Aim: A network meta-analysis (NMA) was performed to determine the effects on the exercise capacity, measured by the 6 MWT, of patients with COPD of (i) different physical activity interventions and (ii) supervised or unsupervised programs. Methods: A literature search was carried out from inception to April 2022. Randomized controlled trials of the effectiveness of physical activity on exercise capacity in patients with COPD were included. The risk of bias was assessed using the Cochrane Risk of Bias (RoB 2.0) tool, and the Grading of Recommendations, Assessment, Development, and Evaluation tool (GRADE) was used to assess the quality of the evidence. A pairwise meta-analysis for direct and indirect effects was carried out. Results: A total of 41 studies were included in this NMA. The highest effects were for urban training pulmonary rehabilitation (PR) programs (ES, 1.50; 95% CI: 0.46 and 2.55) versus the control group. For supervised and unsupervised PR and home-based PR programs, the highest effects were found for supervised PR (ES, 0.85; 95% CI: 0.46 to 1.23) versus the control group. Conclusions: PR implemented with urban circuit training should be considered the most effective strategy to improve exercise capacity in patients with COPD. Supervision of the programs improves exercise capacity.

Metformin is the mainstay treatment for type 2 diabetes mellitus (T2DM), but its use in chronic kidney disease (CKD) remains controversial because of concerns about lactic acidosis. Previous meta-analyses have been limited by heterogeneous comparator groups and lack of CKD-stage stratification. To assess the association between metformin use and the risk of all-cause mortality, major cardiovascular events (MACE), end-stage renal disease (ESRD) and lactic acidosis in population with CKD and T2DM. A systematic search was conducted using Medline, Scopus, Web of Science and the Cochrane Library from inception to September 2025. Observational studies evaluating these associations in populations with CKD and T2DM were included. The associations were expressed as Hazard Ratios (HR) with 95% confidence intervals (95% CI). Meta-analyses were performed with subgroup analyses according to CKD stage. Twelve studies were included. Metformin use was associated with a reduction in both all-cause mortality (HR = 0.76, 95% CI: 0.64, 0.90) and ESRD (HR = 0.61, 95% CI: 0.49, 0.76). However, there was a trend towards an increased lactic acidosis risk in stage 4 (HR = 1.93, 95% CI: 0.95, 3.87). Metformin use was not associated with MACE. In this updated meta-analysis, based on observational evidence with very low certainty, metformin was inversely associated with all-cause mortality and ESRD. However, the results were inconsistent, and a potential increase in lactic acidosis risk in stage 4 was observed. Future studies examining the effect of dosage on these associations are required.

Although aerobic training leads to physiological improvements in older adults, as measured by the VO₂ peak, there is no conclusive evidence on which type of physical exercise intervention is most effective in improving the VO₂ peak. Aim To determine the effects of different physical exercise interventions on oxygen consumption in healthy older adults. A network meta-analysis (NMA) was conducted. The literature search was performed from inception to October 2025. Randomized controlled trials (RCTs) assessing the effectiveness of exercise programs on oxygen consumption in healthy older adults were included. The risk of bias was assessed via the Cochrane risk of bias tool (RoB 2.0), and the quality of the evidence was evaluated via the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) framework. Pairwise meta-analyses and NMA were conducted to synthesize both direct and indirect evidence. A total of 16 studies were included in the NMA. The greatest improvements in oxygen consumption were observed in the endurance exercise group versus the control group (effect size [ES]: 2.68; 95 % CI: 2.00-3.35), followed by the combined exercise group versus the control group (ES: 2.31; 95 % CI: 1.02-3.60). Endurance should be considered the most effective exercise to improve oxygen consumption in healthy older adults, followed by combined exercise. Given the importance of the VO₂ peak as a predictor of quality of life and mortality in older adults, it is essential to include its assessment in clinical guidelines and prioritize the most effective physical activity interventions to enhance it. The protocol for this review was registered in PROSPERO (CRD420251081461).

Cardiac rehabilitation (CR) improves functional capacity and reduces cardiovascular morbidity, yet clinical response remains highly heterogeneous and difficult to stratify using conventional assessment. This study presents a machine-learning framework for the early stratification of CR patients into responders and non-responders based exclusively on pre-intervention baseline characteristics. A total of 122 patients undergoing an 8-week CR program were evaluated using 56 clinical, physiological and metabolic predictors. Multiple classification models were trained under a stratified 10-fold cross-validation scheme. Among them, an SVM-RBF classifier achieved the best performance and retained high discriminative capacity after dimensionality reduction. The final reduced model, based on the ten most informative features identified through convergence between Random Forest and SHAP analyses, preserved >95% of the full-feature performance. The predictors were physiologically coherent, reflecting muscular strength, ventilatory efficiency, chronotropic modulation and metabolic burden. SHAP-based explainability enabled patient-level attribution of improvement likelihood, identifying modifiable variables associated with favorable or limited training response. In parallel, we developed a web-based clinical decision-support prototype that estimates improvement probability and highlights the most influential determinants for each patient, illustrating translational applicability for precision rehabilitation planning. These findings support a transition toward personalized CR strategies guided by explainable AI and baseline phenotyping.