Gender-specific association between night-work exposure and type-2 diabetes: results from longitudinal study of adult health, ELSA-Brasil

Scand J Work Environ Health. 2015 Nov;41(6):569-78. doi: 10.5271/sjweh.3520. Epub 2015 Aug 27.


Objectives: Diabetes is a multifactorial disease of increasing prevalence. The literature suggests an impact of night work on metabolic components, though the relationship with diabetes is unclear. Our aim was to investigate gender-specific associations between night work and type-2 diabetes (DM2) or impaired glucose tolerance (IGT) using baseline data of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil).

Methods: The cohort comprised 15 105 civil servants, aged 35-74 years. Baseline assessments (2008-2010) included clinical and laboratory measurements and interviews on sociodemographic, occupational, and health characteristics.

Results: In the baseline sample (N=14 427), 19.6% were classified as having DM2 and 20.5% as having IGT. Mean age was 52.1 (SD 9.1) years. A total of 2041 participants worked at night for 1-20 years and 687 for >20 years. Among women exposed to night work for >20 years compared with no night work after adjustments for potential confounders, including obesity, the odds ratios (OR) derived from multinomial logistic regression for DM2 and IGT were 1.42 [95% confidence interval (95% CI) 1.39-1.45] and 0.96 (95% CI 0.94-0.99), respectively. Among men exposed to night work for >20 years compared with no night work, the OR for DM2 and IGT were 1.06 (95% CI 1.04-1.08) and 0.99 (95% CI 0.98-1.01), respectively.

Conclusions: The association between years of night work and diabetes is stronger among women than men. Longitudinal studies from ELSA-Brasil will be able to corroborate or refute these findings.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Brazil
  • Diabetes Mellitus, Type 2 / epidemiology*
  • Female
  • Glucose Intolerance / epidemiology*
  • Humans
  • Longitudinal Studies
  • Male
  • Middle Aged
  • Occupational Health
  • Personnel Staffing and Scheduling / statistics & numerical data*
  • Prospective Studies
  • Risk Factors
  • Socioeconomic Factors
  • Time Factors
  • Work Schedule Tolerance*