Efforts for the prevention and management of cardiovascular diseases (CVDs) in workers have been actively pursued. Obesity is one of the important risk factors related to CVDs. Obesity has various metabolic characteristics, and some individuals can be metabolically healthy. Body composition including skeletal muscle mass is known to have protective effect in obesity. The study aims to investigate the association between skeletal muscle mass and Korea Occupational Safety and Health Agency (KOSHA) CVD risk among obese male manufacturing workers in Korea and to identify appropriate indicators of skeletal muscle mass for predicting risk of CVDs.

The study was conducted on 2,007 obese male workers at a manufacturing industry aged more than 19 years. Skeletal muscle mass, skeletal muscle index (SMI), skeletal muscle mass percent (SMM%) and skeletal muscle to body fat ratio (MFR) were used to evaluate body composition and these indicators were divided into quartiles. The odds ratios (ORs) and 95% confidence intervals (CIs) for the KOSHA CVD risk groups according to quartiles of skeletal muscle mass indicators were estimated using ordinal logistic regression analysis.

The OR for the KOSHA CVD risk groups in the highest quartile of SMI was 1.67 (95% CI: 1.42–1.92), while the ORs for the KOSHA CVD risk groups in the highest quartiles of SMM%, SMM/body mass index (BMI), and MFR were 0.47 (95% CI: 0.22–0.72), 0.51 (95% CI: 0.05–0.76), and 0.48 (95% CI: 0.23–0.74), respectively.

We found that high SMI increase the likelihood of high risk of CVDs, while high SMM%, SMM/BMI, and MFR lower the likelihood of high risk of CVDs. Accurate evaluation of skeletal muscle mass can help assess the cardiovascular risk in obese male workers.

Shift work is known to cause changes in the circadian rhythm of the human body and adversely affect not only physical health but also mental health. Some studies have demonstrated the correlation between shift work and thyroid stimulating hormone (TSH), a hormone that changes according to the diurnal rhythm, but few studies have reported the different TSH levels according to the shift work type. This study aimed to investigate changes in TSH according to the shift work type.

This study included 1,318 female workers who had a medical checkup at a university hospital in Changwon from 2015 to 2019. Shift work types were classified as non-shift work, regular 2 shifts, and irregular three shifts, and a TSH ≥ 4.2 mIU/L was defined as abnormal. A general linear model (GLM) was used to compare the TSH levels and the risk of subclinical hypothyroidism in each year, and a binary logistic analysis was performed using a generalized estimation equation (GEE) to compare the risk of subclinical hypothyroidism over the 5-year period.

Of the 1,318 participants included in this study, 363, 711, and 244 were non-shift, two-shift, and irregular three-shift workers, respectively. In the GEE analysis, after adjusting for age, body mass index, smoking, and alcohol consumption, the odds ratios (ORs) were 1.81 (95% confidence interval [CI]: 1.15–2.86; p = 0.011) in 2 shifts and 2.02 (95% CI: 1.23–3.32; p = 0.006) in irregular three shifts, compared to non-shift.

Our results showed that shift work had a higher risk of subclinical hypothyroidism than non-shift work and that there was a significant difference in the risk of subclinical hypothyroidism according to the shift work type. These findings suggest that the shift work type can be considered in future thyroid function tests and evaluations.