Bidirectional link between diabetes mellitus and coronavirus disease 2019 leading to cardiovascular disease: A narrative review

Viswanathan, Vijay; Puvvula, Anudeep; Jamthikar, Ankush; Saba, Luca; Johri, Amer M.; Kotsis, Vasilios; Khanna, Narendra N.; Dhanjil, Surinder; Majhail, Misha; Misra, Durga Prasanna; Agarwal, Vikas; Kitas, George D.; Sharma, Aditya; Kolluri, Raghu; Naidu, Subbaram; Suri, Jasjit S.

doi:10.4239/wjd.v12.i3.215

Cited by 44 publications

(52 citation statements)

References 200 publications

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“…A person's susceptibility to developing atherosclerosis may be identified by the risk factors associated with it. The risk factors responsible for atherosclerotic disease can be categorized into (a) conventional, such as hypertension, obesity, body mass index, ethnicity, gender, ethanol use, and smoking [48] and (b) blood biomarkers such as lipids, hemoglobin A1c (HbA1c) as diabetes index [35,49], estimated glomerular filtration rate (eGFR) as renal index [50], erythrocyte sedimentation rate (ESR) as rheumatoid arthritis index [51], homocysteine, triglycerides, and hCRP/c reactive protein. Fig.…”

Section: Cardiovascular Risk Factorsmentioning

confidence: 99%

“…Imaging has shown to be very useful in understanding COVID-19 severity [241,242]. Comorbidity has been shown to affect the lungs and heart more aggressively [45,46,49]. SARS-CoV-2 causes brain and heart injuries via different pathways (as seen in Fig.…”

Section: Covid-19 and Pathways Leading To Brain/heart Injurymentioning

confidence: 99%

“…These events may transform vulnerable plaque into a complicated and ruptured plaque [267]. Some studies also suggest that if positive for SARS-CoV-2 and asymptomatic, patients could be at increased risk of developing cerebral ischemic strokes, and myocardial infarction due to increased instability of coronary and carotid plaques [49,268].…”

Section: Covid-19 Association With Underlying Conditionsmentioning

confidence: 99%

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

Munjral,

Ahluwalia,

Jamthikar

et al. 2021

Front. Biosci. (Landmark Ed)

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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.

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Section: Cardiovascular Risk Factorsmentioning

confidence: 99%

Section: Covid-19 and Pathways Leading To Brain/heart Injurymentioning

confidence: 99%

Section: Covid-19 Association With Underlying Conditionsmentioning

confidence: 99%

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

Munjral,

Ahluwalia,

Jamthikar

et al. 2021

Front. Biosci. (Landmark Ed)

View full text Add to dashboard Cite

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“…As per the WHO's statistics dated 28 July 2021, more than 196 million people have been infected with Acute Respiratory Distress Syndrome (ARDS), and nearly 4.2 million have lost their lives due to this virus [1]. This SARS-CoV-2 virus affects the respiratory system, damages lungs, travels through the entire body, and causes myocardial infarction or coronary artery syndrome [2,3] or worsening diabetes [4] or causing pulmonary embolism [5]. It was seen that comorbidity had a severe influence on COVID-19 [6].…”

Section: Introductionmentioning

confidence: 99%

COVLIAS 1.0: Lung Segmentation in COVID-19 Computed Tomography Scans Using Hybrid Deep Learning Artificial Intelligence Models

Suri¹,

Agarwal²,

Pathak

et al. 2021

Diagnostics

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Background: COVID-19 lung segmentation using Computed Tomography (CT) scans is important for the diagnosis of lung severity. The process of automated lung segmentation is challenging due to (a) CT radiation dosage and (b) ground-glass opacities caused by COVID-19. The lung segmentation methodologies proposed in 2020 were semi- or automated but not reliable, accurate, and user-friendly. The proposed study presents a COVID Lung Image Analysis System (COVLIAS 1.0, AtheroPoint™, Roseville, CA, USA) consisting of hybrid deep learning (HDL) models for lung segmentation. Methodology: The COVLIAS 1.0 consists of three methods based on solo deep learning (SDL) or hybrid deep learning (HDL). SegNet is proposed in the SDL category while VGG-SegNet and ResNet-SegNet are designed under the HDL paradigm. The three proposed AI approaches were benchmarked against the National Institute of Health (NIH)-based conventional segmentation model using fuzzy-connectedness. A cross-validation protocol with a 40:60 ratio between training and testing was designed, with 10% validation data. The ground truth (GT) was manually traced by a radiologist trained personnel. For performance evaluation, nine different criteria were selected to perform the evaluation of SDL or HDL lung segmentation regions and lungs long axis against GT. Results: Using the database of 5000 chest CT images (from 72 patients), COVLIAS 1.0 yielded AUC of ~0.96, ~0.97, ~0.98, and ~0.96 (p-value < 0.001), respectively within 5% range of GT area, for SegNet, VGG-SegNet, ResNet-SegNet, and NIH. The mean Figure of Merit using four models (left and right lung) was above 94%. On benchmarking against the National Institute of Health (NIH) segmentation method, the proposed model demonstrated a 58% and 44% improvement in ResNet-SegNet, 52% and 36% improvement in VGG-SegNet for lung area, and lung long axis, respectively. The PE statistics performance was in the following order: ResNet-SegNet > VGG-SegNet > NIH > SegNet. The HDL runs in <1 s on test data per image. Conclusions: The COVLIAS 1.0 system can be applied in real-time for radiology-based clinical settings.

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“…More than 231 million people have been infected worldwide, and nearly 4.7 million people have died due to COVID-19 [1]. Although this "severe acute respiratory syndrome coronavirus 2" (SARS-CoV-2) virus specifically targets the pulmonary and vascular system, it has the potential to travel through the body and lead to complications such as pulmonary embolism [2], myocardial infarction, stroke, or mesenteric ischemia [3][4][5]. Comorbidities such as diabetes mellitus, hypertension, and obesity substantially increase the severity and mortality of COVID-19 [6,7].…”

Section: Introductionmentioning

confidence: 99%

Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography

Suri¹,

Agarwal²,

Elavarthi³

et al. 2021

Diagnostics

Self Cite

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Background: For COVID-19 lung severity, segmentation of lungs on computed tomography (CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) models have a bias in the training stage of segmentation because only one set of ground truth (GT) annotations are evaluated. We propose a robust and stable inter-variability analysis of CT lung segmentation in COVID-19 to avoid the effect of bias. Methodology: The proposed inter-variability study consists of two GT tracers for lung segmentation on chest CT. Three AI models, PSP Net, VGG-SegNet, and ResNet-SegNet, were trained using GT annotations. We hypothesized that if AI models are trained on the GT tracings from multiple experience levels, and if the AI performance on the test data between these AI models is within the 5% range, one can consider such an AI model robust and unbiased. The K5 protocol (training to testing: 80%:20%) was adapted. Ten kinds of metrics were used for performance evaluation. Results: The database consisted of 5000 CT chest images from 72 COVID-19-infected patients. By computing the coefficient of correlations (CC) between the output of the two AI models trained corresponding to the two GT tracers, computing their differences in their CC, and repeating the process for all three AI-models, we show the differences as 0%, 0.51%, and 2.04% (all < 5%), thereby validating the hypothesis. The performance was comparable; however, it had the following order: ResNet-SegNet > PSP Net > VGG-SegNet. Conclusions: The AI models were clinically robust and stable during the inter-variability analysis on the CT lung segmentation on COVID-19 patients.

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Bidirectional link between diabetes mellitus and coronavirus disease 2019 leading to cardiovascular disease: A narrative review

Cited by 44 publications

References 200 publications

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

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

COVLIAS 1.0: Lung Segmentation in COVID-19 Computed Tomography Scans Using Hybrid Deep Learning Artificial Intelligence Models

Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography

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