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Flavie Perrier,1 Reza Ghiasvand,2,3 Simon Lergenmuller,1 Trude E Robsahm,3 Adele C Green,4,5 Kristin B Borch,6 Torkjel M Sandanger,6 Elisabete Weiderpass,7 Corina S Rueegg,2 Marit B Veierød1 1Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway; 2Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway; 3Cancer Registry of Norway, Institute of Population-Based Cancer Research, Oslo, Norway; 4QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia; 5Cancer Research UK Manchester and Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK; 6Department of Community Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø, Norway; 7International Agency for Research on Cancer, Lyon, FranceCorrespondence: Flavie Perrier, Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, PO Box 1122, Blindern, Oslo, N-0317, Norway, Email flavie.perrier@medisin.uio.noPurpose: Physical activity (PA) is a cornerstone in disease prevention and varies throughout life. A pooled analysis of cohort studies and a meta-analysis of cohort studies found positive associations between PA and melanoma risk. However, previous studies focused on PA at specific ages and often lacked information on ultraviolet radiation (UVR) exposure. Using the population-based Norwegian Women and Cancer (NOWAC) cohort, including information on PA and UVR exposure, we estimated life-course PA trajectories from adolescence to adulthood and their associations with melanoma.Methods: Total PA across different domains (recreation, occupation, transport, household) was reported for ages 14 and 30 years, and when responding to the questionnaire (31– 76 years) using a 10-point scale, validated to rank PA levels in Norwegian females. We estimated life-course PA trajectories using a latent class mixed model in 152,248 women divided into three subcohorts depending on age at questionnaire completion: 31– 39 (n = 27,098), 40– 49 (n = 52,515) and ≥ 50 years (n = 72,635). The unique 11-digit identity number of Norwegian citizens was used to link NOWAC to the Cancer Registry of Norway for information on cancer diagnoses, emigration and death. Associations between PA trajectories and melanoma risk were estimated in each subcohort using multivariable Cox regression.Results: Five classes of individual life-course PA trajectories were identified in subcohort 31– 39 years (low, moderate, high, decreasing, increasing PA) and four in subcohorts 40– 49 and ≥ 50 years (low, moderate, high, decreasing PA). No significant association was found between life-course PA trajectories and melanoma risk in any subcohort. Hazard ratios (95% confidence intervals) for the high versus moderate trajectory were 0.92 (0.66– 1.29), 1.15 (0.97– 1.37) and 0.90 (0.78– 1.05) for subcohorts 31– 39, 40– 49 and ≥ 50 years, respectively.Conclusion: Our results do not support a positive association between PA and melanoma risk found in previous studies, which is important for public health guidelines promoting regular PA.Keywords: physical activity, cutaneous melanoma, trajectory, cohort, women, Norway

Knowledge about factors associated with melanoma thickness, the most important prognostic factor for localized primary melanoma survival,1 may help reduce the risk of melanoma deaths. Previously reported associations of melanoma thickness with pigmentary characteristics, number of naevi, diet quality and body mass index (BMI)2–4 may be explained by behavioural and biological mechanisms. Physical activity (PA) has been associated with improved outcomes for several cancers,5 but its relation with melanoma thickness and prognosis is unknown.

This is a post-peer-review, pre-copyedit version of an article published in the European Journal of Epidemiology. The final authenticated version is available online at: https://doi.org/10.1007/s10654-019-00559-6. 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. Within the European Prospective Investigation into Cancer and Nutrition cohort, 1113 incident PC (57% women) were diagnosed from 400,577 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. The HRs for a one-standard deviation increment of HLIBMI and HLIWHR were 0.84 (95% CI: 0.79, 0.89; ptrend = 4.3e−09) and 0.77 (0.72, 0.82; ptrend = 1.7e−15), respectively. Exclusions of smoking from HLIWHR resulted in HRs of 0.88 (0.82, 0.94; ptrend = 4.9e−04). The overall PAF estimate was 19% (95% CI: 11%, 26%), and 14% (6%, 21%) when smoking was removed from the score. Adherence to a healthy lifestyle was inversely associated with PC risk, beyond the beneficial role of smoking avoidance. Public health measures targeting compliance with healthy lifestyles may have an impact on PC incidence.

Physical activity (PA) is an important factor in cancer prevention, but positive association between PA and risk of cutaneous melanoma found in recent studies may complicate this strategy. Ultraviolet radiation (UVR) exposure during outdoor PA is a plausible explanation for a positive association. We investigated the associations between PA, UVR and melanoma risk in the Norwegian Women and Cancer cohort. Overall PA was reported by 151,710 women, aged 30–75 at inclusion, using a validated 10-point-scale at enrolment and during follow-up, together with recent numbers of sunburns, indoor tanning sessions and weeks on sunbathing vacations. Seasonal outdoor walking and seasonal PAs were recorded in subsamples (n = 102,671 and n = 29,077, respectively). Logistic and Cox regression were used. Mean follow-up was 18.5 years, and 1565 invasive incident melanoma cases were diagnosed. Overall PA was inversely associated with sunburns, while positively associated with sunbathing vacations and indoor tanning. Overall PA was not associated with melanoma risk in all body sites combined (ptrend = 0.61), but reduced risk was found in upper limb melanomas (hazard ratio (HR) = 0.70, 95% confidence interval (CI) 0.51–0.96; high versus low PA). Non-significant reduced risks were found for seasonal outdoor walking >2 h/day versus 30–60 min/day (summer HR = 0.81, 95% CI 0.66–1.00; autumn HR = 0.74, 95%CI 0.55–1.01). Seasonal PAs were not associated with melanoma risk. In conclusion, we found positive associations between overall PA and sunbathing vacations and indoor tanning, and, unlike literature, inverse association between overall PA and sunburns. Our results do not support a positive association between PA and melanoma risk in Norwegian women.

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 vit ...

Supplemental Digital Content is available in the text., Background: For chemicals with high within-subject temporal variability, assessing exposure biomarkers in a spot biospecimen poorly estimates average levels over long periods. The objective is to characterize the ability of within-subject pooling of biospecimens to reduce bias due to exposure misclassification when within-subject variability in biomarker concentrations is high. Methods: We considered chemicals with intraclass correlation coefficients of 0.6 and 0.2. In a simulation study, we hypothesized that the chemical urinary concentrations averaged over a given time period were associated with a health outcome and estimated the bias of studies assessing exposure that collected 1 to 50 random biospecimens per subject. We assumed a classical type error. We studied associations using a within-subject pooling approach and two measurement error models (simulation extrapolation and regression calibration), the latter requiring the assay of more than one biospecimen per subject. Results: For both continuous and binary outcomes, using one sample led to attenuation bias of 40% and 80% for compounds with intraclass correlation coefficients of 0.6 and 0.2, respectively. For a compound with an intraclass correlation coefficient of 0.6, the numbers of biospecimens required to limit bias to less than 10% were 6, 2, and 2 biospecimens with the pooling, simulation extrapolation, and regression calibration methods (these values were, respectively, 35, 8, and 2 for a compound with an intraclass correlation coefficient of 0.2). Compared with pooling, these methods did not improve power. Conclusion: Within-subject pooling limits attenuation bias without increasing assay costs. Simulation extrapolation and regression calibration further limit bias, compared with the pooling approach, but increase assay costs.

Source at https://doi.org/10.1186/s13148-018-0471-6. © The Author(s). 2018 Background: Methylation 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. Results: A 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). Conclusions: The 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.

Accepted manuscript version, licensed CC BY-NC-ND 4.0. Published version available in European Journal of Cancer, 75, 299-307. 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 caseecontrol study embedded in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort (n Z 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, 1.007e1.076, P Z 0.016). One unit increase in IEAA was associated with a 4% increased odds of developing breast cancer (OR, 1.04; 95% CI, 1.007e1.076). Stratified analysis based on menopausal status revealed that IEAA was associated with development of postmenopausal breast cancers (OR, 1.07; 95% CI, 1.020e1.11, P Z 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 Z 1.20; 95 %CI: 1.03e1.40, P Z 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.