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Principal Investigator: Andrew Francis Laine
Organization: COLUMBIA UNIVERSITY HEALTH SCIENCES
Fiscal Year: 2024
Award: $457,803
Funding agency: National Heart Lung and Blood Institute
Project Summary / Abstract:
Chronic obstructive pulmonary disease (COPD) defined by irreversible airflow limitation, is the 3rd leading cause
of death globally and 4th in the United States. Smoking tobacco is a major extrinsic COPD risk factor, but
despite six decades of declining smoking rates in many countries, the corresponding declines in COPD have
been modest. Only a minority of lifetime smokers develop COPD, and up to 25% occurs in never smokers.
While other factors have been linked to COPD much of the variation in COPD risk remains unexplained. In
addition, personalized risk and therapies are lacking for COPD, due to a lack of reliable COPD subphenotypes.
Airflow obstruction, or reduced airflow from the lungs, is determined in part by airway tree structure and lung
volume, both of which can be imaged with high precision by high resolution computed tomographic (HRCT)
scans. Emerging evidence by our group suggests that airway tree structure variation is common in the general
population and is a major contributor to this unexplained COPD risk. By manual labeling of the airway tree
structure, limited to one airway generation in just 2 of the 5 lung lobes (due to complexity of tree structure),
we found that 26% of the general population has major airway branch variants that differ from the classical
“textbook” structure, increase COPD risk, and have a strong and biologically plausible genetic basis. We further
demonstrated that airway tree caliber variation (dysanapsis) measured on CT was a stronger predictor of COPD
risk than all known risk factors including smoking. Yet there is no standardized approach to characterize the
full scope airway tree variation, making the exact relationship between COPD and individual airway-structure
features unclear. This proposal would apply for the first-time the power of machine learning methods to the
entire airway tree structure imaged on HRCT to build logically upon prior high-impact work to discover new
COPD subphenotypes for risk stratification and biological pathways of intervention.
Also, we will apply sophisticated / rigorous mathematical clustering approaches to airway trees derived from
over 18,000 computed tomography (CT) scans in three highly characterized NIH/NHLBI-funded cohorts – the
Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study, the Subpopulations and Intermediate Outcome
Measures in Chronic Obstructive Pulmonary Disease Study (SPIROMICS), and the Genetic Epidemiology of
COPD (COPDGene) Study, in addition to the Canadian Cohort of Obstructive Lung Disease (CanCOLD) – to
discover and replicate novel and clinically significant airway tree subtypes and their genetic basis.
The proposed study provides a transformative opportunity to define and validate normal and clinically relevant
tree variation in the general population and COPD cohorts. This research would result in robust, reproducible,
image based novel quantitative airway tree structure subtypes from lung CT scans, and understand their role in
COPD risk, prognosis, and their underlying genetic basis to help personalize COPD risk.
Terms: <3-D><3-Dimensional><3D><Active Follow-up><Activities of Daily Living><Activities of everyday life><Air Movements><Asthma><Biological><Bronchial Asthma><CAT scan><COPD><CT X Ray><CT Xray><CT imaging><CT scan><Caliber><Candidate Disease Gene><Candidate Gene><Cause of Death><Cessation of life><Chronic Obstruction Pulmonary Disease><Chronic Obstructive Lung Disease><Chronic Obstructive Pulmonary Disease><Complex><Computed Tomography><Country><Creativeness><Death><Detection><Development><Developmental Biology><Diagnostic tests><Diathesis><Dictionary><Disease Pathway><Disease susceptibility><Event><Exhibits><Fostering><Funding><GWA study><GWAS><General Population><General Public><General Radiology><Generations><Genetic><Grant><Health protection><High Resolution Computed Tomography><Image><Impairment><Individual><Intervention><Intervention Strategies><Investigation><Knowledge><Label><Link><Long-term Follow-up><Longterm Follow-up><Lung><Lung Respiratory System><Machine Learning><Manuals><Math><Mathematics><Measures><Minority><Mission><Modeling><Morphology><Multi-Ethnic Study of Atherosclerosis><NHLBI><NIH><National Heart, Lung, and Blood Institute><National Institutes of Health><Obstructive Lung Diseases><Occupational><Outcome Measure><Pathway interactions><Phenotype><Prognosis><Public Health><Publishing><QOL><Quality of life><Radiology><Radiology Specialty><Reproducibility><Research><Resolution><Respiratory Signs and Symptoms><Risk><Risk Factors><Risk-associated variant><Role><Scanning><Severities><Shapes><Smoker><Smoking><Spirometry><Standardization><Structure><Testing><Textbooks><Time><Tobacco><Tomodensitometry><Trees><United States><United States National Institutes of Health><Variant><Variation><Work><X-Ray CAT Scan><X-Ray Computed Tomography><X-Ray Computerized Tomography><Xray CAT scan><Xray Computed Tomography><Xray computerized tomography><active followup><air flow><airflow><airflow limitation><airflow obstruction><airway limitation><airway obstruction><airway symptom><biologic><catscan><chronic obstructive pulmonary disorder><cigarette smoking><cigarette use><clinical relevance><clinical significance><clinically relevant><clinically significant><cohort><computed axial tomography><computer tomography><computerized axial tomography><computerized tomography><cost><creativity><daily living function><daily living functionality><deep learning><deep learning method><deep learning strategy><detector><developmental><disease prognosis><disease prognostication><disease risk><disorder risk><environmental tobacco smoke><follow up><follow-up><followed up><followup><functional ability><functional capacity><generative models><genetic epidemiologic study><genetic epidemiology><genome wide association><genome wide association scan><genome wide association studies><genome wide association study><genomewide association scan><genomewide association studies><genomewide association study><high resolution CT><high standard><imaging><improved><innovate><innovation><innovative><interventional strategy><liability to disease><long-term followup><longterm followup><lung development><lung function decline><lung lobe><lung volume><machine based learning><machine learning based method><machine learning method><machine learning methodologies><measurable outcome><never smoker><non-contrast CT><noncontrast CT><noncontrast computed tomography><novel><obstructed airflow><obstructed airway><obstructive pulmonary diseases><outcome measurement><pathway><personalization of treatment><personalized medicine><personalized therapy><personalized treatment><pollutant><prognostic significance><pulmonary><pulmonary function decline><resolutions><respiratory><respiratory airway obstruction><respiratory symptom><risk allele><risk gene><risk genotype><risk loci><risk locus><risk stratification><risk variant><second-hand smoke><second-hand tobacco smoke><secondhand smoke><secondhand tobacco smoke><social role><stratify risk><supervised learning><supervised machine learning><three dimensional><tool><unsupervised learning><unsupervised machine learning><whole genome association analysis><whole genome association studies><whole genome association study>