We investigated the associations of aspirin and other nonsteroidal anti-inflammatory drugs (NSAIDs) with breast cancer risk by the status of COX-2 protein expression.This study included 421 cases and 3,166 controls from a nested case–control study within the Nurses’ Health Study (NHS) and Nurses’ Health Study II (NHSII) cohorts. Information on medication use was first collected in 1980 (NHS) and 1989 (NHSII) and was updated biennially. Medication use was defined as none, past or current; average cumulative dose and frequency were calculated for all past or current users using data collected from all biannual questionnaires preceding the reference date. Immunochemistry for COX-2 expression was performed using commercial antibody . We used polychotomous logistic regression to quantify associations of aspirin and NSAIDs with the risk of COX2+ and COX2− breast cancer tumors, while adjusting for known breast cancer risk factors. All tests of statistical significance were two-sided.In multivariate analysis, we found no differences in associations of the aspirin exposures and NSAIDs with breast cancer risk by COX2 expression status. In stratified analyses by COX2 status, significant associations of these medications with breast cancer risk were observed for dosage of aspirin among current users in COX2- tumors . Regular aspirin use was marginally associated with the risk of COX2- tumors (p-trend = 0.06).Our findings suggested no differences in associations of aspirin and other NSAIDs with COX2+ and COX2− tumors.The online version contains supplementary material available at 10.1186/s13058-022-01575-3. Chronic inflammation is a known risk factor for several cancers  and is aEpidemiologic studies on the association between nonsteroidal anti-inflammatory drugs (NSAIDs) and, specifically, aspirin use and breast cancer demonstrated inconsistent findings. While some studies found no association –8, otherIt is unknown whether the associations of anti-inflammatory medications, including aspirin, with breast cancer may differ across COX-2 defined breast tumor subtypes. In this study, we aimed to compare the associations of aspirin and other NSAIDs with COX-2 positive and COX-2 negative tumors using Nurses’ Health Study (NHS) and the Nurses’ Health Study II (NHS II) cohorts.Women included in this study were selected from participants of the NHS and NHSII cohorts. These prospective cohorts followed registered nurses in the USA who were 30–55 years (NHS) or 25–42 years old (NHSII) at enrollment. After administration of the initial questionnaire, the information on breast health risk factors and any cancer diagnoses was updated biennially. More detailed description of the cohort has been published elsewhere .We used a nested case–control study design to examine the association between aspirin and other NSAIDs with breast tumor subtypes defined based on the COX-2 expression status. Details of this nested case–control study have been previously described . BrieflyThe study protocol was approved by the institutional review boards of the Brigham and Women’s Hospital and Harvard T.H. Chan School of Public Health, and those of participating registries as required. Consent was obtained or implied by return of questionnaires.The methods of assessing exposure to aspirin and other NSAIDs have been described in detail elsewhere . BrieflyWomen were classified as current users at each questionnaire in which current use was reported and were considered current users for the subsequent two-year follow-up period (or the 4-year follow-up period from 1989 to 1993). For participants who missed a questionnaire, drug use information was carried forward from the previous cycle. The women who ceased reporting use were classified as past users, but they were eligible to become current users in subsequent follow-up years. Women were classified as nonusers if they did not report analgesic use at baseline or on any of their follow-up questionnaires. Duration of use of each drug was calculated from baseline  to the reference date (date of diagnosis for cases and their matched controls) . To bettWe requested formalin-fixed paraffin-embedded tissue samples from hospitals throughout the US where women underwent primary breast tumor resection. Tumor microarrays (TMAs) were constructed at the Dana Farber Harvard Cancer Center Tissue Microarray Core Facility, Boston, MA. As described previously , TMAs weImmunohistochemistry for COX-2 was conducted on 5 μm paraffin sections of TMA blocks. Cores with fewer than 100 cells were excluded from all analyses. For NHS, the staining was first performed with monoclonal antibodies from Thermo Fisher Scientific . These stained NHS TMAs were subsequently stained with a second monoclonal antibody from Cayman Chemical  at the Pepper Schedin’s laboratory , as previously described [Expression of COX-2 for NHSII TMAs was assessed in Dr. Pepper Schedin’s laboratory  . Based oInformation on breast cancer risk factors was obtained from the biennial questionnaires closest to the date of the mammogram. Women were considered to be postmenopausal if they reported: (1) no menstrual periods within the 12 months before blood collection with natural menopause, (2) bilateral oophorectomy, or (3) hysterectomy with one or both ovaries retained and were 54 years or older for ever-smokers or 56 years or older for never-smokers , 25.To quantify mammographic density, the craniocaudal views of both breasts for all screening mammograms in the NHS and for the first two batches of mammograms in the NHSII were digitized at 261 μm per pixel with a Lumisys 85 laser film scanner . The third batch of NHSII mammograms was digitized using a VIDAR CAD PRO Advantage scanner  and comparable resolution of 150 dots per inch and 12 bit depth. The Cumulus software  was used for computer-assisted determination of the absolute dense area, non-dense area, and percent mammographic density on all mammograms . Percent2), percent breast density , age at menarche , parity and age at first birth , PMH use , family history of breast cancer , alcohol consumption , and study cohort .We used unconditional logistic regression to analyze the association between anti-inflammatory drug use and breast cancer risk, while adjusting for the following potential confounders in the fully adjusted logistic regression models: age at diagnosis , body mass index . The test of heterogeneity from polychotomous logistic regression models was done using STATA version 11.0 .Differences in the association of breast density with COX2-defined tumor subtypes were investigated using polychotomous logistic regression . In thisOur study included 421 cases and 3,166 controls. Age-adjusted characteristics of controls in the study by menopausal status and aspirin intake have been described previously . Based oIn the primary analysis using staining from both antibodies for COX2 Table , we founIn the secondary analysis using either Cayman staining antibody or Thermo Scientific antibody staining-defined COX-2 status (Additional file In this study of associations of anti-inflammatory drug use and breast cancer by the status of COX-2 protein expression, we found no differences in the association patterns of aspirin or other NSAIDs across COX2-defined tumor subtypes.The findings of the studies on associations of aspirin with breast cancer risk have been inconsistent with some suggesting an inverse associations of aspirin use with breast cancer and breast cancer-specific mortality after primary breast cancer diagnosis –15 and oSeveral biological mechanisms were suggested as a possible explanation for potential effects of aspirin and other anti-inflammatory medications on breast cancer risk, including inhibition of COX-2 enzyme activity , 13, 14 We examined, for the first time, associations of anti-inflammatory medications with the risk of COX2-defined breast tumor subtypes. Our study used data from the NHS and NHSII cohorts with more than 25 years of follow-up, ascertainment of disease status, and comprehensive information on breast cancer risk factors and breast density. Our study has a few limitations. Despite the prospective nature of the cohort, potential errors in recall of aspirin and other NSAIDs use are possible. However, given our population of registered nurses with a familiarity of health-related exposures and use of drugs as well as prospective data collection, the medication use data are likely to be accurate.We investigated the associations of aspirin and other NSAIDs use with breast cancer risk by the status of COX-2 protein expression. Our findings suggested no differences in associations of these medications with COX2+ and COX2− tumors.Additional file 1. Supplementary table 1. Associations of aspirin with breast cancer risk, by COX2 status defined using median Cayman staining antibody. Supplementary table 2. Associations of aspirin with breast cancer risk, by COX2 status defined using median, Thermo Scientific staining antibody.


Early detection of breast cancer in asymptomatic women through screening is an important strategy in reducing the burden of breast cancer. In current organized breast screening programs, age is the predominant risk factor. Breast cancer risk assessment tools are numerical models that can combine information on various risk factors to estimate the risk of being diagnosed with breast cancer within a certain time period. These tools could be used to offer risk-based screening. This systematic review assessed, using a variety of methods, how accurately breast cancer risk assessment tools can group women eligible for screening within a population, into risk groups, so that each group could potentially be offered a screening protocol with more benefits and less harms compared to current age-based screening.n = 11), followed by Tyrer-Cuzick (n = 5), BRCAPRO and iCARE-Lit (n = 3). No tool was consistently well-calibrated across multiple studies and breast density or polygenic risk scores did not improve calibration. Most tools identified a risk group with higher rates of observed cancers, but few tools identified lower-risk groups across different settings. All tools demonstrated a high risk of bias. Conclusion: Some risk tools can identify groups of women at higher or lower breast cancer risk, but this is highly dependent on the setting and population.Background: The benefits and harms of breast screening may be better balanced through a risk-stratified approach. We conducted a systematic review assessing the accuracy of questionnaire-based risk assessment tools for this purpose. Methods: Population: asymptomatic women aged ≥40 years; Intervention: questionnaire-based risk assessment tool (incorporating breast density and polygenic risk where available); Comparison: different tool applied to the same population; Primary outcome: breast cancer incidence; Scope: external validation studies identified from databases including Medline and Embase (period 1 January 2008–20 July 2021). We assessed calibration (goodness-of-fit) between expected and observed cancers and compared observed cancer rates by risk group. Risk of bias was assessed with PROBAST. Results: Of 5124 records, 13 were included examining 11 tools across 15 cohorts. The Gail tool was most represented ( Early detection of breast cancer in asymptomatic women through screening is an important strategy in reducing the burden of breast cancer. Mammographic screening programs have decreased mortality for screened women and reduced the intensity of breast cancer treatment and associated sequelae ,2,3,4. NBreast cancer risk assessment tools  use numerical models to combine information on various risk factors (or risk predictors) to estimate the risk or probability of being diagnosed with breast cancer within a certain time period  or from the time of assessment to older age . These tWhile many of these tools have been developed for individual clinical applications or management of higher-risk population groups, such risk assessment tools could potentially be used to stratify screening populations into population-level risk groups, with each group offered a screening protocol to optimise the benefits and minimise the harms of screening . In lineA critical step making the most of available tools is understanding which tools can accurately achieve population-level risk stratification, including the extent to which their accuracy can be generalised to different populations and health settings. Case-control studies frequently report improvements in the discrimination of new or revised risk assessment tools ,19,20,21This systematic review aims to characterise studies which compare breast cancer risk assessment tools and assess their ability to stratify screening populations according to (i) absolute risk of breast cancer and (ii) related outcomes of breast cancer risk . This review was undertaken as part of the Roadmap to Optimising Screening in Australia (ROSA) project  funded bOur Patient, Intervention, Comparison, Outcomes (PICO) question is ‘For asymptomatic women aged ≥40 years, how accurately do different breast cancer risk assessment tools assign women to risk groups?’, where the term ‘risk assessment tool’ is used synonymously for risk prediction tool, prognostic model, risk prediction model, risk model, and breast cancer prediction model. The protocol for this systematic review was registered on the International Prospective Register of Systematic Reviews (PROSPERO) as part of a larger protocol exploring breast cancer risk assessment tools (CRD42020159232). We followed the requirements of the PRISMA 2020 guidelines for conducting and reporting of systematic reviews [The current analysis was confined to articles comparing breast cancer risk assessment tools on the same study cohort; cohorts had to consist of asymptomatic women undergoing population mammographic screening. We excluded articles limited to cohorts of women undergoing diagnostic breast imaging, specific ethnic groups or women with high risk of breast cancer as these represent sub-groups of the screened population. We considered only external validation studies (so that the study cohort was different from that used to develop each tool being compared), We included randomised controlled trials, paired cohort studies or systematic reviews thereof. Due to the need for sufficient follow-up between risk assessment and cancer outcomes, we included prospective or retrospective cohort studies (based on timing of risk predictor data collection in relation to outcome occurrence). All other study designs  were excluded.We included risk assessment tools based on questionnaire data with or without genetic and/or breast density information, where estimated future risk was projected to a minimum of two years . Tools designed to be calibrated to the target population prior to use were included if they were developed on a different population to the study cohort. Tools requiring any non-standardised input  were excluded.We restricted our analysis to articles published from 2008, aiming to include studies likely to use more relevant imaging methods and more recent versions of risk assessment tools while not excluding relatively contemporary studies with longer periods of follow-up. Only English language peer-reviewed publications were included; conference abstracts, reviews, letters, editorials and comments were excluded.The primary outcome was expected versus observed incidence of invasive breast cancer (with or without in situ disease). Secondary outcomes were breast cancer mortality, incidence for different types of breast cancer as defined by characteristics such as tumour subtype, grade, size, nodal involvement, and interval breast cancers . Articles that did not report expected versus observed (E/O) calibration outcomes according to risk groups determined by the risk assessment tool were excluded.Results were excluded from the analysis if risk was projected beyond the period for which the tool was developed. Five-year risk was the primary outcome compared and reported; results for 10-year risk are included in We contacted corresponding authors when there was a lack of clarity around criteria for inclusion in our review, allowing two weeks for a response, after which we sent a reminder in addition to contacting other authors on the paper. If no response was received, the study was excluded. Extracted data is presented in An experienced systematic reviewer (VF) searched on 1 July 2021 for English-language reports published from 1 January 2008 to 29 June 2021 on the following databases: (i) Ovid Medline and Embase; (ii) The Cochrane Database of Systematic Reviews (CDSR) and (iii) PROSPERO. An updated search until 20 July 2021 was also performed for these databases. For Ovid databases, database-specific subject headings and text terms were combined for breast cancer, risk assessment and calibration terms (see Supplementary Methods). The CDSR was searched by combining “breast cancer” and “risk” text terms. Reference lists of relevant systematic reviews and full-text articles were also scanned for additional potentially relevant reports by two systematic reviewers . The search strategy is presented in Titles and abstracts of the articles identified via the literature searches were screened against pre-specified inclusion criteria and split equally between two reviewers  with 20% assessed by both reviewers. The two reviewers independently assessed full-text articles of potential or unclear relevance for inclusion using a form with pre-specified selection criteria. Reviewers were not blinded to journal titles or study authors/institutions. Disagreements were resolved by discussion or adjudication by a third reviewer (SH). Two independent reviewers  equally split the extraction of pre-determined study characteristics and results data from each included study and then reviewed the other’s extractions for accuracy. Disagreements were resolved by discussion or adjudication by a third reviewer ; experienced statisticians were consulted to advise upon or review article methodology or calculations (SE or CN).The following information was extracted: first author, publication year, country, study design, setting, study start, participant inclusion/exclusion criteria, screening protocol, population characteristics, risk assessment tool information, follow-up duration, risk prediction interval, reported relevant outcomes, E/O estimates and 95% confidence intervals (CIs), observed rates  and other relevant information . If E/O ratios, their 95% CIs or data for observed rates were not reported, these were calculated by the systematic reviewers from available data or plots where possible . Ninety-five percent CIs were calculated using the following formula: E/O × exp ^ (±1.96 × sqrt(1/O)) ,24. If tWe also identified high, moderate and low risk groups for each tool in each cohort. These groups were dependent on the number of quantiles the cohort of interest was divided into and whether they had the equivalent number of participants in each one. In general, when the cohort was divided in equal quartiles or deciles, we assumed the high-risk group corresponded to quartile 5 or deciles 9 and 10, the low-risk group corresponded to quartile 1 or deciles 1 and 2 while moderate-risk groups correspond to the remaining quantiles (quartiles 2–4 or deciles 3–8). Prior to analysis, risk assessment tool comparisons were grouped by comparator tool (which could be any version of that tool). Data was extracted into Microsoft Excel and then plotted for each tool, age range and predicted year of risk. A.1.Plotted ratios of expected versus observed cancers, by population percentile. The E/O ratio  with 95% confidence intervals were plotted according to risk group assignment using the mid-point percentile of each risk group in the study population. This facilitated standardisation of comparisons between tools that had a different number of risk groups and/or assigned different proportions of women to each risk group.2.The total number of women in each study cohort in risk groups for which the E/O 95%CIs included unity. This helped indicate the proportion of each study cohort that was well-validated by the tool, noting that this is more likely for smaller studies (and therefore wider CIs).3.p-value <0.05 indicated miscalibration by the tool [Calibration belt goodness-of-fit tests. We assessed goodness of fit between expected (predicted) probabilities of developing breast cancer and observed data using calibration belts  as applithe tool .Goodness of fit between expected (predicted) and observed outcomes:B.1.Observed cancer rates , by mid-point percentile of each risk group in the study population. This helped to standardise comparisons.2.Characterisation of the functional form (curve) of observed cancer incidence rates according to increasing risk group, classified as either: ‘increasing’ (observed rates consistently increasing across risk categories), ‘monotonic’  or ‘fluctuating’ .3.p-values <0.05 indicated a statistically significant difference and, therefore, good allocation of women to the highest risk group. To ensure comparability of findings, if >25% of the study cohort was allocated to the highest risk groups, p-values were reported but not taken into consideration when drawing conclusions regarding a particular tool. Consequently, mid-range risk groups would be expected to include ≥50% of the study cohort.Assessment of whether highest-risk women could be distinguished from women at more moderate-risk. We compared the observed breast cancer rate corresponding to the mid-range risk groups  with the highest risk group (quintile 5 or deciles 9–10). 4.p-values were reported but not taken into consideration when drawing conclusions regarding a particular tool.Assessment of whether lowest-risk women could be distinguished from women at more moderate-risk. As for (3 above), but for the lowest risk group , compared to the remainder . To ensure comparability of findings, if >25% of the study cohort was allocated in the lowest risk groups, Analysis of observed outcomes by risk group classification:We generated various data presentations and metrics to help evaluate and compare studies, as follows: Plots and all statistical analyses were conducted using STATA . Two independent reviewers  assessed the risk of bias for each included study. Differences were resolved by consensus or adjudication from a third reviewer (JS). Risk of bias was assessed using the ‘Prediction model Risk Of Bias ASsessment Tool’ (PROBAST), specifically designed to assess the Risk of Bias for, and the applicability of, diagnostic and prognostic prediction model studies . PROBASTFor each study, a separate risk of bias assessment was conducted for each distinct risk assessment tool validated, for each individual outcome and each cohort included . OutcomeRulings were developed where necessary to account for judgements that required topic-specific knowledge or statistical expertise. These rulings were initially trialled independently over several studies by the same two reviewers  with third reviewer input from a senior researcher (JS) where required. It was decided a priori that: (i) risk of bias domains that contained signalling items relating only to model development would be omitted as the primary interest of this systematic review was risk assessment tool validation and (ii) the applicability of a study would not be formally assessed by the PROBAST tool; instead, concerns would be highlighted where necessary in the discussion. We sought statistical advice to develop rulings for items in the analysis domain as suggested by PROBAST. When assessing the reasonable number of participants with outcome PROBAST recommends that validation studies should include at least 100 participants with outcomes. After consulting statistical experts , it was decided a priori that a study would qualify for a low risk of bias rating if this was the case for every risk category. Where data for observed incidence of breast cancer per risk category was provided by authors or calculated by reviewers from calibration figures, this was used to inform our ratings. Otherwise, risk of bias was appraised based on the information reported in the article and included references. For the handling of missing data, based on methodological advice (QL), it was decided a priori that a study performing multiple imputation would qualify for low risk only if <50% of values were originally missing (and thus imputed) for a predictor and the missing data were missing at random ,28.The remaining 13 articles included in this review examined the prediction of breast cancer across 15 cohort studies applying 11 distinct breast cancer risk assessment tools of different versions. Summary characteristics of included articles are presented in The tools assessed included data from questionnaires, with or without information on mammographic breast density and PRS. The number of risk predictors varied between tools, from as few as five   to as maTwo articles evaluated the effect of adding breast density data: McCarthy et al.  comparedOnly one study assessed the effect of PRS data on existing tools; in Hurson et al.  a 313-vaEvidence was available to compare tools in terms of risk of invasive breast cancer, however, evidence was sparse for in situ breast cancer incidence, while no data was available on breast cancer incidence according to prognostic indicators . Therefore, these outcomes were not able to be assessed.p < 0.05)  in BOADICEA ; ER- [39BOADICEA ; originaBOADICEA ; updatedBOADICEA ).Combining breast density data with a tool score  or integp < 0.05). Addition of PRS data, in the single study that evaluated a specific score , did notNo change was observed in the calibration of most tools  for longer-term risks, with evidence of miscalibration for both 5- and 10-year risk. The only exception was the AABCS tool, for which the goodness-of-fit improved for 10-year risk . The majority of tools, with a few exceptions  unless stated in the text.Tools tended to rate poorly for methods regarding handling of missing data. The main reasons for poor ratings included inappropriate assumptions, omitting predictors with missing data in general or for a particular predictor, and imputation of predictors with >50% missing participant data. This systematic review of studies comparing multiple breast cancer risk assessment tools within general populations examined several metrics to evaluate risk assessment tools, namely: the ratio of the expected over observed number of breast cancer cases; evidence of miscalibration; the proportion of study group where E/O and 95%CI includes unity, and how these related to the observed cancer incidence rates across assigned risk groups. We found that no tool was consistently well-calibrated across multiple studies, and breast density or polygenic risk scores did not improve calibration. While most tools identified a risk group with higher rates of observed cancers, few tools identified lower-risk groups across different settings. We did not apply a single metric to compare tools because the interpretation and value of each metric depends on how the risk assessment tool might be used. Where risk assessment tools are being used to advise an individual woman about her estimated breast cancer risk, specified as, for example, her 5-year or lifetime risk, the tool should have demonstrated very good calibration of E/O rates within her population to ensure a sufficiently accurate estimate. Communication of this information is also important, as these estimates are often misinterpreted as individual level risk so that, for example, an estimated 3% five-year risk is interpreted as the individual woman having a 3% risk of breast cancer in the next five years, when instead it indicates that 3% of women in the risk group to which she belongs would be expected to have a breast cancer diagnosed in the next five years .The individual-level risk estimates generated by risk tools are also used in clinical practice to advise and manage women according to a risk group assignment based on their estimated risk of breast cancer, without necessarily reporting the estimated individual breast cancer risk for each woman. For example, the Royal Australian College of General Practitioners (RACGP) guidelines define women at ‘moderately higher’ risk as those with a 1.5 to 3 times higher than average risk, and women at ‘potentially high’ risk as more than 3 times the average population risk and recommend management based on these risk categories such as screening frequency and/or referral to specific breast imaging surveillance tests, or referral to specialist high-risk services . These tThe issues mentioned above have potential consequences for how risk assessment tools should be evaluated in relation to risk-based population breast screening While GPs and specialists are  able to refer an unlimited number of patients to services to which they are eligible, resource-constrained population risk-based screening programs would benefit from directing screening protocols for higher-risk (or lower-risk) clients to a priori proportions of the screening population. This could mean, for example, that a screening program would provide supplemental or alternative imaging tests to 10% of women deemed to be most likely to benefit from that imaging, based on their short-term breast cancer risk and the expected accuracy of their routine screening test . For this purpose, it should be sufficient to confirm that a risk tool can identify the 10% of screening clients for whom outcomes  under the current approach to screening are significantly higher compared to clients with average outcomes in the screened population, even if that tool is not well calibrated in terms of expected and observed rates; this risk stratification could then be used to trial alternative approaches to screening.This is an important consideration because requiring good E/O calibration of risk assessment tools across the risk spectrum is a difficult standard to reach. For example, a recent evaluation of six established risk models  in over 52,000 Australian women concluded that only one model (BOADICEA) calibrated well across the spectrum of 15-year risk . Even where good E/O calibration is achieved, this does not necessarily mean that observed rates are ranked well or that calibration is good across the risk spectrum. For example, in the study by McCarthy and colleagues , despiteOverall, despite differences between risk assessment tools and study cohorts, most risk tools were able to identify a group of women with the highest risk of breast cancer, with only a few exceptions . For lower-risk women, some tools assessed consistently stratified women in the lowest categories of breast cancer risk across different settings . In the case of BCRAT, this depended on the version used; i.e., BCRAT version 3 was found to be consistent in distinguishing women in the lowest risk group whereas the same was not observed for versions 2 and 1. Of note, for some tools it was not possible to assess this feature across different settings as there was only one relevant study included . Reporting results for more groups provides more detail on how the tool performs as a graded association with increasing risk, which is informative for population-level applications where the availability of resources might be limited. For example, isolating smaller groups of women with very high risk may be more feasible for targeting more costly options (such as MRI) to higher-risk women as part of population breast screening.We found that mammographic breast density has not been shown to improve the accuracy of breast cancer risk assessment tools based on self-reported information collected from questionnaires. We did not review evidence on the accuracy of breast density alone as a risk assessment tool, with an equivalent assessment of whether other risk predictors improved the accuracy of breast density as a risk assessment tool. However, this is a very active research area, and ongoing review of high-quality evidence is warranted. Similarly, we found that the addition of a PRS score did not improve accuracy when added to self-reported information within the tools assessed, although this finding was based on a single study . We did A number of other systematic reviews have been published previously in this field ,46,47,48We observed that tools that were recalibrated to the risk profiles of the population in which they were applied demonstrated an improvement in fit, as exemplified in the study by Chay and colleagues , which cAssessment of the studies included, revealed opportunities to improve standardisation of risk tool evaluations. Not all studies cited the specific version of the tool and package used. When these details were not provided, it was difficult for reviewers to deduce this information even if predictors were listed. For example, one study  providedCritical assessment of studies in terms of risk of bias is required to provide a comprehensive evaluation. We used the recently published PROBAST tool, specifically designed to thoroughly assess the risk of bias in relation to risk assessment tool studies. Only one previous systematic review identified from our searches had included a risk of bias assessment, although a tool for evaluating modelling studies was used instead of PROBAST . All tooOne of the main areas of concern is the domain of predictors with respect to the collection and completeness of data on risk predictors, and the statistical methods used to deal with any data issues. One method that studies used to deal with missing predictors at the time of validation was multiple imputation . Although this is a common method to deal with missing data, the reference dataset is simulated and thus possibly less reliable. This also limits our understanding of how missing data would be addressed at an individual level if the tool were utilised as part of health service provision. In other studies, researchers sometimes stated that missing data was handled according to the specifications of each software application  statement published in 2015 . TRIPOD This systematic review has certain limitations. A number of studies that compared different risk assessment tools on the same population were not included due to the focus of this review to compare risk assessment tools generated from, or calibrated to, a different population to the study validation population of interest or tools specifically calibrated to the study population of interest. However, focusing on the selected studies in this review enabled a fairer comparison between tools and improvement in the quality of the evidence. Secondly, despite meeting the criteria for inclusion, some studies had to be excluded due to some required data being unavailable for full assessment. Nonetheless, efforts were made to contact the authors. Additionally, for studies which did not provide the number of women in each risk category, the calculated estimates may be inaccurate if numbers are distributed unequally between risk categories, as the number of women per category was estimated by dividing the numbers of participants equally among categories. This review did not compare tools in terms of interval cancers , breast cancer mortality, nor incidence of breast cancer defined by different tumour characteristics . We did initially seek to assess these outcomes as this evidence is likely to be of interest for some applications, such as consideration of risk-based screening protocols, however insufficient evidence was available to make these comparisons between tools. Finally, one of the methods used to assess risk assessment tools was based on E/O point estimates and their 95%CI including unity (E/O = 1). Studies where tools were applied on small cohorts of women have wider CIs and therefore be more likely to include this value compared to larger studies which have narrower CIs. Additionally, we characterised the functional form of observed cancer rates according to risk groups based on point estimates reported without uncertainty estimates . However, while we acknowledge these metrics have minor limitations, these were only two of the metrics employed; when evaluated collectively all metrics analysed provide sufficient information to enable a fair and balanced assessment of risk assessment tools.This systematic review identified various questionnaire-based tools (sometimes incorporating mammographic density or genetic information) that are effective in assigning women to risk groups for incident breast cancer, for various metrics of tool performance. The most appropriate metrics to consider depend on how the risk tool is to be applied. While good calibration between expected and observed rates is essential for individual-level estimated breast cancer risk described as a rate over a specified period, tools demonstrating good differentiation of observed breast cancer incidence rates are potentially suitable for triaging women to population-level risk-based interventions such as risk-based breast cancer screening, even if they are not well calibrated in terms of expected versus observed outcomes across the risk spectrum. Current trials such as MyPeBS  and WISD


Associations of birthweight, childhood body size and pubertal timing with breast cancer risks by menopausal status and tumor receptor subtypes are inconclusive. Thus, we investigated these associations in a population-based cohort of Danish women.n = 7510), including estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2) and menopausal status. Hormone replacement therapy use came from the Danish National Prescription Registry. Hazard ratios (HR) and 95% confidence intervals (CI) were estimated by Cox regression.We studied 162,419 women born between 1930 and 1996 from the Copenhagen School Health Records Register. The register includes information on birthweight, measured childhood weights and heights at the age of 7–13 years, and computed ages at the onset of the growth spurt (OGS) and at peak height velocity (PHV). The Danish Breast Cancer Cooperative Group database provided information on breast cancer  and 1.02 (95% CI 0.96–1.09) per height z-score, respectively. In general, childhood height was positively associated with HER2+ and HER2− tumors among pre- and postmenopausal women. Ages at OGS and PHV were not associated with any breast cancer subtypes.We found that birthweight was not associated with any breast cancer subtypes. While childhood BMI was not statistically significantly associated with ER+ tumors nor consistently with ER− tumors among pre-menopausal women, consistent inverse associations were found among postmenopausal women. At the age of 7 years, the HRs for postmenopausal ER+ and ER− tumors were 0.90 (95% CI 0.87–0.93) and 0.84 (95% CI 0.79–0.91) per BMI We showed that a high BMI and short stature in childhood are associated with reduced risks of certain breast cancer subtypes. Thus, childhood body composition may play a role in the development of breast cancer.The online version contains supplementary material available at 10.1186/s13058-022-01578-0. Female breast cancer is the most common cancer type in Denmark as well as globally and accounted for nearly 700,000 deaths in 2020 , 2. EstaIt is established that birthweight, childhood BMI or adiposity (assessed by somatotype) and height, as well as the timing of puberty, are associated with risks of breast cancer –23. NeveWe used data from the Copenhagen School Health Records Register. This register includes computerized information on 200,978 girls  born from 1930 to 1996, which is virtually all children who ever attended a private or public school in Copenhagen, Denmark . Because2) was calculated. BMI and height were transformed into z-scores using internal references. Age at the onset of the growth spurt (OGS) and age at peak height velocity (PHV) were used as markers of pubertal timing and were derived from height measurements using validated methods, which required at least five measurements [From the measured heights and weights, childhood BMI   to ensurn = 1825). Further, women with missing or outlying values of BMI or height at all childhood ages (n = 3551) were excluded , with a further exclusion of women with a birthweight outside the range of 2000–5500 g (n = 2591). The analyses examining associations with the ages at OGS and PHV were restricted to women born between 1930 and 1969 .In the analyses examining associations with pre- and postmenopausal breast cancer as well as ER status, we initiated follow-up on January 1, 1995. Of the 178,844 women with a valid personal identification number, we included women if they were alive and living in Denmark on the start date of follow-up or later and aged 18 years or older. We excluded women who were diagnosed with breast cancer prior to January 1, 1995 , emigration (n = 5867), loss to follow-up (n = 9) or breast cancer prior to this date (n = 4196) and missing or outlying values of BMI and height (n = 3295), 149,121 women were available for these analyses.The analyses examining associations with HER2 status began on January 1, 2007. After exclusions for death (n = 338) or menopausal status (n = 16) were censored at breast cancer diagnosis. In the analyses by HER2 status, women with missing information on HER2 status (n = 117) or menopausal status (n = 14) were censored at breast cancer diagnosis.Follow-up ended on the date of the first diagnosis of breast cancer, emigration, death or loss to follow-up on December 31, 2018, whichever came first. In the analyses by ER status, women with missing information on ER status  and 95% confidence intervals (CI) for the associations between birthweight , childhood BMI and height In all analyses, age was the underlying time scale. Further, the analyses were stratified by three birth cohorts  to allow the baseline hazard to differ by birth cohort. All analyses of postmenopausal breast cancer were adjusted for the use of HRT categorized as ever or never users.z scores as well as ages at OGS and PHV, respectively, with the outcomes were assessed by comparing a restricted cubic spline model with a linear model using likelihood ratio tests. Limited indications of deviations from linearity in the associations were found; therefore, all results are presented linearly. We examined potential birth cohort effects by comparing models with and without interaction terms between the exposure of interest and birth cohort using likelihood ratio tests. Similarly, the proportional hazard assumption was tested using the likelihood ratio test to compare models with and without interaction terms between the exposure and categories of age at diagnosis. There were no consistent indications of birth cohort effects or violations of the proportional hazard assumptions. All statistical analyses were performed using Stata .Potential deviations from linearity in the associations of birthweight, childhood BMI and height This study included 162,419 women  pre-menopausal and in 3273 (88%) postmenopausal women.Birthweight was not associated with breast cancer by HER2 tumor status in either pre- or postmenopausal women Table . SimilarWe found that a woman’s birthweight was not associated with pre- or postmenopausal breast cancer overall or by the ER or HER2 status of the tumors. Further, we showed that the higher the BMI in childhood, the lower the risks of pre- and postmenopausal breast cancer overall and of postmenopausal ER+ and ER− tumors as well as of pre- and postmenopausal HER2− tumors. Additionally, the taller a woman was in childhood, the higher her risks of pre- and postmenopausal breast cancer overall and of ER+ and HER2− tumors as well as postmenopausal HER2 + tumors. Further, we showed that ages at OGS and PHV were not associated pre- or postmenopausal breast cancer overall or when divided by tumor receptor subtype.A large body of evidence supports an association between birthweight and breast cancer, especially at pre-menopausal ages. Meta-analyses showed that the risk estimates per 500 g of birthweight were 1.05 (95% CI 1.02–1.09) for pre-menopausal  and 1.06We found inverse associations between childhood BMI and pre- and postmenopausal breast cancer overall, postmenopausal ER+ and ER− tumors as well as pre- and postmenopausal HER2− tumors. While we did not identify other studies on childhood BMI, our overall findings on pre- and postmenopausal breast cancer extend those in another study based on the same data resource  and are The mechanisms underlying the inverse associations between childhood BMI and risks of breast cancer are speculative. Nevertheless, from the Continuous Update Project Expert Report 2018 from the World Cancer Research Fund/American Institute for Cancer Research it is evident that the timing and duration of excess weight are important for pre- and postmenopausal breast cancer risks . In our We generally found positive associations between childhood height and breast cancer overall and by tumor receptor subtypes in both pre- and postmenopausal women, apart from HER2 + tumors in pre-menopausal women and ER− tumors in postmenopausal women. While our findings on pre- and postmenopausal breast cancer extend those from the previous study using this data resource , we did Our findings of positive associations between childhood height and ER+ tumors are biologically plausible and may be related to growth-regulating hormones such as insulin-like growth factor-1 (IGF-1). Because of its role in promoting cell division and inhibiting apoptosis, it has been associated with breast cancer and ER+ tumors , 40. In In this study, we found that ages at OGS and PHV were not associated with breast cancer when divided by menopausal status or by the tumor receptor subtype. Thus, our findings extend those from the previous study using the same data resource, although age at peak growth was derived using a different methodology than in this study . StudiesOur study has several strengths. We included girls from a large population-based cohort of schoolchildren, of whom the majority were prospectively followed for 24 years through national registers and a national database. Further, the measurements of childhood height and weight were performed by trained staff, which ensures a low risk of measurement error and precludes bias associated with the recall of body size. As the coverage of the DBCG is nearly complete (> 95%), the risk of undetected cases is low. Additionally, because of the mandatory nature of the school health examination program for both public and private schools, and as healthcare access in Denmark is free and universal, the risk of selection bias into the cohort is minimal.Our study also has certain limitations. HER2+ tumors consisted of both luminal B and HER2-enriched cases, and similarly, HER2− tumors were a mix of luminal A, luminal B and triple-negative cases . As the We found that birthweight was not associated with pre- and postmenopausal breast cancer. We also showed that a higher BMI in childhood was associated with lower risks of pre- and postmenopausal breast cancer overall, postmenopausal ER+ and ER− tumors as well as pre- and postmenopausal HER2− tumors. In contrast, the taller a woman was in childhood, the higher her risks of breast cancer overall, ER+, and HER2− tumors among both pre- and postmenopausal women as well as postmenopausal HER2 + tumors. Ages at OGS and PHV were not associated with breast cancer. Our findings highlight that childhood body size may be indicative of breast cancer risks and that an understanding of the underlying mechanisms is warranted to develop preventive efforts.Additional file1. Table S1: Characteristics and prevalence of pre- and postmenopausal breast cancer among women included in the analyses of birthweight and women who could potentially have been included in these analyses. Table S2: Characteristics and prevalence of pre- and postmenopausal breast cancer among women included in the analyses of puberty markers and women who could potentially have been included in these analyses. Table S3: Characteristics of women with and without information on ER status among women diagnosed with breast cancer after January 1995. Table S4: Characteristics of women with and without information on HER2 status among women diagnosed with breast cancer after January 2007. Table S5: Hazard ratios of the associations between childhood BMI and height and risks of overall, ER-positive and ER-negative breast cancers by menopausal status in girls from the Copenhagen School Health Records Register followed from January 1995. Figure S1: Flowchart of included and excluded women from the Copenhagen School Health Records Register (CSHRR).


N = 995) were randomized to (1) control or (2) the PMT-informed intervention. Six weeks post-intervention, 924 (93% retention) self-reported PMT constructs and behavioral intentions. Bootstrapped mediations evaluated the direct effect of the intervention on behavioral intentions and the mediating role of PMT constructs. There was no direct intervention effect on intentions for risk-reducing medication or MRI (p’s ≥ 0.12). There were significant indirect effects on risk-reducing medication intentions via perceived risk, self-efficacy, and response efficacy, and on MRI intentions via perceived risk and response efficacy (p’s ≤ 0.04). The PMT-informed intervention effected behavioral intentions via perceived breast cancer risk, self-efficacy, and response efficacy. Future research should extend these findings from intentions to behavior. ClinicalTrials.gov Identifier: NCT03029286 .The purpose of this study is to evaluate the direct and indirect effects of a web-based, Protection Motivation Theory (PMT)–informed breast cancer education and decision support tool on intentions for risk-reducing medication and breast MRI among high-risk women. Women with ≥ 1.67% 5-year breast cancer risk ( National guidelines present options for breast cancer risk management among women with elevated risk . Women wHigh-risk women with an estimated lifetime breast cancer risk ≥ 20% may also consider annual screening with breast magnetic resonance imaging (MRI) . For theDespite the low rates of risk-reducing medication and MRI among high-risk women, efforts to increase uptake have been few and have had limited success –8. HowevA randomized controlled trial compared the PMT-informed intervention to a control arm that directed participants to relevant online health information , 11. OneGiven these limitations, we wanted to examine the intervention’s impact on a proximal outcome: intentions for risk-reducing medication and breast MRI at 6 weeks post-intervention. Intentions are an important necessary condition for engaging in recommended health behaviors ; thus, eIn the present study, a secondary data analysis examined the direct and indirect effects of the PMT-informed intervention on intentions for risk-reducing medication and/or breast MRI at 6 weeks post-intervention. We hypothesized that (1) the intervention would have a direct effect on intentions for breast cancer risk management, such that women in the intervention arm would report stronger intentions than women in the control arm, and (2) PMT variables would mediate the relationship between study arm and intentions for breast cancer risk management. Our primary outcome of interest was intentions for risk-reducing medication. We also examined intentions for MRI as a secondary outcome.This two-arm randomized controlled trial  has been described in detail elsewhere , 11. TheWomen were randomized 1:1 to the intervention or control arm at study sample identification (prior to recruitment). Women randomized to usual care were instructed to review information on the American Cancer Society website related to breast cancer risk, prevention, and cancer screening. The PMT-informed intervention is described below.n = 924) completed a follow-up survey  and are included in the analyses presented here.A total of 995 women provided verbal informed consent, enrolled in the study, and completed a baseline interview by telephone . Six weeks later, 93% of participants (The PMT-informed intervention has been previously described . In linePMT constructs and intentions for breast cancer risk management were assessed via self-report at the 6-week follow-up time point.getting breast cancer during the past two weeks?). Participants rated each item on a 4-point Likert scale . Items were summed to generate a total score ranging from 1 to 12, with higher scores indicating greater worry.We adapted the 3-item Lerman Breast Cancer Worry Scale  to assesParticipants rated their agreement with the statement “I believe that breast cancer is severe” on a 5-point Likert scale (1 = “strongly disagree” to 5 = “strongly agree”).Patients estimated their personal risk of experiencing breast cancer in the next 5 years on a scale from 0% (no chance) to 100% (definitely will).Self-efficacy is an individual’s confidence in performing a behavior; in the present study, participants responded to items about self-efficacy of using risk-reducing medication and MRI on a 5-point Likert scale (1 = “strongly disagree” to 5 = “strongly agree”). The four items assessed participants’ confidence in their ability to manage medication side effects, take a pill every day, manage discomfort during an MRI, and have an MRI every year. Items were averaged to generate separate self-efficacy scores for MRI and risk-reducing medication. Total scores ranged from 1 to 5; higher scores indicate higher self-efficacy.Response efficacy is an individual’s belief as to whether or not a behavior will avoid a health threat. Participants responded to nine items  on a 5-point Likert scale (1 = “strongly disagree” to 5 = “strongly agree”). Risk-reducing medication items assessed participants’ perceptions that tamoxifen and raloxifene are effective in preventing breast cancer, could significantly improve future health, and are an effective way to reduce breast cancer risk. MRI items assessed participants’ perceptions that MRI is effective in finding breast cancer, could significantly improve future health, and is an effective way to find breast cancer early. Items were averaged to generate separate response efficacy scores for MRI and risk-reducing medication. Total scores ranged from 1 to 5; higher scores indicate higher response efficacy.Response cost is an individual’s perceptions of the downsides of a behavior. Participants responded to three items assessing the costs of risk-reducing medication and four assessing the costs of MRI using a 5-point Likert scale (1 = “strongly disagree” to 5 = “strongly agree”). Risk-reducing medication items included side effects, taking a pill daily, and cost. MRI items included lack of breast cancer risk reduction, discomfort, cost, and potential additional, unneeded tests or treatments. Items were averaged to generate separate response cost scores for MRI and risk-reducing medication. Total scores ranged from 1 to 5; higher scores indicate higher response cost.To measure participants’ intentions to use risk-reducing medication, participants rated their likelihood of using tamoxifen in the next year, and their likelihood of using raloxifene in the next year on a 5-point Likert scale (1 = “strongly disagree” to 5 = “strongly agree”). The two items were averaged to create a single score representing intentions for risk-reducing medication.We measured intentions for MRI by asking participants to rate their likelihood of having a breast MRI in the next year using a 5-point Likert scale (1 = “strongly disagree” to 5 = “strongly agree”).t-tests were used for the continuous variables. To identify variables to include as mediators in bootstrapped mediation models, we examined correlations between PMT constructs and outcomes at the 6-week follow-up; only potential mediators that were significantly correlated with the outcomes of interest (p < 0.05) were included in primary analyses.Descriptive statistics were used to characterize the sample demographicsand the 6-week follow-up assessment of PMT constructs and behavioral intentions. We described categorical variables using frequencies and percentages, and continuous variables using means and standard deviations. Categorical variables were compared using chi-squared tests; Student Direct and indirect effects of PMT variables on intentions for using risk-reducing medication or MRI were examined using the PROCESS macro for SPSS (Model 4) . The PROAll analyses were conducted using IBM SPSS for Windows, version 27 .M = 62 years, range = 40–69), with a college degree or greater (74%) and an annual household income ≥ $70,001 (56%) (see prior descriptions of this sample [The sample was primarily non-Hispanic White (95%), in middle adulthood . ThM = 1.8 versus 1.7, p = 0.03). The intervention and control groups did not significantly differ on intentions for breast MRI .Intentions for risk-reducing medication or MRI at 6 weeks were low overall Table . Comparep’s ≤ 0.001) (Table p = 0.97) or intentions for MRI (p = 0.42). Thus, bootstrapped mediation analyses did not include perceived breast cancer severity as a mediator.In bivariate analyses, intentions for risk-reducing medication and MRI were significantly correlated with cancer worry, perceived breast cancer risk, self-efficacy for risk-reducing medication, response efficacy for risk-reducing medication, and response cost for risk-reducing medication  (Table p = 0.004), self-efficacy (p = 0.04), and response efficacy (p = 0.01). Compared to women in the control arm, women in the intervention arm reported lower perceived breast cancer risk, lower self-efficacy, and higher response efficacy. In turn, perceived breast cancer risk, self-efficacy, and response efficacy were all positively associated with intentions for risk-reducing medication.The bootstrapped mediation model predicting intentions for risk-reducing medication explained 16% of the variance in intentions for risk-reducing medication R = 0.16 (6) Table . NeitherR2 = 0.15) . Neither the total effect nor the direct effect of study arm on intentions for MRI was significant. There were significant indirect effects of study arm on intentions for MRI via perceived breast cancer risk (p = 0.02) and MRI response efficacy (p = 0.01). Compared to women in the control arm, women in the intervention arm reported lower perceived breast cancer risk and higher response efficacy. In turn, perceived breast cancer risk and response efficacy were all positively associated with intentions for breast MRI.The bootstrapped mediation model predicting intentions for breast MRI explained 15% of the variance in intentions for breast MRI R = 0.15 (5) Table . The dirWe evaluated whether a web-based, Protection Motivation Theory–informed breast cancer education and decision support tool could increase intentions for risk-reducing medication and breast MRI compared to an active control arm. The data presented here demonstrate the important role of threat appraisals, like cancer worry and perceived breast cancer risk, on intentions to engage in breast cancer risk mitigation. Coping appraisals—including self-efficacy, response efficacy, and response cost—were also related to women’s intentions for breast cancer risk management.We identified three significant mediators of the relationship between study arm and intentions for breast cancer risk management: perceived breast cancer risk, self-efficacy, and response efficacy. Compared to women in the control arm, women in the intervention arm reported significantly lower perceived breast cancer risk at the 6-week follow-up. As women tend to overestimate their risk of breast cancer , it is lThe intervention group also reported lower self-efficacy for risk-reducing medication at the 6-week follow-up. While there has been little research on the role of self-efficacy in uptake of and adherence to risk-reducing medication, self-efficacy has been shown to play an important role in adherence to other types of medications . Our intCompared to women in the control arm, women in the intervention arm reported greater response efficacy for risk-reducing medication and breast MRI at the 6-week follow-up. Our intervention targeted response efficacy in two ways: presenting tailored risks and benefits of risk-reducing medication and breast MRI, and engaging participants in an interactive values clarification exercise. While a dismantling study would be needed in order to assess the relative effectiveness of these components, it is likely that education about risk-reducing strategies played an important role, given the demonstrated lack of knowledge about risk-reducing medication  and suppThese indirect effects must be interpreted in light of the null total and direct effects of the intervention on intentions for risk-reducing medication and breast MRI. Although traditional approaches to mediation require a direct effect in order to estimate and test hypotheses about indirect effects, current thinking about mediation analysis does not . InsteadOur results support the applicability of PMT to breast cancer risk management. Of the six PMT constructs examined, five were significantly related to intentions for risk-reducing medication and breast MRI. In addition, the direction of the relationships between PMT constructs and behavioral intentions was theoretically consistent. Interestingly, perceived breast cancer severity was not significantly related to intentions for risk-reducing behaviors, and as a result, was not included in the final models. This contrasts with prior meta-analyses examining the relationship between PMT variables and behavioral intentions that have demonstrated a small but significant effect of perceived severity . The disThese results have clinical implications for future interventions in this area. The tendency for women to overestimate their breast cancer risk is well-documented in the literature, and prior risk communication interventions have promoted more accurate breast cancer risk perceptions through the provision of a personalized risk estimates . AccuratPromoting medication self-efficacy has become a focus of interventions to promote adherence to oral medications, not only in cancer but in other chronic conditions such as diabetes  and arthOur study had two key strengths. First, we examined theoretical constructs in the setting of a randomized controlled trial. Second, we had a large study sample with a relatively high retention rate; 93% of the baseline participants completed the 6-week follow-up assessment.Study results must be interpreted in light of some limitations. First, the specified models do not meet the criteria for a “true” test of mediation as the PMT constructs and behavioral intentions were both assessed at the 6-week follow-up time point . Second,In summary, this trial evaluated a novel web-based intervention informed by PMT that provides personalized breast cancer risk communication and decision support. While the intervention did not have a direct effect on intentions for risk-reducing medication or breast MRI, we did observe significant indirect effects of the intervention on breast cancer risk management intentions via changes in perceived breast cancer risk, response efficacy, and self-efficacy. Interventions that address perceived risk and boost self-efficacy and response efficacy may be particularly effective in the context of breast cancer risk management.


Inflammation has been hypothesized to play a role in the development and progression of breast cancer and might differently impact breast cancer risk among pre and postmenopausal women. We performed a nested case-control study to examine whether pre-diagnostic circulating concentrations of adiponectin, leptin, c-reactive protein (CRP), tumour necrosis factor-α, interferon-γ and 6 interleukins were associated with breast cancer risk, overall and by menopausal status.Pre-diagnostic levels of inflammatory biomarkers were measured in plasma from 1558 case-control pairs from the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. We used conditional logistic regression to estimate the odds ratios (ORs) of breast cancer at blood collection, per one standard deviation increase in biomarker concentration.Cases were diagnosed at a mean age of 61.4 years on average 8.6 years after blood collection. No statistically significant association was observed between inflammatory markers and breast cancer risk overall. In premenopausal women, borderline significant inverse associations were observed for leptin, leptin-to-adiponectin ratio and CRP  while positive associations were observed among postmenopausal women . Adjustment for BMI strengthened the estimates in premenopausal women  but attenuated the estimates in postmenopausal women .Associations between CRP, leptin and leptin-to-adiponectin ratio with breast cancer risk may represent the dual effect of obesity by menopausal status although this deserves further investigation.The online version contains supplementary material available at 10.1186/s12916-022-02319-y. Since the discovery of the infiltration of leukocytes into tumour tissue by Rudolf Virchow in 1863, many experimental studies have confirmed the role of the immune system and inflammation in tumour proliferation, angiogenesis and metastasis . Finally, we excluded 2 cases and their matched controls because they were pregnant at blood collection. The final population included 1558 breast cancer cases and 1558 controls , adipokines (leptin and adiponectin) and CRP were measured on plasma samples in the laboratories of the Nutrition and Metabolism Branch at IARC, by Meso Scale Discovery . Samples from cases and matched controls were analysed together in the same analytical batch, and laboratory personnel were blinded as to the case or control status of samples. Three plasma quality control samples were inserted in duplicate within each analytical batch. Mean intra-batch coefficients of variation, calculated on the concentrations from the quality control samples, varied from 2.6% for CRP to 15.8% for IL17-D. Mean inter-batch coefficients of variation varied from 7.8% for IL-8 to 19.1% for IL17-D.No measurement below the lower limit of quantification (LOQ) was observed for leptin, CRP, IL-8 and IL-1-RA. Measurements below the LOQ represented less than 3% of the measurements for adiponectin, IFN-γ and IL-17-D, less than 8% for IL-6, 22% for IL-10, 25% for TNF-α and around 80% for IL-13 , number of live births or still births, oral contraceptive and menopausal hormone therapy (MHT) use.2). Physical activity levels were estimated using a questionnaire focused on past-year physical activity in occupational, leisure and household domains. A four-level validated physical activity index  was derived by combining occupational physical activity together with time participating in cycling and other physical exercises   but positively associated with breast cancer risk in postmenopausal women . Adjustment for BMI strengthened the estimates in premenopausal women  but attenuated the estimates in postmenopausal women . In fully-adjusted models, statistically significant heterogeneity according to menopausal status at blood collection was also observed for IL-10 (Phomogeneity(pre/peri/post) = 0.02). Fully adjusted-models showed a significant positive association with breast cancer risk with levels of IL-10 in premenopausal women [OR1SD = 1.20 (1.03–1.41)]. Among perimenopausal women, none of the biomarkers were associated with breast cancer risk neither in crude, BMI-adjusted or fully-adjusted models , leptin-to-adiponectin ratio (Pnon-linearity = 0.05) and CRP (Pnon-linearity = 0.04). Pnon-linearity for other biomarkers were higher than 0.07. Restricted cubic spline graphical representations for these three biomarkers are presented in Additional file Pnon-linearity=0.54) while in postmenopausal women there seemed to be a threshold before which no significant association was observed, with a linear positive association observed after that threshold.Departure from linearity was indicated for leptin ], a decreased breast cancer risk for cases diagnosed between five and ten years after blood collection [Fully-adjusted models: OR1SD =0.70 (0.53–0.93)] and no significant association after ten years [Fully-adjusted models: OR1SD =0.93 (0.68–1.29)]. Other inflammatory biomarkers-breast cancer associations were similar according to breast cancer subtypes  and OR1SD =1.08 (0.99–1.18), respectively] but not among women with BMI < 25 kg/m2 . Adjustment for BMI attenuated the estimates among women with BMI ≥ 25kg/m2 [leptin: OR1SD = 1.06 (0.96–1.17) and leptin-to-adiponectin ratio: OR1SD = 1.04 (0.95–1.15)]. There was no heterogeneity by WC . The power was too limited to evaluate the inflammation-breast cancer associations by subgroups among premenopausal women.In unadjusted models, statistically significant heterogeneity by BMI was found for leptin and leptin-to-adiponectin ratio . Among women with quantifiable levels of IL-10, no association was observed between IL-10 and breast cancer risk, overall and by menopausal status ], an inverse association among perimenopausal women [Fully-adjusted models: OR1SD = 0.59 (0.33–0.95)] and no association among postmenopausal women [fully-adjusted models: OR1SD = 1.16 (0.89–1.50)].The associations were virtually unchanged when we restricted the analyses to women who were not exogenous hormone users at the time of blood collection  [Q4 vs. Q1 = 0.69 (0.38–1.23) [T3 vs. T1= 0.80 (0.52–1.22) [q4 vs Q1=1.30 (0.80–1.03) [1SD =1.05 (0.79–1.40) [T3 vs. T1= 0.56 (0.28–1.11) [1SD = 1.58 (1.02–2.46)] and TNF-α [RR1SD=1.81 (0.91–3.61)] . Only t0 0.52–1. [q4 vs Qper doubling of circulating CRP concentration= 1.07 (1.02–1.12)] [T3 vs T1=0.65 (0.43–0.99)] [Q4 vs. Q1= 1.94 (1.37–2.75)] and the leptin-to-adiponectin ratio [ORQ4 vs. Q1= 1.91 (1.36–2.68)]  . It is i2–1.12)] , 34–36. 2–1.12)]  or a pos2–1.12)] , 14, 17.2–1.12)] –12, 14 o2–1.12)] , 17. Our2–1.12)] , 23, 24,2–1.12)] , 27, 28 2–1.12)] , 22, 28.3–0.99)]  while th3–0.99)] . Several6–2.68)]  while ot6–2.68)] , 22, 23.6–2.68)] . As In oInflammation has been shown to contribute carcinogenesis by causing genomic instability, increasing the production of free radicals, inducing epigenetic changes, regulating genes involved in cell cycle, angiogenesis, survival and proliferation . In our The main strength of our study is the use of a large European population-based prospective cohort which allowed us to select a large number of cases and controls and to prospectively examine associations between inflammatory biomarkers and breast cancer risk according to menopausal status and breast cancer subtypes. In addition, the long follow-up (median of 8.6 years among cases) allowed us to evaluate potential reverse causality due to undiagnosed breast cancer by exploring whether the inflammation-breast cancer associations differed according to time of follow-up. Another advantage was that we had detailed data on anthropometry, lifestyle and reproductive factors and we were able to evaluate whether those factors were confounding or modifying the biomarker-breast cancer associations. However, some results from analysis subgroups were based on relatively small numbers of cases and, because of the number of comparisons evaluated, they might be due to chance. Our study also had a number of limitations. The EPIC cohort is based on volunteers of European ancestry with probably more health conscious behaviours and results cannot be generalized beyond the range of biomarker levels represented by this population. There were a too large number of values below the LOQ for IL-13 and we had to dichotomize this biomarker into values higher or lower than the LOQ. There were also a large number of values below the LOQ for IL-10 and TNF-α and a significant heterogeneity by menopausal status was observed when we assessed the associations between TNF-α and breast cancer risk among women with quantifiable levels of TNF-α. However, it remains difficult to interpret these results due to the high number of values below the LOQ and small sample size. In our study, although we were able to adjust our models for anthropometric measures, it remains very difficult to completely rule out residual confounding of anthropometric measures because of their high degree of correlations with CRP and adipokines. Though, we did not observe any heterogeneity by BMI and WC levels when our models were adjusted for BMI. We were also unable to evaluate the potential mediating role of CRP and leptin in the obesity-breast cancer association due 1) to the lack of statistically significant association between BMI or WC and premenopausal breast cancer in our study population and 2) to the lack of statistically significant association between CRP or leptin levels and postmenopausal breast cancer risk after BMI adjustment. Another limitation is that it is not known to what extent circulating levels of the inflammatory biomarkers are associated with levels in breast tissue and circulating levels of the inflammatory biomarkers may be poor surrogates for local activity. In addition, we were able to capture only a small part of the inflammation process and did not measure chemokines or other ILs specific in driving inflammatory diseases such as IL-5, IL-2 or IL-4. Future studies should examine a more extensive set of markers from different immune pathways that could be relevant to cancer development. Finally, as in many other studies on biomarker-cancer risk, the main limitation of our study is that inflammatory biomarkers were measured only once, even though most of the inflammatory markers have shown good reproducibility over time .In summary, our study suggests that high levels of CRP, leptin and leptin-to-adiponectin ratio could lower breast cancer risk among premenopausal women but not among postmenopausal women. Whether this simply reflects the dual association of obesity with breast cancer risk by menopausal status requires further investigation in other settings.Additional file 1: Circulating inflammatory biomarkers and breast cancer risk in the EPIC prospective study: Supplementary Tables S1- S7 and Figures S1-S3. Table S1. Geometric mean values for inflammatory biomarkers in breast cancer cases and matched controls. Table S2. Associations between inflammatory biomarkers and breast cancer risk by age at diagnosis. Table S3. Associations between inflammatory biomarkers and breast cancer risk by breast cancer molecular subtypes. Table S4. Associations between inflammatory biomarkers and breast cancer risk by time between blood collection and diagnosis. Table S5. Associations between inflammatory biomarkers and breast cancer risk by body mass index. Table S6. Associations between inflammatory biomarkers and breast cancer risk by waist circumference. Table S7. Associations between inflammatory biomarkers and breast cancer risk among non-hormone users at blood collection. Table S8. Associations between IL-10 and breast cancer risk overall and according to menopausal status at blood collection, after excluding women with values of IL-10 below the LOQ. Table S9. Associations between TNF-α and breast cancer risk overall and according to menopausal status at blood collection, after excluding women with values of TNF-α below the LOQ. Figure S1. Flow chart of the study population. Figure S2. Association between leptin and breast cancer risk, overall and by menopausal status, allowing for nonlinear effects (restricted cubic spline). Figure S3. Association between leptin-to-adiponectin ratio and breast cancer risk, overall and by menopausal status, allowing for nonlinear effects (restricted cubic spline). Figure S4. Association between CRP with breast cancer risk, overall and by menopausal status, allowing for nonlinear effects (restricted cubic spline).


The incidence, mortality, burden of disability-adjusted life years (DALYs), and attributable risk factors of breast cancer vary significantly by country or region, particularly between developing and developed countries. This study aimed to analyze breast cancer development trends in developing countries based on the influence of the different sociodemographic indices (SDIs) and World Bank (WB) income-level disease data from 1990 to 2019.Data on the annual incidence, mortality, DALY, years of life lost (YLL) prematurely, years lived with disability (YLD), and age-standardized rate (ASR) of breast cancer from 1990 to 2019 in different countries and territories were obtained from the 2019 Global Burden of Disease (GBD) Study. A comparative risk assessment (CRA) framework was used to analyze the general risk factors.The global age-standardized incidence rate (ASIR) gradually increased from 21.44 per 100,000 population in 1990 to 24.17 per 100,000 population in 2019. It rose precipitously to 2.91- and 2.49-fold, respectively, for countries with middle SDIs and low-middle SDIs. The ASIR of breast cancer was increasing in the lower-middle-income levels in WB, with an estimated annual percentage change (EAPC) of 0.29 [95% uncertainty interval (UI): 0.20–0.37] and reduced income (EAPC of 0.59 [95% UI: 0.53–0.65]). The Solomon Islands and the United Arab Emirates observed the most significant increase in the magnitude of deaths from breast cancer cases. Compared to the death cases of 1990, percentage changes increased separately by 1,169 and 851%. Compared to developed areas, breast cancer-related deaths increased rapidly in developing regions, especially among the middle-aged and elderly groups. Meanwhile, the long-term burden of breast cancer was ever expanding. Of all the GBD regions, Oceania had the youngest age distribution. The deaths in the young and middle-aged groups accounted for 69% in 1990 and 72% in 2019. Percentage changes in deaths from the seven risk factors in low- to middle-SDI regions increased significantly over time across all age groups. However, a diet with high red meat and high body mass index (BMI) accounted for the most considerable increase in the magnitude.Public health policy regarding breast cancer is fundamental in low- and medium-income countries. The development and adoption of cost-effective screening and therapeutic solutions, the mitigation of risk factors, and the establishment of a cancer infrastructure are essential. Cancer is the second leading cause of death worldwide, placing a significant economic burden on public health systems . HoweverBreast cancer is the leading cause of death among women, and its incidence ranks first among the most prevalent tumors in women. According to the World Health Organization (WHO), ~2.26 million new cases of breast cancer in women were diagnosed worldwide in 2020, accounting for one in every eight cancer patients—making breast cancer the world's top most prevalent type of cancer .Over the past three decades, breast cancer incidence and mortality have increased in developing and developed countries . The gloThe study aimed to analyze breast cancer development trends in developing countries based on the influence of different sociodemographic indices (SDIs) and World Bank (WB) income levels on breast cancer incidence, mortality, and DALYs from the 2019 Global Burden of Disease (GBD) data from 1990 to 2019. Based on GBD in breast cancer, it could help provide timely assessment and guidance to address these serious disease problems.http://ghdx.healthdata.org/gbd-results-tool), an online data source tool. All available epidemiological data were adopted in this ongoing global collaboration to assess health losses caused by 369 diseases and 87 risk factors across 204 countries and 21 GBD regions  query tool  prematurely and years lived with disability (YLD), and age-standardized rate (ASR) of breast cancer from 1990 to 2019. DALYs can be used to estimate the global cancer burden, YLLs can be used to estimate the early mortality burden related to breast cancer, and YLD reflects the number of years living with short- or long-term health losses, weighted by disability severity. In addition, DALY can also be calculated by combining YLL and YLD measures of disease burden .In the GBD Study 2019, a comparative risk assessment (CRA) framework was used to calculate the burden attributable to risk factors, which is based on the amount of disease burden that can be reduced by reducing exposure to specific risk factors to the theoretical minimum risk exposure level (TMREL) . We estiA detailed description of a framework for assessing GBD risk factors is presented in the 2019 GBD risk factor study . We deteTo quantify breast cancer, incidence and mortality, ASRs, DALYs, and the estimated annual percentage changes (EAPCs) were calculated. The calculation formula of ASRs  was developed based on a previous study . The for (wi) in the same age subgroup i of the selected reference standard population).Y = α + βX + ε, Y represents ln (ASR), X represents the calendar year, and ε represents the error term. According to this formula, β determines whether ASR trends are positive or negative. The formula for calculating EAPC is as follows:Age-standardized rate trends can be an important indicator of changing disease and risk factor patterns. To reflect such trends in time, we used a widely used ASR-EAPC measurement , 13. EAPThe linear model provides 95% confidence intervals (CIs) , 15. ASRP-values of < 0.05 were considered statistically significant.We used R  for all statistical analyses. Globally, the number of breast cancer cases increased to 2.00 million : 1.83–2.17) in 2019 . ThereinFrom the WB income level, the incidence followed a global trend. The incidence of cases increased faster at the WB lower-middle- and upper-middle-income levels  between 1990 and 2019. ASIR for breast cancer increased at the WB lower-middle-income level  in 1990 to 0.70 million (95% UI: 0.65–0.75) in 2019 . DALYs iBy country, deaths attributable to breast cancer increased most significantly in the Solomon Islands and the United Arab Emirates . CompareAs shown in The population was divided into four age groups: 15–44 (youth), 45–59 (middle-aged), 60–74 (middle-old), and 75–94 years (elderly). Our analysis and comparison of a percentage change in deaths by the group in different SDI regions from 1990 to 2019 were performed . OverallFrom 1990 to 2019, the proportion of breast cancer deaths among the elderly was higher in the high SDI region than in the low SDI region . Among aThe burden of breast cancer is primarily due to metabolic and behavioral risk factors in the global disease burden database. ASDRs and DALY are attributable to seven risk factors, namely alcohol consumption, high BMI, high FPG, a diet high in red meat, low physical activity, smoking, and secondhand smoke. Compared with the risk factors for ASDR in 1990, smoking and alcohol consumption decreased. In contrast, high FPG and high BMI were ranked as significant contributors to increased ASDR in 2019 . These tIn the global level, there was a tremendous increase in breast cancer mortality rate among the populations with high FPG and high BMI in the middle-aged to elderly groups . The perIn 2019, ASDRs attributable to behavioral risk factors were declining globally –E. HowevGlobally and in major GBD regions, metabolic risk factors contributed to increased deaths , G. NotaAccording to the 2019 GBD Study dataset, which compares mortality, sex, and age groups across regions, countries, ages, and timelines, this study offers an updated analysis of the global burden and risk factors for breast cancer. Globally, the number of breast cancer incidents increased from 0.88 million in 1990 to 2 million in 2019. Furthermore, ASIR changes increased by 12%. Globally, deaths from breast cancer increased from 0.38 million in 1990 to 0.7 million in 2019. However, global ASDR has been declining continuously since 1990, suggesting that aging and population growth may have contributed to this increase.Our results showed that EAPC was positively associated with ASIR and ASDR in low to middle SDI regions. It indicates that some areas showed rapid growth. From the WB income level, EAPC was positively associated with ASIR and ASDR in the WB's low- and lower- and middle-income levels in the period between 1990 and 2019. The results of this study suggest that breast cancer prevention and treatment programs should not be ranked below other public health problems in those countries. For example, The Solomon Islands experienced high ASDRs for decades and a sharp increase, indicating a significant healthcare concern. The situation was especially perilous in Uzbekistan, where ASDR increased rapidly and was the highest in the world.In terms of the overall age distribution, most breast cancer deaths were among people between the ages of 35–54. Compared with developed areas, deaths in developing regions increased faster and significantly in the middle-aged to elderly population. Meanwhile, the long-term burden for breast cancer had been growing. A similar trend was observed for the WB income level. The younger group showed negative values in high-income regions. Low-income regions experience the most significant change in deaths among the elderly, while breast cancer awareness among the general public and healthcare workers was poor in low-income countries . HoweverMeanwhile, high BMI and high FGB were both found to be potential risk factors for breast cancer deaths and DALYs. Globally, these two risk factors showed an increasing contribution trend, with rapid increases from 1990 to 2019 in low, low-middle, and middle SDI regions. Irregular menstruation or fewer ovulatory cycles may contribute to breast cancer risk and may be inversely associated with BMI . In addiIt has been demonstrated that alcohol consumption may contribute to the risk of breast cancer –34. In aCurrent and former smokers both have a higher risk of breast cancer than non-smokers, according to a meta-analysis study . FurtherIn recent years, higher SDI regions experience a reduction in disease burden, while lower SDI regions experience an increase in breast cancer burden. Over time, disparities could widen. Furthermore, it is particularly important to prevent the acceleration of these disparities. In addition, income levels, education, and health insurance status need to be improved, which may affect healthcare access and breast cancer stage at diagnosis. Changes in reproductive patterns, such as having the first baby at a younger age, having increased parity, using less hormone replacement therapy (HRT) during menopause, and extending breastfeeding for a longer period, are also measures that need to be taken. People should also reduce smoking and drinking, exercise regularly, eat healthy food, stay in shape, and have a healthy lifestyle . Non-genThis study examined and revealed the latest trends and patterns in global breast cancer incidence, mortality, DALYs, and the most relevant risk factors, particularly in developing countries, over the period of 1990–2019. Unlike other previous studies, this study updated the global burden data of breast cancer. The results analyzed the changes in trends in disease burden not only from a global scale but also from different social development index regions, different WB income levels, and 21 GBD countries over the past three decades. Furthermore, four different age groups were analyzed in detail. Seven risk factors attributable to breast cancer disease burden were also analyzed in depth from the perspectives of age stratification and geographic regions. This study summarizes breast cancer trends in developing countries and provides more targeted guidance for formulating public health policies.In low- and medium-income regions , breast However, the 2019 GBD Study's detailed and high-quality analysis of GBD and risk factors for death has several limitations. Data collected by various regions and countries may vary in quality, comparability, accuracy, and degree of omission. Some deviations inevitably occur in the estimated values, even with many statistical methods. Furthermore, variations in mortality and incidence may be partly explained by changes in screening protocols rather than changes in age-specific rates.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.LL and BZ: data curation . JK: writing-review and editing (Supporting). SL: supervision (Supporting). HW: conceptualization, supervision, and writing-review and editing (Lead). All authors contributed to the article and approved the submitted version.


Aggregation of lung cancer (LCa) in family members is well-documented. However, little is known on the familial risk of LCa when first-degree relatives  are diagnosed with LCa as a second primary malignancy (LCa-2). We aimed to investigate whether and to what extent a family history of LCa-2 was associated with an increased LCa risk.In this Swedish national cohort we identified 127,865 individuals who had one FDR affected by LCa as a first primary cancer (LCa-1) and 15,490 individuals who had one FDR affected by LCa-2, respectively. We then estimated relative risk (RR) of LCa using those without cancer family history as reference.The number of LCa-2 has been increasing annually and rather similarly in men and women in the last decade. Familial RR of LCa was 1.96  for LCa-1 family history and 1.89 for LCa-2 (1.62–2.21). Risk was especially high when FDR was diagnosed with early-onset LCa-2 and when siblings were affected by LCa-2. The RR was 1.53 (1.10–2.12) when LCa-2 in FDR was diagnosed within 26 months after first primary cancer, and it increased to 2.16 (1.62–2.90) when LCa-2 was diagnosed between 74 to 154 months. Higher risk was observed for first primary cancer of the ovary , nervous system , upper aerodigestive tract  and cervix , and for non-Hodgkin lymphoma .LCa risk is associated with diagnosis of LCa-2 in FDR to a similar degree as LCa-1 in FDRs.The online version contains supplementary material available at 10.1186/s12885-022-10149-7. With the rise in cancer incidence and the continuous improvement in treatment, more and more cancer patients can survive longer time. At the same time, the number of patients having a second primary cancer is increasing. As a common first primary cancer and leading cause of cancer-related death , lung caBy incorporating several Swedish national registers, we aimed to estimate the risk of LCa when FDRs were diagnosed with LCa-2 after other cancers, and compare this familial risk to the risk that was related to family history of LCa-1. Our results will provide the first-hand information on LCa risk prediction for people who have FDRs affected by LCa-2.Multiple Swedish national registers were used in this study and the linkage of them was based on the unique personal identification number that has been replaced by a serial number to preserve confidentiality. Family relationships in FDRs were obtained through the Swedish Multi-Generation Registry. It records all the offspring born since 1932 and individuals still alive since 1961 together with their biological parents. The Swedish Cancer Register, with coverage of over 90%, recorded all incident tumors since 1958 . A totalFamily history of LCa-2 was defined as LCa diagnosis after other first primary cancer in FDR (parents or siblings). As shown in Fig. For the familial risk estimation, we identified individuals who were at risk of first LCa in the offspring generation. We only included those who had a single FDR affected by single LCa-1 or single LCa-2 as the risk groups in order to achieve accurate risk estimation. Therefore, we excluded individuals who had: 1) any FDR affected by multiple primary LCa, 2) any FDR affected by other first primary cancer (except those who further were diagnosed with LCa-2), 3) more than one FDR affected by LCa-1 or LCa-2 and 4) any FDR affected by cancer after LCa-1 or LCa-2. The study population were followed from 1958, birth or immigration, whichever came latest. The follow-up was ended in 2015, first primary LCa diagnosis, death or migration, whichever came earliest.Annual number of LCa-2 diagnosed from 1958 until 2015 in the cancer registry  was counted to show the temporal trend of LCa-2 in Sweden.p value less than 0.05 was considered to be statistically significant. All the statistical analyses were performed with the use of SAS statistical software, version 9.4 (SAS Institute) and R (version 3.6.2).The relative risk (RR) of LCa in offspring were estimated with Poisson regression adjusting for age (5-year group), gender, periods (5-year group), socioeconomic status  and place of residence  as well as hospitalization due to COPD, alcoholism and obesity. We further stratified the risk based on the age at diagnosis of LCa in FDRs (≤ 60 and > 60 years), family relationship (parent–offspring or siblings), the first primary cancer sites in FDRs and time interval (quartiles) between two cancers in FDRs. The cumulative incidence across age and the 95% confidence interval (CI) were calculated, considering death and diagnosis with other cancers as competing events. We tested if there was difference in familial risks associated with LCa-1 and LCa-2 diagnoses in FDRs in two ways: 1) the full model (the Poisson regression model above), 2) full model with additional adjustment for age at diagnosis of LCa in FDRs (5-year group) and family relationship. The results could indicate whether the different age at diagnosis of LCa in FDRs and family relationship contributed the potential different familial risk. To reveal the familial aggregation mainly caused by genetic component for lung cancer, we analyzed the familial risk by excluding individuals with diagnosis of COPD, who were assumed as heavy smokers. A two-sided The median (IQR) diagnostic age for LCa-1 and LCa-2 in the cancer registry was 69 (62–76) and 73 (67–79), respectively. The annual number of LCa-2 has been increasing in both men and women (Fig. A total of 8,171,958 individuals in the offspring generation were identified at risk of first LCa. The maximum age for the offspring in 2015 was 84 years, whereas the age for the parents was not limited. After removing 2,439,821 individuals who did not reach the inclusion criteria (see study population selection in Supplementary Fig. When stratifying the first primary cancer site before LCa-2 in FDRs (Table The principal finding of this nationwide cohort study is that familial RR of LCa was associated with the diagnosis of LCa-2 in FDR similarly to that with LCa1- in FDR. The risk of LCa was high when early-onset LCa-2 was diagnosed in FDRs and when siblings were affected by LCa-2. High risks were observed for first primary cancer in the ovary, nervous system, upper aerodigestive tract and cervix, and in non-Hodgkin lymphoma.We observed that the number of LCa-2 in the last decade has become comparable between men and women, which is consistent with the increasing trend of total LCa incidence among women in Sweden . This isThe overall risk of LCa is similar regardless of the order of LCa in FDRs i.e., first or second primary. A family history of LCa-2 shared similar features with LCa-1; risk was high when early-onset LCa was diagnosed in FDRs and when siblings were affected by LCa. It is well-accepted that an earlier age at diagnosis of cancer is indicative of a higher likelihood of inherited risk . The lowIn the site-stratified analysis, we identified increased risk in individuals whose FDR was diagnosed with LCa-2 after some specific types of first primary cancer. Many of these are smoking-related cancers , such asWhen dealing with multiple primary cancers, it is difficult to differentiate between the new malignancy and metastases from existing cancer. In Sweden, the diagnosis of multiple primary cancers follows the IARC/IACR multiple cancer coding rules . PreviouTo the best of our knowledge, this is the first to study the familial risk of LCa among individuals whose FDRs were diagnosed with LCa-2. This similar risk among individuals with family history of LCa-1 or LCa-2 suggests that they can be managed similarly regarding LCa prevention. Compared to family history of LCa-1, LCa risk associated with family history of LCa-2 can be better predicted based on the cancer diagnosed before LCa-2 in FDRs.Additional file 1: Supplementary Figure 1. Selection of study population. Supplementary Table 1. Association between lung cancer risk and family history of LCa-1 or LCa-2 among individuals without diagnosis of chronic obstructive pulmonary disease.