Late Breaking Abstract - Novel Prognostic Biomarkers in Idiopathic Pulmonary Fibrosis: A Proteomic Study
Bibliographic record
Abstract
Background: Idiopathic pulmonary fibrosis (IPF) is characterized by progressive fibrosis and continuous deterioration in lung function and poor survival. Reliable prognostic biomarkers are needed. Some exist and are approaching clinical use, but further screening is needed to reflect the full complexity of the disease. Aim: To identify and validate circulating protein biomarkers that predict disease progression and increased risk of death. Methods: Serum samples from 9 healthy volunteers (HV) and 61 IPF patients enrolled in a German tertiary ILD referral center were analyzed using the OLINK Explore HT platform, covering 5,416 protein analytes. Statistical learning algorithms were applied to identify prognostics biomarkers and rank their ability to predict disease progression, defined by an absolute decline in forced vital capacity (FVC) ≥10 % or diffusing capacity (DLCO) ≥15%, and/or death over a mean follow-up of 2–28 months, with continued data collection. Results: We highlighted 472 proteins that distinguish IPF patients from HV (adj. pval < 0.01). Among the discriminating analytes, KRT19, SCGB3A2, BPIFB1 and AREG showed the largest effect sizes. Of these, AREG (adj. HR = 3.76, adj. pval = 0.04) and KRT19 (adj HR=3.24, adj. pval=0.04) were associated with mortality. Analogous analysis for progression-free survival and pathway analysis are ongoing. A comparative analysis with recently published studies is also planned. Conclusion: In this proteomic investigation of IPF outcomes, we highlighted several disease biomarkers associated with mortality, their role in disease progression is under investigation. These findings may support the refinement of existing biomarker panels for future clinical use.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".