BMI-related Genetic Factors and COPD Imaging Phenotypes
Bibliographic record
Abstract
Abstract Background While low body mass index (BMI) is associated with emphysema and obesity is associated with airway disease in chronic obstructive pulmonary disease (COPD), the underlying mechanisms are unclear. Methods We aggregated genetic variants from population-based genome-wide association studies to generate a polygenic score of BMI (PGS BMI ). We calculated this score for participants from COPD-enriched and community-based cohorts and examined associations with automated quantification and visual interpretation of computed tomographic emphysema and airway wall thickness (AWT). We summarized the results using meta-analysis. Results In the random-effects meta-analyses combining results of all cohorts (n=16,349), a standard deviation increase of the PGS BMI was associated with less emphysema as quantified by log-transformed percent of low attenuation areas ≤ 950 Hounsfield units (β= -0.062, p <0.0001) and 15 th percentile value of lung density histogram (β=2.27, p <0.0001), and increased AWT as quantified by the square root of wall area of a 10-mm lumen perimeter airway (β=0.016, p =0.0006) and mean segmental bronchial wall area percent (β=0.26, p =0.0013). For imaging characteristics assessed by visual interpretation, a higher PGS BMI was associated with reduced emphysema in both COPD-enriched cohorts (OR for a higher severity grade=0.89, p =0.0080) and in the community-based Framingham Heart Study (OR for the presence of emphysema=0.82, p =0.0034), and a higher risk of airway wall thickening in the COPDGene study (OR=1.17, p =0.0023). Conclusions In individuals with and without COPD, a higher body mass index polygenic risk is associated with both quantitative and visual decreased emphysema and increased AWT, suggesting genetic determinants of BMI affect both emphysema and airway wall thickening.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".