| Literature DB >> 33335527 |
Mohamed Abu-Farha1, Fahd Al-Mulla2, Thangavel Alphonse Thanaraj2, Sina Kavalakatt1, Hamad Ali2,3, Mohammed Abdul Ghani4, Jehad Abubaker1.
Abstract
COVID-19 is a disease caused by the coronavirus SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus-2), known as a highly contagious disease, currently affecting more than 200 countries worldwide. The main feature of SARS-CoV-2 that distinguishes it from other viruses is the speed of transmission combined with higher risk of mortality from acute respiratory distress syndrome (ARDS). People with diabetes mellitus (DM), severe obesity, cardiovascular disease, and hypertension are more likely to get infected and are at a higher risk of mortality from COVID-19. Among elderly patients who are at higher risk of death from COVID-19, 26.8% have DM. Although the reasons for this increased risk are yet to be determined, several factors may contribute to type-2 DM patients' increased susceptibility to infections. A possible factor that may play a role in increasing the risk in people affected by diabetes and/or obesity is the impaired innate and adaptive immune response, characterized by a state of chronic and low-grade inflammation that can lead to abrupt systemic metabolic alteration. SARS patients previously diagnosed with diabetes or hyperglycemia had higher mortality and morbidity rates when compared with patients who were under metabolic control. Similarly, obese individuals are at higher risk of developing complications from SARS-CoV-2. In this review, we will explore the current and evolving insights pertinent to the metabolic impact of coronavirus infections with special attention to the main pathways and mechanisms that are linked to the pathophysiology and treatment of diabetes.Entities:
Keywords: Furin; angiotensin converting enzyme2 (ACE2); coronavirus disease (COVID-19); interferon induced membrane (IFITM3); metformin; serine 2 (TMPRSS2); transmembrane protease; type 2 diabetes
Year: 2020 PMID: 33335527 PMCID: PMC7736089 DOI: 10.3389/fimmu.2020.576818
Source DB: PubMed Journal: Front Immunol ISSN: 1664-3224 Impact factor: 7.561
Figure 1A schematic model summarizing the various mechanisms by which diabetes can impact on COVID-19 poor outcome.
Prevalence (%) of comorbidities in COVID-19 infected patients.
| Study | Sample size (n) | Diabetes (%) | CVD (%) | HTN (%) | CKD (%) | Ref |
|---|---|---|---|---|---|---|
| Li B et al. | 1,527 | 9.7 | 16.4 | 17.1 | NR | ( |
| Covid-19 group, Italy | 481 | 33.9 | 30.1 | 73.8 | 20.2 | ( |
| Onder et al. | 355 | 35.5 | 42.5 | NR | NR | ( |
| Zhou et al. | 191 | 19 | 8 | 30 | 1 | ( |
| Wu C et al. | 201 | 10.9 | 4 | 19.4 | 1 | ( |
| Guan et al. | 1,099 | 7.4 | 3.8 | 15 | 0.7 | ( |
| Bhatraju et al. | 24 | 58 | NR | NR | 21 | ( |
| CDC, USA | 7,162 | 10.9 | 9 | NR | 3 | ( |
| Zhang et al. | 140 | 12.1 | 8.6 | 30 | 1.4 | ( |
| Liu J et al. | 61 | 8.2 | 1.6 | 19.7 | NR | ( |
| Guo et al. | 187 | 15 | 11.2 | 32.6 | 3.2 | ( |
| Huang et al. | 41 | 19.5 | 15 | 14.6 | NR | ( |
| Chen N et al. | 99 | 12.1 | 40 | NR | NR | ( |
| Wang et al. | 138 | 10.1 | 19.6 | 31.2 | 2.9 | ( |
| Yang J et al. | Meta-analysis of eight studies n = 46,248 | 0.08 | 0.05 | 0.17 | NR | ( |
| Yang X et al. | 52 | 17 | 23 | NR | NR | ( |
| Liu K et al. | 137 | 10.2 | 7.3 | 9.5 | NR | ( |
| Chen T et al. | 274 | 17 | 8 | 34 | 1 | ( |
| CDC China | 20,982 | 5.3 | 4.2 | 12.8 | NR | ( |
| Singh et al. | Meta-analysis of 10 studies = 2,209 | 0.11 | 0.07 | 0.21 | NR | ( |
| Hu Y et al. | Meta-analysis of 21 studies n = 47,344 | 7.7 | 4.7 | 15.6 | 2.1 | ( |
CVD, cardiovascular disease; HTN, hypertension; CKD, chronic kidney disease; NR, not reported; CDC, Centers for Disease Control and Prevention.
Prevalence of diabetes amongst non-severe and severe COVID-19 infected patients.
| Study | Sample size(n) | DM (%) | ICU admission (Severe/Critical) (%)* | Significance p value of non-severe | Ref. | ||||
|---|---|---|---|---|---|---|---|---|---|
| Wu et al. | 201 | 10.90% | 19.00% | 0.002 | ( | ||||
| Guan et al. | 1,099 | 7.40% | 16.20% | NR | ( | ||||
| CDC, USA | 7,162 | 10.90% | 32.00% | NR | ( | ||||
| Zhang et al. | 140 | 12.10% | 13.80% | 0.615 | ( | ||||
| Huang et al. | 41 | 15% | 25.00% | 0.160 | ( | ||||
| Wang et al. | 138 | 10.10% | 22.20% | 0.009 | ( | ||||
| Liu J et al. | 61 | 8.20% | 17.60% | 0.094 | ( | ||||
| Hu Y et al. | Meta-analysis of 21 studies n = 47,344 | 7.70% | 44.50% | NR | ( | ||||
DM, diabetes mellitus; Ref., references; CDC, Centers for Disease Control and Prevention. *% is calculated from total population with COVID-19.
Prevalence of diabetes among non-survivor and survivor COVID-19 infected patients.
| Study | Sample size (n) | DM in entire cohort (%) | DM (%) in non-survivors | DM (%) in survivors | Mortality rate | Ref. | |
|---|---|---|---|---|---|---|---|
| Zhou et al. | 191 | 19% | 31.00% | 14.00% | OR 2.8(1.35 to 6.05) p < 0.001 | ( | |
| Wu et al. | 88 | 18.2% | 25.00% | 12.50% | HR 1.58(0.80 to 3.13), p = 0.19 | ( | |
| Guan et al. | 1,099 | 7.4% | 26.90% | 6.10% | NR | ( | |
| Yang X et al. | 52 | 17% | 22% | 10% | NR | ( | |
| Chen N et al. | 274 | 17% | 21.00% | 14.00% | NR | ( | |
Figure 2Illustration of association of the different host-cellular proteins involved in SARS-COV-2 infection with diabetes.