intake, are shown in Table 4. Those signifi- cantly associated with an increased risk were mono-ortho PCBs 28 and 118, each with indi- vidual ORs > 2 for the more exposed category. The OR for serum concentrations of the mono-ortho PCB group that combines PCB-28 and PCB-118 was 1.82 (95% CI, 0.90–3.70) in the middle tertile (LOD, 147 ng/g lipid) and 2.94 (95% CI, 1.39–6.20; p-value for trend = 0.004) for the highest tertile. Concentrations of di-ortho PCBs were not related to disease, nor was the classiﬁcation of OCs according to their lipophilicity. High levels of α-HCH, HCB, and p,p´-DDE showed a nonsigniﬁcant increase in risk. Levels of p,p´-DDT were lower in cases than in controls in this population. OC levels and interaction with other vari- ables. Dietary variables, which are both sources of exposure and risk factors for colorectal can- cer, were explored as potential confounders. Speciﬁc food groups explored were vegetables, fruit, legumes, potatoes, meat, ﬁsh, eggs, dairy products, added fats, and pastries. Spearman’s correlation coefficients between food groups and serum levels of speciﬁc OCs were in general low and nonsigniﬁcant (maximum r = 0.19). Exceptions worth mentioning were dairy prod- ucts, which correlated with p,p´-DDT and PCB-101; fresh meats, which correlated with PCB-101, PCB-153, and PCB-180; and ﬁsh, which correlated with PCB-153 and PCB-180. The significance of these correlations was poor, and in addition to those correlations mentioned, we observed several negative cor- relations. We also used a multivariate logistic regression model with all food groups to generate a “dietary propensity factor,” which could be interpreted as a score integrating the risk of colorectal cancer associated with diet. Adjusting OCs for this dietary propensity fac- tor did not modify the risk estimates, showing again that diet was not a confounding factor. Hence, after careful consideration, adjustment of the analyses for food groups was found to be unnecessary because foods were not clearly related to serum OC levels and, more impor- tant, did not modify the risk estimates. An exploratory analysis of interactions between mono-ortho PCBs and several vari- ables was carried out. The magnitude of the risk of colorectal cancer observed for these OCs was not modiﬁed by sex, age, BMI, smoking, NSAIDs use, or parity in women. Analysis of interactions with other hormonal variables in women could not be performed because only six women were exposed to oral contraceptives and only two to postmenopausal hormone replacement treatments. Because alcohol was an important risk factor in this study and alco- hol intake produces metabolic induction simi- lar to that produced by some OCs (Mochizuki and Yoshida 1989), interactions between OCs and alcohol were explored in detail, but none was signiﬁcant. p,p´-DDE Low Medium High p,p´-DDT Low Medium High HCB Low Medium High α-HCH Low Medium High β-HCH Low Medium High γ-HCH < LOD Medium High PCB-28 < LOD > LOD PCB-118 < LOD Medium High PCB-52 < LOD > LOD PCB-153 Low Medium High PCB-138 Low Medium High PCB-180 Low Medium High ap-Value for trend adjusted for age, sex, energy intake, and BMI. Environmental Health Perspectives • VOLUME 112 | NUMBER 15 | November 2004 Article | Organochlorine exposure and colorectal cancer risk OC levels and mutations at the K-ras and p53 genes. K-ras was mutated in 50 (38%) cases. Most frequent mutations were GAT (n = 16) and GTT (n = 15) in codon 12. In 6 cases, codon 13 was mutated to GAC. Other, less frequent mutations in codon 12 were TGT (n = 5), AGT (n = 4), GCT (n = 3), and CGT (n = 1). Twenty-six mutations were transitions (G:C → A:T), and 24 were trans- versions (G:C → T:A). Tumor suppressor gene p53 was mutated in 59 (60%) of the tumors analyzed (data not available for 34 cases). Twenty-four mutations were located in hotspots: codon 175 (n = 4), codon 245 (n = 3), codon 248 (n = 3), codon 273 (n = 6), and codon 282 (n = 8). Four cases harbored mutations at codon 158, the remaining being detected at 24 different codons with frequencies ≤ 2. Forty mutations were transitions, and 15 were transversions; in the remaining four cases, deletions were detected. The most frequent base changes were C→T and G→A, each in 17 cases, mainly at hotspot sites. G→T changes were observed in eight cases and distributed with a nonclustered profile. The immunohisto- chemical analyses showed that 76% of the cases overexpressed the p53 protein. Table 5 shows the analysis of selected OCs where the cases have been stratiﬁed by muta- tions in K-ras and p53. This analysis is based on polytomous logistic regression, and each group of cases is compared with controls in a uniﬁed model. Exposure to mono-ortho PCBs 28 and 118 increased risk in similar magni- tude for both mutated and wild-type K-ras. PCB-118, but not PCB-28, showed higher risk for p53-mutated tumors, although the Table 4. Risk of colorectal cancer associated with organochlorine body burden. Controls No. (%) 31 (41) 21 (28) 24 (32) 25 (33) 18 (24) 33 (43) 29 (38) 22 (29) 25 (33) 33 (43) 27 (36) 16 (21) 26 (34) 23 (30) 27 (36) 30 (39) 22 (29) 24 (32) 65 (86) 11 (14) 36 (47) 20 (26) 20 (26) 69 (91) 7 (9) 30 (39) 22 (29) 24 (32) 26 (34) 26 (34) 24 (32) 29 (38) 24 (32) 23 (30) Cases No. (%) 38 (29) 49 (37) 45 (34) 44 (33) 52 (39) 36 (27) 40 (30) 47 (36) 45 (34) 48 (36) 37 (28) 47 (36) 44 (33) 45 (34) 43 (33) 61 (46) 36 (27) 35 (27) 91 (69) 41 (31) 43 (33) 39 (30) 50 (38) 120 (91) 12 (9) 39 (30) 48 (36) 45 (34) 43 (33) 44 (33) 45 (34) 41 (31) 44 (33) 47 (36) OR (95% CI) 1.00 2.17 (1.03–4.54) 1.60 (0.79–3.25) 1.00 1.58 (0.74–3.36) 0.56 (0.27–1.17) 1.00 1.72 (0.83–3.54) 1.60 (0.62–4.15) 1.00 0.99 (0.49–1.98) 2.02 (0.95–4.29) 1.00 1.14 (0.55–2.36) 0.88 (0.39–2.02) 1.00 0.79 (0.38–1.61) 0.69 (0.34–1.38) 1.00 2.75 (1.29–5.83) 1.00 1.63 (0.80–3.31) 2.02 (1.00–4.08) 1.00 1.16 (0.42–3.19) 1.00 1.50 (0.74–3.07) 1.22 (0.59–2.52) 1.00 0.85 (0.41–1.75) 0.90 (0.43–1.90) 1.00 1.20 (0.59–2.42) 1.19 (0.57–2.49) p-Valuea 0.19 0.12 0.23 0.081 0.81 0.28 0.006 0.045 0.77 0.57 0.79 0.63 1463