Background: Genome-wide association studies have identified hundreds of loci associated with common vascular diseases, such as coronary artery disease, myocardial infarction, and hypertension. However, the lack of mechanistic insights for many GWAS loci limits their translation into the clinic. Among these loci with unknown functions is -four-and-a-half LIM (LIN-11, Isl-1, MEC-3) domain 5 (; chr6q16.1), which reached genome-wide significance in a recent coronary artery disease/ myocardial infarction GWAS meta-analysis. is also associated with several vascular diseases, consistent with the widespread pleiotropy observed for GWAS loci.
Methods: We apply a multimodal approach leveraging statistical fine-mapping, epigenomic profiling, and ex vivo analysis of human coronary artery tissues to implicate as the top candidate causal gene. We unravel the molecular mechanisms of the cross-phenotype genetic associations through in vitro functional analyses and epigenomic profiling experiments in coronary artery smooth muscle cells.
Results: We prioritized as the top candidate causal gene at the locus through expression quantitative trait locus colocalization methods. gene expression was enriched in the smooth muscle cells and pericyte population in human artery tissues with coexpression network analyses supporting a functional role in regulating smooth muscle cell contraction. Unexpectedly, under procalcifying conditions, FHL5 overexpression promoted vascular calcification and dysregulated processes related to extracellular matrix organization and calcium handling. Lastly, by mapping FHL5 binding sites and inferring FHL5 target gene function using artery tissue gene regulatory network analyses, we highlight regulatory interactions between FHL5 and downstream coronary artery disease/myocardial infarction loci, such as and that have roles in vascular remodeling.
Conclusions: Taken together, these studies provide mechanistic insights into the pleiotropic genetic associations of We show that FHL5 mediates vascular disease risk through transcriptional regulation of downstream vascular remodeling gene programs. These transacting mechanisms may explain a portion of the heritable risk for complex vascular diseases.
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http://dx.doi.org/10.1161/CIRCRESAHA.122.321692 | DOI Listing |
Circ Genom Precis Med
January 2025
Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT (A.A., L.S.D., E.K.O., R.K.).
Background: While universal screening for Lp(a; lipoprotein[a]) is increasingly recommended, <0.5% of patients undergo Lp(a) testing. Here, we assessed the feasibility of deploying Algorithmic Risk Inspection for Screening Elevated Lp(a; ARISE), a validated machine learning tool, to health system electronic health records to increase the yield of Lp(a) testing.
View Article and Find Full Text PDFArterioscler Thromb Vasc Biol
January 2025
Department of Pediatrics, Division of Pediatric Infectious Diseases, Guerin Children's, Cedars-Sinai Medical Center, Los Angeles, CA.(P.K.J., M.A., M.N.R.).
The intestinal microbiota influences many host biological processes, including metabolism, intestinal barrier functions, and immune responses in the gut and distant organs. Alterations in its composition have been associated with the development of inflammatory disorders and cardiovascular diseases, including Kawasaki disease (KD). KD is an acute pediatric vasculitis of unknown etiology and the leading cause of acquired heart disease in children in the United States.
View Article and Find Full Text PDFEur Heart J Digit Health
January 2025
Division of Cardiovascular Medicine, Department of Clinical Sciences, Danderyd Hospital, Karolinska Institutet, SE-182 88 Stockholm, Sweden.
Aims: A simplified version of the history, electrocardiogram, age, risk factors, troponin (HEART) score, excluding troponin, has been proposed to rule-out major adverse cardiac events (MACEs). Computerized history taking (CHT) provides a systematic and automated method to obtain information necessary to calculate the HEAR score. We aimed to evaluate the efficacy and diagnostic accuracy of CHT in calculating the HEAR score for predicting MACE.
View Article and Find Full Text PDFEur Heart J Digit Health
January 2025
Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114, USA.
Aims: Accurate prediction of clinical outcomes following percutaneous coronary intervention (PCI) is essential for mitigating risk and peri-procedural planning. Traditional risk models have demonstrated a modest predictive value. Machine learning (ML) models offer an alternative risk stratification that may provide improved predictive accuracy.
View Article and Find Full Text PDFHealth Sci Rep
January 2025
Department of Cardiac Surgery, School of Medicine Hamadan University of Medical Sciences Hamadan Iran.
Background And Aim: Coronary artery bypass grafting (CABG) is a key treatment for coronary artery disease, but accurately predicting patient survival after the procedure presents significant challenges. This study aimed to systematically review articles using machine learning techniques to predict patient survival rates and identify factors affecting these rates after CABG surgery.
Methods: From January 1, 2015, to January 20, 2024, a comprehensive literature search was conducted across PubMed, Scopus, IEEE Xplore, and Web of Science.
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