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CT Airway-to-Lung Ratio Modifies the Impact of Indoor Air Pollution on Cardiac Autonomic Function in COPD

2025· article· en· W4410268143 on OpenAlexaff
Sarath Raju, Kirsten Koehler, B.M. Smith, Han Woo, Ashraf Fawzy, A. Balasubramanian, N. Putcha, N.N. Hansel, M.C. Mccormack

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineLung functionAirwayCOPDAutonomic functionLungCardiologyAir pollutionIntensive care medicineInternal medicineHeart rate variabilityAnesthesiaHeart rateBlood pressure

Abstract

fetched live from OpenAlex

Abstract Rationale: Indoor air pollution remains a major contributor to cardiovascular and respiratory morbidity in COPD, however the impact of air pollution exposure is not borne equally across populations. Studies suggest that low airway-to-lung ratio(ALR), a CT measure of dysanapsis (mismatch of airway tree caliber to lung size), conveys susceptibility to the respiratory impacts of air pollution. There is limited data surrounding whether ALR alters susceptibility to the cardiovascular effects of indoor pollutants. We aimed to describe associations between indoor air pollution and cardiac autonomic function, assessed as heart rate variability(HRV), and association modification by ALR. Measures of HRV, particularly the RMSSD, are tied to cardiovascular and respiratory morbidity in COPD. Methods: Former smokers with moderate-to-severe obstruction were recruited from a 6-month indoor air cleaner intervention trial to undergo additional cardiovascular characterization. ALR was calculated from chest CT scans as mean of airway lumen diameters divided by lung volume. Participants completed weeklong indoor PM2.5 and NO2 measurement and concomitant 24-hr Holter monitoring(assessing HRV) at up to 5 time points. HRV measures included: SDNN, RMSDD, High Frequency[HF] and Low Frequency[LF]. Previous studies demonstrated that an air cleaner intervention improved RMSDD, HF, and LF. Linear mixed models with interactions between pollutants and ALR, were used to assess effects of indoor pollutants on HRV and modification by ALR, after adjustment for demographics, poverty, FEV1, cardiovascular comorbidities and anti-arrhythmic use. Pollutants and HRV measures were log transformed with effects described per two-fold pollutant increase. Results: 48 participants with CT and HRV measures contributed 148 observations. In adjusted models, participants with low ALR(25th-percentile v 75th-percentile) experienced greater PM2.5- and NO2-associated decreases in HRV as measured by RMSSD, HF and LF power(Figure). Increases in PM2.5 were associated with a significant decrease in RMSDD (-6.3%; P=0.017) among those with low ALR not observed among those with high ALR (1.3%; P=0.55)(P-interaction=0.008). Similarly, ALR modified the impact of NO2, with NO2 associated with decreased RMSSD only among those with low ALR (-15.7 %; P-int=0.018). For SDNN, no ALR-related modification was observed. Conclusion: Our results suggest that airway morphology modifies the impact of indoor pollution on cardiovascular health in COPD, demonstrating that those with lower airway-to-lung ratio had a stronger association between indoor air pollution and reduced heart rate variability. CT airway measures, including ALR, may help to identify those facing a greater burden of multi-morbidity related to indoor pollution and may benefit the most from in-home air quality interventions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.338
Teacher spread0.324 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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