Cardiovascular diseases (CVD) have been recognized as the leading cause of mortality worldwide, accounting for 31% of global mortality. Among the risk factors of CVD, hyperlipidemia has been established as the most potent risk factor. Statins, a class of drug that reduces lower-density lipoprotein cholesterol (LDL-C), are the preferred medical treatment. However, due to the development of statin-associated muscle symptoms, statins are associated with patients' discontinuation and nonadherence. Other statin-induced side effects, such as hepatotoxicity and gastrointestinal upset, all contribute to patients choosing alternative medicines. Squalene (SQ), an unsaturated hydrocarbon naturally synthesized in plants and animals, could become the alternative treatment or supplementary agent for cardiovascular health. SQ has been shown to exert cardioprotective effect via its antioxidant activity. Oxidative stress and inflammatory responses are closely related to each other, which proposes an interdependence relation between antioxidant and anti-inflammatory. Therefore, this review explores the interdependence between the antioxidant and anti-inflammatory effects of SQ implicated on cardiovascular health.
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http://dx.doi.org/10.3390/life11020103 | DOI Listing |
Circ Genom Precis Med
January 2025
Mary and Steve Wen Cardiovascular Division, Department of Medicine, University of California, Los Angeles. (W.F., N.D.W.).
Background: Lp(a; Lipoprotein[a]) is a predictor of atherosclerotic cardiovascular disease (ASCVD); however, there are few algorithms incorporating Lp(a), especially from real-world settings. We developed an electronic health record (EHR)-based risk prediction algorithm including Lp(a).
Methods: Utilizing a large EHR database, we categorized Lp(a) cut points at 25, 50, and 75 mg/dL and constructed 10-year ASCVD risk prediction models incorporating Lp(a), with external validation in a pooled cohort of 4 US prospective studies.
Circ Genom Precis Med
January 2025
Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston. (S.M.U., K.P., B.T., A.C.F., P.N.).
Background: Earlier identification of high coronary artery disease (CAD) risk individuals may enable more effective prevention strategies. However, existing 10-year risk frameworks are ineffective at earlier identification. We sought to understand how the variable importance of genomic and clinical factors across life stages may significantly improve lifelong CAD event prediction.
View Article and Find Full Text PDFCirc Genom Precis Med
January 2025
Centre for Heart Lung Innovation, University of British Columbia, Vancouver. (K.H., M.A., L.R., Y.L., A.S., H.H., L.R.B., Z.W.L.).
Background: Protein-truncating mutations in the titin gene are associated with increased risk of atrial fibrillation. However, little is known about the underlying pathophysiology.
Methods: We identified a heterozygous titin truncating variant (TTNtv) in a patient with unexplained early onset atrial fibrillation and normal ventricular function.
Circ Genom Precis Med
January 2025
Garvan Institute of Medical Research, University of New South Wales, Sydney, Australia. (A.B., J.S., A.C., J.I.).
Background: Females with hypertrophic cardiomyopathy present at a more advanced stage of the disease and have a higher risk of heart failure and death. The factors behind these differences are unclear. We aimed to investigate sex-related differences in clinical and genetic factors affecting adverse outcomes in the Sarcomeric Human Cardiomyopathy Registry.
View Article and Find Full Text PDFFront Public Health
January 2025
Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi Arabia.
Introduction: The growing demand for real-time, affordable, and accessible healthcare has underscored the need for advanced technologies that can provide timely health monitoring. One such area is predicting arterial blood pressure (BP) using non-invasive methods, which is crucial for managing cardiovascular diseases. This research aims to address the limitations of current healthcare systems, particularly in remote areas, by leveraging deep learning techniques in Smart Health Monitoring (SHM).
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