Nutrition, atherosclerosis, arterial imaging, cardiovascular risk stratification, and manifestations in COVID-19 framework: a narrative review.

Stroke Monitoring and Diagnostic Division, AtheroPointTM, Roseville, CA 95678, USA. Max Institute of Cancer Care, Max Superspeciality Hospital, 110058 New Delhi, India. Visvesvaraya National Institute of Technology, 440001 Nagpur, India. Annu's Hospitals for Skin and Diabetes, 24002 Nellore, AP, India. Department of Radiology, Azienda Ospedaliero Universitaria, 09125 Cagliari, Italy. Department of Pathology, AOU of Cagliari, 09125 Cagliari, Italy. The Hanse-Wissenschaftskolleg Institute for Advanced Study, 27749 Delmenhorst, Germany. Department of Medicine, Division of Cardiology, Queen's University, Kingston, ON K7L, Canada. Department of Cardiology, Indraprastha APOLLO Hospitals, 110001 New Delhi, India. University Hospital for Infectious Diseases, 10000 Zagreb, Crotia. Cardiology Clinic, Onassis Cardiac Surgery Center, 106 71 Athens, Greece. Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA 94574, USA. Minimally Invasive Urology Institute, Brown University, Providence, RI 02906, USA. Men's Health Center, Miriam Hospital Providence, RI 02903, USA. Rheumatology Unit, National Kapodistrian University of Athens, 106 71 Athens, Greece. Aristoteleion University of Thessaloniki, 546 30 Thessaloniki, Greece. National & Kapodistrian University of Athens, 106 71 Athens, Greece. Sanjay Gandhi Postgraduate Institute of Medical Sciences, 226018 Lucknow, UP, India. Academic Affairs, Dudley Group NHS Foundation Trust, DY2 8 Dudley, UK. Arthritis Research UK Epidemiology Unit, Manchester University, M13 9 Manchester, UK. OhioHealth Heart and Vascular, OH 43311, USA. Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL 60629, USA. Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON M5H, Canada. MV Center of Diabetes, 600003 Bangalore, India. Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22903, USA. Nephrology Department, Kaiser Permanente, Sacramento, CA 95823, USA. Department of Physiology & Biomedical Engg., Mayo Clinic College of Medicine and Science, MN 55441, USA. Department of Radiology, Mayo Clinic College of Medicine and Science, MN 55441, USA. MV Hospital for Diabetes and Professor MVD Research Centre, 600003 Chennai, India. Neurology Department, Fortis Hospital, 562123 Bangalore, India. Department of Neurology, University Medical Centre Maribor, 2000 Maribor, Slovenia. Electrical Engineering Department, University of Minnesota, Duluth, MN 55812, USA. Vascular Screening and Diagnostic Centre, University of Nicosia Medical School, 999058 Nicosia, Cyprus.

Frontiers in bioscience (Landmark edition). 2021;(11):1312-1339
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Abstract

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.

Methodological quality

Publication Type : Review

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