Relationship of human paraoxonase-1 serum activity and genotype with atherosclerosis in individuals from the Deep South
Bibliographic record
Abstract
OBJECTIVE: Paraoxonase-1 (PON1) is synthesized in the liver and is bound to high-density lipoprotein particles in blood. PON1 protects against the development of atherosclerosis by metabolizing proatherogenic-oxidized lipids. The Southeastern USA (excluding Florida) has the country's highest age-adjusted mortality rate of cardiovascular disease. This study determines the association of PON1 status with atherosclerosis in individuals from the Southeastern USA. METHODS: Eighty African Americans (40 men, 40 women) and 120 Caucasians (60 men, 60 women) were enrolled from a cardiology practice in Northeastern Mississippi. Serum PON1 activities were determined using diazoxon, paraoxon, and phenyl acetate (PhAc) as substrates. The PON1(192) genotype of each individual was also determined. A multivariable logistic regression model was developed to identify the associations of clinical characteristics, serum PON1 activity, and PON1(192) genotype of the study population with atherosclerosis. RESULTS: A core model consisting of age, sex, history of smoking, hypertension, and low-density lipoprotein-cholesterol group was constructed. The maximum-rescaled generalized r(2) value for the core model was 0.35. Addition of PON1 activity assessed by PhAc hydrolysis was the only measure of PON1 enzymatic activity to add significant information to the core model (P=0.0317) with the maximum-rescaled generalized r(2) value increasing to 0.37. Increasing PON1 activity was associated with decreased odds of atherosclerosis. The PON1(192) genotype was not significantly associated with atherosclerosis. CONCLUSION: Increasing PON1 activity assessed by the hydrolysis of PhAc is associated with decreased odds of atherosclerosis in a group of African American and Caucasian Southerners.
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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 | 0.000 |
| 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) | 0.001 | 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".