Comparing biomarker profiles of patients with heart failure: atrial fibrillation vs. sinus rhythm and reduced vs. preserved ejection fraction

Obesity and diabetes can lead to heart failure with preserved ejection fraction (HFpEF), potentially because they both cause expansion and inflammation of epicardial adipose tissue and thus lead to microvascular dysfunction and fibrosis of the underlying left ventricle. The same process also causes an atrial myopathy, which is clinically evident as atrial fibrillation (AF); thus, AF may be the first manifestation of HFpEF. Many patients with apparently isolated AF have latent HFpEF or subsequently develop HFpEF. Most patients with obesity or diabetes who have AF and exercise intolerance have increased left atrial pressures at rest or during exercise, even in the absence of diagnosed HFpEF. Among patients with AF, those who also have latent HFpEF have increased risk for systemic thromboembolism and death. The identification of HFpEF in patients with obesity or diabetes alters the risk‐to‐benefit relationship of commonly prescribed treatments. Bariatric surgery and statins can ameliorate AF and reduce the risk for HFpEF. Conversely, antihyperglycaemic drugs that promote adipogenesis or cause sodium retention (insulin and thiazolidinediones) may increase the risk for heart failure in patients with an underlying ventricular myopathy. Patients with obesity and diabetes who undergo catheter ablation for AF are at increased risk for AF recurrence and for post‐ablation increases in pulmonary venous pressures and worsening heart failure, especially if HFpEF coexists. Therefore, AF may be the earliest indicator of HFpEF in patients with obesity or type 2 diabetes, and recognition of HFpEF alters the management of these patients.

Section: Epidemiological and Clinical Evidence Supporting Atrial Fibrmentioning

confidence: 99%

Do most patients with obesity or type 2 diabetes, and atrial fibrillation, also have undiagnosed heart failure? A critical conceptual framework for understanding mechanisms and improving diagnosis and treatment

Packer

2019

“…Previous analyses in BIOSTAT‐CHF have shown that worse cardiovascular outcomes were seen in heart failure patients with AF compared to sinus rhythm . Nevertheless, after multivariable adjustment, the AF genetic risk score was not associated with all‐cause mortality.…”

Section: Discussionmentioning

confidence: 79%

“…Previous analyses in BIOSTAT-CHF have shown that worse cardiovascular outcomes were seen in heart failure patients with AF compared to sinus rhythm. 24 Nevertheless, after multivariable adjustment, the AF genetic risk score was not associated with all-cause mortality. However, a genetic risk score alone does not capture the clinical significance of AF presence in patients with an extensive cardiac substrate and other underlying risk factors.…”

Section: Atrial Fibrillation Genetic Risk Score and All-cause Mortalitymentioning

confidence: 99%

Genetic risk and atrial fibrillation in patients with heart failure

Kloosterman

Santema

Roselli

et al. 2020

Self Cite

Aims To study the association between an atrial fibrillation (AF) genetic risk score with prevalent AF and all‐cause mortality in patients with heart failure. Methods and results An AF genetic risk score was calculated in 3759 European ancestry individuals (1783 with sinus rhythm, 1976 with AF) from the BIOlogy Study to TAilored Treatment in Chronic Heart Failure (BIOSTAT‐CHF) by summing 97 single nucleotide polymorphism (SNP) alleles (ranging from 0–2) weighted by the natural logarithm of the relative SNP risk from the latest AF genome‐wide association study. Further, we assessed AF risk variance explained by additive SNP variation, and performance of clinical or genetic risk factors, and the combination in classifying AF prevalence. AF was classified as AF or atrial flutter (AFL) at baseline electrocardiogram and/or a history of AF or AFL. The genetic risk score was associated with AF after multivariable adjustment. Odds ratio for AF prevalence per 1‐unit increase genetic risk score was 2.12 (95% confidence interval 1.84–2.45, P = 2.15 × 10−24) in the total cohort, 2.08 (1.72–2.50, P = 1.30 × 10−14) in heart failure with reduced ejection fraction (HFrEF) and 2.02 (1.37–2.99, P = 4.37 × 10−4) in heart failure with preserved ejection fraction (HFpEF). AF‐associated loci explained 22.9% of overall AF SNP heritability. Addition of the genetic risk score to clinical risk factors increased the C‐index by 2.2% to 0.721. Conclusions The AF genetic risk score was associated with increased AF prevalence in HFrEF and HFpEF. Genetic variation accounted for 22.9% of overall AF SNP heritability. Addition of genetic risk to clinical risk improved model performance in classifying AF prevalence.

“…Overall, 100 patients with HF in this study were selected from the EU FP7 funded BIOSTAT‐CHF (A systems BIOlogy Study to TAilored Treatment in Chronic Heart Failure) project. This was a multicentre clinical study in Europe that aimed to identify poor outcomes in HF patients with standard treatment using a systems biology approach (including demographics, biomarkers, genetics, and proteomics) . This project was conducted according to the Declaration of Helsinki that was approved by national and local ethics committees.…”

Section: Methodsmentioning

confidence: 99%

“…This was a multicentre clinical study in Europe 14 that aimed to identify poor outcomes in HF patients with standard treatment using a systems biology approach (including demographics, biomarkers, genetics, and proteomics). [15][16][17][18][19][20][21][22][23][24][25][26][27] This project was conducted according to the Declaration of Helsinki that was approved by national and local ethics committees. All patients in this study had written informed consent.…”

Section: Patient Populationmentioning

confidence: 99%

Plasma proteomic approach in patients with heart failure: insights into pathogenesis of disease progression and potential novel treatment targets

Cao

Jones

Voors

et al. 2019

Self Cite

Aims To provide insights into pathogenesis of disease progression and potential novel treatment targets for patients with heart failure by investigation of the plasma proteome using network analysis. Methods and results The plasma proteome of 50 patients with heart failure who died or were rehospitalised were compared with 50 patients with heart failure, matched for age and sex, who did not have an event. Peptides were analysed on two‐dimensional liquid chromatography coupled to tandem mass spectrometry (2D LC ESI‐MS/MS) in high definition mode (HDMSE). We identified and quantified 3001 proteins, of which 51 were significantly up‐regulated and 46 down‐regulated with more than two‐fold expression changes in those who experienced death or rehospitalisation. Gene ontology enrichment analysis and protein–protein interaction networks of significant differentially expressed proteins discovered the central role of metabolic processes in clinical outcomes of patients with heart failure. The findings revealed that a cluster of proteins related to glutathione metabolism, arginine and proline metabolism, and pyruvate metabolism in the pathogenesis of poor outcome in patients with heart failure who died or were rehospitalised. Conclusions Our findings show that in patients with heart failure who died or were rehospitalised, the glutathione, arginine and proline, and pyruvate pathways were activated. These pathways might be potential targets for therapies to improve poor outcomes in patients with heart failure.