Research ExplorerPublicationsExploring the incremental utility of circulating biomarkers ...

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

Standard

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.

In: EUROPACE, Vol. 25, No. 3, 30.03.2023, p. 812-819.

Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review

Harvard

Toprak, B, Brandt, S, Brederecke, J, Gianfagna, F, Vishram-Nielsen, JKK, Ojeda, FM, Costanzo, S, Börschel, CS, Söderberg, S, Katsoularis, I, Camen, S, Vartiainen, E, Donati, MB, Kontto, J, Bobak, M, Mathiesen, EB, Linneberg, A, Koenig, W, Løchen, M-L, Di Castelnuovo, A, Blankenberg, S, de Gaetano, G, Kuulasmaa, K, Salomaa, V, Iacoviello, L, Niiranen, T, Zeller, T & Schnabel, RB 2023, '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', EUROPACE, vol. 25, no. 3, pp. 812-819. https://doi.org/10.1093/europace/euac260

APA

Toprak, B., Brandt, S., Brederecke, J., Gianfagna, F., Vishram-Nielsen, J. K. K., Ojeda, F. M., Costanzo, S., Börschel, C. S., Söderberg, S., Katsoularis, I., Camen, S., Vartiainen, E., Donati, M. B., Kontto, J., Bobak, M., Mathiesen, E. B., Linneberg, A., Koenig, W., Løchen, M-L., ... Schnabel, R. B. (2023). 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. EUROPACE, 25(3), 812-819. https://doi.org/10.1093/europace/euac260

Vancouver

Toprak B, Brandt S, Brederecke J, Gianfagna F, Vishram-Nielsen JKK, Ojeda FM et al. 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. EUROPACE. 2023 Mar 30;25(3):812-819. https://doi.org/10.1093/europace/euac260

Bibtex

@article{a434caa59e0543f191507db96e5019f6,
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",
abstract = "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.",
keywords = "Humans, Female, Middle Aged, Atrial Fibrillation/diagnosis, Risk Factors, Biomarkers, C-Reactive Protein/metabolism, Natriuretic Peptide, Brain, Inflammation, Peptide Fragments",
author = "Bet{\"u}l Toprak and Stephanie Brandt and Jan Brederecke and Francesco Gianfagna and Vishram-Nielsen, {Julie K K} and Ojeda, {Francisco M} and Simona Costanzo and B{\"o}rschel, {Christin S} and Stefan S{\"o}derberg and Ioannis Katsoularis and Stephan Camen and Erkki Vartiainen and Donati, {Maria Benedetta} and Jukka Kontto and Martin Bobak and Mathiesen, {Ellisiv B} and Allan Linneberg and Wolfgang Koenig and Maja-Lisa L{\o}chen and {Di Castelnuovo}, Augusto and Stefan Blankenberg and {de Gaetano}, Giovanni and Kari Kuulasmaa and Veikko Salomaa and Licia Iacoviello and Teemu Niiranen and Tanja Zeller and Schnabel, {Renate B}",
note = "{\textcopyright} The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology.",
year = "2023",
month = mar,
day = "30",
doi = "10.1093/europace/euac260",
language = "English",
volume = "25",
pages = "812--819",
journal = "EUROPACE",
issn = "1099-5129",
publisher = "Oxford University Press",
number = "3",

}

RIS

TY - JOUR

T1 - 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

AU - Toprak, Betül

AU - Brandt, Stephanie

AU - Brederecke, Jan

AU - Gianfagna, Francesco

AU - Vishram-Nielsen, Julie K K

AU - Ojeda, Francisco M

AU - Costanzo, Simona

AU - Börschel, Christin S

AU - Söderberg, Stefan

AU - Katsoularis, Ioannis

AU - Camen, Stephan

AU - Vartiainen, Erkki

AU - Donati, Maria Benedetta

AU - Kontto, Jukka

AU - Bobak, Martin

AU - Mathiesen, Ellisiv B

AU - Linneberg, Allan

AU - Koenig, Wolfgang

AU - Løchen, Maja-Lisa

AU - Di Castelnuovo, Augusto

AU - Blankenberg, Stefan

AU - de Gaetano, Giovanni

AU - Kuulasmaa, Kari

AU - Salomaa, Veikko

AU - Iacoviello, Licia

AU - Niiranen, Teemu

AU - Zeller, Tanja

AU - Schnabel, Renate B

N1 - © The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology.

PY - 2023/3/30

Y1 - 2023/3/30

N2 - 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.

AB - 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.

KW - Humans

KW - Female

KW - Middle Aged

KW - Atrial Fibrillation/diagnosis

KW - Risk Factors

KW - Biomarkers

KW - C-Reactive Protein/metabolism

KW - Natriuretic Peptide, Brain

KW - Inflammation

KW - Peptide Fragments

U2 - 10.1093/europace/euac260

DO - 10.1093/europace/euac260

M3 - SCORING: Journal article

C2 - 36610061

VL - 25

SP - 812

EP - 819

JO - EUROPACE

JF - EUROPACE

SN - 1099-5129

IS - 3

ER -