Related Experiment Video
Updated: Jun 18, 2026
Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
Published on: April 12, 2024
Integrative Phenogroups Based on Lung Imaging and Spirometry from Young Adulthood to Midlife and Associations With
Deepika Laddu1, Lucia Petito1, Xiaoning Jack Huang2
1Department of Preventive Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL.
Background:
Chronic lung disease and heart failure (HF) commonly co-occur, share modifiable risk factors, and are preceded by a prolonged, heterogeneous, and subclinical phase that is poorly defined.
Research Question:
Does the use of unsupervised machine learning identify distinct lung phenogroups, and are these phenogroups associated with cardiac structure and function?
Study Design And Methods:
Participants from the Coronary Artery Risk Development in Young Adults study who completed CT imaging of the lung, spirometry, and echocardiography were included. Gaussian mixture models were used to cluster 10 lung features from CT imaging and spirometry over 30 years into mutually exclusive phenogroups. Multivariable-adjusted linear and logistic regression estimated associations between lung phenogroups and cardiac structure and function parameters from year 30 echocardiograms.
Results:
Among 2,302 participants (mean [SD] age, 25.1 [3.6] years; 58% female; 44% Black race), 4 lung-heart phenogroups at year 30 were identified: (1) ideal, (2) emphysema-predominant with obstructive physiologic features, (3) mild interstitial or lung injury, and (4) substantial interstitial or lung injury with restrictive physiologic features. Compared with the ideal phenogroup, the substantial interstitial or lung injury group showed higher CT imaging-measured lung injury (39% vs 1%) and interstitial change (12% vs 0.6%), worse cardiac remodeling, including higher left ventricular mass or height (mean difference, 4.4 [95% CI, 2.8-6.1]) and global longitudinal strain (mean difference, 0.7% [95% CI, 0.2%-1.2%]), and higher odds of stage B heart failure (OR, 1.18 [95% CI, 1.08-1.29]; P < .05 for all). These findings were consistent among those who had never smoked.
Interpretation:
Our results show that machine learning identified 4 distinct lung phenogroups in midlife, each defined by diverse subclinical lung and associated with different patterns of cardiac remodeling. Early subclinical lung features are associated with adverse cardiac remodeling and may increase the risk of development of cardiopulmonary diseases.
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