Krämer et al. not shown). Another limitation is that our follow-up was not complete and that women with higher education were overrepresented. Because the incidence of diabetes was lower in higher-educated women, this may have led to an underestimation of diabetes incidence. The association with air pollution, however, is likely not confounded by selective partici- pation because a) we adjusted for education in our analysis, and b) the association of the risk factor C3c with air pollution was the same in participating and nonparticipating women. Another limitation might be that C3c levels were available for only 70% of the cohort. Further inflammatory markers would be required to characterize more accurately the components of subclinical inflamma- tion that are important in the relationship between air pollution and type 2 diabetes. Furthermore, data on diet, diabetes family history, and physical exercise were not avail- able. However, we investigated the effect of BMI, which might also reflect differences in dietary habits and physical exercise. BMI was not positively associated with any of our exposure variables and therefore could not be a confounder. Interestingly, change in BMI was associated with air pollution, that is, the adjusted change in BMI was 0.4 kg/m² per IQR of NO2 exposure (land-use regres- sion). However, the association between air pollution and incident diabetes remained unchanged even after including BMI change into the model. Finally, we had no data on antihypertensive medications or drugs for coronary heart disease available. However, as a sensitivity analysis, we additionally adjusted the analysis for hypertension and excluded persons with CHD at baseline or follow-up from the analysis. The results hardly changed. We therefore assume that the effects of these medications most likely did not confound our results. Adjustment for the different locations in the models led to lower HRs than without adjustment, although they remained posi- tive. Only distance < 100 m from busy road and NO2 from land-use regression remained statistically significant. However, we con- sider this an indication of overadjustment in the model, because air pollution and type of area are closely related. Additionally, we did a stratified Cox regression with two strata (urban and rural living) and did an additional analysis including urban and rural living as fixed effect in the model. The urban areas are of very similar structure and type. The effect estimates were nearly unchanged. This indi- cates that the associations are not due only to confounding by factors associated with urban or rural living, although we cannot preclude residual confounding by socioeconomic and lifestyle factors. We also cannot exclude that the association between diabetes incidence 1278 and living near a major road might be at least partially determined by other factors such as noise. Finally, the cohort comprised mid- dle-aged women only, so the findings may not be representative for other groups of the population. If confirmed, our study results have important implications for prevention of type 2 diabetes. About half of the world’s population now lives in urban areas, and many of the urban centers are expanding rapidly to megacities (Molina and Molina 2004). Air pollution, primarily from traf- fic, has become one of the most important problems of megacities. Vulnerable popula- tions, such as those living in areas with low socioeconomic status, are disproportionately affected by air pollution (Younger et al. 2008). Our findings suggest a potential new pathway to reduce the risk of type 2 diabetes in the population by reductions in traffic- related air pollution. Furthermore, our findings are interest- ing because numerous epidemiological stud- ies have shown a higher prevalence of type 2 diabetes in urban areas compared with rural areas (Ramachandran et al. 1999; Sobngwi et al. 2004). This difference has been observed particularly in developing countries that have undergone a rapid transition from rural to urban lifestyle. It has been largely attrib- uted to broad shifts in diet, physical activity, and obesity in urban areas (Popkin 1999). However, these lifestyle factors fail to com- pletely explain the association with increased diabetes and obesity risk (Popkin 1999). Other environmental factors would also be expected to contribute to these striking dif- ferences, and further studies are needed to investigate the extent to which traffic-related air pollution determines the environmental health burden in cities worldwide. 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