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The COVID-19 pandemic introduced changes in people’s lives that affected their mental health. Our study aimed to explore the level of psychological distress in the academic population during the lockdown period and investigate its association with the new working or studying conditions. The study sample included 9364 students and 2159 employees from five Italian universities from the study IO CONTO 2020. We applied linear regression models to investigate the association between home learning or remote working conditions and psychological distress, separately for students and employees. Psychological distress was assessed using the Hospital Anxiety and Depression Scale (HADS). In both students and employees, higher levels of distress were significantly associated with study/work–family conflicts, concerns about their future careers, and inadequacy of equipment; in employees, higher levels of distress were significantly associated with a lack of clarity on work objectives. Our results are in line with previous research on the impact of spaces and equipment in remote working/studying from home. Moreover, the study contributes to deepening the association between well-being and telework–family conflict, which in the literature is still equivocal. Practical implications require academic governance to promote sustainable environments both in remote and hybrid work conditions, by referring to a specific management by objectives approach.

BACKGROUND: DNA methylation in blood may reflect adverse exposures accumulated over the lifetime and could therefore provide potential improvements in the prediction of cancer risk. A substantial body of research has shown associations between epigenetic aging and risk of disease, including cancer. Here we aimed to study epigenetic measures of aging and lifestyle-related factors in association with risk of breast cancer. METHODS: Using data from four prospective case-control studies nested in three cohorts of European ancestry participants, including a total of 1,655 breast cancer cases, we calculated three methylation-based measures of lifestyle factors (body mass index [BMI], tobacco smoking and alcohol consumption) and seven measures of epigenetic aging (Horvath-based, Hannum-based, PhenoAge and GrimAge). All measures were regression-adjusted for their respective risk factors and expressed per standard deviation (SD). Odds ratios (OR) and 95% confidence intervals (CI) were calculated using conditional or unconditional logistic regression and pooled using fixed-effects meta-analysis. Subgroup analyses were conducted by age at blood draw, time from blood sample to diagnosis, oestrogen receptor-positivity status and tumour stage. RESULTS: None of the measures of epigenetic aging were associated with risk of breast cancer in the pooled analysis: Horvath 'age acceleration' (AA): OR per SD = 1.02, 95%CI: 0.95-1.10; AA-Hannum: OR = 1.03, 95%CI:0.95-1.12; PhenoAge: OR = 1.01, 95%CI: 0.94-1.09 and GrimAge: OR = 1.03, 95%CI: 0.94-1.12, in models adjusting for white blood cell proportions, body mass index, smoking and alcohol consumption. The BMI-adjusted predictor of BMI was associated with breast cancer risk, OR per SD = 1.09, 95%CI: 1.01-1.17. The results for the alcohol and smoking methylation-based predictors were consistent with a null association. Risk did not appear to substantially vary by age at blood draw, time to diagnosis or tumour characteristics. CONCLUSION

Importance: Owing to infrastructural and population characteristics, the prison setting is at increased risk for transmission of SARS-CoV-2 and for severe clinical outcomes. Because of structural and operational reasons, research in prison settings is challenging and available studies are often monocentric and have limited temporal coverage; broader-based research is necessary. Objectives: To assess the extent and dynamics of the COVID-19 pandemic within the prison system of a large Italian region, Lombardy, and report the infection prevention and control measures implemented. Design, Setting, and Participants: This repeated cross-sectional study was carried out from March 1, 2020, through February 28, 2021 (first wave, March-June 2020; second wave, October 2020-February 2021) in the prison system of Lombardy, which includes 18 detention facilities for adults. All incarcerated persons and the prison staff of the penitentiary system of the Lombardy region participated in the study. Exposures: The main exposures of interest were the weekly average number of incarcerated individuals placed in quarantine in single or shared isolation rooms, the rate of sick leave by symptomatic and asymptomatic prison staff reported to the prison occupational medicine department on a weekly basis, and the level of overcrowding. Main Outcomes and Measures: The primary outcome measures were weekly COVID-19 crude case rates, weekly test positivity rate, and the relative risk of acquiring the infection for prison staff, incarcerated persons, and the general population. Results: The study population comprised a mean of 7599 incarcerated individuals and 4591 prison staff. Approximately 5.1% of the prison population were women; demographic characteristics of the prison staff were not available. During the study, COVID-19 occurred in 1564 incarcerated individuals and 661 prison staff. Most of these cases were reported during the second wave (1474 in incarcerated individuals, 529 in prison staff

Mammograms contain information that predicts breast cancer risk. We developed two novel mammogram-based breast cancer risk measures based on image brightness (Cirrocumulus) and texture (Cirrus). Their risk prediction when fitted together, and with an established measure of conventional mammographic density (Cumulus), is not known. We used three studies consisting of: 168 interval cases and 498 matched controls; 422 screen-detected cases and 1197 matched controls; and 354 younger-diagnosis cases and 944 controls frequency-matched for age at mammogram. We conducted conditional and unconditional logistic regression analyses of individually- and frequency-matched studies, respectively. We estimated measure-specific risk gradients as the change in odds per standard deviation of controls after adjusting for age and body mass index (OPERA) and calculated the area under the receiver operating characteristic curve (AUC). For interval, screen-detected and younger-diagnosis cancer risks, the best fitting models (OPERAs [95% confidence intervals]) involved: Cumulus (1.81 [1.41-2.31]) and Cirrus (1.72 [1.38-2.14]); Cirrus (1.49 [1.32-1.67]) and Cirrocumulus (1.16 [1.03 to 1.31]); and Cirrus (1.70 [1.48 to 1.94]) and Cirrocumulus (1.46 [1.27-1.68]), respectively. The AUCs were: 0.73 [0.68-0.77], 0.63 [0.60-0.66], and 0.72 [0.69-0.75], respectively. Combined, our new mammogram-based measures have twice the risk gradient for screen-detected and younger-diagnosis breast cancer (P <= 10-12), have at least the same discriminatory power as the current polygenic risk score, and are more correlated with causal factors than conventional mammographic density. Discovering more information about breast cancer risk from mammograms could help enable risk-based personalised breast screening.Copyright © 2020 UICC

Background: Age-related epigenetic dysregulations are associated with several diseases, including cancer. The number of stochastic epigenetic mutations (SEM) has been suggested as a biomarker of life-course accumulation of exposure-related DNA damage; however, the predictive role of SEMs in cancer has seldom been investigated. Method(s): A SEM, at a given CpG site, was defined as an extreme outlier of DNA methylation value distribution across individuals. We investigated the association of the total number of SEMs with the risk of eight cancers in 4,497 case-control pairs nested in three prospective cohorts. Furthermore, we investigated whether SEMs were randomly distributed across the genome or enriched in functional genomic regions. Result(s): In the three-study meta-analysis, the estimated ORs per one-unit increase in log(SEM) from logistic regression models adjusted for age and cancer risk factors were 1.25; 95% confidence interval (CI), 1.11-1.41 for breast cancer, and 1.23; 95% CI, 1.07-1.42 for lung cancer. In the Melbourne Collaborative Cohort Study, the OR for mature B-cell neoplasm was 1.46; 95% CI, 1.25-1.71. Enrichment analyses indicated that SEMs frequently occur in silenced genomic regions and in transcription factor binding sites regulated by EZH2 and SUZ12 (P < 0.0001 and P 1/4 0.0005, respectively): two components of the polycomb repressive complex 2 (PCR2). Finally, we showed that PCR2-specific SEMs are generally more stable over time compared with SEMs occurring in the whole genome. Conclusion(s): The number of SEMs is associated with a higher risk of different cancers in prediagnostic blood samples. Impact: We identified a candidate biomarker for cancer early detection, and we described a carcinogenesis mechanism involving PCR2 complex proteins worthy of further investigations. The authors are very thankful to Dr. Akram Ghantous (IARC, Lyon, France) for the methylation analyses of PEM-Turin study, produced within the Exposomics EC FP7 grant (gran

BACKGROUND: Menopausal hormone therapy (MHT) is a risk factor for breast cancer (BC). Evidence suggests that its effect on BC risk could be partly mediated by mammographic density. The aim of this study was to investigate the relationship between MHT, mammographic density and BC risk using data from a prospective study. METHODS: We used data from a case-control study nested within the French cohort E3N including 453 cases and 453 matched controls. Measures of mammographic density, history of MHT use during follow-up and information on potential confounders were available for all women. The association between MHT and mammographic density was evaluated by linear regression models. We applied mediation modelling techniques to estimate, under the hypothesis of a causal model, the proportion of the effect of MHT on BC risk mediated by percent mammographic density (PMD) for BC overall and by hormone receptor status. RESULTS: Among MHT users, 4.2% used exclusively oestrogen alone compared with 68.3% who used exclusively oestrogens plus progestogens. Mammographic density was higher in current users (for a 60-year-old woman, mean PMD 33%; 95% CI 31 to 35%) than in past (29%; 27 to 31%) and never users (24%; 22 to 26%). No statistically significant association was observed between duration of MHT and mammographic density. In past MHT users, mammographic density was negatively associated with time since last use; values similar to those of never users were observed in women who had stopped MHT at least 8 years earlier. The odds ratio of BC for current versus never MHT users, adjusted for age, year of birth, menopausal status at baseline and BMI, was 1.67 (95% CI, 1.04 to 2.68). The proportion of effect mediated by PMD was 34% for any BC and became 48% when the correlation between BMI and PMD was accounted for. These effects were limited to hormone receptor-positive BC. CONCLUSIONS: Our results suggest that, under a causal model, nearly half of the effect of MHT on hormone rec

BACKGROUND: Perturbations in circulating metabolites prior to a breast cancer diagnosis are not well characterised. We aimed to gain more detailed knowledge to help understand and prevent the disease. METHODS: Baseline plasma samples from 791 breast cancer cases and 791 matched controls from the E3N (EPIC-France) cohort were profiled by nuclear magnetic resonance (NMR)-based untargeted metabolomics. Partial least-squares discriminant analysis (PLS-DA) models were built from NMR profiles to predict disease outcome, and odds ratios and false discovery rate (FDR)-adjusted CIs were calculated for 43 identified metabolites by conditional logistic regression. RESULTS: Breast cancer onset was predicted in the premenopausal subgroup with modest accuracy (AUC 0.61, 95% CI: 0.49-0.73), and 10 metabolites associated with risk, particularly histidine (OR = 1.70 per SD increase, FDR-adjusted CI 1.19-2.41), N-acetyl glycoproteins (OR = 1.53, FDR-adjusted CI 1.18-1.97), glycerol (OR = 1.55, FDR-adjusted CI 1.11-2.18) and ethanol (OR = 1.44, FDR-adjusted CI 1.05-1.97). No predictive capacity or significant metabolites were found overall or for postmenopausal women. CONCLUSIONS: Perturbed metabolism compared to controls was observed in premenopausal but not postmenopausal cases. Histidine and NAC have known involvement in inflammatory pathways, and the robust association of ethanol with risk suggests the involvement of alcohol intake.

Measures implemented in many countries to contain the COVID-19 pandemic resulted in a change in lifestyle with unpredictable consequences on physical and mental health. We aimed at identifying the variables associated with psychological distress during the lockdown between April and May 2020 in the Italian academic population. We conducted a multicenter cross-sectional online survey (IO CONTO 2020) within five Italian universities. Among about 240,000 individuals invited to participate through institutional communications, 18 120 filled the questionnaire. Psychological distress was measured by the self-administered Hospital Anxiety and Depression Scale (HADS). The covariates collected included demographic and lifestyle characteristics, trust in government, doctors and scientists. Associations of covariates with influenza-like symptoms or positive COVID-19 test and with psychological distress were assessed by multiple regression models at the local level; a meta-analysis of the results was then performed. Severe levels of anxiety or depression were reported by 20% of the sample and were associated with being a student or having a lower income, irrespective of their health condition and worries about contracting the virus. The probability of being severely anxious or depressed also depended on physical activity: compared to those never exercising, the highest OR being for those who stopped during lockdown (1.53; 95% CI, 1.28 to 1.84) and the lowest for those who continued (0.78; 95% CI, 0.64 to 0.95). Up to 21% of severe cases of anxiety or depression might have been avoided if during lockdown participants had continued to exercise as before. Socioeconomic insecurity contributes to increase mental problems related to the COVID-19 pandemic and to the measures to contain it. Maintaining or introducing an adequate level of physical activity is likely to mitigate such detrimental effects. Promoting safe practice of physical activity should remain a public health priority

Interval breast cancers (those diagnosed between recommended mammography screens) generally have poorer outcomes and are more common among women with dense breasts. We aimed to develop a risk model for interval breast cancer. We conducted a nested case-control study within the Melbourne Collaborative Cohort Study involving 168 interval breast cancer patients and 498 matched control subjects. We measured breast density using the CUMULUS software. We recorded first-degree family history by questionnaire, measured body mass index (BMI) and calculated age-adjusted breast tissue aging, a novel measure of exposure to estrogen and progesterone based on the Pike model. We fitted conditional logistic regression to estimate odds ratio (OR) or odds ratio per adjusted standard deviation (OPERA) and calculated the area under the receiver operating characteristic curve (AUC). The stronger risk associations were for unadjusted percent breast density (OPERA = 1.99; AUC = 0.66), more so after adjusting for age and BMI (OPERA = 2.26; AUC = 0.70), and for family history (OR = 2.70; AUC = 0.56). When the latter two factors and their multiplicative interactions with age-adjusted breast tissue aging (p = 0.01 and 0.02, respectively) were fitted, the AUC was 0.73 (95% CI 0.69-0.77), equivalent to a ninefold interquartile risk ratio. In summary, compared with using dense breasts alone, risk discrimination for interval breast cancers could be doubled by instead using breast density, BMI, family history and hormonal exposure. This would also give women with dense breasts, and their physicians, more information about the major consequence of having dense breasts-an increased risk of developing an interval breast cancer.Copyright © 2019 The Authors. International Journal of Cancer published by John Wiley & Sons Ltd on behalf of UICC

DNA methylation may be one of the mechanisms by which alcohol consumption is associated with the risk of disease. We conducted a large-scale, cross-sectional, genome-wide DNA methylation association study of alcohol consumption and a longitudinal analysis of repeated measurements taken several years apart. Using the Illumina HumanMethylation450 BeadChip, DNA methylation was measured in blood samples from 5606 Melbourne Collaborative Cohort Study (MCCS) participants. For 1088 of them, these measures were repeated using blood samples collected a median of 11 years later. Associations between alcohol intake and blood DNA methylation were assessed using linear mixed-effects regression models. Independent data from the London Life Sciences Prospective Population (LOLIPOP) (N = 4042) and Cooperative Health Research in the Augsburg Region (KORA) (N = 1662) cohorts were used to replicate associations discovered in the MCCS. Cross-sectional analyses identified 1414 CpGs associated with alcohol intake at P < 10-7, 1243 of which had not been reported previously. Of these novel associations, 1078 were replicated (P <.05) using LOLIPOP and KORA data. Using the MCCS data, we also replicated 403 of 518 previously reported associations. Interaction analyses suggested that associations were stronger for women, non-smokers, and participants genetically predisposed to consume less alcohol. Of the 1414 CpGs, 530 were differentially methylated (P <.05) in former compared with current drinkers. Longitudinal associations between the change in alcohol intake and the change in methylation were observed for 513 of the 1414 cross-sectional associations. Our study indicates that alcohol intake is associated with widespread changes in DNA methylation across the genome. Longitudinal analyses showed that the methylation status of alcohol-associated CpGs may change with alcohol consumption changes in adulthood.Copyright © 2019 Society for the Study of Addiction

Early-adulthood body size is strongly inversely associated with risk of premenopausal breast cancer. It is unclear whether subsequent changes in weight affect risk. We pooled individual-level data from 17 prospective studies to investigate the association of weight change with premenopausal breast cancer risk, considering strata of initial weight, timing of weight change, other breast cancer risk factors and breast cancer subtype. Hazard ratios (HR) and 95% confidence intervals (CI) were obtained using Cox regression. Among 628,463 women, 10,886 were diagnosed with breast cancer before menopause. Models adjusted for initial weight at ages 18-24 years and other breast cancer risk factors showed that weight gain from ages 18-24 to 35-44 or to 45-54 years was inversely associated with breast cancer overall (e.g., HR per 5 kg to ages 45-54: 0.96, 95% CI: 0.95-0.98) and with oestrogen-receptor(ER)-positive breast cancer (HR per 5 kg to ages 45-54: 0.96, 95% CI: 0.94-0.98). Weight gain from ages 25-34 was inversely associated with ER-positive breast cancer only and weight gain from ages 35-44 was not associated with risk. None of these weight gains were associated with ER-negative breast cancer. Weight loss was not consistently associated with overall or ER-specific risk after adjusting for initial weight. Weight increase from early-adulthood to ages 45-54 years is associated with a reduced premenopausal breast cancer risk independently of early-adulthood weight. Biological explanations are needed to account for these two separate factors.

Adiposity increases estrogen receptor (ER)-positive postmenopausal breast cancer risk. While mechanisms underlying this relationship are uncertain, dysregulated sex-steroid hormone production and insulin signaling are likely pathways. Our aim was to quantify mediating effects of fasting insulin and free estradiol in the adiposity and ER-positive postmenopausal breast cancer association. We used data from a case-cohort study of sex hormones and insulin signaling nested within the Melbourne Collaborative Cohort Study. Eligible women, at baseline, were not diagnosed with cancer, were postmenopausal, did not use hormone therapy and had no history of diabetes or diabetes medication use. Women with ER-negative disease or breast cancer diagnosis within the first follow-up year were excluded. We analyzed the study as a cumulative sampling case-control study with 149 cases and 1,029 controls. Missing values for insulin and free estradiol were multiply imputed with chained equations. Interventional direct (IDE) and indirect (IIE) effects were estimated using regression-based multiple-mediator approach. For women with body mass index (BMI) >30 kg/m2 compared to women with BMI 18.5-25 kg/m2 , the risk ratio (RR) of breast cancer was 1.75 (95% confidence interval [CI] 1.05-2.91). The estimated IDE (RR) not through the mediators was 1.03 (95% CI 0.43-2.48). Percentage mediated effect through free estradiol was 72% (IIE-RR 1.56; 95% CI 1.11-2.19). There was no evidence for an indirect effect through insulin (IIE-RR 1.12; 95% CI 0.68-1.84; 28% mediated). Our results suggest that circulating free estradiol plays an important mediating role in the adiposity-breast cancer relationship but does not explain all of the association.

Mammograms contain information that predicts breast cancer risk. We developed two novel mammogram-based breast cancer risk measures based on image brightness (Cirrocumulus) and texture (Cirrus). Their risk prediction when fitted together, and with an established measure of conventional mammographic density (Cumulus), is not known. We used three studies consisting of: 168 interval cases and 498 matched controls; 422 screen-detected cases and 1197 matched controls; and 354 younger-diagnosis cases and 944 controls frequency-matched for age at mammogram. We conducted conditional and unconditional logistic regression analyses of individually- and frequency-matched studies, respectively. We estimated measure-specific risk gradients as the change in odds per standard deviation of controls after adjusting for age and body mass index (OPERA) and calculated the area under the receiver operating characteristic curve (AUC). For interval, screen-detected and younger-diagnosis cancer risks, the best fitting models (OPERAs [95% confidence intervals]) involved: Cumulus (1.81 [1.41-2.31]) and Cirrus (1.72 [1.38-2.14]); Cirrus (1.49 [1.32-1.67]) and Cirrocumulus (1.16 [1.03 to 1.31]); and Cirrus (1.70 [1.48 to 1.94]) and Cirrocumulus (1.46 [1.27-1.68]), respectively. The AUCs were: 0.73 [0.68-0.77], 0.63 [0.60-0.66], and 0.72 [0.69-0.75], respectively. Combined, our new mammogram-based measures have twice the risk gradient for screen-detected and younger-diagnosis breast cancer (P ≤ 10-12 ), have at least the same discriminatory power as the current polygenic risk score, and are more correlated with causal factors than conventional mammographic density. Discovering more information about breast cancer risk from mammograms could help enable risk-based personalised breast screening.

BACKGROUND: Women with an advantaged socioeconomic position (SEP) have a higher risk of developing breast cancer (BC). The reasons for this association do not seem to be limited to reproductive factors and remain to be understood. We aimed to investigate the impact of lifecourse SEP from childhood and social mobility on the risk of BC considering a broad set of potential mediators. METHODS: We used a discovery-replication strategy in two European prospective cohorts, E3N (N = 83,436) and EPIC-Italy (N = 20,530). In E3N, 7877 women were diagnosed with BC during a median 24.4 years of follow-up, while in EPIC-Italy, 893 BC cases were diagnosed within 15.1 years. Hazard ratios (HR) were estimated using Cox proportional hazard models on imputed data. RESULTS: In E3N, women with higher education had a higher risk of BC (HR [95%CI] = 1.21 [1.12, 1.30]). This association was attenuated by adjusting for reproductive factors, in particular age at first childbirth (HR[95%CI] = 1.13 [1.04, 1.22]). Health behaviours, anthropometric variables, and BC screening had a weaker effect on the association. Women who remained in a stable advantaged SEP had a higher risk of BC (HR [95%CI] = 1.24 [1.07; 1.43]) attenuated after adjustment for potential mediators (HR [95%CI] = 1.13 [0.98; 1.31]). These results were replicated in EPIC-Italy. CONCLUSIONS: These results confirm the important role of reproductive factors in the social gradient in BC risk, which does not appear to be fully explained by the large set of potential mediators, including cancer screening, suggesting that further research is needed to identify additional mechanisms.

Background: It is well established that estrogens and other hormonal factors influence breast cancer susceptibility. We hypothesized that a woman's total lifetime estrogen exposure accumulates changes in DNA methylation, detectable in the blood, which could be used in risk assessment for breast cancer. Method(s): An estimated lifetime estrogen exposure (ELEE) model was defined using epidemiological data from EPIC-Italy (n = 31,864). An epigenome-wide association study (EWAS) of ELEE was performed using existing Illumina HumanMethylation450K Beadchip (HM450K) methylation data obtained from EPIC-Italy blood DNA samples (n = 216). A methylation index (MI) of ELEE based on 31 CpG sites was developed using HM450K data from EPIC-Italy and the Generations Study and evaluated for association with breast cancer risk in an independent dataset from the Generations Study (n = 440 incident breast cancer cases matched to 440 healthy controls) using targeted bisulfite sequencing. Lastly, a meta-analysis was conducted including three additional cohorts, consisting of 1187 case-control pairs. Result(s): We observed an estimated 5% increase in breast cancer risk per 1-year longer ELEE (OR = 1.05, 95% CI 1.04-1.07, P = 3 x 10-12) in EPIC-Italy. The EWAS identified 694 CpG sites associated with ELEE (FDR Q < 0.05). We report a DNA methylation index (MI) associated with breast cancer risk that is validated in the Generations Study targeted bisulfite sequencing data (ORQ4-vs-Q1 = 1.77, 95% CI 1.07-2.93, P = 0.027) and in the meta-analysis (ORQ4-vs-Q1 = 1.43, 95% CI 1.05-2.00, P = 0.024); however, the correlation between the MI and ELEE was not validated across study cohorts. Conclusion(s): We have identified a blood DNA methylation signature associated with breast cancer risk in this study. Further investigation is required to confirm the interaction between estrogen exposure and DNA methylation in the blood.Copyright © 2019 The Author(s).

We conducted a genome-wide association study of blood DNA methylation and smoking, attempted replication of previously discovered associations, and assessed the reversibility of smoking-associated methylation changes. DNA methylation was measured in baseline peripheral blood samples for 5,044 participants in the Melbourne Collaborative Cohort Study. For 1,032 participants, these measures were repeated using blood samples collected at follow-up, a median of 11 years later. A cross-sectional analysis of the association between smoking and DNA methylation and a longitudinal analysis of changes in smoking status and changes in DNA methylation were conducted. We used our cross-sectional analysis to replicate previously reported associations for current (N = 3,327) and former (N = 172) smoking. A comprehensive smoking index accounting for the biological half-life of smoking compounds and several aspects of smoking history was constructed to assess the reversibility of smoking-induced methylation changes. This measure of lifetime exposure to smoking allowed us to detect more associations than comparing current with never smokers. We identified 4,496 cross-sectional associations at P < 10-7, including 3,296 annotated to 1,326 genes that were not previously implicated in smoking-associated DNA methylation changes at this significance threshold. We replicated the majority of previously reported associations (P < 10-7) for current and former smokers. In our data, we observed for former smokers a substantial degree of return to the methylation levels of never smokers, compared with current smokers (median: 74%, IQR = 63-86%), corresponding to small values (median: 2.75, IQR = 1.5-5.25) for the half-life parameter of the comprehensive smoking index. Longitudinal analyses identified 368 sites at which methylation changed upon smoking cessation. Our study demonstrates the usefulness of the comprehensive smoking index to detect associations between smoking and DNA methylation at Cp

Age- and body mass index (BMI)-adjusted mammographic density is one of the strongest breast cancer risk factors. DNA methylation is a molecular mechanism that could underlie inter-individual variation in mammographic density. We aimed to investigate the association between breast cancer risk-predicting mammographic density measures and blood DNA methylation. For 436 women from the Australian Mammographic Density Twins and Sisters Study and 591 women from the Melbourne Collaborative Cohort Study, mammographic density (dense area, nondense area and percentage dense area) defined by the conventional brightness threshold was measured using the CUMULUS software, and peripheral blood DNA methylation was measured using the HumanMethylation450 (HM450) BeadChip assay. Associations between DNA methylation at >400,000 sites and mammographic density measures adjusted for age and BMI were assessed within each cohort and pooled using fixed-effect meta-analysis. Associations with methylation at genetic loci known to be associated with mammographic density were also examined. We found no genome-wide significant (p < 10-7) association for any mammographic density measure from the meta-analysis, or from the cohort-specific analyses. None of the 299 methylation sites located at genetic loci associated with mammographic density was associated with any mammographic density measure after adjusting for multiple testing (all p > 0.05/299 = 1.7 x 10-4). In summary, our study did not find evidence for associations between blood DNA methylation, as measured by the HM450 assay, and conventional mammographic density measures that predict breast cancer risk.Copyright © 2019 UICC

Background: Mammographic breast density, adjusted for age and body mass index, and a polygenic risk score (PRS), comprised of common genetic variation, are both strong risk factors for breast cancer and increase discrimination of risk models. Understanding their joint contribution will be important to more accurately predict risk. Method(s): Using 3628 breast cancer cases and 5126 controls of European ancestry from eight case-control studies, we evaluated joint associations of a 77-single nucleotide polymorphism (SNP) PRS and quantitative mammographic density measures with breast cancer. Mammographic percent density and absolute dense area were evaluated using thresholding software and examined as residuals after adjusting for age, 1/BMI, and study. PRS and adjusted density phenotypes were modeled both continuously (per 1 standard deviation, SD) and categorically. We fit logistic regression models and tested the null hypothesis of multiplicative joint associations for PRS and adjusted density measures using likelihood ratio and global and tail-based goodness of fit tests within the subset of six cohort or population-based studies. Result(s): Adjusted percent density (odds ratio (OR) = 1.45 per SD, 95% CI 1.38-1.52), adjusted absolute dense area (OR = 1.34 per SD, 95% CI 1.28-1.41), and the 77-SNP PRS (OR = 1.52 per SD, 95% CI 1.45-1.59) were associated with breast cancer risk. There was no evidence of interaction of the PRS with adjusted percent density or dense area on risk of breast cancer by either the likelihood ratio (P > 0.21) or goodness of fit tests (P > 0.09), whether assessed continuously or categorically. The joint association (OR) was 2.60 in the highest categories of adjusted PD and PRS and 0.34 in the lowest categories, relative to women in the second density quartile and middle PRS quintile. Conclusion(s): The combined associations of the 77-SNP PRS and adjusted density measures are generally well described by multiplicative models, and both risk fact

BACKGROUND: DNA methylation changes in peripheral blood have recently been identified in relation to lung cancer risk. Some of these changes have been suggested to mediate part of the effect of smoking on lung cancer. However, limitations with conventional mediation analyses mean that the causal nature of these methylation changes has yet to be fully elucidated. METHODS: We first performed a meta-analysis of four epigenome-wide association studies (EWAS) of lung cancer (918 cases, 918 controls). Next, we conducted a two-sample Mendelian randomization analysis, using genetic instruments for methylation at CpG sites identified in the EWAS meta-analysis, and 29 863 cases and 55 586 controls from the TRICL-ILCCO lung cancer consortium, to appraise the possible causal role of methylation at these sites on lung cancer. RESULTS: Sixteen CpG sites were identified from the EWAS meta-analysis [false discovery rate (FDR) < 0.05], for 14 of which we could identify genetic instruments. Mendelian randomization provided little evidence that DNA methylation in peripheral blood at the 14 CpG sites plays a causal role in lung cancer development (FDR > 0.05), including for cg05575921-AHRR where methylation is strongly associated with both smoke exposure and lung cancer risk. CONCLUSIONS: The results contrast with previous observational and mediation analysis, which have made strong claims regarding the causal role of DNA methylation. Thus, previous suggestions of a mediating role of methylation at sites identified in peripheral blood, such as cg05575921-AHRR, could be unfounded. However, this study does not preclude the possibility that differential DNA methylation at other sites is causally involved in lung cancer development, especially within lung tissue.