Relationship Between Rotating Night Shift Work and Anthropometric Markers of Overall and Central Adiposity

This study aimed to investigate possible association between rotating night shift (RNS) work and anthropometric markers of overall and central adiposity among male workers.

Medical records of workers who underwent medical examination at the Occupational Medicine Unit affiliated with Alexandria Faculty of Medicine were reviewed in December 2021 to extract data about RNS, overall adiposity [body mass index (BMI) from 25 to 29.9 kg/m² indicated overweight, while BMI ≥30 kg/m² indicated obesity], central adiposity (waist circumference ≥94 cm). Univariate and multivariate logistic regression were done to model adiposity as a function of potential demographic, lifestyle, and occupational factors.

Data of 647 male workers, including daytime workers (26.9%) and RNS workers (73.1%) who spent 1-5 years, 6-9 years, or ≥10 years working RNS, were analyzed. No association was found between RNS work and overall adiposity (BMI ≥30 kg/m²). In multivariate regression, age, residence, and smoking status were predictors of an increased BMI. Compared with young workers (<30 years old), the odds of an increased BMI (BMI ≥25 kg/m²) were 2.6 and 3.01 times higher among workers who were 30-<40, and 40-<50 years old, respectively [95% CI=(1.35, 5.05) and (1.19, 7.56), respectively]. Workers who lived in urban areas were less likely to have had an increased BMI (OR=0.34, 95% CI=0.16, 0.74). The odds of an increased BMI were 2.14 times higher in workers who never smoked compared with current smokers (95% CI=1.12, 4.11). No association was found between RNS and central adiposity. In multivariate regression, age was the sole predictor of central adiposity.

The study supports the idea that RNS work might not be associated with overall or central adiposity and highlights a possible association between central adiposity and age and association between BMI and age, residence, and smoking behavior. Longitudinal, larger studies are required to explore the relationship between RNS and adiposity.