Fasting blood glucose at admission is an independent predictor for 28-day mortality in patients with COVID-19 without previous diagnosis of diabetes: a multi-centre retrospective study

New Onset Diabetes After COVID 19 (NODAC) is predominantly due to exacerbated Insulin Resistance (IR) rather than beta cell dysfunction: Lessons from tertiary care hospital data during confluence of two epidemics

PURPOSE

To investigate determinants of new onset diabetes after COVID-19 (NODAC) and its recovery at 6 months.

METHODS

This was an observational follow up study conducted from August, 2020 to July, 2023, recruiting patients with preexisting DM and COVID 19 patients with no history of DM. Multivariate regression analysis was used to determine the factors responsible for severity of COVID 19 infection in preexisting DM group. Clinical, laboratory and glycometabolic parameters were estimated at baseline and 6 months in NODAC and euglycemic group to determine the factors responsible for NODAC and its persistence at 6 months.

RESULTS

Of 1310 patients, 855 (65.3%) COVID 19 patients were further divided based on their glycemic status: preexisting DM (19%), NODAC (8.5%) and euglycemia (72.5%). Older age and male gender were independent risk factors for severe COVID 19 disease in patients with preexisting diabetes. Prevalence of NODAC in present study was 8.5%. Patients with NODAC had higher mean fasting blood glucose (FBG), random blood glucose (RBG) and HbA1c at baseline as compared to COVID with euglycemic group with no difference in serum C-peptide levels. Female gender, family history of DM, signs of insulin resistance, higher BMI, WHR, HbA1c, serum insulin levels, FBG and RBG predicted persistence of NODAC at 6 months.

CONCLUSION

Preexisting DM is a risk factor for severe COVID 19 disease. Patients with NODAC have evidence of persistence insulin resistance on follow up, underscoring the need for long term glycemic monitoring.

Glucose Tolerance and the Risk Factors for Transmission in Japanese SARS-CoV-2/WA-1/2020 Epicenter: A Retrospective Study

Purpose

The severe pathogenic ancient-type COVID-19, SARS-CoV-2/WA-1/2020 was the predominant gene variant in early 2020 in Japan, however, its transmissibility was uncertain. The period before the public commenced using any personal protective equipment (PPE) was evaluating to describe the transmissibility of the SARS-CoV-2/WA-1/2020. We analyzed the secondary attack rate (SAR) among close contacts and the risk factor for SAR.

Methods

This retrospective cohort study included a total of 539 patients who were anticipated for the SARS-CoV-2/WA-1/2020 infection at Toho University Medical Center Omori Hospital from February to May 2020. We selected 54 patients with 1) exclude other pathogens infection, 2) include "Three Cs" condition: crowded places between distance< 6 feet, closed spaces indoor and close contact settings involving contact >15min with a person tested positive for SARS-CoV-2/WA-1/2020 without PPE. We evaluated alternative infection risks: the body mass index (BMI) and diabetes (DM) status (non-DM, pre-DM, and DM) as demographic determinants of transmissibility and infectivity of SARS-CoV2/WA-1/2020 cases during the incubation period.

Results

The calculated SAR was 79.3%. BMI was significantly associated with the PCR positivity rate, which was significant in the univariate (CI 95%, 1.02-1.51; P = 0.03) and multivariate (CI 95%, 1.02-1.60; P = 0.03) analyses. Comparing the different BMI groups, the highest BMI group (25.5-35.8 kg/m2) had an elevated risk of SAR compared to the lowest BMI group (14.0-22.8 kg/m2), with an odds ratio of 1.41 (95% CI, 1.02-1.59; P = 0.03). There were no significant differences in the risk of SAR among different DM statuses.

Conclusion

The transmissibility of SARS-CoV2/WA-1/2020 was high (79.3%) among household members without PPE who had "Three Cs" exposure. Although pre-DM and established DM did not confer a risk for transmissibility, higher BMI was associated with an increased risk of SAR.

Trial Registration

UMIN Clinical Trials Registry, UMIN0000 50905.

Effect of fasting plasma glucose level in severe fever and thrombocytopenia syndrome patients without diabetes

Hyperglycemia is correlated with worse in-hospital outcomes in acute infectious diseases such as coronavirus disease 2019 (COVID-19) and severe fever with thrombocytopenia syndrome (SFTS). This study assessed the relationship between fasting plasma glucose (FPG) levels and in-hospital mortality, disease type, and secondary infections among individuals with SFTS without preexisting diabetes. The clinical data and laboratory results upon admission of 560 patients with SFTS without preexisting diabetes meeting the inclusion criteria at Wuhan Union Hospital were collected. FPG levels in surviving patients with SFTS subjects were significantly lower than those in patients with SFTS who had died (P<0.0001). In multivariate Cox regression, high FPG level (≥11.1 mmol/L) was a risk factor independently associated with the in-hospital death of patients with SFTS without preexisting diabetes. Similarly, the FPG levels in general patients with SFTS were significantly lower than those in patients with severe SFTS (P<0.0001). Multivariate logistic regression identified high FPG level (7.0-11.1 mmol/L) as a risk factor independently associated with SFTS severity. While FPG levels were comparable between patients with SFTS with and without secondary infection (P = 0.5521), logistic regression analysis revealed that high FPG levels were not a risk factor for secondary infection in patients with SFTS without preexisting diabetes. High FPG level on admission was an independent predictor of in-hospital death and severe disease in individuals with SFTS without preexisting diabetes. FPG screening upon admission and glycemic control are effective methods for improving the prognosis of patients with SFTS.

Prediction of viral pneumonia based on machine learning models analyzing pulmonary inflammation index scores

OBJECT

To obtain Pulmonary Inflammation Index scores from imaging chest CT and combine it with clinical correlates of viral pneumonia to predict the risk and severity of viral pneumonia using a computer learning model.

METHODS

All patients with suspected viral pneumonia on CT examination admitted to The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University from December 2022 to March 2023 were retrospectively selected. The respiratory viruses were monitored by RT-PCR and categorized into patients with viral pneumonia and those with non-viral pneumonia. The extent of lung inflammation was quantified according to the Pulmonary Inflammation Index score (PII). Information on patient demographics, comorbidities, laboratory tests, pathogenetic testing, and radiological data were collected. Five machine learning models containing Random Forest(RF), Radial Basis Function Neural Network (RBFNN), Support Vector Machine (SVM), K Nearest Neighbour Algorithm (KNN), and Kernel Ridge Regression (KRR) were used to predict the risk of onset and severity of viral pneumonia based on the clinically relevant factors or PII.

RESULTS

Among the five models, the SVM model performed best in ACC (76.75 %), SN (73.99 %), and F1 (72.42 %) and achieved a better area under the receiver operating characteristic curve (ROC) (0.8409) when predicting the risk of developing viral pneumonia. RF had the best overall classification accuracy in predicting the severity of viral pneumonia, especially in predicting pneumonia with a PII classification of grade I, the RF model achieved an accuracy of 98.89%.

CONCLUSION

Machine learning models are valuable in assessing the risk of viral pneumonia. Meanwhile, machine learning models confirm the importance in predicting the severity of viral pneumonia through PII. The establishment of machine learning models for predicting the risk and severity of viral pneumonia promotes the further development of machine learning in the medical field.

Hyperglycaemia and Its Prognostic Value in Patients with COVID-19 Admitted to the Hospital in Lithuania

BACKGROUND AND OBJECTIVES

Increased blood glucose levels atadmission are frequently observed in COVID-19 patients, even in those without pre-existing diabetes. Hyperglycaemia is associated with an increased incidence of severe COVID-19 infection. The aim of this study was to evaluate the association between hyperglycaemia at admission with the need for invasive mechanical ventilation (IMV) and in-hospital mortality in patients without diabetes who were hospitalized for COVID-19 infection.

MATERIALS AND METHODS

This retrospective observational study was conducted at Vilnius University Hospital Santaros Clinics, Lithuania with adult patients who tested positive for severe acute respiratory syndrome coronavirus 2 SARS-CoV-2 and were hospitalized between March 2020 and May 2021. Depersonalized data were retrieved from electronic medical records. Based on blood glucose levels on the day of admission, patients without diabetes were divided into 4 groups: patients with hypoglycaemia (blood glucose below 4.0 mmol/L), patients with normoglycaemia (blood glucose between ≥4.0 mmol/L and <6.1 mmol/L), patients with mild hyperglycaemia (blood glucose between ≥6.1 mmol/L and <7.8 mmol/L), and patients with intermittent hyperglycaemia (blood glucose levels ≥7.8 mmol/L and <11.1 mmol/L). A multivariable binary logistic regression model was created to determine the association between hyperglycaemia and the need for IMV. Survival analysis was performed to assess the effect of hyperglycaemia on outcome within 30 days of hospitalization.

RESULTS

Among 1945 patients without diabetes at admission, 1078 (55.4%) had normal glucose levels, 651 (33.5%) had mild hyperglycaemia, 196 (10.1%) had intermittent hyperglycaemia, and 20 (1.0%) had hypoglycaemia. The oddsratio (OR) for IMV in patients with intermittent hyperglycaemia was 4.82 (95% CI 2.70-8.61, p < 0.001), and the OR was 2.00 (95% CI 1.21-3.31, p = 0.007) in those with mild hyperglycaemia compared to patients presenting normal glucose levels. The hazardratio (HR) for 30-day in-hospital mortality in patients with mild hyperglycaemia was 1.62 (95% CI 1.10-2.39, p = 0.015), while the HR was 3.04 (95% CI 2.01-4.60, p < 0.001) in patients with intermittent hyperglycaemia compared to those with normoglycaemia at admission.

CONCLUSIONS

In COVID-19 patients without pre-existing diabetes, the presence of hyperglycaemia at admission is indicative of COVID-19-induced alterations in glucose metabolism and stress hyperglycaemia. Hyperglycaemia at admission in COVID-19 patients without diabetes is associated with an increased risk of invasive mechanical ventilation and in-hospital mortality. This finding highlights the importance for clinicians to carefully consider and select optimal support and treatment strategies for these patients. Further studies on the long-term consequences of hyperglycaemia in this specific population are warranted.

Hyperglycemia and glucose variability are associated with worse survival in mechanically ventilated COVID-19 patients: the prospective Maastricht Intensive Care Covid Cohort

BACKGROUND

Data on hyperglycemia and glucose variability in relation to diabetes mellitus, either known or unknown in ICU-setting in COVID-19, are scarce. We prospectively studied daily glucose variables and mortality in strata of diabetes mellitus and glycosylated hemoglobin among mechanically ventilated COVID-19 patients.

METHODS

We used linear-mixed effect models in mechanically ventilated COVID-19 patients to investigate mean and maximum difference in glucose concentration per day over time. We compared ICU survivors and non-survivors and tested for effect-modification by pandemic wave 1 and 2, diabetes mellitus, and admission HbA1c.

RESULTS

Among 232 mechanically ventilated COVID-19 patients, 21.1% had known diabetes mellitus, whereas 16.9% in wave 2 had unknown diabetes mellitus. Non-survivors had higher mean glucose concentrations (ß 0.62 mmol/l; 95%CI 0.20-1.06; ß 11.2 mg/dl; 95% CI 3.6-19.1; P = 0.004) and higher maximum differences in glucose concentrations per day (ß 0.85 mmol/l; 95%CI 0.37-1.33; ß 15.3; 95%CI 6.7-23.9; P = 0.001). Effect modification by wave, history of diabetes mellitus and admission HbA1c in associations between glucose and survival was not present. Effect of higher mean glucose concentrations was modified by pandemic wave (wave 1 (ß 0.74; 95% CI 0.24-1.23 mmol/l) ; (ß 13.3; 95%CI 4.3-22.1 mg/dl)) vs. (wave 2 (ß 0.37 (95%CI 0.25-0.98) mmol/l) (ß 6.7 (95% ci 4.5-17.6) mg/dl)).

CONCLUSIONS

Hyperglycemia and glucose variability are associated with mortality in mechanically ventilated COVID-19 patients irrespective of the presence of diabetes mellitus.

Increased risk of poor clinical outcome in COVID-19 patients with diabetes mellitus and in-hospital mortality predictors: A retrospective cohort from a tertiary hospital in Indonesia

AIM

To determine association between diabetes in confirmed cases of COVID-19 and intensive care admission and in-hospital mortality, evaluate several laboratory parameters as mortality predictor and develop predictors of in-hospital mortality among diabetics with COVID-19.

METHODS

This retrospective cohort recruited all cases of COVID-19 hospitalized in Fatmawati General Hospital from March to October 2020. Inclusion criterion was RT-PCR confirmed cases of COVID-19 who aged 18 years and older while exclusion criteria were incomplete medical record or cannot be found and pregnant women.

RESULTS

We enrolled 506 participants to this study with median age of 51 years (IQR:22), female (56.32%), and diabetes (28.46%). Diabetes increased intensive care admission (adjusted OR: 2.57; 95% CI: 3.52-10.43) and in-hospital mortality (adjusted OR: 2.50; 95% CI: 1.61-3.89). In predicting in-hospital mortality, ferritin and lactate dehydrogenase offered an acceptable discrimination, AUC: 0.71 (95% CI: 0.62-0.79) and AUC: 0.70 (95% CI: 0.61-0.78), respectively. The optimal cut-off of predicting mortality for ferritin was 786 g/mL and for LDH was 514.94 u/L. Factors include age above 70 years old, RBGs level on admission above 250 mg/dL or below 140 mg/dL, ferritin level above 786 ng/mL and presence of ARDS increased the odds of mortality among individuals with diabetes.

CONCLUSIONS

Diabetes increases risk intensive care admission and in hospital mortality in COVID-19. Multivariate analysis showed that older age, RBG on admission, high ferritin level, presence of ARDS increased the odds of mortality among individuals with diabetes.

New-Onset Diabetes After COVID-19

There is evidence suggesting that infection with SARS-CoV-2 can lead to several long-term sequelae including diabetes. This mini-review examines the rapidly evolving and conflicting literature on new-onset diabetes after COVID-19, which we term NODAC. We searched PubMed, MEDLINE, and medRxiv from inception until December 1, 2022, using Medical Subject Headings (MeSH) terms and free text words including "COVID-19," "SARS-CoV-2," "diabetes," "hyperglycemia," "insulin resistance," and "pancreatic β-cell." We also supplemented searches by examining reference lists from retrieved articles. Current evidence suggests that COVID-19 increases the risk of developing diabetes, but the attributable risk is uncertain because of limitations of study designs and the evolving nature of the pandemic, including new variants, widespread population exposure to the virus, diagnostic options for COVID-19, and vaccination status. The etiology of diabetes after COVID-19 is likely multifactorial and includes factors associated with host characteristics (eg, age), social determinants of health (eg, deprivation index), and pandemic-related effects both at the personal (eg, psychosocial stress) and the societal-community level (eg, containment measures). COVID-19 may have direct and indirect effects on pancreatic β-cell function and insulin sensitivity related to the acute infection and its treatment (eg, glucocorticoids); autoimmunity; persistent viral residency in multiple organs including adipose tissue; endothelial dysfunction; and hyperinflammatory state. While our understanding of NODAC continues to evolve, consideration should be given for diabetes to be classified as a post-COVID syndrome, in addition to traditional classifications of diabetes (eg, type 1 or type 2), so that the pathophysiology, natural history, and optimal management can be studied.

Lipid metabolism of plasma-derived small extracellular vesicles in COVID-19 convalescent patients

The coronavirus disease 2019 (COVID-19), which affects multiple organs, is causing an unprecedented global public health crisis. Most COVID-19 patients recover gradually upon appropriate interventions. Viruses were reported to utilize the small extracellular vesicles (sEVs), containing a cell-specific cargo of proteins, lipids, and nucleic acids, to escape the attack from the host's immune system. This study aimed to examine the sEVs lipid profile of plasma of recovered COVID-19 patients (RCs). Plasma sEVs were separated from 83 RCs 3 months after discharge without underlying diseases, including 18 recovered asymptomatic patients (RAs), 32 recovered moderate patients (RMs), and 33 recovered severe and critical patients (RSs), and 19 healthy controls (HCs) by Total Exosome Isolation Kit. Lipids were extracted from sEVs and then subjected to targeted liquid chromatography-mass spectrometry. The size, concentration, and distribution of sEVs did not differ in RCs and HCs as validated by transmission electron microscopy, nanoparticle tracking analysis, and immunoblot analysis. Fifteen subclasses of 508 lipids were detected in plasma sEVs from HCs, RAs, RMs, and RSs, such as phosphatidylcholines (PCs) and diacylglycerols (DAGs), etc. Total lipid intensity displayed downregulation in RCs compared with HCs. The relative abundance of DAGs gradually dropped, whereas PCs, lysophosphatidylcholines, and sphingomyelins were higher in RCs relative to HCs, especially in RSs. 88 lipids out of 241 in sEVs of RCs were significantly different and a conspicuous increase was revealed with disease status. The sEVs lipids alternations were found to be significantly correlated with the clinical indices in RCs and HCs, suggesting that the impact of COVID-19 on lipid metabolism lingered for a long time. The lipid abnormalities bore an intimate link with glycerophospholipid metabolism and glycosylphosphatidylinositol anchor biosynthesis. Furthermore, the lipidomic analysis showed that RCs were at higher risk of developing diabetes and sustaining hepatic impairment. The abnormality of immunomodulation in RCs might still exist. The study may offer new insights into the mechanism of organ dysfunction and help identify novel therapeutic targets in the RCs.

Prevalence and impact of chronic dysglycaemia among patients with COVID-19 in Swedish intensive care units: a multicentre, retrospective cohort study

OBJECTIVE

Using glycated haemoglobin A1c (HbA1c) screening, we aimed to determine the prevalence of chronic dysglycaemia among patients with COVID-19 admitted to the intensive care unit (ICU). Additionally, we aimed to explore the association between chronic dysglycaemia and clinical outcomes related to ICU stay.

DESIGN

Multicentre retrospective observational study.

SETTING

ICUs in three hospitals in Stockholm, Sweden.

PARTICIPANTS

COVID-19 patients admitted to the ICU between 5 March 2020 and 13 August 2020 with available HbA1c at admission. Chronic dysglycaemia was determined based on previous diabetes history and HbA1c.

PRIMARY AND SECONDARY OUTCOMES

Primary outcome was the actual prevalence of chronic dysglycaemia (pre-diabetes, unknown diabetes or known diabetes) among COVID-19 patients. Secondary outcome was the association of chronic dysglycaemia with 90-day mortality, ICU length of stay, duration of invasive mechanical ventilation (IMV) and renal replacement therapy (RRT), accounting for treatment selection bias.

RESULTS

A total of 308 patients with available admission HbA1c were included. Chronic dysglycaemia prevalence assessment was restricted to 206 patients admitted ICUs in which HbA1c was measured on all admitted patients. Chronic dysglycaemia was present in 82.0% (95% CI 76.1% to 87.0%) of patients, with pre-diabetes present in 40.2% (95% CI 33.5% to 47.3%), unknown diabetes in 20.9% (95% CI 15.5% to 27.1%), well-controlled diabetes in 7.8% (95% CI 4.5% to 12.3%) and uncontrolled diabetes in 13.1% (95% CI 8.8% to 18.5%). All patients with available HbA1c were included for the analysis of the relationship between chronic dysglycaemia and secondary outcomes. We found no independent association between chronic dysglycaemia and 90-day mortality, ICU length of stay or duration of IMV. After excluding patients with specific treatment limitations, no association between chronic dysglycaemia and RRT use was observed.

CONCLUSIONS

In our cohort of critically ill COVID-19 patients, the prevalence of chronic dysglycaemia was 82%. We found no robust associations between chronic dysglycaemia and clinical outcomes when accounting for treatment limitations.

Is single point HbA1c a reliable predictor for death in severe COVID-19?

Introduction

The severity of COVID-19 infection is affected by several risk factors such as Diabetes Mellitus (DM). The current study aimed to determine the effect of single-point HbA1c on the severity and mortality of hospitalized COVID-19 patients.

Methods

This cross-sectional study was conducted among hospitalized moderate and severe COVID-19 patients in Baharloo Hospital in Iran between December 23rd and February 23rd, 2021. The patients have been diagnosed by Polymerase Chain Reaction (PCR) and Chest Computed Tomography (CT) imaging as COVID-19. Demographic data, clinical presentation, laboratory results, and treatments along with the HbA1c data were included.

Results

165 COVID-19 cases were included in this study; 126 (76.4%) of which were severe cases. 89 (53.9%) patients were male, with a mean age of 59.89 ± 16.59 years. Severe COVID-19 patients were more prone to a longer hospital stay, and a higher level of inflammatory mediators, compared to the moderate COVID-19 patients (p < 0.05). No significant association was found between single point HbA1c, FBS, and severity and mortality of COVID-19 cases (p > 0.05).

Conclusions

Single point HbA1c was not a reliable mediator for the prediction of severity or death in hospitalized COVID-19 patients.

Molecular Mechanisms Responsible for Diabetogenic Effects of COVID-19 Infection-Induction of Autoimmune Dysregulation and Metabolic Disturbances

The COVID-19 pandemic has revealed a significant association between SARS-CoV-2 infection and diabetes, whereby individuals with diabetes are more susceptible to severe disease and higher mortality rates. Interestingly, recent findings suggest a reciprocal relationship between COVID-19 and diabetes, wherein COVID-19 may contribute to developing new-onset diabetes and worsen existing metabolic abnormalities. This narrative review aims to shed light on the intricate molecular mechanisms underlying the diabetogenic effects of COVID-19. Specifically, the review explores the potential role of various factors, including direct damage to β-cells, insulin resistance triggered by systemic inflammation, and disturbances in hormonal regulation, aiming to enhance our understanding of the COVID-19 impact on the development and progression of diabetes. By analysing these mechanisms, the aim is to enhance our understanding of the impact of COVID-19 on the development and progression of diabetes. The binding of SARS-CoV-2 to angiotensin-converting enzyme 2 (ACE2) receptors, which are present in key metabolic organs and tissues, may interfere with glucometabolic pathways, leading to hyperglycaemia, and potentially contribute to the development of new disease mechanisms. The virus's impact on β-cells through direct invasion or systemic inflammation may induce insulin resistance and disrupt glucose homeostasis. Furthermore, glucocorticoids, commonly used to treat COVID-19, may exacerbate hyperglycaemia and insulin resistance, potentially contributing to new-onset diabetes. The long-term effects of COVID-19 on glucose metabolism are still unknown, necessitating further research into the possibility of developing a novel type of diabetes. This article provides a comprehensive overview of the current understanding of the interaction between COVID-19 and diabetes, highlighting potential areas for future research and therapeutic interventions.

The public health impact of poor sleep on severe COVID-19, influenza and upper respiratory infections

BACKGROUND

Poor sleep is associated with an increased risk of infections and all-cause mortality but the causal direction between poor sleep and respiratory infections has remained unclear. We examined if poor sleep contributes as a causal risk factor to respiratory infections.

METHODS

We used data on insomnia, influenza and upper respiratory infections (URIs) from primary care and hospital records in the UK Biobank (N ≈ 231,000) and FinnGen (N ≈ 392,000). We computed logistic regression to assess association between poor sleep and infections, disease free survival hazard ratios, and performed Mendelian randomization analyses to assess causality.

FINDINGS

Utilizing 23 years of registry data and follow-up, we discovered that insomnia diagnosis associated with increased risk for infections (FinnGen influenza Cox's proportional hazard (CPH) HR = 4.34 [3.90, 4.83], P = 4.16 × 10-159, UK Biobank influenza CPH HR = 1.54 [1.37, 1.73], P = 2.49 × 10-13). Mendelian randomization indicated that insomnia causally predisposed to influenza (inverse-variance weighted (IVW) OR = 1.65, P = 5.86 × 10-7), URI (IVW OR = 1.94, P = 8.14 × 10-31), COVID-19 infection (IVW OR = 1.08, P = 0.037) and risk of hospitalization from COVID-19 (IVW OR = 1.47, P = 4.96 × 10-5).

INTERPRETATION

Our findings indicate that chronic poor sleep is a causal risk factor for contracting respiratory infections, and in addition contributes to the severity of respiratory infections. These findings highlight the role of sleep in maintaining sufficient immune response against pathogens.

FUNDING

Instrumentarium Science Foundation, Academy of Finland, Signe and Ane Gyllenberg Foundation, National Institutes of Health.

Glycemic Dysregulation, Inflammation and Disease Outcomes in Patients Hospitalized with COVID-19: Beyond Diabetes and Obesity

INTRODUCTION

During the COVID-19 pandemic, diabetes mellitus (DM) and obesity were associated with high rates of morbidity and mortality. The aim of this study was to investigate the relationship between markers of inflammation, disease severity, insulin resistance, hyperglycemia, and outcomes in COVID-19 patients with and without diabetes and obesity.

MATERIALS AND METHODS

Epidemiological, clinical, and laboratory data were collected from the University Hospital of Ioannina COVID-19 Registry and included hospitalized patients from March 2020 to December 2022. The study cohort was divided into three subgroups based on the presence of DM, obesity, or the absence of both.

RESULTS

In diabetic patients, elevated CRP, IL-6, TRG/HDL-C ratio, and TyG index, severe pneumonia, and hyperglycemia were associated with extended hospitalization. Increased IL-6, NLR, and decreased PFR were associated with a higher risk of death. In the obese subgroup, lower levels of PFR were associated with longer hospitalization and a higher risk of death, while severe lung disease and hyperglycemia were associated with extended hospitalization. In patients without DM or obesity severe pneumonia, NLR, CRP, IL-6, insulin resistance indices, and hyperglycemia during hospitalization were associated with longer hospitalization.

CONCLUSION

Inflammatory markers and disease severity indices were strongly associated with disease outcomes and hyperglycemia across all subgroups.

Increased risk of new-onset diabetes in patients with COVID-19: a systematic review and meta-analysis

Background

There is growing evidence that patients with COVID-19 are at increased risk of new-onset diabetes. The limited preliminary studies do not provide strong evidence. To assess the association of the SARS-CoV-2 virus with new-onset diabetes and to characterize the population.

Methods

Search PubMed, Embase, Cochrane Library, and Web of Science electronic databases for a limited period from December 2019 to July 2022. Two independent reviewers conducted a thorough review of eligible articles and extracted relevant information. Pooled proportions, risk ratios (RR), and 95% confidence intervals (95% CI) indicated the incidence and risk ratios of events.

Results

The incidence of new-onset diabetes and hyperglycemia in patients with COVID-19 was 5% (P < 0.001) (3 and 30% for new-onset diabetes and hyperglycemia, respectively), with age, ethnicity, time of diagnosis, and study type all having an impact on the incidence (P < 0.05). New-onset diabetes and hyperglycemia were 1.75 times higher in COVID-19 patients than in non-COVID-19 patients. In new-onset diabetes and hyperglycemia population, the percentage of men is 60% (40% for women), with a mortality rate of 17%. The proportion of new-onset diabetes and hyperglycemia after infection with COVID-19 was 25% in men and 14% in women.

Conclusions

The incidence and relative risk of new-onset diabetes and hyperglycemia are elevated after COVID-19 infection, especially in the early COVID-19 and male populations.

Systemic review registration

PROSPERO registration no.: CRD42022382989 https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=382989.

COVID-19 Vaccination and Clinical Outcomes at a Secondary Referral Hospital During the Delta Variant-dominant Period in West Sumatra, Indonesia

OBJECTIVES

The second wave of coronavirus disease 2019 (COVID-19) cases in Indonesia, during which the Delta variant predominated, took place after a vaccination program had been initiated in the country. This study was conducted to assess the impact of COVID-19 vaccination on unfavorable clinical outcomes including hospitalization, severe COVID-19, intensive care unit (ICU) admission, and death using a real-world model.

METHODS

This single-center retrospective cohort study involved patients with COVID-19 aged ≥18 years who presented to the COVID-19 emergency room at a secondary referral teaching hospital between June 1, 2021 and August 31, 2021. We used a binary logistic regression model to assess the effect of COVID-19 vaccination on unfavorable clinical outcomes, with age, sex, and comorbidities as confounding variables.

RESULTS

A total of 716 patients were included, 32.1% of whom were vaccinated. The elderly participants (≥65 years) had the lowest vaccine coverage among age groups. Vaccination had an effectiveness of 50% (95% confidence interval [CI], 25 to 66) for preventing hospitalization, 97% (95% CI, 77 to 99) for preventing severe COVID-19, 95% (95% CI, 56 to 99) for preventing ICU admission, and 90% (95% CI, 22 to 99) for preventing death. Interestingly, patients with type 2 diabetes had a 2-fold to 4-fold elevated risk of unfavorable outcomes.

CONCLUSIONS

Among adults, COVID-19 vaccination has a moderate preventive impact on hospitalization but a high preventive impact on severe COVID-19, ICU admission, and death. The authors suggest that relevant parties increase COVID-19 vaccination coverage, especially in the elderly population.

Common Prognostic Biomarkers and Outcomes in Patients with COVID-19 Infection in Saudi Arabia

Background: COVID-19 is a respiratory disease that eventually became a pandemic, with 300 million people infected around the world. Alongside the improvement in COVID-19 management and vaccine development, identifying biomarkers for COVID-19 has recently been reported to help in early prediction and managing severe cases, which might improve outcomes. Our study aimed to find out if there is any correlation between clinical severity and elevated hematological and biochemical markers in COVID-19 patients and its effect on the outcome. Methods: We have collected retrospective data on socio-demographics, medical history, biomarkers, and disease outcomes from five hospitals and health institutions in the Kingdom of Saudi Arabia. Results: Pneumonia was the most common presentation of COVID-19 in our cohort. The presence of abnormal inflammatory biomarkers (D-dimer, CRP, troponin, LDH, ferritin, and t white blood cells) was significantly associated with unstable COVID-19 disease. In addition, patients with evidence of severe respiratory disease, particularly those who required mechanical ventilation, had higher biomarkers when compared to those with stable respiratory conditions (p < 0.001). Conclusion: Identifying biomarkers predicts outcomes for COVID-19 patients and may significantly help in their management.

Diabetes as one of the long-term COVID-19 complications: from the potential reason of more diabetic patients' susceptibility to COVID-19 to the possible caution of future global diabetes tsunami

According to recent researches, people with diabetes mellitus (type 1 and 2) have a higher incidence of coronavirus disease 2019 (COVID-19), which is caused by a SARS-CoV-2 infection. In this regard, COVID-19 may make diabetic patients more sensitive to hyperglycemia by modifying the immunological and inflammatory responses and increasing reactive oxygen species (ROS) predisposing the patients to severe COVID-19 and potentially lethal results. Actually, in addition to COVID-19, diabetic patients have been demonstrated to have abnormally high levels of inflammatory cytokines, increased virus entrance, and decreased immune response. On the other hand, during the severe stage of COVID-19, the SARS-CoV-2-infected patients have lymphopenia and inflammatory cytokine storms that cause damage to several body organs such as β cells of the pancreas which may make them as future diabetic candidates. In this line, the nuclear factor kappa B (NF-κB) pathway, which is activated by a number of mediators, plays a substantial part in cytokine storms through various pathways. In this pathway, some polymorphisms also make the individuals more competent to diabetes via infection with SARS-CoV-2. On the other hand, during hospitalization of SARS-CoV-2-infected patients, the use of some drugs may unintentionally lead to diabetes in the future via increasing inflammation and stress oxidative. Thus, in this review, we will first explain why diabetic patients are more susceptible to COVID-19. Second, we will warn about a future global diabetes tsunami via the SARS-CoV-2 as one of its long-term complications.

Global Diabetes Prevalence in COVID-19 Patients and Contribution to COVID-19- Related Severity and Mortality: A Systematic Review and Meta-analysis

BACKGROUND

COVID-19 and diabetes both contribute to large global disease burdens.

PURPOSE

To quantify the prevalence of diabetes in various COVID-19 disease stages and calculate the population attributable fraction (PAF) of diabetes to COVID-19-related severity and mortality.

DATA SOURCES

Systematic review identified 729 studies with 29,874,938 COVID-19 patients.

STUDY SELECTION

Studies detailed the prevalence of diabetes in subjects with known COVID-19 diagnosis and severity.

DATA EXTRACTION

Study information, COVID-19 disease stages, and diabetes prevalence were extracted.

DATA SYNTHESIS

The pooled prevalence of diabetes in stratified COVID-19 groups was 14.7% (95% CI 12.5-16.9) among confirmed cases, 10.4% (7.6-13.6) among nonhospitalized cases, 21.4% (20.4-22.5) among hospitalized cases, 11.9% (10.2-13.7) among nonsevere cases, 28.9% (27.0-30.8) among severe cases, and 34.6% (32.8-36.5) among deceased individuals, respectively. Multivariate metaregression analysis explained 53-83% heterogeneity of the pooled prevalence. Based on a modified version of the comparative risk assessment model, we estimated that the overall PAF of diabetes was 9.5% (7.3-11.7) for the presence of severe disease in COVID-19-infected individuals and 16.8% (14.8-18.8) for COVID-19-related deaths. Subgroup analyses demonstrated that countries with high income levels, high health care access and quality index, and low diabetes disease burden had lower PAF of diabetes contributing to COVID-19 severity and death.

LIMITATIONS

Most studies had a high risk of bias.

CONCLUSIONS

The prevalence of diabetes increases with COVID-19 severity, and diabetes accounts for 9.5% of severe COVID-19 cases and 16.8% of deaths, with disparities according to country income, health care access and quality index, and diabetes disease burden.

Treating diabetes with combination of phosphodiesterase 5 inhibitors and hydroxychloroquine-a possible prevention strategy for COVID-19?

Type 2 diabetes (T2D) is one of the major risk factors for developing cardiovascular disease and the resultant devastating morbidity and mortality. The key features of T2D are hyperglycemia, hyperlipidemia, insulin resistance, and impaired insulin secretion. Patients with diabetes and myocardial infarction have worse prognosis than those without T2D. Moreover, obesity and T2D are recognized risk factors in developing severe form of COVID-19 with higher mortality rate. The current lines of drug therapy are insufficient to control T2D and its serious cardiovascular complications. Phosphodiesterase 5 (PDE5) is a cGMP specific enzyme, which is the target of erectile dysfunction drugs including sildenafil, vardenafil, and tadalafil. Cardioprotective effects of PDE5 inhibitors against ischemia/reperfusion (I/R) injury were reported in normal and diabetic animals. Hydroxychloroquine (HCQ) is a widely used antimalarial and anti-inflammatory drug and its hyperglycemia-controlling effect in diabetic patients is also under investigation. This review provides our perspective of a potential use of combination therapy of PDE5 inhibitor with HCQ to reduce cardiovascular risk factors and myocardial I/R injury in T2D. We previously observed that diabetic mice treated with tadalafil and HCQ had significantly reduced fasting blood glucose and lipid levels, increased plasma insulin and insulin-like growth factor-1 levels, and improved insulin sensitivity, along with smaller myocardial infarct size following I/R. The combination treatment activated Akt/mTOR cellular survival pathway, which was likely responsible for the salutary effects. Therefore, pretreatment with PDE5 inhibitor and HCQ may be a potentially useful therapy not only for controlling T2D but also reducing the rate and severity of COVID-19 infection in the vulnerable population of diabetics.

Implications of an HbA1c-based Diabetes Screening on Prevalence and Effect of Dysglycemia in Patients With COVID-19

CONTEXT

In patients with severe acute respiratory syndrome coronavirus type 2 infection, diabetes is associated with poor COVID-19 prognosis. However, case detection strategy is divergent and reported prevalence varies from 5% to 35%.

OBJECTIVE

We examined how far the choice of screening tools affects the detection rate of dysglycemia and in consequence the estimation of diagnosis-associated risk for moderate (mo) or severe (s) COVID-19.

METHODS

Non-intensive care unit inpatients with COVID-19 were screened systematically at admission for diabetes (D) and prediabetes (PreD) by glycated hemoglobin A1c (HbA1c) (A), random blood glucose (B), and known history (C) from November 1, 2020 to March 8, 2021. Dysglycemia rate and effect on COVID-19 outcome were analyzed in 2 screening strategies (ABC vs BC).

RESULTS

A total of 578 of 601 (96.2%) of admitted patients were screened and analyzed. In ABC, prevalence of D and PreD was 38.2% and 37.5%, respectively. D was significantly associated with an increased risk for more severe COVID-19 (adjusted odds ratio [aOR] [moCOVID-19]: 2.27, 95% CI, 1.16-4.46 and aOR [sCOVID-19]: 3.26, 95% CI, 1.56-6.38). Patients with PreD also presented more often with more severe COVID-19 than those with normoglycemia (aOR [moCOVID-19]: 1.76, 95% CI, 1.04-2.97 and aOR [sCOVID-19]: 2.41, 95% CI, 1.37-4.23). Screening with BC failed to identify only 96% of PreD (206/217) and 26.2% of D diagnosis (58/221) and missed associations of dysglycemia and COVID-19 severity.

CONCLUSION

Pandemic conditions may hamper dysglycemia detection rate and in consequence the awareness of individual patient risk for COVID-19 severity. A systematic diabetes screening including HbA1c reduces underdiagnosis of previously unknown or new-onset dysglycemia, and enhances the quality of risk estimation and access of patients at risk to a diabetes-specific intervention.

Admission eGFR predicts in-hospital mortality independently of admission glycemia and C-peptide in patients with type 2 diabetes mellitus and COVID-19

OBJECTIVE

Type 2 diabetes mellitus (T2DM) and impaired kidney function are associated with a higher risk of poor outcomes of COVID-19. We conducted a retrospective study in hospitalized T2DM patients with COVID-19 to assess the association between in-hospital mortality and admission values of different hematological/biochemical parameters, including estimated glomerular filtration rate (eGFR), plasma glucose and C-peptide (as a marker of beta-cell function).

METHODS

The study included T2DM patients with confirmed SARS-CoV-2 infection who were consecutively admitted to our Institution between October 1, 2020 and April 1, 2021.

RESULTS

Patients (n = 74) were categorized into survivors (n = 55) and non-survivors (n = 19). Non-survivors exhibited significantly higher median WBC count, D-dimer, neutrophil-to-lymphocyte ratio, hsCRP, and procalcitonin levels, as well as significantly lower median serum 25(OH)D levels compared to survivors. Non-survivors exhibited significantly higher median admission plasma glucose (APG) values compared to survivors (210 vs 166 mg/dL; p = 0.026). There was no statistically significant difference in median values of plasma C-peptide between non-survivors and survivors (3.55 vs 3.24 ng/mL; p = 0.906). A significantly higher percentage of patients with an eGFR < 60 mL/min/1.73 m² was observed in the non-survivor group as compared to the survivor group (57.9% vs 23.6%; p = 0.006). A multivariate analysis performed by a logistic regression model after adjusting for major confounders (age, sex, body mass index, major comorbidities) showed a significant inverse association between eGFR values and risk of in-hospital mortality (OR, 0.956; 95% CI, 0.931-0.983; p = 0.001). We also found a significant positive association between WBC count and risk of in-hospital mortality (OR, 1.210; 95% CI, 1.043-1.404; p = 0.011).

CONCLUSIONS

Admission eGFR and WBC count predict in-hospital COVID-19 mortality among T2DM patients, independently of traditional risk factors, APG and random plasma C-peptide. Hospitalized patients with COVID-19 and comorbid T2DM associated with impaired kidney function at admission should be considered at high risk for adverse outcomes and death.

COVID-19 and Pre-existing Type 2 Diabetes Mellitus: A Retrospective Observational Study From Eastern India on the Association Between Glycaemic Control and Treatment Outcomes

BACKGROUND

Diabetes has emerged as an important risk factor for causing severe illness and death from COVID-19. There is a paucity of structured data from the Indian subcontinent on the impact that glycaemic control (both immediate and remote) has on the degree of required medical intervention and mortality among hospitalized COVID-19 patients with type 2 diabetes mellitus (T2DM).

OBJECTIVES

To evaluate the differences in clinical characteristics and treatment outcomes between well-controlled and poorly controlled patients with T2DM and COVID-19.

METHODS

This was a retrospective observational study. Data on 177 patients who were hospitalized between February 2021 and July 2021 were categorized into four groups using a cut-off admission plasma glucose of <200 mg/dL and glycated hemoglobin (HbA1c) <7.5%.

RESULTS

Patients with poorly controlled diabetes presented at a significantly older age than the other groups. Radiological findings suggested severe lung involvement in them. As a combined group patients with HbA1c ≥7.5% required more ventilatory requirement as compared with the group having HbA1c <7.5% irrespective of admission glucose. They also required prolonged hospitalization and intensive care unit (ICU) stays as compared with the well-controlled diabetes group. In this study, within similar ranges of HbA1c admission glucose seemed to have a numerical impact on mortality without being able to achieve statistical significance.

CONCLUSION

From the current study, it can be concluded that poor glycaemic control, particularly HbA1c ≥7.5%, is an important risk factor for the development of severe COVID-19 and a predictor for the requirement of more intensive treatment and adverse treatment outcomes leading to increased hospital and ICU stay.

Risk Factors of Severe COVID-19: A Review of Host, Viral and Environmental Factors

The clinical course and outcome of COVID-19 are highly variable, ranging from asymptomatic infections to severe disease and death. Understanding the risk factors of severe COVID-19 is relevant both in the clinical setting and at the epidemiological level. Here, we provide an overview of host, viral and environmental factors that have been shown or (in some cases) hypothesized to be associated with severe clinical outcomes. The factors considered in detail include the age and frailty, genetic polymorphisms, biological sex (and pregnancy), co- and superinfections, non-communicable comorbidities, immunological history, microbiota, and lifestyle of the patient; viral genetic variation and infecting dose; socioeconomic factors; and air pollution. For each category, we compile (sometimes conflicting) evidence for the association of the factor with COVID-19 outcomes (including the strength of the effect) and outline possible action mechanisms. We also discuss the complex interactions between the various risk factors.

Host genetics and gut microbiota composition: Baseline gut microbiota composition as a possible prognostic factor for the severity of COVID-19 in patients with familial Mediterranean fever disease

Background

It is known that the gut microbiome of a healthy person affects the process of COVID-19 after getting infected with SARS-CoV-2 virus. It is also believed that colchicine can alleviate the severity of COVID-19.

Objective

Current investigations aimed to evaluate the associations between the baseline gut microbiota composition of healthy and Familial Mediterranean fever (FMF) - carrier Armenian men populations, and the severity of the COVID-19 disease after their infection with the SARS-CoV-2. The study has a purpose of answering three core questions: i. Do the characteristics of gut microbiome of Armenians affect the course of COVID-19 severity? ii. How does the COVID-19 disease course on go for FMF patients who have been taking colchicine as a medication over the years after getting infected with SARS-CoV-2? iii. Is there an initial gut micribiota structure pattern for non-FMF and FMF patients in the cases when COVID-19 appears in mild form?

Methods

The gut microbiota composition in non-FMF and FMF patients before the first infection (mild and moderate course of COVID-19) was considered. COVID-19 was diagnosed by SARS-CoV-2 nucleic acid RT-PCR in nasopharyngeal swab and/or sputum.

Results

The number of patients with male FMF with mild COVID-19 was approximately two times higher than that of non-FMF male subjects with COVID-19. In addition, an association of COVID-19 disease severity with the baseline gut Prevotella, Clostridium hiranonis, Eubacterium biforme, Veillonellaceae, Coprococcus, and Blautia diversities in the non-FMF and FMF populations were revealed by us, which can be used as risk/prognostic factor for the severity of COVID-19.

Impact of heart failure on all-cause mortality in COVID-19: findings from the Eurasian International Registry

AIMS

To study all-cause mortality in patients hospitalized with COVID-19 with or without chronic heart failure (CHF) during hospitalization and at 3 and 6 months of follow-up.

METHODS AND RESULTS

The international registry Analysis of Comorbid Disease Dynamics in Patients with SARS-CoV-2 Infection (ACTIV) was conducted at 26 centres in seven countries: Armenia, Belarus, Kazakhstan, Kyrgyzstan, Moldova, Russian Federation, and Uzbekistan. The primary endpoints were in-hospital all-cause mortality and all-cause mortality at 3 and 6 months of follow-up. Of the 5616 patients hospitalized with COVID-19, 917 (16.3%) had CHF. Total in-hospital mortality was 7.6%. In-hospital mortality was higher in patients with CHF than in patients without a history of CHF [17.7% vs. 4.0%, P < 0.001; odds ratio (OR) 4.614, 95% confidence interval (CI) 3.633-5.859; P < 0.001]. The risk of in-hospital all-cause mortality correlated significantly with the severity of CHF; specifically, the risk of in-hospital all-cause mortality was greater for patients in New York Heart Association functional classes III and IV (OR 6.124, 95% CI 4.538-8.266; P < 0.001 vs. patients without CHF) than for patients in functional classes I and II (OR 2.446, 95% CI 1.831-3.267, P < 0.001 vs. patients without CHF). The risk of mortality in patients with ischemic CHF was 58% higher than in patients with non-ischaemic CHF [OR 1.58 (95% CI 1.05-2.45), P = 0.030]. In the first 3 months of follow-up, the all-cause mortality rate in patients with CHF was 10.32%, compared with 1.83% in patients without CHF (P < 0.001). At 6 months of follow-up, NYHA classes II-IV was a strong risk factor for all-cause mortality [OR 5.343 (95% CI 2.717-10.508); P < 0.001].

CONCLUSIONS

Hospitalized COVID-19 patients with CHF have an increased risk of in-hospital all-cause mortality, which remains high 6 months after discharge.

Prädiabetes – eine Krankheit?

Ist Prädiabetes eine Krankheit? Diese Frage lässt sich nicht einfach beantworten. Zunächst gilt es, sich systematisch mit dem Begriff Prädiabetes auseinanderzusetzen. Dabei müssen folgende Aspekte berücksichtigt werden: 1. Wie wird Prädiabetes definiert? 2. Wie häufig tritt Prädiabetes in der Bevölkerung auf? 3. Ist das Risiko für bestimmte Erkrankungen bei Prädiabetes erhöht? 4. Welche Bedeutung hat Prädiabetes in der täglichen klinischen Praxis? Falls sich unter der Berücksichtigung dieser Aspekte ein klares Bild ergibt, kann man schlussfolgern, ob Prädiabetes eine Erkrankung darstellt.

Molecular and cellular mechanisms involved in tissue-specific metabolic modulation by SARS-CoV-2

Coronavirus disease 2019 (COVID-19) is triggered by the SARS-CoV-2, which is able to infect and cause dysfunction not only in lungs, but also in multiple organs, including central nervous system, skeletal muscle, kidneys, heart, liver, and intestine. Several metabolic disturbances are associated with cell damage or tissue injury, but the mechanisms involved are not yet fully elucidated. Some potential mechanisms involved in the COVID-19-induced tissue dysfunction are proposed, such as: (a) High expression and levels of proinflammatory cytokines, including TNF-α IL-6, IL-1β, INF-α and INF-β, increasing the systemic and tissue inflammatory state; (b) Induction of oxidative stress due to redox imbalance, resulting in cell injury or death induced by elevated production of reactive oxygen species; and (c) Deregulation of the renin-angiotensin-aldosterone system, exacerbating the inflammatory and oxidative stress responses. In this review, we discuss the main metabolic disturbances observed in different target tissues of SARS-CoV-2 and the potential mechanisms involved in these changes associated with the tissue dysfunction.

In-Hospital Hyperglycemia Is Associated With Worse Outcomes in Patients Admitted With COVID-19

OBJECTIVE

Diabetes and the outpatient diabetes treatment regimen have been identified as risk factors for poor outcomes in patients with sepsis. However, little is known about the effect of tight inpatient glycemic control in the setting of coronavirus disease 2019 (COVID-19). Therefore, we examined the effect of hyperglycemia in patients with diabetes hospitalized because of COVID-19.

RESEARCH DESIGN AND METHODS

We analyzed data from 1,938 COVID-19 patients with diabetes hospitalized for COVID-19 from March to May 2020 at a large academic medical center in New York City. Patients were divided into two groups based on their inpatient glycemic values, and a Cox proportional hazards regression model was used to assess the independent association of inpatient glucose levels with mortality (primary outcome) and the risk of requiring mechanical ventilation (MV) (secondary outcome).

RESULTS

In our analysis, 32% of the patients were normoglycemic and 68% hyperglycemic. Moreover, 31% of the study subjects died during hospitalization, and 14% required MV, with inpatient hyperglycemia being significantly associated with both mortality and the requirement for MV. Additionally, in the Cox regression analysis, after adjustment for potential confounders, including age, sex, race, BMI, HbA1c, comorbidities, inflammatory markers, and corticosteroid therapy, patients with uncontrolled hyperglycemia had a higher risk of dying (hazard ratio [HR] 1.54, 95% CI 1.00-2.36, P = 0.049) and of requiring MV (HR 4.41, 95% CI 1.52-2.81, P = 0.006) than those with normoglycemia.

CONCLUSIONS

A tight control of inpatient hyperglycemia may be an effective method for improving outcomes in patients with diabetes hospitalized for COVID-19.

Cardiometabolic Risk after SARS-CoV-2 Virus Infection: A Retrospective Exploratory Analysis

OBJECTIVE

The aim of this study is to characterize the cardiometabolic risk of individuals who were infected with the SARS-CoV-2 virus and subsequently admitted to a hospital in a major city in mainland Portugal.

METHODS

This is a retrospective exploratory study using a sample of 102 patients, with data analysis including descriptive statistics, nonparametric measures of association between variables based on Spearman's rank-order correlation, a logistic regression model for predicting the likelihood that an individual might eventually pass away, and a multiple linear regression model to predict a likely increase in the number of days an infected patient remained in the hospital.

RESULTS

About 62.7% of the individuals required intensive care on the second day of hospitalization, remaining 14.2 days in the intensive care unit (ICU) on average. The likelihood that an individual might eventually pass away due to SARS-CoV-2 virus infection increases for the older than younger ones and increases even more if he/she suffers from cardiometabolic disorders such as obesity, especially cardiovascular disease. Older individuals and those with obesity and hypertension remained more days in the ICU.

CONCLUSIONS

A later age and the prevalence of cardiometabolic disorders severely affect the care pathway of individuals infected with the SARS-CoV-2 virus.

Predictors of New-Onset Diabetes in Hospitalized Patients with SARS-CoV-2 Infection

The pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is one of the world's most disruptive health crises. The presence of diabetes plays an important role in the severity of the infection, and a rise in newly diagnosed diabetes cases has been identified. The aim of this retrospective study was to determine the incidence of new-onset diabetes (NOD) and predictive factors with their cut-off values for patients hospitalized with COVID-19. All patients (n = 219) hospitalized for COVID-19 during three consecutive months were included. NOD was diagnosed in 26.48% of patients. The severity of the infection, hospital admission values for fasting plasma glucose, lactate dehydrogenase (LDH), PaO2/FiO2 ratio, the peak values for leucocytes, neutrophils, C-reactive protein, triglycerides, and the need for care in the intensive care unit were predictors for the occurrence of NOD in univariate analysis, while only LDH level remained a significant predictor in the multivariable analysis. In conclusion, the results of the study showed a high incidence of NOD in patients hospitalized with COVID-19 and identified LDH levels at hospital admission as a significant predictor of NOD during SARS-CoV-2 infection. However, the persistence of NOD after the COVID-19 infection is not known, therefore, the results must be interpreted with caution.

Glucose variability is a marker for COVID-19 severity and mortality

Objective

We aimed to investigate the association between glucose coefficient of variation (CV) and mortality and disease severity in hospitalized patients with coronavirus disease-19 (COVID-19).

Subjects and Methods

Retrospective cohort study in a tertiary center of patients with COVID-19 admitted to designated departments between March 11^th, 2020, and November 2^nd, 2020. We divided patients based on quartiles of glucose CV after stratification to those with and without diabetes mellitus (DM). Main outcomes were length of stay and in-hospital mortality.

Results

The cohort included 565 patients with a mean age of 67.71 ± 15.45 years, and 62.3% were male. Of the entire cohort, 44.4% had DM. The median glucose CV was 32.8% and 20.5% in patients with and without DM, respectively. In patients with DM, higher glucose CV was associated with a longer hospitalization in the unadjusted model (OR = 2.7, 95% CI [1.3,5.6] for Q4), and when adjusted for age, sex, comorbidities, and laboratory markers, this association was no longer statistically significant (OR = 1.3, 95% CI [0.4,4.5] for Q4). In patients with and without DM, higher glucose CV was associated with higher rates of in-hospital mortality in the unadjusted model, but adjustment for comorbidities and laboratory markers eliminated the association (OR = 0.5, 95% CI [0.1,3.4] for Q4 in patients with DM).

Conclusion

Higher glucose CV was associated with increased in-hospital mortality and length of stay, but this association disappeared when the adjustment included laboratory result data. Glucose CV can serve as a simple and cheap marker for mortality and severity of disease in patients with COVID-19.

COVID-19 pneumonia in patients with impaired fasting glucose, newly diagnosed diabetes and pre-existing diabetes: a tertiary center experience

COVID-19 infection is known to increase mortality in patients with diabetes. We aim to demonstrate the differences in disease course and clinical outcomes of patients with COVID-19 regarding the presence of impaired fasting glucose, pre-existing diabetes mellitus (DM) or new-onset DM. 236 patients with positive reverse transcription-PCR tests for SARS-CoV-2 were included in this single-center, retrospective observational study between March 2020 and May 2021. Laboratory results, comorbidities, medications and imaging findings were noted. Logistic regression was used to estimate associated factors for admission to the intensive care unit (ICU). 43 patients with normal glucose, 53 with impaired fasting glucose, 60 with newly diagnosed DM, and 80 with pre-existing DM were classified. Patients with pre-existing DM had higher fasting glucose and glycated hemoglobin than the other groups (p<0.001 for all). Patients with newly diagnosed DM were more likely to need dexamethasone 6 mg (p=0.001). In both newly diagnosed diabetes and impaired fasting glucose groups, 250 mg methylprednisolone was needed at higher rates (p=0.002). Newly diagnosed DM had higher rates of intubation (21.6%) and more mortality (20.0%) (p=0.045 and p=0.028, respectively). Mortality and hospitalization in the ICU were lower in the group receiving antidiabetic treatment. The risk of ICU attendance was higher in patients with impaired fasting glucose (HR=1.71, 95% CI: 0.48 to 6.08) and newly diagnosed DM (HR=1.88, 95% CI: 0.57 to 6.17), compared with pre-existing DM and non-diabetics. Newly diagnosed DM and impaired fasting glucose are associated with increased mortality and intubation in inpatients with COVID-19.

A machine learning-based diagnostic model for children with autism spectrum disorders complicated with intellectual disability

Background

Early detection of children with autism spectrum disorder (ASD) and comorbid intellectual disability (ID) can help in individualized intervention. Appropriate assessment and diagnostic tools are lacking in primary care. This study aims to explore the applicability of machine learning (ML) methods in diagnosing ASD comorbid ID compared with traditional regression models.

Method

From January 2017 to December 2021, 241 children with ASD, with an average age of 6.41 ± 1.96, diagnosed in the Developmental Behavior Department of the Children's Hospital Affiliated with the Medical College of Zhejiang University were included in the analysis. This study trained the traditional diagnostic models of Logistic regression (LR), Support Vector Machine (SVM), and two ensemble learning algorithms [Random Forest (RF) and XGBoost]. Socio-demographic and behavioral observation data were used to distinguish whether autistic children had combined ID. The hyperparameters adjustment uses grid search and 10-fold validation. The Boruta method is used to select variables. The model's performance was evaluated using discrimination, calibration, and decision curve analysis (DCA).

Result

Among 241 autistic children, 98 (40.66%) were ASD comorbid ID. The four diagnostic models can better distinguish whether autistic children are complicated with ID, and the accuracy of SVM is the highest (0.836); SVM and XGBoost have better accuracy (0.800, 0.838); LR has the best sensitivity (0.939), followed by SVM (0.952). Regarding specificity, SVM, RF, and XGBoost performed significantly higher than LR (0.355). The AUC of ML (SVM, 0.835 [95% CI: 0.747-0.944]; RF, 0.829 [95% CI: 0.738-0.920]; XGBoost, 0.845 [95% CI: 0.734-0.937]) is not different from traditional LR (0.858 [95% CI: 0.770-0.944]). Only SVM observed a good calibration degree. Regarding DCA, LR, and SVM have higher benefits in a wider threshold range.

Conclusion

Compared to the traditional regression model, ML model based on socio-demographic and behavioral observation data, especially SVM, has a better ability to distinguish whether autistic children are combined with ID.

COVID-19 associated diabetes mellitus: A review

A significantly higher rate of new-onset diabetes in many coronavirus disease 2019 (COVID-19) patients is a frequently observed phenomenon. The resultant hyperglycemia is known to influence the clinical outcome, thereby increasing the cost of treatment and stay in hospital. This will also affect the post-hospitalization recuperation. It has been observed that new-onset diabetes in COVID-19 patients is associated with considerable increase in morbidity and may be associated with increased mortality in some cases. This mini-review focuses on the possible causes to understand how COVID-19-related diabetes develops, various associated risk factors, and possible mechanism to understand the natural history of the disease process, clinical outcome, associated morbidities and various treatment options in the mana-gement of post COVID-19 diabetes. A literature search was performed in PubMed and other online database using appropriate keywords. A total of 80 articles were found, among which, 53 of the most relevant were evaluated/ analyzed and relevant data were included. The studies show that patients who have had severe acute respiratory syndrome coronavirus 2 infection leading to development of COVID-19 may manifest not only with new-onset diabetes but also worsening of pre-existing diabetes. Cytopathic effect and autoimmune destruction of insulin-secreting pancreatic beta cells, cytokine storm during the active phase of infection causing impaired insulin secretion and resistance, drug-induced hyperglycemia, undetected pre-existing hyperglycemia/diabetic condition, and stress-induced impairment of glucose metabolism are some of the possible potential mechanisms of COVID-19-associated new-onset diabetes mellitus. Many studies published in recent times have found a significantly higher rate of new-onset diabetes mellitus in many COVID-19 patients. Whether it is an inflammatory or immune-mediated response, direct effect of virus or combination of these is unclear. The resultant hyperglycemia is known to influence the clinical outcome and has been associated with considerable increase in morbidity and increased mortality in some cases.

Stress Hyperglycemia Ratio as a Prognostic Marker in Diabetic Patients Hospitalized with COVID-19

Evidence is conflicting about the diabetes characteristics associated with worse outcome among hospitalized COVID-19 patients. We aimed to assess the role of stress hyperglycemia ratio (SHR) as a prognostic marker among them. In our retrospective cohort study, patients were stratified according to SHR, admission glucose, and glycated hemoglobin tertiles. The primary outcome was a composite endpoint of invasive mechanical ventilation, intensive care unit admission, and in-hospital mortality. The study included 395 patients with a mean age of 59 years, and 50.1% were males. Patients in the third tertile of SHR developed more primary events, and the difference was significant compared to the first tertile (p = 0.038) and close to significance compared to the second tertile (p = 0.054). There was no significant difference in the outcomes across admission glucose and glycated hemoglobin tertiles. A higher SHR tertile was an independent risk factor for the primary outcome (OR, 1.364; 95% CI: 1.014–1.836; p = 0.040) after adjustment for other covariables. In hospitalized COVID-19 diabetic patients, SHR third tertile was significantly associated with worse outcome and death. SHR can be a better prognostic marker compared to admission glucose and glycated hemoglobin. A higher SHR was an independent risk factor for worse outcome and in-hospital mortality.

COVID-19 induced Diabetes: A novel presentation

INTRODUCTION

The COVID-19 pandemic disproportionately affected patients who had comorbid diabetes mellitus. COVID-19 patients with diabetes experience significantly higher rates of complications and mortality. COVID-induced diabetes is a novel phenomenon observed in critically ill patients. The aims of this review were to explore the literature about COVID-induced diabetes and the pathophysiological mechanisms that could lead to this novel presentation.

METHODS

A literature search was performed using PUBMED, Google Scholar, MEDLINE and Embase for original studies (meta-analyses, cross-sectional studies, case series, case reports) about new-onset diabetes following COVID infection, and the proposed biochemical pathways behind this presentation. It was assumed that the authors of the studies used the current diagnostic criteria for diagnosis of type 1 and type 2 diabetes.

RESULTS

COVID-19 causes dysregulation of glucose homeostasis leading to new-onset diabetes and hyperglycaemia. This is also seen in patients with no previous risk factors for diabetes mellitus. The atypical glycaemic parameters and increased rates of DKA suggest that COVID-induced diabetes is a novel form of diabetes. A spectrum of COVID-induced diabetes has also been noted. COVID-induced diabetes is associated with remarkably higher mortality rates and worse outcomes compared to COVID-19 patients with pre-existing diabetes. The novel presentation of COVID-induced diabetes could be due to beta cell damage and insulin resistance caused by SARS-CoV-2.

CONCLUSION

COVID-induced diabetes is essential to detect early, owing to its implications on prognosis. Further studies must include follow-up of these patients to better understand the trajectory of COVID-induced diabetes and the best management plan. It is also important to assess the beta cell function and insulin resistance of COVID-induced diabetes patients over time to better understand the underlying biochemical mechanisms.

Hybrid stacked ensemble combined with genetic algorithms for diabetes prediction

Diabetes is currently one of the most common, dangerous, and costly diseases globally caused by increased blood sugar or a decrease in insulin in the body. Diabetes can have detrimental effects on people’s health if diagnosed late. Today, diabetes has become one of the challenges for health and government officials. Prevention is a priority, and taking care of people’s health without compromising their comfort is an essential need. In this study, the ensemble training methodology based on genetic algorithms was used to diagnose and predict the outcomes of diabetes mellitus accurately. This study uses the experimental data, actual data on Indian diabetics on the University of California website. Current developments in ICT, such as the Internet of Things, machine learning, and data mining, allow us to provide health strategies with more intelligent capabilities to accurately predict the outcomes of the disease in daily life and the hospital and prevent the progression of this disease and its many complications. The results show the high performance of the proposed method in diagnosing the disease, which has reached 98.8%, and 99% accuracy in this study.

J-shaped associations and joint effects of fasting glucose with inflammation and cytokines on COVID-19 mortality

OBJECTIVES

The aim of this study was to investigate the dose-response relationship of admission fasting glucose (FBG) with corona virus disease 2019 (COVID-19) mortality and to further evaluate potential interactions of hyperglycemia with inflammation and hypercoagulation on COVID-19 outcomes.

METHODS

This retrospective study included 2555 consecutively hospitalized patients with COVID-19, until death or discharge, in Wuhan Union hospital between January 1 and April 9, 2020. The poor early outcomes included admission to intensive care unit, intubation, and deaths occurring within 28 days. We used splines nested in Cox regression to visualize dose-response associations and generalized additive models to fit three-dimensional (3D) trend plots for joint effects of FBG with markers of inflammation and coagulation.

RESULTS

J-shaped associations existed between hospitalized mortality or poor early outcomes and FBG with a nadir at 5 mmol/L, which were more evident in women. 3D plots demonstrated significant joint effect trends, and patients with hyperglycemia and high neutrophil-lymphocyte ratio, C-reactive protein, lactate dehydrogenase, procalcitonin, d-dimer, and interleukin-6 had 7.4-25.3-fold risks; the proportions of joint associations attributed to additive interactions reached 30% to 54%.

CONCLUSIONS

FBG was associated with hospitalized mortality and poor early outcomes in a J-shaped manner, and a combination of hyperglycemia, inflammation, hypercoagulation, and cytokines conferred a dramatically higher risk.

Hyperglycemia and Hypoglycemia Are Associated with In-Hospital Mortality among Patients with Coronavirus Disease 2019 Supported with Extracorporeal Membrane Oxygenation

Metabolic abnormalities, such as preexisting diabetes or hyperglycemia or hypoglycemia during hospitalization aggravated the severity of COVID-19. We evaluated whether diabetes history, hyperglycemia before and during extracorporeal membrane oxygenation (ECMO) support, and hypoglycemia were risk factors for mortality in patients with COVID-19. This study included data on 195 patients with COVID-19, who were aged ≥19 years and were treated with ECMO. The proportion of patients with diabetes history among nonsurvivors was higher than that among survivors. Univariate Cox regression analysis showed that in-hospital mortality after ECMO support was associated with diabetes history, renal replacement therapy (RRT), and body mass index (BMI) < 18.5 kg/m2. Glucose at admission >200 mg/dL and glucose levels before ventilator >200 mg/dL were not associated with in-hospital mortality. However, glucose levels before ECMO >200 mg/dL and minimal glucose levels during hospitalization <70 mg/dL were associated with in-hospital mortality. Multivariable Cox regression analysis showed that glucose >200 mg/dL before ECMO and minimal glucose <70 mg/dL during hospitalization remained risk factors for in-hospital mortality after adjustment for age, BMI, and RRT. In conclusion, glucose >200 mg/dL before ECMO and minimal glucose level <70 mg/dL during hospitalization were risk factors for in-hospital mortality among COVID-19 patients who underwent ECMO.

Type 2 Diabetes Mellitus and COVID-19: A Narrative Review

Type-2 diabetes mellitus (T2DM) is a chronic metabolic disorder. The incidence and prevalence of patients with T2DM are increasing worldwide, even reaching epidemic values in most high- and middle-income countries. T2DM could be a risk factor of developing complications in other diseases. Indeed, some studies suggest a bidirectional interaction between T2DM and COVID-19. A growing body of evidence shows that COVID-19 prognosis in individuals with T2DM is worse compared with those without. Moreover, various studies have reported the emergence of newly diagnosed patients with T2DM after SARS-CoV-2 infection. The most common treatments for T2DM may influence SARS-CoV-2 and their implication in infection is briefly discussed in this review. A better understanding of the link between TD2M and COVID-19 could proactively identify risk factors and, as a result, develop strategies to improve the prognosis for these patients.

Elevated Fasting Blood Glucose Levels Are Associated with Worse Clinical Outcomes in COVID-19 Patients Than in Pneumonia Patients with Bacterial Infections

Aims: We investigate how fasting blood glucose (FBG) levels affect the clinical severity in coronavirus disease 2019 (COVID-19) patients, pneumonia patients with sole bacterial infection, and pneumonia patients with concurrent bacterial and fungal infections. Methods: We enrolled 2761 COVID-19 patients, 1686 pneumonia patients with bacterial infections, and 2035 pneumonia patients with concurrent infections. We used multivariate logistic regression analysis to assess the associations between FBG levels and clinical severity. Results: FBG levels in COVID-19 patients were significantly higher than in other pneumonia patients during hospitalisation and at discharge (all p < 0.05). Among COVID-19 patients, the odds ratios of acute respiratory distress syndrome (ARDS), respiratory failure (RF), acute hepatitis/liver failure (AH/LF), length of stay, and intensive care unit (ICU) admission were 12.80 (95% CI, 4.80−37.96), 5.72 (2.95−11.06), 2.60 (1.20−5.32), 1.42 (1.26−1.59), and 5.16 (3.26−8.17) times higher in the FBG ≥7.0 mmol/L group than in FBG < 6.1 mmol/L group, respectively. The odds ratios of RF, AH/LF, length of stay, and ICU admission were increased to a lesser extent in pneumonia patients with sole bacterial infection (3.70 [2.21−6.29]; 1.56 [1.17−2.07]; 0.98 [0.88−1.11]; 2.06 [1.26−3.36], respectively). The odds ratios of ARDS, RF, AH/LF, length of stay, and ICU admission were increased to a lesser extent in pneumonia patients with concurrent infections (3.04 [0.36−6.41]; 2.31 [1.76−3.05]; 1.21 [0.97−1.52]; 1.02 [0.93−1.13]; 1.72 [1.19−2.50], respectively). Among COVID-19 patients, the incidence rate of ICU admission on day 21 in the FBG ≥ 7.0 mmol/L group was six times higher than in the FBG < 6.1 mmol/L group (12.30% vs. 2.21%, p < 0.001). Among other pneumonia patients, the incidence rate of ICU admission on day 21 was only two times higher. Conclusions: Elevated FBG levels at admission predict subsequent clinical severity in all pneumonia patients regardless of the underlying pathogens, but COVID-19 patients are more sensitive to FBG levels, and suffer more severe clinical complications than other pneumonia patients.

Sugar is not always sweet: exploring the relationship between hyperglycemia and COVID-19 in a predominantly African American population

INTRODUCTION

The purpose of this study is to examine the effect of admission glucose in patients hospitalized with COVID-19 with and without diabetes mellitus in a largely African American cohort.

DESIGN AND METHODS

This study included 708 adults (89% non-Hispanic Black) admitted with COVID-19 to an urban hospital between 1 March and 15 May 2020. Patients with diabetes were compared with those without and were stratified based on admission glucose of 140 and 180 mg/dL. Adjusted ORs were calculated for outcomes of mortality, intubation, intensive care unit (ICU) admission, acute kidney injury (AKI), and length of stay based on admission glucose levels.

RESULTS

Patients with diabetes with admission glucose >140 mg/dL (vs <140 g/dL) had 2.4-fold increased odds of intubation (95% CI 1.2 to 4.6) and 2.1-fold increased odds of ICU admission (95% CI 1.0 to 4.3). Patients with diabetes with admission glucose >180 mg/dL (vs <180 g/dL) had a 1.9-fold increased mortality (95% CI 1.2 to 3.1). Patients without diabetes with admission glucose >140 mg/dL had a 2.3-fold increased mortality (95% CI 1.3 to 4.3), 2.7-fold increased odds of ICU admission (95% CI 1.3 to 5.4), 1.9-fold increased odds of intubation (95% CI 1.0 to 3.7) and 2.2-fold odds of AKI (95% CI 1.1 to 3.8). Patients without diabetes with glucose >180 mg/dL had 4.4-fold increased odds of mortality (95% CI 1.9 to 10.4), 2.7-fold increased odds of intubation (95% CI 1.2 to 5.8) and 3-fold increased odds of ICU admission (95% CI 1.3 to 6.6).

CONCLUSION

Our results show hyperglycemia portends worse outcomes in patients with COVID-19 with and without diabetes. While our study was limited by its retrospective design, our findings suggest that patients presenting with hyperglycemia require closer observation and more aggressive therapies.

Association between glycemic control and the outcome in hospitalized patients with COVID-19

PURPOSE

Coronavirus disease 2019 (COVID-19) clinical outcome and disease severity affected by several factors; deterioration of glycemic control is one of them. Therefore, achieving optimum blood glucose parameters is hypothesized for better consequences of COVID-19. However, varying data supporting this hypothesis is available in literature. The intention of this study was to investigate the role of glycemic management on the prognosis of hospitalized COVID-19 patients with varying degrees of severity.

METHODS

From April 2020 to January 2021, we carried this retrospective cohort in a clinical care facility in Pakistan.

RESULTS

Mortality was lowest in patients with HbA1c of less than 7% (53 mmol/mol) (p < 0.001). Similarly, mortality was found lowest in patients with fasting blood glucose less than 126 mg/dl and random blood glucose less than 160 mg/dl (p < 0.001 in each). In contrast, need for admission in critical care was found highest in patients with HbA1c between 7 and 10% (53-86 mmol/mol) (p 0.002). However, participants with blood glucose levels during fasting greater than 200 mg/dl and random blood glucose levels greater than 250 mg/dl were found to have a greater need for invasive mechanical ventilation. Cox regression hazard showed no difference in risk of death and invasive mechanical ventilation based on previous glycemic control.

CONCLUSION

Effective diabetic management is correlated with a considerably lower risk of mortality and invasive mechanical ventilation in COVID-19 cases.

Obesity: an evolutionary context

People completely lacking body fat (lipodystrophy/lipoatrophy) and those with severe obesity both show profound metabolic and other health issues. Regulating levels of body fat somewhere between these limits would, therefore, appear to be adaptive. Two different models might be contemplated. More traditional is a set point (SP) where the levels are regulated around a fixed level. Alternatively, dual-intervention point (DIP) is a system that tolerates fairly wide variation but is activated when critically high or low levels are breached. The DIP system seems to fit our experience much better than an SP, and models suggest that it is more likely to have evolved. A DIP system may have evolved because of two contrasting selection pressures. At the lower end, we may have been selected to avoid low levels of fat as a buffer against starvation, to avoid disease-induced anorexia, and to support reproduction. At the upper end, we may have been selected to avoid excess storage because of the elevated risks of predation. This upper limit of control seems to have malfunctioned because some of us deposit large fat stores, with important negative health effects. Why has evolution not protected us against this problem? One possibility is that the protective system slowly fell apart due to random mutations after we dramatically reduced the risk of being predated during our evolutionary history. By chance, it fell apart more in some people than others, and these people are now unable to effectively manage their weight in the face of the modern food glut. To understand the evolutionary context of obesity, it is important to separate the adaptive reason for storing some fat (i.e. the lower intervention point), from the nonadaptive reason for storing lots of fat (a broken upper intervention point). The DIP model has several consequences, showing how we understand the obesity problem and what happens when we attempt to treat it.

COVID-19 in Kidney Transplant Recipients With Diabetes Mellitus: A Propensity Score Matching Analysis

Kidney transplant recipients present higher rates of pre-existing comorbidities, in particular diabetes mellitus (DM), hypertension, and cardiac disease. We aimed to verify the main risk factors related to DM that contribute to COVID-19 progression and mortality in a kidney transplant setting. From March to August 2020, we evaluated 300 kidney transplant recipients affected by COVID-19. We used propensity score matching (PSM) to estimate the impact of DM on COVID-19. After matching, all baseline characteristics were well balanced between those with and without DM (n = 100 in each group). Case fatality rate, the requirement of invasive mechanical ventilation (IMV), and acute kidney injury (AKI) were associated with previous fasting blood glucose, and C-reactive protein (CRP), and lactate dehydrogenase (LDH) levels on admission. These findings were similar in kidney transplant patients with and without DM. Glycemia on admission and estimated glomerular filtration rate (eGFR) either on admission or basal correlated to the need of IMV and development of AKI, respectively. Poor glycaemic control, eGFR, markers of inflammation (CRP) and tissue damage (LDH) were indicative of COVID-19 burden in kidney transplant recipients and may be useful tools for risk-stratifying this population, independently of the DM status, during the pandemic.

Challenges in hyperglycemia management in critically ill patients with COVID-19

Hyperglycemia is commonly associated with adverse outcomes especially in patients requiring intensive care unit stay. Data from the corona virus disease 2019 (COVID-19) pandemic indicates that individuals with diabetes appear to be at similar risk for COVID-19 infection to those without diabetes but are more likely to experience increased morbidity and mortality. The proposed hypothesis for hyperglycemia in COVID-19 include insulin resistance, critical illness hyperglycemia (stress- induced hyperglycemia) secondary to high levels of hormones like cortisol and catecholamines that counteract insulin action, acute cytokine storm and pancreatic cell dysfunction. Diabetic patients are more likely to have severe hyperglycemic complications including diabetic ketoacidosis and hyperosmolar hyperglycemic state. Management of hyperglycemia in COVID-19 is often complicated by use of steroids, prolonged total parenteral or enteral nutrition, frequent acute hyperglycemic events, and restrictions with fluid management due to acute respiratory distress syndrome. While managing hyperglycemia special attention should be paid to mode of insulin delivery, frequency of glucose monitoring based on patient and caregiver safety thereby minimizing exposure and conserving personal protective equipment. In this article we describe the pathophysiology of hyperglycemia, challenges encountered in managing hyperglycemia, and review some potential solutions to address them.

MECHANICAL VENTILATION NEED AND GLYCEMIC STATUS IN PATIENTS WITH COVID -19: A FOLLOW-UP STUDY

Context

Diabetes mellitus is a well known risk factor for COVID-19 patients. However, There is limited data to investigate the association between prediabetes and COVID-19.

Objective

We aimed to evaluate the effect of prediabetes and mechanical ventilation on the course of COVID-19 and determine whether patients who recover from COVID-19 infection show changes in cardiac function and laboratory findings during follow-up.

Patients and Methods

This study included 87 adult patients who were diagnosed with COVID-19 according to the WHO definition and were admitted for inpatient treatment between April 2021 and August 2021. They were classified into 3 groups, normoglycemia (n=40), prediabetes (n=25), and diabetes (n=22), and then divided into groups according to need for mechanical ventilation. Statistical analyses were performed to compare laboratory, echocardiographic findings and COVID-19 outcomes among the groups.

Results

The need of mechanical ventilation was significantly higher in both diabetes and prediabetes groups than the normoglycemic group. Patients with diabetes and prediabetes had significantly higher LV E/Em (p=0.003, p=0.045) and RV MPI (p=0.032, p=0.021) and significantly shorter PAT (p=0.001, p=0.036) and significantly longer RV IVRT (p=0.021, p=0.017), respectively, compared to the normoglycemia group. Patients who required mechanical ventilation had significantly higher CRP (p=0.043), troponin (p<0.001), ferritin (p<0.001), HBA1C (P<0.001), glucose (p=0.019), monocytes (p<0.001), and monocytes-HDL ratio (MHR) (p<0.001) and significantly lower levels of HDL-C (p<0.001). Glucose, HDL-C, troponin, MPV, NLR, PLR level and RV and E/Em were found independently associated with the RVMPI.

Conclusion

Prediabetes was associated with more impaired LV and RV diastolic functions compared to normoglycemic patients, comparable to those seen in diabetes. Our observations suggest that prediabetes should be considered as diabetes in the risk stratification of patients with COVID-19.