Breast cancer (BC) research examining disparities in cancer survivorship and modifiable risk behaviors has been mostly cancer-specific, leaving relevant gaps in disparities research relating to other cancer survivorship outcomes, including cardiovascular disease (CVD). Maintaining healthy lifestyle behaviors is a critical component of successful cancer survivorship, where unhealthy behaviors may increase the risk for recurrence, second primary cancers, and incidence of new comorbid conditions, including CVD. The current study describes BC survivorship factors among an online pilot study of Black BC survivors in Maryland, with a focus on the burden of obesity, comorbidity, and behavioral factors associated with CVD risk.Utilizing social media recruitment strategies and survivor networks, we recruited 100 Black female BC survivors to complete an online survey. Descriptive characteristics  were analyzed in terms of frequencies, means, standard deviations (SD) overall and by county.SD = 10.1) and 49.1 years (SD = 10.2), respectively. More than half of the survivors reported having hypertension (51%); and while only 7% reported being obese at the time of BC diagnosis, 54% reported being obese at the time of survey which was on average 9 years post BC diagnosis. Only 28% of the survivors reported meeting weekly exercise recommendations. While 70% were never smokers, most ever smokers resided in Baltimore City/Baltimore County (n = 18 ever smokers).The average ages at time of survey and at primary BC diagnosis were 58.6 years . These pilot study methods will inform a future statewide multilevel prospective study to improve health behaviors among Black BC survivors. African American/Black female breast cancer patients have the highest breast cancer mortality rates and shortest overall survival than any racial/ethnic group of women in the US . When diThe cause of these disparities in breast cancer and overall survival among Black women is multifactorial and reflects the interplay between biological factors  and socioeconomic and societal disparities. The high prevalence of obesity and obesity-associated comorbid conditions can impact late stage of presentation, cancer progression, choice of treatment, and overall survival –11. UneqPartially due to these psychosocial stressors described, Black women have high rates of hypertension (50%) compared to women of other racial/ethnic groups (39% of non-Hispanic white women and 38% of Hispanic women); and furthermore, among Black women with hypertension, only 26.5% have their blood pressure controlled , 16. Morp-int. < 0.01)  . A notabAs a next step to address this limitation and to acquire a better understanding of the comorbidity burden and prevalence of behavioral risk factors associated with CVD risk among Black breast cancer survivors in Maryland, we conducted a pilot study of Black breast cancer survivors living in Maryland using social media recruitment strategies. Our team piloted study methods to inform a future community-based intervention study of Black breast cancer survivors at high risk of poor outcomes due to comorbid conditions and social inequities . Marylanvia Meta and Instagram began on January 5, 2022 and completed on August 18, 2022. After ~6 months of recruitment, our Facebook ad reached 118,461 individuals on Facebook and 2,650 link clicks, had 3,539 post engagements, 611 post reactions, and 181 post shares. A total of 127 women completed the online screener and consented to participate, and of those women, 100 completed the online survey. All participants had to identify as biologically female, African American/Black, have been diagnosed with breast cancer (stages in situ-IV), and reside in Maryland (identified by geolocation).Detailed recruitment methods of this cross-sectional study have been previously described . Recruitvia institutional email addresses. By private messaging through the “Pages” forum, research administration could view names and information (depending on participants' Meta and Instagram privacy settings) without research administration using their own social media accounts. Therefore, they were directly conversing with participants via a “Page” front. This study was approved by the Johns Hopkins Bloomberg School of Public Health Institutional Review Board (IRB #00018654).Social media accounts were collected but were utilized for ad purposes, as there was only direct contact between participant  and research team through Facebook study “Pages” direct messaging and via REDCap© , clinical characteristics , recent and former cancer screening history , genetic testing information (if they completed genetic testing), health promotion questions , comorbid conditions, COVID-19 impact, healthcare access and utilization, and quality of physician-patient communication . The online survey also collected preferences for future interventions, willingness to participate in medical record abstraction, and email addresses for compensation and future contact.The online survey was administered The following variables were of interest for the current analysis: demographic factors , clinical characteristics , cancer screening history, ever completed genetic testing, behavioral/lifestyle factors , and ever diagnosed with specific comorbid conditions. County of residence was collapsed into the following groups based on frequencies . Comorbid conditions were also grouped by the total number  and then categorically: breast cancer only; 2–5 comorbid conditions; 6+ comorbid conditions. PA was determined using the Centers for Disease Control and Prevention's (CDC) National Health and Nutrition Examination Survey (NHANES) Physical Activity Questionnaire for vigorous- and moderate-intensity and strength training minutes/week, days/week, hours/day, and minutes/day types of PA. These variables were utilized to calculate minutes of each type of activity per week and on how many days per week each activity was completed. Number of days per week and minutes/week were utilized to determine if each participant had met aerobic (moderate- or vigorous-intensity PA) and/or strength training PA per week, determined by the American College of Sports Medicine (ACSM) exercise recommendations for cancer populations . RecommeResponses from the online survey were downloaded from REDCap into SAS version 9.4 and Stata version 16. Descriptive characteristics  were analyzed in terms of frequencies, means, standard deviations (SD) overall and by county. Statistical analyses were performed using Stata version 16.n = 100 completed surveys) overall and by county of residence. The average ages at time of survey and at primary breast cancer diagnosis were 58.6 years (SD = 10.1) and 49.1 years (SD = 10.2), respectively, with an average of 9.2 years (SD = 8.4) between primary breast cancer diagnosis and time of survey. Women reported living in Prince George's County (33%) and Baltimore City/Baltimore County (33%) Maryland, and other counties (34%). For sociodemographic factors, 36% reported having a college degree as their highest level of education, and 47.1% have an annual household income of ≥$75,000. When asked about participation in future research studies, 99% consented to be contacted in the future and 79% reported they would be interested in having their medical records abstracted for research purposes (data not shown).n = 43), followed by arthritis (45%), lymphedema (32%) and self-reported obesity (32%) (ty (32%) , and 28%ty (32%) . General2) at the time of the breast cancer diagnosis, 54% reported being obese at the time of their online survey. Most women (95%) reported drinking one or fewer alcoholic drinks/day and 70% were never smokers; however, most ever smoking participants resided in Baltimore City/Baltimore County (n = 18). Only 6 women reported current smoking overall. Only 28% of the survivors reported meeting ACSM weekly exercise recommendations of more than 150 min/week of vigorous or moderate activity and 35% reported meeting ACSM strength training recommendations weekly.Our pilot study examined a new recruitment strategy that could increase the enrollment and retention of minority breast cancer survivors into community-based studies. Maryland is the ideal state to conduct such a study given that the African American/Black population represents more than 30% of the total population, unlike the US where the African American/Black population is close to 14% . For inin = 3,039), we identified a significant difference in risk of CVD mortality among women with a diagnosis of hypertension within 2 years of breast cancer diagnosis, in comparison with women without hypertension   . While o02–3.44) , 34–38 w02–3.44) .Cancer research examining disparities in cancer survivorship and modifiable risk behaviors has been mostly cancer-specific, leaving relevant gaps in disparities research relating to other cancer survivorship outcomes, including CVD outcomes. Maintaining healthy lifestyle behaviors is a critical component of successful cancer survivorship, where unhealthy behaviors may increase the risk for recurrence, second primary cancers, and incidence of new comorbid conditions, including CVD and impact quality of life post-cancer . A systeThe current analysis has strengths and limitations. Past literature on the use of social media among breast cancer survivors has shown that limited research has focused on non-Caucasian populations . Our stuWe also must consider there could be differences in participation by time since diagnosis, as we did not limit our inclusion criteria by active treatment or time since breast cancer diagnosis. Women who are sicker due to their cancer and/or on active treatment may have been less likely to participate and among those who participated, may have different lifestyle factors and healthcare experiences. Additionally, with respect to collection of data for lifestyle factors, it should be noted that behavioral factors showed the highest prevalence of missing values, which limits the interpretation of this data. Lastly, most of our study population reported both higher incomes and education levels and were from more urban areas. Taking into account these study limitations, our descriptive results may not be generalizable for most Black breast cancer patients and survivors in Maryland; however, our future research studies utilizing this recruitment approach will address these study limitations.Our research goal was to utilize our pilot study of community-based Black breast cancer survivors to gain a better understanding of the comorbidity burden and describe the prevalence of behavioral risk factors associated with CVD risk among Black survivors in Maryland. We identified at-risk breast cancer survivors due to the high prevalence of CVD risk factors including hypertension, current obesity, and the lack of meeting aerobic and strength training PA recommendations. These research methods will inform a statewide multilevel prospective population-based study to improve health behaviors and disease management among Black women breast cancer survivors in Maryland at high risk of poor outcomes due to biological differences and socioeconomic inequities.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Johns Hopkins Bloomberg School of Public Health Institutional Review Board. Passive consent for participation was obtained electronically from the participants. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.AC was responsible for the study design, design of the analysis, and manuscript writing. KD was responsible for the data analysis, tables, and manuscript writing. KV was responsible for reviewing and editing the manuscript. All authors reviewed the manuscript. All authors contributed to the article and approved the submitted version.


GIPR, rs1800437 (E354Q), shown to impair long-term GIPR signaling and lower circulating glucose-dependent insulinotropic peptide concentrations, with risk of 6 cancers influenced by impaired glucose homeostasis  in up to 235,698 cases and 333,932 controls. Each copy of E354Q was associated with a higher risk of overall and luminal A-like breast cancer and this association was consistent in replication and colocalization analyses. E354Q was also associated with higher postprandial glucose concentrations but diminished insulin secretion and lower testosterone concentrations. Our human genetics analysis suggests an adverse effect of the GIPR E354Q variant on breast cancer risk, supporting further evaluation of GIPR signaling in breast cancer prevention.Preclinical and genetic studies suggest that impaired glucose-dependent insulinotropic polypeptide receptor (GIPR) signaling worsens glycemic control. The relationship between GIPR signaling and the risk of cancers influenced by impaired glucose homeostasis is unclear. We examined the association of a variant in   •GIPR signaling may influence cancer risk•GIPR (E354Q) on risk of 6 cancersWe tested the effect of a functional variant in •E354Q was associated with increased risk of overall and luminal A-like breast cancer•Further evaluation of GIPR signaling in breast cancer prevention is warranted Health sciences; Genetics; Cancer Food and Drug Administration (FDA) for type 2 diabetes treatment.,GIPR missense variant rs1800437 , indexing long-term reduced GIPR signaling, has been shown to be associated with increased bone mineral density and increased risk of fractures.,,,,,,,Glucose-dependent insulinotropic peptide (GIP) is one of two incretin hormones, along with glucagon-like peptide-1 (GLP1), that are produced in response to nutrient consumption, maintaining glucose homeostasis by increasing insulin and lowering glucagon secretion.GIPR, previously shown to result in impaired long-term GIPR signaling and decreased fasting and 2-h GIP concentrations, to predict the potential effect of such impaired GIPR signaling on the risk of 6 cancers influenced by hyperinsulinemia .,Here, we used a missense variant in Characteristics of genetic variants used to proxy all traits are presented in −9)(−7) and luminal B HER2 negative-like breast cancer . In hist.82x10−3). When sct cancer .Figure 1GIPR , but little evidence of association of this variant with risk of 5 other cancers examined . In colo−3). In exploratory analyses in BRCA1 or BRCA2 mutation carriers, there was little evidence of association of E354Q with breast cancer risk .Findings for an association of E354Q with breast cancer risk were replicated in an independent sample of 8,401 cases and 99,321 controls in the FinnGen consortium  and lower adult BMI  , although there was no evidence for an association with measured BMI in children aged 2–10  , 3. The = 99.8%) . We also = 0.89) .Figure 2−24, H4 = 99.9%) and lower levels of 3 measures of insulin secretion: AUCins , AUCins/AUCgluc , and Ins30   and Ins30 (BMI adj.) was also supported in colocalization analysis for 2-h, but not fasting, GIP concentrations (H4 = 64.2% and 52.4% for HbA1c and Ins30 (BMI adj.), respectively)  and S3. ectively) and S3.T−10, H4 = 99.8%) and bioavailable testosterone concentrations in women and S4. −3), luminal A-like , and luminal B HER2 negative-like breast cancer risk  (−9), luminal A-like , and luminal B HER2 negative-like breast cancer risk   models, genetically proxied bioavailable testosterone was associated with overall  . Likewis02x10−6) . When em02x10−6) .Figure 5−3), luminal A-like , and luminal B HER2 negative-like breast cancer risk . Genetically proxied smaller comparative body size at age 10 was likewise associated with lower risk of overall , luminal A-like , and luminal B HER2 negative-like breast cancer risk   . However87x10−6) . There wcer risk . Single-−10) but the direct effect of comparative body size at age 10 was retained for overall and luminal B HER2negative-like breast cancer risk .GIPR E354Q missense variant was associated with a higher risk of overall, luminal A-like, and luminal B HER2 negative-like breast cancer risk. These findings were supported in colocalization analysis and were replicated in an independent sample of 8,401 breast cancer cases and 99,321 controls. Although colocalization analyses were performed using fasting GIP concentrations, putative causal effects are unlikely to be driven through fasting GIP concentrations; rather, effects are more likely to reflect the GIPR signaling pathway, of which fasting GIP concentrations are a marker.In this MR analysis of up to 235,698 cancer cases and 333,932 controls, each copy of the E354Q was also associated with higher 2-h glucose concentrations but diminished insulin secretion and lower total and bioavailable testosterone concentrations. These measures confer opposing effects on breast cancer risk, suggesting perturbed glycemic and/or other adverse effects of impaired GIPR signaling through this mechanism offset possible beneficial effects on insulin secretion and circulating testosterone levels. Further work validating these findings and clarifying mechanisms using alternative approaches could help to reconcile these findings. There was little evidence of association of E354Q with the risk of the 5 other cancers examined.GIPR E354Q variant has previously been implicated in increased Glucose-dependent insulinotropic polypeptide-Glucose-dependent insulinotropic polypeptide receptor (GIP-GIPR) residence time, signaling, internalization and thus likely desensitization and downregulation of the signaling pathway long-term in some tissues.,,,The in vitro or in vivo studies have examined the role of GIP signaling in breast cancer to date.,Our findings are not consistent with a previous conventional epidemiological analysis which found little evidence of an association of circulating GIP concentrations with breast cancer risk , though this study was restricted to 109 cancer cases and GIP was measured in non-fasting samples which could result in substantial measurement error.,,In our analyses, E354Q was associated with lower adult BMI levels which is not consistent with weight loss observed in clinical trials of GIPR agonists .The E354Q variant was also associated with smaller self-reported comparative body size at age 10, but not with measured BMI in children aged 2–10. In univariable MR models, both adult BMI and smaller self-reported comparative body size at age 10 were associated with lower breast cancer risk, though only childhood smaller self-reported comparative body size showed evidence of a direct effect on breast cancer in multivariable MR models, consistent with prior MR analysis.There was little evidence of association of E354Q with the risk of the 5 other cancers examined, which could reflect the relatively smaller sample sizes and, consequently, lower power for these other cancer sites. Alternatively, the specificity of the association of E354Q with breast cancer risk could reflect a potentially unique role of GIPR signaling in breast carcinogenesis. Our findings suggest that a potential adverse effect of impaired GIPR signaling on breast cancer risk is unlikely to be mediated via insulinemic and/or hormonal pathways. Along with further evaluation of the potential mediating role of lower childhood adiposity in this relationship, evaluation of the effect of pharmacological GIPR perturbation in breast cancer cell lines and/or animal models could provide further insight into potential mechanisms governing this effect.Strengths of this analysis include the use of an MR approach, which should be less susceptible to issues of confounding and reverse causation than conventional epidemiological analyses; the use of a summary-data MR approach which permitted use to leverage data from several large genome-wide association study (GWAS) consortia, increasing statistical power and precision of causal estimates; and the comprehensive assessment of the effect of GIPR signaling across a large panel of glycemic, hormonal, and lipidomic mediators which enabled us to evaluate potential biological mechanisms through which impaired GIPR signaling may confer an increased risk of breast cancer.GIPR to proxy impaired GIPR signaling, suggest potential adverse effects of downregulated GIPR signaling on breast cancer risk and, thus, possible protective effects of pharmacological GIPR agonism. Given the sparsity of preclinical and epidemiological literature examining the role of GIPR signaling in breast cancer development, further work is warranted to validate and clarify potential mechanisms underpinning this putative effect. In particular, further evaluation of possible non-insulinemic pathways influenced by GIPR signaling could help to reconcile the specificity of the E354 association with breast cancer risk given the important role of metabolic dysfunction across the 5 other cancers examined in this analysis. Though clinical trial data support the efficacy of dual GIPR/GLP1R agonism for glycemic control in type 2 diabetes, it is unclear whether pharmacological GIPR agonism alone would confer similar favorable effects on glucose metabolism.,There is considerable interest in the pharmacological modification of GIPR signaling as treatment for type 2 diabetes and obesity. Our findings, using an established missense variant in GIPR E354Q missense variant on breast cancer risk. In mechanistic analyses, this variant was associated with higher levels of 2-h glucose but diminished insulin secretion and lower total and bioavailable testosterone concentrations. Triangulation of these findings in other settings will inform on the efficacy of pharmacologically modifying GIPR signaling as a potential chemoprevention strategy for breast cancer.In conclusion, our drug-target MR analyses across 6 cancers suggest adverse effects of the ins/AUCgluc, AUCins, Ins30, and Ins30 [BMI adj.]), influenced by E354Q, on breast cancer risk due to the lack of genome-wide significant variants available to serve as instruments for these measures. Furthermore, we were unable to directly test the effects of estrogen and progesterone on breast cancer risk due to a lack of robust instruments for these traits. Sixth, effect estimates were generated from data on participants without type 2 diabetes and therefore findings may not generalize to those with this condition. In addition, our findings did not recapitulate the known weight-loss effect of tirzepatide, which we believe is driven by receptor desensitization, though this could not be verified by the data available to us. Furthermore, while the restriction of participants to those of European ancestry, the use of a functional variant in GIPR to instrument GIPR signaling, and the use of colocalization should help to minimize exchangeability and exclusion restriction violations, these assumptions are unverifiable. In addition, our use of a single genetic variant to instrument GIPR signaling prevented us from employing various pleiotropy-robust methods to evaluate and/or mitigate the presence of horizontal pleiotropy. We selected 50% as a posterior probability threshold for colocalization of traits given the low statistical power of this analysis and the limited power for some anatomical site/subtype-specific cancer analyses. We cannot rule out the possibility that the use of a more liberal threshold to account for the limited power of these analyses may have meant that some traits reported as "colocalized" may represent alternate SNP association patterns in GIPR, such as distinct causal variants influencing traits or only one of two traits having a causal variant in this locus.There are several limitations to these analyses. First, drug-target MR analyses are restricted to examining the “on-target” effects of pharmacological interventions. Second, the effect estimates presented assume linear and time-fixed effects of GIPR signaling and the absence of gene-environment and gene-gene interactions. Third, MR analyses consider the small, lifelong effects exerted by a genetic variant, which may not necessarily translate to the clinical effect observed through pharmacological intervention in adult life. Fourth, statistical power was likely limited for some less common cancer sites  and histological subtypes . Statistical power can also often be limited in colocalization analyses which can reduce the likelihood of shared causal variants across traits being detected. Fifth, we were unable to examine the effect of four measures of insulin secretion (AUCjames.yarmolinsky@bristol.ac.uk)Further information and requests for resources should be directed to and will be fulfilled by the lead contact, James Yarmolinsky  meta-analyses of 6 adult cancers in up to 235,698 cases and 333,932 controls of European ancestry. Cancer sites were selected based on previous genetic epidemiological evidence linking fasting insulin to cancer susceptibility and included the following anatomical sites: breast , colorectum , endometrium , lung , kidney , and pancreas .BRCA1/2 mutation carriers, by obtaining GWAS summary data on 19,306 BRCA1 mutation carriers  and 12,412 BRCA2 mutation carriers  of European ancestry from the Breast Cancer Association Consortium (BCAC) and Consortium of Investigations of Modifiers of BRCA1/2 (CIMBA).,For replication analyses, summary genetic association data were obtained on 8,401 breast cancer cases and 99,321 controls of European ancestry from the Finngen consortium.,,,,,,,,,,,,,,,For analyses investigating the effect of impaired GIPR signalling on putative mediators of the GIPR-breast cancer relationship, we obtained summary genetic association data from previous GWAS of child and adult BMI or smaller self-reported comparative body size, type 2 diabetes, 3 endogenous sex hormones, 4 glycaemic traits measured in the non-postprandial state, 11 glycaemic traits measured following an oral glucose tolerance test, 2 lipid traits, and insulin-like growth factor 1.GIPR, rs1800437 , to proxy impaired GIPR signalling. This variant has been implicated in increased GIP residence time at GIPR, increased internalisation and signalling, and thus desensitisation and impairment of the signalling pathway long-term.P<5.0x10-8) with lower fasting and 2-hour GIP concentrations in a GWAS meta-analysis of 7,828 individuals of European ancestry across the Malmö Diet and Cancer (MDC) and Prevalence, Prediction and Prevention of diabetes (PPP)-Botnia studies. Participants in both studies were not taking anti-diabetic medications.We used a missense variant in P<5.0x10-8) and independent (r2<0.001) SNPs were selected using the 1000 Genomes Phase 3 European reference panel.To generate genetic instruments to proxy potential mediators of the GIPR signalling-cancer relationship, genome-wide significant  were performed using inverse-variance weighted (IVW) random-effects models.,Mendelian randomization (MR) analysis assumes that a genetic instrument (i) is associated with a modifiable exposure or drug target (“relevance”), (ii) does not share a common cause with an outcome (“exchangeability”), and (iii) has no direct effect on the outcome (“exclusion restriction”).2) and F-statistics. An F-statistic >10 is conventionally used to indicate that instruments are unlikely to suffer from weak instrument bias.We assessed the “relevance” assumption by generating estimates of the proportion of variance in each trait explained by the instrument (rP<0.05), to assess whether two traits examined shared a causal variant at a genetic locus .4>50.0% as evidence to support colocalisation of traits.Colocalisation was performed as a sensitivity analysis for primary analyses where there was nominal evidence of an association , which we used as a heuristic to define “strong evidence,” with findings between P≥0.0029 and P<0.05 defined as “weak evidence.”To account for multiple testing across E354Q-cancer analyses, a Bonferroni correction was used to establish a •http://pngu.mgh.harvard.edu/purcell/plink/.PLINK: •LocusZoom: LocusZoom - Create Plots of Genetic Data.


Studies of women of European ancestry have shown that the average familial relative risk for first-degree relatives of women with breast cancer is approximately twofold, but little is known for Asian women. We aimed to provide evidence for the association between family history and breast cancer risk for Asian women by systematically reviewing published literature.Studies reporting the familial relative risk of breast cancer for Asian women were searched in three online databases and complemented by a manual search. Odds ratios (ORs) for the association between family history and breast cancer risk were pooled across all included studies and by subgroups in terms of the type of family history, age, menopausal status and geographical region.P > 0.3). The pooled ORs for women of Asian ancestry with a family history in any relative were similar for those living in non-Asian countries  compared with those living in Asian countries .The pooled OR for women who have a first-degree relative with breast cancer was 2.46 . There was no evidence that the familial risk differed by the type of affected relative (mother versus sisters), the woman’s age (< 50 years versus ≥ 50 years), menopausal status (pre versus post) and geographical region (East and Southeast Asia versus other regions) (all Family history of breast cancer is associated with an approximately twofold relative risk of breast cancer for Asian women, which is of similar magnitude to that observed for women of European ancestry. This implies that similar familial factors are implicated in breast cancer risk between women of European and Asian ancestries. Genetic factors are likely to play a substantial role in explaining the breast cancer familial risk for Asian women, as similar risks were observed across different living environments and cultures.The online version contains supplementary material available at 10.1186/s12916-023-02950-3. Family history is a strong risk factor for breast cancer. Previous studies have shown that the relative risk of breast cancer associated with an affected first-degree relative is approximately twofold, and the risk is higher when the number of affected relatives is greater, the relatives’ age at diagnosis is younger and the women’s age is younger –3. MoreoIt is worth noting that the risk association mentioned above and the development of these risk models are mainly based on data from women of European ancestry. Breast cancer incidence and burden for Asian women have been increasing , and undSome reviews investigated breast cancer risk factors for women living in Asia but only included a few Asian countries  regarding family history , 14; theThis study design is a systematic review with a meta-analysis, which followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Statement (PRISMA) . This syWe searched for studies in three online databases including PubMed, Embase and Web of Science using Medical Subject Headings (MeSH) and Embase Subject Headings (Emtree) from the earliest publication date. Three search strategies were used: 1) (breast neoplasm) AND (family history) AND (Asia), (2) (breast neoplasm) AND (risk factor) AND (Asia) and (3) (breast neoplasms) AND (risk factors) AND (race OR ethnicity)  populations: women of Asian ethnicity; (2) exposure: having a family history of breast cancer; (3) comparator: women without a family history of breast cancer; (4) outcomes: breast cancer; (5) study types: case–control or cohort study that reported risk ratios, odds ratios (ORs), or hazard ratios to assess family history as a risk factor of breast cancer; (6) time frame: published before 31 March 2023; and (7) full text of the articles are available. Conference abstracts, publications in a non-English language or studies of non-human research were excluded.After determining the included studies, the following information was extracted from each publication using an Excel form designed for this study: authors, publication date, study location, study design, information on cases and controls , type of family history (i.e. relatedness and number of affected relatives and the relatives’ age at diagnosis) and risk estimates with corresponding 95% confidence intervals (95% CIs). The data extraction was conducted by one investigator (H.W.) and checked by another investigator (S.L.), and any disagreement was resolved by discussion and consensus.For the studies eligible for inclusion, we adopted the Risk of Bias in Non-randomised Studies of Exposure (ROBINS-E) criteria  to evaluZ-test) between subgroups were conducted only using studies that reported estimates for all subgroups; for subgroups including different studies, we examined the consistency between subgroups by checking the overlap between the 95% CIs of the subgroup risks.The meta-analysis was conducted using Stata version 16.0 (Stata Corporation). The results were presented as ORs with corresponding 95% confidence intervals. We fitted random-effect models to evaluate the association between family history and breast cancer, assuming varying effect sizes across the studies. Apart from investigating the overall association across all studies, we also investigated the association by the type of family history, age and menopausal status of the participants and study location. For age-specified risk comparison, we compared the breast cancer risk of women aged ≥ 50 years with those of women aged < 50 years. The cut-off of 50 years old was chosen for comparison with the results from previous studies ; it was I2 statistic was adopted to quantify the heterogeneity across the included studies. We visually evaluated publication bias using funnel plots and statistically assessed the bias through Egger’s and Begg’s tests.The Grading of Recommendations Assessment, Development and Evaluation (GRADE) was adopted to rate the quality of scientific evidence in our systematic review . The assThe search identified 16,781 articles; of these, 4,652 duplicates were removed, and another 12,129 articles were excluded after screening the titles and abstracts , Iran (11 studies), and Japan (8 studies) accounted for a large proportion of the included studies. There were also three studies from the USA and one from England that studied women of Asian ancestry. A total of 5,184,024 women were included in our analysis, with a mean age of 48.3 years old. A summary of all the included studies is presented in Additional file Forty-nine studies including 48 case–control studies and one cohort study reported the risk of breast cancer associated with having a family history in any relative. The ORs ranged from 0.6 to 9.1, with 92% (45/49) of studies reporting an OR over 1. The pooled OR was 2.21   (Table P = 0.8) that there was a difference for women under 50 years  compared with those over 50 years     (Table P = 0.35) that there was a difference in the association for women with a family history in mothers  and women with a family history in sisters .Thirty-three studies reported the risk of breast cancer for women with a family history in at least one first-degree relative Fig. . The ranTen studies reported the risk of breast cancer in women with a family history in at least one second-degree relative (Table P = 0.11) and Begg’s test (P = 0.11) also suggest there was no appreciable evidence for publication bias.For the association with family history in any relative and breast cancer risk, no evidence of asymmetry was found in the funnel plot Fig. . Egger’sP = 0.06) and Begg’s test (P = 0.04) also suggest there was some evidence for publication bias. Four sets of data in three studies [P = 0.25, Begg’s test P = 0.08). Therefore, the publication bias in the main analysis did not materially impact our estimate of the association with family history in first-degree relatives.For the association with family history in first-degree relatives and breast cancer risk, the funnel plot showed an asymmetric pattern Fig. . Egger’s studies , 70, 85 This systematic review had moderate to high certainty according to the GRADE guidelines  for having an affected mother and 3.18  for having an affected sister [A familial relative risk of two does not necessarily imply that every Asian woman with a family history should participate in cancer screening. However, women who have a strong family history should consider more frequent screenings , 105. AlFamily history, especially multi-generational one, is included as an important predictor in the risk models that are commonly used for women of European ancestry –7. For AOur study has several strengths, including searching published literature in three databases, using three search strategies to minimise the potential omission of eligible studies, quality assessment of the included studies, investigating breast cancer familial risk between several subgroups, and our results having moderate to high certainty according to the GRADE guidelines.Nevertheless, in addition to the limitations mentioned above, other limitations need to be considered when interpreting our results. First, we defined Asian women as those who live or have origin in Asian countries in the United Nations geoscheme, but populations in these countries are not homogenous, especially in genetic ancestry. Second, although the included studies were from 25 countries and regions which is more than previous reviews , 14, dueThis systematic review and meta-analysis provided evidence that family history is associated with an increased risk of breast cancer for Asian women, and the familial risk appears to be similar to those observed for women of European ancestry, suggesting there are similar familial factors implicated in breast cancer risk across ancestries. Genetic risk factors play a substantial role in explaining the familial risk, as similar familial risks were observed across different countries, living environments and cultures.Additional file 1.Additional file 2.Additional file 3.


BRCA1 and BRCA2 genes. There is limited evidence for such associations with moderate/high penetrance BC-risk genes such as ATM, CHEK2, and PALB2.Young age at breast cancer (BC) diagnosis and family history of BC are strongly associated with high prevalence of pathogenic variants (PVs) in We analyzed multi-gene panel testing results (09/2013–12/2019) for women unaffected by any cancer  and those affected with BC  ascertained for suspicion of hereditary breast and/or ovarian cancer. Multivariable logistic regression was used to test association between PV status and age at BC diagnosis (≤ 45 vs. > 45 years) or family history of BC after controlling for personal/family non-BC histories and self-reported ancestry.BRCA1  and moderate for BRCA2 . Modest associations were observed between PVs and young age at diagnosis for ATM  and CHEK2  genes, but not for PALB2 . For women with BC, earliest age of familial BC diagnosis followed a similar pattern. For unaffected women, earliest age of family cancer diagnosis was significantly associated with PV status only for BRCA1  and BRCA2 .An association between young age (≤ 45 years) at diagnosis and presence of PVs was strong for ATM, CHEK2, or PALB2.Young age at BC diagnosis is not a strong risk factor for carrying PVs in BC-associated genes The online version contains supplementary material available at 10.1007/s10549-023-06946-8. BRCA1 and BRCA2 (BRCA1/2) in 1994–5 . This analysis did not meet the requirements for studies involving human subjects and was therefore not subject to oversight by an institutional review board.CHEK2), (3) had a history of prior genetic testing for founder or familial mutations, (4) had a history of breast cancer but were missing age of diagnosis or (5) had a relative with a diagnosis of breast cancer under the age of 20, based on features suggestive of Li-Fraumeni Syndrome [Two subsets of women were selected for analysis: women with a personal diagnosis of breast cancer, and women unaffected with cancer of any type. Women were excluded if (1) they resided in states with laws preventing the use of de-identified genetic data , (2) carried more than one PV in any tested gene . Subsequent additions to the panel test in 2016 and 2019 included GREM1, HOXB13, POLD1, POLE, AXIN2, GALNT12, MSH3, NTHL1, RNF43, and RPS20.Testing was performed at a Clinical Laboratory Improvement Amendments (CLIA) and College of American Pathology-approved (CAP) laboratory . The initial multi-gene panel test included 25 genes (CHEK2 c.470C > T (p.Ile157Thr) and c.1283C > T (p.Ser428Phe) [Variant classification was performed using American College of Medical Genetics and Genomics recommendations . Supportr428Phe) .ATM, BRCA1, BRCA2, CHEK2, and PALB2). PV status was coded as the dependent variable. The multivariable logistic regression models were adjusted using the following independent variables: age of breast cancer diagnosis (≤ 45 years vs > 45 years); personal history of ovarian or pancreatic cancer ; family history  of female breast, male breast, ovarian, pancreatic, and prostate cancer; as well as self-reported ancestry. Self-reported ancestries were coded as quantitative variables representing fractions of reported ancestries as described previously [Among women affected by breast cancer, multivariable logistic regression was used to evaluate how age at breast cancer diagnosis and family history of female breast cancer affected the likelihood of detecting a PV after controlling for personal and family cancer history (first- and second-degree relatives), and self-reported ancestry. A separate analysis was performed for each gene .Among women who were unaffected by cancer of any type, we tested associations of PV status with a presence or absence of breast cancer family history, and with earliest age of diagnosis in affected female relatives using models as described above but excluding the variable for age of personal breast cancer diagnosis.Confidence intervals (CI) were calculated using Wald statistics. Similar methods have been described previously , 25. All. The median age at testing was higher for women with a personal history of breast cancer (53 years) compared to unaffected women (41 years). The median age at diagnosis of affected women was 48 years, and over one third  were diagnosed ≤ 45 years of age. A total of 16,719 (12.8%) affected women reported a second breast cancer. The prevalence of a second breast cancer was similar for those diagnosed ≤ 45 years (12.0%) and those diagnosed > 45 years (13.4%). The percentage of women who met NCCN testing criteria (version 3.2019) was 84% or greater in both subgroups. Self-reported ancestry distributions were similar between women with breast cancer and unaffected women  compared to unaffected women (21.6%). Among affected women, breast cancer in the family was more commonly reported at older ages (14.6% ≤ 45 versus 32.3% > 45). Conversely, the presence of breast cancer in the family was more commonly diagnosed at younger ages for unaffected women (39.1% ≤ 45 versus 29.5% > 45). This difference in age at diagnosis for breast cancer has been reported in other studies and is likely to be a function of how affected and unaffected women met criteria for testing, as a stronger family cancer history is typically required for unaffected women to meet NCCN testing criteria [The proportion of breast cancers diagnosed in women with a family history of breast cancer is shown in Table 2ed women 2.0% comp. PVs were more common in affected women (9.3%) compared to unaffected women (4.9%). Among affected women, PVs in ATM (0.9%), BRCA1 (2.1%), BRCA2 (2.3%), CHEK2 (1.4%), and PALB2 (0.8%) were most common. This trend was similar for unaffected women.PVs were identified in 6.0% N = 30,115) of all women tested . Breast cancer diagnoses ≤ 45 years of age was associated with a more modest, but statistically significant, risk of carrying a PV in BRCA2 . This early age at diagnosis was associated with a small increased risk of carrying a PV in CHEK2  or ATM  and with a non-significant increased risk of carrying a PV in PALB2 .The risk of carrying a PV based on age at breast cancer diagnosis was evaluated for affected women Table . After cBRCA1, BRCA2, PALB2) compared to those with no familial breast cancer , PALB2 , ATM , and CHEK2 . Similar trends were observed for unaffected women, where BRCA1  and BRCA2 , were associated with a significant increase in risk among women with a relative diagnosed ≤ 45 versus > 45 years of age with breast cancer  and PALB2 remained non-significant . There was no notable change to the overall contribution of family history of breast cancer and mutation status in this more limited age sub-group.Women < 40 years old have a greater relative risk of breast cancer attributable to a mutation , whereasmutation , 27, 28.BRCA1/2 carriers. Here we examined the impact of two traditional markers of hereditary cancer risk (age at breast cancer diagnosis and family cancer history) on the risk of carrying a PV in the most commonly mutated breast cancer-risk genes: ATM, BRCA1, BRCA2, CHEK2, and PALB2.With multi-gene panel testing becoming a more common component of cancer prevention and treatment decisions, it is important to more effectively identify patients who are likely to carry a PV as candidates for testing. Historically, these criteria have been based on well-characterized phenotypes for BRCA genes included in this analysis. When collapsing age at diagnosis into a dichotomous variable (≤ 45 and > 45 years) and controlling for family history in multivariable logistic regression models, there was a statistically significant, but very modest association between age at diagnosis and PV status for ATM and CHEK2, as well as stronger associations for BRCA1 and BRCA2. The finding that age at breast cancer diagnosis alone may not be a useful predictor of PVs in some moderate- and high-risk genes is clinically relevant, as genetic testing decisions are often heavily weighted by this factor. Therefore, the clinical significance of using age at diagnosis for these genes must be scrutinized within the wider context of other risk modifiers.Considered alone, PV prevalence was similar across a continuum of five-year age groups at diagnosis for the non-ATM, BRCA1, BRCA2, CHEK2, and PALB2, in affected women (similar to previous studies) [CHEK2 or ATM PV status among unaffected women. For ATM, the PV rates of 0.9% in women affected with breast cancer and 0.5% in unaffected women were similar to those observed in the Women’s Health Initiative  and the CARRIERS study (0.78% in affected women and 0.41% in unaffected women) [BRCA genes, particularly for the high-penetrant gene PALB2.The data presented here showed that the presence of familial breast cancer was more strongly associated with PV carrier status for studies) . Breast d women) , 30. Sim-BRCA genes as a single group was independent of age at breast cancer diagnosis [BRCA1/2 [PALB2 found that the breast cancer log(relative risk) estimate decreased with age from age 25 to age 75 years using a linear trend model [PALB2 and there was no significant difference in positivity rate by age.The present findings support previous work showing that PV prevalence is not solely dependent upon age of diagnosis for many genes. For example, a cohort of 10,000 women with breast cancer being evaluated by genetics providers found a prevalence of PVs in moderate- and high-risk genes of 5.6% among women 65 years and older . Recentl[BRCA1/2 , 31, 32.[BRCA1/2 . Howevernd model . This diBRCA1/2, these and other data demonstrate that these criteria are not necessarily appropriate for a broader range of other moderate- and high-penetrant risk genes. The accumulation of evidence will require adaptations of testing guidelines over time. Based on the findings herein this is particularly relevant for PALB2 PV carriers, as guidelines currently recommend consideration of prophylactic mastectomy [PALB2 could impact the extent of the surgery selected to treat their primary breast cancer and prevention of a secondary breast cancer.While current testing criteria were originally developed based on the clinical features associated with PVs in stectomy . For womBRCA1/2 and PALB2 be made available to every women with a personal history of breast cancer [Previous work has suggested that expanding the age at diagnosis testing criterion may be appropriate to improve the identification of PV carriers. Yadav et al. evaluated alternate age thresholds for selecting candidates for genetic testing and noted that increasing the age to 65 years would identify > 90% of PV-positive patients ; howevert cancer . There ct cancer , the inct cancer . HoweverATM, CHEK2, or PALB2 PVs.One of the strengths of this study is the large sample size of women tested with a multi-gene panel that includes high-risk and moderate-risk genes. However, a limitation is that this is not a population-based study, but one of women who qualified for clinical genetic testing by current guidelines and thus was enriched for high-risk individuals. Through multivariable adjustment for clinical factors that are related to genetic testing, we produced effect estimates that may apply to the general population of breast cancer cases . StatistThe current study data were collected from test requisition forms, which is a limitation. Notably, < 1% of patients were missing family history data, whereas self-reported ancestry data were unavailable for ~ 14% of the patients  or no (PALB2) increased risk of carrying a PV in non-BRCA1/2 breast cancer-associated genes. Risk modifiers such as the presence of a family history of breast and other cancers, tumor characteristics, single-nucleotide polymorphisms and breast density may hold more weight than age at diagnosis for some genes and may inform testing guidelines [Overall, these data contribute to the evidence that age at breast cancer diagnosis is not a strong predictor of carrying a PV across some moderate- and high-penetrance breast cancer genes. While early age at diagnosis was strongly associated with idelines . As moreSupplementary file1 (DOCX 30 KB)Below is the link to the electronic supplementary material.


Evidence concerning intakes of protein or sources of dairy protein and risks of colorectal, breast, and prostate cancers is inconclusive.Using a subsample of UK Biobank participants who completed ≥2 (maximum of 5) 24-h dietary assessments, we estimated intakes of total protein, protein from total dairy products, milk, and cheese, and dietary calcium in 114,217 participants. Hazard ratios (HRs) and 95% confidence intervals (CI) were estimated using multivariable-adjusted Cox regression.Q4 vs Q1:0.80, 95% CI: 0.67–0.94; 0.79, 0.67–0.94; 0.71, 0.58–0.86, respectively). We also observed positive associations of milk protein and dietary calcium with prostate cancer risk . No significant associations were observed between intake of dairy protein and breast cancer risk. When insulin-like growth factor-I concentrations measured at recruitment were added to the multivariable-adjusted models, associations remained largely unchanged. Analyses were also similar when looking at total grams of dairy products, milk, and cheese.After a median of 9.4 years of follow-up, 1193 colorectal, 2024 female breast, and 2422 prostate cancer cases were identified. There were inverse associations of total dairy protein, protein from milk, and dietary calcium intakes with colorectal cancer incidence (HRFurther research is needed to understand the mechanisms underlying the relationships of dairy products with cancer risk and the potential roles of dietary protein and calcium. Colorectal, breast, and prostate cancer are among the most commonly diagnosed cancers in the world, accounting for approximately 28% of all new cancer cases in 2020 . The mosBoth observational and Mendelian randomisation (MR) studies have found that the risks of developing colorectal, breast, and prostate cancer are associated with higher circulating concentrations of IGF-I –21. In cTherefore, this study aimed to assess the associations of intakes of total protein, dairy protein, sources of dairy protein, and total dietary calcium with risks of colorectal, breast, and prostate cancer in a large British cohort. A secondary aim was to assess the potential role of IGF-I concentrations in these associations.In total, over 500,000 individuals (5.5% response rate) aged 37–73 years consented to take part in the UK Biobank study from 2006 to 2010 . In brieEthical approval for the UK Biobank study was obtained from the North West Multi-Centre Research Ethics Committee (reference number 21/NW/0157). A full description of the study recruitment, assessment, protocol, and ethical approval can be found on the UK Biobank website .Dietary intake was assessed in a subsample of participants using a validated web-based 24-h dietary assessment, the Oxford WebQ , 32. Thin = 331,013 of the total UK Biobank sample), the 24-h dietary assessment was also sent every 3–4 months for a total of four times between February 2011 and June 2012 , milk, and cheese, as well as total dietary calcium, were calculated by multiplying the protein or calcium content of each respective food and beverage by the frequency of intake using the UK Nutrient Databank food composition tables , 32. Perth of September 2021 for English participants, 31st of July 2021 for Scottish participants, and 29th of February 2020 for Welsh participants). Cancer registry data were available until 29th of February 2020 for English participants and 31st of January 2021 for Scottish participants, after this time participants from England and Scotland were followed using HES and SMR databases, respectively. For participants from Wales, hospital episode data were not used because the cancer registry had longer follow-up. Participants were coded as having a diagnosis of cancer based on the ICD-10 codes of their first incident cancer: colorectal cancer (C18-C20), female breast cancer (C50), or prostate cancer (C61).Prevalent and incident cancer diagnoses were determined from a combination of linkage to National Health Service Digital , Hospital Episode Statistics (HES) data for English participants, and National Health Service Central Register Scotland and Scottish Morbidity Records (SMR) for Scottish participants . ParticiN = 29,504)), their genetic sex did not match their reported sex (N = 321), or they did not complete a 24-h dietary assessment . Individual 24-h dietary assessments were excluded if an unreliable energy intake was reported  or <3347 kJ ; women >14,644 kJ  or <2092 kJ ) [N = 2439 reported unreliable energy intake and no longer had a 24-h dietary assessment, N = 592 were ill or fasting and no longer had a valid 24-h dietary assessment). To reduce random measurement error, participants who did not complete a minimum of two valid 24-h dietary assessments were excluded . Participants were also excluded if they were censored or had a cancer diagnosis before the completion of their last 24-h dietary assessment (N = 2897). In total, this left 114,217 participants who completed ≥2 (maximum 5) valid 24-h dietary assessments, of which 51,278 were men and 62,939 were women. Supplementary Fig. A total of 922 participants had withdrawn their consent from the UK Biobank and were excluded from this analysis. Participants were also excluded if they had been diagnosed with a prevalent malignant cancer before recruitment )  or the pex N = 32, or theyParticipants were classified into quartiles of percentage of energy intake from total protein, protein from all dairy products, protein from milk, protein from cheese, except for total dietary calcium intake which was expressed in quartiles of mg/day. Intakes of total protein and protein from all dairy products and sources of dairy protein were also modelled as a per 2.5% energy increment whereas total dietary calcium intake was modelled as a 300 mg/day increment. Baseline characteristics were summarised across quartiles of percentage of energy intake from total protein, protein from all dairy, protein from milk, protein from cheese, as well as total dietary calcium intake.Cox proportional hazards regressions were used to estimate hazard ratios (HRs) and 95% CIs, with age as the underlying time variable. When breast cancer was the outcome of interest, models were restricted to women, and when prostate cancer was the outcome, models were restricted to men. The lowest quartile of percentage of energy intake of total protein, dairy protein, or dietary calcium intake (in mg/day) was used as the reference group for analyses by quartile of intake. Minimally adjusted models were stratified by age at recruitment  and by sex , and adjusted for region at recruitment .2, or unknown/missing (0.20%)), and total energy intake measured from the 24-h dietary assessments (sex-specific quintiles).Multivariable Cox regression analyses were further adjusted for height ), physical activity ), Townsend deprivation index ), education ), employment status ), smoking status ), alcohol consumption ), ethnicity ), diabetes ), body mass index ). For prostate cancer analyses, models were also adjusted for marital status  [2 statistics and p-values for including intake of total protein or dairy protein or dairy sources per 2.5% energy increase in the model were estimated using likelihood ratio tests (LRTs) comparing to a model without percentage of energy from protein or dairy protein in the model [p > 0.05).For colorectal cancer analyses, models were also adjusted for red and processed meat intake  estimated from the recruitment touchscreen questionnaire as meat intake was infrequently reported in the 24-h dietary assessments) and women specific covariates: menopausal hormone therapy (MHT) use ) and menopausal status  assigning men to a separate category). For breast cancer analyses, models were additionally adjusted for MHT use (same as above), oral contraceptive use ), age at menarche ), parity and age at first birth ), and menopausal status and BMI  . To assehe model . This waIn additional analyses, we assessed the associations of intakes of total g/day of all dairy products, milk, and cheese with risks of colorectal, breast, and prostate cancer to compare results to previous meta-analyses –7. We ad2), alcohol intake (<10 g/day and ≥10 g/day), and smoking status (ever and never) by using a LRT comparing the main model to a model including an interaction term between dietary intake of protein, dairy protein, or dietary calcium and the subgroup of interest. For colorectal cancer, we further assessed heterogeneity by sex using a LRT. For breast cancer, we assessed heterogeneity by menopausal status by separating follow-up time at age 55 years and their defined menopause status at recruitment so women who were premenopausal at recruitment would change into the postmenopausal risk set at 55 years of age. If a woman was defined as postmenopausal at recruitment, she remained in the postmenopausal risk set regardless of age. We further explored if associations varied by tumour site for colorectal cancer . In 68 instances, the diagnosis of colon and rectal cancer coincided, and these participants were removed from the subgroup analyses by tumour site. For heterogeneity by tumour site, we stratified Cox models using a competing risks approach [For subgroup analyses, we assessed heterogeneity by BMI  and fruit and vegetable intake (quintiles derived from ≥2 24-h dietary assessment) as well as red and processed meat intake (touchscreen questionnaire) for breast and prostate cancer analyses. Moreover, we restricted analyses to participants who completed a minimum of three 24-h dietary assessments to reduce random measurement error in the estimation of intake of all dairy protein and dairy protein sources. Finally, to assess for reverse causality, we excluded the first 2 years of follow-up.P-values were two-sided with p < 0.05 being considered statistically significant.All analyses were conducted using Stata version 17.0 . r = 0.80) and calcium from dairy products contributed to 45.7% of dietary calcium intake.Over a median of 9.4 years of follow-up (IQR: 9.3–9.8 years), 1193 incident colorectal cancer, 2024 breast cancer and 2422 prostate cancer cases were observed. Table Q4 vs. Q1: 0.80, 95% CI: 0.67–0.94, p-trend = 0.001 and 0.79, 0.67–0.94, p-trend = 0.003, respectively; Fig. Q4 vs. Q1: 0.71, 95% CI: 0.58–0.97, p-trend = 0.004). For breast cancer risk, we did not observe evidence of an association across intakes of total protein, protein from all dairy products or dairy sources, or total dietary calcium intake , although these associations were not statistically significant when milk protein or calcium intake were modelled as continuous variables , although there was no statistically significant trend ; Supplementary Fig. In additional analyses on the associations of intake of total g/day (not only from protein) of all dairy products, milk, and cheese with colorectal, breast, and prostate cancer risk, the associations were generally similar to those from analyses looking at protein from all dairy and dairy sources expressed as a 2.5% energy increment. However, there was a suggestion of an inverse association between g/day of total dairy intake and breast cancer risk when comparing the highest quartile to the lowest quartile of intake  whereas no evidence of an association was observed for postmenopausal breast cancer  but no evidence of an association was observed for those consuming <10 g/day of alcohol  but no evidence of an association was observed for those who consumed <10 g/day of alcohol or were never smokers  including 14 prospective studies, intake of total grams of all dairy products combined was associated with a 13% lower risk of colorectal cancer risk per 400 g/day intake 017 inclu.It is likely that the protein content in dairy products or milk is not the driving force for the observed lower risk of colorectal cancer, as there are some other compounds and minerals in milk, such as calcium, for which there is stronger evidence that they may reduce the risk of colorectal cancer . In the In our analyses, we observed heterogeneity by both alcohol intake and smoking status in the association of intake of protein from milk with colorectal cancer risk; the inverse association of milk protein intake with colorectal cancer risk was observed only in individuals who consumed ≥10 g/day of alcohol, or were ever smokers. To our knowledge, previous prospective studies have not explored whether the association of dairy products intake with colorectal cancer risk varies by these lifestyle factors –50. MoreIntakes of total protein, protein from all dairy products and its sources, and calcium were not associated with breast cancer risk in this study. To the best of our knowledge, only one previous prospective study has looked at the association of dairy protein; in a recent prospective analysis of 700,000 postmenopausal UK women from the Million Women Study  , which iWe found a suggestive positive association between intake of protein from milk and prostate cancer risk. However, this association was attenuated when other dietary components were adjusted for in multivariable models. We also observed a similar positive association between total calcium intake and prostate cancer risk. No other associations between intakes of protein from all dairy products or cheese and prostate cancer risk were observed. To the best of our knowledge, only three previous prospective studies have looked at the association of protein from dairy products and prostate cancer risk; two studies including men in the European Prospective Investigation into Cancer and Nutrition (EPIC) , 13 wherThe possible positive association between protein from milk and prostate cancer risk has been hypothesised to be mediated by circulating IGF-I concentrations, which have been shown to be associated with both higher dairy protein intake and prostate cancer risk , 17, 18,cis-SNP, although CIs were wide [Although dairy protein intake, and more specifically milk protein intake, has been associated with higher circulating IGF-I concentrations , 15, theere wide . Based oThe strengths of these analyses include the prospective nature of the study, detailed estimates of intakes of protein, dairy protein, and dietary calcium obtained from 2 to 5 24-h dietary assessments which reduces random measurement error, and use of record linkage limiting loss to follow-up and outcome misclassification. The detailed dietary assessment allowed for the examination of two specific sources of dairy products in association with common cancers. As well, the UK Biobank collected detailed sociodemographic, anthropometric, and lifestyle information from participants at recruitment, allowing adjustment for these potential confounders and other components of diet in the analyses. Moreover, participants in the UK Biobank also provided a blood sample at recruitment in which IGF-I concentrations were estimated, allowing us to assess how IGF-I concentrations influence the associations between dairy products and risk of IGF-I related cancers.There are also some limitations to consider. All self-reported dietary intake data are subject to measurement error, and although online 24-h dietary assessments offer advantages, such as quick completion by participants, multiple 24-h dietary assessments are needed to reliably estimate average intakes due to the substantial day-to-day variation. In this sample, approximately 35% of participants completed only two 24-h dietary assessments, which will increase random measurement error thus biasing the estimates towards null and reducing the statistical power of these analyses. This may be particularly important for cheese intake as previous evidence has shown low reproducibility (intra-class correlation = 0.38) comparing the average of two 24-h dietary assessments to two other independent 24-h dietary assessements . HoweverIn conclusion, we found that higher intakes of protein from dairy products, and protein from milk, were inversely associated with colorectal cancer risk, although based on previous evidence it is possible that these associations are driven by other components present in dairy products such as calcium, and our analyses also showed that higher dietary calcium intake was inversely associated with colorectal cancer risk. A higher intake of protein from milk and dietary calcium were weakly positively associated with prostate cancer risk, although these associations were only significant when we compared the highest with the lowest quartile and we did not find a significant linear trend. We did not observe evidence of any associations between intakes of protein from dairy products sources and breast cancer risk. Further research is needed on both the IGF pathway and other mechanisms that may account for the possible impacts of dairy products and calcium on cancer risk.Supplementary Materials


BackgroundBreast cancer is one of the most prevalent types of cancer in the female population. The cultural diversity, religious beliefs, myths, and misinformation regarding the disease contribute to diagnostic delays and enhanced burden on the healthcare system. This study aimed to ascertain the extent of knowledge and prevalence of erroneous beliefs and misconceptions regarding breast cancer among Pakistani women belonging to diverse socioeconomic and educational backgrounds.MethodologyThis cross-sectional study was performed in a tertiary care hospital in Karachi, Pakistan. A total of 350 women were enrolled in the study as a representative female population, and 300 participants were included who met the inclusion criteria. Participants were conveniently interviewed using a pre-piloted questionnaire designed to assess the prevalent myths and misconceptions about breast cancer. The data were analyzed by SPSS version 23  using descriptive statistics.ResultsThe study findings point to a significant prevalence of erroneous beliefs and a lack of accurate information on breast cancer. The mean age of the participants was 20.8 ± 10.4 years. The majority of the participants belonged to a middle socioeconomic status (70%) and were undergraduates (61.4%). The participants’ friends and family members were the most frequent sources of information regarding breast cancer. The most common myth was “breast-feeding offers immunity to breast cancer completely” (76.6%), followed by “breast cancer spreads after biopsy” (63.8%). Participants also believed that breast tissue biopsy can lead to the spread of cancer (63.4%) and that faith healers and alternative medicine can cure breast cancer (47.5%). One-third (33.3%) of the participants considered all lumps to be breast cancer; however, approximately half (41.6%) of the participants thought that only painful lumps were associated with breast cancer. A significant number of participants believed breast cancer to be a result of God’s curse (31.4%) or evil eye (38.7%).ConclusionsThe findings suggest a critical need for community-based breast health education initiatives that take into account Pakistani women’s distinctive cultural and societal attitudes and work to dispel common misconceptions about the condition. The annual burden of breast cancer worldwide is 12.5% of new cancer cases. Globally, breast cancer is the most common cause of cancer and cancer-related deaths [Cancer is more common in the developed world but the prevalence of cancer is increasing in low-to-middle-income countries (LMICs) like Pakistan . It has Myths and misconceptions contribute to the increased breast cancer burden in Pakistan. The misinformation regarding the clinical presentation, diagnosis, and management of breast cancer hinders early diagnosis, creates a negative doctor-patient relationship, and hinders the approach toward management. Therefore, misinformation, myths, and misconceptions have been labeled as global risks . CulturaIn this scenario, considering the high prevalence of breast cancer misinformation in our population combined with a low literacy rate, it is imperative to eliminate the factors contributing to breast cancer misinformation to enhance the dissemination of correct information regarding breast cancer diagnosis and management . The comWe conducted a study in a tertiary care hospital in Karachi, Pakistan, on healthy females to determine the prevalence of myths and misconceptions about breast cancer in our population. Identifying the popular myths can help in developing awareness programs and newer guidelines to decrease the burden of misinformation contributing to diagnostic delay and management. We also evaluated the relationship between literacy and education status and its association with the prevalence of myths and misconceptions to provide a perspicacious approach to enhancing the development of healthcare policies and education.A total of 350 participants were enrolled in a cross-sectional study conducted at the outpatient department of a tertiary care hospital in Karachi, Pakistan. These participants were selected based on a study conducted at the Aga Khan University Hospital Karachi and Karachi Institute of Radiation and Nuclear Medicine (KIRAN) cancer hospital in 2020 on the patient delay in breast cancer diagnosis in two hospitals in Karachi, Pakistan . InstituThe sample size was calculated using the Open Epi sample size calculator. A random convenience sampling technique was used to select the participants. All patients were interviewed face to face and written consent was taken from all participants. The interview was conducted by the researchers themselves. The purpose of the research and the procedure of data collection were explained to the participants. The patients were asked to fill out a preformed performa designed after a thorough literature search and review. Information was obtained regarding the myths and misconceptions and cultural beliefs about breast cancer, and their approach toward diagnosis and treatment was recorded. The self-designed questionnaire was translated into Urdu, the national language of Pakistan. Translators were arranged for participants who were unable to read both Urdu and English languages. The inclusion criteria included all healthy females aged 20-70 years with or without a history of breast cancer. All participants denying consent were excluded from the study.The data were fed in SPSS version 23  and analyzed using descriptive statistics. The demographics of the participants were recorded. The socioeconomic status was divided into three categories depending upon the income of the participants such as lower class with an income of <50,000 Pakistani Rupees (PKR), middle class with an income of 50,000-100,000 PKR, and high class with an income of >100,000. The education status was divided into four categories such as no education (unable to read and write), primary education (undergraduate), secondary education (graduate), and tertiary education (postgraduate). All questions for myths and misconceptions were scored (one point for a correct response and zero for an incorrect or not sure response). The determinant of the correct responses was based on the review of current guidelines and literature. The categorical variables were evaluated as frequencies and means. The level of correlation between education status and the prevalence of misconceptions was determined using the chi-square test. A p-value of 0.05 was considered significant for all tests.A total of 350 participants were enrolled in the study; however, 50 participants were excluded due to failure to meet the inclusion criteria. Twenty-five participants were excluded as they had no knowledge or awareness regarding breast cancer. Sixteen participants were excluded because they failed to sign the consent form, and nine participants were excluded because of incomplete data.The demographic characteristics of the participants are summarized in Table The sources of breast cancer awareness and their frequencies are shown in Table The myths and their prevalence among the participants are shown in Table The responses of the participants to the myths and misconceptions of the questionnaire are summarized in Table The level of education of the participants and the prevalence of their incorrect responses to the myths and misconceptions are shown in Table In our study, descriptive statistics were used to determine the proportion of myths and misconceptions regarding breast cancer in our population. These misconceptions were selected after a detailed review of the literature. Pakistan being a Muslim country has various religious beliefs contributing to the misinformation and myths regarding the disease. Cultural values also contribute to the misinformation. According to a systematic review, one factor contributing to the misconception is a health-related concept, which implies that an individual is considered in good health until they can perform their daily life activities without seeking the help of a doctor . This heBRCA1 and 2. Therefore, it is imperative to create awareness among the population on the topic of de novo occurrence of breast cancer to guide early detection and management.The first misconception analyzed in our study was the fact that participants considered breast cancer to be contagious. According to the results of our study, it was seen that 25.5% of the participants considered breast cancer to be a contagious disease. According to a study conducted by Elobaid et al. in 2016, breast cancer was viewed as a contagious illness contributing to the fear of undergoing screening and being isolated from society . ConsideUpon determining the presentation of the breast cancer, all participants were asked if all lumps presenting in the breast were considered malignant. One-third (33.3%) of the participants considered all lumps to be breast cancer; however, approximately half (41.6%) of the participants thought that only painful lumps were associated with breast cancer. This finding is in congruence with a study conducted by Altwalbeh et al. in Saudi Arabia in 2015, which also identified that painless breast lumps are perceived as non-malignant and not a threat to life which requires breast cancer screening .It was determined if participants considered the diagnosis of breast cancer to be a sentence to death. In our study, 38.3% of patients considered that patients who have been diagnosed with breast cancer die and that there is no cure or intervention available for breast cancer. On the other hand, it was also observed that almost half (47.5%) of the participants considered that faith healers and alternative and herbal medicine can provide a cure for breast cancer. The use of holy water for treating conditions such as breast cancer is common in the Muslim population . CulturaIn our study, 31.4% and 38.7% of the patients thought of breast cancer as God’s curse and a consequence of the evil eye, respectively. In a study by Saeed et al., the most common misconception identified with the causative factor of cancer was fate (82%), followed by 58.8% of participants blaming the evil eye as the cause of their cancer . It is oThe most prevalent myth, however, was the prevention of breast cancer with breastfeeding. It was observed that 76.6% of the patients considered that breastfeeding their children would protect them from breast cancer. The idea that breastfeeding offers complete immunity to breast cancer is quite prevalent in the population. It is seen that the duration of breastfeeding does reduce the risk of breast cancer, but is not entirely protective . It is iFinally, the fear of the spread of breast cancer with biopsy and the use of mammography and radiation exposure for screening purposes was another factor contributing to the delay in management and early detection. More than 60% of the participants believed in the spread of cancer with biopsy and radiation exposure as a causative factor for breast cancer. It was seen that the majority of the misconceptions were more prevalent in the participants with no formal education. In LMICs like Pakistan, the availability of formal education, schools, and colleges is limited. Moreover, it is seen because most of the population falls in the lower socioeconomic group, with the children earning rather than going to schools to receive education. This is another factor contributing to reduced awareness regarding breast cancer in LMICs like Pakistan.Hakeem for the management of breast cancer was highly linked with a lower education level. Therefore, according to the results of the study, more education and awareness must be created among the masses regarding these misconceptions. This study also suggests utilizing holistic approaches in educating women regarding measures of breast cancer screening and prompt presentation to a healthcare professional to reduce the morbidity and mortality associated with breast cancer.The reasons for the delay in diagnosis include lack of awareness, use of traditional methods, alternative medicine and false belief in the power of faith healers in treating breast cancer, fear of spread with the use of diagnostic modalities, and possible shame and embarrassment. In a study conducted in two hospitals in Pakistan, it was determined that the mean delay in the appearance of a breast lump and first presentation to a primary healthcare physician was 8.79 months . Lower sThe limitations of the study include that the data were collected from only one hospital in a metropolitan city of Pakistan; therefore, the data cannot represent the entire Pakistani population. As a result of this limitation, the results of our study may not be generalizable to the entire Pakistani population.Healthcare providers should be aware of the myths and misconceptions commonly prevalent in our society. The people of Pakistan are influenced by religious and cultural beliefs associated with a disease such as breast cancer. The low literacy rate of the country adds to the increasing prevalence of such misinformation. This misinformation can be highly persuasive and contributes to diagnostic delay. It is essential to design comprehensive breast cancer education campaigns and awareness programs addressing the myths and misconceptions related to breast cancer to decrease the burden of delayed presentation among Pakistani women.


In terms of breast cancer risk, there is no consensus on the effect of uric acid (UA) levels. The aim of our study was to clarify the link between UA and breast cancer risk in a prospective case–control study and to find the UA threshold point.We designed a case–control study with 1050 females (525 newly diagnosed breast cancer patients and 525 controls). We measured the UA levels at baseline and confirmed the incidence of breast cancer through postoperative pathology. We used binary logistic regression to study the association between breast cancer and UA. In addition, we performed restricted cubic splines to evaluate the potential nonlinear links between UA and breast cancer risk. We used threshold effect analysis to identify the UA cut-off point.P < 0.05) in the lowest UA level and 2.245 (95% CI 0.946–5.326) (P > 0.05) in the highest level. Using the restricted cubic bar diagram, we disclosed a J-shaped association between UA and breast cancer risk (P-nonlinear < 0.05) after adjusting for all confounders. In our study, 3.6 mg/dl was found to be the UA threshold which acted as the optimal turning point of the curve. The OR for breast cancer was 0.170 (95% CI 0.056–0.512) to the left and 1.283 (95% CI 1.074–1.532) to the right of 3.6 mg/dl UA (P for log likelihood ratio test < 0.05).After adjusting for multiple confounding factors, we found that compared with the referential level (3.5–4.4 mg/dl), the odds ratio (OR) of breast cancer was 1.946 (95% CI 1.140–3.321) (We found a J-shaped association between UA and breast cancer risk. Controlling the UA level around the threshold point of 3.6 mg/dl provides a novel insight into breast cancer prevention. Uric acid (UA) is the final product of purine metabolism excreted by the kidneys and gut. Elevated UA levels were closely associated with metabolic syndrome (MetS)  all patients were newly diagnosed with invasive ductal breast cancers through postoperative pathology; (2) 20 years of age or older. The exclusion criteria were: (1) presence of other malignant tumors; (2) past surgery, chemotherapy, targeted therapy, endocrine therapy, and other therapy for any malignant tumors; (3) chronic kidney diseases; (4) consumption of any drug that regulates UA level; (5) inadequate clinical information.The inclusion criteria for the control group were: (1) females who had completed a regular physical examination in the same hospital at the same period; (2) age matched with the breast cancer patients. The exclusion criteria were: (1) presence of other malignant tumors; (2) positive ultrasound breast scans and/or mammographic screening results; (3) cancer evidence or history; (4) chronic kidney diseases; (5) consumption of any drug that regulates UA level; (6) inadequate clinical information.Finally, 1050 participants (525 cases and 525 controls) conforming to the above-mentioned criteria were included in the study. The Ethics Committee approved this study.Medical records and comprehensive questionnaires were used to collect data by trained investigators. The questionnaire included age, sex, history of type 2 diabetes mellitus (T2DM), smoking, alcohol drinking, family history of cancer, age at menarche (years), age at first pregnancy (years) and menopause or not. Height (cm) and weight (kg) were measured for all subjects and body mass index (BMI) was also calculated. All participants underwent a physical examination.Metabolic indicators such as low-density lipoprotein cholesterol , fasting glucose (mmol/l), triglycerides , and UA (mg/dl) were collected through medical records. Blood tests were performed during hospitalization through standard methods using an autoanalyzer. Blood samples were taken after fasting for at least 8 h.http://www.empowerstats.com). The Kolmogorov–Smirnov method was used for normality testing. For continuous data with normal deviation, means and standard deviation were used. In the absence of a normal deviation, medians and interquartile ranges were calculated. Categorical data were described by frequency and percentages. Continuous variables with normal distribution and without normal distribution were compared by ANOVA and Kruskal–Wallis test, respectively. A χ2 test was applied to analyze the differences of categorical variables.To perform the statistical analysis, we used R software . The threshold effect of UA was analyzed using the Empower Stats statistical software (We sorted the participants into six groups ordered by increasing UA levels (mg/dl): Group A ≤ 3.5, 3.5 < Group B ≤ 4.4, 4.4 < Group C ≤ 5.4, 5.4 < Group D ≤ 6.4, 6.4 < Group E ≤ 7.4, and Group F > 7.4. Group B, which showed the lowest breast cancer risk, was treated as the reference group.P < 0.05 was regarded as statistically significant. The output table  functions model was conducted. In multivariable logistic regression, we adjusted for all the confounder variables, such as age, BMI, T2DM, family history of cancer, smoking, alcohol drinking, menopause, age at menarche, age at first pregnancy, LDL, fasting glucose, and TG. We used four nodes  in the RCS model to disclose the relationship between UA and breast cancer risk. Then, we applied smooth curve fitting and the threshold effect analysis to obtain the optimal inflection point. We calculated multiple models using a percentile (between 10 and 90%) of UA as inflection point. To achieve the threshold effect of the UA, we established a two-piecewise linear regression model to the left and right of the inflection point. We identified the optimal inflection point according to the maximum log likelihood. To determine whether a threshold exists, we compared the one-line (non-segmented) model with the segmented regression model using the log-likelihood ratio test; le Table  includesParticipants’ characteristicsP < 0.05). Among all the groups, T2DM incidence, family history of cancer, and TG were higher in Group F; menopause incidence and age were higher in Group D. Moreover, in Group E, BMI and LDL-C achieved the highest values.2.Association of UA with breast cancer riskUsing the above criteria, 1050 subjects (525 cases and 525 controls) were enrolled in our study. Participants ranged in age from 21 to 85 at enrollment. The average age of the two groups were both 56 years. All the continuous data conformed to a normal distribution. The general characteristics of the enrolled subjects are recorded in Table n = 144), 3.5 < Group B ≤ 4.4 (n = 276), 4.4 < Group C ≤ 5.4 (n = 327), 5.4 < Group D ≤ 6.4 (n = 184), 6.4 < Group E ≤ 7.4 (n = 69), and Group F > 7.4 (n = 50). The OR between UA and breast cancer risk are presented in Table P < 0.05) through the univariate logistic regression analysis. The OR increased to 5.963 (95% CI 2.974–11.954) in the highest level of UA (> 7.4 mg/dl) (P < 0.05). Regarding model 2, after adjusting for age, BMI, T2DM, family history of cancer, smoking, alcohol drinking, menopause, age at menarche, and age at first pregnancy, compared with the reference group, the OR at the lowest level of UA was 1.928 (95% CI 1.202–3.095) (P < 0.05), then the OR increased to 4.093 (95% CI 1.924–8.709) at the highest level of UA (P < 0.05). Based on the model 2, we further incorporated LDL-C, fasting glucose, and TG to model 3. The results demonstrated that, comparing with the reference group, the OR of the lowest level of UA was 1.946 (95% CI 1.140–3.321) (P < 0.05). The highest UA level was associated with an elevated breast cancer risk as well. However, the result was not statistically significant in Model 3 after incorporating all the confounders  (P > 0.05); details were shown in Table 3.The dose relationship between UA and breast cancerWe sorted the participants into six groups according to their baseline UA levels (mg/dl): Group A ≤ 3.5 .To evaluate the nonlinear association between UA and breast cancer risk, we applied a model which combined multivariate logistic regression analysis with RCS. The RCS displayed an apparent decrease of the breast cancer risk within the lower levels of UA. The lowest risk was achieved at a UA concentration around 3.5–4.5 mg/dl and increased thereafter. Thus, the fully adjusted RCS showed a strong J-shaped association between UA blood levels and breast cancer risk Fig.  (P for nP for log likelihood ratio < 0.05). These results demonstrated that the link between UA and breast cancer risk can be satisfactorily explained by a two-piecewise model, and the threshold was regarded as statistically significant, as shown in Table In addition, we tested the UA threshold effects on breast cancer risk. We established a smooth curve fitting through logistic regression analysis. The RCS and the smooth curve fitting confirmed the existence of a linear relationship between UA and breast cancer. We conducted a two-piecewise logistic regression model and we analyzed every possible model within the range 10–90% of UA as turning point. We selected the model with the optimal inflection point and we conducted a piecewise linear regression model on both the lower and higher levels of the optimal inflection point, which provided maximum log likelihood. Using this method, we found that the optimal inflection point of UA level was 3.6 mg/dl. The OR for breast cancer was 0.170 (95% CI 0.056–0.512) and 1.283 (95% CI 1.074–1.532) to the left and right of 3.6 mg/dl, respectively . The highest UA levels corresponded with a 2.245-fold increase in breast cancer risk, although it was not statistically significant (P > 0.05). This may be explained by the dual role of UA against breast cancer. Notably, the association between UA and breast cancer risk presented a J-shaped trend, even after adjusting baseline characteristics, other common risks, and metabolic factors (P-nonlinear < 0.05). The inflection point in the curve occurred at 3.6 mg/dl. Indeed, the nonlinear association presented as a U-shaped or J-shaped curve has also been found between UA levels and mortality through several cohort studies  are involved in the interplay of UA and the immunity system (Mi et al. To the best of our knowledge, our study is the first to reveal the potential nonlinear J-shaped association between UA and breast cancer risk and, which may also explain the conflicting findings of previous studies. Moreover, we found that the threshold point for the J-shaped breast cancer risk curve was at 3.6 mg/dl UA level. Therefore, especially for those with high risk such as family history, controlling UA around a reasonable level of 3.6 mg/dl rather than blindly reducing UA level is a significant recommendation for the prevention of breast cancer.It should be noted, however, that there are some limitations in our study. Firstly, since this is cross-sectional research, we cannot identify the causality between UA and breast cancer. Secondly, owing to the limited sample size, it is not possible to implement stratified analyzes. Thirdly, we did not record the dietary habits linked with UA. Therefore, larger prospective studies are still needed to make up for the above limitations.In summary, our study demonstrates for the first time that there is a J-shaped association between UA and breast cancer risk. Furthermore, we determined an optimal threshold of 3.6 mg/dl UA, which acted as a trend turning point. The strong J-shaped association between UA and breast cancer provides a novel insight into breast cancer prevention. Therefore, UA levels should always be confirmed to achieve a reasonable range around 3.6 mg/dl. For a progressive defense against BC, a larger prospective study is required to ascertain the accuracy and threshold of the association for predicting breast cancer risk and elucidate the pathogenic mechanisms and explore the value of conventional and novel drugs that have been used merely as UA modulators.