Biomolecules of Muscle Fatigue in Metabolic Myopathies

Pre-Transplant Dual-Energy X-ray Absorptiometry (DXA)-Derived Body Composition Measures as Predictors of Treatment Outcomes and Early Post-Transplant Complications in Patients with Multiple Myeloma (MM) Treated with Autologous Hematopoietic Stem Cell Transplantation (AutoHSCT)

Background/Objectives: Changes in muscle mass and bone density are common in multiple myeloma (MM) patients. Dual-energy X-ray absorptiometry (DXA) offers precise, non-invasive insights into a patient's physical condition before autologous stem cell transplantation (autoHSCT). This study examines how pre-transplant body composition impacts treatment outcomes and early complications in MM patients undergoing autoHSCT. Methods: This study is a single-center, retrospective analysis of patients with MM who were treated with first or second autoHSCT and underwent DXA pre-transplant between 11 August 2019 and 12 June 2024. Results: We conducted a study of pre-transplant body composition in 127 patients with MM. Among them, 108 (85%) qualified for first autoHSCT, while 19 (15%) qualified for a second. The median age of the patients was 64 years (range 50-73). In the Cox proportional hazards regression conducted in the group of women, Total Body %Fat was a statistically significant predictor for progression-free survival (PFS) (HR = 0.07, 95% CI = 0.01,0.6, p = 0.0157). In the Mann-Whitney U test conducted on males, Lean Mass/Height2 and Appen. Lean Height2 were statistically significant predictors of early infections after autoHSCT (Z = 1.98, p = 0.0473 and Z = 2.32, p = 0.0204, respectively). In males, Fat Mass/Height2 was a significant predictor of non-infectious toxicity related to treatment (Z = -1.98, p = 0.0476). Conclusions: In women, higher levels of adipose tissue initially appear to exert a protective effect; however, this benefit diminishes over time, with greater fat mass eventually correlating with an increased risk of disease progression. In men, muscle mass has been identified as a significant predictor of early infection risk post-autoHSCT. Furthermore, our findings indicate that an increased amount of adipose tissue in men is statistically associated with a higher risk of non-infectious treatment-related toxicity. These conclusions highlight the critical need for further investigation into the role of body composition.

6'-Sialyllactose Alleviates Muscle Fatigue through Reduced Blood Lactate Level after Treadmill Exercise in Mice

6'-Sialyllactose (6'-SL), found in human breast milk, exhibits anti-inflammatory, immune function-enhancing, brain development-promoting, and gut health-improving effects. However, its effects on muscle fatigue remain unknown. Here, we aimed to investigate the effects of 6'-SL on blood lactate level, muscle fiber type, and oxidative phosphorylation protein complexes (OXPHOS) in muscle after exercise using C57BL/6J male mice. C57BL/6J mice were randomly assigned to control or 100 mg/kg 6'-SL. After 12 weeks of 6'-SL administration, the mice were made to perform treadmill exercise; their blood lactate and glucose levels were measured at the basal level (rest) and 0, 5, and 10 min after treadmill exercise. Results showed that 6'-SL treatment in C57BL/6J mice significantly reduced blood lactate level and improved blood glucose level. Moreover, 6'-SL increased the expression of slow-myosin heavy chain (MHC) and OXPHOS in gastrocnemius muscle. In addition, 6'-SL treatment for 12 weeks did not affect food intake, serum biomarkers of tissue injury, and lipid profiles compared with those of the controls. These findings indicate that non-toxic 6'-SL suppressed muscle fatigue during exercise by promoting protein expression of muscle fibers, especially slow-twitch muscle fibers characterized by abundant OXPHOS complexes and decreased blood lactate level. This study suggests that 6'-SL holds promise as a nutritional supplement in exercise and clinical settings, subject to further validation.

Revealing the Complexity of Fatigue: A Review of the Persistent Challenges and Promises of Artificial Intelligence

Part I reviews persistent challenges obstructing progress in understanding complex fatigue's biology. Difficulties quantifying subjective symptoms, mapping multi-factorial mechanisms, accounting for individual variation, enabling invasive sensing, overcoming research/funding insularity, and more are discussed. Part II explores how emerging artificial intelligence and machine and deep learning techniques can help address limitations through pattern recognition of complex physiological signatures as more objective biomarkers, predictive modeling to capture individual differences, consolidation of disjointed findings via data mining, and simulation to explore interventions. Conversational agents like Claude and ChatGPT also have potential to accelerate human fatigue research, but they currently lack capacities for robust autonomous contributions. Envisioned is an innovation timeline where synergistic application of enhanced neuroimaging, biosensors, closed-loop systems, and other advances combined with AI analytics could catalyze transformative progress in elucidating fatigue neural circuitry and treating associated conditions over the coming decades.