Whether cytochrome P450 24A1 (CYP24A1) polymorphism is associated with cancer susceptibility, the individual study results are still controversial. Therefore, we performed a comprehensive study to identify the association of CYP24A1 polymorphisms  with cancer susceptibility.Electronic databases including Cochrane Library, PubMed, and Embase were systematically retrieved for relevant publications. Fixed or random-effect model was selected to calculate odds ratios (ORs) with their 95% confidence intervals (95%CI).P=0.035; C vs. T: P=0.016) and Asian population  and breast cancer risk . A significant association was found between rs2296241 polymorphism and esophageal squamous cell carcinoma risk (AA vs. GG+AG: P = 0.023) and prostate cancer susceptibility (A vs. G: P=0.022). Furthermore, rs4809957 polymorphism was associated with prostate cancer susceptibility in Caucasian  and breast cancer susceptibility . Additionally, rs6068816 polymorphism significantly decreased the lung cancer  and breast cancer risk . No association was found for rs2762939 polymorphism with overall cancer risk. However, for rs2296241, rs4809957, and rs6068816 polymorphisms, there were no significant differences after the Bonferroni correction.Eighteen published articles were identified. The results indicated that rs4809960 polymorphism was associated with a decreased cancer risk in Caucasian (TT vs. TC+CC: The meta-analysis suggested that rs4809960 was associated with cancer risk and might be a genetic marker for predicting cancer risk. More large-scale and large-sample studies are necessary to further confirm these results.The online version contains supplementary material available at 10.1186/s12957-023-03156-w. Cancer is a global public health problem, and incidence and mortality are rapidly growing worldwide. According to the data of the International Agency for Research on Cancer, GLOBOCAN 2020 investigation results showed 19.3 million new cancer cases and 10.0 million cancer deaths in 2020 .Vitamin D, an essential fat-soluble vitamin, is mainly come from ultraviolet exposure and diet metabolism . MeanwhiSingle-nucleotide polymorphisms (SNPs) are the most common form of variation in the human genome, which can alter the expression level or function of genes or their encoded products and thus determine the phenotype of the organism , 12. TheThis study was performed under the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) and was registered in PROSPERO (CRD42023446451).The relevant paper was identified (published until Feb. 2023) through Embase, PubMed, and Cochrane Library using the following strategy: (CYP24A1 or rs2296241 or rs4809957 or rs2762939 or rs4809960 or rs6068816) and (polymorphism or SNP or variant or variation or mutation or genotype) and (cancer or carcinoma or tumor or neoplasm). In addition, other potential publications were also searched by scanning the reference list. The details of the search strategy can be found in Supplementary Table Relevant studies were included according to the following criteria: (1) case-control studies, (2) evaluated the association between CYP24A1 polymorphism and cancer risk, (3) provided sufficient data to calculate the OR with 95%CI, and (4) control group conform to the Hardy–Weinberg equilibrium (HWE). The exclusion criteria were (1) review, abstract, comment, or letter; (2) duplication publications; and (3) relevant data not reported. In addition, for studies with repeat data, the study with the largest sample size was included. Each ethnicity was regarded as a separate study when different ethnicities were reported in a study.P value of HWE in the control group, methodology quality of each study was assessed according to the Newcastle–Ottawa Scale (NOS).Two authors independently extracted relevant data from the included studies. The extraction parameters included the first author, publication year, country, ethnicity, sample size, cancer type, genotype, and allele distribution in cases and controls, and the P value of < 0.05 was considered as statistically significant. The chi-square test and I2 statistics were calculated to evaluate the heterogeneity across studies. If heterogeneity was found (P<0.10 or I2> 50%), the random-effect model was adopted. Otherwise, the fixed-effect model was adopted. Bonferroni correction was performed to adjust multiple-test P value [HWE was assessed by the chi-square test. The ORs and 95%CIs were calculated to evaluate the strength under allelic, recessive, dominant, homozygous, and heterozygous models. The  P value . SensitiA total of 258 articles were retrieved in the initial search. Finally, a total of 18 articles , 17–32  and Asian population . Subgroup analyses by cancer type revealed that rs4809960 polymorphism decreased breast cancer risk . However, we only observed that rs4809960 polymorphism was significantly associated with the risk of breast cancer after Bonferroni correction.Six publications , 30–33 iP=0.020, I2 = 53.1%; TC vs. TT: OR 0.84, 95%CI 0.70~0.99, P=0.043, I2 = 60.2%; C vs. T: OR 0.88, 95%CI 0.81~0.95, P=0.001, I2 = 37.2%) when after removed Yi et al. .Significant heterogeneity was found in all genetic models. Sensitivity analysis suggested that a significant association between rs4809960 polymorphism and overall cancer susceptibility was found  and decreased risk in prostate cancer  .Significant heterogeneity was found under the recessive, homozygous, and allelic models. Sensitivity analysis showed that the initial result was not changed by removing each study respectively. No visual publication bias was detected under the allelic genetic model Fig. . In addiP=0.029; GA vs. GG: OR 1.27, 95%CI 1.04, 1.56, P=0.022). Furthermore, a significant association was found in breast cancer   and breast cancer risk  . It is a key gene that converted 1,25(OH)2D3 to 1,24,25(OH)2D3 by 24-hydroxylation25-hydroxyvitamin D 24-hydrolase . AlbertsThe present study indicated that there was no association between CYP24A1 polymorphisms  and overall cancer risk. For rs4809960 polymorphism, it was related to the Caucasian and Asian populations and decreased breast cancer risk. Moreover, our results suggested that rs2296241 polymorphism increased esophageal squamous cell carcinoma risk and decreased prostate cancer risk. For rs4809957 polymorphism, it was associated with pancreas cancer and breast cancer risk. In addition, we found that rs6068816 polymorphism significantly decreased lung cancer and breast cancer risk. However, rs4809960 polymorphism was associated with a decreased breast cancer risk after Bonferroni correction. A previous meta-analysis also reported CYP24A1 rs2296241 polymorphism was associated with prostate cancer risk [The improvements of our meta-analysis are as follows: Firstly, more case-control studies about rs4809960, rs6068816, and rs2296241 polymorphism were included in the meta-analysis. Secondly, this is the first meta-analysis to assess the relationship between CYP24A1  polymorphism and cancer risk. Thirdly, all included studies conform to the HWE, which may improve the reliability and stability of our study. In addition, all CYP24A1 polymorphisms were considered at the beginning. Ultimately, due to a lack of eligible articles and overlapping studies, our further evaluation of other CYP24A1 polymorphisms was limited. Therefore, in this meta-analysis, we only focused on five polymorphisms.There are several limitations should be noted in the present study. First, the sample size of the included studies was relatively small, which might weaken the strength of the results. Second, the number of included studies in the subgroup analysis was also relatively small, which might lead to statistical bias. Third, not sufficient data to analyze whether environmental factors may influence the statistical result. Four, the association of CYP24A1 polymorphism with different types or stages, drinking, smoking, age, gender, exposure factors, or other risk factors was not considered in this study. Five, the patients of included studies mainly come from Caucasians. The African and Asian populations were relatively small.In conclusion, this meta-analysis indicated that rs4809960 polymorphism was associated with a decreased breast cancer risk. No association between rs4809957, rs2296241, rs2762939, rs4809957 polymorphism, and overall cancer risk was found after Bonferroni correction. Considering the above limitations, more large-scale and large-sample studies are necessary to confirm these results.Additional file 1: Supplementary Table 1. Search strategy.


This study investigated how changes in metabolic syndrome (MetS) are associated with the subsequent risk of breast and endometrial cancer according to menopausal status.This cohort study, using data from the National Health Insurance Service database, included women aged ≥40 years who underwent 2 biennial cancer screenings (2009-2010 and 2011-2012) and were followed up until 2020. Participants were grouped into MetS-free, MetS-recovery, MetS-development, and MetS-persistent groups. Menopausal status  was assessed at 2 screenings. Cox proportional hazard regression was used to assess the association between MetS changes and cancer risk.In 3,031,980 women, breast and endometrial cancers were detected in 39,184 and 4,298, respectively. Compared with the MetS-free group, those who recovered, developed, or had persistent MetS showed an increased risk of breast cancer, with adjusted hazard ratios (aHRs) of 1.05, 1.05, and 1.11, respectively (p<0.005). MetS persistence was associated with an increased risk of breast cancer in postmenopausal women  but not in premenopausal or perimenopausal women. MetS persistence was associated with an increased risk of endometrial cancer in premenopausal, perimenopausal, and postmenopausal women, with aHRs of 1.41 , 1.59 , and 1.47 , respectively.Increased breast cancer risk was associated with recovered, developed, and persistent MetS in postmenopausal women. Meanwhile, increased endometrial cancer risk was found in obese women who recovered from MetS or persistently had MetS, regardless of menopausal status, when compared to MetS-free women. Metabolic syndrome (MetS) is a growing global problem ,2. MetS Previous studies have found an increased risk of breast cancer in postmenopausal, but not premenopausal women with MetS, suggesting that the association between MetS and breast cancer might differ depending on menopausal status -13, althDespite the existence of comprehensive evidence suggesting the important role of MetS as a risk factor for breast and endometrial cancer in postmenopausal women, it is unclear how changes in MetS affect the risk of cancer. Thus, this study aimed to assess the associations between longitudinal changes in MetS and the risk of developing breast and endometrial cancer. Because menopause has been proposed as an important factor contributing to an increased prevalence of MetS , we asseThis study used customized data from the National Health Insurance Service (NHIS) database . In KoreMetS was defined based on the modified National Cholesterol Rationale Education Program Adult Treatment Program (NECP-ATP III) , comprisAccording to changes in MetS status between the first screening in 2009-2010 and the second screening in 2011-2012, participants were grouped into the following categories: MetS-free (no MetS at both screenings), MetS-recovered (MetS at the first screening and no MetS at the second screening), MetS-developed (no MetS at the first screening and MetS at the second screening), and MetS-persistent (MetS at both screenings) .In addition, changes in each component of MetS status between the first and second screenings were analyzed according to each menopausal group. Changes in MetS scores and the presence of each component of MetS between the first and second measurements and their associations with breast and endometrial cancer were assessed as described above.The main outcomes were incident breast cancer and endometrial cancer, which were ascertained from the healthcare utilization database of the NHID. In Korea, patients with cancer have a special co-payment reduction program called the rare incurable disease system, and cancer cases are registered in this system from the time of diagnosis. In this study, the incidence of cancer events was defined as cases having both the corresponding International Classification of Diseases code  and a rare incurable disease system code . This apMenopausal status was measured at both screenings using the question: “What is your current menopausal status?” with the following response options: still menstruating, have had a hysterectomy, and have undergone menopause. Women who reported having undergone menopause were further asked to report the age at which they underwent menopause. Based on this information, menopausal status was classified into the following categories: premenopausal, perimenopausal, and postmenopausal. Perimenopausal women included those whose status changed from premenopausal to postmenopausal between the 2 screenings.2), normal (18.5 to < 23.0 kg/m2), overweight (23.0 to < 25.0 kg/m2), and obese (≥ 25.0 kg/m2).Other risk factors measured during the second screening were considered covariates in our analysis. Height and weight were measured on the date of screening and used to calculate the BMI. According to the Asia-Pacific classification, BMI was categorized into the following groups : underweAll participants in the national health screening were asked to complete a self-report questionnaire to assess their health behaviors and conditions before the screening. We included the following variables as covariates in our analysis: age at screening, age at menarche , child delivery , duration of breastfeeding , oral contraceptive use , family history of breast cancer in first-degree relatives (yes and no), vigorous or moderate physical activity per week , smoking status , alcohol consumption , and breast density (4 levels). For perimenopausal and postmenopausal women, we further adjusted for age at menopause  and hormone replacement therapy for postmenopausal women . Only age at screening was expressed as a continuous variable, and the rest of the variables were expressed as categorical variables.Descriptive statistics of the baseline characteristics of the participants, MetS status at the first and second screenings, and changes in MetS according to menopausal status are presented. To quantify the association between changes in MetS and cancer development, we performed Cox proportional hazards regression analysis to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). The time-to-event period was counted in days from the date of the second screening to the date of the event , the development of other types of cancer, death, or the end of the study period. The risk of cancer was calculated with adjustment for other covariates. Missing covariate values were treated as dummy variables (separate categories).We assessed the risk of cancer in the different MetS-change groups compared to the MetS-free group. The MetS-change variable, with 4 values corresponding to the four MetS-change groups, was included in a regression model, and the MetS-free group was used as the reference. We assessed the risk of cancer in the total population and each menopausal group. Three models were used in this study. The first was adjusted for age at the time of screening. The second and third covariates were additionally adjusted for other covariates, with and without adjustment for BMI. Statistical analyses were performed using SAS version 9.4 .This study was approved by the Institutional Review Board of Hanyang University College of Medicine . The NHIS database approved the use of the National Health Insurance Service-National Health Information Database (NHIS-NHID). In this study, all members of the screened population agreed to transfer their screening results to the NHIS-NHID. The NHIS database was constructed for individual identification after anonymization.At the time of screening, the mean± standard deviation (SD) age of 3,031,980 women was 55.5 ± 10.1 years. Among them, 788,926 (26.0%), 229,951 (7.6%), and 2,013,103 (66.4%) women were in the premenopausal, perimenopausal, and postmenopausal phases, respectively . The proAt the first screening, the proportion of women with MetS (≥ 3 criteria) was 26.1% in the total population and was higher in postmenopausal women (32.9%) than in perimenopausal or premenopausal women  . BetweenIn postmenopausal women, those in the MetS-recovered, MetS-developed, or MetS-persistent groups had a higher risk of breast cancer than those in the MetS-free group, with adjusted HRs of 1.19 , 1.19 , and 1.33 , respectively . After aChanges in MetS were also associated with the risk for endometrial cancer . Before This study reports an elevated risk of breast cancer in postmenopausal women who recovered from MetS, developed MetS, and had persistent MetS compared to women who remained free of MetS, after adjusting for BMI and other covariates, but not in premenopausal or perimenopausal women. The risk of endometrial cancer was found to be higher in MetS-persistent and MetS-recovered premenopausal and postmenopausal women. An increased risk of endometrial cancer was also observed in perimenopausal women with MetS-persistent status. When stratifying by obesity status, consistent results were observed in obese women. In non-obese women, an increased endometrial cancer risk associated with MetS development and persistence was only observed in postmenopausal women.The increased risk of breast cancer in postmenopausal women with MetS is similar to that reported in previous studies ,23. A poThe association between MetS and the risk of endometrial cancer has been well documented ,29. An iShared etiologies and risk factors between breast cancer and endometrial cancer have been identified ,37, as hThis study has several limitations. First, the median follow-up time was less than 9 years, which was relatively short and might be insufficient to assess the effect of MetS on the risk of cancer, as a relatively slowly developing disease. Second, our database may have been subjected to selection bias because women who regularly undergo cancer screening may be a particularly health-conscious segment of the population. Third, data on MetS status before the first screening were not available and were therefore not considered in the current findings. This left-truncated feature of the database might explain some of the higher HRs observed in women who recovered from MetS relative to those observed in the MetS-free group. Finally, this study assessed changes in MetS using only 2 biennial cancer screenings. The short period between the 2 measurements and the limited number of MetS assessments (only 2 measurements) may not have fully captured the trajectory of MetS changes over time. As MetS is a chronic condition that can develop over many years, there may have been some misclassification of participants’ MetS statuses between the 2 screenings owing to transient changes in the MetS criteria. This misclassification of MetS status may have led to an underestimation or overestimation of the impact of MetS changes on cancer risk. Thus, future studies with different approaches for assessing MetS changes—either with longer gaps between 2 assessments or more total MetS assessments—may be helpful for overcoming our study’s limitations.While increased breast and endometrial cancer risks associated with MetS have been previously reported ,13, thosDespite these limitations, our study has several strengths. First, this study included a very large, women, population-based database with a longitudinal assessment of MetS status and an accurate assessment of cancer cases. In addition, a wide range of well-established confounding factors associated with breast cancer and endometrial cancer were adjusted for, such as reproductive factors and family history of cancer. We also excluded prevalent cancer cases that developed within 6 months of the last assessment of MetS to avoid the potential inverse causality between changes in MetS and cancer development.In conclusion, this cohort study found that changes in MetS were associated with an increased risk of breast and endometrial cancer in Korean women aged ≥ 40 years, with the highest risk in those who had persistent MetS. MetS-persistent status in postmenopausal women was associated with increased breast cancer risk. A higher risk of endometrial cancer was found in obese women who recovered from MetS or had persistent MetS, whereas in non-obese women, MetS only showed significant associations in the postmenopausal subgroup. Our findings add more evidence to support the idea that not only MetS itself, but also its changes, are important etiologic factors for breast and endometrial cancer. Hence, nutritional and lifestyle modifications to alleviate MetS may help reduce the risk of these cancers.https://nhiss.nhis.or.kr/). We accessed the database after submitting the study protocol, the IRB approval document, and the reviewed request form by the committee. Further information is available from the corresponding author upon request.The data that support the findings of this study are available on the website of the National Health Insurance Sharing Service (


Excess body fatness and hyperinsulinemia are both associated with an increased risk of postmenopausal breast cancer. However, whether women with high body fatness but normal insulin levels or those with normal body fatness and high levels of insulin are at elevated risk of breast cancer is not known. We investigated the associations of metabolically defined body size and shape phenotypes with the risk of postmenopausal breast cancer in a nested case–control study within the European Prospective Investigation into Cancer and Nutrition.2, or WC < 80 cm, or WHR < 0.8) and overweight or obese  status for each of the three anthropometric measures separately: (1) MHNW, (2) MHOW/OB, (3) MUNW, and (4) MUOW/OB. Conditional logistic regression was used to compute odds ratios (ORs) and 95% confidence intervals (CIs).Concentrations of C‐peptide—a marker for insulin secretion—were measured at inclusion prior to cancer diagnosis in serum from 610 incident postmenopausal breast cancer cases and 1130 matched controls. C‐peptide concentrations among the control participants were used to define metabolically healthy  and metabolically unhealthy  status. We created four metabolic health/body size phenotype categories by combining the metabolic health definitions with normal weight  and WC  cut points and there was also a suggestive increased risk for the WHR  definition. Conversely, women with the MHOW/OB and MUNW were not at statistically significant elevated risk of postmenopausal breast cancer risk compared to MHNW women.These findings suggest that being overweight or obese and metabolically unhealthy raises risk of postmenopausal breast cancer while overweight or obese women with normal insulin levels are not at higher risk. Additional research should consider the combined utility of anthropometric measures with metabolic parameters in predicting breast cancer risk. In this nested case‐control study, we found that being overweight or obese and metabolically unhealthy raises risk of postmenopausal breast cancer while overweight or obese women with normal insulin levels are not at higher risk. These findings are consistent with findings from previous cohort studies. In contrast, in France and Germany, cancer cases during follow‐up were identified through a combination of several methods including health insurance records, contacts with cancer and pathology registries, and active follow‐up of participants and their next of kin. During follow‐up, data on vital status in most EPIC study centers were obtained from cancer or mortality registries at the regional or national level. Information on molecular status and stage of breast cancer was collected from each center, where possible.2.3This present study uses data from a previous nested case–control study in which serum C‐peptide levels were measured in 1141 incident breast cancer cases and 2204 matched controls from seven EPIC countries.Case participants were identified among women diagnosed with breast cancer after the initial blood collection and before the end of the follow‐up in each study center. At the time of blood collection, women who had a prevalent cancer (excluding keratinocyte cancer) and those who used any menopausal hormone therapy or any exogenous hormones for contraception or medical purposes were excluded from this study; however, those who used exogenous hormones in the past were not excluded from this analysis. Women who did not provide a blood sample or those with missing information on fasting status and serum C‐peptide were also excluded.For each breast cancer case, two controls were randomly selected among women who were alive and free of cancer (except keratinocyte cancer) at the time of diagnosis of the index case. An incidence density sampling protocol for control selection was used, such that controls could include participants who became a case later in time, while each control could also be sampled more than once. Controls were matched to cases on study center, age at blood collection (continuous), menopausal status , time of day at blood collection (± 1 h), and fasting status at blood collection , in between (3–6 h), fasting (>6 h), unknown).In addition to the previous exclusions, we excluded pre and perimenopausal cases and their matched controls leaving a final study sample of 610 incident postmenopausal breast cancer cases and 1130 matched controls with available information for analysis.2.4Blood samples were collected at baseline according to standardized procedures and stored at the IARC  for all countries except Denmark , and Sweden (at − 80°C in standard freezers).2.5Data on lifestyle and dietary intake were collected at recruitment through questionnaires in all centers.Dietary intake including alcohol intake over the 12 months before recruitment were assessed using validated country‐specific dietary and lifestyle questionnaires designed to reflect local dietary habits.2.6To define metabolic health, we used concentrations of C‐peptide among the control participants as previously done in EPIC studies on other cancer sites.2, or WC < 80 cm, or WHR < 0.80; MHNW); (2) metabolically healthy and overweight/obese ; (3) metabolically unhealthy and normal weight ; (4) metabolically unhealthy and overweight/obese . We considered the metabolically healthy/normal weight (MHNW) status as the reference group.To generate metabolic health/body size and shape phenotype categories, women were classified into four groups by cross‐tabulating anthropometric measures (one at a time) with metabolic health status: (1) metabolically healthy and normal weight . We first compared the baseline characteristics between cases and controls using mean and standard deviation (SD), for continuous data, or frequency distributions for categorical variables. We then performed descriptive analyses according to metabolically defined body size and shape phenotypes among the controls.Odds ratios (ORs) and 95% confidence intervals (CIs) for the associations between metabolically defined body size and shape phenotypes and breast cancer risk were estimated using conditional logistic regression. Model 1 included matching criteria only. The multivariable models were further adjusted for known breast cancer risk factors including age at menarche (> 13 and ≤13 years), number of full‐term pregnancies and age at first full‐term pregnancy ; age < 30 years (≥3 children) and age years ≥ 30), age at menopause (continuous), breastfeeding , previous use of oral contraceptive pills , previous use of menopausal hormonal therapy , physical activity index , alcohol consumption , smoking status , educational level , height (continuous), and energy intake from diet . For all adjustment variables, if values were missing in <5% observations, they were imputed to the median, for continuous variables, or mode, for categorical variables and a “missing” category (for education level only) was introduced otherwise.N = 209) diagnosed within the first 2 years of follow‐up and their matched controls (N = 384). Since information on breast cancer subtypes was missing for a large proportion of breast cancer cases, we were not able to explore breast cancer risk according to molecular subtypes.We evaluated heterogeneity of the associations by fasting status, previous use of oral contraceptives, circulating levels of estradiol or estrone and age at breast cancer diagnosis (<60 and ≥60 years). Heterogeneity tests were assessed using Wald chi‐square tests to compare estimates across strata. In addition, we performed sensitivity analyses by (1) excluding women who reported a history of diabetes, which may affect C‐peptide concentration, (2) restricting analyses to women who had never used menopausal hormone therapy (3) considering only the upper tertile of C‐peptide as metabolically unhealthy group, (4) exploring the phenotypes defined based on quartiles or on median level of C‐peptide cut points and (5) using a cutoff of 88 cm and 0.88 for WC and WHR, respectively. In addition, we assessed separately the association between each of the components of metabolically defined body size phenotypes and the risk of postmenopausal breast cancer. To explore a potential reverse‐causation bias, we assessed the association between metabolically defined body size phenotypes and breast cancer risk by excluding cases , whereas high levels of WHR were not associated with the risk of postmenopausal breast cancer . In addition, circulating C‐peptide concentrations were positively and linearly associated with risk of postmenopausal breast cancer though the OR was only statistically significant .Overweight and obese women had higher risks of postmenopausal breast cancer compared with normal weight women  and WC  cut points and there was also a suggestive risk for the WHR  definition Table .BMI = 2.11, 95% CI = 1.34–3.32, ORWC = 2.15, 95% CI = 1.35–3.41 and ORWHR = 1.93, 95% CI = 1.23–3.02), those who had previously used oral contraceptives  and those aged 60 years or above at diagnosis , whereas no evidence of heterogeneity was detected in findings according to ever use of oral contraceptives  and age at diagnosis  categories. In contrast, our stratified analyses by circulating levels of estradiol or estrone showed no statistically significant associations between MUOW/OB and postmenopausal breast cancer risk, except for among those with lower estrone levels and using the BMI definition , with no heterogeneity detected in findings across categories .In stratified analyses, the positive association between MUOW/OB phenotype and postmenopausal breast cancer risk seemed to be stronger among non‐fasting women  and not in those who had previously used oral contraceptives , although there was no heterogeneity detected in findings (pheterogeneity = 0.32). There was also no heterogeneity in estimates across findings by circulating levels of estradiol and estrone .Women with MUNW were not at statistically significant elevated risk of postmenopausal breast cancer risk compared to MHNW women , WC  and WHR  cut points Table .3.6Exclusion of women who had a history of diabetes did not substantially change the magnitude of the associations, but the associations were attenuated and no longer statistically significant when excluding cases diagnosed within the first 2 years of follow‐up Table . We alsoIn sensitivity analyses considering only the upper tertile of C‐peptide concentration as the MU group, similar, and generally stronger associations were observed for the MUOW/OB group Table . Using CA similar pattern of results compared to those of the main analysis were observed when using a WC cut point of 88 cm, although the magnitude of the associations was attenuated for MUNW and MHOW/OB Table . In cont4In this prospective analysis, we found that women classified as MUOW/OB had elevated risk of postmenopausal breast cancer compared to MHNW women considering BMI, WC and WHR definitions. Our results also suggest that women classified as MHOW/OB or those with the MUNW phenotype were not at elevated risk of postmenopausal breast cancer compared to MHNW women.Both adiposity and insulin resistance have been independently associated with higher postmenopausal breast cancer risk.n ~ 21,000) found that overweight/obese women with metabolic abnormalities  had a higher risk of postmenopausal breast cancer compared with normal weight women with no metabolic abnormalities.n = 3763), Moore and colleagues reported that overweight women with elevated blood glucose had a 2.6‐fold higher risk of reproductive cancers including a large proportion of postmenopausal breast cancers, whereas normal weight women with elevated glucose were not at higher risk.n = 43,599), women classified as MUOW/OB, defined as being overweight or obese with one or more metabolic abnormalities, including central obesity, type 2 diabetes, dyslipidemia, and elevated blood pressure, had higher risk of developing postmenopausal breast cancer compared to normal weight women with no metabolic abnormalities and considering BMI, WC and WHR definitions.So far, four prospective studies have examined the association of BMI or WC, and insulin resistance/hyperinsulinemia in relation to breast cancer risk.A possible mechanism underlying the elevated risk of postmenopausal breast cancer among MUOW/OB women may implicate a direct effect of insulin which acts as a cancer promoter through its known mitotic and anti‐apoptotic activities.Our findings suggest that women with MHOW/OB or those with MUNW did not have a significantly increased risk of postmenopausal breast cancer compared to MHNW women. These findings lend support to those from two previous studies reporting no statistically significant association among normal weight women with metabolic abnormalities,Our results indicate that the elevated risk among MUOW/OB phenotype appeared to be stronger in women who had ever used oral contraceptives, compared to never users, suggesting that sex steroids hormone may influence these associations. When further excluding women who had ever used MHT, the associations were no longer statistically significant with no heterogeneity detected in findings across categories. In addition, we found that the positive associations were restricted to non‐fasting women with evidence of significant heterogeneity across categories of fasting status. Most of the previous studies considered fasting glucose/insulin or fasting biomarkers. Only the analyses from Framingham study considered fasting and non‐fasting samples; and the authors reported that non‐fasting samples also could be used to identify cases of diabetes with sensitivity equal to that of fasting samples.Our study has some limitations that should be taken into consideration when interpreting these findings. First, a single measurement of C‐peptide with a study‐specific cut point was used to define metabolic health categories which may not reflect a complete and objective definition of metabolic healthy status. Therefore, some degree of misclassification cannot be excluded. However, although similar patterns of results compared to those of the main analysis were obtained when considering the first quartile or median of C‐peptide as the metabolically healthy group, the associations were no longer statistically significant—probably due to the small sample size in some categories. In addition, although previous studies have used different markers such as glucose or insulin levels, HOMA‐IR, and the MetS to define “metabolic health”, two recent cohort studies have used concentration of C‐peptide as a marker of metabolic health status since C‐peptide has been reported to be a good indicator for long‐term insulin secretion.In conclusion, our findings suggest that being overweight or obese and metabolically unhealthy is associated with an increased risk of postmenopausal breast cancer compared to metabolically healthy normal weight women and considering BMI, WC and WHR measurements as markers of adiposity and C‐peptide as marker of metabolic health. Overweight or obese women with normal insulin levels are not at higher risk of breast cancer. Although additional research is required, these findings highlighted the importance of assessing the effects of anthropometric measures in conjunction with metabolic parameters to identify women at higher risk of breast cancer.Yahya Mahamat‐Saleh: Conceptualization ; data curation ; formal analysis ; funding acquisition ; investigation ; methodology ; resources ; software ; visualization ; writing – original draft ; writing – review and editing . Sabina Rinaldi: Conceptualization ; data curation ; funding acquisition ; methodology ; resources ; supervision ; writing – review and editing . Rudolf Kaaks: Investigation ; methodology ; resources ; writing – review and editing . Carine Biessy: Data curation ; formal analysis ; methodology ; project administration ; visualization ; writing – review and editing . Esther Gonzalez Gil: Investigation ; methodology ; resources ; writing – review and editing . Neil Murphy: Investigation ; methodology ; resources ; writing – review and editing . Charlotte Le Cornet: Investigation ; methodology ; resources ; writing – review and editing . Jose Maria Huerta: Investigation ; methodology ; resources ; writing – review and editing . Sabina Sieri: Investigation ; methodology ; resources ; writing – review and editing . Anne Tjonneland: Investigation ; methodology ; resources ; writing – review and editing . Lene Mellemkjaer: Investigation ; methodology ; resources ; writing – review and editing . Marcela Guevara: Investigation ; methodology ; resources ; writing – review and editing . Kim Overvad: Investigation ; methodology ; resources ; writing – review and editing . Aurora Perez‐Cornago: Investigation ; methodology ; resources ; writing – review and editing . Sandar Tin Tin: Investigation ; methodology ; resources ; writing – review and editing . Lisa Padroni: Investigation ; methodology ; resources ; writing – review and editing . Vittorio Simeon: Investigation ; methodology ; resources ; writing – review and editing . Giovanna Masala: Investigation ; methodology ; resources ; writing – review and editing . Anne May: Investigation ; methodology ; resources ; writing – review and editing . E. M. Monninkhof: Investigation ; methodology ; resources ; writing – review and editing . Sofia Christakoudi: Investigation ; methodology ; resources ; writing – review and editing . Alicia K. Heath: Investigation ; methodology ; resources ; writing – review and editing . Konstantinos K. Tsilidis: Investigation ; methodology ; resources ; writing – review and editing . Antonio Agudo: Investigation ; methodology ; resources ; writing – review and editing . Matthias B. Schulze: Investigation ; methodology ; resources ; writing – review and editing . Joseph Rothwell: Investigation ; methodology ; resources ; writing – review and editing . Claire Cadeau: Investigation ; methodology ; resources ; writing – review and editing . Gianluca Severi: Investigation ; methodology ; resources ; writing – review and editing . Elisabete Weiderpass: Investigation ; methodology ; resources ; writing – review and editing . Marc Gunter: Conceptualization ; funding acquisition ; investigation ; methodology ; resources ; supervision ; writing – review and editing . Laure Dossus: Conceptualization ; data curation ; funding acquisition ; investigation ; methodology ; resources ; supervision ; writing – original draft ; writing – review and editing .Yahya Mahamat‐Saleh is a postdoctoral scientist at the International Agency for Research on Cancer and supported by the Fondation ARC pour la recherche sur le cancer ARCPOST‐DOC2021080004105. The coordination of EPIC is financially supported by International Agency for Research on Cancer (IARC) and also by the Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London which has additional infrastructure support provided by the NIHR Imperial Biomedical Research Centre (BRC). The national cohorts are supported by: Danish Cancer Society (Denmark); Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Générale de l'Education Nationale, Institut National de la Santé et de la Recherche Médicale ; German Cancer Aid, German Cancer Research Center (DKFZ), German Institute of Human Nutrition Potsdam‐Rehbruecke (DIfE), Federal Ministry of Education and Research ; Associazione Italiana per la Ricerca sul Cancro‐AIRC‐Italy, Compagnia di SanPaolo and National Research Council ; Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); Health Research Fund (FIS)—Instituto de Salud Carlos III (ISCIII), Regional Governments of Andalucía, Asturias, Basque Country, Murcia and Navarra, and the Catalan Institute of Oncology—ICO (Spain); Swedish Cancer Society, Swedish Research Council and County Councils of Skåne and Västerbotten (Sweden); Cancer Research UK , Medical Research Council .The authors have no conflict of interest to disclose.clinicaltrials.gov as NCT03285230.The EPIC study was conducted in accordance with the Declaration of Helsinki. The study was approved by the local ethical committees in participating countries and the IARC ethical committee. All participants provided written informed consent for data collection and storage, as well as individual follow‐up before study entry. This study is listed at Not applicable.Where authors are identified as personnel of the International Agency for Research on Cancer / World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy, or views of the International Agency for Research on Cancer/World Health Organization.Data S1Click here for additional data file.


Mammography images contain a lot of information about not only the mammary glands but also the skin, adipose tissue, and stroma, which may reflect the risk of developing breast cancer. We aimed to establish a method to predict breast cancer risk using radiomics features of mammography images and to enable further examinations and prophylactic treatment to reduce breast cancer mortality. We used mammography images of 4000 women with breast cancer and 1000 healthy women from the ‘starting point set’ of the OPTIMAM dataset, a public dataset. We trained a Light Gradient Boosting Machine using radiomics features extracted from mammography images of women with breast cancer  and healthy women. This model was a binary classifier that could discriminate whether a given mammography image was of the contralateral side of women with breast cancer or not, and its performance was evaluated using five-fold cross-validation. The average area under the curve for five folds was 0.60122. Some radiomics features, such as ‘wavelet-H_glcm_Correlation’ and ‘wavelet-H_firstorder_Maximum’, showed distribution differences between the malignant and normal groups. Therefore, a single radiomics feature might reflect the breast cancer risk. The odds ratio of breast cancer incidence was 7.38 in women whose estimated malignancy probability was ≥0.95. Radiomics features from mammography images can help predict breast cancer risk. The importance of identifying women at high risk of developing breast cancer has increased in recent years. Globally, breast cancer affects more women than any other cancer . Early dBRCA1/2 pathogenic variants [Therefore, using other modalities, such as digital breast tomosynthesis , ultrasovariants . This isvariants ,17.Various models have been devised to calculate the risk of developing breast cancer. The most well-known is the Gail model . HoweverSeveral changes are known to occur in the mammary glands of women at high risk for breast cancer. For example, diabetes, a risk factor for breast cancer development , can cauBRCA1/2 pathogenic variants are reflected in MRI images [Furthermore, I images . In thisI images .Radiomics has been used in various modalities in the field of breast cancer . In receIn fact, some pilot studies have demonstrated the potential for predicting the risk of breast cancer development from radiomics features of mammography using small numbers and closed datasets ,37. In pIn this cross-sectional study, we used a large and publicly accessible dataset. Radiomics features calculated from mammography images of a healthy-side breast in women with breast cancer were compared with those of breast cancer-free women to estimate the individual risk of developing breast cancer and to identify radiomics features associated with the risk of developing breast cancer.Firstly, we hypothesized that bilateral breasts in a woman have the same environment, such as a germline pathogenic variant , exposurBased on this hypothesis, we compared radiomics features of mammography images of patients with breast cancer and breast-cancer-free women to predict the risk of developing breast cancer. For the malignant group, we used the mammography images from the healthy side, whereas for the breast-cancer-free women, we randomly selected mammography images from either breast to match the left–right ratio of the patients with breast cancer during validation. Using radiomics features calculated from whole-breast mammography images in both groups, we could predict the breast cancer risk and identify specific radiomics features associated with an increased risk of breast cancer development.We used the OPTIMAM Mammography Image Database  as our mFor the malignant group, we used mammography images obtained just before biopsy to diagnose breast cancer. For the normal group, we used cases that were judged as normal in mammography images more than one year later. We used the oldest image for the normal group.The proportion of mammography equipment manufacturers for the normal and malignant groups is shown in We also excluded cases with artificial objects in the images , large breast sizes that prevented routine imaging, bilateral breast cancer, and one-sided mammography images. A summary of the excluded cases is shown in All 4111 cases that met the inclusion criteria had mammography images available in two directions, mediolateral oblique (MLO) and craniocaudal (CC). However, because of substantial inter-individual variability in MLO-view images arising from factors such as the breast compression angle, success of pectoral muscle compression, and presence of abdominal subcutaneous fat, we chose to use only CC-view images in our study. For the malignant group, we used CC images from the healthy breast. For the normal group, we randomly selected bilateral CC images to match the left–right ratio of the malignant group.We created a mask image of the breast region for each breast. For each image, a mask was created using a threshold value of 1/100, numerically calculated with Otsu’s binarization method . In somehttp://www.radiomics.io/pyradiomics.html, accessed on 14 September 2023). The radiomics features used in this study were classified into seven categories: shape-based 2D, First Order Statistics, Gray Level Cooccurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), Gray Level Size Zone Matrix (GLSZM), Gray Level Dependence Matrix (GLDM), and Neighborhood Gray Tone Difference Matrix (NGTDM). Shape-based 2D features were calculated only from the original mammography images. The other six categories of radiomics features were calculated from both the original mammography images and the mammography images processed with six filters (log (laplacian of gaussian)-sigma-2-0 mm, log-sigma-3-0 mm, log-sigma-4-0 mm, log-sigma-5-0 mm, wavelet-H, wavelet-L). As a result, a total of 646 radiomics features were obtained. The feature items used in this study  are shown in Radiomics feature values for all breast regions were calculated using Pyradiomics, an open-source Python package platform  for our classification task. LGBM is an open-source distributed gradient-boosting framework developed by Microsoft Corporation that uses supervised learning to compute the objective variable from the explanatory variables using a decision tree method. Unlike the sort-based decision tree algorithm used in eXtreme Gradient Boosting and other implementations, LGBM features a highly optimized histogram-based decision tree learning algorithm, which increases efficiency and reduces memory consumption.We used the Light Gradient Boosting Machine (LGBM) .The cases used in the analysis, categorized by age and by left and right sides of the mammographic image, are shown in We present histograms showing the probability of malignancy for each case in the malignant and normal groups in The normal group has equal numbers of left and right sides, as both sides are available.Some features were found to be important as they were frequently used in decision tree branching across multiple folds . For exap < 0.001), ‘wavelet-H_firstorder_Maximum’ (p< 0.001), and ‘wavelet-H_glcm_Imc2’ (p < 0.001)  .In recent years, deep learning has been widely used in the field of image analysis with the advancement of computational equipment. Deep learning-based methods might also be able to predict the risk of developing breast cancer using mammography images of the contralateral breast. Nevertheless, for the classification tasks in this study, we chose to use LGBM, a decision tree method. There are two reasons for this. One is that there have been reports that machine learning is more useful than deep learning when the difference between the number of radiomics features used as explanatory variables and the number of cases is not sufficient . In thisWhile only the mammary gland area was used to calculate radiomics features in previous studies ,37, the However, this approach reflects mammary gland density, which has certainly been correlated with breast cancer development in previous reports . TherefoTherefore, this suggests that some radiomics features that are not highly associated with mammary gland density are associated with the risk of developing breast cancer. The AUC for the risk of developing breast cancer estimated from mammary gland density alone in a previous study using 1 million mammography images with approximately 10,000 malignant cases was 0.57 , whereasThe relationship between these radiomics features and the risk of developing breast cancer can also be visually observed in histograms of relative frequency densities. For ‘wavelet-H_glcm_Correlation’, high bars for malignant cases can be seen to the left of the peak, whereas for ‘wavelet-H_firstorder_Maximum’, high bars for malignant cases can be seen to the right of the peak. The former feature, ‘wavelet-H_glcm_Correlation’, represents local similarity with neighboring pixels after high-frequency component emphasis (using wavelet transform) . The latSome other radiomics features show differences in distribution between the malignant and normal cases in the relative frequency density histograms, while others do not show much difference. These features that do not show differences in the relative frequency density histograms were considered to be features that do not reflect the risk of developing breast cancer when used as stand-alone radiomics features. However, when combined with other radiomics features, they may contribute to risk estimation for developing breast cancer.In this analysis, using all 646 radiomics features, we were able to calculate the probability of having any cancer in the contralateral breast. When we set the cut-off threshold of this probability value as 0.95, the odds ratio of having the malignancy in the contralateral breast was 7.38. Therefore, this value can be interpreted as the relative risk ratio of the high-risk group, which is defined in this study. As such, it may be possible to recommend additional imaging examinations and closer screening schedules for women whose probability of malignancy values exceed 0.95 but do not show abnormalities on mammography. A sufficiently high pre-test probability may justify following ultrasound and/or contrast-enhanced MRI as a further breast cancer screening.In recent years, prophylactic treatment, medication, and surgical treatment have also been considered for individuals at high risk of developing breast cancer ,50.Identifying high-risk individuals using our method may aid in selecting appropriate prophylactic medication targets. The accuracy of this approach is expected to improve as more cases are accumulated. Furthermore, machine learning using many images generally requires significant data preparation and cleaning efforts, such as setting regions of interest. However, this method is simple and has the advantage that the model can be easily enhanced even after accumulating a large number of cases.In this study, we did not determine what events in the breast tissue were reflected in the radiomics features suggested to be associated with breast cancer risk. These features may reflect changes in the mammary tissue or adipose tissue due to hormonal balance, blood glucose levels, or other factors such as genetic mutations. Further investigation using datasets linked to clinical data or genetic information is needed to clarify this point.Our study’s limitations include the retrospective nature of the investigation and the use of images of the contralateral breast in women with breast cancer. To evaluate the results of this study, observational studies on the population-calculated radiomics features from mammography images are needed in the future. Another limitation is that the mammography images used were from a dataset and a single imaging equipment manufacturer. It is necessary to verify whether the results of this study apply to other datasets and mammography equipment from different manufacturers.Finally, the AUC for breast cancer risk prediction obtained in this study is not very high. It is slightly higher than the AUC of the Gail model (AUC: 0.55 (0.52–0.57)) and the Tyrer–Cuzick model (AUC: 0.57 (0.55–0.59)) alone, and is comparable to the AUC obtained by adding mammary gland density information to the Gail (AUC: 0.59 (0.57–0.61)) and Tyrer–Cuzick models (AUC: 0.61 (0.59–0.63)) . TherefoIn conclusion, our study demonstrates that radiomics features obtained from mammography images using a simple method can predict the risk of developing breast cancer. We identified three radiomics features, ‘wavelet-H_glcm_Correlation’, ‘wavelet-H_firstorder_Maximum’, and ‘wavelet-H_glcm_Imc2’, which might reflect the risk of developing breast cancer. Furthermore, using our method, it is suggested that the subgroup with a 7.38-fold relative cancer prevalence risk can be identified. We hope that incorporating these radiomics features into breast cancer screening will lead to the addition of other examination modalities to reduce mortality rates and help identify individuals at high risk of developing breast cancer for prophylactic treatment.


N = 1,717, 22%) mostly lived in rural areas (55%); 45% of rural and 25% of urban residents had high school education or less and this difference was statistically significant. After adjustment for rurality, lower educational attainment was associated with smoking, difficulty paying for basic necessities, greater financial difficulty during the COVID-19 pandemic, struggling to pay for gas (P < 0.01), fatalistic attitudes toward cancer prevention, and susceptibility to information overload about cancer prevention. Among the 33% of respondents who delayed getting medical care in the past year, this was more often due to lack of transportation in those with lower educational attainment  and more often due to concerns about catching COVID-19 among urban than rural residents . In conclusion, in NH/VT, smoking, financial hardship, and beliefs about cancer prevention are independently associated with lower educational attainment but not rural residence. These findings have implications for the design of interventions to address cancer risk in rural areas.Compared with urban areas, rural areas have higher cancer mortality and have experienced substantially smaller declines in cancer incidence in recent years. In a New Hampshire (NH) and Vermont (VT) survey, we explored the roles of rurality and educational attainment on cancer risk behaviors, beliefs, and other social drivers of health. In February–March 2022, two survey panels in NH and VT were sent an online questionnaire. Responses were analyzed by rurality and educational attainment. Respondents (In NH and VT, the finding that some associations between cancer risk factors and rural residence are more closely tied to educational attainment than rurality suggest that the design of interventions to address cancer risk should take educational attainment into account. Cancer is the second leading cause of death in the United States, with 1.9 million new cases and 609,360 deaths expected in 2022 . Almost Rural areas generally have higher prevalence of cigarette smoking and obesity compared with large metropolitan areas . Disadva, brain, breast, esophageal, and childhood cancer, as well as Hodgkin lymphoma and melanoma); VT ranks 17th for incidence and is in the top three states for three cancer types  Community Outreach and Engagement (COE) team investigated rural residence and educational attainment and their associations with cancer risk factors in the DCC catchment area states of New Hampshire (NH) and Vermont (VT). NH ranks 8th in the nation for cancer incidence overall and ranks in the top three states for seven cancer types . Two email reminders were sent to nonresponders 1 and 3 days after the invitation and the survey was closed on the morning of March 8th. We elicited sociodemographic information and responses to questions about cancer risk behaviors, cancer attitudes and beliefs, and social drivers of health including health care access and financial, food, and transportation security.The survey  was offeThe study was conducted according to recognized ethical guidelines; use of human subjects for the Granite State and Green Mountain State Panels was approved by the Institutional Review Board for the University of New Hampshire , and this study was approved by the Dartmouth College Committee for the Protection of Human Subjects following the ethical principles identified in the Belmont report. Participants provided written consent electronically within the online survey.P(1 − P)/N] where P is the percentage of responses in the answer category being evaluated and N is the total number of persons answering the particular question. The margin of sampling error for our survey is ±2.6%. The design effect for the survey is 2.9%. Because of rounding, percentages may not sum to 100%. The number of respondents in each demographic below may not equal the number reported in cross-tabulation tables as some respondents choose not to answer some questions.Each survey response from the NH and VT population was weighted by the respondent's age, gender, education, and region of the state using targets from the most recent U.S. Census Bureau's American Community Survey conducted by the U.S. Census Bureau, as well as political party registration levels in NH and 2020 election results. NH regions used for weighting are the Central/Lakes Region ; Hillsborough County; Northern NH ; Seacoast ; and Western NH . VT regions are Central VT , Chittenden County; Northern Counties ; Southern VT . Like all surveys, this survey is subject to sampling error due to the fact that all residents in the area were not interviewed. For those questions asked of 500 or so respondents, the error is ±4.4%. For those questions where fewer than 500 persons responded, the sampling error can be calculated as ±1.96√[χ2 tests of independence and t tests, respectively. Domain analysis was used to account for variability of the whole sample to estimate the variance of subgroups. Totals (N) and percentages (%) may not equal 100% due to rounding. Multivariable models assessed the independent associations on each cancer risk factor of educational attainment (high school or less vs. more than high school) and rural (vs. urban) residence. An interaction term  was included in each model and dropped when not statistically significant. Unadjusted and adjusted P values are presented for each association tested. Analyses used SAS version 9.4  and educational attainment (lower vs. higher). Rural and urban areas were defined using Rural and Urban Commuting Areas (RUCA); primary RUCA codes 1–3 (Metropolitan) were classified as urban while 4–10 were considered rural . EducatiThe datasets generated and analyzed from the population survey are not publicly available at the time of publication, as they are still being analyzed for other articles; however, they may be available in the future from the corresponding author on reasonable request.n = 939) lived in rural areas, and 36% (n = 618) had a high school education or less (≤HS). The distributions of rural and urban residence differed in the two states, with a greater proportion of rural residence in VT (P = 0.001), and lower salaries were more common among rural participants  and those with lower educational attainment . Rural participants were significantly more likely than urban participants to be insured through Medicaid (20% vs. 9%). Nine percent reported a history of cancer.The 1,717 survey responses represented a response rate of 27%. When comparing responders and nonresponders, some differences were seen by gender, political party affiliation, education, and age , but thece in VT . Lower eFor the cancer risk factors we assessed, we more often found significant independent associations with educational attainment. When we saw univariate associations between risk factors and rurality, these were not generally significant after adjustment for educational attainment. Education by rurality interaction terms were not significant in any model and were removed.P = 0.03, adjusted for rurality P = 0.05) or to have smoked in the past 15 years . Rural residence and smoking status were not significantly associated. No significant differences were seen for alcohol use or exercise by rurality or educational attainment. Although the use of sunscreen was similar by rurality and education, those with higher (vs. lower) educational attainment were more likely to protect themselves by wearing a long-sleeved shirt and more likely to stay in the shade ; no significant difference was seen between rural and urban participants . When asked whether they would rather not know their chances of getting cancer, almost twice as many rural participants agreed . Eighty percent of participants agreed with the statement “There are things I could change in my life to reduce my risk of cancer,” with little variation by rurality or educational attainment; 59% of participants agreed that “It seems like everything causes cancer,” again with little variation by rurality or educational attainment.When asked their opinions about cancer, 30% of those with lower educational attainment agreed that “There is not much you can do to lower your chances of getting cancer,” compared with 16% of those with higher educational attainment . Rural residence was not significantly associated with financial hardship after adjustment for educational attainment. In univariate analyses, rural populations and those with lower educational attainment were significantly more likely to experience food insecurity, but in the multivariable model, educational attainment was independently associated with concern that the food would run out before getting money to buy more (adjusted P = 0.01). Difficulty paying for gas was more often experienced by those with lower (31%) than higher (12%) educational attainment .Among larger households of three or more people, those living in rural areas were more likely to have a lower income . SignifiP = 0.03; P = 0.02). Interestingly, significantly more urban than rural participants missed or delayed care because of concerns about catching COVID-19 , but significant differences were not seen by educational attainment after adjustment for rurality . Cost and availability of appointments as reasons for delayed care did not differ significantly by rurality or educational attainment.One-third of participants reported delaying medical care during the preceding 12 months; these proportions were lower among those with lower educational attainment (23% vs. 39%, adjusted In this study, we assessed the independent contributions of educational attainment and rural residence to a variety of risk factors that might influence cancer incidence and mortality. Although univariate analyses identified several cancer risk factors associated with both lower educational attainment and rural residence, we found—as others have done —that thoIn our study, smoking prevalence was more than twice as high in those with high school education or less, and was not independently associated with rural residence. Previous studies have shown that some of the overlap in cancer risk between rural individuals and those with lower educational attainment is likely attributable to smoking . We alsoBeliefs about cancer and cancer prevention influence engagement in preventive behaviors . We founOur survey was conducted at the start of the third year of the COVID-19 pandemic. Anxiety about catching COVID-19 was a deterrent to seeking medical care, and—unlike most of the associations that we found—this concern was independently associated with urban residence but not with educational attainment. Although urban populations—which include a greater proportion of individuals with higher educational attainment—were more open to cancer prevention in principle, their concerns about exposure to COVID-19 in health care environments presented substantial barriers to seeking care, a phenomenon also reported elsewhere . Now thaPrevious studies have highlighted travel time to preventive services as a barrier for rural residents , and canSeveral aspects of the study limit its generalizability. The response rate of 22% is low, perhaps in part due to attrition within the survey panels, but higher than response rates typically seen in random digit dialing surveys . The popIn our region, meaningful disparities remain for those with lower educational attainment who comprise a greater proportion of rural than urban populations. Understanding the independent role of educational attainment in NH and VT will help in the design and implementation of interventions to reduce cancer morbidity and mortality in our most disadvantaged populations. Educational attainment is a determinant of economic status and health  and thisSupplementary Data T1Survey instrumentClick here for additional data file.Supplementary Data T2Comparison of key variables between responders and non respondersClick here for additional data file.Supplementary Data T3Cancer risk and prevention behaviors by rurality and educational attainmentClick here for additional data file.Supplementary Data T4Cancer beliefs by rurality and educational attainmentClick here for additional data file.Supplementary Data T5Social factors by rurality and educational attainmentClick here for additional data file.


FANCM confer moderately increased risk of ER-negative and triple-negative breast cancer (TNBC), especially for women with a family history of the disease. Association between FANCM missense variants (MVs) and breast cancer risk has been postulated. In this study, we further used the BRIDGES study to test 689 FANCM MVs for association with breast cancer risk, overall and in ER-negative and TNBC subtypes, in 39,885 cases (7566 selected for family history) and 35,271 controls of European ancestry. Sixteen common MVs were tested individually; the remaining rare 673 MVs were tested by burden analyses considering their position and pathogenicity score. We also conducted a meta-analysis of our results and those from published studies. We did not find evidence for association for any of the 16 variants individually tested. The rare MVs were significantly associated with increased risk of ER-negative breast cancer by burden analysis comparing familial cases to controls . Higher ORs were found for the subgroup of MVs located in functional domains or predicted to be pathogenic. The meta-analysis indicated that FANCM MVs overall are associated with breast cancer risk . Our results support the definition from previous analyses of FANCM as a moderate-risk breast cancer gene and provide evidence that FANCM MVs could be low/moderate risk factors for ER-negative and TNBC subtypes. Further genetic and functional analyses are necessary to clarify better the increased risks due to FANCM MVs.Evidence from literature, including the BRIDGES study, indicates that germline protein truncating variants (PTVs) in  BRCA1 and BRCA2, extensive efforts have tried to identify additional breast cancer predisposition genes. Many candidate genes have been proposed but replication studies have been confirmatory for only few of them [BRCA1, BRCA2 and PALB2 are associated with high-risk of breast cancer, that PTVs in CHEK2 and ATM confer moderate risk especially for the ER-positive disease subtype, and that PTVs in RAD51C, RAD51D and BARD1 are moderate risk variants for ER-negative breast cancer. Lack of evidence of association was detected for PTVs in the great majority of the other tested candidate genes, but for one—namely FANCM—some evidence for association with ER-negative breast cancer was observed [Since the discovery of the high-risk breast cancer predisposition genes  of them . ResultsFANCM PTV and breast cancer risk was initially investigated in 2013 [FANCM PTVs are potential risk variants for ER-negative and TNBC subtypes; more precisely, they suggest that each PTV confers an increase risk with magnitude that may vary depending on its position in the gene or on the population genetic background [The association between a  in 2013 . Since t in 2013 , 6. In a in 2013 . Overall in 2013 , 9—revieckground .FANCM MVs and breast cancer risk has been investigated by three studies in which all the rare variants were combined in burden analyses. Two studies were conducted using familial breast cancer cases with no BRCA1 or BRCA2 pathogenic variants and controls from the general population. The first, based on the analysis of 1207 cases and 1199 controls from France, did not find clear evidence of association with FANCM MVs  [FANCM MVs  were tested in population- and family-based studies combined and separately. An association with breast cancer risk was found when comparing cases selected for family history of breast cancer and controls, with an OR estimate of 1.22 (95% CI 1.05–1.42) [FANCM sequencing in women of European ancestry from population- and family-based studies. Specifically, we assessed 673 rare MVs with allele frequency <0.1% that were combined in burden analyses, but we also assessed individually 16 common MVs with allele frequency ≥0.1%. The burden analyses were based on the MVs’ gene domain location, and their pathogenicity prediction score. Analyses were conducted to assess associations with overall breast cancer but also the ER-negative and TNBC disease subtypes.While PTVs in breast cancer predisposition genes are usually considered bona fide pathogenic, missense variants (MVs) are often referred to as “variants of uncertain significance”. Their effect on protein function and cancer risk is generally unknown and difficult to estimate. Several in silico tools that predict pathogenicity of MVs have been developed that, together with additional evidence, such as frequency data, segregation analyses and functional assays, allow some MVs to be classified. However, MVs are often so infrequent that they have to be combined overall, or in subgroups based on their location in the gene domains or pathogenicity prediction score, in order to generate evidence of pathogenicity. MVs in several established and candidate breast cancer predisposition genes have been tested for association with breast cancer risk in many studies. To date, the potential association between BRCA1 and BRCA2 test and at the moment of the study enrollment were known to carry a pathogenic variant in these genes. We also excluded all carriers of FANCM PTVs and all women with one or more unknown FANCM MV genotypes. Thus, a total of 39,885 breast cancer cases  and 35,271 controls were included in this study. Of the cases, 32,083 (80.4%) were from population-based studies and 7566 (19.0%) were from family-based studies; for the remaining 236 cases (0.6%) this information was not available. Of all cases, 5880 had ER-negative breast cancer and 2176 had TNBC.In this work we included women affected with breast cancer (cases) and unaffected women (controls) from 40 studies participating in the BRIDGES project  with their corresponding 95% confidence intervals (CIs) and P values (P). Multiple testing correction was applied using Benjamini and Hochberg procedure [P < 0.05 were considered statistically significant.To test the association between rocedure . Rare MVrocedure , 12. AllFANCM MVs, of which 16 were common and 673 were rare, were detected in at least one woman from our study sample (Supplementary Table P < 0.05) with breast cancer risk or a protective effect in some of the case groups tested. But none were statistically significant after correction for multiple testing , was found with ER-negative breast cancer in the analysis of family-based studies. These analyses were repeated with subgroups of the variants. We firstly considered the subgroup of the 372 MVs located within the FANCM functional or binding domains but found no evidence of association. We then excluded the 76 MVs located in the FAAP24 domain and found that the 296 remaining MVs were associated with TNBC in familial studies with an OR = 2.27 . We further selected among the 296 MVs the 61 MVs predicted to be pathogenic by at least one of the four in silico tools used and found an association with TNBC with an OR = 3.51  in the familial studies (Table The 673 rare MVs are described and represented, based on their gene location, pathogenicity score according to four in silico tools, and the numbers of variant carriers in cases and controls, in Fig. ly Table . The onlFANCM MVs and breast cancer risk conducted using familial designs and excluding carriers of BRCA1 or BRCA2 pathogenic variants [FANCM MVs combined were associated with familial breast cancer risk with OR = 1.22 (95% CI 1.08–1.38; P = 0.002, Fig. Finally, we considered the two studies published so far testing the association between variants , 12. ThuFANCM sequencing data assessing the breast cancer risk effects of 689 unique MVs in 39,885 European breast cancer cases and 35,271 controls from population- and family-based studies. According to their allele frequencies, these MVs were analysed either individually or by burden analyses, in the latter case combined in groups considering their gene domain location or their pathogenicity score. Also, the cases were analysed in different combinations, by study-design, and overall and for ER-negative or TNBC clinical subtypes.In this study, we re-analysed the BRIDGES FANCM MVs are associated with familial breast cancer risk, suggesting that these variants are low-risk susceptibility variants for breast cancer. This observation was confirmed by the meta-analysis of our and the published results [FANCM MVs was only found in family-based studies. While this supports the hypothesis that FANCM MVs are breast cancer risk factors, the ORs we found in this study are an overestimate of the risks these variants confer. The lack of associations of FANCM MVs with breast cancer risk in the analyses of only population-based studies could be explained by the presence of other unmeasured risk variants aggregating in families that may interact with FANCM MVs. Results from the analysis of family- and population-based studies combined are similar to those of the analysis of only population-based studies as familial cases represent only the 19% of all the cases included.Sixteen common MVs with an allele frequency ≥0.1% were analysed individually but we did not find evidence for association for any of these variants. The remaining 673 MVs were rare, with an allele frequency <0.1%. The best approach to study the risks conferred by these variants is that of combining single variant data in burden analysis and of conducting meta-analyses of different studies. Overall, our results and those from the previously conducted studies , 11, 12,FANCM MVs—in particular those located in some gene domains and classified as pathogenic in silico—could be risk variants for ER-negative breast cancer in familial settings. Larger association studies and, functional assays may be helpful to better clarify these MVs effects on breast cancer risk. Overall, our results showed that perturbation of the FANCM gene has an impact on breast cancer risk, reinforcing the knowledge that FANCM is a breast cancer gene predisposing especially to develop ER-negative and TNBC disease subtypes.In conclusion, our data suggest that at least some of the Supplementary TableSupplementary Table


To evaluate incident cardiovascular outcomes and imaging phenotypes in UK Biobank participants with previous cancer.Cancer and cardiovascular disease (CVD) diagnoses were ascertained using health record linkage. Participants with cancer history  were propensity matched on vascular risk factors to non-cancer controls. Competing risk regression was used to calculate subdistribution HRs (SHRs) for associations of cancer history with incident CVD (ischaemic heart disease (IHD), non-ischaemic cardiomyopathy (NICM), heart failure (HF), atrial fibrillation/flutter, stroke, pericarditis, venous thromboembolism (VTE)) and mortality outcomes  over 11.8±1.7 years of prospective follow-up. Linear regression was used to assess associations of cancer history with left ventricular (LV) and left atrial metrics.We studied 18 714 participants  years, 97% white ethnicities) with cancer history, including 1354 individuals with cardiovascular magnetic resonance. Participants with cancer had high burden of vascular risk factors and prevalent CVDs. Haematological cancer was associated with increased risk of all incident CVDs considered (SHRs: 1.92–3.56), larger chamber volumes, lower ejection fractions, and poorer LV strain. Breast cancer was associated with increased risk of selected CVDs , HF/NICM death, hypertensive disease death, lower LV ejection fraction, and lower LV global function index. Lung cancer was associated with increased risk of pericarditis, HF, and CVD death. Prostate cancer was linked to increased VTE risk.Cancer history is linked to increased risk of incident CVDs and adverse cardiac remodelling independent of shared vascular risk factors. Few studies have reported associations of past cancer with incident cardiovascular outcomes in large population-based cohorts, and none have included cardiovascular imaging.We studied 18 714 UK Biobank participants with history of six common cancers and an equal number of non-cancer comparators propensity matched on vascular risk factors. Our results demonstrate association of cancer history with increased risk of a wide range of incident cardiovascular disease and mortality outcomes over 12 years of prospective follow-up. In participants with cardiovascular magnetic resonance (n=1354), cancer history was linked to adverse cardiac remodelling. The greatest range and magnitude of risk was observed in those with past breast and haematological cancers.People with past cancer have heightened cardiovascular risk, which appears independent of vascular risk factors and persists several years after initial cancer diagnosis. This study highlights the specific cardiovascular care needs of patients with cancer and supports consideration of cancer-specific exposures in cardiovascular risk stratification.Patients with cancer history represent a growing cohort at heightened cardiovascular risk, attributed to shared vascular risk factors, cardiotoxicities of cancer therapies, and biological processes related to the cancer itself.Existing work indicates highest risk of cardiovascular complications to be in the first year after cancer diagnosis.Cardiovascular imaging has a key role in detecting subclinical cardiotoxicity. However, associations of cancer with cardiovascular remodelling in population cohorts have not been previously reported.We evaluated cardiovascular health in 18 714 UK Biobank participants with previous cancer, characterising disease and risk factor burden, incident disease and mortality outcomes, and cardiovascular remodelling patterns.The UK Biobank includes over 500 000 participants aged 40–69 years, characterised in detail at baseline recruitment (2006–2010).Cancer history was ascertained from cancer registry and HES records . We crea10.1136/heartjnl-2022-321888.supp1Supplementary dataWe defined incident CVD (ischaemic heart disease (IHD), stroke, atrial fibrillation (AF)/flutter, heart failure (HF), non-ischaemic cardiomyopathies (NICM), venous thromboembolism , pulmonary embolus (PE)), pericarditis) and mortality outcomes  using HES and death registration records .CMR scans were performed according to predefined protocols and analysed using automated pipelines.https://www.R-project.org/) and Stata V.17.Statistical analysis was performed using R studio V.4.1.0  and 95% CIs for the association of cancer history at baseline with incident disease and mortality outcomes. Participants with the outcome of interest at baseline were excluded from analyses for that outcome . Incident events were first occurrence of the outcome after baseline. Prevalent events were conditions present at baseline. The censor date was 26 March 2021, providing mean prospective follow-up of 11.8±1.7 years. We performed sensitivity analyses using cause-specific Cox regression, limiting to cases with complete data (no imputation), and to cancers diagnosed within 5 years prior to baseline. Given possible heterogeneities within the haematological cancer category, we examined associations with incident outcomes within its subcategories . We tested for interaction of cancer exposure with time by defining time from cancer diagnosis to baseline for cases and assigning the same time to their matched controls.et al . The samples all matched well on overall propensity score; individual covariates that were less well matched were included as covariates in final models, as per Nguyen et al .13 We reWe analysed 18 714 participants with past cancer . SmokingAlmost one-third of participants with cancer developed one of the incident CVDs . The higAmong participants with breast cancer, 22.3% (2130/9531) developed one of the incident CVDs considered and 15.3% (1454/9531) died. The most common incident CVDs were IHD (5.9%), AF/flutter (5.8%), HF (3.5%), VTE (3.2%) and stroke (2.2%). NICMs occurred in 0.9% and pericarditis in 0.8% of participants with breast cancer. A total of 5.1% (74/1454) of all deaths were primary cardiovascular deaths. The most common causes of CVD death were stroke and IHD.−5) ; p=0.0004), 80% greater risk of incident NICM , p=0.0008), and 45% greater risk of incident VTE ; p=6.61×10−5) . Breast Among the cancer sites considered, participants with a history of lung cancer (n=313) had the highest rates of incident CVD (49.4%), all-cause death (51.1%), and CVD death (5.4%). The most common incident CVDs were IHD (12.8%), AF/flutter (12.1%) and HF (10.2%). Among participants with lung cancer who died, 10.1% (17/160) died of a primary cardiovascular cause.Lung cancer was associated with over 12-fold greater risk of incident pericarditis ; p=0.017), 88% greater risk of incident HF ; p=0.029), and almost 2.5-fold greater risk of CVD death ; p=0.05). The risk of IHD death was increased in lung cancer patients, although with wide CIs ; p=0.14).Among 3291 participants with prostate cancer, 40.6% developed incident CVD and 20.8% died. Primary cardiovascular deaths contributed 12.2% (83/683) of all deaths. The most common incident CVDs were IHD (11.7%), AF/flutter (11.6%), and HF (6.2%). Incident stroke and VTE each occurred in 4.5%, NICMs in 1.2% and pericarditis in 0.9%.−16). Associations with all other outcomes were statistically non-significant.Compared with matched non-cancer controls, participants with prostate cancer had increased risk of incident VTE ; p=0.0001) and all-cause death ; p=2.40×10One-third (803/2412) of participants with colorectal cancer developed incident CVD, 20.7% died and 2.2% died of primary cardiovascular causes . The most common incident CVDs were IHD (10.2%), AF/flutter (9.8%), and HF (4.4%).Participants with colorectal cancer had 26% greater risk of incident AF/flutter ; p=0.02) compared with matched non-cancer controls. Colorectal cancer was associated with higher risk of HF/NICM death, but with wide CIs ; p=0.14). Aside from all-cause death, there was no statistically significant difference in risk of any other outcome.Among the 937 participants with uterine cancer, 26.7% developed incident CVD and 12.1% died. Primary cardiovascular deaths contributed 10.6% (12/113) of all deaths. The most common incident CVDs were AF/flutter (7.4%), IHD (7.3%) and HF (4.5%). Incident stroke occurred in 3.2%, VTE in 2.9% and NICMs and pericarditis were each observed in 0.7% of individuals.Compared with matched non-cancer controls, uterine cancer patients had increased  risk of incident NICM ; p=0.12), pericarditis ; p=0.12) and stroke death ; p=0.14).Among 2230 participants with past haematological cancer, 48.4% (n=1080) developed incident CVD and 27.1% died. A total of 7.8% (47/605) of all deaths were attributed to a primary cardiovascular cause. The most common CVDs were IHD (12.8%), AF/flutter (11.6%), and HF (10.2%). Incident stroke and VTE each occurred in 4.6%, NICMs in 2.6% and pericarditis in 2.1% of haematological cancer patients.−19), pericarditis by almost threefold ; p=0.0002)], and there was over 2.5-fold greater risk of both incident VTE ; p=2.47×10−7] and NICM ; p=0.002). There was almost twofold increased risk of incident AF/flutter ; p=2.62×10−10) and IHD ; p=2.02×10−10). Associations with CVD mortality outcomes were statistically non-significant; however, participants with a history of haematological cancer appeared at higher risk of CVD ; p=0.087) and IHD ; p=0.090) death.Participants with past haematological cancer had significantly greater risk of all incident CVDs . The risAssociations with incident events were broadly similar across myeloma, leukaemia, and lymphomas .In analyses limiting to cases with complete data, associations remained similar across all outcomes . The resWe investigated associations of past cancer with cardiovascular phenotypes in 1354 participants who had CMR data available . CompareIn this large population-based study, covering an average of 12 years prospective follow-up, past cancer was linked to increased risk of a wide range of incident cardiovascular outcomes and adverse remodelling, independent of shared vascular risk factors. Previous haematological cancer was linked to increased incidence of all CVDs considered, poorer LV function (by LVEF and GLS), larger LV and LA size, and poorer LA function (lower LAEF). Past breast cancer was linked to increased incidence of NICM, HF, pericarditis, VTE, HF/NICM mortality, hypertensive disease death, and poorer LV function (by LVEF and LVGFI). Lung cancer was associated with increased risk of incident HF, pericarditis and CVD death. Colorectal cancer was associated with increased risk of incident AF/flutter. Prostate cancer was linked to increased VTE risk.The most common incident CVDs in our cancer-exposed cohort were IHD, AF/flutter, and HF. This distribution reflects both the risk factor profile of individuals with cancer and general population trends.Our work extends previous reports by isolating cardiovascular risk associated with cancer independent of shared risk factors. A recent study from the UK used linked primary care and hospitalisation records to examine risk of incident disease-specific CVDs in patients with cancer independent of vascular risk factors.Participants with previous haematological cancer had significantly increased risk of all incident CVDs. They also had increased size and poorer function of both the LA and LV. Haematological cancer patients are exposed to many cardiotoxic cancer therapies such as tyrosine kinase inhibitors,Increased risk of VTE was observed in participants with haematological, breast, and prostate cancer. Many factors promote a prothrombotic state in the setting of cancer, such as the systemic biological processes of the cancer itself, tumour compression effects, chemotherapy, and long-term indwelling venous catheters. Previous studies have documented augmented risk of VTE in patients with cancer.et al,Radiation-induced heart disease has a range of possible manifestations.Participants with previous lung, breast or haematological cancer had increased risk of pericardial disease, with lung cancer patients having a markedly increased risk (over 12-fold). This may reflect metastatic disease presentations. Pericardial disease may also be an adverse consequence of mediastinal radiotherapy,Participants with breast cancer had increased risk of incident HF, incident NICMs and death from HF or NICM. Furthermore, breast cancer history was associated with poorer LV function by LVGFI and LVEF. These observations likely reflect cardiotoxicity linked to breast cancer therapies.Participants with uterine cancer had the highest average body mass index of all cancers, high rates of hypertension and diabetes and increased risk of stroke death. The clustering of cardiometabolic factors has been previously reported in uterine cancer.Patients with cancer have a constellation of demographic and clinical risk factors that place them at higher cardiovascular risk. Our findings underscore the importance of controlling modifiable risk factors for all patients during and after their cancer treatment, as well as specific areas of risk where surveillance and/or preventive strategies should be focused. Importantly, we demonstrate that past cancer confers an increased risk of cardiovascular events, independent of traditional vascular risk factors and that this risk may extend several years beyond the initial cancer diagnosis. Thus, our results support consideration of cancer-specific exposures in cardiovascular risk stratification and lower thresholds for treatment of modifiable risk factors in this patient group. We demonstrate particular vulnerability of individuals with past breast and haematological cancer, who appeared at greatest risk, both with regards risk of incident clinical disease and adverse cardiac remodelling.We found significant associations between breast and haematological cancer history and selected CMR metrics, even in the absence of prevalent CVD. The most consistent associations were observed with LVEF. We also demonstrate potential value of LVGFI, GLS, and LAEF as emerging novel imaging biomarkers of subclinical disease.Ascertainment of incident outcomes from health records may be subject to miscoding. We may be underpowered to detect associations in cancers with small sample sizes . Our dataset does not permit characterisation by cancer histology or stage. Information about specific cancer therapies was not available, and we cannot make inferences about treatment-specific effects. We are unable to consider ethnic disparities as our sample comprises a predominantly white cohort; future studies in more diverse cohorts are needed.Individuals with past cancer have heightened cardiovascular risk, which appears independent of vascular risk factors and persists several years after initial cancer diagnosis. The pattern of CVDs varies by cancer site, likely reflecting specific characteristics of the cancer and its therapies. CMR measures of LV and LA structure and function provide preclinical indicators of cardiovascular health in this context.