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Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Determinants of ascending aortic morphology: Cross-sectional deep learning-based analysis on 25,073 non-contrast-enhanced NAKO MRI studies.
Eur. Heart J. Cardiovasc. Imaging 26, 895–907 (2025)
AIMS: Understanding determinants of thoracic aortic morphology is crucial for precise diagnostics and therapeutic approaches. This study aimed to automatically characterize ascending aortic morphology based on 3D non-contrast-enhanced magnetic resonance angiography (NC-MRA) data from the epidemiological cross-sectional German National Cohort (NAKO) and to investigate possible determinants of mid-ascending aortic diameter (mid-AAoD). METHODS AND RESULTS: Deep learning (DL) automatically segmented the thoracic aorta and ascending aortic length, volume, and diameter was extracted from 25,073 NC-MRAs. Statistical analyses investigated relationships between mid-AAoD and demographic factors, hypertension, diabetes, alcohol, and tobacco consumption. Males exhibited significantly larger mid-AAoD than females (M:35.5±4.8mm, F:33.3±4.5mm). Age and body surface area (BSA) were positively correlated with mid-AAoD (age: male: r²=0.20, p<0.001, female: r²=0.16, p<0.001; BSA: male: r²=0.08, p<0.001, female: r²=0.05, p<0.001). Hypertensive and diabetic subjects showed higher mid-AAoD (ΔHypertension = 2.9 ± 0.5mm; ΔDiabetes = 1.5 ± 0.6mm). Hypertension was linked to higher mid-AAoD regardless of age and BSA, while diabetes and mid-AAoD were uncorrelated across age-stratified subgroups. Daily alcohol consumption (male: 37.4±5.1mm, female: 35.0±4.8mm) and smoking history exceeding 16.5 pack-years (male: 36.6±5.0mm, female: 33.9±4.3mm) exhibited highest mid-AAoD. Causal analysis (Peter-Clark algorithm) suggested that age, BSA, hypertension, and alcohol consumption are possibly causally related to mid-AAoD, while diabetes and smoking are likely spuriously correlated. CONCLUSIONS: This study demonstrates the potential of DL and causal analysis for understanding ascending aortic morphology. By disentangling observed correlations using causal analysis, this approach identifies possible causal determinants, such as age, BSA, hypertension, and alcohol consumption. These findings can inform targeted diagnostics and preventive strategies, supporting clinical decision-making for cardiovascular health.
Impact Factor
Scopus SNIP
Altmetric
6.600
0.000
Anmerkungen
Besondere Publikation
Auf Hompepage verbergern
Publikationstyp
Artikel: Journalartikel
Dokumenttyp
Wissenschaftlicher Artikel
Schlagwörter
Thoracic Aorta ; Aortic Organ ; Automated Shape Analysis ; Causality ; Deep Learning ; Non-contrast-enhanced Magnetic Resonance Angiography; Cardiac Computed-tomography; Thoracic Aorta; Diameter; Age; Population
Sprache
englisch
Veröffentlichungsjahr
2025
HGF-Berichtsjahr
2025
ISSN (print) / ISBN
2047-2404
e-ISSN
2047-2412
Zeitschrift
European heart journal - cardiovascular imaging
Quellenangaben
Band: 26,
Heft: 5,
Seiten: 895–907
Verlag
Oxford University Press
Verlagsort
Oxford
Begutachtungsstatus
Peer reviewed
Institut(e)
Institute of Epidemiology (EPI)
POF Topic(s)
30202 - Environmental Health
Forschungsfeld(er)
Genetics and Epidemiology
PSP-Element(e)
G-504000-010
G-504000-006
G-504000-007
G-504000-006
G-504000-007
Förderungen
Deutsche Forschungsgemeinschaft (DFG)-German Research Foundation
WOS ID
001451234000001
Scopus ID
105004078406
PubMed ID
40052574
Erfassungsdatum
2025-05-06