Effects of paraoxonase activity and gene polymorphism on coronary vasomotion
Bibliographic record
Abstract
BACKGROUND: Paraoxonase 1 [PON1] is recognized as a protective enzyme against LDL oxidation, and PON1 polymorphism has been described as a factor influencing coronary heart disease [CHD] free survival. As coronary vasoreactivity is a surrogate of future cardiovascular events, we aimed at assessing the respective effect of the PON1 genotype and activity on coronary vasoreactivity in a population of type 2 diabetic patients. METHODS: Nineteen patients with type 2 diabetes mellitus underwent 82Rb cardiac PET/CT to quantify myocardial blood flow [MBF] at rest, during cold pressor testing [CPT], and during adenosine-induced hyperaemia to compute myocardial flow reserve [MFR]. They were allocated according to Q192R and L55M polymorphisms into three groups (wild-type and LM/QR heterozygotes, MM homozygotes, and RR homozygotes) and underwent a measurement of plasmatic PON1 activity. Relations between rest-MBF, stress-MBF, MFR, and MBF response to CPT and PON1 genotypes and PON1 activity were assessed using Spearman's correlation and multivariate linear regression analysis. RESULTS: Although PON1 activity was significantly associated with PON1 polymorphism (p < 0.0001), there was no significant relation between the PON1 genotypes and the rest-MBF, stress-MBF, or MBF response to CPT (p ≥ 0.33). The PON1 activity significantly correlated with the HDL plasma level (ρ = 0.63, p = 0.005), age (ρ = -0.52, p = 0.027), and MFR (ρ = 0.48, p = 0.044). Moreover, on multivariate analysis, PON1 activity was independently associated with MFR (p = 0.037). CONCLUSION: Our study supports an independent association between PON1 activity and MFR. Whether PON1 contributes to promote coronary vasoreactivity through its antioxidant activity remains to be elucidated. This putative mechanism could be the basis of the increased risk of CHD in patients with low PON1 activity.
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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".