Causal inference and risk prediction of gestational diabetes mellitus based on case-control study and Mendel randomization

Aim: To evaluate the causal determinants and
their risk predictive efficacy of gestational
diabetes mellitus (GDM) in Chinese population. Methods:
Genotyping data for candidate genetic variants were collected from 554
cases of GDM and 641 pregnant women with normal glucose
tolerance. The associations between these variants and GDM risk
were evaluated with the odds ratios (ORs) and their corresponding 95%
confidence intervals (CIs). Multivariate mendelian randomization (MVMR)
was employed to validate the GDM causal factors. Subsequently, a GDM early
risk prediction nomogram model was developed based on the key
clinical and genetic factors identified. Result: After adjusting age and
pre-pregnancy BMI (pre-BMI), the rs6127416 variant showed a significant
association with susceptibility to GDM. Comparing the AA genotype to the
TT genotype, the adjusted odds ratio (OR) was 2.20 (95%CI = 1.53-3.18, P
<0.001), and comparing AA to TT/TA genotypes, the adjusted OR was
2.35 (95%CI = 1.68-3.30, P <0.001). MVMR analysis
confirmed the positive causal effects of pre-BMI and fasting
plasma glucose (FPG) on GDM (pre-BMI-ORMVMR = 1.80, FPG-ORMVMR =
12.37,* P* < 0.001). A nomogram risk predictive
model incorporating pre-BMI, FPG, and rs6127416 demonstrated an area under
the ROC curve of 0.808. Conclusion: Pre-BMI and FPG were determined to be
causal factors linked to GDM. The prediction model constructed
using key clinical and genetic variables (such as
rs6127416-preBMI-FPG) holds promising utility for personalized risk
assessment of GDM in the initial trimester of pregnancy, with
potential to support early identification of high-risk women and
facilitate timely lifestyle or clinical interventions during antenatal
care.