Hand grip strength-based cachexia index as a predictor of cancer cachexia and prognosis in patients with cancer

Cancer-associated muscle weakness - From triggers to molecular mechanisms

Skeletal muscle weakness is a debilitating consequence of many malignancies. Muscle weakness has a negative impact on both patient wellbeing and outcome in a range of cancer types and can be the result of loss of muscle mass (i.e. muscle atrophy, cachexia) and occur independently of muscle atrophy or cachexia. There are multiple cancer specific triggers that can initiate the progression of muscle weakness, including the malignancy itself and the tumour environment, as well as chemotherapy, radiotherapy and malnutrition. This can induce weakness via different routes: 1) impaired intrinsic capacity (i.e., contractile dysfunction and intramuscular impairments in excitation-contraction coupling or crossbridge cycling), 2) neuromuscular disconnection and/or 3) muscle atrophy. The mechanisms that underlie these pathways are a complex interplay of inflammation, autophagy, disrupted protein synthesis/degradation, and mitochondrial dysfunction. The current lack of therapies to treat cancer-associated muscle weakness highlight the critical need for novel interventions (both pharmacological and non-pharmacological) and mechanistic insight. Moreover, most research in the field has placed emphasis on directly improving muscle mass to improve muscle strength. However, accumulating evidence suggests that loss of muscle function precedes atrophy. This review primarily focuses on cancer-associated muscle weakness, independent of cachexia, and provides a solid background on the underlying mechanisms, methodology, current interventions, gaps in knowledge, and limitations of research in the field. Moreover, we have performed a mini-systematic review of recent research into the mechanisms behind muscle weakness in specific cancer types, along with the main pathways implicated.

Comparison of the cachexia index based on hand-grip strength (H-CXI) with the original CXI for the prediction of cancer cachexia and prognosis in patients who underwent radical colectomy for colorectal cancer

Background and aims

The cachexia index (CXI) is a novel biomarker for estimating cancer cachexia. The cachexia index based on hand-grip strength (H-CXI) has been recently developed as a simple proxy for CXI. The present study aims to compare both the H-CXI and CXI for the prediction of cancer cachexia and postoperative outcomes in patients who underwent radical colectomy for colorectal cancer.

Methods

Patients who underwent radical operations for colorectal cancer were included in this study. Cancer cachexia was diagnosed according to the international consensus outlined by Fearon et al. The cachexia index (CXI) was calculated as [skeletal muscle index (SMI) × serum albumin/neutrophil-to-lymphocyte ratio (NLR)]. The H-CXI was calculated as [hand-grip strength (HGS)/height2 × serum albumin/NLR]. The SMI was measured based on the preoperative CT images at the third lumbar vertebra (L3) level. HGS was measured before surgery.

Results

From July 2014 to May 2021, a total of 1,411 patients were included in the present study, of whom 361 (25.6%) were identified as having cancer cachexia. Patients with cachexia had a lower CXI (p < 0.001) and lower H-CXI (p < 0.001) than those without cachexia. A low CXI but not low H-CXI independently predicted cancer cachexia in the multivariate analysis (OR 1.448, p = 0.024). Both a low CXI (HR 1.476, p < 0.001 for OS; HR 1.611, p < 0.001 for DFS) and low H-CXI (HR 1.369, p = 0.007 for OS; HR 1.642, p < 0.001 for DFS) were independent predictors for overall survival (OS) and disease-free survival (DFS) after adjusting for the same covariates. A low H-CXI but not low CXI was an independent risk factor for postoperative complications (OR 1.337, p = 0.044). No significant association was found between cancer cachexia and postoperative complications.

Conclusion

The CXI and H-CXI exhibited better prognostic value than cancer cachexia for the prediction of postoperative outcomes in patients who underwent radical colectomy for colorectal cancer. The H-CXI was a superior index over the CXI in predicting short-term clinical outcomes, whereas the CXI demonstrated a closer correlation with Fearon's criteria for cancer cachexia. Ideal tools for the assessment of cancer cachexia should incorporate not only weight loss but also muscle mass, physical function, and inflammatory state.

Harnessing the Systemic Biology of Functional Decline and Cachexia to Inform more Holistic Therapies for Incurable Cancers

Options for treatment of incurable cancer remain scarce and are largely focused on limited therapeutic mechanisms. A new approach specific to advanced cancers is needed to identify new and effective treatments. Morbidity in advanced cancer is driven by functional decline and a number of systemic conditions, including cachexia and fatigue. This review will focus on these clinical concepts, describe our current understanding of their underlying biology, and then propose how future therapeutic strategies, including pharmaceuticals, exercise, and rehabilitation, could target these mechanisms as an alternative route to addressing incurable cancer.

Geriatric Nutritional Risk Index Combined with Calf Circumference Can be a Good Predictor of Prognosis in Patients Undergoing Surgery for Gastric or Colorectal Cancer

OBJECTIVES

To explore the effect of combined hematological and physical measurement indicators on the prognosis of patients undergoing surgery for gastric or colorectal cancer and to screen for the best prognostic indicators.

INTRODUCTION

Gastric and colorectal cancer is a widespread health concern worldwide and one of the major contributors to cancer-related death. The hematological and physical measurement indicators have been shown to associate with the prognosis of patients undergoing surgery for gastric or colorectal cancer, respectively, but it is still unclear whether the combination of the two can reflect the prognosis more effectively.

METHODS

Thirteen hematological indicators and 5 physical measurement indicators were selected in this study, and the most promising ones were screened using LASSO regression. Then, the best prognostic indicators were selected by time-ROC curves. Survival curves were constructed using the Kaplan-Meier method, and the effects of hematological and physical measurement indicators on the prognosis of patients undergoing surgery for gastric or colorectal cancers were evaluated by Cox proportional risk regression analysis. In addition, the relationship between hematological and physical measurement indicators on secondary outcomes, including length of stay, hospitalization costs, intensive care unit (ICU) admission, and patients' subjective global assessment scores (PGSGA), was explored.

RESULTS

After initial screening, among the hematological indicators, the geriatric nutritional risk index (GNRI) showed the highest mean area under the curve (AUC) values. Among body measures, calf circumference (CC) showed the highest mean AUC value. Further analyses showed that the combination of combined nutritional prognostic index (GNRI) and calf circumference (CC) (GNRI-CC) had the best performance in predicting the prognosis of patients undergoing surgery for gastric or colorectal cancers. Low GNRI, low CC, and low GNRI-low CC increased the risk of death by 44%, 48%, and 104%, respectively. Sensitivity analyses showed the same trend. In addition, low GNRI-low CC increased the risk of malnutrition by 17%.

CONCLUSION

This study emphasizes that a combination of blood measures and body measures is essential to accurately assess the prognosis of patients undergoing surgery for gastric or colorectal cancers. The GNRI-CC is a good prognostic indicator and can also assess the risk of possible malnutrition.

Superiority of CRP-albumin-lymphocyte index as a prognostic biomarker for patients with gastric cancer

OBJECTIVES

The new C-reactive protein (CRP)-albumin-lymphocyte (CALLY) index is an immune nutrition scoring system based on serum CRP) serum albumin, and lymphocyte counts. The aim of this study was to verify the prognostic value of the CALLY index in patients with gastric cancer and to evaluate the superiority of this new system.

METHODS

We retrospectively analyzed the data of patients with gastric cancer who were followed up from the INSCOC database between May 2013 and December 2018. Through simple random sampling, patients with gastric cancer were placed into one of two groups: the training group (n = 684) or the verification group (n = 290) in a ratio of 7:3. Correlation analysis, Kaplan-Meier method, and cubic spline function were used to analyze the relationship between the CALLY index and overall survival (OS) in these patients. Based on the results of Cox regression analysis of the training cohort, a nomogram model for predicting 1 -, 2 -, 3-, and 5-y OS was established and verified internally. The prediction accuracy and benefit of the nomogram in gastric cancer were evaluated by calibration and clinical decision curve and compared with the traditional TNM gastric cancer staging system.

RESULTS

The CALLY index was negatively correlated with the age of patients with gastric cancer (men, r = -0.1; women, r = -0.1), but positively correlated with body mass index (BMI; men, r = 0.063; women, r = 0.058), and the cutoff value of the CALLY index was determined as 1.12. The OS of patients with gastric cancer and a CALLY index >1.12 was significantly higher than that of patients with gastric cancer and a CALLY index ≤1.12 (P < 0.0001). There was an L-shaped dose-response relationship between the CALLY index and OS in patients with gastric cancer, and age, TNM stage, surgical treatment, chemotherapy, BMI, and the CALLY index were significantly correlated with the prognosis of patients with gastric cancer. Tumor TNM stage, BMI, and the CALLY index were independent risk factors affecting the prognosis of patients with gastric cancer. The CALLY index was a protective factor in the following patient factors: diagnosis of gastric cancer; <65 y of age; male; TNM 3 stage; BMI 18.5 to 23.9 kg/m2; smoker; consumer of alcohol; no radio- or chemotherapy; surgery; presence of diabetes, hypertension, or both; no family history of cancer; experienced a significant interaction with chemotherapy and surgery. A nomogram based on TNM staging, BMI, and the CALLY index has good predictive ability and clinical application value. Compared with traditional TNM staging systems, the nomogram has better resolution and accuracy in predicting 1 -, 2 -, 3-, and 5-year OS.

CONCLUSION

The CALLY index can be used as an independent prognostic factor for patients with gastric cancer, and constructs a nomogram prediction model combining TNM staging, BMI, and CALLY index, which yields better predictions than traditional TNM staging.

Explainable machine learning model for predicting skeletal muscle loss during surgery and adjuvant chemotherapy in ovarian cancer

BACKGROUND

Skeletal muscle loss during treatment is associated with poor survival outcomes in patients with ovarian cancer. Although changes in muscle mass can be assessed on computed tomography (CT) scans, this labour-intensive process can impair its utility in clinical practice. This study aimed to develop a machine learning (ML) model to predict muscle loss based on clinical data and to interpret the ML model by applying SHapley Additive exPlanations (SHAP) method.

METHODS

This study included the data of 617 patients with ovarian cancer who underwent primary debulking surgery and platinum-based chemotherapy at a tertiary centre between 2010 and 2019. The cohort data were split into training and test sets based on the treatment time. External validation was performed using 140 patients from a different tertiary centre. The skeletal muscle index (SMI) was measured from pre- and post-treatment CT scans, and a decrease in SMI ≥ 5% was defined as muscle loss. We evaluated five ML models to predict muscle loss, and their performance was determined using the area under the receiver operating characteristic curve (AUC) and F1 score. The features for analysis included demographic and disease-specific characteristics and relative changes in body mass index (BMI), albumin, neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR). The SHAP method was applied to determine the importance of the features and interpret the ML models.

RESULTS

The median (inter-quartile range) age of the cohort was 52 (46-59) years. After treatment, 204 patients (33.1%) experienced muscle loss in the training and test datasets, while 44 (31.4%) patients experienced muscle loss in the external validation dataset. Among the five evaluated ML models, the random forest model achieved the highest AUC (0.856, 95% confidence interval: 0.854-0.859) and F1 score (0.726, 95% confidence interval: 0.722-0.730). In the external validation, the random forest model outperformed all ML models with an AUC of 0.874 and an F1 score of 0.741. The results of the SHAP method showed that the albumin change, BMI change, malignant ascites, NLR change, and PLR change were the most important factors in muscle loss. At the patient level, SHAP force plots demonstrated insightful interpretation of our random forest model to predict muscle loss.

CONCLUSIONS

Explainable ML model was developed using clinical data to identify patients experiencing muscle loss after treatment and provide information of feature contribution. Using the SHAP method, clinicians may better understand the contributors to muscle loss and target interventions to counteract muscle loss.