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, Jahrgang 25, Nr. 3, 30.03.2023, S. 812-819.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
Harvard
APA
Vancouver
Bibtex
}
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 -