Lymphocytes and related inflammatory factors as predictors of metabolic syndrome risk in shift workers: A machine learning approach based on large-scale population data

作者：Yiren Bao, Rui Wang · 发表于：PLoS ONE · 年份：2025 · DOI：10.1371/journal.pone.0339673 · 被引用次数：2 · 研究领域：Sleep and Work-Related Fatigue、Circadian rhythm and melatonin、Sleep and related disorders

BACKGROUND: Metabolic syndrome (MetS) is characterized by chronic inflammation and can be worsened by circadian disruption, which is common among shift work. Machine learning can predict the risk of MetS in shift workers using inflammatory biomarkers. Most investigations have focused on the general population rather than shift workers, a distinct group that requires continuous health monitoring; therefore, we aimed to examine the relationship between inflammatory indicators and MetS using blood cell counts in this high-risk group of shift workers who require long-term health monitoring and to enhance the biological understanding of MetS by applying machine learning methods. METHODS: In this cross-sectional study, we analyzed data from shift workers included in the National Health and Nutrition Examination Survey between 2005-2010 and 2017-2018. Prediction models, including random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and linear regression (LR), were developed and validated. We compared the model performance and conducted stratified analysis, smooth curve fitting, and threshold effect analysis to further explore the relationship between inflammation and MetS risk in shift workers. RESULTS: The analysis included 3,079 participants in total. Each machine learning model demonstrated good predictive performance in assessing MetS risk among shift workers. LightGBM achieved the area under the curve (AUC) of 0.944 in training da...