Prediction of Gestational Diabetes Mellitus: A Nomogram Model Incorporating Lifestyle, Nutrition and Health Literacy Factors

<b>Background:</b> Over the past several decades, the prevalence of gestational diabetes mellitus (GDM) has risen markedly worldwide, posing serious threats to both maternal and child health by increasing adverse pregnancy outcomes and long-term metabolic risks. Developing effective risk prediction tools for early detection and intervention has become the most important clinical priority in this field. The current GDM prediction models primarily rely on non-modifiable factors, for example age and body mass index, while modifiable factors such as lifestyle and health literacy, although strongly associated with GDM, have not been fully utilized in risk assessment. This study sought to establish and validate a nomogram prediction model combining modifiable and non-modifiable risk factors, with the goal of identifying high-risk Chinese pregnant women with GDM at an early stage and promoting targeted prevention and personalized prenatal management. <b>Methods:</b> A multicenter study was conducted across 7 maternal health institutions in Southern China (2021-2023), enrolling 806 singleton pregnant women (14-23⁺⁶ weeks). The collected data included sociodemographic, clinical history, and modifiable factors collected through validated questionnaires: dietary quality, physical activity level, sleep quality, and nutrition and health literacy. GDM was diagnosed via 75 g oral glucose tolerance test at 24-28 weeks. Predictive factors were identified through multi-variable logistic regression. A nomogram model was developed (70% modeling group) and validated (30% validation group). Receiver operator characteristic curves, calibration curves, and decision curve analysis were used to evaluate the prediction ability, the degree of calibration, and the clinical benefit of the model, respectively. <b>Results:</b> The finalized risk prediction model included non-modifiable factors such as maternal age, pre-pregnancy weight, and maternal polycystic ovary syndrome, as well as modifiable factors including dietary quality, physical activity level, sleep quality, nutrition and health literacy. The application of the nomogram in the modeling group and the validation groups showed that the model had high stability, favorable predictive ability, good calibration effect and clinical practicality. <b>Conclusions:</b> Overall, the integrated model demonstrates significant clinical utility as it facilitates the prompt identification of individuals at heightened risk and offers actionable targets for personalized interventions. In terms of future implementation, this model can be integrated into prenatal care as a rapid scoring table during early pregnancy consultations or incorporated into mobile health applications. This approach fosters precise prevention strategies for GDM in maternal health by emphasizing nutrition and health literacy, supplemented by coordinated adjustments in diet, physical activity, and sleep.