This preprint is under consideration at Journal of Occupational Medicine and Toxicology. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
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Establishment of Metabolic Syndrome Prediction Model for Occupational Population based on the Lasso Regression Algorithm
Yong-Ran Cheng
Hangzhou Medical College
Corresponding Author
ORCiD: https://orcid.org/0000-0003-2646-560X
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Metabolic syndrome (MS) screening is important for the early detection of 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. Finally, the screened biomarkers were used to establish a logistic regression model and calculate the odds ratio (OR) of the corresponding biomarkers.
Results: A total of 2844 occupational workers were included, and 10 biomarkers related to MS were screened. The area under the curve (AUC) value for non-Lasso and Lasso regression was 0.652 and 0.907, respectively. 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).
Conclusion: The risk assessment model based on the Lasso regression algorithm helped identify Metabolic syndrome with high accuracy in physically examining an occupational population.
Keywords
Lasso regression algorithm, metabolic syndrome, occupational population
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Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the latest manuscript can be downloaded and accessed as a PDF.
Due to technical limitations, tables only available as a download in the Supplemental Files section.
This is a list of supplementary files associated with this preprint. Click to download.
- Table1.docx
Types of medical markers included in the study
- Table2.docx
Basic characteristics of the population
- Table3.docx
Basic characteristics of routine physical examination markers
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Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 31 Dec, 2020
No community comments so far
Reviewer #1 agreed
On 10 Jan, 2021
Reviewers invited
Invitations sent on 04 Jan, 2021
Editor assigned
On 25 Dec, 2020
Submission checks complete
On 25 Dec, 2020
Editor invited
On 25 Dec, 2020
First submitted
On 22 Dec, 2020
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Subject Areas
Toxicology
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This preprint is under consideration at Journal of Occupational Medicine and Toxicology. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Establishment of Metabolic Syndrome Prediction Model for Occupational Population based on the Lasso Regression Algorithm
Yong-Ran Cheng
Hangzhou Medical College
Corresponding Author
ORCiD: https://orcid.org/0000-0003-2646-560X
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 31 Dec, 2020
No community comments so far
Reviewer #1 agreed
On 10 Jan, 2021
Reviewers invited
Invitations sent on 04 Jan, 2021
Editor assigned
On 25 Dec, 2020
Submission checks complete
On 25 Dec, 2020
Editor invited
On 25 Dec, 2020
First submitted
On 22 Dec, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Toxicology
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Metabolic syndrome (MS) screening is important for the early detection of 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. Finally, the screened biomarkers were used to establish a logistic regression model and calculate the odds ratio (OR) of the corresponding biomarkers.
Results: A total of 2844 occupational workers were included, and 10 biomarkers related to MS were screened. The area under the curve (AUC) value for non-Lasso and Lasso regression was 0.652 and 0.907, respectively. 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).
Conclusion: The risk assessment model based on the Lasso regression algorithm helped identify Metabolic syndrome with high accuracy in physically examining an occupational population.
Figure 1
Figure 2
Figure 3
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the latest manuscript can be downloaded and accessed as a PDF.
Due to technical limitations, tables only available as a download in the Supplemental Files section.