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Aim of the study A vast majority of human malignancies are associated with ageing, and age is a strong predictor of cancer risk. Recently, DNA methylation-based marker of ageing, known as ‘epigenetic clock’, has been linked with cancer risk factors. This study aimed to evaluate whether the epigenetic clock is associated with breast cancer risk susceptibility and to identify potential epigenetics-based biomarkers for risk stratification. Methods Here, we profiled DNA methylation changes in a nested case–control study embedded in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort (n = 960) using the Illumina HumanMethylation 450K BeadChip arrays and used the Horvath age estimation method to calculate epigenetic age for these samples. Intrinsic epigenetic age acceleration (IEAA) was estimated as the residuals by regressing epigenetic age on chronological age. Results We observed an association between IEAA and breast cancer risk (OR, 1.04; 95% CI, 1.007–1.076, P = 0.016). One unit increase in IEAA was associated with a 4% increased odds of developing breast cancer (OR, 1.04; 95% CI, 1.007–1.076). Stratified analysis based on menopausal status revealed that IEAA was associated with development of postmenopausal breast cancers (OR, 1.07; 95% CI, 1.020–1.11, P = 0.003). In addition, methylome-wide analyses revealed that a higher mean DNA methylation at cytosine-phosphate-guanine (CpG) islands was associated with increased risk of breast cancer development (OR per 1 SD = 1.20; 95 %CI: 1.03–1.40, P = 0.02) whereas mean methylation levels at non-island CpGs were indistinguishable between cancer cases and controls. Conclusion Epigenetic age acceleration and CpG island methylation have a weak, but statistically significant, association with breast cancer susceptibility.

Epidemiological studies have reported inconsistent findings for the association between B vitamins and breast cancer (BC) risk. We investigated the relationship between biomarkers of folate and vitamin B12 and the risk of BC in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Plasma concentrations of folate and vitamin B12 were determined in 2,491 BC cases individually matched to 2,521 controls among women who provided baseline blood samples. Multivariable logistic regression models were used to estimate odds ratios by quartiles of either plasma B vitamin. Subgroup analyses by menopausal status, hormone receptor status of breast tumors (estrogen receptor [ER], progesterone receptor [PR] and human epidermal growth factor receptor 2 [HER2]), alcohol intake and MTHFR polymorphisms (677C > T and 1298A > C) were also performed. Plasma levels of folate and vitamin B12 were not significantly associated with the overall risk of BC or by hormone receptor status. A marginally positive association was found between vitamin B12 status and BC risk in women consuming above the median level of alcohol (OR = 1.26; 95% CI 1.00-1.58; P = 0.05). Vitamin B12 status was also positively associated with BC risk in women with plasma folate levels below the median value (OR = 1.29; 95% CI 1.02-1.62; P = 0.03). Overall, folate and vitamin B12 status was not clearly associated with BC risk in this prospective cohort study. However, potential interactions between vitamin B12 and alcohol or folate on the risk of BC deserve further investigation.

Background. Pancreatic cancer (PC) is a highly fatal cancer with currently limited opportunities for early detection and effective treatment. Modifiable factors may offer pathways for primary prevention. In this study, the association between the healthy lifestyle index (HLI) and PC risk was examined. Methods. Within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, 1,113 incident PC (57% women) were diagnosed from 400,577 cancer-free participants followed-up for 15 years (median). HLI scores combined smoking, alcohol intake, dietary exposure, physical activity and, in turn, overall and central adiposity using BMI (HLIBMI) and waist-to-hip ratio (WHR, HLIWHR), respectively. High values of HLI indicate adherence to healthy behaviors. Cox proportional hazard models with age as primary time variable were used to estimate PC hazard ratios (HR) and 95% confidence intervals (CI). Sensitivity analyses were performed by excluding, in turn, each factor from the HLI score. Population attributable fractions (PAF) were estimated assuming participants' shift to healthier lifestyles. Results. The HRs for a one-standard deviation increment of HLIBMI and HLIWHR were 0.84

BackgroundMethylation measures quantified by microarray techniques can be affected by systematic variation due to the technical processing of samples, which may compromise the accuracy of the measurement process and contribute to bias the estimate of the association under investigation. The quantification of the contribution of the systematic source of variation is challenging in datasets characterized by hundreds of thousands of features.In this study, we introduce a method previously developed for the analysis of metabolomics data to evaluate the performance of existing normalizing techniques to correct for unwanted variation. Illumina Infinium HumanMethylation450K was used to acquire methylation levels in over 421,000 CpG sites for 902 study participants of a case-control study on breast cancer nested within the EPIC cohort. The principal component partial R-square (PC-PR2) analysis was used to identify and quantify the variability attributable to potential systematic sources of variation. Three correcting techniques, namely ComBat, surrogate variables analysis (SVA) and a linear regression model to compute residuals were applied. The impact of each correcting method on the association between smoking status and DNA methylation levels was evaluated, and results were compared with findings from a large meta-analysis.ResultsA sizeable proportion of systematic variability due to variables expressing ‘batch’ and ‘sample position’ within ‘chip’ was identified, with values of the partial R2 statistics equal to 9.5 and 11.4% of total variation, respectively. After application of ComBat or the residuals’ methods, the contribution was 1.3 and 0.2%, respectively. The SVA technique resulted in a reduced variability due to ‘batch’ (1.3%) and ‘sample position’ (0.6%), and in a diminished variability attributable to ‘chip’ within a batch (0.9%). After ComBat or the residuals’ corrections, a larger number of significant sites (k = 600 and k = 427, respectively) were associated to smoking status than the SVA correction (k = 96).ConclusionsThe three correction methods removed systematic variation in DNA methylation data, as assessed by the PC-PR2, which lent itself as a useful tool to explore variability in large dimension data. SVA produced more conservative findings than ComBat in the association between smoking and DNA methylation.Electronic supplementary materialThe online version of this article (10.1186/s13148-018-0471-6) contains supplementary material, which is available to authorized users.