IMR Press / FBL / Volume 26 / Issue 11 / DOI: 10.52586/5026
Open Access Review
Nutrition, atherosclerosis, arterial imaging, cardiovascular risk stratification, and manifestations in COVID-19 framework: a narrative review
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1 Stroke Monitoring and Diagnostic Division, AtheroPointTM, Roseville, CA 95678, USA
2 Max Institute of Cancer Care, Max Superspeciality Hospital, 110058 New Delhi, India
3 Visvesvaraya National Institute of Technology, 440001 Nagpur, India
4 Annu’s Hospitals for Skin and Diabetes, 24002 Nellore, AP, India
5 Department of Radiology, Azienda Ospedaliero Universitaria, 09125 Cagliari, Italy
6 Department of Pathology, AOU of Cagliari, 09125 Cagliari, Italy
7 The Hanse-Wissenschaftskolleg Institute for Advanced Study, 27749 Delmenhorst, Germany
8 Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L, Canada
9 Department of Cardiology, Indraprastha APOLLO Hospitals, 110001 New Delhi, India
10 University Hospital for Infectious Diseases, 10000 Zagreb, Crotia
11 Cardiology Clinic, Onassis Cardiac Surgery Center, 106 71 Athens, Greece
12 Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA 94574, USA
13 Minimally Invasive Urology Institute, Brown University, Providence, RI 02906, USA
14 Men’s Health Center, Miriam Hospital Providence, RI 02903, USA
15 Rheumatology Unit, National Kapodistrian University of Athens, 106 71 Athens, Greece
16 Aristoteleion University of Thessaloniki, 546 30 Thessaloniki, Greece
17 National & Kapodistrian University of Athens, 106 71 Athens, Greece
18 Sanjay Gandhi Postgraduate Institute of Medical Sciences, 226018 Lucknow, UP, India
19 Academic Affairs, Dudley Group NHS Foundation Trust, DY2 8 Dudley, UK
20 Arthritis Research UK Epidemiology Unit, Manchester University, M13 9 Manchester, UK
21 OhioHealth Heart and Vascular, OH 43311, USA
22 Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60629, USA
23 Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON M5H, Canada
24 MV Center of Diabetes, 600003 Bangalore, India
25 Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22903, USA
26 Nephrology Department, Kaiser Permanente, Sacramento, CA 95823, USA
27 Department of Physiology & Biomedical Engg., Mayo Clinic College of Medicine and Science, MN 55441, USA
28 Department of Radiology, Mayo Clinic College of Medicine and Science, MN 55441, USA
29 MV Hospital for Diabetes and Professor MVD Research Centre, 600003 Chennai, India
30 Neurology Department, Fortis Hospital, 562123 Bangalore, India
31 Department of Neurology, University Medical Centre Maribor, 2000 Maribor, Slovenia
32 Electrical Engineering Department, University of Minnesota, Duluth, MN 55812, USA
33 Vascular Screening and Diagnostic Centre, University of Nicosia Medical School, 999058 Nicosia, Cyprus
*Correspondence: (Jasjit S. Suri)
Front. Biosci. (Landmark Ed) 2021, 26(11), 1312–1339;
Submitted: 4 June 2021 | Revised: 17 September 2021 | Accepted: 23 September 2021 | Published: 30 November 2021
Copyright: © 2021 The Author(s). Published by BRI.
This is an open access article under the CC BY 4.0 license (

Background: Atherosclerosis is the primary cause of the cardiovascular disease (CVD). Several risk factors lead to atherosclerosis, and altered nutrition is one among those. Nutrition has been ignored quite often in the process of CVD risk assessment. Altered nutrition along with carotid ultrasound imaging-driven atherosclerotic plaque features can help in understanding and banishing the problems associated with the late diagnosis of CVD. Artificial intelligence (AI) is another promisingly adopted technology for CVD risk assessment and management. Therefore, we hypothesize that the risk of atherosclerotic CVD can be accurately monitored using carotid ultrasound imaging, predicted using AI-based algorithms, and reduced with the help of proper nutrition. Layout: The review presents a pathophysiological link between nutrition and atherosclerosis by gaining a deep insight into the processes involved at each stage of plaque development. After targeting the causes and finding out results by low-cost, user-friendly, ultrasound-based arterial imaging, it is important to (i) stratify the risks and (ii) monitor them by measuring plaque burden and computing risk score as part of the preventive framework. Artificial intelligence (AI)-based strategies are used to provide efficient CVD risk assessments. Finally, the review presents the role of AI for CVD risk assessment during COVID-19. Conclusions: By studying the mechanism of low-density lipoprotein formation, saturated and trans fat, and other dietary components that lead to plaque formation, we demonstrate the use of CVD risk assessment due to nutrition and atherosclerosis disease formation during normal and COVID times. Further, nutrition if included, as a part of the associated risk factors can benefit from atherosclerotic disease progression and its management using AI-based CVD risk assessment.

Arterial imaging
Artificial intelligence
Risk stratification
Plaque tissue charac-terization
Fig. 1.
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