Balkan Medical Journal最新文献

Background: Hyperuricemia is associated with poor clinical outcomes in several cardiovascular diseases, including heart failure (HF). However, whether lowering serum uric acid (SUA) levels improves the prognosis of HF remains insufficiently studied.

Aims: To evaluate whether urate-lowering therapy (ULT) with febuxostat confers clinical benefits in patients with HF and concomitant hyperuricemia.

Study design: Prospective, observational cohort study.

Methods: Patients with chronic HF and hyperuricemia were enrolled and assigned either to a febuxostat group or to a non-ULT group and were followed prospectively for 5 years. The primary endpoint was all-cause mortality or rehospitalization for HF.

Results: Among 2005 patients, those with higher SUA levels experienced more endpoint events. After propensity score matching, we found that febuxostat therapy significantly reduced the incidence of primary endpoints in patients with HF with preserved ejection fraction (HFpEF) [p = 0.012; hazard ratios (HR), 0.744; 95% confidence intervals (CI), 0.589-0.939], but not in those with HF with reduced ejection fraction (HFrEF) or mildly reduced ejection fraction (HFmrEF) (p = 0.234; HR, 0.894; 95% CI, 0.742-1.077). The benefits of febuxostat in HFpEF were most evident in patients within the highest tertiles of B-type natriuretic peptide (BNP) (p = 0.021; HR, 0.647; 95% CI, 0.436-0.960) and SUA (p = 0.025; HR, 0.651; 95% CI, 0.441-0.963).

Conclusion: High SUA levels are associated with increased all-cause mortality and rehospitalization for HF. Febuxostat-mediated SUA reduction significantly improved clinical outcomes in patients with HFpEF, particularly those with elevated SUA and BNP levels.

Background: Cardiac fibrosis plays a critical role in the progression of chronic cardiovascular conditions, with mitochondrial dysfunction acting as a central mechanism underlying pathological myocardial fibrosis. Increasing research shows that microRNAs may modulate the fibrotic process by regulating mitochondrial function via various pathways.

Aims: To examine the involvement of miR-17-5p in modulating mitochondrial autophagy and alleviating pathological cardiac fibrosis.

Study design: Combined in vivo and in vitro study.

Methods: Expression levels of miR-17-5P and BCL2/adenovirus E1B 19 kDa protein-interacting protein 3 (BNIP3) were measured in a mouse model of myocardial fibrosis induced by abdominal aortic constriction, as well as in cardiac fibroblasts (CFs) treated with angiotensin II. CFs were transiently transfected with a miR-17-5p mimic, the pcDNA3.1-BNIP3 plasmid, or both. Cell viability was evaluated using the CCK-8 colorimetric assay. The expression of fibrotic and autophagy-related markers was determined via quantitative real-time reverse transcription polymerase chain reaction and immunoblotting. Intracellular levels of reactive oxygen species (ROS) and adenosine triphosphate (ATP) were also assessed.

Results: Reduced myocardial miR-17-5p expression was associated with diminished left ventricular systolic function and increased collagen accumulation in heart tissue. In vitro, angiotensin II treatment led to decreased miR-17-5p expression, upregulated BNIP3, and excessive mitochondrial autophagy-evidenced by increased ROS, lowered ATP production, and elevated fibrosis-related markers. Rescue experiments demonstrated that miR-17-5p overexpression directly targeted the 3’ untranslated region (3’-UTR) of BNIP3, significantly downregulating its expression, restoring mitochondrial balance, and decreasing collagen production. Conversely, BNIP3 overexpression counteracted the anti-fibrotic and mitochondrial-protective effects of miR-17-5p.

Conclusion: The miR-17-5p/BNIP3 signaling pathway modulates mitochondrial autophagy in CFs and plays a key role in fibrotic remodeling. This axis may serve as a promising therapeutic target for reducing cardiac fibrosis and slowing the progression of heart failure.

Background: Low bone mineral density (BMD) is a common age-related condition that elevates the risk of fractures and mortality. Machine learning (ML) techniques offer a promising approach for early prediction using readily available clinical, biochemical, and demographic data.

Aims: To evaluate the predictive performance of eleven ML models in identifying low BMD and to determine the most influential risk factors using the best-performing model.

Study design: Cross-sectional study.

Methods: Data were obtained from National Health and Nutrition Examination Survey (2005-2010, 2013-2014, and 2017-2020), focusing on individuals aged ≥ 50 years with available femoral neck or total femur BMD data. After applying exclusion criteria, 12,108 participants were included. Supervised ML algorithms were trained using 57 clinical, biochemical, demographic, and behavioral features. Model performance was assessed using accuracy, area under the curve (AUC), recall, precision, and F1 score. SHAP analysis was employed to interpret model outputs and rank predictors.

Results: The extra trees classifier outperformed other ML methods, achieving an accuracy of 76.7% and an AUC of 0.85. Recursive Feature Elimination with Cross-Validation identified 14 key predictors of low BMD in descending order of importance: sex, age, body mass index, race, family income-to-poverty ratio, serum uric acid, diabetes status, HDL cholesterol, urinary creatinine, alkaline phosphatase, mean cell volume, lymphocyte count, diastolic blood pressure, and glycohemoglobin.

Conclusion: Tree-based ML models, particularly Extra Trees, can effectively predict low BMD. The identified risk factors include both established and lesser-studied predictors. These findings support the use of ML for personalized osteoporosis and osteopenia screening and highlight its ability to capture complex, multifactorial relationships in population health data.