Downregulation of Tryptophan-related Metabolomic Profile in Rheumatoid Arthritis Synovial Fluid
Bibliographic record
Abstract
OBJECTIVE: Synovial fluid (SF) is one of the most important materials that reflect the pathophysiological process of arthritis. A metabolomic and lipidomic study of SF was performed with the aim of identifying tentative diagnostic markers or therapeutic candidates for rheumatoid arthritis (RA). METHODS: SF was aspirated from 10 patients with RA and 10 patients with osteoarthritis (OA). RA SF and OA SF were collected and analyzed by ultraperformance liquid chromatography quadruple time-of-flight mass spectrometry. Associations among clinical variables, laboratory results, and metabolic profiles were investigated. RESULTS: The metabolic pathways for carnitine, tryptophan, phenylalanine, arachidonic acid, and glycophospholipid were significantly upregulated in OA SF. The metabolic pathways for taurine, cholesterol ester, and the β-oxidation of pristine acid, linolenic acid, and sphingolipid were activated more in RA SF than in OA SF. In particular, the tryptophan pathway, which comprises kynurenine, indoleacetic acid, indole acetaldehyde, and N'-formylkynurenine, was downregulated. Interestingly, the levels of tryptophan metabolites kynurenine and N'-formylkynurenine, which are involved in immune tolerance, were significantly lower in RA SF compared with OA SF (p < 0.05), but the opposite pattern was observed for erythrocyte sedimentation rate (p < 0.01) and the levels of C-reactive protein (CRP; p < 0.01), rheumatoid factor (p < 0.01), and anticyclic citrullinated peptide antibody (p < 0.05). Kynurenine concentration correlated inversely with CRP concentration in RA SF but not in OA SF (r -0.65, p < 0.05). CONCLUSION: Advances in metabolomic techniques enabled us to delineate distinctive metabolic and lipidomic profiles in RA SF and OA SF. RA SF and OA SF showed distinct metabolic profiles.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".