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

## TL;DR

This study identifies pre-BMI and fasting glucose as causal factors for gestational diabetes and develops a predictive model for early risk assessment in Chinese women.

## Contribution

A novel nomogram model combining clinical and genetic factors for early GDM risk prediction in the Chinese population.

## Key findings

- The rs6127416 genetic variant is significantly associated with increased GDM risk.
- Mendelian randomization confirms pre-BMI and fasting plasma glucose as causal factors for GDM.
- The prediction model achieves an AUC of 0.808 for early GDM risk assessment.

## Abstract

To evaluate the causal determinants and their risk predictive efficacy of gestational diabetes mellitus (GDM) in Chinese population.

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.

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.

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.