Brominated flame retardants (BFR) and per- and polyfluorinated alkylated substances (PFAS) are two groups of substances suspected to act as endocrine disruptors. Such substances could therefore be implicated in the occurrence of breast cancer, nevertheless, previous studies have led to inconstant results. Due to the large correlation between these substances, and the possibly non-linear effects they exert, evaluating their joint impact as mixtures on health remains challenging.This exploratory study aimed to generate hypotheses on the relationship between circulating levels of 7 BFR (6 polybrominated diphenyl ethers and 1 polybrominated biphenyls) and 11 PFAS and the risk of breast cancer in a case–control study nested in the E3N French prospective cohort by performing two methods: Principal Component Regression (PCR) models, and Bayesian Kernel Machine Regression (BKMR) models.194 post-menopausal breast cancer cases and 194 controls were included in the present study. Circulating levels of BFR and PFAS were measured by gas chromatography coupled to high-resolution mass spectrometry and liquid chromatography coupled to tandem mass spectrometry, respectively. The first statistical approach was based on Principal Component Analysis (PCA) followed by logistic regression models that included the identified principal components as main exposure variables. The second approach used BKMR models with hierarchical variable selection, this latter being suitable for highly correlated exposures. Both approaches were also run separately for Estrogen Receptor positive (ER +) and Estrogen Receptor negative (ER-) breast cancer cases.PCA identified four principal components accounting for 67% of the total variance. Component 3 showed a marginal association with ER + breast cancer risk. No clear association between BFR and PFAS mixtures and breast cancer was identified using BKMR models, and the credible intervals obtained were very wide. Finally, the BKMR models suggested a negative cumulative effect of BFR and PFAS on ER- breast cancer risk, and a positive cumulative effect on ER + breast cancer risk.Although globally no clear association was identified, both approaches suggested a differential effect of BFR and PFAS mixtures on ER + and ER- breast cancer risk. However, the results for ER- breast cancer should be interpreted carefully due to the small number of ER- cases included in the study. Further studies evaluating mixtures of substances on larger study populations are needed.The online version contains supplementary material available at 10.1186/s12940-022-00840-4. Polybrominated diphenyl ethers (PBDE) and polybrominated biphenyls (PBB) are two families of brominated flame retardants (BFR) that have been greatly used in a wide range of consumers’ products to reduce their flammability. Although their use has been progressively limited and banned in Europe during the 90 s, due to their resistance to degradation, PBDE and PBB are widespread in the environment . The lonPer- and polyfluorinated alkylated substances (PFAS) are a wide group of synthetic compounds that are water- and oil-repellent and have been used in a large number of industrial and consumer applications . PFAS arThe incidence of breast cancer has risen in the past decades among Western populations but, despite a large body of research, the etiology has not yet been fully delineated, as established risk factors cannot solely explain this trend –9. ThereThere are several challenges to modeling the effects of exposure to chemical mixtures , 33. FirBayesian Kernel Machine Regression (BKMR) is a novel statistical method that addresses the above-mentioned challenges. Indeed BKMR estimates the exposure–response surface accounting for interactions and non-linear relationships by flexibly modeling exposures and it can handle multicollinearity between substances by applying hierarchical variable selection –38.Another classic method used in epidemiology to deal with multicollinearity of multiple exposure variables is Principal Component Analysis (PCA). PCA projects the original data points living in the space of the original, and possibly correlated, variables onto a lower dimensional subspace whose orthogonal axes, referred to as principal components, are uncorrelated, in a way that minimizes the average projection error. Principal components can then be used to predict the outcome of interest by means of classic regression models. , 40. ThiThe objective of this exploratory study is to generate hypotheses on the relationship between circulating levels of 7 BFR (6 PDBE and 1 PBB) and 11 PFAS and the risk of breast cancer in a case–control study nested in the E3N French prospective cohort using two methods: PCR models, and BKMR models. More specifically, PCR models can highlight profiles of exposures associated with breast cancer, accounting for less informative variables and managing the high dimensionality with orthogonal scores . By consMutuelle Générale de l'Education Nationale (MGEN). Women were enrolled in the cohort through a self-administered questionnaire, and were followed by self-administered questionnaires every two years. Detailed cancer risk factor data were collected through questionnaires at different time points during follow-up, including reproductive history, use of hormonal treatments, anthropometric characteristics, smoking habits, alcohol consumption, diet, and physical activity. The average follow-up rate per questionnaire cycle has been of 83%, and to date, the total loss to follow-up since 1990 has been < 3%.The E3N study is a large ongoing French prospective cohort of women, set up in France in 1990. The study was approved by the French National Commission for Data Protection and Privacy ; all participants gave written informed consent. The detailed protocol has been described elsewhere . BrieflyBetween 1994 and 1999, E3N participants were invited to donate blood, resulting in the collection of blood samples from approximately 25 000 participants. Each sample was separated into 28 aliquots  that were stored in plastic straws in liquid nitrogen containers (− 196 °C) in a biobank.Breast cancer cases were identified through self-reporting in the questionnaires, from the MGEN files, or through information from death certificates. Deaths were reported by family members and by searches in the MGEN files and causes of death were obtained from the National Death Index. Pathology reports were obtained for the 93% of incident cases. We also considered cases for which pathology reports have not been obtained, because the proportion of false-positive self-reports was low in our study population (< 5%). Cases were identified up to 2013, which was therefore used as the date of end of follow-up in statistical analyses.n = 27) or who were diagnosed before the blood sampling and/or before the dietary questionnaire (n = 11). Cases of Paget’s disease and benign breast disease were also excluded (n = 3). Finally, 240 incident breast cancer cases were available. Due to budget constraints, among those, 194 incident postmenopausal breast cancer cases were randomly selected and included in the study. For each case, one control was sampled from women who were free of breast cancer at the time of diagnosis of the corresponding case (density sampling method). Controls were matched to cases by age (± 2 years), menopausal status at blood collection , body mass index (BMI) at blood collection (< 25 or ≥ 25 kg/m2) and year of blood collection.As previously described elsewhere , we iden13C labeled analogous as internal standards. All the analyses have been conducted in an ISO 17025:2005 accredited laboratory. Serum and plasma lipids were determined enzymatically . The following 7 BFR and 18 PFAS were measured: 2,2',4,4',5,5'-hexabromobiphenyl (PBB-153), 2,2',4,4',6-pentabromodiphenyl ether (PBDE-100), 2,2',4,4',5,5'-hexabromodiphenyl ether (PBDE-153), 2,2',4,4',5,6'-hexabromodiphenyl ether (PBDE-154), 2,4,4'-tribromodiphenyl ether (PBDE-28), 2,2',4,4'-tetrabromodiphenyl ether (PBDE-47), 2,2',4,4',5-pentabromodiphenyl ether (PBDE-99), perfluorobutane sulfonic acid (PBFS), perfluorodecane sulfonic acid (PFDS), perfluorobutanoic acid (PFBA), perfluoroalkyl phosphonic acid (PFPA), perfluorohexane sulfonic acid (PFHxA), perfluorododecanoic acid (PFDoA), perfluorohexane sulfonic acid (PFHxS), perfluoroheptane sulfonic acid (PFHpS), perfluorooctane sulfonic acid (PFOS), perfluorooctane sulfonamide (PFOSA), N-Methyl perfluorooctane sulfonamidoacetic acid (N_MeFOSAA), N-Ethyl perfluorooctane sulfonamidoacetic acid (N_EtFOSAA), perfluoroheptanoic acid (PFHpA), perfluorooctanoic acid (PFOA), perfluorononanoic acid (PFNA), perfluorodecanoic acid (PFDA) and perfluoroundecanoic acid (PFUnA).Laboratory analysis of BFR and PFAS have been detailed elsewhere , 23. Bri2, continuous), and year of blood draw (continuous), except for menopausal status at blood draw, in order to avoid over-adjustment due to the a priori inclusion of age at menopause in the model.Logistic regression and BKMR models were adjusted for total plasma lipid content by the addition of a separate term in the model , as recommended by Schisterman et al. . The modThe selection of confounders was done a priori, based on the known breast cancer risk factors available in the E3N dataset that are potentially associated with the exposures considered in the present study.In our study population, missing values were < 5% for all variables and were imputed to the median (continuous variables) or modal value .Substances for which more than 25% of the values were below the limit of detection (LOD) were eliminated from the analysis . For those substances which had 25% or less of the values below the LOD, these last were imputed to ½ of the LOD.Supplementary material Table Demographic characteristics of the study participants were reported using means and standard deviations or counts with percentages. Univariate conditional logistic regression models were performed to compare concentrations of PFAS and BFR between cases and controls. Exposure to substances were log-transformed for the analyses. Correlations between log-transformed PFAS and BFR concentrations were assed using Pearson’s correlation tests.A PCA with varimax rotation was performed on the matrix of log-transformed biomarkers, to identify a reduced number of uncorrelated components representing the exposure to substances. The number of retained components was chosen using several indicators: individual and cumulated explained variance, and interpretability and coherence of identified components . Then, mIn order to investigate the hypothesis of a differential relationship between exposure to PFAS and BFR and breast cancer risk based on the tumor expression of estrogen receptors (ER– vs. ER +), the components scores previously identified were also included in two separate models: the first model included ER + cases and all controls, while the second model included ER– cases and all controls. Cases for which information on the estrogen receptors’ expression was missing were excluded from the analysis.All these models were adjusted for the covariates described above, including matching criteria.As a sensitivity analysis, conditional logistic regression models accounting for the matching of cases and controls were run on the overall study population, but not on specific ER + and ER- subpopulations. These models were not adjusted for the matching criteria.bkmr’) exists for this purpose, with the possibility of adapting the model to binary outcomes, like breast cancer [BKMR was proposed as a new approach to assess the effect of exposure to chemical mixtures on health . An R pat cancer . In the The model for binary outcome using the Probit link function with hierarchical variable selection is as follows:For each subject i = 1,…,n:where:−1 = probit link function;Φi = binary outcome (0/1), here breast cancer;Yi1, …,ziM, = exposure variables of group 1, here 11 PFAS (ng / mL of serum) as continuous, log-transformed and centered variables (i.e. subtraction of the mean);zi1,…,viN = exposure variables of group 2, here 7 BFR (ng / L of plasma) as continuous, log-transformed and centered variables (i.e. subtraction of the mean);vh = flexible function of exposure variables, specified using a kernel function (exposure–response function);xi = vector of covariates (see the list below);β = vector of the corresponding coefficients;i = residuals.εFirst, the BKMR model with a hierarchical selection of variables provides two types of posterior inclusion probabilities (PIPs): the PIPs of each of the two groups (BFR and PFAS), and the conditional PIPs of each substance within groups. The PIPs are indicators of the contribution of each group or substance in relation to the outcome.Secondly, the BKMR method estimates a univariate exposure–response function for each substance. This function consists of a section of the function h quantifying the relationship between a given substance and the outcome, while all other exposure variables are set at their median value. In the specific case of a binary outcome, these sections of h can be interpreted as the relationship between the exposure variable and an underlying continuous latent outcome, such as a biomarker of health status underlying the binary outcome .th, 50th, and 80th) and all the others are fixed at their median value. This approach allows to highlight interactions between pairs of substances: an alteration in the dose–response curve of one substance at a different percentile of another substance suggests an interaction, while parallel lines indicate no interaction. With the hierarchical variable selection mode, only interactions between substances not belonging to the same group can be identified.Then, the BKMR method provides bivariable exposure–response functions, which are estimates of the relationship between exposure to a given substance and the outcome while one other substance is fixed at predefined percentiles  and R (version 4.0.3).Table Among BFR, PBDE-47 (4.70 ng/L) was the congener with the highest average concentration in the overall population, followed by PBDE-153 (3.14 ng/L) and PBB-153 2.03 ng/L). PFOS (19.08 ng/mL) and PFOA (7.35 ng/mL) were the two PFAS with the highest average concentration in the study population. The mean and standard deviation of each exposure variable in the total study population, and for cases and controls separately, is presented in Table .03 ng/L.PCA identified four main components accounting respectively for the 29%, 22%, 9%, and 8% of the total variance. Loading factors for each chemical on each component are presented in Supplementary material Table Results for logistic regression models are presented in Table p-value = 0.04). Also, an association was highlighted for Component 4, with OR > 1 for the second and third quintile groups, and OR < 1 for the fourth and fifth quintile groups .Results were similar when including only ER + cases: Component 3 in quintiles was positively associated with ER + breast cancer risk, with OR > 1 for all quintile groups when compared to the first quintile group . In addition, it has to be taken into account that all the confidence intervals were wide for this analysis due to the limited number of cases included (n = 26).Finally, when including only ER- cases, an association was identified for Component 1 in quintiles, with OR > 1 for the second, third, and fourth quintile groups, and OR < 1 for the fifth quintile group , and PBB-153 (0.18), while among PFAS, PFOSA (0.16) was the substance with the highest conditional PIP  to the concentrations corresponding to each 5th percentile from the 25th to the 75th percentile, was close to zero  and PFOS 0.15)  and PBDE-99 (0.18) concerning BFR, while with regards to PFAS the substance with the highest conditional PIP was N_MeFOSAA (0.23) . No interactions were identified between substances across groups (data not shown). The cumulative effect of PFAS, PBDE, and PBB on the risk of breast cancer was close to zero  and ER + breast cancer risk was identified. A non-linear association could be noticed for Component 4 . As expected, results for overall breast cancer risk were similar to those obtained for ER + since these represented the majority (81%) of breast cancer cases included in the study.In the ER- stratum, a non-linear association could be noticed for Component 1 (mainly composed of PBDEs). The small number of ER- cases could explain the lack of statistically significant effect.When performing BKMR analyses for overall and ER + breast cancer risk, results were also very similar, but globally attenuated for overall breast cancer risk. The PFAS group had a slightly higher PIP than the BFR group, suggesting that PFAS could play a more important role than BFR in the occurrence of breast cancer. Moreover, conditional PIPs highlighted that PBDE-153 (positive relationship) and PBB-153 (negative relationship), among the BFR group, and PFOSA (positive relationship), among the PFAS group, played the most important roles in the relationship with overall and ER + breast cancer risk. A positive cumulative effect of BFR and PFAS on ER + breast cancer risk was also highlighted. No effect was seen for overall breast cancer risk, probably due to a dilution effect due to the inclusion of ER- cases.When performing analyses including only ER- breast cancer cases, PIPs were instead slightly higher for the BFR group than the PFAS group. In addition, the substances that had the highest PIPs were PBDE-100 and PBDE-99, in the BFR group, and N_MeFOSAA, in the PFAS group. Regarding the relationship between each substance and ER- breast cancer risk, several differences were noticed in comparison to ER + breast cancer risk. Indeed, PBDE-99 and ER- breast cancer risk showed a negative relationship; stiffer slopes were identified for PBDE-100 (negative relationship) and for N_MeFOSAA (positive relationship) compared to ER + breast cancer risk, while the slopes for PFOSA and PFOS were milder. Finally, the results suggest a negative cumulative effect of BFR and PFAS on ER- breast cancer risk.When comparing the results obtained with the two approaches tested in the present study, some consistency can be noticed. Indeed, with regard to ER + breast cancer risk, an association was shown with Component 3, and a non-linear association for Component 4. This was overall coherent with the results of the BKMR approach, which identified among the most important substances contributing to ER + breast cancer risk, PBDE-153 and PBB-153, with a positive and a negative relationship, respectively. These two substances were in fact contributing strongly to Component 4, and this could explain the non-linear association identified for this Component. Concerning the PFAS group, the BKMR approach identified PFOSA among the substances with higher PIP, highlighting a positive relationship with ER + breast cancer risk. PFOSA, in turn, was also an important contributor to Component 3 and could be responsible for the association identified between this component and ER + breast cancer risk.With regard to ER- breast cancer risk, only a non-linear association was highlighted for Component 1. Among the substances with the higher PIPs identified by the BKMR approach, some were contributing to Component 1, such as PBDE-99 and PBDE-100, having both a negative relationship with ER- breast cancer risk. However, numerous other substances contributed to Component 1, with opposite relationships with ER- breast cancer risks and this could explain the non-linear association found with this Component.PCA, a dimension reduction method classically used in epidemiology to deal with multicollinearity, transforms a large set of variables into a smaller one, minimizing the loss of information. It does so by creating new uncorrelated variables, called components, on the basis of correlations between the initial variables . In our The BKMR approach allows to model non-linear and non-additive relationships between substances and outcome, accounting for confounding variables . For hiePrevious studies have evaluated the associations between internal exposure levels of BFR and PFAS and breast cancer –29. ThesMore precisely, regarding the hypothesis of a positive association between PFOSA and both ER + and all breast cancer risk generated by our study, one previous case–control study has also identified a positive relationship between PFOSA and all breast cancer risk , while aHowever, comparisons between the results of these studies, using a single pollutant approach, and our results, obtained from a mixture approach, must be made with caution. Indeed, the high correlation levels between substances could potentially lead to biased estimates of the associations between individual substances and breast cancer and this could explain the inconstant results between the different studies. Moreover, although the use of sums of substance, instead of individual substances, could have overcome the problems due to collinearity, this approach is based on the strong assumption that the aggregated substances have an additive effect, which is not necessarily true. The limits of these methods and the lack of studies evaluating mixtures of substances rather than individual or sums of substances could explain the global inconsistency of results. More studies assessing the impact of the choice between multi-pollutants versus single-pollutant approaches on results are needed to better understand these differences.This study presents some limitations. First, the study population was composed of women working for the French national education system, with globally a healthier lifestyle than the general population, so the extrapolation of results to the general population should be done with caution. In addition, the small study population size limited the statistical power. In particular, the small number of ER- cases limited the consistency of the results when performing stratified analysis based on hormone receptor status. The presence of chemicals with observations below the limit of detection should also be taken into account. We acknowledge that large left-censored data (i.e. > 25%) may have impacted regression based estimates, especially when using parametric methods constrained by distributional assumptions, which is not the case of BKMR . To overThe present study has also several strengths. First, the availability of numerous information collected in the E3N cohort has permitted to adjust models on main potential confounders for breast cancer. In addition, the present study investigated two methodological approaches, PCR and BKMR, that are adapted to the assessment of the effects of chemical mixtures rather than the single-exposures, and that take into account multicollinearity. These features allowed to highlight some positive trends between some PBDEs and PFAS considered as mixtures and breast cancer risk, which could be elusive in single-pollutant models. In addition, the combination of these two methods allowed generating more robust conclusions than those that could have been obtained applying a single method. In addition, these two approaches can be seen as complementary, PCR detecting associations with some components of correlated substances, and BKMR further allowing to identify interactions between substances and non-linear associations while accommodating confounding.Combining the results of these two approaches has made it possible to formulate hypotheses about the components associated with breast cancer risk and to further hypothesize which substances contributing to these components could be responsible for the association identified, highlighting the direction of the relationship and taking multicollinearity into account. However, the insufficient number of subjects has not allowed identifying some potentially significant association with the BKMR approach, which resulted in very large credible intervals. Further studies evaluating mixtures of substances on larger sample sizes are needed.Additional file 1: Table S1. Percentage of values below the Limit Of Detection (LOD) and associated decision for each substance measured in the present study. Table S2. Loading factors for each substances on each of the 4 components obtained by principal component analysis. Table S3. Associations between adherence to PCA components and all breast cancer risk. Components are used in continuous and in quintiles in conditionnal logistic regression models. Odds Ratio (OR) and 95% Confidence Intervals (CI) are presented. Figure S1. Correlation matrix between log-transformed exposure to substances in the study population. Pearson’s rank correlation coefficients are presented. Figure S2-A. Conditional posterior inclusion probabilities of substances within group for all breast cancer risk. Figure S2-B. Conditional posterior inclusion probabilities of substances within group for ER+ breast cancer risk. Figure S2-C. Conditional posterior inclusion probabilities of substances within group for ER- breast cancer risk. Figure S3. Exposure-response functions between exposure to Substance 1 and probit of probability of having a breast cancer while Substance 2 is fixed at defined percentiles , all other substances being fixed at their median value. Figure S4. Cumulative effect of PFAS, PBB and PBDE for all breast cancer risk.


There are many hereditary breast cancer patients in China, and multigene panel testing has been a new paradigm of genetic testing for these patients and their relatives. However, the magnitude of breast cancer risks related to multiple breast cancer susceptibility genes are largely unknown in Chinese women.We screened pathogenic variants in 15 established or potential breast cancer susceptibility genes from 8,067 consecutive Chinese female breast cancer patients and 13,129 Chinese cancer-free female controls. These breast cancer patients were unselected for age at diagnosis or family history.TP53 ; BRCA2 ; BRCA1 ; and PALB2  were associated with a high risk of breast cancer. ATM, BARD1, CHEK2, and RAD51D were associated with a moderate risk of breast cancer with ORs ranging from 2-fold to 4-fold. In contrast, pathogenic variants of NBN, RAD50, BRIP1, and RAD51C were not associated with increased risk of breast cancer in Chinese women. The pathogenic variants of PTEN, CDH1, and STK11 were very rare, so they had a limited contribution to Chinese breast cancer. Patients with pathogenic variants of TP53, BRCA1, BRCA2, and PALB2 more often had early-onset breast cancer, bilateral breast cancer, and a family history of breast cancer and/or any cancer.We found that pathogenic variants in This study provided breast cancer risk assessment data for multiple genes in Chinese women, which is useful for genetic testing and clinical management of Chinese hereditary breast cancer. It is estimated that nearly 10% of unselected breast cancer patients in China carry pathogenic variants in cancer susceptibility genes4. Detecting these pathogenic variants and precisely estimating their risks for breast cancer will provide the basis for prevention and management of hereditary breast cancers. Advances in sequencing technology have made multigene testing, or “panel testing,” a routine option to detect pathogenic variants for potential breast cancer patients and their relatives. These panels usually contain established breast cancer susceptibility genes, such as BRCA1, BRCA2, PALB2, TP53, CHEK2, and ATM, and potential breast cancer susceptibility genes, such as BARD1, BRIP1, RAD50, RAD51C, and RAD51D6. However, the magnitude of breast cancer risks associated with these known or potential breast cancer genes is largely unknown in Chinese women.Breast cancer is the most frequent malignant tumor in Chinese women, with approximately 268,600 newly-diagnosed cases per year. Furthermore, the burden of breast cancer is still increasing14. However, these data may not be generally applicable to Chinese women. Although the frequencies of pathogenic variants of breast cancer genes in Chinese women are comparable to those in other ethnicities16, studies by our group or other groups have shown substantial differences in the spectrum of pathogenic variants of breast cancer susceptibility genes between Chinese and non-Chinese ethnicities, with up to one-third of the pathogenic variants in Chinese women not being found in Caucasian women22. Moreover, early-onset breast cancer  is more common in Chinese women than Caucasian women1. Therefore, the breast cancer risks in specific genes in Chinese women may differ from those in other populations. In this study, we therefore screened pathogenic variants in 15 established or potential breast cancer susceptibility genes in 8,067 unselected Chinese female breast cancer patients and 13,129 Chinese cancer-free female controls, then compared the risk-related phenotypes between the patients with pathogenic variants and those without a pathogenic variant. We aimed to determine the breast cancer risks of the 15 breast cancer genes in Chinese women.Large case-control association studies that quantify the breast cancer risks of multiple genes from panel testing have been mainly conducted in Caucasian women or other populations4. Eighteen male breast cancer patients were excluded from the analysis, and the remaining 8,067 female breast cancer patients were included in this study. Early-onset breast cancer patients were defined as patients diagnosed at or before the age of 40 years. Family history of breast cancer was defined as the breast cancer patient having 1 or more breast cancer patients in the first-, second-, or third-degree relatives, and family history of any cancer was defined as the breast cancer patient having 1 or more cancer patients (any kind of cancer) in the first-, second-, or third-degree relatives. The family history of breast or other cancer was collected from medical records and/or telephone interviews. A total of 13,129 Chinese women (ages ≥ 18 years) without a personal history of any cancer were recruited from the general population and were considered as a reference control for this case-control study. Written informed consents were obtained from all participants. This study was reviewed and approved by the Ethics Committee of Peking University Cancer Hospital & Institute  and was performed in accordance with the Declaration of Helsinki.A total of 8,085 consecutive breast cancer patients who were treated at the Breast Center of Peking University Cancer Hospital & Institute from October 2003 to May 2015 underwent 62-gene panel sequencing, as described in our previous report4. We selected 15 established or potential breast cancer susceptibility genes from the 62-gene panel for further analysis based on published studies24, including 10 established breast cancer susceptibility genes  and 5 potential breast cancer susceptibility genes . For the control cohort, genomic DNA extracted from buccal swabs was used for whole-exome sequencing at an average depth of 100-fold coverage of the target region. Reads were aligned to the reference human genome, GRCh37. Germline variations were called with GATK. Annotations were defined using ANNOVAR (https://annovar.openbioinformatics.org/en/latest/).For the breast cancer cohort, genomic DNA extracted from peripheral blood was used for 62-gene panel sequencing at an average depth of 200-fold coverage of the target region, as described in our previous studyhttps://www.1000genomes.org), NHLBI Exome Sequencing Project , and the Exome Aggregation Consortium  were collected. Among these, the truncating variants (nonsense and frameshift variants) were included in this study, but the truncating variants in the last 55 base pairs of the penultimate exon or last exon that potentially avoided nonsense-mediated messenger RNA decay and did not influence known functional domains, were excluded. For splice-site, synonymous, nonsynonymous, in-frame, and stop-loss variants, only variants classified as pathogenic or likely pathogenic by ClinVar (http://www.ncbi.nlm.nih.gov/clinvar/) were included in the analysis. The variants with conflicting interpretations of pathogenicity in ClinVar were further annotated according to the ACMG/AMP standards and guidelines25, with supporting data from function prediction software, public literature, and curated databases (Supplementary Table S1). Variants classified to be pathogenic or likely pathogenic were considered as pathogenic in this study.In this study, we analyzed germline variants in the 15 established or potential breast cancer susceptibility genes. Only variants with < 1% population frequency in the population databases including 1,000 Genomes .Case-control association analysis within each gene was performed using logistic regression. The strength of associations with breast cancer was estimated by the odds ratio (OR) and corresponding 95% confidence interval (CI). Genes were categorized as high risk (OR ≥ 5.0), moderate risk (2.0 ≤ OR < 5.0), or of no clinical relevance (OR < 2.0)Table 1). Among them, 18.2% were diagnosed at or before the age of 40 years (early-onset breast cancer), and 10.0% had a family history of breast cancer. A total of 13,129 Chinese women without a personal history of cancer were enrolled as controls. The median age at entry was 33 years (range: 18–84 years) (Table 1). The majority of breast cancer cases (95.8%) and controls (93.6%) were of Chinese Han descent (Table 1).The 8,067 consecutive Chinese female breast cancer patients analyzed in this study were unselected for age at diagnosis or family history. The median age at diagnosis for breast cancer patients was 50 years (range: 19–98 years) (Table 2). Fourteen breast cancer patients and 1 control had pathogenic variants in 2 different genes . For breast cancer cases, BRCA2  was the most frequently mutated gene, followed by other established breast cancer susceptibility genes: BRCA1 , PALB2 , and TP53 . For controls, BRCA2  still ranked as the most frequently mutated gene, followed by the candidate genes, RAD50  and BRIP1 . Notably, pathogenic germline variants of PTEN, CDH1, and STK11 were extremely rare in Chinese breast cancer patients or controls (Table 2). Furthermore, in all 533 pathogenic germline variants of the 15 genes analyzed in this study, 385 (72.2%) variants have been reported in the latest version of the ClinVar dataset (Supplementary Table S2).Pathogenic germline variants of the 15 genes were found in 654 (8.11%) breast cancer cases and 251 (1.91%) controls (Table 2). Among these genes, TP53 ; BRCA2 ; BRCA1 ; and PALB2  were classified as high risk breast cancer susceptibility genes in Chinese women (Table 2). BARD1 ; CHEK2 ; RAD51D ; and ATM  were classified as moderate risk breast cancer risk genes (Table 2). The 8 high or moderate risk susceptibility genes accounted for 7.5% of unselected Chinese breast cancer patients in this study (5.2% for BRCA1/2 and 2.3% for the other genes). In contrast, NBN, RAD50, BRIP1, and RAD51C were not associated with increased risks of breast cancer in Chinese women (Table 2). In addition, pathogenic variants of PTEN (5 cases), CDH1 (1 case), and STK11 (1 case and 1 control) were too rare to estimate their risks of breast cancer in Chinese women (Table 2). The breast cancer risks of susceptibility genes were higher after being adjusted for age (Supplementary Table S3).Breast cancer risks for each of the genes were estimated by comparing the frequency of pathogenic variants identified in breast cancer cases to controls. Logistic regression results showed that 8 genes in this study were significantly associated with increased risk of breast cancer in Chinese women (Supplementary Table S4). Among these, BRCA1, BRCA2, and PALB2 were still classified as high risk, and ATM, BARD1, CHEK2, and RAD51D were still classified as moderate risk breast cancer susceptibility genes in Chinese women (Supplementary Table S4). As the majority of pathogenic variants in the TP53 gene were missense, the truncating variants in the TP53 gene were too rare to estimate the risk of breast cancer in this study (Supplementary Table S4).We further estimated breast cancer risks for each of the genes based on pathogenic truncating variants ; BRCA2 ; BRCA1 ; and PALB2  were still classified as high risk breast cancer susceptibility genes (Table 3). CHEK2  and ATM  were classified as moderate risk susceptibility genes (Table 3), while BARD1 and RAD51D were not significantly associated with risk in this subgroup (Table 3). Among the HER2+ group, only TP53  was classified as a high risk breast cancer susceptibility gene, while BRCA2 ; BRCA1 ; PALB2 ; and CHEK2  were classified as moderate risk breast cancer susceptibility genes (Table 3). Among the triple negative subgroup, TP53, BRCA1, BRCA2, PALB2, BRAD1, and RAD51D were all classified as high risk breast cancer susceptibility genes (Table 3). Of these, BRCA1 had the highest risk of breast cancer susceptibility  (Table 3).We further analyzed the breast cancer risks of the 15 genes stratified by estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor 2 status in this case-control study. Among the ER and/or PR26. We therefore compared these risk-related characteristics between the patients with pathogenic variants and those without any pathogenic variant. Double variant carriers were excluded from the clinical characteristic analysis.Pathogenic variants in breast cancer susceptibility genes are usually associated with risk-related phenotypes, such as early-onset breast cancer, bilateral breast cancer, and higher frequencies of family cancer historyTP53 ; BRCA2 ; BRCA1 ; PALB2 ; and RAD51D  was significantly younger than that of noncarriers (Table 4). Similarly, pathogenic variants of TP53, BRCA2, BRCA1, and RAD51D were significantly associated with early-onset breast cancer (Table 4), and pathogenic variants of TP53, BRCA2, BRCA1, and NBN were significantly associated with premenopausal breast cancer (Table 4). Among these, the TP53 gene, which conferred the highest risk of breast cancer, was also associated with the youngest mean age at diagnosis and the highest frequency of early-onset breast cancer (Table 4). In addition, the high risk genes  were significantly associated with bilateral breast cancer (Table 4). When comparing the family history of cancer, all high risk and moderate risk genes except for RAD51D were significantly associated with higher frequencies of family history of breast cancer and/or family history of any cancer (Table 4). In summary, all 8 high or moderate risk susceptibility genes in this study affected 1 or more risk-related phenotypes. In contrast, the 4 genes that were not associated with increased risk of breast cancer in this study  had no effect on any of the risk-related phenotypes (Table 4).The mean age at diagnosis of breast cancer patients with pathogenic variants of TP53, PTEN, STK11, and CDH1 are associated with Li-Fraumeni syndrome, Cowden syndrome, Peutz-Jeghers syndrome, and hereditary diffuse gastric cancer syndrome30. Among the 31 breast cancer patients with the TP53 pathogenic variant in this study, only 3 patients (9.7%) met the criteria for the Li-Fraumeni syndrome. After excluding the 3 patients with Li-Fraumeni syndrome, the TP53 gene was still significantly associated with a high risk of breast cancer in Chinese women , which suggested that women with TP53 pathogenic variants but without a family history of Li-Fraumeni syndrome still had a high risk of breast cancer. None of the 5 breast cancer patients with the PTEN variant met the Cowden syndrome criteria, and the patient with STK11 variant did not meet the Peutz-Jeghers criteria. The patient with the CDH1 variant in this study did not present with lobular breast cancer, nor with a personal history or family history of gastric cancer, although there were 255 (3.2%) invasive lobular breast cancer patients in this study.TP53, BRCA1, BRCA2, and PALB2 as high risk, and ATM, BARD1, CHEK2, and RAD51D as moderate risk breast cancer susceptibility genes in Chinese women. In contrast, our study revealed that NBN, RAD50, BRIP1, and RAD51C were not associated with an increased risk of breast cancer, and PTEN, CDH1, and STK11 had a very limited contribution to Chinese breast cancer.In this study, we screened pathogenic variants in 15 established or potential breast cancer susceptibility genes in 8,067 Chinese unselected female breast cancer patients and 13,129 Chinese cancer-free female controls. Our results classified TP53 gene conferred the highest risk of breast cancer in Chinese women (OR: 16.9), and patients carrying pathogenic variants of the TP53 gene had the youngest mean age at diagnosis. Pathogenic variants of the TP53 gene were also associated with a high risk of breast cancer in Japanese women9. However, 2 recent large-scale case-control studies of Caucasian women reported that the TP53 gene was not significantly associated with breast cancer risk14. This difference might be explained by the observation that the prevalence of TP53 pathogenic variants in Asian breast cancer patients was much higher than that in Caucasian breast cancer patients31. In addition, a previous study based on patients with Li-Fraumeni syndrome reported a very high relative risk of breast cancer (OR: 105) for the TP53 gene27. This difference might be explained by that the TP53 variant carriers in our study were identified from unselected breast cancer patients, and only a minority of them met the Li-Fraumeni Chompret criteria. Importantly, our results showed that Chinese women with TP53 pathogenic variants but without a family history of Li-Fraumeni syndrome had a high risk of breast cancer (OR = 15.2).We found that the BRCA1/2 pathogenic variant carriers, when compared with noncarriers in Chinese women. Recent case-control studies in Caucasian women have reported relative risks of breast cancer for the BRCA1 and BRCA2 genes of 5.9–10.6-fold and 3.3–5.9-fold, respectively14, which were lower than the magnitude of breast cancer risks estimated in this study. This difference might be explained by sample selection and ethnic differences.This case-control study showed approximately a 10-fold increased risk of breast cancer for PALB2 gene was recently classified as a high risk breast cancer susceptibility gene in Caucasian women, with 53% cumulative risk to age 80 years and 7-fold relative risk for female breast cancer32, which was comparable to the breast cancer risk of the BRCA2 gene in Caucasian women33. However, our results and a recent study from another group34 both suggested that PALB2 conferred 5-fold increased risk for breast cancer in Chinese women, indicating that the risk associated with the PALB2 gene was lower than that of BRCA1/2 genes in Chinese women, although the magnitude of risk for the PALB2 gene reached the threshold of high risk genes.The ATM, BARD1, CHEK2, and RAD51D as moderate risk breast cancer susceptibility genes in Chinese women. Although ATM and CHEK2 were identified as classic moderate risk genes in Caucasian women35, the risks of the 2 genes in Chinese women are still worth investigating because it has been reported that the risk of pathogenic variants of the 2 genes was highly related to location and the type of variants, and considerable differences in the spectrum of ATM and CHEK2 variants were observed between Chinese and Caucasian ethnicities. For example, the dominant negative ATM p.Val2424Gly variant confers a much higher risk than truncating variants of the ATM gene 37. CHEK2 c.1100delC, the most frequent truncating variant in Caucasian women, confers a higher risk than the common missense mutation, CHEK2 p.Ile157Thr40. However, these specific variants, such as ATM p.Val2424Gly and CHEK2 c.1100delC, were absent in Chinese women21. In summary, our study defined ATM and CHEK2 as moderate risk genes for breast cancer in Chinese women. Some large case-control studies in Caucasian women showed that BRAD1 and RAD51D were breast cancer susceptibility genes13, while other studies did not reach this conclusion14. Our results indicated that BRAD1 and RAD51D were moderate risk breast cancer susceptibility genes in Chinese women. In addition, breast cancer patients with the BRAD1 and RAD51D pathogenic variants were associated with a higher frequency of family cancer history and early-onset breast cancer, respectively. These clinical characteristics also suggested that BARD1 and RAD51D were breast cancer susceptibility genes in Chinese women.This study classified PTEN, CDH1, and STK11 were too rare in our study to estimate their risks of breast cancer in Chinese women, which suggested that these genes had a very limited contribution to breast cancer in unselected Chinese women. In addition, the pathogenic variant carriers for PTEN, CDH1, and STK11 identified from unselected breast cancer patients in this study did not meet the criteria for the Cowden syndrome, hereditary diffuse gastric cancer syndrome, and Peutz-Jeghers syndrome, respectively. Additional larger case-control studies or segregation analysis in families might be needed to fully elucidate the breast cancer risks for PTEN, CDH1, and STK11 in Chinese women. NBN serves as an established breast cancer susceptibility gene in the latest guidelines, because many studies reported that NBN c.657del5 (a founder variant in the Slavic population) was associated with increased risk of breast cancer42. However, NBN c.657del5 was absent in Chinese breast cancer patients and cancer-free controls in this study, and our results showed that other truncating variants of NBN were not associated with breast cancer risk. Our study did not support RAD50, BRIP1 and RAD51C as breast cancer susceptibility genes in the Chinese population, consistent with results from case-control studies in Caucasian women14.The pathogenic variants of BRCA1 conferred the highest breast cancer risk in the triple negative subgroup than that in other subgroups. Pathogenic variants in TP53 conferred the highest breast cancer risk in the HER2+ subgroup than that in other subgroups. In addition, the ATM and CHEK2 genes were classified as moderate risk susceptibility genes in the ER and/or PR+ and HER2- subgroups, but were not associated with a risk in the triple negative subgroup. These findings were consistent with previous case-control studies14.This study also showed that breast cancer susceptibility genes conferred different risks in breast cancer molecular subgroups. For example, pathogenic variants in BRCA1/2 carriers up to the age of 70 years were 37.9% and 36.5% in Chinese women, respectively43, which were also 10-fold higher risks than noncarriers (3.6% by the age of 70 years). Similar results based on 2 different methods confirmed the reliability of our findings, although some bias existed in this case-control study.This study had some limitations. The ages of the cancer-free controls were younger than those of the breast cancer cases. It therefore may have underestimated the risks based on logistic regression models. However, our previous study based on the kin-cohort method found that the cumulative breast cancer risks in TP53, BRCA2, BRCA1, and PALB2 as high risk, and ATM, BARD1, CHEK2, and RAD51D as moderate risk breast cancer susceptibility genes in Chinese women. In contrast, the study showed that NBN, RAD50, BRIP1, and RAD51C were not associated with an increased risk of breast cancer, and PTEN, CDH1, and STK11 had a very limited contribution to Chinese breast cancer. These results suggested that the 8 high or moderate risk susceptibility genes should be covered in panel testing for high risk Chinese breast cancer patients and their relatives. Overall, our risk assessment data should be useful for the prevention and early detection of Chinese women who carry a pathogenic variant in the 8 breast cancer susceptibility genes.This large case-control study classified Click here for additional data file.


There is evidence to suggest that reproductive factors play an important role in women’s future cancer risk, particularly hormone-dependent malignancies such as breast cancer. However, some controversy remains regarding their effect on any cancer risk. This study aimed to assess the risk of any cancer in a nested case-control study of fertile women living in northeast Scotland in relation to: total number of pregnancies, cumulative time pregnant, age at first pregnancy and interpregnancy interval. Women who were older at first delivery had an increased risk of breast and gastrointestinal cancer, and a reduced risk of cancers of the respiratory tract and uterine cervix, including carcinoma in situ. These findings warrant the investigation of mechanisms underpinning these associations.We assessed the risk of any and site-specific cancers in a case-control study of parous women living in northeast Scotland in relation to: total number of pregnancies, cumulative time pregnant, age at first delivery and interpregnancy interval. We analysed 6430 women with cancer and 6430 age-matched controls. After adjustment for confounders, women with increasing number of pregnancies had similar odds of cancer diagnosis as women with only one pregnancy. The adjusted odds of cancer diagnosis were no higher in women with cumulative pregnancy time 50–150 weeks compared to those pregnant ≤ 50 weeks. Compared with women who had their first delivery at or before 20 years of age, the adjusted odds ratio (AOR) among those aged 21–25 years was 0.81, 95% CI 0.74, 0.88; 26–30 years AOR 0.77, 95% CI 0.69, 0.86; >30 years AOR 0.63, 95% CI 0.55, 0.73. After adjustment, the odds of having any cancer were higher in women who had an inter-pregnancy interval >3 years compared to those with no subsequent pregnancy . Older age at first pregnancy was associated with increased risk of breast and gastrointestinal cancer, and reduced risk of invasive cervical, carcinoma in situ of the cervix and respiratory cancer. Infertility in women has been linked to hormone-dependent malignancies, such as breast and gynaecological cancer in later life . While sAlthough pregnancy appears to have a protective effect on the development of breast cancer in later life, delayed childbearing seems to negate this effect. In particular, women who have their first child before the age of 20 have a 50% reduction in risk of breast cancer when compared with women who have never had children .Other pregnancy-related factors may play an important role with regard to breast cancer risk in later life. A full-term pregnancy may exert different effects on the risk of breast cancer over time—a short-term increase in risk—possibly due to increased oestrogen production during pregnancy, followed by a long-term protection resulting from the terminal differentiation of mammary tissue ,6. If thThere is some controversy in the literature regarding the effect of a pregnancy that has been interrupted before full term on the risk of breast cancer. While a study published in the late 20th century observed an increased risk of breast cancer in women with a history of spontaneous or induced abortion , Melbye A re-analysis of data from 53 different epidemiological studies, however, came to the conclusion that there are no harms associated with induced abortion with regard to breast cancer risk . SubsequThere is a dearth of information regarding the association between reproductive history and other types of cancers, including endocrine cancers. Some evidence of association exists between reproductive history and other cancers, including gastric , ovarianTotal number of pregnanciesCumulative time pregnantAge at first pregnancyInterpregnancy intervalIn this study, we hypothesise that pregnancy has a protective effect on overall risk of subsequent cancer in later life, which acts in a dose-dependent manner with an increasing number of full-term pregnancies offering greater protection. To test the hypothesis, we assessed the risk of any cancer in a nested case-control study of parous women living in northeast Scotland in relation to:Approvals to carry out the study were obtained from the Aberdeen Maternity and Neonatal Databank (AMND) steering committee and the Privacy Advisory Committee of the Information and Services Division (ISD) of the National Health Service (NHS) Scotland.The AMND records and stores all pregnancy-related information about women living in Aberdeen city and district from 1950 onwards. It now has information on over 250,000 pregnancies occurring in these women. Complete reproductive histories of individual women can be constructed using internal data linkage . RecordsThe ISD collect and collate information about all hospital admissions, as well as cancer, birth and death registrations, in Scotland in the Scottish Morbidity Records (SMR). The cohort of women identified from the AMND were linked by ISD in 2008 to the national Cancer Registry (SMR06) and to death notifications supplied by the General Register Office (GRO-S) using probability matching. This linkage process involves using identifiers such as name, date of birth and post code to find the closest match. After the linkage, all identifiers were removed by ISD and we were supplied with a pseudo-anonymised (but containing the AMND unit number) and linked study database. All women who were linked to the Scottish registers were then traced on the Community Health Index (CHI) register in Scotland by the Data Management Team, University of Aberdeen (who have access to both registers) to make sure that all linked women had their complete reproductive histories recorded in the AMND. Any woman who had moved out of Aberdeen before the age of 55 years was excluded from the analysis. Using the linked dataset as the sampling frame, we conducted a nested case-control study. Cases were all women in the linked dataset with a cancer diagnosis (identified from SMR06) and/or recorded death from cancer (identified from GRO-S). For each case, we randomly identified one control born within two years of the case’s year of birth. Controls did not have a history of cancer at the time of the case’s diagnosis. We excluded cases who had cancer prior to their first pregnancy or during pregnancy. Moreover, women who left Aberdeen before they were likely to have completed their families were also excluded after tracing on the CHI register. This was because we could not be certain of the completeness of their reproductive histories. After applying all the exclusions, there were 6430 cases of cancer and 6430 controls available for statistical analysis.A case was defined as having a record in the Scottish Cancer Registry (SMR06) and/or having a record of death associated with cancer. The site of cancer is coded according to ICD-9 or -10 classifications in this registry and these are presented in the Personal  and reproductive characteristics consisted of total number of pregnancies, number of pregnancies of normal duration, cumulative time pregnant (in weeks), age at first delivery and inter-pregnancy interval, which were compared between cases and controls using appropriate descriptive statistics. We included in the multivariable analyses variables that were potential confounders and did not have a high proportion of missing data (see below). The non-manual social class formed the reference group for analysis. Similarly, current and ex-smokers were classified together as having ever smoked, with the “never smoked” group as the reference category. Conditional logistic regression was used to calculate the odds ratio and corresponding 95% confidence interval for the development of any cancer among women with different reproductive characteristics, adjusted for height and age at first delivery (except where age at first delivery was being examined). Subgroup analyses were then conducted according to the site of cancer .There was a substantial proportion of missing data  in the variables weight, social class and smoking status. A large number of women, mostly those who had their first deliveries in the 1950s, had missing data for smoking. As smoking is likely to be an important confounder for some cancers, an additional subgroup analysis by smoking status was conducted including only those women with known smoking history. For clarity of presentation, only the adjusted odds ratios (AORs) are presented for this subgroup analysis. For the other potential confounders, BMI and social class, the proportion of missing data was too high to consider multiple imputation. We did not conduct any further subgroup analyses for these missing data because where data were complete, as they did not appear to vary by case-control status.Women with cancer were younger at the time of their first delivery than controls , and more likely to be current or ex-smokers at the time of their first pregnancy , there were 4660 live births, 78 stillbirths, 687 miscarriages, 689 terminations and 45 ectopic pregnancies following the first delivery.The inverse relationship between cancer and age at first delivery persisted following adjustment for maternal height. Compared with women who had their first delivery at or before the age of 20 years, the AOR among those who were aged 21–25 years was 0.81, 95% CI 0.74, 0.88; 26–30 years, AOR 0.77, 95% CI 0.69, 0.86; >30 years, AOR 0.63, 95% CI 0.55, 0.73. After adjusting for height and age at first delivery, the odds of having any cancer remained higher in women who had an inter-pregnancy interval greater than 3 years compared with those with no subsequent pregnancy .Of the non-gynaecological sites examined , only caTo the best of our knowledge, this is the first study which explored several pregnancy-related factors and their association with any cancer in parous women. Our results showed that the odds of developing any cancer were statistically significantly reduced with increasing age at first delivery. Cancer diagnosis was also associated with inter-pregnancy intervals longer than 3 years. However, breast cancer risk was increased in women with delayed childbearing and with longer time pregnant, as reflected by higher cumulative time pregnant and shorter interpregnancy intervals. Older age at first delivery was associated with a reduced risk of invasive cancer of the uterine cervix, carcinoma in situ and cancers of the respiratory tract.Although there have been several reports of associations between infertility and hormone-dependent cancer ,20,21, tAlthough the reduction of risk seen with cumulative time pregnant (>101 weeks) on cancer of the uterus was not statistically significant in this study, a recent analysis found that whilst there is a greater magnitude of reduction in risk for a full-term pregnancy than an early pregnancy loss, each additional pregnancy is associated with a further reduction in endometrial cancer risk .The similar reproductive pattern seen in our subgroup analysis of women with gastrointestinal cancer has not been described before, although a Swedish national record linkage study involving 406,439 women did not find a protective effect of increasing parity on the development of stomach cancer .The main strength of this study lies in the quality of the data recorded in the databases (AMND and the Scottish Registers) used for record linkage. These databases are almost 100% complete in terms of case ascertainment and have been in existence since the 1950s, thereby reducing the possibility of selection bias. Many other studies have utilised women’s recall of reproductive events, which may be subject to recall bias. We are confident in the completeness of our records of reproductive history, as they are recorded contemporaneously at the time of pregnancy. Moreover, we excluded all women who could not be traced on the CHI register before they were likely to have completed their families. Cases and controls were matched on year of birth. This controlled for an important confounder, as cancer risk varies by age and over time.An obvious limitation of this study is that we only looked at women who had at least one full-term pregnancy, thereby missing any “all or none” protective effect of pregnancy and term delivery on subsequent development of cancer. Another weakness of this study was the inability to adjust for potential confounding factors, notably breast feeding, BMI, exogenous hormone use and smoking. The AMND contains information about breast feeding at discharge from hospital. We did not adjust for this variable in our analyses for two reasons—first, data were missing in a substantial proportion of pregnancies; second, even where they were available, feeding status at discharge from hospital does not capture the duration of breast feeding, which is of more relevance to breast cancer risk. We were able to do a subgroup analysis where smoking data were available, which achieved similar results overall. The women were identified from the AMND, and although they are representative of the local population, the observed findings may not be generalisable to a wider population. Moreover, we had relatively few women with higher-order parity in our dataset, so our study could have been underpowered to observe the protective effect seen in previous reports. Another limitation is multiple statistical testing and significant findings due to chance alone.Interestingly, breast cancer risk showed a different direction of effect of reproductive factors, such as interpregnancy interval to other cancers risk. As the other cancer categories contained a mixed bag of different cancers, as shown in the Despite the limitations, this study shows that reproductive patterns in parous women could be used to identify women at risk of developing cancer in later life. Together with genetic and environmental risk factors, such as smoking, reproductive patterns may be included in prediction models to assess the risk of cancer in women at an early age. Preventing teenage pregnancies would have the added benefit of reducing the risk of cancer. Future research needs to focus on the mechanistic explanations of the associations found in this and other epidemiological studies. For example, this cohort predated the rollout of HPV immunisations in Scotland. The association shown in this study between younger age at first delivery with cervical cancer may be explained by increased rates of HPV infection in women who were sexually active at a younger age.For women who had been pregnant at least once, there was evidence of a small increase in the risk of any cancer among those having subsequent pregnancies. Older age at first pregnancy was associated with increased risk of breast and gastrointestinal cancer and a reduced risk of invasive cervical, carcinoma in situ of the cervix and respiratory cancer.


Few studies have investigated risk factor heterogeneity by molecular subtypes in indigenous African populations where prevalence of traditional breast cancer (BC) risk factors, genetic background, and environmental exposures show marked differences compared to European ancestry populations.We conducted a case-only analysis of 838 pathologically confirmed BC cases recruited from 5 groups of public, faith-based, and private institutions across Kenya between March 2012 to May 2015. Centralized pathology review and immunohistochemistry (IHC) for key markers  was performed to define subtypes. Risk factor data was collected at time of diagnosis through a questionnaire. Multivariable polytomous logistic regression models were used to determine associations between BC risk factors and tumor molecular subtypes, adjusted for clinical characteristics and risk factors.p = 0.021, comparing ≥ 5 to ≤ 2 children). Compared to patients with luminal A tumors, luminal B patients were more likely to have lower parity ; HER2-enriched patients were less likely to be obese  or older age at menopause . Body mass index (BMI), either overall or by menopausal status, did not vary significantly by ER status. Overall, cumulative or average breastfeeding duration did not vary significantly across subtypes.The median age at menarche and first pregnancy were 14 and 21 years, median number of children was 3, and breastfeeding duration was 62 months per child. Distribution of molecular subtypes for luminal A, luminal B, HER2-enriched, and triple negative (TN) breast cancers was 34.8%, 35.8%, 10.7%, and 18.6%, respectively. After adjusting for covariates, compared to patients with ER-positive tumors, ER-negative patients were more likely to have higher parity , In Kenya, we found associations between parity-related risk factors and ER status consistent with observations in European ancestry populations, but differing associations with BMI and breastfeeding. Inclusion of diverse populations in cancer etiology studies is needed to develop population and subtype-specific risk prediction/prevention strategies.The online version contains supplementary material available at 10.1186/s13058-021-01446-3. Women in Africa have lower incidence rates of breast cancer (BC) than women in developed countries (age-standardized rates (ASR) per 100,000 of 36 vs. 74), but higher mortality rates (ASR of 17 vs. 15) . FurtherAlthough advanced stage at presentation, lack of awareness about BC and limited access to available screening and treatment options  are contBreast cancer can be divided into several molecular subtypes based on gene expression profiling analysis, which are subsequently corroborated by a panel of immunohistochemical (IHC) markers including ER, progesterone receptor (PR), human epidermal growth receptor factor 2 (HER2), proliferation marker Ki-67, cytokeratin (CK) 5/6, and epidermal growth factor receptor (EGFR). Epidemiologic studies have demonstrated that BC risk associated with established risk factors, including genetic and environment/lifestyle factors, differ for different breast cancer subtypes , which hHere, in this study, using carefully annotated risk factor and pathology data collected from 838 BC patients enrolled from multiple hospitals across Kenya, we aimed to evaluate distributions of established BC risk factors across BC subtypes.The study has been previously described but in brief, 838 pathologically confirmed BC cases were collected across Kenya between March 2012 and May 2015 . There wPathologic characteristics including histologic grade, histologic tumor type, tumor size, lymph node stage, lymphovascular invasion, and ER/PR/HER2 status were extracted from the clinical database. Central pathology review and IHC for ER/PR/HER2 of all breast carcinoma tissue were done at AKU Hospital, Nairobi, and interpreted by SS and ZM. AKU Pathology department is a College of American Pathologists accredited laboratory and as such enrolls in proficiency testing schemes for breast biomarkers. Additional slides were cut at 5 μm and subjected to IHC stains for EGFR, CK5/6, and Ki67  according to the manufacturer specifications as previously described , with apER and PR tumor expression were considered positive by IHC with ≥ 1% nuclear staining. HER2 expression was determined by IHC and fluorescence in situ hybridization (FISH), the latter in case of an equivocal HER2 IHC result. An IHC score of 3+ or a FISH-positive test result was defined as HER2-positive . Ki-67 wWe used Ki-67 status (low/high) to discriminate luminal A and B and used tumor grade as a surrogate for patients with missing Ki-67 . For EGFMolecular subtypes were defined based on previous clinically validated guidelines  Fig. 1)1): luminDistributions of breast cancer risk factors, including sociodemographic, reproductive, and tumor pathologic characteristics in the overall study population and by hospital groups, were assessed using the chi-squared test or Fisher’s exact test. Multivariable polytomous logistic regression models were used to determine associations between BC risk factors and tumor molecular subtypes .All regression models were fully adjusted for the same covariates (except for where noted): age at diagnosis, BMI, age at menarche, age at first pregnancy, number of children, averaged breastfeeding duration, age at menopause, family history of breast cancer in 1st degree female relatives, highest education level, and occupation. A two-tailed P value less than 0.05 was considered statistically significant. All analyses were performed with SAS v9.4 statistical software (SAS Institute Inc.).2 at diagnosis and 61% lived in rural areas. Our study population was also characteristic for late age at menarche , young age at first pregnancy , having 3 or more children (68%), high prevalence in breastfeeding (95%), and long breastfeeding duration  (Table There were 838 invasive breast cancer cases with complete data on ER and PR status after exclusion of DCIS cases n=21) and cases without any data for tumor subtype (n=8). Fifty-four percent of patients were diagnosed under 50 years of age, 69% had BMI ≥ 25 kg/m and caseCompared to patients admitted to the other 4 hospital groups, AKU patients were more likely to be overweight or obese (79%), have tertiary education level (45%), start the first pregnancy ≥ 25 years (35%), have < 3 children (39%), and have shorter breastfeeding duration per child, which is as expected given that AKU is a private health facility, and compared to the others, patients are generally from a higher socioeconomic status.P < 0.01). Patients admitted to Kijabe and Nyeri hospitals had higher proportions of tumors with lymphovascular invasion: 71.4% and 69.1%, respectively. There was no statistical difference in distributions of patient molecular subtypes  across hospitals (P = 0.08).The distribution of tumor subtypes defined by IHC markers is presented in Fig. trend = 0.021, comparing ≥ 5 to ≤ 2 children). ER-negative patients were also more likely to have longer cumulative breastfeeding duration ; however, these positive associations became insignificant after adjusting for a number of children. In fact, analyzing parity and breastfeeding variables together showed that the association was driven by parity (Table trend = 0.038), especially among postmenopausal women  (Supplementary Table trend = 0.038) suggesting that the association was unlikely to be due to the reverse causation.Results of adjusted associations between risk factors and ER status are shown in Given that several risk factors and clinical variables varied by hospital groups Tables  and 2, wWe further evaluated the associations between the risk factors and ER in younger < 50 years) and older (≥ 50 years) women separately. In general, the associations with most risk factors were similar in younger and older women, except that we observed an association between older age at menarche and ER-negative patients in older  but not in younger women  . HER2-enriched patients were less likely to be obese  or to have older age at menopause . The HER2-BMI association appeared to be stronger among postmenopausal women  than among premenopausal women. Overall, cumulative or average breastfeeding duration did not vary significantly across subtypes. When looking at a number of children and breastfeeding or age at first birth jointly, it appears that luminal B patients with four or more children seemed to have shorter cumulative breastfeeding duration and later age at birth compared with luminal A patients  and early age at menarche  was much higher in AMBER [There is limited data from Africa where some of the breast cancer-associated risk/protective factors such as parity and breastfeeding have extremely different distributions. The overall risk factor distribution for BC patients in our study is similar to a large case-control study from Ghana , but is in AMBER , 23 thanParity has been reported to have a dual effect on breast cancer risk; it is protective for ER+ women while increases risk for ER− women especially among younger women , 21. DesInvestigations of associations between breastfeeding and breast cancer risk by receptor status have resulted in inconsistent findings, with some showing a similar protective effect for all subtypes , and othp-trend = 0.01) [Obesity is a known risk factor for breast cancer in post-menopausal women but protective in premenopausal women . Obesity = 0.01) . In addi = 0.01) . Our res = 0.01)  and a po = 0.01)  showing  = 0.01) .The strength of our study includes representation of BC cases from multiple hospitals in Kenya, well-annotated risk factor questionnaire and clinical data, and centralized high-quality biomarker assessment in a unique east African population.This study was limited by the retrospective collection of risk factor data and possible reverse causation, as well as the case-only design, which prohibited us from estimating relative risks associated with each risk factor. Further, despite being the largest BC study of this type conducted in Kenya, the sample size was still relatively small to evaluate risk factors in rare tumor subtypes, especially in age-stratified analyses.In summary, our findings, based on data from an indigenous African population with unique risk factor profiles, add to the growing body of knowledge regarding the etiologic heterogeneity of breast cancer molecular subtypes among geographically diverse ethnic groups. Further investigations of genetic and environmental factors that modify breast cancer risk in African populations are recommended. Inclusion of diverse regional population groups from sub-Saharan Africa in global breast cancer studies may help provide a better understanding of the subtype-specific breast cancer risk etiology, which will be critical for the development of risk prediction models in African populations.Supplementary Table 1.. Classifications of the five hospital groupsSupplementary Table 2. Associations between breast cancer risk factors and PR and HER2 status in Kenyan breast cancer patients (N=838)Supplementary Table 3. Associations of key risk factors with ER status by hospitals (N=838)Supplementary Table 4. Associations between BMI and HER2 status stratified by hospitals (N=821)Supplementary Table 5. Associations between key risk factors and ER status by age at diagnosis (N=834)Supplementary Table 6. Associations between breast cancer risk factors and tumor molecular subtypes in Kenyan breast cancer patients (N=776)Supplementary Table 7.. Associations of key risk factors and tumor subtypes without applying tumor grade to tumor subtype classificationSupplementary Table 8. Associations of key risk factors and tumor subtypes after excluding women missing for ki67Supplementary Table 9.. Associations between age at first pregnancy and ER status .in parous womenSupplementary Figure 1.. Associations between BMI and HER2 status stratified by hospital groups


The data presented here were obtained for a New Zealand nationwide population-based case-control analysis undertaken to assess the association between ovarian cancer and depot medroxyprogesterone acetate (DMPA), intrauterine contraceptive devices (IUDs), and vasectomy of a woman's sexual partner . The research involved women aged 35 to 69 years. Controls were randomly selected from the New Zealand electoral roll. Cases were women with a diagnosis of incident ovarian cancer recruited from the New Zealand Cancer Registry and had to be listed on the electoral roll.Data collection was conducted between 1st May 2013 and 31st October, 2015. A structured postal questionnaire was used to gather information. Data were analysed using IBM Statistical Package for the Social Sciences (IBM SPSS statistics 22). Odds ratios adjusted for age were calculated using the method of Mantel and Haensze . For multivariable analyses, binary logistic regression was used.Description of study participants and age-adjusted and multivariable analyses of the association between ever-use and specifics of use of DMPA, IUDs, and vasectomy were presented in a journal article . Here, we present data from analyses of the risk of ovarian cancer by histological type associated with the use of DMPA, IUDs and ever having had a vasectomised partner. In addition, analyses assessing the association between ovarian cancer and these contraceptives restricted to ever-users and never-users of hormonal contraceptives  are presented. Data from analyses of the association between history of tubal ligation and the risk of ovarian cancer are also presented.These data, including the findings of a related study  and the raw data, can be included in a collaborative analysis of existing studies undertaken to assess the association between IUDs, long-acting progestogen-based contraceptives, and partner vasectomy and the risk of ovarian cancer. Contraceptives are widely used and use may span months or years; therefore, even a small effect on the risk of ovarian cancer may have a great impact on the incidence of disease in the general population.•The inverse associations of both partner vasectomy and use of DMPA with the occurrence of ovarian cancer provide additional important non-contraceptive benefits of vasectomy and DMPA. This may provide alternative contraceptive options for women who wish to lower their risk of ovarian cancer but cannot use oral contraceptives.•These data, including the findings of a related study 1Description of research participants and age-adjusted and multivariable analyses of the association between ever-use and specifics of use of depot medroxyprogesterone acetate (DMPA), intrauterine contraceptive devices (IUDs), and vasectomy were presented in an article 1.1Analysis of risk by histological type of ovarian cancer associated with ever-use of DMPA, IUDs and vasectomy was performed . Due to 1.2The association between risk of ovarian cancer and partner vasectomy or use of IUDs was assessed confined to women who had ever-used hormonal contraceptives  . Among wAnalyses restricted to never-users of hormonal contraceptives were also performed  contains all the data collected for this research, including for the published article 2The data presented here are from a national population-based case-control study conducted in New Zealand. Approval to conduct the study was obtained from the Southern Health and Disability Ethics Committee (13/STH/26) and the University of Canterbury Human Ethics Committee (HEC2013/08). Recruitment of participants and data collection were done nationwide. Data were collected from May 2013 through October 2015.Cases were women with newly diagnosed, histologically confirmed ovarian cancer, aged 35 to 69 years, at any stage of disease but capable of effective communication in English. The controls consisted of women free from ovarian cancer with similar age restrictions and capable of effective communication in English. Ovarian cancer is generally a disease of post-menopausal women, with the highest incidence at ages 65 to 74 years. Women use contraceptives during their reproductive years; that is, between menarche and menopause. The age range, 35 to 69 years inclusive, captures the population that is most affected by ovarian cancer and, at the same time allows for best recall of contraceptive usage. Due to logistical constraints, potential participants who were unable to communicate in English were excluded. English being the most spoken language in New Zealand and participants being New Zealand citizens or permanent residents, this requirement was not expected to exclude a significant number of potential participants. Controls were randomly selected from the General and Māori Electoral Rolls, frequency matched to cases by 5-year age groups. Access to electronic data about people on the New Zealand electoral roll for purposes of health research is allowed under Section 112(3) of the Electoral Act 1993. Cases were recruited sequentially from the New Zealand Cancer Registry (NZCR) and were eligible if they were also listed on the electoral rolls. Histology reports for all women with a recent diagnosis of ovarian cancer were forwarded to the research team from the NZCR . The reports were reviewed by a medically qualified member of the research team. All women who met the inclusion criteria for the study were selected. Approval to approach the patients about the study was then sought from each woman's relevant doctor. In NZ, there are statutory requirements for laboratories to report all cancer diagnoses (except non-melanoma skin cancer) to NZCR and for all New Zealand citizens and permanent residents 18 years and above to register on the parliamentary electoral roll. To ensure that the controls represented the population from which the cases came, and that cases and controls could be contacted by mail, all eligible cases and controls were listed on the electoral roll. Participants were stratum-matched by age because the incidence of ovarian cancer increases with age. Approval from the doctors of the women who constitute the cases was sought with the assumption that each doctor knew the woman well and would, therefore, know whether she was well enough to participate in the study. In view of the small number of ovarian cancer cases, more controls than cases were recruited (a case-control ratio of 1:5) in order to enhance the statistical power of the study.The data were collected using a structured questionnaire that included previously validated questions  and questions sourced from suitable questionnaires such as those used in the Million Women Study Each potential participant was sent a letter on University of Canterbury letterhead, signed by two members of the research team. The letter was accompanied by an information and consent form, a copy of the questionnaire, and a postage-paid addressed envelope for returning the questionnaire and signed consent form. Participants were provided with a calendar of major life events, as part of the questionnaire, to assist in recall and to record their use of contraceptives. To facilitate responses, women who did not respond to the initial questionnaire and consent form within three weeks from the date of dispatch, were sent a second letter, information and consent form, questionnaire, and postage-paid addressed envelope. Women who did not respond to the second mail-out were contacted and asked to complete the questionnaire by telephone interview conducted by a trained interviewer. If they were willing to do this, a telephone interview was done, using the same questionnaire. They were requested to post back a signed consent form. Those who returned completed questionnaires without a signed consent form or who had a telephone interview and did not post back a signed consent form were considered to have consented. All questionnaires were checked for completeness; where necessary, participants were contacted to obtain missing data and to clarify discrepant values.Data were entered into an EXCEL spreadsheet. Frequency tables were constructed in order to aid in identifying data entry errors. These errors were corrected by referring to the answers in the questionnaires. In situations where values provided were considered unrealistic, these were treated as missing values. All identifying information was coded before analysis. To ensure that participants were at risk of a first primary ovarian cancer, women with a prior history of ovarian cancer were excluded. Controls with history of bilateral oophorectomy were also excluded because this effectively eliminates the risk of developing ovarian cancer.Data were analysed using the IBM Statistical Package for the Social Sciences (IBM SPSS statistics 22). Descriptive statistics were used to compute frequencies and percentages. All variables collected in the study were assessed for association with the risk of ovarian cancer, adjusted for age in 5-year groups using the method of Mantel and Haenszel In addition, analyses of the risk of ovarian cancer associated with ever having had a vasectomised partner, history of TL, and use of DMPA and IUDs restricted to women who have ever-used and those who have never used hormonal contraceptives (defined as OCs or DMPA) was performed.Approval to conduct the study was obtained from the Southern Health and Disability Ethics Committee (13/STH/26) and the University of Canterbury Human Ethics Committee (HEC 2013/08). Approval to approach the patients (cases) was sought from each woman's doctor. All participants gave informed consent to participate in the study.Jacqueline Chesang: Investigation, Data curation, Formal analysis, Writing – original draft. Ann Richardson: Investigation, Data curation, Formal analysis, Writing – original draft. John Potter: Investigation, Formal analysis. Mary Sneyd: Investigation. Pat Coope: Formal analysis.The authors declare that they have no known competing financial interests or personal relationships which have or could be perceived to have influenced the work reported in this article.This work was supported by Genesis Oncology Trust (E6083) and Wayne Francis Charitable Trust (GOT-1262-RPG). However, Genesis Oncology Trust and Wayne Francis Charitable Trust were not involved in the design of the study and collection, analysis and interpretation of data and in writing the manuscript.


Q4-Q1 ranged from 1.78 for breast cancer, when combining NT-proBNP and HbA1C, to 2.87 for MI when combining NT-proBNP, HbA1C, CRP and cystatin-C). This analysis suggests that combinations of biomarkers related to unhealthy ageing show strong associations with cancer risk, and corroborates published evidence on CVD risk. If confirmed in other studies, using these biomarkers could be useful for the identification of individuals at higher risk of age-related diseases.Biological age is an important risk factor for chronic diseases. We examined the associations between five markers of unhealthy ageing; Growth Differentiation Factor-15 (GDF-15), N-terminal pro-brain natriuretic peptide (NT-proBNP), glycated hemoglobin A1c (HbA1C), C-Reactive Protein (CRP) and cystatin-C; with risks of cancer and cardiovascular disease (CVD). We used a case-cohort design embedded in the EPIC-Heidelberg cohort, including a subcohort of 3792 participants along with 4867 incident cases of cancer and CVD. Hazard ratios (HRs) were computed and the strongest associations were used to build weighted multi-marker combinations, and their associations with cancer and CVD risks were tested. After adjusting for common confounders, we observed direct associations of GDF-15 with lung cancer risk, NT-proBNP with breast, prostate and colorectal cancers, HbA1C with lung, colorectal, and breast cancer risks, and CRP with lung and colorectal cancer risks. An inverse association was observed for GDF-15 and prostate cancer risk. We also found direct associations of all 5 markers with myocardial infarction (MI) risk, and of GDF-15, NT-proBNP, CRP and cystatin-C with stroke risk. A combination of the independently-associated markers showed a moderately strong association with the risks of cancer and CVD (HRThe online version contains supplementary material available at 10.1007/s10654-021-00828-3. Non-communicable diseases (NCDs) are the number one cause of mortality worldwide. According to the Global Burden of Diseases (GBD), 73.4% of the deaths that occurred in 2017 were linked to NCDs, among which approximately 31% linked to cardiovascular diseases (CVD) and 29% to cancer ), a more than fourfold hazard ratio for top vs bottom quartiles of CRP , and a more than threefold hazard ratio for top vs bottom quartiles of HbA1c . Each of these associations with lung cancer risk were strongly attenuated, however, when models were additionally adjusted for smoking status and educational level  and further covariates in model 1. For NT-proBNP, HR associations  were of moderate magnitude, with highest hazard ratio of 2.2 for top vs. bottom quartiles , and for this biomarker there was no change in HR estimates after additional covariate adjustments. Interestingly, levels of GDF-15 showed a significant inverse association with risk of prostate cancer. Cystatin-C levels showed no association with risks of prostate, lung or colorectal cancer risk, but showed a borderline significant inverse association for breast cancer in the multivariable-adjusted model.With regard to cancer, basic risk models adjusted only for age and sex showed increased risks at multiple cancer (organ) sites for higher blood concentrations of NT-proBNP , HbA1C , and CRP , whereas higher GDF-15 levels were associated only with an increased risk of lung cancer. The strongest associations were observed for lung cancer, where minimally adjusted models showed a more than eightfold hazard ratio for top vs. bottom quartiles of GDF-15  and colorectal (p-heterogeneity = 0.02) cancer; where significant and moderately strong associations were observed only in never smokers . On the other hand, for lung cancer, marker associations were only statistically significant in current smokers; however, heterogeneity tests were non-significant, suggesting that absence of significance in never and former smokers might be linked to the limited number of lung cancer cases in these subgroups. Furthermore, although heterogeneity tests were not significant, the direct associations of NT-proBNP with colorectal and breast cancer risk were of a stronger magnitude or statistically significant exclusively among never smokers, respectively .For breast cancer, further exploratory analyses by ER, PR and HER receptor status and by age at tumor diagnosis showed significant associations of HbA1c especially with risk of Her2 + cancers (p-heterogeneity = 0.04), and of tumors diagnosed after age 55, although no significant heterogeneity was observed . No statistically significant heterogeneity was observed for the associations of NT-proBNP with breast and prostate cancer risk, according to tumor subtypes, grade and age at diagnosis . The inverse association between GDF-15 and prostate cancer risk was more pronounced for high-grade disease, with a borderline significant heterogeneity (p-heterogeneity = 0.05).Findings were unchanged when cases diagnosed within the first 2 years of follow-up were excluded. Only the association of cystatin-C with risk of stroke became non-significant .Q4-Q1 = 1.78  for breast cancer (combining NT-proBNP and HbA1C), to HRQ4-Q1 = 2.87  for MI   and NT-proBNP (positive) were associated with prostate cancer, only GDF-15 and HbA1C with lung cancer risk, and only NT-proBNP with colorectal cancer risk. Models combining these biomarkers, weighted by their effect sizes in the mutually adjusted models, and adjusted for BMI, lifetime alcohol consumption, smoking, physical activity level, educational level, baseline self-reported diabetes, and baseline self-reported hypertension, showed moderately strong associations with cancer and CVD risk, ranging from HRIn this prospective analysis, we found risk associations for both CVD  and cancer with five ageing-related biomarkers that were pre-selected on the basis of their measurement reliability, relevance to biological ageing, and ability to predict all-cause mortality. All five markers showed associations with long-term risk of CVD . Regarding cancer, increased risks were observed especially among individuals who had higher blood concentrations of NT-proBNP , HbA1C , and CRP , whereas higher GDF-15 levels were associated with an increased risk of lung cancer and a reduced risk of prostate cancer.NT-proBNP was associated with breast, prostate, colorectal cancer and CVD risks. HbA1C was associated with breast, lung and colorectal (among never smokers) cancer and MI risks. CRP was associated with lung cancer and CVD risks. Cystatin-C was inversely associated with breast cancer risk and directly associated with CVD risk. For each of the chronic disease outcomes, combining the markers showed HRs between 1.78 (breast cancer) and 2.87 (MI) comparing highest to lowest quartiles, after adjustment for smoking, BMI, alcohol consumption, physical activity, and self-reported diabetes or hypertension.. used targeted proteomics to identify strong predictors of CVD and mortality, covering 85 pre-selected protein markers. In mutually adjusted analyses, GDF-15 was the only biomarker associated with CVD risk, whereas twelve further ones were associated with all-cause mortality, among which GDF-15, NT-proBNP, and Cystatin-C, and six (among which NT-proBNP and GDF-15) were associated specifically with CVD death [. [Q4-Q1 = 2.87, after adjustment for other established CVD risk factors) and stroke (HRQ4-Q1 = 2.44).Our findings for MI and stroke are consistent with those from previous studies and published meta-analyses –28, withVD death . Anotherdeath [.  also fouRegarding cancer, our findings for CRP and HbA1C are also quite in line with those from previous prospective studies. For CRP, previous prospective studies have shown increased risks of colorectal cancer , and of Novel observations from our study are the increased risk of lung cancer, and the reduced risk of prostate cancer, among individuals who had higher blood levels of GDF-15. The association with lung cancer in part appeared confounded by smoking, but persisted after smoking was adjusted for (HR = 2.73 after adjustment for smoking and other confounders vs HR = 8.14 in models adjusted only for age and sex). Other studies have found higher levels of circulating GDF-15 in patients with pulmonary fibrosis  – a condFor NT-proBNP, our study showed for the first time in the general population moderately strong associations between higher blood levels and increased risks of breast, prostate and colorectal cancer. These findings are consistent with those from a small prospective study (n = 699 and 24 cancer cases) in coronary disease patients, linking NT-proBNP with overall cancer risk , and supFor cystatin-C our data suggest a possible inverse association with breast cancer risk, although this association was statistically significant only in multi-variate adjusted risk models or when cases diagnosed within the first 2 years were excluded. However, our data showed no evidence for any further associations of cystatin-C with risk of cancers of the colorectum, lung or prostate. We are not aware of other studies, so far, that prospectively examined the association of cystatin-C in initially cancer free subjects with later cancer risk.. found that in a mutually adjusted model, GDF-15 was the only predictor of cancer-specific mortality [To our knowledge, this study is the first to assess how combinations of these five biomarkers associate with cancer incidence. Daniels et alortality . Our res2 ranged between 0.02 for CRP and 0.13 for GDF-15 and CRP); however, our participants were younger at the time of cohort enrollment and blood donation. Interestingly, participants above 50 years old in our cohort (55 for NT-proBNP) had clearly higher levels than younger participants  and cancer risk. The present analyses were well-powered and comprehensively adjusted for potential confounders. Our study included healthy participants from the general population, free of major cardiovascular conditions or cancer at baseline. This has allowed detecting possible associations, before diagnosis was made or symptoms appear. We had data about histological grading for prostate cancer and breast tumor subtypes, as well as a relatively long follow-up duration (median 10y): even though this enabled us to investigate long term associations, a dilution effect could not be entirely ruled out. Nevertheless, this could have led to a non-differential measurement error , most probably leading to an underestimation of the observed associations. Moreover, the risk of residual confounding, linked either to unmeasured factors  or to imperfections in data collection (for smoking for instance) cannot be totally excluded despite adjusting for a 5-category smoking variable. However, the majority of associations with cancer risk were also found in never smokers. Moreover, even if these markers have shown strong associations with chronological age and mortality risk in previous studies, they cannot be interpreted as signatures of biological ageing. In addition, the multi-marker combination we developed was not cross-validated; however, it was not intended for further replication, or clinical practice, but it aimed to examine whether these markers, reflecting different dimensions of ageing hallmarks , jointly show stronger associations with cancer and CVD risk, in our study population, than considering each of them individually. Furthermore, even though these five markers were related to ageing, any direct inference of the observed associations to a specific single dimension of biological ageing is not straightforward, and should be interpreted with caution, as these markers are not specific to ageing, and could reflect other causal pathways  and as biological ageing covers a wide array of mechanisms and pathways. Last, the number of cases might have been limited to detect associations in stratified and tumor subtypes/grades models, leading to a weaker statistical power in some of these analyses.This study allowed us to have an umbrella view of the associations between five biomarkers linked to biological ageing  and cancer and CVD risks. While all of them were independently associated with CVD risk, we observed for the first time that combining NT-proBNP and HbA1C associates strongly with breast cancer risk, GDF-15 and NT-proBNP with prostate cancer risk, and GDF-15 and HbA1C with lung cancer risk. If confirmed in other studies and settings, these findings open up the possibility of using combinations of biological ageing markers to predict cancer, CVD risk and mortality, which might enable risk stratification to identify people at high risk of NCDs.Supplementary file1 (DOCX 692 kb)Below is the link to the electronic supplementary material.