Abstract
Aim: Metabolic syndrome (MS) screening is essential for the early detection of the occupational population. This study aimed to screen out biomarkers related to MS and establish a risk assessment and prediction model for the routine physical examination of an occupational population.Methods: The least absolute shrinkage and selection operator (Lasso) regression algorithm of machine learning was used to screen biomarkers related to MS. Then, the accuracy of the logistic regression model was further verified based on the Lasso regression algorithm. The areas under the receiving operating characteristic curves were used to evaluate the selection accuracy of biomarkers in identifying MS subjects with risk. The screened biomarkers were used to establish a logistic regression model and calculate the odds ratio (OR) of the corresponding biomarkers. A nomogram risk prediction model was established based on the selected biomarkers, and the consistency index (C-index) and calibration curve were derived.Results: A total of 2,844 occupational workers were included, and 10 biomarkers related to MS were screened. The number of non-MS cases was 2,189 and that of MS was 655. The area under the curve (AUC) value for non-Lasso and Lasso logistic regression was 0.652 and 0.907, respectively. The established risk assessment model revealed that the main risk biomarkers were absolute basophil count (OR: 3.38, CI:1.05–6.85), platelet packed volume (OR: 2.63, CI:2.31–3.79), leukocyte count (OR: 2.01, CI:1.79–2.19), red blood cell count (OR: 1.99, CI:1.80–2.71), and alanine aminotransferase level (OR: 1.53, CI:1.12–1.98). Furthermore, favorable results with C-indexes (0.840) and calibration curves closer to ideal curves indicated the accurate predictive ability of this nomogram.Conclusions: The risk assessment model based on the Lasso logistic regression algorithm helped identify MS with high accuracy in physically examining an occupational population.
Highlights
Metabolic syndrome (MS) refers to a group of metabolismrelated diseases, including obesity, dyslipidemia, diabetes/impaired glucose tolerance, hypertension, and other diseases [1]
A total of 2,844 occupational workers were included, and 10 biomarkers related to MS were screened
The established risk assessment model revealed that the main risk biomarkers were absolute basophil count (OR: 3.38, CI:1.05–6.85), platelet packed volume (OR: 2.63, CI:2.31–3.79), leukocyte count (OR: 2.01, CI:1.79–2.19), red blood cell count (OR: 1.99, CI:1.80–2.71), and alanine aminotransferase level (OR: 1.53, CI:1.12–1.98)
Summary
Metabolic syndrome (MS) refers to a group of metabolismrelated diseases, including obesity, dyslipidemia, diabetes/impaired glucose tolerance, hypertension, and other diseases [1]. The occupational population occupies a significant part and continues to increase [3]. Nearly 25 million workers suffer from health hazards, among which MS is already an important risk factor seriously affecting the health of the occupational population [4]. Many studies were conducted on the relationship between the working environment of the occupational population and MS. Ma et al confirmed that exposure to heavy metal elements in the work environment affected the body’s metabolic function and increased the risk of MS in the Chinese population [5]. [6] confirmed that the long-term exposure to noise in the work environment increased the chance of suffering from MS in the Chinese professional population [6]. Performing early MS screening for the occupational population is of great significance
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