Relations of biomarkers of distinct pathophysiological pathways and atrial fibrillation incidence in the community.

BACKGROUND: Biomarkers of multiple pathophysiological pathways have been related to incident atrial fibrillation (AF), but their predictive ability remains controversial. METHODS AND RESULTS: In 3120 Framingham cohort participants (mean age 58.4+/-9.7 years, 54% women), we related 10 biomarkers that represented inflammation (C-reactive protein and fibrinogen), neurohormonal activation (B-type natriuretic peptide [BNP] and N-terminal proatrial natriuretic peptide), oxidative stress (homocysteine), the renin-angiotensin-aldosterone system (renin and aldosterone), thrombosis and endothelial function (D-dimer and plasminogen activator inhibitor type 1), and microvascular damage (urinary albumin excretion; n=2673) to incident AF (n=209, 40% women) over a median follow-up of 9.7 years (range 0.05 to 12.8 years). In multivariable-adjusted analyses, the biomarker panel was associated with incident AF (P<0.0001). In stepwise-selection models (P<0.01 for entry and retention), log-transformed BNP (hazard ratio per SD 1.62, 95% confidence interval 1.41 to 1.85, P<0.0001) and C-reactive protein (hazard ratio 1.25, 95% confidence interval 1.07 to 1.45, P=0.004) were chosen. The addition of BNP to variables recently combined in a risk score for AF increased the C-statistic from 0.78 (95% confidence interval 0.75 to 0.81) to 0.80 (95% confidence interval 0.78 to 0.83) and showed an integrated discrimination improvement of 0.03 (95% confidence interval 0.02 to 0.04, P<0.0001), with 34.9% relative improvement in reclassification analysis. The combined analysis of BNP and C-reactive protein did not appreciably improve risk prediction over the model that incorporated BNP in addition to the risk factors. CONCLUSIONS: BNP is a predictor of incident AF and improves risk stratification based on well-established clinical risk factors. Whether knowledge of BNP concentrations may be used to target individuals at risk of AF for more intensive monitoring or primary prevention requires further investigation.

Duke Scholars

Author Michael J Pencina Biostatistics & Bioinformatics, Division of Biostatistics