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Exploring the incremental utility of circulating biomarkers for robust risk prediction of incident atrial fibrillation in European cohorts using regressions and modern machine learning methods

AIMS: To identify robust circulating predictors for incident atrial fibrillation (AF) using classical regressions and machine learning (ML) techniques within a broad spectrum of candidate variables. METHODS AND RESULTS: In pooled European community cohorts (n = 42 280 individuals), 14 routinely avai...

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Autores principales: Toprak, Betül, Brandt, Stephanie, Brederecke, Jan, Gianfagna, Francesco, Vishram-Nielsen, Julie K K, Ojeda, Francisco M, Costanzo, Simona, Börschel, Christin S, Söderberg, Stefan, Katsoularis, Ioannis, Camen, Stephan, Vartiainen, Erkki, Donati, Maria Benedetta, Kontto, Jukka, Bobak, Martin, Mathiesen, Ellisiv B, Linneberg, Allan, Koenig, Wolfgang, Løchen, Maja-Lisa, Di Castelnuovo, Augusto, Blankenberg, Stefan, de Gaetano, Giovanni, Kuulasmaa, Kari, Salomaa, Veikko, Iacoviello, Licia, Niiranen, Teemu, Zeller, Tanja, Schnabel, Renate B
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10062370/
https://www.ncbi.nlm.nih.gov/pubmed/36610061
http://dx.doi.org/10.1093/europace/euac260
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author Toprak, Betül
Brandt, Stephanie
Brederecke, Jan
Gianfagna, Francesco
Vishram-Nielsen, Julie K K
Ojeda, Francisco M
Costanzo, Simona
Börschel, Christin S
Söderberg, Stefan
Katsoularis, Ioannis
Camen, Stephan
Vartiainen, Erkki
Donati, Maria Benedetta
Kontto, Jukka
Bobak, Martin
Mathiesen, Ellisiv B
Linneberg, Allan
Koenig, Wolfgang
Løchen, Maja-Lisa
Di Castelnuovo, Augusto
Blankenberg, Stefan
de Gaetano, Giovanni
Kuulasmaa, Kari
Salomaa, Veikko
Iacoviello, Licia
Niiranen, Teemu
Zeller, Tanja
Schnabel, Renate B
author_facet Toprak, Betül
Brandt, Stephanie
Brederecke, Jan
Gianfagna, Francesco
Vishram-Nielsen, Julie K K
Ojeda, Francisco M
Costanzo, Simona
Börschel, Christin S
Söderberg, Stefan
Katsoularis, Ioannis
Camen, Stephan
Vartiainen, Erkki
Donati, Maria Benedetta
Kontto, Jukka
Bobak, Martin
Mathiesen, Ellisiv B
Linneberg, Allan
Koenig, Wolfgang
Løchen, Maja-Lisa
Di Castelnuovo, Augusto
Blankenberg, Stefan
de Gaetano, Giovanni
Kuulasmaa, Kari
Salomaa, Veikko
Iacoviello, Licia
Niiranen, Teemu
Zeller, Tanja
Schnabel, Renate B
author_sort Toprak, Betül
collection PubMed
description AIMS: To identify robust circulating predictors for incident atrial fibrillation (AF) using classical regressions and machine learning (ML) techniques within a broad spectrum of candidate variables. METHODS AND RESULTS: In pooled European community cohorts (n = 42 280 individuals), 14 routinely available biomarkers mirroring distinct pathophysiological pathways including lipids, inflammation, renal, and myocardium-specific markers (N-terminal pro B-type natriuretic peptide [NT-proBNP], high-sensitivity troponin I [hsTnI]) were examined in relation to incident AF using Cox regressions and distinct ML methods. Of 42 280 individuals (21 843 women [51.7%]; median [interquartile range, IQR] age, 52.2 [42.7, 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 < 0.001]. Further, hsTnI [HR per SD, 1.18 (95% CI, 1.13–1.22); P < 0.001], cystatin C [HR per SD, 1.16 (95% CI, 1.10–1.23); P < 0.001], and C-reactive protein [HR per SD, 1.08 (95% CI, 1.02–1.14); P = 0.012] correlated positively with incident AF. Applying various ML techniques, a high inter-method consistency of selected candidate variables was observed. NT-proBNP was identified as the blood-based marker with the highest predictive value for incident AF. Relevant clinical predictors were age, the use of antihypertensive medication, and body mass index. CONCLUSION: Using different variable selection procedures including ML methods, NT-proBNP consistently remained the strongest blood-based predictor of incident AF and ranked before classical cardiovascular risk factors. The clinical benefit of these findings for identifying at-risk individuals for targeted AF screening needs to be elucidated and tested prospectively.
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spelling pubmed-100623702023-03-31 Exploring the incremental utility of circulating biomarkers for robust risk prediction of incident atrial fibrillation in European cohorts using regressions and modern machine learning methods Toprak, Betül Brandt, Stephanie Brederecke, Jan Gianfagna, Francesco Vishram-Nielsen, Julie K K Ojeda, Francisco M Costanzo, Simona Börschel, Christin S Söderberg, Stefan Katsoularis, Ioannis Camen, Stephan Vartiainen, Erkki Donati, Maria Benedetta Kontto, Jukka Bobak, Martin Mathiesen, Ellisiv B Linneberg, Allan Koenig, Wolfgang Løchen, Maja-Lisa Di Castelnuovo, Augusto Blankenberg, Stefan de Gaetano, Giovanni Kuulasmaa, Kari Salomaa, Veikko Iacoviello, Licia Niiranen, Teemu Zeller, Tanja Schnabel, Renate B Europace Clinical Research AIMS: To identify robust circulating predictors for incident atrial fibrillation (AF) using classical regressions and machine learning (ML) techniques within a broad spectrum of candidate variables. METHODS AND RESULTS: In pooled European community cohorts (n = 42 280 individuals), 14 routinely available biomarkers mirroring distinct pathophysiological pathways including lipids, inflammation, renal, and myocardium-specific markers (N-terminal pro B-type natriuretic peptide [NT-proBNP], high-sensitivity troponin I [hsTnI]) were examined in relation to incident AF using Cox regressions and distinct ML methods. Of 42 280 individuals (21 843 women [51.7%]; median [interquartile range, IQR] age, 52.2 [42.7, 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 < 0.001]. Further, hsTnI [HR per SD, 1.18 (95% CI, 1.13–1.22); P < 0.001], cystatin C [HR per SD, 1.16 (95% CI, 1.10–1.23); P < 0.001], and C-reactive protein [HR per SD, 1.08 (95% CI, 1.02–1.14); P = 0.012] correlated positively with incident AF. Applying various ML techniques, a high inter-method consistency of selected candidate variables was observed. NT-proBNP was identified as the blood-based marker with the highest predictive value for incident AF. Relevant clinical predictors were age, the use of antihypertensive medication, and body mass index. CONCLUSION: Using different variable selection procedures including ML methods, NT-proBNP consistently remained the strongest blood-based predictor of incident AF and ranked before classical cardiovascular risk factors. The clinical benefit of these findings for identifying at-risk individuals for targeted AF screening needs to be elucidated and tested prospectively. Oxford University Press 2023-01-04 /pmc/articles/PMC10062370/ /pubmed/36610061 http://dx.doi.org/10.1093/europace/euac260 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Clinical Research
Toprak, Betül
Brandt, Stephanie
Brederecke, Jan
Gianfagna, Francesco
Vishram-Nielsen, Julie K K
Ojeda, Francisco M
Costanzo, Simona
Börschel, Christin S
Söderberg, Stefan
Katsoularis, Ioannis
Camen, Stephan
Vartiainen, Erkki
Donati, Maria Benedetta
Kontto, Jukka
Bobak, Martin
Mathiesen, Ellisiv B
Linneberg, Allan
Koenig, Wolfgang
Løchen, Maja-Lisa
Di Castelnuovo, Augusto
Blankenberg, Stefan
de Gaetano, Giovanni
Kuulasmaa, Kari
Salomaa, Veikko
Iacoviello, Licia
Niiranen, Teemu
Zeller, Tanja
Schnabel, Renate B
Exploring the incremental utility of circulating biomarkers for robust risk prediction of incident atrial fibrillation in European cohorts using regressions and modern machine learning methods
title Exploring the incremental utility of circulating biomarkers for robust risk prediction of incident atrial fibrillation in European cohorts using regressions and modern machine learning methods
title_full Exploring the incremental utility of circulating biomarkers for robust risk prediction of incident atrial fibrillation in European cohorts using regressions and modern machine learning methods
title_fullStr Exploring the incremental utility of circulating biomarkers for robust risk prediction of incident atrial fibrillation in European cohorts using regressions and modern machine learning methods
title_full_unstemmed Exploring the incremental utility of circulating biomarkers for robust risk prediction of incident atrial fibrillation in European cohorts using regressions and modern machine learning methods
title_short Exploring the incremental utility of circulating biomarkers for robust risk prediction of incident atrial fibrillation in European cohorts using regressions and modern machine learning methods
title_sort exploring the incremental utility of circulating biomarkers for robust risk prediction of incident atrial fibrillation in european cohorts using regressions and modern machine learning methods
topic Clinical Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10062370/
https://www.ncbi.nlm.nih.gov/pubmed/36610061
http://dx.doi.org/10.1093/europace/euac260
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