Multimarker approach for the prediction of heart failure incidence in the community

Screening for Heart Failure: Biomarkers to Detect Heightened Risk in the General Population

PURPOSE OF REVIEW

Heart failure (HF) represents a growing global burden of morbidity and mortality. Identifying individuals at risk for HF development is increasingly important, particularly given the advent of disease-modifying therapies for HF as well as its major risk factors such as obesity actalnd diabetes. We aim to review the key circulating biomarkers associated with future HF which may contribute to HF risk prediction.

RECENT FINDINGS

While current guidelines recommend the use of natriuretic peptides and cardiac troponins in HF risk stratification, there are a diverse array of other emerging protein, metabolic, transcriptomic, and genomic biomarkers of future HF development. These biomarkers not only lend insight into the underlying pathophysiology of HF, which spans inflammation to cardiac fibrosis, but also offer an opportunity to further refine HF risk in addition to established biomarkers. As evolving techniques in molecular biology enable an increased understanding of the complex biologic contributions to HF pathophysiology, there is an important opportunity to construct integrated clinical and multi-omic models to best capture HF risk. Moving forward, future studies should seek to understand the contributions of sex differences, underlying comorbidity burden, and HF subtypes to an individual's HF risk. Further studies are necessary to fully define the clinical utility of biomarker screening approaches to refine HF risk assessment, as well as to link risk assessment directly to preventive strategies for HF.

Prioritizing the primary prevention of heart failure: Measuring, modifying and monitoring risk

With the rising incidence of heart failure (HF) and increasing burden of morbidity, mortality, and healthcare expenditures, primary prevention of HF targeting individuals in at-risk HF (Stage A) and pre-HF (Stage B) Stages has become increasingly important with the goal to decrease progression to symptomatic (Stage C) HF. Identification of risk based on traditional risk factors (e.g., cardiovascular health which can be assessed with the American Heart Association's Life's Essential 8 framework), adverse social determinants of health, inherited risk of cardiomyopathies, and identification of risk-enhancing factors, such as patients with viral disease, exposure to cardiotoxic chemotherapy, and history of adverse pregnancy outcomes should be the first step in evaluation for HF risk. Next, use of guideline-endorsed risk prediction tools such as Pooled Cohort Equations to Prevent Heart Failure provide quantification of absolute risk of HF based in traditional risk factors. Risk reduction through counseling on traditional risk factors is a core focus of implementation of prevention and may include the use of novel therapeutics that target specific pathways to reduce risk of HF, such as mineralocorticoid receptor agonists (e.g., fineronone), angiotensin-receptor/neprolysin inhibitors, and sodium glucose co-transporter-2 inhibitors. These interventions may be limited in at-risk populations who experience adverse social determinants and/or individuals who reside in rural areas. Thus, strategies like telemedicine may improve access to preventive care. Gaps in the current knowledge base for risk-based prevention of HF are highlighted to outline future research that may target approaches for risk assessment and risk-based prevention with the use of artificial intelligence, genomics-enhanced strategies, and pragmatic trials to develop a guideline-directed medical therapy approach to reduce risk among individuals with Stage A and Stage B HF.

Unveiling the Role of Biomarkers in Cardiovascular Risk Assessment and Prognosis

Cardiovascular diseases (CVDs) remain a leading cause of global morbidity and mortality, necessitating innovative approaches for accurate risk assessment and prognosis. This review explores the evolving role of biomarkers in advancing cardiovascular risk evaluation and prognostication. Utilizing cardiac biomarkers that represent diverse pathophysiological pathways has the potential to enhance risk stratification for CVD. We delve into the intricate molecular signatures indicative of cardiovascular health, focusing on established biomarkers such as troponins, natriuretic peptides, and lipid profiles while also examining emerging candidates like microRNAs and inflammatory markers. This review provides a holistic perspective on the current landscape of cardiovascular biomarkers, offering insights into their applications in risk assessment and prognosis. In evaluating the risk and prognosis of heart failure (HF), the measurement of natriuretic peptides (B-type natriuretic peptide [BNP] or N-terminal pro-B-type natriuretic peptide [NT-proBNP]) or markers of myocardial injury (cardiac troponin I [TnI] or T [TnT]) has demonstrated utility. By elucidating the synergistic interplay between traditional markers and cutting-edge technologies, this work aims to guide future research endeavors and clinical practices, ultimately contributing to more effective strategies for risk assessment and prognosis of cardiovascular disease.

Novel Prediction Equations for Absolute Risk Assessment of Total Cardiovascular Disease Incorporating Cardiovascular-Kidney-Metabolic Health: A Scientific Statement From the American Heart Association

Cardiovascular-kidney-metabolic (CKM) syndrome is a novel construct recently defined by the American Heart Association in response to the high prevalence of metabolic and kidney disease. Epidemiological data demonstrate higher absolute risk of both atherosclerotic cardiovascular disease (CVD) and heart failure as an individual progresses from CKM stage 0 to stage 3, but optimal strategies for risk assessment need to be refined. Absolute risk assessment with the goal to match type and intensity of interventions with predicted risk and expected treatment benefit remains the cornerstone of primary prevention. Given the growing number of therapies in our armamentarium that simultaneously address all 3 CKM axes, novel risk prediction equations are needed that incorporate predictors and outcomes relevant to the CKM context. This should also include social determinants of health, which are key upstream drivers of CVD, to more equitably estimate and address risk. This scientific statement summarizes the background, rationale, and clinical implications for the newly developed sex-specific, race-free risk equations: PREVENT (AHA Predicting Risk of CVD Events). The PREVENT equations enable 10- and 30-year risk estimates for total CVD (composite of atherosclerotic CVD and heart failure), include estimated glomerular filtration rate as a predictor, and adjust for competing risk of non-CVD death among adults 30 to 79 years of age. Additional models accommodate enhanced predictive utility with the addition of CKM factors when clinically indicated for measurement (urine albumin-to-creatinine ratio and hemoglobin A1c) or social determinants of health (social deprivation index) when available. Approaches to implement risk-based prevention using PREVENT across various settings are discussed.

Prediction models for heart failure in the community: A systematic review and meta-analysis

AIMS

Multivariable prediction models can be used to estimate risk of incident heart failure (HF) in the general population. A systematic review and meta-analysis was performed to determine the performance of models.

METHODS AND RESULTS

From inception to 3 November 2022 MEDLINE and EMBASE databases were searched for studies of multivariable models derived, validated and/or augmented for HF prediction in community-based cohorts. Discrimination measures for models with c-statistic data from ≥3 cohorts were pooled by Bayesian meta-analysis, with heterogeneity assessed through a 95% prediction interval (PI). Risk of bias was assessed using PROBAST. We included 36 studies with 59 prediction models. In meta-analysis, the Atherosclerosis Risk in Communities (ARIC) risk score (summary c-statistic 0.802, 95% confidence interval [CI] 0.707-0.883), GRaph-based Attention Model (GRAM; 0.791, 95% CI 0.677-0.885), Pooled Cohort equations to Prevent Heart Failure (PCP-HF) white men model (0.820, 95% CI 0.792-0.843), PCP-HF white women model (0.852, 95% CI 0.804-0.895), and REverse Time AttentIoN model (RETAIN; 0.839, 95% CI 0.748-0.916) had a statistically significant 95% PI and excellent discrimination performance. The ARIC risk score and PCP-HF models had significant summary discrimination among cohorts with a uniform prediction window. 77% of model results were at high risk of bias, certainty of evidence was low, and no model had a clinical impact study.

CONCLUSIONS

Prediction models for estimating risk of incident HF in the community demonstrate excellent discrimination performance. Their usefulness remains uncertain due to high risk of bias, low certainty of evidence, and absence of clinical effectiveness research.

Novel Biomarkers and the Multiple-Marker Approach in Early Detection, Prognosis, and Risk Stratification of Cardiac Diseases: A Narrative Review

Cardiac diseases are a primary cause of mortality worldwide, underscoring the importance of early identification and risk stratification to enhance patient outcomes. Biomarkers have become important tools for the risk assessment of cardiovascular disease and monitoring disease progression. This narrative review focuses on the multiple-marker approach, which involves simultaneously evaluating several biomarkers for the early detection and risk stratification of heart diseases. The review covers the clinical applications of novel biomarkers, such as high-sensitivity troponin, galectin-3, source of tumorigenicity 2, B-type natriuretic peptide and N-terminal pro-B-type natriuretic peptide, growth differentiation factor 15, myeloperoxidase, fatty acid-binding protein, C-reactive protein, lipoprotein-associated phospholipase A2, microRNAs, circulating endothelial cells, and ischemia-modified albumin. These biomarkers have demonstrated potential in identifying people who are at high risk for developing heart disease and in providing prognostic data. Given the complexity of cardiac illnesses, the multiple-marker approach to risk assessment is extremely beneficial. Implementing the multiple-marker strategy can improve risk stratification, diagnostic accuracy, and patient care in heart disease patients.

The Role of Congestion Biomarkers in Heart Failure with Reduced Ejection Fraction

In heart failure with reduced ejection fraction, edema and congestion are related to reduced cardiac function. Edema and congestion are further aggravated by chronic kidney failure and pulmonary abnormalities. Furthermore, together with edema/congestion, sodium/water retention is an important sign of the progression of heart failure. Edema/congestion often anticipates clinical symptoms, such as dyspnea and hospitalization; it is associated with a reduced quality of life and a major risk of mortality. It is very important for clinicians to predict the signs of congestion with biomarkers and, mainly, to understand the pathophysiological findings that underlie edema. Not all congestions are secondary to heart failure, as in nephrotic syndrome. This review summarizes the principal evidence on the possible roles of the old and new congestion biomarkers in HFrEF patients (diagnostic, prognostic, and therapeutic roles). Furthermore, we provide a description of conditions other than congestion with increased congestion biomarkers, in order to aid in reaching a differential diagnosis. To conclude, the review focuses on how congestion biomarkers may be affected by new HF drugs (gliflozins, vericiguat, etc.) approved for HFrEF.

Circ_0000284: A risk factor and potential biomarker for prehypertension and hypertension

CircRNAs have been shown to be involved in the development of certain diseases, but their application in prehypertension and hypertension remains unclear. We aimed to explore the potential role of circ_0000284 in revealing the molecular regulatory mechanisms of prehypertension and hypertension. We enrolled a total of 100 patients with normal blood pressure, 100 patients with prehypertension and 100 patients with hypertension. The expression of circ_0000284 among the groups was detected by real-time fluorescence quantitative polymerase chain reaction (qRT‒PCR). Multivariate logistic models were constructed combining conventional risk factors with circ_0000284. A receiver operating characteristic curve (ROC) was used to analyze the diagnostic value of circRNAs in the clinical model. Spearman correlation was used to analyze the correlation of circ_0000284 and the biochemical characteristics of all subjects. The results showed that circ_0000284 was differentially expressed among the normal blood pressure group, prehypertensive group and hypertensive group and showed a significantly upregulated trend in the progression to hypertension (P < 0.05). The ROC curve revealed a high diagnostic ability of circ_0000284 in hypertension in the clinical model (AUC = 0.812). Circ_0000284 also presented a certain ability for early diagnosis of prehypertension (AUC = 0.628). Spearman correlation showed that circ_0000284 was positively correlated with Na and CKMB. Our study suggested that upregulated expression of circ_0000284 was an independent risk factor for prehypertension and hypertension. Circ_0000284 was a potential fingerprint for the early diagnosis of hypertension and distinguished the intermediate stage of hypertension development. Moreover, our study provided new insight into the correlation between circ_0000284 and cardiac injury in the progression to hypertension.

Albuminuria and Heart Failure: JACC State-of-the-Art Review

Although chronic kidney disease is characterized by low glomerular filtration rate (GFR) or albuminuria, estimated GFR (eGFR) is more widely utilized as a marker of risk profile in cardiovascular diseases, including heart failure (HF). The presence and magnitude of albuminuria confers a strong prognostic association in forecasting risk of incident HF as well as its progression, irrespective of eGFR. Despite the high prevalence of albuminuria in HF, whether it adds incremental prognostic information in clinical practice and serves as an independent risk marker, and whether there are any therapeutic implications of assessing albuminuria in patients with HF is less well-established. In this narrative review, we assess the potential role of albuminuria in risk profiling for development and progression of HF, strengths and limitations of utilizing albuminuria as a risk marker, its ability to serve in HF risk prediction models, and the implications of adopting albuminuria as an effective parameter in cardiovascular trials and practice.

Metabolite-assisted models improve risk prediction of coronary heart disease in patients with diabetes

Background: Patients with diabetes have a two-to four-fold increased incidence of cardiovascular diseases compared with non-diabetics. Currently, there is no recognized model to predict the occurrence and progression of CVDs in diabetics. Objective: This work aimed to develop a metabolic biomarker-assisted model, a combination of metabolic markers with clinical variables, for risk prediction of CVDs in diabetics. Methods: A total of 475 patients with diabetes were studied. Each patient underwent coronary angiography. Plasma samples were analyzed by liquid chromatography-quadrupole time-of-flight mass spectrometry. Ordinal logistic regression and random forest were used to screen metabolites. Receiver operating characteristic (ROC) curve, nomogram, and decision curve analysis (DCA) were employed to evaluate their prediction performances. Results: Ordinal logistic regression screened out 34 differential metabolites (adjusted-false discovery rate p < 0.05) from 2059 ion features by comparisons of diabetics with and without CVDs. Random forest identified methylglutarylcarnitine and lysoPC (18:0) as the metabolic markers (mean decrease gini >1.0) for non-significant CVDs (nos-CVDs) versus normal coronary artery (NCA), 1,3-Octadiene and 3-Octanone for acute coronary syndrome (ACS) versus nos-CVDs, and lysoPC (18:0) for acute coronary syndrome versus normal coronary artery. For risk prediction, the metabolic marker-assisted models provided areas under the curve of 0.962-0.979 by ROC (0.576-0.779 for the base models), and c-indices of 0.8477-0.9537 by nomogram analysis (0.1514-0.5196 for the base models). Decision curve analysis (DCA) showed that the models produced greater benefits throughout a wide range of risk probabilities compared with the base model. Conclusion: Metabolic biomarker-assisted model remarkably improved risk prediction of cardiovascular disease in diabetics (>90%).

Comparison of Five Expressions of Handgrip Strength for Predicting Cardiovascular Disease Risk Factors in Chinese Middle-Aged Community Residents

Objective

To compare the predictive performance of five handgrip strengths for cardiovascular disease (CVD) risk factors.

Methods

A total of 804 Chinese middle-aged community residents' health medical examinations were collected. The absolute handgrip strength was denoted as HGS. HGS/body weight (HGS/BW), HGS/body mass index (HGS/BMI), HGS/lean body mass (HGS/LBM), and HGS/muscle mass (HGS/MM) represented relative handgrip strength (RHGS). To assess predictive performance, receiver operating characteristic (ROC) curves and the area under the curve (AUC) were constructed.

Results

HGS was not associated with most CVD risk biomarkers; however, RHGS showed a negative correlation trend after controlling for covariates (sex, age, smoking, and exercise). HGS/BMI and HGS/BW had better AUCs for predicting CVD risk factors than HGS/LBM or HGS/MM. HGS/BMI and HGS/BW can successfully predict all CVD risk factors in men with AUCs 0.55–0.65; similarly, women may effectively predict arteriosclerosis, hyperglycemia, hyperuricemia, and metabolic syndrome with AUCs 0.59–0.64, all p < 0.05. The optimal HGS/BW cut-off points for identifying different CVD risk factors were 0.59–0.61 in men and 0.41–0.45 in women, while the HGS/BMI were 1.75–1.79 in men and 1.11–1.15 in women.

Conclusions

Almost all CVD risk biomarkers and CVD risk factors were unrelated to HGS. There is, however, a significant inverse relationship between RHGS and CVD risk factors. HGS/BMI or HGS/BW should be recommended to be the best choice for predicting the risk of CVD risk factors in five expressions of handgrip strength. We also acquired the recommended optimal cut-off points of HGS/BMI and HGS/BW for predicting CVD risk factors.

Application of integrated management bundle incorporating with multidisciplinary measures improved in-hospital outcomes and early survival in geriatric hip fracture patients with perioperative heart failure: a retrospective cohort study

Background

In elderly, hip fracture is often complicated by perioperative heart failure, related to worse prognosis. We aimed to analyze the effects of integrated management bundle incorporating with multidisciplinary measures on in-hospital outcomes and early survival in elderly hip fracture patients with perioperative heart failure.

Methods

In this retrospective cohort study, a total of 421 hip fracture patients aged 65 and over who developed perioperative heart failure were included. According to different perioperative management modes applied, patients were retrospectively divided into multidisciplinary management group (Group A), including 277 patients, and integrated management bundle group (Group B), including 144 patients. The B-type natriuretic peptide (BNP) and C-reactive protein (CRP) levels, complications, length of stay, and hospitalization costs were observed and compared between two groups. Overall survival was compared by Kaplan–Meier methods. Cox regression analysis was used to identify prognostic factors associated with overall survival.

Results

A total of 421 patients were enrolled for analysis, including 277 in Group A and 144 in Group B. BNP and CRP levels were significantly decreased compared with admission (P < 0.05). Furthermore, BNP and CRP in Group B decreased much more than those in Group A (P < 0.05). The reductions were observed in length of stay, hospitalization costs and incidence of pulmonary infection, hypoproteinemia, and acute cerebral infarction in Group B (all P < 0.05). The Kaplan–Meier plots showed significantly superior overall survival in Group B. Integrated management bundle was independent favorable prognostic factors.

Conclusions

The integrated management bundle incorporating with multidisciplinary measures significantly improved the therapeutic effect of perioperative heart failure, reduced inflammatory response, and yielded better hospital outcomes. It brought better survival benefits for geriatric hip fracture patients with perioperative heart failure. The results of this study can play an important role in clinical work and provide a valuable theoretical basis for selection of management model in elderly hip fracture patients with perioperative heart failure.

Full-Length Transcriptome Sequencing: An Insight Into the Dog Model of Heart Failure

Heart failure (HF) leads to a progressive increase in morbidity and mortality rates. This study aimed to explore the transcriptional landscape during HF and identify differentially expressed transcripts (DETs) and alternative splicing events associated with HF. We generated a dog model of HF (n = 3) using right ventricular pacemaker implantation. We performed full-length transcriptome sequencing (based on nanopore platform) on the myocardial tissues and analyzed the transcripts using differential expression analysis and functional annotation methods [Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses]. Additionally, we estimated the expression of the selected genes by quantitative real-time PCR (qRT-PCR) and detected the proportion of immune cells using flow cytometry. We found that increased B-type natriuretic peptide reduced ejection fraction, and apparent clinical signs were observed in the dog model of HF. We identified 67,458 transcripts using full-length transcriptome sequencing. A total of 785 DETs were obtained from the HF and control groups. These DETs were mainly enriched in the immune responses, especially Th1, Th2, and Th17 cell differentiation processes. Furthermore, flow cytometry results revealed that the proportion of Th1 and Th17 cells increased in patients with HF compared to controls, while the proportion of Th2 cells decreased. Differentially expressed genes in the HF and control groups associated with Th1, Th2, and Th17 cell differentiation were quantified using qRT-PCR. We also identified variable splicing events of sarcomere genes (e.g., MYBPC3, TNNT2, TTN, FLNC, and TTNI3). In addition, we detected 4,892 transcription factors and 406 lncRNAs associated with HF. Our analysis based on full-length transcript sequencing provided an analysis perspective in a dog model of HF, which is valuable for molecular research in an increasingly relevant large animal model of HF.

PRospective Evaluation of natriuretic peptide-based reFERral of patients with chronic heart failure in primary care (PREFER): a real-world study

Objective

To assess current management practice of heart failure with reduced ejection fraction (HFrEF) in multinational primary care (PC) and determine whether N-terminal-pro-B-type natriuretic peptide (NT-pro-BNP)-guided referral of HFrEF patients from PC to a cardiologist could improve care, defined as adherence to European Society of Cardiology (ESC) guideline-recommended pharmacotherapy.

Methods

PRospective Evaluation of natriuretic peptide-based reFERral of patients with chronic HF in PC (PREFER) study enrolled HFrEF patients from PC considered clinically stable and those with NT-pro-BNP ≥600 pg/mL were referred to a cardiologist for optimisation of HF treatment. The primary outcome of adherence to ESC HF guidelines after referral to specialist was assessed at the second visit within 4 weeks of cardiologist’s referral and no later than 6 months after the baseline visit. Based on futility interim analysis, the study was terminated early.

Results

In total, 1415 HFrEF patients from 223 PCs from 18 countries in Europe were enrolled. Of these, 1324 (96.9%) were considered clinically stable and 920 (65.0%) had NT-pro-BNP ≥600 pg/mL (mean: 2631 pg/mL). In total, 861 (60.8%) patients fulfilled both criteria and were referred to a cardiologist. Before cardiologist consultation, 10.1% of patients were on ESC guideline-recommended HFrEF medications and 2.7% were on recommended dosages of HFrEF medication (defined as ≥50% of ESC guideline-recommended dose). Postreferral, prescribed HFrEF drugs remained largely unchanged except for an increase in diuretics (+4.6%) and mineralocorticoid receptor antagonists (+7.9%). No significant increase in patients’ adherence to guideline-defined drug combinations (11.2% post-referral vs 10.1% baseline) or drug combinations and dosages (3.3% postreferral vs 2.7% baseline) was observed after cardiologist consultation.

Conclusions

PREFER demonstrates substantial suboptimal treatment of HFrEF patients in the real world. Referral of patients with elevated NT-pro-BNP levels from PC to cardiologist did not result in meaningful treatment optimisation for treatments with known mortality and morbidity benefit.

Multiple Cardiac Biomarker Testing Among Patients With Acute Dyspnea From the ICON-RELOADED Study

BACKGROUND

Among patients with acute dyspnea, concentrations of N-terminal pro-B-type natriuretic peptide (NT-proBNP), high-sensitivity cardiac troponin T, and insulin-like growth factor binding protein-7 predict cardiovascular outcomes and death. Understanding the optimal means to interpret these elevated biomarkers in patients presenting with acute dyspnea remains unknown.

METHODS AND RESULTS

Concentrations of NT-proBNP, high-sensitivity cardiac troponin T, and insulin-like growth factor binding protein-7 were analyzed in 1448 patients presenting with acute dyspnea from the prospective, multicenter International Collaborative of NT-proBNP-Re-evaluation of Acute Diagnostic Cut-Offs in the Emergency Department (ICON-RELOADED) Study. Eight biogroups were derived based upon patterns in biomarker elevation at presentation and compared for differences in baseline characteristics. Of 441 patients with elevations in all 3 biomarkers, 218 (49.4%) were diagnosed with acute heart failure (HF). The frequency of acute HF diagnosis in this biogroup was higher than those with elevations in 2 biomarkers (18.8%, 44 of 234), 1 biomarker (3.8%, 10 of 260), or no elevated biomarkers (0.4%, 2 of 513). The absolute number of elevated biomarkers on admission was prognostic of the composite end point of mortality and HF rehospitalization. In adjusted models, patients with one, 2, and 3 elevated biomarkers had 3.74 (95% confidence interval [CI], 1.26-11.1, P = .017), 12.3 (95% CI, 4.60-32.9, P < .001), and 12.6 (95% CI, 4.54-35.0, P < .001) fold increased risk of 180-day mortality or HF rehospitalization.

CONCLUSIONS

A multimarker panel of NT-proBNP, hsTnT, and IGBFP7 provides unique clinical, diagnostic, and prognostic information in patients presenting with acute dyspnea. Differences in the number of elevated biomarkers at presentation may allow for more efficient clinical risk stratification of short-term mortality and HF rehospitalization.

Quantile-specific heritability of plasminogen activator inhibitor type-1 (PAI-1, aka SERPINE1) and other hemostatic factors

BACKGROUND

Plasminogen activator inhibitor type-1 (PAI-1, aka SERPINE1) is a moderately heritable glycoprotein that regulates fibrin clot dissolution (fibrinolysis).

OBJECTIVES

Test whether the heritabilities (h2 ) of PAI-1 and other hemostatic factors are constant throughout their distribution or whether they are quantile-specific (i.e., a larger or smaller h2 depending on whether their concentrations are high or low).

METHODS

Quantile regression was applied to 5606 parent-offspring pairs and 5310 full siblings of the Framingham Heart Study. Quantile-specific heritability was estimated from the parent-offspring regression slope (βPO , h2  = 2βPO /(1+rspouse )) and the full-sib regression slope (βFS , h2  = {(1+8rspouse βFS )0.5 -1}/(2rspouse )).

RESULTS

Heritability (h2  ± SE) increased significantly with increasing percentiles of the offspring's age- and sex-adjusted PAI-1 distribution when estimated from βPO (plinear trend  = 0.0001): 0.09 ± 0.02 at the 10th, 0.09 ± 0.02 at the 25th, 0.16 ± 0.02 at the 50th, 0.29 ± 0.04 at the 75th, and 0.26 ± 0.08 at the 90th percentile of the PAI-1 distribution, and when estimated from βFS (plinear trend  = 6.5x10-7 ). There was no significant evidence for quantile-specific heritability for factor VII (plinear trend  = 0.35), D-dimer (plinear trend  = 0.08), tPA (plinear trend  = 0.74), or von Willebrand factor (plinear trend  = 0.79).

CONCLUSION

Higher mean plasma PAI-1 antigen concentrations tend to accentuate genetic effects (quantile-dependent expressivity), which is consistent with the greater reported differences in PAI-1 concentrations between rs1799889 SERPINE1 (4G/5G) genotypes in patients with osteonecrosis, meningococcal sepsis, obesity, prior myocardial infarction, deep vein thrombosis, and polycystic ovarian syndrome than in healthy controls. It is also consistent with the greater increases in PAI-1 concentrations in 4G-allele carriers than 5G/5G homozygotes following fibrinolytic treatment, low-salt intake, and high saturated fat intake.

Current Use of Cardiac Biomarkers in Various Heart Conditions

Biomarkers are increasingly recognized to have significant clinical value in early identification and progression of various cardiovascular diseases. There are many heart conditions, such as congestive heart failure (CHF), ischemic heart diseases (IHD), and diabetic cardiomyopathy (DCM), and cardiac remodeling, in which the severity of the cardiac pathology can be mirrored through these cardiac biomarkers. From the emergency department (ED) evaluation of acute coronary syndromes (ACS) or suspected acute myocardial infarction (AMI) with cardiac marker Troponin to the diagnosis of chronic conditions like Heart Failure (HF) with natriuretic peptides, like B-type natriuretic peptide (BNP), N-terminal pro-B- type natriuretic peptide (Nt-proBNP) and mid regional pro-atrial natriuretic peptide (MR- proANP), their use is continuously increasing. Their clinical importance has led to the discovery of newer biomarkers, such as the soluble source of tumorigenicity 2 (sST2), galectin-3 (Gal-3), growth differentiation factor-15 (GDF-15), and various micro ribonucleic acids (miRNAs). Since cardiac pathophysiology involves a complex interplay between inflammatory, genetic, neurohormonal, and biochemical levels, these biomarkers could be enzymes, hormones, and biologic substances showing cardiac injury, stress, and malfunction. Therefore, multi-marker approaches with different combinations of novel cardiac biomarkers, and continual assessment of cardiac biomarkers are likely to improve cardiac risk prediction, stratification, and overall patient wellbeing. On the other hand, these biomarkers may reflect coexisting or isolated disease processes in different organ systems other than the cardiovascular system. Therefore, knowledge of cardiac biomarkers is imperative. In this article, we have reviewed the role of cardiac biomarkers and their use in the diagnosis and prognosis of various cardiovascular diseases from different investigations conducted in recent years.

Heart failure risk estimation based on novel biomarkers

Introduction: Despite advances in medical care, heart failure (HF)-associated morbidity and mortality remains high. Consequently, there is increased effort to find better ways for predicting, screening, and prognosticating HF in order to facilitate effective primary and secondary prevention.Areas covered: In this review, we describe the various biomarkers associated with different etiologic pathways implicated in HF, and discuss their roles in screening, diagnosing, prognosticating and predicting HF. We explore the emerging role of multi-omic approaches. We performed electronic searches in databases (PubMed and Google Scholar) through December 2020, using the following key terms: biomarker, novel, heart failure, risk, prediction, and estimation.Circulating BNP and troponin concentrations have been established in clinical care as key biomarkers for diagnosing and prognosticating HF. Emerging biomarkers (such as galectin-3 and ST-2) have gained further recognition for use in evaluating prognosis of HF patients. Promising biomarkers that are yet to be part of clinical recommendations include biomarkers of cardiorenal disease.Expert opinion: Increasing recognition of the complex and interdependent nature of pathophysiological pathways of HF has led to the application of multi-marker approaches including multi-omic high throughput assays. These newer approaches have the potential for new therapeutic discoveries and improving precision medicine in HF.

Biomarkers for the diagnosis and management of heart failure

Heart failure (HF) is a significant cause of morbidity and mortality worldwide. Circulating biomarkers reflecting pathophysiological pathways involved in HF development and progression may assist clinicians in early diagnosis and management of HF patients. Natriuretic peptides (NPs) are cardioprotective hormones released by cardiomyocytes in response to pressure or volume overload. The roles of B-type NP (BNP) and N-terminal pro-B-type NP (NT-proBNP) for diagnosis and risk stratification in HF have been extensively demonstrated, and these biomarkers are emerging tools for population screening and as guides to the start of treatment in subclinical HF. On the contrary, conflicting evidence exists on the role of NPs as a guide to HF therapy. Among the other biomarkers, high-sensitivity troponins and soluble suppression of tumorigenesis-2 are the most promising biomarkers for risk stratification, with independent value to NPs. Other biomarkers evaluated as predictors of adverse outcome are galectin-3, growth differentiation factor 15, mid-regional pro-adrenomedullin, and makers of renal dysfunction. Multi-marker scores and genomic, transcriptomic, proteomic, and metabolomic analyses could further refine HF management.

Heart Disease and Stroke Statistics-2021 Update: A Report From the American Heart Association

BACKGROUND

The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2021 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population, an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, the global burden of cardiovascular disease, and further evidence-based approaches to changing behaviors related to cardiovascular disease.

RESULTS

Each of the 27 chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policy makers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Implications of the ACC/AHA risk score for prediction of heart failure: the Rotterdam Study

BACKGROUND

Despite the growing burden of heart failure (HF), there have been no recommendations for use of any of the primary prevention models in the existing guidelines. HF was also not included as an outcome in the American College of Cardiology/American Heart Association (ACC/AHA) risk score.

METHODS

Among 2743 men and 3646 women aged ≥ 55 years, free of HF, from the population-based Rotterdam Study cohort, 4 Cox models were fitted using the predictors of the ACC/AHA, ARIC and Health-ABC risk scores. Performance of the models for 10-year HF prediction was evaluated. Afterwards, performance and net reclassification improvement (NRI) for adding NT-proBNP to the ACC/AHA model were assessed.

RESULTS

During a median follow-up of 13 years, 429 men and 489 women developed HF. The ARIC model had the highest performance [c-statistic (95% confidence interval [CI]): 0.80 (0.78; 0.83) and 0.80 (0.78; 0.83) in men and women, respectively]. The c-statistic for the ACC/AHA model was 0.76 (0.74; 0.78) in men and 0.77 (0.75; 0.80) in women. Adding NT-proBNP to the ACC/AHA model increased the c-statistic to 0.80 (0.78 to 0.83) in men and 0.81 (0.79 to 0.84) in women. Sensitivity and specificity of the ACC/AHA model did not drastically change after addition of NT-proBNP. NRI(95%CI) was - 23.8% (- 19.2%; - 28.4%) in men and - 27.6% (- 30.7%; - 24.5%) in women for events and 57.9% (54.8%; 61.0%) in men and 52.8% (50.3%; 55.5%) in women for non-events.

CONCLUSIONS

Acceptable performance of the model based on risk factors included in the ACC/AHA model advocates use of this model for prediction of HF risk in primary prevention setting. Addition of NT-proBNP modestly improved the model performance but did not lead to relevant discrimination improvement in clinical risk reclassification.

Risk-Based Approach for the Prediction and Prevention of Heart Failure

Targeted prevention of heart failure (HF) remains a critical need given the high prevalence of HF morbidity and mortality. Similar to risk-based prevention of atherosclerotic cardiovascular disease, optimal HF prevention strategies should include quantification of risk in the individual patient. In this review, we discuss incorporation of a quantitative risk-based approach into the existing HF staging landscape and the clinical opportunity that exists to translate available data on risk estimation to help guide personalized decision making. We first summarize the recent development of key HF risk prediction tools that can be applied broadly at a population level to estimate risk of incident HF. Next, we provide an in-depth description of the clinical utility of biomarkers to personalize risk estimation in select patients at the highest risk of developing HF. We also discuss integration of genomics-enhanced approaches (eg, Titin [TTN]) and other risk-enhancing features to reclassify risk with a precision medicine approach to HF prevention. Although sequential testing is very likely to identify low and high-risk individuals with excellent accuracy, whether or not interventions based on these risk models prevent HF in clinical practice requires prompt attention including randomized placebo-controlled trials of candidate therapies in risk-enriched populations. We conclude with a summary of unanswered questions and gaps in evidence that must be addressed to move the field of HF risk assessment forward.

The Diagnostic and Therapeutic Value of Multimarker Analysis in Heart Failure. An Approach to Biomarker-Targeted Therapy

Background: Heart failure is a pathophysiological state, which is still associated with high morbidity and mortality despite established therapies. Diverse well-known biomarkers fail to assess the variety of individual pathophysiology in the context of heart failure.

Methods: An analysis of prospective, multimarker-specific therapeutic approaches to heart failure based on studies in current literature was performed. A total of 159 screened publications in the field of biomarkers in heart failure were hand-selected and found to be eligible for this study by a team of experts.

Results: Established biomarkers of the inflammatory axis, matrix remodeling, fibrosis and oxidative stress axis, as well as potential therapeutic interventions were investigated. Interaction with end organs, such as cardio-hepatic, cardio-renal and cardio-gastrointestinal interactions show the complexity of the syndrome and could be of further therapeutic value. MicroRNAs are involved in a wide variety of physiologic and pathophysiologic processes in heart failure and could be useful in diagnostic as well as therapeutic setting.

Conclusion: Based on our analysis by a biomarker-driven approach in heart failure therapy, patients could be treated more specifically in long term with a consideration of different aspects of heart failure. New studies evaluating a multimarker – based therapeutic approach could lead in a decrease in the morbidity and mortality of heart failure patients.

Association of Urine Albumin Excretion With Incident Heart Failure Hospitalization in Community-Dwelling Adults

OBJECTIVES

This study examined the association between urinary albumin excretion and incident heart failure (HF) hospitalization.

BACKGROUND

Excess urinary albumin excretion is more strongly associated with incident stroke and coronary heart disease risk in black than in white individuals. Whether similar associations extend to HF is unclear.

METHODS

This study examined the associations between the urinary albumin-to-creatinine ratio (ACR) and incident hospitalization for HF overall in 24,433 REGARDS (Reasons for Geographic and Racial Differences in Stroke) study participants free of suspected HF at baseline; findings were stratified by race and HF subtype (preserved vs. reduced ejection fraction). Models were adjusted for sociodemographic, clinical, and laboratory variables including estimated glomerular filtration rate, and multiple imputation was used to account for missing covariate data.

RESULTS

After a median follow-up of 9.2 years, 881 incident HF events (332 preserved ejection fraction, 447 reduced ejection fraction, 102 unspecified) were observed. Compared to the lowest ACR category (<10 mg/g), the risk of incident HF increased with increasing ACR categories (10 to 29 mg/g hazard ratio [HR]: 1.49; 95% confidence interval [CI]: 1.26 to 1.78; 30 to 300 mg/g HR: 2.32; 95% CI: 1.93 to 2.78; >300 mg/g HR: 4.42; 95% CI: 3.36 to 5.83) in the fully adjusted model. Results did not differ by race. The magnitude of the association between ACR and HF with preserved ejection fraction was greater than with HF with reduced ejection fraction (HR comparing highest vs. lowest ACR category: 6.20; 95% CI: 4.15 to 9.26 vs. HR: 4.37; 95% CI: 3.00 to 6.25, respectively; p = 0.05).

CONCLUSIONS

Higher ACR was associated with greater risk of incident HF hospitalization in community-dwelling black and white adults.

Prognosis of "pre-heart failure" clinical phenotypes

Background

Heart failure (HF) is a clinical syndrome where diagnostic certainty varies. The prognosis of individuals with some clinical features of HF, but without the fully overt syndrome, is unclear. Therefore, we sought to evaluate their natural history.

Methods and results

Between 1990 and 2009, all suspected HF cases in the Framingham Heart Study were adjudicated into 3 groups reflecting varying diagnostic certainty: definite (meeting HF diagnostic criteria; n = 479), possible (meeting HF criteria but with an alternative explanation for findings; n = 135), and probable (insufficient criteria for definite HF; n = 121) HF. Age-and-sex-matched individuals (n = 1112) without HF or cardiovascular disease (CVD) were controls. Using multivariable-adjusted Cox regression, we compared the possible/probable HF groups with controls regarding risk of incident definite HF, coronary heart disease (CHD), other CVD or death; and with definite HF regarding risk of latter three outcomes.

During follow-up (mean 8.6 years), ~90% of individuals with possible, probable and definite HF experienced CVD events or died. Compared with controls, those with possible or probable HF experienced higher hazards for definite HF, CHD, other CVD and death (hazards ratios [HR] 1.35–9.31; p<0.05). The possible/probable groups did not differ from the definite HF group for risk of any outcome. Compared with the possible HF group, the probable HF group had a higher propensity for definite HF (HR 1.64, with a higher proportion of ischemic HF) but lower risk of death (HR 0.69).

Conclusions

Individuals meeting partial criteria for HF are at a substantial risk for progression to HF, CVD, and mortality.

Update in recent clinical trials in heart failure

PURPOSE OF REVIEW

Heart failure represents a major growing health problem in developed world. This article aims to review recent heart failure trials that have significantly impacted the management of heart failure.

RECENT FINDINGS

Despite advances in heart failure, mortality and morbidity remains elevated amongst patients. Recent clinical trials demonstrate promising treatment strategies that likely impact clinical practice; including heart failure prevention with the use of SGLT2-inhibitors in patients with diabetes and cardiovascular risk, new treatments that may abrogate disease progression in cardiac amyloidosis, intravenous iron therapy in iron deficiency anemia in chronic systolic heart failure, predischarge treatment with angiotensin receptor blocker with neprilysin inhibition (ARNi) in patients hospitalized for acute decompensated heart failure, and newer continuous flow left ventricular assist device with increased durability and efficacy in patients with Stage D heart failure.

SUMMARY

Recent clinical trials with SGLT2 inhibitors, therapies targeting transthyretin cardiac amyloidosis, iron, angiotensin receptor blocker with neprilysin inhibition and newer mechanical circulatory support devices are very promising as practice changing new treatment strategies in prevention and treatment of heart failure. This article presents a summary of important trials and should be of practical value to both clinicians and researchers.

Heart Disease and Stroke Statistics-2020 Update: A Report From the American Heart Association

BACKGROUND

The American Heart Association, in conjunction with the National Institutes of Health, annually reports on the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2020 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population, metrics to assess and monitor healthy diets, an enhanced focus on social determinants of health, a focus on the global burden of cardiovascular disease, and further evidence-based approaches to changing behaviors, implementation strategies, and implications of the American Heart Association's 2020 Impact Goals.

RESULTS

Each of the 26 chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policy makers, media professionals, clinicians, healthcare administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Troponins in Heart Failure - a Perpetual Challenge

Increased troponin levels in HF are a frequent and significant finding, as it strongly correlates with the underlying pathogenic mechanisms, diagnosis and prognosis. The advent of hs-cTn testing, as opposed to conventional troponin testing, led to additional difficulties in result interpretation. Most frequently, though not exclusively, increased cTn levels in acute or chronic failure is correlated, with myocardial necrosis (AMI); the diagnosis of AMI is confirmed if other criteria are fulfilled, as described in the fourth Universal Definition of Myocardial Infarction. Increased cTn levels below the cut-off for AMI suggest acute or chronic injury, depending on the ascending and/or descending trend curve or stable levels of cTn on serial testing. In acute or chronic HF with reduced or preserved EF, increased cTn levels carry prognostic value for adverse outcomes. Acute and chronic HF, as well as other ischemic or non-ischemic conditions, may lead to a transient increase in cTn levels: hypertensive crises, tachyarrhythmias, valvular regurgitation, myocarditis, stroke, mandating differential diagnosis with ACS. There are multiple mechanisms that explain increased levels of cTn: myocardial necrosis or coronary thrombosis (type I MI), supply-demand mismatch with subendocardial ischemia/injury, cardiomyocyte apoptosis, inflammatory cytokines, neurohomonal changes. Screening for cTn levels in the population at high cardiovascular risk yields prognostic information on development of de novo HF or other cardiovascular adverse events.

Malondialdehyde and Uric Acid as Predictors of Adverse Outcome in Patients with Chronic Heart Failure

In chronic heart failure (HF), some parameters of oxidative stress are correlated with disease severity. The aim of this study was to evaluate the importance of oxidative stress biomarkers in prognostic risk stratification (death and combined endpoint: heart transplantation or death). In 774 patients, aged 48-59 years, with chronic HF with reduced ejection fraction (median: 24.0 (20-29)%), parameters such as total antioxidant capacity, total oxidant status, oxidative stress index, and concentration of uric acid (UA), bilirubin, protein sulfhydryl groups (PSH), and malondialdehyde (MDA) were measured. The parameters were assessed as predictive biomarkers of mortality and combined endpoint in a 1-year follow-up. The multivariate Cox regression analysis was adjusted for other important clinical and laboratory prognostic markers. Among all the oxidative stress markers examined in multivariate analysis, only MDA and UA were found to be independent predictors of death and combined endpoint. Higher serum MDA concentration increased the risk of death by 103.0% (HR = 2.103; 95% CI (1.330-3.325)) and of combined endpoint occurrence by 100% (HR = 2.000; 95% CI (1.366-2.928)) per μmol/L. Baseline levels of MDA in the 4^th quartile were associated with an increased risk of death with a relative risk (RR) of 3.64 (95% CI (1.917 to 6.926), p < 0.001) and RR of 2.71 (95% CI (1.551 to 4.739), p < 0.001) for the occurrence of combined endpoint as compared to levels of MDA in the 1^st quartile. Higher serum UA concentration increased the risk of death by 2.1% (HR = 1.021; 95% CI (1.005-1.038), p < 0.001) and increased combined endpoint occurrence by 1.4% (HR = 1.014; 95% CI (1.005-1.028), p < 0.001), for every 10 μmol/L. Baseline levels of UA in the 4^th quartile were associated with an increased risk for death with a RR of 3.21 (95% CI (1.734 to 5.931)) and RR of 2.73 (95% CI (1.560 to 4.766)) for the occurrence of combined endpoint as compared to the levels of UA in the 1^st quartile. In patients with chronic HF, increased MDA and UA concentrations were independently related to poor prognosis in a 1-year follow-up.

Point-of-Care Testing Devices for Heart Failure Analyzing Blood and Saliva Samples

Heart failure (HF) is the most rapidly growing cardiovascular condition with an estimated prevalence of >37.7 million individuals globally. HF is associated with increased mortality and morbidity and confers a substantial burden, in terms of cost and quality of life, for the individuals and the healthcare systems, highlighting thus the need for early and accurate diagnosis of HF. The accuracy of HF diagnosis, severity estimation, and prediction of adverse events has improved by the utilization of blood tests measuring biomarkers. The contribution of biomarkers for HF management is intensified by the fact that they can be measured in short time at the point-of-care. This is allowed by the development of portable analytical devices, commonly known as point-of-care testing (POCT) devices, which exploit the advancements in the area of microfluidics and nanotechnology. The aim of this review paper is to present a review of POCT devices used for the measurement of biomarkers facilitating decision making when managing HF patients. The devices are either commercially available or in the form of prototypes under development. Both blood and saliva samples are considered. The challenges concerning the implementation of POCT devices and the barriers for their adoption in clinical practice are discussed.

Predisposing factors to heart failure in diabetic nephropathy: a look at the sympathetic nervous system hyperactivity

Diabetes mellitus (DM) and heart failure (HF) are frequent comorbidities among elderly patients. HF, a leading cause of mortality and morbidity worldwide, is characterized by sympathetic nervous system hyperactivity. The prevalence of diabetes mellitus (DM) is rapidly growing and the risk of developing HF is higher among DM patients. DM is responsible for several macro- and micro-angiopathies that contribute to the development of coronary artery disease (CAD), peripheral artery disease, retinopathy, neuropathy and diabetic nephropathy (DN) as well. Independently of CAD, chronic kidney disease (CKD) and DM increase the risk of HF. Individuals with diabetic nephropathy are likely to present a distinct pathological condition, defined as diabetic cardiomyopathy, even in the absence of hypertension or CAD, whose pathogenesis is only partially known. However, several hypotheses have been proposed to explain the mechanism of diabetic cardiomyopathy: increased oxidative stress, altered substrate metabolism, mitochondrial dysfunction, activation of renin-angiotensin-aldosterone system (RAAS), insulin resistance, and autonomic dysfunction. In this review, we will focus on the involvement of sympathetic system hyperactivity in the diabetic nephropathy.

Relation of multi-marker panel to incident chronic kidney disease and rapid kidney function decline in African Americans: the Jackson Heart Study

Background

Few investigations have evaluated the incremental usefulness of multiple biomarkers representing varying physiological pathways for predicting risk of renal outcomes in African Americans.

Design, setting, participants, and measurements

We related a multi-marker panel to incident chronic kidney disease (CKD) and rapid kidney function decline (RKFD) in 2813 Jackson Heart Study participants without prevalent CKD at exam 1 (2000–2004) and with complete assays at exam 1 for 9 biomarkers: adiponectin, aldosterone, B-natriuretic peptide [BNP], cortisol, high sensitivity C-reactive protein (hsCRP), endothelin, homocysteine, plasma renin activity and mass. Incident CKD was defined as estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m² at exam 3 while RKFD was defined as eGFR ≥30% loss between exams 1 and 3 (8.2 median years). We employed multiple logistic regression model to describe association between the panel and incident CKD and RKFD and used backward elimination strategy to estimate the most parsimonious biomarker model while controlling for conventional risk factors.

Results

The multi-marker panel predicted the risk for both incident CKD (odds ratios [OR], 2.72; 95% confidence intervals [CI], 1.63, 4.56; P = 0.001) and RKFD (2.61; 95% CI, 1.67, 4.08; P < 0.001). Per standard deviation increase in log biomarker concentrations were significantly (multivariable adjusted odds ratios, [95% confidence interval], p-value) associated with incident CKD: plasma adiponectin (1.24 [1.07, 1.44], p = 0.005) and leptin (1.3 [1.06, 1.61], p = 0.011), and with RKFD: plasma adiponectin (1.22 [1.06, 1.40], p = 0.006); hsCRP (1.17 [1.01, 1.36], p = 0.031) and aldosterone (0.85 [0.74, 0.96], p = 0.012). Moderate levels (3rd quartile) of aldosterone were inversely associated with incident CKD (0.54 [0.35, 0.82], p = 0.004) while leptin was associated with RKFD (1.64 [1.10, 2.44], p = 0.015). Biomarkers improved CKD risk prediction (P = 0.003) but not RKFD risk prediction (P = 0.10).

Conclusion

In this community-based sample of African Americans, a multi-marker panel added only moderate predictive improvement compared to conventional risk factors.

Electronic supplementary material

The online version of this article (10.1186/s12882-018-1026-y) contains supplementary material, which is available to authorized users.

Biomarkers for heart failure: small molecules with high clinical relevance

Heart failure (HF) is a rising epidemic due to the ageing population and progress in all areas of medicine. Thus, research efforts are made to ensure a timely diagnosis, to improve prognosis and treatment of the disease and to facilitate risk prediction at the population level. Because of their noninvasive determination with mostly high sensitivity and accuracy, circulating blood biomarkers are becoming increasingly important for daily clinical practice. Natriuretic peptides, especially B-type natriuretic peptide (BNP), N-terminal pro-B-type natriuretic peptide (Nt-proBNP) and midregional pro-atrial natriuretic peptide (MR-proANP) and cardiac troponins are established blood biomarkers in HF diagnosis and prognosis of HF-related outcomes. Inflammatory molecules as C-reactive protein (CRP) may have added value in anti-inflammatory therapy guidance. Next-generation biomarkers including soluble source of tumorigenicity 2 (sST2), growth differentiation factor-15 (GDF-15), galectin-3 (Gal-3) and diverse microribonucleic acids (miRNAs) may have additional benefit in assessment of cardiac remodeling or differentiation of HF subtypes. Multimarker approaches containing different combinations of established and novel biomarkers might improve HF risk prediction at the population level once they are used on top of clinical variables.

Pathophysiological mechanism and therapeutic role of S100 proteins in cardiac failure: a systematic review

S100 proteins are a family of highly acidic calcium-binding proteins involved in calcium handling in many tissues and organs. Some of these proteins are highly expressed in cardiac tissue, and an impairment of some specific S100 proteins has been related to heart failure. To check this hypothesis, we decided to review the literature since 2008 until May 2015. According to the studies collected, recovering S100A1 levels may enhance contractile/relaxing performance in heart failure, reverse negative force-frequency relationship, improve contractile reserve, reverse diastolic dysfunction and protect against pro-arrhythmic reductions of sarcoplasmic reticulum calcium. The safety profile of gene therapy was also confirmed. Increased S100B protein levels were related to a worse outcome in chronic heart failure. S100A8/A9 complex plasma levels, as well as other inflammatory biomarkers, were significantly higher in chronic heart failure patients. S100A2 seems to increase both contractile and relaxation performance in animal cardiomyocytes. Otherwise, S100A6 cardiac expression seems to have no effects on contractility. S100A4 KO mice showed reduced cardiac interstitial fibrosis. Data collected encourage a potential prospective application in human. These proteins could be exploited as biomarkers in stadiation and prognosis of chronic heart failure, as well as therapeutic target to rescue failing heart. Registration details The study protocol has been registered in PROSPERO ( http://www.crd.york.ac.uk/PROSPERO/ ) under registration number CRD42015027932.

Evolving Role of Natriuretic Peptides from Diagnostic Tool to Therapeutic Modality

Natriuretic peptides (NP) are widely recognized as key regulators of blood pressure, water and salt homeostasis. In addition, they play a critical role in physiological cardiac growth and mediate a variety of biological effects including antiproliferative and anti-inflammatory effects in other organs and tissues. The cardiac release of NPs ANP and BNP represents an important compensatory mechanism during acute and chronic cardiac overload and during the pathogenesis of heart failure where their actions counteract the sustained activation of renin-angiotensin-aldosterone and other neurohormonal systems. Elevated circulating plasma NP levels correlate with the severity of heart failure and particularly BNP and the pro-peptide, NT-proBNP have been established as biomarkers for the diagnosis of heart failure as well as prognostic markers for cardiovascular risk. Despite activation of the NP system in heart failure it is inadequate to prevent progressive fluid and sodium retention and cardiac remodeling. Therapeutic approaches included administration of synthetic peptide analogs and the inhibition of NP-degrading enzyme neutral endopeptidase (NEP). Of all strategies only the combined NEP/ARB inhibition with sacubitril/valsartan had shown clinical success in reducing cardiovascular mortality and morbidity in patients with heart failure.

Predictors of incident heart failure in patients after an acute coronary syndrome: The LIPID heart failure risk-prediction model

BACKGROUND

Coronary heart disease is a major cause of heart failure. Availability of risk-prediction models that include both clinical parameters and biomarkers is limited. We aimed to develop such a model for prediction of incident heart failure.

METHODS

A multivariable risk-factor model was developed for prediction of first occurrence of heart failure death or hospitalization. A simplified risk score was derived that enabled subjects to be grouped into categories of 5-year risk varying from <5% to >20%.

RESULTS

Among 7101 patients from the LIPID study (84% male), with median age 61years (interquartile range 55-67years), 558 (8%) died or were hospitalized because of heart failure. Older age, history of claudication or diabetes mellitus, body mass index>30kg/m², LDL-cholesterol >2.5mmol/L, heart rate>70 beats/min, white blood cell count, and the nature of the qualifying acute coronary syndrome (myocardial infarction or unstable angina) were associated with an increase in heart failure events. Coronary revascularization was associated with a lower event rate. Incident heart failure increased with higher concentrations of B-type natriuretic peptide >50ng/L, cystatin C>0.93nmol/L, D-dimer >273nmol/L, high-sensitivity C-reactive protein >4.8nmol/L, and sensitive troponin I>0.018μg/L. Addition of biomarkers to the clinical risk model improved the model's C statistic from 0.73 to 0.77. The net reclassification improvement incorporating biomarkers into the clinical model using categories of 5-year risk was 23%.

CONCLUSION

Adding a multibiomarker panel to conventional parameters markedly improved discrimination and risk classification for future heart failure events.

Cognitive Function and Kidney Disease: Baseline Data From the Systolic Blood Pressure Intervention Trial (SPRINT)

BACKGROUND

Chronic kidney disease is common and is associated with cardiovascular disease, cerebrovascular disease, and cognitive function, although the nature of this relationship remains uncertain.

STUDY DESIGN

Cross-sectional cohort using baseline data from the Systolic Blood Pressure Intervention Trial (SPRINT).

SETTING & PARTICIPANTS

Participants in SPRINT, a randomized clinical trial of blood pressure targets in older community-dwelling adults with cardiovascular disease, chronic kidney disease, or high cardiovascular disease risk and without diabetes or known stroke, who underwent detailed neurocognitive testing in the cognition substudy, SPRINT-Memory and Cognition in Decreased Hypertension (SPRINT-MIND).

PREDICTORS

Urine albumin-creatinine ratio (ACR) and estimated glomerular filtration rate (eGFR).

OUTCOMES

Cognitive function, a priori defined as 5 cognitive domains based on 11 cognitive tests using z scores, and abnormal white matter volume quantified by brain magnetic resonance imaging.

RESULTS

Of 9,361 SPRINT participants, 2,800 participated in SPRINT-MIND and 2,707 had complete data; 637 had brain imaging. Mean age was 68 years, 37% were women, 30% were black, and 20% had known cardiovascular disease. Mean eGFR was 70.8±20.9mL/min/1.73m² and median urine ACR was 9.7 (IQR, 5.7-22.5) mg/g. In adjusted analyses, higher ACR was associated with worse global cognitive function, executive function, memory, and attention, such that each doubling of urine ACR had the same association with cognitive performance as being 7, 10, 6, and 14 months older, respectively. Lower eGFR was independently associated with worse global cognitive function and memory. In adjusted models, higher ACR, but not eGFR, was associated with larger abnormal white matter volume.

LIMITATIONS

Cross-sectional only, no patients with diabetes were included.

CONCLUSIONS

In older adults, higher urine ACR and lower eGFR have independent associations with global cognitive performance with different affected domains. Albuminuria concurrently identifies a higher burden of abnormal brain white matter disease, suggesting that vascular disease may mediate these relationships.

Risk for Incident Heart Failure: A Subject-Level Meta-Analysis From the Heart "OMics" in AGEing (HOMAGE) Study

Background

To address the need for personalized prevention, we conducted a subject‐level meta‐analysis within the framework of the Heart “OMics” in AGEing (HOMAGE) study to develop a risk prediction model for heart failure (HF) based on routinely available clinical measurements.

Methods and Results

Three studies with elderly persons (Health Aging and Body Composition [Health ABC], Valutazione della PREvalenza di DIsfunzione Cardiaca asinTOmatica e di scompenso cardiaco [PREDICTOR], and Prospective Study of Pravastatin in the Elderly at Risk [PROSPER]) were included to develop the HF risk function, while a fourth study (Anglo‐Scandinavian Cardiac Outcomes Trial [ASCOT]) was used as a validation cohort. Time‐to‐event analysis was conducted using the Cox proportional hazard model. Incident HF was defined as HF hospitalization. The Cox regression model was evaluated for its discriminatory performance (area under the receiver operating characteristic curve) and calibration (Grønnesby‐Borgan χ² statistic). During a follow‐up of 3.5 years, 470 of 10 236 elderly persons (mean age, 74.5 years; 51.3% women) developed HF. Higher age, BMI, systolic blood pressure, heart rate, serum creatinine, smoking, diabetes mellitus, history of coronary artery disease, and use of antihypertensive medication were associated with increased HF risk. The area under the receiver operating characteristic curve of the model was 0.71, with a good calibration (χ² 7.9, P=0.54). A web‐based calculator was developed to allow easy calculations of the HF risk.

Conclusions

Simple measurements allow reliable estimation of the short‐term HF risk in populations and patients. The risk model may aid in risk stratification and future HF prevention strategies.

Role of Biomarkers for the Prevention, Assessment, and Management of Heart Failure: A Scientific Statement From the American Heart Association

BACKGROUND AND PURPOSE

Natriuretic peptides have led the way as a diagnostic and prognostic tool for the diagnosis and management of heart failure (HF). More recent evidence suggests that natriuretic peptides along with the next generation of biomarkers may provide added value to medical management, which could potentially lower risk of mortality and readmissions. The purpose of this scientific statement is to summarize the existing literature and to provide guidance for the utility of currently available biomarkers.

METHODS

The writing group used systematic literature reviews, published translational and clinical studies, clinical practice guidelines, and expert opinion/statements to summarize existing evidence and to identify areas of inadequacy requiring future research. The panel reviewed the most relevant adult medical literature excluding routine laboratory tests using MEDLINE, EMBASE, and Web of Science through December 2016. The document is organized and classified according to the American Heart Association to provide specific suggestions, considerations, or contemporary clinical practice recommendations.

RESULTS

A number of biomarkers associated with HF are well recognized, and measuring their concentrations in circulation can be a convenient and noninvasive approach to provide important information about disease severity and helps in the detection, diagnosis, prognosis, and management of HF. These include natriuretic peptides, soluble suppressor of tumorgenicity 2, highly sensitive troponin, galectin-3, midregional proadrenomedullin, cystatin-C, interleukin-6, procalcitonin, and others. There is a need to further evaluate existing and novel markers for guiding therapy and to summarize their data in a standardized format to improve communication among researchers and practitioners.

CONCLUSIONS

HF is a complex syndrome involving diverse pathways and pathological processes that can manifest in circulation as biomarkers. A number of such biomarkers are now clinically available, and monitoring their concentrations in blood not only can provide the clinician information about the diagnosis and severity of HF but also can improve prognostication and treatment strategies.

Risk Prediction Models for Incident Heart Failure: A Systematic Review of Methodology and Model Performance

BACKGROUND

Numerous models predicting the risk of incident heart failure (HF) have been developed; however, evidence of their methodological rigor and reporting remains unclear. This study critically appraises the methods underpinning incident HF risk prediction models.

METHODS AND RESULTS

EMBASE and PubMed were searched for articles published between 1990 and June 2016 that reported at least 1 multivariable model for prediction of HF. Model development information, including study design, variable coding, missing data, and predictor selection, was extracted. Nineteen studies reporting 40 risk prediction models were included. Existing models have acceptable discriminative ability (C-statistics > 0.70), although only 6 models were externally validated. Candidate variable selection was based on statistical significance from a univariate screening in 11 models, whereas it was unclear in 12 models. Continuous predictors were retained in 16 models, whereas it was unclear how continuous variables were handled in 16 models. Missing values were excluded in 19 of 23 models that reported missing data, and the number of events per variable was < 10 in 13 models. Only 2 models presented recommended regression equations. There was significant heterogeneity in discriminative ability of models with respect to age (P < .001) and sample size (P = .007).

CONCLUSIONS

There is an abundance of HF risk prediction models that had sufficient discriminative ability, although few are externally validated. Methods not recommended for the conduct and reporting of risk prediction modeling were frequently used, and resulting algorithms should be applied with caution.

The association of chronic kidney disease and microalbuminuria with heart failure with preserved vs. reduced ejection fraction

AIMS

Chronic kidney disease (CKD) and microalbuminuria are associated with incident heart failure (HF), but their relative contributions to HF with preserved vs. reduced EF (HFpEF and HFrEF) are unknown. We sought to evaluate the associations of CKD and microalbuminuria with incident HF subtypes in the community-based Framingham Heart Study (FHS).

METHODS AND RESULTS

We defined CKD as glomerular filtration rate <60 mL/min/1.73 m² , and microalbuminuria as a urine albumin to creatinine ratio (UACR) ≥17 mg/g in men and ≥25 mg/g in women. We observed 754 HF events (324 HFpEF/326 HFrEF/104 unclassified) among 9889 FHS participants with serum creatinine measured (follow-up 13 ± 4 years). In Cox models adjusted for clinical risk factors, CKD (prevalence = 9%) was associated with overall HF [hazard ratio (HR) 1.24, 95% confidence interval (CI) 1.01-1.51], but was not significantly associated with individual HF subtypes. Among 2912 individuals with available UACR (follow-up 15 ± 4 years), 192 HF events (91 HFpEF/93 HFrEF/8 unclassified) occurred. Microalbuminuria (prevalence = 17%) was associated with a higher risk of overall HF (HR 1.71, 95% CI 1.25-2.34) and HFrEF (HR 2.10, 95% CI 1.35-3.26), but not HFpEF (HR 1.26, 95% CI 0.78-2.03). In cross-sectional analyses, microalbuminuria was associated with LV systolic dysfunction (odds ratio 3.19, 95% CI 1.67-6.09).

CONCLUSIONS

Microalbuminuria was associated with incident HFrEF prospectively, and with LV systolic dysfunction cross-sectionally in a community-based sample. In contrast, CKD was modestly associated with overall HF but not differentially associated with HFpEF vs. HFrEF. The mechanisms responsible for the relationship of microalbuminuria to future development of HFrEF warrant further investigation.

Novel Biomarkers of Heart Failure

Although substantial improvements have been made in majority of cardiac disorders, heart failure (HF) remains a major health problem, with both increasing incidence and prevalence over the past decades. For that reason, the number of potential biomarkers that could contribute to diagnosis and treatment of HF patients is, almost exponentially, increasing over the recent years. The biomarkers that are, at the moment, more or less ready for use in everyday clinical practice, reflect different pathophysiological processes present in HF. In this review, seven groups of biomarkers associated to myocardial stretch (mid-regional proatrial natriuretic peptide, MR-proANP), myocyte injury (high-sensitive troponins, hs-cTn; heart-type fatty acid-binding protein, H-FABP; glutathione transferase P1, GSTP1), matrix remodeling (galectin-3; soluble isoform of suppression of tumorigenicity 2, sST2), inflammation (growth differentiation factor-15, GDF-15), renal dysfunction (neutrophil gelatinase-associated lipocalin, NGAL; kidney injury molecule-1, KIM-1), neurohumoral activation (adrenomedullin, MR-proADM; copeptin), and oxidative stress (ceruloplasmin; myeloperoxidase, MPO; 8-hydroxy-2'-deoxyguanosine, 8-OHdG; thioredoxin 1, Trx1) in HF will be overviewed. It is important to note that clinical value of individual biomarkers within the single time points in both diagnosis and outcome prediction in HF is limited. Hence, the future of biomarker application in HF lies in the multimarker panel strategy, which would include specific combination of biomarkers that reflect different pathophysiological processes underlying HF.