Personalized medicine for cardiovascular diseases

A Thorough Literature Review of the Potential Benefits and Drawbacks of Long-Term Aspirin Use for the Primary Prevention of Cardiovascular Disease

This article examines the role of aspirin in the primary prevention of cardiovascular disease. It highlights findings from major studies such as ASPREE (ASPirin in Reducing Events in the Elderly), ARRIVE (Aspirin to Reduce Risk of Initial Vascular Events), and ASPREE-XT (ASPirin in Reducing Events in the Elderly - eXTension) , among others. The review focuses on aspirin's role in primary prevention for specific populations including older adults, diabetics, hypertension patients, rheumatoid arthritis patients, kidney transplant recipients, and those with specific lipoprotein(a) genotypes, among other groups. We review these studies, noting aspirin's role in reducing events such as myocardial infarctions and its potential for increasing bleeding risks. The review also considers the implications for patients with kidney disease, referencing the Chronic Renal Insufficiency Cohort (CRIC) study and the International Polycap Study-3 (TIPS-3) trial. Additionally, it addresses the shifting paradigms in guidelines from the US Preventive Services Task Force and other entities, underscoring the importance of individualized aspirin use by balancing benefits against bleeding risks. The article further explores the concept of platelet reactivity, discusses strategies for improving adherence to aspirin therapy, and identifies existing research gaps, such as the phenomenon of aspirin resistance. It concludes by suggesting potential areas for future investigation to enhance understanding and application of aspirin in cardiovascular disease prevention.

Cardiovascular disease detection using a novel stack-based ensemble classifier with aggregation layer, DOWA operator, and feature transformation

Due to their widespread prevalence and impact on quality of life, cardiovascular diseases (CVD) pose a considerable global health burden. Early detection and intervention can reduce the incidence, severity, and progression of CVD and prevent premature death. The application of machine learning (ML) techniques to early CVD detection is therefore a valuable approach. In this paper, A stack-based ensemble classifier with an aggregation layer and the dependent ordered weighted averaging (DOWA) operator is proposed for detecting cardiovascular diseases. We propose transforming features using the Johnson transformation technique and normalizing feature distributions. Three diverse first-level classifiers are selected based on their accuracy, and predictions are combined using the aggregation layer and DOWA. A linear support vector machine (SVM) meta-classifier makes the final classification. Adding the aggregation layer to the stacking classifier improves classification accuracy significantly, according to the study. The accuracy is enhanced by 5%, resulting in an impressive overall accuracy of 94.05%. Moreover, the proposed system significantly increases the area under the receiver operating characteristic (ROC) curve compared to recent studies, reaching 97.14%. It further reinforces the classifier's reliability and effectiveness in classifying cardiovascular disease by distinguishing between positive and negative instances. With improved accuracy and a high area under the curve (AUC), the proposed classifier exhibits robustness and superior performance in the detection of cardiovascular diseases.

Developing a model to predict the early risk of hypertriglyceridemia based on inhibiting lipoprotein lipase (LPL): a translational study

Hypertriglyceridemia (HTG) is an independent risk factor for atherosclerotic cardiovascular disease (ASCVD). One of the multiple origins of HTG alteration is impaired lipoprotein lipase (LPL) activity, which is an emerging target for HTG treatment. We hypothesised that early, even mild, alterations in LPL activity might result in an identifiable metabolomic signature. The aim of the present study was to assess whether a metabolic signature of altered LPL activity in a preclinical model can be identified in humans. A preclinical LPL-dependent model of HTG was developed using a single intraperitoneal injection of poloxamer 407 (P407) in male Wistar rats. A rat metabolomics signature was identified, which led to a predictive model developed using machine learning techniques. The predictive model was applied to 140 humans classified according to clinical guidelines as (1) normal, less than 1.7 mmol/L; (2) risk of HTG, above 1.7 mmol/L. Injection of P407 in rats induced HTG by effectively inhibiting plasma LPL activity. Significantly responsive metabolites (i.e. specific triacylglycerols, diacylglycerols, phosphatidylcholines, cholesterol esters and lysophospholipids) were used to generate a predictive model. Healthy human volunteers with the impaired predictive LPL signature had statistically higher levels of TG, TC, LDL and APOB than those without the impaired LPL signature. The application of predictive metabolomic models based on mechanistic preclinical research may be considered as a strategy to stratify subjects with HTG of different origins. This approach may be of interest for precision medicine and nutritional approaches.

Type 2 diabetes and in-hospital sudden cardiac arrest in ST-elevation myocardial infarction in the US

Aims

We aimed to assess the impact of diabetes on sudden cardiac arrest (SCA) in US patients hospitalized for ST-elevation myocardial infarction (STEMI).

Methods

We used the National Inpatient Sample (2005-2017) data to identify adult patients with STEMI. The primary outcome was in-hospital SCA. Secondary outcomes included in-hospital mortality, ventricular tachycardia (VT), ventricular fibrillation (VF), cardiogenic shock (CS), acute renal failure (ARF), and the revascularization strategy in SCA patients.

Results

SCA significantly increased from 4% in 2005 to 7.6% in 2018 in diabetes patients and from 3% in 2005 to 4.6% in 2018 in non-diabetes ones (p < 0.001 for both). Further, diabetes was associated with an increased risk of SCA [aOR = 1.432 (1.336-1.707)]. In SCA patients with diabetes, the mean age (SD) decreased from 68 (13) to 66 (11) years old, and mortality decreased from 65.7% to 49.3% during the observation period (p < 0.001). Compared to non-diabetes patients, those with T2DM had a higher adjusted risk of mortality, ARF, and CS [aOR = 1.72 (1.62-1.83), 1.52 (1.43-1.63), 1.25 (1.17-1.33); respectively] but not VF or VT. Those patients were more likely to undergo revascularization with CABG [aOR = 1.197 (1.065-1.345)] but less likely to undergo PCI [aOR = 0.708 (0.664-0.754)].

Conclusion

Diabetes is associated with an increased risk of sudden cardiac arrest in ST-elevation myocardial infarction. It is also associated with a higher mortality risk in SCA patients. However, the recent temporal mortality trend in SCA patients shows a steady decline, irrespective of diabetes.

Potential Novel RNA-Targeting Agents for Effective Lipoprotein(a) Lowering: A Systematic Assessment of the Evidence From Completed and Ongoing Developmental Clinical Trials

ABSTRACT

An increase in blood lipoprotein (a) [Lp(a)] levels, mostly genetically determined, has been identified as an independent risk factor of atherosclerotic cardiovascular disease. No drug has yet been approved that markedly lowers Lp(a) and thereby reduces residual cardiovascular risk. The aim of this article was to critically review the evidence from clinical development studies to date on the efficacy and safety of new RNA-based therapeutics for targeted lowering of Lp(a). PubMed/MEDLINE, Scopus, Web of Science, and ClinicalTrials.gov were searched without any language or date restriction up to November 5, 2022, and a total of 12 publications and 22 trial records were included. Several drugs were found that are currently in various stages of clinical development, such as the antisense oligonucleotide pelacarsen and the small interfering RNA molecule olpasiran and drugs coded as SLN360 and LY3819469. Among them, pelacarsen has progressed the most, currently reaching phase 3. All these drugs have so far shown satisfactory pharmacokinetic properties, consistently high and stable, dose-dependent efficacy in lowering Lp(a) even by more than 90%, with an acceptable safety profile in subjects with highly elevated Lp(a). In addition, reports of early clinical trials with pelacarsen imply a promising suppressive effect on key mechanisms of atherogenesis. Future research should focus on confirming these beneficial clinical effects in patients with lower average Lp(a) levels and clearly demonstrating the association between lowering Lp(a) and reducing adverse cardiovascular outcomes.

The Potential of Single Nucleotide Polymorphisms (SNPs) as Biomarkers and Their Association with the Increased Risk of Coronary Heart Disease: A Systematic Review

Human genetic analyses and epidemiological studies showed a potential association between several types of gene polymorphism and the development of coronary heart disease (CHD). Many studies on this pertinent topic need to be investigated further to reach an evidence-based conclusion. Therefore, in this current review, we describe several types of gene polymorphisms that are potentially linked to CHD. A systematic review using the databases EBSCO, PubMed, and ScienceDirect databases was searched until October of 2022 to find relevant studies on the topic of gene polymorphisms on risk factors for CHD, especially for the factors associated with single nucleotide polymorphisms (SNPs). The risk of bias and quality assessment was evaluated by Joanna Briggs Institute (JBI) guidelines. From keyword search results, a total of 6243 articles were identified, which were subsequently narrowed to 14 articles using prespecified inclusion criteria. The results suggested that there were 33 single nucleotide polymorphisms (SNPs) that can potentially increase the risk factors and clinical symptoms of CHD. This study also indicated that gene polymorphisms had a potential role in increasing CHD risk factors that were causally associated with atherosclerosis, increased homocysteine, immune/inflammatory response, Low-Density Lipoprotein (LDL), arterial lesions, and reduction of therapeutic effectiveness. In conclusion, the findings of this study indicate that SNPs may increase risk factors for CHD and SNPs show different effects between individuals. This demonstrates that knowledge of SNPs on CHD risk factors can be used to develop biomarkers for diagnostics and therapeutic response prediction to decide successful therapy and become the basis for defining personalized medicine in future.

Machine Learning Identifies New Predictors on Restenosis Risk after Coronary Artery Stenting in 10,004 Patients with Surveillance Angiography

OBJECTIVE

Machine learning (ML) approaches have the potential to uncover regular patterns in multi-layered data. Here we applied self-organizing maps (SOMs) to detect such patterns with the aim to better predict in-stent restenosis (ISR) at surveillance angiography 6 to 8 months after percutaneous coronary intervention with stenting.

METHODS

In prospectively collected data from 10,004 patients receiving percutaneous coronary intervention (PCI) for 15,004 lesions, we applied SOMs to predict ISR angiographically 6-8 months after index procedure. SOM findings were compared with results of conventional uni- and multivariate analyses. The predictive value of both approaches was assessed after random splitting of patients into training and test sets (50:50).

RESULTS

Conventional multivariate analyses revealed 10, mostly known, predictors for restenosis after coronary stenting: balloon-to-vessel ratio, complex lesion morphology, diabetes mellitus, left main stenting, stent type (bare metal vs. first vs. second generation drug eluting stent), stent length, stenosis severity, vessel size reduction, and prior bypass surgery. The SOM approach identified all these and nine further predictors, including chronic vessel occlusion, lesion length, and prior PCI. Moreover, the SOM-based model performed well in predicting ISR (AUC under ROC: 0.728); however, there was no meaningful advantage in predicting ISR at surveillance angiography in comparison with the conventional multivariable model (0.726, p = 0.3).

CONCLUSIONS

The agnostic SOM-based approach identified-without clinical knowledge-even more contributors to restenosis risk. In fact, SOMs applied to a large prospectively sampled cohort identified several novel predictors of restenosis after PCI. However, as compared with established covariates, ML technologies did not improve identification of patients at high risk for restenosis after PCI in a clinically relevant fashion.

The utility of zebrafish cardiac arrhythmia model to predict the pathogenicity of KCNQ1 variants

Genetic testing for inherited arrhythmias and discriminating pathogenic or benign variants from variants of unknown significance (VUS) is essential for gene-based medicine. KCNQ1 is a causative gene of type 1 long QT syndrome (LQTS), and approximately 30% of the variants found in type 1 LQTS are classified as VUS. We studied the role of zebrafish cardiac arrhythmia model in determining the clinical significance of KCNQ1 variants. We generated homozygous kcnq1 deletion zebrafish (kcnq1del/del) using the CRISPR/Cas9 and expressed human Kv7.1/MinK channels in kcnq1del/del embryos. We dissected the hearts from the thorax at 48 h post-fertilization and measured the transmembrane potential of the ventricle in the zebrafish heart. Action potential duration was calculated as the time interval between peak maximum upstroke velocity and 90% repolarization (APD90). The APD90 of kcnq1del/del embryos was 280 ± 47 ms, which was significantly shortened by injecting KCNQ1 wild-type (WT) cRNA and KCNE1 cRNA (168 ± 26 ms, P < 0.01 vs. kcnq1del/del). A study of two pathogenic variants (S277L and T587M) and one VUS (R451Q) associated with clinically definite LQTS showed that the APD90 of kcnq1del/del embryos with these mutant Kv7.1/MinK channels was significantly longer than that of Kv7.1 WT/MinK channels. Given the functional results of the zebrafish model, R451Q could be reevaluated physiologically from VUS to likely pathogenic. In conclusion, functional analysis using in vivo zebrafish cardiac arrhythmia model can be useful for determining the pathogenicity of loss-of-function variants in patients with LQTS.

Disease mechanisms as Subtypes: Mitochondrial and bioenergetic dysfunction

Parkinson disease (PD) is the second most common neurodegenerative disease in the world. Despite its enormous human and societal cost, there is no disease-modifying therapy for PD. This unmet medical need reflects our limited understanding of PD pathogenesis. One of the most important clues comes from the recognition that PD motor symptoms arises from the dysfunction and degeneration of a very select group of neurons in the brain. These neurons have a distinctive set of anatomic and physiologic traits that reflect their role in brain function. These traits elevate mitochondrial stress, potentially making them particularly vulnerable to age, as well as to genetic mutations and environmental toxins linked to PD incidence. In this chapter, the literature supporting this model is outlined, along with gaps in our knowledge base. The translational implications of this hypothesis are then discussed, with a focus on why disease-modification trials have failed to date and what this means for the development of new strategies for altering disease course.

Material basis and integrative pharmacology of danshen decoction in the treatment of cardiovascular diseases

BACKGROUND

Cardiovascular diseases (CVDs) are among the primary and predominant threats to human health with increasing incidence. Danshen Decoction (DSD) as an adjuvant therapy can benefit CVDs patients by improving clinical efficacy.

PURPOSE

The purpose of this study was to identify the active components and potential pharmacological mechanisms of DSD by combining mass spectrometry with a network pharmacology strategy and to review the use of DSD in the treatment of CVDs.

METHOD

First, the composition of DSD was analyzed by ultrahigh-performance liquid chromatography/tandem mass spectrometry (UHPLC-MS/MS). Second, the network pharmacology method was used to elucidate the underlying material basis and possible pharmacological mechanism of DSD for the treatment of CVDs. Finally, clinical and experimental studies on DSD in the past ten years were retrieved from the PubMed and CNKI database, and the content of these studies was used to summarize the latest progress in DSD treatment of CVDs.

OUTCOME

A total of 35 compounds were found in DSD by manual identification from the analysis of MS, which may be the material basis for the therapeutic effect of DSD. After taking the intersection of 2086 targets related to CVDs, these 35 compounds are considered to play a role in the treatment of CVDs through 210 targets including signal transducer and activator of transcription 3 (STAT3), sarcoma (SRC) and phosphoinositide-3-kinase regulatory subunit (PIK3R), and a total of 168 signaling pathways were involved in the regulation of CVDs by DSD, including PI3K-AKT signaling pathway, Alzheimer disease, and Rap1 signaling pathway. A total of 29 clinical studies using DSD in the treatment of CVDs were included in the literature review, and these studies showed the positive significance of DSD as adjuvant therapy, while 14 experimental studies included in the literature review also demonstrated the effectiveness of DSD in the treatment of CVDs.

CONCLUSION

DSD plays a role in the treatment of CVDs through a variety of active ingredients. Large-scale clinical research and more in-depth experimental research will help to further reveal the mechanism of DSD in the treatment of CVDs.

Human genetics and its impact on cardiovascular disease

Cardiovascular disease (CVD) is a major cause of death worldwide. Given that CVD is a highly heritable trait, researchers have attempted to fully understand the genetic basis of CVD for a long time. The human genome comprises 3,100 Mbp per haploid genome and 6,200 Mbp in total (diploid genome). However, there is a tendency for rare genetic variations to exhibit a large effect size, whereas common genetic variations have a small effect on diseases, because of natural selection. In this sense, dividing genetic variations into two groups based on allele frequency (and effect sizes on diseases) is a good idea. We know there are several important genes (especially lipid-related genes) in which rare genetic variations are apparently associated with CVD risk, while a polygenic risk score comprising common genetic variations appears to work quite well among general populations. That information can be used not only for risk stratification but also for discoveries for novel pharmacologic targets. In this review article, we provide the important and simple idea that human genetics is important for CVD because it is a highly heritable trait, and we believe that it will lead to precision medicine in this field.

Identifying homogeneous subgroups of patients and important features: a topological machine learning approach

BACKGROUND

This paper exploits recent developments in topological data analysis to present a pipeline for clustering based on Mapper, an algorithm that reduces complex data into a one-dimensional graph.

RESULTS

We present a pipeline to identify and summarise clusters based on statistically significant topological features from a point cloud using Mapper.

CONCLUSIONS

Key strengths of this pipeline include the integration of prior knowledge to inform the clustering process and the selection of optimal clusters; the use of the bootstrap to restrict the search to robust topological features; the use of machine learning to inspect clusters; and the ability to incorporate mixed data types. Our pipeline can be downloaded under the GNU GPLv3 license at https://github.com/kcl-bhi/mapper-pipeline .

Clinical Genetic Risk Variants Inform a Functional Protein Interaction Network for Tetralogy of Fallot

BACKGROUND

Tetralogy of Fallot (TOF)-the most common cyanotic heart defect in newborns-has evidence of multiple genetic contributing factors. Identifying variants that are clinically relevant is essential to understand patient-specific disease susceptibility and outcomes and could contribute to delineating pathomechanisms.

METHODS

Using a clinically driven strategy, we reanalyzed exome sequencing data from 811 probands with TOF, to identify rare loss-of-function and other likely pathogenic variants in genes associated with congenital heart disease.

RESULTS

We confirmed a major contribution of likely pathogenic variants in FLT4 (VEGFR3 [vascular endothelial growth factor receptor 3]; n=14) and NOTCH1 (n=10) and identified 1 to 3 variants in each of 21 other genes, including ATRX, DLL4, EP300, GATA6, JAG1, NF1, PIK3CA, RAF1, RASA1, SMAD2, and TBX1. In addition, multiple loss-of-function variants provided support for 3 emerging congenital heart disease/TOF candidate genes: KDR (n=4), IQGAP1 (n=3), and GDF1 (n=8). In total, these variants were identified in 63 probands (7.8%). Using the 26 composite genes in a STRING protein interaction enrichment analysis revealed a biologically relevant network (P=3.3×10-16), with VEGFR2 (vascular endothelial growth factor receptor 2; KDR) and NOTCH1 (neurogenic locus notch homolog protein 1) representing central nodes. Variants associated with arrhythmias/sudden death and heart failure indicated factors that could influence long-term outcomes.

CONCLUSIONS

The results are relevant to precision medicine for TOF. They suggest considerable clinical yield from genome-wide sequencing, with further evidence for KDR (VEGFR2) as a congenital heart disease/TOF gene and for VEGF (vascular endothelial growth factor) and Notch signaling as mechanisms in human disease. Harnessing the genetic heterogeneity of single gene defects could inform etiopathogenesis and help prioritize novel candidate genes for TOF.