Circulating C-reactive protein levels as a prognostic biomarker in breast cancer across body mass index groups

Circulating C-reactive protein levels as a prognostic biomarker in breast cancer across body mass index groups | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Circulating C-reactive protein levels as a prognostic biomarker in breast cancer across body mass index groups Jonas Busk Holm, Emma Baggesen, Deirdre Cronin-Fenton, Jan Frystyk, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3996677/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Jun, 2024 Read the published version in Scientific Reports → Version 1 posted You are reading this latest preprint version Abstract Purpose Obesity and systemic inflammation are associated with breast cancer (BC) outcomes. Systemic inflammation is increased in obesity. We examined the association between C-reactive protein (CRP) and disease-free survival (DFS) and overall survival (OS) overall, and according to body mass index (BMI). Methods We assembled a cohort of women with BC (stage I-III) seen at Aarhus University Hospital between 2010 and 2020 who donated blood at BC diagnosis (N = 2,673). CRP levels were measured and divided into quartiles. We followed patients from surgery to recurrence, contralateral BC, other malignancy, death, emigration, or end-of-follow-up. We used Cox regression to estimate hazard ratios (HRs) with 95% confidence intervals (95%CIs) to compare outcomes across CRP quartiles, overall and stratified by BMI (normal-weight (18.5 ≤ BMI < 25 kg/m 2 ), overweight (25 ≤ BMI < 30 kg/m 2 ), and obesity (BMI ≥ 30 kg/m 2 )). Results During follow-up, 368 events (212 recurrences, 38 contralateral BCs, and 118 deaths) occurred (median follow-up 5.55 years). For DFS, high CRP (CRP ≥ 3.24 mg/L) was associated with an increased risk of events (HR adj :1.58 [95%CI = 1.12–2.24]). In BMI-stratified analyses, high CRP was associated with elevated risk of events in normal-weight and overweight (HR adj :1.70 [95%CI = 1.09–2.66]; HR adj :1.75 [95%CI = 1.08–2.86]), but in obesity, the estimate was less precise (HR adj :1.73 [95%CI = 0.78–3.83]). For OS, high CRP was associated with increased risk of death (HR adj :2.47 [95%CI = 1.62–3.76]). The association was strong in normal-weight and overweight (HR adj :3.66 [95%CI = 1.95–6.87]; HR adj :1.92 [95%CI = 1.06–3.46]), but less clear in obesity (HR adj :1.40 [95%CI = 0.64–3.09]). Conclusion High CRP levels at BC diagnosis were associated with inferior prognosis in early BC irrespective of BMI, although less clear in patients with obesity. breast cancer obesity body mass index inflammation C-reactive protein prognosis Figures Figure 1 Figure 2 Figure 3 Introduction In 2020, 2,3 million women were diagnosed with breast cancer (BC) globally, making it the most prevalent cancer type (excluding non-melanoma skin cancer) in the world [ 1 ]. Despite 5-year survival rates approaching 90% in North America for patients with BC, nearly 700,000 women died from BC in 2020 worldwide [ 1 , 2 ]. Alongside, the prevalence of obesity (defined as a body mass index (BMI) ≥ 30 kg/m 2 ) increased excessively worldwide, rising from 7–16% among women between 1975 and 2016 [ 3 ]. Obesity is associated with an increased risk of developing at least 15 types of cancer, including postmenopausal BC [ 4 ]. Also, obesity is a prognostic disadvantage and is associated with shorter disease-free survival (DFS) and overall survival (OS) in BC [ 5 , 6 ]. Inflammation is a hallmark of cancer [ 7 ] and systemic inflammation is associated with poor BC prognosis [ 8 , 9 ]. Obesity is associated with low-grade inflammation [ 10 ] and elevated levels of C-reactive protein (CRP) [ 11 ], also among BC patients [ 12 ]. Thus, both obesity and systemic inflammation are associated with inferior BC outcomes. Yet, it is not clear if BC patients with obesity and high levels of systemic inflammation have a poorer prognosis compared with patients with obesity and lower levels of systemic inflammation. CRP is an acute-phase protein released from the liver upon stimulation from cytokines such as interleukin 6 [ 13 ]. CRP is part of the inflammatory cascade and a marker of systemic inflammation [ 14 , 15 ]. CRP levels are increased in people with obesity compared with their normal weight counterparts, and in cancer patients compared with healthy controls or patients with benign diseases [ 16 ]. A systematic review by Savioli et al concluded that high pre-operative CRP levels were associated with an increased risk of BC-specific mortality and all-cause mortality [ 17 ]. Likewise, in a meta-analysis from 2011, Han et al reported an association between elevated CRP levels and lower OS and DFS [ 18 ]. A meta-analysis by Mikkelsen et al found that high CRP was an indicator of poor prognosis in metastatic BC, but the prognostic value in non-metastatic early BC could not be confirmed [ 19 ]. As such, CRP levels may be prognostic in BC but only three studies have investigated this relationship across BMI groups, and they reported conflicting results [ 20 – 22 ]. Therefore, we investigated the prognostic potential of CRP in BC patients according to BMI groups. We hypothesized that higher circulating CRP levels were associated with poorer BC prognosis and that such an association was most pronounced in patients with obesity. Materials and methods Data sources All data were merged through a unique identification number for each patient, linking all data with 100% accuracy. All patients with BC treated in Denmark are registered in the Danish Breast Cancer Group (DBCG) database [ 23 ]. The completeness of the DBCG database exceeds 95% [ 24 ]. From DBCG and through systemic investigation of medical records, we retrieved baseline data concerning patient-, tumor-, and treatment characteristics. All variables from DBCG were retrieved from “The Danish Clinical Quality Program - National Clinical Registries” (RKKP), which constitutes the infrastructure of the Danish clinical quality registries and the Danish Multidisciplinary Cancer Groups (DMCG) [ 25 ]. Information on emigration was retrieved through RKKP from the Civil Registration System [ 26 ]. Information on comorbidities was retrieved through RKKP from the National Patient Registry [ 27 ]. Information on height, weight, and thereof BMI was extracted from both medical records and the Danish Anesthesia Database (DAD) [ 28 ]. BMI was obtained by merging data from the medical records and the DAD. Regarding follow-up data, we extracted information based on a prespecified codebook. We reviewed all patient electronic medical records, which included pathological reports and digital imaging, to register all recurrences, contralateral BCs, other malignancies, and deaths. Study population Our study cohort was women diagnosed with stage I-III BC between 2010 and 2020, who were referred to the Department of Plastic and Breast Surgery, Aarhus University Hospital (AUH), for primary BC. At this department, all patients with BC were invited to donate blood for future research to the Regional Bio- and Genome Bank Denmark (RBGB) [ 29 ] at the time of BC diagnosis. The blood was drawn between March 2010 and August 2020, median of seven days (interquartile range (IQR): 6–11 days) after the primary invasive BC diagnosis before breast surgery. Patients who received neoadjuvant systemic treatment were excluded from the analyses, as data on tumor characteristics are registered after neoadjuvant treatment and differ substantially from patients receiving up-front breast surgery. The final study population consisted of 2,673 patients, as illustrated in Fig. 1 . C-reactive protein analyses After the blood draw, serum was separated and subsequently stored at -80 o C at the RGBG. In late 2020, serum samples were identified and released for analyses at the Department of Clinical Biochemistry. The CRP levels were analyzed with a high-sensitivity CRP (hs-CRP) test (Siemens ADVIA® Chemistry XPT system with “ADVIA® Chemistry CardioPhase™ High Sensitivity C‑Reactive Protein (hsCRP)-reagents”) [ 30 ]. The lowest detectable level was 0.2 mg/L. Definitions of analytic variables CRP levels were categorised in quartiles (CRP-Q1: \(\le\) 0.59 mg/L, CRP-Q2: 0.6–1.34 mg/L, CRP-Q3: 1.35–3.23 mg/L, CRP-Q4: \(\ge\) 3.24 mg/L). In the statistical analyses, the lowest CRP quartile (CRP-Q1) served as the reference. It should be noted that CRP below 3 mg/L is considered to be within the normal range [ 31 ]. BMI was grouped according to the World Health Organization (WHO) definitions [ 32 ]: underweight (BMI < 18.5 kg/m 2 ), normal-weight (18.5 ≤ BMI < 25 kg/m 2 ), overweight (25 ≤ BMI < 30 kg/m 2 ), and obesity (BMI ≥ 30 kg/m 2 ). The closest registered BMI measurement from the date of the blood sample was used. Registrations within 182 days before or after blood draw were included (median same date as blood draw, IQR: 0–2 days after date of blood draw). Age and menopausal status (defined according to DBCG guidelines) refer to the time of primary invasive BC diagnosis. For tumor size and nodal status, we categorized patients in groups according to the American Joint Committee on Cancer Staging 8th edition [ 33 ]. The histological grade was classified using the standardization from the Nottingham Group [ 33 ]. The histological classification followed the WHO classification of breast tumors 3rd Edition [ 34 ]. Tumors without registration of invasive ductal or lobular carcinoma were categorized as “Other.” Estrogen receptor (ER) status was classified as “Negative,” if tumor cells showed no ER expression, and “Positive” if 1% or more of the tumor cells expressed ER. Human Epidermal Growth Factor Receptor 2 (HER2) expression was defined as either “Positive” or “Negative” through a combination of immunohistochemistry and Fluorescence In Situ Hybridization (FISH)-ratio. The type of breast surgery was categorized as “mastectomy” (including patients with mastectomy after lumpectomy) or “lumpectomy,” based on the final surgery for the primary BC. Adjuvant systemic treatment (endocrine therapy, HER2-targeted treatment, and chemotherapy) and radiotherapy were handled as intention-to-treat variables according to DBCG protocols. Patients registered as treated with neoadjuvant systemic treatment by surgeons or pathologists were classified as “Neoadjuvant”. Recurrence refers to any recurrent invasive BC in the breast, lymph nodes, or elsewhere in the body (apart from the contralateral breast) ≥ 3 months after the final primary surgery date (defined as the last date of surgery for the primary BC). The hierarchy for recurrence date was whatever came first in stating a recurrence in the pathological report, clinical charts, or imaging information. When defining the recurrence as local, regional, or distant, we followed the clinical guidelines from DMCG [ 35 ]. Registration of malignancy in the contralateral breast in the pathological report after final surgery was classified as contralateral BC. Other malignancies apart from non-melanoma skin cancer in the pathological report and death were registered, too. Follow-up and statistical analysis Disease-free-survival (DFS). Follow-up for DFS began on the date of final primary surgery and continued until the first of the following: BC recurrence, contralateral BC, death, other malignancy, emigration, or end-of-follow-up (15th November 2021). We treated BC recurrence, contralateral BC, and death as events. We censored patients at other malignancies, emigration, or end-of-follow-up. However, if recurrence, contralateral BC, or death occurred within 30 days after diagnosis of other malignancy, the event was included in the analyses. Overall survival (OS) . Follow-up for all-cause mortality began at the date of final primary surgery and continued until the first of any death, emigration, or end-of-follow-up (15th November 2021). We treated any death as an event. We censored patients at emigration or end-of-follow-up. We calculated person-years, number of events, and incidence rate per 1000 person-years (with a 95% confidence interval (95%CI)) for each CRP quartile. We used Cox regression models to estimate crude and adjusted hazard ratios (HRs) with 95% CIs for DFS and OS. Patients were followed for a maximum of 10 years in the regression models. We adjusted for patient-, tumor-, and treatment characteristics in the adjusted analysis. Only patients with complete data in all regressed variables were included (N = 2,485). We included the following variables: age (continuous), menopausal status (dichotomous), comorbidities (Charlson Comorbidity Index (CCI)) (categorical), BMI (categorical), histological grade (categorical), histological classification (categorical), tumor size (categorical), nodal status (categorical), ER status (dichotomous), HER2 status (dichotomous), surgery type (dichotomous), intended adjuvant systemic treatment (dichotomous), and intended adjuvant radiotherapy (dichotomous). To explore whether the association between CRP and outcomes differed across BMI groups, we performed DFS and OS analyses stratified for BMI groups as described above. In the stratified analyses, we created CRP quartiles within each BMI group. We adjusted for patient characteristics in the adjusted model. In the stratified analyses, patients with underweight were excluded. In the stratified analyses, we also presented Aalen-Johansen estimates on DFS (events: BC recurrence, contralateral BC, and death; competing risks: other malignancy; censoring points: emigration and end-of-follow-up) and Kaplan-Meier estimates on OS (events: death; censoring points: emigration and end-of-follow-up). Results Our cohort included 2,673 patients with a median age of 62 years at BC diagnosis (see Table 1). The median BMI was lowest in CRP-Q1 (BMI=22.45 kg/m 2 ) and highest in CRP-Q4 (BMI=28.36 kg/m 2 ). In CRP-Q4, more patients were postmenopausal, had higher CCI scores, and larger tumors compared to CRP-Q1. Chemotherapy was more often administered to patients in the CRP-Q1 compared with CRP-Q4. In total, 64 patients (2.39%) had underweight, 1,265 (47.33%) had normal-weight, 818 (30.60%) had overweight, and 486 (18.18%) had obesity. Table 1. Descriptive characteristics of women with breast cancer stage I-III referred to the Department of Plastic and Breast Surgery, Aarhus University Hospital, Denmark for primary breast cancer surgery. Patients were diagnosed with breast cancer and donated blood samples for future research between 2010 and 2020. Total N=2,673 CRP-Q1 0.59 mg/L N=664 CRP-Q2 0.6-1.34 mg/L N=676 CRP-Q3 1.35-3.23 mg/L N=675 CRP-Q4 3.24 mg/L N=658 Age, median (IQR) 62 (52-69) 57 (49-66) 63 (52.5-70) 64 (55-70) 63.5 (55-70) Age (years), categories < 50 50-59 60-69 70 452 (16.91%) 661 (24.73%) 924 (34.57%) 636 (23.79%) 185 (27.86%) 179 (26.96%) 190 (28.61%) 110 (16.57%) 100 (14.79%) 168 (24.85%) 235 (34.76%) 173 (25.59%) 81 (12.00%) 163 (24.15%) 258 (38.22%) 173 (25.63%) 86 (13.07%) 151 (22.95%) 241 (36.63%) 180 (27.36%) Body Mass Index (kg/m 2 ), median (IQR) 24.95 (22.31-28.39) 22.45 (20.74-24.52) 24.49 (22.31-27.10) 25.79 (23.23-29.01) 28.36 (24.84-33.20) Body Mass Index, categories (kg/m 2 ) Underweight < 18.5 Normal-weight 18.5 to < 25 Overweight 25 ≤ to < 30 Obesity ≥ 30 Missing 64 (2.39%) 1265 (47.33%) 818 (30.60%) 486 (18.18%) 40 (1.50%) 31 (4.67%) 488 (73.49%) 118 (17.77%) 19 (2.86%) 8 (1.20%) 16 (2.37%) 348 (51.48%) 235 (34.76%) 66 (9.76%) 11 (1.63%) 6 (0.89%) 269 (39.85%) 255 (37.78%) 136 (20.15%) 9 (1.33%) 11 (1.67%) 160 (24.32%) 210 (31.91%) 265 (40.27%) 12 (1.82%) Menopausal status Premenopausal Postmenopausal Missing 598 (22.37%) 2046 (76.54%) 29 (1.08%) 219 (32.98%) 433 (65.21%) 12 (1.81%) 136 (20.12%) 532 (78.70%) 8 (1.18%) 126 (18.67%) 543 (80.44%) 6 (0.89%) 117 (17.78%) 538 (81.76%) 3 (0.46%) Charlson Comorbidity Index 0 1-2 (mild) 3 (moderate/severe) Missing 346 (12.94%) 1885 (70.52%) 442 (16.54%) 0 (0%) 93 (14.01%) 505 (76.05%) 66 (9.94%) 0 (0%) 91 (13.46%) 488 (72.19%) 97 (14.35%) 0 (0%) 88 (13.04%) 464 (68.74%) 123 (18.22%) 0 (0%) 74 (11.25%) 428 (65.05%) 156 (23.71%) 0 (0%) Tumor size 0-20 mm 21-50 mm >50 mm Missing 1889 (70.67%) 723 (27.05%) 56 (2.10%) 5 (0.19%) 494 (74.40%) 157 (23.64%) 12 (1.81%) 1 (0.15%) 481 (71.15%) 177 (26.18%) 17 (2.51%) 1 (0.15%) 467 (69.19%) 195 (28.89%) 11 (1.63%) 2 (0.30%) 447 (67.93%) 194 (29.48%) 16 (2.43%) 1 (0.15%) Lymph node metastases 0 1-3 4-9 10 Missing 1656 (61.95%) 734 (27.46%) 180 (6.73%) 82 (3.07%) 21 (0.79%) 415 (62.50%) 178 (26.81%) 46 (6.93%) 21 (3.16%) 4 (0.60%) 417 (61.69%) 187 (27.66%) 44 (6.51%) 20 (2.96%) 8 (1.18%) 423 (62.67%) 190 (28.15%) 39 (5.78%) 20 (2.96%) 3 (0.44%) 401 (60.94%) 179 (27.20%) 51 (7.75%) 21 (3.19%) 6 (0.91%) Histological classification Ductal Lobular Other Missing 2008 (75.12%) 331 (12.38%) 334 (12.50%) 0 (0%) 497 (74.85%) 90 (13.55%) 77 (11.60%) 0 (0%) 522 (77.22%) 77 (11.39%) 77 (11.39%) 0 (0%) 493 (73.04%) 93 (13.78%) 89 (13.19%) 0 (0%) 496 (75.38%) 71 (10.79%) 91 (13.83%) 0 (0%) Histological grade N/A Grade 1 Grade 2 Grade 3 Missing 164 (6.14%) 608 (22.75%) 1218 (45.57%) 635 (23.76%) 48 (1.80%) 39 (5.87%) 157 (23.64%) 285 (42.92%) 167 (25.15%) 16 (2.41%) 39 (5.77%) 154 (22.78%) 318 (47.04%) 155 (22.93%) 10 (1.48%) 37 (5.48%) 140 (20.74%) 311 (46.07%) 171 (25.33%) 16 (2.37%) 49 (7.45%) 157 (23.86%) 304 (46.20%) 142 (21.58%) 6 (0.91%) ER status (% positive cells) 0% (negative) 1-100% (positive) Missing 278 (10.40%) 2380 (89.04%) 15 (0.56%) 70 (10.54%) 587 (88.40%) 7 (1.05%) 62 (9.17%) 612 (90.53%) 2 (0.30%) 81 (12.00%) 591 (87.56%) 3 (0.44%) 65 (9.88%) 590 (89.67%) 3 (0.45%) HER2 status Negative Positive Missing 2336 (87.39%) 282 (10.55%) 55 (2.06%) 566 (85.24%) 81 (12.20%) 17 (2.56%) 598 (88.46%) 67 (9.91%) 11 (1.63%) 590 (87.41%) 71 (10.52%) 14 (2.07%) 582 (88.45%) 63 (9.57%) 13 (1.98%) Final primary surgery a Mastectomy Lumpectomy Missing 881 (32.96%) 1778 (66.52%) 14 (0.52%) 231 (34.79%) 426 (64.16%) 7 (1.05%) 223 (32.99%) 450 (66.57%) 3 (0.44%) 222 (32.89%) 450 (66.67%) 3 (0.44%) 205 (31.16%) 452 (68.69%) 1 (0.15%) Adjuvant radiotherapy b No Yes Missing 516 (19.30%) 2078 (77.74%) 79 (2.96%) 123 (18.52%) 517 (77.86%) 24 (3.61%) 139 (20.56%) 517 (76.48%) 20 (2.96%) 125 (18.52%) 535 (79.26%) 15 (2.22%) 129 (19.60%) 509 (77.36%) 20 (3.04%) Endocrine therapy b No Yes Missing 512 (19.15%) 2082 (77.89%) 79 (2.96%) 130 (19.58%) 510 (76.81%) 24 (3.61%) 123 (18.20%) 533 (78.85%) 20 (2.96%) 132 (19.56%) 528 (78.22%) 15 (2.22%) 127 (19.30%) 511 (77.66%) 20 (3.04%) Anti-HER2 treatment b No Yes Missing 2316 (86.64%) 282 (10.55%) 75 (2.81%) 563 (84.79%) 81 (12.20%) 20 (3.01%) 589 (87.13%) 67 (9.91%) 20 (2.96%) 589 (87.26%) 71 (10.52%) 15 (2.22%) 575 (87.39%) 63 (9.57%) 20 (3.04%) Adjuvant chemotherapy b No Yes Missing 1287 (48.15%) 1307 (48.90%) 79 (2.96%) 267 (40.21%) 373 (56.17%) 24 (3.61%) 341 (50.44%) 315 (46.60%) 20 (2.96%) 347 (51.41%) 313 (46.37%) 15 (2.22%) 332 (50.46%) 306 (46.50%) 20 (3.04%) CRP C-reactive protein; Q1 Quartile 1; IQR Interquartile range; N/A Not applicable; ER Estrogen receptor; HER2 Human Epidermal Growth Factor Receptor 2. a: Defined as the last breast surgery procedure for the primary breast cancer. b: All systemic treatment variables and radiotherapy are intention-to-treat variables based on protocol allocation according to the Danish Breast Cancer Group. In DFS analyses, 368 clinical events occurred over 14,962 person-years (median follow-up time 5.55 years). In the mortality analyses, 298 deaths were recorded during 15,803 person-years (median follow-up time=6.02 years). Table 2 presents the estimated DFS hazard ratios across CRP quartiles. In total, 70 events occurred in 3,871 person-years in CRP-Q1, 83 events in 3,854 person-years in CRP-Q2, 100 events in 3,777 person-years in CRP-Q3, and 115 events occurred during 3,460 person-years in CRP-Q4. In the adjusted analyses, we found a positive association between CRP-Q4 and the risk of clinical events compared to CRP-Q1 (CRP-Q4, HR adj : 1.58 [95%CI=1.12-2.24]). Supplementary Tables 1 and 2 present the estimated DFS hazard ratios across CRP quartiles in more adjusted models, and with other malignancy treated as event in Supplementary Table 2. Table 2 shows the estimated mortality hazard ratios across CRP quartiles. In total, 39 deaths were recorded during 4,044 person-years in CRP-Q1, 67 deaths in CRP-Q2 during 4,067 person-years, 87 deaths during 3,987 person-years in CRP-Q3, and 105 deaths in CRP-Q4 during 3,705 person-years. In the adjusted analyses, CRP-Q4 was associated with higher mortality risk compared to CRP-Q1 (CRP-Q4, HR adj : 2.47 [95%CI=1.62-3.76]). Supplementary Table 3 presents the estimated mortality hazard ratios across CRP quartiles in more adjusted models. Figure 2 displays the cumulative incidences of clinical events (BC recurrence, contralateral BC, and death) across BMI groups. We saw an evident increase of incidences in CRP-Q4 compared to the other quartiles in BC patients with normal-weight or overweight, but not in patients with obesity. Table 3 displays the estimated DFS hazard ratios according to CRP quartiles in BMI groups. We demonstrated an increased risk of an event among patients with CRP-Q4 as compared with patients with normal-weight (CRP-Q4, HR adj : 1.70 [95%CI=1.09-2.66]) and overweight (CRP-Q4, HR adj : 1.75 [95%CI=1.08-2.86]). In patients with obesity, we found an increased risk of a clinical event in CRP-Q4 (CRP-Q4, HR adj : 1.73 [95%CI=0.78-3.83]), though the precision of the estimate was less precise. It should be noted that the number of patients was lowest in the obesity group. Figure 3 shows the cumulative incidences of death across BMI groups. We observed a higher number of deaths in CRP-Q4 compared with the other quartiles among patients with normal-weight or overweight. In patients with obesity, we observed an increase in deaths in CRP-Q4 as well, becoming evident after eight years of follow-up. Table 4 displays the estimated mortality hazard ratios according to CRP quartiles in BMI groups. In patients with normal-weight, being in the highest CRP quartile compared to the lowest CRP quartile was associated with an increased risk of death (CRP-Q4, HR adj : 3.66 [95%CI=1.95-6.87]). In patients with overweight, an association was observed between CRP-Q4 and an increased risk of death compared to CRP-Q1 (CRP-Q4, HR adj : 1.92 [95%CI=1.06-3.46]). In patients with obesity, we also observed an increased risk of death for patients in CRP-Q4 (CRP-Q4, HR adj : 1.40 [95%CI=0.64-3.09]) compared with CRP-Q1, however, the precision of the estimate was weaker than in the other BMI groups. Discussion This study demonstrated an association between high CRP levels and inferior outcomes in both DFS and OS analyses. In the BMI stratified analyses, we observed an association between high CRP and inferior DFS in patients with normal-weight, overweight, and obesity, although less evident among patients with obesity, which may be explained by low numbers of patients with obesity. In the OS analyses, we saw over three-fold increased risk of death in patients with normal-weight and high CRP compared with low CRP. In patients with overweight, the increased risk of death was nearly two-fold, whereas a 40% increased risk of death was seen in patients with high CRP and obesity, but the precision of the estimate was lower than in normal-weight and overweight. Prior literature has shown inconsistent results regarding the prognostic value of CRP in BC. The meta-analysis by Mikkelsen et al suggested a limited potential for CRP as a prognostic marker in non-metastatic settings [19]. In studies using CRP as a categorical variable, high CRP was associated with lower DFS and OS, but the estimates were imprecise. A Danish study of 2,910 patients showed that the highest CRP tertile was associated with reduced OS and DFS [36]. The patients had BC stage I-IV diagnosed between 2002 and 2009, and blood was drawn at the time of diagnosis. In a cohort of BC patients with stage I-III disease in the United States (N=2,919), similar results were reported, in which blood was drawn at least 12 months after no evidence of disease (median 21.7 months) [37]. Higher all-cause mortality was observed in patients with CRP levels 10 mg/L compared to patients with CRP levels <1 mg/L. A decrease in OS in pre-diagnostic CRP levels 10 mg/L compared to <10 mg/L was found in a study by Wulaningsih et al (N=6,606) [38]. Contrary to these studies, Frydenberg et al found a decreased risk of all-cause death in the highest pre-diagnostic CRP tertile, and a similar correlation was found in DFS analyses (N=192) [39]. Our findings are consistent with results from the larger studies [36–38]. To our knowledge, only three studies have investigated the association between CRP and BC prognosis stratified by BMI [20–22]. Our study is the first to explore the association with DFS across BMI groups. The latest study, a Chinese prospective multicenter cohort study with BC patients stage I-IV (N=514) by Ruan et al [20], found a strong correlation between CRP >10 mg/L and all-cause mortality. CRP >10 mg/L was associated with lower OS in patients with BMI 24 kg/m 2 and BMI <24 kg/m 2 , however, the precision of the estimate was weaker in patients with BMI <24 kg/m 2 compared to patients with BMI 24 kg/m 2 . In 1,114 BC patients (stage in situ to IV), Nelson et al reported that only patients with higher CRP levels and normal-weight had an increased risk of death (HR: 1.39 [95CI%=1.03–1.89]) for every 1 standard deviation increase in logCRP [21]. Patients with BMI >25 kg/m 2 and higher CRP levels had a slightly lower risk of death, but the precision of the estimate was low. In the NHANES III cohort, Wulaningsih et al included 7,780 females aged ≥20 without a cancer history at baseline [22]. A total of 44 BC deaths were reported. The risk of BC death per log CRP increase was higher in BMI <30 kg/m 2 compared to BMI 30 kg/m 2 (HR 1.94 [95CI%=0.51–7.29] vs. HR 1.40 [95CI%=0.52–3.77]), however, the precision of the estimates was low. Since our findings suggest that increased CRP across all BMI groups may be linked to worse BC prognosis, our results are similar to most of the results from the previous studies cited above. However, variations in study designs make a direct comparison of results difficult. Comparing our results with Ruan et al is problematic since a BMI 24 kg/m 2 includes both normal-weight, overweight, and obesity, and they used CRP as a binary exposure [20]. Also, stage IV patients were included, the cohort was younger (mean 53.7 years), and treatment choice differed (i.e. only 5.3% received radiotherapy)). It is not clear when Ruan et al collected their blood samples. Our study cohort is larger, but based on a single-center cohort study. In the study by Nelson et al , the patients were older (mean age from 70.3-71.5 years in CRP quartiles), and they included patients with in situ and stage IV disease [21]. Blood samples were collected, on average 7.8 years before BC diagnosis which constitutes a major difference to our study [21]. Also, patients with underweight were included in the stratified analyses, and patients with CRP >10 mg/L were excluded. Like Nelson et al , the CRP levels were measured before BC diagnosis by Wulaningsih et al [22]. The authors had no information on BC incidence and disease characteristics [22]. Our findings could have clinical implications. CRP at the time of diagnosis may be used by clinicians to identify BC patients with an increased risk of inferior outcomes. The precision of the estimate in patients with obesity could improve with a larger sample size, as our results in patients with obesity could be due to a type 2 error. However, many other factors are involved in the link between obesity and BC, such as adipokines and estrogens, as we previously reviewed [40], and could potentially be of more significant importance than CRP for patients with obesity. Also, CRP is a surrogate marker for systemic inflammation and many factors (e.g. smoking and blood pressure) influence CRP levels [41], and we were not able to take all these factors into consideration. Furthermore, CRP is not an appropriate marker for the local inflammatory environment in the breast. Our study has limitations. First, it is a single-institutional study and the results may not apply to other institutions and countries. Second, we adjusted for potential confounders, but we cannot rule out the possibility of residual confounding, such as smoking status and alcohol consumption. Third, not all BC patients seen at the Department of Plastic and Breast Surgery, AUH, agreed to donate a blood sample, and we do not have information on the non-participants, which could lead to selection bias. Fourth, BMI is an indicator of general obesity but does not reveal information on body composition, which is important information as the inflammatory signatures differ between abdominal and gynoid obesity [42]. Fifth, we only measured CRP levels at a single time point, so we were unable to evaluate the impact of fluctuations in CRP, for example, due to acute infection or lifestyle factors. Conclusions High circulating CRP levels at the time of BC diagnosis were associated with an inferior BC prognosis in this large Danish cohort. Furthermore, our study suggests that CRP may be a clinically relevant prognostic marker for BC prognosis across BMI groups. Future studies should investigate the relationship between CRP and BC in patients with obesity on a larger scale. Also, we encourage the investigation of other obesity-associated biomarkers in mapping the link between obesity and BC prognosis to identify patients in need of additional intervention. Declarations Competing interests The authors have no conflicts of interest to declare that are relevant to the content of this article. Ethics approval All projects applying for samples at the RBGB need approval from the Danish Data Protection Agency and the Danish Council on Ethics. The conduction of the study is approved by the Danish Council on Ethics (no. 1-10-72-192-20) and registered as a scientific project at Region Midtjylland, Denmark (no. 1-16-02-299-20). Consent to participate Informed consent was obtained from all participants included in the study. Funding This work was supported by the Novo Nordisk Foundation STENO Collaborative Grant (NNF20OC0065928), the NEYE Foundation, the Danish Cancer Society (R288-A16168 & R328-A19070), “Fagerlund Stiftelsen”, and the Department of Oncology Research Foundation. Author Contribution Study conception and design: JBH, DCF, JMB, PC, and SB. Data collection: JBH, EB, PC, and SB. Data analysis: JBH supervised by DCF and SB. Interpretation of data: all authors. Writing – original draft preparation: JBH. Writing – review: all authors. Writing – editing: JBH. All authors approved the final manuscript. Data availability The datasets generated and/or analyzed are not publicly available due to individual privacy could be compromised, but are available from the corresponding author on reasonable request. References Sung H, Ferlay J, Siegel RL et al (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 71:209–249. https://doi.org/10.3322/caac.21660 Allemani C, Matsuda T, Di Carlo V et al (2018) Global surveillance of trends in cancer survival 2000-14 (CONCORD-3): analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries. Lancet 391:1023–1075. https://doi.org/10.1016/S0140-6736(17)33326-3 Sung H, Siegel RL, Torre LA et al (2019) Global patterns in excess body weight and the associated cancer burden. CA Cancer J Clin 69:88–112. https://doi.org/10.3322/caac.21499 American Association for Cancer Research (2022) Preventing cancer: Identifying risk factors. https://cancerprogressreport.aacr.org/progress/cpr22-contents/cpr22-preventing-cancer-identifying-risk-factors/ . Accessed 19 Jul 2023 Lee K, Kruper L, Dieli-Conwright CM, Mortimer JE (2019) The Impact of Obesity on Breast Cancer Diagnosis and Treatment. 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Crit Rev Clin Lab Sci 59:480–500. https://doi.org/10.1080/10408363.2022.2050886 Ruan G-T, Xie H-L, Hu C-L et al (2023) Comprehensive prognostic effects of systemic inflammation and Insulin resistance in women with breast cancer with different BMI: a prospective multicenter cohort. Sci Rep 13:4303. https://doi.org/10.1038/s41598-023-31450-w Nelson SH, Brasky TM, Patterson RE et al (2017) The Association of the C-Reactive Protein Inflammatory Biomarker with Breast Cancer Incidence and Mortality in the Women’s Health Initiative. Cancer Epidemiol Biomarkers Prev 26:1100–1106. https://doi.org/10.1158/1055-9965.EPI-16-1005 Wulaningsih W, Holmberg L, Abeler-Doner L et al (2016) Associations of C-Reactive Protein, Granulocytes and Granulocyte-to-Lymphocyte Ratio with Mortality from Breast Cancer in Non-Institutionalized American Women. PLoS ONE 11:e0157482. https://doi.org/10.1371/journal.pone.0157482 Møller S, Jensen M-B, Ejlertsen B et al (2008) The clinical database and the treatment guidelines of the Danish Breast Cancer Cooperative Group (DBCG); its 30-years experience and future promise. Acta Oncol 47:506–524. https://doi.org/10.1080/02841860802059259 Christiansen P, Ejlertsen B, Jensen M-B, Mouridsen H (2016) Danish Breast Cancer Cooperative Group. Clin Epidemiol 8:445–449. https://doi.org/10.2147/CLEP.S99457 Regionernes Kliniske Kvalitetsudviklingsprogram (2022) In English. https://www.rkkp.dk/in-english/ . Accessed 17 Jan 2022 Schmidt M, Pedersen L, Sørensen HT (2014) The Danish Civil Registration System as a tool in epidemiology. Eur J Epidemiol 29:541–549. https://doi.org/10.1007/s10654-014-9930-3 Sundhedsdatastyrelsen (2022) National health registers. https://sundhedsdatastyrelsen.dk/da/english/health_data_and_registers/national_health_registers . Accessed 28 Dec 2022 Antonsen K, Rosenstock CV, Lundstrøm LH (2016) The Danish Anaesthesia Database. Clin Epidemiol 8:435–438. https://doi.org/10.2147/CLEP.S99517 Bio- and Genome Bank Denmark (2022) Bio- and Genome Bank Denmark. https://www.regioner.dk/rbgben . Accessed 13 Jan 2022 Siemens (2022) ADVIA Chemistry XPT System. https://www.siemens-healthineers.com/dk/clinical-chemistry/systems/advia-chemistry-xpt-system . Accessed 11 Apr 2022 Nehring SM, Goyal A, Patel BC (2023) C Reactive Protein. In: StatPearls. StatPearls Publishing, Treasure Island (FL) Weir CB, Jan A (2023) BMI Classification Percentile And Cut Off Points. In: StatPearls. StatPearls Publishing, Treasure Island (FL) Giuliano AE, Connolly JL, Edge SB et al (2017) Breast Cancer-Major changes in the American Joint Committee on Cancer eighth edition cancer staging manual. CA Cancer J Clin 67:290–303. https://doi.org/10.3322/caac.21393 Sinn H-P, Kreipe H (2013) A Brief Overview of the WHO Classification of Breast Tumors, 4th Edition, Focusing on Issues and Updates from the 3rd Edition. Breast Care (Basel) 8:149–154. https://doi.org/10.1159/000350774 Dansk Multidisciplinære Cancer Gruppe Kliniske Retningslinjer. https://www.dmcg.dk/Kliniske-retningslinjer/kliniske-retningslinjer-opdelt-paa-dmcg/brystcancer/ . Accessed 19 Dec 2023 Allin KH, Nordestgaard BG, Flyger H, Bojesen SE (2011) Elevated pre-treatment levels of plasma C-reactive protein are associated with poor prognosis after breast cancer: a cohort study. Breast Cancer Res 13:R55. https://doi.org/10.1186/bcr2891 10.1186/bcr2891 Villaseñor A, Flatt SW, Marinac C et al (2014) Postdiagnosis C-reactive protein and breast cancer survivorship: findings from the WHEL study. Cancer Epidemiol Biomarkers Prev 23:189–199. https://doi.org/10.1158/1055-9965.EPI-13-0852 Wulaningsih W, Holmberg L, Garmo H et al (2015) Prediagnostic serum inflammatory markers in relation to breast cancer risk, severity at diagnosis and survival in breast cancer patients. Carcinogenesis 36:1121–1128. https://doi.org/10.1093/carcin/bgv096 Frydenberg H, Thune I, Lofterød T et al (2016) Pre-diagnostic high-sensitive C-reactive protein and breast cancer risk, recurrence, and survival. Breast Cancer Res Treat 155:345–354. https://doi.org/10.1007/s10549-015-3671-1 Holm JB, Rosendahl AH, Borgquist S (2021) Local Biomarkers Involved in the Interplay between Obesity and Breast Cancer. Cancers (Basel) 13. https://doi.org/10.3390/cancers13246286 Sproston NR, Ashworth JJ (2018) Role of C-Reactive Protein at Sites of Inflammation and Infection. Front Immunol 9:754. https://doi.org/10.3389/fimmu.2018.00754 Lempesis IG, Hoebers N, Essers Y et al (2023) Distinct inflammatory signatures of upper and lower body adipose tissue and adipocytes in women with normal weight or obesity. Front Endocrinol (Lausanne) 14:1205799. https://doi.org/10.3389/fendo.2023.1205799 Tables Tables 2 to 4 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table24.docx Supplementarymaterial.pdf Cite Share Download PDF Status: Published Journal Publication published 24 Jun, 2024 Read the published version in Scientific Reports → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3996677","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":275530717,"identity":"1e56506c-898c-4a18-aabf-695bec9294b4","order_by":0,"name":"Jonas Busk Holm","email":"data:image/png;base64,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","orcid":"","institution":"Aarhus University","correspondingAuthor":true,"prefix":"","firstName":"Jonas","middleName":"Busk","lastName":"Holm","suffix":""},{"id":275530718,"identity":"c79f665c-55c6-4771-9486-871ddbdba588","order_by":1,"name":"Emma Baggesen","email":"","orcid":"","institution":"Aarhus University","correspondingAuthor":false,"prefix":"","firstName":"Emma","middleName":"","lastName":"Baggesen","suffix":""},{"id":275530719,"identity":"23edd214-f1e0-42ae-88c1-988c2b1dcfd9","order_by":2,"name":"Deirdre Cronin-Fenton","email":"","orcid":"","institution":"Aarhus University","correspondingAuthor":false,"prefix":"","firstName":"Deirdre","middleName":"","lastName":"Cronin-Fenton","suffix":""},{"id":275530720,"identity":"e6fc0d5a-6ea4-4b63-b801-e5c82bb481f7","order_by":3,"name":"Jan Frystyk","email":"","orcid":"","institution":"Odense University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jan","middleName":"","lastName":"Frystyk","suffix":""},{"id":275530721,"identity":"2f513257-21b9-4ec1-9200-36ad4afaffd2","order_by":4,"name":"Jens Meldgaard Bruun","email":"","orcid":"","institution":"Aarhus University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jens","middleName":"Meldgaard","lastName":"Bruun","suffix":""},{"id":275530722,"identity":"a3d27326-fa70-40ff-a1e9-4f338b41aea2","order_by":5,"name":"Peer Christiansen","email":"","orcid":"","institution":"Aarhus University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Peer","middleName":"","lastName":"Christiansen","suffix":""},{"id":275530723,"identity":"5b1ceed9-befb-4897-98aa-8d887d19b70f","order_by":6,"name":"Signe Borgquist","email":"","orcid":"","institution":"Aarhus University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Signe","middleName":"","lastName":"Borgquist","suffix":""}],"badges":[],"createdAt":"2024-02-28 12:46:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3996677/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3996677/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-64428-3","type":"published","date":"2024-06-24T15:29:33+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":52026181,"identity":"02509323-5461-4474-83ab-20b38def15fa","added_by":"auto","created_at":"2024-03-05 15:50:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":37308,"visible":true,"origin":"","legend":"\u003cp\u003eWe identified 3,424 female patients with primary invasive breast cancer who donated blood when seen at the Department of Plastic and Breast Surgery, Aarhus University Hospital between 2010 and 2020. We excluded patients who withdrew their consent. Patients with previous cancer or co-existing cancer (defined as a diagnosis of other malignancy before or within 90 days from the final primary surgery date) were excluded. Non-melanoma skin cancer was not classified as previous or co-existing cancer. As recurrence was an endpoint in the statistical analyses, patients who did not receive surgery or had distant metastasis at baseline were also excluded. Patients were excluded if a blood sample was drawn more than 6 months (182 days) before or after the date of invasive BC diagnosis. If the final primary surgery date was after the last date of follow-up (15\u003csup\u003eth\u003c/sup\u003e of November 2021) we excluded the patients. Finally, patients receiving neoadjuvant systemic therapy were excluded. After exclusion, 2,673 patients were included in the statistical analyses. Abbreviations: \u003cem\u003eBC\u003c/em\u003e Breast cancer\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-3996677/v1/c7727435e7074c8a5eb0be66.png"},{"id":52026177,"identity":"bfde92be-ffc9-40d7-907b-425a6ddeb7ff","added_by":"auto","created_at":"2024-03-05 15:50:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":211047,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative incidences (Aalen-Johansen estimator) of clinical events (recurrence, contralateral breast cancer, and death) in women with breast cancer stage I-III according to C-reactive protein quartiles in patients with a) normal-weight (body mass index 18.5-\u0026lt;25 kg/m\u003csup\u003e2\u003c/sup\u003e), b) overweight (body mass index 25-\u0026lt;30 kg/m\u003csup\u003e2\u003c/sup\u003e), and c) obesity (body mass index ≥30 kg/m\u003csup\u003e2\u003c/sup\u003e). Abbreviations: \u003cem\u003eCRP-Q1\u003c/em\u003e C-reactive protein quartile 1 (lowest quartile); \u003cem\u003eCRP-Q4\u003c/em\u003e C-reactive protein quartile 4 (highest quartile). The figures were made using Stata® version 18\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-3996677/v1/243bfbf878b4049ba274b9da.png"},{"id":52026176,"identity":"a1b80d15-49b9-4131-a1c9-76af05ff1b09","added_by":"auto","created_at":"2024-03-05 15:50:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":131849,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative incidences (Kaplan-Meier estimator) of deaths in women with breast cancer stage I-III according to C-reactive protein quartiles in patients with a) normal-weight (body mass index 18.5-\u0026lt;25 kg/m\u003csup\u003e2\u003c/sup\u003e), b) overweight (body mass index 25-\u0026lt;30 kg/m\u003csup\u003e2\u003c/sup\u003e), and c) obesity (body mass index ≥30 kg/m\u003csup\u003e2\u003c/sup\u003e). Abbreviations: \u003cem\u003eCRP-Q1\u003c/em\u003e C-reactive protein quartile 1 (lowest quartile); \u003cem\u003eCRP-Q4\u003c/em\u003e C-reactive protein quartile 4 (highest quartile). The figures were made using Stata® version 18\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-3996677/v1/159ef0819a297f3dd890c8ab.png"},{"id":59202566,"identity":"6ab0e666-88ce-4844-a601-22217b67e145","added_by":"auto","created_at":"2024-06-27 15:29:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1045458,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3996677/v1/40e59edf-46ae-48e9-9f45-95e822891f72.pdf"},{"id":52026183,"identity":"d734d532-8a07-4685-ae0b-883d749a09ea","added_by":"auto","created_at":"2024-03-05 15:50:46","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":562369,"visible":true,"origin":"","legend":"","description":"","filename":"Table24.docx","url":"https://assets-eu.researchsquare.com/files/rs-3996677/v1/cd0e8ebcf8669dce0e6ca7ea.docx"},{"id":52026180,"identity":"79709942-3e55-49af-bc8a-53bb272209a7","added_by":"auto","created_at":"2024-03-05 15:50:46","extension":"pdf","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":35865,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3996677/v1/80dfe9892f56ce9e276f0f76.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Circulating C-reactive protein levels as a prognostic biomarker in breast cancer across body mass index groups","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn 2020, 2,3\u0026nbsp;million women were diagnosed with breast cancer (BC) globally, making it the most prevalent cancer type (excluding non-melanoma skin cancer) in the world [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite 5-year survival rates approaching 90% in North America for patients with BC, nearly 700,000 women died from BC in 2020 worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Alongside, the prevalence of obesity (defined as a body mass index (BMI)\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e) increased excessively worldwide, rising from 7\u0026ndash;16% among women between 1975 and 2016 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Obesity is associated with an increased risk of developing at least 15 types of cancer, including postmenopausal BC [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Also, obesity is a prognostic disadvantage and is associated with shorter disease-free survival (DFS) and overall survival (OS) in BC [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInflammation is a hallmark of cancer [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and systemic inflammation is associated with poor BC prognosis [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Obesity is associated with low-grade inflammation [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and elevated levels of C-reactive protein (CRP) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], also among BC patients [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Thus, both obesity and systemic inflammation are associated with inferior BC outcomes. Yet, it is not clear if BC patients with obesity and high levels of systemic inflammation have a poorer prognosis compared with patients with obesity and lower levels of systemic inflammation.\u003c/p\u003e \u003cp\u003eCRP is an acute-phase protein released from the liver upon stimulation from cytokines such as interleukin 6 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. CRP is part of the inflammatory cascade and a marker of systemic inflammation [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. CRP levels are increased in people with obesity compared with their normal weight counterparts, and in cancer patients compared with healthy controls or patients with benign diseases [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. A systematic review by Savioli \u003cem\u003eet al\u003c/em\u003e concluded that high pre-operative CRP levels were associated with an increased risk of BC-specific mortality and all-cause mortality [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Likewise, in a meta-analysis from 2011, Han \u003cem\u003eet al\u003c/em\u003e reported an association between elevated CRP levels and lower OS and DFS [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. A meta-analysis by Mikkelsen \u003cem\u003eet al\u003c/em\u003e found that high CRP was an indicator of poor prognosis in metastatic BC, but the prognostic value in non-metastatic early BC could not be confirmed [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs such, CRP levels may be prognostic in BC but only three studies have investigated this relationship across BMI groups, and they reported conflicting results [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Therefore, we investigated the prognostic potential of CRP in BC patients according to BMI groups. We hypothesized that higher circulating CRP levels were associated with poorer BC prognosis and that such an association was most pronounced in patients with obesity.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eData sources\u003c/p\u003e \u003cp\u003eAll data were merged through a unique identification number for each patient, linking all data with 100% accuracy. All patients with BC treated in Denmark are registered in the Danish Breast Cancer Group (DBCG) database [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The completeness of the DBCG database exceeds 95% [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. From DBCG and through systemic investigation of medical records, we retrieved baseline data concerning patient-, tumor-, and treatment characteristics. All variables from DBCG were retrieved from \u0026ldquo;The Danish Clinical Quality Program - National Clinical Registries\u0026rdquo; (RKKP), which constitutes the infrastructure of the Danish clinical quality registries and the Danish Multidisciplinary Cancer Groups (DMCG) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Information on emigration was retrieved through RKKP from the Civil Registration System [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Information on comorbidities was retrieved through RKKP from the National Patient Registry [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Information on height, weight, and thereof BMI was extracted from both medical records and the Danish Anesthesia Database (DAD) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. BMI was obtained by merging data from the medical records and the DAD. Regarding follow-up data, we extracted information based on a prespecified codebook. We reviewed all patient electronic medical records, which included pathological reports and digital imaging, to register all recurrences, contralateral BCs, other malignancies, and deaths.\u003c/p\u003e \u003cp\u003eStudy population\u003c/p\u003e \u003cp\u003eOur study cohort was women diagnosed with stage I-III BC between 2010 and 2020, who were referred to the Department of Plastic and Breast Surgery, Aarhus University Hospital (AUH), for primary BC. At this department, all patients with BC were invited to donate blood for future research to the Regional Bio- and Genome Bank Denmark (RBGB) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] at the time of BC diagnosis. The blood was drawn between March 2010 and August 2020, median of seven days (interquartile range (IQR): 6\u0026ndash;11 days) after the primary invasive BC diagnosis before breast surgery. Patients who received neoadjuvant systemic treatment were excluded from the analyses, as data on tumor characteristics are registered after neoadjuvant treatment and differ substantially from patients receiving up-front breast surgery. The final study population consisted of 2,673 patients, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eC-reactive protein analyses\u003c/p\u003e \u003cp\u003eAfter the blood draw, serum was separated and subsequently stored at -80 \u003csup\u003eo\u003c/sup\u003eC at the RGBG. In late 2020, serum samples were identified and released for analyses at the Department of Clinical Biochemistry. The CRP levels were analyzed with a high-sensitivity CRP (hs-CRP) test (Siemens ADVIA\u0026reg; Chemistry XPT system with \u0026ldquo;ADVIA\u0026reg; Chemistry CardioPhase\u0026trade; High Sensitivity C‑Reactive Protein (hsCRP)-reagents\u0026rdquo;) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The lowest detectable level was 0.2 mg/L.\u003c/p\u003e \u003cp\u003eDefinitions of analytic variables\u003c/p\u003e \u003cp\u003eCRP levels were categorised in quartiles (CRP-Q1: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\le\\)\u003c/span\u003e\u003c/span\u003e0.59 mg/L, CRP-Q2: 0.6\u0026ndash;1.34 mg/L, CRP-Q3: 1.35\u0026ndash;3.23 mg/L, CRP-Q4: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge\\)\u003c/span\u003e\u003c/span\u003e3.24 mg/L). In the statistical analyses, the lowest CRP quartile (CRP-Q1) served as the reference. It should be noted that CRP below 3 mg/L is considered to be within the normal range [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. BMI was grouped according to the World Health Organization (WHO) definitions [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]: underweight (BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e), normal-weight (18.5\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e), overweight (25\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e), and obesity (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e). The closest registered BMI measurement from the date of the blood sample was used. Registrations within 182 days before or after blood draw were included (median same date as blood draw, IQR: 0\u0026ndash;2 days after date of blood draw).\u003c/p\u003e \u003cp\u003e Age and menopausal status (defined according to DBCG guidelines) refer to the time of primary invasive BC diagnosis. For tumor size and nodal status, we categorized patients in groups according to the American Joint Committee on Cancer Staging 8th edition [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The histological grade was classified using the standardization from the Nottingham Group [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The histological classification followed the WHO classification of breast tumors 3rd Edition [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Tumors without registration of invasive ductal or lobular carcinoma were categorized as \u0026ldquo;Other.\u0026rdquo; Estrogen receptor (ER) status was classified as \u0026ldquo;Negative,\u0026rdquo; if tumor cells showed no ER expression, and \u0026ldquo;Positive\u0026rdquo; if 1% or more of the tumor cells expressed ER. Human Epidermal Growth Factor Receptor 2 (HER2) expression was defined as either \u0026ldquo;Positive\u0026rdquo; or \u0026ldquo;Negative\u0026rdquo; through a combination of immunohistochemistry and Fluorescence In Situ Hybridization (FISH)-ratio.\u003c/p\u003e \u003cp\u003eThe type of breast surgery was categorized as \u0026ldquo;mastectomy\u0026rdquo; (including patients with mastectomy after lumpectomy) or \u0026ldquo;lumpectomy,\u0026rdquo; based on the final surgery for the primary BC. Adjuvant systemic treatment (endocrine therapy, HER2-targeted treatment, and chemotherapy) and radiotherapy were handled as intention-to-treat variables according to DBCG protocols. Patients registered as treated with neoadjuvant systemic treatment by surgeons or pathologists were classified as \u0026ldquo;Neoadjuvant\u0026rdquo;.\u003c/p\u003e \u003cp\u003eRecurrence refers to any recurrent invasive BC in the breast, lymph nodes, or elsewhere in the body (apart from the contralateral breast)\u0026thinsp;\u0026ge;\u0026thinsp;3 months after the final primary surgery date (defined as the last date of surgery for the primary BC). The hierarchy for recurrence date was whatever came first in stating a recurrence in the pathological report, clinical charts, or imaging information. When defining the recurrence as local, regional, or distant, we followed the clinical guidelines from DMCG [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Registration of malignancy in the contralateral breast in the pathological report after final surgery was classified as contralateral BC. Other malignancies apart from non-melanoma skin cancer in the pathological report and death were registered, too.\u003c/p\u003e \u003cp\u003eFollow-up and statistical analysis\u003c/p\u003e \u003cp\u003e \u003cem\u003eDisease-free-survival (DFS).\u003c/em\u003e Follow-up for DFS began on the date of final primary surgery and continued until the first of the following: BC recurrence, contralateral BC, death, other malignancy, emigration, or end-of-follow-up (15th November 2021). We treated BC recurrence, contralateral BC, and death as events. We censored patients at other malignancies, emigration, or end-of-follow-up. However, if recurrence, contralateral BC, or death occurred within 30 days after diagnosis of other malignancy, the event was included in the analyses.\u003c/p\u003e \u003cp\u003e \u003cem\u003eOverall survival (OS)\u003c/em\u003e. Follow-up for all-cause mortality began at the date of final primary surgery and continued until the first of any death, emigration, or end-of-follow-up (15th November 2021). We treated any death as an event. We censored patients at emigration or end-of-follow-up.\u003c/p\u003e \u003cp\u003eWe calculated person-years, number of events, and incidence rate per 1000 person-years (with a 95% confidence interval (95%CI)) for each CRP quartile. We used Cox regression models to estimate crude and adjusted hazard ratios (HRs) with 95% CIs for DFS and OS. Patients were followed for a maximum of 10 years in the regression models. We adjusted for patient-, tumor-, and treatment characteristics in the adjusted analysis. Only patients with complete data in all regressed variables were included (N\u0026thinsp;=\u0026thinsp;2,485). We included the following variables: age (continuous), menopausal status (dichotomous), comorbidities (Charlson Comorbidity Index (CCI)) (categorical), BMI (categorical), histological grade (categorical), histological classification (categorical), tumor size (categorical), nodal status (categorical), ER status (dichotomous), HER2 status (dichotomous), surgery type (dichotomous), intended adjuvant systemic treatment (dichotomous), and intended adjuvant radiotherapy (dichotomous).\u003c/p\u003e \u003cp\u003eTo explore whether the association between CRP and outcomes differed across BMI groups, we performed DFS and OS analyses stratified for BMI groups as described above. In the stratified analyses, we created CRP quartiles within each BMI group. We adjusted for patient characteristics in the adjusted model. In the stratified analyses, patients with underweight were excluded. In the stratified analyses, we also presented Aalen-Johansen estimates on DFS (events: BC recurrence, contralateral BC, and death; competing risks: other malignancy; censoring points: emigration and end-of-follow-up) and Kaplan-Meier estimates on OS (events: death; censoring points: emigration and end-of-follow-up).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eOur cohort included 2,673 patients with a median age of 62 years at BC diagnosis (see Table 1). The median BMI was lowest in CRP-Q1 (BMI=22.45\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e) and highest in CRP-Q4 (BMI=28.36\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e). In CRP-Q4, more patients were postmenopausal, had higher CCI scores, and larger tumors compared to CRP-Q1. Chemotherapy was more often administered to patients in the CRP-Q1 compared with CRP-Q4. In total, 64 patients (2.39%) had underweight, 1,265 (47.33%) had normal-weight, 818 (30.60%) had overweight, and 486 (18.18%) had obesity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eDescriptive characteristics of women with breast cancer stage I-III referred to the Department of Plastic and Breast Surgery, Aarhus University Hospital, Denmark for primary breast cancer surgery. Patients were diagnosed with breast cancer and donated blood samples for future research between 2010 and 2020.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"736\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.93877551020408%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.374149659863946%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eN=2,673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRP-Q1\u003c/strong\u003e\u0026nbsp;\u003cbr\u003e\u0026nbsp; 0.59 mg/L\u0026nbsp;\u003cbr\u003e\u0026nbsp;N=664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRP-Q2\u003c/strong\u003e\u0026nbsp;\u003cbr\u003e\u0026nbsp;0.6-1.34 mg/L\u003c/p\u003e\n \u003cp\u003eN=676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRP-Q3\u003c/strong\u003e\u0026nbsp;\u003cbr\u003e\u0026nbsp;1.35-3.23 mg/L\u003c/p\u003e\n \u003cp\u003eN=675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRP-Q4\u003c/strong\u003e\u0026nbsp;\u003cbr\u003e\u0026nbsp; 3.24 mg/L N=658\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.93877551020408%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, median\u003c/strong\u003e \u003cstrong\u003e(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.374149659863946%\" valign=\"top\"\u003e\n \u003cp\u003e62 (52-69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e57 (49-66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e63 (52.5-70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e64 (55-70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e63.5 (55-70)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.93877551020408%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years), categories\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026lt; 50\u003c/p\u003e\n \u003cp\u003e50-59\u003c/p\u003e\n \u003cp\u003e60-69\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; 70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.374149659863946%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e452 (16.91%)\u003c/p\u003e\n \u003cp\u003e661 (24.73%)\u003c/p\u003e\n \u003cp\u003e924 (34.57%)\u003c/p\u003e\n \u003cp\u003e636 (23.79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e185 (27.86%)\u003c/p\u003e\n \u003cp\u003e179 (26.96%)\u003c/p\u003e\n \u003cp\u003e190 (28.61%)\u003c/p\u003e\n \u003cp\u003e110 (16.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e100 (14.79%)\u003c/p\u003e\n \u003cp\u003e168 (24.85%)\u003c/p\u003e\n \u003cp\u003e235 (34.76%)\u003c/p\u003e\n \u003cp\u003e173 (25.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e81 (12.00%)\u003c/p\u003e\n \u003cp\u003e163 (24.15%)\u003c/p\u003e\n \u003cp\u003e258 (38.22%)\u003c/p\u003e\n \u003cp\u003e173 (25.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e86 (13.07%)\u003c/p\u003e\n \u003cp\u003e151 (22.95%)\u003c/p\u003e\n \u003cp\u003e241 (36.63%)\u003c/p\u003e\n \u003cp\u003e180 (27.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.93877551020408%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBody Mass Index (kg/m\u003csup\u003e2\u003c/sup\u003e), median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.374149659863946%\" valign=\"top\"\u003e\n \u003cp\u003e24.95 (22.31-28.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e22.45 (20.74-24.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e24.49 (22.31-27.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e25.79 (23.23-29.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e28.36 (24.84-33.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.93877551020408%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBody Mass Index, categories (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eUnderweight \u0026lt; 18.5 \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNormal-weight \u0026nbsp; 18.5 to \u0026lt; 25\u003c/p\u003e\n \u003cp\u003eOverweight 25 \u0026le; \u0026nbsp;to \u0026lt; 30\u003c/p\u003e\n \u003cp\u003eObesity \u0026ge; 30\u003c/p\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.374149659863946%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e64 (2.39%)\u003c/p\u003e\n \u003cp\u003e1265 (47.33%)\u003c/p\u003e\n \u003cp\u003e818 (30.60%)\u003c/p\u003e\n \u003cp\u003e486 (18.18%)\u003c/p\u003e\n \u003cp\u003e40 (1.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e31 (4.67%)\u003c/p\u003e\n \u003cp\u003e488 (73.49%)\u003c/p\u003e\n \u003cp\u003e118 (17.77%)\u003c/p\u003e\n \u003cp\u003e19 (2.86%)\u003c/p\u003e\n \u003cp\u003e8 (1.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e16 (2.37%)\u003c/p\u003e\n \u003cp\u003e348 (51.48%)\u003c/p\u003e\n \u003cp\u003e235 (34.76%)\u003c/p\u003e\n \u003cp\u003e66 (9.76%)\u003c/p\u003e\n \u003cp\u003e11 (1.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6 (0.89%)\u003c/p\u003e\n \u003cp\u003e269 (39.85%)\u003c/p\u003e\n \u003cp\u003e255 (37.78%)\u003c/p\u003e\n \u003cp\u003e136 (20.15%)\u003c/p\u003e\n \u003cp\u003e9 (1.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e11 (1.67%)\u003c/p\u003e\n \u003cp\u003e160 (24.32%)\u003c/p\u003e\n \u003cp\u003e210 (31.91%)\u003c/p\u003e\n \u003cp\u003e265 (40.27%)\u003c/p\u003e\n \u003cp\u003e12 (1.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.93877551020408%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMenopausal status\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ePremenopausal\u003c/p\u003e\n \u003cp\u003ePostmenopausal\u003c/p\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.374149659863946%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e598 (22.37%)\u003c/p\u003e\n \u003cp\u003e2046 (76.54%)\u003c/p\u003e\n \u003cp\u003e29 (1.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e219 (32.98%)\u003c/p\u003e\n \u003cp\u003e433 (65.21%)\u003c/p\u003e\n \u003cp\u003e12 (1.81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e136 (20.12%)\u003c/p\u003e\n \u003cp\u003e532 (78.70%)\u003c/p\u003e\n \u003cp\u003e8 (1.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e126 (18.67%)\u003c/p\u003e\n \u003cp\u003e543 (80.44%)\u003c/p\u003e\n \u003cp\u003e6 (0.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e117 (17.78%)\u003c/p\u003e\n \u003cp\u003e538 (81.76%)\u003c/p\u003e\n \u003cp\u003e3 (0.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.93877551020408%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharlson Comorbidity Index\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e1-2 (mild)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;3 (moderate/severe)\u003c/p\u003e\n \u003cp\u003eMissing\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.374149659863946%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e346 (12.94%)\u003c/p\u003e\n \u003cp\u003e1885 (70.52%)\u003c/p\u003e\n \u003cp\u003e442 (16.54%)\u003c/p\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e93 (14.01%)\u003c/p\u003e\n \u003cp\u003e505 (76.05%)\u003c/p\u003e\n \u003cp\u003e66 (9.94%)\u003c/p\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e91 (13.46%)\u003c/p\u003e\n \u003cp\u003e488 (72.19%)\u003c/p\u003e\n \u003cp\u003e97 (14.35%)\u003c/p\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e88 (13.04%)\u003c/p\u003e\n \u003cp\u003e464 (68.74%)\u003c/p\u003e\n \u003cp\u003e123 (18.22%)\u003c/p\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e74 (11.25%)\u003c/p\u003e\n \u003cp\u003e428 (65.05%)\u003c/p\u003e\n \u003cp\u003e156 (23.71%)\u003c/p\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.93877551020408%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTumor size\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0-20 mm\u003c/p\u003e\n \u003cp\u003e21-50 mm\u003c/p\u003e\n \u003cp\u003e\u0026gt;50 mm\u003c/p\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.374149659863946%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1889 (70.67%)\u003c/p\u003e\n \u003cp\u003e723 (27.05%)\u003c/p\u003e\n \u003cp\u003e56 (2.10%)\u003c/p\u003e\n \u003cp\u003e5 (0.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e494 (74.40%)\u003c/p\u003e\n \u003cp\u003e157 (23.64%)\u003c/p\u003e\n \u003cp\u003e12 (1.81%)\u003c/p\u003e\n \u003cp\u003e1 (0.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e481 (71.15%)\u003c/p\u003e\n \u003cp\u003e177 (26.18%)\u003c/p\u003e\n \u003cp\u003e17 (2.51%)\u003c/p\u003e\n \u003cp\u003e1 (0.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e467 (69.19%)\u003c/p\u003e\n \u003cp\u003e195 (28.89%)\u003c/p\u003e\n \u003cp\u003e11 (1.63%)\u003c/p\u003e\n \u003cp\u003e2 (0.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e447 (67.93%)\u003c/p\u003e\n \u003cp\u003e194 (29.48%)\u003c/p\u003e\n \u003cp\u003e16 (2.43%)\u003c/p\u003e\n \u003cp\u003e1 (0.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.93877551020408%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLymph node metastases\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e1-3\u003c/p\u003e\n \u003cp\u003e4-9\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;10\u003c/p\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.374149659863946%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1656 (61.95%)\u003c/p\u003e\n \u003cp\u003e734 (27.46%)\u003c/p\u003e\n \u003cp\u003e180 (6.73%)\u003c/p\u003e\n \u003cp\u003e82 (3.07%)\u003c/p\u003e\n \u003cp\u003e21 (0.79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e415 (62.50%)\u003c/p\u003e\n \u003cp\u003e178 (26.81%)\u003c/p\u003e\n \u003cp\u003e46 (6.93%)\u003c/p\u003e\n \u003cp\u003e21 (3.16%)\u003c/p\u003e\n \u003cp\u003e4 (0.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e417 (61.69%)\u003c/p\u003e\n \u003cp\u003e187 (27.66%)\u003c/p\u003e\n \u003cp\u003e44 (6.51%)\u003c/p\u003e\n \u003cp\u003e20 (2.96%)\u003c/p\u003e\n \u003cp\u003e8 (1.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e423 (62.67%)\u003c/p\u003e\n \u003cp\u003e190 (28.15%)\u003c/p\u003e\n \u003cp\u003e39 (5.78%)\u003c/p\u003e\n \u003cp\u003e20 (2.96%)\u003c/p\u003e\n \u003cp\u003e3 (0.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e401 (60.94%)\u003c/p\u003e\n \u003cp\u003e179 (27.20%)\u003c/p\u003e\n \u003cp\u003e51 (7.75%)\u003c/p\u003e\n \u003cp\u003e21 (3.19%)\u003c/p\u003e\n \u003cp\u003e6 (0.91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.93877551020408%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistological classification\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eDuctal\u003c/p\u003e\n \u003cp\u003eLobular\u003c/p\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.374149659863946%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2008 (75.12%)\u003c/p\u003e\n \u003cp\u003e331 (12.38%)\u003c/p\u003e\n \u003cp\u003e334 (12.50%)\u003c/p\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e497 (74.85%)\u003c/p\u003e\n \u003cp\u003e90 (13.55%)\u003c/p\u003e\n \u003cp\u003e77 (11.60%)\u003c/p\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e522 (77.22%)\u003c/p\u003e\n \u003cp\u003e77 (11.39%)\u003c/p\u003e\n \u003cp\u003e77 (11.39%)\u003c/p\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e493 (73.04%)\u003c/p\u003e\n \u003cp\u003e93 (13.78%)\u003c/p\u003e\n \u003cp\u003e89 (13.19%)\u003c/p\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e496 (75.38%)\u003c/p\u003e\n \u003cp\u003e71 (10.79%)\u003c/p\u003e\n \u003cp\u003e91 (13.83%)\u003c/p\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.93877551020408%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistological grade\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003cp\u003eGrade 1\u003c/p\u003e\n \u003cp\u003eGrade 2\u003c/p\u003e\n \u003cp\u003eGrade 3\u003c/p\u003e\n \u003cp\u003eMissing\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.374149659863946%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e164 (6.14%)\u003c/p\u003e\n \u003cp\u003e608 (22.75%)\u003c/p\u003e\n \u003cp\u003e1218 (45.57%)\u003c/p\u003e\n \u003cp\u003e635 (23.76%)\u003c/p\u003e\n \u003cp\u003e48 (1.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e39 (5.87%)\u003c/p\u003e\n \u003cp\u003e157 (23.64%)\u003c/p\u003e\n \u003cp\u003e285 (42.92%)\u003c/p\u003e\n \u003cp\u003e167 (25.15%)\u003c/p\u003e\n \u003cp\u003e16 (2.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e39 (5.77%)\u003c/p\u003e\n \u003cp\u003e154 (22.78%)\u003c/p\u003e\n \u003cp\u003e318 (47.04%)\u003c/p\u003e\n \u003cp\u003e155 (22.93%)\u003c/p\u003e\n \u003cp\u003e10 (1.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e37 (5.48%)\u003c/p\u003e\n \u003cp\u003e140 (20.74%)\u003c/p\u003e\n \u003cp\u003e311 (46.07%)\u003c/p\u003e\n \u003cp\u003e171 (25.33%)\u003c/p\u003e\n \u003cp\u003e16 (2.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e49 (7.45%)\u003c/p\u003e\n \u003cp\u003e157 (23.86%)\u003c/p\u003e\n \u003cp\u003e304 (46.20%)\u003c/p\u003e\n \u003cp\u003e142 (21.58%)\u003c/p\u003e\n \u003cp\u003e6 (0.91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.93877551020408%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eER status (% positive cells)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0% (negative)\u003c/p\u003e\n \u003cp\u003e1-100% (positive)\u003c/p\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.374149659863946%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e278 (10.40%)\u003c/p\u003e\n \u003cp\u003e2380 (89.04%)\u003c/p\u003e\n \u003cp\u003e15 (0.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e70 (10.54%)\u003c/p\u003e\n \u003cp\u003e587 (88.40%)\u003c/p\u003e\n \u003cp\u003e7 (1.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e62 (9.17%)\u003c/p\u003e\n \u003cp\u003e612 (90.53%)\u003c/p\u003e\n \u003cp\u003e2 (0.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e81 (12.00%)\u003c/p\u003e\n \u003cp\u003e591 (87.56%)\u003c/p\u003e\n \u003cp\u003e3 (0.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e65 (9.88%)\u003c/p\u003e\n \u003cp\u003e590 (89.67%)\u003c/p\u003e\n \u003cp\u003e3 (0.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.93877551020408%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHER2 status\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.374149659863946%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2336 (87.39%)\u003c/p\u003e\n \u003cp\u003e282 (10.55%)\u003c/p\u003e\n \u003cp\u003e55 (2.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e566 (85.24%)\u003c/p\u003e\n \u003cp\u003e81 (12.20%)\u003c/p\u003e\n \u003cp\u003e17 (2.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e598 (88.46%)\u003c/p\u003e\n \u003cp\u003e67 (9.91%)\u003c/p\u003e\n \u003cp\u003e11 (1.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e590 (87.41%)\u003c/p\u003e\n \u003cp\u003e71 (10.52%)\u003c/p\u003e\n \u003cp\u003e14 (2.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e582 (88.45%)\u003c/p\u003e\n \u003cp\u003e63 (9.57%)\u003c/p\u003e\n \u003cp\u003e13 (1.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.93877551020408%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFinal primary surgery\u003c/strong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eMastectomy\u003c/p\u003e\n \u003cp\u003eLumpectomy\u003c/p\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.374149659863946%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e881 (32.96%)\u003c/p\u003e\n \u003cp\u003e1778 (66.52%)\u003c/p\u003e\n \u003cp\u003e14 (0.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e231 (34.79%)\u003c/p\u003e\n \u003cp\u003e426 (64.16%)\u003c/p\u003e\n \u003cp\u003e7 (1.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e223 (32.99%)\u003c/p\u003e\n \u003cp\u003e450 (66.57%)\u003c/p\u003e\n \u003cp\u003e3 (0.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e222 (32.89%)\u003c/p\u003e\n \u003cp\u003e450 (66.67%)\u003c/p\u003e\n \u003cp\u003e3 (0.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e205 (31.16%)\u003c/p\u003e\n \u003cp\u003e452 (68.69%)\u003c/p\u003e\n \u003cp\u003e1 (0.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.93877551020408%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjuvant radiotherapy\u003c/strong\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.374149659863946%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e516 (19.30%)\u003c/p\u003e\n \u003cp\u003e2078 (77.74%)\u003c/p\u003e\n \u003cp\u003e79 (2.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e123 (18.52%)\u003c/p\u003e\n \u003cp\u003e517 (77.86%)\u003c/p\u003e\n \u003cp\u003e24 (3.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e139 (20.56%)\u003c/p\u003e\n \u003cp\u003e517 (76.48%)\u003c/p\u003e\n \u003cp\u003e20 (2.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e125 (18.52%)\u003c/p\u003e\n \u003cp\u003e535 (79.26%)\u003c/p\u003e\n \u003cp\u003e15 (2.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e129 (19.60%)\u003c/p\u003e\n \u003cp\u003e509 (77.36%)\u003c/p\u003e\n \u003cp\u003e20 (3.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.93877551020408%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEndocrine therapy\u003c/strong\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.374149659863946%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e512 (19.15%)\u003c/p\u003e\n \u003cp\u003e2082 (77.89%)\u003c/p\u003e\n \u003cp\u003e79 (2.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e130 (19.58%)\u003c/p\u003e\n \u003cp\u003e510 (76.81%)\u003c/p\u003e\n \u003cp\u003e24 (3.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e123 (18.20%)\u003c/p\u003e\n \u003cp\u003e533 (78.85%)\u003c/p\u003e\n \u003cp\u003e20 (2.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e132 (19.56%)\u003c/p\u003e\n \u003cp\u003e528 (78.22%)\u003c/p\u003e\n \u003cp\u003e15 (2.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e127 (19.30%)\u003c/p\u003e\n \u003cp\u003e511 (77.66%)\u003c/p\u003e\n \u003cp\u003e20 (3.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.93877551020408%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnti-HER2 treatment\u003c/strong\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.374149659863946%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2316 (86.64%)\u003c/p\u003e\n \u003cp\u003e282 (10.55%)\u003c/p\u003e\n \u003cp\u003e75 (2.81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e563 (84.79%)\u003c/p\u003e\n \u003cp\u003e81 (12.20%)\u003c/p\u003e\n \u003cp\u003e20 (3.01%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e589 (87.13%)\u003c/p\u003e\n \u003cp\u003e67 (9.91%)\u003c/p\u003e\n \u003cp\u003e20 (2.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e589 (87.26%)\u003c/p\u003e\n \u003cp\u003e71 (10.52%)\u003c/p\u003e\n \u003cp\u003e15 (2.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e575 (87.39%)\u003c/p\u003e\n \u003cp\u003e63 (9.57%)\u003c/p\u003e\n \u003cp\u003e20 (3.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.93877551020408%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjuvant chemotherapy\u003c/strong\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.374149659863946%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1287 (48.15%)\u003c/p\u003e\n \u003cp\u003e1307 (48.90%)\u003c/p\u003e\n \u003cp\u003e79 (2.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e267 (40.21%)\u003c/p\u003e\n \u003cp\u003e373 (56.17%)\u003c/p\u003e\n \u003cp\u003e24 (3.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e341 (50.44%)\u003c/p\u003e\n \u003cp\u003e315 (46.60%)\u003c/p\u003e\n \u003cp\u003e20 (2.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e347 (51.41%)\u003c/p\u003e\n \u003cp\u003e313 (46.37%)\u003c/p\u003e\n \u003cp\u003e15 (2.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.421768707482993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e332 (50.46%)\u003c/p\u003e\n \u003cp\u003e306 (46.50%)\u003c/p\u003e\n \u003cp\u003e20 (3.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eCRP\u003c/em\u003e C-reactive protein; \u003cem\u003eQ1\u003c/em\u003e Quartile 1; \u003cem\u003eIQR\u003c/em\u003e Interquartile range; \u003cem\u003eN/A\u003c/em\u003e Not applicable; \u003cem\u003eER\u003c/em\u003e Estrogen receptor; \u003cem\u003eHER2\u003c/em\u003e Human Epidermal Growth Factor Receptor 2. a: Defined as the last breast surgery procedure for the primary breast cancer. b: All systemic treatment variables and radiotherapy are intention-to-treat variables based on protocol allocation according to the Danish Breast Cancer Group.\u003c/p\u003e\n\u003cp\u003eIn DFS analyses, 368 clinical events occurred over 14,962 person-years (median follow-up time 5.55 years). In the mortality analyses, 298 deaths were recorded during 15,803 person-years (median follow-up time=6.02 years).\u003c/p\u003e\n\u003cp\u003eTable 2 presents the estimated DFS hazard ratios across CRP quartiles. In total, 70 events occurred in\u0026nbsp;3,871 person-years in\u0026nbsp;CRP-Q1, 83 events in 3,854 person-years in CRP-Q2, 100 events in 3,777 person-years in CRP-Q3, and 115 events occurred during 3,460 person-years in\u0026nbsp;CRP-Q4.\u0026nbsp;In the adjusted analyses, we found a positive association between CRP-Q4 and the risk of clinical events compared to CRP-Q1 (CRP-Q4, HR\u003csub\u003eadj\u003c/sub\u003e: 1.58 [95%CI=1.12-2.24]). Supplementary Tables 1 and 2 present the estimated DFS hazard ratios across CRP quartiles in more adjusted models, and with other malignancy treated as event in Supplementary Table 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Table 2 shows the estimated mortality hazard ratios across CRP quartiles. In total, 39 deaths were recorded during 4,044 person-years in CRP-Q1, 67 deaths in CRP-Q2 during 4,067 person-years, 87 deaths during 3,987 person-years in CRP-Q3, and 105 deaths in CRP-Q4 during 3,705 person-years. In the adjusted analyses, CRP-Q4 was associated with higher mortality risk compared to CRP-Q1 (CRP-Q4, HR\u003csub\u003eadj\u003c/sub\u003e: 2.47 [95%CI=1.62-3.76]).\u0026nbsp;Supplementary Table 3 presents the estimated mortality hazard ratios across CRP quartiles in more adjusted models.\u003c/p\u003e\n\u003cp\u003eFigure 2 displays the cumulative incidences of clinical events (BC recurrence, contralateral BC, and death) across BMI groups. We saw an evident increase of incidences in CRP-Q4 compared to the other quartiles in BC patients with normal-weight or overweight, but not in patients with obesity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 3 displays the estimated DFS hazard ratios according to CRP quartiles in BMI groups. We demonstrated an increased risk of an event among patients with CRP-Q4 as compared with patients with normal-weight (CRP-Q4,\u0026nbsp;HR\u003csub\u003eadj\u003c/sub\u003e: 1.70 [95%CI=1.09-2.66]) and overweight (CRP-Q4,\u0026nbsp;HR\u003csub\u003eadj\u003c/sub\u003e: 1.75 [95%CI=1.08-2.86]). In patients with obesity, we found an increased risk of a clinical event in CRP-Q4 (CRP-Q4, HR\u003csub\u003eadj\u003c/sub\u003e: 1.73 [95%CI=0.78-3.83]), though the precision of the estimate was less precise. It should be noted that the number of patients was lowest in the obesity group.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 3 shows the cumulative incidences of death across BMI groups. We observed a higher number of deaths in CRP-Q4 compared with the other quartiles among patients with normal-weight or overweight. In patients with obesity, we observed an increase in deaths in CRP-Q4 as well, becoming evident after eight years of follow-up.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4 displays the estimated mortality hazard ratios according to CRP quartiles in BMI groups. In patients with normal-weight, being in the highest CRP quartile compared to the lowest CRP quartile was associated with an increased risk of death (CRP-Q4, HR\u003csub\u003eadj\u003c/sub\u003e:\u0026nbsp;3.66 [95%CI=1.95-6.87]).\u0026nbsp;In patients with overweight, an association was observed between\u0026nbsp;CRP-Q4 and an increased risk of death compared to\u0026nbsp;CRP-Q1\u0026nbsp;(CRP-Q4, HR\u003csub\u003eadj\u003c/sub\u003e:\u0026nbsp;1.92 [95%CI=1.06-3.46]). In patients with obesity, we also observed an increased risk of death for patients in CRP-Q4 (CRP-Q4, HR\u003csub\u003eadj\u003c/sub\u003e: 1.40 [95%CI=0.64-3.09]) compared with CRP-Q1, however, the precision of the estimate was weaker than in the other BMI groups.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrated an association between high CRP levels and inferior outcomes in both DFS and OS analyses. In the BMI stratified analyses, we observed an association between high CRP and inferior DFS in patients with normal-weight, overweight, and obesity, although less evident among patients with obesity, which may be explained by low numbers of patients with obesity. In the OS analyses, we saw over three-fold increased risk of death in patients with normal-weight and high CRP compared with low CRP. In patients with overweight, the increased risk of death was nearly two-fold, whereas a 40% increased risk of death was seen in patients with high CRP and obesity, but the precision of the estimate was lower than in normal-weight and overweight.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrior literature has shown inconsistent results regarding the prognostic value of CRP in BC. The meta-analysis by Mikkelsen \u003cem\u003eet al\u003c/em\u003e suggested a limited potential for CRP as a prognostic marker in non-metastatic settings\u0026nbsp;[19]. In studies using CRP as a categorical variable, high CRP was associated with lower DFS and OS, but the estimates were imprecise. A Danish study of 2,910 patients showed that the highest CRP tertile was associated with reduced OS and DFS\u0026nbsp;[36]. The patients had BC stage I-IV diagnosed between 2002 and 2009, and blood was drawn at the time of diagnosis. In a cohort of BC patients with stage I-III disease in the United States (N=2,919), similar results were reported, in which blood was drawn at least 12 months after no evidence of disease (median 21.7 months)\u0026nbsp;[37]. Higher all-cause mortality was observed in patients with CRP levels\u0026nbsp;\u0026nbsp;10 mg/L compared to patients with CRP levels \u0026nbsp;\u0026lt;1 mg/L. A decrease in OS in pre-diagnostic CRP levels\u0026nbsp;\u0026nbsp;10 mg/L compared to \u0026lt;10 mg/L was found in a study by Wulaningsih \u003cem\u003eet al\u003c/em\u003e (N=6,606)\u0026nbsp;[38]. Contrary to these studies, Frydenberg \u003cem\u003eet al\u003c/em\u003e found a decreased risk of all-cause death in the highest pre-diagnostic CRP tertile, and a similar correlation was found in DFS analyses (N=192)\u0026nbsp;[39]. Our findings are consistent with results from the larger studies\u0026nbsp;[36\u0026ndash;38].\u003c/p\u003e\n\u003cp\u003eTo our knowledge, only three studies have investigated the association between CRP and BC prognosis stratified by BMI\u0026nbsp;[20\u0026ndash;22]. Our study is the first to explore the association with DFS across BMI groups. The latest study, a Chinese prospective multicenter cohort study with BC patients stage I-IV (N=514)\u0026nbsp;by Ruan \u003cem\u003eet al\u003c/em\u003e [20], found a strong correlation between CRP \u0026gt;10 mg/L and all-cause mortality. CRP \u0026gt;10 mg/L was associated with lower OS in patients with BMI\u0026nbsp;\u0026nbsp;24 kg/m\u003csup\u003e2\u003c/sup\u003e and BMI \u0026lt;24 kg/m\u003csup\u003e2\u003c/sup\u003e, however, the precision of the estimate was weaker in patients with BMI \u0026lt;24 kg/m\u003csup\u003e2\u0026nbsp;\u003c/sup\u003ecompared to patients with BMI\u0026nbsp;\u0026nbsp;24 kg/m\u003csup\u003e2\u003c/sup\u003e. In 1,114 BC patients (stage in situ to IV), Nelson \u003cem\u003eet al\u003c/em\u003e reported that only patients with higher CRP levels and normal-weight had an increased risk of death (HR: 1.39 [95CI%=1.03\u0026ndash;1.89]) for every 1 standard deviation increase in logCRP\u0026nbsp;[21]. Patients with BMI \u0026gt;25 kg/m\u003csup\u003e2\u003c/sup\u003e and higher CRP levels\u0026nbsp;had a slightly lower risk of death, but the precision of the estimate was low. In the NHANES III cohort, Wulaningsih \u003cem\u003eet al\u003c/em\u003e included 7,780 females aged \u0026ge;20 without a cancer history at baseline\u0026nbsp;[22]. A total of 44 BC deaths were reported. The risk of BC death per log CRP increase was higher in BMI \u0026lt;30 kg/m\u003csup\u003e2\u003c/sup\u003e compared to BMI\u0026nbsp;\u0026nbsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e (HR 1.94 [95CI%=0.51\u0026ndash;7.29] vs. HR 1.40 [95CI%=0.52\u0026ndash;3.77]), however, the precision of the estimates was low.\u003c/p\u003e\n\u003cp\u003eSince our findings suggest that increased CRP across all BMI groups may be linked to worse BC prognosis, our results are similar to most of the results from the previous studies cited above. However, variations in study designs make a direct comparison of results difficult. Comparing our results with Ruan \u003cem\u003eet al\u003c/em\u003e is problematic since a BMI\u0026nbsp;\u0026nbsp;24 kg/m\u003csup\u003e2\u003c/sup\u003e includes both normal-weight, overweight, and obesity, and they used CRP as a binary exposure\u0026nbsp;[20]. Also, stage IV patients were included, the cohort was younger (mean 53.7 years), and treatment choice differed (i.e. only 5.3% received radiotherapy)). It is not clear when Ruan \u003cem\u003eet al\u003c/em\u003e collected their blood samples. Our study cohort is larger, but based on a single-center cohort study. In the study by Nelson \u003cem\u003eet al\u003c/em\u003e, the patients were older (mean age from 70.3-71.5 years in CRP quartiles), and they included patients with in situ and stage IV disease\u0026nbsp;[21]. Blood samples were collected, on average 7.8 years before BC diagnosis which constitutes a major difference to our study\u0026nbsp;[21]. Also, patients with underweight were included in the stratified analyses, and patients with CRP \u0026gt;10 mg/L were excluded. Like Nelson \u003cem\u003eet al\u003c/em\u003e, the CRP levels were measured before BC diagnosis by Wulaningsih \u003cem\u003eet al\u003c/em\u003e [22]. The authors had no information on BC incidence and disease characteristics\u0026nbsp;[22].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur findings could have clinical implications. CRP at the time of diagnosis may be used by clinicians to identify BC patients with an increased risk of inferior outcomes. The precision of the estimate in patients with obesity could improve with a larger sample size, as our results in patients with obesity could be due to a type 2 error. However, many other factors are involved in the link between obesity and BC, such as adipokines and estrogens, as we previously reviewed\u0026nbsp;[40], and could potentially be of more significant importance than CRP for patients with obesity. Also, CRP is a surrogate marker for systemic inflammation and many factors (e.g. smoking and blood pressure) influence CRP levels\u0026nbsp;[41], and we were not able to take all these factors into consideration. Furthermore, CRP is not an appropriate marker for the local inflammatory environment in the breast.\u003c/p\u003e\n\u003cp\u003eOur study has limitations. First, it is a single-institutional study and the results may not apply to other institutions and countries. Second, we adjusted for potential confounders, but we cannot rule out the possibility of residual confounding, such as smoking status and alcohol consumption. Third, not all BC patients seen at the Department of Plastic and Breast Surgery, AUH, agreed to donate a blood sample, and we do not have information on the non-participants, which could lead to selection bias. Fourth, BMI is an indicator of general obesity but does not reveal information on body composition, which is important information as the inflammatory signatures differ between abdominal and gynoid obesity [42]. Fifth, we only measured CRP levels at a single time point, so we were unable to evaluate the impact of fluctuations in CRP, for example, due to acute infection or lifestyle factors.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eHigh circulating CRP levels at the time of BC diagnosis were associated with an inferior BC prognosis in this large Danish cohort. Furthermore, our study suggests that CRP may be a clinically relevant prognostic marker for BC prognosis across BMI groups. Future studies should investigate the relationship between CRP and BC in patients with obesity on a larger scale. Also, we encourage the investigation of other obesity-associated biomarkers in mapping the link between obesity and BC prognosis to identify patients in need of additional intervention.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors have no conflicts of interest to declare that are relevant to the content of this article.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics approval\u003c/h2\u003e \u003cp\u003eAll projects applying for samples at the RBGB need approval from the Danish Data Protection Agency and the Danish Council on Ethics. The conduction of the study is approved by the Danish Council on Ethics (no. 1-10-72-192-20) and registered as a scientific project at Region Midtjylland, Denmark (no. 1-16-02-299-20).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to participate\u003c/strong\u003e \u003cp\u003e Informed consent was obtained from all participants included in the study.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the Novo Nordisk Foundation STENO Collaborative Grant (NNF20OC0065928), the NEYE Foundation, the Danish Cancer Society (R288-A16168 \u0026amp; R328-A19070), \u0026ldquo;Fagerlund Stiftelsen\u0026rdquo;, and the Department of Oncology Research Foundation.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eStudy conception and design: JBH, DCF, JMB, PC, and SB. Data collection: JBH, EB, PC, and SB. Data analysis: JBH supervised by DCF and SB. Interpretation of data: all authors. Writing \u0026ndash; original draft preparation: JBH. Writing \u0026ndash; review: all authors. Writing \u0026ndash; editing: JBH. All authors approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe datasets generated and/or analyzed are not publicly available due to individual privacy could be compromised, but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL et al (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. 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Front Endocrinol (Lausanne) 14:1205799. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fendo.2023.1205799\u003c/span\u003e\u003cspan address=\"10.3389/fendo.2023.1205799\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 2 to 4 are available in the Supplementary Files section.\u003c/p\u003e\n"}],"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"breast cancer, obesity, body mass index, inflammation, C-reactive protein, prognosis","lastPublishedDoi":"10.21203/rs.3.rs-3996677/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3996677/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eObesity and systemic inflammation are associated with breast cancer (BC) outcomes. Systemic inflammation is increased in obesity. We examined the association between C-reactive protein (CRP) and disease-free survival (DFS) and overall survival (OS) overall, and according to body mass index (BMI).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe assembled a cohort of women with BC (stage I-III) seen at Aarhus University Hospital between 2010 and 2020 who donated blood at BC diagnosis (N\u0026thinsp;=\u0026thinsp;2,673). CRP levels were measured and divided into quartiles. We followed patients from surgery to recurrence, contralateral BC, other malignancy, death, emigration, or end-of-follow-up. We used Cox regression to estimate hazard ratios (HRs) with 95% confidence intervals (95%CIs) to compare outcomes across CRP quartiles, overall and stratified by BMI (normal-weight (18.5\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e), overweight (25\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e), and obesity (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e)).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eDuring follow-up, 368 events (212 recurrences, 38 contralateral BCs, and 118 deaths) occurred (median follow-up 5.55 years). For DFS, high CRP (CRP\u0026thinsp;\u0026ge;\u0026thinsp;3.24 mg/L) was associated with an increased risk of events (HR\u003csub\u003eadj\u003c/sub\u003e:1.58 [95%CI\u0026thinsp;=\u0026thinsp;1.12\u0026ndash;2.24]). In BMI-stratified analyses, high CRP was associated with elevated risk of events in normal-weight and overweight (HR\u003csub\u003eadj\u003c/sub\u003e:1.70 [95%CI\u0026thinsp;=\u0026thinsp;1.09\u0026ndash;2.66]; HR\u003csub\u003eadj\u003c/sub\u003e:1.75 [95%CI\u0026thinsp;=\u0026thinsp;1.08\u0026ndash;2.86]), but in obesity, the estimate was less precise (HR\u003csub\u003eadj\u003c/sub\u003e:1.73 [95%CI\u0026thinsp;=\u0026thinsp;0.78\u0026ndash;3.83]). For OS, high CRP was associated with increased risk of death (HR\u003csub\u003eadj\u003c/sub\u003e:2.47 [95%CI\u0026thinsp;=\u0026thinsp;1.62\u0026ndash;3.76]). The association was strong in normal-weight and overweight (HR\u003csub\u003eadj\u003c/sub\u003e:3.66 [95%CI\u0026thinsp;=\u0026thinsp;1.95\u0026ndash;6.87]; HR\u003csub\u003eadj\u003c/sub\u003e:1.92 [95%CI\u0026thinsp;=\u0026thinsp;1.06\u0026ndash;3.46]), but less clear in obesity (HR\u003csub\u003eadj\u003c/sub\u003e:1.40 [95%CI\u0026thinsp;=\u0026thinsp;0.64\u0026ndash;3.09]).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eHigh CRP levels at BC diagnosis were associated with inferior prognosis in early BC irrespective of BMI, although less clear in patients with obesity.\u003c/p\u003e","manuscriptTitle":"Circulating C-reactive protein levels as a prognostic biomarker in breast cancer across body mass index groups","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-05 15:50:35","doi":"10.21203/rs.3.rs-3996677/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"61c168a9-5a8f-4ade-b032-937f07ec38cf","owner":[],"postedDate":"March 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-06-27T15:29:33+00:00","versionOfRecord":{"articleIdentity":"rs-3996677","link":"https://doi.org/10.1038/s41598-024-64428-3","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-06-24 15:29:33","publishedOnDateReadable":"June 24th, 2024"},"versionCreatedAt":"2024-03-05 15:50:35","video":"","vorDoi":"10.1038/s41598-024-64428-3","vorDoiUrl":"https://doi.org/10.1038/s41598-024-64428-3","workflowStages":[]},"version":"v1","identity":"rs-3996677","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3996677","identity":"rs-3996677","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}