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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,김원,Sae Kyung Joo,Sue Shin,Chan-Wook Park,Taesung 대한간학회 2022 Clinical and Molecular Hepatology(대한간학회지) Vol.28 No.1
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)
Kim, Sun Min,Park, Joong Shin,Norwitz, Errol R.,Lee, Seung Mi,Kim, Byoung Jae,Park, Chan-Wook,Jun, Jong Kwan,Kim, Chul-Woo,Syn, Hee Chul Springer-Verlag 2012 REPRODUCTIVE SCIENCES Vol.19 No.2
<P>This study is designed to identify proteomic biomarkers that predict the subsequent development of gestational diabetes mellitus (GDM).</P>
김선민,Byoung-Kyu Cho,Byoung Jae Kim,Ha Yun Lee,Errol R. Norwitz,Min Jueng Kang,Seung Mi Lee,박찬욱,Jong Kwan Jun,Eugene C. Yi,박중신 대한의학회 2020 Journal of Korean medical science Vol.35 No.10
Background: Twin-to-twin transfusion syndrome (TTTS) is a serious complication of monochorionic twin pregnancies. It results from disproportionate blood supply to each fetus caused by abnormal vascular anastomosis within the placenta. Amniotic fluid (AF) is an indicator reflecting the various conditions of the fetus, and an imbalance in AF volume is essential for the antenatal diagnosis of TTTS by ultrasound. In this study, two different mass spectrometry quantitative approaches were performed to identify differentially expressed proteins (DEPs) within matched pairs of AF samples. Methods: We characterized the AF proteome in pooled AF samples collected from donor and recipient twin pairs (n = 5 each) with TTTS by a global proteomics profiling approach and then preformed the statistical analysis to determine the DEPs between the two groups. Next, we carried out a targeted proteomic approach (multiple reaction monitoring) with DEPs to achieve high-confident TTTS-associated AF proteins. Results: A total of 103 AF proteins that were significantly altered in their abundances between donor and recipient fetuses. The majority of upregulated proteins identified in the recipient twins (including carbonic anhydrase 1, fibrinogen alpha chain, aminopeptidase N, alpha-fetoprotein, fibrinogen gamma chain, and basement membrane-specific heparan sulfate proteoglycan core protein) have been associated with cardiac or dermatologic disease, which is often seen in recipient twins as a result of volume overload. In contrast, proteins significantly upregulated in AF collected from donor twins (including IgGFc-binding protein, apolipoprotein C-I, complement C1q subcomponent subunit B, apolipoprotein C-III, apolipoprotein A-II, decorin, alpha-2-macroglobulin, apolipoprotein A-I, and fibronectin) were those previously shown to be associated with inflammation, ischemic cardiovascular complications or renal disease. Conclusion: In this study, we identified proteomic biomarkers in AF collected from donor and recipient twins in pregnancies complicated by TTTS that appear to reflect underlying functional and pathophysiological challenges faced by each of the fetuses.
홍수빈,이승미,곽수헌,김병재,구자남,오익환,오소희,김선민,신수,김원,주세경,Norwitz Errol R.,Louangsenlath Souphaphone,박찬욱,전종관,박중신 대한당뇨병학회 2020 Diabetes and Metabolism Journal Vol.44 No.5
Background: The definition of the high-risk group for gestational diabetes mellitus (GDM) defined by the American College of Obstetricians and Gynecologists was changed from the criteria composed of five historic/demographic factors (old criteria) to the criteria consisting of 11 factors (new criteria) in 2017. To compare the predictive performances between these two sets of criteria. Methods: This is a secondary analysis of a large prospective cohort study of non-diabetic Korean women with singleton pregnancies designed to examine the risk of GDM in women with nonalcoholic fatty liver disease. Maternal fasting blood was taken at 10 to 14 weeks of gestation and measured for glucose and lipid parameters. GDM was diagnosed by the two-step approach. Results: Among 820 women, 42 (5.1%) were diagnosed with GDM. Using the old criteria, 29.8% (n=244) of women would have been identified as high risk versus 16.0% (n=131) using the new criteria. Of the 42 women who developed GDM, 45.2% (n=19) would have been mislabeled as not high risk by the old criteria versus 50.0% (n=21) using the new criteria (1-sensitivity, 45.2% vs. 50.0%, P>0.05). Among the 778 patients who did not develop GDM, 28.4% (n=221) would have been identified as high risk using the old criteria versus 14.1% (n=110) using the new criteria (1-specificity, 28.4% vs. 14.1%, P<0.001). Conclusion: Compared with the old criteria, use of the new criteria would have decreased the number of patients identified as high risk and thus requiring early GDM screening by half (from 244 [29.8%] to 131 [16.0%]).