Response to micronized fenofibrate treatment is associated with the peroxisome???proliferator-activated receptors alpha G/C intron7 polymorphism in subjects with type 2 diabetes
Bibliographic record
Abstract
OBJECTIVE: The association between polymorphisms in candidate genes related to lipoprotein metabolism and the reduction in plasma triglyceride (TG) in response to fenofibrate treatment was evaluated in subjects with type 2 diabetes treated with micronized fenofibrate (200 mg/day) for at least 3 years in the Diabetes Atherosclerosis Intervention Study. METHODS: The cholesteryl ester transfer protein Taq1B, LPL S447X, hepatic lipase -514 C-->T, peroxisome-proliferator-activated receptors alpha (PPARA) L162V and G/C intron 7 polymorphisms and the apolipoprotein E2/E3/E4 alleles were genotyped using PCR and restriction enzyme digestion. Subjects were divided into high TG-responders (with > 30% TG relative reduction after treatment) and low TG-responders. RESULTS: The frequency of the PPARA intron 7 G/G genotype was higher in high TG-responders than in low TG-responders (85% vs. 69%, P < 0.05). There was no significant difference between the percentage of high TG-responders and low TG-responders for any of the other genetic polymorphisms examined. In stepwise logistic regression, baseline TG and only the PPARA intron 7 polymorphism among the others were selected in the model as significant predictors of TG-response (odds ratio: 3.10, 95% CI: 1.28-7.52, P = 0.012 for PPARA polymorphism). With age, gender, body mass index, smoking status and HbA1c as additional factors, baseline TG (P< 0.0001), intron 7 (P = 0.013), body mass index (P = 0.040) and LPL-S447X (P = 0.084) were significant predictors of TG-response. CONCLUSION: These results indicate that elevated baseline TG levels and PPARA gene intron 7 G/G genotype were associated with TG reduction > 30% after fenofibrate treatment in patients with type 2 diabetes.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".