The positive association between white blood cell count and metabolic syndrome is independent of insulin resistance among a Chinese population: a cross-sectional study - Ren, ZhongYu; Luo, Shi; Liu, Lian (Frontiers in Immunology, 2023)
Aims: To examine the cross-sectional association between white cell counts and metabolic syndrome (MS) among national population and to describe the performance to predict MS using machine learning (ML) models. Methods: We analyzed 8305 data from China Health and Nutrition Survey (CHNS). White blood cell count was analyzed using an automatic hematology analyzer and MS was defined according to the criteria of the American Heart Association scientific statements of 2009. Variables on sociodemographic characteristics (sex, age and residence), clinical laboratory (BMI and HOMA-IR), lifestyle characteristics (smoking and drinking status) and history of diseases (diabetes, hypertension, apoplexy and myocardial infarction) were used to construct ML models to predict MS [logistic regression (LR) and multilayer perceptron neural network (MLP)]. Results: We found that 26.4% of participants (2192/8305) were classified as having MS. In multivariate logistic regression, the result revealed a significant positive association between white cell counts and MS. The odds ratios (95% CI) for MS with increasing white cell counts level were 1.00 (reference), 1.70 (1.25, 2.31), and 2.45 (1.59, 3.78) (P for trend: <0.001). For two ML algorithms, two models showed adequate calibration and good discrimination, but the MLP showed better performance (AUC-ROC = 0.862 and 0.867). Conclusion: The findings suggest that maintaining normal white cell counts level is helpful to prevent the development of MS. The results also showed that MPL algorithm represented more prominent predictive performance to predict MS.