Predicting the Risk of Insulin-Requiring Gestational Diabetes before Pregnancy: A Model Generated from a Nationwide Population-Based Cohort Study in Korea
Background The severity of gestational diabetes mellitus (GDM) is associated with adverse pregnancy outcomes. We aimed to generate a risk model for predicting insulin-requiring GDM before pregnancy in Korean women. Methods A total of 417,210 women who received a health examination within 52 weeks be...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Korean Endocrine Society
2023-02-01
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| Series: | Endocrinology and Metabolism |
| Subjects: | |
| Online Access: | http://www.e-enm.org/upload/pdf/enm-2022-1609.pdf |
| _version_ | 1811158248560001024 |
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| author | Seung-Hwan Lee Jin Yu Kyungdo Han Seung Woo Lee Sang Youn You Hun-Sung Kim Jae-Hyoung Cho Kun-Ho Yoon Mee Kyoung Kim |
| author_facet | Seung-Hwan Lee Jin Yu Kyungdo Han Seung Woo Lee Sang Youn You Hun-Sung Kim Jae-Hyoung Cho Kun-Ho Yoon Mee Kyoung Kim |
| author_sort | Seung-Hwan Lee |
| collection | DOAJ |
| description | Background The severity of gestational diabetes mellitus (GDM) is associated with adverse pregnancy outcomes. We aimed to generate a risk model for predicting insulin-requiring GDM before pregnancy in Korean women. Methods A total of 417,210 women who received a health examination within 52 weeks before pregnancy and delivered between 2011 and 2015 were recruited from the Korean National Health Insurance database. The risk prediction model was created using a sample of 70% of the participants, while the remaining 30% were used for internal validation. Risk scores were assigned based on the hazard ratios for each risk factor in the multivariable Cox proportional hazards regression model. Six risk variables were selected, and a risk nomogram was created to estimate the risk of insulin-requiring GDM. Results A total of 2,891 (0.69%) women developed insulin-requiring GDM. Age, body mass index (BMI), current smoking, fasting blood glucose (FBG), total cholesterol, and γ-glutamyl transferase were significant risk factors for insulin-requiring GDM and were incorporated into the risk model. Among the variables, old age, high BMI, and high FBG level were the main contributors to an increased risk of insulin-requiring GDM. The concordance index of the risk model for predicting insulin-requiring GDM was 0.783 (95% confidence interval, 0.766 to 0.799). The validation cohort’s incidence rates for insulin-requiring GDM were consistent with the risk model’s predictions. Conclusion A novel risk engine was generated to predict insulin-requiring GDM among Korean women. This model may provide helpful information for identifying high-risk women and enhancing prepregnancy care. |
| first_indexed | 2024-04-10T05:20:09Z |
| format | Article |
| id | doaj.art-548defe7064744c0a621d39e664e60f5 |
| institution | Directory Open Access Journal |
| issn | 2093-596X 2093-5978 |
| language | English |
| last_indexed | 2024-04-10T05:20:09Z |
| publishDate | 2023-02-01 |
| publisher | Korean Endocrine Society |
| record_format | Article |
| series | Endocrinology and Metabolism |
| spelling | doaj.art-548defe7064744c0a621d39e664e60f52023-03-08T07:43:20ZengKorean Endocrine SocietyEndocrinology and Metabolism2093-596X2093-59782023-02-0138112913810.3803/EnM.2022.16092364Predicting the Risk of Insulin-Requiring Gestational Diabetes before Pregnancy: A Model Generated from a Nationwide Population-Based Cohort Study in KoreaSeung-Hwan Lee0Jin Yu1Kyungdo Han2Seung Woo Lee3Sang Youn You4Hun-Sung Kim5Jae-Hyoung Cho6Kun-Ho Yoon7Mee Kyoung Kim8 Division of Endocrinology and Metabolism, Department of Internal Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea Division of Endocrinology and Metabolism, Department of Internal Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea Department of Statistics and Actuarial Science, Soongsil University, Seoul, Korea Department of Medical Statistics, College of Medicine, The Catholic University of Korea, Seoul, Korea College of Medicine, The Catholic University of Korea, Seoul, Korea Division of Endocrinology and Metabolism, Department of Internal Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea Division of Endocrinology and Metabolism, Department of Internal Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea Division of Endocrinology and Metabolism, Department of Internal Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea Division of Endocrinology and Metabolism, Department of Internal Medicine, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, KoreaBackground The severity of gestational diabetes mellitus (GDM) is associated with adverse pregnancy outcomes. We aimed to generate a risk model for predicting insulin-requiring GDM before pregnancy in Korean women. Methods A total of 417,210 women who received a health examination within 52 weeks before pregnancy and delivered between 2011 and 2015 were recruited from the Korean National Health Insurance database. The risk prediction model was created using a sample of 70% of the participants, while the remaining 30% were used for internal validation. Risk scores were assigned based on the hazard ratios for each risk factor in the multivariable Cox proportional hazards regression model. Six risk variables were selected, and a risk nomogram was created to estimate the risk of insulin-requiring GDM. Results A total of 2,891 (0.69%) women developed insulin-requiring GDM. Age, body mass index (BMI), current smoking, fasting blood glucose (FBG), total cholesterol, and γ-glutamyl transferase were significant risk factors for insulin-requiring GDM and were incorporated into the risk model. Among the variables, old age, high BMI, and high FBG level were the main contributors to an increased risk of insulin-requiring GDM. The concordance index of the risk model for predicting insulin-requiring GDM was 0.783 (95% confidence interval, 0.766 to 0.799). The validation cohort’s incidence rates for insulin-requiring GDM were consistent with the risk model’s predictions. Conclusion A novel risk engine was generated to predict insulin-requiring GDM among Korean women. This model may provide helpful information for identifying high-risk women and enhancing prepregnancy care.http://www.e-enm.org/upload/pdf/enm-2022-1609.pdfdiabetes, gestationalinsulinnomogramsrisk |
| spellingShingle | Seung-Hwan Lee Jin Yu Kyungdo Han Seung Woo Lee Sang Youn You Hun-Sung Kim Jae-Hyoung Cho Kun-Ho Yoon Mee Kyoung Kim Predicting the Risk of Insulin-Requiring Gestational Diabetes before Pregnancy: A Model Generated from a Nationwide Population-Based Cohort Study in Korea Endocrinology and Metabolism diabetes, gestational insulin nomograms risk |
| title | Predicting the Risk of Insulin-Requiring Gestational Diabetes before Pregnancy: A Model Generated from a Nationwide Population-Based Cohort Study in Korea |
| title_full | Predicting the Risk of Insulin-Requiring Gestational Diabetes before Pregnancy: A Model Generated from a Nationwide Population-Based Cohort Study in Korea |
| title_fullStr | Predicting the Risk of Insulin-Requiring Gestational Diabetes before Pregnancy: A Model Generated from a Nationwide Population-Based Cohort Study in Korea |
| title_full_unstemmed | Predicting the Risk of Insulin-Requiring Gestational Diabetes before Pregnancy: A Model Generated from a Nationwide Population-Based Cohort Study in Korea |
| title_short | Predicting the Risk of Insulin-Requiring Gestational Diabetes before Pregnancy: A Model Generated from a Nationwide Population-Based Cohort Study in Korea |
| title_sort | predicting the risk of insulin requiring gestational diabetes before pregnancy a model generated from a nationwide population based cohort study in korea |
| topic | diabetes, gestational insulin nomograms risk |
| url | http://www.e-enm.org/upload/pdf/enm-2022-1609.pdf |
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