131 papers
Cardiovascular medicine focuses on the heart and blood vessels, exploring how to prevent, diagnose, and treat conditions that affect our circulation. This vital field ranges from understanding high blood pressure and heart failure to investigating the latest breakthroughs in surgical techniques and lifestyle interventions. Because these discoveries directly impact public health, staying informed about emerging research is more important than ever for both specialists and curious readers.
At Gist.Science, we process every new preprint in this category as it appears on medRxiv, ensuring you have immediate access to the latest findings before they undergo formal peer review. For each study, we provide both a plain-language explanation to clarify the core concepts and a detailed technical summary for those seeking deeper scientific context. Below are the latest papers in cardiovascular medicine, organized to help you navigate the most recent developments shaping the future of heart health.
The paper introduces DELTA, a rigorous N=1 digital health study that integrates lifestyle interventions, dynamic biomarker challenges, and AI-driven analytics to assess and enhance human biological resilience, aiming to extend healthspan and inform future population-level trials.
This systematic review and meta-analysis of 45 randomized controlled trials suggests that vitamin D supplementation may modestly improve key cardiometabolic risk factors, including systolic blood pressure, LDL cholesterol, and glycemic control, with effects that appear to be modulated by the recipient's age and baseline vitamin D status, though the clinical significance of these findings remains uncertain.
This retrospective study from Hospital Clinic de Barcelona demonstrates that uncontrolled donation after circulatory death (uDCD) following failed ECPR is a viable source of transplantable organs, particularly kidneys, where shorter warm ischemia times and the central role of perfusionist nurses significantly enhance donation effectiveness.
By analyzing arterial transcriptome data and validating findings in plasma proteomics, this study identified four novel protein biomarkers (ACP5, LEFTY2, LILRA5, and PSME2) that are differentially expressed in people with diabetes and improve the accuracy of cardiovascular disease risk prediction models.
This study demonstrates that explainable deep learning models can accurately predict left ventricular function from wearable 1-lead ECGs with performance nearly matching 12-lead ECGs, thereby enabling broader clinical applications and enhanced patient autonomy in remote or resource-limited settings.
This study demonstrates that the novel Monocyte Atherosclerotic Risk Score (MARS), derived from flow cytometric profiling of monocyte subsets and aggregates, significantly outperforms traditional cardiovascular risk calculators (QRISK3 and China-PAR) in detecting silent atherosclerosis in two independent asymptomatic cohorts.
This study demonstrates that genetic testing for HFE variants in patients with early-onset atrial fibrillation can identify individuals with hereditary hemochromatosis and iron overload, enabling early intervention through therapeutic phlebotomy despite the absence of cardiac siderosis.
This study identifies specific circulating and cardiac N-glycan signatures, particularly the glycan A2G2S2, as novel biomarkers that reflect inflammation-driven atrial remodeling and accurately classify atrial fibrillation.
This study demonstrates that the left atrial to left ventricular (LA:LV) volume ratio is a superior marker for atrial cardiopathy compared to indexed left atrial volume (LAVi), showing stronger associations with ischemic stroke risk, cognitive dysfunction, and the identification of atrial fibrillation as a stroke etiology.
Leveraging the Million Veteran Program, this study identified causal DNA methylation markers for abdominal aortic aneurysm, elucidated their regulation through inflammatory and cardiometabolic pathways, and demonstrated that a methylation-based risk score significantly improves prediction accuracy beyond clinical models.