Background: Sodium-glucose co-transporter 2 inhibitors (SGLT2i) are effective anti-diabetic drugs improving cardiovascular outcomes in type 2 diabetes mellitus (T2DM) patients. This study investigated cardiovascular, cerebrovascular and cognitive outcomes of SGLT2i therapy in patients with atrial fibrillation (AF) and T2DM. Methods: Observational study using TriNetX, a global health research network of anonymised electronic medical records from real-world patients between January 2018 and December 2019. The network includes healthcare organisations globally but predominately in the United States. AF patients (ICD-10-CM code: I48) with T2DM were divided according to SGLT2i use or not, and balanced using propensity score matching (PSM). Patients were followed-up for 3-years. The primary endpoints were ischaemic stroke/transient ischemic attack (TIA), intracranial haemorrhage (ICH), and incident dementia. Secondary endpoints were incident heart failure and mortality. Results: We identified 89,356 AF patients with T2DM of which 5061 (5.7%) were taking a SGLT2i. After PSM, 5049 patients (mean age 66.7 ± 10.6 years; 28.9% female) were included in each group. At 3-years follow-up, the risk of ischaemic stroke/TIA was higher in patients not receiving SGLT2i (HR 1.12, 95% CI 1.01–1.24) and for ICH (HR 1.57, 95% CI 1.25–1.99) and incident dementia (HR 1.66, 95% CI 1.30–2.12). Incident heart failure (HR 1.50, 95% CI 1.34–1.68) and mortality (HR 1.77, 95% CI 1.58–1.99) risks were increased in AF patients not receiving SGLT2i. Conclusions: In our large ‘real world’ analysis of patients with concomitant AF and T2DM, SGLT2i reduced the risk of cerebrovascular events, incident dementia, heart failure and death.

Background: Cardiovascular disease is the leading cause of death in chronic kidney disease populations. The risk of major adverse cardiovascular events (MACE) is greater than that of progression to end-stage kidney disease. An exponential increase in mortality risk is associated with declining kidney function. This study aimed to review the current landscape of traditional and novel blood biomarkers in predicting MACE in ESKD patients. Methods: The systematic review was registered on PROSPERO (CRD42024497403). Standard and extensive Cochrane search methods were used. The latest search date was July 2023. Participants were aged ≥18 years with end-stage kidney disease. Descriptive analysis was performed and data was presented in tabular form. The hazard ratio or odds ratio was presented for potential biomarkers discovered. Results: Overall, 14 studies (4965 participants) were included for analysis; 12 focused on participants requiring haemodialysis and 2 on haemodialysis and peritoneal dialysis. The biomarkers analysed were Troponin I (n = 3), Troponin T (n = 3), B-type natriuretic peptide (n = 2), N-Terminal Pro-Brain-Natriuretic Peptide (n = 7), soluble receptors for advanced glycation end products (n = 2), Galectin 3 (n = 4), and the serum-soluble suppression of tumorigenicity-2 (n = 2). Reported study outcomes included all-cause mortality (n = 11), MACE (n = 5), cardiac specific mortality (n = 6), sudden cardiac death (n = 2), and first cardiovascular event (n = 3). Conclusions: This review outlines the potential role of traditional and novel biomarkers in predicting MACE in end-stage kidney disease. Further larger-scale research is required to establish the validity of the study outcomes to develop new methods of cardiovascular risk prediction in this high-risk population.

Background: Despite its many advantages, haemodialysis (HD) has been shown to be associated with significant cardiovascular events, especially in patients commencing HD. Currently, there is no specific method to risk-stratify incident HD patients. Blood-based biomarkers provide insight into myocardial injury and stress. We aimed to evaluate the association of increased circulating biomarker concentration in incident HD with incident major adverse cardiac events (MACE). Methods: This was a retrospective cohort study of incident haemodialysis cases within 3 months of treatment initiation (≥18 years) from the TriNetX database. Cohorts were grouped by biomarker thresholds: Troponin I: ≥50 ng/L, BNP ≥ 100 pg/mL and 1:1 propensity-score matched for demographic characteristics, baseline cardiovascular risk, laboratory values, and cardiovascular medication. Primary outcome: Incidence of major adverse cardiac events (MACE) censored prior to index event of HD. Secondary outcome: Risk of each individual component of the composite outcome. Cox regression reported hazard ratios (95% CI) for the outcomes. Results: In total, 62,206 and 10,476 patients were included in the troponin I and BNP cohorts, respectively. In the troponin I cohort, 5878 developed MACE (HR 1.33 (95% CI 1.26–1.41, p < 0.0001)). In the BNP cohort, 1050 developed MACE (HR 1.28 (95% CI 1.13–1.44, p < 0.0001)). Conclusions: In incident HD, routine clinical laboratory biomarkers can predict incident MACE. The results suggest the clinical need for CV mortality and morbidity risk profiling in incident HD using a combination of clinical and laboratory variables.

Background: Most patients with hypertension exhibit elevated and detectable levels of natriuretic peptides, particularly BNP and NT-proBNP, as well as troponin concentrations. However, the prognostic relevance of this finding has not been clearly established in patients who have hypertension without heart failure (HF). In this review, we aimed to evaluate the prognostic utility of BNP/NT-proBNP alongside troponin T/I for risk stratification in hypertensive patients, excluding those with HF. Methods: This systematic review was registered in PROSPERO (CRD42024552031). A systematic literature search was conducted using two online databases, Ovid Medline and Web of Science, to identify studies. Data retrieved from articles were used in line with the PRISMA statement guidelines. Participants were aged ≥ 18 years with hypertension. The primary end point was a major adverse cardiac event (MACE) and its individual components. Descriptive synthesis was performed, and data are presented in tabular form. Results: Seventeen studies (70,021 participants) were retrieved for analysis comprising eight prospective cohort studies, six randomized controlled trials, and three retrospective studies. The review evaluated cardiac biomarkers: BNP (n = 6), NT proBNP (n=9), troponin T (n = 4), and troponin I (n = 7). Studies predicted composite MACE (n = 8), all-cause mortality (n = 7), HF (n = 6), and atrial fibrillation (n = 3) outcomes. Cardiac biomarkers showed a strong association with reported outcomes. However, heterogeneity in biomarker thresholds and methodologies limited comparability. Conclusions: The obtained results suggest that elevated cardiac biomarkers BNP/NT-proBNP and troponin I are associated with significantly higher risk of MACE and are powerful predictors in clinical setting. However, large-scale studies are required to validate the robustness and prognostic utility of these biomarkers

Aims: Hypertension is a major contributor to cardiovascular morbidity and mortality, yet identifying individuals at risk before clinical diagnosis remains challenging. Here, we present a multi-horizon machine learning framework designed to model incident hypertension risk across multiple clinically meaningful time windows using data from 246 286 participants in the UK Biobank. The framework systematically compares predictive performance across five horizons under severe class imbalance, enabling analysis of how discrimination, precision, and risk drivers evolve as outcome prevalence changes over time. Methods and results: Seven classification algorithms were evaluated, including logistic regression, random forest, naïve Bayes, and four boosting-based ensemble methods. To enhance interpretability, we integrate SHapley Additive exPlanations (SHAP) with generative topographic mapping (GTM), combining feature-level attribution with population-level visualization of model predictions. Ensemble boosting models consistently achieved the strongest performance, with average precision increasing from 0.04 for the ≤2-year horizon to 0.22 for the ≤10-year horizon, while ROC-AUC remained relatively stable (∼0.75–0.79). Together, this framework reveals consistent predictors of hypertension risk (including baseline blood pressure, age, body mass index, medication burden, and cardiometabolic multimorbidity) and illustrates how combinations of risk factors organize hypertension risk across time horizons. Conclusions: Our results demonstrate how multi-horizon modelling and complementary explainability approaches can provide deeper insight into evolving disease risk patterns in large biomedical cohorts, supporting more interpretable and scalable strategies for population-level cardiovascular prevention. Such approaches may enable earlier identification of high-risk individuals and inform targeted screening and preventive interventions in routine care settings.