Relations of Biomarkers of Distinct Pathophysiological Pathways and Atrial Fibrillation Incidence in the Community
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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.
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Diakantonis A, Verras C, Bezati S, Bistola V, Ventoulis I, Velliou M Biomedicines. 2025; 12(12.
PMID: 39767800 PMC: 11727096. DOI: 10.3390/biomedicines12122895.
Nayak S, Kuriyakose D, Polisetty L, Patil A, Ameen D, Bonu R Cardiovasc Diabetol. 2024; 23(1):310.
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