ArticleDiabetology & metabolic syndrome2022
An early prediction model for gestational diabetes mellitus based on genetic variants and clinical characteristics in China.
Article in Diabetology & metabolic syndrome, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 26 citations in OpenAlex.
- Insights into the genetic aetiology of diabetes in Africa.EBioMedicine · 2026Review
- Polygenic risk score as a tool to predict gestational weight gain and gestational diabetes among pregnant women in Taiwan.Journal of human genetics · 2025Article
- Polygenic Risk Score Associated with Gestational Diabetes Mellitus in an AmericanIndian Population.Journal of personalized medicine · 2025Article
- Correlation between serum advanced glycation end-products and their receptor-mediated oxidative stress and perinatal outcomes in gestational diabetes mellitus.World journal of diabetes · 2025Article
- Genetic architecture and risk prediction of gestational diabetes mellitus in Chinese pregnancies.Nature communications · 2025Article
- Article
- Functional genetic variants and susceptibility and prediction of gestational diabetes mellitus.Scientific reports · 2024Article
- Article
- A Simplified Screening Model to Predict the Risk of Gestational Diabetes Mellitus in Pregnant Chinese Women.Diabetes therapy : research, treatment and education of diabetes and related disorders · 2023Article
- Genetics and Epigenetics: Implications for the Life Course of Gestational Diabetes.International journal of molecular sciences · 2023Review
- ABO blood groups are not associated to gestational diabetes mellitus in Mexican women.PloS one · 2023Article
- Polygenic Risk Score and Risk Factors for Gestational Diabetes.Journal of personalized medicine · 2022Article
- Automated Machine Learning (AutoML)-Derived Preconception Predictive Risk Model to Guide Early Intervention for Gestational Diabetes Mellitus.International journal of environmental research and public health · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
Abstract
objectivesTo evaluate the influence of genetic variants and clinical characteristics on the risk of gestational diabetes mellitus (GDM) and to construct and verify a prediction model of GDM in early pregnancy.
methodsFour hundred seventy five women with GDM and 487 women without, as a control, were included to construct the prediction model of GDM in early pregnancy. Both groups had complete genotyping results and clinical data. They were randomly divided into a trial cohort (70%) and a test cohort (30%). Then, the model validation cohort, including 985 pregnant women, was used for the external validation of the GDM early pregnancy prediction model.
resultsWe found maternal age, gravidity, parity, BMI and family history of diabetes were significantly associated with GDM (OR > 1; P < 0.001), and assisted reproduction was a critical risk factor for GDM (OR = 1.553, P = 0.055). MTNR1B rs10830963, C2CD4A/B rs1436953 and rs7172432, CMIP rs16955379 were significantly correlated with the incidence of GDM (AOR > 1, P < 0.05). Therefore, these four genetic susceptible single nucleotide polymorphisms (SNPs) and six clinical characteristics were included in the construction of the GDM early pregnancy prediction model. In the trial cohort, a predictive model of GDM in early pregnancy was constructed, in which genetic risk score was independently associated with GDM (AOR = 2.061, P < 0.001) and was the most effective predictor with the exception of family history of diabetes. The ROC-AUC of the prediction model was 0.727 (95% CI 0.690-0.765), and the sensitivity and specificity were 69.9% and 64.0%, respectively. The predictive power was also verified in the test cohort and the validation cohort.
conclusionsBased on the genetic variants and clinical characteristics, this study developed and verified the early pregnancy prediction model of GDM. This model can help screen out the population at high-risk for GDM in early pregnancy, and lifestyle interventions can be performed for them in a timely manner in early pregnancy.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.