Heart failure subtypes: Pathophysiology and definitions

Role of CETP, PCSK-9, and CYP7-alpha in cholesterol metabolism: Potential targets for natural products in managing hypercholesterolemia

Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide, primarily affecting the heart and blood vessels, with atherosclerosis being a major contributing factor to their onset. Epidemiological and clinical studies have linked high levels of low-density lipoprotein (LDL) emanating from distorted cholesterol homeostasis as its major predisposing factor. Cholesterol homeostasis, which involves maintaining the balance in body cholesterol level, is mediated by several proteins or receptors, transcription factors, and even genes, regulating cholesterol influx (through dietary intake or de novo synthesis) and efflux (by their conversion to bile acids). Previous knowledge about CVDs management has evolved around modulating these receptors' activities through synthetic small molecules/antibodies, with limited interest in natural products. The central roles of the cholesteryl ester transfer protein (CETP), proprotein convertase subtilisin/kexin type 9 (PCSK9), and cytochrome P450 family 7 subfamily A member 1 (CYP7A1), among other proteins or receptors, have fostered growing scientific interests in understanding more on their regulatory activities and potential as drug targets. We present up-to-date knowledge on the contributions of CETP, PCSK9, and CYP7A1 toward CVDs, highlighting the clinical successes and failures of small molecules/antibodies to modulate their activities. In recommendation for a new direction to improve cardiovascular health, we have presented recent findings on natural products (including functional food, plant extracts, phytochemicals, bioactive peptides, and therapeutic carbohydrates) that also modulate the activities of CETP, PCSK-9, and CYP7A1, and emphasized the need for more research efforts redirected toward unraveling more on natural products potentials even at clinical trial level for CVD management.

Development and validation of a model for predicting the early occurrence of RF in ICU-admitted AECOPD patients: a retrospective analysis based on the MIMIC-IV database

BACKGROUND

This study aims to construct a model predicting the probability of RF in AECOPD patients upon hospital admission.

METHODS

This study retrospectively extracted data from MIMIC-IV database, ultimately including 3776 AECOPD patients. The patients were randomly divided into a training set (n = 2643) and a validation set (n = 1133) in a 7:3 ratio. First, LASSO regression analysis was used to optimize variable selection by running a tenfold k-cyclic coordinate descent. Subsequently, a multifactorial Cox regression analysis was employed to establish a predictive model. Thirdly, the model was validated using ROC curves, Harrell's C-index, calibration plots, DCA, and K-M curve.

RESULT

Eight predictive indicators were selected, including blood urea nitrogen, prothrombin time, white blood cell count, heart rate, the presence of comorbid interstitial lung disease, heart failure, and the use of antibiotics and bronchodilators. The model constructed with these 8 predictors demonstrated good predictive capabilities, with ROC curve areas under the curve (AUC) of 0.858 (0.836-0.881), 0.773 (0.746-0.799), 0.736 (0.701-0.771) within 3, 7, and 14 days in the training set, respectively and the C-index was 0.743 (0.723-0.763). Additionally, calibration plots indicated strong consistency between predicted and observed values. DCA analysis demonstrated favorable clinical utility. The K-M curve indicated the model's good reliability, revealed a significantly higher RF occurrence probability in the high-risk group than that in the low-risk group (P < 0.0001).

CONCLUSION

The nomogram can provide valuable guidance for clinical practitioners to early predict the probability of RF occurrence in AECOPD patients, take relevant measures, prevent RF, and improve patient outcomes.

Precision Cardiology: Phenotype-targeted Therapies for HFmrEF and HFpEF

Heart failure with mid-range ejection fraction (HFmrEF) and preserved ejection fraction (HFpEF) represent over half of heart failure cases but lack proven effective therapies beyond sodium-glucose cotransporter 2 inhibitor and diuretics. HFmrEF and HFpEF are heterogeneous conditions requiring precision phenotyping to enable tailored therapies. This review covers concepts on precision medicine approaches for HFmrEF and HFpEF. Areas discussed include HFmrEF mechanisms, anti-inflammatory and antifibrotic treatments for obesity-related HFpEF, If inhibition for HFpEF with atrial fibrillation, and mineralocorticoid receptor antagonism for chronic kidney disease-HFpEF. Incorporating precision phenotyping and matched interventions in HFmrEF and HFpEF trials will further advance therapy compared to blanket approaches.

CardiOmics signatures reveal therapeutically actionable targets and drugs for cardiovascular diseases

Cardiovascular diseases are the leading cause of death worldwide, with heart failure being a complex condition that affects millions of individuals. Single-nucleus RNA sequencing has recently emerged as a powerful tool for unraveling the molecular mechanisms behind cardiovascular diseases. This cutting-edge technology enables the identification of molecular signatures, intracellular networks, and spatial relationships among cardiac cells, including cardiomyocytes, mast cells, lymphocytes, macrophages, lymphatic endothelial cells, endocardial cells, endothelial cells, epicardial cells, adipocytes, fibroblasts, neuronal cells, pericytes, and vascular smooth muscle cells. Despite these advancements, the discovery of essential therapeutic targets and drugs for precision cardiology remains a challenge. To bridge this gap, we conducted comprehensive in silico analyses of single-nucleus RNA sequencing data, functional enrichment, protein interactome network, and identification of the shortest pathways to physiological phenotypes. This integrated multi-omics analysis generated CardiOmics signatures, which allowed us to pinpoint three therapeutically actionable targets (ADRA1A1, PPARG, and ROCK2) and 15 effective drugs, including adrenergic receptor agonists, adrenergic receptor antagonists, norepinephrine precursors, PPAR receptor agonists, and Rho-associated kinase inhibitors, involved in late-stage cardiovascular disease clinical trials.

Current Landscape of Gene Therapy for the Treatment of Cardiovascular Disorders

Cardiovascular disorders (CVD) are the primary cause of death worldwide. Multiple factors have been accepted to cause cardiovascular diseases; among them, smoking, physical inactivity, unhealthy eating habits, age, and family history are flag-bearers. Individuals at risk of developing CVD are suggested to make drastic habitual changes as the primary intervention to prevent CVD; however, over time, the disease is bound to worsen. This is when secondary interventions come into play, including antihypertensive, anti-lipidemic, anti-anginal, and inotropic drugs. These drugs usually undergo surgical intervention in patients with a much higher risk of heart failure. These therapeutic agents increase the survival rate, decrease the severity of symptoms and the discomfort that comes with them, and increase the overall quality of life. However, most individuals succumb to this disease. None of these treatments address the molecular mechanism of the disease and hence are unable to halt the pathological worsening of the disease. Gene therapy offers a more efficient, potent, and important novel approach to counter the disease, as it has the potential to permanently eradicate the disease from the patients and even in the upcoming generations. However, this therapy is associated with significant risks and ethical considerations that pose noteworthy resistance. In this review, we discuss various methods of gene therapy for cardiovascular disorders and address the ethical conundrum surrounding it.

Utilization rates and predictors of sodium glucose cotransporter 2 inhibitor use in patients with heart failure with or without type 2 diabetes

PURPOSE

Sodium glucose cotransporter 2 (SGLT2) inhibitors have been demonstrated to reduce cardiovascular deaths and heart failure (HF) hospitalizations in patients with HF. Despite this, utilization remains low. The purpose of this study was to characterize SGLT2 inhibitor utilization rates and predictors of use in a population of patients with or without type 2 diabetes (T2D).

METHODS

This was a retrospective, single-center, descriptive chart review study. Individuals 18 years of age or older with HF were eligible for inclusion. Charts were reviewed between August 2021 and February 2022. The primary objective was to identify rates of SGLT2 inhibitor prescribing for patients with HF within a large academic medical center. Logistic regression analyses were conducted to identify potential SGLT2 inhibitor utilization predictors (demographic characteristics, medical history, laboratory results, specialty provider visits, medication use, and medication coverage).

RESULTS

A total of 800 patients with HF were included: 377 with HF with reduced ejection fraction (HFrEF), 88 with mildly reduced EF, and 335 with preserved EF. Key baseline characteristics were as follows: 43% female; 47% Hispanic; 42% with T2D; 49% with established atherosclerotic cardiovascular disease; and mean age, 65 years. SGLT2 inhibitor utilization was 6.5% overall. Key predictors of utilization were as follows: T2D (odds ratio [OR], 33.4; 95% CI, 8.01-139.55), HFrEF (OR, 2.8; 95% CI, 1.45-5.51), HF clinic visit (OR, 2.5; 95% CI, 1.40-4.60), visit with pharmacist with prescriptive authority (OR, 5.8; 95% CI, 3.14-10.88), and enrollment in the hospital patient assistance program (OR, 2.3; 95% CI, 1.08-4.97).

CONCLUSION

Despite guideline recommendations, SGLT2 inhibitors are underutilized in patients with HF with or without T2D.

MapperPlus: Agnostic clustering of high-dimension data for precision medicine

One of the goals of precision medicine is to classify patients into subgroups that differ in their susceptibility and response to a disease, thereby enabling tailored treatments for each subgroup. Therefore, there is a great need to identify distinctive clusters of patients from patient data. There are three key challenges to three key challenges of patient stratification: 1) the unknown number of clusters, 2) the need for assessing cluster validity, and 3) the clinical interpretability. We developed MapperPlus, a novel unsupervised clustering pipeline, that directly addresses these challenges. It extends the topological Mapper technique and blends it with two random-walk algorithms to automatically detect disjoint subgroups in patient data. We demonstrate that MapperPlus outperforms traditional agnostic clustering methods in key accuracy/performance metrics by testing its performance on publicly available medical and non-medical data set. We also demonstrate the predictive power of MapperPlus in a medical dataset of pediatric stem cell transplant patients where a number of cluster is unknown. Here, MapperPlus stratifies the patient population into clusters with distinctive survival rates. The MapperPlus software is open-source and publicly available.

Predictors of early heart failure rehospitalization among older adults with preserved and reduced ejection fraction: A review and derivation of a conceptual model

BACKGROUND

Heart failure (HF) is prevalent among older adults who suffer with either heart failure preserved ejection fraction (HFpEF) or heart failure reduced ejection fraction (HFrEF) and have a high rate of early HF rehospitalization. Preventing early rehospitalization is complex because of major differences between the two subtypes of HF as well as inadequate predictive models to identify key contributing factors.

OBJECTIVE

To present research addressing relationships between selected clinical, hemodynamic, social factors, and early (≤ 60-day) HF rehospitalization in older adults with HFpEF and HFrEF, derive a conceptual model of predictors of rehospitalization, and understand to what extent the literature addresses these predictors among older women.

METHODS

Four computerized databases were searched for research addressing clinical, hemodynamic, and social factors relevant to early HF rehospitalization and older adults post index hospitalization for HF.

RESULTS

21 full-text articles were included in the final review and organized thematically. Most studies focused on early (≤ 30-day) HF rehospitalizations, with limited attention given to the 31 to 60-day period. Specific clinical, hemodynamic, and social factors which influenced early HF rehospitalization were identified. The existing literature confirms that risk predictors or their combinations which influence early (≤ 60-day) HF rehospitalization after an index HF hospitalization remains inconsistent. Further, the literature fails to capture the influence of these predictors solely among older women. A conceptual model of risk predictors is proposed for clinical intervention.

CONCLUSION

Further evaluation to understand risk predictors of early (31 to 60-day) HF rehospitalizations among older women is needed.

Diabetes-induced chronic heart failure is due to defects in calcium transporting and regulatory contractile proteins: cellular and molecular evidence

Heart failure (HF) is a major deteriorating disease of the myocardium due to weak myocardial muscles. As such, the heart is unable to pump blood efficiently around the body to meet its constant demand. HF is a major global health problem with more than 7 million deaths annually worldwide, with some patients dying suddenly due to sudden cardiac death (SCD). There are several risk factors which are associated with HF and SCD which can negatively affect the heart synergistically. One major risk factor is diabetes mellitus (DM) which can cause an elevation in blood glucose level or hyperglycaemia (HG) which, in turn, has an insulting effect on the myocardium. This review attempted to explain the subcellular, cellular and molecular mechanisms and to a lesser extent, the genetic factors associated with the development of diabetes- induced cardiomyopathy due to the HG which can subsequently lead to chronic heart failure (CHF) and SCD. The study first explained the structure and function of the myocardium and then focussed mainly on the excitation-contraction coupling (ECC) processes highlighting the defects of calcium transporting (SERCA, NCX, RyR and connexin) and contractile regulatory (myosin, actin, titin and troponin) proteins. The study also highlighted new therapies and those under development, as well as preventative strategies to either treat or prevent diabetic cardiomyopathy (DCM). It is postulated that prevention is better than cure.

The Relationship between Angiotensin–Neprilysin Treatment, Echocardiographic Parameters, and NT-proBNP Levels in HFpEF Patients with Acute Decompensated Heart Failure

Background

The valsartan-sacubitril therapy improved the outcomes of patients with acute decompensated heart failure (ADHF) of a reduced ejection fraction (HFrEF). In ADHF patients with preserved ejection fraction (HFpEF), it is not yet clear whether the same treatment regimen may be safely used to treat ADHF.

Methods

For this study, HFpEF patients hospitalized due to ADHF were enrolled. Following hemodynamic stabilization, patients were randomized into two groups that were treated with enalapril or sacubitril-valsartan. In this trial, the primary efficacy outcomes were changes in echocardiographic parameters and NT-proBNP levels from baseline to 8 weeks treatment.

Results

ARNI treatment resulted in a significant decrease in NT-proBNP levels and an increase in LVEF in patients with HFpEF. However, HFpEF patients that underwent ARNI treatment achieved better outcomes than did patients that underwent ACEI treatment.

Conclusion

Sacubitril-valsartan treatment, which lowered NT-proBNP levels and improved cardiac function, was more effective in HFpEF patients with acute decompensated heart failure than enalapril.

The Comprehensive Machine Learning Analytics for Heart Failure

Background: Early detection of heart failure is the basis for better medical treatment and prognosis. Over the last decades, both prevalence and incidence rates of heart failure have increased worldwide, resulting in a significant global public health issue. However, an early diagnosis is not an easy task because symptoms of heart failure are usually non-specific. Therefore, this study aims to develop a risk prediction model for incident heart failure through a machine learning-based predictive model. Although African Americans have a higher risk of incident heart failure among all populations, few studies have developed a heart failure risk prediction model for African Americans. Methods: This research implemented the Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression, support vector machine, random forest, and Extreme Gradient Boosting (XGBoost) to establish the Jackson Heart Study's predictive model. In the analysis of real data, missing data are problematic when building a predictive model. Here, we evaluate predictors' inclusion with various missing rates and different missing imputation strategies to discover the optimal analytics. Results: According to hundreds of models that we examined, the best predictive model was the XGBoost that included variables with a missing rate of less than 30 percent, and we imputed missing values by non-parametric random forest imputation. The optimal XGBoost machine demonstrated an Area Under Curve (AUC) of 0.8409 to predict heart failure for the Jackson Heart Study. Conclusion: This research identifies variations of diabetes medication as the most crucial risk factor for heart failure compared to the complete cases approach that failed to discover this phenomenon.