Polyphenols, plant-derived secondary metabolites, play crucial roles in plant stress responses, growth regulation, and environmental interactions. In humans, polyphenols are associated with various health benefits, particularly in cardiometabolic health. Despite growing evidence of polyphenols' health-promoting effects, their mechanisms remain poorly understood due to high interindividual variability in bioavailability and metabolism. Recent research highlights the bidirectional relationship between dietary polyphenols and the gut microbiota, which can influence polyphenol metabolism and, conversely, be modulated by polyphenol intake. In this concise review, we summarized recent advances in this area, with a special focus on isoflavones and ellagitannins and their corresponding metabotypes, and their effect on cardiovascular health. Human observational studies published in the past 10 years provide evidence for a consistent association of isoflavones and ellagitannins and their metabotypes with better cardiovascular risk factors. However, interventional studies with dietary polyphenols or isolated microbial metabolites indicate that the polyphenol-gut microbiota interrelationship is complex and not yet fully elucidated. Finally, we highlighted various pending research questions that will help identify effective targets for intervention with precision nutrition, thus maximizing individual responses to dietary and lifestyle interventions and improving human health.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11673714 | PMC |
http://dx.doi.org/10.3390/antiox13121552 | 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).
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!