Article
Obstetrics & Gynecology
Yiling Wei, Andong He, Chaoping Tang, Haixia Liu, Ling Li, Xiaofeng Yang, Xiufang Wang, Fei Shen, Jia Liu, Jing Li, Ruiman Li
Summary: This study aimed to build and evaluate models for predicting gestational diabetes mellitus (GDM) using routine indexes. The results showed that the models had good discrimination and calibration in both the training and validation cohorts, indicating their potential use in predicting the risk of GDM in early pregnancy.
BMC PREGNANCY AND CHILDBIRTH
(2022)
Article
Endocrinology & Metabolism
Z. -R Niu, L. -W Bai, Q. Lu
Summary: This study evaluated the risk factors for gestational diabetes mellitus (GDM) and developed an early risk prediction model for GDM by comparing indicators in the first trimester of pregnancy between women with GDM and non-GDM. The model showed satisfactory predictive efficacy in both the modeling and validation cohorts.
JOURNAL OF ENDOCRINOLOGICAL INVESTIGATION
(2023)
Review
Endocrinology & Metabolism
Yumeng Tian, Ping Li
Summary: This article reviews the research progress in the use of genetic risk score (GRS) in diabetes mellitus in recent years and discusses future prospects.
FRONTIERS IN ENDOCRINOLOGY
(2022)
Article
Health Care Sciences & Services
Zheqing Zhang, Luqian Yang, Wentao Han, Yaoyu Wu, Linhui Zhang, Chun Gao, Kui Jiang, Yun Liu, Huiqun Wu
Summary: This study conducted a meta-analysis and comparison of published prognostic models for predicting the risk of gestational diabetes mellitus (GDM) and identified several predictors applicable to these models. The results showed that machine learning models are more attractive than current screening strategies for predicting GDM, but the importance of quality assessments and unified diagnostic criteria should be further emphasized.
JOURNAL OF MEDICAL INTERNET RESEARCH
(2022)
Article
Obstetrics & Gynecology
Yingdi Yuan, Qingyi Zhu, Xiaodie Yao, Zhonghua Shi, Juan Wen
Summary: This study identified differential metabolic biomarkers between women with gestational diabetes mellitus (GDM) and macrosomia (GDM-M) and those with GDM and normal neonatal weight (GDM-N). Using LASSO regression and Logistic regression, a predictive model was constructed with nine predictors that displayed good performance in predicting GDM-M risk. This model is of significant importance for predicting the risk of macrosomia in women with GDM.
BMC PREGNANCY AND CHILDBIRTH
(2023)
Article
Endocrinology & Metabolism
Melissa Razo-Azamar, Rafael Nambo-Venegas, Noemi Meraz-Cruz, Martha Guevara-Cruz, Isabel Ibarra-Gonzalez, Marcela Vela-Amieva, Jaime Delgadillo-Velazquez, Xanic Caraza Santiago, Rafael Figueroa Escobar, Felipe Vadillo-Ortega, Berenice Palacios-Gonzalez
Summary: This study aimed to develop an early prediction model to identify women who will later be diagnosed with gestational diabetes mellitus (GDM) during pregnancy. Using targeted metabolomics, two short-chain acylcarnitines were identified as predictive biomarkers for GDM. The model showed a high classification performance and successfully identified all GDM cases.
DIABETOLOGY & METABOLIC SYNDROME
(2023)
Article
Endocrinology & Metabolism
Yingting Wu, Siyu Ma, Yin Wang, Fangfang Chen, Feilong Zhu, Wenqin Sun, Weiwei Shen, Jun Zhang, Huifen Chen
Summary: The study developed a GDM risk stratification prediction model in Chinese pregnant women using machine learning algorithm, showing reliable ability to predict GDM risk of population and the ability to screen for GDM before 16 gestational weeks. The model included 15 parameters and had an AUC of 0.746.
DIABETES RESEARCH AND CLINICAL PRACTICE
(2021)
Article
Health Care Sciences & Services
Shamil D. Cooray, Kushan De Silva, Joanne C. Enticott, Shrinkhala Dawadi, Jacqueline A. Boyle, Georgia Soldatos, Eldho Paul, Vincent L. Versace, Helena J. Teede
Summary: This study temporally validated and updated the Monash gestational diabetes risk prediction model, improving its performance and generalizability. The updated model C2 demonstrated better performance and highlighted the value and versatility of prediction models for guiding risk-stratified diabetes care.
JOURNAL OF CLINICAL EPIDEMIOLOGY
(2023)
Article
Multidisciplinary Sciences
Bernice Man, Alan Schwartz, Oksana Pugach, Yinglin Xia, Ben Gerber
Summary: A clinical prediction model was developed for personalized treatment decisions for prediabetes in women with a history of gestational diabetes mellitus. The study found that higher levels of fasting glucose and hemoglobin A1C were associated with an increased risk of developing diabetes, and metformin was more effective for individuals with higher BMI.
Article
Obstetrics & Gynecology
Peilin Ouyang, Siqi Duan, Yiping You, Xiaozhou Jia, Liqin Yang
Summary: This retrospective cohort study found that Hemoglobin A1c, age, total cholesterol, low-density lipoprotein cholesterol, systolic blood pressure, family history, body mass index, and testosterone were predictive factors of gestational diabetes mellitus in women with polycystic ovary syndrome.
BMC PREGNANCY AND CHILDBIRTH
(2023)
Review
Medicine, General & Internal
Wenqian Lu, Cheng Hu
Summary: This review summarizes recent studies on biomolecules associated with GDM and postpartum diabetes, focusing on their predictive value in screening and diagnosis.
CHINESE MEDICAL JOURNAL
(2022)
Article
Endocrinology & Metabolism
Xiaoqi Hu, Xiaolin Hu, Ya Yu, Jia Wang
Summary: The XG Boost machine learning model was developed for predicting gestational diabetes mellitus (GDM) and showed better predictive ability than the traditional logistic regression model. Both models had good calibration performance.
FRONTIERS IN ENDOCRINOLOGY
(2023)
Article
Medical Laboratory Technology
Yuqi Wang, Ling Li, Ping Li
Summary: The association between gestational diabetes mellitus (GDM) and single nucleotide polymorphisms (SNPs) has been widely studied. SNPs can provide insights into the pathogenesis of GDM, help predict the risk of GDM, and guide the management of GDM patients. This review focuses on recent studies investigating the association between SNPs and GDM, identifying several SNPs that have been associated with GDM. However, further research is needed to explore the role of SNPs in the prediction, diagnosis, treatment, and prognosis of GDM in diverse ethnic populations.
CLINICA CHIMICA ACTA
(2023)
Article
Environmental Sciences
Zhigang Li, Rongrong Xu, Zhanshan Wang, Nianfeng Qian, Yan Qian, Jianhao Peng, Xiaojing Zhu, Chen Guo, Xiaoqian Li, Qiujin Xu, Yongjie Wei
Summary: This study found that ozone exposure, especially in the first month before pregnancy, is associated with an increased risk of gestational diabetes mellitus (GDM). The odds of GDM also increase with increasing ozone concentration. These findings highlight the importance of stricter air pollution controls to improve the health of pregnant women and their offspring.
Review
Biochemistry & Molecular Biology
Monika Ruszala, Magdalena Niebrzydowska, Aleksandra Pilszyk, Zaneta Kimber-Trojnar, Marcin Trojnar, Bozena Leszczynska-Gorzelak
Summary: Gestational diabetes mellitus (GDM) is a common metabolic disease in pregnant women, early diagnosis is crucial for the health of both mother and fetus. Identifying biopredictors at the start of pregnancy is key, with potential use of less known biomolecules. High FABP4 levels, low irisin levels, and high under-carboxylated osteocalcin levels may be predictive markers for GDM diagnosis.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2021)