Abstract
Breast cancer, the most common malignancy in women, often requires modified radical mastectomy, which can lead to complications like postoperative pulmonary infections. These infections, with an incidence of 11.26 to 20.19%, significantly impact prognosis and mortality. Frailty and nutritional status (Controlling Nutritional Status [CONUT] score) are key predictors of these complications, highlighting the need for their early assessment. This study assesses the predictive value of preoperative frailty combined with the CONUT score for postoperative pulmonary infections in breast cancer patients. Patients who underwent elective modified radical mastectomy for breast cancer at Fuyang Cancer Hospital between January 2022 to February 2024 were included. Frailty and nutritional status were evaluated within 24 hours of admission using the frailty scale and CONUT score, respectively. Multivariable logistic regression was employed to identify independent risk factors for postoperative pulmonary infections. The predictive performance of the combined frailty and CONUT score was assessed using receiver operating characteristic curves and decision curve analysis. A total of 416 patients were analyzed, with 84 exhibiting preoperative frailty and 39 experiencing postoperative pulmonary infections. Preoperative frailty, CONUT score, age, and a history of combined chemoradiotherapy were identified as independent risk factors for postoperative pulmonary infections. The combined assessment of preoperative frailty and CONUT score demonstrated strong predictive value, with an area under the curve of 0.777 (95% confidence interval: 0.700–0.854). The combination of preoperative frailty and the CONUT score is an effective tool for predicting postoperative pulmonary infections in breast cancer patients.
Keywords: breast cancer, Controlling Nutritional Status score, preoperative frailty
1. Introduction
In 2022, breast cancer became the second most common cancer globally and the most frequently diagnosed malignancy in women, accounting for 11.6% of all cancer cases worldwide.[1] Surgical intervention remains one of the primary treatments for early and intermediate-stage breast cancer. Modified radical mastectomy effectively removes lymph nodes between the axilla and the pectoralis major and minor muscles, reducing the risk of postoperative recurrence and metastasis, and significantly improving patient survival rates.[2] However, the invasive nature of surgery can lead to local tissue damage, potentially causing postoperative infections, pain, and limited limb mobility.
Among various postoperative complications, the incidence of pulmonary infections ranges from approximately 11.26 to 20.19%.[3,4] Breast cancer patients may reduce their physical activity due to postoperative pain and psychological distress, coupled with an immunocompromised state induced by surgical stress. Additionally, elderly patients often have comorbidities, further increasing the risk of postoperative pulmonary infections. Previous studies have shown that pulmonary infections following radical mastectomy can diminish surgical outcomes, severely impact patient prognosis, and even lead to mortality.[3] Therefore, effectively predicting and preventing postoperative pulmonary infections in breast cancer patients is crucial for improving their quality of life and extending their survival.
Existing research has found that frailty is a crucial factor influencing the occurrence of postoperative complications. Frailty is a multidimensional syndrome characterized by organ dysfunction, reduced physiological reserves, and diminished stress resistance.[5,6] Patients with frailty exhibit poor treatment tolerance, and malignancy-related factors, coupled with the long-term impact of surgery, chemotherapy, and other stressors, significantly increase the risk of frailty. Frail breast cancer patients, due to diminished physical function, are more prone to falls, severe chemotherapy toxicity, increased risk of readmission, and elevated mortality rates.[7–9] A systematic review and meta-analysis revealed a frailty incidence rate of up to 43% in breast cancer patients.[8] Therefore, preoperative assessment of frailty is vital for predicting postoperative complications. Early identification of frailty in breast cancer patients can effectively complement routine medical history and examinations by identifying unnoticed physical impairments. This aims to optimize treatment decisions for elderly cancer patients, develop personalized treatment plans, and enhance patient quality of life.[10,11]
In addition to frailty, nutritional status also plays a critical role in the occurrence of postoperative complications. The Controlling Nutritional Status (CONUT) score, comprising lymphocyte count, serum albumin (ALB), and total cholesterol (TC), reflects a patient’s compromised immune defense, protein reserves, and energy expenditure.[12] As a comprehensive nutritional assessment tool, the CONUT score not only evaluates patients’ nutritional status but also provides information on their immune function. Previous studies have shown that the CONUT score can provide accurate prognostic and survival information for various solid organ and hematological malignancies.[13–16] Prognosis in breast cancer is closely associated with the degree of systemic inflammation and nutritional status. A retrospective study of 1364 breast cancer patients confirmed that regardless of tumor stage, the CONUT score independently predicts prognosis in breast cancer patients.[17] Furthermore, preoperative nutritional status can also predict complications after other types of surgeries. For instance, in elderly patients with hip fractures, preoperative nutritional status assessment has been shown to predict the occurrence of postoperative pulmonary infections.[18] Providing nutritional support during the perioperative period can effectively reduce patients’ physiological stress responses, thereby enhancing their tolerance to surgical trauma and reducing postoperative complications.[19]
Against this background, this study aims to explore the predictive value of preoperative frailty combined with the CONUT score for postoperative pulmonary infections in breast cancer. By early identification and assessment of patients’ frailty and nutritional status, this study aims to provide reference criteria for the early detection of postoperative pulmonary infections in breast cancer patients, thereby improving their quality of life and survival.
2. Methods
2.1. Patients
This prospective cohort study included patients aged ≥18 years with pathologically confirmed breast cancer undergoing elective modified radical mastectomy at Fuyang Cancer Hospital from January 2022 to February 2024. Patients provided informed consent and were capable of independent communication and cooperation. Exclusion criteria included: preoperative acute infection, defined as fever (≥38.3°C), elevated white blood cell count (WBC) (>10 × 10⁹/L), elevated C-reactive protein (>10 mg/L), or local infection symptoms (e.g., cough, sputum production, dysuria, or skin redness/swelling) within 2 weeks prior to surgery, confirmed by imaging or microbiological evidence;[20,21] preoperative lung diseases, including COPD (FEV1/FVC < 0.70), asthma (reversible airflow obstruction or clinically confirmed history), interstitial lung disease (diagnosed via high-resolution CT), or active pneumonia/bronchitis within 4 weeks prior to surgery (confirmed by imaging or sputum culture);[22,23] breast cancer recurrence, metastasis, or concurrent malignancies; severe heart, liver, kidney, or hematological diseases; history of lung surgery; and severely incomplete clinical data. Infection status and lung diseases were confirmed through preoperative medical records, laboratory findings, imaging reports, and pulmonary function tests, with data verified by 2 independent investigators.
2.2. Data collection and assessment
2.2.1. Basic patient information
The following baseline characteristics were collected: age, BMI, smoking history, comorbidities (hypertension, diabetes, coronary heart disease), and history of combined chemotherapy.
2.2.2. Peripheral blood biomarker detection
Blood samples were collected 3 days before surgery to measure the following indicators: WBC, neutrophil count, lymphocyte count, albumin, TC, and C-reactive protein.
2.2.3. Perioperative indicators
The following perioperative data were recorded: intraoperative blood loss, surgery duration, and postoperative indicators (postoperative drainage volume, duration of postoperative catheter placement, and occurrence of postoperative pulmonary infections).
2.2.4. Pathology methods and molecular subtypes
Estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) statuses, and Ki-67 expression were assessed by immunohistochemical staining. The following monoclonal antibodies were used: ER (clone SP1; Ventana, Tucson, AZ), PR (clone 1E2; Ventana), Ki-67 (clone 30–9; Ventana), and HER2 (clone 4B5; Roche, Sandhofer, Mannheim, Germany). Positive ER or PR was defined as having ≥1% of immunoreactive tumor cell nuclei, following the American Society of Clinical Oncology and College of American Pathologists Guideline Recommendations from 2010.[24] The cutoff value for Ki-67 was set at ≥14%. HER2 status was considered negative for scores of 0 or 1+, and positive for a score of 3+. Fluorescence in situ hybridization was performed to determine HER2 status in cases with a score of 2+. Molecular subtypes were classified as follows: Luminal A: ER + and/or PR+, HER2−, Ki-67 < 14%; Luminal B: ER + and/or PR+, HER2 + and/or HER2−, any Ki-67; HER2-enriched: ER−, PR−, HER2+, any Ki-67; triple-negative breast cancer: ER−, PR−, HER2−, any Ki-67.
2.2.5. Preoperative frailty status
All patients were assessed for frailty within 24 hours of admission using the FRAIL scale,[25] which includes the following 5 items: Fatigue: feeling fatigued in the past 4 weeks; Resistance: inability to climb stairs; Ambulation: inability to walk 100 m without assistance; Weight loss: unintentional weight loss of ≥3 kg in the past 6 months; Illness: presence of 5 or more illnesses such as hypertension, diabetes, heart disease, malignancy, asthma, or kidney disease. Each item is scored as 1 point, with a total score of 0 indicating no frailty, 1 to 2 points indicating pre-frailty, and ≥3 points indicating frailty. In this study, patients classified as pre-frail and those with no frailty were combined into the non-frail category.[26]
2.2.6. Preoperative CONUT score
Peripheral venous blood samples were collected from patients 1 week before surgery. The preoperative nutritional status was assessed using the CONUT score, which evaluates ALB concentration, lymphocyte count, and cholesterol level. The detailed scoring criteria are provided in Table 1.
Table 1.
Components and scoring criteria of the CONUT system.
| Parameters | Normal | Light | Moderate | Severe |
|---|---|---|---|---|
| Serum albumin (g/dL) | ≥3.50 | 3.00–3.49 | 2.50–2.99 | <2.50 |
| score | 0 | 2 | 4 | 6 |
| Total lymphocyte count | ≥1600 | 1200–1599 | 800–1199 | <800 |
| score | 0 | 1 | 2 | 3 |
| Total cholesterol (mg/dL) | >180 | 140–180 | 100–139 | <100 |
| Score | 0 | 1 | 2 | 3 |
| CONUT score (total) | 0–1 | 2–4 | 5–8 | 9–12 |
| Assessment | Normal | Light | Moderate | Severe |
CONUT = Controlling Nutritional Status.
2.3. Follow-up
Patients were followed up according to the National Comprehensive Cancer Network (2023) guidelines[27]: every 3 months for the first 2 years post-surgery; every 6 months from 3 to 5 years post-surgery; and annually after 5 years, until the patient’s death. Patients were advised to visit the outpatient department for routine blood tests, blood biochemistry, chest X-rays, CT scans, and color Doppler ultrasound examinations.
The primary outcome observed was pulmonary infection. Pulmonary infection was diagnosed based on the following criteria: Pronounced symptoms of respiratory diseases. Physical examination revealed dry and moist rales on lung auscultation, and dullness on percussion. Positive sputum culture under sterile conditions. Elevated WBC and body temperature > 38°C. Chest CT indicating pulmonary infiltrates, consolidation, or ground-glass opacities. A diagnosis of pulmonary infection was confirmed if 2 or more of the above criteria were met. The follow-up period extended from the date of surgery to the final follow-up or the date of patient death. All pulmonary infections occurred within 60 days postoperatively. The median follow-up duration was 14.5 months (interquartile range: 9.2–19.8), ensuring that no delayed-onset infections were missed.
2.4. Data collection methods
Data were collected by 2 trained investigators using standardized instructions. Patients were interviewed item by item, and measurements were taken and recorded using standardized methods and equipment. Serological indicators were obtained from the electronic medical record system. During the follow-up period, if patients visited the hospital, their medical information was reviewed, and the date of any pulmonary infection was recorded. If patients did not visit the hospital during the follow-up period, they were contacted by phone to remind them to return for follow-up and to record their prognosis. The data collection process is illustrated in Figure 1.
Figure 1.
Flowchart of patient selection and data collection process for the study.
2.5. Ethical approval and consent to participate
This research adhered to the ethical standards outlined in the Helsinki Declaration[28] and received ethical approval from the Institutional Review Board of Fuyang Cancer Hospital, Anhui, China. (Approval No.: 2022FYSZLYY-IRB-6). All participants provided written informed consent before the interviews, with the assurance that they could decline to answer questions or exit the study at any point.
2.6. Statistical analysis
Statistical analyses were performed using SPSS software (version 26.0; IBM Corp., Armonk) and R software (version 4.2.1; R Foundation for Statistical Computing, Vienna, Austria). Continuous variables following a normal distribution were expressed as mean ± SD () and compared using the independent samples t test. For non-normally distributed variables, the median and interquartile range (M[Q]) were used, and comparisons between groups were made using the rank-sum test. Categorical variables were compared using the chi-square (χ²) test. Variables that were statistically significant in univariate analysis were included in multivariable logistic regression analysis. Collinearity among the independent variables was assessed using linear regression, with a variance inflation factor > 5 or 10 and tolerance (Tol) < 0.1 indicating significant multicollinearity. The predictive performance of the combined CONUT score and frailty for postoperative pulmonary infections in breast cancer patients was evaluated using receiver operating characteristic (ROC) curves and decision curve analysis (DCA). The Z test was used to compare the areas under different ROC curves (AUC), with a 2-tailed P value < .05 considered statistically significant.
3. Results
3.1. Patient characteristics
A total of 466 patients were initially included in the study. Forty-three patients were lost to follow-up, and 7 patients declined to participate. Ultimately, 416 patients were included in the statistical analysis. Among these, 84 patients (20.2%) were classified as frail preoperatively, and 39 patients (9.8%) experienced postoperative pulmonary infections. Patients were divided into an infection group and a non-infection group. There were statistically significant differences between the 2 groups in terms of age, hypertension, diabetes, coronary heart disease, TNM stage, history of combined chemoradiotherapy, preoperative frailty, and preoperative nutritional status (P < .05), as shown in Table 2.
Table 2.
Baseline characteristics of included patients (N = 416).
| Variable | Infection group N = 39 | Non-infection group N = 377 |
Z/χ2 | P value |
|---|---|---|---|---|
| Age [M(Q), yr] | 71.0 (18.0) | 66.0 (12.7) | 11.104 | <.001 |
| BMI [M(Q), kg/m2] |
25.2 (3.4) | 25.2 (3.6) | 0.256 | .613 |
| ASA classification [n (%)] | 0.036 | .849 | ||
| II | 9 (23.1) | 82 (21.8) | ||
| III | 30 (76.9) | 295 (78.2) | ||
| Smoking [n (%)] | 1.922 | .166 | ||
| Yes | 26 (66.7) | 203 (53.8) | ||
| No | 13 (33.3) | 174 (46.2) | ||
| Alcohol consumption [n (%)] | 0.061 | .805 | ||
| Yes | 2 (5.1) | 24 (6.4) | ||
| No | 37 (94.9) | 353 (93.6) | ||
| Hypertension [n (%)] | 4.812 | .028 | ||
| Yes | 25 (64.1) | 189 (50.1) | ||
| No | 14 (35.9) | 188 (49.9) | ||
| Diabetes [n (%)] | 6.109 | .013 | ||
| Yes | 13 (33.3) | 52 (13.8) | ||
| No | 26 (66.7) | 325 (86.2) | ||
| Coronary heart disease [n (%)] | 8.885 | .003 | ||
| Yes | 12 (30.8) | 32 (8.5) | ||
| No | 27 (69.2) | 345 (91.5) | ||
| TNM stage [n (%)] | 10.378 | .001 | ||
| I/II | 23 (59.0) | 69 (18.3) | ||
| III/IV | 16 (41.0) | 308 (81.7) | ||
| Combined chemotherapy history [n (%)] | 8.923 | .003 | ||
| Yes | 16 (41.0) | 74 (19.6) | ||
| No | 23 (59.0) | 303 (80.4) | ||
| Preoperative frailty [n (%)] | 22.108 | <.001 | ||
| Yes | 20 (51.3) | 64 (17.0) | ||
| No | 19 (48.7) | 313 (83.0) | ||
| Preoperative CONUT score [M(Q)] | 5.0 (7.0) | 3.0 (3.0) | 25.733 | <.001 |
| Intraoperative blood loss [M(Q), mL] | 82.0 (32.0) | 70.0 (26.0) | 3.649 | .056 |
| Surgery duration [M(Q), min] | 115.0 (60.0) | 108.0 (55.0) | 0.601 | .438 |
| Postoperative drainage volume [M(Q), mL] | 738.9 (56.9) | 749.4 (53.1) | 0.354 | .552 |
| Duration of postoperative catheterization [M(Q), d] | 9.0 (2.0) | 7.0 (2.0) | 0.072 | .908 |
| ER [n (%)] | 0.026 | .872 | ||
| + | 24 (61.5) | 227 (60.2) | ||
| − | 15 (38.5) | 150 (39.8) | ||
| PR [n (%)] | 0.055 | .815 | ||
| + | 22 (56.4) | 220 (58.4) | ||
| − | 17 (43.6) | 157 (41.6) | ||
| Ki-67 status [n (%)] | 0.044 | .833 | ||
| + | 25 (64.1) | 248 (65.8) | ||
| − | 14 (35.9) | 129 (34.2) | ||
| Molecular subtype [n (%)] | 0.579 | .447 | ||
| Luminal A | 11 (28.2) | 84 (22.3) | ||
| Luminal B | 17 (43.6) | 217 (57.6) | ||
| HER2-enriched | 7 (17.9) | 65 (17.2) | ||
| TNBC | 4 (10.3) | 11 (2.9) | ||
| Histological grade [n (%)] | 0.159 | .690 | ||
| I–II | 26 (66.7) | 263 (69.8) | ||
| III | 13 (33.3) | 114 (30.2) | ||
| Menopause [n (%)] | 0.007 | .933 | ||
| Yes | 17 (43.6) | 167 (44.3) | ||
| No | 22 (56.4) | 210 (55.7) | ||
| Tumor size [n (%)] | 0.032 | .858 | ||
| ≤2 cm | 12 (30.8) | 118 (31.3) | ||
| >2 and <5 cm | 20 (51.3) | 197 (52.3) | ||
| ≥ 5 cm | 7 (17.9) | 62 (16.4) |
CONUT = Controlling Nutritional Status, ER = estrogen receptor, HER2= human epidermal growth factor receptor 2, PR = progesterone receptor.
3.2. Incidence of postoperative pulmonary infections in frail patients with different nutritional status
Among the included patients, 124 had normal preoperative nutritional status, 181 had mild malnutrition, and 111 had moderate to severe malnutrition. In the moderate to severe malnutrition group, frail patients had a significantly higher risk of developing postoperative pulmonary infections compared to non-frail patients (P < .05), as shown in Table 3.
Table 3.
Incidence of postoperative pulmonary infections in frail versus non-frail patients by nutritional status.
| Nutritional status | Frailty status | N | Incidence of postoperative pulmonary infections (%) | χ² | P value |
|---|---|---|---|---|---|
| Normal | Frail | 24 | 2.7 | 0.003 | .995 |
| Non-frail | 100 | 2.8 | |||
| Mild malnutrition | Frail | 36 | 7.0 | 0.817 | .366 |
| Non-frail | 145 | 12.5 | |||
| Moderate to severe malnutrition | Frail | 24 | 45.8 | 8.127 | .004 |
| Non-frail | 87 | 14.9 |
3.3. Independent risk factors for postoperative pulmonary infections
Variables demonstrating statistical significance were included in the collinearity analysis. All variance inflation factors were <5, confirming no significant multicollinearity among the predictors (see Table 4). The results of multivariable logistic regression analysis indicated that advanced age (≥60 yr), preoperative combined chemoradiotherapy history, preoperative frailty, and high CONUT score (moderate to severe preoperative malnutrition) were independent risk factors for postoperative pulmonary infections following modified radical mastectomy for breast cancer (P < .05), as shown in Table 5.
Table 4.
Collinearity analysis among predictor variables.
| Risk factor | unstandardized coefficients | standardized coefficients | t | P value | Collinearity statistics | ||
|---|---|---|---|---|---|---|---|
| β | SD | β | Tol | VIF | |||
| Age (≥60 yr) | 0.004 | 0.003 | 0.139 | 1.391 | .165 | 0.210 | 4.764 |
| Hypertension | −0.030 | 0.021 | −0.133 | −1.463 | .144 | 0.252 | 3.967 |
| Diabetes | −0.015 | 0.027 | −0.042 | −0.550 | .583 | 0.364 | 2.751 |
| Coronary heart disease | 0.000 | 0.030 | 0.000 | −0.005 | .996 | 0.359 | 2.785 |
| TNM stage | 0.053 | 0.047 | 0.073 | 1.121 | .263 | 0.495 | 2.019 |
| Preoperative frailty | 0.176 | 0.048 | 0.242 | 3.644 | .000 | 0.475 | 2.105 |
| Combined chemotherapy history | 0.046 | 0.046 | 0.065 | 0.999 | .319 | 0.501 | 1.994 |
| High CONUT score | 0.031 | 0.006 | 0.231 | 4.937 | .000 | 0.959 | 1.043 |
CONUT = Controlling Nutritional Status, VIF = variance inflation factor.
Table 5.
Multivariable logistic regression analysis of independent risk factors for postoperative pulmonary infections.
| Risk factor | P value | Odds ratio (OR) | 95% confidence interval (CI) |
|---|---|---|---|
| Age (≥60 yr) | .045 | 1.093 | 1.209–1.988 |
| Hypertension | .113 | 0.613 | 0.334–1.124 |
| Diabetes | .671 | 0.875 | 0.473–1.620 |
| Coronary heart disease | .854 | 0.940 | 0.448–1.813 |
| TNM stage | .315 | 1.672 | 0.614–4.544 |
| Preoperative frailty | .013 | 4.549 | 1.384–14.953 |
| Combined chemotherapy history | .028 | 1.733 | 1.640–4.691 |
| High CONUT score | <.01 | 1.450 | 1.244–1.718 |
CONUT = Controlling Nutritional Status.
3.4. Predictive performance of frailty, CONUT score, and their combination for postoperative pulmonary infections in breast cancer patients
The predictive performance of frailty, CONUT score, and their combination was assessed using ROC analysis. The results showed that the difference in AUC between frailty alone and the combination of frailty and CONUT score was 0.105 (Z = 2.358, P = .018), and the difference in AUC between CONUT score alone and the combination of frailty and CONUT score was 0.085 (Z = 3.669, P < .001). The combined prediction using frailty and CONUT score had a better predictive ability for postoperative pulmonary infections following modified radical mastectomy for breast cancer than either individual indicator (see Fig. 2 and Table 6).
Figure 2.
Receiver operating characteristic (ROC) curves for predicting postoperative pulmonary infections: frailty, CONUT score, and combined model. CONUT = Controlling Nutritional Status.
Table 6.
Predictive performance of frailty, CONUT score, and their combination for postoperative pulmonary infections.
| Indicator | AUC (95% CI) | Sensitivity | Specificity | P value |
|---|---|---|---|---|
| Frailty | 0.672 (0.573–0.770) | 0.813 | 0.543 | <.01 |
| CONUT score | 0.692 (0.600–0.785) | 0.815 | 0.584 | <.01 |
| Combination of frailty and CONUT score | 0.777 (0.700–0.854) | 0.946 | 0.680 | <.01 |
AUC = area under the curve, CI = confidence interval, CONUT = Controlling Nutritional Status.
The Hosmer–Lemeshow goodness-of-fit test indicated that the combined prediction model of frailty and CONUT score had a good fit for predicting postoperative pulmonary infections in breast cancer patients (χ² = 0.265, P = .880). DCA showed that when the high-risk threshold was between 0.16 and 0.30, the combined prediction model of frailty and CONUT score had better clinical benefits (see Fig. 3).
Figure 3.
Decision curve analysis (DCA) for the combined prediction model of frailty and CONUT score in assessing postoperative pulmonary infection risk. CONUT = Controlling Nutritional Status.
4. Discussion
Postoperative pulmonary infection is a common complication following modified radical mastectomy for breast cancer, significantly impacting patients’ overall prognosis. Firstly, pulmonary infections prolong hospital stays, increase medical expenses, and impose a financial burden on patients and their families. Secondly, these infections delay postoperative recovery, negatively affecting patients’ quality of life and increasing readmission rates. More critically, pulmonary infections can further compromise the immune system of breast cancer patients, affecting the efficacy of subsequent treatments and potentially increasing mortality rates.[29] Therefore, predicting and preventing postoperative pulmonary infections is crucial for the postoperative recovery of breast cancer patients.
This single-center prospective cohort study adopted a sample size (n = 416) based on the actual number of eligible participants, without a formal preliminary calculation. Nevertheless, the event-to-predictor ratio (39 pulmonary infections vs 4 predictors) was 9.75, closely aligning with the 10:1 recommendation for logistic regression,[30] suggesting that the sample size is sufficient to ensure the model’s robustness. Furthermore, the model’s AUC was 0.777 (95% confidence interval: 0.700–0.854), and the Hosmer–Lemeshow goodness-of-fit test demonstrated a satisfactory fit (χ² = 0.265, P = .880), reinforcing the adequacy of the sample size. In this study, the incidence of postoperative pulmonary infections among breast cancer patients was 9.5%. After adjusting for all risk variables, including age, hypertension, diabetes, coronary heart disease, TNM stage, history of combined chemoradiotherapy, preoperative frailty, and CONUT score, multivariable logistic regression analysis indicated that age ≥ 60 years, preoperative frailty, high CONUT score, and history of combined chemoradiotherapy were independent risk factors for postoperative pulmonary infections in breast cancer patients. Linear regression analysis showed no multicollinearity among the variables. Although no significant multicollinearity was observed statistically, preoperative frailty and the CONUT score may exhibit a certain degree of clinical association.
Frailty is a multidimensional syndrome characterized by reduced muscle function, fatigue, and diminished physiological reserves, while the CONUT score reflects the patient’s nutritional status and immune function. Previous studies have suggested that malnutrition may exacerbate the severity of frailty. Specifically, inadequate protein intake can impair muscle protein synthesis and promote muscle breakdown, leading to a decline in muscle mass and function. Additionally, malnutrition may trigger a systemic inflammatory response, further depleting physiological reserves and aggravating frailty.[31,32] Moreover, elderly patients are more prone to frailty and malnutrition due to decreased metabolic capacity and accelerated muscle loss, while combined chemoradiotherapy may further worsen nutritional status by suppressing appetite and altering metabolism, thereby indirectly exacerbating frailty.[33,34] Among patients with the same CONUT score, those with preoperative frailty had a higher incidence of pulmonary infections compared to non-frail patients, suggesting that frailty increases the risk of postoperative pulmonary infections in breast cancer patients.
4.1. Mechanisms linking frailty and postoperative pulmonary infections in breast cancer
Frailty is considered a multidimensional syndrome with complex causes. These causes generally arise from the interplay of genetic, biological, physical, psychological, social, and environmental factors.[35] In breast cancer patients, the impact of frailty is particularly significant. This phenomenon may be attributed to the prolonged tumor burden, physical exhaustion from treatment, and psychological stress, which collectively exacerbate frailty.
Multivariable logistic regression analysis identified preoperative frailty as an independent risk factor for postoperative pulmonary infections in breast cancer patients. Frailty increased the incidence of postoperative pulmonary infections, with frail breast cancer patients being 5 times more likely to develop these infections compared to non-frail patients. The area under the ROC curve (AUC) for preoperative frailty was 0.672, indicating its predictive value for postoperative pulmonary infections. However, the pathophysiological mechanisms linking frailty and pulmonary infections are not yet fully understood. The following points may contribute to this association: Disease Burden and Immune Function: malignant tumor patients experience significant disease-related consumption and treatment-induced decline in physiological reserves, resulting in impaired immune function.[36] Environmental Instability and Muscle Weakness: frail patients often exhibit disrupted internal environments, which increases their susceptibility to infections.[37] Furthermore, frailty is frequently associated with sarcopenia. This condition weakens respiratory muscles, including the diaphragm, thereby reducing lung function. Impaired respiratory muscle strength further compromises effective coughing, leading to decreased airway clearance. Consequently, secretion blockage, atelectasis, and pulmonary infections may occur.[38] Chemotherapy and Immune Suppression: Patients who have received preoperative chemotherapy experience further immune suppression and damage to the respiratory ciliary clearance system, increasing the risk of postoperative pulmonary infections. Inflammaging and Inflammatory Response: Frail elderly breast cancer patients often exhibit elevated levels of pro-inflammatory cytokines such as interleukin 6 and tumor necrosis factor alpha (TNF-α), as well as acute phase proteins like C-reactive protein, along with decreased levels of interleukin 10.[39,40] This imbalance, known as inflammaging, impairs immunological homeostasis and is proposed as an underlying mechanism of frailty. During stress events such as surgery, frail patients have higher circulating levels of inflammatory factors compared to non-frail patients. This leads to alveolar and pulmonary capillary damage, increased capillary permeability, reduced alveolar surfactant, and ultimately alveolar collapse and ventilation-perfusion mismatch, increasing the risk of pulmonary infections.[41]
4.2. Relationship between CONUT score and postoperative pulmonary infections in breast cancer
The results of this study indicate that, after controlling for all risk variables, a high preoperative CONUT score is an independent risk factor for postoperative pulmonary infections in breast cancer patients. For each unit increase in the CONUT score, the risk of postoperative pulmonary infections increases by 1.5 times. The CONUT score comprises 3 components: total lymphocyte count, ALB, and TC. These biomarkers reflect impaired immune defense, protein reserves, and energy expenditure, respectively.[12] Importantly, these indicators not only assess nutritional status but also provide insights into the severity of systemic inflammation.
Among these components, ALB level is a critical part of the CONUT score. It is closely associated with the incidence of postoperative complications, mortality, and overall survival rates in cancer patients. A decrease in ALB weakens cellular and humoral immunity, phagocytosis, and other defense mechanisms in cancer patients, leading to an inflammatory response. Conversely, inflammation also reduces ALB levels. Numerous studies have confirmed that low ALB levels predict poorer morbidity and mortality in both solid tumors and hematologic malignancies.[42–45]
Peripheral blood lymphocytes, commonly used as inflammation markers, play a crucial role in tumor immunity, such as cytotoxic cell death, inhibition of tumor cell proliferation, and migration.[46] A decrease in lymphocytes reflects suppressed tumor immune function, allowing tumor cells to proliferate unchecked, leading to tumor metastasis and poor prognosis.
Cholesterol is an essential lipid for maintaining cellular homeostasis, participating in acquired and adaptive immune responses, and playing a key role in cell membrane formation and various biochemical pathways crucial for normal biological functions.[47,48] Total serum cholesterol levels have been linked to tumorigenesis in multiple cancer types. Moreover, emerging evidence suggests that cholesterol may influence prognostic outcomes and chemotherapy resistance in malignancies.[49–51]
The CONUT score integrates 3 biomarkers: ALB, lymphocyte count, and TC. Our findings demonstrate that this composite score effectively predicts postoperative pulmonary infections in breast cancer patients. ROC analysis shows that combining preoperative frailty with the CONUT score provides high accuracy in predicting postoperative pulmonary infections. DCA analysis further reveals that the predictive model combining preoperative frailty and the CONUT score demonstrates significant clinical utility within the high-risk threshold range of 0.16 to 0.30. This implies that using the combined scoring model in clinical practice can more effectively identify high-risk patients, enabling targeted preventive measures to reduce the incidence of postoperative pulmonary infections.
This study is the first to propose a combined assessment method incorporating preoperative frailty and the CONUT score for predicting postoperative pulmonary infections in breast cancer patients. By enhancing the accuracy and clinical utility of the predictive model, this combined scoring model holds promise as an effective tool in clinical practice. It can assist clinicians in better identifying high-risk patients and implementing targeted preventive measures. This approach not only helps reduce the incidence of postoperative pulmonary infections but also improves the overall prognosis and quality of life for breast cancer patients.
4.3. Relationship between frailty and malnutrition (CONUT score)
Frailty and malnutrition, as assessed by the CONUT score, often exhibit a clinical interrelationship, forming a vicious cycle that may significantly increase the risk of postoperative infections. Frailty is a multidimensional syndrome characterized by reduced muscle strength, fatigue, and diminished physiological reserves, while the CONUT score, based on ALB, lymphocyte count, and cholesterol levels, reflects nutritional status and indirectly indicates immune function and inflammation levels. Studies have shown that malnutrition can reduce muscle protein synthesis, thereby exacerbating sarcopenia and frailty, and impairing immune defenses.[52,53]
Inadequate protein intake inhibits muscle protein synthesis and accelerates sarcopenia, leading to weakness in respiratory muscles (e.g., diaphragm and intercostal muscles), which reduces cough efficiency and impairs airway clearance, consequently increasing the risk of pulmonary infections. Furthermore, malnutrition compromises both innate and adaptive immunity, rendering frail patients more susceptible to infections. Frailty is often accompanied by chronic low-grade inflammation, a condition further aggravated by malnutrition. The systemic inflammation induced by malnutrition, marked by elevated levels of C-reactive protein and interleukin 6, not only depletes physiological reserves but also disrupts immune homeostasis, further elevating infection risk.[54]
In summary, malnutrition depletes physiological reserves, exacerbating frailty, while frailty-related functional decline perpetuates the worsening of malnutrition. This bidirectional relationship underscores the clinical value of jointly assessing frailty and the CONUT score to identify high-risk patients. Clinicians should conduct comprehensive preoperative evaluations of nutritional status and frailty, and consider implementing personalized nutritional support and resistance training to optimize postoperative outcomes.
Although age ≥ 60 years and preoperative combined chemoradiotherapy history were independent risk factors for postoperative pulmonary infections, their clinical utility is limited by non-modifiability and mechanistic overlap with frailty and CONUT scores. Aging and chemoradiotherapy may exacerbate frailty and malnutrition through shared pathways such as chronic inflammation and metabolic dysregulation. Therefore, we prioritized the 2-factor model (frailty + CONUT score) targeting modifiable preoperative factors to optimize resource allocation and guide actionable interventions (e.g., protein-rich nutritional supplementation combined with resistance training). Future studies should explore dynamic models integrating perioperative biomarkers to further refine predictive accuracy.
5. Conclusion
Preoperative frailty, high preoperative CONUT score, age ≥ 60 years, and a history of combined chemoradiotherapy are independent risk factors for postoperative pulmonary infections in breast cancer patients. The combination of preoperative frailty and the CONUT score effectively predicts the risk of postoperative pulmonary infections and has significant clinical utility.
6. Limitations
The incidence of postoperative pulmonary infections in breast cancer patients in this study was 9.5%, slightly lower than reported in previous studies.[3] This discrepancy may be attributed to the study design. As a prospective study, it allowed for comprehensive collection of patient variables and exclusion of preexisting pulmonary diseases caused by other factors before surgery. Additionally, some patients received nutritional support preoperatively. This study was conducted at a single center, which may limit the generalizability of the findings. Future research should involve larger sample sizes and multicenter prospective studies to validate these results.
Acknowledgments
We are very grateful to the people who gave help to this study.
Author contributions
Conceptualization: Feng Liu, Dawang Xiao.
Data curation: Zhe Zhang, Tianpeng Wang, Feng Liu, Dawang Xiao, Guangqun Yu, Zhongfeng Jia, Rong Yang, Wenwen Zhang, Jing Bai.
Formal analysis: Zhe Zhang, Tianpeng Wang.
Investigation: Zhe Zhang, Tianpeng Wang, Feng Liu, Dawang Xiao.
Methodology: Zhe Zhang, Tianpeng Wang.
Project administration: Feng Liu, Dawang Xiao, Guangqun Yu.
Resources: Zhe Zhang, Tianpeng Wang.
Supervision: Dawang Xiao.
Visualization: Zhe Zhang.
Writing – original draft: Zhe Zhang, Tianpeng Wang.
Writing – review & editing: Guangqun Yu, Zhongfeng Jia.
Abbreviations:
- ALB
- serum albumin
- AUC
- areas under different ROC curves
- CONUT
- Controlling Nutritional Status
- DCA
- decision curve analysis
- ER
- estrogen receptor
- HER2
- human epidermal growth factor receptor 2
- PR
- progesterone receptor
- ROC
- receiver operating characteristic
- TC
- total cholesterol
- WBC
- white blood cell count
This study was approved by the ethics committee of Fuyang Cancer Hospital, Anhui, China. (Approval No.: 2022FYSZLYY-IRB-6). Written informed consent was obtained from all participants.
The authors have no funding and conflicts of interest to disclose.
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
How to cite this article: Zhang Z, Wang T, Liu F, Xiao D, Yu G, Jia Z, Yang R, Zhang W, Bai J. Predictive value of preoperative frailty combined with Controlling Nutritional Status score for postoperative lung infection in breast cancer patients. Medicine 2025;104:27(e43037).
Contributor Information
Zhe Zhang, Email: 827501392@qq.com.
Tianpeng Wang, Email: 1076092100@qq.com.
Feng Liu, Email: 12402806@qq.com.
Guangqun Yu, Email: 13956802612@139.com.
Zhongfeng Jia, Email: ahjzf@163.com.
Rong Yang, Email: 776893062@qq.com.
Wenwen Zhang, Email: 827501392@qq.com.
Jing Bai, Email: 1463554853@qq.com.
References
- [1].Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74:229–63. [DOI] [PubMed] [Google Scholar]
- [2].Aitken GL, Correa G, Samuels S, Gannon CJ, Llaguna OH. Assessment of textbook oncologic outcomes following modified radical mastectomy for breast cancer. J Surg Res. 2022;277:17–26. [DOI] [PubMed] [Google Scholar]
- [3].Xie J, Li Y, Qiu M, Liu X, Zhou S, Jiang J. Risk factors and nursing countermeasures of postoperative pulmonary infection in patients with breast cancer: a retrospective analysis. Medicine (Baltim). 2021;100:e26952. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [4].Lu Y, Li FF, Song AF, Liu Y, Yu XH. Risk factors for postoperative pulmonary infection in breast cancer patients and changes of peripheral blood cytokines. Chin J Nosocomiol. 2020;30:893–7. [Google Scholar]
- [5].Dent E, Martin FC, Bergman H, Woo J, Romero-Ortuno R, Walston JD. Management of frailty: opportunities, challenges, and future directions. Lancet. 2019;394:1376–86. [DOI] [PubMed] [Google Scholar]
- [6].Hoogendijk EO, Afilalo J, Ensrud KE, Kowal P, Onder G, Fried LP. Frailty: implications for clinical practice and public health. Lancet. 2019;394:1365–75. [DOI] [PubMed] [Google Scholar]
- [7].Tsai HH, Yu JC, Hsu HM, et al. The impact of frailty on breast cancer outcomes: evidence from analysis of the Nationwide Inpatient Sample, 2005-2018. Am J Cancer Res. 2022;12:5589–98. [PMC free article] [PubMed] [Google Scholar]
- [8].Wang S, Yang T, Qiang W, et al. The prevalence of frailty among breast cancer patients: a systematic review and meta-analysis. Support Care Cancer. 2022;30:2993–3006. [DOI] [PubMed] [Google Scholar]
- [9].Yan CH, Coleman C, Nabulsi NA, et al. Associations between frailty and cancer-specific mortality among older women with breast cancer. Breast Cancer Res Treat. 2021;189:769–79. [DOI] [PubMed] [Google Scholar]
- [10].Coleman C, Yan CH, Ko NY, et al. Physical functioning, frailty and risks of locally-advanced breast cancer among older women. Breast. 2022;64:19–28. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [11].Williams GR, Deal AM, Sanoff HK, et al. Frailty and health-related quality of life in older women with breast cancer. Support Care Cancer. 2019;27:2693–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [12].Chen J, Song P, Peng Z, et al. The controlling nutritional status (CONUT) score and prognosis in malignant tumors: a systematic review and meta-analysis. Nutr Cancer. 2022;74:3146–63. [DOI] [PubMed] [Google Scholar]
- [13].Hayama T, Ozawa T, Okada Y, et al. The pretreatment controlling nutritional status (CONUT) score is an independent prognostic factor in patients undergoing resection for colorectal cancer. Sci Rep. 2020;10:13239. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [14].Li Y, Zhang C, Ji R, et al. Prognostic significance of the controlling nutritional status (CONUT) score in epithelial ovarian cancer. Int J Gynecol Cancer. 2020;30:74–82. [DOI] [PubMed] [Google Scholar]
- [15].Peng L, Meng C, Li J, et al. The prognostic significance of controlling nutritional status (CONUT) score for surgically treated renal cell cancer and upper urinary tract urothelial cancer: a systematic review and meta-analysis. Eur J Clin Nutr. 2022;76:801–10. [DOI] [PubMed] [Google Scholar]
- [16].Lu C, Chen Q, Fei L, Wang J, Wang C, Yu L. Prognostic impact of the controlling nutritional status score in patients with hematologic malignancies: a systematic review and meta-analysis. Front Immunol. 2022;13:952802. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [17].Li W, Li M, Wang T, et al. Controlling nutritional status (CONUT) score is a prognostic factor in patients with resected breast cancer. Sci Rep. 2020;10:6633. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [18].Yang C, Zhang Y, Zhou Y, Shen J. Correlation between nutritional status and postoperative pneumonia in geriatric hip fracture. J Trauma Surg. 2021;23:372–4. [Google Scholar]
- [19].Malafarina V, Reginster JY, Cabrerizo S, et al. Nutritional status and nutritional treatment are related to outcomes and mortality in older adults with hip fracture. Nutrients. 2018;10:555. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [20].Dellinger RP, Levy MM, Rhodes A, et al. Surviving Sepsis Campaign: international guidelines for management of severe sepsis and septic shock, 2012. Intensive Care Med. 2013;39:165–228. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [21].Horan TC, Andrus M, Dudeck MA. CDC/NHSN surveillance definition of health care-associated infection and criteria for specific types of infections in the acute care setting. Am J Infect Control. 2008;36:309–32. [DOI] [PubMed] [Google Scholar]
- [22].Metlay JP, Waterer GW, Long AC, et al. Diagnosis and treatment of adults with community-acquired pneumonia. An official clinical practice guideline of the American Thoracic Society and Infectious Diseases Society of America. Am J Respir Crit Care Med. 2019;200:e45–67. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [23].Raghu G, Remy-Jardin M, Myers JL, et al. Diagnosis of idiopathic pulmonary fibrosis. An official ATS/ERS/JRS/ALAT clinical practice guideline. Am J Respir Crit Care Med. 2018;198:e44–68. [DOI] [PubMed] [Google Scholar]
- [24].Hammond ME, Hayes DF, Dowsett M, et al. American Society of Clinical Oncology/College Of American Pathologists guideline recommendations for immunohistochemical testing of estrogen and progesterone receptors in breast cancer. J Clin Oncol. 2010;28:2784–95. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [25].Aprahamian I, Cezar NOC, Izbicki R, et al. Screening for frailty with the FRAIL sale: a comparison with the phenotype criteria. J Am Med Dir Assoc. 2017;18:592–6. [DOI] [PubMed] [Google Scholar]
- [26].Sia TY, Wen T, Cham S, Friedman AM, Wright JD. The effect of frailty on postoperative readmissions, morbidity, and mortality in endometrial cancer surgery. Gynecol Oncol. 2021;161:353–60. [DOI] [PubMed] [Google Scholar]
- [27].Gradishar WJ, Moran MS, Abraham J, et al. NCCN Guidelines® insights: breast cancer, version 4.2023. J Natl Compr Canc Netw. 2023;21:594–608. [DOI] [PubMed] [Google Scholar]
- [28].World Medical Association. World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA. 2013;310:2191–4. [DOI] [PubMed] [Google Scholar]
- [29].Ho AY, Ballangrud A, Li G, et al. Long-term pulmonary outcomes of a feasibility study of inverse-planned, multibeam intensity modulated radiation therapy in node-positive breast cancer patients receiving regional nodal irradiation. Int J Radiat Oncol Biol Phys. 2019;103:1100–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [30].Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol. 1996;49:1373–9. [DOI] [PubMed] [Google Scholar]
- [31].Bauer J, Morley JE, Schols A, et al. Sarcopenia: a time for action. An SCWD position paper. J Cachexia Sarcopenia Muscle. 2019;10:956–61. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [32].Cruz-Jentoft AJ, Bahat G, Bauer J, et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing. 2019;48:16–31. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [33].Clegg A, Young J, Iliffe S, Rikkert MO, Rockwood K. Frailty in elderly people. Lancet (London). 2013;381:752–62. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [34].Arends J, Baracos V, Bertz H, et al. ESPEN expert group recommendations for action against cancer-related malnutrition. Clin Nutr. 2017;36:1187–96. [DOI] [PubMed] [Google Scholar]
- [35].Marcos-Pérez D, Sánchez-Flores M, Proietti S, et al. Association of inflammatory mediators with frailty status in older adults: results from a systematic review and meta-analysis. Geroscience. 2020;42:1451–73. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [36].Ikeda H, Togashi Y. Aging, cancer, and antitumor immunity. Int J Clin Oncol. 2022;27:316–22. [DOI] [PubMed] [Google Scholar]
- [37].Vetrano DL, Triolo F, Maggi S, et al. Fostering healthy aging: the interdependency of infections, immunity and frailty. Ageing Res Rev. 2021;69:101351. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [38].Zhou R, Tian G, Guo X, Li R. Lung function and the risk of frailty in the European population: a Mendelian randomization study. Eur J Med Res. 2024;29:95. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [39].Cybularz M, Wydra S, Berndt K, et al. Frailty is associated with chronic inflammation and pro-inflammatory monocyte subpopulations. Exp Gerontol. 2021;149:111317. [DOI] [PubMed] [Google Scholar]
- [40].Mitchell A, Malmgren L, Bartosch P, McGuigan FE, Akesson KE. Pro-Inflammatory proteins associated with frailty and its progression-a longitudinal study in community-dwelling women. J Bone Miner Res. 2023;38:1076–91. [DOI] [PubMed] [Google Scholar]
- [41].Meyer KC. The role of immunity and inflammation in lung senescence and susceptibility to infection in the elderly. Semin Respir Crit Care Med. 2010;31:561–74. [DOI] [PubMed] [Google Scholar]
- [42].Akkiz H, Carr BI, Bag HG, et al. Serum levels of inflammatory markers CRP, ESR and albumin in relation to survival for patients with hepatocellular carcinoma. Int J Clin Pract. 2021;75:e13593. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [43].Gradel KO, Larsen TS, Frederiksen H, et al. Impact of C-reactive protein and albumin levels on short, medium, and long term mortality in patients with diffuse large B-cell lymphoma. Ann Med. 2022;54:713–22. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [44].Gupta D, Lis CG. Pretreatment serum albumin as a predictor of cancer survival: a systematic review of the epidemiological literature. Nutr J. 2010;9:69. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [45].Yin L, Cui J, Lin X, et al. Triceps skinfold-albumin index significantly predicts the prognosis of cancer cachexia: a multicentre cohort study. J Cachexia Sarcopenia Muscle. 2023;14:517–33. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [46].Stanton SE, Disis ML. Clinical significance of tumor-infiltrating lymphocytes in breast cancer. J ImmunoTher Cancer. 2016;4:59. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [47].Huang B, Song BL, Xu C. Cholesterol metabolism in cancer: mechanisms and therapeutic opportunities. Nat Metab. 2020;2:132–41. [DOI] [PubMed] [Google Scholar]
- [48].Xu H, Zhou S, Tang Q, Xia H, Bi F. Cholesterol metabolism: new functions and therapeutic approaches in cancer. Biochim Biophys Acta Rev Cancer. 2020;1874:188394. [DOI] [PubMed] [Google Scholar]
- [49].Llaverias G, Danilo C, Mercier I, et al. Role of cholesterol in the development and progression of breast cancer. Am J Pathol. 2011;178:402–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [50].Soucek P, Vrana D, Ueng YF, Wei S, Kozevnikovova R, Guengerich FP. Selective changes in cholesterol metabolite levels in plasma of breast cancer patients after tumor removal. Clin Chem Lab Med. 2018;56:e78–81. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [51].Sarhadi S, Ganjali S, Pirro M, Sahebkar A. The role of high-density lipoproteins in antitumor drug delivery. IUBMB Life. 2019;71:1442–52. [DOI] [PubMed] [Google Scholar]
- [52].Sieber CC. Malnutrition and sarcopenia. Aging Clin Exp Res. 2019;31:793–8. [DOI] [PubMed] [Google Scholar]
- [53].Ligthart-Melis GC, Luiking YC, Kakourou A, Cederholm T, Maier AB, de van der Schueren MAE. Frailty, sarcopenia, and malnutrition frequently (Co-)occur in hospitalized older adults: a systematic review and meta-analysis. J Am Med Dir Assoc. 2020;21:1216–28. [DOI] [PubMed] [Google Scholar]
- [54].Bano G, Trevisan C, Carraro S, et al. Inflammation and sarcopenia: a systematic review and meta-analysis. Maturitas. 2017;96:10–5. [DOI] [PubMed] [Google Scholar]