Diabetes and COVID-19: evidence, current status and unanswered research questions

COVID-19 related complications

The COVID-19 pandemic has significantly impacted global healthcare systems, revealed vulnerabilities and prompted a re-evaluation of medical practices. Acute complications from the virus, including cardiovascular and neurological issues, have underscored the necessity for timely medical interventions. Advances in diagnostic methods and personalized therapies have been pivotal in mitigating severe outcomes. Additionally, Long COVID has emerged as a complex challenge, affecting various body systems and leading to respiratory, cardiovascular, neurological, psychological, and musculoskeletal problems. This broad spectrum of complications highlights the importance of multidisciplinary management approaches that prioritize therapy, rehabilitation, and patient-centered care. Vulnerable populations such as paediatric patients, pregnant women, and immunocompromised individuals face unique risks and complications, necessitating continuous monitoring and tailored management strategies to reduce morbidity and mortality associated with COVID-19.

The impact of the COVID-19 pandemic shelter in place on glycemic control, blood pressure control, and body mass index among diabetic patients at Kaiser Permanente Northern California: a retrospective cohort study

BACKGROUND/ INTRODUCTION

Regional shelter-in-place (SiP) mandates had a substantial impact on physical health and access to care. The impact of SiP on chronic disease management, specifically diabetes mellitus, is unknown. We sought to quantify the impact of California's 2020 SiP order on various health parameters in the Kaiser Permanente Northern California (KPNC) diabetic population.

METHODS

This retrospective cohort study included 168,621 diabetic patients, stratified by pre-pandemic HbA1c level. Our primary outcome was the difference in HbA1c, systolic blood pressure (SBP), and diastolic blood pressure (DBP) comparing the pre- (September 1, 2019, to March 31, 2020) to post-SiP period (June 1, 2020, to December 31, 2020). Our secondary endpoints included use of insulin or oral hypoglycemic agents (OHAs) during the post-SiP period and change in body mass index (BMI). This study utilized a paired t-test and chi-squared testing in order to assess for statistically significant differences in pre- versus post-SiP values.

RESULTS

Patients in this cohort were 52.29% male with 37.68% White, 22.35% Hispanic, 9.30% Black and 23.54% Asian and a mean age of 63 years. In this cohort, 44.02% of patients had a HbA1c < 7%, while 29.17%, 13.67%, 6.34% and 6.81% had an HbA1c of 7-7.9%, 8-8.9%, 9-9.9% and ≥ 10%, respectively (range 4.10 to 19.50%). Mean HbA1c, SBP, and DBP increased significantly across all groups; OHA use and insulin utilization also increased overall. Patients with lower pre-SiP HbA1c demonstrated larger increases in HbA1c and OHA utilization, while patients with higher pre-SiP HbA1c demonstrated increased rates of insulin initiation and decreases in their post-SiP HbA1c. Notably, mean BMI decreased in every HbA1c subgroup. Due to the large sample size, all p-values were < 0.0001.

CONCLUSIONS

Among the KPNC diabetic population, several metrics for diabetes health management were significantly worsened after California's SiP. However, diabetic patients with the highest HbA1c values showed clinically significant improvement in their HbA1c, indicating a differential effect of the SiP on diabetes management. Our study helps identify higher-risk diabetic patients who require more intensive monitoring in the setting of the recent pandemic and highlights the importance of considering long-term implications of policy decisions on diabetes care.

Users' perspectives about challenges of using telemedicine for patient with diabetes during the COVID-19 pandemic

Purpose

Telemedicine services have been recognized as a safe and affordable method for providing continuous healthcare services, especially to patients with chronic diseases. Despite all advantages, the use of this technology has faced several challenges particularly during COVID-19. The aim of this study was to investigate physicians' and patients' with diabetes perspectives about challenges of using telemedicine during the COVID-19 pandemic.

Methods

This survey study was conducted in 2023. The participants were endocrinologists, internal medicine specialists, general practitioners (GPs), and patients with diabetes in five teaching hospitals located in one of the underprivileged areas in Iran. Data were collected using a 5-point Likert-scale questionnaire and analyzed using descriptive statistics and the Kruskal-Wallis test.

Results

In this study, 95 questionnaires were completed by 60 patients with diabetes (30%), 21 GPs (70%), 10 internal medicine specialists (66.6%), and 4 endocrinologists (80%). The results showed that technical (4.42 ± 0.57), clinical (4.25 ± 0.52), organizational (4.23 ± 0.56), and individual (4.02 ± 0.42) challenges were the most important challenges, respectively. However, there was a statistically significant difference between the perspectives of the specialists, GPs, and patients with diabetes in terms of individual, clinical, technical, and organizational challenges.

Conclusions

Since the use of telemedicine for patients with diabetes faced several challenges during the COVID-19 pandemic, it is necessary to consider practical solutions to overcome these challenges in order to improve quality of services and increase the use of this technology, particularly in underprivileged areas. Moreover, the effectiveness of these services in different contexts, and for people with different economic conditions should be investigated in the future.

Metformin use and its association with various outcomes in COVID-19 patients with diabetes mellitus: a retrospective cohort study in a tertiary care facility

BACKGROUND

Evidence shows that diabetes raises the probability of contracting COVID-19 and associated complications. We hypothesize that metformin, being pleiotropic, may improve COVID-19 in diabetics.

METHODS

A retrospective cohort study was conducted with 421 COVID-19 patients with diabetes, hospitalized between 1st April 2020 and 31st March 2022 in a tertiary-care hospital. Patients with metformin or its combination constituted the study cohort (SC; n = 221), while other antidiabetics constituted the reference cohort (RC; n = 200).

RESULTS

SC and RC were matched for mean age ± SD (SC: 53.3 ± 5.7 vs. RC: 54.3 ± 8.2 years). The mean length of hospitalization (days) was significantly shorter in SC (9.0 ± 5.7) than in RC (12.7 ± 6) (p < 0.02). Metformin use was associated with reduction in mortality risk (OR: 0.106, 95% CI = 0.039-0.287; p < 0.001). Moreover, SC also improved levels of LDH (OR: 0.243, 95% CI = 0.104-0.566; p < 0.001), CRP (OR: 0.281, 95% CI = 0.120-0.659; p < 0.004), and D-dimer (OR: 0.220, 95% CI = 0.089-0.539; p < 0.001) than RC. The calculated number needed to treat for metformin was 3.1.

CONCLUSION

Metformin users have a decrease in hospital stay and mortality rates and improvement in LDH, CRP, and D-dimer levels. Therefore, metformin might protect against mortality in COVID-19 with diabetes.

Analyzing Diabetes Detection and Classification: A Bibliometric Review (2000-2023)

Bibliometric analysis is a rigorous method to analyze significant quantities of bibliometric data to assess their impact on a particular field. This study used bibliometric analysis to investigate the academic research on diabetes detection and classification from 2000 to 2023. The PRISMA 2020 framework was followed to identify, filter, and select relevant papers. This study used the Web of Science database to determine relevant publications concerning diabetes detection and classification using the keywords "diabetes detection", "diabetes classification", and "diabetes detection and classification". A total of 863 publications were selected for analysis. The research applied two bibliometric techniques: performance analysis and science mapping. Various bibliometric parameters, including publication analysis, trend analysis, citation analysis, and networking analysis, were used to assess the performance of these articles. The analysis findings showed that India, China, and the United States are the top three countries with the highest number of publications and citations on diabetes detection and classification. The most frequently used keywords are machine learning, diabetic retinopathy, and deep learning. Additionally, the study identified "classification", "diagnosis", and "validation" as the prevailing topics for diabetes identification. This research contributes valuable insights into the academic landscape of diabetes detection and classification.

Role of renin-angiotensin system/angiotensin converting enzyme-2 mechanism and enhanced COVID-19 susceptibility in type 2 diabetes mellitus

Coronavirus disease 2019 (COVID-19) is a disease that caused a global pandemic and is caused by infection of severe acute respiratory syndrome coronavirus 2 virus. It has affected over 768 million people worldwide, resulting in approximately 6900000 deaths. High-risk groups, identified by the Centers for Disease Control and Prevention, include individuals with conditions like type 2 diabetes mellitus (T2DM), obesity, chronic lung disease, serious heart conditions, and chronic kidney disease. Research indicates that those with T2DM face a heightened susceptibility to COVID-19 and increased mortality compared to non-diabetic individuals. Examining the renin-angiotensin system (RAS), a vital regulator of blood pressure and pulmonary stability, reveals the significance of the angiotensin-converting enzyme (ACE) and ACE2 enzymes. ACE converts angiotensin-I to the vasoconstrictor angiotensin-II, while ACE2 counters this by converting angiotensin-II to angiotensin 1-7, a vasodilator. Reduced ACE2 expression, common in diabetes, intensifies RAS activity, contributing to conditions like inflammation and fibrosis. Although ACE inhibitors and angiotensin receptor blockers can be therapeutically beneficial by increasing ACE2 levels, concerns arise regarding the potential elevation of ACE2 receptors on cell membranes, potentially facilitating COVID-19 entry. This review explored the role of the RAS/ACE2 mechanism in amplifying severe acute respiratory syndrome coronavirus 2 infection and associated complications in T2DM. Potential treatment strategies, including recombinant human ACE2 therapy, broad-spectrum antiviral drugs, and epigenetic signature detection, are discussed as promising avenues in the battle against this pandemic.

Prevalence of Diabetes, Ketosis, and Ketoacidosis and Their Correlation With Mortality in Critical COVID-19 Patients: A Single-Center Retrospective Study

Aim We aimed to find out the prevalence of diabetes, ketosis, and ketoacidosis in coronavirus disease 2019 (COVID-19) critically ill patients and to explore the clinical impact of the development of ketosis and ketoacidosis on the outcome of COVID-19 critically ill patients and identify them as potential risk factors for these patients. Methods We collected data on COVID-19 patients admitted to the intensive care unit (ICU) retrospectively. The study population will be classified into two groups based on the presence of diabetes or ketosis. Results The study comprises data on 253 ICU patients admitted with COVID-19 pneumonia. Two hundred patients (79.05%) had diabetes or prediabetes on admission. Seventy-six patients (30%) presented with ketosis. Nine patients had progressed to diabetic ketoacidosis during their ICU stay. Concerning the outcome, among 150 patients who died (59.3%), there was significantly higher mortality among the ketotic patients (69.7%) compared to nonketotic patients (54.8%) with a P-value < 0.027. We noted that the peak blood glucose level during ICU stay was statistically significantly higher in nonsurvivors (mean 345 mg/dl) compared to survivors (mean 298 mg/dl) with a P-value of 0.006. Our data showed that peak serum levels of lactate, procalcitonin (PCT), C-reactive protein, white blood cells (WBC), D dimer, and lactate dehydrogenase strongly positively correlated to the length of ICU stay. We used the ROC curve (receiver operating characteristic curve) to assess the relation between many laboratories and mortality. We noted that uncontrolled hyperglycemia and other laboratory variables are significant predictors of mortality of COVID-19 patients (e.g., peak blood glucose (P = 0.004), PCT (P = 0.047), and P < 0.001 of other laboratories (e.g. lactate, PH, WBC, D dimer, ferritin). Conclusion We reported a high prevalence of diabetes and ketosis among COVID-19 patients admitted to the ICU. Ketosis is associated with an increased mortality risk. Uncontrolled hyperglycemia is a significant predictor of mortality in critically ill COVID-19 patients.

COVID-19 testing and vaccination uptake among youth and young adults: Disparities by age, race/ethnicity, sexual orientation, and gender identity

BACKGROUND

The COVID-19 pandemic disproportionately impacts youth and young adults (YYA) and YYA with multiple marginalized identities, yet little is known about differences in uptake, testing access, and vaccine concerns among YYA by diverse demographic identities.

METHODS

Between 2/2021 and 2/2022, we conducted a national, cross-sectional online survey focused on diverse YYA ages 14 to 24 (n = 983). We explored the prevalence of COVID-19 testing and vaccination among YYA by age, race/ethnicity, and sexual and gender identities. Bivariate and multivariable logistic regression models were developed to estimate associations between individual variables and COVID-19 testing and vaccination.

RESULTS

The overall COVID-19 testing and vaccination rates in our sample were high (75.99% and 69.07%, respectively). No differences in testing by demographics were found. Compared to individuals aged 14 to 17 years, those aged 18 to 21 years and 22 to 24 years were over 2 times and 4 times as likely to report receiving a vaccine, respectively. All race/ethnicity groups except for Asian individuals were more likely to report being vaccinated compared to their white peers.

CONCLUSIONS

Our findings showed critical disparities in COVID-19 vaccination among YYA with marginalized identities and emphasized the urgency for data collection and research on pandemic prevention for vulnerable YYA populations.

The role of SARS-CoV-2-mediated NF-κB activation in COVID-19 patients

The SARS-CoV-2 pandemic, now in its third year, has had a profound impact on public health and economics all over the world. Different populations showed varied susceptibility to this virus and mortality after infection. Clinical and laboratory data revealed that the uncontrolled inflammatory response plays an important role in their poor outcome. Herein, we summarized the role of NF-κB activation during SARS-CoV-2 invasion and replication, particularly the angiotensin-converting enzyme 2 (ACE2)-mediated NF-κB activation. Then we summarized the COVID-19 drugs' impact on NF-κB activation and their problems. A favorable prognosis is linked with timely treatment with NF-κB activation inhibitors, such as TNFα, IL-1β, and IL-6 monoclonal antibodies. However, further clinical researches are still required to clarify the time window, dosage of administration, contraindication, and potential side effects of these drugs, particularly for COVID-19 patients with hypertension, hyperglycemia, diabetes, or other chronic diseases.

The Effect of Sodium-Glucose Cotransporter-2 Inhibitors on COVID-19 Patients with Type 2 Diabetes Mellitus: A Retrospective Cohort Study Using the Common Data Model

BACKGROUND

There is no clinical evidence about the effect of sodium-glucose cotransporter-2 (SGLT2) inhibitors on diabetic patients who have been diagnosed with coronavirus disease 19 (COVID-19).

METHODS

The dataset is based on insurance benefit claims sent to the Health Insurance Review and Assessment Service of Korea from January, 2018 to April, 2022. Among 9,822,577 patients who were involved in the claims, diabetic patients were divided into two groups based on whether they had a prescription for an SGLT2 inhibitor. The primary outcome was major adverse cardiac and cerebrovascular events (MACCEs), which were a composite of all-cause mortality, myocardial infarction, stroke, and revascularization over 90 days.

RESULTS

A total of 172,682 patients was analyzed. In the propensity score-matched analysis, the rate of MACCE was lower in the SGLT2 inhibitor group compared to the non-SGLT2 inhibitor group (0.89% vs. 1.31%; hazard ratio, 0.71; 95% confidence interval, 0.53-0.94; p =0.020). Each of the MACCEs showed no differences between the two groups. The rate of pneumonia was similar between the two groups (4.45% vs. 4.39%; hazard ratio, 1.06; 95% confidence interval, 0.91-1.16; p = 0.620).

CONCLUSIONS

In the diabetic patients who were diagnosed with COVID-19, SGLT2 inhibitors were associated with improved clinical outcomes in terms of MACCEs. SGLT2 inhibitors might be considered for prescription to diabetic patients in the current context of long COVID-19.

COVID-19: A novel holistic systems biology approach to predict its molecular mechanisms (in vitro) and repurpose drugs

PURPOSE

COVID-19 strangely kills some youth with no history of physical weakness, and in addition to the lungs, it may even directly harm other organs. Its complex mechanism has led to the loss of any significantly effective drug, and some patients with severe forms still die daily. Common methods for identifying disease mechanisms and drug design are often time-consuming or reductionist. Here, we use a novel holistic systems biology approach to predict its molecular mechanisms (in vitro), significant molecular relations with SARS, and repurpose drugs.

METHODS

We have utilized its relative phylogenic similarity to SARS. Using the available omics data for SARS and the fewer data for COVID-19 to decode the mechanisms and their significant relations, We applied the Cytoscape analyzer, MCODE, STRING, and DAVID tools to predict the topographically crucial molecules, clusters, protein interaction mappings, and functional analysis. We also applied a novel approach to identify the significant relations between the two infections using the Fischer exact test for MCODE clusters. We then constructed and analyzed a drug-gene network using PharmGKB and DrugBank (retrieved using the dgidb).

RESULTS

Some of the shared identified crucial molecules, BPs and pathways included Kaposi sarcoma-associated herpesvirus infection, Influenza A, and NOD-like receptor signaling pathways. Besides, our identified crucial molecules specific to host response against SARS-CoV-2 included FGA, BMP4, PRPF40A, and IFI16.

CONCLUSION

We also introduced seven new repurposed candidate drugs based on the drug-gene network analysis for the identified crucial molecules. Therefore, we suggest that our newly recommended repurposed drugs be further investigated in Vitro and in Vivo against COVID-19.

A nonlinear mathematical model on the Covid-19 transmission pattern among diabetic and non-diabetic population

In this paper, a three tier mathematical model describing the interactions between susceptible population, Covid-19 infected, diabetic population and Covid-19 infected, non diabetic population is proposed. Basic properties of such a dynamic model, namely, non negativity, boundedness of solutions, existence of disease-free and disease equilibria are studied and sufficient conditions are obtained. Basic reproduction number for the system is derived. Sufficient conditions on functionals and parameters of the system are obtained for the local as well as global stability of equilibria, thus, establishing the conditions for eventual prevalence of disease free or disease environment, as the case may be. The stability aspects are discussed in the context of basic reproduction number and vice versa. An important contribution of this article is that a novel technique is presented to estimate some key, influencing parameters of the system so that a pre-specified, assumed equilibrium state is approached eventually. This enables the society to prepare itself with the help of these key, influencing parameters so estimated. Several examples are provided to illustrate the results established and simulations are provided to visualize the examples.

Age, Sex and BMI Relations with Anti-SARS-CoV-2-Spike IgG Antibodies after BNT162b2 COVID-19 Vaccine in Health Care Workers in Northern Greece

The aim of this work was to study age, sex, and BMI (Body Mass Index)-related differences in the development of anti-SARS-CoV-2-Spike IgG antibodies, after vaccination with the BNT162b2 COVID-19 vaccine, in health care workers of a General Hospital in a city in Northern Greece. Blood sampling was drawn two to four weeks following the second dose of the vaccine, and six months after the first blood sample collection. Measurement of serum IgG antibodies against the spike domain of SARS-CoV-2 was performed using the SARS-CoV-2 IgG II Quant assay. All participants had sufficient serum IgG titers in the first measurement. Women developed higher IgG titers than men. The IgG titers were inversely related to age in both sexes; there was also a small, insignificant tendency to be inversely related to BMI. Six months after the first measurement, the IgG titers decreased dramatically to values less than 5% of the initial. This decrease was observed in both men and women and was inversely related to age. Multivariate regression analysis showed that age and sex explained with statistical significance 9% of the variance in SARS-CoV-2 IgG titers in our study population; the role of BMI was limited and insignificant.

Delay in Regular Visits of Chronic Disease Patients and Its Associated Factors during the COVID-19 Pandemic

Background

Chronic patients need regular follow-ups. During the COVID-19 pandemic, these regular visits can be affected. The delay of chronic patients and its contributing factors in their periodic visits during the COVID-19 pandemic are examined here.

Methods

This cross-sectional study was conducted between February and June 2021, in Fars, Iran. Two hundred and eighty-six households with at least one chronic patient were recruited. Then, several trained questioners called the studied households and asked about the studied variables. The dependent variable was the number of delays in regular visits during the COVID-19 pandemic. The results were analyzed through Poisson regression by SPSS Statistics version 22 and GraphPad Prism software version 9. A significance level of 0.05 was considered for this study.

Results

Out of 286 households 113 (73.4%) fathers, 138 (70.1%) mothers, and 17 (58.6%) children in the households reported delayed referral. In fathers, referring to the health center was significantly associated with a decrease in the number of delays (P=0.033). The higher age of the householder (P=0.005), the higher number of children (P=0.043), and having a family physician (P=0.007) in the mothers' group, also the number of children in households (P=0.001) in the children group were significantly associated with increasing the number of delays.

Conclusion

COVID-19 pandemic not only creates direct harmful effects but also adversely affects people in danger of chronic diseases. Delays in follow-ups are taken into account as a major challenge during the COVID-19 pandemic. This issue is not limited to rural or urban residency.

Caffeic acid attenuates neuroinflammation and cognitive impairment in streptozotocin-induced diabetic rats: Pivotal role of the cholinergic and purinergic signaling pathways

The present study evaluated the effect of caffeic acid (CA) on behavioral learning and memory tasks in the diabetic state. We also evaluated the effect of this phenolic acid on the enzymatic activities of acetylcholinesterase, ecto-nucleoside triphosphate diphosphohydrolase, ecto-5-nucleotidase and adenosine deaminase as well as on the density of M1R, α7nAChR, P2×7R, A1R, A2AR, and inflammatory parameters in the cortex and hippocampus of diabetic rats. Diabetes was induced by a single intraperitoneal dose of streptozotocin (55 mg/kg). The animals were divided into six groups: control/vehicle; control/CA 10 and 50 mg/kg; diabetic/vehicle; diabetic/CA 10 and 50 mg/kg, treated by gavage. The results showed that CA improved learning and memory deficits in diabetic rats. Also, CA reversed the increase in acetylcholinesterase and adenosine deaminase activities and reduced ATP and ADP hydrolysis. Moreover, CA increased the density of M1R, α7nAChR, and A1R receptors and reversed the increase in P2×7R and A2AR density in both evaluated structures. In addition, CA treatment attenuated the increase in NLRP3, caspase 1, and interleukin 1β density in the diabetic state; moreover, it increased the density of interleukin-10 in the diabetic/CA 10 mg/kg group. The results indicated that CA treatment positively modified the activities of cholinergic and purinergic enzymes and the density of receptors, and improved the inflammatory parameters of diabetic animals. Thus, the outcomes suggest that this phenolic acid could improve the cognitive deficit linked to cholinergic and purinergic signaling in the diabetic state.

Interpretable machine learning analysis to identify risk factors for diabetes using the anonymous living census data of Japan

Purpose

Diabetes mellitus causes various problems in our life. With the big data boom in our society, some risk factors for Diabetes must still exist. To identify new risk factors for diabetes in the big data society and explore further efficient use of big data, the non-objective-oriented census data about the Japanese Citizen's Survey of Living Conditions were analyzed using interpretable machine learning methods.

Methods

Seven interpretable machine learning methods were used to analysis Japan citizens' census data. Firstly, logistic analysis was used to analyze the risk factors of diabetes from 19 selected initial elements. Then, the linear analysis, linear discriminate analysis, Hayashi's quantification analysis method 2, random forest, XGBoost, and SHAP methods were used to re-check and find the different factor contributions. Finally, the relationship among the factors was analyzed to understand the relationship among factors.

Results

Four new risk factors: the number of family members, insurance type, public pension type, and health awareness level, were found as risk factors for diabetes mellitus for the first time, while another 11 risk factors were reconfirmed in this analysis. Especially the insurance type factor and health awareness level factor make more contributions to diabetes than factors: hypertension, hyperlipidemia, and stress in some interpretable models. We also found that work years were identified as a risk factor for diabetes because it has a high coefficient with the risk factor of age.

Conclusions

New risk factors for diabetes mellitus were identified based on Japan's non-objective-oriented anonymous census data using interpretable machine learning models. The newly identified risk factors inspire new possible policies for preventing diabetes. Moreover, our analysis certifies that big data can help us find helpful knowledge in today's prosperous society. Our study also paves the way for identifying more risk factors and promoting the efficiency of using big data.

Angiotensin converting enzyme 2 level and its significance in COVID-19 and other diseases patients

BACKGROUND

Angiotensin-converting enzyme 2 (ACE2) expressions and its modulation are of great interest as being a key receptor for severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) and the protective arm of the rennin-angiotensin axis, maintaining cardiovascular homeostasis. However, ACE2 expressions and their modulation in the healthy and disease background are yet to be explored.

METHOD

We performed a meta-analysis, extracting the data for ACE2 expression in human subjects with various diseases, including SARS-CoV2 infection without or with co-morbidity. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Out of 203 studies, 39 met the inclusion criteria with SARS-CoV2 patients without co-morbidity, SARS-CoV2 patients with co-morbidity, cardiovascular (CVD) patients, diabetes patients, kidney disorders patients, pulmonary disease patients, and other viral infections patients.

RESULTS

Angiotensin-converting enzyme 2 expression was significantly increased in all diseases. There was an elevated level of ACE2, especially membrane-bound ACE2, in COVID-19 patients compared to healthy controls. A statistically significant increase in ACE2 expression was observed in CVD patients and patients with other viral diseases compared to healthy subjects. Moreover, subgroup analysis of ACE2 expression as soluble and membrane-bound ACE2 revealed a remarkable increase in membrane-bound ACE2 in CVD patients, patients with viral infection compared to soluble ACE2 and pooled standard mean difference (SMD) with the random-effects model was 0.37 and 2.23 respectively.

CONCLUSION

It was observed that utilizing the ACE2 by SARS-CoV2 for its entry and its consequence leads to several complications. So there is a need to investigate the underlying mechanism along with novel therapeutic strategies.

Crosstalk between SARS-CoV-2 Infection and Type II Diabetes

Since the outbreak of coronavirus disease (COVID-19) in Wuhan, China, triggered by severe acute respiratory coronavirus 2 (SARS-CoV-2) in late November 2019, spreading to more than 200 countries of the world, the ensuing pandemic to an enormous loss of lives, mainly the older population with comorbidities, like diabetes, cardiovascular disease, chronic obstructive pulmonary disease, obesity, and hypertension. Amongst these immune-debilitating diseases, SARS-CoV-2 infection is the most common in patients with diabetes due to the absence of a normal active immune system to fight the COVID-19. Recovery of patients having a history of diabetes from COVID-19 encounters several complications, and their management becomes cumbersome. For control of coronavirus, antiviral medications, glucose-lowering agents, and steroids have been carefully evaluated. In the present review, we discuss the crosstalk between SARS-CoV-2 infection and patients with a history of diabetes. We mainly emphasize the molecular factors that are involved in diabetic individuals recently infected by SARS-CoV-2 and developed COVID-19 disease. Lastly, we examine the medications available for the long-term management of diabetic patients with SARS-CoV-2 infection.

Effect of Antidiabetic Therapy on Clinical Outcomes of COVID-19 Patients With Type 2 Diabetes: A Systematic Review and Meta-Analysis

Background:

No study has yet systematically evaluated the effect of antidiabetic therapy on clinical outcomes of COVID-19 patients with type 2 diabetes (T2D).

Objective:

We aimed to evaluate the effect of different antidiabetic therapy on clinical outcomes of COVID-19 patients with T2D.

Methods:

We comprehensively retrieved the published research which examined the effect of antidiabetic therapy on clinical outcomes of COVID-19 patients with T2D. The odds ratio (OR) and its 95% confidence interval (95% CI) for clinical outcomes were calculated using the random-effects model, and meta-regression was adopted to evaluate the potential sources of heterogeneity between studies.

Results:

A total of 54 studies were included in this study. We found that the use of metformin (OR = 0.66, 95% CI: 0.58-0.75), SGLT-2i (OR = 0.80, 95% CI: 0.73-0.88), and GLP-1ra (OR = 0.83, 95% CI: 0.70-0.98) were significantly associated with lower mortality risk in COVID-19 patients with T2D, while insulin use might unexpectedly increase the ICU admission rate (OR = 2.32, 95% CI: 1.34-4.01) and risk of death (OR = 1.52, 95% CI: 1.32-1.75). No statistically significant associations were identified for DPP-4i, SUs, AGIs, and TZDs.

Conclusion and Relevance:

We demonstrated that the usage of metformin, SGLT-2i, and GLP-1ra could significantly decrease mortality in COVID-19 patients with T2D. The heterogeneity across the studies, baseline characteristics of the included patients, shortage of dosage and the duration of antidiabetic drugs and autonomy of drug selection might limit the objectivity and accuracy of results. Further adequately powered and high-quality randomized controlled trials are warranted for conclusive findings.

Hypocalcemia: a key biomarker in hospitalized COVID-19 patients

Background

At the end of 2019 a new respiratory syndrome emerged in China named Coronavirus disease 2019 (COVID-19) due to SARS-CoV-2 infection. Considering the severity of the disease in adult subjects with one or more chronic pathologies, it was mandatory to find simple and effective biomarkers for negative prognosis of the disease easily available at the admission to the hospital.

Methods

To identify possible parameters showing association with the outcome in COVID-19 patients with pre-existing chronic diseases, blood biochemical profiles of 511 patients, enrolled from March to June 2020, were retrospectively evaluated. The pathological conditions taken into consideration were diabetes, arterial hypertension, chronic kidney disease, cardiovascular diseases, chronic obstructive pulmonary disease, obesity, and cancer. All the data were collected upon admission to the emergency room (ER) during the indicated period.

Results

We observed that serum and ionized calcium were prevalently altered in our cohort. We determined that hypocalcemia was a major parameter associated with mechanical ventilation and poor prognosis, correlating also with the presence of comorbidities such as cardiovascular diseases, chronic kidney disease, and cancer. In addition, we found a positive correlation between hypocalcemia and clinical complications during hospitalizations.

Conclusions

Our results strengthen the relevance of serum calcium concentration as a useful prognostic biomarker in hospitalized COVID-19 patients.

What hematological and endocrinal indicators are important in COVID-19 infection?

Background: Clinical evidence of endocrine involvement in coronavirus disease needs further investigation. The aim of the present study was to assess the relationship between hematology and endocrine parameters in coronavirus disease 2019 (COVID-19) infection. Methods: In the present cross-sectional study, a total of 320 patients (215 survivors and 105 non-survivors) with confirmed COVID-19 infection were enrolled. After isolation of serum samples, hematological, biochemical, and hormonal parameters were analyzed. Results: The mean age of survivors and non-survivors was 58.92 (SD: 15.28) and 63.65 (SD: 16.62) years, respectively. The results demonstrated significant differences in free triiodothyronine (FT3) [MD (95% CI): 0.40 (0.10, 0.71), P=0.009], total calcium [MD (95% CI): 0.53 (0.21, 0.86), P=0.003], vitamin D [MD (95% CI): 7.72 (6.38, 9.05), P=0.003], erythrocyte sedimentation rate (ESR) [MD (95% CI): 17.09 (9.38, 22.05), P=0.004) and serum ferritin [Median difference: -1091.9, P<0.001), between survivors and non-survivors, respectively. Conclusion: The results revealed that some hematological and endocrine factors play an important role in prognosis of COVID-19 infection. However, further studies with a larger population are required to clarify the exact effects of COVID-19 on the endocrine system.

Evaluation of Impact of a Pharmacist-Led Educational Campaign on Disease Knowledge, Practices and Medication Adherence for Type-2 Diabetic Patients: A Prospective Pre- and Post-Analysis

Type 2 Diabetes mellitus is a major public health concern with an alarming global growth rate. According to the World Health Organization (WHO), Saudi Arabia ranks seventh in the world and second in the Middle East for the largest estimated burden of diabetic cases. Evidence shows that pharmacist-led care programs can be beneficial for the effective treatment of diabetes mellitus. Current study was aimed to evaluate the impact of Pharmacist-Based Diabetic Intervention (PDIM) for Type 2 Diabetes patients on knowledge of the disease, adherence to medications and self-care practices during the first wave of COVID-19. A multi-arm pre-post study was conducted among type 2 diabetic patients from April to October 2021 in Sakaka, Saudi Arabia. Patients were randomly divided into an intervention and a control group. The intervention group received the PDIM, whereas the control group only received the usual care. The pharmacist-based diabetes intervention model consisted of a diabetic educational module and medication improvement strategies. Furthermore, the intervention group also received specific telepharmacy services (calls, messages or emails) to address their medication-related problems, inquire about medication adherence and follow-up. At the end of six months, disease knowledge, self-care practices, and medication adherence score were analyzed. Furthermore, HbA1c and lipid profile were also compared. A total of 109 patients were included in the study. A significant difference was observed in the knowledge score between the intervention and control group (16.89 ± 2.01 versus 15.24 ± 2.03, p-value < 0.001). Similarly, self-care practices also improved in the intervention group as compared to the control group (4.39 ± 1.10 versus 3.16 ± 0.97, p-value < 0.001). Furthermore, the medication adherence and HbA1c significantly improved during between the group analysis (p < 0.05). Our study demonstrates that pharmacist-based diabetes intervention model is effective in improving patients’ knowledge of diabetes, self-care practices, medication adherence and glycemic control.

A web-based survey assessing perceived changes in diet, physical activity and sleeping behaviours in adults with type 1 and type 2 diabetes during the COVID-19 pandemic in the UK

Background

The COVID-19 pandemic may have contributed to poorer self-management (ie, diet, physical activity and sleep) of diabetes mellitus (DM), which might predispose individuals to more severe COVID-19 outcomes.

Objective

The first objective was to capture perceived changes in diet, physical activity and sleeping during the COVID-19 pandemic in adults with type 1 (T1DM) and type 2 diabetes mellitus (T2DM) in the UK. A second objective was to explore differences between individuals with DM compared with 'no' or 'other' health conditions.

Methods

Participants aged >18 years were selected by convenience. Individuals subscribed to the Diabetes.co.uk community were sent a web-based survey including questions about demographics and health, followed by 5-point Likert-type scale questions relating to lifestyle-related behaviours during the COVID-19 pandemic. Individuals were grouped by diagnosis of DM, 'other' or 'no' health condition and responses were compared.

Results

4764 individuals responded, with 2434 (51.3%) being female and 1550 (32.6%) aged 55-64 years. T2DM (2974; 62.7%), hypertension (2147; 45.2%) and T1DM (1299; 27.4%) were most frequently reported. Compared with T1DM, 'no' or 'other' health conditions, respondents with T2DM reported making a less conscious effort to get outside and exercise daily (p<0.001) and spending no time outdoors (p=0.001). Weight loss was more frequently reported in respondents with T2DM (p=0.005). More individuals with T2DM reported consuming convenience foods (p=0.012) and sugary foods (p=0.021), yet eating more fresh foods (p=0.001) and drinking less alcohol than normal (p<0.001). More individuals with T1DM and T2DM reported worse sleep quality (p=0.004).

Conclusions

Our study highlighted important differences in lifestyle by individuals with T1DM, T2DM, other and no health conditions in relation to the COVID-19 pandemic. Establishing surveillance systems and conducting repeated assessments are required to analyse how the situation shifted over time and whether adverse collateral effects of the pandemic were sustained in those with chronic health conditions.

Nonmedical Interventions For Type 2 Diabetes: Evidence, Actionable Strategies, And Policy Opportunities

This systematic review identified studies of nonmedical interventions designed to reduce risk for and improve clinical outcomes for type 2 diabetes. Specifically, this review sought to identify interventions that target structural racism and social determinants of health. To be included, studies were published in English; published between database initiation and January 2022; conducted in the United States; measured an intervention effect using a clinical trial, quasi-experimental, or pre-post design; included a population of adults at risk for or with type 2 diabetes; and targeted hemoglobin A1c levels, blood pressure, lipids, self-care, or quality of life as outcomes. The findings of our review indicate that interventions with targeted, multicomponent designs that combine both medical and nonmedical approaches can reduce risk for and improve clinical outcomes for type 2 diabetes. HbA1c levels improved significantly with the use of food supplementation with referral and diabetes support; the use of financial incentives with education and skills training; the use of housing relocation with counseling support; and the integration of nonmedical interventions into medical care using the electronic medical record. Our findings demonstrate that the literature on nonmedical interventions designed to address relevant social factors and target structural racism is limited. The article offers actionable strategies and identifies policy opportunities for targeting structural inequalities and decreasing social risk among adults with type 2 diabetes.

Clinical management of COVID-19 in hospitals and the community: A snapshot from a medical insurance database in South Africa

BACKGROUND

Little is known about the clinical care, use of medicines, and risk factors associated with mortality among the population with private health insurance with COVID-19 in South Africa.

METHODS

This was a retrospective cross-sectional study using claims data of patients with confirmed COVID-19. Sociodemographics, comorbidities, severity, concurrent/progressive comorbidity, drug treatment, and outcomes were extracted from administrative data. Univariate and multivariate logistic regression models were used to explore the risk factors associated with in-hospital death.

RESULTS

This study included 154,519 patients with COVID-19; only 24% were categorized as severe because they received in-hospital care. Antibiotic (42.8%) and steroid (30%) use was high in this population. After adjusting for known comorbidities, concurrent/progressive diagnosis of the following conditions were associated with higher in-hospital death odds: acute respiratory distress syndrome (aOR = 1.55; 95% CI = 1.44-1.68), septic shock (aOR = 1.55; 95% CI = 2.00-4.12), pneumonia (aOR = 1.35; 95% CI = 1.24-1.47), acute renal failure (aOR = 2.30; 95% CI = 2.09-2.5), and stroke (aOR = 2.09; 95% CI = 1.75-2.49). The use of antivirals (aOR = 0.47; 95% CI= 0.40-0.54), and/or steroids (aOR = 0.46; 95% CI = 0.43-0.50) were associated with decreased death odds. The use of antibiotics in-hospital was not associated with increased survival (aOR = 0.97; 95% CI = 0.91-1.04).

CONCLUSIONS

Comorbidities remain significant risk factors for death mediated by organ failure. The use of antibiotics did not change the odds of death, suggesting inappropriate use.

SELF-CARE AND QUALITY OF LIFE PEOPLE WITH TYPE 2 DIABETES DURING THE COVID-19: CROSS-SECTIONAL STUDY

Background

. Social distancing and emotional stress during the COVID-19 pandemic have affected the self-care management of type 2 diabetes mellitus patients. There was a shift in self-care management during the COVID-19 that will impact the quality of life of type 2 diabetes mellitus (T2DM) patients. This study aims to determine the relationship between self-care management and quality of life of T2DM patients in the community health center during the lockdown.

Methods

. This research was a quantitative study with a cross-sectional study approach. The sampling technique used a non-probability sampling with total sampling of 89 respondents. The instruments used in this study were WHO Quality of Life-BREF and Summary of Diabetes Self Care Activities.

Findings

The results showed a significant relationship between self-care management and the quality of life of people with type 2 diabetes (p = 0.000) with moderate strength and a positive direction.

Conclusions

People with diabetes coped well with their disease management which was supported by family and health professionals. Sustainable health education and motivation for patients and families are important so that the patients always carry out self-care management to improve the quality of life of type 2 diabetes mellitus patients.

The COVID-19 Vaccination Behavior and Correlates in Diabetic Patients: A Health Belief Model Theory-Based Cross-Sectional Study in China, 2021

The population with diabetes is more susceptible to severe acute respiratory syndrome-associated coronavirus (SARS-CoV)-2, and have a significantly higher coronavirus disease-2019 (COVID-19) mortality rate. Previous studies have shown low willingness for the COVID-19 vaccination, and there are limited reports on the behavior and relevance of the COVID-19 vaccination. This study aimed to determine the uptake behavior and associated factors of the COVID-19 vaccine. In our cross-sectional questionnaire-based clinical study, 645 diabetes patients affiliated with two affiliated hospitals of Changzhi Medical College completed the questionnaire between June to October 2021. The health belief model (HBM) was used in examining factors influencing vaccination behavior. After adjusting for covariates with significant differences in social background characteristics, a multivariable logistic regression was used to determine predictors related to uptake in COVID-19 vaccination. A total of 162 vaccinated and 483 unvaccinated eligible diabetic patients were recruited. Patients who believed that the COVID-19 syndrome is severe (aOR3.67, 95%CI 1.88−7.17; p < 0.001), believe that vaccination can significantly reduce the risk of SARS-Cov-2 infection (aOR3.48, 95%CI 1.80−6.73; p < 0.001), believe that vaccination is beneficial to themselves and others (aOR 4.53, 95%CI 1.71−11.99; p = 0.002), think that relatives’ vaccination status has a positive impact on their vaccination behavior (aOR 5.68, 95%CI 2.83−11.39; p < 0.001), and were more likely to be vaccinated; worrying about the adverse health effects of COVID-19 vaccination (aOR 0.18, 95%CI 0.09−0.35; p < 0.001) was negatively correlated with COVID-19 vaccination behavior. Health care workers should provide targeted informative interventions based on the safety and protective effects theory of HBM to improve vaccination behavior in patients with diabetes.

An Overview of Systematic Reviews of the Role of Vitamin D on Inflammation in Patients with Diabetes and the Potentiality of Its Application on Diabetic Patients with COVID-19

Almost two years have passed since the outbreak reported for the first time in Wuhan of coronavirus disease 2019 (COVID-19), due to severe acute respiratory syndrome (SARS)-CoV-2 coronavirus, rapidly evolved into a pandemic. This infectious disease has stressed global health care systems. The mortality rate is higher, particularly in elderly population and in patients with comorbidities such as hypertension, diabetes mellitus, cardiovascular disease, chronic lung disease, chronic renal disease, and malignancy. Among them, subjects with diabetes have a high risk of developing severe form of COVID-19 and show increased mortality. How diabetes contributes to COVID-19 severity remains unclear. It has been hypothesized that it may be correlated with the effects of hyperglycemia on systemic inflammatory responses and immune system dysfunction. Vitamin D (VD) is a modulator of immune-response. Data from literature showed that vitamin D deficiency in COVID-19 patients increases COVID-19 severity, likely because of its negative impact on immune and inflammatory responses. Therefore, the use of vitamin D might play a role in some aspects of the infection, particularly the inflammatory state and the immune system function of patients. Moreover, a piece of evidence highlighted a link among vitamin D deficiency, obesity and diabetes, all factors associated with COVID-19 severity. Given this background, we performed an overview of the systematic reviews to assess the association between vitamin D supplementation and inflammatory markers in patients with diabetes; furthermore, vitamin D’s possible role in COVID-19 patients was assessed as well. Three databases, namely MEDLINE, PubMed Central and the Cochrane Library of Systematic Reviews, were reviewed to retrieve the pertinent data. The aim of this review is to provide insight into the recent advances about the molecular basis of the relationship between vitamin D, immune response, inflammation, diabetes and COVID-19.

SARS-CoV-2 Infections, Impaired Tissue, and Metabolic Health: Pathophysiology and Potential Therapeutics

The SARS-CoV-2 enters the human airways and comes into contact with the mucous membranes lining the mouth, nose, and eyes. The virus enters the healthy cells and uses cell machinery to make several copies of the virus. Critically ill patients infected with SARS-CoV-2 may have damaged lungs, air sacs, lining, and walls. Since COVID-19 causes cytokine storm, it damages the alveolar cells of the lungs and fills them with fluid, making it harder to exchange oxygen and carbon dioxide. The SARS-CoV-2 infection causes a range of complications, including mild to critical breathing difficulties. It has been observed that older people suffering from health conditions like cardiomyopathies, nephropathies, metabolic syndrome, and diabetes instigate severe symptoms. Many people who died due to COVID-19 had impaired metabolic health [IMH], characterized by hypertension, dyslipidemia, and hyperglycemia, i.e., diabetes, cardiovascular system, and renal diseases making their retrieval challenging. Jeopardy stresses for increased mortality from COVID-19 include older age, COPD, ischemic heart disease, diabetes mellitus, and immunosuppression. However, no targeted therapies are available as of now. Almost two-thirds of diagnosed coronavirus patients had cardiovascular diseases and diabetes, out of which 37% were under 60. The NHS audit revealed that with a higher expression of ACE-2 receptors, viral particles could easily bind their protein spikes and get inside the cells, finally causing COVID-19 infection. Hence, people with IMH are more prone to COVID-19 and, ultimately, comorbidities. This review provides enormous information about tissue [lungs, heart and kidneys] damage, pathophysiological changes, and impaired metabolic health of SARS-CoV-2 infected patients. Moreover, it also designates the possible therapeutic targets of COVID-19 and drugs which can be used against these targets.

Diabetes Mellitus during the Pandemic Covid-19: Prevalence, Pathophysiology, Mechanism, and Management: An updated overview

BACKGROUND

Diabetes mellitus (DM) is among the most frequently reported comorbidities in patients tainted with the pandemic coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). With a high pervasiveness of diabetes mellitus, there is an urgency to understand the special aspects of COVID-19 in hyperglycemic patients. Diabetic patients are at higher risk than the general population of viral or bacterial infections, thus require special attention since diabetes is linked with severe, critical, and lethal modes of COVID-19.

OBJECTIVE

The objective of this study was to focus on epidemiology, pathophysiology, mechanism, and management of DM with COVID-19.

METHODS

The search was carried out on databases portals such as Pubmed, EMBASE, Google Scholar, and CINAHL with the keywords, i.e., COVID-19, coronavirus, SARS-CoV-2, diabetes, covid-19, etc. Result: DM and COVID-19 disease conditions can impact each other in terms of clinical progression and outcome. Available laboratory/clinical observations suggest that hyperglycemia-induced immune dysfunction, inflated lactate grades, and cytokines storm may play critical roles in the seriousness of COVID-19 in patients with diabetes; however, the exact mechanisms linking diabetes and COVID-19 remain to be further clarified.

CONCLUSION

Standards to constrain the disease spread at the individual and community level are the key to extenuate the speedily rising pandemic, while definitive treatment, like plasma therapy, chemoprophylaxis, or vaccine for COVID-19, has yet to be discovered.

County-level societal predictors of COVID-19 cases and deaths changed through time in the United States: A longitudinal ecological study

People of different racial/ethnic backgrounds, demographics, health, and socioeconomic characteristics have experienced disproportionate rates of infection and death due to COVID-19. This study tests if and how county-level rates of infection and death have changed in relation to societal county characteristics through time as the pandemic progressed. This longitudinal study sampled monthly county-level COVID-19 case and death data per 100,000 residents from April 2020 to March 2022, and studied the relationships of these variables with racial/ethnic, demographic, health, and socioeconomic characteristics for 3125 or 97.0% of U.S. counties, accounting for 96.4% of the U.S. population. The association of all county-level characteristics with COVID-19 case and death rates changed significantly through time, and showed different patterns. For example, counties with higher population proportions of Black, Native American, foreign-born non-citizen, elderly residents, households in poverty, or higher income inequality suffered disproportionately higher COVID-19 case and death rates at the beginning of the pandemic, followed by reversed, attenuated or fluctuating patterns, depending on the variable. Patterns for counties with higher White versus Black population proportions showed somewhat inverse patterns. Counties with higher female population proportions initially had lower case rates but higher death rates, and case and death rates become more coupled and fluctuated later in the pandemic. Counties with higher population densities had fluctuating case and death rates, with peaks coinciding with new variants of COVID-19. Counties with a greater proportion of university-educated residents had lower case and death rates throughout the pandemic, although the strength of this relationship fluctuated through time. This research clearly shows that how different segments of society are affected by a pandemic changes through time. Therefore, targeted policies and interventions that change as a pandemic unfolds are necessary to mitigate its disproportionate effects on vulnerable populations, particularly during the first six months of a pandemic.

Diabetes in COVID-19 patients: challenges and possible management strategies

Background

The recent pandemic of coronavirus disease 19 (COVID-19) has been causing intense stress among the global population. In the case of hospitalized and ICU-admitted COVID-19 patients with comorbidities, it has been observed that a major portion of them are diabetic. Therefore, researchers had indicated a link between diabetes mellitus (DM) and COVID-19. Furthermore, DM is a potential risk factor for the severity of COVID-19 cases. Thus, in this study, the correlation existing between diabetic patients and COVID-19 was summarized.

Main body of the abstract

Diabetic patients have a weaker immune system, less viral clearance rate, malfunctions of metabolic activity due to their high blood glucose level, and other associated problems. This does not increase the susceptibility for the patients to be infected with COVID-19. However, the severity of COVID-19 can worsen due to the comorbidity of DM.

Short conclusion

Proper management, appropriate use of drugs that do not increase the ACE2 expression, lowering blood glucose level, decreasing the susceptibility of SARS-CoV-2, and maintaining a healthy lifestyle could be effective.

ACE2 function in the pancreatic islet: Implications for relationship between SARS-CoV-2 and diabetes

The molecular link between SARS-CoV-2 infection and susceptibility is not well understood. Nonetheless, a bi-directional relationship between SARS-CoV-2 and diabetes has been proposed. The angiotensin-converting enzyme 2 (ACE2) is considered as the primary protein facilitating SARS-CoV and SARS-CoV-2 attachment and entry into the host cells. Studies suggested that ACE2 is expressed in the endocrine cells of the pancreas including beta cells, in addition to the lungs and other organs; however, its expression in the islets, particularly beta cells, has been met with some contradiction. Importantly, ACE2 plays a crucial role in glucose homoeostasis and insulin secretion by regulating beta cell physiology. Given the ability of SARS-CoV-2 to infect human pluripotent stem cell-derived pancreatic cells in vitro and the presence of SARS-CoV-2 in pancreatic samples from COVID-19 patients strongly hints that SARS-CoV-2 can invade the pancreas and directly cause pancreatic injury and diabetes. However, more studies are required to dissect the underpinning molecular mechanisms triggered in SARS-CoV-2-infected islets that lead to aggravation of diabetes. Regardless, it is important to understand the function of ACE2 in the pancreatic islets to design relevant therapeutic interventions in combatting the effects of SARS-CoV-2 on diabetes pathophysiology. Herein, we detail the function of ACE2 in pancreatic beta cells crucial for regulating insulin sensitivity, secretion, and glucose metabolism. Also, we discuss the potential role played by ACE2 in aiding SARS-COV-2 entry into the pancreas and the possibility of ACE2 cooperation with alternative entry factors as well as how that may be linked to diabetes pathogenesis.

The Potential Role of Vitamin B12 in the Prevention of COVID-19 Complications: A Narrative Review

The elderly are more prone to mortality from COVID-19 infection, as they are susceptible to develop acute respiratory distress syndrome (ARDS). For COVID-19 patients with ARDS caused by sepsis or septic shock, high-dose parenteral vitamin B12 appears to be a potential new treatment option. Vitamin B12 may play a substantial impact in COVID-19 morbidity and mortality reduction owing to its function in DNA synthesis, cellular control, improvement of anti-inflammatory immune responses, and decrease of pro-inflammatory responses. This review aims to assess the functional role of Vitamin B12 in COVID-19 in terms of its immunomodulatory effect, role in cellular and humoral immunity and maintaining the gut microbe homeostasis. From data inception to June 2021, accessible electronic databases were searched for research/review articles reporting on the function of Vitamin B12 in COVID-19. Scopus, Web of Science, PubMed, WHO worldwide research on COVID-19 and the clinical trials registration “https://clinicaltrials.gov/” were used to conduct the systematic search by using keywords: “COVID-19 and “Vitamin B12”. Also, based on these outcomes, it can be concluded that Vitamin B12 may have a potential role in preventing COVID-19 complications. Further, studies evaluating the role of Vitamin B12 in COVID-19 may open a new array of ideas on the optimal and the well-tolerated dose and timing of its administration in COVID-19 patients.

Correlation Between CT Severity Scoring and Diabetes Mellitus in Patients With COVID-19 Infection

Background and objective

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19), was first identified in Wuhan, China in December 2019. Since then, It has spread across multiple countries and was declared a pandemic by WHO in March 2020. Patients with underlying diabetes mellitus (DM) are deemed at-risk for developing severe COVID-19 infection. In light of this, we aimed to evaluate the correlation between DM and chest CT severity scores (CTSS) in COVID-19 patients.

Methods

This was a hospital-based descriptive, analytical retrospective study conducted at our tertiary care hospital. A quantitative severity score was calculated among 220 patients with COVID-19 infection based on the degree of lung lobe involvement on CT chest scans. Based on CTSS, the patients were classified into groups of mild, moderate, and severe lung involvement. The association between DM and CTSS was evaluated using the chi-square test.

Results

The severity of lung involvement was higher among COVID-19 patients with a co-diagnosis of DM (29.3%) compared to those without DM (11.7%). This association of severe lung involvement with DM was statistically significant (p=0.002).

Conclusion

Based on our findings, diabetic patients are at an increased risk of developing the severe form of COVID-19 with a higher CT lung involvement score compared to non-diabetic patients.

COVID-19 and Diabetes: A Comprehensive Review of Angiotensin Converting Enzyme 2, Mutual Effects and Pharmacotherapy

The potential relationship between diabetes and COVID-19 has been evaluated. However, new knowledge is rapidly emerging. In this study, we systematically reviewed the relationship between viral cell surface receptors (ACE2, AXL, CD147, DC-SIGN, L-SIGN and DPP4) and SARS-CoV-2 infection risk, and emphasized the implications of ACE2 on SARS-CoV-2 infection and COVID-19 pathogenesis. Besides, we updated on the two-way interactions between diabetes and COVID-19, as well as the treatment options for COVID-19 comorbid patients from the perspective of ACE2. The efficacies of various clinical chemotherapeutic options, including anti-diabetic drugs, renin-angiotensin-aldosterone system inhibitors, lipid-lowering drugs, anticoagulants, and glucocorticoids for COVID-19 positive diabetic patients were discussed. Moreover, we reviewed the significance of two different forms of ACE2 (mACE2 and sACE2) and gender on COVID-19 susceptibility and severity. This review summarizes COVID-19 pathophysiology and the best strategies for clinical management of diabetes patients with COVID-19.

Multi-Mechanistic In Vitro Evaluation of Antihyperglycemic, Antioxidant and Antiglycation Activities of Three Phenolic-Rich Indian Red Rice Genotypes and In Silico Evaluation of Their Phenolic Metabolites

The study evaluated the antidiabetic potential of three traditional Indian red rice genotypes/RR (Kattuyanam/KA, Chennangi/CH & Karungkuruvai/KU) using a combination of in vitro, metabolomics (Quadrupole-Time of Flight-Liquid chromatography-Mass spectrometry/Q-TOF-LC-MS/MS), and in silico techniques. In terms of antihyperglycemic potential, KA exhibited the highest inhibitory activity against α-amylase; CH against α-glucosidase; and KU against DPPIV and PTP1B enzymes. KA exhibited the highest antioxidant activity (DPPH, FRAP, and ABTS) and greater inhibition of protein glycation compared to other RR indicating its potential to mitigate diabetic complications. The metabolomic analysis confirmed the presence of 99 phenolics in the sample extracts (KU-71, KA-70, CH-68). Molecular docking studies revealed seven metabolites to be good inhibitors of the four target enzymes and activators of insulin receptor substrate/IRS. The antihyperglycemic and oxidation-glycation reduction composite index revealed KA to have the highest overall antidiabetic potential. Hence, the RR could be utilized in functional foods with a multi-barrelled strategy for diabetes prevention/management.

Consumer and food changes in Mexican households during maximal contingency in the COVID-19 pandemic

The Coronavirus disease (COVID-19) rapidly expanded throughout the world affecting human life in the health, social, cultural, economic and environmental aspects. On an international scale, some works have addressed the importance of the agri-food topic during the pandemic, especially about food consumption. The Mexican case is particularly relevant since it is the country with the highest proportion of overweight people. The objective of this work was to know the choices and consumption of foods in Mexican households during the period of maximal contingency. An on-line questionnaire was responded by 867 volunteers who answered about the aspects taken in consideration in their food consumption, the changes in the consumption of certain foods and the socioeconomic and health aspects of the family. Four groups were identified: “Budget consumers”, “Hedonics”, “Price conscious” and “Conscious”. Changes in the attitudes to food are not related to the health state, overweight, or family size; but are related to income level of households and above all, to the education level. Results show an opportunity to change for healthier food habits in Mexico, mainly in the most vulnerable sectors.

Transcriptomic studies revealed pathophysiological impact of COVID-19 to predominant health conditions

Despite the association of prevalent health conditions with coronavirus disease 2019 (COVID-19) severity, the disease-modifying biomolecules and their pathogenetic mechanisms remain unclear. This study aimed to understand the influences of COVID-19 on different comorbidities and vice versa through network-based gene expression analyses. Using the shared dysregulated genes, we identified key genetic determinants and signaling pathways that may involve in their shared pathogenesis. The COVID-19 showed significant upregulation of 93 genes and downregulation of 15 genes. Interestingly, it shares 28, 17, 6 and 7 genes with diabetes mellitus (DM), lung cancer (LC), myocardial infarction and hypertension, respectively. Importantly, COVID-19 shared three upregulated genes (i.e. MX2, IRF7 and ADAM8) with DM and LC. Conversely, downregulation of two genes (i.e. PPARGC1A and METTL7A) was found in COVID-19 and LC. Besides, most of the shared pathways were related to inflammatory responses. Furthermore, we identified six potential biomarkers and several important regulatory factors, e.g. transcription factors and microRNAs, while notable drug candidates included captopril, rilonacept and canakinumab. Moreover, prognostic analysis suggests concomitant COVID-19 may result in poor outcome of LC patients. This study provides the molecular basis and routes of the COVID-19 progression due to comorbidities. We believe these findings might be useful to further understand the intricate association of these diseases as well as for the therapeutic development.

A Fractional Ordered COVID-19 Model Incorporating Comorbidity and Vaccination

The primary goal of this research is to investigate COVID-19 transmission patterns in West Bengal, India in 2021; the first Coronavirus illness (COVID-19) in West Bengal was revealed on 17 March 2020. We employed the modified Susceptible-Asymptomatic-Vaccinated-Comorbidity-Infectious-Recovered (SAVICR) compartmental model as part of fractional orders because of the uncertainty created by the limited Coronavirus (COVID-19) information. In this article, two sub-compartments (Normal Infected and Infected with Co-morbidity) has been considered with vaccinated class, which is relevant in the present situation. We have studied the dynamical analysis of the system and also studied sensitivity of the parameters for West Bengal framework. We have also considered an optimal control problem taking social distancing (non-pharmaceutical treatments) as a control parameter along with vaccination.

COVID-19 Mechanisms in the Human Body-What We Know So Far

More than one and a half years have elapsed since the commencement of the coronavirus disease 2019 (COVID-19) pandemic, and the world is struggling to contain it. Being caused by a previously unknown virus, in the initial period, there had been an extreme paucity of knowledge about the disease mechanisms, which hampered preventive and therapeutic measures against COVID-19. In an endeavor to understand the pathogenic mechanisms, extensive experimental studies have been conducted across the globe involving cell culture-based experiments, human tissue organoids, and animal models, targeted to various aspects of the disease, viz., viral properties, tissue tropism and organ-specific pathogenesis, involvement of physiological systems, and the human immune response against the infection. The vastly accumulated scientific knowledge on all aspects of COVID-19 has currently changed the scenario from great despair to hope. Even though spectacular progress has been made in all of these aspects, multiple knowledge gaps are remaining that need to be addressed in future studies. Moreover, multiple severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants have emerged across the globe since the onset of the first COVID-19 wave, with seemingly greater transmissibility/virulence and immune escape capabilities than the wild-type strain. In this review, we narrate the progress made since the commencement of the pandemic regarding the knowledge on COVID-19 mechanisms in the human body, including virus-host interactions, pulmonary and other systemic manifestations, immunological dysregulations, complications, host-specific vulnerability, and long-term health consequences in the survivors. Additionally, we provide a brief review of the current evidence explaining molecular mechanisms imparting greater transmissibility and virulence and immune escape capabilities to the emerging SARS-CoV-2 variants.

Role of toll-like receptors in modulation of cytokine storm signaling in SARS-CoV-2-induced COVID-19

Balanced immune regulation is crucial for recognizing an invading pathogen, its killing, and elimination. Toll‐like receptors (TLRs) are the key regulators of the innate immune system. It helps in identifying between self and nonself‐molecule and eventually eliminates the nonself. Endosomal TLR, mainly TLR3, TLR7, TLR8, and membrane‐bound TLR4, has a role in the induction of cytokine storms. TLR7/8 recognizes the ssRNA SARS‐COV‐2 and when it replicates to dsRNA, it is recognized by TLR3 and drives the TRIF‐mediated inflammatory signaling like NF‐κB, MAPK. Such signaling leads to significant transcription and translation of pro‐inflammatory genes, releasing inflammatory molecules into the systemic circulation, causing an imbalance in the system. So, whenever an imbalance occurs, a surge in the pro‐inflammatory mediators is observed in the blood, including cytokines like interleukin (IL)‐2, IL‐4, IL‐6, IL‐1β, IL‐8, interferon (IFN)‐γ, tumor necrosis factor (TNF)‐α. IL‐6 and IL‐1β are one of the driving factors for bringing the cytokine storm into the systemic circulation, which migrates into the other organs, causing multiple organ failures leading to the death of the individual with severe illness.

The imbalanced and hyper responsive immune system leads to a surge leading to death of the infected patients in COVID‐19.

It has been observed that cytokine surge is TLR induced, mainly through activation of TLR3, TLR4, TLR7, TLR8 receptors.

The cytokine storm migrates into the other organ through systemic circulation. The inflammation and the organ damage occur due to the TLR mediated NF‐κB, MAPK pathway. Hence blocking these specific TLRs may alleviate the chance of SARS‐COV‐2 infection.

Diabetes mellitus and COVID-19: Understanding the association in light of current evidence

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections have posed a problematic healthcare situation worldwide since December 2019. Diabetes mellitus is associated with an increased risk and severity of coronavirus disease 2019 (COVID-19). While interacting with various other risk factors, high blood sugar was found to reduce immunity and increase the replication of SARS-CoV-2. Oxidative stress and the release of pro-inflammatory cytokines are greater in diabetic individuals than in healthy people, worsening the outcome of SARS-CoV-2 infection in diabetics. Increased expression of furin and angiotensin converting enzyme 2 (ACE-2) receptor in the hyperglycemic environment may promote the entry of SARS-CoV-2 in the host cell. COVID-19 infection primarily modulates immune and inflammatory responses, and may cause a cytokine storm, resulting in possible lethal outcomes in diabetics. An experimental report suggests that ACE expressed in the pancreas and the SARS-CoV-2 virus invariably destroy β-cells which contain ACE-2 receptors and results in acute diabetes. Moreover, COVID-19 also causes hyperglycemia in an individual with diabetes which may be related to insulin resistance and destruction of β-cells during SARS-CoV-2 infection. Early observations also suggest a correlation between oral hypoglycemic agents and the risk of COVID-19. This review focused on the possible cause and mechanism involved in SARS-CoV-2 infection in diabetics and the role of antidiabetic drugs in COVID-19.

Uncovering Clinical Risk Factors and Predicting Severe COVID-19 Cases Using UK Biobank Data: Machine Learning Approach

Background

COVID-19 is a major public health concern. Given the extent of the pandemic, it is urgent to identify risk factors associated with disease severity. More accurate prediction of those at risk of developing severe infections is of high clinical importance.

Objective

Based on the UK Biobank (UKBB), we aimed to build machine learning models to predict the risk of developing severe or fatal infections, and uncover major risk factors involved.

Methods

We first restricted the analysis to infected individuals (n=7846), then performed analysis at a population level, considering those with no known infection as controls (ncontrols=465,728). Hospitalization was used as a proxy for severity. A total of 97 clinical variables (collected prior to the COVID-19 outbreak) covering demographic variables, comorbidities, blood measurements (eg, hematological/liver/renal function/metabolic parameters), anthropometric measures, and other risk factors (eg, smoking/drinking) were included as predictors. We also constructed a simplified (lite) prediction model using 27 covariates that can be more easily obtained (demographic and comorbidity data). XGboost (gradient-boosted trees) was used for prediction and predictive performance was assessed by cross-validation. Variable importance was quantified by Shapley values (ShapVal), permutation importance (PermImp), and accuracy gain. Shapley dependency and interaction plots were used to evaluate the pattern of relationships between risk factors and outcomes.

Results

A total of 2386 severe and 477 fatal cases were identified. For analyses within infected individuals (n=7846), our prediction model achieved area under the receiving-operating characteristic curve (AUC–ROC) of 0.723 (95% CI 0.711-0.736) and 0.814 (95% CI 0.791-0.838) for severe and fatal infections, respectively. The top 5 contributing factors (sorted by ShapVal) for severity were age, number of drugs taken (cnt_tx), cystatin C (reflecting renal function), waist-to-hip ratio (WHR), and Townsend deprivation index (TDI). For mortality, the top features were age, testosterone, cnt_tx, waist circumference (WC), and red cell distribution width. For analyses involving the whole UKBB population, AUCs for severity and fatality were 0.696 (95% CI 0.684-0.708) and 0.825 (95% CI 0.802-0.848), respectively. The same top 5 risk factors were identified for both outcomes, namely, age, cnt_tx, WC, WHR, and TDI. Apart from the above, age, cystatin C, TDI, and cnt_tx were among the top 10 across all 4 analyses. Other diseases top ranked by ShapVal or PermImp were type 2 diabetes mellitus (T2DM), coronary artery disease, atrial fibrillation, and dementia, among others. For the “lite” models, predictive performances were broadly similar, with estimated AUCs of 0.716, 0.818, 0.696, and 0.830, respectively. The top ranked variables were similar to above, including age, cnt_tx, WC, sex (male), and T2DM.

Conclusions

We identified numerous baseline clinical risk factors for severe/fatal infection by XGboost. For example, age, central obesity, impaired renal function, multiple comorbidities, and cardiometabolic abnormalities may predispose to poorer outcomes. The prediction models may be useful at a population level to identify those susceptible to developing severe/fatal infections, facilitating targeted prevention strategies. A risk-prediction tool is also available online. Further replications in independent cohorts are required to verify our findings.

Reliable and Interpretable Mortality Prediction With Strong Foresight in COVID-19 Patients: An International Study From China and Germany

Cohort-independent robust mortality prediction model in patients with COVID-19 infection is not yet established. To build up a reliable, interpretable mortality prediction model with strong foresight, we have performed an international, bi-institutional study from China (Wuhan cohort, collected from January to March) and Germany (Würzburg cohort, collected from March to September). A Random Forest-based machine learning approach was applied to 1,352 patients from the Wuhan cohort, generating a mortality prediction model based on their clinical features. The results showed that five clinical features at admission, including lymphocyte (%), neutrophil count, C-reactive protein, lactate dehydrogenase, and α-hydroxybutyrate dehydrogenase, could be used for mortality prediction of COVID-19 patients with more than 91% accuracy and 99% AUC. Additionally, the time-series analysis revealed that the predictive model based on these clinical features is very robust over time when patients are in the hospital, indicating the strong association of these five clinical features with the progression of treatment as well. Moreover, for different preexisting diseases, this model also demonstrated high predictive power. Finally, the mortality prediction model has been applied to the independent Würzburg cohort, resulting in high prediction accuracy (with above 90% accuracy and 85% AUC) as well, indicating the robustness of the model in different cohorts. In summary, this study has established the mortality prediction model that allowed early classification of COVID-19 patients, not only at admission but also along the treatment timeline, not only cohort-independent but also highly interpretable. This model represents a valuable tool for triaging and optimizing the resources in COVID-19 patients.

Blood glucose regulation in context of infection

The immune and endocrine systems collectively control homeostasis in the body. The endocrine system ensures that values of essential factors and nutrients such as glucose, electrolytes and vitamins are maintained within threshold values. The immune system resolves local disruptions in tissue homeostasis, caused by pathogens or malfunctioning cells. The immediate goals of these two systems do not always align. The immune system benefits from optimal access to nutrients for itself and restriction of nutrient availability to all other organs to limit pathogen replication. The endocrine system aims to ensure optimal nutrient access for all organs, limited only by the nutrients stores that the body has available. The actual state of homeostatic parameters such as blood glucose levels represents a careful balance based on regulatory signals from the immune and endocrine systems. This state is not static but continuously adjusted in response to changes in the current metabolic needs of the body, the amount of resources it has available and the level of threats it encounters. This balance is maintained by the ability of the immune and endocrine systems to interact and co-regulate systemic metabolism. In context of metabolic disease, this system is disrupted, which impairs functionality of both systems. The failure of the endocrine system to retain levels of nutrients such as glucose within threshold values impairs functionality of the immune system. In addition, metabolic stress of organs in context of obesity is perceived by the immune system as a disruption in local homeostasis, which it tries to resolve by the excretion of factors which further disrupt normal metabolic control. In this chapter, we will discuss how the immune and endocrine systems interact under homeostatic conditions and during infection with a focus on blood glucose regulation. In addition, we will discuss how this system fails in the context of metabolic disease.

Influenza co-infection associated with severity and mortality in COVID-19 patients

BACKGROUND

In COVID-19 patients, undetected co-infections may have severe clinical implications associated with increased hospitalization, varied treatment approaches and mortality. Therefore, we investigated the implications of viral and bacterial co-infection in COVID-19 clinical outcomes.

METHODS

Nasopharyngeal samples were obtained from 48 COVID-19 patients (29% ICU and 71% non-ICU) and screened for the presence of 24 respiratory pathogens using six multiplex PCR panels.

RESULTS

We found evidence of co-infection in 34 COVID-19 patients (71%). Influenza A H1N1 (n = 17), Chlamydia pneumoniae (n = 13) and human adenovirus (n = 10) were the most commonly detected pathogens. Viral co-infection was associated with increased ICU admission (r = 0.1) and higher mortality (OR 1.78, CI = 0.38-8.28) compared to bacterial co-infections (OR 0.44, CI = 0.08-2.45). Two thirds of COVID-19 critically ill patients who died, had a co-infection; and Influenza A H1N1 was the only pathogen for which a direct relationship with mortality was seen (r = 0.2).

CONCLUSIONS

Our study highlights the importance of screening for co-infecting viruses in COVID-19 patients, that could be the leading cause of disease severity and death. Given the high prevalence of Influenza co-infection in our study, increased coverage of flu vaccination is encouraged to mitigate the transmission of influenza virus during the on-going COVID-19 pandemic and reduce the risk of severe outcome and mortality.