Low-Blood Lymphocyte Number and Lymphocyte Decline as Key Factors in COPD Outcomes: A Longitudinal Cohort Study
Bibliographic record
Abstract
BACKGROUND: Smokers with and without chronic obstructive pulmonary disease (COPD) are at risk of severe outcomes like exacerbations, cancer, respiratory failure, and decreased survival. The mechanisms for these outcomes are unclear; however, there is evidence that blood lymphocytes (BL) number might play a role. OBJECTIVE: The objective of this study is to investigate the relationship between BL and their possible decline over time with long-term outcomes in smokers with and without COPD. METHODS: In 511 smokers, 302 with COPD (COPD) and 209 without COPD (noCOPD), followed long term, we investigated whether BL number and BL decline over time might be associated with long-term outcomes. Smokers were divided according to BL number in high-BL (≥1,800 cells/µL) and low-BL (<1,800 cells/µL). Clinical features, cancer incidence, and mortality were recorded during follow-up. BL count in multiple samples and BL decline over time were calculated and related to outcomes. RESULTS: BL count was lower in COPD (1,880 cells/µL) than noCOPD (2,300 cells/µL; p < 0.001). 43% of COPD and 23% of noCOPD had low-BL count (p < 0.001). BL decline over time was higher in COPD than noCOPD (p = 0.040). 22.5% of the whole cohort developed cancer which incidence was higher in low-BL subjects and in BL decliners than high-BL (31 vs. 18%; p = 0.001) and no decliners (32 vs. 19%; p = 0.002). 26% in the cohort died during follow-up. Furthermore, low-BL count, BL decline, and age were independent risk factors for mortality by Cox regression analysis. CONCLUSION: BL count and BL decline are related to worse outcomes in smokers with and without COPD, which suggests that BL count and decline might play a mechanistic role in outcomes deterioration. Insights into mechanisms inducing the fall in BL count could improve the understanding of COPD pathogenesis and point toward new therapeutic measures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".