Background: Renin angiotensin aldosterone system inhibitors (RAASi) are a mainstay treatment in patients with heart failure with reduced ejection fraction (HFrEF) in part to prevent hospitalizations. However, whether RAASi reduce the risk of hospitalization in Black patients is not entirely clear because enrollment of Black patients in previous clinical trials was low and a previous meta-analysis showed a significant racial disparity: reduction in hospitalizations with an RAASi in White patients but not Black patients. Previous studies relied on the use of self-identified race instead of genomic ancestry. Therefore, this study aimed to investigate the role of self-identified race and genomic ancestry in the racial disparity in RAASi-associated reductions in HFrEF hospitalizations.
Methods: The primary outcome was time to first heart failure hospitalization. Data from the Henry Ford Heart Failure Pharmacogenomic Registry (HFPGR) and the GUIDE-IT multi-center randomized control trial were analyzed with Cox proportional hazards models un/adjusted for clinical risk factors, death as a competing risk, and time-varying RAASi exposure. The proportion of Yoruba African ancestry was quantified. Analyses of self-identified race were performed in both the HFPGR and GUIDE-IT. Analysis of genomic ancestry was only performed in the HFPGR since this information was not available in GUIDE-IT. A fixed effect meta-analysis combined results of both the HFPGR and GUIDE-IT for race.
Results: The HFPGR had 1010 total HFrEF patients (Black = 509 and White = 501) with 852 having ancestry quantification (>80% Yoruba African Ancestry = 381 and <5% Yoruba African Ancestry = 471). GUIDE-IT had 810 HFrEF patients (Black = 322 and White = 488). There was no significant difference in the association of RAASi exposure with heart failure hospitalization by race (meta-analysis P value for race*RAASi exposure interaction = .49; Black patients hazard ratio [HR, 95% confidence interval] for RAASi exposure = 0.89 [0.64-1.23)], P = .47; White patients = 1.20 [0.83-1.75], P = .34). Results were similar when analyzed by ancestry (P value for ancestry*RAASi exposure interaction = 0.57; >80% Yoruba African Ancestry = 0.93 [0.51-1.69], P = .80; <5% Yoruba African Ancestry = 1.29 [0.57-2.92], P = .54).
Conclusions: In contrast to a previous meta-analysis, this more contemporary analysis of 2 HFrEF patient datasets demonstrates the absence of a racial disparity in RAASi-associated reductions in heart failure hospitalizations. The difference in this racial disparity over time may be due to improvements in background heart failure therapies, racial differences in health care usage, and the use of more advanced statistical approaches.
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http://dx.doi.org/10.1016/j.cardfail.2024.09.012 | DOI Listing |
Circ Res
January 2025
Key Laboratory of Drug Targets and Translational Medicine for Cardio-cerebrovascular Diseases, Key Laboratory of Targeted Intervention of Cardiovascular Disease, Collaborative Innovation Center for Cardiovascular Disease Translational Medicine, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Jiangsu, China (X.T., X.L., X.S., Y. Zhang, Y. Zu, Q.F., L.H., S.S., F.C., L.X., Y.J.).
Background: The decrease in S-nitrosoglutathione reductase (GSNOR) leads to an elevation of S-nitrosylation, thereby exacerbating the progression of cardiomyopathy in response to hemodynamic stress. However, the mechanisms under GSNOR decrease remain unclear. Here, we identify NEDD4 (neuronal precursor cell expressed developmentally downregulated 4) as a novel molecule that plays a crucial role in the pathogenesis of pressure overload-induced cardiac hypertrophy, by modulating GSNOR levels, thereby demonstrating significant therapeutic potential.
View Article and Find Full Text PDFEur Heart J Digit Health
January 2025
Klaus Tschira Institute for Integrative Computational Cardiology, University Hospital Heidelberg, Im Neuenheimer Feld 669, 69120 Heidelberg, Germany.
Aims: Data availability remains a critical challenge in modern, data-driven medical research. Due to the sensitive nature of patient health records, they are rightfully subject to stringent privacy protection measures. One way to overcome these restrictions is to preserve patient privacy by using anonymization and synthetization strategies.
View Article and Find Full Text PDFEur Heart J Digit Health
January 2025
Cardiology Department, Dr Balmis General University Hospital, Alicante Institute for Health and Biomedical Research (ISABIAL), C/Maestro Alonso s/n, Alicante 03010, Spain.
Aims: Evidence regarding the safety of early discharge following transcatheter aortic valve implantation (TAVI) is limited. The aim of this study was to evaluate the safety of very early (<24) and early discharge (24-48 h) as compared to standard discharge (>48 h), supported by the implementation of a voice-based virtual assistant using artificial intelligence (AI) and natural language processing.
Methods And Results: Single-arm prospective observational study that included consecutive patients who underwent TAVI in a tertiary hospital in 2023 and were discharged under an AI follow-up programme.
Eur Heart J Digit Health
January 2025
Kolling Institute, Royal North Shore Hospital, University of Sydney, St Leonards, Sydney, NSW 2065, Australia.
Aims: An explainable advanced electrocardiography (A-ECG) Heart Age gap is the difference between A-ECG Heart Age and chronological age. This gap is an estimate of accelerated cardiovascular aging expressed in years of healthy human aging, and can intuitively communicate cardiovascular risk to the general population. However, existing A-ECG Heart Age requires sinus rhythm.
View Article and Find Full Text PDFLancet Reg Health West Pac
January 2025
Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing 100191, China.
Background: Existing studies have not provided robust evidence about the CVD risk of non-smoking patients with restrictive spirometric pattern (RSP) or airflow obstruction (AFO), and how the risk is modified by body shape. We aimed to bridge the gap.
Methods: We used never-smokers' data from the China Kadoorie Biobank (CKB) and performed Cox models by sex (278,953 females and 50,845 males).
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