To investigate whether MRI-based measurements of fibro-glandular tissue volume, breast density (MRBD), and background parenchymal enhancement (BPE) could be used to stratify two cohorts of healthy women: BRCA carriers and women at population risk of breast cancer.Pre-menopausal women aged 40–50 years old were scanned at 3 T, employing a standard breast protocol including a DCE-MRI . The dynamic range of the DCE protocol was characterised and both breasts were masked and segmented with minimal user input to produce measurements of fibro-glandular tissue volume, MRBD, and voxelwise BPE. Statistical tests were performed to determine inter- and intra-user repeatability, evaluate the symmetry between metrics derived from left and right breasts, and investigate MRBD and BPE differences between the high- and low-risk cohorts.Intra- and inter-user reproducibility in estimates of fibro-glandular tissue volume, MRBD, and median BPE estimations were good, with coefficients of variation < 15%. Coefficients of variation between left and right breasts were also low (< 25%). There were no significant correlations between fibro-glandular tissue volume, MRBD, and BPE for either risk group. However, the high-risk group had higher BPE kurtosis, although linear regression analysis did not reveal significant associations between BPE kurtosis and breast cancer risk.This study found no significant differences or correlations in fibro-glandular tissue volume, MRBD, or BPE metrics between the two groups of women with different levels of breast cancer risk. However, the results support further investigation into the heterogeneity of parenchymal enhancement.A semi-automated method enabled quantitative measurements of fibro-glandular tissue volume, breast density, and background parenchymal enhancement with minimal user intervention.• Background parenchymal enhancement was quantified over the entire parenchyma, segmented in pre-contrast images, thus avoiding region selection.• No significant differences and correlations in fibro-glandular tissue volume, breast density, and breast background parenchymal enhancement were found between two cohorts of women at high and low levels of breast cancer risk.• The online version contains supplementary material available at 10.1007/s00330-023-09592-2. Breast cancer is the second most diagnosed cancer and the second most common cause of female cancer death in the UK, with similar incidence and mortality rates in other developed countries . The maiMRI has several advantages over XRM for measuring breast density. MRI provides high-resolution, three-dimensional (3D) images while XRM relates to a two-dimensional (2D) projection image that may be affected by tissue overlap effects . AdditioIn addition to measuring breast density, MRI can also be used to detect a phenomenon called background parenchymal enhancement (BPE). BPE refers to the increased signal increase of fibro-glandular tissue on MRI following contrast uptake. Some studies have suggested that BPE measurements could be used as additional biomarkers for predicting breast cancer risk , but theIn this study, we aim to compare semi-quantitative measurements of MRBD and voxelwise BPE metrics between two groups of radiologically normal subjects: BRCA carriers and subjects at population risk of breast cancer. To the best of our knowledge, no other studies have directly compared MRBD or BPE measures between these two groups, where subjects at population risk of breast cancer are not usually included in MRI studies. By examining potential differences between the two groups, we hope to determine whether MRBD and BPE measurements could provide useful biomarkers for cancer risk in risk stratification models to optimise population screening programs. To determine the consistency of our measurements, we plan to conduct statistical evaluations of the inter- and intra-user repeatability and agreement of metrics derived from the right and left breasts.Two cohorts of women were recruited from separate sites: one group at high risk for breast cancer (August 2015–February 2019) and another group at low risk (May 2017–November 2019). The first, high-risk, group consisted of 35 women with genetically proven BRCA1 or BRCA2 mutations. The MRI scans for this group were obtained retrospectively from a high-risk screening program, with a gap of less than 6 months between XRM and MRI. The low-risk group comprised 30 women at population-level risk (i.e. no proven BRCA1/2 mutations), who were found to be radiologically normal after being referred to the Rapid Diagnostic and Assessment Centre for breast investigations. This second group of women was recruited prospectively and underwent breast MRI within 6 weeks of XRM.Inclusion and exclusion criteria were established to control for factors that may contribute to variations in XRM breast density and BPE within the population. The primary difference between the two groups was their genetic predisposition to develop breast cancer. Inclusion criteria for both groups included the following: (i) age between 39.5 and 50.5 years, (ii) pre-menopausal status, and (iii) MRI screening within 6 weeks of XRM. Exclusion criteria included the following: (i) previous breast cancer diagnosis or treatment, (ii) treatment or medication between XRM and MRI, and (iii) hormonal treatment and bilateral salpingo-oophrectomy.This study was approved by the National Research Ethics Committee (REC 14/LO/1908) and complies with the Declaration of Helsinki and local data protection regulations. All prospective participants provided written consent. The study has been registered in the NIH research database (NCT03684733).All MRI scans were performed on 3-T scanners  using 7-element rigid biopsy-compatible breast coils. The protocol for high-risk breast screening at site 1 complied with national guidelines  and was Although the protocols were closely aligned, possible sources of bias were investigated. BPE is known to depend on pulse sequence parameters which are often overlooked . In thisImage processing was performed with in-house software developed in MATLAB (Mathworks). Fibro-glandular volume, MRBD, and BPE measurements were optimised separately.To generate left and right breast masks, we applied a semi-automated, 3D region-growing algorithm  to non-fBreast tissue was segmented on non-FS T2-weighted images Fig.  to excluFibro-glandular volume was calculated as the number of fibro-glandular voxels multiplied by the voxel size. MRBD was quantified as the percentage of the breast volume occupied by fibro-glandular tissue.To estimate breast percent enhancement (BPE), we used a similar segmentation method as described above (fuzzy c-means clustering with seven clusters) to segment the fibro-glandular tissue on the pre-contrast, fat-suppressed T1-weighted images obtained from the DCE-MRI Fig. . These sBy segmenting the breast parenchyma on pre-contrast images rather than post-contrast images, we aimed to probe enhancement as a distribution of values and avoid bias by not presuming enhancement, which may be position dependent. The operator intervention involved selecting which clusters to include as breast parenchyma and excluding areas of poor fat suppression that might be misinterpreted by the software. The measurement was designed to be as operator independent as possible and to include as much of the breast parenchyma as possible.BPE maps were calculated voxelwise in segmented fibro-glandular tissue with the formula:In our analysis, we primarily considered the MTP, although data from all time points were available. The reason for this decision was that the subjects in our study were radiologically normal. In contrast, it is recommended to assess BPE in MRI of breast cancer patients at an early post-contrast time point, where cancers exhibit peak enhancement, in order to avoid including malignant tumours in the segmented fibro-glandular tissue . HoweverStatistical BPE distribution metrics were calculated from the enhancement map across one breast fibro-glandular tissue segmentation. The median, inter-quartile range, skewness, and kurtosis of the BPE distribution were measured independently in the left and right breasts for each subject.To determine the inter- and intra-user repeatability of our method, two different software operators conducted independent measurements of fibro-glandular tissue volume, MRBD, and median BPE. Ten subjects (five from each group), with a range of breast tissue densities, were used to calculate the coefficient of variation (CV) as Bland–Altman statistics were used to explore the agreement between right and left fibro-glandular tissue volume, MRBD, and median BPE. We also considered age and weight differences between the two groups, as these factors have been associated with breast density .t-test) and non-parametric test (Wilcoxon signed-rank test). Parametric (t-test) and non-parametric (Mann–Whitney U test) tests were also used to investigate the differences in all parameters between the high-risk and the population-risk groups. Spearman’s rank correlation coefficient, r, was also used to examine parameter correlations.For all estimated parameters , histograms were plotted and assessed visually to inspect deviations from normal distribution. Differences between right and left breasts were explored accordingly using paired parametric test .The coefficients of reproducibility (RPC) for fibro-glandular tissue volume, MRBD, and median BPE between the right and left breasts were found to be 0.0 cc, ± 3.5%, and ± 7.0%, respectively, when both subject groups were combined  between the high-risk group (41.1 ± 2.6 years) and the population-risk group (44.6 ± 2.6 years). However, there was no statistically significant difference in weight between the two groups (p = 0.1) (Table r = 0.17 (p = 0.4), − 0.03 (p = 0.9), − 0.05 (p = 0.8) and high-risk group: r =  − 0.06 (p = 0.7), − 0.03 (p = 0.9), − 0.18 (p = 0.3)) or together (r = 0.15 (p = 0.25), 0.002 (p = 1), − 0.02 (0.9), Fig. The image quality in breast and axillae is often better on the right side, due to less residual ghosting from cardiac motion. Due to this and the agreement observed between right and left breasts, comparisons between the two groups were made using only the right breast. Although the age range for the two groups was restricted within 10 years, we found statistically significant age difference (1) Table . In addir = 0.4, p = 0.06; high-risk group: r = 0.18, p = 0.29).The fibro-glandular tissue volume, MRBD, and BPE distribution metrics  were compared between the high-risk and the population-risk groups. Only one statistically significant difference was observed: the BPE kurtosis Table . For botSubsequently, we used linear regression analysis to investigate if the statistically significant higher BPE kurtosis for the high-risk group could be associated with age and breast cancer risk. The multivariable linear regression model showed no evidence of associations between BPE kurtosis and age, breast cancer risk, or their interaction Table . SimilarTwo groups of radiologically normal women at different levels of risk for breast cancer were compared in terms of quantitative measures of fibro-glandular tissue volume, MRBD, and BPE. To the best of our knowledge, this is the first study of this kind. By considering radiologically normal women only, we were able to exclude changes to the breast associated with the development of cancer. This study has two notable methodological features: (i) the assessment of the dynamic range of the DCE-MRI pulse sequence used on all scanners and (ii) the segmentation of the breast parenchyma in pre-contrast images. Our segmentation methods are accessible and use data from standard clinical breast MRI exams, and we took steps to control for hormonal factors and make our quantitative measurements as operator independent as possible. To characterise the DCE-MRI pulse sequence, we used test objects to measure the level of T1 weighting, which determines the dynamic range of spoiled gradient-echo sequences used in DCE-MRI. Our BPE measurement method used pre-contrast images to perform fibro-glandular tissue segmentation to include as much breast parenchyma in the evaluation as practicable. This is important because segmenting post-contrast images (or subtracted datasets) is more likely to exclude portions of the breast parenchyma where the enhancement is low or absent. We characterised the BPE pattern of each subject by a distribution of % enhancement values associated with parenchyma voxels. Our methodology was semi-automated, allowing the user to make adjustments, as validated by an intra- and inter-observer reproducibility evaluation. Although we found small differences in repeated measurements, likely due to differences in interpretation of parenchyma segmentation, the correlation between left and right measurements remained very high, indicating consistent measurements for fibro-glandular tissue volume, MRBD, and BPE.Further validation to the design of the study is offered by both subject populations being limited to pre-menopausal women within a 10-year age range. The significant difference in age between the two groups was not expected and led us to verify that none of the measured breast attributes varied with age within the small range of ages considered, for all patient groups, as shown in Fig. No significant differences were found in fibro-glandular tissue volume, MRBD, and median BPE when the two groups of patients were compared. Our results on MRBD are consistent with those of Hu et al who did not find significant differences in fibro-glandular tissue in the breast cancer group compared with the normal control group . There wPreviously published studies have investigated BPE as a prognostic factor of breast cancer risk. Two large recent meta-analyses found associations between increased BPE with a higher risk of breast cancer , 15. SimWe believe our study contributed towards the standardisation of protocols for BPE measurements by considering the dynamic range of the sequences employed. Clinically, BPE is mainly estimated qualitatively in the four broad categories ; however, the estimations are subjective and suffer from large inter-reader variability . Where qOne limitation of this study is that it is only powered to detect large differences in BPE between the two breast cancer risk groups recruited here. It does not support any significant differences in BPE between the two groups and does not suggest any differences in the perfusion of the normal breast which is associated with the BRCA genes in radiologically normal women. Furthermore, this study has highlighted some of the difficulties in assessing BPE. As expected, there were difficulties in making the measurement operator independent and reproducible. Despite our best efforts, the inter-observer CV was 8.4% for median BPE. However, our high-quality data was acquired with current technology and our methods were optimised for the purpose of BPE calculation. This suggests an intrinsic level of uncertainty in BPE measurements that will hinder its use.In conclusion, this carefully designed study did not reveal significant differences or correlations in fibro-glandular tissue volume, MRBD, or median BPE between the two cohorts of radiologically normal women at different levels of breast cancer risk. However, our work supports further investigation to probe the heterogeneity of the parenchymal enhancement. In future work, this non-uniform nature of breast enhancement could be explored in developing alternative quantitative MRI approaches for breast cancer risk stratification.Supplementary file1 (PDF 107 KB)Below is the link to the electronic supplementary material.


Genetic and lifestyle factors are associated with cancer risk. We investigated the benefits of adhering to lifestyle advice by the World Cancer Research Fund (WCRF) with the risk of 13 types of cancer and whether these associations differ according to genetic risk using data from the UK Biobank.n = 195 822). Polygenic risk scores (PRSs) were computed for 13 cancer types and these cancers combined , and a lifestyle index was calculated from WCRF recommendations. Associations with cancer incidence were estimated using Cox regression, adjusting for relevant confounders. Additive and multiplicative interactions between lifestyle index and PRSs were assessed.In 2006–2010, participants aged 37–73 years had their lifestyle assessed and were followed up for incident cancers until 2015–2019. Analyses were restricted to those of White European ancestry with no prior history of malignant cancer .There were 15 240 incident cancers during the 1 926 987 person-years of follow-up (median follow-up = 10.2 years). After adjusting for confounders, the lifestyle index was associated with a lower risk of overall cancer [hazard ratio per standard deviation increase (95% CI) = 0.89 ] and of eight specific cancer types. There was no evidence of interactions on the multiplicative scale. There was evidence of additive interactions in risks for colorectal, breast, pancreatic, lung and bladder cancers, such that the recommended lifestyle was associated with greater change in absolute risk for persons at higher genetic risk (The recommended lifestyle has beneficial associations with most cancers. In terms of absolute risk, the protective association is greater for higher genetic risk groups for some cancers. These findings have important implications for persons most genetically predisposed to those cancers and for targeted strategies for cancer prevention. Key MessagesCommon genetic variations captured by polygenic risk scores (PRSs) are associated with the risk of multiple cancers, providing opportunities for the identification of high-risk groups.We observed positive additive interactions in the associations between an index of recommended lifestyle and PRSs, consistent with some lifestyles being more beneficially associated with the risk of colorectal, breast, pancreatic, lung and bladder cancers in those at higher genetic risk.If the recommended lifestyle factors are causally related to risk, our data suggest that screening for genetic predisposition followed by lifestyle intervention might provide a viable strategy for targeted risk mitigation for some cancers.,Cancer is the second leading cause of death worldwide.An estimated 30–50% of all cancer cases could be prevented through lifestyle changes, such as eating more fruit, vegetables and wholegrains, and less red and processed meat; being physically active; maintaining a normal bodyweight; and avoiding tobacco and alcohol.,Recent evidence suggests that lifestyle may help reduce cancer risk, even among those with a higher genetic risk of cancer.This prospective cohort study utilizes data from the UK Biobank, which is a large population-based cohort of >500 000 adults aged 37–73 years and living in the UK at the time of the initial assessment in 2006–2010.To reduce the effects of population stratification, our analyses were restricted to participants of White European ancestries and we included participants with no history of any malignant cancer at baseline and sufficient data available for the lifestyle factors and other covariates of interest .et al.r2 < 0.3 to ensure independence  selected by Graff n is the number of SNPs in the set of independent SNPs for that cancer type, ilx is the number of risk alleles  for the i-th individual at the l-th SNP and lOR is the estimated OR for the l-th SNP using a logistic regression.We calculated PRSs as the sum of the number of risk alleles multiplied by the log of the odds ratio (OR) for each SNP, as implemented in PLINK softwarehttps://www.cancerresearchuk.org/health-professional/cancer-statistics/incidence#heading-Four).Each PRS was z-standardized, with higher scores representing a greater genetic risk. A PRS for overall cancer was also derived as the sum of the z-standardized PRSs for the 13 cancer types, each weighted according to the distribution of incident cases of these cancers reported by Cancer Research UK in 2017 (IJE online).A lifestyle index was constructed based on the WCRF/American Institute for Cancer Research (WCRF/AICR) cancer prevention recommendations and standardized scoring system  codes, phenotype and tumour behaviour information obtained through linkage to national cancer registries in England, Wales and Scotland. Incident cancer events were those diagnosed after baseline assessment. Cancer information was complete up to 31 July 2019 for England and Wales, and 31 October 2015 for Scotland. Hence, follow-up time was defined as being from the date of baseline assessment to the earliest of the date of diagnosis, date of death (obtained via linkage to death registries) or 31 July 2019 for England and Wales residents and 31 October 2015 for Scotland residents.Baseline characteristics were summarized for the total study population, those diagnosed with any of the 13 cancers examined  and individually for each cancer type. Characteristics were summarized as percentages for all variables for greater interpretability.IJE online, for a list of the additional covariates and definitions). Additional analyses exploring the risk of cancer in the top 5% of PRSs were conducted using Cox regression with the bottom tertile of PRSs as the reference and adjusting for the same covariates.Cox proportional hazard regression models were used to investigate associations between PRS, lifestyle index and incidence of overall cancer, and separately for each cancer. PRSs and lifestyle index were used as continuous variables, standardized to estimate hazard ratio per SD change. Incidences of cancers that are sex-specific were assessed in the relevant sex only . Breast cancer analyses were further restricted to post-menopausal women only (self-reported at baseline). All models were adjusted for age at baseline (continuous); sex (where relevant); assessment centre; socio-economic status (Townsend Index—continuous); education ; household income ; birth location (north south coordinate and east west coordinate—both in deciles) and population stratification measured by the first 40 principal components. Models with and without mutual adjustment for lifestyle index/PRS were examined. Subsequent analyses adjusting for additional covariates specific to each cancer type were also performed  was applied to interaction models to account for multiple testing. All analyses were performed using Stata SE version 16 . Results are presented in order of power determined from the number of cases for each cancer type.For each cancer outcome, we investigated multiplicative interactions between lifestyle and genetic risk by using a likelihood ratio test to compare Cox models with and without an interaction term between PRS and lifestyle index. Meanwhile, additive interactions were assessed by using the relative excess risk due to interaction (RERI), which was calculated from Cox models with the interaction term.A summary of the baseline characteristics of the 195 822 participants included in the analysis and for those diagnosed with any of the 13 types of cancer during the follow-up period are provided in IJE online). Further adjustment for other potential confounders specific to cancer type had little effect on the estimates presented (IJE online).After adjusting for confounders, a higher lifestyle index was associated with a lower risk of overall cancer [hazard ratio (HR) 95% CI per SD increase in lifestyle index = 0.89 ], colorectal cancer [0.85 ], post-menopausal breast cancer [0.85 ], lung cancer [0.57 ], kidney cancer [0.78 ], uterine cancer [0.82 ], pancreatic cancer [0.79 ], bladder cancer [0.72 ] and oral cavity/pharyngeal cancer [0.78 ] . There wIJE online). Further exploration of genetic risk found that those in the top 5% of PRS for prostate, colorectal, melanoma and pancreatic cancers had an estimated 2.5-, 2.5-, 3- and 4-fold higher risk, respectively . However, some evidence for an additive interaction was observed for colorectal, breast, lung, pancreatic and bladder cancers (IJE online). Forest plots of risk with a combined lifestyle/genetic risk variable were constructed to visually depict these interactions. These plots show a greater change in absolute risk across lifestyle tertiles in individuals with a higher genetic risk of these cancers ]. The additive interaction between lifestyle index and PRS was attenuated by excluding information on smoking status .We further investigated the association between lifestyle and lung cancer by removing smoking status from the lifestyle index and including it as a confounder. Although strongly attenuated, a higher lifestyle index was associated with a lower risk of lung cancer also when smoking was excluded [HR (95%CI) per SD increase = 0.91 , ,,,In this study, adhering to lifestyle advice by the WCRF was associated with a lower risk of several cancers and a higher genetic risk was associated with an increased risk. These associations were independent of each other. There were no multiplicative interactions between lifestyle and genetic risk on the relative risk scale, while we observed some evidence for risk differences (additive interaction) suggesting that adherence to lifestyle recommendations for cancer prevention may be of greater benefit in those with a higher genetic susceptibility to colorectal, breast and pancreatic cancers. We found that risks of lung and bladder cancers do not differ by genetic risk among individuals with higher adherence to lifestyle recommendations. Our findings are consistent with the limited comparable research for breastBRCA1 and BRCA2 gene variants and breast cancer risk, which are far less common  and trigger more frequent cancer screening.A higher genetic risk of cancer was associated with an increased risk of overall cancer and 11 cancer types: prostate, colorectal, breast (post-menopause), lung, melanoma, NHL, kidney, uterine, pancreatic, bladder and lymphocytic leukaemia. The highest genetic risk was observed for pancreatic cancer, with those in the top 5% of PRS having a 4-fold higher risk compared with those in the bottom tertile of genetic risk. This increase is similar in magnitude to ,Lower adherence to lifestyle recommendations was associated with a higher risk of overall cancer and eight cancer types: colorectal, post-menopausal breast, lung, kidney, uterine, pancreatic, bladder and oral cavity/pharyngeal cancers. These findings are consistent with previously published research for breast and colorectal cancers, and add to the limited and inconclusive research on overall lifestyle and other cancers.,We found no association between the lifestyle recommendations and risk of melanoma, NHL, ovarian cancer and lymphocytic leukaemia. These results were not unexpected; none of the lifestyle factors included in the lifestyle index has been conclusively linked with the risk of lymphocytic leukaemia, melanoma or NHL and only obesity is positively associated with ovarian cancer risk.,We found that higher adherence to lifestyle recommendations was associated with a higher risk of prostate cancer. This finding is inconsistent with the majority of previous research suggesting either a negative association or no association for combined or individual components of the lifestyle indexThis large prospective cohort study has some limitations. The length of the follow-up and age range of the study population have limited the number of incident cancer cases. Consequently, some statistical models may not have been adequately powered to observe a modifying effect of genetic risk on lifestyle–cancer associations. There are likely to be measurement errors in the components of the lifestyle score as they are almost all measured via self-report; however, this is likely to attenuate the findings to the null. We also cannot rule out the possibility of residual confounding, although we adjusted for multiple covariates. The measure of genetic risk was limited by the SNPs included in the PRS, which may not be exhaustive. Lastly, this study was conducted in adults of European ancestries, so the relevance of these findings to populations of other ethnicities is unclear.In summary, our findings indicate that adhering to the WCRF/AICR lifestyle recommendations is associated with a lower cancer risk and, for some specific cancer types, greater adherence to lifestyle recommendations may be more beneficial among population groups with a higher genetic susceptibility to that cancer type. Our findings also highlight the potential benefit of using a PRS to identify those with a higher genetic risk of pancreatic cancer, and possibly other cancers, as PRSs are further refined. PRSs could be used in conjunction with other tools to increase the accuracy of identifying high-risk individuals who may then undergo regular screening and be targeted by prevention intervention strategies. This prospect requires further investigation using cohort studies conducted across various ethnic populations.This study is a secondary analysis of UK Biobank data. The UK Biobank cohort study was approved by the National Health Service North West Multi-centre Research Ethics Committee (11/NW/0382), the National Information Governance Board for Health and Social Care in England and Wales, and the Community Health Index Advisory Group in Scotland. All participants provided written informed consent.dyac238_Supplementary_DataClick here for additional data file.


Moderate to heavy alcohol consumption is associated with an increased risk of breast cancer. The etiologic role of genetic variation in genes involved in ethanol metabolism has not been established, with little information available among women of African ancestry.ADH, ALDH, CYP2E1, and ALDH2). We used generalized estimating equations to calculate genetic effects, gene* alcohol consumption (≥ 7drinks/week vs. < 7/week) interactions, and joint main plus interaction effects of up to 23,247 variants in ethanol metabolism genomic regions on odds of breast cancer.Our analysis from the African American Breast Cancer Epidemiology and Risk (AMBER) Consortium included 2889 U.S. Black women who were current drinkers at the time of breast cancer diagnosis (N cases = 715) and had available genetic data for four ethanol metabolism genomic regions . In addition, there was a statistically significant interaction of rs3858704-A in ALDH2 with consumption of ≥ 7 drinks/week on odds of triple negative breast cancer .Among current drinkers, 21% of cases and 14% of controls reported consuming ≥ 7 drinks per week. We identified statistically significant genetic effects for rs79865122-C in CYP2E1 and odds of ER- and triple negative breast cancer. Replication of these findings is warranted.There is a paucity of information on the impact of genetic variation in alcohol metabolism genes on odds of breast cancer among Black women. Our analysis of variants in four genomic regions harboring ethanol metabolism genes in a large consortium of U.S. Black women identified significant associations between rs79865122-C in The online version contains supplementary material available at 10.1186/s13058-023-01660-1. Alcohol consumption has consistently been recognized as a risk factor for breast cancer , 2. PrevADH7, are highly expressed in the liver   = 6.28 , pint = 8.97 × 10–5]. Stratified analysis of the ALDH2 interaction revealed an increased odds of triple negative breast cancer in women who consumed ≥ 7 drinks per week (OR (95% CI) = 4.41 , p = 1.26 × 10–3), and decreased odds in women who consumed < 7 drinks per week (OR (95% CI) = 0.57 , p = 0.013). These associations were driven by Phase 1, as rs79865122 was filtered out of the Phase 2 results due to EffNcases < 2, and rs3858704 was not present in Phase 2. Using LD Link, we identified rs78085062 as in LD with rs3858704 in 1000 Genomes AFR + EUR populations (r2 = 0.986). This variant was also not present in our Phase 2 results, nor were 6 other variants with r2 > 0.7. Significant results for each phase are presented in Additional file In the genetic main effects meta-analysis, we identified multiple testing-corrected statistically significant per-allele associations  in our meta-analysis results for main genetic effects on invasive breast cancer. For each known variant extracted from the literature, we identified proxies in 1000 Genomes Phase 3 African and European populations using LD Link   . We wereumption] , 15, 17.ADH, CYP2E1, ALDH1, and ALDH2. Variants in these genes have been previously investigated for influencing ethanol metabolism or modifying the effect of alcohol on breast cancer risk, including ADH1B*2 (rs1229984), ADH1B*3 (rs2066702), ADH1C*1 (rs698), CYP2E1*5 , CYP2E1*6 (rs6413432-A) and ALDH2*2 (rs671) [ADH1B*2 (rs1229984-T) and ALDH2*2 (rs671-A) than other populations [ADH1B*2 (rs1229984) and ALDH2*2 (rs671) variants are fixed or very low frequency  ≤ 1%) in 1000 Genomes Phase 3 populations of African ancestry, while the ADH1B*3 (rs2066702) and ADH1C*1 (rs698) variants are common (MAF 6.94–28.24%) in these groups. In our lookups of known variants with odds of invasive breast cancer in our meta-analysis results, we replicated the association at rs2075633 in the ADH region on chromosome 4, which was nominally significant in our meta-analysis results and directionally consistent in both phases [ORSNP (95% CI) = 1.37 , pSNP = 0.016).Our analysis focused on the four major human alcohol metabolism genes or gene clusters, including  (rs671) , 57. Somulations . The ADHThis study had several strengths including a focus on breast cancer risk among African American women, breast cancer hormone receptor status information, the use of the MEGA array in Phase 2, comprehensive coverage of genomic regions containing ethanol metabolism genes and gene clusters, and examination of gene-alcohol interactions using both interaction and joint main effects + interaction models.The analysis also had some limitations. Although the Phase 2 chip included variants designed to capture the genetic variation of global populations , compareCYP2E1 region of chromosome 10 with ER- and PR- and breast cancer, potentially pointing to genomic regions influencing the associations identified in our exposure-outcome models examining the impact of consumption of ≥ 7 drinks per week on odds of breast cancer. Although we used the largest available genetics resource relevant to breast cancer in Black women, additional research to further validate the interaction of genetic variants in alcohol metabolism genes and alcohol consumption on odds of breast cancer is warranted.In the present study, we examined the relationship between genetic variation in key ethanol metabolism genes with odds of breast cancer. We also evaluated interactions between alcohol intake and genetic variation on odds of breast cancer. As has been reported in a previous AMBER analysis , we idenAdditional file 1. Table S1: Characteristics of AMBER analytic sample participants by study and phase; Table S2. Significant main, interaction, and joint (2df) effects of SNP and alcohol intake on breast cancer hormone receptor subtypes in AMBER by phase;  Table S3: Statistical significance thresholds by phase in AMBER.Additional file 2. Power plots. Fig. S1: Power to detect main genetic effects for minor allele frequencies= 0.01–0.25. OR = 1.5–4.5 in: A Phase 1 sample size 1847, B Phase 1 sample size 1392, and alpha = 6.79E-5; C Phase 2 sample size 1043, D Phase 2 sample size 950, and alpha = 1.32E-4; and E meta-analysis sample size 2889, F sample size 2494, and alpha = 1.50E-4. Fig. S2: Power to detect interaction effects in A Phase 1: N = 1800, case proportion = 32%, alpha = 6.79E-5; B Phase 2: N = 1000, case proportion = 12%, alpha=1.32E-4; C meta-analysis: N = 2900, case proportion = 25%, alpha=1.50E-4. OR_G = genetic main effect, OR_GE = G*E interaction effect.


Thyroid and breast cancers are the two most frequent types of endocrine-related tumors among women worldwide, and their incidence is still on the rise. Observational studies have shown a relationship between thyroid and breast cancers. Nevertheless, many confounders predispose the results to interference. Accordingly, we performed a two-sample Mendelian randomization (MR) study to investigate the causal association between thyroid and breast cancers.We acquired breast cancer data from the UK Biobank  and the Breast Cancer Association Consortium , and thyroid cancer data from FinnGen Biobank . Then, the multiplicative random effects inverse variance weighting (IVW), weight median (WM), and MR Egger methods were executed for MR analysis.Overall, IVW showed a causal effect of breast cancer on thyroid cancer using the BCAC dataset , and this relationship was also supported by the UK Biobank dataset , which showed that breast cancer patients were more likely to be diagnosed with thyroid cancer. On the whole, the reverse MR analysis did not show a causal effect of breast cancer on thyroid cancer. However, IVW showed a causal effect of thyroid cancer on estrogen receptor -negative breast cancer using the BCAC dataset , which suggested that people with thyroid cancer were more likely to develop breast cancer.Breast cancer represents a possible risk factor for thyroid cancer and thyroid cancer also represents a possible risk factor for ER-negative breast cancer. Future studies using powerful genetic tools to determine the causal relationship between breast and thyroid cancers are required. It is the fifth leading cause of cancer-related deaths worldwide, resulting in 685,000 deaths  studies, genetic variants have been utilized as proxies or instrumental variables for putative risk factors, which overcame these limitations. As a result of the random assignment of genetic variants, the MR approach eliminates the possibility of confounding. In addition, it can avoid reverse causality bias associated with genetic variants assigned before a disease develops. As a result, MR studies are increasingly being used to measure the effects of interventions on disease risk.For this study, we used publicly available pooled statistics from a large genome-wide association study (GWAS) with a two-sample MR design to assess the relationship between thyroid and breast cancers.22.1The overall study design of the two-way, two-sample MR analysis is shown in 2.2The features of the data sources are listed in 2.3-8). Linkage disequilibrium (LD) clustering was conducted to obtain independent SNPs with the standard . Following screening, 142 and 34 SNPs for breast cancer from the UK Biobank and BCAC, respectively were identified as IV candidates. In selecting genome-wide SNPs significantly associated with thyroid cancer, only three SNPs were identified; therefore, we used an optional threshold of implicative correlation (P < 1 × 10-5) and consequently selected 20 SNPs. And then we removed SNPs related to outcome variables based on a threshold of 0.05. Last, we further searched the PhenoScaner database to exclude SNPs that may be related to confounding factors. The obtained instrumental variables were all in line with the above screening process.In order to study the first MR hypothesis, the selected Single Nucleotide Polymorphism (SNPs) must be genome-wide and significantly related to the exposure under interest  were eliminated to minimize underlying polymorphisms. F-statistics of each exposure were calculated with the formula truments , 14. All2.4The inverse variance weighting approach provides the highest accuracy estimation, but may be subjective to ineffective instrumental variables and multivariate impacts. Therefore, with regard to the two cancers and their associations reaching regular significance levels (P < 0.05), three additional sensitivity and multi-effect analyses were performed using the weighted median, MR-Egger, and MR-pleiotropy residuals and outliers (MR-PRESSO) methods to check and rectify likely multivariance. The weighted median method provides a precise estimation if at least 50% of instrumental variables are effective. The MR-Egger regression method detects and adjusts polymorphisms. The MR-PRESSO test detects possible outliers and corrects for the estimates of horizontal polymorphism obtained from the MR-PRESSO analysis. Therefore, in this study, outliers were rejected by correcting the estimates of the horizontal polymorphism obtained by the MR-PRESSO analysis. All statistical analyses were performed in the Windows environment using “TwoSampleMR” (version 0.5.6) and “forestplot” in R 4.1.0. P < 0.05 was considered statistically significant.33.1In general, the included GWAS data were published in 2021 and mainly based on European descent Table 1,3.2First, we selected 125 SNPs associated with breast cancer from the BCAC datasets as valid IVs. Following a critical screening process, 110 SNPs related to breast cancer were selected as effective IVs for thyroid cancer. Causal correlation analysis of breast and thyroid cancers used the multiplicative random effects inverse variance weighting (IVW), weighted median (WM), and MR-Egger methods. The results from the IVW method showed a causal relationship between breast and thyroid cancers , which indicated that patients with breast cancer were more likely to be diagnosed with thyroid cancer Figure 23.3Following a critical screening process, 13 and 14 SNPs related to thyroid cancer in the BCAC and UK Biobank, respectively were selected as valid IVs for breast cancer. Causal effect analysis of thyroid cancer on breast cancer also used the multiplicative random effects IVW, WM, and MR-Egger methods. However, there was no evidence suggesting the causal effects of thyroid cancer on breast cancer using various MR methods in both the BCAC and UK Biobank datasets. A in the BCAC database, patients with breast cancer were divided into ER-positive and ER-negative subgroups according to ER expression in patients with breast cancer. Although a causal effect of thyroid cancer on ER-positive breast cancer was not observed, the IVW method showed a causal relationship between thyroid cancer and ER-negative breast cancer , which indicated that patients with thyroid cancer had an increased risk of ER-negative breast cancer Figure 34To the best of our knowledge, this was the first two-sample MR analysis to investigate the bidirectional association between thyroid and breast cancers. We identified genetic susceptibility to breast cancer that was strongly correlated with an enhanced risk of thyroid cancer using summary data from a previous GWAS. The relationship between the two cancers was also validated using validation datasets. Although the results of the reverse MR analysis did not indicate causal effects of thyroid cancer on breast cancer in general, through subgroup analysis, we found that thyroid cancer was associated with a higher risk of ER-negative breast cancer.Thyroid cancer is one of the most prevalent endocrine malignancies, and it is ranked the fifth most prevalent cancer in women. In 2020, thyroid cancer had the ninth-highest incidence worldwide, and incidence rates continue to rise , 16. MulAs hormonally reactive tissues, the mammary and thyroid glands share similar endocrine signals. Mammary cells respond to signals from thyroid hormones and can be affected by changes in the thyroid hormone levels. Similarly, thyroid cells respond to sex hormones, especially estrogen, and may undergo carcinogenic transformation upon estrogen stimulation. Both thyroid and sex hormones also show important levels of transcriptional cross-talk, influencing tumorigenesis and sensitization to treatment .Additionally, studies have explored the effects of thyroid cancer treatment with radioactive iodine (RAI) on breast cancer. In a meta-analysis, 14 studies with 200,247 differentiated thyroid cancer(DTC) individuals were included , which indicated that DTC patients receiving RAI did not have a higher risk of developing primary breast cancer than those who were not treated with RAI. These results indicated that RAI therapy did not affect the probability of developing breast cancer .These results of these studies support the those of the present study. SNPs used as IVs are unaffected by numerous confounding factors, making the MR technique particularly advantageous. Additionally, the sequential order used in causal inference supports the validity of the conclusions.As previously described, causal estimations are valid when the three assumptions of the MR model are satisfied. In the first assumption, independent SNPs that closely related to the breast and thyroid cancer were selected. In addition, the F-statistic was >10 for each exposure, demonstrating that the selected SNPs were reliable for IV. In addition, the data in this study were obtained from European populations, thus avoiding bias caused by different populations to a certain extent. Second, we used the MR-Egger regression method to assess the bias created by polymorphism in MR and found that the intercept was close to 0 (P > 0.05), showing that there was no polymorphism caused by unknown factors. In addition, no polymorphisms were observed using the MR-PRESSO method. Third, the results of the heterogeneity test confirmed the absence of heterogeneity. In summary, the selected IVs were reasonable, and the study results were convincing. Our research shows that patients with breast cancer are more likely to develop thyroid cancer, and that patients with thyroid cancer have an increased risk of developing ER-negative breast cancer. Therefore, for patients with breast cancer, more emphasis should be placed on preventing thyroid cancer. Similarly, patients with thyroid cancer should be cautious of developing breast cancer by seeking early diagnosis and treatment for a good prognosis.However, our study had some limitations. First, the study analyzed data from a European population to ensure that the results were not skewed; however, there are limitations to extrapolating our results to other populations. Second, we screened and examined risk factors that were potentially modifiable and reported in previous studies. Those that had not been reported might also influence the pathogenesis of the resulting tumor. Third, the MR method could only be used to analyze cause and effect relationships; the mechanisms behind the increased risk of cancer could not be studied. More comprehensive studies are required to further explore the mechanisms that influence the increased risk for each type of cancer.The datasets presented in this study can be found in online repositories. The original contributions presented in the study are included in the article/LL: conception, design, acquisition, interpretation of data, writing—original draft, and writing—review and editing. JL: data acquisition, analysis, interpretation of data, and writing—original draft. All authors contributed to the article and approved the submitted version.


CLOCK gene and breast cancer risk were inconsistent. This study performed a meta-analysis of the association between CLOCK gene polymorphisms and breast cancer risk. PubMed, Cochrane Library, and Embase databases were electronically searched to collect studies on the association between CLOCK gene polymorphisms and breast cancer risk from inception to February 14, 2022. The quality of the included literature was assessed using the Newcastle–Ottawa Scale. For statistical analysis, odds ratio (OR) and 95% confidence intervals (CIs) were calculated using STATA 14.0. In addition, publication bias was performed by the funnel diagram and the Harbord’s regression test. And sensitivity analysis was assessed by the trim and fill method. A total of 6 eligible studies, including 10,164 subjects (5488 breast cancer cases and 4676 controls), were screened in this meta-analysis. Though we did not find a significant association between the polymorphisms in the overall CLOCK gene with breast cancer risk , we found that compared with T/T types of rs3749474 in CLOCK, T/C and C/C types of rs3749474 were associated with lower risk of breast cancer . The sensitivity analysis confirmed the robustness of the results. The funnel plot showed no significant publication bias. Polymorphisms in the CLOCK gene might be associated with breast cancer risk. More studies are needed to confirm the conclusion.Previous studies found that the circadian clock gene participated in the genesis and development of breast cancer. However, research findings on the relationship between polymorphisms in the  Breast cancer is one of the most common malignancies in women worldwide, and its incidence is increasing yearly , Circadian Locomotor Output Cycles Kaput (CLOCK), cryptochrome circadian regulator 1/2, period circadian regulator 1/2/3.  = 0.64 ] among 563 breast cancer cases and 619 controls  principle  odds ratio (OR), hazard ratio and 95% confidence intervals that could be obtained directly or indirectly calculated based on the data provided in the graphics or tables; (5) study in English.Inclusion criteria were as follows: (1) patients definitively diagnosed with breast cancer; (2) definitive detection of CLOCK genotype and allele frequency that were provided in the study; (3) relationships between Exclusion criteria were as follows: (1) duplicate publications or incomplete data; (2) reviews, meta-analysis, only abstracts, conference proceedings, expert reviews, and case reports; (3) literature with unclear diagnostic criteria of breast cancer and genotyping methods; (4) control group with unclear population or history of cervical cancer-related diseases; (5) cell or animal studies.CLOCK polymorphisms, sample size, clinical stage, detection method, age, and the number of subjects.Two investigators independently screened the literature and extracted the required information from the literature. The required information included: first author, year of publication, geographic region, Two researchers independently evaluated the quality of the included literature according to the Newcastle–Ottawa Scale (NOS) criteria and the total score ranging from 0 to 9 Stang . StudiesCLOCK gene polymorphism and the risk of breast cancer. The dominant model, the recessive model, and the over-dominant model were used in the meta-analysis. We used the Q test and I2 statistic to evaluate the heterogeneity across studies. If P ≥ 0.1 and I2 < 50%, the results indicated that the homogeneity among studies was good, and the fixed-effect model was chosen for the meta-analysis; if P < 0.1 and I2 ≥ 50%, the results indicated that there was heterogeneity among studies, and the random-effect model was chosen. Sensitivity analysis was used to evaluate the effect of individual studies on the final results and to determine the reliability of the results. Funnel plots and Harbord’s test was used to assess whether there was significant publication bias. Meta-analyses were performed using STATA 14.0 . Except for the heterogeneity test, two-sided P values < 0.05 were considered statistically significant.In this meta-analysis, the pooled odds ratio (OR) with its corresponding 95% confidence intervals (CIs) were used to assess the effect size for studies that reported the association between CLOCK were determined by Polymerase Chain Reaction (PCR). Characteristics of the included 6 studies are shown in Table According to the search strategy, a total of 122 studies were obtained from various databases. Among them, 13 studies were excluded due to duplication; 34 studies were removed due to being reviews, only abstracts, conference literature, or systematic evaluation; 15 animal experiments were excluded; 19 studies with non-specified genes were excluded. Therefore, seven studies were obtained provisionally. In addition, two studies were obtained from reference list searching. After excluding three studies with incomplete data, six studies comprising 10,164 subjects, including 5488 breast cancer cases and 4676 controls, remained in the meta-analysis. The literature search process is detailed in Fig. CLOCK gene SNPs were included. The dominant model, over-dominant model, and recessive model were used in the meta-analysis. However, we did not find a significant association between CLOCK polymorphisms and breast cancer 2 < 0.001 and Heterogeneity = 0.971). The results of the meta-analysis are shown in Fig. A total of three studies examined the association between rs3749474 and breast cancer risk. In the sub-meta-analysis of rs3749474, we found that compared with T/T types of rs3749474, T/C and C/C types of rs3749474 were associated with lower risk of breast cancer , with non-significant heterogeneity across studies (I%CI = 0.9P = 0.864) indicated no publication bias.The Cochrane Collaboration’s tool for assessing the risk of bias is shown in Fig. The leave-one-out sensitivity analysis showed that no single study significantly affected the pooled correlation from the meta-analysis (the OR ranging from 0.98 to 1.24), which indicated the reliability of the findings. The trim and fill method suggests funnel plot symmetry  = 0.93 ]. These results uncover the important role of CLOCK polymorphisms in breast cancer.To provide insight into the relationship between Breast cancer is one of the most common cancer types among women. Further understanding of progress, treatments, and biomarkers of breast cancer is urgently needed. A number of risk factors have been found to be associated with the risk of breast cancer these years, including cytokine, miRNA, and trace elements in food, etc. (Çetin and Topçul It has been reported that disruption in sleep and circadian rhythm disorders are significantly associated with the risk of breast cancer (He et al. CLOCK is one of the core circadian clock genes. Its corresponding protein belongs to the basic helix-loop-helix PAS family of transcription factors and forms heterodimers with BMAL1 to enhance target gene expression (Benna et al. CLOCK has been identified as a significant modifier of breast cancer incidence (Sancar and Gelder c-Myc could be controlled by CLOCK; on the other hand, oncogenes c-Myc, P53, and Ras could affect the face of CLOCK (Sancar and Gelder CLOCK could induce remodeling of the tumor microenvironment cells by disturbing the cellular metabolism, altering gene expression, and aberrantly activating signaling pathways (Malla et al. CLOCK contributed to T cell exhaustion and global upregulation of immune inhibitory molecules (Wu et al. Evidence has also shown that individual genes of the circadian clock play a role in controlling tumorigenesis. CLOCK 3’-untranslated region. In the current study, we performed the sub-group meta-analysis only for rs3749474, and we found that compared with wild type T/T, the homozygous mutant-type C/C and heterozygous-type T/C of rs3749474 were associated with a lower risk of breast cancer. However, rs3749474 is not specific to breast cancer. Zhou et al. found that the homozygous mutant-type of rs3749474 was associated with better survival of colorectal cancer (F. Zhou et al. SNP rs3749474 is located in the CLOCK polymorphisms and breast cancer risk, which incorporates multiple SNPs. Our findings may gain insights into breast cancer genetics and uncover the roles of the circadian clock gene on breast cancer, which helps provide a basis for further research in breast cancer diagnosis and treatment. However, the present study has some limitations. First, we could not conduct a subgroup meta-analysis given the limited data in the included studies, so our results should be interpreted cautiously. Second, only studies published in English were included; therefore, we might have missed some relevant studies in other languages. Third, bias might have been introduced due to confounding factors among different studies.To our knowledge, this is the first meta-analysis of the association between CLOCK gene rs3749474 are negatively associated with the risk of breast cancer. More studies are still needed to confirm this conclusion.The mutation types of


P = 0.007), high-density lipoprotein cholesterol (HDL-C)  and triglycerides (TG)  were causally related to breast cancer risk. In multivariable MR, only HDL-C  retained a robust effect, suggesting that the genetic association between BMI, HDL-C and TG with breast cancer risk in univariable analysis was explained via HDL-C. These findings suggest a possible causal role of HDL-C in breast cancer etiology.Observational studies showed that metabolic phenotypes were associated with the risk of developing breast cancer (BC). However, those results are inconsistent regarding the magnitude of the association, particularly by subtypes of breast cancer. Furthermore, the mechanisms of the association remain unclear. We performed two-sample Mendelian randomization (MR) analyses to evaluate the causal effect of metabolic risk factors on breast cancer in the European population. Assessed individually using MR, body mass index (BMI) (odds ratio [OR] 0.94, 95% Confidence interval [CI] 0.90–0.98,  Breast cancer (BC) is the most prevalent invasive cancer and the second leading cause of cancer death in women. The National Cancer Institute and the Centers for Disease Control and Prevention claim that breast cancer risk was 129.7 per 100,000 women in the US in 2019 . The mortality rate was 19.4 per every 100,000 women (95% CI 19.2–19.6). The burden of breast cancer motivates scientists and researchers to comprehend better the etiology of breast cancer and provide more effective prevention strategies.4. For example, a common feature of many malignancies, including breast cancer, is the reprogramming of lipid metabolism6. The mortality rate among women diagnosed with breast cancer has been associated with type 2 diabetes (T2D)7. Breast cancer frequently occurs with a set of metabolic risk factors including diabetes, central obesity, and poor lipid profiles. Those observational studies indicated that those risk variables are all likely to be regulated by genetic and environmental risk factors9.While the exact cause of breast cancer is not fully understood, factors such as age, genetics, and lifestyle choices have been linked to its development. There is convincing evidence that metabolic characteristics are linked to a higher risk of developing breast cancer8. It is challenging to completely protect observational studies from bias due to reverse causation or confounding variables. Obesity has been identified as a risk factor for postmenopausal women's breast cancer10. However, for premenopausal women, a greater body mass index (BMI) may operate as a protective factor in reducing the incidence of breast cancer11. Although T2D raises the breast cancer risk, other researchers hypothesized it may actually be a marker for the adiposity-breast cancer relationship rather than the true cause of this cancer12. Observational epidemiological studies have found a range of associations between high-density lipoprotein cholesterol (HDL-C) and breast cancer, including positive13, negative15, and no relationships16. Mendelian randomization (MR) analysis makes it possible to evaluate the causal relationship between a particular exposure and disease risk while minimizing any biases that may be present in traditional observational studies. It is being utilized more frequently because it can overcome an important drawback of evidence from observational studies: unmeasured confounding . It requires that (1) the genetic variants are associated with the exposure, (2) the genetic variants are independent of a measured or unmeasured confounder, and (3) the genetic variants can only influence the outcome through the exposure  is one of the most used techniques that is resistant to horizontal pleiotropy but suffers from loss of power21. On the other hand, the weighted median technique can produce reliable casual estimates if the invalid IVs account for up to 50% of the weight of the studies22. Mendelian Randomization Pleiotropy Residual Sum and Outlier (MR-PRESSO) method is able to find outliers for preventing potential horizontal pleiotropy and correct the causal estimates23. The constrained maximum likelihood and model averaging (cML-MA) approach is resistant to invalid IVs as well as uncorrelated or correlated pleiotropy24.Several MR analysis techniques have been developed to determine the causal relationship between risk variables and complex diseases. The inverse variance weighted (IVW) method  is most effective when all IVs are legitimate, but it can produce biased inference when horizontal pleiotropy is presented30. However, those studies have limitations. First, the causal effect was initially estimated using the IVW approach. The IVW requires all the genetic variants to be valid IVs, the estimation may be biased if horizontal pleiotropy is present, which is a violation of assumption 3. When IVW is used, most studies conduct sensitivity analyses such as MR-Egger or weighted median to detect or account for potential biases due to horizontal pleiotropy, the consistency between the estimates from the IVW analyses and sensitivity analyses should be assessed. Studies have shown that complex diseases frequently exhibit horizontal pleiotropy, which can lead to severe bias in MR settings23. Second, the causal relationships between metabolic risk factors and breast cancer were investigated separately. Evidence indicated that these exposures were closely related, which makes it more difficult to determine whether one exposure mediates another exposure, or whether both exposures have a simultaneous causal effect on the outcome31  method. The cML-MA method produces a robust estimate by eliminating invalid IVs (i.e. pleiotropic variants)cer Fig. c. ThirdlP = 0.007) for breast cancer  and the I2 statistic (I2 > 75%)  of 0.94  Table . We obse%) Table .Figure 2–11)  Fig. . 19 outl11) Fig. . After r11) Fig. . The assP = 1.58 × 10–6)  Fig. . The ass–6) Fig. . A formaP = 0.018). The causal estimate of both BMI and TG was diminished. The adjusted OR for BMI is 0.98 , and for TG is 0.96 . Both P-values were not significant, suggesting the causal estimates were not distinguishable from the null. The multivariable MR results indicated that there was robust evidence for a potential causal role of HDL-C in the development of BC after adjusting for BMI and TG.When BMI, HDL-C and TG were assessed together in multivariable MR, only the causal relationship between HDL-C and BC risk remained robust Fig. a. The adP = 0.034) and with ER- . Height is found only to be significantly related to ER + . There is compelling evidence that HDL-C is positively correlated with both ER +  and ER- . TG is also found to be negatively correlated with both ER +  and ER − .Next, we performed the MR analysis after stratifying the breast cancer by ER + and ER- status. The six metabolic-related traits' effect size estimates on the BC subtypes were comparable to those on the overall risk for breast cancer Table  and S3. P = 0.013)      Fig. . Associa–4) Fig. . The cauWhen BMI, HDL-C and TG were assessed together in multivariable MR, the causal estimate of all three traits was diminished Fig. b and c. In this study, a two-sample MR study was used to examine the causal relationship of six metabolic risk factors with breast cancer risk. Using univariable MR analysis, we found BMI, HDL-C and TG causally linked to the breast cancer risk. When BMI, HDL-C and TG were taken into account in multivariable MR, the relationship between BMI and TG on breast cancer risk was attenuated. Only HDL-C retained a robust effect with breast cancer risk, indicating that HDL-C was responsible for the genetic association between BMI and TG with breast cancer risk in the univariable study. Our study offers empirical proof from human genetics that HDL-C is essential for metabolic risk factors to have a causal effect on breast cancer risk. These results add to our understanding of the relationships between BMI, HDL-C, TG and breast cancer and highlight the relevance of preventing high HDL-C in obese patients to reduce breast cancer risk.32. However, breast cancer is rare in women before menopause33. That unlikely benefit is offset by the health risks of being overweight or obese including heart disease, diabetes, and an increased lifetime risk for several other cancers . Although BMI doesn't directly measure body fat, it does indicate if there is excess body fat. Body fat can influence breast cancer risk through an effect on hormones34. By elevating estrogen levels, having more fat tissue can raise your risk of developing breast cancer. Additionally, obese women have higher insulin levels than normal, another hormone whose elevated levels have been connected to a variety of malignancies, including breast cancer.Studies suggested that extra weight reduces breast cancer risk in premenopausal women28. Higher levels of HDL cholesterol have been linked to a lower risk of heart disease. However, there’s been debate in lipid-related therapy whether targeting raised HDL-C levels is beneficial37. Our findings provide additional evidence against general use of HDL-C therapy.Our study found that high HDL-C levels may increase breast cancer risk. This result is somewhat surprising given that increasing HDL-C is thought to be healthier15. Similarly, BMI also been found to be associated with breast cancer in observational studies32. Those studies were conducted individually and did not consider the correlations among those metabolic risk factors. When analyzing those risk factors together, we observed that SNPs associated with one metabolic-related trait tend to associate with other metabolic traits, highlighting their pleiotropic nature. Therefore, assessing those metabolic risk factors together is needed. Although, in univariable analysis, we observed significant causal effects of both BMI and TG with breast cancer risk, those associations were diminished when we adjusted for HDL-C. Those findings have clarified the underlying biological pathways of BMI, HDL-C, TG and breast cancer association. Our study provided additional insight into the genetic and biological basis of the association between metabolic risk factors and breast cancer risk.The association between HDL-C and breast cancer has been reported intensivelyIn the univariable analysis, there was evidence of horizontal pleiotropy for all the risk factors, which reflected the shared biological pathways between metabolic risk factors and breast cancer. We addressed this issue by using the cML-MA method, which removed the potential pleiotropy variants and provided a more accurate causal estimate. The pleiotropic variants identified in univariable analysis were further removed when conducting multivariable MR analysis. By taking horizontal pleiotropy into account, we have considered many of the relevant factors in our analyses. Thus, our results provided robust estimates between HDL-C and BC risk after adjusting for BMI and TG.We performed MR analysis to explore the potential causal effects on breast cancer subtypes of six metabolic risk factors. In univariable analysis, we observed that height was only causally associated with ER + , while BMI was suggestively associated with both ER + and ER −. We found strong evidence that both HDL-C and TG causally linked to both ER + and ER- breast cancer. After multivariable analysis, only the causal effect of HDL-C remained for ER-, this is consistent with our observation of BC risk. It is interesting to note that for ER + , only the causal effect of TG remained in the multivariable MR analysis.We did not find any significant causal relationship between T2D or LDL-C and BC risk. One possible explanation is the IVs we selected for T2D and LDL-C in our analyses are also associated with breast cancer. We noted that the funnel plots show asymmetry for both T2D and LDL-C, indicating the presence of horizontal pleiotropy. Another possibility is that the SNPs associated with T2D or LDL-C only account for a small proportion of the heritability, so there’s not enough power to detect the causal effect, as has been seen in traditional observational data.Finding the causal link between metabolic characteristics and breast cancer risk still presents several difficulties. To continue implicating additional loci, larger sample sizes will be required, and if cohorts are pooled in the original GWAS heterogeneity among studies may become an issue. When evaluating the causal effect, horizontal pleiotropy may provide challenges. It will be required to carefully take into account the prospective IVs and statistical techniques.Assumption 1 the genetic variants must be significantly associated with the exposure. SNPs that have been replicated in an independent sample and pass genome-wide significance (P < 5 × 10–8) are typically employed as instruments in MR investigations. The employment of weak instruments can cause MR estimates to be biased towards the confounded observational estimate. It may be beneficial to use multiple SNPs as instrument variables in MR research because common genetic variations only account for a small fraction of a trait's variance.Assumption 2 the IVs ought to be independent of any confounding factors. It is technically impossible to demonstrate that this assumption is true in an MR study, but by looking at the link between the variant and known confounders of the exposure-outcome relationship, it may be possible to do so.Assumption 3 there were no alternative paths by which the IVs directly impacted the outcome (breast cancer), only the exposure. This is also known as the exclusion restriction criterion or the "no pleiotropy" assumption. This presumption may be violated by horizontal pleiotropy, in which an SNP is linked to several phenotypes independently. While it is impossible to demonstrate that this assumption is true in an MR study, there are several adaptations to the standard MR design that can be utilized to identify its presence and estimate the causal effect of the exposure even when the assumption is violated.To calculate the causal inference between metabolic risk factors and breast cancer risk, this study used a two-sample MR design. The MR study required to employ three hypotheses Fig. b.AssumptP < 5 × 10–8) in the European population as instrumental variants for each exposure. For BMI, we selected 941 variants genome wide significantly associated with BMI from a GWAS from the Genetic Investigation of ANthropometric Traits (GIANT) consortium of 681,275 European individuals38. We selected 697 variants that were genome wide significantly associated with height from 253,288 European individuals that also came from of GWAS of the GIANT consortium38. For T2D, there were 139 common variants that were genome wide significantly associated with T2D from a sample of 655,666 Europeans38. Lastly, we selected 534 variants from a 403,943 sample of European individuals that were genome wide significantly associated with high density lipoprotein cholesterol levels, and 220 variants from a 440,546 sample of European individuals that were genome wide significantly associated with low density lipoprotein cholesterol levels 38. For sex-specific MR analysis, we selected sex-specific SNPs associated with the exposure at genome-wide significance level (P < 5 × 10–8) in the European population.Summary statistics for all six exposures were available from public GWAS repositories Table . All exp45. This was accomplished by further clumping the exposure-related SNPs in the European sample based on the linkage disequilibrium structure of the 1000 Genomes Project (r2 < 0.001 with any other associated SNP within 10 Megabase (Mb)).Next, to ensure assumption 2, the IVs ought to be independent of any confounding factors. Unless steps are made in the MR analysis to account for any correlation structures that occur through linkage disequilibrium (LD), it is crucial to make sure the instruments chosen for an exposure are independent46. It is possible to substitute SNPs that are LD 'proxies' if a chosen SNP was unavailable from the breast cancer GWAS study. Using data from the 1000 Genomes Project, we chose proxy SNPs that had high correlations (r2 > 0.8) with the desired variant.The Breast Cancer Association Consortium (BCAC), which included 122,977 cases and 105,974 controls of European ancestry, provided effect estimates for those chosen SNPs on breast cancer risk45. Palindromic SNPs with an intermediate minor allele frequency (MAF > 0.42) were further eliminated45.To ensure that the effect alleles for the SNP effects in the exposure and outcome are the same, we harmonized exposure and outcome SNP effects. We flipped the sign of breast cancer effect estimates if the effect allele in the study associated with the exposure is the non-effect allele in the study associated with breast cancer. Palindromic SNPs, which have A/T or G/C alleles, can make it difficult to determine the identification of the effect allele in exposure and outcome GWASs24. We understand that such pruning could possibly remove variants that are in the same causal pathway with the risk of breast cancer, affecting the strength and validity of study instruments. Additionally, we applied four other MR techniques, MR-PRESSO, MR-Egger, IVW and Weighted median as sensitivity analysis. We presented results from analyses using the instruments that either included or excluded pleiotropic SNPs.First, we conducted two-sample mendelian randomization analyses to assess the relationship between each of the six metabolic risk factors and breast cancer risk individually. In order to accurately evaluate the causal relationship, we applied a cutting-edge approach called cML-MA which produces a robust estimate by eliminating invalid IVs (i.e. pleiotropic variants)31.Second, individual MR analyses results were carried out to further elucidate which of the metabolic traits is of fundamental relevance to breast cancer. We further employed the multivariable MR Fig. b to examP-value between 0.008 and 0.05 was regarded as suggestively significant and a P-value less than 0.008 (0.05/6 after Bonferroni adjustment) as statistically significant.The odds ratios and corresponding 95% confidence intervals for the results were displayed. To account for multiple comparisons, the Bonferroni method is used to handle the estimation of the chance of error in multiple testing for the six exposures. In the univariable MR analyses, a 2 statistic to quantify heterogeneity among causal estimates across all IVs. The corresponding P-value of Q statistic (P > 0.05) offers more reliability for causal effects47. The SNPs were also assessed by the F-statistics for week IV bias48. Additionally, we conducted a leave-one-out analysis to identify any potential high influence variant driving the causal association between metabolic traits and breast cancer risk.Besides just applying all the MR analyses described above to demonstrate sensitivity to different patterns of assumption violations, we calculated the Cochran’s Q statistic and the IIn conclusion, our findings demonstrated that HDL-C was critical in facilitating the causal effects of breast cancer risk. Additional MR studies of breast cancer subtypes  helped to establish the exact nature of this relationship. BMI, HDL-C and TG may be utilized as prognostic indicators of breast cancer and have significant clinical significance.Supplementary Information 1.Supplementary Information 2.


Higher mammographic breast density in premenopausal and postmenopausal women is related to a higher breast cancer risk. In this review, we analyze the correlation between estrogen, progesterone, and mammographic density in postmenopausal women and clarify whether these findings are consistent across different types of mammographic breast density. We extracted data concerning mammographic density increases in the populations treated with estrogen-only hormone replacement therapy and those treated with estrogen and progestin hormone replacement therapy. Postmenopausal women treated with estrogen and progesterone regimens had a statistically significant lesser mammographic density increase than estrogen-only hormone replacement therapy regimens. Higher mammographic breast density in premenopausal and postmenopausal women signifies a higher breast cancer risk and development -3. When Women with a breast density area of ≥75% have a three-to-six-fold risk of developing breast cancer compared to women with a breast density area of ≤5% . PremenoIn postmenopausal women, progesterone promotes epithelial and stromal growth in the mammary gland and is associated positively with the increased risk of developing breast cancer . VariousThe main objective of this review is to analyze the correlation between estrogen, progesterone, and mammographic density in postmenopausal women and clarify whether these findings are consistent across different types of mammographic breast density. To achieve this, we found six articles from the relevant bibliography. We extracted the relevant patient characteristics and estrogen/progesterone hormonal replacement therapy (HRT) data correlating to mammographic density. With the help of a review tool, we analyzed the data. We attempted to determine whether there is a correlation between estrogen, progesterone, various hormonal replacement therapies, and mammographic density levels in postmenopausal women.Materials and methodsSearch Strategyhttp://www.prismastatement.org/; accessed on August 12th, 2023).The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines , Scopus , and Web of Science . For our included studies, only those published in the last 20 years were considered relevant and up-to-date. This decision was taken to ensure consistency in evidence across the studies. No other restrictions were applied to the query.We searched for ((“progesterone” OR “progestogen”) OR (“estrogen” OR “estrogens”) AND (“mammary” OR “mammography” OR “mammographic” OR “mammary glands” OR “breast” OR “breasts”) AND  AND (“mammographic density”)). The search on PubMed yielded  by reading their abstracts. For all relevant articles, the authors reviewed the entire paper, and all inclusion and exclusion criteria were applied to narrow down the result set to only the pertinent studies of interest. Any disagreements were resolved following consensus. Furthermore, the snowball procedure was applied in all selected papers for other studies that might have been relevant.Inclusion and Exclusion CriteriaThe eligible studies considered by the authors were case-control, cross-sectional, and cohort studies that examined mammographic density, progesterone, and/or estrogen HRT in postmenopausal women. Two authors (AK and IB) manually assessed the relevant articles to properly report the patients' hormonal replacement therapy, mammographic density reference, and appropriate population characteristics . All cell culture studies, case series, and case reports were considered irrelevant and were excluded from the result set. Any studies that lacked information on density or hormonal therapy treatment were also excluded. Finally, only studies in the English language were considered.Data Extraction and Quality AssessmentAfter the inclusion and exclusion criteria were applied to the result set, the result set was narrowed down to six relevant studies. Two authors (AK and IB) independently reviewed the studies and extracted all relevant data of interest using a customized data extraction form. The form included the following characteristics: author, year of publication, number of patients, the mean age of the population group, parity (nulliparous or parous), BMI, smoking habits, mammographic density, and HRT. Based on the extracted characteristics, each study was rated poor, fair, or good, emphasizing the sample size and the appropriate reporting of the outcome variables. The authors also performed a statistical analysis based on the study population and mean breast density using the Review Manager 5.4 Tool .ResultsStudy CharacteristicsWe identified 772 studies eligible for this review, as seen in the PRISMA study design diagram in Figure The six studies that were identified for inclusion referred to 40649 postmenopausal women ,17-21. MWe also extracted data presenting mammographic density increases in the populations treated with estrogen-only hormonal replacement therapy and those treated with estrogen and progestin hormone replacement therapy. The data reported on the percentage of increase in mammographic density, and from a first glimpse, we couldn't see estrogen and progestin hormonal replacement regimens presenting many different results in breast density increase compared to the estrogen-only regimens. The extracted mammographic density data can be seen in Table We decided to perform a statistical analysis and see if a statistical tool would accurately represent the data and extracted conclusions. We utilized Review Manager 5.4 (RevMan) for the analysis, and we reformed the percentage data to actual population numbers so the representation of the analysis would be as accurate as possible. Because RevMan does not allow decimal numbers, we had to multiply our data by 100 for the software to accurately represent the data and ensure we hadn't rounded any of the extracted data.After introducing the data in RevMan, we observed an obvious, statistically significant result in the forest plot the software produced. Postmenopausal women treated with estrogen and progesterone HRT regimens had lesser mammographic density increases than estrogen-only combined HRT regimens. The forest plot data and results can be seen in Figure DiscussionIn recent years, there has been an emphasis on establishing HRT as a preventive measure to avoid cardiovascular events and osteoporotic fractures . It is cThe data from the existing literature demonstrate that HRT, in most cases, can lead to an increase in breast density . More spAzam et al., in 2020, carried out the first extensive literature report on the possible association of HRT with changes in breast density. It summarized the effect of combinations of estrogens and their derivatives with or without the addition of progesterone . This stAdditionally, as part of the literature review, we studied the addition of dydrogesterone to HRT regimens. Despite the difficulties encountered, there are research papers that conclude the beneficial addition of dydrogesterone to HRT regimens with a prophylactic effect on breast cancer . The proIn summary, it is crucial to answer whether the addition of progesterone to estrogenic HRT regimens increases the risk of breast cancer. Our study through the performed meta-analysis shows that the association of progesterone addition is statistically significant as it limits the change in mammary density. Another question is the choice of the appropriate progesterone; although medroxyprogesterone is most preferred in HRT, new literature recognizes dydrogesterone and natural progesterone as possible additions. In any case, primary research and clinical studies are needed to answer the research question. Until then, from the existing literature, progesterone appears to be an ally in preventing the increase in breast density and, possibly, breast cancer.


LEP and ADIPOQ genes, as well as their receptor-encoded genes with risk for breast cancer. Breast cancer has a strong genetic predisposition, and its genetic architecture is not fully understood thus far. In this study, we aimed to perform a meta-analysis to evaluate the association of genetic alterations in Only published studies conducted in humans and written in English were identified by searching PubMed, SCOPUS, CINAHIL and Embase from their inception to October 2022. Eligibility assessment and data collection were completed independently by two researchers. Statistical analyses were done using the STATA software. LEP gene rs7799039-G allele  and ADIPOQ gene rs1501299-T allele  were associated with the significant risk of breast cancer. In subgroup analyses, differences in menopausal status, obesity, race, study design, diagnosis of breast cancer, genotyping method and sample size might account for the divergent observations of individual studies. Circulating leptin levels were comparable across genotypes of LEP gene rs7799039, as well as that of LEPR gene rs1137101 (P>0.05). Begg’s funnel plots seemed symmetrical, with the exception of LEPR gene rs1137100 and ADIPOQ gene rs1501299. After literature search, 33 publications were eligible for inclusion. Overall, LEP gene rs7799039 and ADIPOQ gene rs1501299 were two promising candidate loci in predisposition to breast cancer risk. Taken together, we found, in this meta-analysis, that  Breast cancer is a leading cause of death in women . Global LEP) and adiponectin (ADIPOQ) and their receptors are promising candidates in predisposition to breast cancer risk.It is widely recognized that obesity is linked to an enhanced risk of tumorigenesis . Leptin LEP and ADIPOQ genes as well as their receptor-encoded genes by pooling published summary data, aiming to evaluate their association with risk for breast cancer, as well as circulating leptin and adiponectin levels.To test this hypothesis, we conducted a meta-analysis on genetic alterations in The conduct of this meta-analysis complied with the PRISMA  guideline .Only peer-reviewed published studies were retrieved in this meta-analysis by searching PubMed, SCOPUS, CINAHIL and Embase electronic datasets from their inception to October 2022. The key words used for indexing studies in above datasets were formulated from the MeSH  database, and they are expressed in logistic relations, that is, (“breast cancer” or “breast neoplasm” or “breast tumor” or “breast carcinoma” or “cancer or breast” or “mammary cancer”) and (“leptin” or “lep” or “leptin receptor” or “adiponectin” or “ADIPOQ” or “adiponectin receptor” or “ADIPOQR”) and . The search process was completed by two researchers (X.L. and C.L.) independently. Search results from different datasets were managed by the ENDNOTE software version X9.3.3, and duplicate records were deleted.In addition, potential missing studies were complemented by checking the references of reviews, meta-analyses and major original articles in search results.LEP or ADIPOQ genes or their receptor-encoded genes between patients with breast cancer and controls or mean or median values of circulating leptin or adiponectin levels for single genotypes or their combination . Items of data covered surname of first author, year of publication, study design, race, country where study participants resided in, menopause status, source of control participants, factors matched between patients and controls, genotyping method, diagnostic criteria of breast cancer, sample size, chronological age, age at menarche, age of first delivery, nulliparous, percentage of ER, PR and Her-2 of patients with breast cancer, height, weight, body mass index, cigarette smoking, alcohol drinking, family history of breast cancer and genotypes of genetic alteration between patients and controls.If there was disagreement between the two researchers, original article was assessed, and if necessary, a third researcher (W.P.) was involved.I2), and I2 over 50% or the probability of associated χ2 test less than 0.1 was indicative of statistical significance. Exploring sources of heterogeneity was implemented by using subgroup analyses according to categorical items of interest. Contribution of each study to overall OR was illustrated by sensitivity analyses.Summary data from identified eligible studies were pooled by the Stata software version 15.0. To derive a sufficient power to detect significance, a minimum number of eligible studies was set at 3 for genetic alterations analyzed in this study. The association between genetic alterations and breast cancer was expressed as odds ratio (OR) and 95% confidence interval (95% CI). The association between genetic alterations and circulating leptin or adiponectin levels was expressed as standardized mean difference (SMD) and its 95% CI. Effect-size estimates were generated using the DerSimonian-Laird method and under the random-effects model. Heterogeneity between studies was justified by using a percent, inconsistency index  and breast cancer risk, 4 studies for the association between rs7799039 genotypes and circulating leptin levels, and 8 studies for the association between rs1137101 genotypes and circulating leptin levels.Initial search of 4 public datasets identified a total of 596 publications after deleting duplicates. Only 33 of these publications were eligible for inclusion , 19–50. The baseline characteristics of 55 studies in this meta-analysis are presented in LEP gene rs7799039-G allele and LEPR gene rs1137100-A allele were associated with reduced breast cancer risk relative to the corresponding reference alleles, and the risk was close to statistical significance. By contrast, ADIPOQ gene rs1501299-T allele increased breast cancer risk significantly by 26%  relative to the corresponding G allele. The I2 ranged from 62% to 85.4%, denoting the moderate-strong evidence of heterogeneity between studies.The association of 5 genetic alterations with breast cancer risk was displayed in the form of forest plots under allele mode of inheritance Figure 2LEP gene rs7799039 GG plus GA genotypes and LEPR gene rs1137100 AA plus AG genotypes on breast cancer risk dwindled, and the risk conferred by ADIPOQ gene rs1501299 TT plus TG genotypes was enhanced, with OR of 1.41 (95% CI: 1.06 to 1.88). Under genotype mode, LEP gene rs7799039-GG was associated with a 22% reduced risk of breast cancer significantly , and no significance was detected for the other comparisons.Besides allele mode, pooled estimates under dominant and genotype modes of inheritance are shown in The association of 5 genetic alterations with breast cancer stratified by menopause and obesity is summarized in LEPR gene rs1137100-AA genotype carriers conferred a significantly reduced risk of breast cancer compared with GG genotype carriers  in both premenopausal and postmenopausal women. For ADIPOQ gene rs1501299, the risk for breast cancer was significant under both allele  and dominant  modes of inheritance in both premenopausal and postmenopausal women.By menopausal status, LEP gene rs7799039 GG plus GA genotypes with breast cancer was substantiated in normal-weight women .By obesity, the association of LEP rs7799039, the association was significant in studies with prospective design, with histologically-confirmed breast cancer, and involving sample size exceeding 300 under three genetic modes of inheritance. For ADIPOQ rs1501299, significance was noticed in women from East Asia, in studies involving hospital-sourced controls, in studies adopting RFLP technique, and in studies with histologically-confirmed breast cancer under both allele and dominant modes.Sensitivity analyses were performed for 5 genetic alterations associated with breast cancer under allele mode of inheritance, respectively . Filled funnel plots revealed that two studies and one study were theoretically missing for LEPR gene rs1137100 and ADIPOQ gene rs1501299, respectively. After taking these missing studies into consideration, effect-size estimates were changed slightly.Publication bias was assessed in the form of funnel plots and regression tests. As shown in LEP gene rs7799039 and LEPR gene rs1137101. There was no noticeable difference for all comparisons (P>0.05).LEP and ADIPOQ genes, as well as their receptor-encoded genes, with breast cancer risk and circulating leptin levels. Importantly, we found that LEP gene rs7799039 and ADIPOQ gene rs1501299 were two promising candidate loci in predisposition to breast cancer risk. Additionally, we found that differences in menopausal status, obesity, race, study design, diagnosis of breast cancer, genotyping method and sample size might account for the divergent results of previous studies in the literature. To the best of our knowledge, this meta-analysis is thus far the most comprehensive on the susceptibility of LEP and ADIPOQ genes to breast cancer.The aim of this meta-analysis was to examine the association of 5 genetic alterations in BRCA)  . Howevert cancer . It is pt cancer  and the To seek possible reasons behind the irreproducible findings of previous studies, we further conducted subgroup analyses for the association of 5 genetic alterations with breast cancer under three genetic modes of inheritance. It is of importance to see that menopausal status, obesity and race were potential attributes responsible for this irreproducibility. The impact of menopausal status and obesity on breast carcinogenesis has been well established, with evidence from both clinical and experimental aspects –57. The LEP, LEPR and ADIPOQ genes and breast cancer risk, we did not notice remarkable differences in circulating leptin levels across genotypes of their genetic alterations. The possibility for this phenomenon might be the limited number of studies measuring and comparing circulating leptin levels across genotypes. Another possibility is that studies for the association with breast cancer and circulating leptin levels are not identical, and differences in study designs, sample sizes and participant characteristics may matter. Practically, it is expected to validate the association with circulating leptin levels by large, well-designed cohorts in the future.Although the significant association between Some possible limitations needed to be addressed for this meta-analysis. The first is the probability of selection bias. This meta-analysis merely retrieved published studies in English, and studies written the other languages known as “grey” literature were not covered. The second limitation is the cross-sectional nature of all retrieved studies, and the association derived in this meta-analysis cannot imply the cause-and-effect relationship, calling for further investigations to fill this gap in knowledge. The third limitation is the insufficient power in most subgroup association analyses. The fourth limitation is that this meta-analysis is based on summary estimates, instead of individual participant data, which made the statistical correction for some confounding factors such as menopausal status and body mass index impractical. The fifth limitation is the possibility of publication bias for two of five genetic alterations assessed in this meta-analysis; however, incorporation of adding theoretically missing studies did not materially change our effect-size estimates.LEP gene rs7799039 and ADIPOQ gene rs1501299 were two promising candidate loci in predisposition to breast cancer risk. Additionally, we found that differences in menopausal status, obesity, race, study design, diagnosis of breast cancer, genotyping method and sample size might account for the divergent results of previous studies in the literature. We agree that further investigations from genetic and experimental points of view are necessary to ascertain the implication of these genes in the pathogenesis of breast cancer, which might shed more light on knowledge and preferences toward breast cancer screening for high-risk women.In conclusion, through a comprehensive analysis of 33 publications, we found that The original contributions presented in the study are included in the article/W-zP conceived the study; XL and C-fL conducted the literature search. XL and C-fL extracted the required data. XL and C-fL performed data analysis and interpretation. W-zP and JZ did statistical analyses. W-zP and XL drafted the manuscript. All authors contributed to the writings and approved the final version of the manuscript.