Erythrocytes induce vascular dysfunction in COVID-19

Background

Vascular injury has been implicated as a major cause of clinical complications in patients with coronavirus disease 2019 (COVID-19). Autopsy studies have revealed destruction of the endothelial cell lining, which might explain cardiovascular alterations arising from the infection. However, data demonstrating endothelial dysfunction during ongoing infection are sparse, and the underlying mechanisms are still largely unknown. Red blood cells (RBCs) are affected by COVID-19 with alterations in their structure and function, possibly contributing to vascular injury via increased oxidative stress.

Purpose

To determine the presence of endothelial dysfunction in patients with COVID-19 and to explore the RBC as a possible mediator of such dysfunction.

Methods

The study was performed on 17 patients hospitalized for moderate COVID-19 infection and age- and sex-matched healthy subjects. Inclusion criteria of the COVID-19 patients were PCR-verified SARS-CoV2 infection, pulmonary infiltrates on x-ray, oxygen demand during hospital stay and ≤ one cardiovascular co-morbidity. Microvascular endothelial function in vivo was assessed with a pulse amplitude tonometry device on each index finger at baseline and during reactive hyperemia and expressed as reactive hyperemia index (RHI). RBCs from COVID-19 patients (C19-RBCs) and healthy subjects (H-RBCs) were incubated with isolated rat aortic segments for evaluation of endothelium-dependent and -independent relaxation.

Results

COVID-19 patients displayed profound impairment in endothelial function in vivo with RHI 1.56 (1.30–1.81, median and interquartile range) compared to healthy subjects 2.36 (1.97–2.79, p<0.001). C19-RBCs induced severe impairment in both endothelium-dependent (27% maximal relaxation) and -independent relaxations (54%) compared to H-RBCs (67% and 95% relaxation, respectively). Further, C19-RBCs induced upregulation of vascular arginase 1 (∼2 fold increase compared to H-RBCs) and markers of oxidative stress (∼6 fold). Consequently, inhibition of vascular arginase or superoxide attenuated the impairment in endothelial function induced by C19-RBCs. C19-RBCs were characterized by increased production of reactive oxygen species (∼1.4 fold) and reduced export of the nitric oxide metabolite nitrate. Following pre-incubation with interferon-γ, but not interleukin-6 or tumor necrosis factor-α, H-RBCs induced impairment in endothelial function.

Conclusions

This study demonstrates the presence of marked endothelial dysfunction in an otherwise mainly healthy patient group hospitalized for COVID-19, and clearly implicates a central role of the RBC as a mediator of endothelial injury through enhancement of reactive oxygen species and arginase. These data shed light on a new pathological mechanism underlying vascular dysfunction in COVID-19 patients and may lay the foundation for future therapeutic developments.

Funding Acknowledgement

Type of funding sources: Foundation. Main funding source(s): Swedish Heart and Lung foundationSwedish Research Council

Remote ischemic conditioning fails to protect against ischemia-reperfusion injury in patients with untreated familial hypercholesterolemia

Background/Introduction

Remote ischemic conditioning (RIC) is the action of brief periods of ischemia to a remote tissue and has been suggested to protect against myocardial ischemia-reperfusion (IR) injury. The outcomes of clinical trials in terms of clinical endpoints and infarct size reduction have been variable, which may be related to influence of comorbidities on the effect of RIC. Animal studies suggest that hypercholesterolemia attenuates the cardioprotective effect of RIC, but no data from study on patients are available. Hence, our aim was to investigate the response of RIC on IR-induced endothelial dysfunction in patients with familial hypercholesterolemia (FH).

Purpose

To investigate if RIC protects against endothelial dysfunction induced by IR in patients with FH with high (≥5.5 mmol/L) and low (≤2.5 mmol/L) LDL-cholesterol levels.

Methods

All subjects with FH (n=12) with LDL ≥5.5 mmol/L, FH with LDL <2.5 mmol/L (n=12), and age-matched healthy control subjects (n=12) participated in two protocols separated by at least one week. In both protocols, endothelium-dependent vasodilatation was assessed by determination of flow-mediated vasodilatation (FMD) of the brachial artery at baseline and again after 20 minutes of forearm ischemia and 20 minutes of reperfusion. Forearm ischemia was induced by inflating a blood pressure cuff on the upper arm to 200 mmHg. An additional inflatable cuff was placed around the left thigh. In one protocol (IR+sham), this cuff was left uninflated. In the second protocol (IR+RIC), it was inflated to 200 mmHg in four cycles of 5 minutes inflation and 5 min deflation with the first cycle starting at the onset of forearm ischemia.

Results

Plasma mean LDL-cholesterol was significantly higher in the FH group with high LDL (6.6±1.4 mmol/L) compared to the control group (2.4±0.7 mmol/L; p<0.01) and the low LDL FH group (2.0±0.6 mmol/L; p<0.001). FMD was markedly reduced (p<0.05) in all subjects following IR+sham, indicating IR-induced endothelial dysfunction in all three groups. As expected, RIC prevented the reduction in FMD after IR in the control group (Fig 1). By contrast, in the FH group with high LDL, RIC failed to protect from IR-induced endothelial dysfunction. Thus, in this group the decrease in FMD was similar after IR+RIC and IR+ sham (Fig 1). In the FH group with LDL <2.5 mmol/L, the decrease in FMD induced by IR was attenuated by RIC (p=0.05).

Conclusion

These observations suggest that RIC, which protects from IR-induced endothelial dysfunction in healthy controls, fails to protect from IR-induced endothelial dysfunction in patients with FH and LDL-cholesterol >5.5 mmol/L. The protective effect of RIC is restored after treatment of hypercholesterolemia. This finding may have bearings on the clinical efficacy of RIC in patients with ST-elevation myocardial infarction.

Funding Acknowledgement

Type of funding sources: Foundation. Main funding source(s): Heart-lung foundation

P655High lipoprotein(a) plasma levels is associated with higher prevalence of cardiovascular disease and poor metabolic control in patients with type 1 diabetes

Background

Lipoprotein(a) [Lp(a)] is a cardiovascular risk factor that has been shown to correlate to cardiovascular disease and aortic valve disease. Plasma levels of Lp(a) has a skewed distribution, is highly influenced by genetic inheritance and is not considered to be affected by age, sex or lifestyle. Its importance for the development of vascular complications in patients with type 1 diabetes is unknown.

Purpose

To assess the contribution of Lp(a) to cardiovascular disease, microvascular complications, aortic valve disease in patients with type 1 diabetes mellitus, and to assess the relationship between diabetes metabolic control and Lp(a) levels.

Methods

We included 1857 consecutive type 1 diabetes patients receiving regular care at our out-patient clinic, department of Endocrinology, into an observational cross sectional registry study. Patient characteristics, cardiovascular history and Lp(a) measurement was extracted from their electronic medical charts. Patients were divided into four groups according to their Lp(a) levels in nmol/L (Very Low <10; Low 10–30; Intermediate 30–120; High >120) and statistical analysis was performed comparing the prevalence of mikro- and makrovascular complications between the groups. The relationship between Lp(a) and diabetes control measured as HbA1c (mmol/mol) was studied by comparing the subgroups with good (<52), average (52–70) and poor (>70) metabolic control.

Results

The mean (SD) age and diabetes duration in the cohort was 49.9 (15.8) years and 26.7 (15.5) years, respectively, and the Lp(a) median (inter quartile range) was 20.4 (7.8–75.1) nmol/L. Patients in the high Lp(a) group had significantly higher prevalence of cardiovascular and microvascular complications compared to patients with very low levels. The relative risk (confidence interval) increase to be affected by ischemic heart disease was 2.42 (1.41–4.15) (p=0.001), by albuminuria 1.87 1.26–2.78) (p=0.002) and by aortic valve disease 2.96 (1.53–5.78) (p=0.001). The relationship between Lp(a) and vascular complications was sustained when data was adjusted for age and smoking status between the groups albeit at a weaker level. No significant relationship was detected between cerebrovascular disease or the microvascular complications retinopathy and neuropathy and Lp(a) levels. Significantly higher Lp(a) levels were observed in patients with poor and average metabolic control compared to patients with good control (p<0.05). The 80:th percentile of Lp(a) was 78.6 nmol/L, 105.2 nmol/L and 100.6 nmol/L for good, average and poor metabolic control respectively (Figure 1).

Figure 1. Lp(a) levels in relation to HbA1c

Conclusions

Lp(a) is a significant risk factor for cardiovascular complications and aortic valve disease in patients with type 1 diabetes. Poor metabolic control of the diabetes disease is associated to high Lp(a) levels.

Acknowledgement/Funding

Swedish Heart Foundation (PP), The Swedish Research Council (PP), Karolinska Institutet (PP, JB), Investigator initiated study grant from Sanofi (JB)

Artificial intelligence for direct-to-physician reporting of ambulatory electrocardiography

Developments in ambulatory electrocardiogram (ECG) technology have led to vast amounts of ECG data that currently need to be interpreted by human technicians. Here we tested an artificial intelligence (AI) algorithm for direct-to-physician reporting of ambulatory ECGs. Beat-by-beat annotation of 14,606 individual ambulatory ECG recordings (mean duration = 14 ± 10 days) was performed by certified ECG technicians (n = 167) and an ensemble AI model, called DeepRhythmAI. To compare the performance of the AI model and the technicians, a random sample of 5,235 rhythm events identified by the AI model or by technicians, of which 2,236 events were identified as critical arrhythmias, was selected for annotation by one of 17 cardiologist consensus panels. The mean sensitivity of the AI model for the identification of critical arrhythmias was 98.6% (95% confidence interval (CI) = 97.7-99.4), as compared to 80.3% (95% CI = 77.3-83.3%) for the technicians. False-negative findings were observed in 3.2/1,000 patients for the AI model versus 44.3/1,000 patients for the technicians. Accordingly, the relative risk of a missed diagnosis was 14.1 (95% CI = 10.4-19.0) times higher for the technicians. However, a higher false-positive event rate was observed for the AI model (12 (interquartile range (IQR) = 6-74)/1,000 patient days) as compared to the technicians (5 (IQR = 2-153)/1,000 patient days). We conclude that the DeepRhythmAI model has excellent negative predictive value for critical arrhythmias, substantially reducing false-negative findings, but at a modest cost of increased false-positive findings. AI-only analysis to facilitate direct-to-physician reporting could potentially reduce costs and improve access to care and outcomes in patients who need ambulatory ECG monitoring.