Serum Immunoglobulin G Levels Are Associated with Risk for Exacerbations: An Analysis of SPIROMICS
Bibliographic record
Abstract
Abstract Rationale Serum IgG deficiency is associated with morbidity in chronic obstructive pulmonary disease (COPD), but it is unclear whether concentrations in the lower end of the normal range still confer risk. Objectives To determine if levels above traditional cutoffs for serum IgG deficiency are associated with exacerbations among current and former smokers with or at risk for COPD. Methods Former and current smokers in SPIROMICS (the Subpopulations and Intermediate Outcome Measures of COPD study) (n = 1,497) were studied: 1,026 with COPD and 471 at risk for COPD. In a subset (n = 1,031), IgG subclasses were measured. Associations between total IgG or subclasses and prospective exacerbations were evaluated with multivariable models adjusting for demographic characteristics, current smoking, smoking history, FEV1 percent predicted, inhaled corticosteroids, and serum IgA. Measurements and Main Results The 35th percentile (1,225 mg/dl in this cohort) of IgG was the best cutoff by Akaike information criterion. Below this, there was increased exacerbation risk (incidence rate ratio [IRR], 1.28; 95% confidence interval [CI], 1.08–1.51). Among subclasses, IgG1 and IgG2 below the 35th percentile (354 and 105 mg/dl, respectively) were associated with increased risks of severe exacerbation (IgG1, IRR, 1.39; 95% CI, 1.06–1.84; IgG2, IRR, 1.50; 95% CI, 1.14–1.1.97). These associations remained significant when additionally adjusting for a history of exacerbations. Conclusions Lower serum IgG is prospectively associated with exacerbations in individuals with or at risk for COPD. Among subclasses, lower IgG1 and IgG2 are prospectively associated with severe exacerbations. The optimal IgG cutoff was substantially higher than traditional cutoffs for deficiency, suggesting that subtle impairment of humoral immunity may be associated with exacerbations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".