Alcohol consumption is an established cause of female breast cancer. This systematic review examines in detail the association between alcohol and female breast cancer overall and among the described subgroups, using all of the evidence to date. A systematic review of PubMed and Embase was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The search included articles published up to November 15, 2023. Meta-analyses and regressions were performed for alcohol consumption of less than 1 standard drink (10 g of ethanol) per day and for a range of alcohol consumption categories in relation to breast cancer. Analyses by menopausal status, hormone receptor status, human epidermal growth factor receptor 2 status, and molecular subtype were performed. The search yielded 5645 publications, of which 23 publications of individual and pooled studies examined the association between overall alcohol consumption and breast cancer incidence. The meta-regression showed a positive association; relative risks (RR) of breast cancer were 1.05 (95% CI: 1.04, 1.06), 1.10 (95% CI: 1.08, 1.12), 1.18 (95% CI: 1.15, 1.21), and 1.22 (95% CI: 1.19, 1.25) for 0.5, 1, 2, and 3 standard drinks per day compared with nondrinking, respectively. A meta-analysis of nine studies indicated that for consumption of less than one standard drink per day, the RR estimate of breast cancer was 1.04 (95% CI: 1.01, 1.07) compared with nondrinking. Consumption of an additional 1 standard drink per day was associated with a higher risk of premenopausal (RR: 1.03 (95% CI: 1.01, 1.06)) and postmenopausal (RR: 1.10 (95% CI: 1.08, 1.12)) breast cancer. Alcohol consumption increases female breast cancer risk, even for women who consume one drink per day. Furthermore, alcohol consumption is associated with both pre- and postmenopausal breast cancer risk. These findings support evidence-based cancer prevention guidelines to reduce alcohol-related risks.

Because alcohol consumption is an established cause of female breast cancer, understanding whether cessation affects risk is of public health importance. In a recent meta-analysis, compared with continuing consumption, the relative risk (RR) for cessation was 0.95 (95

In 2018, the authors reported estimates of the number and proportion of cancers attributable to potentially modifiable risk factors in 2014 in the United States. These data are useful for advocating for and informing cancer prevention and control. Herein, based on up-to-date relative risk and cancer occurrence data, the authors estimated the proportion and number of invasive cancer cases (excluding nonmelanoma skin cancers) and deaths, overall and for 30 cancer types among adults who were aged 30 years and older in 2019 in the United States, that were attributable to potentially modifiable risk factors. These included cigarette smoking; second-hand smoke; excess body weight; alcohol consumption; consumption of red and processed meat; low consumption of fruits and vegetables, dietary fiber, and dietary calcium; physical inactivity; ultraviolet radiation; and seven carcinogenic infections. Numbers of cancer cases and deaths were obtained from data sources with complete national coverage, risk factor prevalence estimates from nationally representative surveys, and associated relative risks of cancer from published large-scale pooled or meta-analyses. In 2019, an estimated 40.0% (713,340 of 1,781,649) of all incident cancers (excluding nonmelanoma skin cancers) and 44.0% (262,120 of 595,737) of all cancer deaths in adults aged 30 years and older in the United States were attributable to the evaluated risk factors. Cigarette smoking was the leading risk factor contributing to cancer cases and deaths overall (19.3% and 28.5%, respectively), followed by excess body weight (7.6% and 7.3%, respectively), and alcohol consumption (5.4% and 4.1%, respectively). For 19 of 30 evaluated cancer types, more than one half of the cancer cases and deaths were attributable to the potentially modifiable risk factors considered in this study. Lung cancer had the highest number of cancer cases (201,660) and deaths (122,740) attributable to evaluated risk factors, followed by female breast cancer (83,840 cases), skin melanoma (82,710), and colorectal cancer (78,440) for attributable cases and by colorectal (25,800 deaths), liver (14,720), and esophageal (13,600) cancer for attributable deaths. Large numbers of cancer cases and deaths in the United States are attributable to potentially modifiable risk factors, underscoring the potential to substantially reduce the cancer burden through broad and equitable implementation of preventive initiatives.

Supplementary Table S1: Cohorts participating in analysis of coffee variables, Liver Cancer Pooling Project. Supplementary Table S2: Assessment of coffee consumption, Liver Cancer Pooling Project. Supplementary Table S3: Characteristics of participants in the Liver Cancer Pooling Project by coffee drinking. Supplementary Table S4: Adjusted Hazard Ratios (HR) and 95% Confidence Intervals (CI) for Associations Between Coffee Consumption and Intrahepatic Cholangiocarcinoma Incidence by Sex, Smoking Status, Body Mass Index, and Diabetes, Liver Cancer Pooling Project. Supplementary Table S5: Adjusted Hazard Ratios (HR) and 95% Confidence Intervals (CI) for Associations Between Coffee Consumption and Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma Incidence by Caffeine Content; Sensitivity Analysis Excluding WHI and Jointly Modeling Caffeine Content and Drinking Intensity, Liver Cancer Pooling Project. Supplementary Table S6: Adjusted Hazard Ratios (HR) and 95% Confidence Intervals (CI) for Associations Between Coffee Consumption and Suspected Hepatocellular Carcinoma Incidence by Caffeine Content, Liver Cancer Pooling Project. Supplementary Table S7: Adjusted Hazard Ratios (HR) and 95% Confidence Intervals (CI) for Associations Between Coffee Consumption and Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma Incidence by Caffeine Content in the AARP Study, Liver Cancer Pooling Project. Supplementary Table S8: Adjusted Hazard Ratios (HR) and 95% Confidence Intervals (CI) for Associations Between Coffee Consumption and Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma Incidence by Sex in the AARP Study, Liver Cancer Pooling Project. Supplementary Table S9: Adjusted Hazard Ratios (HR) and 95% Confidence Intervals (CI) for Associations Between Coffee Consumption and Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma Incidence by Caffeine Content excluding the AARP Study, Liver Cancer Pooling Project. Supplementary Table S10: Adjusted Hazard Ratios (HR) and 95% Confidence Intervals (CI) for Associations Between Coffee Consumption and Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma Incidence by Sex excluding the AARP Study, Liver Cancer Pooling Project. Supplementary Figure S1: Meta-influence Graph of the Influence of Individuals Studies for Associations between Coffee Consumption versus No Consumption and Hepatocellular Carcinoma, Liver Cancer Pooling Project.

Supplementary Figure 1: Electrophoretic mobility shift assays (EMSA) for CCNE1 SNPs rs8102137 and rs7257330. Supplementary Figure 2: Alignment of cyclin E protein isoforms - WT1, WT2 and ES and ET. Supplementary Figure 3: Functional analysis of cyclin E isoforms.Supplementary Table 1: Description of sub-studies included in NCI-GWAS1 and GWAS2 of bladder cancer. Supplementary Table 2: Characteristics of bladder tissue samples used for mRNA expression analysis. Supplementary Table 3: PCR primers, genotyping and gene expression assays, EMSA probes and antibodies. Supplementary Table 4: Bladder cancer stage and grade information for patients in the combined GWAS1+2 set. Supplementary Table 6: Association with bladder cancer risk with mutual adjustment for CCNE1 variants. Supplementary Table 7: Association with bladder cancer risk for CCNE1 variants previously associated with other cancers and for two non-synonymous coding variants. Supplementary Table 8: Association between cyclin E protein expression (IHC scores), bladder cancer patient characteristics and CCNE1 variants.

Supplementary Figure S1. LocusZoom regional association plots for the seven new cross-cancer loci that were > 1 Mb from known index SNPs. Supplementary Figure S2A-B. Box plots showing eQTL associations between (A) rs9375701 and L3MBTL3 in normal breast and prostate tissues and (B) rs8037137 and RCCD1 in normal breast and ovarian tissues. Supplementary Figure S3. Interactions between BCL2L11 and the 32 Biocarta "Death Pathway" genes. Interactions were identified using the GeneMania server. Circles contain gene names, lines represent interactions, and the color of the line indicates a specific type of interaction as listed in the legend.

Supplementary Table 1. Characteristics of the Studies Included in the Male Breast Cancer Pooling Project

The IARC convened a group of scientists to review the evidence on reduction or cessation of alcohol beverage consumption in relation to alcohol-related cancer incidence or mortality.

Supplementary Figure 1: PCoA for quality control samples; Supplementary Figure 2: Rarefaction curves of richness and the Shannon index; Supplementary Figure 3: Forest plot of odds ratios by cohort; Supplementary Table 1: Quality control sample CVs and ICCs; Supplementary Table 2: alpha- and beta-diversity and EAC/ESCC; Supplementary Table 3: Oral taxa and EAC by years to case diagnosis; Supplementary Table 4: Oral taxa and EAC by cohort; Supplementary Table 5: Oral taxa and EAC by smoking; Supplementary Table 6: Oral taxa and EAC by obesity; Supplementary Table 7: Oral taxa and EAC by fruit/vegetable intake.

Abstract Background: Case–control studies conducted in North America, Europe, and Asia provided evidence of increased lung cancer risk due to radon in homes. Here, the association between residential radon and lung cancer mortality was examined in a large-scale cohort study. Methods: Nearly 1.2 million Cancer Prevention Study-II participants were recruited in 1982. Mean county-level residential radon concentrations were linked to study participants according to ZIP code information at enrollment [mean (SD) = 53.5 Bq/m3 (38.0)]. Cox proportional hazards regression models were used to obtain adjusted HR and 95% CI for lung cancer mortality associated with radon. Potential effect modification by cigarette smoking, ambient sulfate concentrations, and other risk factors was assessed on both the additive and multiplicative scales. Results: Through 1988, 3,493 lung cancer deaths were observed among 811,961 participants included in the analysis. A significant positive linear trend was observed between categories of radon concentrations and lung cancer mortality (P = 0.02). A 15% (95% CI, 1–31) increase in the risk of lung cancer mortality was observed per 100 Bq/m3 increase in radon. Participants with mean radon concentrations above the EPA guideline value (148 Bq/m3) experienced a 34% (95% CI, 7–68) increase in risk for lung cancer mortality relative to those below the guideline value. Conclusions: This large prospective study showed positive associations between ecological indicators of residential radon and lung cancer. Impact: These results further support efforts to reduce radon concentrations in homes to the lowest possible level. Cancer Epidemiol Biomarkers Prev; 20(3); 438–48. ©2011 AACR.

Nighttime light exposure may increase cancer risk by disrupting the circadian system. However, there is no well-established survey method for measuring ambient light. In the Cancer Prevention Study-3, 732 men and women answered a light survey based on seven environments. The light environment in the past year was assessed twice, one year apart, and four one-week diaries were collected between the annual surveys. A total of 170 participants wore a meter to measure photopic illuminance and circadian stimulus (CS). Illuminance and CS values were estimated for lighting environments from measured values and evaluated with a cross validation approach. The kappas for self-reported light environment comparing the two annual surveys were 0.61 on workdays and 0.49 on non-workdays. Kappas comparing the annual survey to weekly diaries were 0.71 and 0.57 for work and non-workdays, respectively. Agreement was highest for reporting of darkness (95.3%), non-residential light (86.5%), and household light (75.6%) on workdays. Measured illuminance and CS identified three peaks of light (darkness, indoor lighting, and outdoor daytime light). Estimated illuminance and CS were correlated with the measured values overall (r = 0.77 and r = 0.67, respectively) but were less correlated within each light environment (r = 0.23–0.43). The survey has good validity to assess ambient light for studies of human health.

Supplementary Tables 1-3 and Supplemenary Figure 1 from Radon and Lung Cancer in the American Cancer Society Cohort

Key characteristics of the pooled studies

Although short-term feeding studies demonstrated effects of grains, fiber, and gluten on gut microbiome composition, the impact of habitual intake of these dietary factors is poorly understood. We examined whether habitual intakes of whole and refined grains, fiber, and gluten are associated with gut microbiota in a cross-sectional study. This study included 779 participants from the multi-ethnic Food and Microbiome Longitudinal Investigation study. Bacterial 16SV4 rRNA gene from baseline stool was amplified and sequenced using Illumina MiSeq. Read clustering and taxonomic assignment was performed using QIIME2. Usual dietary intake was assessed by a 137-item food frequency questionnaire. Association of diet with gut microbiota was assessed with respect to overall composition and specific taxon abundances. Whole grain intake was associated with overall composition, as measured by the Jensen–Shannon divergence (multivariable-adjusted Ptrend for quartiles = 0.03). The highest intake quartile was associated with higher abundance of Bacteroides plebeius, Faecalibacterium prausnitzii, Blautia producta, and Erysipelotrichaceae and lower abundance of Bacteroides uniformis. These bacteria also varied by dietary fiber intake. Higher refined grain and gluten intake was associated with lower Shannon diversity (Ptrend < 0.05). These findings suggest that whole grain and dietary fiber are associated with overall gut microbiome structure, largely fiber-fermenting microbiota. Higher refined grain and gluten intakes may be associated with lower microbial diversity.Significance:Regular consumption of whole grains and dietary fiber was associated with greater abundance of gut bacteria that may lower risk of colorectal cancer. Further research on the association of refined grains and gluten with gut microbial composition is needed to understand their roles in health and disease.

<p>Association between CCNE1 variants and bladder cancer</p>

Supplemental Table 1 - Association of self-reported T2DM at blood draw with risk of all-cancers combined and separately in CPS-II LifeLink participants; Supplemental Table 2 - Association BMI per 5 kg/m2 at blood draw with risk of all-cancers combined and separately in CPS-II LifeLink participants. Excludes those with BMI <18.5 kg/m2; Supplementary Table 3 - Associations of c-peptide with risk of all cancers combined and for the specific cancers of interest by sex in the CPS-II LifeLink cohort; Supplementary Table 4 - Associations of HbA1c with risk of all cancers combined and for the specific cancers of interest by sex in the CPS-II LifeLink cohort.

Little is known regarding the potential relationship between clonal hematopoiesis (CH) of indeterminate potential (CHIP), which is the expansion of hematopoietic stem cells with somatic mutations, and risk of prostate cancer, the fifth leading cause of cancer death of men worldwide. We evaluated the association of age-related CHIP with overall and aggressive prostate cancer risk in two large whole-exome sequencing studies of 75 047 European ancestry men, including 7663 prostate cancer cases, 2770 of which had aggressive disease, and 3266 men carrying CHIP variants. We found that CHIP, defined by over 50 CHIP genes individually and in aggregate, was not significantly associated with overall (aggregate HR = 0.93, 95% CI = 0.76-1.13, P = 0.46) or aggressive (aggregate OR = 1.14, 95% CI = 0.92-1.41, P = 0.22) prostate cancer risk. CHIP was weakly associated with genetic risk of overall prostate cancer, measured using a polygenic risk score (OR = 1.05 per unit increase, 95% CI = 1.01-1.10, P = 0.01). CHIP was not significantly associated with carrying pathogenic/likely pathogenic/deleterious variants in DNA repair genes, which have previously been found to be associated with aggressive prostate cancer. While findings from this study suggest that CHIP is likely not a risk factor for prostate cancer, it will be important to investigate other types of CH in association with prostate cancer risk.

S1. Gut microbiome alpha- and beta-diversity according to energy-adjusted quartiles of dietary fiber intake in the Food and Microbiome Longitudinal Investigation (FAMiLI). S2. Gut microbiome alpha- and beta-diversity according to energy-adjusted quartiles of whole grain intake in the Food and Microbiome Longitudinal Investigation (FAMiLI), stratified by race/ethnicity. S3. Gut microbiome alpha- and beta-diversity according to energy-adjusted quartiles of whole grain intake in the Food and Microbiome Longitudinal Investigation (FAMiLI). S4. Gut microbiome alpha- and beta-diversity according to energy-adjusted quartiles of whole grain intake in the Food and Microbiome Longitudinal Investigation (FAMiLI), excluding participants with self-reported inflammatory bowel disease (N=21) at enrollment. S5. Gut microbiome alpha- and beta-diversity according to energy-adjusted quartiles of refined grain (with sweets/desserts) intake in the Food and Microbiome Longitudinal Investigation (FAMiLI). S6. Gut microbiome alpha- and beta-diversity according to energy-adjusted quartiles of gluten (with sweets/desserts) intake in the Food and Microbiome Longitudinal Investigation (FAMiLI).

Background: Few prospective studies have examined biomarkers of glucose homeostasis or inflammation with prostate cancer risk by tumor stage or grade. Methods: We conducted a case-cohort study to examine associations of prediagnosis hemoglobin A1c (HbA(1c)), C-peptide, and C-reactive protein (CRP) with prostate cancer risk overall and stratified by tumor stage and grade. The study included 390 nonaggressive (T1-2, N0, M0, and Gleason score <8) and 313 aggressive cases (T3-4, or N1, or M1, or Gleason score 8-10) diagnosed after blood draw (1998-2001) and up to 2013, and a random subcohort of 1,303 cancer-free men at blood draw in the Cancer Prevention Study-II Nutrition Cohort. Prentice-weighted Cox proportional hazards regression models were used to estimate HRs and 95% confidence intervals (CI). Results: In the multivariable-adjusted model without body mass index, HbA(1c) was inversely associated with nonaggressive prostate cancer (HR per unit increase, 0.89; 95% CI, 0.80-1.00; P = 0.04). Analyses stratified by tumor stage and grade separately showed that HbA(1c) was inversely associated with low-grade prostate cancer (HR per unit increase, 0.89; 95% CI, 0.80-1.00) and positively associated with high-grade prostate cancer (HR per unit increase, 1.15; 95% CI, 1.01-1.30). C-peptide and CRP were not associated with prostate cancer overall or by stage or grade. Conclusions: The current study suggests that associations of hyperglycemia with prostate cancer may differ by tumor grade and stage. Impact: Future studies need to examine prostate cancer by tumor stage and grade, and to better understand the role of hyperglycemia in prostate cancer progression.