RESUMO
BACKGROUND: Noise has been found to be associated with endocrine changes and cardiovascular disease. Increased cortisol levels and chronic sleep problems due to noise may increase the risk of obesity. OBJECTIVES: We investigated the relationship between road traffic noise and obesity markers. Furthermore, we explored the modifying role of noise sensitivity, noise annoyance, and sleep disturbances. METHODS: We used data from a population-based study, HUBRO (N=15,085), and its follow-up study HELMILO (N=8410) conducted in Oslo, Norway. Measurements were used to define body mass index (BMI), waist circumference (WC), waist-hip ratio (WHR), and these binary outcomes: BMI≥30kg/m(2), WC≥102cm (men)/88cm (women), and WHR≥0.90 (men)/0.85 (women). Modelled levels of road traffic noise (Lden) were assigned to each participant's home address. Linear and logistic regression models were used to examine the associations. RESULTS: The results indicated no significant associations between road traffic noise and obesity markers in the total populations. However, in highly noise sensitive women (n=1106) a 10dB increase in noise level was associated with a slope (=beta) of 1.02 (95% confidence interval (CI): 1.01, 1.03) for BMI, 1.01 (CI: 1.00, 1.02) for WC, and an odds ratio (OR) of 1.24 (CI: 1.01, 1.53) for WHR ≥0.85. The associations appeared weaker in highly noise sensitive men. We found no effect modification of noise annoyance or sleep disturbances. In a sub-population with bedroom facing a road, the associations increased in men (e.g. an OR of 1.25 (CI: 0.88, 1.78) for BMI ≥30kg/m(2)), but not in women. Among long-term residents the associations increased for BMI ≥30kg/m(2) (OR of 1.07 (CI: 0.93, 1.24) in men and 1.10 (CI: 0.97, 1.26) in women), but not for the other outcomes. CONCLUSION: In an adult urban Scandinavian population, road traffic noise was positively associated with obesity markers among highly noise sensitive women. The associations appeared stronger among men with bedroom facing a street, representing a population with more accurately assigned exposure.