Clinical and Molecular Hepatology

"Sun Min Kim"

Original Articles

Steatotic liver disease

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; Clin Mol Hepatol 2022;28(1):105-116.
Published online October 15, 2021; DOI: https://doi.org/10.3350/cmh.2021.0174

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 NAFLDassociated variables significantly improved the performance of GDM prediction. (ClinicalTrials.gov Identifier: NCT02276144)

Citations

Citations to this article as recorded by

Artificial intelligence methods in gestational diabetes mellitus prediction: A systematic literature review
Valentina Ivanovic, Md Abu Jafar Sujan, Ole Jakob Mengshoel, Trine Moholdt
International Journal of Medical Informatics.2026; 206: 106158. CrossRef
What role does metabolic disfunction-associated fatty liver disease play in the metabolic landscape of pregnancy?
Annunziata Lapolla, Maria Grazia Dalfrà
Expert Review of Endocrinology & Metabolism.2026; 21(1): 1. CrossRef
Predictive Performance of Artificial Intelligence Algorithms for Gestational Diabetes Mellitus in Pregnant Women: Systematic Review and Meta-Analysis
Yingni Liang, Anran Dai, Meiyan Luo, Zhuolian Zheng, Jiayu Shen, Yinhua Su, Zhongyu Li
Journal of Medical Internet Research.2026; 28: e79729. CrossRef
Metabolic factor-based machine learning model for mortality prediction in acute hepatitis E: Development and validation from a dual-center cohort
Haoshuang Fu, Shuying Song, Yuelin Xiao, Bingying Du, Gangde Zhao, Tianhui Zhou, Yanan Du
Digestive and Liver Disease.2026; 58(5): 660. CrossRef
Sucralose Exposure During Pregnancy Elevates Gestational Diabetes Risk via Gut Microbiota‐Metabolic Axis in Mice
Jiajia Song, Juhui He, Zhaoxia Liang, Hannah Wesley
Journal of Diabetes Research.2026;[Epub] CrossRef
Establishment of a predictive model for spontaneous preterm birth in primiparas with grade A1 gestational diabetes mellitus
Ting Sun, Yangyang Zhang, Chunzhi Xie, Anyi Teng, Shi Lin, Hui Zhang, Yan Li
Frontiers in Global Women's Health.2025;[Epub] CrossRef
Machine learning based model for the early detection of Gestational Diabetes Mellitus
Hesham Zaky, Eleni Fthenou, Luma Srour, Thomas Farrell, Mohammed Bashir, Nady El Hajj, Tanvir Alam
BMC Medical Informatics and Decision Making.2025;[Epub] CrossRef
Metabolomic profiling reveals early biomarkers of gestational diabetes mellitus and associated hepatic steatosis
Youngae Jung, Seung Mi Lee, Jinhaeng Lee, Yeonjin Kim, Woojoo Lee, Ja Nam Koo, Ig Hwan Oh, Kue Hyun Kang, Byoung Jae Kim, Sun Min Kim, Jeesun Lee, Ji Hoi Kim, Yejin Bae, Sang Youn Kim, Gyoung Min Kim, Sae Kyung Joo, Dong Hyeon Lee, Joon Ho Moon, Bo Kyung
Cardiovascular Diabetology.2025;[Epub] CrossRef
GDM-BC: Non-invasive body composition dataset for intelligent prediction of Gestational Diabetes Mellitus
Chen Zheng, Tong Qing, Mao Li, Shujuan Liao, Biru Luo, Chenwei Tang, Jiancheng Lv
Computers in Biology and Medicine.2025; 192: 110176. CrossRef
Metabolic dysfunction–associated steatotic liver disease and pregnancy
Monika Sarkar, Tatyana Kushner
Journal of Clinical Investigation.2025;[Epub] CrossRef
Maternal liver fibrosis indices as predictors of adverse perinatal outcomes in patients with gestational diabetes mellitus
Murad Gezer, Ümit Taşdemir, Ömer Gökhan Eyisoy, Sevdenur Yiğit, Mucize Eriç Özdemir, Oya Demirci
Acta Diabetologica.2025; 62(12): 2055. CrossRef
Associations of hepatic steatosis index in early pregnancy with perinatal outcomes: A prospective birth cohort study
Shaofei Su, Enjie Zhang, Shen Gao, Yue Zhang, Jianhui Liu, Shuanghua Xie, Jinghan Yu, Qiutong Zhao, Wentao Yue, Ruixia Liu, Chenghong Yin
Clinical Medicine.2025; 25(4): 100343. CrossRef
Artificial Intelligence in Gestational Diabetes Care: A Systematic Review
Rawan AlSaad, Ali Elhenidy, Aliya Tabassum, Nour Odeh, Eman AboArqoub, Aya Odeh, Maya AlTamimi, Alaa Abd-alrazaq, Rajat Thomas, Mohammed Bashir, Javaid Sheikh
Journal of Diabetes Science and Technology.2025;[Epub] CrossRef
Sex and gender differences in MASLD: pathophysiological mechanisms, clinical implications, and future directions
Mohamad Jamalinia, Samira Saeian, Nima Nikkhoo, Amirhossein Nazerian, Kamran Bagheri Lankarani
Metabolism and Target Organ Damage.2025;[Epub] CrossRef
Evaluating the performance of maternal risk factors in predicting gestational diabetes mellitus: a systematic review and meta-analysis
Alemu Degu Ayele, Getnet Gedefaw Azeze, Beklau Kassie Alemu, Yao Wang, Chi Chiu Wang
BMJ Evidence-Based Medicine.2025; : bmjebm-2025-114065. CrossRef
Adverse pregnancy outcomes as a risk factor for new-onset metabolic dysfunction-associated steatotic liver disease in postpartum women: A nationwide study
Young Mi Jung, Seung Mi Lee, Wonyoung Wi, Min-Jeong Oh, Joong Shin Park, Geum Joon Cho, Won Kim
JHEP Reports.2024; 6(4): 101033. CrossRef
The early prediction of gestational diabetes mellitus by machine learning models
Yeliz Kaya, Zafer Bütün, Özer Çelik, Ece Akça Salik, Tuğba Tahta, Arzu Altun Yavuz
BMC Pregnancy and Childbirth.2024;[Epub] CrossRef
Could Machine Learning-Based Gestational Diabetes Mellitus Prediction Models Replace Traditional Screening Test?
Jong Yun Hwang
Journal of Korean Maternal and Child Health.2024; 28(4): 153. CrossRef
The Role of Adiponectin during Pregnancy and Gestational Diabetes
Brittany L. Moyce Gruber, Vernon W. Dolinsky
Life.2023; 13(2): 301. CrossRef
Liver biomarkers, lipid metabolites, and risk of gestational diabetes mellitus in a prospective study among Chinese pregnant women
Ping Wu, Yi Wang, Yi Ye, Xue Yang, Yichao Huang, Yixiang Ye, Yuwei Lai, Jing Ouyang, Linjing Wu, Jianguo Xu, Jiaying Yuan, Yayi Hu, Yi-Xin Wang, Gang Liu, Da Chen, An Pan, Xiong-Fei Pan
BMC Medicine.2023;[Epub] CrossRef
Performance Analysis and Assessment of Type 2 Diabetes Screening Scores in Patients with Non-Alcoholic Fatty Liver Disease
Norma Latif Fitriyani, Muhammad Syafrudin, Siti Maghfirotul Ulyah, Ganjar Alfian, Syifa Latif Qolbiyani, Chuan-Kai Yang, Jongtae Rhee, Muhammad Anshari
Mathematics.2023; 11(10): 2266. CrossRef
Synergistic effect of non-alcoholic fatty liver disease and history of gestational diabetes to increase risk of type 2 diabetes
Yoosun Cho, Yoosoo Chang, Seungho Ryu, Sarah H. Wild, Christopher D. Byrne
European Journal of Epidemiology.2023; 38(8): 901. CrossRef
Identification of influence factors in overweight population through an interpretable risk model based on machine learning: a large retrospective cohort
Wei Lin, Songchang Shi, Huiyu Lan, Nengying Wang, Huibin Huang, Junping Wen, Gang Chen
Endocrine.2023; 83(3): 604. CrossRef
Nonalcoholic fatty liver disease-based risk prediction of adverse pregnancy outcomes: Ready for prime time?
Seung Mi Lee, Won Kim
Clinical and Molecular Hepatology.2022; 28(1): 47. CrossRef

12,692 View
272 Download
24 Web of Science
Crossref

Viral hepatitis

Changes in the seroprevalence of IgG anti-hepatitis A virus between 2001 and 2013: experience at a single center in Korea: Sung Jun Chung, Tae Yeob Kim, Sun Min Kim, Min Roh, Mi Yeon Yu, Jung Hoon Lee, ChangKyo Oh, Eun Young Lee, Seung Lee, Yong Cheol Jeon, Kyo-Sang Yoo, Joo Hyun Sohn; Clin Mol Hepatol 2014;20(2):162-167.
Published online June 30, 2014; DOI: https://doi.org/10.3350/cmh.2014.20.2.162

Abstract
PDF

Background/Aims

The incidence of symptomatic hepatitis A reportedly increased among 20- to 40-year-old Korean during the late 2000s. Vaccination against hepatitis A was commenced in the late 1990s and was extended to children aged <10 years. In the present study we analyzed the changes in the seroprevalence of IgG anti-hepatitis A virus (HAV) over the past 13 years.

Methods

Overall, 4903 subjects who visited our hospital between January 2001 and December 2013 were studied. The seroprevalence of IgG anti-HAV was analyzed according to age and sex. In addition, the seroprevalence of IgG anti-HAV was compared among 12 age groups and among the following time periods: early 2000s (2001-2003), mid-to-late 2000s (2006-2008), and early 2010s (2011-2013). The chi-square test for trend was used for statistical analysis.

Results

The seroprevalence of IgG anti-HAV did not differ significantly between the sexes. Furthermore, compared to the seroprevalence of IgG anti-HAV in the early 2000s and mid-to-late 2000s, that in the early 2010s was markedly increased among individuals aged 1-14 years and decreased among those aged 25-44 years (P<0.01). We also found that the seroprevalence of IgG anti-HAV in individuals aged 25-44 years in the early 2010s was lower than that in the early 2000s and mid-to-late 2000s.

Conclusions

The number of symptomatic HAV infection cases in Korea is decreasing, but the seroprevalence of IgG anti-HAV is low in the active population.

Citations

Citations to this article as recorded by

The chronological changes in the seroprevalence of anti-hepatitis A virus IgG from 2005 to 2019: Experience at four centers in the capital area of South Korea
Dae Hyun Lim, Won Sohn, Jae Yoon Jeong, Hyunwoo Oh, Jae Gon Lee, Eileen L. Yoon, Tae Yeob Kim, Seungwoo Nam, Joo Hyun Sohn
Medicine.2022; 101(48): e31639. CrossRef
Seropositive rate of the anti-hepatitis A immunoglobulin G antibody in maintenance hemodialysis subjects from two hospitals in Korea
Hyunsuk Kim, Jiwon Ryu, Young-Ki Lee, Myung Jin Choi, Ajin Cho, Ja-Ryong Koo, Sae Yun Baik, Eun Hee Lee, Jong-Woo Yoon, Jung-Woo Noh
The Korean Journal of Internal Medicine.2019; 34(6): 1297. CrossRef
Seroprevalence of Hepatitis A Virus among Healthy Individuals in Birjand, Eastern Region of Iran
Neda Mahavar, Mohammad Fereidouni, Masood Ziaee
Hepatitis Monthly.2018;[Epub] CrossRef
Seroepidemiology of Hepatitis Viruses and Hepatitis B Genotypes of Female Marriage Immigrants in Korea
Jae-Cheol Kwon, Hye Young Chang, Oh Young Kwon, Ji Hoon Park, In Soo Oh, Hyung Joon Kim, Jun Hyung Lee, Ha-Jung Roh, Hyun Woong Lee
Yonsei Medical Journal.2018; 59(9): 1072. CrossRef
Nationwide Seropositivity of Hepatitis A in Republic of Korea from 2005 to 2014, before and after the Outbreak Peak in 2009
Kyung-Ah Kim, Anna Lee, Moran Ki, Sook-Hyang Jeong, Yury E. Khudyakov
PLOS ONE.2017; 12(1): e0170432. CrossRef
Hepatitis A Outbreak Among Men Who Have Sex With Men in a Country of Low Endemicity of Hepatitis A Infection
Guan-Jhou Chen, Kuan-Yin Lin, Chien-Ching Hung, Shan-Chwen Chang
The Journal of Infectious Diseases.2017; 215(8): 1339. CrossRef
CXCL10 is produced in hepatitis A virus-infected cells in an IRF3-dependent but IFN-independent manner
Pil Soo Sung, Seon-Hui Hong, Jeewon Lee, Su-Hyung Park, Seung Kew Yoon, Woo Jin Chung, Eui-Cheol Shin
Scientific Reports.2017;[Epub] CrossRef
Current status and strategies for the control of viral hepatitis A in Korea
Eileen L. Yoon, Dong Hyun Sinn, Hyun Woong Lee, Ji Hoon Kim
Clinical and Molecular Hepatology.2017; 23(3): 196. CrossRef
Seroprevalence and disease burden of acute hepatitis A in adult population in South Korea
Jin Gu Yoon, Min Joo Choi, Jae Won Yoon, Ji Yun Noh, Joon Young Song, Hee Jin Cheong, Woo Joo Kim, Sheng-Nan Lu
PLOS ONE.2017; 12(10): e0186257. CrossRef
Hepatitis A in Korea from 2011 to 2013: Current Epidemiologic Status and Regional Distribution
Shinje Moon, Jun Hee Han, Geun-Ryang Bae, Enhi Cho, Bongyoung Kim
Journal of Korean Medical Science.2016; 31(1): 67. CrossRef
Comparative Analysis of Liver Injury-Associated Cytokines in Acute Hepatitis A and B
So Youn Shin, Sook-Hyang Jeong, Pil Soo Sung, Jino Lee, Hyung Joon Kim, Hyun Woong Lee, Eui-Cheol Shin
Yonsei Medical Journal.2016; 57(3): 652. CrossRef
Travel Pattern and Prescription Analysis at a Single Travel Clinic Specialized for Yellow Fever Vaccination in South Korea
Bum Sik Chin, Jae Yoon Kim, Sara Gianella, Myunghee Lee
Infection & Chemotherapy.2016; 48(1): 20. CrossRef
Age-period-cohort analysis of hepatitis A incidence rates in Korea from 2002 to 2012
Joo Yeon Seo, Sungyong Choi, BoYoul Choi, Moran Ki
Epidemiology and Health.2016; 38: e2016040. CrossRef
Seroprevalence of Hepatitis A and B Virus Antibody of Employees among Three Companies with Different Health Policy
Hyun Min Koh, Jun Seok Son
Journal of Korean Society of Occupational and Environmental Hygiene.2015; 25(2): 229. CrossRef