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Metabolites
Special Issues
Exploring Emerging Novel Metabolic Biomarkers of Atherosclerosis
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Exploring Emerging Novel Metabolic Biomarkers of Atherosclerosis

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Advances in Metabolomics".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 8492

Special Issue Editors

Prof. Dr. Maciej Banach

E-Mail Website
Guest Editor
1. Department of Preventive Cardiology and Lipidology, Medical University of Lodz (MUL), 93-338 Lodz, Poland
2. Department of Cardiology and Congenital Diseases of Adults, Polish Mother’s Memorial Hospital Research Institute (PMMHRI), 93-338 Lodz, Poland
Interests: disorders of lipid metabolism; atherosclerosis; hypertension; preventive medicine; drug development; risk stratification
Special Issues, Collections and Topics in MDPI journals
Dr. Mohammad Qadura

E-Mail Website
Guest Editor
1. Division of Vascular Surgery, St. Michael’s Hospital, Unity Health Toronto, University of Toronto, Toronto, ON M5B 1W8, Canada
2. Department of Surgery, University of Toronto, Toronto, ON M5S 1A1, Canada
Interests: vascular surgery; peripheral arterial disease; biomarkers; prognostication; abdominal aortic aneurysm; thrombosis; atherosclerosis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Atherosclerotic diseases remain a leading cause of disability, morbidity and mortality worldwide, partly due to a paucity of adequate diagnostic tools. The critical need for novel and improved diagnostic modalities for certain atherosclerotic diseases (peripheral arterial disease, carotid artery stenosis etc.) warrant the discovery and emergence of specific yet sensitive biomarkers, but this area of research has historically been understudied.

New genetic biomarkers, novel biochemical biomarkers that might significantly add to better risk stratification and the study of metabolomics represent unique opportunities to advance precision-based medicine for atherosclerotic diseases by investigating their molecular phenotype. For this Special Issue titled “Exploring Emerging Novel Metabolic Biomarkers of Atherosclerosis”, we invite submissions exploring novel biomarkers and personalized medicine options for a variety of atherosclerotic diseases.

Dr. Maciej Banach
Dr. Mohammad Qadura
Guest Editors

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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Metabolites 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 2700 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

  • metabolomics
  • metabolites
  • biomarkers
  • atherosclerosis
  • molecular biomarkers
  • mass spectrometry
  • nuclear magnetic resonance
  • peripheral artery disease
  • risk stratification
  • coronary artery disease
  • carotid artery stenosis

Published Papers (2 papers)

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Review

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17 pages, 1743 KiB  
Review
Genetic Markers of Insulin Resistance and Atherosclerosis in Type 2 Diabetes Mellitus Patients with Coronary Artery Disease
by Sangeetha Perumalsamy, Hasniza Zaman Huri, Bashar Mudhaffar Abdullah, Othman Mazlan, Wan Azman Wan Ahmad and Shireene Ratna D. B. Vethakkan
Metabolites 2023, 13(3), 427; https://doi.org/10.3390/metabo13030427 - 14 Mar 2023
Cited by 6 | Viewed by 2161
Abstract
Type 2 diabetes mellitus (T2DM) is characterized by impaired insulin secretion on a background of insulin resistance (IR). IR and T2DM are associated with atherosclerotic coronary artery disease (CAD). The mechanisms of IR and atherosclerosis are known to share similar genetic and environmental [...] Read more.
Type 2 diabetes mellitus (T2DM) is characterized by impaired insulin secretion on a background of insulin resistance (IR). IR and T2DM are associated with atherosclerotic coronary artery disease (CAD). The mechanisms of IR and atherosclerosis are known to share similar genetic and environmental roots. Endothelial dysfunction (ED) detected at the earliest stages of IR might be the origin of atherosclerosis progression. ED influences the secretion of pro-inflammatory cytokines and their encoding genes. The genes and their single nucleotide polymorphisms (SNPs) act as potential genetic markers of IR and atherosclerosis. This review focuses on the link between IR, T2DM, atherosclerosis, CAD, and the potential genetic markers CHI3L1, CD36, LEPR, RETN, IL-18, RBP-4, and RARRES2 genes. Full article
(This article belongs to the Special Issue Exploring Emerging Novel Metabolic Biomarkers of Atherosclerosis)
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Other

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30 pages, 3307 KiB  
Systematic Review
Cardiovascular/Stroke Risk Stratification in Parkinson’s Disease Patients Using Atherosclerosis Pathway and Artificial Intelligence Paradigm: A Systematic Review
by Jasjit S. Suri, Sudip Paul, Maheshrao A. Maindarkar, Anudeep Puvvula, Sanjay Saxena, Luca Saba, Monika Turk, John R. Laird, Narendra N. Khanna, Klaudija Viskovic, Inder M. Singh, Mannudeep Kalra, Padukode R. Krishnan, Amer Johri and Kosmas I. Paraskevas
Metabolites 2022, 12(4), 312; https://doi.org/10.3390/metabo12040312 - 31 Mar 2022
Cited by 21 | Viewed by 4996
Abstract
Parkinson’s disease (PD) is a severe, incurable, and costly condition leading to heart failure. The link between PD and cardiovascular disease (CVD) is not available, leading to controversies and poor prognosis. Artificial Intelligence (AI) has already shown promise for CVD/stroke risk stratification. However, [...] Read more.
Parkinson’s disease (PD) is a severe, incurable, and costly condition leading to heart failure. The link between PD and cardiovascular disease (CVD) is not available, leading to controversies and poor prognosis. Artificial Intelligence (AI) has already shown promise for CVD/stroke risk stratification. However, due to a lack of sample size, comorbidity, insufficient validation, clinical examination, and a lack of big data configuration, there have been no well-explained bias-free AI investigations to establish the CVD/Stroke risk stratification in the PD framework. The study has two objectives: (i) to establish a solid link between PD and CVD/stroke; and (ii) to use the AI paradigm to examine a well-defined CVD/stroke risk stratification in the PD framework. The PRISMA search strategy selected 223 studies for CVD/stroke risk, of which 54 and 44 studies were related to the link between PD-CVD, and PD-stroke, respectively, 59 studies for joint PD-CVD-Stroke framework, and 66 studies were only for the early PD diagnosis without CVD/stroke link. Sequential biological links were used for establishing the hypothesis. For AI design, PD risk factors as covariates along with CVD/stroke as the gold standard were used for predicting the CVD/stroke risk. The most fundamental cause of CVD/stroke damage due to PD is cardiac autonomic dysfunction due to neurodegeneration that leads to heart failure and its edema, and this validated our hypothesis. Finally, we present the novel AI solutions for CVD/stroke risk prediction in the PD framework. The study also recommends strategies for removing the bias in AI for CVD/stroke risk prediction using the PD framework. Full article
(This article belongs to the Special Issue Exploring Emerging Novel Metabolic Biomarkers of Atherosclerosis)
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