| Literature DB >> 34070672 |
Francesco Nappi1, Adelaide Iervolino2, Sanjeet Singh Avtaar Singh3.
Abstract
Severe Acute Respiratory Syndrome (SARS) Coronavirus (CoV)-2 is a recently identified positive sense single-strand RNA (ssRNA) β-coronavirus. The viral spike proteins infect human hosts by binding to the cellular receptor angiotensin-converting enzyme 2 (ACE2). The infection causes a systemic illness involving cell metabolism. This widespread involvement is implicated in the pathophysiology of the illness which ranges from mild to severe, requiring multi organ support, ranging from oxygen supplementation to full cardiovascular and respiratory support. Patients with multiple co-existing comorbidities are also at a higher risk. The aim of this review is to explore the exact mechanisms by which COVID-19 affects patients systemically with a primary focus on the bleeding and thrombotic complications linked with the disease. Issues surrounding the thrombotic complications following administration of the ChAdOx1 nCoV-19 (Astra-Zeneca-Oxford) vaccine have also been illustrated. Risk stratification and treatment options in these patients should be tailored according to clinical severity with input from a multidisciplinary team.Entities:
Keywords: SARS-CoV-2 infection; cytokines; inflammation; metabolism; thrombosis
Year: 2021 PMID: 34070672 PMCID: PMC8229698 DOI: 10.3390/metabo11060341
Source DB: PubMed Journal: Metabolites ISSN: 2218-1989
2020 case-control retrospective studies comparing risk factors for thrombosis development in hospitalized patients with severe Covid-19 (controls) versus hospitalized patients with both severe infection and DVT or ATE (cases). VTE: venous thromboembolism, ATE: arterial thromboembolism, WBCs: white blood cells, INR: international normalized ratio, aPTT: activated partial thromboplastin time, CRP: C reactive protein, ICU: intensive care unit, CTPA: CT pulmonary angiography, IL-6: interleukin-6, DVT: deep vein thrombosis, IMV: invasive mechanical ventilation.
| Reference | Total SARS-CoV-2 + Hospitalized Patients | VTE and ATE Cases | Risk Factors More Present in Cases ( | Risk Factors Similar in Cases and Controls ( | Conclusions |
|---|---|---|---|---|---|
| Stoneham et al., 2020 [ | 208 | 21 | High Wbcs, high D-dimer, high INR. | aPTT ratio, fibrinogen. | Comorbidities were not associated with a higher risk of thrombosis. Monitoring of D-dimer and anti-factor Xa levels may be relevant for management. |
| Zuo et al., 2020 [ | 44 | 11 | High calprotectin, markers of NETs (myeloperoxidase-DNA complexes) high D-dimer, high platelets. | Troponins, Wbcs. | There was a significant difference between peak D-dimer, calprotectin and cell free DNA levels between the populations. |
| Zhang et al., 2020 [ | 143 | 66 | High Wbcs, older age, low oxygenation index, high rate of cardiac injury, CURB-65 score 3 to 5, Padua score ≥ 4, high D-dimer. | Platelets count. | COVID-19 is suspected to cause an additional risk factor for DVT in hospitalized patients. |
| Planquette et al., 2020 [ | 1042 | 59 | High CRP, fibrinogen, D-dimer. IMV. | Comorbidities: BMI, previous VTE, ATE, Cancer, hypertension, Cardiovascular diseases. | Similar prevalence of VTE risk factors in cases and controls was found. In both groups, altered coagulation parameters were found. |
| Trimaille et al., 2020 [ | 289 | 49 | High Improve score, high WBCs, D-dimer, low haemoglobin at discharge. | Padua score of 4 or more, CRP | Lack of thromboprophylaxis is a major determinant of VTE in non-ICU COVID-19 patients. Comorbidities were not found to affect the event occurrence. |
| Shah et al., 2020 [ | 187 | 81 | High troponins, ferritin, D-dimer. | Platelets count, Wbcs, thromboelastography parameters. | Elevated D-dimer, ferritin, troponin and white cell count at ICU admission may reflect undiagnosed altered coagulation and be used to identify patients for CTPA. |
| Kolielat et al., 2021 [ | 117 | 18 | High D-dimer, fibrinogen, ferritin. | Wbcs, platelets, troponins, Il-6. | Elevated D-dimer and a less elevated fibrinogen are associated with DVT despite conventional thromboprophylactic treatment. |
| Kampouri et al., 2020 [ | 443 | 41 | High D-dimer, positive Wells criteria, bilateral infiltrates on X-rays or CT scan, mechanical ventilation. | Wbcs, platelets, CRP, Padua score, Geneva score. | The combination of Wells ≥ 2 score and D—dimer ≥ 3000 ng/L is predictive of VTE at admission. Hospitalization in the ICU and especially mechanical ventilation were associated with VTE occurrence. The combination of Wells’ score and D-dimer value can be used for guiding empiric anticoagulation therapy. |
Figure 1Interaction between antiviral agents and antiplatelet drugs on CYP3A4 metabolism. In red, antiviral agents are depicted. Lopinavir and ritonavir exert an inhibitory action on the cytochrome. This increases the exposure of ticagrelor leading to a dysregulation of hemostasis (highlighted in the picture being it the only depicted potential effect). Remdesivir is instead an inducer of CYP3A4 function. Differently from ticagrelor, prasugrel is metabolized by several cytochromes (2C19, 2C9, 3A4/3A5, 2B6, 2D6), thus its effects seem to be unmodified by ritonavir or lopinavir interaction. CYP3A4: Cytochrome P450 3A4, ADP P2Y12 receptor: adenosine 5′diphosphate P2Y12 receptor.
Figure 2Pathophysiology of SARS-CoV-2 coagulopathy. DIC has been frequently noticed in Covid-19 severe patients but bleeding diathesis was a less present feature. Cytokine storm and inflammatory-driven thrombogenesis is the most known hypothesis, due to IL-2R, IL-6, IL-8, and IL-10 cascade generation. A direct viral damage has also been acknowledged to start endothelitis and endothelial damage. MAS is instead an added mechanism present on an already compromised immune condition where hyperferritinaemia and increase in IL-6 production are pathognomonic of macrophages overactivation. RAAS system, complement and platelets also drive uncontrollable responses by generating hypercytokinemia and dysregulating fibrinolysis. Increased levels of PAI-1 and tissue factor have been demonstrated. Meta-inflammation is another possible trigger for coagulopathy. Obesity, hyperinsulinemia and metabolic syndrome are strong risk factors for severity of infection in hospitalized patients. Primary conditions developing after viral infection are depicted with a darker background. RAAS overactivation, platelets and complement activation are mainly secondary mechanisms, thus they appear in a lighter background. Abbreviations: DIC: disseminated intravascular coagulation, FDPs: fibrin degradation products, ACE: angiotensin converting enzyme, AngII: angiotensin II, PAI-1: plasminogen activator inhibitor, IF-γ: interferon- γ, TNF-α: tumor necrosis factor-α, IL-6, IL-8, IL-10: interleukin 6, interleukin 8, interleukin 10. IL-2R: interleukin 2 receptor.
Figure 3Hyperinsulinemia, CVD and vitamin D have a strong impact on homeostatic equilibrium. Both hyperinsulinemia (depicted in bold and colored font, in order to emphasize it) and hyperglycemia generate states of increased coagulation and decreased fibrinolysis. By driving the development of CVD, diabetes mellitus and obesity, they contribute to the inflammatory substrate of cytokines. They increase the ROS production due to the damage in decreasing both NAD+ and reduced glutathione (GSH). A reduction in vitamin D, due to sequestration into the adipocytes, leads to decreased levels of ChS and HSPG, regulators of RBCs deformation, increasing cells agglutination. These mechanisms are all responsible for thrombosis initiation. Vascular inflammation and decreasing levels of Ch-S and HSPG are represented with colored backgrounds being the main actors of thrombogenesis trigger. Also, thrombogenesis, the main effect, is outlined with a different color too. Abbreviations: Ch-S: cholesterol sulfate, HSPG: heparan sulfate proteoglycans, NAD+: nicotinamide adenine dinucleotide, PAI-1 plasminogen activator inhibitor type 1, ROS: reactive oxygen species, T2DM: type 2 diabetes mellitus, SIRT3: sirtuin 3, TNF-α: tumor necrosis factor-α, IL-6: interleukin 6.
Investigational studies related to ChAdOx1 nCov-19-associated thrombosis cases. We reported major findings, age (as median or mean) and female gender prevalence in number &/or percentage. PF: platelet factor.
| Published Study | Vaccine Type | Patient (N) | Women (N) | Age | Time Span | Cases’ Etiology | Major Findings |
|---|---|---|---|---|---|---|---|
| Greinacher et al. [ | ChAdOx1 nCov-19 | 11 | 9 | 36 yrs (median) | 5 to 16 days after 1st dose | 10 multiple thrombosis, 9 cerebral venous thrombosis. | Immune thrombotic thrombocytopenia. |
| Schultz et al. [ | ChAdOx1 nCov-19 | 5 | 4/5 | 40.8 ys (mean) | 7 to 10 days after | 2 thromboses (sigmoid cerebral sinuses), | High levels of antibodies to platelet factor (PF) 4-polyanion complexes. Authors propose the acronym VITT (vaccine-induced immune thrombotic thrombocytopenia) as causative mechanism. |
Recent rare cases’ reports of thrombosis following administration of anti-SARS-CoV-2 vaccines. Data for ChAdOx1 nCov-19 vaccine are reported by 4 April 2021. Data for JNJ-78436735/Ad26.COV2.S are reported by 26 April 2021. CVST: cerebral venous sinus thrombosis, EEA: European Economic Area, UK: United Kingdom, US: United States.
| Vaccine Type | Thrombosis Cases | Total Administrations | Associated Factors |
|---|---|---|---|
| ChAdOx1 nCov-19 | 169 CVST, 53 splanchnic vein thrombosis | 34 million people had been vaccinated in the EEA and UK | <60 years of age, symptoms onset within 3 weeks after vaccination, female gender, thrombocytopenia. |
| JNJ-78436735/Ad26. | 6 CVST (1 death) | 8.09 million in US | Thrombocytopenia, women between 18 and 48 ys. symptoms onset between 6 to 13 days after vaccination |
Selected studies investigating efficacy and adverse events of most administered anti-SARS-CoV-2 vaccines worldwide. For each study, the total participants number, women/total number and age are presented as baseline characteristics. Major findings have been divided into two columns: adverse events and efficacy data in terms of prevention of Covid-19 and antibodies titers. Data are number (percentages).
| Published Study | Vaccine Type | Participant ( | Women ( | Age | Vaccine Components | Adverse Events | Efficacy |
|---|---|---|---|---|---|---|---|
| Voysey et al. (interim analysis of | ChAdOx1 nCoV-19 | 11,636 | 3525/5807 | mostly 18–55 yrs | dsDNA encoding for the Spike protein protected in an adenoviral particle | 175 severe adverse events | 2 standard doses efficacy was 62.1%. Low boosted dose efficacy was 90.0%. |
| Ramasamy et al. (phase 2 of COV002) [ | ChAdOx1 nCoV-19 | 560 | 104 low-d | 100 (18–55 yrs) | dsDNA encoding for the Spike protein protected in an adenovirus. | Systemic reactions | 14 days after the 2nd dose, 208 of 209 boosted participants had neutralising antibody responses. |
| Logunov et al. (phase 3) [ | rAd26 and rAd5 vector-based vaccine (Sputnik V) | 21.977 | 5821 (38.9%) | mostly 18–60 yrs | dsDNA encoding for the Spike protein protected in Ad26 vector for the 1st dose, Ad5 for the 2nd one). | 4 deaths, none was related to the vaccine. | Vaccine efficacy was 91.6% (95% CI 85.6–95.2). |
| Zhang et al. (phase 1/2) [ | CoronaVac (Sinovac Life Sciences, Beijing, China) | 743 | 397/743 | phase 1 42.6 yrs | Inactivated virus vaccine with beta-propiolactone | In the phase 2 trial adverse reactions was 19% with 3 μg 19% with 6 μg group, and 18% with placebo. | In the phase 2.97% seroconversion with 3 μg, 100% with 6 μg group, and 0% with placebo group. |
| Sadoff et al. (interim analysis of phase 1–2) [ | Ad26.COV2. S/JNJ-78436735 (Johnson&Johnson) | 805 | 169 in cohort 1, 159 in cohort 3 | 35.4 ± 10.2 (cohort 1) | dsDNA encoding for the Spike protein protected in an adenoviral particle (Ad26) | The most frequent systemic adverse event was fever. Systemic adverse events were less common in cohort 3 than in cohort 1. | Reactogenicity was lower after the second dose. Neutralizing-antibody titers were detected in 90% or more of all participants on day 29 after the first vaccine dose |
| Polack et al. [ | BNT162b2 mRNA | 43.548 | 9221 (48.9) | 52.0 | mRNA vaccine encoding for Spike protein protected in a lipid nanoparticle | Serious adverse events incidence was low and similar between the vaccine and placebo groups. | BNT162b2 95% effective in preventing the disease. Similar efficacy for age, sex, race, ethnicity, baseline body-mass index, and the presence of coexisting conditions. |
| Baden et al. [ | mRNA-1273 vaccine (Moderna) | 30.351 | 7108 (46.9) | 51.3 (18–95) | encapsulated mRNA vaccine encoding for Spike protein protected in a lipid nanoparticle | Transient local systemic reactions. No safety concerns were identified. | mRNA-1273 94.1% effective in preventing severe and mild disease development. |
Abbreviation. Ca = control administration; dsDNA = double strand DNA, va: vaccine administration.
Total administrations of anti-SARS-CoV-2 vaccines according to selected countries and type of vaccine. Data are only available for countries which provided official reports. Time span is 24 December 2020–26 April 2021.
| - | Adenoviral Vector | mRNA | ||||
|---|---|---|---|---|---|---|
| Country | ChAdOx1 nCov-19 (Astrazeneca) | Sputnik V/Gam-Covid-Vac | CoronaVac (SinoVac) | JNJ-78436735/Ad26.COV2.S (Johnson&Johnson) | BNT162b2 mRNA (Pfizer) | mRNA-1273 (Moderna) |
| France | 3.72 million | 0 | 0 | 2004 | 14.33 million | 1.59 million |
| Germany | 5.60 million | 0 | 0 | 0 | 18.81 million | 1.47 million |
| Italy | 3.97 million | 0 | 0 | 20700 | 12.83 million | 1.27 million |
| United States | 0 | 0 | 0 | 8.09 million | 116.19 million | 100.83 million |
Source of data: https://ourworldindata.org/covid-vaccinations (accessed on 10 April 2021); mRNA-1273 Moderna: https://github.com/owid/covid19data/tree/master/public/data/vaccinations/locations.csv (accessed on 10 April 2021); ChAdOx1 nCoV-19: https://github.com/owid/covid-19-data/tree/master/public/data/vaccinations/locations.csv (accessed on 10 April 2021); BNT162b2 mRNA: https://github.com/owid/covid-19-data/tree/master/public/data/vaccinations/locations.csv (accessed on 10 April 2021); CoronaVac (SinoVac): https://github.com/owid/covid-19-data/tree/master/public/data/vaccinations/locations.csv (accessed on 10 April 2021); JNJ-78436735/Ad26.COV2.S (Johnson & Johnson): https://github.com/owid/covid-19-data/tree/master/public/data/vaccinations/locations.csv (accessed on 10 April 2021).