IL-8 correlates with reduced baseline femoral neck bone mineral density in adults with cystic fibrosis: a single center retrospective study
Bibliographic record
Abstract
Abstract Cystic fibrosis (CF) is a multi-system disease that is characterized by lung disease due to recurrent airway infection and inflammation. Endocrine complications, such as CF bone disease (CFBD), are increasingly identified as patients are living longer. The cause of CFBD is multifactorial with chronic systemic inflammation theorized to be a contributing factor. Thus, we attempted to identify inflammatory biomarkers that are associated with CFBD. We conducted a retrospective observational study of 56 adult patients with CF with an average percentage predictive forced expiratory volume in one second (ppFEV1) of 73.7% (standard deviation: 30.0) who underwent baseline serum analysis for osteoprotegerin (OPG) and pro-inflammatory biomarkers (IL-1β, IL-6, IL-8 and TNF-α), and had repeated dual-energy x-ray absorptiometry (DXA) scans separated by at least 2 years to examine correlations between serum biomarkers and bone mineral density (BMD) measurements. Univariate linear regression model analysis demonstrated that serum IL-1β and IL-8, but not other pro-inflammatory markers, were negatively correlated with baseline BMD results. However, after accounting for confounding variables, only the relationship between IL-8 and left femoral neck BMD remained statistically significant. Additionally, IL-8 level was associated with BMD decline over time. These results suggest that IL-8 might play a unique role in the pathophysiology of CFBD relative to other pro-inflammatory cytokines but further study is warranted before firm conclusions can be made.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".