Diabetes patients with comorbidities had unfavorable outcomes following COVID-19: A retrospective study

Acarbose reduces Pseudomonas aeruginosa respiratory tract infection in type 2 diabetic mice

BACKGROUND

Type 2 diabetes mellitus (T2DM) is widely prevalent worldwide, and respiratory tract infections (RTIs) have become the primary cause of death for T2DM patients who develop concurrent infections. Among these, Pseudomonas aeruginosa infection has been found to exhibit a high mortality rate and poor prognosis and is frequently observed in bacterial infections that are concurrent with COVID-19. Studies have suggested that acarbose can be used to treat T2DM and reduce inflammation. Our objective was to explore the effect of acarbose on P. aeruginosa RTI in T2DM individuals and elucidate its underlying mechanism.

METHODS

High-fat diet (HFD) induction and P. aeruginosa inhalation were used to establish a RTI model in T2DM mice. The effect and mechanism of acarbose administered by gavage on P. aeruginosa RTI were investigated in T2DM and nondiabetic mice using survival curves, pathological examination, and transcriptomics.

RESULTS

We found that P. aeruginosa RTI was more severe in T2DM mice than in nondiabetic individuals, which could be attributed to the activation of the NF-κB and TREM-1 signaling pathways. When acarbose alleviated P. aeruginosa RTI in T2DM mice, both HIF-1α and NF-κB signaling pathways were inhibited. Furthermore, inhibition of the calcium ion signaling pathway and NF-κB signaling pathway contributed to the attenuation of P. aeruginosa RTI by acarbose in nondiabetic mice.

CONCLUSIONS

This study confirmed the attenuating effect of acarbose on P. aeruginosa RTIs in T2DM and nondiabetic mice and investigated its mechanism, providing novel support for its clinical application in related diseases.

In Vitro Anti-Oxidant, In Vivo Anti-Hyperglycemic, and Untargeted Metabolomics-Aided-In Silico Screening of Macroalgae Lipophilic Extracts for Anti-Diabetes Mellitus and Anti-COVID-19 Potential Metabolites

COVID-19 patients with comorbid DM face more severe outcomes, indicating that hyperglycemic conditions exacerbate SARS-CoV-2 infection. Negative side effects from existing hyperglycemia treatments have urged the need for safer compounds. Therefore, sourcing potential compounds from marine resources becomes a new potential approach. Algal lipids are known to possess beneficial activities for human health. However, due to limitations in analyzing large amounts of potential anti-hyperglycemic and anti-COVID-19-related marine metabolites, there is an increasing need for new approaches to reduce risks and costs. Therefore, the main aim of this study was to identify potential compounds in macroalgae Sargassum cristaefolium, Tricleocarpa cylindrica, and Ulva lactuca lipophilic extracts for treating DM and COVID-19 by an integrated approach utilizing in vitro anti-oxidant, in vivo anti-hyperglycemic, and metabolomic-integrated in silico approaches. Among them, S. cristaefolium and T. cylindrica showed potential anti-hyperglycemic activity, with S. cristaefolium showing the highest anti-oxidant activity. A GC-MS-based untargeted metabolomic analysis was used to profile the lipophilic compounds in the extracts followed by an in silico molecular docking analysis to examine the binding affinity of the compounds to anti-DM and anti-COVID-19 targets, e.g., α-amylase, α-glucosidase, ACE2, and TMPRSS2. Notably, this study reveals for the first time that steroid-derived compounds in the macroalgae T. cylindrica had higher binding activity than known ligands for all the targets mentioned. Studies on drug likeliness indicate that these compounds possess favorable drug properties. These findings suggest the potential for these compounds to be further developed to treat COVID-19 patients with comorbid DM. The information in this study would be a basis for further in vitro and in vivo analysis. It would also be useful for the development of these candidate compounds into drug formulations.

Predictors of mortality and ICU requirement in hospitalized COVID-19 patients with diabetes: A multicentre study

AIM

This study aimed to identify the predictors of mortality and ICU requirements in hospitalized COVID-19 Patients with Diabetes.

DESIGN

Cross-sectional study.

METHODS

It was a retrospective study of patients hospitalized with COVID-19 infection from October 2020-February 2021 in four hospitals in Sylhet, Bangladesh. Logistic regression analysis was applied to explore the predictors of ICU requirement and in-hospital mortality.

RESULTS

In the whole cohort (n = 500), 11% of patients died and 24% of patients required intensive care unit (ICU) support. Non-survivors had significantly higher prevalence of lymphopenia, thrombocytopenia and leukocytosis. Significant predictors of in-hospital mortality were older age, neutrophil count, platelet count and admission peripheral capillary oxygen saturation (SpO2). Older age, ischemic heart disease, WBC count, D-dimer and admission SpO2 were identified as significant predictors for ICU requirement.

PATIENT OR PUBLIC CONTRIBUTION

No.

Association of Glucose-Lowering Drugs With Outcomes in Patients With Diabetes Before Hospitalization for COVID-19: A Systematic Review and Network Meta-analysis

IMPORTANCE

Patients with COVID-19 have a high prevalence of diabetes, and diabetes and blood glucose control are determinants of intensive care unit admission and mortality.

OBJECTIVE

To evaluate the association between COVID-19-related adverse outcomes and 8 antihyperglycemic drugs in patients with diabetes who were subsequently diagnosed and hospitalized with COVID-19.

DATA SOURCES

Data were retrieved and collected in PubMed, Embase, Cochrane Central Register, Web of Science, and ClinicalTrials.gov from database inception to September 5, 2022.

STUDY SELECTION

For this systematic review and network meta-analysis, randomized clinical trials and observational studies conducted among patients with diabetes while receiving glucose-lowering therapies for at least 14 days before the confirmation of COVID-19 infection were included after blinded review by 2 independent reviewers and consultations of disagreement by a third independent reviewer. Of 1802 studies initially identified, 31 observational studies met the criteria for further analysis.

DATA EXTRACTION AND SYNTHESIS

This study follows the Preferred Reporting Items for Systematic Reviews and Meta-analyses reporting guideline. Bayesian network meta-analyses were performed with random effects.

MAIN OUTCOMES AND MEASURES

A composite adverse outcome, including the need for intensive care unit admission, invasive and noninvasive mechanical ventilation, or in-hospital death.

RESULTS

Thirty-one distinct observational studies (3 689 010 patients with diabetes hospitalized for COVID-19) were included. The sodium-glucose cotransporter-2 inhibitors (SGLT-2is) were associated with relatively lower risks of adverse outcomes compared with insulin (log of odds ratio [logOR], 0.91; 95% credible interval [CrI], 0.57-1.26), dipeptidyl peptidase-4 inhibitors (logOR, 0.61; 95% CrI, 0.28-0.93), secretagogues (logOR, 0.37; 95% CrI, 0.02-0.72), and glucosidase inhibitors (logOR, 0.50; 95% CrI, 0.00-1.01). Based on the surface under the cumulative ranking curves value, SGLT-2is were associated with the lowest probability for adverse outcomes (6%), followed by glucagon-like peptide-1 receptor agonists (25%) and metformin (28%). A sensitivity analysis revealed that the study was reliable.

CONCLUSIONS AND RELEVANCE

These findings suggest that the use of an SGLT-2i before COVID-19 infection is associated with lower COVID-19-related adverse outcomes. In addition to SGLT-2is, glucagon-like peptide-1 receptor agonists and metformin were also associated with relatively low risk of adverse outcomes.

Preadmission use of antidiabetic medications and mortality among patients with COVID-19 having type 2 diabetes: A meta-analysis

BACKGROUND

Diabetes is an independent predictor of poor outcomes in patients with COVID-19. We compared the effects of the preadmission use of antidiabetic medications on the in-hospital mortality of patients with COVID-19 having type 2 diabetes.

METHODS

A systematic search of PubMed, EMBASE, Scopus and Web of Science databases was performed to include studies (except case reports and review articles) published until November 30, 2021. We excluded papers regarding in-hospital use of antidiabetic medications. We used a random-effects meta-analysis to calculate the pooled OR (95% CI) and performed a sensitivity analysis to confirm the robustness of the meta-analyses.

MAIN FINDINGS

We included 61 studies (3,061,584 individuals), which were rated as having low risk of bias. The OR (95% CI) indicated some medications protective against COVID-related death, including metformin [0.54 (0.47-0.62), I² 86%], glucagon-like peptide-1 receptor agonist (GLP-1RA) [0.51 (0.37-0.69), I² 85%], and sodium-glucose transporter-2 inhibitor (SGLT-2i) [0.60 (0.40-0.88), I² 91%]. Dipeptidyl peptidase-4 inhibitor (DPP-4i) [1.23 (1.07-1.42), I² 82%] and insulin [1.70 (1.33-2.19), I² 97%] users were more likely to die during hospitalization. Sulfonylurea, thiazolidinedione, and alpha-glucosidase inhibitor were mortality neutral [0.92 (95% CI 0.83-1.01, I² 44%), 0.90 (95% CI 0.71-1.14, I² 46%), and 0.61 (95% CI 0.26-1.45, I² 77%), respectively]. The sensitivity analysis indicated that our findings were robust.

CONCLUSIONS

Metformin, GLP-1RA, and SGLT-2i were associated with lower mortality rate in patients with COVID-19 having type 2 diabetes. DPP-4i and insulin were linked to increased mortality. Sulfonylurea, thiazolidinedione, and alpha-glucosidase inhibitors were mortality neutral. These findings can have a large impact on the clinicians' decisions amid the COVID-19 pandemic.