Diabetic cardiomyopathy (DCM) is a significant cause of heart failure in patients with diabetes, and its pathogenesis is closely related to myocardial mitochondrial injury and functional disability. Studies have shown that the development of diabetic cardiomyopathy is related to disorders in mitochondrial metabolic substrates, changes in mitochondrial dynamics, an imbalance in mitochondrial Ca²⁺ regulation, defects in the regulation of microRNAs, and mitochondrial oxidative stress. Physical activity may play a role in resistance to the development of diabetic cardiomyopathy by improving myocardial mitochondrial biogenesis, the level of autophagy and dynamic changes in fusion and division; enhancing the ability to cope with oxidative stress; and optimising the metabolic substrates of the myocardium. This paper puts forward a new idea for further understanding the specific mitochondrial mechanism of the occurrence and development of diabetic cardiomyopathy and clarifying the role of exercise‐mediated myocardial mitochondrial changes in the prevention and treatment of diabetic cardiomyopathy. This is expected to provide a new theoretical basis for exercise to reduce diabetic cardiomyopathy symptoms.

Background: Altitude training stimulates erythropoietin hormone (EPO) release and increases blood hemoglobin (Hb) mass, which may result in improved oxygen (O₂) transport capacity. It was hypothesized in the present study that periodic inhalation of carbon monoxide (CO) might elicit similar physiological adaptations compared to altitude training. Methods: Twelve male college student athletes, who were well-trained soccer players, participated. They performed a 4-week treadmill-training program, five times a week. Participants were randomly assigned into an experimental group with inhaling CO (INCO) (1 mL/kg body weight for 2 min) in O₂ (4 L) before all training sessions and a control group without inhaling CO (NOCO). CO and EPO concentrations in venous blood were first measured acutely at the 1st, 2nd, 4th, 6th, and 8th hour after INCO, and total hemoglobin mass (tHb), running economy and VO₂max were measured before and after the 4 weeks training intervention. Results: HbCO% increased from 0.7 to 4.4% (P < 0.05) after 1 h of CO inhalation and EPO increased from 1.9 to 2.7 mIU/mL after 4 h post CO inhalation (P < 0.05) acutely before the intervention. After the training, the tHb and VO₂max in the INCO group increased significantly by 3.7 and 2.7%, respectively, while no significant differences were observed in the NOCO condition. O₂ uptake at given submaximal speeds declined by approximately 4% in the INCO group. Conclusion: Acutely, EPO increased sharply post CO inhalation, peaking at 4 h post inhalation. 4-weeks of training with CO inhalation before exercise sessions improved tHb and VO₂max as well as running economy, suggesting that moderate CO inhalation could be a new method to improve the endurance performance in athletes.

Myocarditis is a common disease of the cardiovascular and immune systems, but the relationship between relevant blood metabolites and the risk of myocarditis has not been well-established. To identify potential biometabolic markers associated with myocarditis, we conducted a two-sample Mendelian randomization (MR) study. We performed preliminary MR analysis using the inverse variance weighted (IVW) method, supplemented by MR-Egger, weighted median, and weighted mode methods to adjust for false discovery rate (FDR). Confounders were screened using the GWAS Catalog website. Sensitivity analyses included Cochrane Q-test, Egger regression, Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO), scatterplots, funnel plots, and forest plots. For genetic and directional analysis, we employed co-localization analysis and the Steiger test. MR analysis was performed using the FinnGen database and meta-analysis was performed using the IEU database. MR analysis identified significant correlations for five metabolic biomarkers after FDR correction. These included four known metabolites: kynurenine, 1-stearoyl-GPE (18:0), deoxycarnitine, and 5-acetylamino-6-formylamino-3-methyluracil, as well as one unknown metabolite, X-25,422. Among these, kynurenine (OR = 1.441, 95%CI = 1.089–1.906, p-value = 0.018) and 1-stearoyl-GPE (18:0) (OR = 1.263, 95%CI = 1.029–1.550, p-value = 0.029) were identified as risk factors for myocarditis, while deoxycarnitine (OR = 0.813, 95%CI = 0.676–0.979, p-value = 0.029), 5-acetylamino-6-formylamino-3-methyluracil (OR = 0.864, 95% CI = 0.775–0.962, p-value = 0.018), and X-25,422 (OR = 0.721, 95%CI = 0.587–0.886, p-value = 0.009) were found to be protective factors. No evidence of heterogeneity, horizontal pleiotropy, or sensitivity issues was observed, and no shared genetic factors between exposure and outcome were detected. The causality was in the correct direction. Meta-analysis further confirmed the causal relationship between the five metabolites and myocarditis. This study identifies a causal relationship between five circulating metabolites and myocarditis. Kynurenine, 1-stearoyl-GPE (18:0), deoxycarnitine, X-25,422, and 5-acetylamino-6-formylamino-3-methyluracil may serve as potential drug targets for myocarditis, providing a theoretical basis for the prevention, diagnosis, and treatment of the condition.

Lactate anaerobic threshold has been a commonly used metric in the field of training monitoring, however its invasiveness has been overwhelming. Therefore, this study utilized sweat sensors to monitor Na⁺ and K⁺ in sweat to investigate the possibility of using sweat for anaerobic threshold monitoring. Fifty-five subjects were asked to complete an incremental load riding test. The test started at 100 W and each level of load lasted 3 min with one minute of rest in increments of 25 W/3min until exhaustion. Sweat collection was performed on the left chest throughout the ride to test sweat Na⁺ and K⁺ concentrations at each level, and fingertip blood collection was performed to measure blood lactate concentrations. For high and middle level populations, sST and sPT showed higher correlation and agreement with bLT (HL: sST vs. bLT: r = 0.559 p < 0.05,sPT vs. bLT: r = 0.667 p < 0.05;ML: sST vs. bLT: r = 0.802 ,p < 0.01, sPT vs. bLT: r = 0.723 p < 0.01), whereas for low level populations the method may not predict anaerobic threshold. The findings validates the possibility of sweat monitoring on anaerobic threshold testing, which suggests that sweat metrics are linked to energy metabolism metrics, which could help to advance sweat monitoring in the field of exercise practice. Further research is needed to advance exploration in this field.

Background: Previous observational studies have shown a correlation between leisure sedentary behaviors (LSB) and physical activity (PA) with the incidence of obstructive sleep apnea (OSA). However, the causal associations remain unknown. Therefore, our study used bidirectional two-sample Mendelian randomization (MR) to identify potential causal relationships between LSB/PA and OSA.Methods: We sourced genetic variation data for LSB and PA from the UK Biobank, while data on OSA were collected from the FinnGen study. The primary analysis method employed was the inverse variance weighted (IVW) approach, complemented by the weighted median and MR-Egger methods. For sensitivity analyses, we conducted Cochran’s Q test, the MR-Egger intercept test, the MR-PRESSO global test, and the leave-one-out analysis.Results: IVW analyses showed that genetically predicted leisure television watching (odds ratio [OR] = 1.38, 95% confidence interval [CI] = 1.09–1.75, p = 0.007) and computer use (OR = 1.48, 95% CI = 1.15–1.92, p = 0.002) significantly increased the risk of OSA. Conversely, self-reported vigorous physical activity (VPA) (OR = 0.33, 95% CI = 0.11–0.98, p = 0.046) may reduce the risk of OSA. No causal effects on OSA risk were observed for driving or self-reported moderate-to-vigorous physical activity. Furthermore, the reverse MR analysis indicated no significant causal relationship between OSA and any LSB/PA phenotype. Sensitivity tests showed no significant heterogeneity or horizontal pleiotropy.Conclusion: This study suggests that leisurely television watching and computer use are risk factors for OSA, while VPA may be a protective factor. Additionally, OSA does not affect PA or LSB levels. We recommend reducing sedentary activities, particularly television watching and computer use, and prioritizing VPA to reduce the risk of OSA. Further research in diverse populations and settings is needed to validate these findings.

This preliminary study examined the effects of wearing an elevation training mask^® 2.0 (ETM) during high-intensity interval training (HIIT) on the aerobic capacity of young men. Twenty-six young men were recruited and randomly divided into an experimental group (EG, n = 13) and a control group (CG, n = 13). The former performed HIIT on a running platform while wearing an ETM, and the latter performed HIIT only. Before and after the intervention, the following parameters were measured: exercise performance indicator is a 3000-m running test; aerobic capacity indicators are maximal oxygen uptake (VO_2max), ventilatory anaerobic threshold (VT), and running economy (RE); the physiological indicators are blood, pulmonary function, and cardiac function. After 6 weeks of intervention, there were significant differences (p < 0.05) between the time and time × group interaction for the 3000-m running time. There were no significant differences (p > 0.05) in VO_2max between the time and time × group interaction. The VT and RE demonstrated significant differences (p < 0.05) between the time and time × group interaction. Among the physiological indicators, pulmonary and cardiac function differed significantly (p < 0.05) be-tween the time and time × group interaction. Blood parameters showed no significant differences (p > 0.05) between the time points and time × group interaction. These preliminary results suggest that using ETM in conjunction with HIIT for 6 weeks increased young men’s aerobic performance much more than conventional HIIT. This effect may be attributed to the significantly improved respiratory function and cardiac function caused by the combination of ETM with HIIT.