Paraoxonase 1 Polymorphisms 172T→A and 584A→G Modify the Association between Serum Concentrations of the Antioxidant Lycopene and Bone Turnover Markers and Oxidative Stress Parameters in Women 25–70 Years of Age
Bibliographic record
Abstract
BACKGROUND/AIMS: Polymorphisms of the paraoxonase 1 (PON1) enzyme affect the ability to protect LDL from oxidation. Oxidative stress is a risk factor for osteoporosis and antioxidants may be beneficial for prevention. The aim of this study was to determine whether PON1 genotypes modified the association between lycopene and bone turnover markers and oxidative stress parameters. METHODS: Blood samples from 107 women 25-70 years of age were analyzed for serum carotenoid concentrations, bone-specific alkaline phosphatase (BAP), N-telopeptide of type I collagen (NTx) and oxidative stress parameters. Subjects were genotyped for the 172T→A and 584A→G polymorphisms of PON1. RESULTS: The 172T→A polymorphism modified the association between lycopene and NTx (p < 0.05 for interaction). In the 172TT genotype, high serum lycopene was associated with decreased NTx (p < 0.05). The 584A→G polymorphism modified the association between lycopene and BAP (p < 0.05 for interaction). Additionally, in participants with the 584GG genotype, high serum lycopene was associated with high TBA-reactive substances (p < 0.05). CONCLUSIONS: These findings show that PON1 polymorphisms modify the association between serum concentrations of lycopene and oxidative stress parameters and bone turnover markers and may, therefore, moderate the risk of osteoporosis.
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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) | 0.002 | 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".