Prognostic Nutritional Index as a Predictor of Mortality Risk in Breast Cancer Survivors: A Population-Based Study

Prognostic Nutritional Index as a Predictor of Mortality Risk in Breast Cancer Survivors: A Population-Based Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prognostic Nutritional Index as a Predictor of Mortality Risk in Breast Cancer Survivors: A Population-Based Study Xin Zhang, Junlong Wang, Siyu Liao, Yuehua Huang, Youjiang Tan, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8399826/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background: The Prognostic Nutritional Index (PNI) is widely utilized to evaluate nutritional and inflammatory status in predicting cancer prognosis, yet its impact on mortality among breast cancer patients remains incompletely understood. This study aimed to assess the association between PNI and all-cause as well as cancer-specific mortality in breast cancer survivors. Material and methods: Data were retrieved from the National Health and Nutrition Examination Survey (NHANES) covering the period 2005 to 2018. A cross-sectional study design was employed to examine the association between PNI and breast cancer prevalence, with a cohort design utilized for mortality follow-up. Moreover, weighted logistic regression was applied to quantify the relationship between PNI and breast cancer survivor status. Multivariate Cox proportional hazards models, restricted cubic spline (RCS) analysis, and two-piecewise Cox proportional hazards models were used to evaluate the correlations of PNI with all-cause mortality and cancer-specific mortality. Finally, subgroup analyses were performed to validate the robustness. Results: A total of 16,688 participants were ultimately enrolled. Among 479 female breast cancer survivors, 122 all-cause deaths occurred during follow-up, including 43 cancer deaths (median 6.21 years). After multivariate adjustment, RCS analysis revealed non-linear relationships, with inflection points: 46 for all-cause and cancer-specific mortality. When PNI < 46, higher PNI significantly reduced all-cause mortality (HR = 0.74, 95% CI: 0.64 - 0.86, P < 0.001) and cancer-specific (HR = 0.34, 95% CI: 0.20 - 0.58, P < 0.001). When PNI ≥ 46, the association between elevated PNI and the risk of all-cause mortality (HR = 0.97, 95% CI: 0.89 - 1.06, P = 0.489) as well as cancer-specific mortality (HR = 1.04, 95% CI: 0.84 - 1.28, P = 0.741) was no longer statistically significant. The final subgroup analysis further supported the robustness of the results. Conclusions: PNI was nonlinearly associated with mortality in breast cancer survivors. Its threshold facilitates risk stratification, and a PNI below this threshold increases the risk of both all-cause and cancer-specific mortality. However, this specific finding requires validation in larger cohorts due to substantial statistical uncertainty. Breast cancer Prognostic Nutritional Index (PNI) NHANES All-Cause Mortality population-based study Figures Figure 1 Figure 2 Figure 3 Introduction Breast cancer is the most common malignancy in women around the world ( 1 ). It is a heterogeneous disease in which genetic and environmental factors are involved with molecular subtypes that have biological distinctness and different behavior ( 2 ). Breast cancer is life-threatening disease in females ( 3 ). Breast cancer incidence continued an upward trend. Between 1990 to 2021, the global incidence, death, and DALYs, of female breast, cervical, uterine and ovarian cancer both to varying degrees of elevation ( 4 ). Certain patients with breast cancer often experience a poor prognosis due to metastasis progression despite receiving advanced treatment strategies such as surgery, endocrine therapy, radiotherapy and chemotherapy ( 5 ). Recent studies have shown that the prognosis of various cancer types is also affected by patient-related inflammation, immunocompetence, and nutrition. The correlation between nutrition and cancer prognosis is particularly evident ( 6 ). Malnutrition is common in patients with breast cancer, which can suppress immune function and reduce resistance to disease. Therefore, further exploration of biomarkers with potential prognostic value to assess the risk of death in breast cancer survivors holds significant clinical and public health significance. The Prognostic Nutritional Index (PNI) is an indicator that comprehensively assesses a patient's nutritional and immune status. The PNI simultaneously reflects visceral protein reserves and cellular immune function. Initially developed to predict the risk of postoperative complications in surgical patients ( 7 ), the PNI has since been confirmed as a reliable biomarker for prognosis across multiple diseases. In the cardiovascular field, the PNI can independently predict the risk of major adverse cardiac events in patients with peripartum cardiomyopathy ( 8 ). In kidney diseases, a lower PNI is significantly associated with more severe renal histopathological damage and a higher risk of progression to end-stage renal disease in patients with diabetic nephropathy ( 9 ). In hip fracture patients, a higher preoperative PNI is independently associated with a 39% reduction in the risk of postoperative complications and a 39% decrease in all-cause mortality at 2 years ( 10 ). The importance of PNI is increasingly recognized in oncology research. For instance, in patients with metastatic renal cell carcinoma receiving targeted therapy, a lower PNI before treatment is an independent predictor of shorter overall survival and progression-free survival, with predictive power even superior to some traditional risk stratification systems ( 11 ). A meta-analysis of 17 articles involving 2,883 patients with gastrointestinal cancer found that patients with high PNI levels had longer OS and PFS, higher objective response rates, and better disease control rates ( 12 ). A meta-analysis of 8 studies involving 2,322 breast cancer patients showed that a lower preoperative PNI was significantly associated with poorer OS and disease-free survival ( 13 ). Although multiple studies have reported that the PNI is associated with disease progression and poor prognostic outcomes in various cancers ( 11 – 13 ), there remains a lack of in-depth research on the prognostic value of PNI in patients with breast cancer survivors. Given the large volume of breast cancer cases, identifying biomarkers with potential prognostic value holds exploratory significance for optimizing risk stratification and informing future research on individualized treatment strategies. Therefore, our research team, utilizing nationally representative data from the NHANES, aimed to preliminarily investigate the association between PNI and all-cause and cancer-specific mortality in breast cancer survivors; we analyzed the potential prognostic threshold and dose-response curve of PNI, with the goal of providing preliminary observational evidence for research on prognosis assessment in breast cancer survivors. Material and methods Study population NHANES, a nationwide cross-sectional survey led by the U.S. Centers for Disease Control and Prevention (CDC), employs a multistage stratified probability sampling strategy to dynamically monitor health status, nutritional levels, and related lifestyle factors in the U.S. general population. Ongoing since 1999, the program collects multidimensional data via standardized questionnaires, physical examinations, and laboratory tests. All participants provided written informed consent, and the study protocol was approved by the National Center for Health Statistics Institutional Review Board. This study integrated NHANES data from 2005 - 2018, strictly following NHANES guidelines analysis. Initially, 70,190 eligible participants were recruited. Subsequently, 4,787 respondents lacking follow-up information were excluded, resulting in 65,403 participants. A total of 30,229 participants with missing data on PNI, cancer status, and covariates were removed, leaving 35,174 participants. Further exclusions were performed to eliminate 2,675 individuals diagnosed with other cancers (not breast cancer) and 15,301 male participants (Figure 1) . Exposure Variable In this study, the exposure factor was PNI, calculated using the following formula: PNI = Albumin (g/L) + 5 × Lymphocyte number(10⁹/L). Lymphocyte number was primarily determined by complete blood cell count test, which used Beckman Coulter counting and sizing methods for measurement. Serum albumin levels were analyzed using an automated chemistry analyzer, the Beckman Synchron LX20. The patients were then divided into tertiles based on their PNI, categorized into three distinct groups: T1 group (35 - 48), T2 group (48 - 52), T3 group (52 - 68). Outcome Variables Breast cancer survivors were categorized based on previously published diagnostic criteria: participants with a self-reported history of breast cancer were classified as breast cancer survivors, whereas those who denied a breast cancer diagnosis were defined as non-cases. Participants responded with “refuse” or “don’t know” to the breast cancer diagnosis inquiry, were excluded from the analysis. All-cause mortality and cancer-specific mortality data were obtained through the NHANES database linked to the National Death Index system. The follow-up period ranged from the date of participant enrollment in the survey to December 31, 2019. The cause of death was classified according to the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD - 10). All-cause mortality included all deaths recorded in the NDI, while cancer-specific mortality was defined by ICD - 10 codes C00 - C97 and further verified by tumor registry data. Covariates We included various covariates that might affect the results. Age, gender, race, education level, smoking status and disease status were collected from standardized household interview questionnaires. The laboratory indicators used included white blood cells (10⁹/L), segmented neutrophils percent (%), lymphocyte number (10⁹/L), platelets (10⁹/L), serum albumin (g/L) and total calcium (mmol/L). Detailed information on the relevant definitions is provided in the Supplementary Methods . In this study, the covariate check and screening module of the EmpowerStats software was used to explore the covariates of all-cause mortality risk factors. The final fully adjusted model used the following variables: age, gender, race, education level, BMI (Kg/m 2 ), overweight(yes, no), smoking status (yes, no), total calcium (mmol/L), white blood cell count (10⁹/L), segmented neutrophil percentage (%), hypertension (yes, no), diabetes (yes, no), heart attack (yes, no), stroke history (yes, no), platelet (10⁹/L). Statistical analysis All statistical analyses were conducted in accordance with the guidelines of the Centers for Disease Control and Prevention (https://wwwn.cdc.gov/nchs/nhanes/tutorials/default.aspx). Each participant in the NHANES was assigned a sample weight. Therefore, to account for significant variations, weighted methods were adopted in this study. Continuous variables were expressed as weighted means (95% confidence intervals), and categorical variables as weighted percentages. We used weighted χ2 tests (for categorical variables) or weighted linear regression models (for continuous variables) to calculate the differences between different PNI group. To explore the association between the PNI and mortality, adjusted univariate and multivariate Cox proportional hazards regression analyses were employed in this study. To examine the independent association of PNI with all-cause and cause-specific mortality, multivariate Cox proportional hazards regression models were used. Covariate selection for multivariable adjustment was performed using the covariate adjustment module in EmpowerStats software. Baseline variables were included in the final model if they: (1) showed potential association with outcomes in univariate Cox regression ( P < 0.1); or (2) were clinically relevant to survival based on established literature, regardless of univariate significance. Hierarchical Model Construction: To clarify adjustment effects on the PNI-mortality association, progressively adjusted Cox models were fitted: Model 1 : Unadjusted. Model 2 : Adjusted for age, smoking status, and key serum parameters [total calcium (mmol/L), lymphocyte count (10⁹/L), white blood cell count (10⁹/L), segmented neutrophil percentage (%)]. Model 3 : Adjusted for age, race, education, BMI (Kg/m 2 ), overweight(yes, no), smoking status (yes, no), total calcium (mmol/L), lymphocyte count (10⁹/L), white blood cell count (10⁹/L), segmented neutrophil percentage (%), platelet (10⁹/L), hypertension (yes, no), diabetes (yes, no), history of heart attack (yes, no) and history of stroke (yes, no). Nonlinear Relationship Analysis: To explore potential nonlinear associations between PNI and mortality risk, Cox models with restricted cubic splines (RCS) were used, with 3 knots at predefined percentiles (10th, 50th, 90th). Dose-response relationships were visualized using penalized smoothing splines. Threshold points characterizing nonlinear patterns were determined via an iterative recursive algorithm maximizing model likelihood. Nonlinearity significance was assessed by comparing RCS Cox models with linear Cox models using log-likelihood ratio tests. Subgroup and Interaction Analyses: Subgroup analyses for all-cause and cancer-specific mortality were performed across strata: age (<65/≥65 years), race, education, smoking status (yes, no), hypertension (yes, no), diabetes (yes, no), history of heart attack (yes, no), history of stroke (yes, no), overweight (yes, no). Interaction effects were tested by including multiplicative interaction terms in fully adjusted Cox models. Statistical Software: Analyses were performed using R software (version 4.2.0) for core modeling and EmpowerStats (version 4.2.0) for automated threshold detection and visualization. Statistical significance was defined as two-sided P < 0.05. Results Baseline characteristics of study population This study included a total of 16,688 participants with a mean age of (46.99 ± 16.89) years. Of these, 8.4% were Mexican American, 5.9% were other Hispanic, 65.7% were non-Hispanic White, 12.1% were non-Hispanic Black, and 8.0% were of other races. The mean PNI among all participants was (52.54 ± 5.42). Compared with the group without a history of breast cancer, the group of breast cancer survivors was significantly older and had a significantly higher proportion of comorbidities including heart attack, stroke, diabetes, and hypertension. Additionally, the proportion of non-Hispanic White individuals and those with a college degree or higher education was significantly higher in the breast cancer history group. Meanwhile, this group had significantly lower PNI levels, follow-up time, lymphocyte count, white blood cell count, and platelet count, whereas the percentage of segmented neutrophils and total calcium levels were significantly higher. Statistical analysis revealed no significant differences between the two groups in terms of smoking status or albumin levels ( P > 0.05) (Table 1 ). Further analyzed the breast cancer survivors were grouped according to the three of PNI. The ranges for PNI for Tertile 1 through Tertile 3 were 35–48, 48–52 and 52–68, respectively. We found significant differences between PNI tertiles for characteristics ( Table S1 ). Compared with the PNI tertile 1 group, participants in tertile 2 and 3 groups were younger, had a lower percentage of neutrophils, and higher levels of serum albumin, lymphocyte counts, and platelet counts (all P < 0.05). There were no significant differences among the three groups in terms of race, education level, smoking status, or complications such as heart attack, stroke, hypertension, and diabetes (all P > 0.05) ( Table S1 ). Table 1 Baseline characteristics of study population with and without breast cancer Characteristics All participants Breast cancer survivors No history of breast cancer P-Value N a 16688 479 16209 Estimated N 98724006 3031034.2 95692971.8 PNI 52.54 ± 5.42 50.58 ± 4.53 52.60 ± 5.43 < 0.001 Age 46.99 ± 16.89 65.72 ± 11.82 46.40 ± 16.69 < 0.001 BMI(Kg/m2) 29.14 (7.56) 29.06 (6.78) 29.15 (7.58) 0.846 Race/ethnicity (%) < 0.001 Mexican American 8269504.7 (8.4) 105866.0 (3.5) 8163638.7 (8.5) Other Hispanic 5802173.0 (5.9) 80012.5 (2.6) 5722160.6 (6.0) Non-Hispanic White 64826476.7 (65.7) 2518035.9 (83.1) 62308440.8 (65.1) Non-Hispanic Black 11952929.0 (12.1) 208503.5 (6.9) 11744425.5 (12.3) Other Race 7872922.7 (8.0) 118616.4 (3.9) 7754306.3 (8.1) Education level (%) 0.042 Less than 9th grade 5291150.8 (5.4) 140965.0 (4.7) 5150185.8 (5.4) 9-11th grade 9940861.2 (10.1) 249667.7 (8.2) 9691193.5 (10.1) High school graduate 21932480.8 (22.2) 669342.7 (22.1) 21263138.1 (22.2) Some college or AA degree 32916799.1 (33.3) 874155.6 (28.8) 32042643.5 (33.5) College graduate or above 28642714.2 (29.0) 1096903.2 (36.2) 27545811.0 (28.8) Smoke (%) 0.88 Yes 36537564.7 (37.0) 1135841.7 (37.5) 35401723.0 (37.0) No 62186441.3 (63.0) 1895192.5 (62.5) 60291248.8 (63.0) Heart attack (%) < 0.001 Yes 2114661.1 (2.1) 225728.7 (7.4) 1888932.4 (2.0) No 96609344.9 (97.9) 2805305.5 (92.6) 93804039.4 (98.0) Stroke (%) < 0.001 Yes 2725230.2 (2.8) 216386.1 (7.1) 2508844.0 (2.6) No 95998775.9 (97.2) 2814648.1 (92.9) 93184127.8 (97.4) Hypertension (%) < 0.001 Yes 29675043.3 (30.1) 1543180.6 (50.9) 28131862.7 (29.4) No 69048962.7 (69.9) 1487853.6 (49.1) 67561109.1 (70.6) Diabetes (%) < 0.001 Yes 10335122.3 (10.5) 586428.0 (19.3) 9748694.3 (10.2) No 88388883.7 (89.5) 2444606.2 (80.7) 85944277.5 (89.8) Overweight 0.227 Yes 38008389.9 (38.5) 1278958.0 (42.2) 36729431.8 (38.4) No 60715616.1 (61.5) 1752076.2 (57.8) 58963540.0 (61.6) Laboratory data Albumin (g/L) 41.61 ± 3.41 41.29 ± 2.95 41.62 ± 3.43 0.094 Lymphocyte number (10⁹/L) 2.19 ± 0.85 1.86 ± 0.69 2.20 ± 0.85 < 0.001 White blood cell count (10⁹/L) 7.35 ± 2.23 6.93 ± 2.10 7.36 ± 2.23 0.003 Segmented neutrophils percent (%) 58.64 ± 9.10 60.48 ± 9.33 58.58 ± 9.09 0.001 Platelet (10⁹/L) 262.61 ± 66.93 245.53 ± 64.23 263.15 ± 66.95 < 0.001 Total calcium (mmol/L) 2.34 ± 0.09 2.36 ± 0.10 2.34 ± 0.09 < 0.001 Follow-up time (years) 7.52 ± 4.02 6.21 ± 4.03 7.57 ± 4.02 < 0.001 Note: Mean ± standard deviation for continuous variables: P-Value was calculated by weighted linear regression model. Number (%) for categorical variables: P-Value was calculated by weighted χ2 test. a : Unweighted number of observations in dataset. Association of PNI with all-cause and cancer-specific mortality in breast cancer survivors. During a median follow-up of 6.21 years (standard deviation: ± 4.03 years), 122 all-cause deaths occurred, including 43 cancer deaths. Kaplan-Meier survival plots showed lower all-cause, cancer-specific mortality in PNI quartiles Tertile 2–3 compared with Tertile 1( P < 0.05) ( Supplementary Figure S1 ). The results as shown in Table 2 , in Model 3, each 1-unit increase in PNI was associated with a 9% reduction in all-cause mortality (HR = 0.91, 95%CI: 0.84–0.99, P = 0.035), a 19% reduction in cancer-specific mortality (HR = 0.81, 95%CI: 0.68–0.96, P = 0.016). Table 2 Association between PNI with all-cause and cancer-specific mortality in breast cancer survivors. Mortality No. of Events Model 1 Model 2 Model 3 HR (95% CI) P Value HR (95% CI) P Value HR (95% CI) P Value All-cause mortality in breast cancer survivors PNI 122 0.91; (0.86–0.97) 0.003 0.93; (0.86–1.00) 0.041 0.91; (0.84–0.99) 0.035 Tertile 1 56 Ref Ref Ref Ref Ref Ref Tertile 2 36 0.43; (0.27–0.71) <0.001 0.62; (0.40–0.98) 0.039 0.60; (0.36–1.01) 0.052 Tertile 3 30 0.46; (0.27–0.79) 0.005 0.59; (0.31–1.10) 0.099 0.55; (0.29–1.07) 0.081 P for trend 122 0.64; (0.49–0.86) 0.003 0.74; (0.54–1.01) 0.058 0.34; (0.14–0.83) 0.018 cancer-specific mortality in breast cancer survivors PNI 43 0.95; (0.84–1.01) 0.368 0.75; (0.64–0.89) <0.001 0.81; (0.68–0.96) 0.016 Tertile 1 19 Ref Ref Ref Ref Ref Ref Tertile 2 12 0.33; (0.15–0.73) 0.007 0.27; (0.11–0.67) 0.005 0.13; (0.05–0.34) <0.001 Tertile 3 12 0.61; (0.25–1.49) 0.276 0.27; (0.06–1.29) 0.100 0.19; (0.04–1.03) 0.055 P for trend 43 0.72; (0.42–1.23) 0.232 0.42; (0.20–0.89) 0.023 0.34; (0.14–0.83) 0.018 Nonlinear association of PNI with All-cause and cancer-specific mortality in breast cancer survivors Restricted cubic spline (RCS) analysis combined with Cox models (adjusted for age, race, educational level, smoking status, serum total calcium, lymphocyte count, white blood cell count, segmented neutrophil percentage, platelet count, as well as hypertension, diabetes, history of heart attack, and history of stroke) showed that among breast cancer survivors, the PNI was nonlinearly and negatively associated with all-cause mortality and cancer-specific mortality (Fig. 2 ). Threshold effect analysis of PNI on all-cause and cancer-specific mortality in breast cancer survivors We used the Cox proportional hazards model and the two-segment Cox proportional hazards model to fit the relationship between the PNI and the mortality rate of survivors with breast cancer. The results are shown in Table 3 . There was a non-linear association between PNI and all-cause and cancer-specific mortality (log-likelihood ratio test, P < 0.05). We determined the PNI inflection point. When PNI < 46, an increase in PNI was significantly associated with a decreased risk of all-cause mortality (HR = 0.74, 95% CI: 0.64–0.86, P < 0.001) and cancer-specific mortality (HR = 0.34, 95% CI: 0.20–0.58, P < 0.001) in survivors with breast cancer. At PNI ≥ 46, the association between PNI and risks of all-cause mortality (HR = 0.97, 95% CI: 0.89–1.06, P = 0.489) and cancer-specific mortality (HR = 1.04, 95% CI: 0.84–1.28, P = 0.741) lost statistical significance in breast cancer survivors. Table 3 Threshold effect analysis of PNI on all-cause and cancer mortality. Variable Adjusted HR (95% CI) a , P Value Model 1 Model 2 Model 3 All-cause mortality in breast cancer PNI 0.92 (0.88–0.96); <0.0001 0.92 (0.86–0.99) 0.025 0.91 (0.85–0.98); 0.015 Inflection point (K) 46 46 46 <K 0.72 (0.64–0.81); <0.001 0.76 (0.66–0.88); <0.001 0.74 (0.64–0.86); K 0.97 (0.92–1.02); 0.251 0.98 (0.90–1.05); 0.536 0.97 (0.89–1.06); 0.489 P for log likelihood ratio test < 0.001 0.005 0.005 Cancer-specific mortality in breast cancer PNI 0.92 (0.88–0.96) < 0.001 0.80 (0.70–0.92); 0.0021 0.82 (0.69–0.97); 0.023 Inflection point (K) 46 46 46 <K 0.72 (0.64–0.81); <0.001 0.47 (0.34–0.64); <0.001 0.34 (0.20–0.58); K 0.97 (0.92–1.02); 0.251 0.91 (0.78–1.07); 0.263 1.04 (0.84–1.28); 0.741 P for log likelihood ratio test < 0.001 < 0.001 < 0.001 Subgroup analysis of PNI and all-cause mortality and cancer-specific mortality in breast cancer survivors As shown in Fig. 3 , we conducted subgroup analyses to examine whether demographic characteristics and comorbidities could modify the association between PNI and all-cause mortality or cancer-specific mortality among breast cancer survivors. When stratified by age, race, education level, smoking status, diabetes, history of heart attack, history of stroke, and overweight, the results remained consistent (all P -values for interaction were > 0.05). A significant interaction was observed between PNI and hypertension with regard to all-cause mortality ( P -value for interaction < 0.05). Discussion The NHANES database of the United States, as one of the world's widely used national epidemiological databases, serves as a valuable resource for population-based epidemiological research, facilitating investigations into potential links between health indicators and clinical outcomes ( 14 ). This study, using data from NHANES, aims to provide a preliminary assessment of the associations between PNI and all-cause and cancer mortality in breast cancer survivors. Through multivariate Cox regression and RCS analysis, we preliminarily confirmed that the PNI exhibits a non-linear negative correlation with all-cause mortality and cancer-specific mortality in breast cancer survivors: when PNI is below 46, it may be associated with an increased risk of all-cause mortality and cancer-specific mortality in this population, and this threshold can provide preliminary reference for mortality risk stratification in breast cancer survivors. The PNI has emerged as a validated biomarker that integrates immune and nutritional status and has been shown to have prognostic value in various malignancies, including gastric cancer ( 12 , 13 , 15 ), colorectal cancer ( 16 , 17 ), and hepatocellular carcinoma ( 12 ). In non-tumor populations, low PNI independently predicts an increased risk of cardiovascular mortality ( 18 ). However, evidence directly linking PNI to survival outcomes in breast cancer survivors remains limited. Our study found a negative correlation between PNI and the risk of all-cause mortality and cancer-specific mortality in survivors with breast cancer. However, most studies have confirmed that the PNI threshold is disease-specific. Previous studies have determined the optimal preoperative PNI cutoff value for colorectal cancer patients to be 48.65 (training cohort) through ROC curve analysis, and found that a low PNI (< 48.65) was significantly associated with poor prognosis (HR = 2.78, P = 0.005) ( 17 ). Japanese researchers determined the optimal PNI cutoff value for gastric cancer patients to be 45 (high PNI group ≥ 45, low PNI group < 45) through ROC curve analysis, and found that the 5-year OS and cancer-specific survival in the low PNI group were significantly lower than those in the high PNI group (76.7% and 87.0%, P < 0.01) ( 19 ). Therefore, we further analyzed and confirmed the PNI thresholds for predicting all-cause mortality and cancer-specific mortality in breast cancer. The results suggest that the PNI threshold for all-cause mortality and cancer-specific mortality in breast cancer survivors is 46. When PNI is below the inflection point, an increase in PNI significantly reduces the risk of all-cause mortality and cancer-specific mortality; while above the inflection point, the association between PNI and breast cancer mortality weakens. This underscores the importance of dynamically monitoring PNI levels in breast cancer survivors to reduce the risks of all-cause mortality and cancer-specific mortality. Subgroup analysis demonstrated that the predictive efficacy of the PNI threshold remained stable across strata of age, race, education level, smoking status, diabetes, heart disease, stroke, and overweight (all P for interaction > 0.05). Notably, a significant interaction was observed between PNI and hypertension with respect to all-cause mortality ( P for interaction < 0.05): the protective association of PNI with all-cause mortality was substantially attenuated in breast cancer survivors with hypertension. This significant interaction underscores the need for clinicians to pay special attention to breast cancer survivors with hypertension when using PNI to stratify all-cause mortality risk. In conclusion, the present study found that PNI levels may serve as a biomarker for assessing mortality risk in breast cancer survivors, which warrants further validation in large-scale clinical studies. PNI is calculated based on serum albumin and lymphocyte count, and it can comprehensively reflect the nutritional status, immune function and chronic inflammation level of the body. This characteristic makes it an important indicator for prognosis assessment in various diseases. In survivors with breast cancer, the relationship between PNI and the risk of death may be attributed to the following interrelated biological pathways. The cascade events triggered by low PNI may amplify the risk of death through impaired immune function and chronic inflammation. Lymphocytes, as a key component of the immune system, mainly include T lymphocytes, B lymphocytes and natural killer cells. A decrease in lymphocyte count directly weakens cell-mediated immune responses, including impaired differentiation of CD4 + T cells and reduced cytotoxicity of NK cells, which may reduce the body's surveillance ability against tumor cells and accelerate tumor development ( 20 ). In addition, lymphocytes produce cytokines such as interferon-γ, tumor necrosis factor-α and interleukin-12, which play important roles in activating immune cells and regulating inflammatory responses. Serum albumin is the main protein component in human serum and plays a crucial role in maintaining colloid osmotic pressure in the blood, transporting metabolic substances in the body and providing nutrition. Over the past few decades, serum albumin has been widely used to assess the general nutritional status of patients and has been proven to be related to the prognosis of cancer patients ( 21 – 24 ). Low albumin levels not only indicate malnutrition but are also associated with systemic inflammation. Inflammatory factors inhibit albumin synthesis, and oxidative stress causes albumin denaturation, further reducing serum albumin levels ( 25 , 26 ). The coexistence of hypoalbuminemia reflects IL-6/TNF-α-driven systemic inflammation ( 27 ), which may activate muscle-specific E3 ubiquitin ligases (MuRF-1/MAFbx) and accelerate the development of cancer cachexia ( 28 , 29 ). These processes synergistically amplify treatment toxicity - hypoalbuminemia increases the bioavailability of free drugs ( 30 , 31 ), while lymphopenia impairs mucosal repair function ( 32 ), jointly leading to increased infection-related and chemotherapy-induced mortality. In summary, PNI, by integrating immune and nutritional status, can explain the non-linear relationship between PNI and mortality in breast cancer survivors from multiple dimensions such as immune function, chronic inflammation and metabolic modification, providing a quantitative basis for prognosis assessment of breast cancer survivors from an immunonutritional perspective. This is consistent with the characteristic of PNI as an effective prognostic indicator in other diseases. This study has several potential limitations that warrant consideration. First, the observational design inherently restricts causal inference. Second, due to the absence of clinical data—such as tumor stage and treatment modalities in the NHANES database—these variables could not be incorporated into the analysis. These unmeasured factors might indirectly influence mortality by affecting tumor progression or nutritional status, potentially introducing residual confounding. Third, the relatively limited sample size constrained our capacity to investigate the relationship between PNI and mortality from specific causes; therefore, larger-scale studies are warranted. Additionally, survival data available from NCHS is limited to records from 2019, thus leaving no access to more recent follow-up information. Nevertheless, this study offers notable strengths. It identifies a significant non-linear relationship between PNI and mortality among breast cancer survivors, with a threshold effect that provides an objective quantitative reference for clinical risk stratification. Future research could further validate the clinical value of PNI through prospective study designs, improved clinical data collection, and intervention trials. Conclusion This study preliminary confirmed that the PNI has a nonlinear negative correlation with the all-cause mortality and cancer-specific mortality of breast cancer survivors. The nonlinear relationship provides threshold values for risk stratification, PNI below this threshold (PNI＜46) may be associated with an increased risk of all-cause mortality and cancer-specific mortality in this population. Declarations Data availability statement The dataset used in this study is available through an online repository. The relevant repository information and corresponding identifiers are as follows: This analytical dataset is a publicly available resource, accessible at: https: //wwwn.cdc.gov/nchs/nhanes/tutorials/default.aspx. Funding This study was supported by Natural Science Foundation of Guangxi Province (No. 2025GXNSFHA069073; 2025GXNSFHA069187); Startup Fund for Scientific Research from Fujian Medical University (2023QH1317); Innovation Project of Guangxi Graduate Education (NO.YCSW2024532); Scientific Research and Technology Development Plan of Baise (No.20241541); Project to improveme basic research ability of Young and middle-aged teachers of Guangxi Universities (2025KY0568). Ethics statement All survey protocols were approved by the National Center for Health Statistics Institutional Review Board. All participants provided written informed consent prior to enrollment. Therefore, no additional institutional ethics committee approval was required. Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Clinical trial number Not applicable. 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Wiedermann: Hypoalbuminemia as Surrogate and Culprit of Infections. Int J Mol Sci , 22(9) (2021) doi:10.3390/ijms22094496 R. M. Bologa, D. M. Levine, T. S. Parker, J. S. Cheigh, D. Serur, K. H. Stenzel and A. L. Rubin: Interleukin-6 predicts hypoalbuminemia, hypocholesterolemia, and mortality in hemodialysis patients. Am J Kidney Dis , 32(1), 107-14 (1998) doi:10.1053/ajkd.1998.v32.pm9669431 S. C. Bodine and L. M. Baehr: Skeletal muscle atrophy and the E3 ubiquitin ligases MuRF1 and MAFbx/atrogin-1. Am J Physiol Endocrinol Metab , 307(6), E469-84 (2014) doi:10.1152/ajpendo.00204.2014 D. Neyroud, O. Laitano, A. Dasgupta, C. Lopez, R. E. Schmitt, J. Z. Schneider, D. W. Hammers, H. L. Sweeney, G. A. Walter, J. Doles, S. M. Judge and A. R. Judge: Blocking muscle wasting via deletion of the muscle-specific E3 ligase MuRF1 impedes pancreatic tumor growth. Commun Biol , 6(1), 519 (2023) doi:10.1038/s42003-023-04902-2 A. Dasgupta: Clinical utility of free drug monitoring. 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Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 20 Jan, 2026 Editor invited by journal 01 Jan, 2026 Editor assigned by journal 28 Dec, 2025 Submission checks completed at journal 28 Dec, 2025 First submitted to journal 18 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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1","display":"","copyAsset":false,"role":"figure","size":99515,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow diagram of the selection of eligible participants from NHANES 2005 - 2018.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8399826/v1/d9205d9adb875a7ba823afae.png"},{"id":100867798,"identity":"0e13a96a-9295-4a99-8d4c-634883df2a33","added_by":"auto","created_at":"2026-01-22 08:45:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":43875,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNonlinear association of PNI with all-cause and cancer-specific mortality. A.\u003c/strong\u003e All-cause mortality in model 3; \u003cstrong\u003eB. \u003c/strong\u003eCancer-specific mortality in model 3.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8399826/v1/75a10f54aa7a563bc09f20f2.png"},{"id":100867786,"identity":"a97b274d-2d12-4932-9dea-3b0938d688ea","added_by":"auto","created_at":"2026-01-22 08:45:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":280922,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubgroup Analyses and Interaction Assessment.\u003c/strong\u003e (A) Subgroup analyses examining the relationship between PNI and all-cause mortality risk. (B) Subgroup analyses examining the relationship between PNI and cancer-specific mortality.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8399826/v1/284f04610a7cf6f6d92a86ff.png"},{"id":100952562,"identity":"6e3b39d6-f7ed-4993-bb5b-4bc694774976","added_by":"auto","created_at":"2026-01-23 07:16:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1913682,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8399826/v1/6dc216de-18de-4369-99e6-b1578fd041aa.pdf"},{"id":100867801,"identity":"2ec97a7f-bf74-4a55-a559-2a4129edc3a9","added_by":"auto","created_at":"2026-01-22 08:45:12","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2369547,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8399826/v1/073ec91d018b48ce4c10b9a3.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic Nutritional Index as a Predictor of Mortality Risk in Breast Cancer Survivors: A Population-Based Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer is the most common malignancy in women around the world (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). It is a heterogeneous disease in which genetic and environmental factors are involved with molecular subtypes that have biological distinctness and different behavior (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Breast cancer is life-threatening disease in females (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Breast cancer incidence continued an upward trend. Between 1990 to 2021, the global incidence, death, and DALYs, of female breast, cervical, uterine and ovarian cancer both to varying degrees of elevation (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Certain patients with breast cancer often experience a poor prognosis due to metastasis progression despite receiving advanced treatment strategies such as surgery, endocrine therapy, radiotherapy and chemotherapy (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Recent studies have shown that the prognosis of various cancer types is also affected by patient-related inflammation, immunocompetence, and nutrition. The correlation between nutrition and cancer prognosis is particularly evident (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Malnutrition is common in patients with breast cancer, which can suppress immune function and reduce resistance to disease. Therefore, further exploration of biomarkers with potential prognostic value to assess the risk of death in breast cancer survivors holds significant clinical and public health significance.\u003c/p\u003e \u003cp\u003eThe Prognostic Nutritional Index (PNI) is an indicator that comprehensively assesses a patient's nutritional and immune status. The PNI simultaneously reflects visceral protein reserves and cellular immune function. Initially developed to predict the risk of postoperative complications in surgical patients (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), the PNI has since been confirmed as a reliable biomarker for prognosis across multiple diseases. In the cardiovascular field, the PNI can independently predict the risk of major adverse cardiac events in patients with peripartum cardiomyopathy (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). In kidney diseases, a lower PNI is significantly associated with more severe renal histopathological damage and a higher risk of progression to end-stage renal disease in patients with diabetic nephropathy (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). In hip fracture patients, a higher preoperative PNI is independently associated with a 39% reduction in the risk of postoperative complications and a 39% decrease in all-cause mortality at 2 years (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The importance of PNI is increasingly recognized in oncology research. For instance, in patients with metastatic renal cell carcinoma receiving targeted therapy, a lower PNI before treatment is an independent predictor of shorter overall survival and progression-free survival, with predictive power even superior to some traditional risk stratification systems (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). A meta-analysis of 17 articles involving 2,883 patients with gastrointestinal cancer found that patients with high PNI levels had longer OS and PFS, higher objective response rates, and better disease control rates (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). A meta-analysis of 8 studies involving 2,322 breast cancer patients showed that a lower preoperative PNI was significantly associated with poorer OS and disease-free survival (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough multiple studies have reported that the PNI is associated with disease progression and poor prognostic outcomes in various cancers (\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), there remains a lack of in-depth research on the prognostic value of PNI in patients with breast cancer survivors. Given the large volume of breast cancer cases, identifying biomarkers with potential prognostic value holds exploratory significance for optimizing risk stratification and informing future research on individualized treatment strategies. Therefore, our research team, utilizing nationally representative data from the NHANES, aimed to preliminarily investigate the association between PNI and all-cause and cancer-specific mortality in breast cancer survivors; we analyzed the potential prognostic threshold and dose-response curve of PNI, with the goal of providing preliminary observational evidence for research on prognosis assessment in breast cancer survivors.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003e\u003cstrong\u003eStudy population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNHANES, a nationwide cross-sectional survey led by the U.S. Centers for Disease Control and Prevention (CDC), employs a multistage stratified probability sampling strategy to dynamically monitor health status, nutritional levels, and related lifestyle factors in the U.S. general population. Ongoing since 1999, the program collects multidimensional data via standardized questionnaires, physical examinations, and laboratory tests. All participants provided written informed consent, and the study protocol was approved by the National Center for Health Statistics Institutional Review Board. This study integrated NHANES data from 2005 - 2018, strictly following NHANES guidelines analysis. Initially, 70,190 eligible participants were recruited. Subsequently, 4,787 respondents lacking follow-up information were excluded, resulting in 65,403 participants. A total of 30,229 participants with missing data on PNI, cancer status, and covariates were removed, leaving 35,174 participants. Further exclusions were performed to eliminate 2,675 individuals diagnosed with other cancers (not breast cancer) and 15,301 male participants \u003cstrong\u003e(Figure 1)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExposure Variable\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, the exposure factor was PNI, calculated using the following formula:\u0026nbsp;PNI = Albumin (g/L) + 5 × Lymphocyte number(10⁹/L). Lymphocyte number was primarily determined by complete blood cell count test, which used Beckman Coulter counting and sizing methods for measurement.\u0026nbsp;Serum albumin levels were analyzed using an automated chemistry analyzer, the Beckman Synchron LX20. The patients were then divided into tertiles based on their PNI, categorized into three distinct groups: T1 group (35 - 48), T2 group (48 - 52), T3 group (52 - 68).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcome Variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBreast cancer survivors were categorized based on previously published diagnostic criteria: participants with a self-reported history of breast cancer were classified as breast cancer survivors, whereas those who denied a breast cancer diagnosis were defined as non-cases. Participants responded with “refuse” or “don’t know” to the breast cancer diagnosis inquiry, were excluded from the analysis.\u0026nbsp;All-cause mortality and cancer-specific mortality data were obtained through the NHANES database linked to the National Death Index system. The follow-up period ranged from the date of participant enrollment in the survey to December 31, 2019. The cause of death was classified according to the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD - 10). All-cause mortality included all deaths recorded in the NDI, while\u0026nbsp;cancer-specific mortality\u0026nbsp;was defined by ICD\u0026nbsp;-\u0026nbsp;10 codes C00\u0026nbsp;-\u0026nbsp;C97 and further verified by tumor registry data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCovariates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe included various covariates that might affect the results. Age, gender, race, education level, smoking status and disease status were collected from standardized household interview questionnaires. The laboratory indicators used included white blood cells (10⁹/L), segmented neutrophils percent (%), lymphocyte number (10⁹/L), platelets (10⁹/L), serum albumin (g/L) and total calcium (mmol/L). Detailed information on the relevant definitions is provided \u003cstrong\u003ein the Supplementary Methods\u003c/strong\u003e. In this study, the covariate check and screening module of the EmpowerStats software was used to explore the covariates of all-cause mortality risk factors. The final fully adjusted model used the following variables: age, gender, race, education level, BMI (Kg/m\u003csup\u003e2\u003c/sup\u003e), overweight(yes, no), smoking status (yes, no), total calcium (mmol/L), white blood cell count (10⁹/L), segmented neutrophil percentage (%), hypertension (yes, no), diabetes (yes, no), heart attack (yes, no), stroke history (yes, no), platelet (10⁹/L).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were conducted in accordance with the guidelines of the Centers for Disease Control and Prevention (https://wwwn.cdc.gov/nchs/nhanes/tutorials/default.aspx). Each participant in the NHANES was assigned a sample weight. Therefore, to account for significant variations, weighted methods were adopted in this study. Continuous variables were expressed as weighted means (95% confidence intervals), and categorical variables as weighted percentages. We used weighted χ2 tests (for categorical variables) or weighted linear regression models (for continuous variables) to calculate the differences between different PNI group. To explore the association between the PNI and mortality, adjusted univariate and multivariate Cox proportional hazards regression analyses were employed in this study.\u003c/p\u003e\n\u003cp\u003eTo examine the independent association of PNI with all-cause and cause-specific mortality, multivariate Cox proportional hazards regression models were used. Covariate selection for multivariable adjustment was performed using the covariate adjustment module in EmpowerStats software. Baseline variables were included in the final model if they: (1) showed potential association with outcomes in univariate Cox regression (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.1); or (2) were clinically relevant to survival based on established literature, regardless of univariate significance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Hierarchical Model Construction:\u0026nbsp;\u003c/strong\u003eTo clarify adjustment effects on the PNI-mortality association, progressively adjusted Cox models were fitted: \u003cstrong\u003eModel 1\u003c/strong\u003e: Unadjusted. \u003cstrong\u003eModel 2\u003c/strong\u003e: Adjusted for age, smoking status, and key serum parameters [total calcium (mmol/L), lymphocyte count (10⁹/L), white blood cell count (10⁹/L), segmented neutrophil percentage (%)]. \u003cstrong\u003eModel 3\u003c/strong\u003e: Adjusted for age, race, education, BMI (Kg/m\u003csup\u003e2\u003c/sup\u003e), overweight(yes, no), smoking status (yes, no), total calcium (mmol/L), lymphocyte count (10⁹/L), white blood cell count (10⁹/L), segmented neutrophil percentage (%), platelet (10⁹/L), hypertension (yes, no), diabetes (yes, no), history of heart attack (yes, no) and history of stroke (yes, no).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNonlinear Relationship Analysis:\u0026nbsp;\u003c/strong\u003eTo explore potential nonlinear associations between PNI and mortality risk, Cox models with restricted cubic splines (RCS) were used, with 3 knots at predefined percentiles (10th, 50th, 90th). Dose-response relationships were visualized using penalized smoothing splines. Threshold points characterizing nonlinear patterns were determined via an iterative recursive algorithm maximizing model likelihood. Nonlinearity significance was assessed by comparing RCS Cox models with linear Cox models using log-likelihood ratio tests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubgroup and Interaction Analyses:\u0026nbsp;\u003c/strong\u003eSubgroup analyses for all-cause and cancer-specific mortality were performed across strata: age (\u0026lt;65/≥65 years), race, education, smoking status (yes, no), hypertension (yes, no), diabetes (yes, no), history of heart attack (yes, no), history of stroke (yes, no), overweight (yes, no). Interaction effects were tested by including multiplicative interaction terms in fully adjusted Cox models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Software:\u0026nbsp;\u003c/strong\u003eAnalyses were performed using R software (version 4.2.0) for core modeling and EmpowerStats (version 4.2.0) for automated threshold detection and visualization. Statistical significance was defined as two-sided \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics of study population\u003c/h2\u003e \u003cp\u003eThis study included a total of 16,688 participants with a mean age of (46.99\u0026thinsp;\u0026plusmn;\u0026thinsp;16.89) years. Of these, 8.4% were Mexican American, 5.9% were other Hispanic, 65.7% were non-Hispanic White, 12.1% were non-Hispanic Black, and 8.0% were of other races. The mean PNI among all participants was (52.54\u0026thinsp;\u0026plusmn;\u0026thinsp;5.42). Compared with the group without a history of breast cancer, the group of breast cancer survivors was significantly older and had a significantly higher proportion of comorbidities including heart attack, stroke, diabetes, and hypertension. Additionally, the proportion of non-Hispanic White individuals and those with a college degree or higher education was significantly higher in the breast cancer history group. Meanwhile, this group had significantly lower PNI levels, follow-up time, lymphocyte count, white blood cell count, and platelet count, whereas the percentage of segmented neutrophils and total calcium levels were significantly higher. Statistical analysis revealed no significant differences between the two groups in terms of smoking status or albumin levels (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurther analyzed the breast cancer survivors were grouped according to the three of PNI. The ranges for PNI for Tertile 1 through Tertile 3 were 35\u0026ndash;48, 48\u0026ndash;52 and 52\u0026ndash;68, respectively. We found significant differences between PNI tertiles for characteristics (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Compared with the PNI tertile 1 group, participants in tertile 2 and 3 groups were younger, had a lower percentage of neutrophils, and higher levels of serum albumin, lymphocyte counts, and platelet counts (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). There were no significant differences among the three groups in terms of race, education level, smoking status, or complications such as heart attack, stroke, hypertension, and diabetes (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of study population with and without breast cancer\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll participants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBreast cancer survivors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo history of breast cancer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEstimated N\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98724006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3031034.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95692971.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePNI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.54\u0026thinsp;\u0026plusmn;\u0026thinsp;5.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.58\u0026thinsp;\u0026plusmn;\u0026thinsp;4.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.60\u0026thinsp;\u0026plusmn;\u0026thinsp;5.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.99\u0026thinsp;\u0026plusmn;\u0026thinsp;16.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.72\u0026thinsp;\u0026plusmn;\u0026thinsp;11.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.40\u0026thinsp;\u0026plusmn;\u0026thinsp;16.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI(Kg/m2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.14 (7.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.06 (6.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.15 (7.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace/ethnicity (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMexican American\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8269504.7 (8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105866.0 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8163638.7 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOther Hispanic\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5802173.0 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80012.5 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5722160.6 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-Hispanic White\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64826476.7 (65.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2518035.9 (83.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62308440.8 (65.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-Hispanic Black\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11952929.0 (12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e208503.5 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11744425.5 (12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOther Race\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7872922.7 (8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118616.4 (3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7754306.3 (8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation level (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLess than 9th grade\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5291150.8 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e140965.0 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5150185.8 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e9-11th grade\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9940861.2 (10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e249667.7 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9691193.5 (10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHigh school graduate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21932480.8 (22.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e669342.7 (22.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21263138.1 (22.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSome college or AA degree\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32916799.1 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e874155.6 (28.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32042643.5 (33.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCollege graduate or above\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28642714.2 (29.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1096903.2 (36.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27545811.0 (28.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoke (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36537564.7 (37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1135841.7 (37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35401723.0 (37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62186441.3 (63.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1895192.5 (62.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60291248.8 (63.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHeart attack (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2114661.1 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e225728.7 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1888932.4 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96609344.9 (97.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2805305.5 (92.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93804039.4 (98.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStroke (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2725230.2 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e216386.1 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2508844.0 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95998775.9 (97.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2814648.1 (92.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93184127.8 (97.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29675043.3 (30.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1543180.6 (50.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28131862.7 (29.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69048962.7 (69.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1487853.6 (49.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67561109.1 (70.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10335122.3 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e586428.0 (19.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9748694.3 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88388883.7 (89.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2444606.2 (80.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85944277.5 (89.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOverweight\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38008389.9 (38.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1278958.0 (42.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36729431.8 (38.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60715616.1 (61.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1752076.2 (57.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58963540.0 (61.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlbumin (g/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.61\u0026thinsp;\u0026plusmn;\u0026thinsp;3.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.29\u0026thinsp;\u0026plusmn;\u0026thinsp;2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.62\u0026thinsp;\u0026plusmn;\u0026thinsp;3.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLymphocyte number (10⁹/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWhite blood cell count (10⁹/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.35\u0026thinsp;\u0026plusmn;\u0026thinsp;2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.93\u0026thinsp;\u0026plusmn;\u0026thinsp;2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.36\u0026thinsp;\u0026plusmn;\u0026thinsp;2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSegmented neutrophils percent (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.64\u0026thinsp;\u0026plusmn;\u0026thinsp;9.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.48\u0026thinsp;\u0026plusmn;\u0026thinsp;9.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.58\u0026thinsp;\u0026plusmn;\u0026thinsp;9.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlatelet (10⁹/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e262.61\u0026thinsp;\u0026plusmn;\u0026thinsp;66.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e245.53\u0026thinsp;\u0026plusmn;\u0026thinsp;64.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e263.15\u0026thinsp;\u0026plusmn;\u0026thinsp;66.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal calcium (mmol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFollow-up time (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.52\u0026thinsp;\u0026plusmn;\u0026thinsp;4.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.21\u0026thinsp;\u0026plusmn;\u0026thinsp;4.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.57\u0026thinsp;\u0026plusmn;\u0026thinsp;4.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation for continuous variables: \u003cem\u003eP-Value\u003c/em\u003e was calculated by weighted linear regression model. Number (%) for categorical variables: \u003cem\u003eP-Value\u003c/em\u003e was calculated by weighted χ2 test.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003e: Unweighted number of observations in dataset.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAssociation of PNI with all-cause and cancer-specific mortality in breast cancer survivors.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eDuring a median follow-up of 6.21 years (standard deviation: \u0026plusmn; 4.03 years), 122 all-cause deaths occurred, including 43 cancer deaths. Kaplan-Meier survival plots showed lower all-cause, cancer-specific mortality in PNI quartiles Tertile 2\u0026ndash;3 compared with Tertile 1(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (\u003cb\u003eSupplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). The results as shown \u003cb\u003ein\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, in Model 3, each 1-unit increase in PNI was associated with a 9% reduction in all-cause mortality (HR\u0026thinsp;=\u0026thinsp;0.91, 95%CI: 0.84\u0026ndash;0.99, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035), a 19% reduction in cancer-specific mortality (HR\u0026thinsp;=\u0026thinsp;0.81, 95%CI: 0.68\u0026ndash;0.96, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between PNI with all-cause and cancer-specific mortality in breast cancer survivors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMortality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNo. of Events\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eAll-cause mortality in breast cancer survivors\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePNI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91; (0.86\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.93; (0.86\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.91; (0.84\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTertile 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTertile 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.43; (0.27\u0026ndash;0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.62; (0.40\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.60; (0.36\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTertile 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.46; (0.27\u0026ndash;0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.59; (0.31\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.55; (0.29\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e \u003cb\u003efor trend\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.64; (0.49\u0026ndash;0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.74; (0.54\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.34; (0.14\u0026ndash;0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ecancer-specific mortality in breast cancer survivors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePNI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95; (0.84\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.75; (0.64\u0026ndash;0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.81; (0.68\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTertile 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTertile 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.33; (0.15\u0026ndash;0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.27; (0.11\u0026ndash;0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.13; (0.05\u0026ndash;0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTertile 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.61; (0.25\u0026ndash;1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.27; (0.06\u0026ndash;1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.19; (0.04\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e \u003cb\u003efor trend\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.72; (0.42\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.42; (0.20\u0026ndash;0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.34; (0.14\u0026ndash;0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eNonlinear association of PNI with All-cause and cancer-specific mortality in breast cancer survivors\u003c/h3\u003e\n\u003cp\u003eRestricted cubic spline (RCS) analysis combined with Cox models (adjusted for age, race, educational level, smoking status, serum total calcium, lymphocyte count, white blood cell count, segmented neutrophil percentage, platelet count, as well as hypertension, diabetes, history of heart attack, and history of stroke) showed that among breast cancer survivors, the PNI was nonlinearly and negatively associated with all-cause mortality and cancer-specific mortality (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eThreshold effect analysis of PNI on all-cause and cancer-specific mortality in breast cancer survivors\u003c/h2\u003e \u003cp\u003eWe used the Cox proportional hazards model and the two-segment Cox proportional hazards model to fit the relationship between the PNI and the mortality rate of survivors with breast cancer. The results are shown \u003cb\u003ein\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. There was a non-linear association between PNI and all-cause and cancer-specific mortality (log-likelihood ratio test, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). We determined the PNI inflection point. When PNI\u0026thinsp;\u0026lt;\u0026thinsp;46, an increase in PNI was significantly associated with a decreased risk of all-cause mortality (HR\u0026thinsp;=\u0026thinsp;0.74, 95% CI: 0.64\u0026ndash;0.86, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and cancer-specific mortality (HR\u0026thinsp;=\u0026thinsp;0.34, 95% CI: 0.20\u0026ndash;0.58, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in survivors with breast cancer. At PNI\u0026thinsp;\u0026ge;\u0026thinsp;46, the association between PNI and risks of all-cause mortality (HR\u0026thinsp;=\u0026thinsp;0.97, 95% CI: 0.89\u0026ndash;1.06, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.489) and cancer-specific mortality (HR\u0026thinsp;=\u0026thinsp;1.04, 95% CI: 0.84\u0026ndash;1.28, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.741) lost statistical significance in breast cancer survivors.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThreshold effect analysis of PNI on all-cause and cancer mortality.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eAdjusted HR (95% CI) \u003csup\u003ea\u003c/sup\u003e, \u003cem\u003eP\u003c/em\u003e Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eAll-cause mortality in breast cancer\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePNI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.92 (0.88\u0026ndash;0.96); \u0026lt;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92 (0.86\u0026ndash;0.99) 0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91 (0.85\u0026ndash;0.98); 0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflection point (K)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.72 (0.64\u0026ndash;0.81); \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76 (0.66\u0026ndash;0.88); \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.74 (0.64\u0026ndash;0.86); \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.92\u0026ndash;1.02); 0.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98 (0.90\u0026ndash;1.05); 0.536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97 (0.89\u0026ndash;1.06); 0.489\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for log likelihood ratio test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCancer-specific mortality in breast cancer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePNI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.92 (0.88\u0026ndash;0.96)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.80 (0.70\u0026ndash;0.92); 0.0021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82 (0.69\u0026ndash;0.97); 0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflection point (K)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.72 (0.64\u0026ndash;0.81); \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.47 (0.34\u0026ndash;0.64); \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.34 (0.20\u0026ndash;0.58); \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.92\u0026ndash;1.02); 0.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91 (0.78\u0026ndash;1.07); 0.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04 (0.84\u0026ndash;1.28); 0.741\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for log likelihood ratio test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analysis of PNI and all-cause mortality and cancer-specific mortality in breast cancer survivors\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, we conducted subgroup analyses to examine whether demographic characteristics and comorbidities could modify the association between PNI and all-cause mortality or cancer-specific mortality among breast cancer survivors. When stratified by age, race, education level, smoking status, diabetes, history of heart attack, history of stroke, and overweight, the results remained consistent (all \u003cem\u003eP\u003c/em\u003e-values for interaction were \u0026gt;\u0026thinsp;0.05). A significant interaction was observed between PNI and hypertension with regard to all-cause mortality (\u003cem\u003eP\u003c/em\u003e-value for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe NHANES database of the United States, as one of the world's widely used national epidemiological databases, serves as a valuable resource for population-based epidemiological research, facilitating investigations into potential links between health indicators and clinical outcomes (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). This study, using data from NHANES, aims to provide a preliminary assessment of the associations between PNI and all-cause and cancer mortality in breast cancer survivors. Through multivariate Cox regression and RCS analysis, we preliminarily confirmed that the PNI exhibits a non-linear negative correlation with all-cause mortality and cancer-specific mortality in breast cancer survivors: when PNI is below 46, it may be associated with an increased risk of all-cause mortality and cancer-specific mortality in this population, and this threshold can provide preliminary reference for mortality risk stratification in breast cancer survivors.\u003c/p\u003e \u003cp\u003eThe PNI has emerged as a validated biomarker that integrates immune and nutritional status and has been shown to have prognostic value in various malignancies, including gastric cancer (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), colorectal cancer (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), and hepatocellular carcinoma (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). In non-tumor populations, low PNI independently predicts an increased risk of cardiovascular mortality (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). However, evidence directly linking PNI to survival outcomes in breast cancer survivors remains limited. Our study found a negative correlation between PNI and the risk of all-cause mortality and cancer-specific mortality in survivors with breast cancer. However, most studies have confirmed that the PNI threshold is disease-specific. Previous studies have determined the optimal preoperative PNI cutoff value for colorectal cancer patients to be 48.65 (training cohort) through ROC curve analysis, and found that a low PNI (\u0026lt;\u0026thinsp;48.65) was significantly associated with poor prognosis (HR\u0026thinsp;=\u0026thinsp;2.78, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005) (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Japanese researchers determined the optimal PNI cutoff value for gastric cancer patients to be 45 (high PNI group\u0026thinsp;\u0026ge;\u0026thinsp;45, low PNI group\u0026thinsp;\u0026lt;\u0026thinsp;45) through ROC curve analysis, and found that the 5-year OS and cancer-specific survival in the low PNI group were significantly lower than those in the high PNI group (76.7% and 87.0%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Therefore, we further analyzed and confirmed the PNI thresholds for predicting all-cause mortality and cancer-specific mortality in breast cancer. The results suggest that the PNI threshold for all-cause mortality and cancer-specific mortality in breast cancer survivors is 46. When PNI is below the inflection point, an increase in PNI significantly reduces the risk of all-cause mortality and cancer-specific mortality; while above the inflection point, the association between PNI and breast cancer mortality weakens. This underscores the importance of dynamically monitoring PNI levels in breast cancer survivors to reduce the risks of all-cause mortality and cancer-specific mortality. Subgroup analysis demonstrated that the predictive efficacy of the PNI threshold remained stable across strata of age, race, education level, smoking status, diabetes, heart disease, stroke, and overweight (all \u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Notably, a significant interaction was observed between PNI and hypertension with respect to all-cause mortality (\u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.05): the protective association of PNI with all-cause mortality was substantially attenuated in breast cancer survivors with hypertension. This significant interaction underscores the need for clinicians to pay special attention to breast cancer survivors with hypertension when using PNI to stratify all-cause mortality risk. In conclusion, the present study found that PNI levels may serve as a biomarker for assessing mortality risk in breast cancer survivors, which warrants further validation in large-scale clinical studies.\u003c/p\u003e \u003cp\u003ePNI is calculated based on serum albumin and lymphocyte count, and it can comprehensively reflect the nutritional status, immune function and chronic inflammation level of the body. This characteristic makes it an important indicator for prognosis assessment in various diseases. In survivors with breast cancer, the relationship between PNI and the risk of death may be attributed to the following interrelated biological pathways. The cascade events triggered by low PNI may amplify the risk of death through impaired immune function and chronic inflammation. Lymphocytes, as a key component of the immune system, mainly include T lymphocytes, B lymphocytes and natural killer cells. A decrease in lymphocyte count directly weakens cell-mediated immune responses, including impaired differentiation of CD4\u0026thinsp;+\u0026thinsp;T cells and reduced cytotoxicity of NK cells, which may reduce the body's surveillance ability against tumor cells and accelerate tumor development (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). In addition, lymphocytes produce cytokines such as interferon-γ, tumor necrosis factor-α and interleukin-12, which play important roles in activating immune cells and regulating inflammatory responses. Serum albumin is the main protein component in human serum and plays a crucial role in maintaining colloid osmotic pressure in the blood, transporting metabolic substances in the body and providing nutrition. Over the past few decades, serum albumin has been widely used to assess the general nutritional status of patients and has been proven to be related to the prognosis of cancer patients (\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Low albumin levels not only indicate malnutrition but are also associated with systemic inflammation. Inflammatory factors inhibit albumin synthesis, and oxidative stress causes albumin denaturation, further reducing serum albumin levels (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). The coexistence of hypoalbuminemia reflects IL-6/TNF-α-driven systemic inflammation (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), which may activate muscle-specific E3 ubiquitin ligases (MuRF-1/MAFbx) and accelerate the development of cancer cachexia (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). These processes synergistically amplify treatment toxicity - hypoalbuminemia increases the bioavailability of free drugs (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), while lymphopenia impairs mucosal repair function (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), jointly leading to increased infection-related and chemotherapy-induced mortality. In summary, PNI, by integrating immune and nutritional status, can explain the non-linear relationship between PNI and mortality in breast cancer survivors from multiple dimensions such as immune function, chronic inflammation and metabolic modification, providing a quantitative basis for prognosis assessment of breast cancer survivors from an immunonutritional perspective. This is consistent with the characteristic of PNI as an effective prognostic indicator in other diseases.\u003c/p\u003e \u003cp\u003eThis study has several potential limitations that warrant consideration. First, the observational design inherently restricts causal inference. Second, due to the absence of clinical data\u0026mdash;such as tumor stage and treatment modalities in the NHANES database\u0026mdash;these variables could not be incorporated into the analysis. These unmeasured factors might indirectly influence mortality by affecting tumor progression or nutritional status, potentially introducing residual confounding. Third, the relatively limited sample size constrained our capacity to investigate the relationship between PNI and mortality from specific causes; therefore, larger-scale studies are warranted. Additionally, survival data available from NCHS is limited to records from 2019, thus leaving no access to more recent follow-up information. Nevertheless, this study offers notable strengths. It identifies a significant non-linear relationship between PNI and mortality among breast cancer survivors, with a threshold effect that provides an objective quantitative reference for clinical risk stratification. Future research could further validate the clinical value of PNI through prospective study designs, improved clinical data collection, and intervention trials.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study preliminary confirmed that the PNI has a nonlinear negative correlation with the all-cause mortality and cancer-specific mortality of breast cancer survivors. The nonlinear relationship provides threshold values for risk stratification, PNI below this threshold (PNI＜46) may be associated with an increased risk of all-cause mortality and cancer-specific mortality in this population.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset used in this study is available through an online repository. The relevant repository information and corresponding identifiers are as follows: This analytical dataset is a publicly available resource, accessible at: https: //wwwn.cdc.gov/nchs/nhanes/tutorials/default.aspx.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Natural Science Foundation of Guangxi Province (No. 2025GXNSFHA069073; 2025GXNSFHA069187); Startup Fund for Scientific Research from Fujian Medical University (2023QH1317); Innovation Project of Guangxi Graduate Education (NO.YCSW2024532); Scientific Research and Technology Development Plan of Baise (No.20241541); Project to improveme basic research ability of Young and middle-aged teachers of Guangxi Universities (2025KY0568).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll survey protocols were approved by the National Center for Health Statistics Institutional Review Board. All participants provided written informed consent prior to enrollment. Therefore, no additional institutional ethics committee approval was required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eF. Bray, M. Laversanne, H. Sung, J. Ferlay, R. L. Siegel, I. Soerjomataram and A. 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Neurath: Immune Cell Circuits in Mucosal Wound Healing: Clinical Implications. \u003cem\u003eVisc Med\u003c/em\u003e, 36(2), 129-136 (2020) doi:10.1159/000506846\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Breast cancer, Prognostic Nutritional Index (PNI), NHANES, All-Cause Mortality, population-based study","lastPublishedDoi":"10.21203/rs.3.rs-8399826/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8399826/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThe Prognostic Nutritional Index (PNI) is widely utilized to evaluate nutritional and inflammatory status in predicting cancer prognosis, yet its impact on mortality among breast cancer patients remains incompletely understood. This study aimed to assess the association between PNI and all-cause as well as cancer-specific mortality in breast cancer survivors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterial and methods:\u003c/strong\u003e Data were retrieved from the National Health and Nutrition Examination Survey (NHANES) covering the period 2005 to 2018. A cross-sectional study design was employed to examine the association between PNI and breast cancer prevalence, with a cohort design utilized for mortality follow-up. Moreover, weighted logistic regression was applied to quantify the relationship between PNI and breast cancer survivor status. Multivariate Cox proportional hazards models, restricted cubic spline (RCS) analysis, and two-piecewise Cox proportional hazards models were used to evaluate the correlations of PNI with all-cause mortality and cancer-specific mortality. Finally, subgroup analyses were performed to validate the robustness.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e A total of 16,688 participants were ultimately enrolled. Among 479 female breast cancer survivors, 122 all-cause deaths occurred during follow-up, including 43 cancer deaths (median 6.21 years). After multivariate adjustment, RCS analysis revealed non-linear relationships, with inflection points: 46 for all-cause and cancer-specific mortality. When PNI \u0026lt; 46, higher PNI significantly reduced all-cause mortality (HR = 0.74, 95% CI: 0.64 - 0.86, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001) and cancer-specific (HR = 0.34, 95% CI: 0.20 - 0.58, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). When PNI ≥ 46, the association between elevated PNI and the risk of all-cause mortality (HR = 0.97, 95% CI: 0.89 - 1.06, \u003cem\u003eP\u003c/em\u003e = 0.489) as well as cancer-specific mortality (HR = 1.04, 95% CI: 0.84 - 1.28, \u003cem\u003eP\u003c/em\u003e = 0.741) was no longer statistically significant. The final subgroup analysis further supported the robustness of the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003ePNI was nonlinearly associated with mortality in breast cancer survivors. Its threshold facilitates risk stratification, and a PNI below this threshold increases the risk of both all-cause and cancer-specific mortality. However, this specific finding requires validation in larger cohorts due to substantial statistical uncertainty.\u003c/p\u003e","manuscriptTitle":"Prognostic Nutritional Index as a Predictor of Mortality Risk in Breast Cancer Survivors: A Population-Based Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-22 08:45:00","doi":"10.21203/rs.3.rs-8399826/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-01-20T14:13:25+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-01T08:39:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-29T02:24:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-29T02:23:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Women's Health","date":"2025-12-19T02:20:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"10eb667f-3027-4748-bc2f-050d415a7529","owner":[],"postedDate":"January 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-22T08:45:00+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-22 08:45:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8399826","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8399826","identity":"rs-8399826","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}