Occupational Assessments of Risk Factors for Cardiovascular Diseases in Labors: An Application of Metabolic Syndrome Scoring Index

Lin, Ching-Yuan; Lin, Chih‐Ming

doi:10.3390/ijerph17207539

Cited by 4 publications

(4 citation statements)

References 31 publications

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“…(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). At the same time, some related studies confirmed the relationship of MS with the type of work in different occupational groups (7)(8)(9). Therefore, performing early MS screening for the occupational population is of great significance.…”

Section: Introductionmentioning

confidence: 65%

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

Xie

Wang

et al. 2021

Front. Public Health

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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.

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Section: Introductionmentioning

confidence: 65%

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

Xie

Wang

et al. 2021

Front. Public Health

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“…This may be because senior workers or those with a higher level of education may be more likely to be assigned to managerial roles with greater responsibilities, especially during emergencies. A Taiwanese study found that managers have higher risks of MS and CVDs [ 40 ]. The authors also suggested that these managers might have been promoted due to their own professional expertise, so might endure more work-related stress and health risks.…”

Section: Discussionmentioning

confidence: 99%

Cardiovascular Risk Factors in Hospital Workers during the COVID-19 Pandemic: A Hospital-Based Repeated Measures Study

Liao

Lai

Lin

2022

IJERPH

Self Cite

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Although many studies have investigated burnout, stress, and mental health issues among health care workers (HCWs) during the COVID-19 pandemic, few have linked these relationships to chronic physiological illnesses such as cardiovascular diseases. This study assessed changes in cardiovascular risk factors in HCWs and other hospital workers during the COVID-19 pandemic and identified vulnerable groups at a higher risk of increased adverse cardiovascular conditions. Five hundred and fourteen hospital employees ≥ 20 years of age underwent physical examinations and laboratory testing once before and once after the first wave of the pandemic in Taiwan during 2020 and 2021. Their sociodemographic characteristics and cardiovascular risk factors, including blood pressure, blood biochemical parameters, and body mass index, were collected. The differences between pre- and post-pandemic measurements of their biophysical and blood biochemical parameters were analyzed using pairwise tests. The post-pandemic increases in their parameter levels and cardiovascular risk as a function of underlying factors were estimated from multivariate regressions. HCWs showed significant increases in levels and abnormal rates of BMI, blood pressure, plasma glucose, and total cholesterol after the pandemic. Post-pandemic increases in BMI, waist circumference, and blood pressure were higher in females than in males. Workers with higher levels of education or longer job tenure had greater increases in BMI, triglyceride, and total cholesterol levels than other workers. Females had a higher incidence of abnormal BMI and hypertension than males (adjusted odds ratios [AORs] of 8.3 and 2.9, respectively). Older workers’ incidence of hypertension was higher than younger workers’ (AOR = 3.5). Preventive strategies should be implemented for HCWs susceptible to cardiovascular diseases during emerging infectious disease outbreaks.

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“…Huang et al 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]. At the same time, some related studies confirmed the relationship of MS with the type of work in different occupational groups [7][8][9]. Therefore, performing early MS screening for the occupational population is of great significance.…”

Section: Introductionmentioning

confidence: 98%

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

Xie¹,

Wang

Hu³

et al. 2020

SSRN Journal

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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.

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Occupational Assessments of Risk Factors for Cardiovascular Diseases in Labors: An Application of Metabolic Syndrome Scoring Index

Cited by 4 publications

References 31 publications

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

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

Cardiovascular Risk Factors in Hospital Workers during the COVID-19 Pandemic: A Hospital-Based Repeated Measures Study

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

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