Bibliographic record
Abstract
Welcome to this special issue of PPAR Research dedicated to the “Genetic Variation of PPARs.” Since PPARs are nuclear transcription factors regulating multiple genes involved in energy production, glucose and lipid metabolism, polymorphisms in these receptors may influence the pathology of numerous diseases including obesity, diabetes, atherosclerosis, inflammation and cancer. The first section of this special issue of PPAR Research contains a series of four original research articles followed by a review article examining the impact of PPAR gene polymorphisms on various metabolic diseases. First is an article by Deeb and Brunzell describing the impact of the Gly482Ser polymorphism in the PPARG coactivator-1 alpha (PPARGC1A) on weight gain in a diabetic population. A second article by Dallongeville and coworkers examines the association of PPARG gene polymorphisms with coronary heart disease. Third, Wieczorek's research group investigates the consequences of polymorphisms in RXRB, PPARA, and PPARG on Wegener's Granulomatosis. Fourth, a study by Ereqat et al. presents the results of an investigation of the impact of the PPARG Pro12Ala polymorphism on the metabolic and clinical characteristics in Palestinian type 2 diabetic patients. Finally this section ends with a review by Weimin He on the influence of the PPARG Pro12Ala polymorphism on insulin sensitivity, as well as other diseases including cancer, polycystic ovary syndrome, Alzheimer disease, and aging. We are also pleased that this special issue contains two articles that describe the functional effects of the PPAR gene polymorphisms. First, Rudkowska and co-researchers describe the differences in transcriptional activation observed in two allelic variants of PPARA (L162V) after omega-3 fatty acids treatment. Second, McCleelland et al. discern the regulation of translational efficiency by disparate 5′ UTRs of PPARG splice variants. These two articles lead to a more complete understanding of the role and functional repercussions of various PPAR gene polymorphisms in the prevention and treatment of diseases. In conclusion, while the influence and impact of PPAR polymorphisms on health and disease is still mostly uncertain, recent evidence suggests that these genetic variations play an important role in the initiation/progression of disease as well as in the efficacy of specific treatments in particular individuals/populations. We are fortunate to have received contributions from such well-renowned experts in the field, and hope that you will find that this special issue of PPAR Research produces greater interest in this critical and evolving field of research. Marie-Claude Vohl Mostafa Badr Stefan Wieczorek
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.043 | 0.013 |
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".