Dataset Information

The association of body composition with the risk of gestational diabetes mellitus in Chinese pregnant women: A case-control study.

ABSTRACT: Studies have found that the measurement of body composition can be used to identify the gestational diabetes mellitus (GDM) risk in pregnant women. However, few studies focused on the relationship between body composition and GDM development in low GDM risk population. Thus, the objective of this study was to examine the association between body composition and the development of GDM in pregnant women with low risk of gestational diabetes.A retrospective case-control study was conducted. We reviewed the medical records of 3965 pregnant women who had body composition measurement from March, 2016 to May, 2018 in our hospital. Their sociodemographic, clinical data, and body composition information were collected from medical record. Multiple logistic regression analyses were used.A total of 2698 subjects were eligible for the study. The mean age of the gravidas was 30.95?±?4.01 years old. Of all gravidas, 462 had gestational diabetes. Percentage body fat was the strongest risk factor for gestational diabetes after adjusting pre-pregnancy body mass index (BMI) (odds ratio?=?1.786, 95% confidence interval?=?1.112-2.866, P?=?.02). The age and extracellular water/intracellular water ratio were independently associated with gestational diabetes.Percentage body fat was the strongest risk factor for gestational diabetes after adjusting pre-pregnancy BMI. Assessment of body composition may provide important guidance to identify gestational diabetes in pregnant women with low gestational diabetes risk.

SUBMITTER: Wang Y

PROVIDER: S-EPMC6824814 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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The association of body composition with the risk of gestational diabetes mellitus in Chinese pregnant women: A case-control study.

Wang Yan Y Luo Bi-Ru BR

Medicine 20191001 42

Studies have found that the measurement of body composition can be used to identify the gestational diabetes mellitus (GDM) risk in pregnant women. However, few studies focused on the relationship between body composition and GDM development in low GDM risk population. Thus, the objective of this study was to examine the association between body composition and the development of GDM in pregnant women with low risk of gestational diabetes.A retrospective case-control study was conducted. We revi ...[more]

PMID: 31626126

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Project description:Gestational diabetes mellitus (GDM) is closely related to adverse pregnancy outcomes and other diseases. Early intervention in pregnant women who are at high risk of developing GDM could help prevent adverse health consequences. The study aims to develop a simple model using the stacking ensemble method to predict GDM for women in the first trimester based on easily available factors. We used the data from the Chinese Pregnant Women Cohort Study from July 2017 to November 2018. A total of 6,848 pregnant women in the first trimester were included in the analysis. Logistic regression (LR), random forest (RF), and extreme gradient boosting (XGBoost) were considered as base learners. Optimal feature subsets for each learner were chosen by using recursive feature elimination cross-validation. Then, we built a pipeline to process imbalance data, tune hyperparameters, and evaluate model performance. The learners with the best hyperparameters were employed in the first layer of the proposed stacking method. Their predictions were obtained using optimal feature subsets and served as meta-learner's inputs. Another LR was used as a meta-learner to obtain the final prediction results. Accuracy, specificity, error rate, and other metrics were calculated to evaluate the performance of the models. A paired samples t-test was performed to compare the model performance. In total, 967 (14.12%) women developed GDM. For base learners, the RF model had the highest accuracy (0.638 (95% confidence interval (CI) 0.628-0.648)) and specificity (0.683 (0.669-0.698)) and lowest error rate (0.362 (0.352-0.372)). The stacking method effectively improved the accuracy (0.666 (95% CI 0.663-0.670)) and specificity (0.725 (0.721-0.729)) and decreased the error rate (0.333 (0.330-0.337)). The differences in the performance between the stacking method and RF were statistically significant. Our proposed stacking method based on easily available factors has better performance than other learners such as RF.

Dataset Information

The association of body composition with the risk of gestational diabetes mellitus in Chinese pregnant women: A case-control study.

Publications

The association of body composition with the risk of gestational diabetes mellitus in Chinese pregnant women: A case-control study.

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