The impacts of biologic treatment on metabolic profiling in psoriasis
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Bibliographic record
Abstract
Psoriasis is an immune-mediated inflammatory disease commonly accompanied by various metabolic disorders. It is widely known that biologics could affect the metabolic status and comorbidities in psoriasis patients, however, the effects of biologics on metabolism in psoriasis patients remain poorly understood. The aim of this study was to elucidate the characteristic changes of metabolic profiling in psoriasis vulgaris (PsV) patients before and after applying biologics. Plasma samples were collected from a retrospective cohort of 43 PsV patients. Non-targeted metabolomics analyses were performed using liquid chromatography-mass spectrometry (LC-MS) to compare the metabolic profiles before and after applying adalimumab (ADA) or ixekizumab (IXE) for 4 weeks. Additionally, correlation analyses were conducted to investigate the associations between metabolite expression levels and clinical characteristics. The biologics significantly affected the metabolic profiles of PsV patients especially in glycerophospholipids (GPs). First, phosphatidylcholine (PC), unsaturated lysophosphatidylcholine (LPC), unsaturated lysophosphatidic acid (LPA) and unsaturated lysophosphatidylethanolamine (LPE) were significantly up-regulated, whereas phosphatidylethanolamine (PE), saturated LPC, saturated LPA and saturated LPE were predominantly down-regulated after biologic treatment. What is more, the changes in PE and LPA were mainly observed after applying IXE instead of ADA. Second, we also found GPs including PC, unsaturated LPC, unsaturated LPA and unsaturated LPE were primarily negatively correlated with disease severity, whereas, PE, saturated LPC, saturated LPA and saturated LPE displayed inverse correlations. Biologics could affect GP metabolism and facilitate the transition of metabolic status from a pro-inflammatory to an anti-inflammatory phenotype in PsV patients.
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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) | 0.000 | 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 it