Exploring circulating biomarkers for risk prediction of incident atrial fibrillation – insights from the BiomarCaRE project
Authors: Toprak B , Brandt S , Brederecke J , Ojeda F , Soderberg S , Linneberg A , Koenig W , Lochen M L , Blankenberg S , Kuulasmaa K , Salomaa V , Iacoviello L , Niiranen T , Zeller T , Schnabel R 
Published: October 2022

Abstract

Background
Atrial fibrillation (AF) remains a major health issue in Europe and worldwide. Risk prediction is crucial to identify at-risk individuals and prevent subsequent complications of AF such as stroke and heart failure. Biomarker-enriched, personalized risk prediction offers great potential for population-wide prevention beyond traditional cardiovascular risk factors.

Purpose
We aimed to identify robust predictors for incident AF using classical regressions and machine learning (ML) techniques within a broad spectrum of candidate variables.

Methods
Three European community cohorts from the Biomarkers for Cardiovascular Risk Assessment in Europe (BiomarCaRE) consortium were included to explore the predictive utility of 14 biomarkers mirroring distinct pathophysiological pathways of AF including lipids, inflammation (C-reactive protein [CRP]), renal, and myocardium-specific markers (N-terminal pro B-type natriuretic peptide [NT-proBNP], high-sensitivity troponin I [hsTnI]) within a population-based sample of 42,280 individuals free of AF at baseline. Investigated biomarkers were examined in relation to incident AF using Cox regressions adjusted for multiple cardiovascular risk factors, and additionally by C-indices and net reclassification improvement (NRI) when compared to a reference model incorporating clinical variables. Their predictive utility for incident AF was further analyzed using different ML methods, including Least Absolute Shrinkage and Selection Operator (LASSO) and Random Survival Forest (RSF).

Results
Of 42,280 individuals (21,843 women [51.7%]; median [interquartile range, IQR] age, 52.2 [42.6, 62.0] years), 1496 (3.5%) developed AF during a median follow-up time of 5.7 years. In multivariable-adjusted Cox regression analysis, NT-proBNP was the strongest circulating predictor of incident AF (hazard ratio [HR] per standard deviation [SD] 1.93, 95% CI 1.82–2.04; P&lt;0.001). Further, hsTnI (HR per SD 1.18, 95% CI 1.13–1.22; P&lt;0.001), cystatin C (HR per SD 1.16, 95% CI 1.10–1.23; P&lt;0.001) and CRP (HR per SD 1.08, 95% CI 1.02–1.14, P=0.012) correlated positively with new-onset AF. NT-proBNP enhanced model discrimination (ΔC-index 0.037, 95% CI 0.029–0.044) markedly and yielded the best reclassification improvement (NRI 0.237, 95% CI 0.187–0.287) when compared to the clinical model. Neither the addition of hsTnI to NT-proBNP, nor a model comprising all investigated biomarkers further increased discrimination or reclassification substantially. In different ML models, NT-proBNP and age were the strongest predictors of incident AF.

Conclusions
Using a dual approach with both classical regressions and modern ML methods, NT-proBNP consistently remained the strongest blood-based predictor of incident AF with relevant discriminative ability and reclassification yield beyond classical cardiovascular risk factors. The clinical benefit of these findings for AF risk prediction needs to be tested prospectively.

Funding Acknowledgement
Type of funding sources: Public grant(s) – EU funding. Main funding source(s): BiomarCaRE (FP7, HEALTH-F2-2011-278913)European Union's Horizon 2020 research and innovation programme (grant agreement number 847770, AFFECT-EU)