regression modeling compared participants with and without diabetes by quintiles of the ΣPCBs. Participants were excluded from multivariable analyses if they had prediabetes (n = 171) because many of their disease and demographic characteristics were intermediate to those observed in individuals with diabetes and those with normoglycemia. Associations between PCB levels and established diabetes risk factors (BMI, family history of diabetes, age, race/ethnicity), sex, other possible confounders (i.e., education, marital status, smoking, duration of residence in Anniston), and diabetes were assessed through bivariate logistic regression analysis. Confounding was assessed in a second series of models that included PCBs and one of the established diabetes risk factors or potential confounders. Confounding was defined as a > 10% change in the β-coefficient and was evaluated by comparing point estimates for the PCB– diabetes associations with and without the potential confounder. Final multivariable models included PCB levels (quintiles or log transformed), age (continuous), sex (female/ male), race/ethnicity (white/nonwhite), family history of diabetes (no/yes), BMI (continuous or categorical: ≤ 24.9, 25.0–29.9, 30.0–34.9, 3 5 . 0 – 3 9 . 9 , ≥ 4 0 k g / m 2) , t o t a l l i p i d s (continuous), and taking lipid-lowering drugs (no/yes). Stratified analyses were performed by sex, race/ethnicity (white and nonwhite), BMI (≤ 24.9, 25.0–29.9, 30.0–39.9, ≥ 40 kg/m2), and age (< 55 and ≥ 55 years of age) because our initial data showed a striking number of younger adults with diabetes. The median age of the study population (55 years) was chosen as the cut point in the age-stratified analyses to have relatively equal sample sizes. The analytic strategy employed in the strati- fied analyses was similar to that for the entire group of participants. Effect modification was assessed in full models using a backward step- wise procedure and the likelihood ratio test; a p-value of ≤ 0.05 indicated the presence of effect modification. Initial analyses were conducted using quintiles of the ΣPCB variable followed by analyses using the log-transformed wet-weight PCB variable to assess trend; odds ratios (ORs) associated with the log-transformed variables (ΣPCBs, PCB subsets, and DDE) were reported as a 1 SD increase. A gener- alized additive model (GAM) (Hastie and Tibshirani 1990) was fitted using a cubic regression spline for the ΣPCBs to further examine the shape of the dose–response curve. Analyses evaluating subsets of PCBs (e.g., mono-ortho) and DDE were fitted using log-transformed versions of these variables. Covariables included in the GAM and the adjusted PCB subset models were identical to those used in the ΣPCB model. All final models report ORs and 95% confidence intervals (CIs). Analyses were conducted using SPSS (version 19; IBM, Chicago, IL) and R software (version 2.10.1; R Development Core Team 2010). Log binomial models were fitted to assess the overall association between PCBs and diabetes because the prevalence of diabetes is 27%; ORs are more difficult to interpret than relative risks when the prevalence of the outcome is > 10–20% because they over- estimate the effects. These binomial models failed to converge; consequently, in this article we present logistic regression results. Results Participant characteristics are shown in Table 1. Nonwhites (47%) were African American except for four Native Americans. The mean age of participants was 55 years (range, 18–93 years). Approximately 30% of the group had less than a high school educa- tion, and 40% were high school graduates with no college. The mean duration of resi- dence in Anniston among participants was 28.8 years, with a range of 0.5–79 years. At the time of data collection, 85% of partici- pants had lived in Anniston for ≥ 5 years, and 11% were lifetime residents. PCBs and diabetes in Anniston Diabetes was present in 27% of 774 participants (self-report, n = 177; negative self- report and fasting glucose values > 125 mg/dL, n = 30). Seventy-five percent of individuals self-reporting diabetes were taking glycemic control medications (data not shown). On average, participants classified as having diabetes or prediabetes were older than those classified as normoglycemic (Table 1). The prevalences of prediabetes and diabetes were greatest in those between 45 and 74 years of age. The ratio of white to nonwhite participants was 1.3 in those without diabetes and 0.89 in those with diabetes (p = 0.05), suggesting a greater prevalence of diabetes in the nonwhite population. A greater proportion of participants with diabetes had BMIs ≥ 35 kg/m2, positive family histories of diabetes, and heart disease. Relationships between PCB levels and diabetes status are shown in Tables 2 and 3. Participants with diabetes had significantly higher age-adjusted GM PCB levels than did those with prediabetes or normoglycemia (Table 2); age-adjusted arithmetic means did not differ significantly by diabetes status. Age-adjusted GM PCB levels also were sig- nificantly higher for both females and males classed as having diabetes compared with Table 2. PCB quintiles by diabetes status and age-adjusted PCB levels by diabetes status, sex, and race: Anniston Community Health Survey. Characteristic PCB quintile (ppb)a Q1 (0.11–1.15) Q2 (1.16–2.42) Q3 (2.43–4.33) Q4 (4.34–9.33) Q5 (9.34–170) ΣPCBs (ppb)a,b* Sex Females** Males Race/ethnicity White# Nonwhite Normoglycemia (n = 392) 105 (27) 82 (21) 65 (17) 84 (21) 56 (14) 6.31 ± 0.58 (2.85) 6.42 ± 0.63 (2.83) 5.99 ± 0.88 (2.91) 3.45 ± 0.64 (1.88) 10.30 ± 0.72 (5.09) Diabetes status Prediabetes (n = 169) 32 (19) 27 (16) 39 (23) 36 (21) 35 (21) 6.14 ± 0.87 (3.13) 6.31 ± 0.95 (3.09) 5.89 ± 1.02 (3.18) 2.80 ± 0.92 (1.92) 9.65 ± 0.93 (5.21) Diabetes (n = 205) 16 (8) 44 (22) 50 (24) 33 (16) 62 (30) 7.71 ± 0.80 (3.62) 7.81 ± 0.83 (3.59) 7.39 ± 1.05 (3.69) Total (n = 766) 153 (20) 153 (20) 154 (20) 153 (20) 153 (20) 6.72 ± 0.44 (3.18) 6.85 ± 0.52 (3.16) 6.42 ± 0.76 (3.24) 3.99 ± 0.87 (2.11) 10.84 ± 0.84 (5.71) 3.41 ± 0.57 (1.97) 10.26 ± 0.58 (5.33) Data are n (%) or mean ± SE (GM). aPCB quintiles were derived from the ΣPCBs. bPCB levels were adjusted for age; separate age-adjusted models are presented for diabetes status alone, for diabetes status with sex, and for diabetes status with race/ethnicity. *p = 0.014 for GMs compared across diabetes status. **p = 0.014 for GMs compared across diabetes status with sex in the model; no difference by sex. (p = 0.71). #p < 0.001 for GMs compared by race/ethnicity; no difference across diabetes status. (p = 0.25). Table 3. PCB levels (log ΣPCBs) by diabetes status, age, and BMI: Anniston Community Health Survey. Diabetes status BMI (kg/m2) ≤ 24.9 25–29.9 30–34.9 35–39.9 ≥ 40 Normoglycemia (n = 392) Prediabetes (n = 169) Diabetes (n = 205) Total (n = 766) < 55 years ≥ 55 years < 55 years ≥ 55 years < 55 years ≥ 55 years < 55 years ≥ 55 years 70 (1.13) 44 (1.58) 51 (1.19) 25 (1.27) 26 (1.23) 57 (6.31) 61 (4.90) 34 (4.18) 14 (5.98) 9 (6.01) 7 (3.87) 17 (3.17) 19 (1.45) 13 (1.99) 11 (1.30) 11 (5.23) 38 (5.48) 33 (5.24) 13 (4.29) 6 (6.87) 7 (4.45) 14 (2.41) 16 (2.76) 17 (2.06) 23 (1.87) 17 (4.16) 29 (7.25) 35 (5.44) 26 (9.43) 21 (7.26) 84 (1.40) 75 (2.00) 86 (1.45) 55 (1.64) 60 (1.46) 85 (5.67) 128 (5.54) 102 (4.92) 53 (6.90) 36 (6.86) Data are [n (GM)]. This GLM model assessed age (< 55, ≥ 55), diabetes status (normoglycemia, prediabetes, diabetes), BMI (five categories above), and log PCB levels. Parameter p-values from the GLM: age, p < 0.001; diabetes status, p < 0.001; BMI, p = 0.33. Environmental Health Perspectives • volume 120 | number 5 | May 2012 729