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Alcohol consumption is causally linked to several cancers but the evidence for stomach cancer is inconclusive. In our study, the association between long-term alcohol intake and risk of stomach cancer and its subtypes was evaluated. We performed a pooled analysis of data collected at baseline from 491 714 participants in the European Prospective Investigation into Cancer and Nutrition and the Melbourne Collaborative Cohort Study. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated for incident stomach cancer in relation to lifetime alcohol intake and group-based life course intake trajectories, adjusted for potential confounders including Helicobacter pylori infection. In all, 1225 incident stomach cancers (78% noncardia) were diagnosed over 7 094 637 person-years; 984 in 382 957 study participants with lifetime alcohol intake data (5 455 507 person-years). Although lifetime alcohol intake was not associated with overall stomach cancer risk, we observed a weak positive association with noncardia cancer (HR = 1.03, 95% CI: 1.00-1.06 per 10 g/d increment), with a HR of 1.50 (95% CI: 1.08-2.09) for ≥60 g/d compared to 0.1 to 4.9 g/d. A weak inverse association with cardia cancer (HR = 0.93, 95% CI: 0.87-1.00) was also observed. HRs of 1.48 (95% CI: 1.10-1.99) for noncardia and 0.51 (95% CI: 0.26-1.03) for cardia cancer were observed for a life course trajectory characterized by heavy decreasing intake compared to light stable intake (Phomogeneity = .02). These associations did not differ appreciably by smoking or H pylori infection status. Limiting alcohol use during lifetime, particularly avoiding heavy use during early adulthood, might help prevent noncardia stomach cancer. Heterogeneous associations observed for cardia and noncardia cancers may indicate etiologic differences.

Evidence suggests an influence of sex hormones on cutaneous melanoma risk, but epidemiologic findings are conflicting. We examined the associations between use of oral contraceptives (OCs) and menopausal hormone therapy (MHT) and melanoma risk in women participating in the European Prospective Investigation into Cancer and Nutrition (EPIC). EPIC is a prospective cohort study initiated in 1992 in 10 European countries. Information on exogenous hormone use at baseline was derived from country-specific self-administered questionnaires. We used Cox proportional hazards regression models to calculate hazard ratios (HRs) and 95% confidence intervals (CIs). Over 1992-2015, 1,696 melanoma cases were identified among 334,483 women, whereof 770 cases among 134,758 postmenopausal women. There was a positive, borderline-significant association between OC use and melanoma risk (HR = 1.12, 95% CI = 1.00-1.26), with no detected heterogeneity across countries (phomogeneity = 0.42). This risk increased linearly with duration of use (ptrend = 0.01). Among postmenopausal women, ever use of MHT was associated with a nonsignificant increase in melanoma risk overall (HR = 1.14, 95% CI = 0.97-1.43), which was heterogeneous across countries (phomogeneity = 0.05). Our findings do not support a strong and direct association between exogenous hormone use and melanoma risk. In order to better understand these relations, further research should be performed using prospectively collected data including detailed information on types of hormone, and on sun exposure, which may act as an important confounder or effect modifier on these relations.

Previous in vitro and case-control studies have found an association between the insulin-like growth factor (IGF)-axis and bladder cancer risk. Circulating concentrations of IGF-I have also been found to be associated with an increased risk of several cancer types; however, the relationship between pre-diagnostic circulating IGF-I concentrations and bladder cancer has never been studied prospectively. We investigated the association of pre-diagnostic plasma concentrations of IGF-I with risk of overall bladder cancer and urothelial cell carcinoma (UCC) in a case-control study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. A total of 843 men and women diagnosed with bladder cancer between 1992 and 2005 were matched with 843 controls by recruitment centre, sex, age at recruitment, date of blood collection, duration of follow-up, time of day and fasting status at blood collection using an incidence density sampling protocol. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using conditional logistic regression with adjustment for smoking status. No association was found between pre-diagnostic circulating IGF-I concentration and overall bladder cancer risk (adjusted OR for highest versus lowest fourth: 0.91, 95% CI: 0.66-1.24, ptrend = 0.40) or UCC (n of cases = 776; 0.91, 0.65-1.26, ptrend = 0.40). There was no significant evidence of heterogeneity in the association of IGF-I with bladder cancer risk by tumour aggressiveness, sex, smoking status, or by time between blood collection and diagnosis (pheterogeneity > 0.05 for all). This first prospective study indicates no evidence of an association between plasma IGF-I concentrations and bladder cancer risk.

Noninvasive biomarkers for early pancreatic ductal adenocarcinoma (PDAC) diagnosis and disease risk stratification are greatly needed. We conducted a nested case-control study within the Prospective Investigation into Cancer and Nutrition (EPIC) cohort to evaluate prediagnostic microRNAs (miRs) as biomarkers of subsequent PDAC risk. A panel of eight miRs (miR-10a, -10b, -21-3p, -21-5p, -30c, -106b, -155 and -212) based on previous evidence from our group was evaluated in 225 microscopically confirmed PDAC cases and 225 controls matched on center, sex, fasting status and age/date/time of blood collection. MiR levels in prediagnostic plasma samples were determined by quantitative RT-PCR. Logistic regression was used to model levels and PDAC risk, adjusting for covariates and to estimate area under the receiver operating characteristic curves (AUC). Plasma miR-10b, -21-5p, -30c and -106b levels were significantly higher in cases diagnosed within 2 years of blood collection compared to matched controls (all p-values <0.04). Based on adjusted logistic regression models, levels for six miRs (miR-10a, -10b, -21-5p, -30c, -155 and -212) overall, and for four miRs (-10a, -10b, -21-5p and -30c) at shorter follow-up time between blood collection and diagnosis (≤5 yr, ≤2 yr), were statistically significantly associated with risk. A score based on the panel showed a linear dose-response trend with risk (p-value = 0.0006). For shorter follow-up (≤5 yr), AUC for the score was 0.73, and for individual miRs ranged from 0.73 (miR-212) to 0.79 (miR-21-5p).

Endometrial cancer risk prediction models including lifestyle, anthropometric and reproductive factors have limited discrimination. Adding biomarker data to these models may improve predictive capacity; to our knowledge, this has not been investigated for endometrial cancer. Using a nested case-control study within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, we investigated the improvement in discrimination gained by adding serum biomarker concentrations to risk estimates derived from an existing risk prediction model based on epidemiologic factors. Serum concentrations of sex steroid hormones, metabolic markers, growth factors, adipokines and cytokines were evaluated in a step-wise backward selection process; biomarkers were retained at p < 0.157 indicating improvement in the Akaike information criterion (AIC). Improvement in discrimination was assessed using the C-statistic for all biomarkers alone, and change in C-statistic from addition of biomarkers to preexisting absolute risk estimates. We used internal validation with bootstrapping (1000-fold) to adjust for over-fitting. Adiponectin, estrone, interleukin-1 receptor antagonist, tumor necrosis factor-alpha and triglycerides were selected into the model. After accounting for over-fitting, discrimination was improved by 2.0 percentage points when all evaluated biomarkers were included and 1.7 percentage points in the model including the selected biomarkers. Models including etiologic markers on independent pathways and genetic markers may further improve discrimination.