Obesity is a Dominant Driver of ER-Negative Breast Cancer: Epidemiological and Mechanistic Evidence from a High-Risk Cohort

Obesity is a Dominant Driver of ER-Negative Breast Cancer: Epidemiological and Mechanistic Evidence from a High-Risk Cohort | 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 Obesity is a Dominant Driver of ER-Negative Breast Cancer: Epidemiological and Mechanistic Evidence from a High-Risk Cohort Naser Elkum, Ali Saeed Alzahrani, Noura Alraouji, Taher AL-Tweigeri, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7617890/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Dec, 2025 Read the published version in Breast Cancer Research → Version 1 posted 9 You are reading this latest preprint version Abstract Background The biological mechanisms linking modifiable risk factors to aggressive breast cancer subtypes remain poorly defined, particularly in underrepresented populations which experience a disproportionate burden. Regions with high obesity prevalence and early-onset disease provide a unique setting to investigate these drivers. Methods In a case-control study of 567 premenopausal breast cancer cases and 906 controls from a high-risk population, tumors were classified into molecular subtypes. Logistic regression estimated subtype-specific risks, and population-attributable fractions (PAF) were calculated. To provide mechanistic validation, we examined paracrine effects of fibroblasts from obese versus lean women on ER-α expression in breast epithelial cells using co-culture and qRT-PCR analysis. Results Obesity was the strongest modifiable factor, with adjusted ORs ranging from 2.29 (luminal B) to 4.32 (TNBC), corresponding to a PAF of 65.2% for TNBC—an effect size exceeding most previous reports. Family history was independently associated with TNBC. Mechanistically, fibroblasts from obese donors downregulated ER-α mRNA expression in malignant epithelial cells, providing a biologically coherent mechanism for obesity-driven ER-negative tumorigenesis. Conclusions Obesity is a dominant, actionable driver of TNBC, with its effect magnified in high-risk populations. The mechanistic link to ER suppression underscores a global biological pathway, highlighting metabolic health as a critical target for prevention strategies worldwide. Breast Cancer Molecular Subtypes Triple-negative Breast Cancer Premenopausal Obesity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Breast cancer is the most common malignancy among women worldwide and is a leading cause of cancer-related mortality [ 1 ]. Molecular classification has revealed distinct subtypes with divergent etiologies and prognoses, with triple-negative breast cancer (TNBC) representing an aggressive form associated with poorer outcomes and a higher incidence in younger women and certain underrepresented populations [ 2 – 5 ]. This challenge is exemplified in populations such as Arab women, who present with unique epidemiological features, including earlier onset and a disproportionately high burden of TNBC [ 6 , 7 ]. In Saudi Arabia, for instance, more than one-third of breast cancer cases are diagnosed before age 50, with TNBC comprising ~ 21% of premenopausal cases—nearly double the global average [ 6 , 8 ]. Obesity has emerged as a prime candidate driver of this disparity and is a leading modifiable risk factor for cancer development and progression [ 9 – 12 ]. Beyond its established role in postmenopausal breast cancer [ 13 ], accumulating evidence implicates obesity in the etiology of aggressive molecular subtypes, including TNBC [ 9 , 10 , 14 – 16 ]. Proposed mechanisms include chronic low-grade inflammation, adipokine dysregulation, and metabolic reprogramming of the tumor microenvironment [ 10 , 12 , 16 ]. However, the majority of this epidemiologic and mechanistic evidence originates from Western populations [ 15 , 17 ]. This represents a critical gap in knowledge, as the drivers of aggressive disease may be obscured in lower-risk populations. Studying high-risk cohorts is therefore essential to fully characterize the potent effects of factors like obesity and to understand the underlying biology. To address this, we conducted a comprehensive subtype-specific case-control study in a high-risk Middle Eastern cohort characterized by significant metabolic stress. We quantified the obesity-attributable risk across molecular subtypes and complemented these analyses with direct biological validation using primary breast fibroblasts. This dual-level approach provides robust evidence on the magnitude of obesity's effect and offers a biologically plausible mechanism with global relevance for targeted prevention strategies. Methods Study Design and Population We conducted a case–control study including 567 premenopausal women diagnosed with breast cancer at King Abdullah Oncology Center, King Faisal Specialist Hospital & Research Centre (KFSHRC), and 906 healthy controls. Participants were recruited between 2000 and 2007, a period that preceded national screening programs and thus reflects baseline subtype distribution. All cases were histologically confirmed, and controls were frequency matched to cases by age (± 5 years) and region of residence. The study was approved by the institutional review board of KFSHRC (RAC#2031091), and written informed consent was obtained from all participants. Data Collection Trained interviewers administered standardized questionnaires to obtain demographic, reproductive, and lifestyle information, including marital status, parity, breastfeeding practices, hormone therapy use, and smoking history. Clinical records provided ER, PR, and HER2 status, with molecular subtyping performed by immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH) following established guidelines. Fibroblast and epithelial cell experiments To assess biological plausibility, we investigated the paracrine effects of breast stromal fibroblasts from obese (BMI ≥ 30) versus lean donors on estrogen receptor (ER-α) expression in breast epithelial cells. Primary fibroblasts were isolated from reduction mammoplasty specimens with informed consent and ethical approval (RAC#2140017). Normal luminal epithelial cells were cultured under standard conditions, and breast cancer cells (MCF-7) were obtained from ATCC. Fibroblast–epithelial interactions were assessed using transwell co-culture and serum-free conditioned media (SFCM). After 24 hours of exposure, epithelial cells were harvested for RNA extraction. ER-α levels were quantified by real-time quantitative PCR (qRT-PCR), with GAPDH as a housekeeping control. Experiments were performed in biological triplicates with ≥ 3 technical replicates. Statistical Analysis Unconditional logistic regression was applied to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for breast cancer risk factors by molecular subtype, using the common control group. Multivariable models adjusted for age group, BMI, parity, total pregnancies, breastfeeding, hormone therapy use, marital status, family history of breast cancer, and region. Population-attributable fractions (PAF) for obesity in TNBC were calculated using the formula: \(\:PAF=\frac{{P}_{e}\left(OR-1\right)}{{P}_{e}\left(OR-1\right)+1}\) ​ where P e ​ represents the proportion of obesity among TNBC cases and OR is the adjusted odds ratio. Analyses were conducted using R version 4.4.3 and SAS version 9.4. A two-sided p < 0.05 was considered statistically significant. Reporting followed the STROBE guidelines for case–control studies. Results Subtype Distribution Among the 567 premenopausal breast cancer cases, luminal A was the most common subtype (39.9%), followed by luminal B (24.7%), TNBC (21.3%), and HER2-enriched (14.1%) (Table 1 ). The distribution differed significantly by age at diagnosis (p = 0.001), with luminal A and HER2-enriched subtypes more frequent in women ≥ 35 years, while TNBC and luminal B were relatively enriched in women < 35 years (Fig. 1 ). Associations with Demographic, Reproductive, and Lifestyle Factors In univariate analyses, older age, obesity, and hormone replacement therapy (HRT) use were associated with increased risk across most subtypes, while family history was significant only for TNBC (Table 2 ). In multivariable models, obesity emerged as the strongest modifiable risk factor, with adjusted odds ratios ranging from 2.29 for luminal B to 4.32 for TNBC (Table 3 ). Age ≥ 35 years remained independently associated with all subtypes, with the highest effect in HER2-enriched disease (OR 5.52; 95% CI 2.71–11.26). HRT use was significantly associated with luminal A (OR 1.75; 95% CI 1.19–2.58), but not with other subtypes. Family history of breast cancer was independently associated with TNBC (OR 2.50; 95% CI 1.48–4.20). These subtype-specific associations are illustrated in a forest plot (Fig. 2 ) and a heatmap summary (Fig. 3 ). Population-attributable fraction of obesity Using prevalence and adjusted odds ratios, we estimated that 65.2% of TNBC cases in this cohort could be attributed to obesity, assuming a causal relationship (Fig. 5 ). This proportion was markedly higher than for luminal or HER2-enriched subtypes, underscoring the unique vulnerability of TNBC to obesity. Biological validation: effects of fibroblasts from obese donors To explore mechanistic underpinnings, we examined the paracrine impact of fibroblasts from obese versus lean women on estrogen receptor expression in breast epithelial cells. Co-culture experiments demonstrated that fibroblasts from obese donors significantly downregulated ER-α expression in MCF-7 cells compared with lean fibroblast controls (Fig. 6 ). Quantitative RT-PCR analysis revealed approximately a 45% reduction in ER-α transcript levels in cells co-cultured with obese fibroblasts (p ≤ 0.05, n = 3). These findings indicate that fibroblasts from obese women can downregulate ER-α expression in breast epithelial cells, providing a cellular mechanism consistent with the observed epidemiologic association between obesity and TNBC. Discussion This study demonstrates that obesity is a dominant driver of estrogen receptor-negative breast carcinogenesis. The effect size observed in this high-risk cohort (OR 4.32, PAF 65.2% for TNBC) is among the strongest reported and significantly exceeds estimates from many Western studies [ 15 , 17 ]. This suggests that the potent, biology-altering impact of obesity on breast cancer risk is fully visible in this setting of high metabolic stress, revealing a risk relationship that may be generalizable but is often attenuated in lower-risk populations Critically, our epidemiological findings are supported by a biologically coherent mechanism. We demonstrate that fibroblasts from obese women can downregulate ER-α expression in breast epithelial cells at the transcript level. This aligns with global evidence that obesity promotes a tumor-promoting microenvironment through inflammatory cytokines and metabolic reprogramming [ 9 , 16 , 18 ]. Our data provide direct experimental evidence from a human model that links obesity to the loss of estrogen receptor expression, the defining feature of TNBC and related aggressive subtypes The public health implications are substantial and extend beyond this cohort. Integrating obesity prevention into cancer control frameworks is a universal imperative [ 19 ]. Our data suggest that interventions targeting metabolic health could have a particularly profound impact on reducing the incidence of aggressive, ER-negative cancers across diverse populations. Beyond lifestyle modification, emerging pharmacologic strategies such as GLP-1 receptor agonists offer promising new tools for precision prevention, as evidenced by both real-world data and randomized controlled trials [ 20 , 21 ]. Family history was also independently associated with TNBC, reflecting the contribution of genetic predisposition, particularly BRCA1 mutations, in this subtype [ 22 , 23 ]. In settings with higher rates of inherited susceptibility, such as regions where consanguinity is more common, genetic risk may compound the impact of obesity-driven pathways. Together, these findings highlight the dual importance of genetic counseling and metabolic health management as complementary strategies for TNBC prevention. Although reproductive variables such as parity and breastfeeding showed protective effects in univariate analyses, they did not attenuate the obesity–TNBC relationship in adjusted models. This underscores obesity as a dominant, overriding driver of TNBC risk in this population. The clinical and policy implications are significant. National cancer control frameworks should prioritize obesity prevention as an essential component of breast cancer prevention strategies. Early identification of women at high metabolic risk can enable targeted interventions before cancer develops. In practice, this could include community-based weight management and nutrition programs integrated within women’s health clinics, earlier metabolic health screening in primary care settings, and culturally tailored education campaigns linking obesity with breast cancer risk. Such initiatives would not only reduce the burden of TNBC but also advance broader non-communicable disease prevention goals. At the research level, translational studies into obesity-driven TNBC biology—such as our fibroblast experiments—should be expanded to inform biomarker discovery and guide preventive interventions, including novel metabolic agents such as GLP-1 receptor agonists. Together, these results align with emerging global evidence that metabolic interventions may represent a new frontier in breast cancer prevention [ 20 , 21 ]. Strengths and limitations Several limitations should be considered. The case-control design is inherently susceptible to recall bias, and recruitment occurred prior to the widespread adoption of national screening, which may influence subtype distribution. However, this pre-screening era cohort provides a unique window to study baseline, intrinsic risk factors without the confounding effect of screen-detection. Additionally, while our mechanistic experiments provide direct biological plausibility, they were limited in scale and focused on a key pathway (ER-α suppression); future work should explore additional signaling pathways (e.g., JAK/STAT, NF-κB) in larger mechanistic cohorts. Importantly, the consistency between the strong epidemiologic associations and the coherent cellular mechanism strengthens causal inference. Future prospective studies that integrate omics-based profiling with metabolic health data in diverse populations will be essential to validate these findings and identify biomarkers for obesity-driven TNBC risk. Conclusion In summary, our study demonstrates that obesity is a dominant and modifiable driver of estrogen receptor-negative breast carcinogenesis. By leveraging a high-risk cohort where this effect is magnified, we reveal the profound potential of metabolic health to influence cancer risk. The integration of robust epidemiology with mechanistic validation provides a biologically coherent model for how obesity promotes aggressive disease. These findings underscore that targeting metabolic health must be a cornerstone of global cancer prevention strategies. They also highlight that studying high-risk populations is a powerful strategy to elucidate fundamental biological drivers and emerging opportunities for precision prevention, including lifestyle interventions and novel pharmacologic approaches. Declarations Ethical Approval and Consent to participate This study was approved by the Institutional Review Board at King Faisal Specialist Hospital & Research Centre (RAC#2031091 and RAC#2140017). Written informed consent was obtained from all participants and tissue donors in accordance with institutional guidelines and the Declaration of Helsinki. Consent for publication Not applicable. Availability of supporting data Data used in the present study were extracted from the Oncology Data Unit and Tumor Registry and linked to the Saudi National Cancer Registry. The data cannot be shared publicly because the individual-level data contain potentially identifying and sensitive patient information, and disclosure is restricted by legislation and ethical approval. Saudi regulations do not permit the public sharing of personal sensitive data. Data may be made available to qualified researchers who meet the legal and institutional requirements for access. Requests regarding data access should be directed to the King Faisal Specialist Hospital & Research Centre Institutional Review Board ( [email protected] ). Competing interests The authors declare that they have no competing interests. Funding No funding was received for this study. Authors’ contributions NE participated in the conception and overall supervision of the study, handled data management, data analysis, and wrote the manuscript. TT selected cases, reviewed medical records, and edited the manuscript. NAA performed cell culture, RNA purification, and qRT-PCR. AA and AAZ contributed to study conception, data interpretation, and manuscript development. All authors have read and approved the final version of the manuscript. Acknowledgments We wish to thank the National Cancer Institute and the Tumor Registry at King Faisal Hospital and Research Center for their assistance in providing the essential information for this study. This work was conducted under RAC proposal number 2031091. References Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A: Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries . CA Cancer J Clin 2024, 74 (3):229-263. 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Supplementary Files Tables.docx Cite Share Download PDF Status: Published Journal Publication published 29 Dec, 2025 Read the published version in Breast Cancer Research → Version 1 posted Editorial decision: Revision requested 14 Oct, 2025 Reviews received at journal 10 Oct, 2025 Reviews received at journal 06 Oct, 2025 Reviewers agreed at journal 06 Oct, 2025 Reviewers agreed at journal 04 Oct, 2025 Reviewers invited by journal 02 Oct, 2025 Editor assigned by journal 22 Sep, 2025 Submission checks completed at journal 21 Sep, 2025 First submitted to journal 15 Sep, 2025 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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16:13:32","extension":"tiff","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":155520206,"visible":true,"origin":"","legend":"","description":"","filename":"Figure4BCR.tiff","url":"https://assets-eu.researchsquare.com/files/rs-7617890/v1/60457534e6fb821fe2923ebf.tiff"},{"id":93611565,"identity":"d83eb527-8cf3-4ff5-b6a3-3d8e9ff7c20c","added_by":"auto","created_at":"2025-10-15 16:13:30","extension":"tiff","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1774310,"visible":true,"origin":"","legend":"","description":"","filename":"Figure5BCR.tiff","url":"https://assets-eu.researchsquare.com/files/rs-7617890/v1/0592f14eeedee5f5386d72c3.tiff"},{"id":93611569,"identity":"3c59c754-81dd-4b68-96d4-a106c28489c1","added_by":"auto","created_at":"2025-10-15 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16:13:30","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":50312,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure2BCR.png","url":"https://assets-eu.researchsquare.com/files/rs-7617890/v1/f3af77ab4a2231b2463cd579.png"},{"id":93612627,"identity":"80694063-e4cc-4141-bcb1-4401dba5445d","added_by":"auto","created_at":"2025-10-15 16:21:30","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":52790,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure3BCR.png","url":"https://assets-eu.researchsquare.com/files/rs-7617890/v1/41514891f298979c4442ead7.png"},{"id":93611579,"identity":"dab8fa81-e7f5-4a38-a4f0-906e30eb37ae","added_by":"auto","created_at":"2025-10-15 16:13:31","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":227624,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure4BCR.png","url":"https://assets-eu.researchsquare.com/files/rs-7617890/v1/e7d3a43978c0ac9cf8d77e7a.png"},{"id":93611563,"identity":"0c119fb8-c33f-48db-a4a3-a4afc8b31aa5","added_by":"auto","created_at":"2025-10-15 16:13:30","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":95637,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure5BCR.png","url":"https://assets-eu.researchsquare.com/files/rs-7617890/v1/7fbdfc52486d8e7012d431f2.png"},{"id":93611556,"identity":"63383d24-5bdb-4daa-8012-268e1a7a3f98","added_by":"auto","created_at":"2025-10-15 16:13:30","extension":"png","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":37957,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure6qRTPCR.png","url":"https://assets-eu.researchsquare.com/files/rs-7617890/v1/fbc85b6eca77ed89e66d137b.png"},{"id":93611562,"identity":"4ab316d1-d893-44e2-93ce-58506ba6ca54","added_by":"auto","created_at":"2025-10-15 16:13:30","extension":"xml","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":101317,"visible":true,"origin":"","legend":"","description":"","filename":"c79485fd0d34446e8c132febfe8a6bea1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7617890/v1/8e51af2360a1c19b76c52ddd.xml"},{"id":93611570,"identity":"aa052b84-343d-4a47-9771-d1fe8a883007","added_by":"auto","created_at":"2025-10-15 16:13:31","extension":"html","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":111393,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7617890/v1/33ec6737dd85ee73a2cf1e36.html"},{"id":93612626,"identity":"a47dabc4-2230-4e01-abb7-e4b02dc502d4","added_by":"auto","created_at":"2025-10-15 16:21:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":585696,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of breast cancer molecular subtypes by age group among premenopausal cases\u003c/p\u003e\n\u003cp\u003eStacked bar chart showing the proportion of molecular subtypes—Luminal A, Luminal B, HER2-enriched, and triple-negative breast cancer (TNBC)—by age at diagnosis (\u0026lt;35 vs ≥35 years) among premenopausal cases (n=567). The distribution differed by age (χ² test, p=0.001). Abbreviations: HER2, human epidermal growth factor receptor 2; TNBC, triple-negative breast cancer.\u003c/p\u003e","description":"","filename":"Figure1BCR.png","url":"https://assets-eu.researchsquare.com/files/rs-7617890/v1/226ee1d8fc2690a67e91eb4c.png"},{"id":93611554,"identity":"84854ad8-0277-46b7-8848-e0a696373ced","added_by":"auto","created_at":"2025-10-15 16:13:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":986754,"visible":true,"origin":"","legend":"\u003cp\u003eAdjusted odds ratios for risk factors by molecular subtype\u003c/p\u003e\n\u003cp\u003eForest plot of adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for key risk factors across subtypes, each subtype compared with the common control group (n=906). Estimates from unconditional multivariable logistic regression adjusted for age group, body-mass index (BMI) category, parity, total pregnancies, breastfeeding (ever and duration), menopausal hormone therapy (HRT) use, marital status, family history of breast cancer, and region. Vertical line indicates OR=1. Abbreviations: BMI, body-mass index; CI, confidence interval; HRT, hormone replacement therapy; OR, odds ratio; TNBC, triple-negative breast cancer.\u003c/p\u003e","description":"","filename":"Figure2BCR.png","url":"https://assets-eu.researchsquare.com/files/rs-7617890/v1/82deade4e45071a501e63761.png"},{"id":93611552,"identity":"5bd24c64-4a04-4ef1-b052-64af6d71c1e6","added_by":"auto","created_at":"2025-10-15 16:13:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":542209,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of adjusted associations between risk factors and molecular subtypes\u003c/p\u003e\n\u003cp\u003eHeatmap displaying adjusted ORs for major risk factors (rows) across molecular subtypes (columns) from the multivariable models described in Figure 2. Color intensity reflects direction and magnitude of association (cool colors \u0026lt;1; warm colors \u0026gt;1); cell values show ORs. Associations were considered statistically significant at two-sided p\u0026lt;0.05. Abbreviations: OR, odds ratio; TNBC, triple-negative breast cancer.\u003c/p\u003e","description":"","filename":"Figure3BCR.png","url":"https://assets-eu.researchsquare.com/files/rs-7617890/v1/8305ea94c265b87a67eb8422.png"},{"id":93611573,"identity":"697dd97b-dbbc-4953-b0af-77e591651f6e","added_by":"auto","created_at":"2025-10-15 16:13:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":6798880,"visible":true,"origin":"","legend":"\u003cp\u003eIntegrated summary of subtype epidemiology and putative obesity–TNBC mechanisms\u003c/p\u003e\n\u003cp\u003ePanel A: Distribution of molecular subtypes in premenopausal cases (n=567). Panel B: Adjusted ORs (95% CIs) for key risk factors by subtype (from models in Figure 2). Panel C: Conceptual schematic linking obesity-related inflammation (e.g., IL-6/TNF-α/CRP) to JAK/STAT and NF-κB pathway activation, immune evasion, and ER-loss consistent with TNBC development; schematic is hypothesis-generating and not drawn to scale. Abbreviations: CI, confidence interval; CRP, C-reactive protein; ER, estrogen receptor; IL-6, interleukin-6; NF-κB, nuclear factor-κB; OR, odds ratio; TNF-α, tumor necrosis factor-α; TNBC, triple-negative breast cancer.\u003c/p\u003e","description":"","filename":"Figure4BCR.png","url":"https://assets-eu.researchsquare.com/files/rs-7617890/v1/8fc168bafa239174c54680ed.png"},{"id":93611559,"identity":"59b1b365-6655-4d5d-b8e9-dde766d00459","added_by":"auto","created_at":"2025-10-15 16:13:30","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1303634,"visible":true,"origin":"","legend":"\u003cp\u003eObesity-attributable burden of TNBC in premenopausal women\u003c/p\u003e\n\u003cp\u003ePanel A: Distribution of TNBC cases by BMI category at diagnosis (lean, overweight, obese); 56.4% of TNBC cases were obese. Panel B: Population-attributable fraction (PAF) for obesity in TNBC estimated using the adjusted OR from multivariable models and the formula PAF = P_e(OR–1) / [P_e(OR–1)+1], where P_e is the prevalence of obesity among TNBC cases. Point estimate shown (65.2%); methods as detailed in Statistical Analysis. Abbreviations: BMI, body-mass index; OR, odds ratio; PAF, population-attributable fraction; TNBC, triple-negative breast cancer.\u003c/p\u003e","description":"","filename":"Figure5BCR.png","url":"https://assets-eu.researchsquare.com/files/rs-7617890/v1/31fc82753b54c0ffc556b96d.png"},{"id":93611574,"identity":"062b4dc8-cb5b-4f7d-ac04-56cb215f0aec","added_by":"auto","created_at":"2025-10-15 16:13:31","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":357693,"visible":true,"origin":"","legend":"\u003cp\u003eBreast stromal fibroblasts from obese donors downregulate ER-α mRNA expression in breast epithelial cells.\u003c/p\u003e\n\u003cp\u003eMCF-7 breast cancer cells were co-cultured for 24 h with fibroblasts derived from obese (BFO-3) or lean (BFL-3) women, followed by total RNA extraction and quantitative RT-PCR analysis of ER-α transcript levels. Results show a significant ~45% decrease in ER-α mRNA expression in MCF-7 cells co-cultured with BFO-3 relative to controls (*p ≤ 0.05, n = 3). Data are presented as mean ± SD. Statistical comparisons were performed using a two-tailed Student’s t-test. Abbreviations: BFL, breast fibroblasts from lean donors; BFO, breast fibroblasts from obese donors; ER-α, estrogen receptor-alpha; qRT-PCR, quantitative reverse-transcription polymerase chain reaction.\u003c/p\u003e","description":"","filename":"Figure6qRTPCR.png","url":"https://assets-eu.researchsquare.com/files/rs-7617890/v1/05181cdec8ded7325ce07c4b.png"},{"id":99545376,"identity":"1b7ba652-1c28-42d9-8ff3-38a870cd94be","added_by":"auto","created_at":"2026-01-05 16:06:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11835624,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7617890/v1/df018dbc-3f07-4572-8d01-fa90eb7f8c58.pdf"},{"id":93611549,"identity":"da2e7b78-4fc5-4fc1-bdf0-b205632a44f5","added_by":"auto","created_at":"2025-10-15 16:13:29","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":27747,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7617890/v1/e381911747f1f2cc451cd7f2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Obesity is a Dominant Driver of ER-Negative Breast Cancer: Epidemiological and Mechanistic Evidence from a High-Risk Cohort","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer is the most common malignancy among women worldwide and is a leading cause of cancer-related mortality [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Molecular classification has revealed distinct subtypes with divergent etiologies and prognoses, with triple-negative breast cancer (TNBC) representing an aggressive form associated with poorer outcomes and a higher incidence in younger women and certain underrepresented populations [\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e–\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis challenge is exemplified in populations such as Arab women, who present with unique epidemiological features, including earlier onset and a disproportionately high burden of TNBC [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In Saudi Arabia, for instance, more than one-third of breast cancer cases are diagnosed before age 50, with TNBC comprising ~ 21% of premenopausal cases—nearly double the global average [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eObesity has emerged as a prime candidate driver of this disparity and is a leading modifiable risk factor for cancer development and progression [\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e–\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Beyond its established role in postmenopausal breast cancer [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], accumulating evidence implicates obesity in the etiology of aggressive molecular subtypes, including TNBC [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e–\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Proposed mechanisms include chronic low-grade inflammation, adipokine dysregulation, and metabolic reprogramming of the tumor microenvironment [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHowever, the majority of this epidemiologic and mechanistic evidence originates from Western populations [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This represents a critical gap in knowledge, as the drivers of aggressive disease may be obscured in lower-risk populations. Studying high-risk cohorts is therefore essential to fully characterize the potent effects of factors like obesity and to understand the underlying biology.\u003c/p\u003e\u003cp\u003eTo address this, we conducted a comprehensive subtype-specific case-control study in a high-risk Middle Eastern cohort characterized by significant metabolic stress. We quantified the obesity-attributable risk across molecular subtypes and complemented these analyses with direct biological validation using primary breast fibroblasts. This dual-level approach provides robust evidence on the magnitude of obesity's effect and offers a biologically plausible mechanism with global relevance for targeted prevention strategies.\u003c/p\u003e\n\n\n\n"},{"header":"Methods","content":"\u003ch3\u003eStudy Design and Population\u003c/h3\u003e\u003cp\u003eWe conducted a case–control study including 567 premenopausal women diagnosed with breast cancer at King Abdullah Oncology Center, King Faisal Specialist Hospital \u0026amp; Research Centre (KFSHRC), and 906 healthy controls. Participants were recruited between 2000 and 2007, a period that preceded national screening programs and thus reflects baseline subtype distribution. All cases were histologically confirmed, and controls were frequency matched to cases by age (± 5 years) and region of residence. The study was approved by the institutional review board of KFSHRC (RAC#2031091), and written informed consent was obtained from all participants.\u003c/p\u003e\u003ch2\u003eData Collection\u003c/h2\u003e\u003cp\u003eTrained interviewers administered standardized questionnaires to obtain demographic, reproductive, and lifestyle information, including marital status, parity, breastfeeding practices, hormone therapy use, and smoking history. Clinical records provided ER, PR, and HER2 status, with molecular subtyping performed by immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH) following established guidelines.\u003c/p\u003e\u003ch3\u003eFibroblast and epithelial cell experiments\u003c/h3\u003e\u003cp\u003eTo assess biological plausibility, we investigated the paracrine effects of breast stromal fibroblasts from obese (BMI ≥ 30) versus lean donors on estrogen receptor (ER-α) expression in breast epithelial cells. Primary fibroblasts were isolated from reduction mammoplasty specimens with informed consent and ethical approval (RAC#2140017). Normal luminal epithelial cells were cultured under standard conditions, and breast cancer cells (MCF-7) were obtained from ATCC.\u003c/p\u003e\u003cp\u003eFibroblast–epithelial interactions were assessed using transwell co-culture and serum-free conditioned media (SFCM). After 24 hours of exposure, epithelial cells were harvested for RNA extraction. ER-α levels were quantified by real-time quantitative PCR (qRT-PCR), with GAPDH as a housekeeping control. Experiments were performed in biological triplicates with ≥ 3 technical replicates.\u003c/p\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eUnconditional logistic regression was applied to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for breast cancer risk factors by molecular subtype, using the common control group. Multivariable models adjusted for age group, BMI, parity, total pregnancies, breastfeeding, hormone therapy use, marital status, family history of breast cancer, and region.\u003c/p\u003e\u003cp\u003ePopulation-attributable fractions (PAF) for obesity in TNBC were calculated using the formula:\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:PAF=\\frac{{P}_{e}\\left(OR-1\\right)}{{P}_{e}\\left(OR-1\\right)+1}\\)\u003c/span\u003e\u003c/span\u003e​\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e​ represents the proportion of obesity among TNBC cases and \u003cem\u003eOR\u003c/em\u003e is the adjusted odds ratio.\u003c/p\u003e\u003cp\u003eAnalyses were conducted using R version 4.4.3 and SAS version 9.4. A two-sided p \u0026lt; 0.05 was considered statistically significant. Reporting followed the STROBE guidelines for case–control studies.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eSubtype Distribution\u003c/p\u003e\n\u003cp\u003eAmong the 567 premenopausal breast cancer cases, luminal A was the most common subtype (39.9%), followed by luminal B (24.7%), TNBC (21.3%), and HER2-enriched (14.1%) (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The distribution differed significantly by age at diagnosis (p\u0026thinsp;=\u0026thinsp;0.001), with luminal A and HER2-enriched subtypes more frequent in women\u0026thinsp;\u0026ge;\u0026thinsp;35 years, while TNBC and luminal B were relatively enriched in women\u0026thinsp;\u0026lt;\u0026thinsp;35 years (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv\u003eAssociations with Demographic, Reproductive, and Lifestyle Factors\u003c/div\u003e\n\u003cp\u003eIn univariate analyses, older age, obesity, and hormone replacement therapy (HRT) use were associated with increased risk across most subtypes, while family history was significant only for TNBC (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIn multivariable models, obesity emerged as the strongest modifiable risk factor, with adjusted odds ratios ranging from 2.29 for luminal B to 4.32 for TNBC (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Age\u0026thinsp;\u0026ge;\u0026thinsp;35 years remained independently associated with all subtypes, with the highest effect in HER2-enriched disease (OR 5.52; 95% CI 2.71\u0026ndash;11.26).\u003c/p\u003e\n\u003cp\u003eHRT use was significantly associated with luminal A (OR 1.75; 95% CI 1.19\u0026ndash;2.58), but not with other subtypes. Family history of breast cancer was independently associated with TNBC (OR 2.50; 95% CI 1.48\u0026ndash;4.20). These subtype-specific associations are illustrated in a forest plot (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) and a heatmap summary (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003ePopulation-attributable fraction of obesity\u003c/p\u003e\n\u003cp\u003eUsing prevalence and adjusted odds ratios, we estimated that 65.2% of TNBC cases in this cohort could be attributed to obesity, assuming a causal relationship (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). This proportion was markedly higher than for luminal or HER2-enriched subtypes, underscoring the unique vulnerability of TNBC to obesity.\u003c/p\u003e\n\u003cp\u003eBiological validation: effects of fibroblasts from obese donors\u003c/p\u003e\n\u003cp\u003eTo explore mechanistic underpinnings, we examined the paracrine impact of fibroblasts from obese versus lean women on estrogen receptor expression in breast epithelial cells. Co-culture experiments demonstrated that fibroblasts from obese donors significantly downregulated ER-\u0026alpha; expression in MCF-7 cells compared with lean fibroblast controls (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). Quantitative RT-PCR analysis revealed approximately a 45% reduction in ER-\u0026alpha; transcript levels in cells co-cultured with obese fibroblasts (p\u0026thinsp;\u0026le;\u0026thinsp;0.05, n\u0026thinsp;=\u0026thinsp;3).\u003c/p\u003e\n\u003cp\u003eThese findings indicate that fibroblasts from obese women can downregulate ER-\u0026alpha; expression in breast epithelial cells, providing a cellular mechanism consistent with the observed epidemiologic association between obesity and TNBC.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrates that obesity is a dominant driver of estrogen receptor-negative breast carcinogenesis. The effect size observed in this high-risk cohort (OR 4.32, PAF 65.2% for TNBC) is among the strongest reported and significantly exceeds estimates from many Western studies [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This suggests that the potent, biology-altering impact of obesity on breast cancer risk is fully visible in this setting of high metabolic stress, revealing a risk relationship that may be generalizable but is often attenuated in lower-risk populations\u003c/p\u003e\u003cp\u003eCritically, our epidemiological findings are supported by a biologically coherent mechanism. We demonstrate that fibroblasts from obese women can downregulate ER-α expression in breast epithelial cells at the transcript level. This aligns with global evidence that obesity promotes a tumor-promoting microenvironment through inflammatory cytokines and metabolic reprogramming [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Our data provide direct experimental evidence from a human model that links obesity to the loss of estrogen receptor expression, the defining feature of TNBC and related aggressive subtypes\u003c/p\u003e\u003cp\u003eThe public health implications are substantial and extend beyond this cohort. Integrating obesity prevention into cancer control frameworks is a universal imperative [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Our data suggest that interventions targeting metabolic health could have a particularly profound impact on reducing the incidence of aggressive, ER-negative cancers across diverse populations. Beyond lifestyle modification, emerging pharmacologic strategies such as GLP-1 receptor agonists offer promising new tools for precision prevention, as evidenced by both real-world data and randomized controlled trials [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFamily history was also independently associated with TNBC, reflecting the contribution of genetic predisposition, particularly BRCA1 mutations, in this subtype [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In settings with higher rates of inherited susceptibility, such as regions where consanguinity is more common, genetic risk may compound the impact of obesity-driven pathways. Together, these findings highlight the dual importance of genetic counseling and metabolic health management as complementary strategies for TNBC prevention.\u003c/p\u003e\u003cp\u003eAlthough reproductive variables such as parity and breastfeeding showed protective effects in univariate analyses, they did not attenuate the obesity\u0026ndash;TNBC relationship in adjusted models. This underscores obesity as a dominant, overriding driver of TNBC risk in this population.\u003c/p\u003e\u003cp\u003eThe clinical and policy implications are significant. National cancer control frameworks should prioritize obesity prevention as an essential component of breast cancer prevention strategies. Early identification of women at high metabolic risk can enable targeted interventions before cancer develops. In practice, this could include community-based weight management and nutrition programs integrated within women\u0026rsquo;s health clinics, earlier metabolic health screening in primary care settings, and culturally tailored education campaigns linking obesity with breast cancer risk. Such initiatives would not only reduce the burden of TNBC but also advance broader non-communicable disease prevention goals. At the research level, translational studies into obesity-driven TNBC biology\u0026mdash;such as our fibroblast experiments\u0026mdash;should be expanded to inform biomarker discovery and guide preventive interventions, including novel metabolic agents such as GLP-1 receptor agonists. Together, these results align with emerging global evidence that metabolic interventions may represent a new frontier in breast cancer prevention [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eStrengths and limitations\u003c/p\u003e\u003cp\u003eSeveral limitations should be considered. The case-control design is inherently susceptible to recall bias, and recruitment occurred prior to the widespread adoption of national screening, which may influence subtype distribution. However, this pre-screening era cohort provides a unique window to study baseline, intrinsic risk factors without the confounding effect of screen-detection. Additionally, while our mechanistic experiments provide direct biological plausibility, they were limited in scale and focused on a key pathway (ER-α suppression); future work should explore additional signaling pathways (e.g., JAK/STAT, NF-κB) in larger mechanistic cohorts. Importantly, the consistency between the strong epidemiologic associations and the coherent cellular mechanism strengthens causal inference. Future prospective studies that integrate omics-based profiling with metabolic health data in diverse populations will be essential to validate these findings and identify biomarkers for obesity-driven TNBC risk.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, our study demonstrates that obesity is a dominant and modifiable driver of estrogen receptor-negative breast carcinogenesis. By leveraging a high-risk cohort where this effect is magnified, we reveal the profound potential of metabolic health to influence cancer risk. The integration of robust epidemiology with mechanistic validation provides a biologically coherent model for how obesity promotes aggressive disease. These findings underscore that targeting metabolic health must be a cornerstone of global cancer prevention strategies. They also highlight that studying high-risk populations is a powerful strategy to elucidate fundamental biological drivers and emerging opportunities for precision prevention, including lifestyle interventions and novel pharmacologic approaches.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthical Approval and Consent to participate\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Review Board at King Faisal Specialist Hospital \u0026amp; Research Centre (RAC#2031091 and RAC#2140017). Written informed consent was obtained from all participants and tissue donors in accordance with institutional guidelines and the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of supporting data\u003c/p\u003e\n\u003cp\u003eData used in the present study were extracted from the Oncology Data Unit and Tumor Registry and linked to the Saudi National Cancer Registry. The data cannot be shared publicly because the individual-level data contain potentially identifying and sensitive patient information, and disclosure is restricted by legislation and ethical approval. Saudi regulations do not permit the public sharing of personal sensitive data. Data may be made available to qualified researchers who meet the legal and institutional requirements for access. Requests regarding data access should be directed to the King Faisal Specialist Hospital \u0026amp; Research Centre Institutional Review Board ([email protected]).\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eNo funding was received for this study.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions\u003c/p\u003e\n\u003cp\u003eNE participated in the conception and overall supervision of the study, handled data management, data analysis, and wrote the manuscript. TT selected cases, reviewed medical records, and edited the manuscript. NAA performed cell culture, RNA purification, and qRT-PCR. AA and AAZ contributed to study conception, data interpretation, and manuscript development. All authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eWe wish to thank the National Cancer Institute and the Tumor Registry at King Faisal Hospital and Research Center for their assistance in providing the essential information for this study. This work was conducted under RAC proposal number 2031091. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A: \u003cstrong\u003eGlobal cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries\u003c/strong\u003e. \u003cem\u003eCA Cancer J Clin \u003c/em\u003e2024, \u003cstrong\u003e74\u003c/strong\u003e(3):229-263.\u003c/li\u003e\n\u003cli\u003eSorlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, Hastie T, Eisen MB, van de Rijn M, Jeffrey SS\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eGene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications\u003c/strong\u003e. \u003cem\u003eProc Natl Acad Sci U S A \u003c/em\u003e2001, \u003cstrong\u003e98\u003c/strong\u003e(19):10869-10874.\u003c/li\u003e\n\u003cli\u003eRakha EA, Reis-Filho JS, Ellis IO: \u003cstrong\u003eCombinatorial biomarker expression in breast cancer\u003c/strong\u003e. \u003cem\u003eBreast Cancer Res Treat \u003c/em\u003e2010, \u003cstrong\u003e120\u003c/strong\u003e(2):293-308.\u003c/li\u003e\n\u003cli\u003eGoldhirsch A, Winer EP, Coates AS, Gelber RD, Piccart-Gebhart M, Th\u0026uuml;rlimann B, Senn HJ, Members P: \u003cstrong\u003ePersonalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013\u003c/strong\u003e. \u003cem\u003eAnnals of Oncology \u003c/em\u003e2013, \u003cstrong\u003e24\u003c/strong\u003e(9):2206-2223.\u003c/li\u003e\n\u003cli\u003eSali AP, Sharma N, Verma A, Beke A, Shet T, Patil A, Pai T, Nair N, Parmar V, Gupta S\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eIdentification of Luminal Subtypes of Breast Carcinoma Using Surrogate Immunohistochemical Markers and Ascertaining Their Prognostic Relevance\u003c/strong\u003e. \u003cem\u003eClin Breast Cancer \u003c/em\u003e2020, \u003cstrong\u003e20\u003c/strong\u003e(5):382-389.\u003c/li\u003e\n\u003cli\u003eAl-Shamsi HO, Abdelwahed N, Abyad A, Abu-Gheida I, Afrit M, Abu ElFuol T, Alasas R, Lababidi B, Dash P, Ahmad M\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eBreast Cancer in the Arabian Gulf Countries\u003c/strong\u003e. \u003cem\u003eCancers (Basel) \u003c/em\u003e2023, \u003cstrong\u003e15\u003c/strong\u003e(22).\u003c/li\u003e\n\u003cli\u003eAl-Thoubaity FK: \u003cstrong\u003eMolecular classification of breast cancer: A retrospective cohort study\u003c/strong\u003e. \u003cem\u003eAnn Med Surg (Lond) \u003c/em\u003e2020, \u003cstrong\u003e49\u003c/strong\u003e:44-48.\u003c/li\u003e\n\u003cli\u003eCouncil SH: \u003cstrong\u003eCancer Incidence Report 2022\u003c/strong\u003e. 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Epidemiol Glob Health \u003c/em\u003e2025, \u003cstrong\u003e15\u003c/strong\u003e(1):36.\u003c/li\u003e\n\u003cli\u003ePierobon M, Frankenfeld CL: \u003cstrong\u003eObesity as a risk factor for triple-negative breast cancers: a systematic review and meta-analysis\u003c/strong\u003e. \u003cem\u003eBreast Cancer Res Treat \u003c/em\u003e2013, \u003cstrong\u003e137\u003c/strong\u003e(1):307-314.\u003c/li\u003e\n\u003cli\u003ePicon-Ruiz M, Morata-Tarifa C, Valle-Goffin JJ, Friedman ER, Slingerland JM: \u003cstrong\u003eObesity and adverse breast cancer risk and outcome: Mechanistic insights and strategies for intervention\u003c/strong\u003e. \u003cem\u003eCA Cancer J Clin \u003c/em\u003e2017, \u003cstrong\u003e67\u003c/strong\u003e(5):378-397.\u003c/li\u003e\n\u003cli\u003eTurkoz FP, Solak M, Petekkaya I, Keskin O, Kertmen N, Sarici F, Arik Z, Babacan T, Ozisik Y, Altundag K: \u003cstrong\u003eAssociation between common risk factors and molecular subtypes in breast cancer patients\u003c/strong\u003e. \u003cem\u003eBreast \u003c/em\u003e2013, \u003cstrong\u003e22\u003c/strong\u003e(3):344-350.\u003c/li\u003e\n\u003cli\u003eSankofi BM, Valencia-Rincon E, Sekhri M, Ponton-Almodovar AL, Bernard JJ, Wellberg EA: \u003cstrong\u003eThe impact of poor metabolic health on aggressive breast cancer: adipose tissue and tumor metabolism\u003c/strong\u003e. \u003cem\u003eFront Endocrinol (Lausanne) \u003c/em\u003e2023, \u003cstrong\u003e14\u003c/strong\u003e:1217875.\u003c/li\u003e\n\u003cli\u003eAnazco D, Acosta A, Cathcart-Rake EJ, D\u0026apos;Andre SD, Hurtado MD: \u003cstrong\u003eWeight-centric prevention of cancer\u003c/strong\u003e. \u003cem\u003eObes Pillars \u003c/em\u003e2024, \u003cstrong\u003e10\u003c/strong\u003e:100106.\u003c/li\u003e\n\u003cli\u003eWang L, Xu R, Kaelber DC, Berger NA: \u003cstrong\u003eGlucagon-Like Peptide 1 Receptor Agonists and 13 Obesity-Associated Cancers in Patients With Type 2 Diabetes\u003c/strong\u003e. \u003cem\u003eJAMA Netw Open \u003c/em\u003e2024, \u003cstrong\u003e7\u003c/strong\u003e(7):e2421305.\u003c/li\u003e\n\u003cli\u003eSilverii GA, Marinelli C, Bettarini C, Del Vescovo GG, Monami M, Mannucci E: \u003cstrong\u003eGLP-1 receptor agonists and the risk for cancer: A meta-analysis of randomized controlled trials\u003c/strong\u003e. \u003cem\u003eDiabetes Obes Metab \u003c/em\u003e2025, \u003cstrong\u003e27\u003c/strong\u003e(8):4454-4468.\u003c/li\u003e\n\u003cli\u003eCouch FJ, Hart SN, Sharma P, Toland AE, Wang X, Miron P, Olson JE, Godwin AK, Pankratz VS, Olswold C\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eInherited mutations in 17 breast cancer susceptibility genes among a large triple-negative breast cancer cohort unselected for family history of breast cancer\u003c/strong\u003e. \u003cem\u003eJ Clin Oncol \u003c/em\u003e2015, \u003cstrong\u003e33\u003c/strong\u003e(4):304-311.\u003c/li\u003e\n\u003cli\u003eArun B, Couch FJ, Abraham J, Tung N, Fasching PA: \u003cstrong\u003eBRCA-mutated breast cancer: the unmet need, challenges and therapeutic benefits of genetic testing\u003c/strong\u003e. \u003cem\u003eBr J Cancer \u003c/em\u003e2024, \u003cstrong\u003e131\u003c/strong\u003e(9):1400-1414.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"breast-cancer-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brcr","sideBox":"Learn more about [Breast Cancer Research](http://breast-cancer-research.biomedcentral.com)","snPcode":"13058","submissionUrl":"https://submission.nature.com/new-submission/13058/3","title":"Breast Cancer Research","twitterHandle":"@BCRJournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Breast Cancer, Molecular Subtypes, Triple-negative Breast Cancer, Premenopausal, Obesity","lastPublishedDoi":"10.21203/rs.3.rs-7617890/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7617890/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe biological mechanisms linking modifiable risk factors to aggressive breast cancer subtypes remain poorly defined, particularly in underrepresented populations which experience a disproportionate burden. Regions with high obesity prevalence and early-onset disease provide a unique setting to investigate these drivers.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eIn a case-control study of 567 premenopausal breast cancer cases and 906 controls from a high-risk population, tumors were classified into molecular subtypes. Logistic regression estimated subtype-specific risks, and population-attributable fractions (PAF) were calculated. To provide mechanistic validation, we examined paracrine effects of fibroblasts from obese versus lean women on ER-α expression in breast epithelial cells using co-culture and qRT-PCR analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eObesity was the strongest modifiable factor, with adjusted ORs ranging from 2.29 (luminal B) to 4.32 (TNBC), corresponding to a PAF of 65.2% for TNBC\u0026mdash;an effect size exceeding most previous reports. Family history was independently associated with TNBC. Mechanistically, fibroblasts from obese donors downregulated ER-α mRNA expression in malignant epithelial cells, providing a biologically coherent mechanism for obesity-driven ER-negative tumorigenesis.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eObesity is a dominant, actionable driver of TNBC, with its effect magnified in high-risk populations. The mechanistic link to ER suppression underscores a global biological pathway, highlighting metabolic health as a critical target for prevention strategies worldwide.\u003c/p\u003e","manuscriptTitle":"Obesity is a Dominant Driver of ER-Negative Breast Cancer: Epidemiological and Mechanistic Evidence from a High-Risk Cohort","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-15 16:13:25","doi":"10.21203/rs.3.rs-7617890/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-14T12:18:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-10T19:21:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-06T18:30:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"120117148911815345196955400805492383734","date":"2025-10-06T12:12:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"318311326885977489303158083353208050053","date":"2025-10-04T15:19:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-02T11:55:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-22T22:41:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-22T01:12:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Breast Cancer Research","date":"2025-09-15T08:06:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"breast-cancer-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brcr","sideBox":"Learn more about [Breast Cancer Research](http://breast-cancer-research.biomedcentral.com)","snPcode":"13058","submissionUrl":"https://submission.nature.com/new-submission/13058/3","title":"Breast Cancer Research","twitterHandle":"@BCRJournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2cf45aa2-70bc-4083-b787-f160c974993d","owner":[],"postedDate":"October 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-05T16:02:02+00:00","versionOfRecord":{"articleIdentity":"rs-7617890","link":"https://doi.org/10.1186/s13058-025-02189-1","journal":{"identity":"breast-cancer-research","isVorOnly":false,"title":"Breast Cancer Research"},"publishedOn":"2025-12-29 15:58:29","publishedOnDateReadable":"December 29th, 2025"},"versionCreatedAt":"2025-10-15 16:13:25","video":"","vorDoi":"10.1186/s13058-025-02189-1","vorDoiUrl":"https://doi.org/10.1186/s13058-025-02189-1","workflowStages":[]},"version":"v1","identity":"rs-7617890","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7617890","identity":"rs-7617890","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}