Krämer et al. gradient did not exist for women who lived further away from busy streets. Association between ambient air pollution and diabetes. All crude and adjusted HRs for the associations between exposure to ambient air pollution and incident type 2 diabetes were > 1.0 and were statistically significant except exposure to broad-scale PM10 (Table 3). The risk of incident type 2 diabetes was increased by 15% (95% CI, 4–27) per 1 IQR of traffic- related PM exposure (p-value = 0.009). The associations between exposure and incident diabetes depended on exposure metric and were highest for the metric with the highest spatial resolution. For each exposure met- ric, the associations with NO2 were stronger than (or at least equal with) PM: The HRs for NO2 were increased by 15% (95% CI, 4–27) to 42% (95% CI, 16–73) (p-values < 0.001–0.005). In a sensitivity analysis, we performed a stratified Cox regression with two strata, (SALIA study). Variable Monitoring stations PM10 NO2 Emission inventory PM from traffic NO2 from traffic Land-use regression Soot NO2 Distance/education < 100 m from busy road/lowc < 100 m from busy road/highd Table 3. Association between incidence of type 2 diabetes (1990–2006) and exposure to ambient air pollu‑ tion, especially traffic‑related air pollutants, assessed on four spatial scales in a cohort of elderly women namely, urban and rural living. The urban areas are all in the Ruhr area and of very simi- lar structure and type. The estimated effects in the urban areas were nearly unchanged from the estimates for all areas combined and were all significant, whereas the effect esti- mates were greater in the rural area but not significant. We also adjusted for urban and rural living, which had hardly any impact on the estimates. This result further indicates that the associations are not due only to con- founding by factors related to urban and rural living. As additional sensitivity analysis, we fitted regression models that included fixed effects for the nine different locations (seven towns, including two towns with two differ- ent study areas). This type of analysis was not possible for exposure based on monitoring stations, because only one monitoring sta- tion per location was available. The HRs were slightly smaller than those without adjusting for location. They all remained positive, but p-Value adjustedb 0.4146 0.0377 0.0087 0.0052 0.0014 0.0006 0.0057e 0.7379e p-Value (interaction) 0.0088 0.0118 0.0208 0.0095 only distance < 100 m from busy road and NO2 from land-use regression remained sta- tistically significant (data not shown). We did a further sensitivity analysis and conducted Cox regression analysis that included only women who did not change their addresses during follow-up (n = 1,492). The HRs for the single-exposure variables were not altered systematically, and overall the results did not change (data not shown). A change of residency was fairly seldom; thus, assigning pollution exposure based on resi- dence at the beginning of the study probably introduced no systematic bias. In further analyses, we adjusted for hyper- tension and change in BMI or excluded per- sons with coronary heart disease at baseline or follow-up and found hardly any change in the results (data not shown). Women with low education were more exposed to PM pollution when living near a busy road than women with high education. Therefore, we included an interaction term (education*living near a busy road) in the regression models. Type 2 diabetes incidence among women with low education more than doubled when they lived near a busy road (adjusted HR = 2.54; 95% CI, 1.31–4.91; p = 0.006) compared with those women with low education who did not live near a busy road. The corresponding adjusted HR (95% CI) for women with high education near a busy road was 0.92 (0.58–1.47) compared with those who did not live near a busy road (p for interaction = 0.0141). HRs for the associations of the included covariates (listed in Table 3) with incident type 2 diabetes are given in the Supplemental Material, Table 2 (doi:10.1289/ehp.0901689). After adjusting for all confounders, single- room heating with fossil fuels and former smoking as indicators of non–traffic-related air pollution were significantly associated with increased diabetes risk. Age, education, and workplace exposure (dust or fumes or extreme temperatures), which had been significant predictors in univariate analyses, lost signifi- cance after adjusting for all other covariates. Ambient air pollution, subclinical inflam‑ mation, and diabetes. Serum concentrations of C3c at baseline were significantly associated with PM10 levels (1985–1989) after adjust- ing for age, BMI, heating with fossil fuels, workplace exposure to dust or fumes, extreme temperatures, smoking, and education (linear regression with log-transformed values; ratio of means and 95% CI) for a 1-IQR PM10 increase: 1.09 (1.06–1.12, p < 0.001). This association was comparable in the two non- responder groups (death before 2006: 1.13 (1.06–1.20), n = 284; p < 0.001; alive, but no participation in 2006: 1.09 (1.06–1.13), n = 890; p < 0.001). Moreover, elevated lev- els of C3c were associated with an increased Soot 1.08 (0.88–1.32) 1.22 (1.02–1.47) 0.0064 NO2 1.03 (0.75–1.41) 1.31 (1.01–1.70) 0.0099 Distance/education < 100 m from busy road/lowc 1.06 (0.14–8.24) 3.51 (1.50–8.23) 0.2796 < 100 m from busy road/highd 0.32 (0.08–1.31) 0.85 (0.42–1.70) 0.2155 aAdjusted for age, BMI, heating with fossil fuels, workplace exposure with dust/fumes, extreme temperatures, smoking, education [see Supplemental Material, Table 2 (doi:10.1289/ehp.0901689)]. bRespective quartiles are given in Table 2. cMore than 10,000 cars/day; estimates for women with low educational status. dMore than 10,000 cars/day; estimates for women with high educational status. 1276 ovlume 118 | number 9 | September 2010 • Environmental Health Perspectives HR (95% CI) per IQR of exposurea Unadjusted 1.64 (1.20–2.25) 1.53 (1.20–1.95) 1.23 (1.12–1.35) 1.22 (1.11–1.34) 1.28 (1.12–1.47) 1.47 (1.22–1.77) 2.32 (1.29–4.17) 0.86 (0.55–1.36) Adjustedb 1.16 (0.81–1.65) 1.34 (1.02–1.76) 1.15 (1.04–1.27) 1.15 (1.04–1.27) 1.27 (1.09–1.48) 1.42 (1.16–1.73) 2.54 (1.31–4.91) 0.92 (0.58–1.47) (n = 634) 1.21 (0.70–1.64) 1.29 (0.93–1.79) 1.24 (1.08–1.41) 1.24 (1.08–1.41) aThe respective quartiles are given in Table 2. bAdjusted for age, BMI, heating with fossil fuels, workplace exposure with dust/fumes, extreme temperatures, smoking, education [see Supplemental Material, Table 2 (doi:10.1289/ehp.0901689)]. cMore than 10,000 cars/day; HR for the subgroup of women with low educational status. dMore than 10,000 cars/day; HR for the subgroup of women with high educational status. epinteraction = 0.0141. Table 4. Association between the incident type 2 diabetes (1990–2006) and exposure to ambient air pollu‑ tion, especially traffic‑related air pollutants, assessed on four spatial scales in a cohort of elderly women (SALIA study) with high and low C3c: results of Cox regression analyses using a study sample of 1,251 subjects with complete information on all covariates and data of C3c concentrations. Adjusteda HR (95% CI) per interquartile range of exposureb C3c < median (< 100.4 mg/dL) C3c ≥ median (≥ 100.4 mg/dL) Variable Monitoring stations PM10 NO2 Emission inventory PM from traffic NO2 from traffic Land-use regression (n = 617) 1.07 (0.70–1.64) 0.92 (0.62–1.38) 0.91 (0.70–1.18) 0.85 (0.64–1.13)