Alcohol is an established risk factor for several cancers, but modest alcohol-cancer associations may be missed because of measurement error in self-reported assessments. Biomarkers of habitual alcohol intake may provide novel insight into the relationship between alcohol and cancer risk. Untargeted metabolomics was used to identify metabolites correlated with self-reported habitual alcohol intake in a discovery dataset from the European Prospective Investigation into Cancer and Nutrition (EPIC; n = 454). Statistically significant correlations were tested in independent datasets of controls from case-control studies nested within EPIC (n = 280) and the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC; n = 438) study. Conditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for associations of alcohol-associated metabolites and self-reported alcohol intake with risk of pancreatic cancer, hepatocellular carcinoma (HCC), liver cancer, and liver disease mortality in the contributing studies. Two metabolites displayed a dose-response association with self-reported alcohol intake: 2-hydroxy-3-methylbutyric acid and an unidentified compound. A 1-SD (log₂) increase in levels of 2-hydroxy-3-methylbutyric acid was associated with risk of HCC (OR = 2.54, 95% CI = 1.51 to 4.27) and pancreatic cancer (OR = 1.43, 95% CI = 1.03 to 1.99) in EPIC and liver cancer (OR = 2.00, 95% CI = 1.44 to 2.77) and liver disease mortality (OR = 2.16, 95% CI = 1.63 to 2.86) in ATBC. Conversely, a 1-SD (log₂) increase in questionnaire-derived alcohol intake was not associated with HCC or pancreatic cancer in EPIC or liver cancer in ATBC but was associated with liver disease mortality (OR = 2.19, 95% CI = 1.60 to 2.98) in ATBC. 2-hydroxy-3-methylbutyric acid is a candidate biomarker of habitual alcohol intake that may advance the study of alcohol and cancer risk in population-based studies.

Background: Epidemiological studies of associations between metabolites and cancer risk have typically focused on specific cancer types separately. Here, we designed a multivariate pan-cancer analysis to identify metabolites potentially associated with multiple cancer types, while also allowing the investigation of cancer type-specific associations.Methods: We analysed targeted metabolomics data available for 5828 matched case-control pairs from cancer-specific case-control studies on breast, colorectal, endometrial, gallbladder, kidney, localized and advanced prostate cancer, and hepatocellular carcinoma nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. From pre-diagnostic blood levels of an initial set of 117 metabolites, 33 cluster representatives of strongly correlated metabolites and 17 single metabolites were derived by hierarchical clustering. The mutually adjusted associations of the resulting 50 metabolites with cancer risk were examined in penalized conditional logistic regression models adjusted for body mass index, using the data-shared lasso penalty.Results: Out of the 50 studied metabolites, (i) six were inversely associated with the risk of most cancer types: glutamine, butyrylcarnitine, lysophosphatidylcholine a C18:2, and three clusters of phosphatidylcholines (PCs); (ii) three were positively associated with most cancer types: proline, decanoylcarnitine, and one cluster of PCs; and (iii) 10 were specifically associated with particular cancer types, including histidine that was inversely associated with colorectal cancer risk and one cluster of sphingomyelins that was inversely associated with risk of hepatocellular carcinoma and positively with endometrial cancer risk.Conclusions: These results could provide novel insights for the identification of pathways for cancer development, in particular those shared across different cancer types.

Background: Mechanisms underlying the adiposity–cancer relationship are incompletely understood. We quantified the mediating roles of C‐reactive protein (CRP), leptin, fasting insulin, and estradiol in the effect of adiposity on estrogen receptor (ER)‐positive breast, endometrial, and colorectal cancer risk in postmenopausal women.Methods: We used a case–cohort study within the Women's Health Initiative Observational Study, analyzed as a cumulative sampling case–control study. The study included 188 breast cancer cases, 98 endometrial cancer cases, 193 colorectal cancer cases, and 285 controls. Interventional indirect and direct effects on the risk ratio (RR) scale were estimated using causal mediation analysis.Results: For breast cancer, the total effect RR for BMI ≥30 versus ≥18.5–<25 kg/m² was 1.87 (95%CI,1.11–3.13). The indirect effect RRs were 1.38 (0.79–2.33) through leptin and CRP, 1.58 (1.17–2.43) through insulin, and 1.11 (0.98–1.30) through estradiol. The direct effect RR was 0.82 (0.39–1.68). For endometrial cancer, the total effect RR was 2.12 (1.12–4.00). The indirect effect RRs were 1.72 (0.85–3.98) through leptin and CRP, 1.42 (0.96–2.26) through insulin, and 1.24 (1.03–1.65) through estradiol. The direct effect RR was 0.70 (0.23–2.04). For colorectal cancer, the total effect RR was 1.70 (1.03–2.79). The indirect effect RRs were 1.04 (0.61–1.72) through leptin and CRP, 1.36 (1.00–1.88) through insulin, and 1.02 (0.88–1.17) through estradiol. The direct effect RR was 1.16 (0.58–2.43).Conclusion: Leptin, CRP, fasting insulin, and estradiol appear to mediate the effect of high BMI on cancer risk to different extents, with likely varying degrees of importance between cancers. These insights might be important in developing interventions to modify obesity‐associated cancer risk in postmenopausal women.

Since the first version of the dietary inflammatory index (DII®) developed in the past decade, several other versions have been developed. However, to date no study has attempted to compare these versions with respect to their associations with biomarkers of inflammation. We aimed to investigate the relationship between four dietary inflammatory scores [DII, two energy-adjusted derivatives (E-DII and E-DII_r), and the Inflammatory Score of the Diet (ISD)], and circulating levels of several inflammatory markers and adipokines. This study included 17 637 participants from the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort with at least one marker of inflammation measured in blood. Associations between the four scores and C-reactive protein (CRP), interleukin (IL)6, IL10, IL1RA, tumor necrosis factor-α (TNFα), soluble tumor necrosis factor receptor-1 (sTNFR1), sTNFR2, leptin, soluble leptin receptor (sLeptin R), adiponectin, and High Molecular Weight (HMW) adiponectin were evaluated using multivariable linear regressions adjusted for potential confounders. Positive associations were observed between the four dietary inflammatory scores and levels of CRP, IL6, sTNFR1, sTNFR2 and leptin. However, only the DII and the ISD were positively associated with IL1RA levels and only the DII and the E-DII_r were positively associated with TNFα levels. The proportion of variance of each biomarker explained by the scores was lower than 2%, which was equivalent to the variance explained by smoking status but much lower than that explained by body mass index. Our results suggest that the four dietary inflammatory scores were associated with some biomarkers of inflammation and could be used to assess the inflammatory potential of diet in European adults but are not sufficient to capture the inflammatory status of an individual. These findings can help to better understand the inflammatory potential of diet, but they need to be replicated in studies with repeated dietary measurements.

Background: Adiposity increases endometrial cancer risk, possibly through inflammation, hyperinsulinemia, and increasing estrogens. We aimed to quantify the mediating effects of adiponectin (anti-inflammatory adipocytokine); IL6, IL1-receptor antagonist, TNF receptor 1 and 2, and C-reactive protein (inflammatory status biomarkers); C-peptide (hyperinsulinemia biomarker); and free estradiol and estrone (estrogen biomarkers) in the adiposity–endometrial cancer link in postmenopausal women.Methods: We used data from a case–control study within the European Prospective Investigation into Cancer and Nutrition (EPIC). Eligible women did not have cancer, hysterectomy, and diabetes; did not use oral contraceptives or hormone therapy; and were postmenopausal at recruitment. Mediating pathways from adiposity to endometrial cancer were investigated by estimating natural indirect (NIE) and direct (NDE) effects using sequential mediation analysis.Results: The study included 163 cases and 306 controls. The adjusted OR for endometrial cancer for body mass index (BMI) ≥30 versus ≥18.5−<25 kg/m2 was 2.51 (95% confidence interval, 1.26–5.02). The ORsNIE were 1.95 (1.01–3.74) through all biomarkers [72% proportion mediated (PM)] decomposed as: 1.35 (1.06–1.73) through pathways originating with adiponectin (33% PM); 1.13 (0.71–1.80) through inflammation beyond (the potential influence of) adiponectin (13% PM); 1.05 (0.88–1.24) through C-peptide beyond adiponectin and inflammation (5% PM); and 1.22 (0.89–1.67) through estrogens beyond preceding biomarkers (21% PM). The ORNDE not through biomarkers was 1.29 (0.54–3.09). Waist circumference gave similar results.Conclusions: Reduced adiponectin and increased inflammatory biomarkers, C-peptide, and estrogens mediated approximately 70% of increased odds of endometrial cancer in women with obesity versus normal weight.Impact: If replicated, these results could have implications for identifying targets for intervention to reduce endometrial cancer risk in women with obesity.