PALB2  and an average PRS (10–90th percentile) have a lifetime risk of breast cancer at 55% (95% CI 49–61%), which increases to 84% (71–97%) with a high PRS ( > 90th percentile), and decreases to 49% (30–68%) with a low PRS ( < 10th percentile). Similarly, for c.1100delC in CHEK2 , the respective lifetime risks are 29% (27–32%), 59% (52–66%), and 9% (5–14%). The PRS also refines the risk assessment of women with first-degree relatives diagnosed with breast cancer, particularly among women with positive family history of early-onset breast cancer. Here we demonstrate the opportunities for a comprehensive way of assessing genetic risk in the general population, in breast cancer patients, and in unaffected family members.Polygenic risk scores (PRS) for breast cancer have potential to improve risk prediction, but there is limited information on their utility in various clinical situations. Here we show that among 122,978 women in the FinnGen study with 8401 breast cancer cases, the PRS modifies the breast cancer risk of two high-impact frameshift risk variants. Similarly, we show that after the breast cancer diagnosis, individuals with elevated PRS have an elevated risk of developing contralateral breast cancer, and that the PRS can considerably improve risk assessment among their female first-degree relatives. In more detail, women with the c.1592delT variant in  Identifying women at high risk of breast cancer has important implications for screening. Here, the authors demonstrate that polygenic risk scores improve breast cancer risk prediction in the population, in women with mutations in high-risk genes and in women with close relatives with the disease. Approximately 5–10% of all breast cancers are estimated to develop due to high-impact germline mutations in breast cancer susceptibility genes, with up to 30% due to pathogenic mutations in BRCA1 and BRCA2 and with a smaller proportion carrying mutations in other susceptibility genes, such as PTEN, TP53, CHEK2, PALB2 and STK112. While pathogenic mutations in BRCA1 and BRCA2 are less common in Finns3, two frameshift mutations, c.1592delT (rs180177102) in PALB2 and c.1100delC (rs555607708) in CHEK2 have an unusually high allele frequency in Finland, which provides a unique opportunity to explore the impact of these mutations in the population. PALB2  encodes a key tumour suppressor protein that functions through affecting BRCA2 nuclear localisation and DNA damage response functions, and through interacting with BRCA14. The second gene, CHEK2 (Checkpoint kinase 2), is a tumour suppressor gene encoding a serine/threonine-protein kinase involved in DNA repair, cell cycle arrest and apoptosis5.In women, breast cancer is the most commonly diagnosed cancer and the leading cause of cancer-related deaths6. These variants, and many more yet to be discovered, represent common genetic variation acting through a wide range of molecular pathways, in contrast to the rare, high-risk pathogenic variants in high-risk breast cancer susceptibility genes that often disrupt a specific pathway involved in maintaining integrity of DNA repair processes. Individually, the common variants have very small effect sizes with odds ratios usually ranging from 0.85 to 1.20, but their cumulative impact in breast cancer risk has been shown to be considerably larger7. This cumulative effect can be captured in a single measure by a polygenic risk score (PRS), the summed contribution of many common risk variants, which is able to identify women at over 3-fold risk of breast cancer, compared to women with an average risk8. By improving identification of these women at high risk of breast cancer, it could serve as a new tool for personalised, risk-based breast cancer screening10.Beyond genetic predisposition caused by high-risk mutations in breast cancer susceptibility genes, breast cancer has a highly polygenic mode of inheritance. Large-scale genetic screens have to date identified over a hundred loci associated with risk of breast cancerPALB2 and CHEK2 genes and (3) that the PRS has utility for informing about risk of contralateral breast cancer, and about the risk in first-degree relatives. We use data from the FinnGen study, which combines nationwide health registries with genomic information for 122,978 women from across the country, representing 5% of the Finnish adult female population.Here, we comprehensively assess the impact of germline genetic variation on risk of breast cancer and show (1) how a high breast cancer PRS compares to high-risk mutations in breast cancer susceptibility genes, (2) how the PRS modifies the risk of breast cancer in women carrying pathogenic mutations in the 7, a genome-wide score by Mars et al.10 derived using LDpred software and a new genome-wide score derived using PRS-CS11. In our data, the genome-wide scores outperformed the 313 SNP score with hazard ratio (HR) estimates per standard deviation at 1.55 , 1.63 (CI 1.60–1.67) and 1.71 (CI 1.68–1.75) of the PRS for 313 SNP score, LDpred score and PRS-CS score, each scaled separately to mean zero and unit variance. We therefore chose the PRS built with PRS-CS for subsequent analyses . In FinnGen, 8401 (6.8%) women have been diagnosed with breast cancer, with mean age at disease onset of 58.6 . We first tested the association of three polygenic risk scores on breast cancer risk: a 313 SNP scorePALB2) was 0.0014 , with 336 heterozygote mutation carriers included in the analyses. The allele frequency for rs555607708 (CHEK2) was 0.0064 (3.7 times enriched in Finns compared to NFEE), with 1641 heterozygotes and 7 homozygote individuals.We then studied the allele frequencies, geographic variation and risk estimates for the two Finnish-enriched, high-impact breast cancer mutations. The allele frequency for rs180177102 , corresponding to a lifetime risk by age 80 of 56.1% (95% CI 50.8–61.4%). The CHEK2 variant conferred a risk increase for breast cancer with HR 2.19 (95% CI 1.91–2.51), p = 3.90 × 10−29), corresponding to a lifetime risk of 31.7% (95% CI 29.5–33.9%). Comparing to women with a PRS between the 10th and 90th percentiles , women with PRS above the 90th percentile had a similar effect size as CHEK2 mutation carriers  and their similar lifetime risk was similar . However, a high PRS affected a nearly 7-fold larger group of women  had a lifetime risk of 55.3% (95% CI 49.4–61.2%), which increased to 83.9% (71.2–96.6%) among women with a high PRS (>90th percentile), and decreased to 49.1% (30.6–67.6%) in women with a low PRS  which doubled to 59.2% (52.1–66.3%) in women with a high PRS and decreased to 9.3% (4.5–14.1%) in women with low PRS.Next, we estimated how the PRS modifies breast cancer risk in the mutation carriers. For both % 95% CI 9.4–61.2%PALB2 and CHEK2) and in non-carriers. In both carriers and non-carriers, hazard ratios for the top and bottom decile of the PRS were very similar , in carriers of CHEK2, 1.86 , and in carriers of neither the PALB2 nor the CHEK2 mutation, the HR was 1.71 . Similarly, in a formal test for interaction by introducing an interaction term in the regression model, we found no evidence of an interaction between the PRS and mutations for neither the PALB2 variant (p = 0.18), nor the CHEK2 variant (p = 0.45).To test for possible interaction between mutation carriers and the PRS, we first compared the PRS effect size in pooled mutation carriers (p = 4.08 × 10−6), to HR 2.32 . Also for late-onset, the association was attenuated, from HR 1.30 , to HR 1.09 .Next, we evaluated how the PRS modifies the risk conferred by a positive first-degree family history. Family history was assessed in 7715 mother–daughter pairs and 12,086 pairs of sisters, separately for family history of early-onset age < 45) and late-onset (age ≥ 45) breast cancer. For both, PRS stratified women for breast cancer risk, but the stratification was more pronounced in family history of early-onset disease , with 97 individuals out of 1604 cases with a high PRS being diagnosed with contralateral breast cancer.Lastly, we tested the association between the PRS contralateral breast cancer among breast cancer patients. With PRS between the 10th and 90th percentile as reference, a high PRS (>90th percentile) was associated with risk of contralateral breast cancer with HR 1.60 , as well as to the national hospital discharge registry (1968-), the national death registry (1969-) and the medication reimbursement registry (1964-). These registries cover the whole population.The data comprised of 122,978 Finnish women in the FinnGen, Data Freeze 5. FinnGen comprises prospective epidemiological cohorts (initiated as far back as 1992), disease-based cohorts, and hospital biobank samples , from the drug reimbursement registry by selecting individuals with a reimbursement code for breast cancer, and from the death registry with ICD-10 C50. Contralateral breast cancer was defined as breast cancer in the opposite breast diagnosed over 6 months after the date of the primary breast cancer diagnosis, obtained from the Cancer Registry.p-value (<1e-6) and minor allele count (MAC < 3). Array data pre-phasing was carried out with Eagle 2.3.527 with the number of conditioning haplotypes set to 20,000. Genotype imputation was done with Beagle 4.128 (as described in 10.17504/protocols.io.xbgfijw) by using the SISu v3 population-specific reference panel developed from high-quality data for 3,775 high-coverage (25-30x) whole-genome sequences in Finns.FinnGen samples were genotyped with Illumina and Affymetrix arrays , and genotype calls were made with the GenCall or zCall (for Illumina) and the AxiomGT1 algorithm for Affymetrix data. Individuals with ambiguous gender, high genotype missingness (>5%), excess heterozygosity (+-4SD) and non-Finnish ancestry were excluded, as well as all variants with high missingness (>2%), low Hardy–Weinberg equilibrium PALB2 and rs555607708 (c.1100delC) in CHEK2. Genotype data batches with an imputation INFO score <0.8 were excluded. This excluded 13,607 women from analyses involving the PALB2 variant (mainly older disease-based cohorts), but no exclusions were needed for CHEK2. PALB2 mutation carrier status was ignored in analyses involving the CHEK2 variant, and vice versa. Women homozygous for the CHEK2 variant were analysed jointly with the heterozygotes.We chose two previously reported Finnish-enriched frameshift variants for our main analyses, rs180177102 (c.1592delT) in 7, (2) another previously published, genome-wide PRS10 built with the software LDpred29 and (3) a genome-wide PRS we built with the software PRS-CS  using HapMap3 variants11. For the LDpred and PRS-CS PRSs, the input weights came from a large independent genome-wide association study (GWAS)6. To have a PRS independent of the PALB2 and CHEK2 variants, we excluded the variants within the CHEK2 gene ±3 Mb, and variants within the PALB2 gene ±2 Mb  a previously published PRS with 313 SNPs a previo23. Correspondingly, we defined a PRS below the 10th percentile as a low PRS. Individuals between the 10th to 90th percentiles served as the reference category.A high PRS was defined as a PRS above the 90th percentile, as it corresponds to a lifetime risk of ≥30%, which guidelines consider as the threshold for high riskPALB2 or CHEK2 variants, and (2) high PRS (>90th percentile). The benchmark for these analyses was age-standardised (age in 2014) breast cancer incidence for the whole Finnish population, calculated as the mean of 5-year incidences for each hospital district over 1998–2007. The incidence data was obtained from the Finnish Cancer Registry (publicly available at https://cancerregistry.fi/statistics/). Polygon data for the Finnish map were obtained from GADM (https://gadm.org/data.html).Geographic variation is reported by region of birth (obtained from Statistics Finland) as the proportion of individuals with (1) the frameshift mutations in the 30. In brief, the method measures the accumulation of PRS differences between the Western and Eastern subpopulations of Finland using a “random PRS”, made from a randomly chosen set of independent (r2 < 0.1) variants with minor allele frequency >0.05 that are not associated with breast cancer . If such random PRS accumulated differences between the subpopulations, that could indicate a population genetic bias in effect estimates of the GWAS, rather than a real difference in genetic susceptibility of breast cancer between the subpopulations. We found no evidence of such bias . To analyse the impact of family history in first-degree relatives, we randomly sampled one female relative for each woman who had at least one first-degree relative in the dataset. For mother–daughter pairs, the mother was assigned as the index relative. For sisters, we randomly assigned one to be the index relative, irrespective of age. If both women in the pair were breast cancer cases, we used the year of diagnosis to assign the woman diagnosed earlier as the index. Some individuals appeared several times as non-index individuals, which may occur when, for instance, a woman is the daughter of one index individual and the sister of another – we therefore randomly sampled the data to contain each non-index individual only once. We then inferred the risk of breast cancer in these unique non-index individuals. We analysed separately family history of early-onset (age < 45) and late-onset (age ≥ 45) breast cancer.The pairs of first-degree relatives were inferred with KING v2.2.4We estimated HRs and 95% CIs with the Cox proportional hazards model, and used Schoenfeld residuals and log–log inspection for assessing the proportional hazards assumption. Start of follow-up was set at birth, and follow-up ended at the first record of the endpoint of interest, death or at the end of follow-up on 31 December 2018, whichever came first. All tests were two-tailed. In all survival analyses, we used age as the time scale, with 63 batches and the first 10 principal components as covariates. The only exception was the analysis on contralateral breast cancer, where follow-up started from the diagnosis, and age was included as a covariate.32. In line with previous studies on breast cancer susceptibility genes, we assessed lifetime risk (cumulative incidence without competing risks) by age 8033. Lifetime risk was estimated from the adjusted survival curves, with 95% CIs obtained by normal approximation. The adjusted survival curves were plotted with the R package survminer. This presents the expected survival curves separately for subgroups, based on the Cox model. To estimate the covariate-adjusted cumulative incidence functions in the presence of competing risks, we used the Stata module stcompadj34. The competing event was non-breast cancer causes of death and covariates were assumed to have similar effects the main and competing event.Goodness-of-fit for the PRS was assessed with a method proposed by May & Hosmer for a Cox proportional hazards modelInteractions between the PRS and the pathogenic mutations were assessed (1) by comparing the PRS effect sizes in pooled and non-pooled mutation carriers and non-carriers , and (2) formally by introducing an interaction term for the mutation and the continuous PRS. For data and variant handling and PRS calculation, we used BCFtools versions 1.7 and 1.9, and PLINK 2.0. For statistical analyses, we used R 3.6.3 and Stata 16.0 . Cromwell and WOMtool were used for workflow handling.The FinnGen project is approved by the Finnish Institute for Health and Welfare (THL), approval number THL/2031/6.02.00/2017, amendments THL/1101/5.05.00/2017, THL/341/6.02.00/2018, THL/2222/6.02.00/2018, THL/283/6.02.00/2019), Digital and population data service agency VRK43431/2017-3, VRK/6909/2018-3, the Social Insurance Institution (KELA) KELA 58/522/2017, KELA 131/522/2018, KELA 70/522/2019 and Statistics Finland TK-53-1041-17.Patients and control subjects in FinnGen provided informed consent for biobank research, based on the Finnish Biobank Act. Alternatively, older research cohorts, collected prior the start of FinnGen (in August 2017), were collected based on study-specific consents and later transferred to the Finnish biobanks after approval by Valvira, the National Supervisory Authority for Welfare and Health. Recruitment protocols followed the biobank protocols approved by Valvira. The Ethics Review Board of the Hospital District of Helsinki and Uusimaa approved the FinnGen study protocol Nr HUS/990/2017.The Biobank Access Decisions for FinnGen samples and data utilised in FinnGen Data Freeze 5 include: THL Biobank BB2017_55, BB2017_111, BB2018_19, BB_2018_34, BB_2018_67, BB2018_71, BB2019_7 Finnish Red Cross Blood Service Biobank 7.12.2017, Helsinki Biobank HUS/359/2017, Auria Biobank AB17-5154, Biobank Borealis of Northern Finland_2017_1013, Biobank of Eastern Finland 1186/2018, Finnish Clinical Biobank Tampere MH0004, Central Finland Biobank 1-2017 and Terveystalo Biobank STB 2018001. Analyses of potential geographic bias of PRS were done with THL biobank permission BB2019_44.Further information on research design is available in the Supplementary InformationReporting Summary


Cancer is a major cause of death in low- and middle-income countries. A large number of studies have shown that some of the metabolic risk factors (MRFs) tend to cluster in individuals. We examined the synergistic effects of multiple MRFs and cancer risk among Iranian adults.Among 8593 (3929 men) participants aged ≥ 30 years, the self-organizing map (SOM) was applied to clustering of four MRFs including high fasting plasma glucose (HFPG), high total cholesterol (HTC), high systolic blood pressure (HSBP), and high body mass index (HBMI). The Cox proportional hazards model was used to investigate the association between clusters with cancer incidence during a median of 14.0 years of follow-up.During the study period, 265 new cases of cancer were identified among participants at risk. The incidence density rate was 2.5 per 1000 person years in total population. About 32 and 40% of men and women, respectively, had three or four MRFs. We identified seven clusters of MRFs in both men and women. In both genders, MRFs were clustered in those with older age. Further, inverse associations were found between current smoking in men, and education level and passive smoking in women and clustering of MRFs. In men, a cluster with 100% HSBP and HBMI had the highest risk for overall cancer. While, among women, a cluster with 100% HFPG and 93% HBMI yielded the highest risk for cancer. The risk was decreased when HBMI accompanied by HTC.Clustering patterns may reflect underlying link between MRFs and cancer and could potentially facilitate tailored health promotion interventions. Cancer is already a major cause of death in low- and middle-income countries , 2. In 2A large number of studies have repeatedly shown that some of the MRFs tend to cluster and co-occur in individuals , 7, and We applied the self-organizing map (SOM)  to identn = 9553) were included. Participants with previous cancer at baseline (n = 52), without any follow-up data until the end of the study (n = 893), and with survival time under 365 days (n = 15) were excluded. The remaining 8593 (3929 men) participants (90% of eligible sample) contributed until the censoring date, first cancer occurrence, or death from any cause . Sitting blood pressure (BP) was measured twice by trained technicians, at least 15 min apart, and the mean value was considered as the subject’s BP. A single blood sample was drawn following an overnight fast to determine the FPG and TC following a standardized protocol [Standardized interviewer-administered questionnaires were used to obtain data on demographic characteristics, smoking status, educational level, physical activity, and prescription medications. Height and weight were measured in standardized ways, and BMI was calculated as weight (kg)/heightprotocol  questionprotocol .Education attainment was categorized into three groups: 0–8 years, 9–12 years, and over 12 years. Current smoker was considered as someone who is currently using cigarettes or other smoking implements daily, non-daily, and occasionally. Former smokers were defined as subjects who have smoked daily or occasionally and those who have quit smoking. Passive smokers were non-smokers who were exposed to environmental tobacco smoking. Never smokers were adults who reported not smoking any product during their entire life. In the first phase, low physical activity was defined as doing exercise or labor < three times a week, and in the second phase, it was defined as achieving a scores ≤ 600 MET  − minutes per week .2), HBP (SBP ≥ 120 mmHg), HTC (serum TC ≥ 5.18 mmol/L), and HFPG (FPG level ≥ 5.55 mmol/L).Four MRFs were defined based on the well-known cut-offs  as folloSubjects with no history of cancer were followed annually from baseline examinations until their first cancer diagnosis, death, emigration, or end of study , whichever occurred first. The main outcome was a composite of cancer types. Death certificates and the medical records of the hospitalized patients were used to supplement the information on cancer incidence. Only patients with pathologically proven cancer leading to hospitalization were enrolled. The collected information was then evaluated by an outcome committee consisting of an endocrinologist, an internist, a cardiologist, an epidemiologist, and other experts. The diagnoses of cancers, including histologic types, were coded according to the International Classification of Diseases 10th Revision. Time to event was defined as time of censoring or having the cancer, whichever occurred first. We censored individuals at the time of other causes of death, loss to follow-up during the study period (who had at least one successful phone contact during the study period), and being in the study until 20 March 2014 without cancer occurrence.t-test, and one-way analysis of variance (ANOVA) were used to compare the categorical and continuous variables, as appropriate. Incidence density rate of cancers was calculated by dividing the number of events by the person years at risk. Missing data (after applying the exclusion criteria) were 2.5, 1.4, 2.7, and 3.0% in men, and 3.7, 1.6, 6.0, and 5.8% in women for smoking status, BMI, TC, and physical activity level, respectively. Therefore, the multivariate imputation by chained equations (MICE) (mice package in R software) [A chi-square goodness of fit test, independent samples oftware)  was implThe SOM was applied to identify clusters of individuals with similar patterns of four MRFs separately for men and women. SOM is a nonparametric and unsupervised learning based on a neural network technique which groups similar individuals based on multivariate distance and forms a low-dimensional map of training dataset. Typically, SOM consisted in neurons (units or cells) organized on a regular two-dimensional grid, usually represented as cells on hexagonal or rectangular lattice. Similar individuals in terms of their characteristics are placed close together on the SOM grid, while individuals far apart on the map are different from each other . For assk level (k is the number of cluster) was specified and the association between cluster membership with the cancer incidence was assessed by the Cox proportional hazards model without and with adjusting for aforementioned baseline characteristics. The proportional hazard assumption was checked using statistical tests based on the scaled Schoenfeld residuals. All models were stratified by sex. Analyses were performed in the R statistical package, v.3.4.0 (www.r-project.org) using packages kohonen [P < 0.05 was considered significant.The multinomial logistic regression analysis were conducted to identify which factors  were associated with being a member of a cluster at baseline. Finally, one categorical variables with  kohonen , mice [2 kohonen , surviva kohonen , and clu kohonen . Two-taiAfter exclusions, the study sample consisted of 8593 total individuals (3929 men and 4664 women) aged ≥ 30 years. The mean (standard deviation (SD)) age at cohort entry were 48.1 (13.1) and 46.5 (11.7) years for men and women, respectively. During a median of 14.0-year follow-up , 265 new cases of cancer (132 in men and 133 in women) were identified among participants at risk. The incidence density rate was 2.5  2.2–2.8) per 1000 person years in total population and 2.8 (2.3–3.3) and 2.3 (1.9–2.7) in men and women, respectively. Among men, the most common cancers in terms of new cases were tumors of the digestive system (40%), tumors of hematopoietic and lymphoid tissues (16%), male genital system (15%), urinary system (14%), and respiratory system (6%). Tumors of the female genital system (46%), digestive system (24%), and hematopoietic and lymphoid tissues (7%) were the three most common cancer site groups among females.Table The frequency and percentage of different MRFs in population by each of the four MRFs are shown in Fig. We obtained seven apparent clusters in both genders by the SOM based on hexagonal topology with size 7 × 1. The average silhouette width was 0.61 and 0.74 for men and women, respectively, indicating a strong and reasonable clustering structure Figs.  and 4.FThe baseline characteristics of the clusters in men are presented in Table Table According to multinomial logistic regression models, age, smoking, and physical activity were significant predictors for cluster membership in men Table . ConsideIn women, age, physical activity, educational level, and smoking status were predictors of cluster membership at baseline Table . Older wAmong men, individuals in cluster 5 had the lowest incidence rate of cancer Table  during mAmong women, subjects in cluster 2 had the lowest incidence rate of cancer Table  during mFinally, we repeated the analysis while considering the clusters with highest incidence rate of cancer as reference groups. Accordingly, among men, only cluster 5 showed a lower risk of cancer  compared with cluster 3 as reference. However, among women, all clusters except cluster 1 had lower risk of cancer than did women in cluster 6 as reference (Supplementary Table 1).In this longitudinal study of 8593 Iranian adults with a median of 14 years follow-up, the prevalence and clustering of four major MRFs were identified. Moreover, sociodemographic determinants related to cluster membership were identified. We also investigated how different clusters of MRFs were associated with increases or decreases in cancer development. In both genders, seven distinct clusters of four MRFs were identified by SOM. These clusters differed substantially from each other in terms of total number of risk factors, the associations between identified clusters with four sociodemographic factors , and incidence of composite of cancer types. Among men, cluster 3 including those with 100% HSBP had significantly greater risk of incident cancer compared with cluster 5 as the reference group. In females, cluster 6 including individuals with 100% HFPG had significantly higher risk of cancer than cluster 2 as the reference group.The present study found that the presence of four MRFs, individually or in combination, is highly prevalent in Iranian adults, as we have previously shown . About 8A large number of studies have previously examined the association between MRFs and cancer incidence. However, they have focused on only one MRF , 25 or pAmong male participants, a relatively healthy subgroup (cluster 7) with the lowest number of MRFs was identified in which 41.6% of subjects had only one MRF. In particular, we identified two unhealthy subgroups (clusters 1 and 4), of whom 100% had at least two MRFs. Association analysis showed that each 1-year increment in age was associated with about 5% increase in chance of being in clusters 1 to 4, with high number of MRFs. It is assumed that aging is the result of the accumulation of multiple forms of damage and pathology in different tissues .Surprisingly, we found that smoking decreased the chance of being in unhealthy clusters (clusters 1 to 6) compared to healthy cluster (cluster 7). In fact, our results suggest that smoking decreases the aggregation of MRFs. Our results confirm the findings of previous studies that suggest that smoking has a protective effect against some MRFs . The inv. In a large pooled cohort of Australian adults, BMI was associated with the development of overall, colorectal, and obesity-related cancers in men [2) increased the risk of gastric and liver cancer, and obesity (BMI ≥ 28.0 kg/m2) increased the risk of colon cancer. However, overweight (BMI 24–28 kg/m2) showed a protective role in lung and bladder cancer incidence in males [Recent studies showed a convincing association between metabolic syndrome, as aggregation of three or more metabolic disorders, and certain types of cancer, including prostate  and breas in men . Also, ss in men . A Chinein males . Severalin males .In recent years, HTC has been linked to the development of several different cancers although the results are inconsistent. A number of studies have reported a positive association between TC and cancers , 34. HowAnother interesting finding emerges from the comparison of four clusters 1, 2, 3, and 4 with three other clusters ; the highest incidence of cancer was observed in clusters at which all or most of the subjects had HSBP (clusters 1 to 4). Also the identified patterns in clusters 3 and 4 suggest that HTC may modify the adverse effect of HSBP, because the risks of cancer were not significantly different between clusters 3 and 4, despite the 100% prevalence of HSBP and HTC in cluster 4. In some studies, arterial hypertension was associated with a higher risk of colorectal , prostatAmong females, cluster 1 was found to be relatively healthier than the others, with the lowest mean number of MRFs (0.8). About 43% of women in this cluster had no MRFs. Furthermore, we found two clusters with multiple MRFs (clusters 5 and 7), of whom 100% had at least two MRFs. Association analysis showed a positive relation between aging and clustering of MRFs, similar association was found in men. In addition, moderate education decreased the chance of being in unhealthy clusters  compared with high education. This finding may be attributable to sedentary lifestyle among highly educated women. One study reported that those with high education had lower total physical activity than those with moderate education . InteresIn women, the highest incidence rate of cancer was observed in cluster 6, although the number of MRFs was relatively smaller than clusters 5 and 7. Thus, unlike some previous studies , our finEvidence has suggested that BMI is an important predictor of cancer risk : a populThe relation between BMI and cancer are complex and are not yet fully understood. For example, some studies have shown that increased BMI is associated with an increased risk of breast cancer in women after menopause ; howeverVery few studies have examined the effects of various combinations of BMI and other MRFs on cancer risk. Our study showed that healthy overweight/obese women (cluster 2) had the lowest risk for incidence of cancer but the risk significantly increased only when HFPG is added to HBMI (clusters 6 and 1). All individuals (100%) and 7.7% of individuals in cluster 6 and cluster 1, respectively, had HFPG; in contrast, nobody had HFPG in cluster 2, reinforcing the positive association between HFPG and cancer risk.While many observational studies suggest that people with pre-diabetes and diabetes are at a significantly higher risk of some types of cancer , but theInterestingly, all individuals in cluster 7 had also HFPG; however, no statistically significant differences in risk of cancer were observed between cluster 7 and cluster 2. Unlike cluster 2, all individuals in cluster 7 had HTC which suggests the protective role of HTC against cancer risk, as we have discussed in the previous section.higher risk for most cancers than men with diabetes. The possible mechanisms include as follows: (1) women often spend longer duration than men in the pre-diabetic stage, (2) diabetic women have poor glycemic control compared with men with diabetes, (3) women are often undertreated or not getting the same level of treatment as men. Therefore, women with diabetes may be at greater risk of developing cancer than men due to carcinogenic effects of hyperglycemia and DNA damage [In sum, we observed the sex-specific risk patterns of overall cancers. It was found that combination of HBMI and HFPG put women at the highest risk for overall cancer. A recent meta-analysis  found thA damage . InteresA damage . HoweverA damage . AlthougSeveral limitations should be noted. First, MRFs were measured only once at cohort entry, so we were unable to assess changes over time. Second, due to the small number of cancer cases, we were unable to stratify results by cancer site. Third, the Iranian background of study participants may limit the generalizability of our findings to more diverse ethnic groups of population. Strengths of our study include its long duration of follow-up and population-based sample. Also, a comprehensive physical exam and questionnaire were completed at cohort entry, and complete and reliable outcome data obtained through the outcome committee team. This is the first study that identified multiple clusters of MRFs using SOM in a well-characterized cohort of Iranian population.Clustering of MRFs is common in Iran. The majority of men had more than one metabolic risk factor. Multiple modifiable factors such as educational level, physical activity level, and smoking are responsible for the clustering of MRFs. In general, a gradient between the number of MRFs and cancer risk was not observed in both men and women. Instead, some combinations of four MRFs were significantly associated with an increased risk of overall cancer. Our study shows that co-occurrence of HBMI and HFPG in women, and HSBP and HBMI in men are powerful indicators of overall cancer. However, HBMI in combination with HTC has a protective effect against cancer development in both genders. These findings suggest that the combined information from a few variables related to cancer development is superior to measurement of only one metabolic risk factor. The cumulative and clustered nature of MRFs helps identify potential mechanisms of cancer and modifiable factors that can serve as important ways for intervention initiatives.This study provides new evidence that the presence of a MRF alone is not a reliable predictor for cancer risk. Instead, clusters of MRF signal an opportunity for screening and primary prevention programs. From a public health perspective, our results have important implication because metabolic risk factors are highly prevalent in our country with inadequate control.Additional file 1: Supplementary Table 1. Cluster membership in relation to cancer incidence in Iranian population in the Tehran Lipid and Glucose Study (1999-2014)


My Guide) and the other to promote healthy lifestyle behaviors .Latina breast cancer survivors experience poorer health-related quality of life (HRQoL), greater symptom burden, and more psychosocial needs compared to non-Latina breast cancer survivors. eHealth platforms such as smartphone apps are increasingly being used to deliver psychosocial interventions to cancer survivors. However, few psychosocial eHealth interventions have been developed specifically for Latina breast cancer survivors. Further, little is known about how Latinas, in general, engage with eHealth interventions and whether specific participant characteristics are associated with app use in this population. We evaluated the use of 2 culturally informed, evidence-based smartphone apps for Latina breast cancer survivors—one that was designed to improve HRQoL and reduce symptom burden , immediately after the 6-week intervention (T2), and 2 weeks after T2 (T3). Specific study outcomes included subdomains of HRQoL, symptom burden, cancer-specific distress, cancer-relevant self-efficacy, and breast cancer knowledge.Eighty Latina breast cancer survivors were randomized to use the My Health group. Content related to common physical and emotional symptoms of breast cancer survivors as well as recommendations for nutrition and physical activity were most frequently accessed by My Guide and My Health participants, respectively. Lastly, clinically meaningful improvements were demonstrated in breast cancer well-being among low app users  of My Guide and social well-being among high app users  of My Health.On average, participants used their assigned app for more than 1 hour per week. Sociodemographic or psychological characteristics were not significantly associated with app use, except for employment status in the My Guide and My Health. Furthermore, since sociodemographic variables, excluding employment status, and baseline HRQoL  were not related to app use, My Guide and My Health may be accessible to diverse Latina breast cancer survivors.The favorable rates of participant use across both apps suggest that Latina breast cancer survivors are interested in the content delivered across both ClinicalTrials.gov NCT03645005; https://clinicaltrials.gov/ct2/show/NCT03645005 Breast cancer is the most prevalent cancer and the leading cause of cancer-related deaths among Latina women in the United States, with an estimated 24,000 new cases diagnosed and 3200 deaths expected annually . CurrenteHealth platforms are increasingly being used to deliver psychosocial interventions to general cancer survivors and have demonstrated positive effects related to psychosocial outcomes and lifestyle behaviors ,23. ReseMy Guide, was designed to improve HRQoL, reduce symptom burden, and reduce cancer-specific distress, and the attention-control app, My Health, was designed to promote general health and healthy lifestyle behaviors  minutes per week) and My Health . See Latina breast cancer survivors used their assigned smartphone app for mean (SD) time of 478.15 (385.84) minutes over the 6-week study period. The mean (SD) weekly app use did not differ between P>.10), except for employment status. Specifically, for My Health, participants who were employed used the app for a lesser duration than those who were unemployed .There were no significant relationships between the sociodemographic characteristics or baseline HRQoL and total app use , followed by Managing My Symptoms . The vast majority of My Guide participants  accessed content related to at least one symptom within the Managing My Symptoms domain. Participants clicked on the least number of links within the Breast Cancer Medications domain .Over the 6-week study period, My Health participants clicked on a total of 7167 links or webpages of study content within the app. The top 3 domains that participants most frequently accessed were Diet and Nutrition  and Exercise . The Preventing Diabetes and Heart Disease domain had the least number of links clicked .Over the 6-week study period, My Guide or My Health, respectively. For My Guide, scores on breast cancer well-being exceeded the minimally important difference threshold from T1 to T2 and T1 to T3 among low app users, while for My Health, scores on social well-being exceeded the minimally important difference threshold from T1 to T2 among high app users.My Guide and My Health apps for 8.66 hours and 7.28 hours, respectively. In line with prior research, our study integrated strategies to improve participant engagement, including telecoaching to promote optimal adherence to app use [My Guide or My Health, which is comparable to the amount of time, if not more, patients would typically spend with an in-person counselor. These findings suggest that both apps are of interest to Latina breast cancer survivors.This study evaluated the patterns of use of 2 culturally informed, evidence-based smartphone apps designed specifically for Latina breast cancer survivors. On average, participants used their  app use , cultura app use  such as My Guide and My Health demonstrated longer total durations of app use over a shorter time period compared to 2 other web-based tools for breast cancer survivors in general, which reported an average of 5.6 hours [The past decade has seen a significant increase in technology-assisted interventions for patients with cancer ,55. Howe.6 hours  and 15.2.6 hours  of user My Guide and My Health apps are accessible to a broad group of Latina breast cancer survivors who speak English or Spanish and have varying educational and income backgrounds, age, ancestry, marital status, and baseline HRQoL. These findings also suggest that the scalability of these apps is feasible. Women who were employed were less likely to use the My Health app, and this finding may be explained by employed breast cancer survivors having less time to devote to the app. Results also demonstrated clinically meaningful improvements in breast cancer well-being and social well-being among low users of My Guide and high users of My Health, respectively. Descriptively, low My Guide users also reported reduced symptom burden over time. However, it is unclear whether these changes are clinically meaningful due to the lack of established cutoffs for minimally important differences in symptom burden. Additional research is needed to further evaluate the associations between low app use versus high app use and improvements in HRQoL subdomains as well as to determine the optimal level of weekly app use. The finding that high My Health users reported improved social well-being over time also warrant further evaluation, as the My Health app focuses on promoting general health and healthy behaviors and does not include specific content related to social support or relationships. Follow-up interviews with participants may help contextualize study results and identify specific aspects of the intervention that were beneficial in terms of study outcomes.With the exception of employment status, sociodemographic factors did not predict app use. These findings suggest that the My Guide most frequently accessed content related to psychosocial aspects of the cancer experience followed by physical symptoms, whereas users of My Health most frequently accessed content related to nutrition and exercise. These most-clicked domains are consistent with commonly documented concerns among the general population of cancer survivors [My Guide app were most likely to click on the Managing My Emotions module. Issues regarding coping with loss of health status, bodily changes, fear of recurrence, and reduced medical oncology visits can make the transition to survivorship an emotionally challenging time [Managing My Emotions module.Click-level data revealed that users of urvivors -61, and urvivors ,63. Womeing time , which mMy Guide with a larger and more diverse sample of Latina breast cancer survivors, include other objective and comprehensive measures of participant engagement or eHealth usage  [The results from this study should be evaluated within the context of the study limitations. First, click-level data only provide the total number of clicks for each domain and subdomain and do not account for differences in the amount of content across the domains and subdomains. For example, the most or least accessed topics may simply reflect domains that had the most or least number of links, rather than indicating which topics had more or less engagement or interest from participants. Second, rather than the total time spent using the app, another measure of app use such as intensity or depth of engagement with the intervention   or some pletion) ,65, and My Guide and My Health. Given the paucity of smartphone apps that have been developed for Latina patients, these click-level data may provide useful insights on the most important cancer topics for this historically understudied patient population. The click-level data provide information on the most accessed topics within the study apps, and these findings may lend insights into some of the most relevant survivorship topics for Latina breast cancer survivors. Furthermore, sociodemographic variables, excluding employment status, or HRQoL at study entry were not related to app use for My Guide and My Health, which suggest the potential for larger uptake among Latina breast cancer survivors. Therefore, these apps may be accessible to diverse Latina breast cancer survivors. Additional research is needed to determine the effect of eHealth use on psychosocial outcomes among Latina breast cancer survivors and whether a longer intervention time frame is needed for optimal improvements.In summary, this study contributes to the scarce research regarding eHealth supportive care interventions among Latina breast cancer survivors. The favorable rates of participant use across the study apps suggest that Latina breast cancer survivors are interested in the content delivered across both


The National Institute of health and Care Excellence issued guidelines on familial breast cancer screening in 2004. Such guidelines should be uniformly implemented to ensure that members of the same family with the same level of risk, but living in different areas, have the same access to screening. We assessed uptake by creating a short, six question online survey designed to assess compliance in each regional area. We used this to conduct a survey of all 22 regional genetics services. There was a 100% response to the survey allowing a complete map to be created. The devolved nations had near complete compliance with the sole exception of SW Scotland, but in England the picture was fragmented with regions representing a combined population of 26.6 million (48%) not implementing the full NICE recommendations. Fifteen years after the publication of the original guidelines, major inequity in provision for screening still occurs and a postcode lottery exists for the management of women from families with a history of breast cancer. We estimate that up to 73 preventable breast cancer deaths occur each year due to the current inequity of access. It may be time to consider alternative funding and implementation models to ensure consistent access across the country. In the UK  the National Institute of health and Care Excellence (NICE) was created 20 years ago with a strapline of ‘Improving health and social care through evidence-based guidance’. When the term ‘offer’ is utilised this infers that health commissioners should implement the recommendation as it has usually passed the test of being cost effective. Although guidelines are just ‘guidance’, the purpose of NICE guidelines was to avoid postcode lotteries where an individual in one part of a country was able to access an aspect of healthcare, whereas in another part they would be refused access. While some have intimated that that individual guidance could be ignored if it was not in the patient’s interest to follow it,2 Many countries have produced guidelines including frequently updated guidance in the USA,3 Netherlands4 and France.5 The first NICE guideline group for familial breast cancer was constituted in 2002 and produced its first report in May 2004.6 This recommended that women at moderate  and high risk  should be ‘offered’ annual mammography screening aged 40–49 years of age. It was acknowledged that a national study (FH01) was in progress and would inform a future update.8 A short update in 2006,6 recommended MRI screening for women at very high risk including carriers of pathogenic variants in BRCA1, BRCA2 and TP53 aged 30–49 years based at least in part on the UK MARIBS study results.9 The 2013 update6 was able to include the results of the FH01 mammography screening trial in women aged 40–49 and strengthen the recommendation to offer annual mammography aged 40–49 for moderate-risk and 40–59 for high-risk women. The FH01 study8 showed a projected mortality advantage for annual mammography surveillance compared to age matched women with no screening based on tumour characteristics at diagnosis. Approximately 3% of women aged 40 years are at moderate or high-risk of breast cancer based on family history alone,9 and at least half of these come forward with concerns.10Breast screening is an important element of what can be offered to women at increased risk of breast cancer by virtue of their family history. Targeting such screening at those at higher risk reduces the negative elements of screening such as concerns regarding what is often termed ‘overdiagnosis’. Women who have seen a mother or sister die from breast cancer before 50 years of age will be concerned about having to wait until age 50, the usual starting age for national programmes to undergo mammography screening.8 that included 76 centres across the UK, but anecdotal evidence between 2006–2011 pointed to patchy implementation of MRI screening. As a result, the National Health Service Breast Screening Programme (NHSBSP) was asked to deliver the MRI aspects of NICE guidance. In January 2012 the Advisory Committee on Breast Cancer Screening agreed to implement this and there is now equitable provision of MRI screening (aged 30–49) and annual mammography screening from 40-69 years in BRCA1, BRCA2 and TP53 carriers (no mammography for TP53) in England.11 The protocol for MRI screening included those at ‘equivalent’ high risk to a BRCA1/BRCA2 mutation carrier. This was misinterpreted by a number of clinics to include all those at high-risk of breast cancer. In reality only about 8–10% of high-risk women qualify for the NHSBSP higher risk programme12 and the protocols have now been rewritten to clarify this. However, this uncertainty has led to many family history clinics (FHCs) not being commissioned to deliver the NICE defined high-risk annual screening to age 59 years.Some women at moderate risk were still able to access mammographic screening aged 40–49 years at least through the FH01 trialThese concerns over the reduction in availability of NICE recommended mammography screening led us to survey all UK genetics centres delivering cancer genetics services.http://www.ukcgg.org/) covering 22 regional centres.A short, six question online survey was designed (supplementary table) to assess compliance in each regional area. The invitation was sent out on 14 March 2019 to all cancer genetics leads from the Cancer Genetics Group  look to save money and therefore are not willing to adopt all NICE guidelines. Not implementing this guidance would save on not having to provide funding for family history clinics for women to be assessed for BC risk. While CGGs appear to be able to ignore guidance, there are precedents to suggest that they could be successfully sued for not implementing NICE guidance without good reason (https://www.nice.org.uk/news/article/court-warns-ccg-over-disagreeing-with-nice-guidance). If CGGs were forced to adopt NICE guidelines this would reduce inequalities of access. Alternatively, all familial breast screening could be incorporated into the NHSBSP, which would also lead to more uniform access. Finally, it is important to educate and inform all women to have a breast cancer risk assessment by age 40 years, either through their GP or by developing tailored online tools to enable a self-assessment.In the USA in 2007, the American Cancer Society (ACS) published recommendations for annual breast MRI along with annual mammography screening for women who have ≥20% lifetime risk for developing breast cancer incorporating both the majority of the UK moderate risk and all of the high-risk group.We have shown that there has been only partial compliance with NICE approved mammographic screening surveillance in women at familial risk with less than half of England is now fully compliant. This has created a postcode lottery where women in one part of the UK get access to surveillance that their sister at the same risk cannot. It is a concern that even in a fully funded public health system such as the UK that major inequity in provision occurs even with the highest levels of evidence provided by NICE guidelines.Supplementary Table


Pancreatic cancer has a poor survival rate and its modifiable risks are poorly understood. We investigated the association between both traditional  and novel (standard UK clothing sizes) adiposity indicators as predictors of pancreatic cancer risk among women enrolled in the UK women’s cohort study (UKWCS). When adjusted for known confounders like age, education, smoking and physical activity, hip circumference and skirt size were both significant predictors of pancreatic cancer risk in the median follow-up of approximately 19 years. BMI became a significant predictor of pancreatic cancer risk when potential latent cases of pancreatic cancer were excluded from the analysis. Thus, adiposity indicators, specifically hip circumference and standard skirt size, are useful to predict pancreatic cancer among women and should therefore be routinely documented in both national surveys and epidemiological studies.p = 0.009) and each size increase in skirt size  at baseline increased pancreatic cancer risk. Baseline BMI became a significant predictor of pancreatic cancer risk  when latent pancreatic cancer cases were removed. Only baseline hip circumference was associated with pancreatic cancer risk  when participants with diabetes at baseline were excluded to control for reverse causality. (4) Conclusion: Hip circumference and skirt size were significant predictors of pancreatic cancer risk in the primary analysis. Thus, hip circumference is useful to assess body shape relationships. Additionally, standard skirt sizes offer an economical and objective alternative to conventional obesity indices for evaluating pancreatic cancer risk in women.(1) Background: We studied the association of both conventional  and novel (UK clothing sizes) obesity indices with pancreatic cancer risk in the UK women’s cohort study (UKWCS). (2) Methods: The UKWCS recruited 35,792 women from England, Wales and Scotland from 1995 to 1998. Cancer diagnosis and death information were obtained from the National Health Service (NHS) Central Register. Cox’s proportional hazards regression was used to evaluate the association between baseline obesity indicators and pancreatic cancer risk. (3) Results: This analysis included 35,364 participants with a median follow-up of 19.3 years. During the 654,566 person-years follow up, there were 136 incident pancreatic cancer cases. After adjustments for age, smoking, education and physical activity, each centimetre increase in hip circumference (HR: 1.03, 95% CI: 1.01–1.05,  Pancreatic cancer has one of the poorest 5-year survival statistics, especially if diagnosed late ,2,3. WhiConventional adiposity indicators, namely weight, body mass index (BMI), waist circumference and waist-to-hip ratio, are routinely used in research and in practice to identify the risk of chronic diseases. These indicators require trained personnel to perform the measurements, and thus, from a public health perspective, they are labour-intensive. Therefore, for population studies or community screening purposes, self-reported standard clothing sizes are an objective, easy-to-measure alternative adiposity indicator and can be reliably reported by respondents ,16,17,18Clothing sizes have been found useful to predict the risk of various cancers in both men and women. Among a sub-cohort of weight-stable women from the Netherlands Cohort Study, self-reported Dutch skirt size data significantly predicted the risk of endometrial cancer in women . In the Given the lack of research in relation to risk factors for pancreatic cancer worldwide, there is a need for more data to gauge the magnitude of association between pancreatic cancer risk and obesity indicators. Most studies are too small or short-term to be able to explore the risk factors associated with pancreatic cancer. Furthermore, the low incidence rate of pancreatic cancer ) requires inevitably large and long-term studies to understand these associations. The UKWCS, with its sufficiently large enrolment and long follow-up, is one of a few studies which can be used to explore these associations. Thus, we explored the relationships between pancreatic cancer risk and both conventional  and novel (skirt and blouse sizes) adipose indicators using the UK Women’s Cohort Study (UKWCS) data.The recruitment and characteristics of the UKWCS participants have been described previously . BrieflySubjects were flagged with the NHS Central Register for both cancer diagnosis and death notification. Incident cancers and cause of death were coded according to the International Classification of Diseases 9 and 10. Pancreatic cancer incidence data were obtained from the linked data from the NHS Central Register for both cancer diagnosis and death notification.Anthropometric measures at baseline, including weight, height, waist and hip circumference, were self-recorded at the time of enrolment. BMI and waist–hip ratio were calculated. BMI was categorised into the four groups of underweight, normal weight, overweight and obese according to the WHO criteria. Skirt and blouse sizes, according to UK clothing standards, were reported by the participants at baseline, with the clothing sizes recorded using even numbers between 6 and 28.Pearson correlation coefficients between adiposity measures were calculated. The association between obesity and pancreatic cancer risk within the UKWCS was explored using Cox’s proportional hazards regression. Linear trends were also studied using the adiposity indices as continuous variables. Confounding variables to be included in the analysis were identified using a directed acyclic graph (DAG). These variables were age, education, smoking status and hours spent on vigorous physical activity per day, measured at the time of participant enrolment.A preliminary simple model presented the age-adjusted hazard ratio (HR) for the adiposity indicators. A second fully adjusted model controlled for age, smoking status, education and physical activity. Two additional sensitivity analyses were carried out to rule out reverse causality: (i) excluding all participants who were censored or were diagnosed with pancreatic cancer within 3 years of enrolment, to account for latent pancreatic cancer cases at baseline; and (ii) excluding all participants with diabetes at baseline, to account for potential reverse causality.All statistical analyses were conducted using STATA version 15 .The UKWCS has ethical approval as a research database (REC reference: 17/YH/0144) with a Public Health England data sharing contract (ODR1718_148). The UKWCS data are held securely on the Integrated Research Campus of the University of Leeds. The data were accessed through a virtual research environment using a token and handled as per the requirements of the European Union General Data Protection Regulation (GDPR). Institutional approvals were also obtained from the University of Leeds and the International Medical University (IMU) before the start of the project.n = 15) at baseline, the total number of participants available for this analysis was 35,364, with a median follow-up of 19.3 years. In the follow-up of 654,566 person-years, there were 136 incidents of pancreatic cancer cases, resulting in an incidence rate of pancreatic cancer in the UKWCS of 0.21 per 1000 person-years.After excluding the prevalent cases of pancreatic cancer  had an education above O-Level and 17,781 (53%) of the women were postmenopausal. At enrolment, 3810 (11%) of the participants were smokers, 9593 (28%) reported being on a vegetarian diet and 441 (1%) reported being vegan. Subjects’ self-reported participation in vigorous activity was highly variable and ranged from 0 to 14 h/day, with a mean (SD) of approximately 15 (29) minutes per day. Two percent (n = 644) of the participants reported having diabetes at baseline. Descriptive characteristics of participants at baseline data collection are summarised by pancreatic cancer incidence in The mean (SD) age of the participants at enrolment was 52 (9) years, the majority of the participants (2. The mean (SD) waist and hip circumference in centimetres were 73.5 (9.3) and 98.6 (8.4), respectively, and the waist–hip ratio was 0.75 (0.06). At enrolment, the reported blouse and skirt sizes ranged between 6 and 28.Anthropometric details of participants at baseline data collection are summarised by pancreatic cancer incidence in Pearson’s correlation coefficients between conventional anthropometric measures and UK clothing sizes are shown in 2) at recruitment, had the highest incidence rates of pancreatic cancer. In the simple model adjusted only for age, when compared to normal-weight individuals, individuals with obesity had a 72% higher risk of pancreatic cancer . However, in the fully adjusted model that was adjusted for age, education, smoking status and physical activity, the increased cancer risk with higher BMI was no longer statistically significant. In the fully adjusted model, only hip circumference remained significantly associated with pancreatic cancer risk (p = 0.009). Waist circumference and waist–hip ratio showed no significant associations with pancreatic cancer incidence in either model  increased the HR of pancreatic cancer by 8%  and 12% , respectively .After the exclusion of pancreatic cancer cases reported within 3 years of recruitment, the association between BMI and pancreatic cancer risk became statistically significant . However, the results of this sensitivity analysis did not greatly affect the magnitude of these associations. A second sensitivity analysis that excluded participants with diabetes at baseline also did not change the magnitude of these associations between obesity indices and pancreatic cancer. Nevertheless, the analysis attenuated the statistical significance of all these associations. Both sensitivity analyses also revealed the non-linear nature of the association of BMI with the risk of pancreatic cancer, which was not observed in the primary analysis. The risk of pancreatic cancer was higher among the underweight as well as the overweight and obese categories when compared to individuals in the normal-weight category .In the current analysis, we investigated the associations between conventional adiposity indicators  and pancreatic cancer risk among the women enrolled in the UKWCS. We also investigated UK clothing sizes as proxy measures for the conventional adiposity indices and evaluated their association with pancreatic cancer risk. In the primary analysis, while BMI and waist and hip circumference showed trends for positive associations with pancreatic cancer risk, in the fully adjusted model, only hip circumference remained a significant predictor of pancreatic cancer risk. The association between BMI and pancreatic cancer risk became statistically significant in the sensitivity analysis that removed potential latent cases of pancreatic cancer at baseline. This was expected given that weight loss precedes the development of other symptoms of pancreatic cancer .The significant association between hip circumference and pancreatic cancer seen in the primary analysis remained unaltered in the sensitivity analyses. These findings are interesting in the context of waist circumference and waist–hip ratio being widely viewed as more effective indicators of central obesity-related health risks compared to hip circumference . The lacp < 0.001) increase in breast cancer risk over the course of 17 years [p = 0.006) increase in postmenopausal breast cancer risk in the fully adjusted model, over a median 4-year follow-up of older women [p = 0.06) increase in relative risk (RR) for endometrial cancer was reported for each size increase in skirt size (used as a continuous variable) during a 13-year follow-up [p = 0.041) increase in pancreatic cancer risk over a median follow-up of 19 years when the self-reported UK skirt size at baseline went up a size.The highest incidence of pancreatic cancer in the UKWCS occurred in women with UK blouse or skirt sizes ≥20. However, increases in skirt size were more significantly associated with pancreatic cancer risk compared to similar increases in blouse size. The magnitude of the association between skirt size and pancreatic cancer risk is in the range of the previously reported estimates for other cancers ,16. Earler women . Similarollow-up . In the In the UKWCS, a higher skirt size, but not blouse size, was associated with a significantly increased risk of pancreatic cancer. This could be because blouse size is more affected by upper-body fat distribution, while skirt size considers both waist and hip measurements and therefore is more reflective of visceral obesity. This argument is supported by the higher degree of correlation observed in this study for skirt rather than blouse size with traditional adiposity indices. Several studies have implicated insulin resistance and the subsequent chronic pancreatic inflammation associated with visceral obesity in the pathogenesis of pancreatic cancer ,10,11,12The strengths of using the UKWCS data for the current analysis include the prospective design, the involvement of a large single-gender population, and the sufficiently long follow-up period to account for the incidence of pancreatic cancer. Moreover, the UKWCS collected data on weight, waist and hip circumference and UK clothing sizes for blouse and skirt., thus facilitating the evaluation of both measures.We also acknowledge the following limitations. Due to the rare nature of the disease, pancreatic cancer cases were limited even within this large cohort, potentially reducing the power to evaluate the associations of interest. Post-hoc power calculations showed that the current analysis of both hip circumference and skirt size with pancreatic cancer incidence yielded powers of 77% and 67%, respectively. The limited number of cases also considerably increased the probability of committing a type II error when evaluating the association between BMI and pancreatic cancer. Hence, these results must be interpreted with caution. However, it should be noted that there is a consistency in the magnitude of associations between all adiposity indicators studied and pancreatic cancer incidence, suggesting that the point estimates may be close to the actual values. Secondly, this study used self-reported measures and clothing sizes. Self-reported body weight and measurements are likely to be misreported due to social desirability bias ,24. HoweFurthermore, changes in body weight, shape and health behaviour throughout the study were not factored in the modelling. Since weight loss is a symptom and consequence of pancreatic cancer , such anPancreatic cancer incidence increased with increases in conventionally used obesity indices . In our primary analysis, only hip circumference remained a significant predictor of cancer risk when adjusted for known confounders. Hip circumference measurements should be included in population studies to further assess the effects of fat storage location and body shape in determining disease risks. When the analysis excluded potential latent pancreatic cancer cases at baseline, BMI was also significantly positively associated with pancreatic cancer risk. The risk for pancreatic cancer increased with each size increase in skirt size. Therefore, when standardised clothing sizes are widely used in a population, skirt sizes offer an objective, economical and robust alternative to traditional obesity indices used to evaluate pancreatic cancer risk in women. Thus, this study contributes further evidence to the utility of self-reported clothing size as an indicator of obesity-related disease risk among women and makes a case for its routine documentation in epidemiological studies and national surveys.


Although overall alcohol consumption is known to increase the risk of a number of cancers internationally, evidence for Australia and evidence regarding the pattern of drinking and cancer risk is limited.Adjusted hazard ratios (HR) and 95% confidence intervals (CI) for cancer risk in relation to overall alcohol consumption (drinks/week) and pattern of drinking were calculated using Cox proportional hazard regressions for 226,162 participants aged ≥45 years (2006–2009) in the 45 and Up Study, an Australian prospective cohort study. Incident primary cancer cases were ascertained by linkage to the New South Wales Cancer Registry to 2013 by the Centre for Health Record Linkage.Pinteraction = 0.049).Over a median of 5.4 years, 17,332 cancers were diagnosed. Increasing levels of alcohol intake were associated with increased risk of cancers of the upper aerodigestive tract , mouth and pharynx , oesophagus , colorectum , colon , liver  and breast . Breast cancer risk was marginally associated with drinking pattern, with higher risk when intake was concentrated on 1–3 days/week compared to the same amount spread over 4–7 days (Alcohol consumption confers a significant risk of cancer, and drinking pattern may be independently related to breast cancer risk. Alcohol consumption is an important risk factor for cancer and has been estimated to account for 2.8% of cancers in Australia and 5.5% of cancers globally,4 as well as 4.5% of the cancer burden in Australia.5 Studies first identified a relationship between alcohol and cancer as far back as 1903,6 and in 1988, the International Agency for Research on Cancer classified alcohol as a Group 1 carcinogen (the highest IARC classification) for cancers of the mouth, pharynx, larynx, oesophagus and liver. In 2010–2012, IARC reviewed the evidence and determined that there is a causal relationship between alcohol and colorectal and female breast cancer.8 In addition, the World Cancer Research Fund (WCRF) concluded it is probable that consuming >45 g of alcohol per day  increases the risk of stomach cancer, and consuming up to 30 g of alcohol per day decreases the risk of kidney cancer.10 Further, both organisations report there is suggestive evidence that heavy drinking causes pancreatic cancer, with recent meta-analyses supporting this conclusion.14 The evidence for other cancer types is inconclusive, although meta-analyses have reported increased risk of myelodysplastic syndromes,15 melanoma11 and cancers of the gallbladder,11 lung11 and prostate16 with increased intake, and inverse associations with thyroid cancer,17 Hodgkin lymphoma18 and non-Hodgkin lymphoma (NHL).19Alcohol consumption is commonplace in Australia, with the most recent national survey reporting that 79% of the adult population consumed alcohol in 2017–2018.21 While the types of drinking patterns in the literature have varied greatly,23 there have been two main patterns of interest in relation to cancer outcomes: (1) frequency of drinking and (2) consumption of a large number of drinks on drinking occasions, which is commonly referred to as ‘heavy episodic’ drinking and/or ‘binge’ drinking.38 A number of cancers have been linked to specific drinking patterns, including IARC Group 1 alcohol-related cancers  and other cancers, including stomach, pancreas, lung and prostate cancer.33 Large prospective studies that have used a consistent methodology to examine multiple cancer types are few. Previous reports have generally varied with respect to the methodology used to assess the relationship between drinking patterns and a single cancer type, making comparisons between studies difficult.The strongest evidence for the link between alcohol and cancer has come predominantly from observational studies that compared drinking and non-drinking cohorts and/or dose–response relationships. Less well studied is the influence of the pattern of drinking.11 due in part to regional variation in gene polymorphisms that relate to the metabolism of alcohol.39 In Australia, the relationship between alcohol consumption and a number of individual cancer types has been investigated,40 but to our knowledge, there are no population-based, prospective studies that quantify the risk of alcohol consumption on many cancer types in a single study, nor are there studies that investigate the impact of drinking pattern on cancer risk. We used a large prospective cohort study in Australia, the 45 and Up Study, to quantify the relationship between self-reported alcohol consumption and primary incident cancer diagnoses captured by record linkage to cancer registry data.The relationship between alcohol consumption and cancer risk is likely to vary across different regions of the world,41 In summary, men and women aged ≥45 years were randomly sampled from the general population of New South Wales (NSW), Australia, between 2006 and 2009 using the Services Australia  enrolment database. The database has records for all Australian citizens and permanent residents, as well as some temporary residents and refugees. Persons aged ≥80 years and persons living in rural and remote areas were oversampled. Participants completed a postal questionnaire (available at https://www.saxinstitute.org.au/our-work/45-up-study/questionnaires/), which contained items on demographic factors, lifestyle and health behaviours , and medical history . Participants completed a 5-year follow-up questionnaire .The Sax Institute’s 45 and Up Study is a prospective cohort study of 267,153 participants conceived to investigate healthy ageing, with methods previously described.Ethics approval for the 45 and Up Study was provided by the University of NSW Human Research Ethics Committee (reference: 10186) and for this specific analysis by the NSW Population Health Services Research Ethics Committee (reference: 2014/08/551).http://www.cherel.org.au/). The CHeReL used a best-practice approach in privacy-preserving record linkage42 along with the open-source probabilistic record linkage software Choice Maker.43 The probabilistic matching process is highly accurate  and a detailed explanation of the linkage process has been published elsewhere.44Personal identifiers were used to probabilistically link records from participants in the 45 and Up Study to records from the NSW Cancer Registry (NSWCR) and the NSW Registry of Births Deaths and Marriages by the NSW Ministry of Health’s Centre for Health Record Linkage  and non-alcohol-related cancers combined  were also examined. Cancers of the colon and rectum were examined both separately and combined, due to evidence that the risk relationship may differ for these two sites.8 Cancers of the mouth and pharynx, oesophagus and larynx were also combined as upper aerodigestive tract cancer. Fact of death was ascertained from the NSW Registry of Births, Deaths and Marriages, to December 2013. Cause of death data was not used for analysis.Cancer incidence was ascertained from the NSWCR, which captures all primary invasive cancers diagnosed in NSW residents apart from non-melanoma skin cancer. Cancer diagnoses were available from January 1994 to December 2013, and were classified according to the International Classification of Diseases, version 10.Alcohol consumption was ascertained from two items on the baseline and follow-up questionnaires: ‘About how many alcoholic drinks do you have each week? One drink = a glass of wine, middy of beer or nip of spirits ' and, ‘On how many days each week do you usually drink alcohol?'. Participants reported their response as an integer. Responses to these were used to derive two independent variables: (1) to assess the total amount of alcohol consumed in a week, and (2) to assess the pattern of consumption across the week.The following categories and stratification criteria were defined for each of the independent variables at baseline:47 This variable was categorised with cut points aimed to align with the Australian alcohol consumption guidelines to minimise the risk of long-term harm (≤2 standard drinks/day) and short-term harm (≤4 standard drinks/day), where a standard drink contains 10 g of ethanol.9(1) Number of drinks per week : ‘0 to <1 drink’, ‘≥1 to ≤3.5 drinks’, ‘>3.5 to ≤7 drinks’, ‘>7 to ≤14 drinks’, ‘>14 to ≤28 drinks’ and ‘>28 drinks’. When analysing breast, endometrial and ovarian cancer ‘>14 drinks/week’ was used as the maximum category as there were few heavy drinkers among women. Light drinkers (≥1 to ≤3.5 drinks per week) were used as the reference group rather than non-drinkers due to the potential for bias from the ‘sick-quitter effect’, whereby non-drinkers may have quit drinking due to symptoms from undiagnosed cancer, which would potentially result in an underestimate of relative risks in relation to heavier drinking.‘≥4 to <7 drinks/week over 4–7 days’ ‘≥7 to <14 drinks/week over 4–7 days’ ‘≥14 drinks/week over 4–7 days’ ‘≥4 to <7 drinks/week over 1–3 days’ ‘≥7 to <14 drinks/week over 1–3 days’ ‘≥14 drinks/week over 1–3 days’ (2) Pattern of drinking:Participants who consumed <4 drinks/week were excluded from the pattern of drinking analysis. The cut point of 1–3 versus 4–7 days was chosen to classify participants as those who consumed alcohol on more than half or less than half of days of the week, and to capture participants who only drink on the weekend and Friday evening, versus those who also drink during the week.49 Participants with missing alcohol consumption data or linkage errors were also excluded.Participants who had cancer other than non-melanoma skin cancer prior to joining the study (either self-reported or reported in the NSWCR from January 1994) were excluded from analysis. An exception was participants with self-reported melanoma that was not confirmed by the NSWCR, who were included (since skin cancers are known to be self-reported inaccurately).50 Censoring occurred at cancer diagnosis, death or the end of study period (December 2013), whichever occurred first. If a participant was diagnosed with more than one primary cancer at different sites, they were counted as a case in the analysis of each cancer type. If a participant reported a hysterectomy, bilateral oophorectomy or radical prostatectomy at baseline, they were excluded from the analysis for endometrium, ovary and prostate cancer, respectively, but were included in the analysis of all cancers combined (regardless of their specific cancer diagnosis).Hazard ratios (HR) and 95% confidence intervals (CI) of a primary cancer diagnosis in relation to weekly alcohol consumption and pattern of drinking were calculated using Cox proportional hazard regressions using age as the underlying time variable.51 to reduce the potential for misclassification and to mitigate the impact of outliers on the linear trend.The risk for each cancer type in relation to weekly alcohol consumption was assessed both categorically, and as a continuous variable in log-linear Cox regressions with hazard ratios representing the change in risk per seven-drink increase in weekly alcohol consumption (among drinkers). For the continuous variable, participants within each category of alcohol consumption at baseline were assigned the mean level of alcohol consumption they reported at the 5-year follow-up questionnaire. This was to allow for regression dilution,11 two-way statistical interaction tests between alcohol consumption as a continuous variable among drinkers and both sex and smoking status (never-smoking vs. ever-smoking) were conducted for each alcohol-related cancer type and for ARCC, with the inclusion of alcohol consumption as a categorical variable in the model as the main effect. The results were stratified where relevant.Because sex differences have been reported for alcohol and colorectal cancer risk, and differences between never- and ever-smokers for alcohol and cancers of the mouth, pharynx, oesophagus, liver and larynx,The cumulative absolute risk of cancer diagnosis from age 25 to 85 years in Australia in 2014 by sex and level of alcohol consumption was calculated for each of the IARC-determined alcohol-related cancer types and for these combined . This calculation was based on the results of the continuous variable analysis, and three categories of drinking were used . The methods used to calculate cumulative absolute risk are detailed in the supplementary material.29 A P value for interaction by the pattern of drinking was calculated, defined as the test of interaction between days per week and drinks per week.The risk of all cancers combined, all ARCC and cancers of the colorectum, breast, lung and prostate were assessed in relation to the pattern of drinking. Breast and colorectal cancer were the only alcohol-related cancer types with sufficient cases for analysis, while lung and prostate cancer were included due to prior reports of associations with drinking pattern.52 Melanoma risk was additionally adjusted for time spent outdoors and skin tone. Dietary-related variables  were included as potential confounders for cancer types where the WCRF has found convincing or probable evidence of a causal relationship.10 Parity and age at first birth, breastfeeding duration, menopausal status, hormonal contraceptive use and use of menopausal hormone therapy (MHT) were included as potential confounders for cancers of the breast, endometrium and ovary. Hormonal contraceptive use was also included for liver cancer,53 and MHT use for colorectal cancer.54 Adjustment for aspirin use was made for oesophageal and colorectal cancer.55 Adjustment for history of bowel, breast and prostate screening was made for colorectum, breast and prostate cancer, respectively. All potential confounders except sex had a missing indicator category.All regressions were adjusted for potential confounders that were self-reported in the baseline questionnaire, including sex, education, household income, health insurance status, partner status, country of birth, smoking status and intensity, body mass index and physical activity , the model was divided into two age groups with equal person-years of follow-up (<60 and ≥60 years) to investigate differences in HRs. If HRs were similar across strata, then models without stratification were reported.Sensitivity analyses examined the impact of excluding the first year of follow-up (to assess the possibility of reverse causation), and the impact of excluding participants with self-reported melanoma at baseline on estimates of melanoma risk. For the analyses of drinks per week as a continuous variable, sensitivity analyses examined the impact of using the mean number of drinks per week reported at baseline rather than at 5-year follow-up (to assess the impact of regression dilution), and the impact of using the actual number of drinks per week reported by participants rather than the mean of each category of intake.Analyses were performed using SAS 9.4 and STATA 16.0.A total of 226,162 of 267,153 participants in the 45 and Up Study (84.7%) were included for analysis after excluding 268 (0.1%) participants who withdrew their consent to participate after baseline, 175 (0.07%) from a pilot study, 5 (0.002%) aged <45 years at baseline, 36,216 (13.6%) with a previous cancer diagnosis, 4273 (1.6%) with missing information on overall alcohol consumption and 54 (0.02%) with data linkage errors. Of included participants, 17,332 (7.7%) had at least one incident cancer by December 2013 over a median follow-up of 5.4 years.56 for men aged 45 years and over , but similar for women  participants consumed at least one alcoholic drink per week, including 32,647 (14.4%) who consumed >14 drinks per week. The proportion of participants who consumed alcohol at least weekly was higher than in the 2007–2008 Australian National Health Survey57) and used aspirin, and a lower proportion had a university degree, a household income ≥ $70,000 per year, private health insurance or were married or living with a partner. Among women who had high numbers of alcoholic drinks per week, a higher proportion were nulliparous, had never breastfed and had ever used hormonal contraceptives compared to women who had fewer drinks per week. A higher proportion of participants who drank on 4–7 days of the week had a history of cancer screening, and a lower proportion were overweight or obese, compared to those who drank on only 1–3 days of the week. Among women who consumed alcohol on more days of the week, a higher proportion were postmenopausal and had ever used MHT compared to those who drank on fewer days of the week.Among participants who had high numbers of alcoholic drinks per week, a higher proportion were men, Australian-born, consumed processed meat more than once per week and usually spent 2 or more hours outdoors per day, compared to participants who had fewer drinks per week Table . ParticiTable P = 0.04) and smoking status (P = 0.03) in relation to alcohol consumption for ARCC, but not for any individual cancer type. Risk was higher in women  compared to men , and in ever-smokers  compared to never-smokers .There were statistical interactions with sex .19 When we examined the impact of drinking pattern on cancer risk, there was a marginally significant finding of an independent effect on the risk of breast cancer for any level of alcohol consumption that was consumed on few days of the week compared to an equivalent amount of intake consumed over many days of the week, but not for other cancer types.In our study, the relative risk of incident cancers known to be caused by alcohol increased by 10% with every additional seven drinks per week, with consumption of more than 14 drinks a week estimated to account for a 4.4% higher absolute cumulative risk in men compared to consumption of 0 to <1 drink per week , and 5.4% higher risk in women , up to age 85 years. This finding is likely to be of significant public health importance, given that 16% of Australian adults were estimated to consume more than two standard drinks per day on average in 2017–2018.3 Based on the relative risks reported here, the difference in the cumulative lifetime risk of diagnosis with an alcohol-related cancer between those consuming 0 to <1 drink per week and those consuming >14 drinks per week was found to be 4.4% and 5.4% for men and women, respectively. These estimates are higher than those reported for the British population;58 specifically, consumption of three bottles of wine per week  was associated with an increased absolute risk of cancer of 1.9% in men and 3.6% in women to age 80 years. This may reflect differences in relative risks used between the two studies, differences in population cancer incidence rates and that our study used an endpoint of 85 years rather than 80 years.Our results add to the evidence that alcohol consumption is an important risk factor for cancer in Australia. It was estimated that 2.8% of all cancers in Australia were attributed to alcohol consumption in 2010.38 Due to the questionnaire design, we were not able to examine heavy episodic drinking directly, but rather we examined whether participants who concentrated intake on 1–3 days per week had a different risk profile to those who consumed the same amount of alcohol on average over 4–7 days of the week. We report a marginally significant finding of an independent effect of pattern of drinking on breast cancer risk among women, suggestive of an increased risk in relation to ‘binge’ or ‘heavy episodic’ drinking . At least two previous studies have reported similar conclusions. One study of 74,854 American women (1988–2008) reported that the highest number of drinks consumed in one day in a typical month was associated with increased risk of breast cancer (Ptrend = 0.04).25 Another study of 17,647 Danish women (1993–2001) reported that heavy episodic drinking  increased breast cancer risk .30 In contrast, null results in relation to drinking frequency have been reported in two studies.34 Overall, the evidence in relation to an association between heavy episodic drinking and breast cancer remains limited, and further research is required to understand the potential mechanistic basis of the relationship.Previous studies have suggested that a heavy episodic ‘binge’ drinking pattern  may increase cancer risk, independent of the overall amount of alcohol consumed.P = 0.06), it is possible that these relationships may change with longer follow-up and additional cases. The methods used in previous studies have differed greatly in the assessment of drinking patterns. Of studies that have accounted for overall alcohol consumption, it has been reported that consuming ≥5 drinks on drinking days was associated with increased pancreatic cancer risk,24 that the highest number of drinks consumed in one day in a typical month was associated with increased risk of ARCC in women,20 that higher intake per drinking day was associated with mortality from all cancers combined in men38 and that greater drinking frequency was associated with higher risk of ARCC in men20 and mortality from all cancers combined,38 while one study reported null results for ARCC37 in relation to drinking frequency. A number of studies that did not, or only partially, accounted for overall alcohol consumption, found that varying definitions of heavy episodic drinking increased risk for cancers of the stomach,26 lung27 and prostate29 and mortality from all cancers combined,36 while significant differences in the risk of mouth and pharyngeal cancer,31 mouth cancer,32 stomach cancer,26 prostate cancer28 and mortality from oesophageal cancer in men and pancreatic cancer in women33 have been reported in relation to drinking frequency, and some studies reported null results.35 As most studies of drinking pattern did not account for overall alcohol consumption, their findings are difficult to interpret, given that it is impossible to differentiate the effects of overall alcohol consumption from the impact of drinking pattern. If there is an effect of drinking pattern on cancer risk, it would be evidence of a mechanism where acute, heavy alcohol exposure impacts some body sites differently than chronic exposure, and that risk relationships may be missed if overall alcohol consumption is examined alone. Ultimately, well-designed and adequately powered studies that account for overall alcohol consumption are needed to address this research gap.While we found potential evidence of an independent effect of pattern of drinking on risk for breast cancer, we note that our estimates for some outcomes were imprecise. For example, the confidence intervals for breast cancer and ARCC were compatible with substantial increased risk for those concentrating their intake on fewer days of the week. Although the association between drinking pattern and risk of all cancers combined was not statistically significant in this analysis  on cancer risk, nor could we investigate differences by beverage type. Further, our drinking pattern analyses relied on a quantity-frequency construct rather than specific drinking patterns that were ascertained directly. Because the quantity-frequency construct was an average of the number of drinks consumed over the week, it could not be used to differentiate participants by daily changes in intake. For example, participants who consume a moderate amount of alcohol every day were grouped with those who drink heavily on the weekend and also consume small amounts during the week, even though the risk profile of these participants is likely to differ. Persons who participate in cohort studies may be more health conscious than the general population, and so heavier drinkers may be underrepresented in our study. Another limitation of our study was that we were not able to ascertain alcohol consumption and drinking patterns over the life course. For instance, there is evidence that alcohol consumption in young adulthood contributes to the accumulation of breast cancer risk in later life.61 This could bias results, particularly if underreporting differs by consumption levels and drinking patterns. In both Australia and Canada, low-risk and non-heavy episodic drinkers were found to underreport consumption to a greater extent than higher-risk and heavy episodic drinkers.63 On the other hand, an English study found underreporting to be disproportionately associated with heavy drinking, frequent drinking and non-routine drinking compared to participants without these drinking behaviours.64 It is unclear how underreporting may impact our results; however, if indeed participants who were lighter drinkers underreported their alcohol consumption to a greater degree than heavier drinkers, HRs attributed to a given level of drinking may instead reflect higher levels of intake, with estimates for lighter drinkers being more vulnerable to this effect than those for heavier drinkers.A potential problem common to all observational studies measuring alcohol consumption is the tendency for participants to underreport intake.65 Evaluation of social marketing strategies has shown mixed results and challenges in influencing behaviour, particularly in younger people.66 However, a mass media campaign conducted in Australia in 2010 (Alcohol and Cancer), which focused on cancer rather than injury or other short-term harm, evaluated comparatively well among persons aged 18–64 years.66 Finally, interventions focusing on long-term harms such as cancer risk may be especially important during the COVID-19 pandemic, given recent reports that a portion of the Australian population has experienced an increase in alcohol consumption,68 with a risk that acquired drinking behaviour during COVID-19 may continue for a longer term.In Australia, interventions aimed at reducing the health risks of alcohol consumption have often focused on short-term harm minimisation. These include mass media campaigns, education programmes, industry-regulated responsible service of alcohol policies, ‘lockout’ laws and random breath testing. The focus of these measures has been on the short-term harms of heavy episodic drinking such as accidents and injury, and they are largely aimed at younger people. Our study demonstrates that the long-term harms of alcohol also affect older people in Australia, and it is important to target this population given evidence that more than half of risky drinkers aged ≥50 years in Australia do not perceive their level of drinking to be harmful, and identify as light, occasional or social drinkers.69 Our results could help to inform future health guidelines by providing evidence that the relationship between drinking and cancer risk in Australia matches that of international evidence. Based on the results of our analysis, this may also include ongoing consideration of the evidence for pattern of drinking and breast cancer risk, particularly the avoidance of heavy episodic ‘binge’ drinking, which may be associated with increased risk above that related to the total amount consumed. Our findings also strengthen the case for adding cancer risk to alcoholic beverage labelling, targeted education programmes and for creating more informative national health guidelines for communication to the public about the prevention of chronic disease. Moreover, a general increase in awareness of the relationship between alcohol and cancer, at any age, may help to ‘de-normalise’ risky consumption across the life course. Our results strengthen the evidence associating alcohol consumption with cancer risk, and can be used in public health campaigns, to discourage risky alcohol consumption in Australia and internationally, with wider-ranging benefits to population health.The Australian alcohol consumption guidelines are about to change.Supplementary information