Circulating fibronectin to C-reactive protein ratio and mortality: a biomarker in COPD?
Bibliographic record
Abstract
The balance between inflammatory and repair processes is important in maintaining lung homeostasis in chronic obstructive pulmonary disease (COPD). The aim of the present study was to determine whether or not an integrated index of a biomarker involved in inflammation, C-reactive protein (CRP), and another involved in wound repair, fibronectin, may be a good measure to predict clinical outcomes in COPD. Circulating blood levels of CRP and fibronectin were measured in 4,787 individuals with mild-to-moderate COPD who were prospectively followed for >7 yrs after blood collection as part of the Lung Health Study. To assess the balance between repair and inflammation, a simple ratio was calculated by dividing fibronectin levels by CRP levels and a Cox proportional hazards model was used to determine the relationship between this ratio and all-cause and disease-specific causes of mortality. The relationship between the fibronectin to CRP ratio and all-cause mortality was L-shaped. There was an exponential decay in the adjusted hazard function (i.e. the risk of mortality) as the ratio decreased until a value of 148 was reached, beyond which point the hazard function did not change significantly. Similar results were observed for the risk of coronary and cardiovascular mortality. Circulating fibronectin to CRP ratio is significantly associated with all-cause mortality of COPD patients. However, in contrast to other biomarkers, the relationship appears to be L-shaped (and not linear), suggesting a threshold at approximately 150. While promising, future studies are needed to validate this simple index as a biomarker in COPD.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".