Silverstone et al. those with prediabetes or normoglycemia. Although mean PCB levels for whites and nonwhites were higher in those with diabetes than in those without diabetes, the differences were not statistically significant. GM PCB lev- els were similar for males and females, whereas nonwhites had significantly higher PCB levels than did whites. In general, PCB levels were higher in participants ≥ 55 years of age than in those < 55 years of age (Table 3). Within specific age strata, PCB levels remained simi- lar across BMI categories for those ≥ 55 years of age. PCB levels showed inverse associations with BMI in younger individuals with diabe- tes, and in those with prediabetes, although the trends were not statistically significant. In a separate linear regression model, age, total lipids, total years living in Anniston, and nonwhite race showed significant positive associations with ΣPCBs, whereas we observed a significant inverse association for education. No association was observed for BMI in this model (data not shown). Results from unadjusted logistic regression modeling indicated a significant increase in the prevalence of diabetes across each quintile of PCB exposure relative to the referent (first quintile); unadjusted ORs (95% CIs) for the second through fifth quintiles were 3.51 (1.84, 6.67), 4.83 (2.53, 9.25), 2.66 (1.37, 5.18), and 7.19 (3.79, 13.63), respectively. After adjustment for standard diabetes-related risk factors (family history, age, BMI, race/ethnicity), sex, total lipids, and lipid-lowering medications, the PCB point estimates for the second through fifth quintiles remained elevated but were similar in magnitude (Table 4). An attenuation in the log-transformed ΣPCB OR also was observed after adjustment for standard factors: The unadjusted OR was 1.65 (95% CI: 1.38, 1.99) per 1 SD increase in log PCBs, whereas the adjusted OR for the PCB–diabetes association was 1.23 (95% CI: 0.88, 1.72). The GAM results in Figure 1 are similar to the quintile results, suggesting increasing diabetes risk with increasing serum PCBs, but possibly via a nonmonotonic relationship, with a decrease between about 3 ppb and 7 ppb. The sharp downtown in dose response Characteristic PCB quintile (ppb) Q1 (0.11–1.15) Q2 (1.16–2.42) Q3 (2.43–4.33) Q4 (4.34–9.33) Q5 (9.34–170) Log PCB per 1 SD increasea 730 Table 4. Logistic regression models of PCB quintiles and diabetes for the total group and with stratification by age: Anniston Community Health Survey. at the high end of exposure is based on seven participants with PCB levels > 50 ppb, which precludes precise estimation. Models stratified by age (< 55 and ≥ 55 years) are shown in Table 4. In the younger participants, after adjustment for established diabetes-related risk factors, the likelihood of diabetes remained significant among those in the highest quintile relative to the lowest (OR = 4.78; 95% CI: 1.11, 20.60). Point estimates in the second and third quin- tiles also were elevated among the younger participants but to a lesser extent. In the older participants, the ORs were elevated in each of the second through fifth quintiles, but the estimates were imprecise, with wide CIs, because the referent category (first quintile) comprised only five individuals. Seventy percent of the study partici- pants were female; analyses stratified by sex revealed a positive association between PCBs and diabetes in females and an inverse asso- ciation in males. This was apparent in both the quintile and the log-transformed ΣPCB models (p < 0.05 for sex × PCB interaction terms). In the quintile model, three of four ORs were significantly elevated in females, whereas three of four ORs were < 1.0 in men; each CI included the value in the analysis of males. The adjusted OR associated with a 1 SD increase in log-transformed PCBs was 1.52 (95% CI: 1.01, 2.28) in females and 0.68 (95% CI: 0.33, 1.41) in males (Table 5). Results from the analyses of log-trans- formed PCB subsets (e.g., mono-ortho, estro- genic), DDE, and diabetes (Table 5) were comparable to results for ΣPCBs in the total group and in the sex-stratified analyses. In females, adjusted ORs were significantly elevated for each congener subset, with the exception of the estrogenic PCBs. ORs were < 1.0 for males and were nonsignificant for all congener subsets; there were 153 males in this analysis compared with 427 females. DDE was associated with a significantly increased log OR of diabetes in women (OR = 1.42; 95% CI: 1.03, 1.95) but not in men (OR = 0.61; 95% CI: 0.32, 1.15), a finding also reported by Turyk et al. (2009a, 2009b). Discussion We found a high prevalence of diabetes in our study population—residents of an area surrounding the oldest former PCB pro- duction facility in the United States. Using logistic and GAM modeling, we observed a nonmonotonic increase in the prevalence of diabetes with increasing PCB congener levels in the total group after adjustment for estab- lished diabetes risk factors. We also observed a significantly elevated OR in the highest quintile among individuals < 55 years of age. Among individuals ≥ 55 years of age, adjusted ORs were elevated in all quintiles relative to the first quintile; however, the CIs were imprecise because only five participants in this age group had PCB values in the lowest quintile. In the total study group, the adjusted ORs for the PCB subsets and DDE were similar to the ΣPCB estimate. Women had an increased likelihood of diabetes not only for ΣPCB but also for DDE and each subset of PCBs examined, with the exception of the estrogenic subset; men had a decreased likeli- hood of diabetes based on a much smaller number of observations. PCB levels in our participants exceeded those observed in the general U.S. population and in previously reported community exposures. Lee et al. (2007) found an increased prevalence of diabetes in NHANES participants 40–59 years of age in the top quarter of the distribution of a mixture of six persistent organic pollutants, including pesticides and dioxins. Although adjusted for most risk factors, their model was not adjusted for family history of diabetes; moreover, individuals with prediabetes were not excluded. For comparison, the top value in the lowest quartile in the present study (1.43 ppb) is within the NHANES third quartile (50th to 75th percentile), whereas the third and fourth quartiles are several times higher (quartile 3 = 3.29–7.42 ppb; quartile 4 = 7.43–171 ppb) (Pavuk et al. 2009). In other studies that evaluated the association of diabetes (Codru et al. 2007; Lee et al. 2006, 2010, 2011a; Turyk et al. 2009a, 2009b; Vasiliu et al. 2006), PCB serum concentrations were mostly less than those found 35 years ovlume 120 | number 5 | May 2012 • Environmental Health Perspectives Total group (n = 580) < 55 years (n = 288) ≥ 55 years (n = 292) n (diabetes/control) OR (95% CI) n (diabetes/control) OR (95% CI) n (diabetes/control) OR (95% CI) 16/102 44/80 47/62 33/79 62/55 202/378 1.00 (reference) 2.18 (0.98, 4.82) 2.91 (1.24, 6.83) 1.64 (0.64, 4.19) 2.78 (1.00, 7.73) 1.23 (0.88, 1.72) 15/99 25/46 17/30 7/25 12/12 76/212 1.00 (reference) 2.44 (0.91, 6.53) 2.29 (0.71, 7.39) 1.14 (0.36, 5.52) 4.78 (1.11, 20.6) 1.52 (0.89, 2.58) 1/4 19/34 30/32 26/54 50/43 126/166 1.00 (reference) 4.47 (0.28, 71.38) 8.45 (0.53, 134.99) 3.74 (0.23, 59.62) 4.19 (0.26, 68.12) 0.93 (0.58, 1.58) PCB quintiles and the log-transformed PCB variable were derived from the ΣPCBs, wet weight. Individuals with prediabetes were excluded from all models. Covariables were age, sex, BMI (continuous), total lipids, race/ethnicity, family history of diabetes, and taking lipid-lowering medication. Controls are individuals with normoglycemia. aThe same covariables were used in both log-transformed PCB models and quintile models.