholic fatty liver disease; GDM, gestational diabetes mellitus; ADA, American Diabetes Association.
Figure 3.
Figure 3.
Variable importance of the top 11 selected variables in support vector machine model. TG, triglycerides; HDL, high-density lipoprotein; ALT, alanine aminotransferase; NAFLD, nonalcoholic fatty liver disease; PCOS, polycystic ovarian syndrome; GDM, gestational diabetes; AUC, area under the receiver operating characteristic curve.
https://www.ncbi.nlm.nih.gov/pmc/articles/instance/8755469/bin/cmh-2021-0174f4.jpg

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Source: PubMed

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