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DOI: 10.1055/s-0037-1605745
Road traffic noise and incident diabetes mellitus after 5 years of follow-up – Results from the Heinz Nixdorf Recall Study
Publikationsverlauf
Publikationsdatum:
01. September 2017 (online)
Background/Aim:
Road traffic noise affects the health of a large number of people in urbanized areas and increases the risk of cardiovascular disease. Recent studies suggested a possible link between exposure to noise and metabolic outcomes. However, most studies lack information for modeling indoor noise. This study investigated the effect of outdoor and indoor residential road traffic noise on incident diabetes mellitus (T2DM).
Methods:
We used data from 2,748 participants 45 – 75 years of age in the prospective Heinz Nixdorf Recall study who were non-diabetic at baseline (2000 – 2003) and completed follow-up (2005 – 2008) examinations. Road traffic noise (weighted 24-h (LDEN) and night-time (LNIGHT) means) was assessed according to the EU directive 2002/49/EC. Indoor noise exposure for LDEN and LNIGHT was modeled considering living-room/bedroom orientation, window insulation and ventilation behavior for different seasons. Noise annoyance was assessed with a 5-scale questionnaire. Poisson regression with robust variance was applied to estimate relative risks (RRs) of developing T2DM adjusting for demographic characteristics, lifestyle, body mass index, waist circumference, air pollution (PM2.5) and noise annoyance.
Results:
In the fully adjusted model including PM2.5, a 10 dB-increase in average outdoor traffic noise (LDEN and LNIGHT) was associated with a RR of 1.07 (95% CI, 0.93 – 1.23) for T2DM. Models for indoor road traffic noise exposure showed very similar, but more precise point estimates (LDEN: 1.08 (0.97 – 1.20), LNIGHT: 1.07 (0.97 – 1.20)). Models including annoyance yielded slightly lower RRs (e.g., 1.06 (0.91 – 1.23) for outdoor LDEN).
Conclusions:
Our analyses suggest a positive association between long-term road traffic noise and T2DM incidence. Exposure assessment might be improved by modeling indoor noise exposures.