Early Detection Of Heart Failure Research Articles

Early Detection of Heart Failure with Autonomous AI-Based Model Using Chest Radiographs: A Multicenter Study

The opportunistic use of radiological examinations for disease detection can potentially enable timely management. We assessed if an index created by an AI software to quantify chest radiography (CXR) findings associated with heart failure (HF) could distinguish between patients who would develop HF or not within a year of the examination. Our multicenter retrospective study included patients who underwent CXR without an HF diagnosis. We included 1117 patients (age 67.6 ± 13 years; m:f 487:630) that underwent CXR. A total of 413 patients had the CXR image taken within one year of their HF diagnosis. The rest (n = 704) were patients without an HF diagnosis after the examination date. All CXR images were processed with the model (qXR-HF, Qure.AI) to obtain information on cardiac silhouette, pleural effusion, and the index. We calculated the accuracy, sensitivity, specificity, and area under the curve (AUC) of the index to distinguish patients who developed HF within a year of the CXR and those who did not. We report an AUC of 0.798 (95%CI 0.77–0.82), accuracy of 0.73, sensitivity of 0.81, and specificity of 0.68 for the overall AI performance. AI AUCs by lead time to diagnosis (<3 months: 0.85; 4–6 months: 0.82; 7–9 months: 0.75; 10–12 months: 0.71), accuracy (0.68–0.72), and specificity (0.68) remained stable. Our results support the ongoing investigation efforts for opportunistic screening in radiology.

Chemiluminescence immunoassay amplified by functional acridinium ester for the detection of NT-proBNP in clinical samples

High sensitivity and accuracy of heart failure biomarker, N-terminal prohormone brain natriuretic peptide (NT-proBNP), is critical for the early detection of heart failure. However, traditional chemiluminescent (CL) probe still face the challenges of poor solubility, in particular, significant disappointing bioavailability, which profoundly limit its clinical applications. In this paper, we demonstrate functional CL probe acridinium ester (AE-2) for the detection of NT-proBNP, through introducing a hydrophilic unit for ensuring high water dispersity and reactive group for promoting better bioavaiability with antibody. Compared to the traditional CL probe, the functional CL probe AE-2 exhibits extraordinary features, such as better biocompatibility, higher label rate with antibody, as well as significant chemiluminescence signal. In addition, this CL probe exhibits excellent linearity with the test data of Longhua Hospital, suggesting that AE-2 could be used as a clinical examination assay to detect NT-proBNP in real clinical samples. Thus, this functional CL probe AE-2 can be a powerful tool for elucidating the roles of NT-proBNP in clinical samples.

Serum metabolomics improves risk stratification for incident heart failure.

Prediction and early detection of heart failure (HF) is crucial to mitigate its impact on quality of life, survival, and healthcare expenditure. Here, we explored the predictive value of serum metabolomics (168 metabolites detected by proton nuclear magnetic resonance [1H-NMR] spectroscopy) for incident HF. Leveraging data of 68 311 individuals and >0.8 million person-years of follow-up from the UK Biobank cohort, we (i) fitted per-metabolite Cox proportional hazards models to assess individual metabolite associations, and (ii) trained and validated elastic net models to predict incident HF using the serum metabolome. We benchmarked discriminative performance against a comprehensive, well-validated clinical risk score (Pooled Cohort Equations to Prevent HF [PCP-HF]). During a median follow-up of ≈12.3 years, several metabolites showed independent association with incident HF (90/168 adjusting for age and sex, 48/168 adjusting for PCP-HF). Performance-optimized risk models effectively retained key predictors representing highly correlated clusters (≈80% feature reduction). Adding metabolomics to PCP-HF improved predictive performance (Harrel's C: 0.768 vs. 0.755, ΔC = 0.013, [95% confidence interval [CI] 0.004-0.022], continuous net reclassification improvement [NRI]: 0.287 [95% CI 0.200-0.367], relative integrated discrimination improvement [IDI]: 17.47% [95% CI 9.463-27.825]). Models including age, sex and metabolomics performed almost as well as PCP-HF (Harrel's C: 0.745 vs. 0.755, ΔC = 0.010 [95% CI -0.004 to 0.027], continuous NRI: 0.097 [95% CI -0.025 to 0.217], relative IDI: 13.445% [95% CI -10.608 to 41.454]). Risk and survival stratification was improved by integrating metabolomics. Serum metabolomics improves incident HF risk prediction over PCP-HF. Scores based on age, sex and metabolomics exhibit similar predictive power to clinically-based models, potentially offering a cost-effective, standardizable, and scalable single-domain alternative.

Optimizing Heart Failure Detection: A Comparison between Naive Bayes and Particle Swarm Optimization

This research focuses on the importance of early detection of heart failure which is a serious global health problem. Given the variety of symptoms of heart failure, accurate early detection methods are needed with the aim of reducing the impact of this disease. This study uses the Naïve Bayes (NB) method which has been proven effective in classifying heart failure with significant variations in accuracy by integrating Particle Swarm Optimization (PSO) to improve the model. The evaluation model involves a confusion matrix including accuracy, precision, recall, and Area Under the Curve. The research results show that the integration of PSO in NB results in an increase in accuracy of 7.73%, an increase in precision of 6.42%, and an increase in recall of 1.93%. Although there was a small decrease in AUC. This research shows that the success of NB with PSO can help improve the performance of early detection of heart failure. This indicates the importance of this research in developing more accurate and effective detection methods for critical health conditions such as heart failure.

Novel Biomarkers in Early Detection of Heart Failure: A Narrative Review.

Heart failure (HF) represents a significant global health challenge, characterized by a variety of symptoms resulting from cardiac dysfunction. This dysfunction often leads to systemic and pulmonary congestion. The pathophysiology of HF is complex, involving stimulation of the sympathetic nervous system, which is insufficiently balanced by the release of natriuretic peptide. This imbalance leads to progressive hypertrophy and dilatation of the heart's chambers, impairing its pumping efficiency and increasing the risk of arrhythmias and conduction disorders. The prevalence of HF is exceptionally high in industrialized nations and is expected to increase owing to an aging population and advancements in diagnostic methods. This study emphasizes the critical role of early diagnosis in reducing morbidity and mortality associated with HF, focusing specifically on the evolving importance of biomarkers in managing this condition. Biomarkers have played a key role in transforming the diagnosis and treatment of HF. Traditional biomarkers such as b-type natriuretic peptide and N-terminal pro-b-type natriuretic peptide have been widely adopted for their cost-effectiveness and ease of access. However, the rise of novel biomarkers such as growth differentiation factor 15 and adrenomedullin has shown promising results, offering superior sensitivity and specificity. These new biomarkers enhance diagnostic accuracy, risk stratification, and prognostic evaluation in HF patients. Despite these advancements, challenges remain, such as limited availability, high costs, and the need for further validation in diverse patient populations. Through a comprehensive literature review across databases such as PubMed, Google Scholar, and the Cochrane Library, this study compiles and analyzes data from 18 relevant studies, offering a detailed understanding of the current state of HF biomarkers.The study examines both traditional and emerging biomarkers such as galectin-3 and soluble suppression of tumorigenicity 2 in HF, exploring their clinical roles and impact on patient outcomes.

Enhancing heart disease prediction using a self-attention-based transformer model

Cardiovascular diseases (CVDs) continue to be the leading cause of more than 17 million mortalities worldwide. The early detection of heart failure with high accuracy is crucial for clinical trials and therapy. Patients will be categorized into various types of heart disease based on characteristics like blood pressure, cholesterol levels, heart rate, and other characteristics. With the use of an automatic system, we can provide early diagnoses for those who are prone to heart failure by analyzing their characteristics. In this work, we deploy a novel self-attention-based transformer model, that combines self-attention mechanisms and transformer networks to predict CVD risk. The self-attention layers capture contextual information and generate representations that effectively model complex patterns in the data. Self-attention mechanisms provide interpretability by giving each component of the input sequence a certain amount of attention weight. This includes adjusting the input and output layers, incorporating more layers, and modifying the attention processes to collect relevant information. This also makes it possible for physicians to comprehend which features of the data contributed to the model's predictions. The proposed model is tested on the Cleveland dataset, a benchmark dataset of the University of California Irvine (UCI) machine learning (ML) repository. Comparing the proposed model to several baseline approaches, we achieved the highest accuracy of 96.51%. Furthermore, the outcomes of our experiments demonstrate that the prediction rate of our model is higher than that of other cutting-edge approaches used for heart disease prediction.

High-sensitivity C-reactive protein as a predictor, indicator of severity and prognostic indicator of chronic heart failure

Cardio vascular diseases (CVD) are one of the most serious problems of modern medicine both throughout the world and in the Russian Federation, due to their high prevalence and high risk of death. A signif-icant contribution to the rates of hospitalization and mortality from cardiovascular diseases is made by chronic heart failure (CHF), which is a severe pathology. The key strategic direction to reduce mortality from CHF and increase life expectancy of the population is the search for methods aimed at prevention and early detection of heart failure. This literature review summarizes research data on the relationship between high-sensitivity C-reactive protein (hs-CRP) and various aspects of cardiovascular events (CVS) (risk of development, severity of the condition, disease prognosis), in particular chronic heart failure. The authors of the article carried out a de-scriptive synthesis of the literature to present the main characteristics of the studied biomarker, including the purpose of the measurement, the relationship with other risk factors (RF), laboratory and instrumental indicators; categories of patients for whom it is the most informative. The study was able to analyze 25 literature sources for the availability of information showing the effect of hs-CRP values in the blood of patients on the course of CVD. A threshold concentration of this biomarker (3 mg/l) was established, which is associated with an increased risk of developing CHF, while the severity of its course directly correlated with the degree of CRP increase, as well as the risk of its progression and death from this disease (complications).

Application analysis of machine learning and data visualization in heart failure prediction

Heart failure is a non-eligible global health challenge, characterized by increased morbidity, mortality, and health expenditures. Early detection of heart failure can help prevent disease progression and improve outcomes. Logistic regression is a machine learning technique widely used in binary classification problems. In this paper, the patient record dataset was used to predict heart failure using logistic regression. The most important features for predicting heart failure are also determined through detailed analysis, mainly including age, serum creatinine, and ejection fraction. The results suggest that logistic regression can be a valuable tool for predicting heart failure and improving patient outcomes. Further research could explore other machine learning algorithms and more sophisticated feature selection techniques to further improve the prediction of heart failure.

СРАВНЕНИЕ ИНФОРМАТИВНОСТИ ОПРЕДЕЛЕНИЯ NT-PROBNP ПОЛУКОЛИЧЕСТВЕННЫМ И КОЛИЧЕСТВЕННЫМ МЕТОДАМИ ДЛЯ РАННЕЙ ДИАГНОСТИКИ ХРОНИЧЕСКОЙ СЕРДЕЧНОЙ НЕДОСТАТОЧНОСТИ

Вackground. Heart failure (HF) is one of the main causes of morbidity and mortality worldwide. Since it is important to diagnose CH early and quickly, at the outpatient appointment, there is a need to create and introduce into practice a portable highly sensitive method for the determination of BNP, NT-proBNP. Purpose of the study. To determine the possibility of using reagents for semi-quantitative determination of N-terminal brain natriuretic peptide antigen for early detection of heart failure. Methods of statistical analysis. Microsoft Office Excel 2019 program was used. Patient Characterization and Research Methods. An open cross-sectional non-randomized cohort study included 50 patients (mean age 63.5 years) who applied on an outpatient basis for consultation to a cardiologist. In addition to standard clinical, biochemical and instrumental examinations, the concentration of N-terminal brain natriuretic peptide (NT-proBNP) was determined by quantitative and semi-quantitative immunochromatographic method (LLC NPO "BioTest", Novosibirsk). The diagnosis of chronic heart failure (CHF) was established in accordance with national and European recommendations. Methods of statistical analysis. Microsoft Office Excel 2019 program was used. Results and conclusion. In the course of the study, it was found that semi-quantitative method is highly sensitive for detection of small NT-proBNP elevations, which is important for early diagnosis of heart failure, including subclinical forms. Semi-quantitative method also allows to distinguish a group of patients with high NT-proBNP values. The relationship of NT-proBNP level with the course and prognosis of CHF requires further study.

Techniques of Machine Learning for Detecting Heart Failure

Heart failure is a major health problem affecting millions of people worldwide. Early detection of heart failure is crucial to ensure timely treatment and reduce the risk of complications. Machine learning approaches have shown promise in detecting heart failure at an early stage. In this study, we aimed to develop a machine learning-based approach for early detection of heart failure using clinical and laboratory data collected from secondary data source in Kaggle. We used a supervised learning approach to develop a predictive model for heart failure. Six machine learning techniques, including logistic regression, decision trees, Random Forest, support vector machines, K-Nearest Neighbor, and Naive Bayes, had their performance evaluated. We have compared the performance of these algorithms to more well-known risk prediction models that only depend on demographic and medical information. Our results showed that the random forest algorithm had the best performance in detecting heart failure, with an accuracy of 86.23%, precision of 86.19%, recall of 86.23%, and F1 score 86.19%. In conclusion, our study demonstrated the potential of machine learning approaches for early detection of heart failure using clinical and laboratory data. The developed model has the potential to be used as a screening tool for early detection of heart failure, which could lead to improved outcomes for patients.

Heart Failure Prediction using Gaussian Naïve Bayes Algorithm

Heart failure affects a minimum of 26 million individuals and its occurrence has been increasing day-by-day. Heart failure occurs when the heart cannot pump enough blood to meet the body's needs. This is caused due to various reasons such as coronary heart disease, heart valve malfunctioning, diabetes, anaemia etc. So, it is important to predict heart failure in its early stage to reduce the mortality rate. Cardiovascular disease is the major contributing factor for the prediction of heart failure. This research uses Gaussian Naïve Bayes technique which comes under supervised learning algorithm to predict heart failure. Gaussian Naïve Bayes algorithm treats each feature to be independent and each feature has equal importance in predicting heart failure. For the purpose of predicting heart failure, the mean and standard deviation of each column in a dataset is used. Different datasets are used for the implementation of heart failure prediction, and performance metrics are identified for each dataset. The findings of this study suggest that the Gaussian Naive Bayes algorithm can be a useful algorithm in predicting heart failure, and it has the potential to improve patient outcomes by facilitating early detection of heart failure. There are various metrics used for the performance analysis of the system such as, accuracy, recall, precision, confusion matrix and ROC curve. Finally, this study concludes by presenting the implementation of heart failure using Gaussian Naïve Bayes.

A classification framework using filter–wrapper based feature selection approach for the diagnosis of congenital heart failure

Premature mortality from cardiovascular disease can be reduced with early detection of heart failure by analysing the patients’ risk factors and assuring accurate diagnosis. This work proposes a clinical decision support system for the diagnosis of congenital heart failure by utilizing a data pre-processing approach for dealing missing values and a filter-wrapper based method for selecting the most relevant features. Missing values are imputed using a missForest method in four out of eight heart disease datasets collected from the Machine Learning Repository maintained by University of California, Irvine. The Fast Correlation Based Filter is used as the filter approach, while the union of the Atom Search Optimization Algorithm and the Henry Gas Solubility Optimization represent the wrapper-based algorithms, with the fitness function as the combination of accuracy, G-mean, and Matthew’s correlation coefficient measured by the Support Vector Machine. A total of four boosted classifiers namely, XGBoost, AdaBoost, CatBoost, and LightGBM are trained using the selected features. The proposed work achieves an accuracy of 89%, 84%, 83%, 80% for Heart Failure Clinical Records, 81%, 80%, 83%, 82% for Single Proton Emission Computed Tomography, 90%, 82%, 93%, 80% for Single Proton Emission Computed Tomography F, 80%, 80%, 81%, 80% for Statlog Heart Disease, 80%, 85%, 83%, 86% for Cleveland Heart Disease, 82%, 85%, 85%, 82% for Hungarian Heart Disease, 80%, 81%, 79%, 82% for VA Long Beach, 97%, 89%, 98%, 97%, for Switzerland Heart Disease for four classifiers respectively. The suggested technique outperformed the other classifiers when evaluated against Random Forest, Classification and Regression Trees, Support Vector Machine, and K-Nearest Neighbor.

Design and Analysis of an Inductive Coupling System for the Early Detection of Heart Failure

Heart failure is a common, complex clinical syndrome with high morbidity and mortality. Hemodynamic parameter evaluation is useful for early detection, clinical outcome monitoring, timely treatment, and the overall prognosis of heart failure patients. Therefore, continuous monitoring of hemodynamic parameters helps in the evaluation of patients with suspected heart failure. The hemodynamic parameters change with respect to the contraction and expansion of the heart. Hence, in this research, two circuit-less 30 mm spherical receiver coils were implanted in both the left and right sides of the heart and an external transceiver coil was placed above the chest. The changes in the reflection coefficient of the transceiver coil at the resonant frequency changed depending on the distance between the implanted coils, allowing the contraction and expansion of the heart to be determined. This work was carried out at 13.56 MHz, considering the safety limits imposed by the FCC. The proposed reflection coefficient monitoring technique may distinguish healthy patients from heart failure and heart attack patients. The reflection coefficients at a maximum distance of 50 mm for simulation and measurement are −10.3 dB and −10.6 dB, respectively, at the resonant frequency.

Abstract 12917: Reproducibility of Use by Physicians and Patients of a Smart Stethoscope for Artificial-Intelligence Enhanced Diagnosis of Heart Failure

Introduction: Early detection of heart failure (HF) is a global health system priority. An artificial intelligence-enhanced stethoscope (‘Eko DUO’), is capable of recording and analysing a single-lead ECG to detect HF with reduced left ventricular ejection fraction (LVEF≤40%) after only 15 seconds of apposition to the chest. Given this simplicity, patient-administered remote HF diagnosis and screening is feasible if performance and reproducibility of use of the Eko DUO is adequate. Hypothesis: We tested hypotheses that: 1. Eko DUO predictions for LVEF≤40% have good intra- and inter- operator reproducibility (intra-class correlation coefficient > 0.75) for both patient and clinician deployment. 2. Eko DUO performance for detection of LVEF≤40% is comparable between patients and clinicians Methods: 98 hospital inpatients undergoing echocardiography at Imperial College Healthcare NHS Trust, UK, performed self-examination with the Eko DUO, using instructions to take three recordings from the left parasternal 2nd intercostal position. A clinician then repeated three recordings. The intra-class correlation coefficient was calculated using the raw numerical artificial intelligence predictions for LVEF≤40%. Performance for detection of LVEF≤40% was measured by calculating the area under the receiver-operating characteristic curves (AUC) using a reference standard of echocardiography-derived percentage LVEF. Results: There was good intra-operator reproducibility for both patients (ICC = 0.817) and for clinicians (ICC = 0.888), and good inter-operator reproducibility between patient and clinicians (ICC = 0.887). The performance of the Eko DUO was comparable between patients and clinicians (AUC = 0.887 vs. AUC = 0.800, respectively). Conclusions: The Eko DUO predictions for LVEF≤40% have good intra- and inter-operator reproducibility between patients and clinicians.

Female-specific reference values for blood pressure and its relationship with subclinical echocardiographic findings in women with a history of preeclampsia: the Queen of Hearts study

Abstract Introduction Hypertension is the most crucial modifiable risk factor for cardiovascular disease (CVD). Sex differences in what is considered optimal blood pressure (BP) might deviate from current sex-blinded guidelines for hypertension. In women, an obstetric history of preeclampsia relates to postpartum hypertension and substantially increases the risk of premature CVD. Purpose To establish female-specific reference values for BP in relatively young women and assess its association with subclinical aberrant echocardiographic findings in a high-risk female population. Methods Data was used from the Queen of Hearts study; a cross-sectional cohort study on early detection of heart failure among young women with a history of preeclampsia and a control group of low-risk healthy women with a history of uncomplicated pregnancy. The control group was used to non-parametrically establish reference values for BP (95th percentile). The association between the derived reference values and the risk of subclinical aberrant echocardiographic findings was evaluated in the former preeclamptic group using logistic regression. All women were admitted to a standardized cardiovascular assessment including transthoracic echocardiography and 30-minutes BP measurement. Subclinical echocardiographic findings included left ventricular (LV) hypertrophy (LVH), LV concentric remodelling (LCVR), diastolic dysfunction (DD) and impaired LV ejection fraction (LVEF, <55%). Results A total of 429 women in the healthy control group and 856 in the former preeclamptic group were included (44±8 and 38±8 years, respectively). In the healthy control group, 97.7% and 99.8% of women had a systolic (SBP) and diastolic BP (DBP) value below the European Society of Cardiology (ESC) guidelines for hypertension. Overall, the derived reference value for SBP and DBP in our control group was 133 and 82 mmHg, respectively. Age-specific reference curves are presented in Figure 1 and 2. In the subgroup of former preeclamptic women 10.4% and 14.6% had SBP and DBP levels above 133 mmHg and 82mmHg, respectively. In this group, the Odds Ratio (OR) for subclinical aberrant echocardiographic findings when having SBP ≥133mmHg was 5.3 (95% CI 2.3–12.3) for LVH, 1.4 (95% CI 0.6–3.2) for LVCR, 5.9 (95% CI 3.3–10.7) for DD and 1.3 (95% CI 0.3–6.0) for impaired LVEF. For DBP ≥82mmHg, the OR for LVH was 3.5 (95% CI 1.5–8.0), LVCR 1.3 (95% CI 0.6–2.7), DD 3.4 (95% CI 1.9–6.0) and impaired LVEF 1.5 (95% CI 0.4–5.3). Conclusions Upper limits for SBP and DBP values in current guidelines correspond with the value of less than 2% of women in our healthy (no pre-eclampsia) population. We derived new reference values with the upper limit corresponding with the upper 5th centile. Values above this level, even below decision limits for hypertension, are associated with abnormal subclinical cardiac function and geometry in a high-risk young female population. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Dutch Heart Foundation

Prediction model for hypertension after preeclampsia in initially normotensive women; the Queen of Hearts cohort

Abstract Background Timely detection and treatment of hypertension prevents cardiovascular diseases. Formerly preeclamptic women are at increased risk for hypertension, but targeted and structural follow-up of these women is lacking. Purpose To develop a prediction model for the development of hypertension in the decade following preeclampsia in women who were initially normotensive shortly after pregnancy. Methods In this longitudinal cohort study, formerly preeclamptic women were invited twice for a cardiovascular assessment. The first visit took place at a median interval of 10 months after delivery and a second visit 10 years later as part of the Queen of Hearts cohort study on early detection of heart failure among young women with a history of preeclampsia (ClinicalTrials.gov Identifier NCT02347540). Normotensive women at the first visit were divided in two groups whether or not they had developed hypertension during visit 2. Hypertension was defined as SBP ≥130 mmHg and/or DBP ≥80 mmhg and/or use of antihypertensive medication. We developed a prediction model using multivariable logistic regression analysis. The model was internally validated with bootstrapping techniques. Results Of the 259 women, 185 (71%) were normotensive at first visit of which 49 (26%) had developed hypertension at the second visit. The prediction model, based on birth weight centile, mean arterial pressure, total cholesterol, left ventricular mass index and left ventricular ejection fraction, had a good to excellent discriminative ability of AUC-ROC-curve 0.82 (95% CI 0.75–0.89) with an optimism corrected AUC of 0.80. Sensitivity and specificity of our model to predict yearly 10% risk to develop hypertension was 98% and 65% respectively and positive- and negative-predictive values were 50% and 99% respectively. Conclusions Based on five easily available variables, we developed a good-to-excellent performing predictive tool to identify incident hypertension following preeclampsia in women that were normotensive shortly after pregnancy. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): This work was partially supported by funding of de Nederlandse Hartstichting (Dutch Heart Foundation, grant number: 2013T084, Queen of Hearts study)

AdaDiag: Adversarial Domain Adaptation of Diagnostic Prediction with Clinical Event Sequences

Early detection of heart failure (HF) can provide patients with the opportunity for more timely intervention and better disease management, as well as efficient use of healthcare resources. Recent machine learning (ML) methods have shown promising performance on diagnostic prediction using temporal sequences from electronic health records (EHRs). In practice, however, these models may not generalize to other populations due to dataset shift. Shifts in datasets can be attributed to a range of factors such as variations in demographics, data management methods, and healthcare delivery patterns. In this paper, we use unsupervised adversarial domain adaptation methods to adaptively reduce the impact of dataset shift on cross-institutional transfer performance. The proposed framework is validated on a next-visit HF onset prediction task using a BERT-style Transformer-based language model pre-trained with a masked language modeling (MLM) task. Our model empirically demonstrates superior prediction performance relative to non-adversarial baselines in both transfer directions on two different clinical event sequence data sources.

Diagnostic potential of a circulating miRNA model associated with therapeutic effect in heart failure

Heart failure (HF), as the leading cause of death, is continuing to increase along with the aging of the general population all over the world. Identification of diagnostic biomarkers for early detection of HF is considered as the most effective way to reduce the risk and mortality. Herein, we collected plasma samples from HF patients (n = 40) before and after medical therapy to determine the change of circulating miRNAs through a quantitative real-time PCR (QRT-PCR)-based miRNA screening analysis. miR-30a-5p and miR-654-5p were identified as the most significantly changed miRNAs in the plasma of patients upon treatment. In consistence, miR-30a-5p showed upregulation and miR-654-5p showed downregulation in the circulation of 30 HF patients, compared to 15 normal controls in the training phase, from which a two-circulating miRNA model was developed for HF diagnosis. Next, we performed the model validation using an independent cohort including 50 HF patients and 30 controls. As high as 98.75% of sensitivity and 95.00% of specificity were achieved. A comparison between the miRNA model and NT-pro BNP in diagnostic accuracy of HF indicated an upward trend of the miRNA model. Moreover, change of the two miRNAs was further verified in association with the therapeutic effect of HF patients, in which miR-30a-5p showed decrease while miR-654-5p showed increase in the plasma of patients after LVAD implantation. In conclusion, the current study not only identified circulating miR-654-5p for the first time as a novel biomarker of HF, but also developed a novel 2-circulating miRNA model with promising potentials for diagnosis and prognosis of HF patients, and in association with therapeutic effects as well.

Imaging Anaplerosis in the Failing Heart by PET

ObjectiveHeart failure is often preceded by distinct metabolic alterations. However, this shift cannot be sustained indefinitely and typically leads to cardiac dysfunction and arrest. These alterations are thought to be caused by mitochondrial dysfunction leading to the accumulation of acyl‐CoAs. To compensate, the injured heart shifts from oxidation of long chain fatty acids (C16) to metabolism of substrates that can replenish the TCA cycle. This is because these fatty acids are restricted to catabolism to acetyl‐CoA, for which the enzymes are saturated. By contrast, odd chain fatty acids are metabolized to propionate. The enzymes specific for propionate are freely available for metabolism to succinate leading to TCA cycle anaplerosis. Propionate conversion to propionyl‐CoA is the first and major step for its sequestration and metabolism. Moreover, the mitochondrial acyl‐CoA synthetase (ACSS) responsible for this conversion is highly expressed in the heart. Hence, we hypothesize that 2‐[18F]‐fluoropropionate ([18F]‐FPA) would significantly accumulate in the failing heart and can be imaged by positron emission tomography (PET). We addressed our hypothesis by studying the substrate behavior of FPA for ACSS, and by metabolic profiling of HepG2 cells incubated with FPA. We also successfully imaged heart uptake of [18F]‐FPA in healthy rats by PET.MethodsThe ability of acetate, propionate, and FPA to act as ACSS substrates was determined by a colorimetric pyrophosphate assay. Metabolites were determined by LC/MS in lysates of HepG2 cells incubated with basal media, 10 mM propionate or 10 mM FPA. Racemic 18F‐FPA was synthesized from ethyl‐2‐bromopropionate by standard nucleophilic substitution. For PET, Sprague‐Dawley rats were injected with 250‐500 µCi (9.25‐18.5 MBq) of [18F]‐FPA, and a 60‐minute dynamic PET scan was immediately acquired.ResultsThe kinetic characteristics for the reaction of ACSS acting on acetate (Km = 4.94 mM, Vmax = 0.25 nmols/sec), propionate (Km = 24.20 mM, Vmax = 0.19 nmols/sec), and 2‐fluoropropionoate (V = 0.05 nmols/sec) were determined. We also observed a reduction of free CoA in HepG2 cells incubated in 10 mM propionate (4.4 ± 1.2‐fold) or 10 mM FPA (3.09 ± 0.96‐fold). The kinetics of 18F‐FPA uptake into the healthy heart was assessed by PET and determined to be maximal at 10 minutes post‐injection.ConclusionsWe identified [18F]‐FPA, and potentially other odd chain fatty acids, as promising metabolic tracers for imaging anaplerosis in the injured heart. FPA effectively decreases free CoA in HepG2 cells, which indicates the formation of acyl‐CoAs. Moreover, FPA exhibits some substrate activity for ACSS, representing a major conduit by which it is metabolized in the heart. The poor substrate activity of FPA is ideal as it precludes metabolism by the highly active and specific ACSS enzymes in the liver. Albeit we expect FPA to accumulate in the heart due to its high ACSS expression which is induced in energy deficient states that are characteristic of heart failure. In support of this idea, we demonstrated that [18F]‐FPA is taken up by the healthy heart. We expect the metabolic alterations resulting from ensuing heart failure are sufficient to increase the uptake of [18F]‐FPA in these tissues. Our long‐term aim is to develop 18F‐labeled short chain fatty acids, such as [18F]‐FPA, that can be used to diagnose at‐risk patients. The early detection of heart failure by non‐invasive means is critical for the development of interventions to reduce the severity and aid recovery from cardiac disease.

Abstract WMP16: Race, Gender, And Cognition In Heart Failure

Background: Cognitive impairment is prevalent in the heart failure (HF) population, eventually complicating the disease course in over 50%. Disease management and risk factor control are thought to play a role. Disparities in healthcare utilization and access may also contribute. We hypothesize gender, ethnic and racial disparities in HF contribute to accelerated cognitive decline. Methods: We performed a cross-sectional analysis of study participants age ≥ 60 years in the biannual NHANES survey. Medical history, clinical variables, and Digit Symbol Substitution Test (DSST) scores from cycles 1999, 2001, 2011, and 2013 were extracted. The DSST assesses visuomotor, attention, and processing speed, and in prior studies was found to be strongly associated with vascular-related cognitive impairment. Survey-weighted regression models adjusting for age, race, gender, education, vascular risk factors and history of stroke were used to generate nationally representative estimates. Results: 6,651 study participants representing 51,841,223 adults ≥ 60 years were included, 7% with prevalent HF. Participants with HF were older (73.8 vs 70.0, P <0.001) but there were no significant differences in the proportion of women or in the racial/ethnic makeup of participants with and without HF. Stroke was 3 times more prevalent in the HF population (20.1% vs 6.5%, P <0.001). Individuals with HF had significantly lower DSST scores (mean 39.6 [0.97] vs 50.2 [0.45], P<0.001). We found a significant interaction between HF and sex (P <0.10). Among men, prevalent HF was associated with a 2.95 (95% CI: -4.75, -1.16) decrease in DSST. In women, larger decreases in cognition were observed (-5.18, 95% CI: -7.34, -3.02). There was no interaction between heart failure and race. Conclusions: Women with HF experience greater levels of HF-related cognitive impairment. These differences may be a consequence of known differential rates of early HF detection and disease maintenance across genders.