Immune Cell Ratio Biomarkers of Non-type 2 Inflammation in COPD
Bibliographic record
Abstract
Abstract Background: Blood eosinophil count (BEC) has emerged as a clinically relevant biomarker of type 2 inflammation in COPD. However, only a minority of COPD patients have elevated blood eosinophils. The majority of COPD patients are characterized by non-type 2 inflammation, yet there are no available non-type 2 biomarkers. We hypothesized that readily obtainable immune cell ratios may serve as biomarkers for clinical phenotypes in COPD. Methods: Using complete blood counts (CBC) with differential collected from subjects at the Phase 2 (5-year visit) in the COPDGene Study, we calculated three immune cell ratios that have been previously described in COPD or other inflammatory diseases: the neutrophil-lymphocyte ratio (NLR; n=6004), the platelet-lymphocyte ratio (PLR; n=5999), and the Systemic Immune-Inflammation Index (SII, defined as NLR[asterisk]platelets; n=5999). We tested for associations with COPD outcomes, including lung function, chest CT phenotypes, and exacerbations. Results: In univariate analyses in all subjects and restricted to subjects with moderate to severe COPD (GOLD 2-4), all three ratios were negatively correlated with FEV1 %predicted and positively correlated with %emphysema (LAA950: low attenuation area at -950HU) on chest CT scans. NLR and SII, but not PLR, were correlated with airway wall thickness (Pi10: square root wall area of a hypothetical airway with 10mm internal perimeter). NLR and PLR were negatively correlated with BEC, but the correlations were weak (r = -0.03 and -0.07, respectively); SII was uncorrelated with BEC. In multivariable models adjusted for age, sex, race, current smoking, pack-years of smoking, and FEV1 %predicted, all three ratios were positively associated with exacerbation frequency (defined by use of antibiotics or systemic corticosteroids) and the presence of severe exacerbations (emergency department visit or hospitalization) in the past year, in all subjects and in subjects with COPD. PLR values showed the highest consistency between measurements at the 5-year and 10-year visit (correlation coefficients: PLR r=0.54, NLR r=0.34, SII r=0.35). Conclusions: The neutrophil-lymphocyte ratio, platelet-lymphocyte ratio, and Systemic Immune-Inflammation Index can be calculated from routine CBC with differential and were associated with lung function, emphysema, and COPD exacerbations. Further research is necessary to determine optimal cutoffs to allow for use as COPD biomarkers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".