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Life
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Precision Medicine in Cardiovascular Diseases
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Precision Medicine in Cardiovascular Diseases

A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Genetics and Genomics".

Deadline for manuscript submissions: closed (30 March 2026) | Viewed by 5847

Special Issue Editor

Dr. Brian Xiangzhi Wang

E-Mail Website
Guest Editor
Department of Cardiology, Jersey General Hospital, Gloucester Street, St. Helier JE1 3QS, UK
Interests: cardiology; stem cell therapy; electrophysiology; congenital heart disease

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the cutting-edge advancements in and applications of precision medicine in the realm of cardiovascular health. With a focus on tailoring treatment strategies to individual patients based on their genetic makeup, lifestyle factors, and environmental influences, this issue seeks to highlight the transformative potential of precision medicine in optimizing patient outcomes and revolutionizing cardiovascular care.

Through the collection of research articles, reviews, and case studies, this Special Issue will delve into various aspects of precision medicine, including molecular profiling, biomarker identification, personalized risk assessment, and targeted therapies for cardiovascular diseases. By elucidating the underlying mechanisms of cardiovascular conditions at the molecular level and identifying patient-specific factors that contribute to disease susceptibility and progression, this issue aims to pave the way for more precise diagnostic methods and therapeutic interventions.

Authors are encouraged to submit original research contributions, comprehensive reviews, and innovative perspectives that shed light on the role of precision medicine in reshaping the landscape of cardiovascular healthcare. By fostering interdisciplinary collaboration and sharing groundbreaking discoveries, this Special Issue endeavors to accelerate the translation of precision medicine concepts into clinical practice, ultimately improving patient outcomes and advancing the field of cardiovascular medicine.

Dr. Brian Xiangzhi Wang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Life is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • genomic
  • cardiovascular disease
  • congenital
  • multi-omics studies
  • precision medicine
  • personalized medicine
  • biomarker

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

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20 pages, 2407 KB  
Article
Integrated Clinical, Molecular, and Machine Learning Assessment of Familial Hypercholesterolemia
by Mustafa Tarık Alay, Atakan Deniz, Hanife Saat and Haktan Bağış Erdem
Life 2026, 16(4), 633; https://doi.org/10.3390/life16040633 - 9 Apr 2026
Viewed by 373
Abstract
Background: In clinical practice, LDL-dominant familial hypercholesterolemia (FH) may overlap phenotypically with triglyceride-dominant or mixed familial dyslipidemia. Rule-based diagnostic approaches like the Dutch Lipid Clinic Network (DLCN) and Simon Broome (SB) criteria are frequently used in countries with limited genetic testing, but [...] Read more.
Background: In clinical practice, LDL-dominant familial hypercholesterolemia (FH) may overlap phenotypically with triglyceride-dominant or mixed familial dyslipidemia. Rule-based diagnostic approaches like the Dutch Lipid Clinic Network (DLCN) and Simon Broome (SB) criteria are frequently used in countries with limited genetic testing, but their concordance with molecular confirmation is inconsistent. In a large Turkish tertiary-care cohort, we studied phenotype-related discordance between clinical criteria and molecular data and tested whether machine learning (ML) models could improve the prediction of reportable pathogenic/likely pathogenic variant positivity among patients with a clinical FH phenotype. Methods: Patients referred for suspected familial hyperlipidemia underwent targeted next-generation sequencing with a 9-gene panel. For the ML analysis, we focused on FH cases with a definitive molecular status (pathogenic/likely pathogenic vs. no reportable variant; variants of uncertain significance were excluded) and applied an 80/20 stratified split (n = 200; 82 molecular-positive cases). Elastic-net logistic regression, random forest, and XGBoost models trained on routinely available clinical variables were compared with dichotomized SB and DLCN classifications. Results: SB positivity was significantly more frequent in triglyceride-dominant phenotypes than in FH (68.4% vs. 52.3%, p = 0.041), despite the substantially lower molecular positivity (14.0% vs. 36.9%, p = 0.002), indicating FH-like false-positive clinical classification in mixed dyslipidemia. In the FH test set, the ML models showed higher discrimination for reportable pathogenic/likely pathogenic variant positivity than dichotomized rule-based criteria (AUC: XGBoost 0.808; random forest 0.769; elastic-net 0.747 vs. SB 0.639; and DLCN 0.598). Thirteen novel variants absent from gnomAD were identified, predominantly in LDLR. Conclusions: In this real-world Turkish cohort, within clinically defined FH cases, ML models performed better at predicting LP/P variant positivity than dichotomized DLCN and Simon Broome criteria. ML-based risk stratification may support prioritization for genetic testing; however, external validation is warranted. Full article
(This article belongs to the Special Issue Precision Medicine in Cardiovascular Diseases)
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Review

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41 pages, 1037 KB  
Review
Clinical Applications of Artificial Intelligence in Cardiovascular Imaging: Where Do We Stand?
by Archit A. Singhal, Tiffany Bowyer-Howell, Nikant Sabharwal, Andrew Lewis, Andrew R. J. Mitchell, Oliver Rider and John A. Henry
Life 2026, 16(3), 507; https://doi.org/10.3390/life16030507 - 19 Mar 2026
Viewed by 919
Abstract
Cardiovascular imaging is essential in the diagnosis, phenotyping and prognostic assessment of cardiovascular disease. However, longstanding limitations constrain the accuracy, throughput, and scalability of cardiovascular imaging techniques. Artificial intelligence (AI) has demonstrated a diverse range of potential benefits across modalities, including echocardiography, computerised [...] Read more.
Cardiovascular imaging is essential in the diagnosis, phenotyping and prognostic assessment of cardiovascular disease. However, longstanding limitations constrain the accuracy, throughput, and scalability of cardiovascular imaging techniques. Artificial intelligence (AI) has demonstrated a diverse range of potential benefits across modalities, including echocardiography, computerised tomography, nuclear imaging, and magnetic resonance imaging. These benefits include automated quantification of key heart parameters, ability to improve traditional disease detection and phenotyping, and image reconstruction. While the use of AI in clinical workflows is still largely emerging, its significance is becoming established through numerous promising studies. The evidence reviewed indicates that AI can meaningfully enhance disease management, clinical operations and patient experience when used alongside physician expertise. However, several challenges restrict the widespread clinical implementation of AI, including a lack of robust prospective evidence, regulatory hurdles, bias in training datasets, and ethical drawbacks such as data privacy and accountability. Future developments should prioritise large-scale prospective and multicentre validation and address practical and ethical barriers to ensure AI can be utilised safely and effectively in clinical settings. This narrative review comprehensively analyses advances in AI-driven cardiovascular imaging with a focus on clinical implementation. Full article
(This article belongs to the Special Issue Precision Medicine in Cardiovascular Diseases)
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26 pages, 6992 KB  
Review
Endovascular Repair of Thoracic Aortic Atresia in Adults: A Narrative Review of a Rare Entity and Emerging Technique
by Claudiu Florin Rășinar, Petru Liuba, Alina Diduța Brie, Alexandru Tîrziu, Cristian Mornoș, Daniel Miron Brie, Dan Ion Gaiță and Constantin Tudor Luca
Life 2025, 15(11), 1651; https://doi.org/10.3390/life15111651 - 23 Oct 2025
Viewed by 1010
Abstract
Thoracic aortic atresia in adults represents a rare and extreme manifestation of aortic coarctation, marked by complete luminal occlusion and frequently compensated by extensive collateral circulation. This narrative review critically evaluates existing literature and institutional experience concerning surgical and endovascular repair strategies for [...] Read more.
Thoracic aortic atresia in adults represents a rare and extreme manifestation of aortic coarctation, marked by complete luminal occlusion and frequently compensated by extensive collateral circulation. This narrative review critically evaluates existing literature and institutional experience concerning surgical and endovascular repair strategies for aortic atresia, synthesizing evidence from related aortic arch pathologies due to the absence of direct comparative studies. Both treatment modalities—open surgical repair and catheter-based recanalization with stenting—have evolved significantly, presenting distinct advantages and limitations influenced by patient anatomy, age, and comorbidities. While surgical repair remains the standard for neonates, infants, and complex cases due to superior long-term durability and blood pressure control, endovascular procedures using chronic total occlusion technique and covered stents offer less invasive alternatives with rapid recovery, particularly in adults with suitable anatomic characteristics. The review highlights procedural considerations, including technical approaches, stent selection, and potential complications such as restenosis, hypertension, and vascular injury. Individualized, multidisciplinary decision-making remains essential, with shared consensus guiding therapy in the absence of randomized trials. The article identifies critical gaps in knowledge, emphasizing the need for multicenter, long-term studies and technological advances—including hybrid and personalized strategies for optimal management and for improving outcomes in this challenging congenital condition. Full article
(This article belongs to the Special Issue Precision Medicine in Cardiovascular Diseases)
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19 pages, 314 KB  
Review
Investigating Inherited Heart Diseases Using Human Induced Pluripotent Stem Cell-Based Models
by Brian Xiangzhi Wang
Life 2024, 14(11), 1370; https://doi.org/10.3390/life14111370 - 25 Oct 2024
Cited by 2 | Viewed by 2653
Abstract
Inherited heart diseases (IHDs) are caused by genetic mutations that disrupt the physiological structure and function of the heart. Understanding the mechanisms behind these diseases is crucial for developing personalised interventions in cardiovascular medicine. Development of induced pluripotent stem cells, which can then [...] Read more.
Inherited heart diseases (IHDs) are caused by genetic mutations that disrupt the physiological structure and function of the heart. Understanding the mechanisms behind these diseases is crucial for developing personalised interventions in cardiovascular medicine. Development of induced pluripotent stem cells, which can then be differentiated to any nucleated adult cell type, has enabled the creation of personalised single-cell and multicellular models, providing unprecedented insights into the pathophysiology of IHDs. This review provides a comprehensive overview of recent advancements in human iPSC models used to dissect the molecular and genetic underpinnings of common IHDs. We examine multicellular models and tissue engineering approaches, such as cardiac organoids, engineered heart tissue, and multicellular co-culture systems, which simulate complex intercellular interactions within heart tissue. Recent advancements in stem cell models offer a more physiologically relevant platform to study disease mechanisms, enabling researchers to observe cellular interactions, study disease progression, and identify therapeutic strategies. By leveraging these innovative models, we can gain deeper insights into the molecular and cellular mechanisms underlying IHDs, ultimately paving the way for more effective diagnostic and therapeutic strategies. Full article
(This article belongs to the Special Issue Precision Medicine in Cardiovascular Diseases)
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