Integrating Coronary Atherosclerosis Burden and Progression with Coronary Artery Disease Risk Factors to Guide Therapeutic Decision Making

A Promising Pathway Toward Mitigation and Eradication of Coronary Artery Disease

Cardiovascular disease remains the leading cause of death in the United States and globally. Significant advances have been made throughout the history of cardiology and the treatment of this disease; however, these efforts have not halted the alarming statistics. Emerging approaches, such as artificial intelligence applied to cardiac imaging, genetic testing, and genetic silencing, may offer essential additional steps in treating the disease. Moreover, new pathways of the disease are being identified, which differ from traditional risk factors and offer a fresh, innovative approach. This paper focuses on a novel strategy that includes identifying and treating multiple pathways of the disease using both new and traditional interventions. These interventions include plaque-directed therapy rather than surrogate therapy, with the potential to mitigate consequences and possibly eradicate the disease through personalized, multi-approach treatments similar to those used in cancer treatment.

eIF6 deficiency regulates gut microbiota, decreases systemic inflammation, and alleviates atherosclerosis

Altered composition of the gut microbiota affects immunity and metabolism. This study previously found that eIF6 gene knockdown changes the composition of the intestinal flora in the eIF6 gene knockdown mouse model. Lactobacillus acidophilus is significantly increased in the model. This study was designed to investigate the role of L. acidophilus in the pathogenesis of atherosclerosis. Transcriptomic data from 117 patients with coronary artery disease (CAD) and 79 healthy individuals were obtained. ApoE-/- and ApoE-/-/eIF6+/- mice on normal chow diet or a high-fat diet were treated for 16 weeks; eIF6 deficiency was evaluated atherosclerosis. ApoE-/- mice on normal chow diet or a high-fat diet were treated with L. acidophilus by daily oral gavage for 16 weeks. Moreover, one group was treated with lipopolysaccharide at 12 weeks. The levels of eIF6, RNASE3, and RSAD2 were notably higher in the patients with CAD than in the healthy individuals. eIF6 deficiency altered the composition of gut microbiota. eIF6 deficiency reduced the atherosclerotic lesion formation in ApoE-/-/eIF6+/- mice compared with the ApoE-/- mice. The microbial sequencing and metabolomics analysis demonstrated some beneficial bacterial (L. acidophilus, Ileibacterium, and Bifidobacterium) and metabolic levels significantly had deference in ApoE-/-/eIF6+/- mice compared with the ApoE-/- mice. Correlational studies indicated that L. acidophilus had close correlations with low-density lipoprotein cholesterol, lesion area, and necrotic area. L. acidophilus inhibited high-fat diet-induced inflammation and atherosclerotic lesion, increasing the expression of tight junction proteins (ZO-1 and claudin-1) and reducing the gut permeability. However, lipopolysaccharide reversed the protective effect of L. acidophilus against atherosclerosis. eIF6 deficiency protected against atherosclerosis by regulating the composition of gut microbiota and metabolites. L. acidophilus attenuated atherosclerotic lesions by reducing inflammation and increasing gut permeability.IMPORTANCEeIF6 deficiency modulates the gut microbiota and multiple metabolites in atherosclerotic ApoE-/- mice. L. acidophilus was reduced in the gut of atherosclerotic ApoE-/- mice, but administration of Lactobacillus acidophilus reversed intestinal barrier dysfunction and vascular inflammation. Our findings suggest that targeting individual species is a beneficial therapeutic strategy to prevent inflammation and atherosclerosis.

Rapidly progressive coronary atherosclerosis in a young male: a retrospective advanced CCT phenotype analysis

We present a real-life case of a very young man with multiple risk factors who progressed rapidly from minimally obstructive non-calcified plaque on computed tomography angiography (CCTA) to severe three-vessel coronary disease presenting with STEMI. It questions the reliability of zero coronary calcium in high-risk subgroups like familial hypercholesterolemia, high Lp(a), and the young. While CCTA can accurately visualize non-calcified plaque, its interpretation requires expertise and clinical judgment should consider both imaging and clinical risk factors for management. Advanced plaque quantification, peri-coronary (PCAT), and epicardial (EAT) adipose tissue could help better-stratified patients but the evidence-based clinical application remains unknown.

Atherosclerosis quantification and cardiovascular risk: the ISCHEMIA trial

BACKGROUND AND AIMS

The aim of this study was to determine the prognostic value of coronary computed tomography angiography (CCTA)-derived atherosclerotic plaque analysis in ISCHEMIA.

METHODS

Atherosclerosis imaging quantitative computed tomography (AI-QCT) was performed on all available baseline CCTAs to quantify plaque volume, composition, and distribution. Multivariable Cox regression was used to examine the association between baseline risk factors (age, sex, smoking, diabetes, hypertension, ejection fraction, prior coronary disease, estimated glomerular filtration rate, and statin use), number of diseased vessels, atherosclerotic plaque characteristics determined by AI-QCT, and a composite primary outcome of cardiovascular death or myocardial infarction over a median follow-up of 3.3 (interquartile range 2.2-4.4) years. The predictive value of plaque quantification over risk factors was compared in an area under the curve (AUC) analysis.

RESULTS

Analysable CCTA data were available from 3711 participants (mean age 64 years, 21% female, 79% multivessel coronary artery disease). Amongst the AI-QCT variables, total plaque volume was most strongly associated with the primary outcome (adjusted hazard ratio 1.56, 95% confidence interval 1.25-1.97 per interquartile range increase [559 mm3]; P = .001). The addition of AI-QCT plaque quantification and characterization to baseline risk factors improved the model's predictive value for the primary outcome at 6 months (AUC 0.688 vs. 0.637; P = .006), at 2 years (AUC 0.660 vs. 0.617; P = .003), and at 4 years of follow-up (AUC 0.654 vs. 0.608; P = .002). The findings were similar for the other reported outcomes.

CONCLUSIONS

In ISCHEMIA, total plaque volume was associated with cardiovascular death or myocardial infarction. In this highly diseased, high-risk population, enhanced assessment of atherosclerotic burden using AI-QCT-derived measures of plaque volume and composition modestly improved event prediction.

Serum N-glycomic profiling identifies candidate biomarker panels for assessing coronary artery stenosis severity

Stenosis severity may escalate over the course of coronary artery disease (CAD), increasing the risk of death for the patient. Conventionally, the assessment of stenosis degree relies on invasive coronary angiography (ICA), an invasive examination unsuitable for patients in poor physical condition or those with contrast allergies and one that imposes a psychological burden on patients. Although abnormal serum N-glycan profiles have exhibited robust associations with various cardiovascular diseases, including CAD, their potential in diagnosing CAD stenosis remains to be determined. In this study, we performed a comprehensive analysis of serum N-glycome from 132 patients who underwent ICA and 27 healthy controls using MALDI-TOF-mass spectrometry. The patients who underwent ICA examination were categorized into four groups based on stenosis severity: no/mild/moderate/severe stenosis. Twenty-seven N-glycans were directly quantified, and 47 derived glycan traits were obtained. Notably, among these 74 glycan features, 18 exhibited variations across the study groups. Using a combination of least absolute shrinkage and selection operator and logistic regression analyses, we developed five diagnostic models for recognizing stenosis degree. Our results suggested that alterations in serum N-glycosylation modifications might be valuable for identifying stenosis degree and monitoring disease progression in individuals with CAD. It is expected to offer a noninvasive alternative for those who could not undergo ICA because of various reasons. However, the diagnostic potential of serum N-glycan panels as biomarkers requires multicenter, large cohort validation in the future.

AI-Guided Quantitative Plaque Staging Predicts Long-Term Cardiovascular Outcomes in Patients at Risk for Atherosclerotic CVD

BACKGROUND

The recent development of artificial intelligence-guided quantitative coronary computed tomography angiography analysis (AI-QCT) has enabled rapid analysis of atherosclerotic plaque burden and characteristics.

OBJECTIVES

This study set out to investigate the 10-year prognostic value of atherosclerotic burden derived from AI-QCT and to compare the spectrum of plaque to manually assessed coronary computed tomography angiography (CCTA), coronary artery calcium scoring (CACS), and clinical risk characteristics.

METHODS

This was a long-term follow-up study of 536 patients referred for suspected coronary artery disease. CCTA scans were analyzed with AI-QCT and plaque burden was classified with a plaque staging system (stage 0: 0% percentage atheroma volume [PAV]; stage 1: >0%-5% PAV; stage 2: >5%-15% PAV; stage 3: >15% PAV). The primary major adverse cardiac event (MACE) outcome was a composite of nonfatal myocardial infarction, nonfatal stroke, coronary revascularization, and all-cause mortality.

RESULTS

The mean age at baseline was 58.6 years and 297 patients (55%) were male. During a median follow-up of 10.3 years (IQR: 8.6-11.5 years), 114 patients (21%) experienced the primary outcome. Compared to stages 0 and 1, patients with stage 3 PAV and percentage of noncalcified plaque volume of >7.5% had a more than 3-fold (adjusted HR: 3.57; 95% CI 2.12-6.00; P < 0.001) and 4-fold (adjusted HR: 4.37; 95% CI: 2.51-7.62; P < 0.001) increased risk of MACE, respectively. Addition of AI-QCT improved a model with clinical risk factors and CACS at different time points during follow-up (10-year AUC: 0.82 [95% CI: 0.78-0.87] vs 0.73 [95% CI: 0.68-0.79]; P < 0.001; net reclassification improvement: 0.21 [95% CI: 0.09-0.38]). Furthermore, AI-QCT achieved an improved area under the curve compared to Coronary Artery Disease Reporting and Data System 2.0 (10-year AUC: 0.78; 95% CI: 0.73-0.83; P = 0.023) and manual QCT (10-year AUC: 0.78; 95% CI: 0.73-0.83; P = 0.040), although net reclassification improvement was modest (0.09 [95% CI: -0.02 to 0.29] and 0.04 [95% CI: -0.05 to 0.27], respectively).

CONCLUSIONS

Through 10-year follow-up, AI-QCT plaque staging showed important prognostic value for MACE and showed additional discriminatory value over clinical risk factors, CACS, and manual guideline-recommended CCTA assessment.

Utility of Artificial Intelligence Plaque Quantification: Results of the DECODE Study

Background

Artificial Intelligence Plaque Analysis (AI-QCPA, HeartFlow) provides, from a CCTA, quantitative plaque burden information including total plaque and plaque subtype volumes. We sought to evaluate the clinical utility of AI-QCPA in clinical decision making.

Methods

One hundred cases were reviewed by 3 highly experienced practicing cardiologists who are SCCT level 3 CCTA readers. Patients had varying levels of calcium (median CACS: 99.5) and CAD-RADS scores. Initial management plan for each case was a majority decision based upon patient demographics, clinical history, and CCTA report. AI-QCPA was then provided for each patient, and the plan was reconsidered. The primary endpoint was the reclassification rate (RR). In a secondary analysis of 40 cases, the above process was repeated but the initial plan was based upon review of the actual CCTA images.

Results

RR following AI-QCPA review was 66% (66/100) of cases (95% CI, 56.72%-75.28%). RR ranged from 47% in cases with CACS 0 to 96% in cases with CACS >400, and from 40% in CAD-RADS 1 cases to 94% in CAD-RADS 4 cases. RR was higher in cases with coronary stenoses ≥50% (89.5%) vs cases with stenoses <50% (51.6%). RR was 39% in cases with LDL <70 mg/dL vs 70% in LDL ≥70 mg/dL. Following review of the CCTA images rather than the CCTA report, the RR was 50% (95% CI of 34.51% - 65.49%). The primary reclassification effect was to intensify preventative medical therapy.

Conclusions

Adding AI-QCPA to CCTA alone leads to a change in clinical care in two-thirds of patients.

The role of early-phase trials and real-world evidence in drug development

Phase 3 randomized controlled trials (RCTs), while the gold standard for treatment efficacy and safety, are not always feasible, are expensive, can be prolonged and can be limited in generalizability. Other under-recognized sources of evidence can also help advance drug development. Basic science, proof-of-concept studies and early-phase RCTs can provide evidence regarding the potential for clinical benefit. Real-world evidence generated from registries or observational datasets can provide insights into the treatment of rare diseases that often pose a challenge for trial recruitment. Pragmatic trials embedded in healthcare systems can assess the treatment effects in clinical settings among patient populations sometimes excluded from trials. This Perspective discusses potential sources of evidence that may be used to complement explanatory phase 3 RCTs and to speed the development of new cardiovascular medications. Content is derived from the 19th Global Cardiovascular Clinical Trialists meeting (December 2022), involving clinical trialists, patients, clinicians, regulators, funders and industry representatives.

miRNAs in Heart Development and Disease

Cardiovascular diseases (CVD) are a group of disorders that affect the heart and blood vessels. They include conditions such as myocardial infarction, coronary artery disease, heart failure, arrhythmia, and congenital heart defects. CVDs are the leading cause of death worldwide. Therefore, new medical interventions that aim to prevent, treat, or manage CVDs are of prime importance. MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression at the posttranscriptional level and play important roles in various biological processes, including cardiac development, function, and disease. Moreover, miRNAs can also act as biomarkers and therapeutic targets. In order to identify and characterize miRNAs and their target genes, scientists take advantage of computational tools such as bioinformatic algorithms, which can also assist in analyzing miRNA expression profiles, functions, and interactions in different cardiac conditions. Indeed, the combination of miRNA research and bioinformatic algorithms has opened new avenues for understanding and treating CVDs. In this review, we summarize the current knowledge on the roles of miRNAs in cardiac development and CVDs, discuss the challenges and opportunities, and provide some examples of recent bioinformatics for miRNA research in cardiovascular biology and medicine.

Myocardial ischemia/reperfusion: Translational pathophysiology of ischemic heart disease

Ischemic heart disease is the greatest health burden and most frequent cause of death worldwide. Myocardial ischemia/reperfusion is the pathophysiological substrate of ischemic heart disease. Improvements in prevention and treatment of ischemic heart disease have reduced mortality in developed countries over the last decades, but further progress is now stagnant, and morbidity and mortality from ischemic heart disease in developing countries are increasing. Significant problems remain to be resolved and require a better pathophysiological understanding. The present review attempts to briefly summarize the state of the art in myocardial ischemia/reperfusion research, with a view on both its coronary vascular and myocardial aspects, and to define the cutting edges where further mechanistic knowledge is needed to facilitate translation to clinical practice.

The expanding role of HLA gene tests for predicting drug side effects

Adverse drug reactions can be either dose-dependent (Type A) or idiosyncratic (Type B). Type B adverse drug reactions tend to be extremely rare and difficult to predict. They are usually immune-mediated. Examples include severe skin reactions and drug-induced liver injury. For many commonly prescribed drugs (such as antibiotics), the risk of developing an idiosyncratic adverse drug reaction is influenced by variability in the human leukocyte antigen (HLA) genes. Because these HLA-mediated adverse drug reactions can be lethal, there is growing interest in defining which specific drug-gene relationships might benefit from pre-emptive HLA genotyping and automated clinical decision support. This review summarizes the literature for HLA-mediated adverse reactions linked to common drugs.

Determination of lipid-rich plaques by artificial intelligence-enabled quantitative computed tomography using near-infrared spectroscopy as reference

BACKGROUND AND AIMS

Artificial intelligence quantitative CT (AI-QCT) determines coronary plaque morphology with high efficiency and accuracy. Yet, its performance to quantify lipid-rich plaque remains unclear. This study investigated the performance of AI-QCT for the detection of low-density noncalcified plaque (LD-NCP) using near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS).

METHODS

The INVICTUS Registry is a multi-center registry enrolling patients undergoing clinically indicated coronary CT angiography and IVUS, NIRS-IVUS, or optical coherence tomography. We assessed the performance of various Hounsfield unit (HU) and volume thresholds of LD-NCP using maxLCBI4mm ≥ 400 as the reference standard and the correlation of the vessel area, lumen area, plaque burden, and lesion length between AI-QCT and IVUS.

RESULTS

This study included 133 atherosclerotic plaques from 47 patients who underwent coronary CT angiography and NIRS-IVUS The area under the curve of LD-NCP<30HU was 0.97 (95% confidence interval [CI]: 0.93-1.00] with an optimal volume threshold of 2.30 mm3. Accuracy, sensitivity, and specificity were 94% (95% CI: 88-96%], 93% (95% CI: 76-98%), and 94% (95% CI: 88-98%), respectively, using <30 HU and 2.3 mm3, versus 42%, 100%, and 27% using <30 HU and >0 mm3 volume of LD-NCP (p < 0.001 for accuracy and specificity). AI-QCT strongly correlated with IVUS measurements; vessel area (r2 = 0.87), lumen area (r2 = 0.87), plaque burden (r2 = 0.78) and lesion length (r2 = 0.88), respectively.

CONCLUSIONS

AI-QCT demonstrated excellent diagnostic performance in detecting significant LD-NCP using maxLCBI4mm ≥ 400 as the reference standard. Additionally, vessel area, lumen area, plaque burden, and lesion length derived from AI-QCT strongly correlated with respective IVUS measurements.

Differential Expression of Serum Proinflammatory Cytokine TNF-α and Genetic Determinants of TNF-α, CYP2C19*17, miR-423 Genes and Their Effect on Coronary Artery Disease Predisposition and Progression

Coronary artery disease (CAD) is the leading cause of death and hospitalization worldwide and represents a problem for public health systems everywhere. In Saudi Arabia, the prevalence of CAD is estimated to be 5.5%. Risk factors for CAD include older age, male gender, obesity, high blood pressure, smoking, diabetes, hyperlipidemia, and genetic factors. Reducing the risk factors in susceptible individuals will decrease the prevalence of CAD. Genome wide association studies have helped to reveal the association of many loci with diseases like CAD. In this study, we examined the link between single nucleotide variations (SNVs) of TNF-α-rs1800629 G>A, CYP2C19*17 (rs12248560) C>T, and miR-423 rs6505162 C>A and the expression of TNF-α with CAD. We used the mutation specific PCR, ARMS-PCR, and ELISA. The results showed that the A allele of the TNF-α rs1800629 G>A SNP is linked to CAD with odd ratio (OR) (95% CI) = 2.10, p-value = 0.0013. The T allele of the CYP2C19*17 (rs12248560) C>T is linked to CAD with OR (95% CI) = 2.02, p-value = 0.003. In addition, the A allele of the miR-423 rs6505162 C>A SNV is linked to CAD with OR (95% CI) = 1.49, p-value = 0.036. The ELISA results indicated that the TNF-α serum levels are significantly increased in CAD patients compared to healthy controls. We conclude the TNF-α rs1800629 G>A, CYP2C19*17, and miR-423 rs6505162 C>A are potential genetic loci for CAD in the Saudi population. These findings require further verification in future studies. After being verified, our results might be utilized in genetic testing to identify individuals that are susceptible to CAD and, therefore, for whom reducing modifiable risk factors (e.g., poor diet, diabetes, obesity, and smoking) would result in prevention or delay of CAD.

Assessment of the Dimensions of Coronary Arteries for the Manifestation of Coronary Artery Disease

INTRODUCTION

The size of the coronary artery influences the effective outcome of therapeutic measures like coronary artery bypass graft (CABG) surgery, percutaneous coronary interventions (PCI), and diagnosis of coronary artery disease. Patients' age, gender, BMI, anatomical variations, and increased left ventricular size all have an effect on coronary artery parameters.

OBJECTIVE

This study aims to compare the average size of the coronary arteries of the Pakistani population in both sexes for manifestation of coronary artery disease.

METHODOLOGY

For the analysis of the coronary arteries, 100 patients of both sexes, male and female, were taken. X-ray angiography was performed for two-dimensional images of coronary arteries. For diameter measurement, images were visualized on quantitative coronary angiography (QCA) in different views (caudal and cranial views). The diameters of the left main coronary artery (left main stem/LMS), left anterior descending (LAD), left circumflex (LCx), and right coronary artery (RCA) were measured on angiograms. Data about the dimensions of the coronary artery was gathered through quantitative angiography. Data analysis was done through SPSS version 26 (IBM Corp., Armonk, NY).

RESULTS

 There is a notable distinction in the average diameters among the proximal LAD (3.12), mid-LAD (2.40), and distal LAD (1.29). A statistically significant difference is evident among mid-LCx, distal LCx, and proximal LCx (p-value < 0.001). Likewise, the average diameter of the distal RCA (1.89) was smaller when compared to the mid-RCA (3.19) and proximal RCA (3.78). However, there was no significant difference in the average diameter among mid-LMS, distal LMS, and proximal LMS (p-value = 0.09).

CONCLUSION

The average diameter of distal RCA was smaller when compared to mid-RCA and proximal RCA. The average size of proximal LAD and proximal LCx was comparatively larger than mid- and distal LAD and LCx. The findings of current research will be beneficial for the diagnosis and management of coronary artery disease patients.

Opioid Impacts on Cardiovascular Health

The prevalence of opioid use in the current opioid epidemic era has led to a public health emergency due to the ties to mortality and morbidity. Studies have investigated opioids' impacts on different aspects of cardiovascular health, although there seems to be a lack of a current concise review. Therefore, the aim of this literature review is to provide a summary of the most recent studies from the past decade that postulate a connection between opioids and their impact on cardiovascular health while highlighting conflicting areas among published research. For this literature review, three databases, PubMed (NLM), EMBASE, and Web of Science (Core Collection), were searched for full peer-reviewed articles written in English about human subjects and published between 2013 and 2023 inclusive. The following initial approach was to search for terms in the title of articles: "opioid AND ("vascular" OR "artery" OR "vein" OR "heart rate" OR "infarct" OR "stroke" OR "aortic" OR "cardiovascular disease"). After assessing for duplicate articles from the three databases, the remaining articles were assessed for inclusion eligibility. In the present review, a brief description of the overall role of opioid receptors is provided followed by the literature findings. These findings indicate potentially important negative impacts of opioid use on cardiovascular health in a number of areas. These include opioid-associated increases in the following: (1) vascular aging based on demonstrated increases in arterial stiffness, (2) opioid-related reductions in heart rate variability (HRV) and its implications on morbidity and mortality, (3) opioid's impacts on coronary artery and coronary heart disease (CHD), (4) opioids as a risk factor for atrial fibrillation (AF) and (5) opioid use as a risk factor for vascular occlusion processes. In addition to these broad cardiovascular effects, other aspects of concern are related to the potential impacts of withdrawal from opioid use, which, when done rapidly, are associated with increases in blood pressure and a decrease in HRV.