Metabolomics Studies in Psoriatic Disease: A Review
Bibliographic record
Abstract
Metabolomics investigates a broad range of small molecules, allowing researchers to understand disease-related changes downstream of the genome and proteome in response to external environmental stimuli. It is an emerging technology that holds promise in identifying biomarkers and informing the practice of precision medicine. In this review, we summarize the studies that have examined endogenous metabolites in patients with psoriasis and/or psoriatic arthritis using nuclear magnetic resonance (NMR) or mass spectrometry (MS) and were published through 26 January 2021. A standardized protocol was used for extracting data from full-text articles identified by searching OVID Medline ALL, OVID Embase, OVID Cochrane Central Register of Controlled Trials and BIOSIS Citation Index in Web of Science. Thirty-two studies were identified, investigating various sample matrices and employing a wide variety of methods for each step of the metabolomics workflow. The vast majority of studies identified metabolites, mostly amino acids and lipids that may be associated with psoriasis diagnosis and activity. Further exploration is needed to identify and validate metabolomic biomarkers that can accurately and reliably predict which psoriasis patients will develop psoriatic arthritis, differentiate psoriatic arthritis patients from patients with other inflammatory arthritides and measure psoriatic arthritis activity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".