Looking back and thinking forwards - 15 years of cardiology and cardiovascular research

Independent Relationship of Lipoprotein(a) and Carotid Atherosclerosis With Long-Term Risk of Cardiovascular Disease

BACKGROUND

Lipoprotein(a) (Lp(a)) is considered to be a causal risk factor of atherosclerotic cardiovascular disease (ASCVD), but whether there is an independent or joint association of Lp(a) and atherosclerotic plaque with ASCVD risk remains uncertain. This study aims to assess ASCVD risk independently or jointly conferred by Lp(a) and carotid atherosclerotic plaque.

METHODS AND RESULTS

A total of 5471 participants with no history of cardiovascular disease at baseline were recruited and followed up for ASCVD events (all fatal and nonfatal acute coronary and ischemic stroke events) over a median of 11.5 years. Independent association of Lp(a), or the joint association of Lp(a) and carotid plaque with ASCVD risk, was explored using Cox proportional hazards models. Overall, 7.6% of the participants (60.0±7.9 years of age; 2649 [48.4%] men) had Lp(a) ≥50 mg/dL, and 539 (8.4/1000 person-years) incident ASCVD events occurred. Lp(a) concentrations were independently associated with long-term risk of total ASCVD events, as well as coronary events and ischemic stroke events. Participants with Lp(a) ≥50 mg/dL had a 62% higher risk of ASCVD incidence (95% CI, 1.19-2.21) than those with Lp(a) <10 mg/dL, and they exhibited a 10-year ASCVD incidence of 11.7%. This association exists even after adjusting for prevalent plaque. Moreover, participants with Lp(a) ≥30 mg/dL and prevalent plaque had a significant 4.18 times higher ASCVD risk than those with Lp(a) <30 mg/dL and no plaque.

CONCLUSIONS

Higher Lp(a) concentrations are independently associated with long-term ASCVD risk and may exaggerate cardiovascular risk when concomitant with atherosclerotic plaque.

Activation of transient receptor potential vanilloid 4 is involved in pressure overload-induced cardiac hypertrophy

Previous studies, including our own, have demonstrated that transient receptor potential vanilloid 4 (TRPV4) is expressed in hearts and implicated in cardiac remodeling and dysfunction. However, the effects of TRPV4 on pressure overload-induced cardiac hypertrophy remain unclear. In this study, we found that TRPV4 expression was significantly increased in mouse hypertrophic hearts, human failing hearts, and neurohormone-induced hypertrophic cardiomyocytes. Deletion of TRPV4 attenuated transverse aortic constriction (TAC)-induced cardiac hypertrophy, cardiac dysfunction, fibrosis, inflammation, and the activation of NFκB - NOD - like receptor pyrin domain-containing protein 3 (NLRP3) in mice. Furthermore, the TRPV4 antagonist GSK2193874 (GSK3874) inhibited cardiac remodeling and dysfunction induced by TAC. In vitro, pretreatment with GSK3874 reduced the neurohormone-induced cardiomyocyte hypertrophy and intracellular Ca²⁺ concentration elevation. The specific TRPV4 agonist GSK1016790A (GSK790A) triggered Ca²⁺ influx and evoked the phosphorylation of Ca²⁺/calmodulin-dependent protein kinase II (CaMKII). But these effects were abolished by removing extracellular Ca²⁺ or GSK3874. More importantly, TAC or neurohormone stimulation-induced CaMKII phosphorylation was significantly blocked by TRPV4 inhibition. Finally, we show that CaMKII inhibition significantly prevented the phosphorylation of NFκB induced by GSK790A. Our results suggest that TRPV4 activation contributes to pressure overload-induced cardiac hypertrophy and dysfunction. This effect is associated with upregulated Ca²⁺/CaMKII mediated activation of NFκB-NLRP3. Thus, TRPV4 may represent a potential therapeutic drug target for cardiac hypertrophy and dysfunction after pressure overload.

Precision Nomothetic Medicine in Depression Research: A New Depression Model, and New Endophenotype Classes and Pathway Phenotypes, and A Digital Self

Machine learning approaches, such as soft independent modeling of class analogy (SIMCA) and pathway analysis, were introduced in depression research in the 1990s (Maes et al.) to construct neuroimmune endophenotype classes. The goal of this paper is to examine the promise of precision psychiatry to use information about a depressed person’s own pan-omics, environmental, and lifestyle data, or to tailor preventative measures and medical treatments to endophenotype subgroups of depressed patients in order to achieve the best clinical outcome for each individual. Three steps are emerging in precision medicine: (1) the optimization and refining of classical models and constructing digital twins; (2) the use of precision medicine to construct endophenotype classes and pathway phenotypes, and (3) constructing a digital self of each patient. The root cause of why precision psychiatry cannot develop into true sciences is that there is no correct (cross-validated and reliable) model of clinical depression as a serious medical disorder discriminating it from a normal emotional distress response including sadness, grief and demoralization. Here, we explain how we used (un)supervised machine learning such as partial least squares path analysis, SIMCA and factor analysis to construct (a) a new precision depression model; (b) a new endophenotype class, namely major dysmood disorder (MDMD), which is a nosological class defined by severe symptoms and neuro-oxidative toxicity; and a new pathway phenotype, namely the reoccurrence of illness (ROI) index, which is a latent vector extracted from staging characteristics (number of depression and manic episodes and suicide attempts), and (c) an ideocratic profile with personalized scores based on all MDMD features.

Dissecting miRNA–Gene Networks to Map Clinical Utility Roads of Pharmacogenomics-Guided Therapeutic Decisions in Cardiovascular Precision Medicine

MicroRNAs (miRNAs) create systems networks and gene-expression circuits through molecular signaling and cell interactions that contribute to health imbalance and the emergence of cardiovascular disorders (CVDs). Because the clinical phenotypes of CVD patients present a diversity in their pathophysiology and heterogeneity at the molecular level, it is essential to establish genomic signatures to delineate multifactorial correlations, and to unveil the variability seen in therapeutic intervention outcomes. The clinically validated miRNA biomarkers, along with the relevant SNPs identified, have to be suitably implemented in the clinical setting in order to enhance patient stratification capacity, to contribute to a better understanding of the underlying pathophysiological mechanisms, to guide the selection of innovative therapeutic schemes, and to identify innovative drugs and delivery systems. In this article, the miRNA–gene networks and the genomic signatures resulting from the SNPs will be analyzed as a method of highlighting specific gene-signaling circuits as sources of molecular knowledge which is relevant to CVDs. In concordance with this concept, and as a case study, the design of the clinical trial GESS (NCT03150680) is referenced. The latter is presented in a manner to provide a direction for the improvement of the implementation of pharmacogenomics and precision cardiovascular medicine trials.

Analysis of Immune Associated Co-Expression Networks Reveals Immune-Related Long Non-Coding RNAs during MI in the Presence and Absence of HDC

Myocardial infarction (MI) is one of the most common cardiovascular diseases. Although previous studies have shown that histidine decarboxylase (HDC), a histamine-synthesizing enzyme, is involved in the stress response and heart remodeling after MI, the mechanism underlying it remains unclear. In this study, using Hdc-deficient mice (Hdc-/- mice), we established an acute myocardial infarction mouse model to explore the potential roles of Hdc/histamine in cardiac immune responses. Comprehensive analysis was performed on the transcriptomes of infarcted hearts. Differentially expressed gene (DEG) analysis identified 2126 DEGs in Hdc-deficient groups and 1013 in histamine-treated groups. Immune related pathways were enriched in Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Then we used the ssGSEA algorithm to evaluate 22 kinds of infiltrated immunocytes, which indicated that myeloid cells and T memory/follicular helper cells were tightly regulated by Hdc/histamine post MI. The relationships of lncRNAs and the Gene Ontology (GO) functions of protein-coding RNAs and immunocytes were dissected in networks to unveil immune-associated lncRNAs and their roles in immune modulation after MI. Finally, we screened out and verified four lncRNAs, which were closely implicated in tuning the immune responses after MI, including ENSMUST00000191157, ENSMUST00000180693 (PTPRE-AS1), and ENSMUST-00000182785. Our study highlighted the HDC-regulated myeloid cells as a driving force contributing to the government of transmission from innate immunocytes to adaptive immunocytes in the progression of the injury response after MI. We identified the potential role of the Hdc/histamine-lncRNAs network in regulating cardiac immune responses, which may provide novel promising therapeutic targets for further promoting the treatment of ischemic heart disease.

Novel molecular insights and public omics data in pulmonary hypertension

Pulmonary hypertension is a rare disease with high morbidity and mortality which mainly affects women of reproductive age. Despite recent advances in understanding the pathogenesis of pulmonary hypertension, the high heterogeneity in the presentation of the disease among different patients makes it difficult to make an accurate diagnosis and to apply this knowledge to effective treatments. Therefore, new studies are required to focus on translational and personalized medicine to overcome the lack of specificity and efficacy of current management. Here, we review the majority of public databases storing 'omics' data of pulmonary hypertension studies, from animal models to human patients. Moreover, we review some of the new molecular mechanisms involved in the pathogenesis of pulmonary hypertension, including non-coding RNAs and the application of 'omics' data to understand this pathology, hoping that these new approaches will provide insights to guide the way to personalized diagnosis and treatment.

YKL-40 as a novel biomarker in cardio-metabolic disorders and inflammatory diseases

Dyslipidaemia is associated with numerous health problems that include the combination of insulin resistance, hypertension and obesity, ie, metabolic syndrome. Although the use of statins to decrease serum low density lipoprotein cholesterol (LDL-C) has been an effective therapeutic in treating atherosclerosis, the persistence of high atherosclerotic risk, ie, residual risk, is notable and is not simply explained as a phenomenon of dyslipidaemia. As such, it is imperative that we identify new biomarkers to monitor treatment and more accurately predict future cardiovascular events. This athero-protective strategy includes the assessment of novel inflammatory biomarkers such as YKL-40. Recent evidence has implicated YKL-40 in patients with inflammatory diseases and cardio-metabolic disorders, making it potentially useful to evaluate disease severity, prognosis and survival. In this review, we summarize role of YKL-40 in the pathogenesis of cardio-metabolic disorders and explore its use as a novel biomarker for monitoring athero-protective therapy.

Danshen (Salvia miltiorrhiza) restricts MD2/TLR4-MyD88 complex formation and signalling in acute myocardial infarction-induced heart failure

Heart failure (HF) represents a major public health burden. Inflammation has been shown to be a critical factor in the progression of HF, regardless of the aetiology. Disappointingly, the majority of clinical trials targeting aspects of inflammation in patients with HF have been largely negative. Many clinical researches demonstrate that danshen has a good efficacy on HF, and however, whether danshen exerts anti‐inflammatory effects against HF remains unclear. In our study, the employment of a water extracted and alcohol precipitated of danshen extract attenuated cardiac dysfunction and inflammation response in acute myocardial infarction‐induced HF rats. Transcriptome technique and validation results revealed that TLR4 signalling pathway was involved in the anti‐inflammation effects of danshen. In vitro, danshen reduced the release of inflammatory mediators in LPS‐stimulated RAW264.7 macrophage cells. Besides, the LPS‐stimulated macrophage conditioned media was applied to induce cardiac H9C2 cells injury, which could be attenuated by danshen. Furtherly, knock‐down and overexpression of TLR4 were utilized to confirm that danshen ameliorated inflammatory injury via MyD88‐dependent TLR4‐TRAF6‐NF‐κB signalling pathway in cardiomyocytes. Furthermore, by utilizing co‐immunoprecipitation, danshen was proved to suppress MD2/TLR4 complex formation and MyD88 recruitment. In conclusion, our results demonstrated that danshen ameliorates inflammatory injury by controlling MD2/TLR4‐MyD88 complex formation and TLR4‐TRAF6‐NF‐κB signalling pathway in acute myocardial infarction‐induced HF.

Machine Learning in Cardiology-Ensuring Clinical Impact Lives Up to the Hype

Despite substantial advances in the study, treatment, and prevention of cardiovascular disease, numerous challenges relating to optimally screening, diagnosing, and managing patients remain. Simultaneous improvements in computing power, data storage, and data analytics have led to the development of new techniques to address these challenges. One powerful tool to this end is machine learning (ML), which aims to algorithmically identify and represent structure within data. Machine learning's ability to efficiently analyze large and highly complex data sets make it a desirable investigative approach in modern biomedical research. Despite this potential and enormous public and private sector investment, few prospective studies have demonstrated improved clinical outcomes from this technology. This is particularly true in cardiology, despite its emphasis on objective, data-driven results. This threatens to stifle ML's growth and use in mainstream medicine. We outline the current state of ML in cardiology and outline methods through which impactful and sustainable ML research can occur. Following these steps can ensure ML reaches its potential as a transformative technology in medicine.