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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.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Thoracic Aorta ; Aortic Organ ; Automated Shape Analysis ; Causality ; Deep Learning ; Non-contrast-enhanced Magnetic Resonance Angiography; Cardiac Computed-tomography; Thoracic Aorta; Diameter; Age; Population
Language
english
Publication Year
2025
HGF-reported in Year
2025
ISSN (print) / ISBN
2047-2404
e-ISSN
2047-2412
Quellenangaben
Volume: 26,
Issue: 5,
Pages: 895–907
Publisher
Oxford University Press
Publishing Place
Oxford
Reviewing status
Peer reviewed
Institute(s)
Institute of Epidemiology (EPI)
POF-Topic(s)
30202 - Environmental Health
Research field(s)
Genetics and Epidemiology
PSP Element(s)
G-504000-010
G-504000-006
G-504000-007
G-504000-006
G-504000-007
Grants
Deutsche Forschungsgemeinschaft (DFG)-German Research Foundation
WOS ID
001451234000001
Scopus ID
105004078406
PubMed ID
40052574
Erfassungsdatum
2025-05-06