Using a Systems Approach to Explore the Mechanisms of Interaction Between Severe Covid-19 and Its Coronary Heart Disease Complications

Abstract

Frontiers requested research on how a systems approach can explore the mechanisms of cardiovascular complications in Covid-19. The focus of this paper will thus be on these detailed mechanisms. It will elucidate the integrated pathogenic pathways based on an extensive review of literature. Many severe Covid-19 cases and deaths occur in patients with chronic cardiovascular comorbidities. To help understand all the mechanisms of this interaction, Covid-19 complications were integrated into a pre-existing systems-based coronary heart disease (CHD) model. Such a complete model could not be found in literature. A fully integrative view could be valuable in identifying new pharmaceutical interventions, help understand how health factors influence Covid-19 severity and give a fully integrated explanation for the Covid-19 death spiral phenomenon seen in some patients. Covid-19 data showed that CHD hallmarks namely, Hypercoagulability, Hypercholesterolemia, Hyperglycemia/Hyperinsulinemia, Inflammation and Hypertension have an important effect on disease severity. The pathogenic pathways that Covid-19 activate in CHD were integrated into the CHD model. This fully integrated model presents a visual explanation of the mechanism of interaction between CHD and Covid-19 complications. This includes a detailed integrated explanation of the death spiral as a result of interactions between Inflammation, endothelial cell injury, Hypercoagulability and hypoxia. Additionally, the model presents the aggravation of this death spiral through the other CHD hallmarks namely, Hyperglycemia/Hyperinsulinemia, Hypercholesterolemia, and/or Hypertension. The resulting model further suggests systematically how the pathogenesis of nine health factors (stress, exercise, smoking, etc.) and seven pharmaceutical interventions (statins, salicylates, thrombin inhibitors, etc.) may either aggravate or suppress Covid-19 severity. A strong association between CHD and Covid-19 for all the investigated health factors and pharmaceutical interventions, except for β-blockers, was found. It is further discussed how the proposed model can be extended in future to do computational analysis to help assess the risk of Covid-19 in cardiovascular disease. With insight gained from this study, recommendations are made for future research in potential new pharmacotherapeutics. These recommendations could also be beneficial for cardiovascular disease, which killed five times more people in the past year than Covid-19.

Keywords: COVID-19; SARS-CoV-2; cardiovascular comorbidities; coronary heart disease; systems-approaches.

Figure 1

Existing model showing the mechanisms of coronary heart disease (–28). The affective pathway of pharmaceuticals, blue boxes, is shown in Figure and salient serological biomarkers are indicated by the red tags (). The blunted blue arrows denote antagonize or inhibit and pointed blue arrows denote up-regulate or facilitate. ACE, angiotensin-converting-enzyme; β-blocker, beta-adrenergic antagonists; BDNF, brain-derived neurotrophic factor; BNP, B-type natriuretic peptide; OX, cyclooxygenase; CRP, C-reactive protein; D-dimer, fibrin degradation product D; FFA, free fatty acids; GCF, gingival crevicular fluid; HbA1c, glycated hemoglobin A1c; HDL, high-density lipoprotein; Hs, homocysteine; ICAM, intracellular adhesion molecule; IGF-1, insulin-like growth factor-1; IL, interleukin; LDL, low-density lipoprotein; MAPK, mitogen-activated protein (MAP) kinase; MCP, monocyte chemoattractant protein; MIF, macrophage migration inhibitory factor; MMP, matrix metalloproteinase; MPO, myeloperoxidase; NFκβ, nuclear factor-κβ; NLRP3, Inflammasome responsible for activation of inflammatory processes as well as epithelial cell regeneration and microflora; NO, nitric oxide; NO-NSAIDs, combinational NO-non-steroidal anti-inflammatory drug; OPG, osteoprotegerin; oxLDL, oxidized LDL; PAI, plasminogen activator inhibitor; PDGF, platelet-derived growth factor; P. gingivalis, Porphyromonas gingivalis; PI3K, phosphatidylinositol 3-kinase; RANKL, receptor activator of nuclear factor kappa-beta ligand; ROS, reactive oxygen species; SCD-40, recombinant human sCD40 ligand; SMC, smooth muscle cell; SSRI, serotonin reuptake inhibitors; TF, tissue factor; TMAO, an oxidation product of trimethylamine (TMA); TNF-α, tumor necrosis factor-α; VCAM, vascular cell adhesion molecule; vWF, von Willebrand factor.

Figure 2

Methodology used to develop an Integrated Covid-19/CHD model.

Figure 3

Integration of Covid-19 pathogenic pathways into the pathways of CHD.

Figure 4

Proposed integrated Covid-19/CHD model.

Figure 5

Death spiral evident in some critical Covid-19 patients.

Figure 6

Simplified schematic of the death spiral evident in some critical Covid-19 patients. The death spiral can be summarized as follows: Increased (1) inflammation at the lungs causes (2) EC injury, which can result in activation of the (3a) coagulation cascade and/or (3b) vascular leakage at the lungs, thereby causing (4) hypoxia which further increases inflammation, creating two closed positive feedback loops and causing severe Covid-19 through a death spiral. *As described in the text, this inflammation is initiated by various factors, primarily by a hyperimmune response to infection of SARS-CoV-2 (cytokine storm) but also other factors such as hyperinsulinaemia/hyperglycaemia or hypercholesterolaemia.

Figure 7

Qualitative comparison of risk factors between coagulation and Covid-19 severity. (An accurate quantitative comparison is not possible, mostly due to differences in study design and size).

Figure 8

CHD related aggravation of severe Covid-19 in patients with high cholesterol.

Figure 9

CHD related aggravation of severe Covid-19 in patients with high blood glucose levels.

Figure 10

CHD related aggravation of severe Covid-19 in patients with chronic hypertension.

Figure 11

The qualitative effect which different health factors have on CHD risk and Covid-19 severity. (An accurate quantitative comparison is not possible, mostly due to differences in study design and size).

Figure 12

The qualitative effect that different pharmaceuticals have on CHD risk and Covid-19 severity. (An accurate quantitative comparison is not possible, mostly due to differences in study design and size).

Figure 13

Schematics of typical simulation networks for (A) engineering and (B) CHD (23). (A) provides a small section of computer model of a relatively complex deep mine. (B) shows the initial computer model (23) developed from the existing CHD model in Figure 1 (23) using the simulation software developed for (A). The CHD computer model includes all the known interactions for health factors, measured elements (salient biomarkers) and controls (pharmaceutical interventions). Remember it is the measured biomarkers that individualize a patient.