Details

Autor(en) / Beteiligte

• Munjral, Smiksha
• Ahluwalia, Puneet
• Jamthikar, Ankush D
• Puvvula, Anudeep
• Saba, Luca
• Faa, Gavino
• Singh, Inder M
• Chadha, Paramjit S
• Turk, Monika
• Johri, Amer M
• Khanna, Narendra N
• Viskovic, Klaudija
• Mavrogeni, Sophie
• Laird, John R
• Pareek, Gyan
• Miner, Martin
• Sobel, David W
• Balestrieri, Antonella
• Sfikakis, Petros P
• Tsoulfas, George
• Protogerou, Athanasios
• Misra, Prasanna
• Agarwal, Vikas
• Kitas, George D
• Kolluri, Raghu
• Teji, Jagjit
• Al-Maini, Mustafa
• Dhanjil, Surinder K
• Sockalingam, Meyypan
• Saxena, Ajit
• Sharma, Aditya
• Rathore, Vijay
• Fatemi, Mostafa
• Alizad, Azra
• Viswanathan, Vijay
• Krishnan, P K
• Omerzu, Tomaz
• Naidu, Subbaram
• Nicolaides, Andrew
• Suri, Jasjit S

Titel

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

Ist Teil von

• Frontiers in bioscience (Landmark. Print), 2021-11, Vol.26 (11), p.1312-1339

Ort / Verlag

IMR Press

Erscheinungsjahr

2021

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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.