Abstract
Background
Metabolic syndrome is common among women with breast cancer and may adversely affect prognosis. However, prospective evidence on changes in metabolic health after breast cancer diagnosis and their association with survival remains limited, particularly in routine public healthcare settings. This study evaluated metabolic health during the first two years after diagnosis and examined the association between metabolic syndrome at diagnosis and overall survival in women with breast cancer.
Methods
This prospective cohort study was conducted in a Brazilian public healthcare service and included women aged ≥ 40 years with newly diagnosed stage I–III invasive breast carcinoma who were followed for two years. Clinical, anthropometric, and laboratory assessments were performed at baseline and at 6, 12, 18, and 24 months. Metabolic syndrome was defined according to the National Cholesterol Education Program Adult Treatment Panel III criteria. Overall survival was defined as the time from breast cancer diagnosis to death from any cause or last follow-up. Survival according to metabolic syndrome status at diagnosis was evaluated using Kaplan–Meier analysis and Cox proportional hazards models.
Results
A total of 165 women were recruited, of whom 126 were included in the analysis and 111 completed the two-year assessment. Among participants with longitudinal data, significant reductions were observed in the prevalence of dysglycemia, elevated systolic blood pressure, and low HDL cholesterol. No significant changes were found in obesity, increased waist circumference, hypertriglyceridemia, or overall metabolic syndrome prevalence. During follow-up, 15 patients died. Women who died had a higher prevalence of metabolic syndrome at diagnosis than survivors (73% vs. 41%, p = 0.020) and higher baseline fasting glucose levels (p = 0.021). Metabolic syndrome at diagnosis was associated with lower overall survival (log-rank p = 0.021) and a higher risk of mortality (hazard ratio 9.09; 95% confidence interval 1.73–47.9), although this estimate should be interpreted as exploratory because of the limited number of events.
Conclusions
In this prospective cohort of women with breast cancer treated in a Brazilian public healthcare setting, metabolic syndrome was highly prevalent at diagnosis and was associated with poorer two-year overall survival in an exploratory analysis. Although favorable changes were observed in selected metabolic parameters during follow-up, obesity and the overall prevalence of metabolic syndrome remained largely unchanged. These findings support the incorporation of metabolic assessment and multidisciplinary cardiometabolic care into breast cancer management and survivorship programs and require confirmation in larger cohorts with longer follow-up.
Supplementary Information
The online version contains supplementary material available at https://doi.org/10.1186/s12885-026-16532-y.
Keywords: Breast cancer, Metabolic syndrome, Cardiometabolic health, Survival, Prospective cohort, Public healthcare
Introduction
Breast cancer remains one of the leading causes of cancer-related morbidity and mortality among women worldwide. According to the World Health Organization (WHO) Global Cancer Observatory (GLOBOCAN), approximately 2.3 million new cases of breast cancer in women were estimated for 2022 [1]. Early diagnosis through screening programs, combined with advances in treatment, has led to a significant improvement in patient survival [2]. As survival has improved, greater attention has been directed toward factors that may influence prognosis after diagnosis, including metabolic health and its interaction with cancer outcomes [3, 4]. In this context, metabolic dysfunction has emerged not only as a potentially modifiable risk factor for breast cancer development, but also as a clinically relevant determinant of prognosis and overall survival [5].
Metabolic dysfunction is defined by the diagnosis of metabolic syndrome (MetS) or by the presence of one or more of its components, with or without associated inflammatory abnormalities [6]. Determining the impact of metabolic dysfunction on cancer recurrence and mortality may be crucial for designing interventions aimed at improving prognosis among cancer survivors [6]. Recent studies have incorporated metabolic health and body composition into a broader assessment of breast cancer risk [7, 8], revealing a substantial proportion of women at increased risk due to metabolic dysfunction and/or excess adiposity [9, 10]. Park et al. categorized 50,884 women aged 35–74 years according to metabolic health and body weight using the components of MetS. Women classified as metabolically unhealthy—those with any metabolic abnormality—had a higher risk of developing breast cancer compared with metabolically healthy women of normal weight [11].
MetS has been increasingly associated with unfavorable outcomes in various types of cancer, including breast cancer [12, 13]. Recent evidence shows that the presence of MetS in patients with breast cancer is linked to a significantly higher risk of all-cause and breast cancer-specific mortality, particularly among postmenopausal women and those with luminal tumor subtypes [12, 14, 15]. These findings underscore the need for clinical guidelines that incorporate the assessment of metabolic health in breast cancer survivors. Future research should aim to identify effective interventions to reduce the prevalence of MetS among survivors, thereby improving both metabolic health and cancer outcomes [15].
Given the high prevalence of excess weight, central adiposity, dysglycemia, and other cardiometabolic abnormalities among women with breast cancer, metabolic assessment may be clinically relevant during oncologic care and survivorship. Multidisciplinary strategies that include nutritional counseling, promotion of physical activity, and management of cardiometabolic risk factors have been associated with improved general health and may contribute to better outcomes among cancer survivors [16–18].
With the growing number of breast cancer survivors, evaluating metabolic health and understanding the occurrence of metabolic dysfunction have become essential. The deterioration of metabolic health is known to have a negative impact on oncologic prognosis [15]. However, prospective data evaluating the evolution of metabolic health after breast cancer diagnosis and its relationship with survival remain limited, particularly in routine public healthcare settings. Based on current evidence, the present prospective cohort study was designed to evaluate metabolic health over the first two years after diagnosis and to investigate its association with survival in women with breast cancer.
Methods
Study design and sample selection
This prospective cohort study was conducted in a Brazilian public healthcare service from 2021 to 2024. Participants were recruited consecutively at the Center for Assessment and Support in Mastology (CEAD, Centro de Avaliação e Apoio em Mastologia), an outpatient breast care service linked to the Brazilian Unified Health System. This was a single-center prospective clinical cohort conducted in a routine-care setting, not a study based on claims or electronic health-record databases. Women were referred to CEAD for initial assessment after a new diagnosis of breast cancer and were invited to participate at their first consultation at the service, before or at the beginning of routine oncologic treatment. Eligible women were invited by the study team, and those who agreed were followed prospectively for two years, with planned assessments at baseline, 6, 12, 18, and 24 months.
The sample size was calculated based on the prevalence of MetS reported by Buttros et al., who found MetS in approximately 50% of postmenopausal women treated for breast cancer [19]. The formula used for estimating a single proportion was n = Z2 × p × (1 − p)/d2, where Z = 1.96 for a 95% confidence level, p = 0.50, and d = 0.10. This calculation yielded 96 participants. Considering possible follow-up losses, a larger number of women was recruited. Inclusion criteria were age ≥ 40 years; histological diagnosis of primary invasive breast carcinoma, including invasive carcinoma of no special type and invasive lobular or other epithelial breast carcinoma subtypes; clinical stage I, II, or III disease; and absence of established cardiovascular disease. Established cardiovascular disease was defined as a previous medical diagnosis or documented history of myocardial infarction, angina, coronary revascularization, stroke, or heart failure, ascertained through medical records and baseline clinical interview. Exclusion criteria included ductal carcinoma in situ, non-epithelial breast tumors such as lymphoma or sarcoma involving the breast, metastatic disease at diagnosis, and previous history of other malignancies.
Clinical data
The following clinical variables were collected through individual interviews, medical-record review, and a standardized clinical assessment form developed specifically for this study (Supplementary File 1): age (years), menopausal status (yes/no), prior use of menopausal hormone therapy (yes/no), family history of cardiovascular disease (yes/no), smoking status (yes/no), physical activity (yes/no), current use of antihypertensive, lipid-lowering, or glucose-lowering medication, and blood pressure (mmHg). Women were classified as physically active if they engaged in moderate-intensity aerobic physical activity for at least 150–300 min per week, vigorous-intensity aerobic activity for at least 75–150 min per week, or regular resistance exercise [20]. Although all participants received routine interdisciplinary counseling regarding diet, physical activity, and general health, adherence to these recommendations was not formally measured. Data on the dose, duration, and changes in cardiometabolic medications during follow-up were not systematically captured, and this was considered in the interpretation of longitudinal metabolic changes.
Metabolic health was evaluated according to the presence of obesity (BMI ≥ 30 kg/m2), elevated blood pressure (BP ≥ 130/85 mmHg or current antihypertensive medication), hypertriglyceridemia (TG ≥ 150 mg/dL or lipid-lowering treatment for hypertriglyceridemia), low HDL cholesterol (< 50 mg/dL or lipid-lowering treatment), fasting glucose ≥ 100 mg/dL or glucose-lowering medication, and the presence of MetS. Throughout the manuscript, metabolic dysfunction indicators refer to MetS and to each individual altered metabolic component assessed longitudinally; the prognostic exposure was baseline MetS. MetS was defined according to the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria as the presence of at least three of the following components: waist circumference > 88 cm, TG ≥ 150 mg/dL or treatment, HDL cholesterol < 50 mg/dL or treatment, BP ≥ 130/85 mmHg or antihypertensive treatment, and fasting glucose ≥ 100 mg/dL or glucose-lowering treatment [21]. Anthropometric data included body weight, height, body mass index (BMI = weight/height2), and waist circumference. BMI classification followed the 2002 World Health Organization criteria: ≤ 24.9 kg/m2 (normal), 25–29.9 kg/m2 (overweight), and ≥ 30 kg/m2 (obesity). Waist circumference was measured at the midpoint between the lower rib and the iliac crest, with the participant standing; values > 88 cm were considered increased [21].
Biochemical analysis
Participants were instructed to fast for 12 h prior to laboratory testing. Lipid and glycemic profiles were assessed by measuring total cholesterol (TC), HDL, LDL, triglycerides (TG), and fasting glucose. All tests were performed at baseline and repeated at 6, 12, 18, and 24 months. Anthropometric and blood pressure measurements were performed by the same trained investigator using a standardized protocol and the same equipment across planned visits whenever possible, in order to reduce interobserver variability. Biochemical analyses were performed in the same institutional laboratory using routine internal quality-control procedures. Analyses of TC, HDL, TG, and glucose were conducted using an automated biochemical analyzer (RAXT, Technicon®, USA) and quantified via the colorimetric method, with commercial reagents (Sera-Pak®, Bayer, USA). LDL cholesterol was calculated using the Friedewald formula, which is not valid when TG values exceed 400 mg/dL. LDL was estimated as total cholesterol minus HDL cholesterol minus TG/5. Optimal reference values were: TC < 200 mg/dL, HDL > 50 mg/dL, LDL < 100 mg/dL, TG < 150 mg/dL, and glucose < 100 mg/dL [21].
Oncologic data
The following data were extracted from medical records: histopathological diagnosis, histological grade, hormonal receptor status (estrogen receptor [ER] and progesterone receptor [PR]), human epidermal growth factor receptor 2 (HER2) status, epithelial proliferative index (Ki67), tumor stage, and treatments received (surgery, radiotherapy, chemotherapy, and endocrine therapy). Tumor diameter was obtained from pathology reports, and histological grade was classified as grade I (well differentiated), II (moderately differentiated), or III (poorly differentiated). Tumor staging followed the 8th edition of the American Joint Committee on Cancer (AJCC) TNM classification system (tumor size, nodal status, metastasis) [22]. Immunohistochemical analysis was performed using tissue blocks and reagents according to the manufacturer’s recommendations (DakoCytomation®, Glostrup, Denmark). Tumors were classified into five molecular subtypes based on immunohistochemical profiles: Luminal A: ER +, PR +, HER2 −, Ki67 < 14%; Luminal B HER2 − : ER +, PR + or −, HER2 −, Ki67 ≥ 14%; Luminal B HER2 + : ER +, PR + or −, HER2 +, any Ki67; Non-luminal HER2: ER −, PR −, HER2 +, any Ki67; Triple-negative: ER −, PR −, HER2 −, any Ki67 [22].
Statistical analysis
The normality of data distribution was assessed using the Shapiro–Wilk test, and homogeneity of variances was evaluated using the Levene test. Data distribution was also verified using histograms and Q–Q plots. Continuous variables were expressed as mean ± standard deviation or median and interquartile range, as appropriate. Categorical variables were expressed as frequencies and percentages. Longitudinal changes in continuous metabolic variables were evaluated descriptively across the five assessment points and tested using nonparametric methods appropriate for repeated observations among participants with available follow-up data. The presence of MetS and its components over time was analyzed using Cochran’s Q test among participants with longitudinal categorical data. Because deaths and losses to follow-up could introduce selection bias in the longitudinal metabolic analyses, changes over time were interpreted as descriptive and exploratory rather than causal.
Overall survival was the main outcome for the prognostic analysis and was defined as the time from breast cancer diagnosis to death from any cause. Participants alive at the end of follow-up were censored at the date of their last clinical assessment. Deaths and causes of death were ascertained from medical records and follow-up information available at CEAD. Comparisons between survivors and deceased patients were performed using the Wilcoxon rank-sum test or Pearson’s chi-squared test, as appropriate. Survival analysis was conducted using the Kaplan–Meier method, and the log-rank test was used to compare survival curves between patients with and without MetS at diagnosis. The Cox proportional hazards model was applied to assess the association between baseline MetS and time to death, adjusted for age and treatment type. Results were expressed as hazard ratios (HR) and 95% confidence intervals (CI). Because only 15 deaths occurred during follow-up, multivariable models were intentionally parsimonious, and the HR estimates were considered exploratory. All statistical tests were two-tailed, with a significance level of 5%. Analyses were performed using R software, version 4.4.2 (R Foundation for Statistical Computing, Vienna, Austria, 2024).
Results
A total of 185 women were assessed for eligibility, of whom 165 were recruited consecutively. Twenty women declined participation before enrollment; because these women did not provide consent, detailed clinical and tumor characteristics were not systematically available for comparison with participants. Thirty-nine participants subsequently withdrew or were lost to follow-up, mainly because of difficulty attending scheduled visits, resulting in 126 patients included in the analytical cohort. During follow-up, 15 women died, and 111 completed the 2-year assessment (Fig. 1). At baseline, participants had a mean age of 50.9 ± 12.0 years (standard deviation) and a mean BMI of 29.7 ± 5.8 kg/m2. Most women were postmenopausal (90%), 19% had previously used menopausal hormone therapy, 17% were smokers, and 83% were physically inactive (Table 1). Luminal tumors were the predominant subtype (79%); 46% of tumors measured ≤ 2 cm, 91% were stage I–II, and 71% had negative axillary nodes. Regarding treatment, 79% underwent breast-conserving surgery, 75% received radiotherapy, 56% received chemotherapy, and 70% received endocrine therapy (Table 2). Time-varying oncologic treatment exposure at each metabolic assessment was not analyzed separately.
Fig. 1.
Study follow-up flowchart
Table 1.
Baseline clinical and oncologic characteristics of women with breast cancer
| Variable | N = 126 |
|---|---|
| Age, years | 50.9 ± 12.0 |
| BMI, kg/m2 | 29.7 ± 5.8 |
| Waist circumference, cm | 99.1 ± 12.7 |
| Postmenopausal, % | 113 (90%) |
| Smoker, % | 22 (17%) |
| Physical activity, % | 21 (16%) |
| Previous MHT, % | 24 (19%) |
| Family history of CVD, % | 46 (37%) |
| Metabolic syndrome, % | 57 (45%) |
Values are presented as mean ± SD or number (%)
BMI Body mass index, MHT, Menopausal hormone therapy, CVD Cardiovascular disease
Table 2.
Descriptive oncological characteristics of the 126 women with breast cancer
| Variable | N = 126 | N = 15 | N = 111 | |
|---|---|---|---|---|
| Total | Deaths | No Deaths | P value | |
| Immunohistochemistry | < 0,001* | |||
| Luminal A | 41 (33%) | 0 (0%) | 41 (37%) | |
| Luminal B | 46 (37%) | 4 (27%) | 42 (38%) | |
| Luminal HER | 11 (9%) | 3 (20%) | 8 (7%) | |
| Pure HER2 | 7 (5%) | 2 (13%) | 5 (5%) | |
| Triple Negative | 21 (16%) | 6 (40%) | 15 (14%) | |
| Tumor size | < 0,001* | |||
| T1 ≤ 2 cm | 58 (46%) | 4 (27%) | 54 (49%) | |
| T2 > 2 cm and ≤ 5 cm | 60 (48%) | 6 (40%) | 54 (49%) | |
| T3 > 5 cm | 8 (6%) | 5 (33%) | 3 (3%) | |
| Stage | O,006* | |||
| I | 59 (47%) | 6 (40%) | 53 (48%) | |
| II | 56 (44%) | 4 (27%) | 52 (47%) | |
| III | 11 (9%) | 5 (33%) | 6 (5%) | |
| Surgery | 0,510* | |||
| Conservadora | 100 (79%) | 11 (73%) | 89 (80%) | |
| Mastectomia | 26 (21%) | 4 (27%) | 22 (20%) | |
| Axillary involvement | 0,033* | |||
| No | 90 (71%) | 7 (47%) | 83 (75%) | |
| Yes | 36 (29%) | 8 (53%) | 28 (25%) | |
| Radiotherapy | < 0,001* | |||
| No | 31 (25%) | 12 (80%) | 19 (17%) | |
| Yes | 95 (75%) | 3 (20%) | 93 (84%) | |
| Chemotherapy | 0,854** | |||
| No | 56 (44%) | 7 (47%) | 49 (44%) | |
| Yes | 70 (56%) | 8 (53%) | 62 ((56%) | |
| Endocrine therapy | < 0,001* | |||
| No | 38 (30%) | 14 (93%) | 24 (22%)) | |
| Yes | 88 (70%) | 1 (7%) | 87 (78%) |
Values are presented as number and frequency (%)
*Significant difference if p < 0.05 (Fisher’s test);
**Significant difference if p < 0.05 (Chi-square test)
Among women with longitudinal follow-up data, median HDL cholesterol increased over time (p = 0.003), while systolic blood pressure (p = 0.010) and mean arterial pressure (p = 0.045) decreased. No significant changes were detected in BMI, waist circumference, total cholesterol, LDL cholesterol, triglycerides, fasting glucose, or diastolic blood pressure (Table 3). Figure 2 shows the occurrence of metabolic dysfunction indicators across the five assessment points. The proportion of participants with fasting glucose ≥ 100 mg/dL decreased over follow-up (p = 0.04), systolic blood pressure ≥ 130 mmHg decreased (p < 0.001), and HDL cholesterol < 50 mg/dL decreased (p = 0.03). No significant changes were observed in MetS status (p = 0.40), obesity (p = 0.70), increased waist circumference (p = 0.09), hypertriglyceridemia (p = 0.09), or diastolic blood pressure ≥ 85 mmHg (p = 0.30). Assessments at time points 1, 2, 3, 4, and 5 correspond to first consultation, 6, 12, 18, and 24 months, respectively. These longitudinal findings should be interpreted cautiously because estimates at later time points include only women who remained alive and in follow-up.
Table 3.
Analysis of body composition, blood pressure and biochemical parameters during two-year follow-up in women with breast cancer
| Time point/variable | Baseline N = 126* | 6 months N = 119* | 12 months N = 117* | 18 months N = 112* | 24 months N = 111* | P-value** |
|---|---|---|---|---|---|---|
| Weight, kg | 73 (61; 85) | 70 (62; 83) | 71 (63; 83) | 70 (62; 83) | 70 (62; 82) | > 0.9 |
| BMI, kg/m2 | 28.9 (25.5; 33.3) | 28.4 (25.2; 32.1) | 28.0 (25.2; 32.5) | 28.4 (24.8; 32.8) | 28.1 (24.8; 32.9) | > 0.9 |
| WC, cm | 99 (90; 106) | 96 (90; 106) | 96 (89; 106) | 98 (88; 106) | 98 (89; 106) | 0.8 |
| TC, mg/dL | 202 (183; 231) | 208 (180; 240) | 203 (181; 240) | 204 (174; 231) | 199 (169; 227) | 0.4 |
| HDL, mg/dL | 55 (48; 65) | 53 (46; 63) | 55 (47; 68) | 60 (49; 72) | 60 (50; 73) | 0.003 |
| LDL, mg/dL | 120 (102; 145) | 122 (98; 152) | 118 (94; 139) | 116 (93; 141) | 112 (89; 134) | 0.054 |
| TG, mg/dL | 117 (91; 157) | 130 (96; 174) | 131 (98; 179) | 124 (91; 166) | 115 (90; 161) | 0.084 |
| Glycemia, mg/dL | 100 (91; 113) | 98 (89; 110) | 95 (88; 106) | 98 (88; 105) | 95 (87; 105) | 0.07 |
| SBP, mmHg | 140 (130; 150) | 130 (120; 140) | 130 (120; 140) | 130 (120; 140) | 130 (120; 140) | 0.01 |
| DBP, mmHg | 80 (80; 90) | 80 (70; 90) | 80 (70; 90) | 80 (70; 90) | 80 (70; 90) | 0.2 |
| MBP. mmHg | 100 (93; 110) | 97 (90; 107) | 100 (90; 105) | 97 (87; 107) | 97 (87; 107) | 0.045 |
Values are presented as median and interquartile range (Q1; Q3)
*Number of women with available data at each time point, considering deaths and losses during follow-up
BMI Body mass index, WC Waist circumference, TC Total cholesterol, TG Triglycerides, SBP Systolic blood pressure, DBP Diastolic blood pressure, MBP Mean blood pressure
**Significant difference if p < 0.05 (Friedman test for repeated continuous observations)
Fig. 2.
Occurrence of metabolic dysfunction indicators during 2-year follow-up in women with breast cancer. A, presence of metabolic syndrome (MetS, p = 0.40); B, obesity, body mass index (BMI) ≥ 30 kg/m.2 (p = 0.70); C, waist circumference (WC) > 88 cm (p = 0.09); D, HDL < 50 mg/dL (p = 0.03); E, triglycerides (TG) ≥ 150 mg/dL (p = 0.09); F, glycemia ≥ 100 mg/dL (p = 0.04); G, systolic blood pressure (SBP) ≥ 130 mmHg (p < 0.001); H, diastolic blood pressure (DBP) ≥ 85 mmHg (p = 0.30). Assessments at time points 1, 2, 3, 4, and 5 correspond to first consultation, 6, 12, 18, and 24 months. * Significant difference if p < 0.05 (Cochran's Q test)
During the 2-year follow-up, 15 women (11.9%) died—11 due to breast cancer and 4 due to other causes (acute myocardial infarction, hypertensive crisis, hematemesis, and pneumonia). Women who died had a higher prevalence of MetS at baseline (73% vs. 41%, p = 0.020) and higher median fasting glucose levels (114 mg/dL vs. 99 mg/dL, p = 0.021) compared with survivors. No other baseline variables differed significantly between groups (p > 0.05) (Table 4). Women with MetS at breast cancer diagnosis had a significantly higher risk of mortality (HR 9.09, 95% CI 1.73–47.9, p = 0.009). When analyzing the individual components of MetS, none was independently associated with increased mortality risk (Supplementary 1). Figure 3 shows the Kaplan–Meier overall survival curve according to baseline MetS status. Women with MetS at diagnosis exhibited lower overall survival compared with those without MetS (log-rank p = 0.021). Because only 15 deaths occurred, the magnitude of the HR should be considered an exploratory estimate.
Table 4.
Comparison of baseline clinical profile of patients who died vs. survivors with breast cancer
| Variable | Died N = 15 | Survivors N = 111 | P-value* |
|---|---|---|---|
| Age. years | 57 (41; 67) | 60 (51; 67) | 0.4 |
| BMI, kg/m2 | 30.6 (26.5; 35.6) | 28.9 (25.5; 33.2) | 0.6 |
| WC. cm | 102 (86; 117) | 98 (90; 106) | 0.5 |
| Total cholesterol, mg/dL | 218 (187; 245) | 201 (183; 227) | 0.12 |
| HDL. mg/dL | 61 (44; 62) | 55 (48; 65) | 0.9 |
| LDL. mg/dL | 136 (109; 158) | 119 (100; 144) | 0.11 |
| Triglycerides. mg/dL | 155 (84; 195) | 113 (91; 151) | 0.4 |
| Glycemia. mg/dL | 114 (98; 128) | 99 (91; 111) | 0.021 |
| SBP. mmHg | 140 (120; 160) | 140 (130; 150) | 0.8 |
| DBP. mmHg | 85 (80; 100) | 80 (80; 90) | 0.4 |
| Metabolic syndrome (%) | 0.020 | ||
| No | 4 (27%) | 65 (59%) | |
| Yes | 11 (73%) | 46 (41%) |
Values are presented as median and interquartile range (Q1; Q3) or number (%)
BMI Body mass index, WC Waist circumference, SBP Systolic blood pressure, DBP Diastolic blood pressure
*Significant difference if p < 0.05 (Wilcoxon rank-sum test or Pearson’s chi-squared test)
Fig. 3.
Kaplan–Meier curve for two-year overall survival in breast cancer patients according to baseline metabolic syndrome (MetS) status. The legend was repositioned to avoid overlap with the curves. *Significance between presence or absence of MetS: p = 0.021 (log-rank test)
Discussion
In the present study, women with breast cancer who underwent prospective follow-up in a routine public healthcare setting showed improvements in selected metabolic parameters over two years, including blood pressure, fasting glucose classification, and HDL cholesterol, while obesity and the overall prevalence of MetS remained largely unchanged. Importantly, MetS was highly prevalent at diagnosis and was associated with lower overall survival in an exploratory analysis. The prevalence of MetS at recruitment was 45%, consistent with the findings of Buttros et al., who reported a high prevalence of 54.2% in women treated for breast cancer compared with 30.7% in age- and menopause-matched controls without breast cancer [19].
The presence of MetS was associated with worse survival outcomes in women with breast cancer in our study. Among participants who died during the two-year follow-up, 73% had MetS compared with 41% of survivors. Women with MetS at diagnosis showed a significantly higher risk of mortality, with an HR of 9.09. This estimate is clinically relevant but should be interpreted with caution because of the small number of deaths and the consequent wide confidence interval. Metabolic syndrome has been recognized as a poor prognostic factor in women treated for breast cancer, associated with reduced overall and cancer-specific survival [14, 15]. A study assessing the impact of MetS, BMI, and other anthropometric factors on mortality in 955 Italian women with recent breast cancer found that MetS was significantly associated with an increased risk of breast cancer-specific and all-cause mortality [14]. Another major study examining the association between MetS, obesity, and breast cancer outcomes in 63,330 postmenopausal women from the Women’s Health Initiative demonstrated that a higher metabolic dysfunction score—defined by the presence of three or four MetS components—was significantly associated with a 53% higher rate of deaths after breast cancer and a 44% increase in breast cancer-specific mortality, even after adjustment for BMI [12]. A recent meta-analysis and systematic review including 17 studies and 42,135 women with breast cancer showed that those who had MetS at the time of breast cancer diagnosis experienced a higher risk of recurrence, breast cancer mortality, and shorter disease-free survival compared with survivors without MetS [15]. Taken together, these findings support the clinical relevance of metabolic assessment at the time of breast cancer diagnosis.
In our study, the Kaplan–Meier overall survival curve demonstrated that women with MetS had significantly lower survival during post-diagnosis follow-up. Consistent with our findings, Bobin-Dubigeon et al. [23] reported that women with MetS had significantly lower overall and disease-free survival compared with those without MetS, with HRs of 4.70 and 3.58, respectively. These findings reinforce the role of MetS as an adverse prognostic factor in this patient population and suggest that the presence of metabolic comorbidities may negatively influence disease trajectory [23]. Similarly, Buono et al. [24], in a 120-month follow-up of 955 women with early-stage breast cancer, demonstrated significant differences in Kaplan–Meier survival curves between patients with and without MetS. Overall survival was 75.9% among women with MetS compared with 91.1% among those without MetS, indicating a markedly higher risk of mortality in the presence of MetS [24].
The association between metabolic dysfunction and poorer survival outcomes underscores the need for a multidisciplinary approach to the management of breast cancer patients, including assessment and intervention on metabolic risk factors to optimize clinical outcomes [23]. A recent systematic review including 98 studies and 1,461,834 cancer patients demonstrated the positive impact of lifestyle modifications on cancer survival. Specifically, adherence to healthy dietary patterns reduced cancer-specific mortality. Furthermore, engagement in physical activity, smoking cessation, and moderation of alcohol consumption were all associated with significant reductions in cancer mortality and recurrence. Regarding breast cancer, general dietary improvements were beneficial for breast cancer-specific mortality across 17 cohort studies. These findings reinforce the importance of integrating metabolic risk assessment and lifestyle-oriented care into survivorship strategies [18].
Although our study was observational and did not include a structured intervention introduced for research purposes, all patients received standard interdisciplinary care involving breast specialists, nutritionists, and psychologists, with encouragement to adopt healthy eating habits and regular physical activity. During the two-year follow-up, selected metabolic indicators improved among women who remained in follow-up, including hyperglycemia classification, elevated systolic blood pressure, and low HDL cholesterol. These findings suggest that prospective follow-up in a multidisciplinary setting may be accompanied by favorable changes in selected metabolic parameters. However, causal inferences cannot be established, and the results may be affected by survivorship and retention bias because women who died or were lost to follow-up did not contribute complete longitudinal data.
A recent study investigated the independent and combined effects of MetS and healthy lifestyle factors—based on behaviors such as smoking, alcohol consumption, physical activity, and diet—on cancer risk in a cohort of 289,557 participants from the UK Biobank [13]. Over a median follow-up of 11.7 years, 11,190 individuals developed cancer. The presence of MetS was associated with an increased cancer risk. Compared with unfavorable lifestyles, regardless of metabolic status, a healthy lifestyle was significantly associated with reduced overall cancer risk (without MetS: HR 0.757; with MetS: HR 0.702). For breast cancer specifically, a modest protective effect was observed (HR 0.912), which was enhanced among those adopting favorable lifestyle behaviors (HR 0.833). These findings highlight the need to develop management strategies stratified by metabolic status and to implement proactive prevention of MetS [13]. The National Comprehensive Cancer Network (NCCN) recommends that all cancer patients should be evaluated for metabolic health and body composition [25].
Among the strengths of this study, to our knowledge, this is the first Brazilian prospective cohort to evaluate overall survival according to the presence of MetS in women with breast cancer, while also describing the evolution of metabolic parameters during the first two years after diagnosis in a routine public healthcare setting. However, some limitations should be acknowledged. The relatively small sample size, together with the limited number of deaths, restricted more robust analyses of the effects of individual MetS components on survival among women undergoing breast cancer treatment. Therefore, the HR for mortality associated with MetS should be interpreted as an important but exploratory finding with limited precision. The short follow-up period is also relevant, particularly because most participants had early-stage and luminal tumors, for which recurrence and mortality may occur beyond two years. Residual confounding by clinical stage, axillary involvement, molecular subtype, tumor grade, HER2 status, and hormone receptor status cannot be excluded because these variables could not be incorporated simultaneously in the survival model without overfitting. In addition, the all-cause mortality endpoint combined breast cancer deaths with cardiovascular and other non-cancer deaths; because MetS may be related to non-cancer mortality, this may have contributed to the observed association. Losses to follow-up may have influenced the longitudinal assessment of metabolic parameters, and missingness may not have been completely random. Medication changes, including antihypertensive, lipid-lowering, glucose-lowering, corticosteroid, and weight-loss treatments, were not comprehensively evaluated over time. Finally, the observational design precludes causal interpretation of the associations observed.
Overall, our findings support the relevance of metabolic health assessment at breast cancer diagnosis and suggest that MetS may identify a subgroup of women requiring closer cardiometabolic surveillance. These results reinforce the potential value of incorporating cardiometabolic assessment and multidisciplinary management into breast cancer care and survivorship programs, while emphasizing that larger studies with longer follow-up and more complete adjustment for oncologic prognostic factors are needed.
Conclusion
In this prospective cohort of women with breast cancer treated in a Brazilian public healthcare setting, metabolic syndrome was highly prevalent at diagnosis and was associated with poorer two-year overall survival in an exploratory analysis. Although favorable changes were observed in selected metabolic parameters, including fasting glucose classification, HDL cholesterol, and blood pressure, obesity and the overall prevalence of metabolic syndrome remained largely unchanged. These findings underscore the clinical relevance of assessing metabolic health at the time of breast cancer diagnosis and support the integration of multidisciplinary cardiometabolic care into breast cancer management and survivorship programs. Further studies with larger samples, longer follow-up, and adjustment for major oncologic prognostic factors are warranted to confirm these exploratory findings and to determine whether targeted metabolic interventions can improve long-term outcomes.
Supplementary Information
Authors’ contributions
The study concept and design were developed by V.P., D. B., E. N. Data collection and analysis were performed by V. P., G. E., G. N., B. M. All authors saw and approved the final version and no other person made a substantial contribution to the paper.
Funding
This study was not supported by any sponsor or funder.
Data availability
The datasets generated and/or analyzed during the current study are not publicly available due to ethical and confidentiality restrictions related to individual patient data. However, de-identified data may be made available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
The study was approved by the Research Ethics Committee of the Botucatu Medical School – UNESP (approval number: 3.493.971, CAAE: 1574019.8.0000.5411), and conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants before enrollment.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets generated and/or analyzed during the current study are not publicly available due to ethical and confidentiality restrictions related to individual patient data. However, de-identified data may be made available from the corresponding author upon reasonable request.