Abstract Background: Dyslipidemia, characterized by abnormal blood lipid levels, significantly enhances the risk for cardiovascular diseases (CVDs). Body composition plays a critical role in the modulation of lipid metabolism and therefore in the pathogenesis of dyslipidemia. This study explores the prevalence of dyslipidemia among medical staff and analyzes the association between body composition and lipid profiles. The objective is to furnish actionable insights for the prevention and management of dyslipidemia in this demographic. Methods: A number of 696 medical staff members, whoparticipated in a health examination conducted from July to October 2023 was selected in this study. The Inbody720 body composition analyzer was utilized to evaluate body fat composition. Serum concentrations of triglycerides and total cholesterol were also measured. A comprehensive suite of statistical analyses was performed, including independent t-tests, non-parametric tests, Spearman rank correlations, logistic regression, and receiver operating characteristic (ROC) curve analysis, to explore the associations between body composition indicators and dyslipidemia. Results: The prevalence of dyslipidemia among the medical staff amounted to 37.55%. Notably, several key body composition parameters, including body mass index (BMI), body fat percentage, protein content, muscle mass, skeletal muscle mass index, waist-to-hip fat ratio, visceral fat area, and body water content, were significantly higher in individuals with dyslipidemia (p < 0.05). Serum total cholesterol showed a positive correlation with BMI, body fat percentage, waist-to-hip fat ratio, and visceral fat area. Similar trend of positive correlation was observed for triglycerides and low-density lipoprotein cholesterol (LDL-C) with these aforementioned body composition indicators, whereas high-density lipoprotein cholesterol (HDL-C) exhibited negative correlations. Logistic regression analysis identified age, waist-to-hip fat ratio as significant predictors of dyslipidemia. The ROC curve analysis provided optimal cut-off values for these indicators in diagnosing dyslipidemia. Conclusion: The study highlights a robust correlation between body composition and the occurrence of dyslipidemia among medical staff. The risk of dyslipidemia can be predicted by assessing body composition. This paper also lays a groundwork for further research on the development of targeted prevention and intervention strategies based on body composition.
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Yao et al. (2024) studied this question.
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