Development of a machine learning model for predicting cardiovascular risk factors and major adverse cardiovascular events in young adults with glucose metabolism disorders

Background

Glucose metabolism disorders (GMDs) are a serious global public health issue, characterized by a high incidence rate and youthfulness. GMDs contribute to the occurrence of major adverse cardiovascular events (MACEs), meanwhile with the rest of the remaining cardiovascular risk factors (CVRFs) act synergistically to influence MACEs. Although numerous studies have explored CVRFs and their prognostic role in cardiovascular diseases, there is a lack of predictive models for novel cardiovascular risk factors and MACEs in the young population, particularly among patients with GMDS. This study aims to investigate important CVRFs affecting MACEs in young patients with GMDs by means of LASSO regression, randomized forest, and XGBoost, providing new evidence to support the early adoption of proactive and comprehensive interventions and management.

Methods

The study included 411 young patients with GMDs who visit the First People's Hospital of Anqing Affiliated to Anhui Medical University, between September 2022 and June 2023. The patients were randomly divided into a training set and a testing set in a 7:3. Comprehensive analysis was performed using LASSO regression, random forest, and XGBoost methods. The performance of the models was evaluated and validated, and the important cardiovascular risk factors identified by the three models were compared.

Results

After one year of follow-up, the incidence of events was higher in men compared to women (85.2% vs. 14.8%). LASSO regression analysis identified BP, TYg, BMI, FBG, and Tg/HDL as significant variables associated with MACEs. The random forest method highlighted BMI, TYg, Tg/HDL, Tg, and FBG as key factors related to MACEs. The XGBoost model also emphasized the important roles of BMI, TYg, TG, and BP. Combining the results from all three models, BMI, TYg, and BP consistently demonstrated significant importance across all models.

Conclusions

To some extent, males are more likely to experience adverse cardiovascular events compared to females. Combining the three major predictive models suggests that traditional CVRFs (BP, BMI) and novel CVRFs (TYg) play an important role in the development of distant MACEs in young GMDs, and that interventions for them may have implications for preventing DM transformation.