DNA methylation in blood may reflect adverse exposures accumulated over the lifetime and could therefore provide potential improvements in the prediction of cancer risk. A substantial body of research has shown associations between epigenetic aging and risk of disease, including cancer. Here we aimed to study epigenetic measures of aging and lifestyle-related factors in association with risk of breast cancer.PhenoAge and GrimAge). All measures were regression-adjusted for their respective risk factors and expressed per standard deviation (SD). Odds ratios (OR) and 95% confidence intervals (CI) were calculated using conditional or unconditional logistic regression and pooled using fixed-effects meta-analysis. Subgroup analyses were conducted by age at blood draw, time from blood sample to diagnosis, oestrogen receptor-positivity status and tumour stage.Using data from four prospective case–control studies nested in three cohorts of European ancestry participants, including a total of 1,655 breast cancer cases, we calculated three methylation-based measures of lifestyle factors  and seven measures of epigenetic aging : OR per SD = 1.02, 95%CI: 0.95–1.10; AA-Hannum: OR = 1.03, 95%CI:0.95–1.12; We found no evidence that methylation-based measures of aging, smoking or alcohol consumption were associated with risk of breast cancer. A methylation-based marker of BMI was associated with risk and may provide insights into the underlying associations between BMI and breast cancer.The online version contains supplementary material available at 10.1186/s13058-022-01554-8. Numerous studies have investigated the association of blood DNA methylation and breast cancer risk, for example, at breast cancer-specific genes –3, and oPhenoAge [GrimAge [PhenoAge and GrimAge are methylation-based predictors of composite measures  that are predictive of mortality. The residual of each of these measures on chronological age, named ‘age acceleration’, best reflects the concept of biological aging. A positive association between epigenetic aging (Horvath first-generation measure) and risk of breast cancer was first reported in an EPIC-IARC study [PhenoAge measures [GrimAge [PhenoAge and GrimAge than for the first-generation measures [Methylation-based markers of aging, such as Horvath-based , Hannum-PhenoAge  and Grim[GrimAge , have beRC study , and latmeasures , but not[GrimAge . Only th[GrimAge  was thermeasures , 24.Factors other than age, mainly tobacco smoking , 25, alcThe aim of this study was to examine the association of previously derived 1) seven methylation-based measures of aging, and 2) methylation-based measures of body mass index, alcohol consumption and tobacco smoking, with breast cancer risk in a meta-analysis of individual-participant data including 1,655 breast cancer cases sampled from the MCCS, EPIC and PLCO.We used data from four methylation studies nested within three prospective cohorts of European ancestry participants: the Melbourne Collaborative Cohort Study (MCCS) , the Eurminfi. β-values were calculated for each CpG site for each sample using the R package minfi. Methylation measures with a detection P-value higher than 0.01 were considered missing. Samples with > 5% of CpG methylation measures missing were excluded, and CpGs with values missing for more than 20% of samples were excluded. White blood cell proportions were estimated using the Houseman algorithm modified by Jaffe and Irizarry [estimateCellCounts implemented in minfi, or Horvath’s calculator, to derive the proportion of CD8 + T cells, CD4 + T cells, NK cells, B cells, monocytes and granulocytes.Methods relating to DNA extraction and bisulphite conversion, and DNA methylation data processing have been described previously and are detailed in Additional file Irizarry , 32, usiPhenoAge [GrimAge [https://dnamage.genetics.ucla.edu/new [We used the normalised DNA methylation data to calculate the epigenetic measures of aging developed by Horvath  and HannPhenoAge , and Gri[GrimAge   or unconditional (PLCO) logistic regression to estimate the odds ratio (OR) and 95% confidence interval (95%CI) for breast cancer risk per one standard deviation (SD) increase for each of the age acceleration, smoking, alcohol intake and BMI methylation-based measures. Associations were also calculated per five-year AA increase for comparison with other studies.Models adjusted were appropriate for the matching variables specific to each study  for the following case characteristics: age at blood draw , time between blood draw and diagnosis , oestrogen receptor (ER) positivity status, stage .P-values were calculated using the Wald test statistic. Heterogeneity in the ORs across studies was examined using the I2 statistic.For all analyses, estimates of pooled OR and 95%CI were calculated using fixed-effects meta-analysis, and PhenoAge: 32% to 50%; GrimAge: 50% to 69%. The variance explained by methylation-based predictors for BMI ranged from 14 to 22%; for smoking from 41 to 54% and for alcohol consumption from 3 to 9%. All measures were strongly associated with their respective risk factor  and 8.4 years (PLCO). Most tumours were ER positive (71% in the PLCO to 83% in EPIC samples) and diagnosed at low stage (~ 60%). For all studies, there were no large case–control differences in terms of smoking, alcohol consumption and BMI  and OR = 1.03 (0.94–1.12), respectively), Table Although the Horvath measures of epigenetic aging AA and IEAA were associated with risk in EPIC-IARC . This association was virtually not attenuated when considering the adjusted measure, i.e. BMI predictor regressed on BMI: OR = 1.09, 95%CI: 1.01–1.17 (I2 = 12%). Estimates were similar in unadjusted models  and alcohol consumption , or their respective adjusted measures, were associated with breast cancer risk . The adjusted BMI measures appeared somewhat more strongly associated with risk for women diagnosed within five years from blood draw  compared with those diagnosed more than five years after blood draw .None of the associations of epigenetic aging or lifestyle measures with breast cancer risk showed substantial heterogeneity by age at blood draw, time to diagnosis, ER positivity or tumour stage at diagnosis Table ; for AA-We have assessed seven measures of epigenetic aging and three methylation-based predictors of lifestyle for their association with breast cancer risk in a large sample  of women from Western countries . We found overall no associations between measures of epigenetic aging and risk of breast cancer. A positive association was observed for the BMI methylation score, but not for smoking and alcohol consumption.AA-PhenoAge: HR = 1.15, 95%CI = 1.07–1.23. In our study, the ORs expressed per 5-year AA were compatible for AA-Hannum and AA-Horvath  and more discrepant for AA-PhenoAge: HR = 1.00, 95%CI = 0.95–1.06. Similar to our findings, the authors did not find substantial heterogeneity by, for example, menopausal or ER-positivity status. For AA-GrimAge, the authors expressed the association per standard deviation [AA-GrimAge appeared somewhat more strongly associated with risk for postmenopausal women in the Sister Study, the evidence for heterogeneity was weak and there was no indication of this in our data (HR = 1.03 for women aged ≥ 50 years at blood draw).To our knowledge few studies have investigated the association of epigenetic aging with breast cancer risk. We included in this meta-analysis the samples for which an association was reported previously in EPIC-IARC . Slightleviation  and founAA-PhenoAge and AA-GrimAge, respectively). The study of Durso and colleagues [P = 0.95), AA-Hannum: HR = 1.24 (P = 0.07), AA-PhenoAge: HR = 1.36 (P = 0.01), and AA-GrimAge = 1.19 (P = 0.16), respectively, in age-adjusted models. The literature to date therefore includes, to our knowledge, approximately 3,550 breast cancer cases and is consistent with a weak (of roughly 8% increase per 5-year AA for AA-PhenoAge) or null association between epigenetic aging measured in blood and breast cancer risk.The main differences between the cohorts included in our meta-analysis and the Sister Study are that it was enriched for family history of breast cancer and had substantially shorter length of follow-up than ours . We nevertheless did not observe that OR estimates were larger when blood was collected closer to diagnosis . Other methylation-based measures of lifestyle have been developed showing similar accuracy, e.g. for alcohol [There has not been to our knowledge any study examining methylation-based predictors of lifestyle-related factors with risk of breast cancer. A handful of studies have examined risk of overall mortality , surviva alcohol , or smok alcohol , 40, and alcohol . In anot alcohol . We did That we observed only weak or null associations may be explained by the fact that none of BMI, alcohol consumption or smoking are strong risk factors for breast cancer. Previous studies have generally found weak to moderate associations , 37, 38,The main strength of our study is the largest sample size to date of ~ 1,650 cases with long follow-up and comprehensive assessment of epigenetic measures for association with risk. The same analysis method was applied across cohort datasets and participants were representative of the general population. Limitations of our study include the relative heterogeneity of the pooled samples; even though most participants were of European ancestry, there was some variation in terms of age at inclusion, follow-up time or sample processing. All studies used the same pipeline for normalisation of the data, but PLCO used the EPIC assay, which may result in small measurement differences.Our study found overall weak associations of methylation-based measures of aging and lifestyle-related with breast cancer risk. The association observed for a BMI methylation score might provide insights in the underlying association between BMI and breast cancer and should be further investigated in additional studies.Additional file 1. Supplementary Methods and Tables.


Mammogram risk scores, based on the area of a mammogram covered by white or bright areas defined at different levels of pixel brightness, predict breast cancer risk. They are correlated in relatives, and this might be due to genetic factors that cause breast cancer. We found that the known genetic markers associated with breast cancer risk were weakly correlated with the studied mammogram risk scores. Our findings suggest that less than 1% of the variance of the studied mammogram risk scores is explained by the known genetic markers associated with breast cancer risk. Discovering the genetic determinants of the bright, not white, regions of a mammogram could reveal new information about the genetic causes of breast cancer.p < 0.04) with all the MRSs except the Cumulus-white MRS based on the “white but not bright area” . After adjusting for its association with the Altocumulus MRS, the PRS was not associated with the Cumulus MRS. There were MRS associations (Bonferroni-adjusted p < 0.04) with one SNP in the ATXN1 gene and nominally with some ESR1 SNPs. Less than 1% of the variance of the MRSs is explained by the genetic markers currently known to be associated with breast cancer risk. Discovering the genetic determinants of the bright, not white, regions of the mammogram could reveal substantial new genetic causes of breast cancer.Cumulus, Altocumulus, and Cirrocumulus are measures of mammographic density defined at increasing pixel brightness thresholds, which, when converted to mammogram risk scores (MRSs), predict breast cancer risk. Twin and family studies suggest substantial variance in the MRSs could be explained by genetic factors. For 2559 women aged 30 to 80 years (mean 54 years), we measured the MRSs from digitized film mammograms and estimated the associations of the MRSs with a 313-SNP breast cancer polygenic risk score (PRS) and 202 individual SNPs associated with breast cancer risk. The PRS was weakly positively correlated (correlation coefficients ranged 0.05–0.08; all  Conventionally, mammographic density refers to the white or bright regions on a mammographic image and can be measured using the computer software CUMULUS . In thisOver the last few years, we have introduced other mammographic density measures based on defining density at, in effect, higher pixel brightness thresholds ,3,4,5. IAge and body mass index (BMI) are confounders of the association between mammographic density measures and breast cancer risk, and these risk factors are always adjusted for. Therefore, when we are talking about a mammographic density measure in terms of its association with breast cancer risk, we are essentially talking about the mammographic density measure adjusted for age and BMI.We define a mammogram risk score (MRS) to be the residual of a mammographic measure  after making an adjustment for age and BMI. In this study, we name the MRSs calculated from Cumulus, Cumulus-percent, Altocumulus, and Cirrocumulus density measures as the Cumulus MRS, Cumulus-percent MRS, Altocumulus MRS, and Cirrocumulus MRS, respectively.The Cumulus MRS and Cumulus-percent MRS have been established as predictors of breast cancer risk . From a The potential role of familial, including genetic, factors in explaining the variance of the MRSs has been considered for more than two decades. Studies have found that the familial correlations in both the Cumulus MRS and Cumulus-percent MRS were about 0.6–0.7 for monozygotic (MZ) twin pairs and about one-half of this (~0.3) for dizygotic (DZ) twin pairs and sister pairs ,11,12,13We recently found that the familial correlation in the Altocumulus MRS was about 0.6 for MZ twin pairs and about 0.25 for DZ twin pairs and sister pairs, while the familial correlation in the Cirrocumulus MRS was about 0.4 for MZ twin pairs and about 0.2 for DZ twin pairs and sister pairs. This is consistent with additive genetic factors explaining about 60% of the variance of the Altocumulus MRS and 40% of the variance of the Cirrocumulus MRS .Based on the relationship between familial risk factors and familial aggregation of diseases ,15,16,17Studies trying to find the genetic determinants of MRSs have been only conducted for the Cumulus MRS and Cumulus-percent MRS. To date, genome-wide association studies (GWAS) have found SNPs at 33 loci to be associated with the Cumulus MRS and SNPs at 25 loci with the Cumulus-percent MRS ,23,24,25In this study, we aimed to investigate the association between the MRSs and the polygenic determinants of breast cancer risk. We investigated the latest breast cancer PRS and the individual SNPs that had been found to be associated with breast cancer risk from GWAS.We used data from the Australian Mammographic Density Twins and Sisters Study (AMDTSS) ,13. BrieAll available episodes of mammograms were retrieved with the participants’ written consent, mostly from Australian BreastScreen services but also from private clinics and private hospitals. We also retrieved mammograms from the participants themselves. The craniocaudal views for left and right breasts were selected and digitized using the Lumysis 85 scanner at the Australian Mammographic Density Research Facility. For each woman, the most recent right breast craniocaudal view was selected for mammographic density measurement, and the left breast craniocaudal view was selected if the right breast mammogram was missing or unavailable. Mammographic measurements were performed using CUMULUS 4.0 , a computer-assisted thresholding technique, after randomization and blind to information, by three independent measurers  with high repeatability; see, for example, . Total bGermline DNA extracted from the blood samples was used to perform genome-wide SNP genotyping using the OncoArray (Illumina)  and follp < 10−7), with a minor allele frequency (MAF) <0.01, or on X chromosome. A total of 399,364 SNPs passed the quality control.For duplicated samples, we excluded the ones with a lower call rate. We excluded samples with a call rate < 95%, extreme heterozygosity (4.89 standard deviations from the mean), or sex discrepancy. Ancestry was computed using a principal component analysis, and women of non-European ancestry were excluded by visualizing the multidimensional scaling plot of the first two principal components, with reference to the 1000 Genomes Project populations . We excluded SNPs with a call rate < 95%, not in Hardy–Weinberg Equilibrium (https://imputationserver.sph.umich.edu/index.html (accessed on 14 September 2021)) with the 1000 Genomes Project Phase 3 (Version 5) as the reference panel. Overall, 14.0 million SNPs with a MAF > 0.1% and an imputation quality score > 0.3 were imputed.Imputation was conducted using Minmac4 on the Michigan Imputation Server  is the weight of the ith SNP, and iX is the number of effect alleles of the ith SNP. We calculated the PRS for overall breast cancer, estrogen receptor negative (ER−) breast cancer, and estrogen receptor positive (ER+) breast cancer, respectively, using the relevant weights for each PRS reported by Mavaddat et al. [All the 313 SNPs of the breast cancer PRS  were inct et al. .The Box–Cox procedure was used to test the normality of the distributions of the mammographic density measures and if necessary to select an appropriate power transformation. As a result, the Cumulus, Cumulus-percent, Altocumulus, Cirrocumulus, and Cumulus-white density measures were cube root, square root, cube root, log, and log transformed, respectively. The five density measures were then used to calculate their relevant MRSs, which were the standardized residuals in the transformed density measures after adjusting for age and the inverse of BMI.We estimated the Pearson correlations between the MRSs and the PRSs. A total of 15 correlations (5 MRSs × 3 PRSs) were estimated.We used a mixed-effect model to estimate the associations (regression coefficients) between the MRSs and the PRSs, in which an MRS was the outcome variable, and a PRS was the predictor variable. The first 10 principal components of the GWAS data were adjusted in the model as fixed effects, and family and zygosity were modeled as random effects to account for the relatedness between the study subjects. Given age and BMI were already adjusted when calculating the MRS, and they were not related to the PRSs, they were not included in the regression analyses. A total of 15 associations (regression coefficients) were estimated. For each MRS, we also estimated its association with the PRSs after adjusting for other MRSs, using the same mixed-effect model with other MRSs as additional fixed effects. The regression coefficients were reported using the odds ratio per adjusted standard deviation (OPERA) method , i.e., aWe used the Bayesian Information Criterion (BIC) to compare the evidence strength between the MRSs for their correlations/associations with the PRSs.We estimated the association between the MRSs and the SNPs known to be associated with breast cancer risk, using the same mixed-effect model with the PRS being replaced as the SNP. Kappor et al.  providedAll statistical analyses were conducted using R 4.0.2 .2 (standard deviation: 5.2 kg/m2). MZ twins, DZ twins, and non-twins did not differ substantially from one another in age, BMI, or any of the PRSs or mammogram density measures.p < 0.05) except those of the Cumulus-white MRS with the PRS for overall breast cancer or with the PRS for ER+ breast cancer. For each MRS, there was no substantial difference between the correlation coefficients with the three PRSs. For each PRS, the correlation coefficient point estimates were the largest for the Altocumulus MRS, less for the Cumulus-percent MRS and Cumulus MRS, and the smallest for the Cirrocumulus MRS and Cumulus-white MRS, although their 95% confidence intervals (CIs) overlapped with each other. These results suggest that the PRSs explained less than 1% of the variance of the MRSs.For each PRS, the BIC for the correlation with the Altocumulus MRS was smaller than the BIC for the correlation with the Cumulus-white MRS. Note that, for both the PRS for overall breast cancer and PRS for ER+ breast cancer, the smallest BIC was observed for correlations with the Altocumulus MRS, and the largest BIC was observed for the correlations with the Cumulus-white MRS. The BIC differences between the correlations with the Altocumulus MRS and Cumulus-white MRS ranged from 6.78 to 15.54 across the three PRSs, suggesting that there was strong evidence that the correlations of the PRSs with the Altocumulus MRS were stronger than those with the Cumulus-white MRS.p < 0.05) except those for the Cumulus-white MRS with the PRS for overall breast cancer and with the PRS for ER+ breast cancer. For the Cumulus MRS, the association point estimates were similar across the three PRSs, while for both the Cumulus-percent MRS and Cumulus-white MRS, the association point estimate with the PRS for ER− breast cancer was the largest. For both the Altocumulus MRS and Cirrocumulus MRS, the association point estimates were similar between the PRS for overall breast cancer and the PRS for ER+ breast cancer, and the smallest for the PRS for ER− breast cancer.For each PRS, the smallest BIC was observed for the association with the Altocumulus MRS. The BIC differences between the associations with the Altocumulus MRS and Cumulus-white MRS ranged from 7.43 to 16.97 across the three PRSs, suggesting that there was strong evidence that the associations of the PRSs with the Altocumulus MRS were stronger than those with the Cumulus-white MRS.ATXN1 locus, was associated with Cumulus MRS, Altocumulus MRS, and Cumulus-white MRS, respectively , and no SNP was found to be associated with the Cumulus-percent MRS or Cirrocumulus MRS. The numbers of SNPs with a nominal p < 0.05 were 36, 26, 25, 18, and 34 for the Cumulus MRS, Cumulus-percent MRS, Altocumulus MRS, Cirrocumulus MRS, and Cumulus-white MRS, respectively, all more than the number expected by chance alone, i.e., 202 × 0.05 = 10.1 . This suggests that there was some association between one or more of the breast cancer SNPs and the MRSs. However, only two-thirds of the SNP associations were in the same direction as the direction of the association between relevant MRS and breast cancer risk: 24, 18, 17, 10, and 22 SNPs for the Cumulus MRS, Cumulus-percent MRS, Altocumulus MRS, Cirrocumulus MRS, and Cumulus-white MRS, respectively. That is, about 5% of the breast cancer SNPs were nominally associated at the p = 0.05 threshold with the Cirrocumulus MRS, while about 10% of the SNPs were nominally associated with the other MRSs. Two SNPs of the ESR1 locus were nominally associated with the MRS at the p = 0.05 threshold.We investigated the associations between two types of breast cancer risk scores, namely genetic-based and mammogram-based, and found that they were almost independent with one another. Such independence implies that the PRSs and the MRSs predict breast cancer risk through different pathways so that when they are combined, the two types of risk scores will likely improve the disease risk prediction . This inTwin and family studies suggest that approximately 40–70% of the variance of the MRSs could be explained by additive genetic factors ,11,12,13Taking all the evidence into consideration, the vast majority of the genetic variance of the MRSs remains unexplained. DNA methylation has been proposed to explain part of the genetic variance of human traits, but our previous study did not find evidence for an association of peripheral blood DNA methylation with the Cumulus MRS or Cumulus-white MRS . More stBRCA1 and BRCA2 germline mutations, could explain part of the variance in the MRSs. However, findings about the association between BRCA1 and BRCA2 mutations and Cumulus-percent density measure are inconsistent [It is also possible that inherited rare mutations implicating in breast cancer risk, such as nsistent ,34. ThesWe found that the Altocumulus MRS remained associated with the breast cancer PRSs after adjusting for the Cumulus MRS although the associations decreased by approximately 65–80%. This is consistent with our findings that the association of the Altocumulus MRS with breast cancer risk remained after adjusting for the Cumulus MRS . On the We found, consistently across the three PRSs, that their associations with the Altocumulus MRS were stronger than with the Cumulus-white MRS. This suggests that the bright, not white, regions of the mammogram are more strongly associated with breast cancer polygenic susceptibility, and studying these regions could reveal more about breast cancer genetic susceptibility. The currently known major predisposition genes and the current PRS discovered to date explain less than half the familial aggregation of breast cancer , and newAs to lifestyle- and hormone-related risk factors, they are weakly correlated in relatives and have a weaker association with breast cancer risk than the MRSs, so they do not explain much familial aggregation of breast cancer ,29. TherATXN1 and ESR1 loci [From the analysis of the SNPs known to be associated with breast cancer risk, we replicated the previously reported associations with the SR1 loci ,21,25. TIn conclusion, our study suggests that less than 1% of the variance of the MRSs is explained by the genetic markers currently known to be associated with breast cancer risk. Discovering the genetic determinants of the bright, not white, regions of the mammogram could reveal substantial new genetic determinants of breast cancer.


Concerns have been raised regarding a potentially increased risk of cancer associated with treatment with glucagon-like peptide-1 (GLP-1) receptor agonists. Here, we explored whether fasting and oral glucose tolerance test post-challenge glucose-dependent insulinotropic peptide (GIP) and GLP-1 levels were associated with incident first cancer. Within the cardiovascular re-examination arm of the population-based Malmö Diet Cancer study (n = 3734), 685 participants with a previous cancer diagnosis were excluded, resulting in 3049 participants , of whom 485 were diagnosed with incident first cancer (median follow-up time 9.9 years). Multivariable Cox-regression and competing risk regression (death as competing risk) were used to explore associations between incretin levels and incident first cancer. Higher levels of fasting GLP-1 (462 incident first cancer cases/2417 controls) showed lower risk of incident first cancer in competing risk regression . No association was seen for fasting GIP, post-challenge GIP, or post-challenge GLP-1 and incident first cancer. In this prospective study, none of the fasting and post-challenge levels of GIP and GLP-1 were associated with higher risk of incident first cancer; by contrast, higher levels of fasting GLP-1 were associated with lower risk of incident first cancer. Patients with diabetes suffer from impaired incretin effects. During the last two decades, incretin-based therapies (glucagon-like peptide 1 receptor agonists (GLP-1 RA) and dipeptidyl peptidase inhibitors (DPP-4i)) have provided safe and effective treatment options for patients with diabetes, along with beneficial effects such as weight loss2. In addition, several studies support therapeutic benefits of GLP-1 RA with regard to cardiovascular outcomes in type 2 diabetes3. However, as incretins stimulate cellular proliferation, concerns have been raised regarding a potentially increased risk of thyroid and pancreatic cancer, associated with GLP-1 RA usage6. Studies have reported increased risk of pancreatic cancer associated with GLP-1 RA usage9, while other studies did not find such an association11. The observed associations are often attributed to an occult pancreatic cancer that provokes or aggravates diabetes7. Considering the relatively short follow-up time in these studies, we hypothesize that it is likely that the mechanism is promotion of existing lesions, through the presence of supra-physiological levels of GLP-1, rather than induction of new tumors. Additionally, the risk of pancreatic cancer is increased in type 2 diabetes, further complicating the interplay between diabetes and cancer12. Further, Ligumsky et al. identified GLP-1 as a potent inducer of cyclic adenosine monophosphate and an inhibitor of breast cancer cell proliferation, suggesting that reduced GLP-1 levels may serve as a novel link between obesity, diabetes mellitus, and breast cancer13. Globally, cancer constitutes a huge burden on society and accounts for a significant proportion of deaths14. Substantial progress has been made during the last three decades in diagnosis and treatment of numerous cancers, while prevention is still behind the progress seen in relation to cardiovascular disease14. Pancreatic cancer remains a clinical challenge, especially regarding early diagnosis and surveillance15. There is an ongoing debate about the potential involvement of incretins in pancreatic cancer initiation and progression. To date, no reliable data exist on the association between endogenous levels of incretins and risk of cancer. Herein, we explored whether endogenous circulating GIP and GLP-1 levels are associated with incident first cancer(s) in the population-based Malmö Diet and Cancer Study.Following nutrient intake, incretin hormones (glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1)) enhance glucose-dependent insulin response, resulting in a lowering of blood glucose16. This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Research Ethical Review Board at Lund University. A written informed consent was obtained from all participants prior to commencement of the study.Between 1991 and 1996, baseline examinations including anthropometrical measurements and blood sample donations were performed as part of the Malmö Diet and Cancer Study (MDC), a prospective population-based study  in the city of Malmö, Sweden. A sample of the study population (n = 6103) was randomized into a sub-study “The Malmö Diet and Cancer Cardiovascular Cohort” (MDC-CC). During 2007–2012, a clinical re-examination including blood sampling was performed in 3734 participants within the MDC-CC cohort, with the addition of oral glucose tolerance test (OGTT) in participants without any diabetes diagnosis and fasting plasma glucose levels ≤ 7 mmol/L. Data from this re-examination was used for analyses. A complete description of the study population has been published elsewhere17. Physical activity was self-reported via questionnaire, concerned average activity during spare time in the last year, and defined as (1) Sedentary Leisure time primarily devoted to reading, needlework, TV, cinema or other sedentary activities; (2) moderate exercise Leisure time primarily devoted to walking, cycling or exercising at least 4 h per week; (3) regular exercise Leisure time primarily devoted to running, swimming, tennis, badminton, fitness exercises, or similar at least 3 h per week; and (4) strenuous exercise Leisure time primarily devoted to strenuous exercise and competition in running, orienteering, skiing, swimming, football, handball and the like, regularly and at least 4 times per week.Clinical assessment included anthropometric measurements and blood samples drawn after overnight fast. Body mass index (BMI) was calculated as weight in kilograms divided by the square of the height in meters. Waist circumference (cm) was measured between the lowest rib margin and the iliac crest. Diabetes mellitus was identified through a self-reported physician’s diagnosis of diabetes, use of anti-diabetic medications, or by oral glucose tolerance testing  ≥ 7.0 mmol/L or ≥ 12.2 mmol/L following OGTT). Smoking was self-reported and defined as present smoker/non-smoker. Use of menopausal hormone therapy (MHT) was self-reported and defined as never/ever/present. Insulin resistance was estimated by Homeostatic Model Assessment for Insulin Resistance (HOMA-IR)18 was performed after an overnight fast. Individuals with known diabetes and individuals with fasting plasma glucose ≥ 7 mmol/L did not undergo the OGTT and subsequently no post-challenge blood sampling. During OGTT, blood samples were drawn at 0 and 120 min for plasma glucose, serum insulin, glucagon, GIP, and GLP-1 analyses (2007–2012). Blood samples for GIP analyses were collected in serum tubes with a clot activator and serum gel separator and stored at − 20 °C until analyses (carried out November 2011 and June 2012). Blood samples for GLP-1 were collected in tubes with addition of a DPP-4i (Diprotin A) and stored at − 80 °C until analyses (during 2014).A standardized 75 g OGTT19 . Identical quality controls and identical batches for all reagents in each analysis set were used in a consecutive sample analysis for two months. Serum GIP was analyzed using Millipore’s Human GIP Total ELISA #EZHGIP-54K . FPG was analyzed after an overnight fast using the Hemocue Glucose System . Serum insulin was assayed with Dako ELISA kit  at the Department of Clinical Chemistry, Malmö University Hospital. Plasma glucagon was assayed with RIA GL-32 K .Total plasma GLP-1 concentrations (intact GLP-1 and the metabolite GLP-1 9–36 amide) were determined radio immunologically as described previously20. Participants were followed from study entry  until first cancer diagnosis  or otherwise censored  using ICD7, ICD9 and ICD10 codes.Data on cancer diagnoses were identified by record linkage with regional and national cancer registries in Sweden, which have high coverage and accuracy of diagnosisModel 2a for the following endpoints: all incident cancers, hormone- and obesity-related cancers. The hazards were not proportional for fasting GLP-1 in relation to incident obesity-related cancers (p = 0.016). Subsequently, an interaction with time was included, both as a continuous variable and with a cut-off > 4 years. There were no other violations of the proportional hazard assumption.Non-normally distributed variables were ln-transformed  and thereafter z-transformed. Correlations were explored using Spearman correlations. Linearity assumptions for variables in the Cox regression models examining associations between incretins and incident first cancer were checked by plotting Martingale residuals. Schoenfeld’s residuals were used to assess whether the proportional hazard assumption was violated for fasting and post challenge incretins and factors of metabolic health in Model 1); and (2) further adjusted for waist circumference, diabetes, smoking status and physical activity (Model 2a). Sensitivity analyses of associations between concentrations of fasting and post-challenge (1) GIP, (2) GLP-1, (3) glucose, (4) insulin and (5) glucagon and any first cancer were carried out after exclusion of 297 participants with prevalent diabetes using Cox-regression models (1) adjusted for age and sex (Model 1); further adjusted for waist circumference, smoking status, physical activity, and HOMA-IR (Model 2b). Additional sensitivity analyses were carried out in participants with diabetes, in whom only fasting values of GIP and GLP-1 were available, exploring associations between fasting GIP and GLP-1 and incident first cancer, using Cox-regression models adjusted according to Model 1 and Model 2b. Incident hormone-sensitive cancers were defined as first incident cancer of incident ovarian cancer, endometrial cancer, or incident breast cancer for women, or incident prostate cancer for men. Incident obesity-related cancers were defined as any incident first breast cancer, incident ovarian cancer, incident gastric cancer, incident colorectal cancer, incident endometrial cancer, incident kidney cancer and incident pancreatic cancer. Analyses of incretins association with hormone-sensitive cancers and with obesity-related cancers were carried out in Cox-regressions using Model 1 and Model 2a. In analyses of hormone-sensitive cancers in women, MHT was added on top of Model 2a.Prior to any analyses, all prevalent cases of cancer other than nonmelanoma skin cancer and cervical cancer in situ by the time of MDC-CC re-examination were excluded (n = 685), resulting in 3049 participants, among whom there were 485 incident cases of first cancer within a maximum follow-up period of 12.6 years  8.6–11.4 years). For associations between fasting and post-challenge (1) GIP, (2) GLP-1, (3) glucose, (4) insulin and (5) glucagon with incident first cancer, participants with missing data for any variable in the following analyses were excluded. Cox regression models were used to obtain Hazard Ratios (HR) with 95% confidence intervals (95%CI), and significant findings were further analyzed in model (1) adjusted for age and sex (Model 1 and Model 2a).To account for death as a competing risk, Fine-Gray sub-distribution hazard models were used to assess the cumulative incidence of the following endpoints . The multivariable models used were the same as previously mentioned .Model 1 and Model 2a.Prior to analyses of associations between post-challenge concentrations and any first cancer, participants with diabetes were excluded (n = 297). Analyses of associations between (1) post-challenge GIP, (2) post-challenge GLP-1, (3) post-challenge glucose, (4) post-challenge insulin and (5) post-challenge glucagon were carried out in Analyses of specific cancers are described in Supplementary Methods.p-values were two-tailed. A p-value < 0.05 was considered statistically significant. Due to the exploratory nature of this study, nominal p-values without adjustment for multiple testing are presented.All analyses were carried out using SPSS 26.0 (IBM) or STATA 17.0 (StataCorp). All The characteristics of the study population across quintiles of fasting GLP-1 are presented in Table Spearman rank correlation analyses between all continuous variables explored in this study are presented in Supplementary Table Model 1, but the association did not remain significant upon further adjustment (Table Each 1 standard deviation (SD) increment of fasting GLP-1 was associated with decreased risk of incident first cancer during the maximum follow-up period of 12.6 years  in Model 2a : HR 0.91 (95% CI 0.83–0.99); P = 0.031 (Table Model 2b). Fasting GLP-1 was associated with lower risk of incident first cancer in participants without diabetes  when adjusted for age, sex, waist circumference, and smoking status but the association was attenuated when physical activity was entered upon Model 2b  in Prior to analyses of associations between post-challenge biomarkers and incident first cancer, participants with diabetes were excluded n = 297). No associations were seen for any of the post-challenge incretins and incident first cancer  and fasting GLP-1  and incident first cancer were observed but the numbers were small.Further, as several cancers might present with unexpected weight loss, we carried out additional analyses with participants categorized into 1) underweight (BMI ≤ 18.5), normal BMI (BMI 18.5–24.9), overweight (25–29.9) and obese (≥ 30) and found no increased risk of first incident cancer in underweight participants .In this explorative, population-based study of elderly participants, originally free from prevalent cancers and followed for a median of 9.9 years, we found no increased risk of incident first cancer associated with either fasting or post-OGTT challenge GIP and GLP-1. In addition, higher endogenous fasting GLP-1 levels were found to be associated with lower risk of any incident first cancer, whereas both fasting glucose and GIP-levels were marginally significantly associated with increased risk of incident cancer risk when adjusted for age and sex. These associations were attenuated upon further adjustment.13, whereas no effect on tumor initiation has been reported. In addition, several cancer types defined as obesity-related in our work are gastrointestinal cancers, whose presence in the gastrointestinal tract might alter GLP-1 secretion by disturbing enteroendocrine cell stimulation21.Further, fasting GIP was neither associated with obesity-related, nor hormone-sensitive cancers. In contrast, fasting GLP-1 was associated with a lower risk of obesity-related cancers during the first 4 years of follow-up and a lower risk of hormone-sensitive cancers during the entire follow-up. Those findings might be attributed to the possible role of GLP-1 as an inhibitor of cancer cell proliferationCompeting risk (death as competing risk) analyses indicated that the effect was stable. To our knowledge, this is the first study to explore the associations of endogenous incretin levels and incident first cancer.There might be several plausible explanations for the observed associations between higher endogenous GLP-1 levels and lower risk of incident cancer. The mechanisms are probably multifactorial and complex. In our study, we found no significant association between GLP-1 and reduced risk of breast cancer, but this might be attributable to the low event rate. We did not analyze associations with prevalent cancer in our study and excluded participants with a cancer diagnosis at study entrance since we had no information on disease status or treatment.24. A recent real-world study using FAERS data from the first quarter of 2004 to the second quarter of 2020 reported significant signals between GLP-1RA and certain tumors, including medullary thyroid cancer, papillary thyroid cancer, malignant pancreatic neoplasms, islet cell neoplasms and APUDoma NEC. They suggested the combination of GLP-1 RA and DPP4i might have caused the increased reporting rate in tumors6. On the contrary, Ruette et al. did not find any increased risk of lung cancer for users of DPP4i and GLP-1 RA, compared to users of second-/third line drugs25. Further, it has been shown that GLP-1 RA can inhibit breast cancer cells growth13, and two recent meta-analyses concluded that there was no evidence of GLP-1 RA usage and increased risk of biliary tract cancer26, or malignant neoplasia27. Our observed neutral or cancer-protective associations between endogenous incretin levels and incident first cancer should be viewed in the light of the fact that associations between endogenous levels of incretins and incident first cancer were explored here, whereas incretin-based drug usage results in supraphysiological incretin levels. Overweight/obesity is a condition more prevalent in participants with diabetes, and an established risk factor for cancer28. However, in this study population, there was no significant difference in BMI between participants with and without incident cancer. The BMI presented here is, however, measured at one specific point in time (baseline), and we had no follow-up data on BMI, which changes over the years. Participants with, and without diabetes, however, differed in almost all aspects except for age, smoking, MHT-usage, and cancer incidence. Supraphysiological GLP-1 levels achieved by GLP-1 agonist treatment have been shown to exert protective effects on β-cells31. However, our study does not offer insight into whether GLP-1 is protective with regard to tumor initiation, or inhibited promotion of existing tumors. Nevertheless, given that it takes 10–30 years from tumor initiation to detectable cancer32, and that the current study population had a mean age of > 70 years, GLP-1 might, if anything, have inhibiting effects on existing tumor promotion.Previous findings regarding possible pro-oncogenic characteristics of GLP-1 RA have been inconsistent. Several studies have shown that incretin-based drugs are associated with increased risk of multiple cancer types33. The sensitivity analysis in participants without diabetes showed similar results, with fasting GLP-1 levels borderline associated with lower risk of any incident cancer. Due to the known sex-specific differences in cancer incidence36 with approximately 20% higher cancer incidence in men36, we carried out additional sex-specific analyses for associations between incretins and risk of any incident cancer, finding a borderline association between higher fasting GLP-1 and lower cancer risk only in men.We carried out our analyses of fasting incretins in all participants, irrespective of diabetes and additionally, separately in participants with and without diabetes, as individuals with diabetes are at significantly higher risk of developing many forms of cancer39. We carried out an OGTT in participants free from known diabetes at baseline, allowing us to be certain about participants’ diabetes status. In this context, this is of great importance, since the GLP-1 response to OGTT may be reduced in established type 2 diabetes and obesity40, while GIP secretion is near normal in diabetes, but its effect on insulin secretion is impaired41.One strength of this study is the relatively large study population, followed for a median of 9.9 years. Further, we used validated and robust national registries of high quality and data completeness (96%)20, there is a delay of approximately 5% in general reporting coverage, making it comparable to other high quality registers in northern Europe. However, a comparison between the Cancer Registry data and the Cause of Death Registry shows that underreporting is somewhat dependent on the cancer site, where pancreatic and lung cancers tend to be missed more often than breast cancer20. Another weakness was the many missing variables on self-reported alcohol consumption. Therefore, we were not able to adjust for alcohol consumption, which is a risk factor for cancer. Multiple imputation to account for missing data on alcohol consumption was deemed inappropriate since data were assumed to be missing not at random. Also, survivorship bias must be considered in this setting, resulting in participants that did not survive long enough to attend the re-examination study of the MDC-CC being excluded. In addition, physical activity was self-reported, and although this question had high response rate, individuals may overestimate or underestimate their true rates of activity42.A limitation of this study is the low number of events in analyses of specific cancers, in particular in analyses regarding risk of pancreatic cancer, which would have been of particular interest to explore. However, with an event frequency of 0.4%, no meaningful analyses could be carried out. According to the Swedish Cancer RegistryFurther, being an observational study, we cannot draw any conclusions about causality. Our participants were of mostly white European descent and within a narrow older age range, therefore the conclusions may not be generalizable to other populations or age groups.In this prospective, community-based study, no associations with higher risk of incident cancer were observed either for fasting or for post-challenge endogenous GIP and GLP-1. In addition, elevated levels of endogenous fasting GLP-1 were associated with lower risk of any incident first cancer within the 12.6 years of follow up. Further studies exploring incretins’ associations with cancer are warranted.Supplementary Information.


Breast parenchymal texture features, including grayscale variation (V), capture the patterns of texture variation on a mammogram and are associated with breast cancer risk, independent of mammographic density (MD). However, our knowledge on the genetic basis of these texture features is limited.We conducted a genome-wide association study of V in 7040 European-ancestry women. V assessments were generated from digitized film mammograms. We used linear regression to test the single-nucleotide polymorphism (SNP)-phenotype associations adjusting for age, body mass index (BMI), MD phenotypes, and the top four genetic principal components. We further calculated genetic correlations and performed SNP-set tests of V with MD, breast cancer risk, and other breast cancer risk factors.ECT2L, rs79670367 (8q24.22) in LINC01591, and rs113174754 (12q22) near PGAM1P5. 6q24.1 and 8q24.22 have not previously been associated with MD phenotypes or breast cancer risk, while 12q22 is a known locus for both MD and breast cancer risk. Among known MD and breast cancer risk SNPs, we identified four variants that were associated with V at the Bonferroni-corrected thresholds accounting for the number of SNPs tested: rs335189 (5q23.2) in PRDM6, rs13256025 (8p21.2) in EBF2, rs11836164 (12p12.1) near SSPN, and rs17817449 (16q12.2) in FTO. We observed significant genetic correlations between V and mammographic dense area , percent density , and adult BMI . Additional significant relationships were observed for non-dense area , estrogen receptor-positive breast cancer , and childhood body fatness  from the SNP-set tests.We identified three genome-wide significant loci associated with V: rs138141444 (6q24.1) in These findings provide new insights into the genetic basis of mammographic texture variation and their associations with MD, breast cancer risk, and other breast cancer risk factors.The online version contains supplementary material available at 10.1186/s13058-022-01570-8. MD ph studies , 5. Geno studies , includi studies .Yet, MD is a global metric that ignores local patterns of variability in breast density . Women wIn the present study, we performed a GWAS of mammographic texture variation within the Nurses’ Health Studies and Mayo Mammography Health Study cohorts. We also leveraged summary statistics of breast cancer risk and MD phenotypes from previous GWAS to identify shared susceptibility loci for V, MD, and breast cancer risk. We further assessed the genetic relationships of V with MD phenotypes, breast cancer risk, and other breast cancer risk factors by estimating genetic correlations and performing single-nucleotide polymorphism (SNP)-set tests.The Nurses’ Health Study (NHS) is a prospective cohort study established in 1976. A total of 121,700 female registered nurses aged 30 to 55 residing in 11 states within the USA completed an initial questionnaire at that time. NHSII was established in 1989 when 116,671 female registered nurses aged 25 to 42 residing in 14 states completed an initial questionnaire. Blood samples were collected from 32,826 women in NHS cohort from 1989 to 1990 and 29,611 women in NHSII cohort from 1996 to 1999, which form the blood subcohorts. Women in each cohort have been followed by self-administered questionnaires to collect updated exposure and newly diagnosed disease information every two years.The Mayo Mammography Health Study (MMHS) is a prospective cohort study of 19,924 women who had a screening mammogram from 2003 to 2006 at the Mayo Clinic in Rochester, MN and agreed to participate in the study. Participants were at least 35 years old, residents of Minnesota, Iowa, or Wisconsin, and had no personal history of breast cancer. Participants completed a baseline questionnaire and provided consent to access any residual blood samples from clinical tests over the time period. Breast cancer diagnostic information was obtained through linkage to state-wide cancer registry data and mailed questionnaires.The mammogram collection and processing procedure in NHS and NHSII is described elsewhere , 17 and Details of mammogram acquisition, retrieval, and digitization for MMHS are described elsewhere , 19. BriV is an automated measure of the grayscale variation on a mammogram. The algorithm for generating V has been described previously by Heine et al. , 20, 21.We generated four assessments of V with different proportions of erosion and image resolutions: V with 35% erosion and low resolution (V65L), V with 25% erosion and low resolution (V75L), V with 35% erosion and high resolution (V65H), and V with 25% erosion and high resolution (V75H). These four V assessments were highly correlated with each other  outliers or samples with call rate < 90%, gender discordance, or extreme heterozygosity were excluded.The full genotyping and quality control pipeline for NHS and NHSII is described elsewhere . In the The full genotyping and quality control pipeline for MMHS is also described elsewhere . Here, wAll genotype data were imputed to the 1000 Genomes Phase 3 version 5 reference panel separately by study and platform . The numV assessments were standardized to have mean zero and unit standard deviation before analysis. SNP association analyses were performed within each study by platform using linear regression assuming an additive dosage effect. RVtests  was usedP values and corresponding Z scores using test statistics from the meta-analysis results for each V assessment and model, accounting for sample overlaps of the four V assessments. We referred to this summary phenotype as VSUM, which was used as our primary multivariate outcome. SNPs with P < 5 × 10−8 in any of the six models for any of the four univariate V assessments or the multivariate VSUM were considered genome-wide significant.Given that the four V assessments were highly correlated with each other and might be proxies for an underlying latent phenotype, we performed multivariate phenotype association tests to pool association evidence across the four V assessments and get a single summary test statistic for each variant. We used R package MPAT  to obtaiP < 0.05/72) or breast cancer (P < 0.05/195) SNPs tested in Model 0 for any V assessment.We evaluated whether the identified V loci were also associated with MD phenotypes or breast cancer risk using GWAS results from Breast Cancer Association Consortium (BCAC) , 25, 32.Genetic correlations of V with MD phenotypes, breast cancer risk, overall and stratified by estrogen receptor (ER) status, adult BMI, childhood body fatness, age at menarche, and age at natural menopause were estimated using linkage disequilibrium (LD) score regression , 34. Sour2 > 0.1) with SNPs of smaller P value . Therefore, we further performed SNP-set tests to assess the genetic relationship between V and the above-mentioned traits using only reported genome-wide significant SNPs for those traits. SNPs for each trait were collected from published GWAS followed by LD clumping to remove any SNPs in LD r2 > 0. with SNPZ score from the SNP-specific association with V and where Our study population contains both women who developed breast cancer and women who did not develop breast cancer during the follow-up period after mammogram collection. We therefore further adjusted for breast cancer case–control status in Model 5 to assess its impact on the genetic associations. We performed a multicollinearity check for the identified genome-wide significant SNPs for Model 5, where we adjusted for all three MD phenotypes, by calculating the variance inflation factor (VIF). To assess the potential impact of outliers on the association results at the identified GWAS loci, we calculated the studentized residuals for all samples for each genome-wide significant SNP. Samples with absolute studentized residual greater than 3 were considered as outliers.10(P) for all tested SNPs across chromosomes are presented in Additional file P value indicate there was limited evidence of systematic heterogeneity in the test results across studies and platforms , 8q24.22 (LINC01591), and 12q22 (PGAM1P5)  is a novel locus that has not previously been associated with MD phenotypes or breast cancer risk. Figure P values. 8q24.22  is neither a MD nor breast cancer risk locus. Figure P = 4.42 × 10−8 for VSUM, Model 3) has previously been associated with NDA  [P = 1.08 × 10−24), and ER + breast cancer risk  [P = 1.30 × 10−13, r2 = 0.47 with rs113174754) [In total, we identified three independent loci that reached the genome-wide significant threshold of 5) Table . 6q24.1 × 10−18) . This lo3174754) . Figure P < 0.05/72 = 6.94 × 10−4 for MD, P < 0.05/195 = 2.56 × 10−4 for breast cancer risk) in Model 0: 5q23.2 (PRDM6), 8p21.2 (EBF2), 12p12.1 (SSPN), and 16q12.2 (FTO)  is a known locus for DA (P = 2.84 × 10−11) and PD (P = 5.78 × 10−10) [P = 5.74 × 10−5 for VSUM, Model 0) has previously been associated with breast cancer risk (P = 1.40 × 10−8) [P value of the association between the lead SNP and PD was close to the genome-wide significant threshold (P = 4.46 × 10−7) [P = 6.69 × 10−5 for VSUM, Model 0) is a known locus for DA (P = 1.66 × 10−9) [P = 1.12 × 10−6 for VSUM, Model 0) is a known locus for PD (P = 5.06 × 10−9) [P = 2.52 × 10−21), ER + (P = 5.59 × 10−14), and ER − breast cancer risk (P = 1.80 × 10−10). This locus is also significantly associated with BMI  [P = 3.70 × 10−7, r2 = 0.89 with rs17817449) [We identified four additional loci that had previously been associated with MD phenotypes or breast cancer risk and reached the Bonferroni-corrected thresholds accounting for the number of MD or breast cancer SNPs tested  × 10−8) . The ass . The as× 10−19)  and brea7817449) . The assrg = 0.79, P = 5.91 × 10−5 for VSUM, Model 0) and percent density   and ER +  breast cancer and became non-significant when adjusting for PD. We also observed a significant negative association with adult BMI , which became non-significant when adjusting for BMI. A strong negative correlation was observed for NDA  before adjusting for PD. Genetic correlation results were similar across V assessments; the full results are summarized in Additional file We observed significant positive genetic correlations between V and dense area  and a significant negative association between V and childhood body fatness from the SNP-set test using genome-wide significant SNPs for childhood body fatness   if we further adjust for BMI and PD in the SNP-set test. Plots showing Z scores from GWAS of V and GWAS of MD phenotypes [In addition to the genetic relationships of V with DA, NDA, PD, and breast cancer risk identified by genetic correlations, we further identified a significant positive association between V and ER + breast cancer , 8q24.22 (LINC01591), and 12q22 (PGAM1P5), the first two of which have not previously been associated with MD or breast cancer risk. Four additional loci for MD or breast cancer risk, 5q23.2 (PRDM6), 8p21.2 (EBF2), 12p12.1 (SSPN), and 16q12.2 (FTO), were also found associated with V.While MD continues to be one of the most well-established and widely used mammographic risk factors for breast cancer, there are gaps in our knowledge of mammographic features themselves and their relationship with breast cancer risk. Current MD measures do not capture the heterogeneity in the distribution of dense breast tissue on a mammogram, known as texture variation. Increasing evidence has shown that the performance of texture variation on discriminating breast cancer outcomes is either comparable or even higher than the performance of MD measures , 37, 38.Different models of the SNP-V association were fit to capture different effects Table . Model 012q22 is also associated with NDA and breast cancer risk in consistent direction, suggesting that at least part of its genetic effect on V is mediated by NDA or the genetic effect on NDA is mediated by V, and there are potential shared biological pathways between these three traits. These hypotheses are further supported by the fact that 12q22 is also associated with total breast size and its association with V was most significant when adjusting for DA and became non-significant when adjusting for NDA. The lead variant rs113174754 at 12q22 is an indel near pseudogene PGAM1P5 and is 30 kb upstream of protein coding gene NTN4  has been found to downregulate NTN4 in mammary tissue [NTN4 has also been identified as a candidate breast cancer risk gene by colocalization analysis, where the C allele of SNP rs61938093  at this region reduced NTN4 promoter activity and knockdown of NTN4 promoted breast cell proliferation and tumor growth [6q24.1 and 8q24.22 are V loci that have not been seen associated with MD phenotypes or breast cancer risk. The lead variant rs138141444 at 6q24.1 is an intronic indel in ECT2L. The lead variant rs79670367 at 8q24.22 is an intronic SNP in LINC01591. Neither these two genes nor nearby genes have been associated with breast cancer risk. The genetic effects of these two loci on V are therefore likely through mechanisms not mediated by MD. It should also be noted that the effect allele frequency for rs79670367 is less than 5% and the outlier analysis indicated that the association results might be influenced by influential outliers. Moreover, only about half of the samples have genotype data on this variant (available in NHS/NHSII Illumina HumanHap and MMHS OncoArray). Further studies are needed to confirm the findings at these two loci.Among the three genome-wide significant V loci, r growth . These f8p21.2 is an intronic SNP in protein coding gene EBF2. EBF2 encodes well-conserved DNA-binding helix–loop–helix transcription factors, which involved in differentiation of osteoblasts. Although little is known about the role of EBF2 in breast cancer development, studies have shown that inactivation of EBF genes can lead to tumorigenesis via accumulation and expansion of undifferentiated progenitor cells [16q12.2 is associated with both PD and breast cancer risk in the same direction with its lead SNP rs17817449 located in FTO. FTO is a well-established susceptibility gene for obesity [FTO is overexpressed in breast cancer cells, which affects the energy metabolism of the cells [5q23.2 is a known locus for DA and PD. The lead variant rs335189 is an intronic SNP in PRDM6. PRDM6 encodes a transcriptional repressor involved in the regulation of endothelial cell proliferation, survival, and differentiation and may play a role in breast cancer tumorigenesis [12p12.1 is an intronic SNP near SSPN and is only associated with DA. Functional analysis needs to be performed to further investigate the role of identified V SNPs in mammary development and breast cancer etiology.Four additional V loci have previously been associated with breast cancer risk or MD phenotypes. The lead variant rs13256025 at or cells . 16q12.2 obesity . In our he cells . 5q23.2 igenesis . The leaConsistent with the phenotypic relationships we observed for V and MD measures, there were strong positive genetic correlations of V with DA and PD, and negative genetic correlations with NDA. The positive genetic correlations between V and breast cancer risk  were also nominally significant, further supporting that the observed phenotypic association between V and breast cancer risk can at least be partially explained by shared genetic components. The magnitude of these genetic correlations is comparable to those between MD and breast cancer risk . A genetOur study focuses on a summary texture measure, V, but there are also many other texture features that may capture more of the parenchymal complexity. For example, Manduca et al. systematically evaluated 1443 textural features and identified six independently validated strongest features . Malkov PGAM1P5) that are associated with MD phenotypes and breast cancer risk and 6q24.1 (ECT2L) and 8q24.22 (LINC01591) that are novel V susceptibility loci. Four additional V loci were identified from looking up MD and breast cancer susceptibility SNPs in GWAS of V, including 5q23.2 (PRDM6), 8p21.2 (EBF2), 12p12.1 (SSPN), and 16q12.2 (FTO). These findings provide the first evidence of the genetic basis of V and shared genetic components between V, MD, and breast cancer risk. Future studies are needed to confirm our findings and further improve our understanding of the mechanisms underlying the relationship between texture features, MD, and breast cancer development.In conclusion, we performed a GWAS of breast parenchymal texture variation, V, and identified three independent loci at genome-wide significance, including 12q22 (Additional file 1: Fig. S1. Correlations between the four V assessments. Fig. S2. Quantile-quantile plots of the GWAS meta-analysis results. Fig. S3. Manhattan plots of the GWAS meta-analysis results. Fig. S4. Quantile-quantile plots of the P value of heterogeneity. Table S2. Sources of summary statistics of breast cancer risk and breast cancer risk factors for calculating genetic correlation. Table S3. Sources of summary statistics of breast cancer risk and breast cancer risk factors for the SNP-set test.Additional file 2: Table S1. Number of GWAS subjects by study and platform. Table S4. GWAS results of significant V loci for all V assessments and models. Table S5. Lookup results of 72 MD phenotype SNPs in GWAS of V. Table S6. Lookup results of 195 breast cancer SNPs in GWAS of V. Table S7. Genetic correlation results of all V assessments and models. Table S8. SNP-set test results of all V assessments and models.


Early identification of individuals with an increased risk of cancer is an important challenge. Danish administrative registers may be useful in this respect because they cover the entire population and include comprehensive and consistently coded long-term data. We aimed to develop a predictive model based on Danish administrative registers to facilitate the automated identification of individuals at risk of any type of cancer. In addition to age, almost all the included factors contributed statistically significantly, but also only marginally, to the prediction models, which means that we have not overlooked obvious information available in the register. Future prediction studies should focus on specific cancer types where more precise risk estimations might be expected. It is our ultimate ambition that an effective model can be used at the point of care, integrated into electronic patient record systems to alert physicians of patients at a high risk of cancer.Purpose: To develop a predictive model based on Danish administrative registers to facilitate automated identification of individuals at risk of any type of cancer. Methods: A nationwide register-based cohort study covering all individuals in Denmark aged +20 years. The outcome was all-type cancer during 2017 excluding nonmelanoma skin cancer. Diagnoses, medication, and contact with general practitioners in the exposure period (2007–2016) were considered for the predictive model. We applied backward selection to all variables by logistic regression to develop a risk model for cancer. We applied the models to the validation cohort, calculated the receiver operating characteristic curves, and estimated the corresponding areas under the curve (AUC). Results: The study population consisted of 4.2 million persons; 32,447 (0.76%) were diagnosed with cancer in 2017. We identified 39 predictive risk factors in women and 42 in men, with age above 30 as the strongest predictor for cancer. Testing the model for cancer risk showed modest accuracy, with an AUC of 0.82 (95% CI 0.81–0.82) for men and 0.75 (95% CI 0.74–0.75) for women. Conclusion: We have developed and tested a model for identifying the individual risk of cancer through the use of administrative data. The models need to be further investigated before being applied to clinical practice. Early identification of individuals at a high risk of cancer is an important challenge for all healthcare systems. In recent years, the focus has been on using healthcare data for risk assessment models in cancer ,2. DenmaAn advantage of register-based tools is that the use of existing administrative data eliminates extensive data collection and recall bias. Such tools lend themselves to automatic risk calculations, which may be useful for the systematic detection of high-risk individuals.Cancer is a major disease burden due to the high morbidity, mortality, diagnosis, and treatment costs and hence the substantial financial costs . AlthougThe objective of the study was to identify the relevant data available in Danish registers for inclusion in a predictive model (the Cancer Risk Assessment Model (CRAM)) for automated case finding of individuals at risk of any type of cancer. Second, the aim was to assess the performance of CRAM in a validation cohort stratified by sex, and, thirdly, to investigate the impact of including socioeconomic status in the model, using the data available from the Danish registers.This study was a nationwide register-based cohort study using data from the Danish national registers covering all individuals in Denmark aged 20 years or above in 2017 with a 10-year look-back period (2007 to 2016).In Denmark, all inhabitants are provided with a unique civil registration number (CRN) issued at birth or when immigrating to Denmark, which is used as the key identifier in all health and social registers. The Danish healthcare system is tax-funded and provides equal access to universal healthcare services .Statistics Denmark is a national organization in Denmark that is responsible for collecting statistical information about Danish society. We used data on demographic factors, vital status, employment status, education, and personal income ,15. DataThe Danish Civil Registration System (CRS) includes all persons living in Denmark  and was The Danish Cancer Registry (DCR) contains the data of all cases of cancer in the Danish population, including date of diagnosis and tumor characteristics . We usedThe Danish National Patient Register (NPR) , The DanThe NHSR contains information about activities in primary healthcare, including all general practitioner (GP) contacts . We obtaCoding details on age, sex, marital status, country of origin, income, educational and occupational status, and comorbidity can be found in The study population characteristics are reported as numbers and frequencies for categorical variables and as means and standard deviations or medians and interquartile ranges for numerical variables. Age on 1 January 2017 was categorized into 5-year categories.p-value cut-off of 0.01 combining the selected conditions/ATC codes/contacts with GPs from the second step and age (in 5-year age groups) (Model A).The study population was randomly split into 50% as a development cohort, 25% as a validation cohort, and 25% as a test cohort stratified by group (cancer versus control) and sex. To enable accurate comparisons with competing prediction models, we withheld the test dataset for future analysis. We applied a three-step variable selection procedure, stratified by sex, on the development datasets. In the first step, we excluded conditions and ATC codes that occurred in <0.1% of the development cohort during the exposure period. In the second step, we carried out a backward selection with a p-value cut-off of 0.05 on variables remaining after the first step by logistic regression for cancer in 2017 separately for hospital diagnoses, ATC codes, and number of contacts per year with GPs and specialists. In the third step, we carried out a similar backward selection with a To investigate the impact of socioeconomic status (SES) on cancer risk, the third step was repeated, including civil status, income, education level, occupation, and country of origin (Model B). Moreover, we constructed a model including age groups only (Model Age), and a fourth model including SES variables only (Model SES) to determine the predictive power of these aspects on their own.To evaluate the resulting models, we applied the models to the validation cohort and calculated a receiver operating characteristic (ROC) curve based on the predicted probabilities and estimated the corresponding area under the curve (AUC). For predicted risk strata , we calculated the observed cancer frequencies. Furthermore, we evaluated the prediction models by determining the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for 1-year cancer risk cut-offs of 1% as well as 5%.Denmark had a total population of 5.7 million individuals in 2017. After excluding persons who did not meet the inclusion criteria, the study population consisted of 4.2 million persons . The chaIn total, 32,447 (0.76%) individuals in the study population were diagnosed with first-time cancer in 2017, of whom 648 (1.99%) individuals were diagnosed with more than one cancer type during 2017. Overall, the highest frequency for first-time cancer was seen for gastrointestinal cancer (23.9%). When stratified by sex, the most frequent type of cancer was breast cancer (28.4%) in women and cancer of the genital organs (26.3%) in men.We identified 39 predictive risk factors in women  and 42 iAge above 30 was a strong predictor for cancer in both sexes. In total, 11 of the 39 identified risk factors in women and 13 of the 42 in men were associated with a lower risk of cancer. Eight risk factors were consistent across the two sexes, whereas none of the GP services (by year) or practicing specialists were identical for men and women.The models including SES differed slightly . Early rValidating Model A on the corresponding cohort resulted in an AUC of 0.82 (95% CI 0.81–0.82) for men and 0.75 (95% CI 0.74–0.75) for women .When comparing the observed and predicted frequency of cancer cases, we found that individuals with a predicted 1-year cancer risk above and below the cut-off of 1% had an observed cancer risk in the predicted low-risk group of 0.22% in men and 0.36% in women compared with an observed risk (PPV) in the high-risk group of 2.14% in men and 1.64% in women . SimilarThe odds ratio for being diagnosed with any cancer in 2017 was 9.78  with a 1% cut-off and 4.96  with a 5% cut-off among men. For women, the model resulted in an odds ratio of 4.49  with a 1% cut-off and 3.34  with a 5% cut-off. The predictive performance of Model A appears in Including the socioeconomic details (Model B) only marginally improved the AUC. Models including age alone resulted in an AUC of 0.81 for men and 0.74 for women, while the model only including socioeconomics resulted in AUCs of 0.75 for men and 0.70 for women .This study is the first study based on a vast number of already available Danish register data, with overall cancer as the outcome. In addition to age, almost all the included factors contributed statistically significantly, but also only marginally, to the prediction models, which means that we have not overlooked obvious register-available information. Given the inclusion of overall cancer as the outcome and the large dataset, the identified predictive risk factors may, to some extent, represent random findings. Future prediction studies should therefore focus on specific cancer types, staging, and testing in clinical care.The ROC showed that it is possible to make moderately precise models; however, ‘all-type cancer’ is a difficult case due to the heterogenous outcome. Omitting SES from the models only weakened the model marginally. This is a sign of how the different factors in the registers are related. Our results show that age is a strong predictor of cancer: the model including age alone had a higher precision than the model based on SES exclusively. The CRAM model predicts the 1-year cancer risk well up to 5% risk, which is a clinically relevant spectrum of risk, as the 1-year risk of cancer in the general population is below 1%. However, given a predicted risk above 5%, the model may overestimate the risk of cancer.This is the first study developing and validating a risk prediction model for cancer in a Danish setting following up on a similar prediction model developed for osteoporotic fractures . A main We extracted data from administrative registries and thereby reduced the likelihood of information bias. Due to the public national registries, the results are applicable to everyone with access to the healthcare system. The CRAM is consequently transparent, can be used on an individual level according to the information presented in this article, and can be tested in future cohorts.A limitation to our study is that we were not able to include information about ‘online’ covariates as symptom of presentation, or lifestyle , as this information is not available from the administrative registries. The results should therefore be perceived as a supplement to the information received during patient presentation in clinical practice.Overall, the cancer group had a higher median age than the control group. This age difference was expected, as age is a known risk factor for cancer. Hence, matching the case and control group would have limited the generalizability of the results to the general population level.We did not take time to event during 2017 and the competing risk of death into account. Although this might have influenced the results slightly, we found that the effect of these factors would be limited, as our 1-year outcome period was short, implying a limited risk of death during the period, and time to cancer diagnosis is neither very informative nor clinically relevant.We used a classical backward selection method instead of, e.g., machine learning algorithms. This relatively simple approach has the drawback that more complicated patterns of risk prediction could have been overlooked by the model. On the other hand, the advantage was a transparent methodology, resulting in a final model which can easily be reported, interpreted, and implemented without any privacy concerns with respect to the development data.We excluded patients with previous cancer, as secondary cancer was considered to be clinically different from primary cancer and because we expect patients with earlier cancers to be followed closely in clinical practice, and hence they are not relevant for general screening. We included any type of cancer for this first study. However, risk factors may play different roles for different types of cancers, and a model should be investigated in relation to specific cancer types in future studies.The British QCancer prediction models are, to some extent, comparable with our study ; howeverOur findings of AUC 0.82 for men and 0.75 for women are comparable with a review of studies regarding prediction models for lung cancer, where the included studies found an AUC between 0.57 and 0.879 .In a prospective observational study among patients with hematuria from 110 hospitals across 26 countries referred to secondary care, a prediction model for urinary tract cancer showed an AUC of 0.86  .The modest contribution of SES to our prediction model is somewhat in contradiction to the findings of other studies of prediction models for nonmalignant diseases, where SES in terms of education and income was found to improve the accuracy of predicting cardiovascular disease risk ,27 and dA growing number of studies have aimed to improve the risk stratification of patients with cancer through multimodal data integration. There has been much recent interest in the use of machine learning (ML) and different artificial intelligence algorithms for cancer predictions. Studies comparing ML with classical statistical models for risk prediction have already been published ,30,31, aWe have verified that the Danish administrative registers are useful for developing a cancer risk prediction model (CRAM) for identifying individuals at risk of having any cancer. The CRAM showed moderately accuracy in the validation cohort and included 39 and 42 risk factors for cancer for women and men, respectively. In addition to age, almost all the included factors contributed statistically significantly, but also only marginally, to the prediction models. Future prediction studies should focus on specific cancer types, where more precise risk estimations might be expected.


The relationship between tea consumption and the risk of breast cancer is inconsistent in previous observational studies and is still in dispute. We intended to detect the causal association between tea consumption and breast cancer risk using two-sample Mendelian randomization (MR) analysis.The summary statistics of tea consumption was obtained from the UK Biobank Consortium with 349,376 individuals and breast cancer information was obtained from the Breast Cancer Association Consortium (BCAC) . Sensitivity analyses of evaluating the influence of outliers and pleiotropy effects were performed by a variety of MR methods under different model assumptions.P = 0.2086], 1.050 , and 1.081 , respectively. The results were consistent with a sensitivity analysis that excluded SNPs associated with other phenotypes, manifesting that the findings were convincing and robust. Moreover, in the multivariable MR analysis, the null associations for breast cancer risk remained after adjusting for smoking and alcohol consumption separately or together.After potentially excluding pleiotropic single nucleotide polymorphisms (SNPs) using the MR Pleiotropy RESidual Sum and Outlier method, the odds ratio (OR) for per extra daily cup of tea intake for overall, estrogen receptor (ER)-positive, and ER-negative breast cancer risk was 1.029 [95% confidence interval (CI) = 0.983–1.077, Our MR results based on genetic data did not support a causal relationship between tea consumption and breast cancer risk. Breast cancer is the most common malignant tumor in women and the second leading cause of death worldwide , with anTea is one of the most widely consumed beverages around the world. Tea has been believed to have a variety of health benefits and has been applied to medical purposes . SeveralMendelian randomization (MR) design employs genetic variants as instrumental variables (IVs) for exposure and can enhance causal inference. Residual confounding is minimized, and MR is less affected by reverse causation because genetic variants are randomly assigned at conception, so that a trait is generally not related to other traits . Therefore, we performed an MR study to assess the association of tea intake with breast cancer risk.1 We obtained the data of habitual tea consumption from a dietary questionnaire. The questionnaire included a question: “How many cups of tea (including black and green tea) do you drink per day?” Summary statistics of daily unconverted tea consumption by GWAS were employed to identify single nucleotide polymorphisms (SNPs) associated with tea drinking. The GWAS was adjusted for age, sex, and the first 20 ancestral principal components. We chose autosomal biallelic SNPs with P < 5 × 10–8 and further conducted quality control at a minor frequency >1%. In addition, based on the reference data from the European sample in the 1000 Genomes Project, we clumped these SNPs with linkage disequilibrium r2 < 0.01 at a 1,000 kb window. At last, 45 independent SNPs strongly related to tea consumption were used in our primary analysis , and no additional ethics approval and informed consent were required . The conP-value < 5 × 10–8, following clumping for LD at r2 < 0.01.When the previous MR studies showed significant associations with common causes of tea consumption and breast cancer risk, we performed multivariable MR analysis to adjust for indirect pathways, which were likely to introduce correlated pleiotropy . The preF-value > 10 was suggested to be a strong genetic IV  model was used as the main analytical method to examine the causal association. The random-effect IVW method included individual MR effects of SNPs to derive overall weighted effects. Potential violation of the second and third assumptions of MR was examined using several approaches, such as the weighted median, simple median, and the MR-Egger regression methods . We removed these SNPs and performed the second sensitivity analysis. Multivariable MR can then be used to adjust for pleiotropy (P = 0.05/3 outcomes) was used to adjust for multiple testing (P = 0.017) in this MR. All statistical tests were two-sided and performed using the TwoSampleMR and MR-PRESSO packages in R Software 3.6.1.We calculated the variance in tea consumption explained by the IVs, and used the F-statistics to test weak IVs bias. netic IV , and we ne tool2 . We appl methods –29. When methods . We alsoner3 web . SNPs reeiotropy , 32. We F-values of the obtained SNPs were all greater than 10, suggesting that there could be no bias caused by weak IVs (The weak IVs .P > 0.05).The Steiger-MR analysis was applied to detect the robustness of the causal effect estimates. Steiger-MR identified that the SNPs explained more variance in exposure than outcome  = 1.001–1.126, P = 0.0446] and ER-positive breast cancer  was observed, but there was no association between tea consumption and ER-negative breast cancer risk  (The scatter plot of the SNP-breast cancer association against the SNP-tea association is shown in = 0.065) . Using tP for MR-PRESSO distortion tests >0.05). The OR of overall, ER-positive, and ER-negative breast cancer was 1.030 , 1.050 , and 1.025  in the outlier-corrected MR-PRESSO analysis, respectively. Using the existing SNPs as IVs, the results indicated that tea consumption was not associated with overall , ER-positive , and ER-negative breast cancer risk   . In addi 0.6513) .We performed multivariable IVW analyses by adjusting for tobacco smoking and alcohol drinking separately or together. We did not observe statistically significant associations between genetic liability to tea consumption and breast cancer risk in any of these analyses .In this study, we applied a two-sample MR analysis using summary-level data from BCAC Consortium to evaluate the causal relationship of tea consumption with overall, ER-positive, and ER-negative breast cancer. The MR analysis showed that an extra daily cup of tea consumption seemed to be not protective against breast cancer.Our research findings were consistent with several previous epidemiological studies, which found a null relationship between tea intake and breast cancer. A recent meta-analysis included one clinical trial and four cohort studies . In one However, the relationship between tea consumption and the risk of breast cancer remains inconsistent in previous meta-analyses –16. ThreMultiple potential biological mechanisms may explain the inverse relationship between tea consumption and breast cancer risk. Polyphenols or tea catechins are the main antioxidants in tea, which act as reactive oxygen species scavengers and may affect transcription factors and enzymatic activities . EpigallThis study has several advantages. First, this is the first attempt to explore the causal association between tea consumption and breast cancer through a two-sample MR analysis using GWAS summary-level statistics. Two-sample method, with large summary-level genetic data, is able to minimize potential confounders and reverse causality. Second, the findings were verified through various sensitivity analyses that applied multiple MR methods with different model assumptions, and the effects of outliers and pleiotropy were evaluated comprehensively. All the analyses indicated that the findings were consistent and robust.However, this study had some limitations. First, we emphasize that our analytical power was relatively limited in observing the weak effect of tea consumption on breast cancer, which might be due in part to the lower proportion of tea consumption variability explained by valid SNPs. Second, it is important to note that our study was mainly based on the available GWAS data. There are no GWASs for different types of tea, making it difficult to infer the differential effects of tea type on the causal relationship between tea consumption and breast cancer. Third, the results cannot be generalized to other populations due to biases with limited data from the European population. Fourth, we could not conduct analyses of non-linear association or stratification effects due to the use of summary-level data. Fourth, we did not find sex-specific genetic IVs for tea consumption from the currently publicly available GWAS, which only included female subjects. Therefore, sex-combined IVs might lead to biased causal effect estimates in sex-specific two-sample MR studies. Future GWAS studies on tea consumption need to distinguish between male and female subjects. Finally, the MR results reflect the effect of lifelong tea intake on breast cancer. Therefore, the short-term effect of tea consumption on breast cancer requires further research.In conclusion, this MR study provides genetic evidence for a null causal relationship between tea consumption and breast cancer. Our results suggest that the evidence for tea consumption as a preventive measure for breast cancer is still deficient. Previous findings on the relationship between tea consumption and breast cancer risk may be influenced by confounding factors. More experimental studies are needed to confirm our findings.The original contributions presented in this study are included in the article/Our study only adopts publicly available data. Ethical approval could be found in the original publications. Informed consent was obtained from all subjects involved in the study.JZ and HX conceived and designed the study and interpreted the results of the data analyses. YD performed the data analyses. YD and WG wrote the manuscript. All authors read and approved the final version of the manuscript.