Bone Morphogenetic Protein 10-A Novel Biomarker to Predict Adverse Outcomes in Patients With Atrial Fibrillation

Atrial fibrillation burden: a new outcome predictor and therapeutic target

Atrial fibrillation (AF), the most common sustained cardiac arrhythmia, is not a dichotomous disease trait. Technological innovations enable long-term rhythm monitoring in many patients and can estimate AF burden. These technologies are already used to detect and monitor AF. This review describes the relation between AF burden and outcomes and potential effects of AF burden reduction. A lower AF burden is associated with a lower risk of stroke and heart failure in patients with AF: stroke risk without anticoagulation is lower in patients with device-detected AF and a low AF burden (stroke rate 1%/year) than in patients with persistent and permanent AF (stroke rate 3%/year). Paroxysmal AF shows intermediate stroke rates (2%/year). Atrial fibrillation burden-reducing interventions can reduce cardiovascular outcomes in patients with AF: early rhythm control reduces cardiovascular events including stroke and heart failure in patients with recently diagnosed AF and cardiovascular conditions. In patients with heart failure and AF, early rhythm control and AF ablation, interventions that reduce AF burden, reduce mortality and heart failure events. Recent technological innovations allow to estimate AF burden in clinical care, creating opportunities and challenges. While evidence remains limited, the existing data already suggest that AF burden reduction could be a therapeutic goal. In addition to anticoagulation and treatment of cardiovascular conditions, AF burden reduction emerges as a therapeutic goal. Future research will define the AF burden that constitutes a relevant risk of stroke and heart failure. Technologies quantifying AF burden need careful validation to advance the field.

Blood-based cardiometabolic phenotypes in atrial fibrillation and their associated risk: EAST-AFNET 4 biomolecule study

AIMS

Atrial fibrillation (AF) and concomitant cardiometabolic disease processes interact and combine to lead to adverse events, such as stroke, heart failure, myocardial infarction, and cardiovascular death. Circulating biomolecules provide quantifiable proxies for cardiometabolic disease processes. The aim of this study was to test whether biomolecule combinations can define phenotypes in patients with AF.

METHODS AND RESULTS

This pre-specified analysis of the EAST-AFNET 4 biomolecule study assigned patients to clusters using polytomous variable latent-class analysis based on baseline concentrations of 13 precisely quantified biomolecules potentially reflecting ageing, cardiac fibrosis, metabolic dysfunction, oxidative stress, cardiac load, endothelial dysfunction, and inflammation. In each cluster, rates of cardiovascular death, stroke, or hospitalization for heart failure or acute coronary syndrome, the primary outcome of EAST-AFNET 4, were calculated and compared between clusters over median 5.1 years follow-up. Findings were independently validated in a prospective cohort of 748 patients with AF (BBC-AF; median follow-up 2.9 years).Unsupervised biomolecule analysis assigned 1586 patients (71 years old, 46% women) into four clusters. The highest risk cluster was dominated by elevated bone morphogenetic protein 10, insulin-like growth factor-binding protein 7, N-terminal pro-B-type natriuretic peptide, angiopoietin 2, and growth differentiation factor 15. Patients in the lowest risk cluster showed low concentrations of these biomolecules. Two intermediate-risk clusters differed by high or low concentrations of C-reactive protein, interleukin-6, and D-dimer. Patients in the highest risk cluster had a five-fold higher cardiovascular event rate than patients in the low-risk cluster. Early rhythm control was effective across clusters (Pinteraction = 0.63). Sensitivity analyses and external validation in BBC-AF replicated clusters and risk gradients.

CONCLUSION

Biomolecule concentrations identify cardiometabolic subphenotypes in patients with AF at high and low cardiovascular risk.

Biomarkers to predict improvement of left ventricular ejection fraction after atrial fibrillation ablation

BACKGROUND

Atrial fibrillation (AF) and heart failure frequently coexist. Prediction of left ventricular ejection fraction (LVEF) recovery after catheter ablation (CA) for AF remains difficult.

OBJECTIVE

The purpose of this study was to evaluate the value of biomarkers, alone and in combination with the Antwerp score, to predict LVEF recovery after CA for AF.

METHODS

Patients undergoing CA for AF with depressed LVEF (<50%) were included. Plasma levels of 13 biomarkers were measured immediately before CA. Patients were categorized into "responders" and "nonresponders" in a similar fashion to the Antwerp score performance derivation and validation cohorts. The predictive power of the biomarkers alone and combined in outcome prediction was evaluated.

RESULTS

A total of 208 patients with depressed LVEF were included (median age 63 years; 39-19% female; median indexed left atrial volume 42 (33-52) mL/m2; median LVEF 43 (38-46)%). At a median follow-up time of 30 (20-34) months, 161 (77%) were responders and 47 (23%) were nonresponders. Of 13 biomarkers, -4-angiopoietin 2 (ANG2), growth differentiation factor 15 (GDF15), fibroblast growth factor 23, and myosin binding protein C3-were significantly different between responders and nonresponders (P ≤ .001) and their combination could predict the end point with an area under the curve of 0.72 (95% confidence interval [CI] 0.64-0.81) overall, 0.69 (95% CI 0.59-0.78) in heart failure with mildly reduced ejection fraction, and 0.88 (95% CI 0.77-0.98) in heart failure with reduced ejection fraction. Only ANG2 and GDF15 remained significantly associated with LVEF recovery after adjustment for age, sex, and Antwerp score and significantly improved the accuracy of the Antwerp score predictions (P < .001). The area under the curve of the Antwerp score in the outcome prediction improved from 0.75 (95% CI 0.67-0.83) to 0.78 (95% CI 0.70-0.86).

CONCLUSION

A biomarker panel (ANG2 and GDF15) significantly improved the accuracy of the Antwerp score.

Repeated Measurement of the Novel Atrial Biomarker BMP10 (Bone Morphogenetic Protein 10) Refines Risk Stratification in Anticoagulated Patients With Atrial Fibrillation: Insights From the ARISTOTLE Trial

BACKGROUND

BMP10 (bone morphogenic protein 10) has emerged as a novel biomarker associated with the risk of ischemic stroke and other outcomes in patients with atrial fibrillation (AF). The study aimed to determine if repeated BMP10 measurements improve prognostication of cardiovascular events in patients with AF.

METHODS AND RESULTS

BMP10 was measured using a prototype Elecsys immunoassay in plasma samples collected at randomization and after 2 months in patients with AF randomized to apixaban or warfarin in the ARISTOTLE (Apixaban for Reduction in Stroke and Other Thromboembolic Events in Atrial Fibrillation) trial (n=2878). Adjusted Cox-regression models were used to evaluate the association between 2-month BMP10 levels and outcomes. BMP10 levels increased by 7.8% (P<0.001) over 2 months. The baseline variables most strongly associated with BMP10 levels at 2 months were baseline BMP10 levels, body mass index, sex, age, creatinine, diabetes, warfarin treatment, and AF-rhythm. During median 1.8 years follow-up, 34 ischemic strokes/systemic embolism, 155 deaths, and 99 heart failure hospitalizations occurred. Comparing the third with the first sample quartile, higher BMP10 levels at 2 months were associated with higher risk of ischemic stroke (hazard ratio [HR], 1.33 [95% CI, 0.67-2.63], P=0.037), heart failure (HR, 1.91 [95% CI, 1.17-3.12], P=0.012) and all-cause death (HR, 1.61 [95% CI, 1.17-2.21], P<0.001). Adding BMP10 levels at 2 months on top of established risk factors and baseline BMP10 levels improved the C-indices for ischemic stroke/systemic embolism (from 0.73 to 0.75), heart failure hospitalization (0.76-0.77), and all-cause mortality (0.70-0.72), all P<0.05.

CONCLUSIONS

Elevated levels of BMP10 at 2 months strengthened the associations with the risk of ischemic stroke, hospitalization for heart failure, and all-cause mortality. Repeated measurements of BMP10 may further refine risk stratification in patients with AF.

Bone morphogenetic protein 10 and atrial fibrillation

Background

The association between bone morphogenetic protein 10 (BMP10) and atrial fibrillation (AF) has been widely investigated by observational studies, but their causal relationships remain inconclusive. Here, we aimed to evaluate the causal effect of BMP10 on the risk of AF through single-nucleotide polymorphisms.

Methods

A Mendelian randomization (MR) analytic framework was applied to data from two BMP10-specific genome-wide association studies comprising a total of 11,036,163 single-nucleotide polymorphisms of European ancestry. Instrument genetic variants associated with BMP10 were selected. A total of 12 AF-specific genome-wide association studies comprising a total of 5,095,117 European participants were included. Summary statistic-based methods of inverse variance weighted, MR Egger, weighted median, simple mode, and weighted mode methods were used. Pleiotropy and sensitivity were assessed.

Results

Specific to AF-specific genome-wide association studies, we found that BMP10 was not associated with AF among different methods (all P > 0.05). We further identified no significant horizontal pleiotropy (all P > 0.05) and no fundamental impact among various data.

Conclusions

This large-scale population study upon data from BMP10- and AF-specific genome-wide association studies and a longitudinal biobank cohort indicates plausible non-causal associations between BMP10 and AF in the European populations. Further studies regarding ancestral diversity are warranted to validate such causal associations.

Metabolomics and Biomarkers for Paroxysmal and Persistent Atrial Fibrillation

BACKGROUND

Atrial fibrillation (AF) is the most common type of arrhythmia worldwide and is associated with serious complications. This study investigated the metabolic biomarkers associated with AF and the differences in metabolomics and associated metabolic biomarkers between paroxysmal AF (AFPA) and persistent AF.

METHODS AND RESULTS

Plasma samples were prospectively collected from patients with AF and patients in sinus rhythm with negative coronary angiography. The patients were divided into 3 groups: AFPA, persistent AF, and sinus rhythm (N=54). Metabolomics (n=36) using ultra-high-performance liquid chromatography mass spectrometry was used to detect differential metabolites that were validated in a new cohort (n=18). The validated metabolites from the validation phase were further analyzed by receiver operating characteristic. Among the 36 differential metabolites detected by omics assay, 4 were successfully validated with area under the curve >0.8 (P<0.05). Bioinformatics analysis confirmed the enrichment pathways of unsaturated fatty acid biosynthesis, glyoxylate and dicarboxylate metabolism, and carbon metabolism. Arachidonic acid was a potential biomarker of AFPA, glycolic acid and L-serine were biomarkers of AFPA and persistent AF, and palmitelaidic acid was a biomarker of AFPA.

CONCLUSIONS

In this metabolomics study, we detected 36 differential metabolites in AF, and 4 were validated with high sensitivity and specificity. These differential metabolites are potential biomarkers for diagnosis and monitoring of disease course. This study therefore provides new insights into the precision diagnosis and management of AF.

An angiopoietin 2, FGF23, and BMP10 biomarker signature differentiates atrial fibrillation from other concomitant cardiovascular conditions

Early detection of atrial fibrillation (AF) enables initiation of anticoagulation and early rhythm control therapy to reduce stroke, cardiovascular death, and heart failure. In a cross-sectional, observational study, we aimed to identify a combination of circulating biomolecules reflecting different biological processes to detect prevalent AF in patients with cardiovascular conditions presenting to hospital. Twelve biomarkers identified by reviewing literature and patents were quantified on a high-precision, high-throughput platform in 1485 consecutive patients with cardiovascular conditions (median age 69 years [Q1, Q3 60, 78]; 60% male). Patients had either known AF (45%) or AF ruled out by 7-day ECG-monitoring. Logistic regression with backward elimination and a neural network approach considering 7 key clinical characteristics and 12 biomarker concentrations were applied to a randomly sampled discovery cohort (n = 933) and validated in the remaining patients (n = 552). In addition to age, sex, and body mass index (BMI), BMP10, ANGPT2, and FGF23 identified patients with prevalent AF (AUC 0.743 [95% CI 0.712, 0.775]). These circulating biomolecules represent distinct pathways associated with atrial cardiomyopathy and AF. Neural networks identified the same variables as the regression-based approach. The validation using regression yielded an AUC of 0.719 (95% CI 0.677, 0.762), corroborated using deep neural networks (AUC 0.784 [95% CI 0.745, 0.822]). Age, sex, BMI and three circulating biomolecules (BMP10, ANGPT2, FGF23) are associated with prevalent AF in unselected patients presenting to hospital. Findings should be externally validated. Results suggest that age and different disease processes approximated by these three biomolecules contribute to AF in patients. Our findings have the potential to improve screening programs for AF after external validation.