Association of Serum Soluble Transferrin Receptor Concentration With Markers of Inflammation: Analysis of 1001 Patients From a Tertiary Rheumatology Center
Bibliographic record
Abstract
Objective Soluble transferrin receptor (sTfR) is considered to be a useful biomarker for the diagnosis of iron deficiency, especially in the setting of inflammation, as it is thought to not be affected by inflammation. We analyzed the relationship between sTfR levels and inflammatory markers in patients with known or suspected inflammatory rheumatic disease (IRD). Methods Blood samples of 1001 patients with known or suspected IRD referred to a tertiary rheumatology center were analyzed. Study participants were classified as patients with active IRD and patients with inactive IRD or without IRD. Correlation analyses were used to explore the relationship between sTfR levels and inflammatory markers (ie, C-reactive protein [CRP], erythrocyte sedimentation rate [ESR]). We applied multiple linear regression analysis to evaluate the predictive value of CRP levels for sTfR concentrations after adjustment for potential confounding factors. Results There were positive correlations between inflammatory markers (CRP, ESR) and serum sTfR levels (ρ 0.44, ρ 0.43, respectively; P < 0.001), exceeding the strength of correlation between inflammatory markers and the acute phase reactant ferritin (ρ 0.30, ρ 0.23, respectively; P < 0.001). Patients with active IRD demonstrated higher serum sTfR levels compared to patients with inactive or without IRD (mean 3.99 [SD 1.69] mg/L vs 3.31 [SD 1.57] mg/L; P < 0.001). After adjustment for potential confounding factors, CRP levels are predictive for serum sTfR concentrations ( P < 0.001). Conclusion The study provides evidence against the concept that sTfR is a biomarker not affected by inflammation.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".