Development and visualization of a risk prediction model for metabolic syndrome: a longitudinal cohort study based on health check-up data in China.

ABSTRACT:

Aim

Our study aimed to construct a practical risk prediction model for metabolic syndrome (MetS) based on the longitudinal health check-up data, considering both the baseline level of physical examination indicators and their annual average cumulative exposure, and to provide some theoretical basis for the health management of Mets.

Methods

The prediction model was constructed in male and female cohorts, separately. The shared set of predictive variables screened out from 49 important physical examination indicators by the univariate Cox model, Lasso-Cox model and the RSF algorithm collectively was further screened by Cox stepwise regression method. The screened predictors were used to construct prediction model by the Cox proportional hazards regression model and RSF model,

SUBMITTER: Liu W 

PROVIDER: S-EPMC10702500 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature