cardiovascular disease

Overview

Cardiovascular disease (CVD) is an umbrella term encompassing a broad spectrum of disorders affecting the heart and blood vessels, including coronary artery disease (CAD), heart failure, stroke, peripheral artery disease, and arrhythmias such as atrial fibrillation/flutter. As the leading cause of morbidity and mortality worldwide, CVD arises from a complex and multifactorial interplay of genetic predisposition, metabolic dysfunction, chronic inflammation, oxidative stress, and environmental exposures. Pathophysiologically, the disease is driven by endothelial dysfunction, atherosclerotic plaque formation, thrombosis, and mitochondrial impairment, with upstream contributors including dyslipidemia (particularly elevated LDL cholesterol and lipoprotein(a)), insulin resistance, hypertension, and systemic inflammation mediated through pathways such as the PI3K/AKT/mTOR axis, NF-κB signaling, and Wnt/β-catenin signaling. These shared mechanisms mean CVD rarely occurs in isolation—it is deeply interconnected with type 2 diabetes, chronic kidney disease (CKD), metabolic dysfunction-associated steatotic liver disease (MASLD), and obesity, a convergence now formalized under the Cardiovascular-Kidney-Metabolic (CKM) syndrome framework.

Beyond classical risk factors, emerging research has identified roles for epigenetic regulation, including blood DNA methylation, gut microbiota composition, mitochondrial biogenesis, and autophagy dysregulation in CVD pathogenesis. The involvement of sirtuin 1, PRKAA1, prostaglandin E2, Gasdermin D-mediated pyroptosis, and proinflammatory cytokines such as IL-17A illustrates the molecular complexity underlying cardiovascular pathology. Gene Editing Technologies including CRISPR-Cas12a, alongside multi-omics approaches integrating genomics, proteomics, and metabolomics, are increasingly being deployed to disentangle this complexity and identify novel therapeutic targets. CVD thus represents one of the most intensively studied domains in modern biomedical science, with translational research spanning from basic molecular biology to population-scale epidemiology.

Focus of Latest Publications

Recent cardiovascular disease research emphasizes multifactorial risk stratification across diverse clinical populations. Studies investigate CVD within the context of metabolic dysfunction, particularly in patients with metabolic syndrome, type 1 and type 2 diabetes, and early-stage Cardiovascular-Kidney-Metabolic (CKM) syndrome. Environmental exposures, including air pollution mixtures and humidity metrics, emerge as modifiable risk drivers in middle-aged and older adults. Additionally, specific high-risk populations—such as survivors of hematopoietic stem cell transplantation, patients with substance use disorders, and those with diabetic complications—demonstrate elevated CVD burden and mortality, reflecting the disease's interconnection with systemic comorbidities and accelerated biological aging.

Inflammatory and metabolic biomarkers are increasingly refined to improve CVD risk assessment. Chronic low-grade inflammation, measured by high-sensitivity C-reactive protein, acts synergistically with metabolic syndrome to elevate coronary artery disease risk, particularly in type 1 diabetes. Emerging biomarkers such as asialoglycoprotein receptor 1 (ASGR1) and insulin resistance indices (SPISE) show diagnostic and prognostic utility in hypertensive and early CKM-stage patients, respectively. Proteomic profiling of blood also models vascular biological aging with precision, enabling identification of patients at highest risk for incident cardiovascular events independent of traditional blood pressure measures.

Mechanistic investigation of therapeutic targets yields mixed findings. Pharmacological inhibition of sclerostin, applied clinically for osteoporosis, shows conflicting associations with cardiovascular safety across randomized trials, highlighting the need for careful evaluation of off-target cardiovascular effects in bone-directed therapies. In contrast, behavioral interventions—including gamification strategies to improve medication adherence in patients with prior nonadherence—represent low-cost approaches to mitigate CVD risk factors and reduce disease progression.

Computational and predictive methodologies advance CVD detection and management. Machine learning frameworks employing modular deep learning architectures demonstrate competitive performance for cardiovascular risk prediction across diverse datasets, offering interpretable decision-support tools applicable in real-world clinical settings. Furthermore, multi-state models integrating phenotypic age acceleration with genetic risk and lifestyle factors clarify the temporal progression from asthma and accelerated aging to incident CVD and mortality, informing targeted prevention strategies in vulnerable populations.