PPAR-gamma gene polymorphisms and psoriatic arthritis.
Bibliographic record
Abstract
OBJECTIVE: Peroxisome proliferator-activated receptor-gamma (PPAR-gamma) activation has been shown to play a role in suppressing angiogenesis and inflammation, both important pathological features of psoriatic arthritis (PsA). Given the potential physiological role for PPAR-gamma in PsA, we examined known coding polymorphisms in the PPAR-gamma gene in a Caucasian population. METHODS: PsA was diagnosed as an inflammatory arthritis in patients with psoriasis, in the absence of other etiologies for inflammatory arthritis. Control subjects were ascertained from the same population and were all Caucasian. DNA samples were genotyped for 4 PPAR-gamma variants by time-of-flight mass spectrometry using the Sequenom platform. All 4 single-nucleotide polymorphisms (SNP) were previously-reported coding variations, 3 of which caused an amino acid change: Pro12Ala (rs1801282), Pro40Ala (rs1805192), and Pro115Gln (rs1800571); the fourth SNP, C161T (rs3856806), was synonymous. All primers were designed using Sequenom SpectroDesigner software, and scanned using a mass spectrometry workstation. RESULTS: Of the 4 SNP examined, Pro40Ala and Pro115Gln were found to be nonpolymorphic in our population. Minor allele frequency for patients with PsA and controls for Pro12Ala (G) were 9.0% vs 13.8% (p = 0.017) and for C161T (T) 10.7% vs 12.0% (p = 0.56), respectively. All genotypes satisfied Hardy-Weinberg equilibrium. CONCLUSION: An association between PsA and a known coding SNP of the PPAR-gamma gene was observed in our Caucasian population. Further studies are now warranted for validation of our findings in an independent cohort.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".