Serum Hepcidin: A Direct Link Between Anemia of Inflammation and Coronary Artery Atherosclerosis in Patients with Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To investigate the role of hepcidin as an inducer of anemia of inflammation in patients with rheumatoid arthritis (RA), and its correlation to coronary artery atherosclerosis. METHODS: Our study included 60 patients with RA and 20 healthy controls. Anemic RA patients with serum transferrin receptors/log ferritin (sTfR-F) index value < 1.5 were classified as having pure anemia of chronic disease (ACD), and patients with sTfR-F index value > 1.5 were classified as having anemia of chronic disease with coexistent iron deficiency anemia (ACD+IDA). Measurements were taken for Disease Activity Score for 28 joints (DAS28), Modified Health Assessment Questionnaire (MHAQ), erythrocyte sedimentation rate (ESR), high sensitivity C-reactive protein (hsCRP), rheumatoid factor (RF), lipid profile, serum interleukin 6 (IL-6), tumor necrosis factor-α, iron studies, and serum hepcidin. Coronary calcium score (CCS) was measured using multislice spiral computed tomography as a marker of atherosclerosis. RESULTS: Serum hepcidin was found to be higher in anemic patients with RA than in controls (p < 0.001), and higher in the pure ACD subgroup than in the ACD+IDA subgroup (p < 0.001). Hepcidin concentration was positively correlated with disease duration, ESR, hsCRP, RF, DAS28, MHAQ, serum ferritin, IL-6, and mean CCS and inversely correlated with hemoglobin, sTfR, and the sTfR-F index. CONCLUSION: Hepcidin can be considered a key inducer of anemia of inflammation in patients with RA. This inflammation was proved to be directly linked to coronary artery atherosclerosis. The correlations between serum hepcidin with disease activity and IL-6 raise the possibility of using it as a surrogate marker for disease activity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".