Establishment of Metabolic Syndrome Prediction Model for Occupational Population based on the Lasso Regression Algorithm

Objective: This study aimed to screen out biomarkers related to metabolic syndrome (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 screened biomarkers were used to establish a logistic regression model and calculate the odds ratio (OR) of the corresponding biomarkers. Finally, the accuracy of the logistic regression model was further verified based on the Lasso regression algorithm. Results: A total of 2844 occupational workers were included, and 10 biomarkers related to MS were screened. The established risk assessment model revealed that the main risk factors were basophil absolute count (OR: 3.38), platelet packed volume (OR: 2.63), leukocyte count (OR: 2.01), red blood cell count (OR: 1.99), and alanine aminotransferase level (OR: 1.53). The area under the curve (AUC) value for non-Lasso and Lasso regression was 0.652 and 0.907, respectively. Conclusion: The risk assessment model based on the Lasso regression algorithm helped identify MS with high accuracy in physically examining an occupational population. Funding Statement: The presented study was supported by the Hangzhou Science and technology development plan projects (20140633B32;20200834M29); Youth fund of Zhejiang Academy of Medical Sciences (No.2019Y009); Medical and Technology Project of Zhejiang Province (No.2021HY127,No.2020362651,No.2021KY890); Hangzhou science and Technology Bureau fund (No.20191203B96;No.20191203B105); Clinical Research Fund of Zhejiang Medical Association(No.2020ZYC-A13); Hangzhou Health and Family Planning Technology Plan key projects（2017ZD02). Declaration of Interests: The authors declare that there is no conflict of interests regarding the publication of this paper. Ethics Approval Statement: Not applicable. My manuscript does not report on or involve the use of any animal or human data or tissue.