Mechanistic Insights Into Inflammation-Induced Arrhythmias: A Simulation Study

Abstract

Cardiovascular diseases are the primary cause of death of humans, and among these, ventricular arrhythmias are the most common cause of death. There is plausible evidence implicating inflammation in the etiology of ventricular fibrillation (VF). In the case of systemic inflammation caused by an overactive immune response, the induced inflammatory cytokines directly affect the function of ion channels in cardiomyocytes, leading to a prolonged action potential duration (APD). However, the mechanistic links between inflammatory cytokine-induced molecular and cellular influences and inflammation-associated ventricular arrhythmias need to be elucidated. The present study aimed to determine the potential impact of systemic inflammation on ventricular electrophysiology by means of multiscale virtual heart models. The experimental data on the ionic current of three major cytokines [i.e., tumor necrosis factor-α (TNF-α), interleukin-1 (IL-1β), and interleukin-6 (IL-6)] were incorporated into the cell model, and the effects of each cytokine and their combined effect on the cell action potential (AP) were evaluated. Moreover, the integral effect of these cytokines on the conduction of excitation waves was also investigated in a tissue model. The simulation results suggested that inflammatory cytokines significantly prolonged APD, enhanced the transmural and regional repolarization heterogeneities that predispose to arrhythmias, and reduced the adaptability of ventricular tissue to fast heart rates. In addition, simulated pseudo-ECGs showed a prolonged QT interval-a manifestation consistent with clinical observations. In summary, the present study provides new insights into ventricular arrhythmias associated with inflammation.

Keywords: COVID-19; cardiac simulation; inflammation; rat ventricle; ventricular arrhythmia.

FIGURE 1

Effects of different inflammatory cytokines on action potentials in (A) rat and (B) human models. Top panels plot the APs in (Ai) rat ENDO (Aii) rat EPI, (Bi) human ENDO, (Bii) human MID, and (Biii) human EPI cells. The bottom panels are the APD₉₀ of different cells for (Aiii) rats and (Biv) humans. Note: “inflammation” represents the combined effects of three cytokines.

FIGURE 2

Simulation results of the combined effects of three inflammatory cytokines on calcium handling. Corresponding profiles for I _CaL (A) and the concentration of Ca²⁺ in the cytosol (B) and sarcoplasmic reticulum (C).

FIGURE 3

Simulation results under different levels of inflammation. (A) APs of ENDO (Ai), MID (Aii), and EPI (Aiii) cells at different levels of inflammation. (B) APD₉₀ of three types of cells under normal and inflammatory conditions.

FIGURE 4

Measurement of the VW in the 1-D strand model under normal, mild inflammatory, and extreme inflammatory conditions. (A) The three subplots from left to right show the bidirectional conduction, the unidirectional conduction block, and the bidirectional conduction block. (B) Distributions of VWs across the strand. Black and red belts represent the control and inflammatory conditions, respectively. (C) Comparison of the average width of the VWs in the three groups.

FIGURE 5

Induced reentry arrhythmias in rat ventricular slices under conditions of (A) control and (B) local inflammation. The S1 and S2 stimuli are marked by white arrows, while the inflammatory region is indicated by a black rectangle.

FIGURE 6

Induced reentry arrhythmias in rat ventricular slices under conditions of (A) control and (B) local inflammation. The S1 and S2 stimuli are marked by white arrows, while the inflammatory region is indicated by a black rectangle.

FIGURE 7

The performance of the model at high frequency. Conduction waves under physiological and inflammatory conditions at high frequency (A) for rats and (B) for humans. The white arrow represents stimulus S1, which failed to pace.

FIGURE 8

Comparison of pseudo-ECGs between normal and inflammatory conditions.