Publications by authors named "Leyao Jian"
Objective: This study aimed to explore the relationships between residential greenness and cardiometabolic risk factors among rural adults in Xinjiang Uygur Autonomous Region (Xinjiang) and thus provide a theoretical basis and data support for improving the health of residents in this region.
Methods: We recruited 9,723 adult rural residents from the 51st Regiment of the Third Division of the Xinjiang Production and Construction Corps in September 2016. The normalized difference vegetation index (NDVI) was used to estimate residential greenness.
Article Synopsis
- Missing data is a common challenge in cohort studies that can hinder accurate findings, so this study evaluates eight imputation methods to address this issue in cardiovascular disease (CVD) risk models using real-world data from Xinjiang, China.
- The methods assessed include simple imputation, regression, expectation-maximization (EM), multiple imputation (MICE), K nearest neighbor (KNN), clustering, random forest (RF), and decision tree (Cart), with performance measured by Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and area under the curve (AUC) for predictive accuracy.
- Results indicate that KNN and RF are the most effective imputation techniques for handling missing data, yielding
Article Synopsis
- This study compared obesity-related indicators to see which ones best identify metabolic syndrome (MetS) in normal-weight adults living in rural Xinjiang.
- The research involved 4315 participants and utilized questionnaires, biochemical tests, and measurements to determine each indicator's association with MetS.
- Results showed that the Triglyceride-glucose index (TyG) and Lipid accumulation product (LAP) were the most effective indicators for diagnosing MetS, with LAP performing slightly better across both genders.