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Screening the Influence of Biomarkers for Metabolic Syndrome in Occupational Population Based on the Lasso Algorithm

Authors :
Zhan-Hui Feng
Xing-Wei Zhang
Yong-Ran Cheng
Yan-Ming Chu
Qiao-Ying Xie
Cheng-Jian Cao
Xin-Yan Fu
Jing-Yu Kang
Cong Wang
Zu-Ying Hu
Ming-Wei Wang
Source :
Frontiers in Public Health, Vol 9 (2021), Frontiers in Public Health
Publication Year :
2021
Publisher :
Frontiers Media S.A., 2021.

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.

Details

Language :
English
ISSN :
22962565
Volume :
9
Database :
OpenAIRE
Journal :
Frontiers in Public Health
Accession number :
edsair.doi.dedup.....00382fd36c66e48cd8f5ba4375a27d0d
Full Text :
https://doi.org/10.3389/fpubh.2021.743731/full