Telemedicine for diabetes management during COVID-19: what we have learnt, what and how to implement.

The past two decades have witnessed telemedicine becoming a crucial part of health care as a method to facilitate doctor-patient interaction. Due to technological developments and the incremental acquisition of experience in its use, telemedicine's advantages and cost-effectiveness has led to it being recognised as specifically relevant to diabetology. However, the pandemic created new challenges for healthcare systems and the rate of development of digital services started to grow exponentially. It was soon discovered that COVID-19-infected patients with diabetes had an increased risk of both mortality and debilitating sequelae. In addition, it was observed that this higher risk could be attenuated primarily by maintaining optimal control of the patient's glucose metabolism. As opportunities for actual physical doctor-patient visits became restricted, telemedicine provided the most convenient opportunity to communicate with patients and maintain delivery of care. The wide range of experiences of health care provision during the pandemic has led to the development of several excellent strategies regarding the applicability of telemedicine across the whole spectrum of diabetes care. The continuation of these strategies is likely to benefit clinical practice even after the pandemic crisis is over.

Glucometabolic Perturbations in Type 2 Diabetes Mellitus and Coronavirus Disease 2019: Causes, Consequences, and How to Counter Them Using Novel Antidiabetic Drugs - The CAPISCO International Expert Panel.

The growing amount of evidence suggests the existence of a bidirectional relation between coronavirus disease 2019 (COVID-19) and type 2 diabetes mellitus (T2DM), as these two conditions exacerbate each other, causing a significant healthcare and socioeconomic burden. The alterations in innate and adaptive cellular immunity, adipose tissue, alveolar and endothelial dysfunction, hypercoagulation, the propensity to an increased viral load, and chronic diabetic complications are all associated with glucometabolic perturbations of T2DM patients that predispose them to severe forms of COVID-19 and mortality. Severe acute respiratory syndrome coronavirus 2 infection negatively impacts glucose homeostasis due to its effects on insulin sensitivity and ß-cell function, further aggravating the preexisting glucometabolic perturbations in individuals with T2DM. Thus, the most effective ways are urgently needed for countering these glucometabolic disturbances occurring during acute COVID-19 illness in T2DM patients. The novel classes of antidiabetic medications (dipeptidyl peptidase 4 inhibitors (DPP-4is), glucagon-like peptide-1 receptor agonists (GLP-1 RAs), and sodium-glucose co-transporter-2 inhibitors (SGLT-2is) are considered candidate drugs for this purpose. This review article summarizes current knowledge regarding glucometabolic disturbances during acute COVID-19 illness in T2DM patients and the potential ways to tackle them using novel antidiabetic medications. Recent observational data suggest that preadmission use of GLP-1 RAs and SGLT-2is are associated with decreased patient mortality, while DPP-4is is associated with increased in-hospital mortality of T2DM patients with COVID-19. Although these results provide further evidence for the widespread use of these two classes of medications in this COVID-19 era, dedicated randomized controlled trials analyzing the effects of in-hospital use of novel antidiabetic agents in T2DM patients with COVID-19 are needed.

Associations between Periodontitis, COVID-19, and Cardiometabolic Complications: Molecular Mechanisms and Clinical Evidence.

Periodontitis is a microbially driven, host-mediated disease that leads to loss of periodontal attachment and resorption of bone. It is associated with the elevation of systemic inflammatory markers and with the presence of systemic comorbidities. Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although the majority of patients have mild symptoms, others experience important complications that can lead to death. After the spread of the COVID-19 pandemic, several investigations demonstrating the possible relationship between periodontitis and COVID-19 have been reported. In addition, both periodontal disease and COVID-19 seem to provoke and/or impair several cardiometabolic complications such as cardiovascular disease, type 2 diabetes, metabolic syndrome, dyslipidemia, insulin resistance, obesity, non-alcoholic fatty liver disease, and neurological and neuropsychiatric complications. Therefore, due to the increasing number of investigations focusing on the periodontitis-COVID-19 relationship and considering the severe complications that such an association might cause, this review aims to summarize all existing emerging evidence regarding the link between the periodontitis-COVID-19 axis and consequent cardiometabolic impairments.

Post-COVID syndrome, inflammation, and diabetes.

The raging COVID-19 pandemic is in its third year of global impact. The SARS CoV 2 virus has a high rate of spread, protean manifestations, and a high morbidity and mortality in individuals with predisposing risk factors. The pathophysiologic mechanisms involve a heightened systemic inflammatory state, cardiometabolic derangements, and varying degrees of glucose intolerance. The latter can be evident as significant hyperglycemia leading to new-onset diabetes or worsening of preexisting disease. Unfortunately, the clinical course beyond the acute phase of the illness may persist in the form of a variety of symptoms that together form the so-called "Long COVID" or "Post-COVID Syndrome". It is thought that a chronic, low-grade inflammatory and immunologic state persists during this phase, which may last for weeks or months. Although numerous insights have been gained into COVID-related hyperglycemia and diabetes, its prediction, course, and management remain to be fully elucidated.

Post-COVID syndrome, inflammation, and diabetes☆

The raging COVID-19 pandemic is in its third year of global impact. The SARS CoV 2 virus has a high rate of spread, protean manifestations, and a high morbidity and mortality in individuals with predisposing risk factors. The pathophysiologic mechanisms involve a heightened systemic inflammatory state, cardiometabolic derangements, and varying degrees of glucose intolerance. The latter can be evident as significant hyperglycemia leading to new-onset diabetes or worsening of preexisting disease. Unfortunately, the clinical course beyond the acute phase of the illness may persist in the form of a variety of symptoms that together form the so-called “Long COVID” or “Post-COVID Syndrome”. It is thought that a chronic, low-grade inflammatory and immunologic state persists during this phase, which may last for weeks or months. Although numerous insights have been gained into COVID-related hyperglycemia and diabetes, its prediction, course, and management remain to be fully elucidated.

Molecular and pro-inflammatory aspects of COVID-19: The impact on cardiometabolic health.

Obesity, type 2 diabetes (T2DM), hypertension (HTN), and Cardiovascular Disease (CVD) often cluster together as "Cardiometabolic Disease" (CMD). Just under 50% of patients with CMD increased the risk of morbidity and mortality right from the beginning of the COVID-19 pandemic as it has been reported in most countries affected by the SARS-CoV2 virus. One of the pathophysiological hallmarks of COVID-19 is the overactivation of the immune system with a prominent IL-6 response, resulting in severe and systemic damage involving also cytokines such as IL2, IL4, IL8, IL10, and interferon-gamma were considered strong predictors of COVID-19 severity. Thus, in this mini-review, we try to describe the inflammatory state, the alteration of the adipokine profile, and cytokine production in the obese state of infected and not infected patients by SARS-CoV2 with the final aim to find possible influences of COVID-19 on CMD and CVD. The immunological-based discussion of the molecular processes could inspire the study of promising targets for managing CMD patients and its complications during COVID-19.

The Dual Pandemics of COVID-19 and Obesity: Bidirectional Impact.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), responsible for the COVID-19 pandemic, has been shown to disrupt many organ systems in the human body. Though several medical disorders have been affected by this infection, a few illnesses in addition may also play a role in determining the outcome of COVID-19. Obesity is one such disease which is not only affected by the occurrence of COVID-19 but can also result in a worse clinical outcome of COVID-19 infection. This manuscript summarizes the most recent evidence supporting the bidirectional impact of COVID-19 and obesity. It highlights how the presence of obesity can be detrimental to the outcome of COVID-19 in a given patient because of the mechanical limitations in lung compliance and also by the activation of several thrombo-inflammatory pathways. The sociodemographic changes brought about by the pandemic in turn have facilitated the already increasing prevalence of obesity. This manuscript highlights the importance of recognizing these pathways which may further help in policy changes that facilitate appropriate measures to prevent the further worsening of these two pandemics.

Cardiovascular Risk Stratification in Diabetic Retinopathy via Atherosclerotic Pathway in COVID-19/Non-COVID-19 Frameworks Using Artificial Intelligence Paradigm: A Narrative Review.

Diabetes is one of the main causes of the rising cases of blindness in adults. This microvascular complication of diabetes is termed diabetic retinopathy (DR) and is associated with an expanding risk of cardiovascular events in diabetes patients. DR, in its various forms, is seen to be a powerful indicator of atherosclerosis. Further, the macrovascular complication of diabetes leads to coronary artery disease (CAD). Thus, the timely identification of cardiovascular disease (CVD) complications in DR patients is of utmost importance. Since CAD risk assessment is expensive for low-income countries, it is important to look for surrogate biomarkers for risk stratification of CVD in DR patients. Due to the common genetic makeup between the coronary and carotid arteries, low-cost, high-resolution imaging such as carotid B-mode ultrasound (US) can be used for arterial tissue characterization and risk stratification in DR patients. The advent of artificial intelligence (AI) techniques has facilitated the handling of large cohorts in a big data framework to identify atherosclerotic plaque features in arterial ultrasound. This enables timely CVD risk assessment and risk stratification of patients with DR. Thus, this review focuses on understanding the pathophysiology of DR, retinal and CAD imaging, the role of surrogate markers for CVD, and finally, the CVD risk stratification of DR patients. The review shows a step-by-step cyclic activity of how diabetes and atherosclerotic disease cause DR, leading to the worsening of CVD. We propose a solution to how AI can help in the identification of CVD risk. Lastly, we analyze the role of DR/CVD in the COVID-19 framework.

Managing Diabetic Foot Complications During COVID-19 Lockdown in India: A Survey.

To understand the management of diabetic foot complications by the Diabetic Foot Research India (DFRI) members during the lockdown period. An online survey link was created in "Survey Monkey", and the link was sent to all the members of Diabetic Foot Research India (DFRI) who are staying in different parts of India and data were collected from May 2020 to June 2020. The survey included questions on the type of consultation they provided to their patients, management of a patient with an active foot ulcer, and the various difficulties encountered by the doctors during the lockdown. A total of 33 diabetologists from all over the country participated in this survey. Among them, 26 doctors had attended to active diabetic foot infection at the time of the online survey. Almost three fourth of the (n = 24; 72.7%) doctors recorded difficulties during the inpatient consultations. Difficulty in regular follow-ups, the facility's workforce shortage was reported to be a significant concern. In managing active foot ulcer cases, 15 doctors (45.5%) opted for in-person consultation in their hospital as they felt the infection cannot be handled over a tele-consultation. Amputation was not performed by 78.7% of doctors, 15% (n = 5) of the doctors performed less than five amputations, and 6% (n = 2) of the doctors performed more than five amputations during the lockdown period. In the case of SMBG (Self-monitoring blood glucose) values, the regularity of patients reporting the values varied significantly. Only 8 (24.2%) doctors reported that 75% of their patients regularly shared their SMBG values while all the others mentioned that their patients were not performing SMBG regularly. Most of the physicians were able to manage the diabetic foot complications by tele-consultation during the lockdown and only a few asked the patients to get hospitalized for surgical intervention. All doctors should recommend SMBG to continuously monitor patients' blood glucose levels and prevent complications of hyperglycemia, particularly during pandemic situations.

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

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.

Major Lower-Limb Amputation During the COVID Pandemic in South India.

People with diabetes have a higher risk of lower-limb amputations than people without diabetes. The risk of avoidable lower-limb amputations has increased in the coronavirus disease 2019 (COVID-19) lockdown period. Hence, we conducted a retrospective, single-centered study on major amputations during the prepandemic period (March 25, 2019-December 31, 2019) and pandemic period (March 25, 2020-December 31, 2020). During the prepandemic period, 24 major amputations (below-knee and above-knee amputations) were performed and during the pandemic period, 37 major amputations were performed. There was a 54.1% increase in major amputations noted in the pandemic period more than the prepandemic period. This increase may also be due to irregular/missed hospital visits, improper diet, nonadherence to the medications, and physical inactivity. This study shows the indirect effect of the COVID-19 pandemic on people with diabetes, resulting in the increased incidence of lower-extremity amputations (below-knee and above-knee amputations) which might cause a drastic impact on their quality of life. This study also emphasizes the importance of easy and routine access to foot-care specialists to prevent avoidable amputations.

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

Coronavirus disease 2019 (COVID-19) is a global pandemic where several comorbidities have been shown to have a significant effect on mortality. Patients with diabetes mellitus (DM) have a higher mortality rate than non-DM patients if they get COVID-19. Recent studies have indicated that patients with a history of diabetes can increase the risk of severe acute respiratory syndrome coronavirus 2 infection. Additionally, patients without any history of diabetes can acquire new-onset DM when infected with COVID-19. Thus, there is a need to explore the bidirectional link between these two conditions, confirming the vicious loop between "DM/COVID-19". This narrative review presents (1) the bidirectional association between the DM and COVID-19, (2) the manifestations of the DM/COVID-19 loop leading to cardiovascular disease, (3) an understanding of primary and secondary factors that influence mortality due to the DM/COVID-19 loop, (4) the role of vitamin-D in DM patients during COVID-19, and finally, (5) the monitoring tools for tracking atherosclerosis burden in DM patients during COVID-19 and "COVID-triggered DM" patients. We conclude that the bidirectional nature of DM/COVID-19 causes acceleration towards cardiovascular events. Due to this alarming condition, early monitoring of atherosclerotic burden is required in "Diabetes patients during COVID-19" or "new-onset Diabetes triggered by COVID-19 in Non-Diabetes patients".

A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence.

COVID-19 has infected 77.4 million people worldwide and has caused 1.7 million fatalities as of December 21, 2020. The primary cause of death due to COVID-19 is Acute Respiratory Distress Syndrome (ARDS). According to the World Health Organization (WHO), people who are at least 60 years old or have comorbidities that have primarily been targeted are at the highest risk from SARS-CoV-2. Medical imaging provides a non-invasive, touch-free, and relatively safer alternative tool for diagnosis during the current ongoing pandemic. Artificial intelligence (AI) scientists are developing several intelligent computer-aided diagnosis (CAD) tools in multiple imaging modalities, i.e., lung computed tomography (CT), chest X-rays, and lung ultrasounds. These AI tools assist the pulmonary and critical care clinicians through (a) faster detection of the presence of a virus, (b) classifying pneumonia types, and (c) measuring the severity of viral damage in COVID-19-infected patients. Thus, it is of the utmost importance to fully understand the requirements of for a fast and successful, and timely lung scans analysis. This narrative review first presents the pathological layout of the lungs in the COVID-19 scenario, followed by understanding and then explains the comorbid statistical distributions in the ARDS framework. The novelty of this review is the approach to classifying the AI models as per the by school of thought (SoTs), exhibiting based on segregation of techniques and their characteristics. The study also discusses the identification of AI models and its extension from non-ARDS lungs (pre-COVID-19) to ARDS lungs (post-COVID-19). Furthermore, it also presents AI workflow considerations of for medical imaging modalities in the COVID-19 framework. Finally, clinical AI design considerations will be discussed. We conclude that the design of the current existing AI models can be improved by considering comorbidity as an independent factor. Furthermore, ARDS post-processing clinical systems must involve include (i) the clinical validation and verification of AI-models, (ii) reliability and stability criteria, and (iii) easily adaptable, and (iv) generalization assessments of AI systems for their use in pulmonary, critical care, and radiological settings.

Six artificial intelligence paradigms for tissue characterisation and classification of non-COVID-19 pneumonia against COVID-19 pneumonia in computed tomography lungs.

BACKGROUND: COVID-19 pandemic has currently no vaccines. Thus, the only feasible solution for prevention relies on the detection of COVID-19-positive cases through quick and accurate testing. Since artificial intelligence (AI) offers the powerful mechanism to automatically extract the tissue features and characterise the disease, we therefore hypothesise that AI-based strategies can provide quick detection and classification, especially for radiological computed tomography (CT) lung scans. METHODOLOGY: Six models, two traditional machine learning (ML)-based (k-NN and RF), two transfer learning (TL)-based (VGG19 and InceptionV3), and the last two were our custom-designed deep learning (DL) models (CNN and iCNN), were developed for classification between COVID pneumonia (CoP) and non-COVID pneumonia (NCoP). K10 cross-validation (90% training: 10% testing) protocol on an Italian cohort of 100 CoP and 30 NCoP patients was used for performance evaluation and bispectrum analysis for CT lung characterisation. RESULTS: Using K10 protocol, our results showed the accuracy in the order of DL > TL > ML, ranging the six accuracies for k-NN, RF, VGG19, IV3, CNN, iCNN as 74.58 ± 2.44%, 96.84 ± 2.6, 94.84 ± 2.85%, 99.53 ± 0.75%, 99.53 ± 1.05%, and 99.69 ± 0.66%, respectively. The corresponding AUCs were 0.74, 0.94, 0.96, 0.99, 0.99, and 0.99 (p-values < 0.0001), respectively. Our Bispectrum-based characterisation system suggested CoP can be separated against NCoP using AI models. COVID risk severity stratification also showed a high correlation of 0.7270 (p < 0.0001) with clinical scores such as ground-glass opacities (GGO), further validating our AI models. CONCLUSIONS: We prove our hypothesis by demonstrating that all the six AI models successfully classified CoP against NCoP due to the strong presence of contrasting features such as ground-glass opacities (GGO), consolidations, and pleural effusion in CoP patients. Further, our online system takes < 2 s for inference.

Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence.

Artificial Intelligence (AI), in general, refers to the machines (or computers) that mimic "cognitive" functions that we associate with our mind, such as "learning" and "solving problem". New biomarkers derived from medical imaging are being discovered and are then fused with non-imaging biomarkers (such as office, laboratory, physiological, genetic, epidemiological, and clinical-based biomarkers) in a big data framework, to develop AI systems. These systems can support risk prediction and monitoring. This perspective narrative shows the powerful methods of AI for tracking cardiovascular risks. We conclude that AI could potentially become an integral part of the COVID-19 disease management system. Countries, large and small, should join hands with the WHO in building biobanks for scientists around the world to build AI-based platforms for tracking the cardiovascular risk assessment during COVID-19 times and long-term follow-up of the survivors.

Insulinization in T2DM with Basal Analogues During COVID-19 Pandemic: Expert Opinion from an Indian Panel.

The ongoing global pandemic of the coronavirus disease 2019 (COVID-19) has placed a severe strain on the management of chronic conditions like diabetes. Optimal glycemic control is always important, but more so in the existing environment of COVID-19. In this context, timely insulinization to achieve optimal glycemic control assumes major significance. However, given the challenges associated with the pandemic like restrictions of movement and access to healthcare resources, a simple and easy way to initiate and optimize insulin therapy in people with uncontrolled diabetes is required. With this premise, a group of clinical experts comprising diabetologists and endocrinologists from India discussed the challenges and potential solutions for insulin initiation, titration, and optimization in type 2 diabetes mellitus (T2DM) during the COVID-19 pandemic and how basal insulin can be a good option in this situation owing to its unique set of advantages like lower risk of hypoglycemia, ease of training, need for less monitoring, better adherence, flexibility of using oral antidiabetic drugs, and improved quality of life compared to other insulin regimens. The panel agreed that the existing challenges should not be a reason to delay insulin initiation in people with uncontrolled T2DM and provided recommendations, which included potential solutions for initiating insulin in the absence or restriction of in-person consultations; the dose of insulin at initiation; the type of insulin preferred for simplified regimen and best practices for optimal titration to achieve glycemic targets during the pandemic. Practical and easily implementable tips for patients and involvement of stakeholders (caregivers and healthcare providers) to facilitate insulin acceptance were also outlined by the expert panel. Simplified and convenient insulin regimens like basal insulin analogues are advised during and following the pandemic in order to achieve glycemic control in people with uncontrolled T2DM.

COVID-19 pathways for brain and heart injury in comorbidity patients: A role of medical imaging and artificial intelligence-based COVID severity classification: A review.

Artificial intelligence (AI) has penetrated the field of medicine, particularly the field of radiology. Since its emergence, the highly virulent coronavirus disease 2019 (COVID-19) has infected over 10 million people, leading to over 500,000 deaths as of July 1st, 2020. Since the outbreak began, almost 28,000 articles about COVID-19 have been published (https://pubmed.ncbi.nlm.nih.gov); however, few have explored the role of imaging and artificial intelligence in COVID-19 patients-specifically, those with comorbidities. This paper begins by presenting the four pathways that can lead to heart and brain injuries following a COVID-19 infection. Our survey also offers insights into the role that imaging can play in the treatment of comorbid patients, based on probabilities derived from COVID-19 symptom statistics. Such symptoms include myocardial injury, hypoxia, plaque rupture, arrhythmias, venous thromboembolism, coronary thrombosis, encephalitis, ischemia, inflammation, and lung injury. At its core, this study considers the role of image-based AI, which can be used to characterize the tissues of a COVID-19 patient and classify the severity of their infection. Image-based AI is more important than ever as the pandemic surges and countries worldwide grapple with limited medical resources for detection and diagnosis.