Background Night shift work has been associated with adverse neurobehavioral and cardiometabolic outcomes, but the role of cumulative and long-term exposure, particularly among female healthcare workers, remains incompletely characterized. This study adopted a multidimensional approach to examine associations between night shift exposure and integrated health profiles. Methods This cross-sectional study included 243 female healthcare workers (96 day-shift, 147 night-shift). Night shift exposure was assessed using multiple metrics: status, cumulative lifetime shifts (500 vs. ≥ 500), and duration (1–10 vs. 11–35 years). Sociodemographic, reproductive, lifestyle, and occupational data were collected. Neurobehavioral outcomes were evaluated using validated questionnaires (sleep quality, psychological well-being, work ability), while cardiometabolic assessment included BMI, WHtR, insulin resistance indices (METS-IR, TyG, WyG), and cardiovascular risk index (IRCV). Analyses comprised stratification, correlation, and multivariable regression adjusted for age, smoking, and menopausal status. Results Higher cumulative exposure was associated with lower physical activity ( p = 0.012), poorer sleep ( p = 0.019), reduced work ability ( p = 0.023), and early insulin resistance ( p = 0.043). Long-term exposure was linked to poorer psychological well-being ( p = 0.009), higher adiposity ( p = 0.034; p = 0.028), and increased IRCV ( p = 0.006). In adjusted models, sleep quality was consistently associated with all exposure metrics, while metabolic outcomes were more strongly linked to duration of exposure and individual factors. Correlation analyses revealed clustering of sleep, neurobehavioral, and cardiometabolic variables, with strength of the associations increasing with exposure level. Conclusion Night shift work, particularly with cumulative and long-term exposure, is associated with sleep disruption, reduced well-being, and early cardiometabolic alterations in female healthcare workers. Sleep impairment emerges as a robust exposure-related marker, while metabolic risk reflects combined occupational and individual susceptibility. These findings support life-course exposure assessment and suggests the use of early subclinical indicators for implementation of precision prevention, as well as early intervention strategies. Future research may further benefit from a life-course exposome framework integrating environmental, occupational, and biological exposures.
Vivarelli et al. (Wed,) studied this question.