Weight loss during treatment is associated with reduced disease-free survival in non-metastatic breast cancer

Introduction

Breast cancer remains the most frequently diagnosed malignancy among women worldwide (1) and is the leading cause of cancer death in the female population in Brazil (2). While significant advancements in early detection and multimodal treatments—including surgery, chemotherapy, radiotherapy, hormone therapy, and targeted agents—have substantially improved survival rates (3,4), patient prognosis is still heavily influenced by host-related factors that extend beyond standard tumor clinicopathological characteristics like stage and molecular subtype (5,6). Among these emerging prognostic factors, body composition dynamics and systemic inflammatory status during treatment have garnered increasing attention (7-10).

Obesity at diagnosis is a well-established risk factor for the development of postmenopausal breast cancer and is associated with poorer overall survival (OS) (11,12). However, the impact of weight variation during oncological treatment is more complex and paradoxical. While significant weight gain is a common and distressing side effect of adjuvant chemotherapy and endocrine therapy (13,14), potentially compounding cardiovascular risks and recurrence (15), unintentional weight loss is often an ominous sign (16). In the context of cancer, involuntary weight loss frequently indicates cancer-associated cachexia—a multifactorial syndrome characterized by systemic inflammation, metabolic dysregulation, and the progressive loss of skeletal muscle mass (sarcopenia), which is strongly linked to increased treatment toxicity, reduced quality of life, and decreased survival (16). The relationship between weight loss and age is also clinically relevant: older patients, particularly those who are postmenopausal, tend to have reduced physiological and nutritional reserve, making them more vulnerable to treatment-related weight loss, sarcopenia, and functional decline during oncological therapy (15,16).

Intricately linked to metabolic changes is the host’s systemic inflammatory response. The tumor microenvironment actively promotes systemic inflammation, which in turn can facilitate tumor progression, metastasis, and treatment resistance (5,17). Routine hematological markers available from standard complete blood counts (CBC), such as the absolute neutrophil count, lymphocyte count, and platelet count, reflect this inflammatory state (17). Composite ratios derived from these parameters—specifically the neutrophil-to-lymphocyte ratio (NLR) and the platelet-to-lymphocyte ratio (PLR)—have emerged as cost-effective, easily accessible biomarkers (18,19). Elevated NLR and PLR, reflecting a protumoral inflammatory milieu and an impaired antitumor immune response, have been associated with poor prognosis across various solid tumors, including breast cancer (20,21).

Despite the individual recognition of weight variation and inflammatory markers as prognostic indicators, their interrelationship and combined impact on clinical outcomes in a real-world clinical setting remain under-explored, particularly within the Brazilian public health context. We hypothesize that weight variation during treatment is independently associated with disease-free survival (DFS) in non-metastatic breast cancer, and that baseline systemic inflammatory markers correlate with weight change. Given the retrospective design, we acknowledge that causal mechanisms cannot be established. Therefore, this study aimed to evaluate the association between weight variation during treatment and inflammatory markers [NLR, PLR, C-reactive protein (CRP)] in women with breast cancer treated at a public hospital, and to determine the prognostic value of these factors for DFS and OS. The primary objective was to assess the association between percentage weight variation and DFS; secondary exploratory analyses included inflammatory markers (NLR, PLR, and CRP). We present this article in accordance with the STROBE reporting checklist (available at https://abs.amegroups.com/article/view/10.21037/abs-2026-1-0005/rc).

Methods

Study design and patient selection

This was an observational, analytical, retrospective cohort study conducted at Hospital Anchieta in São Bernardo do Campo, São Paulo, Brazil. Data collection was carried out between June 2025 and August 2025 through a review of electronic medical records. Patients included in this study were diagnosed with breast cancer between January 2020 and December 2024.

The study population consisted of adult female patients (aged 18 years or older) diagnosed with non-metastatic breast cancer [stages I–III, International Classification of Diseases, 10th Revision (ICD-10) code C50]. To be eligible for inclusion, patients must have received systemic oncological treatment—defined as chemotherapy, hormone therapy, immunotherapy, or targeted therapy—at the hospital’s oncology outpatient clinic. Furthermore, inclusion required the availability of essential data in the medical records, specifically anthropometric measurements (weight and height) and relevant laboratory test results over the course of treatment.

Patients were excluded if they were male, under 18 years of age at diagnosis, or if their medical records were incomplete, lacking the essential data points necessary to address the study’s primary objectives. Patients with confirmed metastatic disease (stage IV) at diagnosis were included for descriptive purposes but excluded from all survival analyses. The patient selection process is summarized in Figure S1.

Data collection and variables

Following the application of eligibility criteria, clinical, pathological, and anthropometric data were extracted from patient charts. The variables analyzed included age at diagnosis, menopausal status, comorbidities (such as hypertension, diabetes, dyslipidemia, and hypothyroidism), lifestyle factors (smoking and alcohol consumption), histological subtype, molecular subtype (luminal A: ER+ and/or PR+, HER2−, Ki-67 <20%; luminal B: ER+ and/or PR+, HER2−, Ki-67 ≥20%, or HER2+; HER2-enriched: ER−, PR−, HER2+; triple negative: ER−, PR−, HER2−; luminal hybrid: ER+ and/or PR+, HER2+), TNM staging at diagnosis, and treatment modalities (neoadjuvant, adjuvant, and palliative chemotherapy, hormone therapy, radiotherapy, and surgery).

Anthropometric assessment included weight (kg) and height (m). Body mass index (BMI) was calculated as weight divided by height squared (kg/m²) and categorized according to World Health Organization standards: underweight (<18.5 kg/m²), normal weight (18.5–24.9 kg/m²), overweight (25.0–29.9 kg/m²), and obesity (≥30.0 kg/m²). Weight variation was calculated as the percentage difference and absolute difference (kg) between the baseline weight recorded at the beginning of treatment and the last available weight measurement during the follow-up period. Percentage weight variation was defined as: [(last weight − baseline weight)/baseline weight] ×100%, where negative values indicate weight loss and positive values indicate weight gain. For categorical analyses, patients were classified into: weight loss (≥5% decrease), stable weight (within ±5%), and weight gain (≥5% increase). Weight was measured using calibrated clinical scales at each oncology consultation, with frequency varying by treatment modality (typically every 2–4 weeks during chemotherapy and at each follow-up visit thereafter). Weight variation was computed using the first recorded weight (at treatment initiation) and the last available weight during follow-up.

For a refined analysis, percentage weight variation during chemotherapy was additionally calculated for patients receiving systemic treatment. In this analysis, weight change was defined as the percentage difference between baseline weight (measured prior to chemotherapy initiation) and the closest available weight measurement at the end of chemotherapy. This analysis was performed separately for patients receiving neoadjuvant and adjuvant chemotherapy. Patients who did not receive chemotherapy were not included in this analysis, as baseline weight measurements prior to surgery were not consistently available, precluding reliable estimation of perioperative weight variation.

Laboratory data included hematological parameters (erythrocytes, hemoglobin, leukocytes, neutrophils, lymphocytes, platelets) and metabolic markers [fasting glucose, glycated hemoglobin (HbA1c), total cholesterol, high-density lipoprotein (HDL), low-density lipoprotein (LDL), triglycerides]. Inflammatory markers were assessed using CRP and derived cellular ratios: the NLR, the PLR, and the absolute lymphocyte count (ALC).

Assessment of systemic inflammatory markers

Systemic inflammatory status was evaluated using hematological parameters derived from routine baseline CBC obtained prior to the initiation of systemic treatment. The NLR was defined as the absolute neutrophil count divided by the ALC. The PLR was calculated by dividing the absolute platelet count by the ALC. Serum CRP levels (mg/L) were recorded as a biochemical marker of inflammation.

Statistical analysis

Data analysis was performed using Stata software. Categorical variables were described as frequencies and percentages, while continuous variables were presented as mean ± standard deviation (SD) or median and interquartile range (IQR). Comparisons between groups were performed using the Chi-squared test or Fisher’s exact test for categorical variables, and the Mann-Whitney U test or t-test for continuous variables. Correlations were assessed using Pearson’s (r) or Spearman’s rank correlation coefficient. Survival analysis was conducted using the Kaplan-Meier method. Prognostic factors for DFS and OS were evaluated using univariate and multivariate Cox proportional hazards regression models, reporting hazard ratio (HR) and 95% confidence interval (CI). Statistical significance was set at P<0.05 for all analyses.

For multivariable Cox regression, the number of variables included in the model was limited in relation to the number of observed events to reduce the risk of overfitting. The final model included four variables selected based on clinical relevance and data availability: percentage weight variation, molecular subtype, tumor-node-metastasis (TNM) stage group (I–II vs. III), and Ki-67 (continuous).

Ethical consideration

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Research Ethics Committee of Centro Universitário FMABC (CAAE: 86344925.0.0000.0082) and informed consent was waived due to the retrospective nature of the study.

Results

Patient characteristics

A total of 184 female patients were included in the analysis, with a mean age at diagnosis of 56.3±12.2 years. Stage IV patients were included for descriptive purposes but excluded from all survival analyses. At baseline, most patients presented with excess body weight, with 38.2% classified as overweight and 45.5% as obese, resulting in a mean BMI of 30.0±6.3 kg/m². Luminal B was the most prevalent molecular subtype (46.4%), followed by triple negative disease (22.4%). Most patients were diagnosed with stages IIA (24.5%) and IIB (19.0%), while 8.2% presented with metastatic disease at diagnosis. Baseline characteristics are summarized in Table 1 and stratified by molecular subtype in Table S1.

Factors associated with disease progression and survival

Disease recurrence occurred in 27.7% of patients, and 20.2% died during the follow-up period. In univariate analyses, higher Ki-67 expression (P=0.004) and advanced TNM stage (P<0.001) were significantly associated with disease recurrence. Additionally, advanced TNM stage was significantly associated with recurrence in categorical analysis (P<0.001), whereas menopausal status and histological type were not (Table 2). For DFS, Kaplan-Meier analysis identified molecular subtype (P<0.001) and BMI category (P=0.02) as significant prognostic factors (Table 3; Figure S2). However, when BMI was analyzed as a continuous variable in Cox regression models, it was not significantly associated with DFS (HR 1.03; P=0.21); the correlation between baseline BMI and percentage weight variation is illustrated in Figure S3. Inflammatory markers, including NLR, PLR, and CRP, were not significant predictors of DFS. Regarding OS, molecular subtype remained significant (P=0.007), and Ki-67 (HR 1.02; P=0.003), NLR (P=0.03), and PLR (P=0.006) were associated with OS in univariate Cox analysis (Table 3; Figure S4; Table S2).

A focused multivariate Cox regression model including four pre-specified variables (weight variation, molecular subtype, TNM stage group, and Ki-67), selected in accordance with events-per-variable conventions, identified percentage weight variation as a significant predictor of DFS among non-metastatic patients (stages I–III) (HR 0.95; 95% CI: 0.92–0.98; P=0.002), indicating an approximate 5% reduction in the risk of disease recurrence for each percentage point of weight maintenance or gain (Table 4). No variables were independently associated with OS in the adjusted model.

When analyzed categorically, patients with ≥5% weight loss had higher rates of disease recurrence and mortality compared to those with stable weight or weight gain (Table 5).When restricted to non-metastatic patients (stages I–III) who received neoadjuvant or adjuvant chemotherapy (n=134), the association between weight loss and worse DFS was maintained. Patients with ≥5% weight loss exhibited higher rates of disease recurrence (39.6%) compared to those with stable weight (15.5%) or weight gain (14.3%) (Table S3). These findings are consistent with the overall cohort and support the robustness of the observed relationship, although this subgroup analysis should be interpreted with caution due to limited statistical power. Weight change during chemotherapy by treatment setting (neoadjuvant vs. adjuvant) is detailed in Tables S4,S5.

Correlation between inflammatory markers and weight variation

Baseline inflammatory marker data were incomplete. CRP, ALC, NLR and PLR data were available for only a limited subset within the predefined baseline window, whereas weight data were available for most patients. Due to the high degree of missingness in inflammatory markers (Table S6), formal multivariable analyses were not performed. An exploratory correlation between leukocyte count and continuous NLR is presented in Figure S5 and should be interpreted with caution given variability in the timing of laboratory assessments.

In the analysis including all available patients, a weak but statistically significant inverse correlation was observed between baseline neutrophil count and weight variation as percentage change (Spearman rho =−0.244; P=0.01; n=98) and as absolute weight change in kg (rho =−0.250; P=0.01). Higher neutrophil counts were associated with greater weight loss. No other inflammatory markers showed significant correlations with weight variation.

In analyses restricted to non-metastatic patients (stages I–III), weak but statistically significant positive correlations were observed between percentage weight variation and absolute neutrophil count (r=0.2035; P=0.01), total leukocyte count (r=0.1875; P=0.02), and continuous NLR (r=0.1654; P=0.041). In exploratory analyses, simple (unadjusted) linear regression models were performed within pre-specified subgroups to assess the association between inflammatory markers (neutrophil count, leukocyte count, and continuous NLR) and percentage weight variation. No statistically significant associations were observed in any subgroup (Table S7). These findings should be interpreted with caution given the limited sample size and missing data.

Discussion

This retrospective cohort study provides real-world evidence regarding the complex interplay between weight dynamics, systemic inflammation, and oncological outcomes in women treated for breast cancer at a public institution in Brazil. Our primary finding indicates that weight variation during treatment is a significant prognostic factor for DFS, particularly among patients with non-metastatic disease (stages I–III).

We observed that patients who maintained a stable weight or experienced weight gain during treatment had a significantly lower risk of disease recurrence compared to those who lost weight. In the categorical analysis, patients with ≥5% weight loss had higher rates of disease recurrence (36.1%) compared to those with stable weight (13.7%) or weight gain (20.0%), supporting that the observed effect of weight variation is primarily driven by the weight-loss group (Table 4). For every percentage point of weight preserved or gained in the non-metastatic setting, the risk of recurrence decreased by approximately 5% in multivariate analysis adjusted for molecular subtype, TNM stage group (I/II vs. III), and Ki-67. This finding aligns with a growing body of literature suggesting that while obesity at diagnosis is a negative risk factor, weight loss during active therapy is often detrimental (11,22,23). Unintentional weight loss in this context is frequently a surrogate marker for treatment toxicity, poor nutritional intake, or cancer cachexia (24-27). Conversely, the ability to maintain weight may reflect better physiological resilience and tolerance to therapeutic regimens (24,27). Importantly, this association remained consistent in a subgroup analysis restricted to patients receiving neoadjuvant or adjuvant chemotherapy, in which a similar directional relationship between weight loss and worse prognosis was observed. It is crucial to note that our study used BMI and total weight, which do not differentiate between muscle and fat mass (11,23).

In our univariate analysis restricted to non-metastatic patients, we found weak but statistically significant positive correlations between weight balance and both absolute neutrophil count and NLR. However, these associations disappeared in multivariable linear regression models adjusted for confounders. This counterintuitive finding should be interpreted with caution: the weak effect size (Spearman rho =0.23) and the lack of consistent associations in exploratory analyses suggest that this relationship may reflect confounding by tumor burden, treatment intensity, or corticosteroid use, rather than a direct biological mechanism. We caution against over-interpreting this unadjusted correlation.

While elevated NLR and PLR are widely recognized as markers of poor prognosis in various meta-analyses (18-20), they did not emerge as significant predictors in our Cox regression models for non-metastatic patients. This may be attributed to the relatively modest sample size, the heterogeneous nature of treatments received, or the overshadowing influence of established prognostic factors. Importantly, with baseline inflammatory marker data available for only 8.1% of patients, formal multivariable testing of NLR and PLR was not feasible, and their independent prognostic value could not be adequately assessed; all conclusions regarding these markers should therefore be considered exploratory. Furthermore, the absence of longitudinal inflammatory measurements represents a key limitation, as baseline values do not allow assessment of dynamic changes over time, precluding inference of temporal or mechanistic relationships between systemic inflammation and weight variation.

From a clinical perspective, routine weight monitoring throughout oncological treatment is a simple, non-invasive tool to identify patients at risk. Clinicians should distinguish treatment-related weight loss (correlates temporally with chemotherapy cycles, may be reversible with antiemetic optimization and nutritional support) from cancer-associated cachexia (progressive, associated with systemic inflammation, anorexia, and muscle wasting). Early referral to clinical nutrition services and, where available, exercise oncology programs should be considered for patients experiencing significant weight loss (≥5%) during treatment (24,27).

The results of this study must be interpreted considering its limitations, primarily inherent to its retrospective observational design. Data collection relied on electronic medical records, leading to missing data points for some laboratory and anthropometric parameters. Specifically, baseline inflammatory marker data (NLR/PLR/ALC) was available for only 15/184 patients (8.1%) and CRP for only 5/184 patients (2.7%), severely limiting the ability to test their independent prognostic value. A formal a priori power calculation was not performed; with 51 DFS events in 169 non-metastatic patients, the study has approximately 80% power to detect a HR of ≤0.92 per unit change in weight variation at alpha =0.05. Weight variation was computed using the first and last available measurements, which may not fully capture the trajectory of weight change. We also acknowledge that reverse causation cannot be fully excluded: weight loss immediately preceding detected recurrence may partly reflect underlying disease activity. The lack of precise body composition data limits our ability to distinguish between muscle loss (sarcopenia) and fat loss. Furthermore, the single-center nature of the study may limit generalizability. The high rates of overweight (38.2%) and obesity (45.5%) observed in our cohort are consistent with the high prevalence of excess body weight in the Brazilian adult population, where obesity has been estimated at approximately 20% in population-based studies (28), although the proportion observed in our sample was higher, likely reflecting the specific clinical and demographic characteristics of this cohort.

Despite these limitations, the study reflects the real-world scenario of breast cancer care in the Brazilian public health system, including patients often excluded from clinical trials due to comorbidities.

Conclusions

Weight variation during treatment may be a valuable, easily obtainable clinical prognostic marker in non-metastatic breast cancer. Preventing significant weight loss during therapy should be a priority in supportive care. While inflammatory markers showed associations with metabolic status in univariate analyses, their formal independent prognostic role could not be tested due to data limitations and requires clarification in larger, prospective studies. Integrating nutritional surveillance into routine oncological practice could help identify high-risk patients who might benefit from early supportive interventions. These findings should be prospectively validated in larger, multicenter studies with standardized weight measurement protocols and formal a priori power calculations.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://abs.amegroups.com/article/view/10.21037/abs-2026-1-0005/rc

Data Sharing Statement: Available at https://abs.amegroups.com/article/view/10.21037/abs-2026-1-0005/dss

Peer Review File: Available at https://abs.amegroups.com/article/view/10.21037/abs-2026-1-0005/prf

Funding: This study was supported by a research scholarship from the Brazilian National Council for Scientific and Technological Development (CNPq, Brazil; No. 147483/2025-5). The funding agency had no role in the study design, data collection, analysis, interpretation, or manuscript preparation.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://abs.amegroups.com/article/view/10.21037/abs-2026-1-0005/coif). A.d.G. serves as an unpaid editorial board member of Annals of Breast Surgery from May 2025 to December 2026. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Research Ethics Committee of Centro Universitário FMABC (CAAE: 86344925.0.0000.0082) and informed consent was waived due to the retrospective nature of the study.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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