Metabolomics profile and 10-year atherosclerotic cardiovascular disease (ASCVD) risk score

Dehghanbanadaki, Hojat; Dodangeh, Salimeh; Roudsari, Peyvand Parhizkar; Hosseinkhani, Shaghayegh; Khashayar, Pouria; Noorchenarboo, Mohammad; Rezaei, Negar; Dilmaghani‐Marand, Arezou; Yoosefi, Moein; Arjmand, Babak; Khalagi, Kazem; Najjar, Niloufar; Kakaei, Ardeshir; Bandarian, Fatemeh; Meybodi, Hamid Aghaei; Larijani, Bagher; Razi, Farideh

doi:10.3389/fcvm.2023.1161761

Cited by 4 publications

(1 citation statement)

References 41 publications

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“…To strengthen these findings further, incorporating more diversity by considering both male and female participants and including metabolomics and lipidomics data is important. [ 340 , 341 ]…”

Section: Critical Outlook and Future Directionsmentioning

confidence: 99%

Integrating Computational and Biological Hemodynamic Approaches to Improve Modeling of Atherosclerotic Arteries

Vuong,

Bartolf‐Kopp,

Andelovic

et al. 2024

Advanced Science

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Atherosclerosis is the primary cause of cardiovascular disease, resulting in mortality, elevated healthcare costs, diminished productivity, and reduced quality of life for individuals and their communities. This is exacerbated by the limited understanding of its underlying causes and limitations in current therapeutic interventions, highlighting the need for sophisticated models of atherosclerosis. This review critically evaluates the computational and biological models of atherosclerosis, focusing on the study of hemodynamics in atherosclerotic coronary arteries. Computational models account for the geometrical complexities and hemodynamics of the blood vessels and stenoses, but they fail to capture the complex biological processes involved in atherosclerosis. Different in vitro and in vivo biological models can capture aspects of the biological complexity of healthy and stenosed vessels, but rarely mimic the human anatomy and physiological hemodynamics, and require significantly more time, cost, and resources. Therefore, emerging strategies are examined that integrate computational and biological models, and the potential of advances in imaging, biofabrication, and machine learning is explored in developing more effective models of atherosclerosis.

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Section: Critical Outlook and Future Directionsmentioning

confidence: 99%

Integrating Computational and Biological Hemodynamic Approaches to Improve Modeling of Atherosclerotic Arteries

Vuong,

Bartolf‐Kopp,

Andelovic

et al. 2024

Advanced Science

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Bioinformatic platforms for clinical stratification of natural history of atherosclerotic cardiovascular diseases

Benincasa

Suades

Padró

et al. 2023

European Heart Journal - Cardiovascular Pharmacotherapy

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Although bioinformatic methods gained a lot of attention in the latest years, their use in real-world studies for primary and secondary prevention of atherosclerotic cardiovascular diseases (ASCVD) is still lacking. Bioinformatic resources have been applied to thousands of individuals from the Framingham Heart Study as well as health care-associated biobanks such as the UK Biobank, the Million Veteran Program, and the CARDIoGRAMplusC4D Consortium and randomized controlled trials (i.e. ODYSSEY, FOURIER, ASPREE, and PREDIMED). These studies contributed to the development of polygenic risk scores (PRS), which emerged as novel potent genetic-oriented tools, able to calculate the individual risk of ASCVD and to predict the individual response to therapies such as statins and proprotein convertase subtilisin/kexin type 9 inhibitor. ASCVD are the first cause of death around the world including coronary heart disease (CHD), peripheral artery disease, and stroke. To achieve the goal of precision medicine and personalized therapy, advanced bioinformatic platforms are set to link clinically useful indices to heterogeneous molecular data, mainly epigenomics, transcriptomics, metabolomics, and proteomics. The DIANA study found that differential methylation of ABCA1, TCF7, PDGFA, and PRKCZ significantly discriminated patients with acute coronary syndrome from healthy subjects and their expression levels positively associated with CK-MB serum concentrations. The ARIC Study revealed several plasma proteins, acting or not in lipid metabolism, with a potential role in determining the different pleiotropic effects of statins in each subject. The implementation of molecular high-throughput studies and bioinformatic techniques into traditional cardiovascular risk prediction scores is emerging as a more accurate practice to stratify patients earlier in life and to favour timely and tailored risk reduction strategies. Of note, radiogenomics aims to combine imaging features extracted for instance by coronary computed tomography angiography and molecular biomarkers to create CHD diagnostic algorithms useful to characterize atherosclerotic lesions and myocardial abnormalities. The current view is that such platforms could be of clinical value for prevention, risk stratification, and treatment of ASCVD.

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Threonine In Different Phenotypes Of Chronic Heart Failure With Preserved Ejection Fraction

Maria F.,

Anastasia V.,

Vita Yu.

et al. 2024

Russ Open Med J

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Background — Chronic heart failure with preserved ejection fraction (CHFpEF) develops as a result of many diseases that lead to significant metabolic disorders. Given the heterogeneity of this group of patients, therapeutic options for this syndrome are extremely limited. In this regard, it seems promising to study the metabolomic profile in patients with CHFpEF to identify biomarkers, examine their roles in the pathogenesis of the syndrome, and search for potential targets for targeted therapy. Objective — The study aimed at testing the correlation between the threonine level and the features of the clinical course of CHFpEF. Methods — The study included a total of 154 patients: 82 with CHFpEF, 45 with hypertension and/or coronary artery disease (comparison group), and 27 healthy volunteers (control group). Threonine levels were assessed using high-performance liquid chromatography-mass spectrometry. Results — The threonine concentration was significantly reduced in patients with CHFpEF (1) vs. comparison group (2) and control group (3): p<0.001; p1-3<0.001; p2-3=0.037). A reduction in the threonine level was characteristic for patients with diabetes mellitus vs. patients without it (p=0.029). Conclusion — Given the importance of threonine in energy metabolism and significant changes in its level in various pathophysiological processes, it should be considered as an additional diagnostic and prognostic criterion for CHFpEF. Additional studies are needed to better understand the role of threonine in the pathophysiology of cardiovascular diseases.

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Metabolomics profile and 10-year atherosclerotic cardiovascular disease (ASCVD) risk score

Cited by 4 publications

References 41 publications

Integrating Computational and Biological Hemodynamic Approaches to Improve Modeling of Atherosclerotic Arteries

Integrating Computational and Biological Hemodynamic Approaches to Improve Modeling of Atherosclerotic Arteries

Bioinformatic platforms for clinical stratification of natural history of atherosclerotic cardiovascular diseases

Threonine In Different Phenotypes Of Chronic Heart Failure With Preserved Ejection Fraction

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