Transcriptome profiling of fast/glycolytic and slow/oxidative muscle fibers in aging and obesity.

Aging and obesity pose significant threats to public health and are major contributors to muscle atrophy. The trends in muscle fiber types under these conditions and the transcriptional differences between different muscle fiber types remain unclear. Here, we demonstrate distinct responses of fast/glycolytic fibers and slow/oxidative fibers to aging and obesity. We found that in muscles dominated by oxidative fibers, the proportion of oxidative fibers remains unchanged during aging and obesity. However, in muscles dominated by glycolytic fibers, despite the low content of oxidative fibers, a significant decrease in proportion of oxidative fibers was observed. Consistently, our study uncovered that during aging and obesity, fast/glycolytic fibers specifically increased the expression of genes associated with muscle atrophy and inflammation, including Dkk3, Ccl8, Cxcl10, Cxcl13, Fbxo32, Depp1, and Chac1, while slow/oxidative fibers exhibit elevated expression of antioxidant protein Nqo-1 and downregulation of Tfrc. Additionally, we noted substantial differences in the expression of calcium-related signaling pathways between fast/glycolytic fibers and slow/oxidative fibers in response to aging and obesity. Treatment with a calcium channel inhibitor thapsigargin significantly increased the abundance of oxidative fibers. Our study provides additional evidence to support the transcriptomic differences in muscle fiber types under pathophysiological conditions, thereby establishing a theoretical basis for modulating muscle fiber types in disease treatment.

Muscle attenuation, not skeletal muscle index, is an independent prognostic factor for survival in gastric cancer patients with overweight and obesity.

OBJECTIVES: Skeletal muscle index (SMI) is insufficient for evaluating muscle in obesity, and muscle attenuation (MA) may be a preferred indicator. This study aimed to investigate whether MA has greater prognostic value than SMI in gastric cancer patients with overweight and obesity. METHODS: Clinical parameters of 1312 patients with gastric cancer who underwent radical gastrectomy were prospectively collected between 2013 and 2019. MA and SMI were analyzed by computed tomography scan. Overweight/obesity was defined as body mass index (BMI) ≥24 kg/m2. The hazard ratio (HR) for death was calculated using Cox regression analysis. RESULTS: Among all patients, 405 were identified as overweight and obese, and 907 were identified as normal and underweight. MA was inversely associated with BMI and visceral fat area. Among the 405 patients with overweight and obesity, 212 patients (52%) were diagnosed with low MA. In the overweight/obese group, MA was an independent predictor for overall survival (HR, 1.610; P = 0.021) in multivariate Cox regression analyses, whereas SMI did not remain in the model. In the normal/underweight group, both low MA (HR, 1.283; P = 0.039) and low SMI (HR, 1.369; P = 0.008) were independent factors of overall survival. Additionally, 318 patients were identified as having visceral obesity in the overweight/obese group, and low MA was also an independent prognostic factor for survival in these patients (HR, 1.765; P = 0.013). CONCLUSION: MA had a higher prognostic value than SMI in overweight and obese patients with gastric cancer after radical gastrectomy.

Establishment and validation of novel nomograms to predict muscle quality in colorectal cancer patients.

OBJECTIVES: The skeletal muscle mass index and skeletal muscle radiodensity have promise as specific diagnostic indicators for muscle quality. However, the difficulties in measuring low skeletal muscle mass index and low skeletal muscle radiodensity limit their use in routine clinical practice, impeding early screening and diagnosis. The objective of this study is to develop a nomogram that incorporates preoperative factors for predicting low skeletal muscle mass index and low skeletal muscle radiodensity. METHODS: A total of 1692 colorectal cancer patients between 2015 and 2021 were included. The patients were randomly divided into a training cohort (n = 1353) and a validation cohort (n = 339). Nomogram models were calibrated using the area under the curve, calibration curves, and the Hosmer-Lemeshow test to assess their predictive ability. Finally, a decision curve was applied to assess the clinical usefulness. RESULTS: In a prediction model for low skeletal muscle mass index, age, body mass index, and grip strength were incorporated as variables. For low skeletal muscle radiodensity, age, sex, body mass index, serum hemoglobin level, and grip strength were included as predictors. In the training cohort, the area under the curve value for low skeletal muscle mass index was 0.750 (95% CI, 0.726-0.773), whereas for low skeletal muscle radiodensity, it was 0.763 (95% CI, 0.739-0.785). The Hosmer-Lemeshow test confirmed that both models fit well in both cohorts. Decision curve analysis was applied to assess the clinical usefulness of the model. CONCLUSIONS: The incorporation of preoperative factors into the nomogram-based prediction model represents a significant advancement in the muscle quality assessment. Its implementation has the potential to early screen patients at risk of low skeletal muscle mass index and low skeletal muscle radiodensity.

Discovery of distinct cancer cachexia phenotypes using an unsupervised machine-learning algorithm.

OBJECTIVES: Cancer cachexia is a debilitating condition with widespread negative effects. The heterogeneity of clinical features within patients with cancer cachexia is unclear. The identification and prognostic analysis of diverse phenotypes of cancer cachexia may help develop individualized interventions to improve outcomes for vulnerable populations. The aim of this study was to show that the machine learning-based cancer cachexia classification model generalized well on the external validation cohort. METHODS: This was a nationwide multicenter observational study conducted from October 2012 to April 2021 in China. Unsupervised consensus clustering analysis was applied based on demographic, anthropometric, nutritional, oncological, and quality-of-life data. Key characteristics of each cluster were identified using the standardized mean difference. We used logistic and Cox regression analysis to evaluate 1-, 3-, 5-y, and overall mortality. RESULTS: A consensus clustering algorithm was performed for 4329 patients with cancer cachexia in the discovery cohort, and four clusters with distinct phenotypes were uncovered. From clusters 1 to 4, the clinical characteristics of patients showed a transition from almost unimpaired to mildly, moderately, and severely impaired. Consistently, an increase in mortality from clusters 1 to 4 was observed. The overall mortality rate was 32%, 40%, 54%, and 68%, and the median overall survival time was 21.9, 18, 16.7, and 13.6 mo for patients in clusters 1 to 4, respectively. Our machine learning-based model performed better in predicting mortality than the traditional model. External validation confirmed the above results. CONCLUSIONS: Machine learning is valuable for phenotype classifications of patients with cancer cachexia. Detection of clinically distinct clusters among cachexic patients assists in scheduling personalized treatment strategies and in patient selection for clinical trials.