Data-driven discovery and validation of circulating blood-based biomarkers associated with prevalent atrial fibrillation.

Chua, Winnie, Purmah, Yanish, Cardoso, Victor R, Gkoutos, Georgios V, Tull, Samantha P, Neculau, Georgiana, Thomas, Mark R, Kotecha, Dipak, Lip, Gregory Y H, Kirchhof, Paulus and Fabritz, Larissa (2019) Data-driven discovery and validation of circulating blood-based biomarkers associated with prevalent atrial fibrillation. European heart journal, 40 (16). pp. 1268-1276. ISSN 1522-9645. This article is available to all UHB staff and students via ASK Discovery tool http://tinyurl.com/z795c8c by using their UHB Athens login IDs.

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Official URL: https://academic.oup.com/eurheartj/article/40/16/1...

Abstract

AIMS

Undetected atrial fibrillation (AF) is a major health concern. Blood biomarkers associated with AF could simplify patient selection for screening and further inform ongoing research towards stratified prevention and treatment of AF.

METHODS AND RESULTS

Forty common cardiovascular biomarkers were quantified in 638 consecutive patients referred to hospital [mean ± standard deviation age 70 ± 12 years, 398 (62%) male, 294 (46%) with AF] with known AF or ≥2 CHA2DS2-VASc risk factors. Paroxysmal or silent AF was ruled out by 7-day ECG monitoring. Logistic regression with forward selection and machine learning algorithms were used to determine clinical risk factors, imaging parameters, and biomarkers associated with AF. Atrial fibrillation was significantly associated with age [bootstrapped odds ratio (OR) per year = 1.060, 95% confidence interval (1.04-1.10); P = 0.001], male sex [OR = 2.022 (1.28-3.56); P = 0.008], body mass index [BMI, OR per unit = 1.060 (1.02-1.12); P = 0.003], elevated brain natriuretic peptide [BNP, OR per fold change = 1.293 (1.11-1.63); P = 0.002], elevated fibroblast growth factor-23 [FGF-23, OR = 1.667 (1.36-2.34); P = 0.001], and reduced TNF-related apoptosis-induced ligand-receptor 2 [TRAIL-R2, OR = 0.242 (0.14-0.32); P = 0.001], but not other biomarkers. Biomarkers improved the prediction of AF compared with clinical risk factors alone (net reclassification improvement = 0.178; P < 0.001). Both logistic regression and machine learning predicted AF well during validation [area under the receiver-operator curve = 0.684 (0.62-0.75) and 0.697 (0.63-0.76), respectively].

CONCLUSION

Three simple clinical risk factors (age, sex, and BMI) and two biomarkers (elevated BNP and elevated FGF-23) identify patients with AF. Further research is warranted to elucidate FGF-23 dependent mechanisms of AF.

Item Type: Article
Additional Information: This article is available to all UHB staff and students via ASK Discovery tool http://tinyurl.com/z795c8c by using their UHB Athens login IDs.
Subjects: WG Cardiovascular system. Cardiology
Divisions: Emergency Services > Cardiology
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Depositing User: Mrs Noomi Tyholdt-Pidgley
Date Deposited: 19 Feb 2021 11:08
Last Modified: 19 Feb 2021 11:08
URI: http://www.repository.uhblibrary.co.uk/id/eprint/3996

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