Physical Activity in High-Risk Women reduces Breast Cancer Risk: UK Biobank Study

Physical Activity in High-Risk Women reduces Breast Cancer Risk: UK Biobank Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Physical Activity in High-Risk Women reduces Breast Cancer Risk: UK Biobank Study Estee Rebibo, Noa Amiel, Noam Shomron, Eitan Friedman This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8862660/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose Physical activity (PA) is associated with reduced breast cancer (BC) risk in average-risk women. Its effect on genetically predisposed high-risk women remains unclear. Methods BC cases (n = 17,409) and controls (n = 26,907) were identified from the UK Biobank with BC-related Polygenic risk score (PRS) and/or pathogenic variants (PVs) in cancer susceptibility genes (CSG), International Physical Activity Questionnaire (IPAQ) data, and/or accelerometer-measured PA. Logistic regression was used to estimate odds ratios (ORs) for developing BC. PA and PRS were stratified into tertiles (low, moderate, high), and for carriers of BRCA1/BRCA2 and other high-penetrance CSG PVs. Results PA was associated with reduced BC risk. In the full cohort, high PA conferred a 25.8%–14.8% risk reduction by accelerometer and IPAQ, respectively. BC risk-reducing effect was maximal among women with moderate PRS (46.0%–23.7% reduction by accelerometer and IPAQ, respectively). BRCA1/BRCA2 PV carriers demonstrated 50.7% risk reduction (95% CI: 6.6–73.9%). No significant effect was observed among women with low PRS or among carriers of other CSG PVs. . Conclusion Both objectively and subjectively measured PA were associated with reduced BC risk among genetically predisposed women. These findings support PA as a feasible risk-reducing strategy for high-risk women. Physical activity breast cancer genetic predisposition BRCA1 BRCA2 polygenic risk score UK Biobank Figures Figure 1 Figure 2 Introduction Physical activity (PA) confers multiple health benefits, including reduced mortality and decreased risk of chronic diseases[ 1 , 2 ]. The World Health Organization recommends at least 150 minutes of moderate-intensity exercise per week for adults and 300 minutes per week for adolescents to reduce non-communicable disease risk[ 3 ]. PA association with BC risk modification has extensively been studied, with most evidence indicating approximately a 25% reduction in risk among the most active compared with inactive women[ 4 – 7 ]. However, research on genetically predisposed women remains limited and inconsistent. Some studies suggest a protective effect of PA on BC risk in BRCA1/BRCA2 germline pathogenic variant (PV) carriers or women with a family history of BC, with PA during adulthood associated with a 20% risk reduction regardless of BRCA mutational status[ 8 ], and adolescent moderate PA (ages 12–17) linked to a 38% lower risk of premenopausal BC among BRCA carriers[ 9 ]. Yet, other studies report a 25% increased risk in premenopausal women with family history engaging in high PA levels compared with physically inactive women[ 10 ]. Notably, prior studies have not assessed the impact of PA stratified by polygenic risk score (PRS). Leveraging data from the UK Biobank, the current study examined the association between PA, assessed both via self-report (International Physical Activity Questionnaire, IPAQ)[ 11 ] and objective accelerometer measures, and BC risk, stratified by PRS as well as the presence of germline PVs in high-penetrance BC susceptibility genes, primarily BRCA1/BRCA2 . Methods Study population - Data were retrieved from the UK Biobank[ 12 ]. Female BC cases were identified using ICD-10 code C50, and controls were women without malignant neoplasm codes and without a personal history of cancer. Inclusion required availability of genetic data in the form of PRS and/or PV in a CSG[ 13 ]. PA information obtained from IPAQ[ 14 ] and/or accelerometer recordings [ 15 ] was required for eligibility. Women missing PA data or whose BC diagnosis occurred before PA assessment were excluded. A total of 17,409 cases initially met inclusion criteria. After exclusions related to IPAQ availability or timing, 8,192 were eligible for IPAQ analyses. For accelerometer analyses, 1,383 cases remained after removing participants without valid accelerometer data or with diagnoses preceding device wear. The control cohort included 26,907 women meeting all inclusion criteria. Figure 1 summarizes cohort selection steps. Exposure assessment - PA was assessed using IPAQ and/or wrist-worn accelerometers. IPAQ data were converted to weekly MET-minutes using standard scoring procedures[ 14 ]. Accelerometer PA was defined as average acceleration in milli-gravity units. For both instruments, PA was categorized into tertiles representing the lower 30%, middle 40%, and upper 30% of the distribution. Genetic data - PRS was categorized into tertiles based on its distribution within the full dataset[ 16 , 13 ]. Cut-off points defined the lowest tertile ( ≤ − 0.657385), highest tertile (≥ 0.4115), and a mid-range tertile in between. High risk genetic status was assigned to carriers of pathogenic variants in BRCA1, BRCA2, TP53, STK11, PTEN , or PALB2 . Statistical analysis - Associations between PA and BC were examined using logistic regression models comparing high versus low PA tertiles. Models produced odds ratios (ORs) and 95% confidence intervals (CIs) and were adjusted for age at PA assessment, BMI, standing height, and Townsend index. Analyses were stratified by PRS tertiles and CSG PV status. Statistical significance was defined as p < 0.05. Analyses were performed in Python. Results Study population characteristics - The analytic cohorts consisted of 8,192 BC cases and 26,907 controls for IPAQ analyses, and 1,383 cases with the same 26,907 controls for accelerometer analyses. (Fig. 1 ). Table 1 presents baseline characteristics across PA tertiles for both PA measurement modalities. Women in higher PA categories were younger and had lower BMI and whole-body fat mass, with minimal variation observed across reproductive factors, socioeconomic indicators, or family history of BC. As expected, MET-minutes and accelerometer-based PA-related values increased progressively across tertiles, confirming good internal consistency between self-reported and device-based PA measures. Table 1 Participant characteristics by activity level (IPAQ and accelerometer). Baseline characteristics of the analytic cohort stratified by PA tertiles, presented separately for accelerometer- and IPAQ-derived activity levels. Variable Acc Low Acc Mod Acc High p-value Acc IPAQ Low IPAQ High p-value IPAQ Cohort size 6,077 12,270 9,943 – 15,569 13,588 – Age at activity measurement (mean ± SD) 63.4 (± 7.6) 61.3 (± 7.7) 58.8 (± 7.5) < 0.001 55.7 (± 7.8) 55.8 (± 7.9) NS BMI (mean ± SD) 28.1 (± 5.6) 26.1 (± 4.5) 24.6 (± 3.9) < 0.001 26.3 (± 4.8) 25.6 (± 4.4) < 0.001 Standing height (mean ± SD) 163.3 (± 6.3) 163.5 (± 6.2) 163.6 (± 6.1) 0.017 163.5 (± 6.2) 163.4 (± 6.2) NS Townsend index (mean ± SD) -1.6 (± 2.9) -1.7 (± 2.8) -1.7 (± 2.8) 0.035 -1.6 (± 2.9) -1.6 (± 2.8) NS Summed MET-min/week (mean ± SD) 1,915 (± 1,986) 2,352 (± 2,204) 2,947 (± 2,543) < 0.001 1,439 (± 626) 4,569 (± 2,489) < 0.001 Overall acceleration avg (mean ± SD) 19.5 (± 2.7) 26.9 (± 2.3) 37.5 (± 6.4) < 0.001 28.3 (± 7.5) 31.3 (± 26.0) 0.05). MET = metabolic equivalent of task. Among all participants, 17,409 were diagnosed with BC and 26,907 were cancer-free. The prevalence of monogenic CSG PVs was 1.70% among BC cases (296 carriers) and 1.56% among controls (419 carriers). Carrier counts for specific genes in cases versus controls were: BRCA2 , 191 vs. 129 (1.10% vs. 0.48%); BRCA1 , 147 vs. 91 (0.84% vs. 0.34%); TP53 , 26 vs. 15 (0.15% vs. 0.06%); STK11 , 21 vs. 13 (0.12% vs. 0.05%); PTEN , 22 vs. 7 (0.13% vs. 0.03%); and PALB2 , 27 vs. 56 (0.16% vs. 0.21%). Association between PA and BC - Higher PA scores were associated with lower odds for BC diagnosis. As shown in Table 2 and visualized in Fig. 2 , high accelerometer-derived PA scores were associated with a 25.8% reduction in BC odds ratio (95% CI: 13.50–36.40%) compared with low PA scores. IPAQ-derived high PA scores were associated with a 14.8% reduction in odds for developing BC (95% CI: 8.37–20.75%) compared with low PA scores. Table 2 Odds ratios for BC associated with high versus low PA (IPAQ and accelerometer). Multivariable-adjusted odds ratios comparing high Vs low PA, overall and within genetic subgroups. Subgroup Accelerometer IPAQ Full cohort 25.8% ↓ (95% CI: 13.5–36.4%) * 14.8% ↓ (95% CI: 8.4–20.8%) * PRS-high 19.6% ↓ (95% CI: 0.3–35.2%) * 11.2% ↓ (95% CI: 1.3–20.1%) * PRS-moderate 46.0% ↓ (95% CI: 28.2–59.5%) * 23.7% ↓ (95% CI: 12.9–33.2%) * PRS-low 1.57% ↑ (95% CI: -29.37↓-46.07%↑) NS 11.92% ↓ (95% Cl: 25.81↓- 4.57↑) NS CSG all 46.29% ↓ (95% CI : -84.64↓-87.78%↑) NS 34.37% ↓ (95% CI : -62.55↓-15.03%↑) NS BRCA1/2 carriers 31.97% ↓ (-83.10↓-173.84%↑) NS 50.7% ↓ (95% CI: 6.6–73.9%) * * p 0.05). ↓ indicates decreased odds of BC. Stratification by PRS - The magnitude of association differed across PRS tertiles (Table 2 ). Among women with moderate PRS, high PA levels were associated with a 46.0% reduction in BC odds in accelerometer analyses (95% CI: 28.17–59.46%) and a 23.72% reduction in IPAQ analyses (95% CI: 12.92–33.19%), compared with low PA levels. In the high PRS tertile, reductions of BC risk were 19.64% (95% CI: 0.35–35.20%) and 11.18% (95% CI: 1.30-20.08%) for accelerometer and IPAQ determinations, respectively. Associations were not statistically significant in the low PRS tertile for either PA modality (Fig. 2 ). Stratification by CSG PV status - For combined CSG PV carriers, high PA showed no statistically significant associations with BC risk in either modality (Table 2 ). Among BRCA1/2 carriers in the IPAQ cohort (n = 14 BC cases), high PA was associated with a 50.7% reduction in odds (95% CI: 6.6–73.9%). Accelerometer analyses in BRCA1/2 carriers yielded non-significant results (31.97% reduction; 95% CI: −83.1 to 173.8%) due to insufficient sample size. Discussion The present study demonstrates that both objectively and subjectively measured physical activity (PA) are associated with lower breast cancer (BC) risk overall and among women with moderate or high polygenic risk scores (PRS). Among BRCA1/BRCA2 pathogenic variant (PV) carriers, a protective association was observed only with self-reported PA, whereas accelerometer-based estimates were not statistically significant, likely reflecting limited sample size. The strongest associations were seen in women with moderate PRS, suggesting that PA may confer the greatest benefit when inherited susceptibility is elevated but not extreme. In the highest PRS stratum, the weaker association may indicate that strong genetic predisposition attenuates the impact of modifiable behavioral factors. These findings are consistent with prior evidence linking PA to reduced BC risk. A meta-analysis of 57 studies including over 4.5 million cases reported approximately 10% lower risk among women with the highest activity levels compared with the lowest, with a dose–response relationship[ 18 ]. An umbrella review similarly classified PA among the few exposures supported by highly suggestive evidence for BC risk reduction[ 19 ]. Evidence in genetically predisposed populations remains comparatively limited. In the Prospective Family Study Cohort of 15,550 women enriched for hereditary risk, increasing recreational PA was associated with a ~ 20% reduction in BC risk without attenuation among BRCA1/2 carriers or women at the highest familial risk levels[ 8 ]. The Sister Study likewise demonstrated a 23–25% lower postmenopausal BC risk among women reporting ≥ 7 hours/week of recreational exercise, independent of family history strength[ 10 ]. Among confirmed BRCA1/2 PV carriers, a multinational matched case-control study reported a 38% reduction in premenopausal BC associated with moderate adolescent PA[ 9 ]. A systematic review of BRCA-focused studies also found consistent protective associations of approximately 40% in highly active adolescent carriers, although dose–response trends were inconsistent[ 20 ]. PRS-stratified analyses from the UK Biobank further suggest that PA as part of a healthy lifestyle may reduce BC risk across genetic risk categories. In one study of 2,728 cases and 88,489 controls, a favorable lifestyle score including regular PA was associated with significantly lower BC risk across PRS strata[ 22 ]. By incorporating both objective and self-reported activity measures and including women across genetic-risk categories, the present analysis extends these observations and helps clarify the magnitude of association between PA and BC risk among genetically susceptible individuals. Several limitations should be considered. The number of PV carriers was limited, reducing precision for gene-specific estimates. Accelerometer data were available only in a subset and for a restricted time window, limiting evaluation of long-term activity patterns and critical exposure periods. Information on detailed family history and risk-reducing interventions was unavailable, PA was assessed only at recruitment, and the cohort was predominantly Caucasian, restricting generalizability. Residual confounding and reverse causation cannot be excluded. However, the UK Biobank design minimizes selection bias[ 23 ], and the ability to evaluate both PRS-defined and monogenic genetic risk represents a key strength. In summary, these findings support PA as a potentially modifiable factor associated with reduced BC risk among women with inherited susceptibility. Prospective studies with longitudinal activity assessment and more diverse populations are warranted to clarify causality and clinical implications. Declarations Acknowledgements This research has been conducted using the UK Biobank Resource under Application Number 716708. We thank the participants and those involved in building and maintaining the UK Biobank. Funding No external funding was received to carry out this study. Authors' contributions Study concept and design: ER, NS, EF; Statistical analysis: ER; Interpretation of the data: ER, NA, NS, EF. Drafting of the initial manuscript: NA. Critical revision of the manuscript and approval of the final submitted version: ER, NA, NS, EF. Authors and Affiliations Gray Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel Estee Rebibo, Noa Amiel, Noam Shomron, Eitan Friedman Edmond J. Safra Center for Bioinformatics, Tel Aviv University, Tel Aviv, Israel Estee Rebibo, Noam Shomron Assuta Medical Center, Tel Aviv, Israel Eitan Friedman Ethics declarations The study was conducted upon specific approval from the UKBB authorities, under the regulatory system that governs data collection and making data accessible in a manner that maintains confidentiality and participant deidentification. Competing interests The authors declare no competing interests. Data availability Data is available through the UK Biobank upon application via their access management system https://www.ukbiobank.ac.uk/register-apply/. The code used for the analysis can be found at https://github.com/esteeliat/breast_cancer_physical_activity . References Garcia L, Pearce M, Abbas A, Mok A, Strain T, Ali S, et al. Non-occupational physical activity and risk of cardiovascular disease, cancer and mortality outcomes: a dose-response meta-analysis of large prospective studies. Br J Sports Med 2023;57(15):979-989. doi:10.1136/bjsports-2022-105669. Ekelund U, Sanchez-Lastra MA, Dalene KE, Tarp J. Dose-response associations, physical activity intensity and mortality risk: a narrative review. J Sport Health Sci 2024;13(1):24-29. doi:10.1016/j.jshs.2023.09.006. Bull FC, Al-Ansari SS, Biddle S, Borodulin K, Buman MP, Cardon G, et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. 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Kehm RD, Genkinger JM, MacInnis RJ, John EM, Phillips KA, Dite GS, et al. Recreational physical activity is associated with reduced breast cancer risk in adult women at high risk for breast cancer. Cancer Res 2020;80(1):116-125. doi:10.1158/0008-5472.CAN-19-1847. Lammert J, Lubinski J, Gronwald J, Huzarski T, Armel S, Eisen A, et al. Physical activity during adolescence and young adulthood and the risk of breast cancer in BRCA1 and BRCA2 mutation carriers. Breast Cancer Res Treat 2018;169(3):561-571. doi:10.1007/s10549-018-4694-1. Niehoff NM, Nichols HB, Zhao S, White AJ, Sandler DP. Adult physical activity and breast cancer risk in women with a family history of breast cancer. Cancer Epidemiol Biomarkers Prev 2019;28(1):51-58. doi:10.1158/1055-9965.EPI-18-0674. Craig CL, Marshall AL, Sjöström M, Bauman AE, Booth ML, Ainsworth BE, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc 2003;35(8):1381-1395. UK Biobank [Internet]. 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Genetic basis of breast and ovarian cancer: approaches and lessons learnt from three decades of inherited predisposition testing. Genes 2024;15(2):219. doi:10.3390/genes15020219. Diao X, Ling Y, Zeng Y, Wu Y, Guo C, Jin Y, et al. Physical activity and cancer risk: a dose-response analysis for the Global Burden of Disease Study 2019. Cancer Commun (Lond) 2023;43(11):1229-1243. doi:10.1002/cac2.12488. PMID:37743572. Yiallourou A, Pantavou K, Markozannes G, Bonovas S, Nikolopoulos GK. Non-genetic factors and breast cancer: an umbrella review of meta-analyses. Eur J Epidemiol 2024;39(7):803-820. Bucy AM, Valencia CI, Howe CL, Larkin TJ, Conard KD, Anderlik EW, et al. Physical activity in young BRCA carriers and reduced risk of breast cancer. Am J Prev Med 2022;63:837–845. doi:10.1016/j.amepre.2022.04.022. Pijpe A, Manders P, Brohet RM, et al. Physical activity and the risk of breast cancer in BRCA1/2 mutation carriers. Breast Cancer Res Treat 2010;120(1):235–44. doi:10.1007/s10549-009-0476-0. Al Ajmi K, Lophatananon A, Mekli K, Ollier W, Muir KR. Association of nongenetic factors with breast cancer risk in genetically predisposed groups of women in the UK Biobank cohort. JAMA Netw Open 2020;3(4):e203760. doi:10.1001/jamanetworkopen.2020.3760. UK Biobank Consortium. UK Biobank prospective cohort design and analytical considerations. UK Biobank; 2025. Available from: https://www.ukbiobank.ac.uk/wp-content/uploads/2025/06/UK-Biobank-prospective-cohort-design-and-analytical-considerations-UK-Biobank-authored-paper.pdf. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8862660","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":594100090,"identity":"7d022f3a-f6a9-4390-a2b2-a7575d213447","order_by":0,"name":"Estee Rebibo","email":"","orcid":"","institution":"Tel Aviv University","correspondingAuthor":false,"prefix":"","firstName":"Estee","middleName":"","lastName":"Rebibo","suffix":""},{"id":594100094,"identity":"d4dcad98-1d88-42f8-a4c6-443885e612c8","order_by":1,"name":"Noa Amiel","email":"","orcid":"","institution":"Tel Aviv University","correspondingAuthor":false,"prefix":"","firstName":"Noa","middleName":"","lastName":"Amiel","suffix":""},{"id":594100099,"identity":"7ade8ce5-5dd2-4116-8fac-19a4e0c0adae","order_by":2,"name":"Noam Shomron","email":"","orcid":"","institution":"Tel Aviv University","correspondingAuthor":false,"prefix":"","firstName":"Noam","middleName":"","lastName":"Shomron","suffix":""},{"id":594100104,"identity":"6ea0e443-28fc-4077-b772-80eaa0711f80","order_by":3,"name":"Eitan Friedman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYBAC9gbGBiBlgyacUIBbC88BxkagnjQJqFKYFgN8WhhA1hxG08KATwt7c/uDnzvO1/HPSD4mwfjDLrGB/fADhgf4tPAcbGzsPXNbQuJGWrIBQ0JyYgNPmgFeh9lLJDY28LbdlmC4kWP4gCGBObGBIYeAX+QfNjb+bTsnIX8j/8MBhoT6xAb+NwS0SDA2NvO2HZAwuJHDCLTlcGKDBCFbeBIbZ8u2JUtuPPPM2CAh7bhxm8QzgwN4tbAff/DxbZsdv9zx5GcSH2yqZfv5kx8+/FGBWwsqSABiNiA+QKyGUTAKRsEoGAXYAQDnk09dOjmuKAAAAABJRU5ErkJggg==","orcid":"","institution":"Tel Aviv University","correspondingAuthor":true,"prefix":"","firstName":"Eitan","middleName":"","lastName":"Friedman","suffix":""}],"badges":[],"createdAt":"2026-02-12 13:38:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8862660/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8862660/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103259219,"identity":"ee1eca8a-b4dc-4bd1-ba49-48a0a0b4ec03","added_by":"auto","created_at":"2026-02-23 17:37:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":56665,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart describing cohort selection for IPAQ- and accelerometer-based analyses.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eLegend- Female participants with PRS data [13] were screened for eligibility. BC cases were identified using ICD-10 code C50, and controls were women without malignant neoplasm codes. Participants missing PA data or with BC diagnoses preceding PA assessment were excluded. Final analytic samples included 8,192 cases and 26,907 controls for IPAQ analyses and 1,383 cases and 26,907 controls for accelerometer analyses.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8862660/v1/fd40f78a33517b8edf9e3264.png"},{"id":103259218,"identity":"4aefb45c-02b9-4acf-acf0-8f1b2ca334ae","added_by":"auto","created_at":"2026-02-23 17:37:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":29267,"visible":true,"origin":"","legend":"\u003cp\u003eOdds ratios for BC associated with high versus low PA, overall and stratified by PRS and \u003cem\u003eBRCA1/2\u003c/em\u003e PV status.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eLegend - Forest plot presenting odds ratios (ORs) and 95% confidence intervals comparing high physical activity to low physical activity (reference group). Analyses were conducted separately using accelerometer-derived activity and IPAQ-reported activity. Results are shown for the full cohort, PRS tertiles (low, moderate, high), CSG all carriers, and \u003cem\u003eBRCA1/2\u003c/em\u003e PV carriers. Models were adjusted for age, BMI, standing height, and Townsend deprivation index.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8862660/v1/79bd00b26a8bce8739581435.png"},{"id":104924908,"identity":"ae9aff26-ba4f-4353-bbba-201c8f03fd1a","added_by":"auto","created_at":"2026-03-18 18:40:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":622636,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8862660/v1/35c5b3bb-e572-4282-a4f4-b24d2cc358e6.pdf"},{"id":103505838,"identity":"816e778b-0460-4c07-b02d-d3a9185024db","added_by":"auto","created_at":"2026-02-26 13:33:12","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":17921,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8862660/v1/681f57e0b8302f8a38d9fd11.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Physical Activity in High-Risk Women reduces Breast Cancer Risk: UK Biobank Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePhysical activity (PA) confers multiple health benefits, including reduced mortality and decreased risk of chronic diseases[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The World Health Organization recommends at least 150 minutes of moderate-intensity exercise per week for adults and 300 minutes per week for adolescents to reduce non-communicable disease risk[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePA association with BC risk modification has extensively been studied, with most evidence indicating approximately a 25% reduction in risk among the most active compared with inactive women[\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, research on genetically predisposed women remains limited and inconsistent. Some studies suggest a protective effect of PA on BC risk in \u003cem\u003eBRCA1/BRCA2\u003c/em\u003e germline pathogenic variant (PV) carriers or women with a family history of BC, with PA during adulthood associated with a 20% risk reduction regardless of \u003cem\u003eBRCA\u003c/em\u003e mutational status[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], and adolescent moderate PA (ages 12\u0026ndash;17) linked to a 38% lower risk of premenopausal BC among \u003cem\u003eBRCA\u003c/em\u003e carriers[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Yet, other studies report a 25% increased risk in premenopausal women with family history engaging in high PA levels compared with physically inactive women[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNotably, prior studies have not assessed the impact of PA stratified by polygenic risk score (PRS). Leveraging data from the UK Biobank, the current study examined the association between PA, assessed both via self-report (International Physical Activity Questionnaire, IPAQ)[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and objective accelerometer measures, and BC risk, stratified by PRS as well as the presence of germline PVs in high-penetrance BC susceptibility genes, primarily \u003cem\u003eBRCA1/BRCA2\u003c/em\u003e.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy population - Data were retrieved from the UK Biobank[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Female BC cases were identified using ICD-10 code C50, and controls were women without malignant neoplasm codes and without a personal history of cancer. Inclusion required availability of genetic data in the form of PRS and/or PV in a CSG[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. PA information obtained from IPAQ[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and/or accelerometer recordings [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] was required for eligibility. Women missing PA data or whose BC diagnosis occurred before PA assessment were excluded.\u003c/p\u003e \u003cp\u003eA total of 17,409 cases initially met inclusion criteria. After exclusions related to IPAQ availability or timing, 8,192 were eligible for IPAQ analyses. For accelerometer analyses, 1,383 cases remained after removing participants without valid accelerometer data or with diagnoses preceding device wear. The control cohort included 26,907 women meeting all inclusion criteria. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes cohort selection steps.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eExposure assessment - PA was assessed using IPAQ and/or wrist-worn accelerometers. IPAQ data were converted to weekly MET-minutes using standard scoring procedures[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Accelerometer PA was defined as average acceleration in milli-gravity units. For both instruments, PA was categorized into tertiles representing the lower 30%, middle 40%, and upper 30% of the distribution.\u003c/p\u003e \u003cp\u003eGenetic data - PRS was categorized into tertiles based on its distribution within the full dataset[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Cut-off points defined the lowest tertile (\u0026thinsp;\u0026le;\u0026thinsp;\u0026minus;\u0026thinsp;0.657385), highest tertile (\u0026ge;\u0026thinsp;0.4115), and a mid-range tertile in between. High risk genetic status was assigned to carriers of pathogenic variants in \u003cem\u003eBRCA1, BRCA2, TP53, STK11, PTEN\u003c/em\u003e, or \u003cem\u003ePALB2\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eStatistical analysis - Associations between PA and BC were examined using logistic regression models comparing high versus low PA tertiles. Models produced odds ratios (ORs) and 95% confidence intervals (CIs) and were adjusted for age at PA assessment, BMI, standing height, and Townsend index. Analyses were stratified by PRS tertiles and CSG PV status. Statistical significance was defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Analyses were performed in Python.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eStudy population characteristics - The analytic cohorts consisted of 8,192 BC cases and 26,907 controls for IPAQ analyses, and 1,383 cases with the same 26,907 controls for accelerometer analyses. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents baseline characteristics across PA tertiles for both PA measurement modalities. Women in higher PA categories were younger and had lower BMI and whole-body fat mass, with minimal variation observed across reproductive factors, socioeconomic indicators, or family history of BC. As expected, MET-minutes and accelerometer-based PA-related values increased progressively across tertiles, confirming good internal consistency between self-reported and device-based PA measures.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eParticipant characteristics by activity level (IPAQ and accelerometer).\u003c/b\u003e Baseline characteristics of the analytic cohort stratified by PA tertiles, presented separately for accelerometer- and IPAQ-derived activity levels.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcc Low\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAcc Mod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAcc High\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value Acc\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIPAQ Low\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIPAQ High\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep-value IPAQ\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCohort size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12,270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15,569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13,588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at activity measurement (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.4 (\u0026plusmn;\u0026thinsp;7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.3 (\u0026plusmn;\u0026thinsp;7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.8 (\u0026plusmn;\u0026thinsp;7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55.7 (\u0026plusmn;\u0026thinsp;7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e55.8 (\u0026plusmn;\u0026thinsp;7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.1 (\u0026plusmn;\u0026thinsp;5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.1 (\u0026plusmn;\u0026thinsp;4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.6 (\u0026plusmn;\u0026thinsp;3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.3 (\u0026plusmn;\u0026thinsp;4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.6 (\u0026plusmn;\u0026thinsp;4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStanding height (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e163.3 (\u0026plusmn;\u0026thinsp;6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e163.5 (\u0026plusmn;\u0026thinsp;6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e163.6 (\u0026plusmn;\u0026thinsp;6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e163.5 (\u0026plusmn;\u0026thinsp;6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e163.4 (\u0026plusmn;\u0026thinsp;6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTownsend index (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.6 (\u0026plusmn;\u0026thinsp;2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.7 (\u0026plusmn;\u0026thinsp;2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.7 (\u0026plusmn;\u0026thinsp;2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.6 (\u0026plusmn;\u0026thinsp;2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.6 (\u0026plusmn;\u0026thinsp;2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSummed MET-min/week (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,915 (\u0026plusmn;\u0026thinsp;1,986)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,352 (\u0026plusmn;\u0026thinsp;2,204)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,947 (\u0026plusmn;\u0026thinsp;2,543)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,439 (\u0026plusmn;\u0026thinsp;626)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4,569 (\u0026plusmn;\u0026thinsp;2,489)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall acceleration avg (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.5 (\u0026plusmn;\u0026thinsp;2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.9 (\u0026plusmn;\u0026thinsp;2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.5 (\u0026plusmn;\u0026thinsp;6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.3 (\u0026plusmn;\u0026thinsp;7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.3 (\u0026plusmn;\u0026thinsp;26.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNS\u0026thinsp;=\u0026thinsp;not statistically significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). MET\u0026thinsp;=\u0026thinsp;metabolic equivalent of task.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAmong all participants, 17,409 were diagnosed with BC and 26,907 were cancer-free. The prevalence of monogenic CSG PVs was 1.70% among BC cases (296 carriers) and 1.56% among controls (419 carriers). Carrier counts for specific genes in cases versus controls were: \u003cem\u003eBRCA2\u003c/em\u003e, 191 vs. 129 (1.10% vs. 0.48%); \u003cem\u003eBRCA1\u003c/em\u003e, 147 vs. 91 (0.84% vs. 0.34%); \u003cem\u003eTP53\u003c/em\u003e, 26 vs. 15 (0.15% vs. 0.06%); \u003cem\u003eSTK11\u003c/em\u003e, 21 vs. 13 (0.12% vs. 0.05%); \u003cem\u003ePTEN\u003c/em\u003e, 22 vs. 7 (0.13% vs. 0.03%); and \u003cem\u003ePALB2\u003c/em\u003e, 27 vs. 56 (0.16% vs. 0.21%).\u003c/p\u003e \u003cp\u003eAssociation between PA and BC - Higher PA scores were associated with lower odds for BC diagnosis. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, high accelerometer-derived PA scores were associated with a 25.8% reduction in BC odds ratio (95% CI: 13.50\u0026ndash;36.40%) compared with low PA scores. IPAQ-derived high PA scores were associated with a 14.8% reduction in odds for developing BC (95% CI: 8.37\u0026ndash;20.75%) compared with low PA scores.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eOdds ratios for BC associated with high versus low PA (IPAQ and accelerometer).\u003c/b\u003e Multivariable-adjusted odds ratios comparing high Vs low PA, overall and within genetic subgroups.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubgroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccelerometer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIPAQ\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFull cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.8% \u0026darr; (95% CI: 13.5\u0026ndash;36.4%) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.8% \u0026darr; (95% CI: 8.4\u0026ndash;20.8%) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRS-high\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.6% \u0026darr; (95% CI: 0.3\u0026ndash;35.2%) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.2% \u0026darr; (95% CI: 1.3\u0026ndash;20.1%) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRS-moderate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.0% \u0026darr; (95% CI: 28.2\u0026ndash;59.5%) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.7% \u0026darr; (95% CI: 12.9\u0026ndash;33.2%) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRS-low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.57% \u0026uarr; (95% CI: -29.37\u0026darr;-46.07%\u0026uarr;) NS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.92% \u0026darr; (95% Cl:\u003c/p\u003e \u003cp\u003e25.81\u0026darr;- 4.57\u0026uarr;) NS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSG all\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.29% \u0026darr; (95% CI : -84.64\u0026darr;-87.78%\u0026uarr;) NS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.37% \u0026darr; (95% CI : -62.55\u0026darr;-15.03%\u0026uarr;) NS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBRCA1/2\u003c/em\u003e carriers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.97% \u0026darr; (-83.10\u0026darr;-173.84%\u0026uarr;) NS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.7% \u0026darr; (95% CI: 6.6\u0026ndash;73.9%) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. NS\u0026thinsp;=\u0026thinsp;not statistically significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). \u0026darr; indicates decreased odds of BC.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eStratification by PRS - The magnitude of association differed across PRS tertiles (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among women with moderate PRS, high PA levels were associated with a 46.0% reduction in BC odds in accelerometer analyses (95% CI: 28.17\u0026ndash;59.46%) and a 23.72% reduction in IPAQ analyses (95% CI: 12.92\u0026ndash;33.19%), compared with low PA levels.\u003c/p\u003e \u003cp\u003eIn the high PRS tertile, reductions of BC risk were 19.64% (95% CI: 0.35\u0026ndash;35.20%) and 11.18% (95% CI: 1.30-20.08%) for accelerometer and IPAQ determinations, respectively. Associations were not statistically significant in the low PRS tertile for either PA modality (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStratification by CSG PV status - For combined CSG PV carriers, high PA showed no statistically significant associations with BC risk in either modality (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among BRCA1/2 carriers in the IPAQ cohort (n\u0026thinsp;=\u0026thinsp;14 BC cases), high PA was associated with a 50.7% reduction in odds (95% CI: 6.6\u0026ndash;73.9%). Accelerometer analyses in BRCA1/2 carriers yielded non-significant results (31.97% reduction; 95% CI: \u0026minus;83.1 to 173.8%) due to insufficient sample size.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study demonstrates that both objectively and subjectively measured physical activity (PA) are associated with lower breast cancer (BC) risk overall and among women with moderate or high polygenic risk scores (PRS). Among BRCA1/BRCA2 pathogenic variant (PV) carriers, a protective association was observed only with self-reported PA, whereas accelerometer-based estimates were not statistically significant, likely reflecting limited sample size. The strongest associations were seen in women with moderate PRS, suggesting that PA may confer the greatest benefit when inherited susceptibility is elevated but not extreme. In the highest PRS stratum, the weaker association may indicate that strong genetic predisposition attenuates the impact of modifiable behavioral factors.\u003c/p\u003e \u003cp\u003eThese findings are consistent with prior evidence linking PA to reduced BC risk. A meta-analysis of 57 studies including over 4.5\u0026nbsp;million cases reported approximately 10% lower risk among women with the highest activity levels compared with the lowest, with a dose\u0026ndash;response relationship[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. An umbrella review similarly classified PA among the few exposures supported by highly suggestive evidence for BC risk reduction[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEvidence in genetically predisposed populations remains comparatively limited. In the Prospective Family Study Cohort of 15,550 women enriched for hereditary risk, increasing recreational PA was associated with a\u0026thinsp;~\u0026thinsp;20% reduction in BC risk without attenuation among BRCA1/2 carriers or women at the highest familial risk levels[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The Sister Study likewise demonstrated a 23\u0026ndash;25% lower postmenopausal BC risk among women reporting\u0026thinsp;\u0026ge;\u0026thinsp;7 hours/week of recreational exercise, independent of family history strength[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Among confirmed BRCA1/2 PV carriers, a multinational matched case-control study reported a 38% reduction in premenopausal BC associated with moderate adolescent PA[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. A systematic review of BRCA-focused studies also found consistent protective associations of approximately 40% in highly active adolescent carriers, although dose\u0026ndash;response trends were inconsistent[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePRS-stratified analyses from the UK Biobank further suggest that PA as part of a healthy lifestyle may reduce BC risk across genetic risk categories. In one study of 2,728 cases and 88,489 controls, a favorable lifestyle score including regular PA was associated with significantly lower BC risk across PRS strata[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. By incorporating both objective and self-reported activity measures and including women across genetic-risk categories, the present analysis extends these observations and helps clarify the magnitude of association between PA and BC risk among genetically susceptible individuals.\u003c/p\u003e \u003cp\u003eSeveral limitations should be considered. The number of PV carriers was limited, reducing precision for gene-specific estimates. Accelerometer data were available only in a subset and for a restricted time window, limiting evaluation of long-term activity patterns and critical exposure periods. Information on detailed family history and risk-reducing interventions was unavailable, PA was assessed only at recruitment, and the cohort was predominantly Caucasian, restricting generalizability. Residual confounding and reverse causation cannot be excluded. However, the UK Biobank design minimizes selection bias[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and the ability to evaluate both PRS-defined and monogenic genetic risk represents a key strength.\u003c/p\u003e \u003cp\u003eIn summary, these findings support PA as a potentially modifiable factor associated with reduced BC risk among women with inherited susceptibility. Prospective studies with longitudinal activity assessment and more diverse populations are warranted to clarify causality and clinical implications.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research has been conducted using the UK Biobank Resource under Application Number 716708. We thank the participants and those involved in building and maintaining the UK Biobank.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo external funding was received to carry out this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy concept and design: ER, NS, EF; Statistical analysis: ER; Interpretation of the data: ER, NA, NS, EF. Drafting of the initial manuscript: NA. Critical revision of the manuscript and approval of the final submitted version: ER, NA, NS, EF.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGray Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEstee Rebibo, Noa Amiel, Noam Shomron, Eitan Friedman\u003c/p\u003e\n\u003cp\u003eEdmond J. Safra Center for Bioinformatics, Tel Aviv University, Tel Aviv, Israel\u003cbr\u003e\u0026nbsp;Estee Rebibo, Noam Shomron\u003cbr\u003e\u003csup\u003e\u0026nbsp;\u003cbr\u003e\u0026nbsp;\u003c/sup\u003eAssuta Medical Center, Tel Aviv, Israel\u003cbr\u003e\u0026nbsp;Eitan Friedman\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted upon specific approval from the UKBB authorities, under the regulatory system that governs data collection and making data accessible in a manner that maintains confidentiality and participant deidentification.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is available through the UK Biobank upon application via their access management system https://www.ukbiobank.ac.uk/register-apply/. The code used for the analysis can be found at https://github.com/esteeliat/breast_cancer_physical_activity\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGarcia L, Pearce M, Abbas A, Mok A, Strain T, Ali S, et al. Non-occupational physical activity and risk of cardiovascular disease, cancer and mortality outcomes: a dose-response meta-analysis of large prospective studies. Br J Sports Med 2023;57(15):979-989. doi:10.1136/bjsports-2022-105669.\u003c/li\u003e\n\u003cli\u003eEkelund U, Sanchez-Lastra MA, Dalene KE, Tarp J. Dose-response associations, physical activity intensity and mortality risk: a narrative review. J Sport Health Sci 2024;13(1):24-29. doi:10.1016/j.jshs.2023.09.006.\u003c/li\u003e\n\u003cli\u003eBull FC, Al-Ansari SS, Biddle S, Borodulin K, Buman MP, Cardon G, et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. 2020;54(24):1451-1462. doi: 10.1136/bjsports-2020-102955. \u003c/li\u003e\n\u003cli\u003eGammon MD, John EM, Britton JA. Recreational and occupational physical activities and risk of breast cancer. J Natl Cancer Inst 1998;90(2):100-117. doi:10.1093/jnci/90.2.100.\u003c/li\u003e\n\u003cli\u003eHoward RA, Leitzmann MF, Linet MS, Freedman DM. Physical activity and breast cancer risk among pre- and postmenopausal women in the US Radiologic Technologists cohort. Cancer Causes Control 2009;20(3):323-333. doi:10.1007/s10552-008-9246-2.\u003c/li\u003e\n\u003cli\u003eNeilson HK, Farris MS, Stone CR, Vaska MM, Brenner DR, Friedenreich CM. Moderate-vigorous recreational physical activity and breast cancer risk, stratified by menopause status: a systematic review and meta-analysis. Menopause 2017;24(3):322-344. doi:10.1097/GME.0000000000000745.\u003c/li\u003e\n\u003cli\u003eGuo W, Fensom GK, Reeves GK, Key TJ. Physical activity and breast cancer risk: results from the UK Biobank prospective cohort. Br J Cancer 2020;122(5):726-732. doi:10.1038/s41416-019-0700-6.\u003c/li\u003e\n\u003cli\u003eKehm RD, Genkinger JM, MacInnis RJ, John EM, Phillips KA, Dite GS, et al. Recreational physical activity is associated with reduced breast cancer risk in adult women at high risk for breast cancer. Cancer Res 2020;80(1):116-125. doi:10.1158/0008-5472.CAN-19-1847.\u003c/li\u003e\n\u003cli\u003eLammert J, Lubinski J, Gronwald J, Huzarski T, Armel S, Eisen A, et al. Physical activity during adolescence and young adulthood and the risk of breast cancer in BRCA1 and BRCA2 mutation carriers. Breast Cancer Res Treat 2018;169(3):561-571. doi:10.1007/s10549-018-4694-1.\u003c/li\u003e\n\u003cli\u003eNiehoff NM, Nichols HB, Zhao S, White AJ, Sandler DP. Adult physical activity and breast cancer risk in women with a family history of breast cancer. Cancer Epidemiol Biomarkers Prev 2019;28(1):51-58. doi:10.1158/1055-9965.EPI-18-0674.\u003c/li\u003e\n\u003cli\u003eCraig CL, Marshall AL, Sj\u0026ouml;str\u0026ouml;m M, Bauman AE, Booth ML, Ainsworth BE, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc 2003;35(8):1381-1395.\u003c/li\u003e\n\u003cli\u003eUK Biobank [Internet]. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. Available from: https://www.ukbiobank.ac.uk/.\u003c/li\u003e\n\u003cli\u003eUK Biobank Resource [Internet]. Genetic data (PRS and PV in CSG) data field. Available from: https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=26220.\u003c/li\u003e\n\u003cli\u003eUK Biobank Resource [Internet]. Accelerometer-measured physical activity data field. Available from: https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=90012.\u003c/li\u003e\n\u003cli\u003eUK Biobank Resource [Internet]. IPAQ data field. Available from: https://biobank.ndph.ox.ac.uk/showcase/refer.cgi?id=540.\u003c/li\u003e\n\u003cli\u003eUK Biobank Project [Internet]. Risk profiles for cancers: polygenic risk scores with and without non-genetic risk factors. Available from: https://www.ukbiobank.ac.uk/projects/risk-profiles-for-cancers-polygenic-risk-scores-with-and-without-non-genetic-risk-factors.\u003c/li\u003e\n\u003cli\u003eBarili V, Ambrosini E, Bortesi B, Minari R, De Sensi E, Cannizzaro IR, et al. Genetic basis of breast and ovarian cancer: approaches and lessons learnt from three decades of inherited predisposition testing. Genes 2024;15(2):219. doi:10.3390/genes15020219.\u003c/li\u003e\n\u003cli\u003eDiao X, Ling Y, Zeng Y, Wu Y, Guo C, Jin Y, et al. Physical activity and cancer risk: a dose-response analysis for the Global Burden of Disease Study 2019. Cancer Commun (Lond) 2023;43(11):1229-1243. doi:10.1002/cac2.12488. PMID:37743572.\u003c/li\u003e\n\u003cli\u003eYiallourou A, Pantavou K, Markozannes G, Bonovas S, Nikolopoulos GK. Non-genetic factors and breast cancer: an umbrella review of meta-analyses. Eur J Epidemiol 2024;39(7):803-820.\u003c/li\u003e\n\u003cli\u003eBucy AM, Valencia CI, Howe CL, Larkin TJ, Conard KD, Anderlik EW, et al. Physical activity in young BRCA carriers and reduced risk of breast cancer. Am J Prev Med 2022;63:837\u0026ndash;845. doi:10.1016/j.amepre.2022.04.022.\u003c/li\u003e\n\u003cli\u003ePijpe A, Manders P, Brohet RM, et al. Physical activity and the risk of breast cancer in BRCA1/2 mutation carriers. Breast Cancer Res Treat 2010;120(1):235\u0026ndash;44. doi:10.1007/s10549-009-0476-0.\u003c/li\u003e\n\u003cli\u003eAl Ajmi K, Lophatananon A, Mekli K, Ollier W, Muir KR. Association of nongenetic factors with breast cancer risk in genetically predisposed groups of women in the UK Biobank cohort. JAMA Netw Open 2020;3(4):e203760. doi:10.1001/jamanetworkopen.2020.3760. \u003c/li\u003e\n\u003cli\u003eUK Biobank Consortium. UK Biobank prospective cohort design and analytical considerations. UK Biobank; 2025. Available from: https://www.ukbiobank.ac.uk/wp-content/uploads/2025/06/UK-Biobank-prospective-cohort-design-and-analytical-considerations-UK-Biobank-authored-paper.pdf.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Physical activity, breast cancer, genetic predisposition, BRCA1, BRCA2, polygenic risk score, UK Biobank","lastPublishedDoi":"10.21203/rs.3.rs-8862660/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8862660/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003ePhysical activity (PA) is associated with reduced breast cancer (BC) risk in average-risk women. Its effect on genetically predisposed high-risk women remains unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eBC cases (n\u0026thinsp;=\u0026thinsp;17,409) and controls (n\u0026thinsp;=\u0026thinsp;26,907) were identified from the UK Biobank with BC-related Polygenic risk score (PRS) and/or pathogenic variants (PVs) in cancer susceptibility genes (CSG), International Physical Activity Questionnaire (IPAQ) data, and/or accelerometer-measured PA. Logistic regression was used to estimate odds ratios (ORs) for developing BC. PA and PRS were stratified into tertiles (low, moderate, high), and for carriers of \u003cem\u003eBRCA1/BRCA2\u003c/em\u003e and other high-penetrance CSG PVs.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePA was associated with reduced BC risk. In the full cohort, high PA conferred a 25.8%\u0026ndash;14.8% risk reduction by accelerometer and IPAQ, respectively. BC risk-reducing effect was maximal among women with moderate PRS (46.0%\u0026ndash;23.7% reduction by accelerometer and IPAQ, respectively). \u003cem\u003eBRCA1/BRCA2\u003c/em\u003e PV carriers demonstrated 50.7% risk reduction (95% CI: 6.6\u0026ndash;73.9%). No significant effect was observed among women with low PRS or among carriers of other CSG PVs. .\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eBoth objectively and subjectively measured PA were associated with reduced BC risk among genetically predisposed women. These findings support PA as a feasible risk-reducing strategy for high-risk women.\u003c/p\u003e","manuscriptTitle":"Physical Activity in High-Risk Women reduces Breast Cancer Risk: UK Biobank Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-23 17:37:03","doi":"10.21203/rs.3.rs-8862660/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dcd9229a-1729-402d-b70c-02ed66ecde2e","owner":[],"postedDate":"February 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-18T18:39:23+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-23 17:37:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8862660","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8862660","identity":"rs-8862660","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}