Body composition as a novel biomarker of recurrence risk in patients with triple-negative breast cancer

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De Vis, Cong Wang, Kirsten V. Nguyen, Lili Sun, Brigitte Jia, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5437121/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Mar, 2025 Read the published version in Breast Cancer Research and Treatment → Version 1 posted 9 You are reading this latest preprint version Abstract Background and Hypothesis Triple-negative breast cancer (TNBC) patients are at increased risk for recurrence compared to other subtypes of breast cancer. Previous evidence showed that adiposity may contribute to worsened cancer control. Current measures of obesity, such as body-mass index (BMI), are poor surrogates of adiposity, while visceral-to-subcutaneous adiposity ratio (VSR), which can be measured from routine computed tomography (CT) imaging, is a direct adiposity measure. We hypothesized that VSR is a stronger predictor of recurrence compared with BMI in patients with TNBC. Materials and Methods This study includes 162 women with stage I-III TNBC who completed standard of care therapy. Measures of body composition, including VSR, visceral adiposity (VA), and subcutaneous adiposity (SA), were estimated using a semi-automated quantitative imaging tool on CT images of the abdomen at the level of L2-L3. Anthropometric measures included BMI and waist circumference and were obtained from CT images. Associations of adiposity measures and recurrence risk were assessed using Fine and Gray competing risk models with death as a competing risk and age at diagnosis and clinical disease stage as covariates. Results During a median follow-up time of 3.6 years, 55 patients had recurrence. The median BMI at baseline was 30.2 [Quartiles: 26.3–35.2]. Body composition was not associated with overall or locoregional recurrence. VSR was significantly associated with an increased risk of distant recurrence, with a subdistribution hazard ratio of 4.25 (95% CI: 1.06–17.02), p = 0.041. By contrast, BMI was not associated with any recurrence risk. Conclusion Consistent with our hypothesis, VSR was associated with a significant risk of distant recurrence and therefore may be a prognostic biomarker. Future directions include interventions targeting VSR reduction among patients with TNBC and VSR-directed therapy modulation. Prognostic markers visceral-to-subcutaneous adiposity ratio triple-negative breast cancer Figures Figure 1 Figure 2 Figure 3 Introduction Breast cancer (BC) has a heterogeneous course that can largely be attributed to differences in clinical, histological, and molecular characteristics. About 15% of BC patients have triple-negative disease 1 , characterized by the absence of estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2. Triple-negative breast cancer (TNBC) has a more aggressive course with increased risk of early recurrence, including distant recurrence 2 as well as locoregional recurrence 3 – 5 , despite systemic treatment, surgery, and radiation. Currently, risk factors for recurrence of TNBC remain understudied. Therefore, TNBC would benefit from the identification of biomarkers for risk stratification and modulation. Obesity has been identified as a risk factor for both hormone receptor positive as well as TNBC occurrence 6 , recurrence 6 , 7 , and BC-related mortality 8 . Higher levels of circulating estrogens 9 , insulin resistance 10 , 11 , increased oxidative stress 12 , 13 , chronic inflammation 14 – 16 and changes in adipocytokines 16 , 17 have been attributed to these risks. Generally, anthropometric measures such as body mass index (BMI) or waist circumference that simultaneously capture visceral and subcutaneous adiposity (VA and SA) are used as biomarkers in epidemiologic studies investigating the effect of obesity on treatment outcomes. Whereas estrogen production, which is the main driver of recurrence risk in hormone receptor positive BC, is driven by both VA and SA, inflammation may be a more important factor in TNBC, which is largely caused by VA and has an inverse relation with SA 18 . Therefore, anthropometric measures may not accurately capture TNBC occurrence and outcome risk. Indeed, studies evaluating anthropometric measures as biomarkers in TNBC are conflicting with some studies reporting positive associations between obesity and TNBC occurrence 19 and TNBC outcome 7 , 20 , 21 , while others have found negative associations 22 , 23 . A study that evaluated both central obesity and BMI as a biomarker for TNBC occurrence found central obesity to be a valid biomarker, while BMI was not 23 , suggesting that VA may be a more accurate biomarker in TNBC patients. The goal of this study was twofold: (1) to elucidate whether obesity is a predictive biomarker for TNBC recurrence, and (2) to identify measures of obesity that most reliably detect an increased risk of TNBC recurrence. We hypothesized that an elevated visceral-to-subcutaneous adiposity ratio (VSR), a measure of body composition, is associated with an increased risk of TNBC recurrence. This hypothesis was tested by examining the relationship between TNBC recurrence and anthropometric measures of obesity, BMI or waist circumference as compared to body composition measures, including VA, SA and VSR, to determine more accurate measures that can better predict patient outcomes. Methods Study subjects Imaging and clinical data was collected from patients who had consented to enroll on an IRB approved Breast Tissue Repository (BRE03103, https://clinicaltrials.gov/study/NCT00899301 ). The inclusion criteria for our specific research question were women, at least 18 years of age, with histologically confirmed, invasive, stage I-III TNBC 24 , treated with standard-of-care treatment. Inclusion criteria also included patients for whom radiation was a component of management as both preclinical 5 as well as retrospective studies 4 have shown that radiation can increase the potential for inflammatory-associated recurrence risk. CT-simulation scans were used to determine measures of body composition through calculating VA, SA, and VSR. Data collection Baseline characteristics including race, gender, age at diagnosis, and date of last follow-up or date of death were collected through electronic medical records (EMR) review. BC specifics including histology, hormone receptor status, human epidermal growth factor receptor 2 (HER2) status, pathologic stage, histologic grade, proliferative rate and lymphovascular invasion (LVI) were gathered. Radiation data included radiation dose to the breast or chest wall and regional lymph nodes, as well as dose to the tumor bed or scar and fractionation regimen. Recurrence was assessed as first site of recurrence being either locoregional with or without distant disease versus distant disease only. Time to recurrence and time to last follow-up were calculated as time from date of diagnosis to date of recurrence and date of last follow up or death, respectively. Obesity assessment Body Mass Index (BMI) was retrieved from the EMR, and waist circumference and body composition were evaluated using abdominal CT images, if available. All measurements were made at the time of initiating RT. A semi-automatic open-source MATLAB-based (MathWorks Inc., Natick, Massachusetts) segmentation tool was used to retrieve quantitative measures of waist circumference, VA, SA, and muscle tissue (Fig. 1 ) from the CT images. In brief, the tool allows for selection of a Digital Imaging and Communications in Medicine (DICOM) image of interest after which the body circumference is detected, and the intra-abdominal cavity is delineated semi-automatically using active contouring with boundary detection. Subcutaneous fat, muscle and visceral fat are then detected using fuzzy c-means clustering, boundary detection and Hounsfield Units thresholds. Results of the tool were validated by the developers through comparison with manual measurements, Aquarius (TeraRecon, Inc., Durham, NC, USA) and ImageJ (National Institutes of Health, Bethesda, MD, USA), with good performance (intraclass correlation coefficients ranging from 0.854 to 0.996) 25 . The above method does not allow for whole abdominal quantitative analysis but allows for single-level analysis which expanded our data collection from patients with early-stage TNBC disease who do not typically get staging CT scans. We focused on the intervertebral disc of lumbar vertebrae L2-L3, which has been shown to correlate best with total intra-abdominal fat 26 , 27 . To reduce noise in measurements, we processed three adjacent imaging slices and averaged the obtained measurements. Then, the visceral-to-subcutaneous fat ratio (VSR = VA/SA) was calculated to reflect the direct and inverse relation of VA and SA, respectively 18 . Statistical analysis Patient baseline BMI, waist circumference and body composition characteristics (SA, VA, and VSR) were summarized using the median and quartiles for continuous variables or frequency and proportion for categorical variables. Pearson correlation was computed to estimate the association between two continuous variables. Comparisons between subjects with and without recurrence (for locoregional, distant and all recurrences) were conducted using chi-squared test for categorical variables and a linear-model analysis-of-variance (ANOVA) test for continuous variables. The Fine and Gray competing risk models 28 were fitted to analyze time-to-event data, where the primary event of interest was recurrence (any, locoregional, and/or distant) with mortality treated as a competing risk. For graphical presentation of cumulative incidence curves, BMI, waist circumference, SA, VA, and VSR were categorized to high and low using the respective median values as the cut point. In each model, age at diagnosis and stage were included as covariates. The cumulative incidence functions were estimated for each type of event, and subdistribution hazard ratios were calculated to assess the effect of covariates on the risk of the primary event. All statistical analyses were performed using R version 4.3. The competing risk analyses were performed using the cmprsk package 29 . Two-sided P < 0.05 was considered statistically significant. Results Baseline characteristics One hundred sixty-two women were included in this study (Table 1 ). Median age at the time of diagnosis was 54 [Quartile: 47–62] years old. Most lesions were invasive mammary carcinoma (98%), there were 2 metaplastic carcinomas (1%), and 2 subjects presented with inflammatory carcinoma (1%). Thirty percent of patients presented with stage I disease, 48% with stage II, and 22% of patients had stage III disease. Subject outcomes During a median follow-up time of 7.1 [IQR 3.6–12.2] years, 56 (35%) patients developed recurrence; 31 patients (55%) presented with locoregional recurrence with or without distant recurrence at time of first recurrence, and 24 (43%) had distant recurrence only. Forty-four (27%) subjects died and, of these subjects, 35 (80%) were known to have disease recurrence. Median time to any recurrence was 1.9 [Quartiles: 1.5–3.0] years, with a median of 1.7 [Quartiles: 1.3–2.5] years for local recurrence and 2.1 [Quartiles: 1.8–3.7] years for distant recurrence. Table 1 Baseline patient characteristics N (%) Age Median 54, Quartiles (47–62) Race Caucasian 119 (73) Black 35 (22) Hispanic 4 (2) Asian 1 (1) Unknown 3 (2) Laterality Left 79 (49) Right 83 (51) Stage I 46 (30) II 74 (48) III 34 (22) Grade High 127 (79) Intermediate 29 (18) Low 5 (3) Lymphovascular invasion Yes 28 (18) No 102 (65) Unknown 26 (17) Body Mass Index (kg/m 2 ) 30 (26–35) Waist Circumference (cm) 102 (93–113) Subcutaneous Adiposity (cm 3 ) 232 (177–313) Visceral Adiposity (cm 3 ) 133 (91–203) Visceral-to-Subcutaneous Adiposity Ratio 0.56 (0.38–0.81) Continuous variables are summarized with median followed by quartiles in parenthesis. Anthropometric and body composition measurements All subjects had BMI data available, while waist circumference and body composition measurements were available in 109 subjects (67%). The correlations between anthropometric measures (BMI and waist circumference) and body composition measures (VA, SA, and VSR) are shown in Fig. 2 . SA strongly correlated with both BMI and waist circumference (r = 0.78 and 0.82, respectively, p < 0.001) (Fig. 2 A, B) while the correlation of VA with BMI and waist circumference was moderate (r = 0.43 and 0.62, respectively) (Fig. 2 C, D). In contrast, VSR was not correlated with BMI or waist circumference (r = -0.038 and 0.069, respectively) (Fig. 2 E, F). Analysis of variables predictive of recurrence BMI, waist circumference, VA, SA, and VSR were not significantly different on univariate analysis between patients with recurrence (locoregional, distant, or any recurrence) versus patients without recurrence (Table 2 ). Table 2 Anthropometric values (BMI, waist circumference) and body composition measurements (VA, SA, VSR) in patients with and without recurrence. No recurrence Locoregional recurrence Distant recurrence Any recurrence Body Mass Index (kg/m 2 ) 30 [26–35] 31 [28–37] 30 [26–32] 31 [27–35] Waist Circumference (cm) 102 [93–114] 103 [96–113] 105 [96–112] 104 [95–113] Subcutaneous Adiposity (cm 3 ) 229 [179–311] 262 [183–316] 235 [167–293] 246 [178–314] Visceral Adiposity (cm 3 ) 130 [79–178] 138 [91–238] 157 [92–212] 140 [91–222] Visceral-to-Subcutaneous Adiposity Ratio 0.5 [0.4–0.8] 0.6 [0.5–0.8] 0.6 [0.4–0.9] 0.6 [0.4–0.8] Higher VSR was associated with a significantly increased risk of distant recurrence (Table 3 ), with a subdistribution hazard ratio of 4.25 (p = 0.04, 95% CI: 1.06–17.02). When dichotomized by median (0.57), VSR was not significantly associated with risk for distant recurrence (Fig. 3 ). We did not observe anthropometric or body composition measures including BMI to be associated with overall recurrence risk (Fig. 3 , Supplemental Fig. S1 and S2 ). Table 3 Subdistribution Hazard Ratio and 95% confidence interval of competing risk models. Locoregional recurrence Distant recurrence Any recurrence Body Mass Index (kg/m 2 ) 1.01 (0.97–1.06) p = 0.55 0.98 (0.92–1.04) p = 0.49 0.99 (0.96–1.03) p = 0.85 Waist Circumference (cm) 1.00 (0.98–1.02) p = 0.98 0.99 (0.98–1.01) p = 0.60 0.99 (0.98–1.01) p = 0.71 Subcutaneous Adiposity (cm 3 ) 1.00 (1.00–1.00) P = 0.74 0.99 (0.99-1.00) p = 0.36 0.99 (0.99-1.00) p = 0.35 Visceral Adiposity (cm 3 ) 1.00 (0.99–1.01) P = 0.96 1.00 (0.99–1.01) p = 0.23 1.00 (0.99–1.01) p = 0.63 Visceral-to-Subcutaneous Adiposity Ratio 0.91 (0.26–3.21) P = 0.88 4.25 (1.06–17.02) p = 0.04 1.53 (0.64–3.62) p = 0.34 Discussion In patients with TNBC, we evaluated obesity and adiposity measures as biomarkers for recurrence risk. Despite a largely obese cohort, we demonstrated that VSR, uniquely differs from anthropometric and other body composition measures, and may be a valuable biomarker for recurrence risk in TNBC patients. We demonstrated that anthropometric measures (i.e. BMI and waist circumference) poorly correlate with VSR, and VSR may have potential prognostic ability to distinguish distant recurrence risk. We confirmed previously published data assessing the relationship between body composition assessment and anthropometric measures. A meta-analysis by Mouchti et al. found BMI and waist circumference to be strongly correlated with Magnetic Resonance Imaging-derived SA (r = 0.83–0.85) while the correlation with VA was less strong (r = 0.76–0.79), which is consistent with our data that shows a strong correlation between SA and anthropometric measures (r = 0.78 and 0.28 for BMI and waist circumference, respectively) and a moderate correlation with VA between BMI and waist circumference (r = 0.43 and 0.62, respectively) 30 . Work from Kaess et al. assessing VSR in the Framingham Heart Study cohort demonstrated a weak, positive correlation of VSR with BMI and waist circumference in women (r = 0.06 and 0.10, respectively) 31 . Our data also found that VSR is not correlated with BMI (r = -0.038) or waist circumference (r = 0.069), highlighting the validity of this body composition marker over anthropometric measures. Our data build on earlier reports that demonstrated increased risk of TNBC with elevated visceral adiposity 19 , and increased risk of cancer progression in BC patients with high visceral fat 32 , highlighting the importance of body composition assessments as biomarkers for disease recurrence through their association with transcriptome profiles 33 , and reinforcing the association of visceral or central obesity with cancer risk due to hyperinsulinemia 34 , 35 , chronic low-grade inflammation 33 and oxidative stress 36 . Our study was not able to reproduce the effect of VA on tumor recurrence, likely due to our small sample size and largely obese cohort. However, our data suggest that VSR, quantifying visceral versus subcutaneous adiposity, may be a more sensitive biomarker for disease recurrence. This is consistent with data evaluating obesity measures and their association with cardiometabolic risk factors, which has also demonstrated that VSR is more highly correlated with cardiometabolic risk factors than BMI and VA 31 . The increased sensitivity of VSR for both cancer recurrence risk and cardiovascular disease risk may be explained by the ability of VSR to simultaneously capture the positive association of visceral adipose tissue, as well as the inverse relation of subcutaneous adipose tissue with insulin resistance 18 . Accordingly, visceral fat is thought to cause hepatic insulin resistance via release of non-esterified fatty acids and inflammatory mediators in the portal venous system 37 , 38 in contrast to subcutaneous fat 39 . Insulin resistance or hyperinsulinemia is a known important risk factor for both cardiovascular disease 40 and TNBC risk 41 . Hyperinsulinemia drives cancer risk by (1) promoting cellular proliferation and neoangiogenesis, (2) overproducing reactive oxygen species which introduce mutagenesis and carcinogenesis, and (3) inhibiting apoptosis 42 , 43 . Interestingly, a prior study demonstrated a correlation between VSR and vascular endothelial growth factor (VEGF), a potent angiogenic factor, which supports the above hypothesis 44 . Our study has some limitations. First, our findings are restricted by a small sample size and skewed towards an obese Caucasian cohort related to the geographical location of our institution 45 . Validation of these findings with a larger sample size in a different geographic area with a wider range of body composition and a more diverse population is important. Data processing of a larger cohort may be facilitated by implementing artificial intelligence to assess body composition 46 . Next, patients included in our study were diagnosed and received treatment between 2004 and 2021, which means that many of our patients with locally advanced disease did not receive current standard-of-care treatment with immune checkpoint inhibition (ICI). Some reports suggest that the association between obesity and cancer progression may be altered in patients receiving ICI, and future studies should evaluate whether this is the case for TNBC patients 47 – 49 . Lastly, prospective studies are needed to assess the effect of lifestyle modifications on body composition and disease recurrence risk. Thiazolinediones, a class of insulin sensitizers, have been shown to expand SA while reducing VA, 50 resulting in improved insulin sensitivity 51 and thus may be a potential protective agent. Despite these limitations, we have shown that evaluating VSR may be an improved method of quantifying obesity compared to the current anthropometric standard and may predict recurrence risk in TNBC patients. While we present correlative evidence, future studies with increased power and more diverse patient populations may lead to using VSR not only as a prognostic factor but also to effect change using FDA-approved semaglutide products to meaningfully reduce patient body weight 52 . Conclusions The data in this study demonstrate that the ratio of visceral-to-subcutaneous adipose tissue is a potential prognostic factor for risk of distant recurrence risk in TNBC patients. Future work will validate this finding in a larger cohort and may focus on therapeutic strategies to improve this ratio by decreasing contribution of visceral fat to total body fat. Declarations Funding This study was supported by the National Cancer Institute of the National Institutes of Health under awards #R00CA201304 (M. Rafat), #P30CA068485 (A. B. Chakravarthy), and #P50CA098131 (A. B. Chakravarthy). Contributions Conceptualization: M.R., A.B.C., J.D.V., T.K.; Methodology: J.D.V., C.W., K.V.N., L.S., J.B., A.D.S., M.N.A-H., M.L.B.; Formal Analysis: J.D.V., C.W., L.S., T.K.; Resources: M.R., A.B.C.; Writing—original draft preparation: J.D.V.; Writing—review and editing: J.D.V., M.R., A.B.C.; Supervision: M.R., A.B.C., T.K. Ethics Declarations Conflict of Interest The authors have no conflicts of interest to declare. Ethical Approval The study adhered to the principles of the Declaration of Helsinki and approval was obtained from the Institutional Review Board of Vanderbilt University Medical Center. Consent to Participate Informed consent was obtained from all individual participants included in the study through enrollment on an IRB approved Breast Tissue Repository (BRE03103, https://clinicaltrials.gov/study/NCT00899301). References Almansour NM, Triple-Negative (2022) Breast Cancer: A Brief Review About Epidemiology, Risk Factors, Signaling Pathways, Treatment and Role of Artificial Intelligence. Front Mol Biosci 9 Dent R et al (2007) Triple-negative breast cancer: clinical features and patterns of recurrence. Clin Cancer Res 13:4429–4434 Zhang C et al (2015) Higher locoregional recurrence rate for triple-negative breast cancer following neoadjuvant chemotherapy, surgery and radiotherapy. Springerplus 4 Sherry AD et al (2020) Systemic Inflammation After Radiation Predicts Locoregional Recurrence, Progression, and Mortality in Stage II-III Triple-Negative Breast Cancer. Int J Radiat Oncol Biol Phys 108:268–276 Rafat M et al (2018) Macrophages Promote Circulating Tumor Cell-Mediated Local Recurrence following Radiotherapy in Immunosuppressed Patients. Cancer Res 78:4241–4252 Harborg S et al (2023) Obesity and breast cancer prognosis: pre-diagnostic anthropometric measures in relation to patient, tumor, and treatment characteristics. Cancer Metab 11 Bao PP et al (2016) Body mass index and weight change in relation to triple-negative breast cancer survival. Cancer Causes Control 27:229–236 Bonet C et al (2023) The association between body fatness and mortality among breast cancer survivors: results from a prospective cohort study. Eur J Epidemiol 38:545–557 TJ K et al (2013) Sex hormones and risk of breast cancer in premenopausal women: a collaborative reanalysis of individual participant data from seven prospective studies. Lancet Oncol 14:1009–1019 Crudele L, Piccinin E, Moschetta A (2021) Visceral Adiposity and Cancer: Role in Pathogenesis and Prognosis. Nutrients 13 McLaughlin T et al (2016) Adipose Cell Size and Regional Fat Deposition as Predictors of Metabolic Response to Overfeeding in Insulin-Resistant and Insulin-Sensitive Humans. Diabetes 65:1245–1254 Dai Q et al (2009) Oxidative stress, obesity, and breast cancer risk: Results from the Shanghai women’s health study. J Clin Oncol 27:2482–2488 Matsuda M, Shimomura I (2013) Increased oxidative stress in obesity: implications for metabolic syndrome, diabetes, hypertension, dyslipidemia, atherosclerosis, and cancer. Obes Res Clin Pract 7 Iyengar NM, Hudis CA, Dannenberg AJ (2013) Obesity and inflammation: new insights into breast cancer development and progression. Am Soc Clin Oncol Educ Book 33:46–51 DeNardo DG, Coussens LM (2007) Inflammation and breast cancer. Balancing immune response: crosstalk between adaptive and innate immune cells during breast cancer progression. Breast Cancer Res 9 Gunter MJ et al (2015) Circulating Adipokines and Inflammatory Markers and Postmenopausal Breast Cancer Risk. J Natl Cancer Inst 107 Macis D, Guerrieri-Gonzaga A, Gandini S (2014) Circulating adiponectin and breast cancer risk: a systematic review and meta-analysis. Int J Epidemiol 43:1226–1236 Hardy OT, Czech MP, Corvera S (2012) What causes the insulin resistance underlying obesity? Curr Opin Endocrinol Diabetes Obes 19:81–87 Aduse-Poku L et al (2022) Associations of Computed Tomography Image-Assessed Adiposity and Skeletal Muscles with Triple-Negative Breast Cancer. Cancers (Basel) 14 Chen H, liang, Ding A, Wang M (2016) li. Impact of central obesity on prognostic outcome of triple negative breast cancer in Chinese women. Springerplus 5 Pajares B et al (2013) Obesity and survival in operable breast cancer patients treated with adjuvant anthracyclines and taxanes according to pathological subtypes: a pooled analysis. Breast Cancer Res 15 Sparano JA et al (2012) Obesity at diagnosis is associated with inferior outcomes in hormone receptor-positive operable breast cancer. Cancer 118:5937–5946 Nag S et al Risk factors for the development of triple-negative breast cancer versus non-triple-negative breast cancer: a case-control study. (123AD) 10.1038/s41598-023-40443-8 Amin AB, E. S. G. F. et al (eds) (2017) AJCC Cancer Staging Manual. Springer, New York Kim SS et al (2019) Semiautomatic software for measurement of abdominal muscle and adipose areas using computed tomography: A STROBE-compliant article. Medicine 98 Abate N, Garg A, Coleman R, Grundy SM, Peshock RM (1997) Prediction of total subcutaneous abdominal, intraperitoneal, and retroperitoneal adipose tissue masses in men by a single axial magnetic resonance imaging slice. Am J Clin Nutr 65:403–408 Han TS, Kelly IE, Walsh K, Greene RME, Lean ME (1997) J. Relationship between volumes and areas from single transverse scans of intra-abdominal fat measured by magnetic resonance imaging. Int J Obes Relat Metab Disord 21:1161–1166 Fine JP, Gray RJ (1999) A Proportional Hazards Model for the Subdistribution of a Competing Risk. J Am Stat Assoc 94:496–509 Gray B Subdistribution Analysis of Competing Risks. R package version 2.2–10 2020. Available at : https://cran.r-project.org/package=cmprsk Mouchti S, Orliacq J, Reeves G, Chen Z (2023) Assessment of correlation between conventional anthropometric and imaging-derived measures of body fat composition: a systematic literature review and meta-analysis of observational studies. BMC Med Imaging 23 Kaess BM et al (2012) The ratio of visceral to subcutaneous fat, a metric of body fat distribution, is a unique correlate of cardiometabolic risk. Diabetologia 55:2622–2630 Wang W, Gao Y, Cui J (2022) High Visceral Fat in Female Breast Cancer Patients Correlates with the Risk of Progression after Adjuvant Chemotherapy. Nutr Cancer 74:2038–2048 Haffa M et al (2019) Transcriptome Profiling of Adipose Tissue Reveals Depot-Specific Metabolic Alterations Among Patients with Colorectal Cancer. J Clin Endocrinol Metab 104:5225–5237 Rose DP, Komninou D, Stephenson GD (2004) Obesity, adipocytokines, and insulin resistance in breast cancer. Obes Rev 5:153–165 Harvie M, Hooper L, Howell AH (2003) Central obesity and breast cancer risk: A systematic review. Obes Rev 4:157–173 Pou KM et al (2007) Visceral and subcutaneous adipose tissue volumes are cross-sectionally related to markers of inflammation and oxidative stress: the Framingham Heart Study. Circulation 116:1234–1241 Bjorntorp P (1990) Portal’ adipose tissue as a generator of risk factors for cardiovascular disease and diabetes. Arteriosclerosis: Official J Am Heart Association Inc 10:493–496 Després JP, Lemieux I (2006) Abdominal obesity and metabolic syndrome. Nature 2006 444:7121 444, 881–887 Booth AD et al (2018) Subcutaneous adipose tissue accumulation protects systemic glucose tolerance and muscle metabolism. Adipocyte 7:261 Fazio S, Mercurio V, Tibullo L, Fazio V, Affuso F (2024) Insulin resistance/hyperinsulinemia: an important cardiovascular risk factor that has long been underestimated. Front Cardiovasc Med 11:1380506 Zhang K, Chen L, Zheng H, Zeng Y (2022) Cytokines secreted from adipose tissues mediate tumor proliferation and metastasis in triple negative breast cancer. BMC Cancer 22 Arcidiacono B et al (2012) Insulin resistance and cancer risk: an overview of the pathogenetic mechanisms. Exp Diabetes Res (2012) Escudero CA et al (2017) Pro-angiogenic Role of Insulin: From Physiology to Pathology. Front Physiol 8 Himbert C et al (2019) Body Fatness, Adipose Tissue Compartments, and Biomarkers of Inflammation and Angiogenesis in Colorectal Cancer: The ColoCare Study. Cancer Epidemiol Biomarkers Prev 28:76–82 American Cancer Society, Cancer Statistics Center: Tennessee 2024. 2024, Atlanta, GA: American Cancer Society, Inc. Xu K et al (2023) AI Body Composition in Lung Cancer Screening: Added Value Beyond Lung Cancer Detection. Radiology 308 Pingili AK et al (2021) Immune checkpoint blockade reprograms systemic immune landscape and tumor microenvironment in obesity-associated breast cancer. Cell Rep 35 Wang Z et al (2019) Paradoxical effects of obesity on T cell function during tumor progression and PD-1 checkpoint blockade. Nat Med 25:141–151 Roccuzzo G et al (2023) Obesity and immune-checkpoint inhibitors in advanced melanoma: A meta-analysis of survival outcomes from clinical studies. Semin Cancer Biol 91:27–34 Miyazaki Y et al (2002) Effect of pioglitazone on abdominal fat distribution and insulin sensitivity in type 2 diabetic patients. J Clin Endocrinol Metab 87:2784–2791 Nakamura T et al (2001) Thiazolidinedione derivative improves fat distribution and multiple risk factors in subjects with visceral fat accumulation - Double-blind placebo-controlled trial. Diabetes Res Clin Pract 54:181–190 Wilding JPH et al (2021) Once-Weekly Semaglutide in Adults with Overweight or Obesity. N Engl J Med 384:989–1002 Additional Declarations No competing interests reported. Supplementary Files BodyCompositionSIfinal.docx Cite Share Download PDF Status: Published Journal Publication published 11 Mar, 2025 Read the published version in Breast Cancer Research and Treatment → Version 1 posted Editorial decision: Revision requested 09 Feb, 2025 Reviews received at journal 08 Feb, 2025 Reviews received at journal 30 Jan, 2025 Reviewers agreed at journal 30 Jan, 2025 Reviewers agreed at journal 30 Jan, 2025 Reviewers invited by journal 08 Dec, 2024 Editor assigned by journal 12 Nov, 2024 Submission checks completed at journal 12 Nov, 2024 First submitted to journal 12 Nov, 2024 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5437121","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":379753326,"identity":"f1e1a6d3-4a39-46a9-8485-f5c4a0bfe55e","order_by":0,"name":"Jill B. De Vis","email":"","orcid":"","institution":"Vanderbilt University Medical Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jill","middleName":"B. De","lastName":"Vis","suffix":""},{"id":379753327,"identity":"187f18ad-26bf-41d0-9644-f2377bc176ed","order_by":1,"name":"Cong Wang","email":"","orcid":"","institution":"Vanderbilt Epidemiology Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Cong","middleName":"","lastName":"Wang","suffix":""},{"id":379753328,"identity":"5a9787aa-2b50-4f7a-a9ed-3c0f2579155d","order_by":2,"name":"Kirsten V. Nguyen","email":"","orcid":"","institution":"Vanderbilt University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kirsten","middleName":"V.","lastName":"Nguyen","suffix":""},{"id":379753329,"identity":"fd5cbdf8-ffab-4af0-b96b-7303eee00543","order_by":3,"name":"Lili Sun","email":"","orcid":"","institution":"Vanderbilt University Medical Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lili","middleName":"","lastName":"Sun","suffix":""},{"id":379753330,"identity":"16c5a8e3-7593-4d71-97eb-f7450e60cd1f","order_by":4,"name":"Brigitte Jia","email":"","orcid":"","institution":"Vanderbilt University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Brigitte","middleName":"","lastName":"Jia","suffix":""},{"id":379753331,"identity":"b72b0c67-1e8a-4970-8716-b2a1af337fb7","order_by":5,"name":"Alexander D. Sherry","email":"","orcid":"","institution":"MD Anderson Cancer Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Alexander","middleName":"D.","lastName":"Sherry","suffix":""},{"id":379753332,"identity":"231c16c1-a19e-437c-911f-d97fa3398b3a","order_by":6,"name":"Mason N. Alford-Holloway","email":"","orcid":"","institution":"Vanderbilt University Medical Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mason","middleName":"N.","lastName":"Alford-Holloway","suffix":""},{"id":379753333,"identity":"6d171e12-8ce2-4489-aae6-ed44b97c856a","order_by":7,"name":"Meredith L. Balbach","email":"","orcid":"","institution":"Vanderbilt University Medical Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Meredith","middleName":"L.","lastName":"Balbach","suffix":""},{"id":379753334,"identity":"4800f5e5-0f14-43dd-839a-9d3b3d30ea88","order_by":8,"name":"Tatsuki Koyama","email":"","orcid":"","institution":"Vanderbilt University Medical Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Tatsuki","middleName":"","lastName":"Koyama","suffix":""},{"id":379753335,"identity":"6f3856bc-cb39-42dc-ab14-503894f3e9d2","order_by":9,"name":"A. Bapsi Chakravarthy","email":"","orcid":"","institution":"Vanderbilt-Ingram Cancer Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"A.","middleName":"Bapsi","lastName":"Chakravarthy","suffix":""},{"id":379753336,"identity":"db10df6e-c783-4ee4-bf5b-2aa0bdd398c2","order_by":10,"name":"Marjan Rafat","email":"data:image/png;base64,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","orcid":"","institution":"Vanderbilt University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Marjan","middleName":"","lastName":"Rafat","suffix":""}],"badges":[],"createdAt":"2024-11-12 07:23:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5437121/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5437121/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10549-025-07675-w","type":"published","date":"2025-03-11T15:57:26+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":71612775,"identity":"90a89e24-7504-4a7f-a2a4-6524b8639668","added_by":"auto","created_at":"2024-12-17 07:03:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":169019,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative computed tomography (CT) image to assess body composition. (\u003cstrong\u003eA\u003c/strong\u003e) CT image at the level of intervertebral disc lumbar vertebrae L2-L3. (\u003cstrong\u003eB\u003c/strong\u003e) Visual representation of the segmentation results with visceral fat in blue, subcutaneous fat in red, and muscle tissue in green.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5437121/v1/c6cc9484a4061b0910146b53.png"},{"id":71612774,"identity":"dd331f7f-a85d-4b91-92dc-535cad27bdc8","added_by":"auto","created_at":"2024-12-17 07:03:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":343268,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between body mass index, waist circumference, and body composition measurements (subcutaneous adiposity (SA), visceral adiposity (VA) and visceral-to-subcutaneous adiposity ratio (VSR). Scatter plots show SA (\u003cstrong\u003eA-B\u003c/strong\u003e), VA (\u003cstrong\u003eC-D\u003c/strong\u003e), and VSR (\u003cstrong\u003eE-F\u003c/strong\u003e) versus BMI and waist circumference with a simple regression line (blue) and 95% confidence interval in gray.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5437121/v1/308964fe498d1e92415c8c41.png"},{"id":71611985,"identity":"e0ea4b7d-219a-4ebd-852f-535498e818ef","added_by":"auto","created_at":"2024-12-17 06:55:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":126898,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative incidence of distant recurrence stratified based on median anthropometric measures or VSR. Distant recurrence over time is shown for (\u003cstrong\u003eA\u003c/strong\u003e) body mass index (BMI; blue line, BMI ≤ 30 kg/m\u003csup\u003e2\u003c/sup\u003e; orange line, BMI \u0026gt; 30 kg/m\u003csup\u003e2\u003c/sup\u003e), (\u003cstrong\u003eB\u003c/strong\u003e) waist circumference (WC; blue line, WC \u0026lt; 103 cm; orange line, WC ≥ 103 cm), and (\u003cstrong\u003eC\u003c/strong\u003e) visceral-to-subcutaneous adiposity ratio (VSR; blue line, VSR ≤ 0.57; orange line, VSR \u0026gt; 0.57). A non-significant separation of the curves can be noted for VSR. At risk subjects are indicated along the x-axis.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5437121/v1/6b68ab405d8daa4a353507c5.png"},{"id":78689103,"identity":"7073ed08-9f57-4901-bb1b-713d2e4b573e","added_by":"auto","created_at":"2025-03-17 16:11:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1236046,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5437121/v1/12893f86-c456-42ec-81d6-e1d1c3b6fc7f.pdf"},{"id":71611988,"identity":"1686972e-85a7-43db-aabc-43fbbe9b0537","added_by":"auto","created_at":"2024-12-17 06:55:48","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":413140,"visible":true,"origin":"","legend":"","description":"","filename":"BodyCompositionSIfinal.docx","url":"https://assets-eu.researchsquare.com/files/rs-5437121/v1/d56a3a8d407a76621b2434e7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Body composition as a novel biomarker of recurrence risk in patients with triple-negative breast cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer (BC) has a heterogeneous course that can largely be attributed to differences in clinical, histological, and molecular characteristics. About 15% of BC patients have triple-negative disease\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, characterized by the absence of estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2. Triple-negative breast cancer (TNBC) has a more aggressive course with increased risk of early recurrence, including distant recurrence\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e as well as locoregional recurrence\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, despite systemic treatment, surgery, and radiation. Currently, risk factors for recurrence of TNBC remain understudied. Therefore, TNBC would benefit from the identification of biomarkers for risk stratification and modulation.\u003c/p\u003e \u003cp\u003eObesity has been identified as a risk factor for both hormone receptor positive as well as TNBC occurrence \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, recurrence\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, and BC-related mortality\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Higher levels of circulating estrogens\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, insulin resistance\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, increased oxidative stress\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, chronic inflammation\u003csup\u003e\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e and changes in adipocytokines\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e have been attributed to these risks. Generally, anthropometric measures such as body mass index (BMI) or waist circumference that simultaneously capture visceral and subcutaneous adiposity (VA and SA) are used as biomarkers in epidemiologic studies investigating the effect of obesity on treatment outcomes. Whereas estrogen production, which is the main driver of recurrence risk in hormone receptor positive BC, is driven by both VA and SA, inflammation may be a more important factor in TNBC, which is largely caused by VA and has an inverse relation with SA\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Therefore, anthropometric measures may not accurately capture TNBC occurrence and outcome risk. Indeed, studies evaluating anthropometric measures as biomarkers in TNBC are conflicting with some studies reporting positive associations between obesity and TNBC occurrence\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e and TNBC outcome\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, while others have found negative associations\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. A study that evaluated both central obesity and BMI as a biomarker for TNBC occurrence found central obesity to be a valid biomarker, while BMI was not\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003c/sup\u003e suggesting that VA may be a more accurate biomarker in TNBC patients.\u003c/p\u003e \u003cp\u003eThe goal of this study was twofold: (1) to elucidate whether obesity is a predictive biomarker for TNBC recurrence, and (2) to identify measures of obesity that most reliably detect an increased risk of TNBC recurrence. We hypothesized that an elevated visceral-to-subcutaneous adiposity ratio (VSR), a measure of body composition, is associated with an increased risk of TNBC recurrence. This hypothesis was tested by examining the relationship between TNBC recurrence and anthropometric measures of obesity, BMI or waist circumference as compared to body composition measures, including VA, SA and VSR, to determine more accurate measures that can better predict patient outcomes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy subjects\u003c/p\u003e \u003cp\u003eImaging and clinical data was collected from patients who had consented to enroll on an IRB approved Breast Tissue Repository (BRE03103, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://clinicaltrials.gov/study/NCT00899301\u003c/span\u003e\u003cspan address=\"https://clinicaltrials.gov/study/NCT00899301\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The inclusion criteria for our specific research question were women, at least 18 years of age, with histologically confirmed, invasive, stage I-III TNBC\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, treated with standard-of-care treatment. Inclusion criteria also included patients for whom radiation was a component of management as both preclinical\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e as well as retrospective studies\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e have shown that radiation can increase the potential for inflammatory-associated recurrence risk. CT-simulation scans were used to determine measures of body composition through calculating VA, SA, and VSR.\u003c/p\u003e \u003cp\u003eData collection\u003c/p\u003e \u003cp\u003eBaseline characteristics including race, gender, age at diagnosis, and date of last follow-up or date of death were collected through electronic medical records (EMR) review. BC specifics including histology, hormone receptor status, human epidermal growth factor receptor 2 (HER2) status, pathologic stage, histologic grade, proliferative rate and lymphovascular invasion (LVI) were gathered. Radiation data included radiation dose to the breast or chest wall and regional lymph nodes, as well as dose to the tumor bed or scar and fractionation regimen. Recurrence was assessed as first site of recurrence being either locoregional with or without distant disease versus distant disease only. Time to recurrence and time to last follow-up were calculated as time from date of diagnosis to date of recurrence and date of last follow up or death, respectively.\u003c/p\u003e \u003cp\u003eObesity assessment\u003c/p\u003e \u003cp\u003eBody Mass Index (BMI) was retrieved from the EMR, and waist circumference and body composition were evaluated using abdominal CT images, if available. All measurements were made at the time of initiating RT. A semi-automatic open-source MATLAB-based (MathWorks Inc., Natick, Massachusetts) segmentation tool was used to retrieve quantitative measures of waist circumference, VA, SA, and muscle tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) from the CT images. In brief, the tool allows for selection of a Digital Imaging and Communications in Medicine (DICOM) image of interest after which the body circumference is detected, and the intra-abdominal cavity is delineated semi-automatically using active contouring with boundary detection. Subcutaneous fat, muscle and visceral fat are then detected using fuzzy c-means clustering, boundary detection and Hounsfield Units thresholds. Results of the tool were validated by the developers through comparison with manual measurements, Aquarius (TeraRecon, Inc., Durham, NC, USA) and ImageJ (National Institutes of Health, Bethesda, MD, USA), with good performance (intraclass correlation coefficients ranging from 0.854 to 0.996)\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. The above method does not allow for whole abdominal quantitative analysis but allows for single-level analysis which expanded our data collection from patients with early-stage TNBC disease who do not typically get staging CT scans. We focused on the intervertebral disc of lumbar vertebrae L2-L3, which has been shown to correlate best with total intra-abdominal fat\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. To reduce noise in measurements, we processed three adjacent imaging slices and averaged the obtained measurements. Then, the visceral-to-subcutaneous fat ratio (VSR\u0026thinsp;=\u0026thinsp;VA/SA) was calculated to reflect the direct and inverse relation of VA and SA, respectively\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003ePatient baseline BMI, waist circumference and body composition characteristics (SA, VA, and VSR) were summarized using the median and quartiles for continuous variables or frequency and proportion for categorical variables. Pearson correlation was computed to estimate the association between two continuous variables. Comparisons between subjects with and without recurrence (for locoregional, distant and all recurrences) were conducted using chi-squared test for categorical variables and a linear-model analysis-of-variance (ANOVA) test for continuous variables. The Fine and Gray competing risk models\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e were fitted to analyze time-to-event data, where the primary event of interest was recurrence (any, locoregional, and/or distant) with mortality treated as a competing risk. For graphical presentation of cumulative incidence curves, BMI, waist circumference, SA, VA, and VSR were categorized to high and low using the respective median values as the cut point. In each model, age at diagnosis and stage were included as covariates. The cumulative incidence functions were estimated for each type of event, and subdistribution hazard ratios were calculated to assess the effect of covariates on the risk of the primary event. All statistical analyses were performed using R version 4.3. The competing risk analyses were performed using the cmprsk package\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Two-sided P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eBaseline characteristics\u003c/p\u003e \u003cp\u003eOne hundred sixty-two women were included in this study (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Median age at the time of diagnosis was 54 [Quartile: 47\u0026ndash;62] years old. Most lesions were invasive mammary carcinoma (98%), there were 2 metaplastic carcinomas (1%), and 2 subjects presented with inflammatory carcinoma (1%). Thirty percent of patients presented with stage I disease, 48% with stage II, and 22% of patients had stage III disease.\u003c/p\u003e \u003cp\u003eSubject outcomes\u003c/p\u003e \u003cp\u003eDuring a median follow-up time of 7.1 [IQR 3.6\u0026ndash;12.2] years, 56 (35%) patients developed recurrence; 31 patients (55%) presented with locoregional recurrence with or without distant recurrence at time of first recurrence, and 24 (43%) had distant recurrence only. Forty-four (27%) subjects died and, of these subjects, 35 (80%) were known to have disease recurrence. Median time to any recurrence was 1.9 [Quartiles: 1.5\u0026ndash;3.0] years, with a median of 1.7 [Quartiles: 1.3\u0026ndash;2.5] years for local recurrence and 2.1 [Quartiles: 1.8\u0026ndash;3.7] years for distant recurrence.\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\u003eBaseline patient characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian 54, Quartiles (47\u0026ndash;62)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaucasian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119 (73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaterality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79 (49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83 (51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 (48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127 (79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphovascular invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102 (65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody Mass Index (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (26\u0026ndash;35)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist Circumference (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102 (93\u0026ndash;113)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubcutaneous Adiposity (cm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e232 (177\u0026ndash;313)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisceral Adiposity (cm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133 (91\u0026ndash;203)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisceral-to-Subcutaneous Adiposity Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.56 (0.38\u0026ndash;0.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eContinuous variables are summarized with median followed by quartiles in parenthesis.\u003c/p\u003e \u003cp\u003eAnthropometric and body composition measurements\u003c/p\u003e \u003cp\u003eAll subjects had BMI data available, while waist circumference and body composition measurements were available in 109 subjects (67%). The correlations between anthropometric measures (BMI and waist circumference) and body composition measures (VA, SA, and VSR) are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. SA strongly correlated with both BMI and waist circumference (r\u0026thinsp;=\u0026thinsp;0.78 and 0.82, respectively, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, B) while the correlation of VA with BMI and waist circumference was moderate (r\u0026thinsp;=\u0026thinsp;0.43 and 0.62, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, D). In contrast, VSR was not correlated with BMI or waist circumference (r = -0.038 and 0.069, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE, F).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAnalysis of variables predictive of recurrence\u003c/p\u003e \u003cp\u003eBMI, waist circumference, VA, SA, and VSR were not significantly different on univariate analysis between patients with recurrence (locoregional, distant, or any recurrence) versus patients without recurrence (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003eAnthropometric values (BMI, waist circumference) and body composition measurements (VA, SA, VSR) in patients with and without recurrence.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo recurrence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLocoregional recurrence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDistant recurrence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAny recurrence\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody Mass Index (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 [26\u0026ndash;35]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 [28\u0026ndash;37]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 [26\u0026ndash;32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31 [27\u0026ndash;35]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist Circumference\u003c/p\u003e \u003cp\u003e(cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102 [93\u0026ndash;114]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103 [96\u0026ndash;113]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105 [96\u0026ndash;112]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e104 [95\u0026ndash;113]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubcutaneous Adiposity\u003c/p\u003e \u003cp\u003e(cm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e229 [179\u0026ndash;311]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e262 [183\u0026ndash;316]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e235 [167\u0026ndash;293]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e246 [178\u0026ndash;314]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisceral Adiposity\u003c/p\u003e \u003cp\u003e(cm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e130 [79\u0026ndash;178]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138 [91\u0026ndash;238]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e157 [92\u0026ndash;212]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e140 [91\u0026ndash;222]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisceral-to-Subcutaneous Adiposity Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5 [0.4\u0026ndash;0.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6 [0.5\u0026ndash;0.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6 [0.4\u0026ndash;0.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6 [0.4\u0026ndash;0.8]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHigher VSR was associated with a significantly increased risk of distant recurrence (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), with a subdistribution hazard ratio of 4.25 (p\u0026thinsp;=\u0026thinsp;0.04, 95% CI: 1.06\u0026ndash;17.02). When dichotomized by median (0.57), VSR was not significantly associated with risk for distant recurrence (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). We did not observe anthropometric or body composition measures including BMI to be associated with overall recurrence risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cb\u003eSupplemental Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and S2\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSubdistribution Hazard Ratio and 95% confidence interval of competing risk models.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocoregional recurrence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDistant recurrence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAny recurrence\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody Mass Index\u003c/p\u003e \u003cp\u003e(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.97\u0026ndash;1.06)\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98 (0.92\u0026ndash;1.04)\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99 (0.96\u0026ndash;1.03) p\u0026thinsp;=\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist Circumference\u003c/p\u003e \u003cp\u003e(cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.98\u0026ndash;1.02)\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99 (0.98\u0026ndash;1.01)\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99 (0.98\u0026ndash;1.01) p\u0026thinsp;=\u0026thinsp;0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubcutaneous Adiposity\u003c/p\u003e \u003cp\u003e(cm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99 (0.99-1.00)\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99 (0.99-1.00) p\u0026thinsp;=\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisceral Adiposity\u003c/p\u003e \u003cp\u003e(cm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.99\u0026ndash;1.01)\u003c/p\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (0.99\u0026ndash;1.01)\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (0.99\u0026ndash;1.01)\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisceral-to-Subcutaneous Adiposity Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.91 (0.26\u0026ndash;3.21)\u003c/p\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.25 (1.06\u0026ndash;17.02)\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.53 (0.64\u0026ndash;3.62) p\u0026thinsp;=\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn patients with TNBC, we evaluated obesity and adiposity measures as biomarkers for recurrence risk. Despite a largely obese cohort, we demonstrated that VSR, uniquely differs from anthropometric and other body composition measures, and may be a valuable biomarker for recurrence risk in TNBC patients. We demonstrated that anthropometric measures (i.e. BMI and waist circumference) poorly correlate with VSR, and VSR may have potential prognostic ability to distinguish distant recurrence risk.\u003c/p\u003e \u003cp\u003eWe confirmed previously published data assessing the relationship between body composition assessment and anthropometric measures. A meta-analysis by Mouchti \u003cem\u003eet al.\u003c/em\u003e found BMI and waist circumference to be strongly correlated with Magnetic Resonance Imaging-derived SA (r\u0026thinsp;=\u0026thinsp;0.83\u0026ndash;0.85) while the correlation with VA was less strong (r\u0026thinsp;=\u0026thinsp;0.76\u0026ndash;0.79), which is consistent with our data that shows a strong correlation between SA and anthropometric measures (r\u0026thinsp;=\u0026thinsp;0.78 and 0.28 for BMI and waist circumference, respectively) and a moderate correlation with VA between BMI and waist circumference (r\u0026thinsp;=\u0026thinsp;0.43 and 0.62, respectively)\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Work from Kaess \u003cem\u003eet al.\u003c/em\u003e assessing VSR in the Framingham Heart Study cohort demonstrated a weak, positive correlation of VSR with BMI and waist circumference in women (r\u0026thinsp;=\u0026thinsp;0.06 and 0.10, respectively)\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Our data also found that VSR is not correlated with BMI (r = -0.038) or waist circumference (r\u0026thinsp;=\u0026thinsp;0.069), highlighting the validity of this body composition marker over anthropometric measures. Our data build on earlier reports that demonstrated increased risk of TNBC with elevated visceral adiposity\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, and increased risk of cancer progression in BC patients with high visceral fat\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, highlighting the importance of body composition assessments as biomarkers for disease recurrence through their association with transcriptome profiles\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, and reinforcing the association of visceral or central obesity with cancer risk due to hyperinsulinemia\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, chronic low-grade inflammation\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e and oxidative stress\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Our study was not able to reproduce the effect of VA on tumor recurrence, likely due to our small sample size and largely obese cohort. However, our data suggest that VSR, quantifying visceral versus subcutaneous adiposity, may be a more sensitive biomarker for disease recurrence. This is consistent with data evaluating obesity measures and their association with cardiometabolic risk factors, which has also demonstrated that VSR is more highly correlated with cardiometabolic risk factors than BMI and VA\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. The increased sensitivity of VSR for both cancer recurrence risk and cardiovascular disease risk may be explained by the ability of VSR to simultaneously capture the positive association of visceral adipose tissue, as well as the inverse relation of subcutaneous adipose tissue with insulin resistance\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Accordingly, visceral fat is thought to cause hepatic insulin resistance via release of non-esterified fatty acids and inflammatory mediators in the portal venous system\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e in contrast to subcutaneous fat\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Insulin resistance or hyperinsulinemia is a known important risk factor for both cardiovascular disease\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e and TNBC risk\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Hyperinsulinemia drives cancer risk by (1) promoting cellular proliferation and neoangiogenesis, (2) overproducing reactive oxygen species which introduce mutagenesis and carcinogenesis, and (3) inhibiting apoptosis\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Interestingly, a prior study demonstrated a correlation between VSR and vascular endothelial growth factor (VEGF), a potent angiogenic factor, which supports the above hypothesis\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur study has some limitations. First, our findings are restricted by a small sample size and skewed towards an obese Caucasian cohort related to the geographical location of our institution\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Validation of these findings with a larger sample size in a different geographic area with a wider range of body composition and a more diverse population is important. Data processing of a larger cohort may be facilitated by implementing artificial intelligence to assess body composition\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Next, patients included in our study were diagnosed and received treatment between 2004 and 2021, which means that many of our patients with locally advanced disease did not receive current standard-of-care treatment with immune checkpoint inhibition (ICI). Some reports suggest that the association between obesity and cancer progression may be altered in patients receiving ICI, and future studies should evaluate whether this is the case for TNBC patients\u003csup\u003e\u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Lastly, prospective studies are needed to assess the effect of lifestyle modifications on body composition and disease recurrence risk. Thiazolinediones, a class of insulin sensitizers, have been shown to expand SA while reducing VA,\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e resulting in improved insulin sensitivity\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e and thus may be a potential protective agent. Despite these limitations, we have shown that evaluating VSR may be an improved method of quantifying obesity compared to the current anthropometric standard and may predict recurrence risk in TNBC patients. While we present correlative evidence, future studies with increased power and more diverse patient populations may lead to using VSR not only as a prognostic factor but also to effect change using FDA-approved semaglutide products to meaningfully reduce patient body weight\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe data in this study demonstrate that the ratio of visceral-to-subcutaneous adipose tissue is a potential prognostic factor for risk of distant recurrence risk in TNBC patients. Future work will validate this finding in a larger cohort and may focus on therapeutic strategies to improve this ratio by decreasing contribution of visceral fat to total body fat.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Cancer Institute of the National Institutes of Health under awards #R00CA201304 (M. Rafat), #P30CA068485 (A. B. Chakravarthy), and #P50CA098131 (A. B. Chakravarthy).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: M.R., A.B.C., J.D.V., T.K.; Methodology: J.D.V., C.W., K.V.N., L.S., J.B., A.D.S., M.N.A-H., M.L.B.; Formal Analysis: J.D.V., C.W., L.S., T.K.; Resources: M.R., A.B.C.; Writing\u0026mdash;original draft preparation: J.D.V.; Writing\u0026mdash;review and editing: J.D.V., M.R., A.B.C.; Supervision: M.R., A.B.C., T.K.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConflict of Interest\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003eEthical Approval\u003c/p\u003e\n\u003cp\u003eThe study adhered to the principles of the Declaration of Helsinki and approval was obtained from the Institutional Review Board of Vanderbilt University Medical Center.\u003c/p\u003e\n\u003cp\u003eConsent to Participate\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study through enrollment on an IRB approved Breast Tissue Repository (BRE03103, https://clinicaltrials.gov/study/NCT00899301).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlmansour NM, Triple-Negative (2022) Breast Cancer: A Brief Review About Epidemiology, Risk Factors, Signaling Pathways, Treatment and Role of Artificial Intelligence. Front Mol Biosci 9\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDent R et al (2007) Triple-negative breast cancer: clinical features and patterns of recurrence. Clin Cancer Res 13:4429\u0026ndash;4434\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang C et al (2015) Higher locoregional recurrence rate for triple-negative breast cancer following neoadjuvant chemotherapy, surgery and radiotherapy. Springerplus 4\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSherry AD et al (2020) Systemic Inflammation After Radiation Predicts Locoregional Recurrence, Progression, and Mortality in Stage II-III Triple-Negative Breast Cancer. Int J Radiat Oncol Biol Phys 108:268\u0026ndash;276\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRafat M et al (2018) Macrophages Promote Circulating Tumor Cell-Mediated Local Recurrence following Radiotherapy in Immunosuppressed Patients. Cancer Res 78:4241\u0026ndash;4252\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarborg S et al (2023) Obesity and breast cancer prognosis: pre-diagnostic anthropometric measures in relation to patient, tumor, and treatment characteristics. Cancer Metab 11\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBao PP et al (2016) Body mass index and weight change in relation to triple-negative breast cancer survival. Cancer Causes Control 27:229\u0026ndash;236\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBonet C et al (2023) The association between body fatness and mortality among breast cancer survivors: results from a prospective cohort study. Eur J Epidemiol 38:545\u0026ndash;557\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTJ K et al (2013) Sex hormones and risk of breast cancer in premenopausal women: a collaborative reanalysis of individual participant data from seven prospective studies. Lancet Oncol 14:1009\u0026ndash;1019\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrudele L, Piccinin E, Moschetta A (2021) Visceral Adiposity and Cancer: Role in Pathogenesis and Prognosis. \u003cem\u003eNutrients\u003c/em\u003e 13\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcLaughlin T et al (2016) Adipose Cell Size and Regional Fat Deposition as Predictors of Metabolic Response to Overfeeding in Insulin-Resistant and Insulin-Sensitive Humans. Diabetes 65:1245\u0026ndash;1254\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDai Q et al (2009) Oxidative stress, obesity, and breast cancer risk: Results from the Shanghai women\u0026rsquo;s health study. J Clin Oncol 27:2482\u0026ndash;2488\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatsuda M, Shimomura I (2013) Increased oxidative stress in obesity: implications for metabolic syndrome, diabetes, hypertension, dyslipidemia, atherosclerosis, and cancer. Obes Res Clin Pract 7\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIyengar NM, Hudis CA, Dannenberg AJ (2013) Obesity and inflammation: new insights into breast cancer development and progression. Am Soc Clin Oncol Educ Book 33:46\u0026ndash;51\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeNardo DG, Coussens LM (2007) Inflammation and breast cancer. Balancing immune response: crosstalk between adaptive and innate immune cells during breast cancer progression. Breast Cancer Res 9\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGunter MJ et al (2015) Circulating Adipokines and Inflammatory Markers and Postmenopausal Breast Cancer Risk. J Natl Cancer Inst 107\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMacis D, Guerrieri-Gonzaga A, Gandini S (2014) Circulating adiponectin and breast cancer risk: a systematic review and meta-analysis. Int J Epidemiol 43:1226\u0026ndash;1236\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHardy OT, Czech MP, Corvera S (2012) What causes the insulin resistance underlying obesity? Curr Opin Endocrinol Diabetes Obes 19:81\u0026ndash;87\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAduse-Poku L et al (2022) Associations of Computed Tomography Image-Assessed Adiposity and Skeletal Muscles with Triple-Negative Breast Cancer. Cancers (Basel) 14\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen H, liang, Ding A, Wang M (2016) li. Impact of central obesity on prognostic outcome of triple negative breast cancer in Chinese women. \u003cem\u003eSpringerplus\u003c/em\u003e 5\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePajares B et al (2013) Obesity and survival in operable breast cancer patients treated with adjuvant anthracyclines and taxanes according to pathological subtypes: a pooled analysis. Breast Cancer Res 15\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSparano JA et al (2012) Obesity at diagnosis is associated with inferior outcomes in hormone receptor-positive operable breast cancer. Cancer 118:5937\u0026ndash;5946\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNag S et al Risk factors for the development of triple-negative breast cancer versus non-triple-negative breast cancer: a case-control study. (123AD) \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-023-40443-8\u003c/span\u003e\u003cspan address=\"10.1038/s41598-023-40443-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmin AB, E. S. G. F. et al (eds) (2017) AJCC Cancer Staging Manual. Springer, New York\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim SS et al (2019) Semiautomatic software for measurement of abdominal muscle and adipose areas using computed tomography: A STROBE-compliant article. Medicine 98\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbate N, Garg A, Coleman R, Grundy SM, Peshock RM (1997) Prediction of total subcutaneous abdominal, intraperitoneal, and retroperitoneal adipose tissue masses in men by a single axial magnetic resonance imaging slice. Am J Clin Nutr 65:403\u0026ndash;408\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan TS, Kelly IE, Walsh K, Greene RME, Lean ME (1997) J. Relationship between volumes and areas from single transverse scans of intra-abdominal fat measured by magnetic resonance imaging. Int J Obes Relat Metab Disord 21:1161\u0026ndash;1166\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFine JP, Gray RJ (1999) A Proportional Hazards Model for the Subdistribution of a Competing Risk. J Am Stat Assoc 94:496\u0026ndash;509\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGray B Subdistribution Analysis of Competing Risks. \u003cem\u003eR package version 2.2\u0026ndash;10 2020. Available at\u003c/em\u003e: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.r-project.org/package=cmprsk\u003c/span\u003e\u003cspan address=\"https://cran.r-project.org/package=cmprsk\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMouchti S, Orliacq J, Reeves G, Chen Z (2023) Assessment of correlation between conventional anthropometric and imaging-derived measures of body fat composition: a systematic literature review and meta-analysis of observational studies. BMC Med Imaging 23\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaess BM et al (2012) The ratio of visceral to subcutaneous fat, a metric of body fat distribution, is a unique correlate of cardiometabolic risk. Diabetologia 55:2622\u0026ndash;2630\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang W, Gao Y, Cui J (2022) High Visceral Fat in Female Breast Cancer Patients Correlates with the Risk of Progression after Adjuvant Chemotherapy. Nutr Cancer 74:2038\u0026ndash;2048\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaffa M et al (2019) Transcriptome Profiling of Adipose Tissue Reveals Depot-Specific Metabolic Alterations Among Patients with Colorectal Cancer. J Clin Endocrinol Metab 104:5225\u0026ndash;5237\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRose DP, Komninou D, Stephenson GD (2004) Obesity, adipocytokines, and insulin resistance in breast cancer. Obes Rev 5:153\u0026ndash;165\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarvie M, Hooper L, Howell AH (2003) Central obesity and breast cancer risk: A systematic review. Obes Rev 4:157\u0026ndash;173\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePou KM et al (2007) Visceral and subcutaneous adipose tissue volumes are cross-sectionally related to markers of inflammation and oxidative stress: the Framingham Heart Study. Circulation 116:1234\u0026ndash;1241\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBjorntorp P (1990) Portal\u0026rsquo; adipose tissue as a generator of risk factors for cardiovascular disease and diabetes. Arteriosclerosis: Official J Am Heart Association Inc 10:493\u0026ndash;496\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDespr\u0026eacute;s JP, Lemieux I (2006) Abdominal obesity and metabolic syndrome. \u003cem\u003eNature 2006 444:7121\u003c/em\u003e 444, 881\u0026ndash;887\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBooth AD et al (2018) Subcutaneous adipose tissue accumulation protects systemic glucose tolerance and muscle metabolism. Adipocyte 7:261\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFazio S, Mercurio V, Tibullo L, Fazio V, Affuso F (2024) Insulin resistance/hyperinsulinemia: an important cardiovascular risk factor that has long been underestimated. Front Cardiovasc Med 11:1380506\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang K, Chen L, Zheng H, Zeng Y (2022) Cytokines secreted from adipose tissues mediate tumor proliferation and metastasis in triple negative breast cancer. BMC Cancer 22\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArcidiacono B et al (2012) Insulin resistance and cancer risk: an overview of the pathogenetic mechanisms. \u003cem\u003eExp Diabetes Res\u003c/em\u003e (2012)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEscudero CA et al (2017) Pro-angiogenic Role of Insulin: From Physiology to Pathology. Front Physiol 8\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHimbert C et al (2019) Body Fatness, Adipose Tissue Compartments, and Biomarkers of Inflammation and Angiogenesis in Colorectal Cancer: The ColoCare Study. Cancer Epidemiol Biomarkers Prev 28:76\u0026ndash;82\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cem\u003eAmerican Cancer Society, Cancer Statistics Center: Tennessee 2024. 2024, Atlanta, GA: American Cancer Society, Inc.\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu K et al (2023) AI Body Composition in Lung Cancer Screening: Added Value Beyond Lung Cancer Detection. \u003cem\u003eRadiology\u003c/em\u003e 308\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePingili AK et al (2021) Immune checkpoint blockade reprograms systemic immune landscape and tumor microenvironment in obesity-associated breast cancer. Cell Rep 35\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Z et al (2019) Paradoxical effects of obesity on T cell function during tumor progression and PD-1 checkpoint blockade. Nat Med 25:141\u0026ndash;151\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoccuzzo G et al (2023) Obesity and immune-checkpoint inhibitors in advanced melanoma: A meta-analysis of survival outcomes from clinical studies. Semin Cancer Biol 91:27\u0026ndash;34\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiyazaki Y et al (2002) Effect of pioglitazone on abdominal fat distribution and insulin sensitivity in type 2 diabetic patients. J Clin Endocrinol Metab 87:2784\u0026ndash;2791\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNakamura T et al (2001) Thiazolidinedione derivative improves fat distribution and multiple risk factors in subjects with visceral fat accumulation - Double-blind placebo-controlled trial. Diabetes Res Clin Pract 54:181\u0026ndash;190\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilding JPH et al (2021) Once-Weekly Semaglutide in Adults with Overweight or Obesity. N Engl J Med 384:989\u0026ndash;1002\u003c/span\u003e\u003c/li\u003e\u003c/ol\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-and-treatment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brea","sideBox":"Learn more about [Breast Cancer Research and Treatment](https://www.springer.com/journal/10549)","snPcode":"10549","submissionUrl":"https://submission.nature.com/new-submission/10549/3","title":"Breast Cancer Research and Treatment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Prognostic markers, visceral-to-subcutaneous adiposity ratio, triple-negative breast cancer","lastPublishedDoi":"10.21203/rs.3.rs-5437121/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5437121/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground and Hypothesis\u003c/p\u003e \u003cp\u003eTriple-negative breast cancer (TNBC) patients are at increased risk for recurrence compared to other subtypes of breast cancer. Previous evidence showed that adiposity may contribute to worsened cancer control. Current measures of obesity, such as body-mass index (BMI), are poor surrogates of adiposity, while visceral-to-subcutaneous adiposity ratio (VSR), which can be measured from routine computed tomography (CT) imaging, is a direct adiposity measure. We hypothesized that VSR is a stronger predictor of recurrence compared with BMI in patients with TNBC.\u003c/p\u003e \u003cp\u003eMaterials and Methods\u003c/p\u003e \u003cp\u003eThis study includes 162 women with stage I-III TNBC who completed standard of care therapy. Measures of body composition, including VSR, visceral adiposity (VA), and subcutaneous adiposity (SA), were estimated using a semi-automated quantitative imaging tool on CT images of the abdomen at the level of L2-L3. Anthropometric measures included BMI and waist circumference and were obtained from CT images. Associations of adiposity measures and recurrence risk were assessed using Fine and Gray competing risk models with death as a competing risk and age at diagnosis and clinical disease stage as covariates.\u003c/p\u003e \u003cp\u003eResults\u003c/p\u003e \u003cp\u003eDuring a median follow-up time of 3.6 years, 55 patients had recurrence. The median BMI at baseline was 30.2 [Quartiles: 26.3\u0026ndash;35.2]. Body composition was not associated with overall or locoregional recurrence. VSR was significantly associated with an increased risk of distant recurrence, with a subdistribution hazard ratio of 4.25 (95% CI: 1.06\u0026ndash;17.02), p\u0026thinsp;=\u0026thinsp;0.041. By contrast, BMI was not associated with any recurrence risk.\u003c/p\u003e \u003cp\u003eConclusion\u003c/p\u003e \u003cp\u003eConsistent with our hypothesis, VSR was associated with a significant risk of distant recurrence and therefore may be a prognostic biomarker. Future directions include interventions targeting VSR reduction among patients with TNBC and VSR-directed therapy modulation.\u003c/p\u003e","manuscriptTitle":"Body composition as a novel biomarker of recurrence risk in patients with triple-negative breast cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-17 06:55:43","doi":"10.21203/rs.3.rs-5437121/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-02-09T18:24:02+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-08T06:58:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-30T20:58:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"220462131604658043495703802768935302715","date":"2025-01-30T14:48:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"68515091519407696175000469306765881565","date":"2025-01-30T05:41:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-12-08T18:41:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-13T02:01:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-13T02:01:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Breast Cancer Research and Treatment","date":"2024-11-12T07:21:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"breast-cancer-research-and-treatment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brea","sideBox":"Learn more about [Breast Cancer Research and Treatment](https://www.springer.com/journal/10549)","snPcode":"10549","submissionUrl":"https://submission.nature.com/new-submission/10549/3","title":"Breast Cancer Research and Treatment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"de0b6b51-61ff-4722-8ea8-488626977147","owner":[],"postedDate":"December 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-17T16:05:41+00:00","versionOfRecord":{"articleIdentity":"rs-5437121","link":"https://doi.org/10.1007/s10549-025-07675-w","journal":{"identity":"breast-cancer-research-and-treatment","isVorOnly":false,"title":"Breast Cancer Research and Treatment"},"publishedOn":"2025-03-11 15:57:26","publishedOnDateReadable":"March 11th, 2025"},"versionCreatedAt":"2024-12-17 06:55:43","video":"","vorDoi":"10.1007/s10549-025-07675-w","vorDoiUrl":"https://doi.org/10.1007/s10549-025-07675-w","workflowStages":[]},"version":"v1","identity":"rs-5437121","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5437121","identity":"rs-5437121","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}