Adhesion Molecules as Prognostic Biomarkers in Coronary Artery Disease

Atherosclerosis is a progressive disease, culminating in the production of atherosclerotic plaques in arteries through intricate pathophysiological processes. The progression of this disorder is based on the effect of triggering factors -mainly hyperlipidemia, diabetes mellitus, arterial hypertension, and smoking- on the endothelium. Coronary artery disease (CAD) is an atherosclerotic disease with a higher prevalence among individuals. Pro- and anti-inflammatory cytokines are the main contributors to atherosclerotic plaque formation. CAD and its manifestations multifactorial affect patients' quality of life, burdening the global healthcare system. Recently, the role of adhesion molecules in CAD progression has been recognized. Physicians delve into the pathophysiologic basis of CAD progression, focusing on the effect of adhesion molecules. They are proteins that mediate cell-cell andcell-extracellular matrix interaction and adhesion, driving the formation of atherosclerotic plaques. Several studies have assessed their role in atherosclerotic disease in small cohorts and in experimental animal models as well. Furthermore, several agents, such as nanoparticles, have been introduced modifying the main atherosclerotic risk factors as well as targeting the endothelial inflammatory response and atherosclerotic plaque stabilization. In this review, we discuss the role of adhesion molecules in atherosclerosis and CAD progression, as well as the potential to be used as targeting moieties for individualized treatment.

Myocardial tissue phenotyping by radiomic features of native T1 maps and machine learning enhances disease detection and classification

Background

Myocardial T1 mapping by cardiac magnetic resonance (CMR) is a useful technique to detect diffuse myocardial fibrosis, but a major limitation of T1 mapping is the significant overlap in native T1 values between health and disease.

Purpose

We explored whether radiomic features from T1 maps could enhance the diagnostic value of T1 mapping in distinguishing health from disease and classifying cardiac disease phenotypes.

Methods

In a total of 149 patients (n=30 with no evidence of heart disease, n=30 with LVH of various etiologies, n=61 with hypertrophic cardiomyopathy (HCM) and n=28 with cardiac amyloidosis) undergoing a CMR scan for various indications were included in this study. In addition to measuring native myocardial T1 values from T1 maps, we extracted a total of 843 radiomic features of myocardial texture and explored their value in disease classification.

Results

We first demonstrated that T1 mapping images are a rich source of extractable, quantifiable data. The first three principal components of the T1 radiomics were significantly and distinctively correlated with cardiac disease type. Unsupervised hierarchical clustering of the population by myocardial T1 radiomics was significantly associated with myocardial disease type (chi2=55.98, p<0.0001). After machine learning for feature selection, training with internal validation and external testing, a model of T1 radiomics had good diagnostic performance (AUC 0.753) for multinomial classification of disease phenotype (normal vs. LVH vs. HCM vs. amyloid). A subset of seven radiomic features outperformed mean native T1 values for classification between myocardial health vs. disease and HCM phenocopies (for normal: T1 AUC 0.549 vs. radiomics AUC 0.888, for LVH: T1 AUC 0.645 vs. radiomics AUC 0.790, for HCM T1 AUC 0.541 vs. radiomics AUC 0.638 and for amyloid T1 AUC 0.769 vs. radiomics AUC 0.840).

Conclusions

We have shown that specific imaging patterns in myocardial native T1 maps are linked to features of cardiac disease and we have provided for the first-time evidence that radiomic phenotyping can be used to enhance the diagnostic yield of native T1 mapping for myocardial disease detection and classification.

Funding Acknowledgement

Type of funding sources: None.

Prognostic value of vascular inflammation biomarkers over clinical risk factors for cardiovascular risk : a meta-analysis

Funding Acknowledgements

Type of funding sources: None.

Background

Measurement of vascular inflammation biomarkers is supported for estimation of residual inflammatory risk and cardiovascular risk stratification, but to date there is no systematic assessment of the added value of such biomarkers in predicting cardiovascular events and their comparative performance.

Methods

We systematically searched in MEDLINE published literature before Apr 14, 2020 for prospective cohort studies assessing the prognostic value of common biomarkers of vascular inflammation in stable patients with or without cardiovascular disease. The primary outcome was the difference in the c-index (Δ[c-index]) of the best clinical model with the use of inflammatory biomarkers for the prediction of the composite endpoint of major adverse cardiovascular events (MACEs) and mortality. The secondary outcome was the Δ[c-index] for MACEs only. We calculated I² to test heterogeneity. We used random-effects modelling for the meta-analyses to assess the primary and secondary outcome.

Results

We identified 92,507 studies in MEDLINE after duplicates were removed, of which 90,882 (96%) studies were excluded after screening the titles and abstracts, and 1,507 (93%) of the 1,625 remaining studies were excluded after assessment of the full texts. We included 93 (6%) studies in our quantitative evaluation, in which 351,628 individuals participated. The combination of high-risk plaque features and Fat attenuation Index (FAI) by CCTA was associated with the highest prognostic value i.e. Δ[c-index] for the composite endpoint per biomarker type (A). In meta-analysis, both plasma and imaging biomarkers of vascular inflammation offered incremental prognostic value for the primary outcome (pooled estimate for Δ[c-index]% 2.9, 95%CI 2.1-3.6, B) and for MACEs only (pooled estimate for Δ[c-index]% 2.9, 95%CI 2.1-3.8). The prognostic value of imaging biomarkers significantly surpassed that of plasma biomarkers for the primary outcome (Δ[c-index]% 11.3, 95%CI 8.3-14.3 vs. 1.4, 95%CI 0.9-1.8 respectively, p = 1.7x10-10, C). Notably, biomarkers of vascular inflammation offered higher incremental prognostic value in studies with a shorter duration of follow-up (i.e. <5 years), in primary CHD prevention setting and lower cardiovascular risk populations i.e. (studies with <5% cumulative event incidence, D)

Conclusions

The combination of HRP features and FAI by CCTA imaging had the highest prognostic value for cardiovascular events among plasma or imaging biomarkers of vascular inflammation. CCTA imaging to detect residual inflammatory risk and the vulnerable patient at risk for events is a rational approach to improve risk stratification and prognostication.

Abstract Figure.

Hydroxychloroquine for colchicine-resistant glucocorticoid-dependent idiopathic recurrent pericarditis: an observational prospective study

Background

Glucocorticoid (GC)-dependent, colchicine-resistant idiopathic recurrent pericarditis (IRP) remains a clinical challenge. We assessed for the first time the efficacy and safety of hydroxychloroquine (HCQ) in IRP.

Methods

This is a single center, post hoc analysis of prospectively collected data of 15 patients with refractory to standard therapy (colchicine plus either GC or anakinra) IRP (≥3 recurrences, disease duration ≥12 months and inability to wean off treatment) treated with HCQ (400 mg/day). These patients were matched 1:1 for age, sex, and treatment type to IRP patients receiving standard-of-care treatment (control group, n=15). Pericarditis recurrence, the time and C-reactive protein (CRP) levels at 1st flare, the % of patients able to achieve a ≥50% reduction of baseline GC dose and the % reduction of GC dose were compared between groups.

Results

Almost all patients (n=29) but one in the HCQ group (14/15) relapsed during follow-up. However, HCQ treatment was associated with an increased median time of flare-free survival (increase by 4 weeks compared to controls) and reduced hazard ratio for flare in survival analysis (HR=0.36, 95% CI 0.16–0.77, p=0.009). HCQ was also associated with a higher proportion of patients obtaining a ≥50% dose reduction of GCs (33.3% vs. 0% in the control group, p=0.037) and reduced GC dose (HCQ: −43.5% vs. control: −4.5%, p<0.001). There were no signficant differences in CRP levels at flare between groups (p=0.615).

Conclusions

In this prospective study, HCQ use was associated with a GC-sparing effect and an increased flare-free survival period in patients with colchicine resistant GC-dependent IRP.

Funding Acknowledgement

Type of funding source: None