A Meta-Analysis on the Relationship of the PON Genes and Alzheimer Disease
Bibliographic record
Abstract
AIM: This study aimed to evaluate the association of the paraoxonase (PON) gene variants and Alzheimer disease (AD) using meta-analysis. METHODS: Relevant studies were identified by searching English and Chinese databases extensively. Allele and genotype frequencies for each included study were extracted. Newcastle-Ottawa Scale (NOS) was employed to assess the quality of included studies. The odds ratio (OR) with 95% confidence interval (95% CI) was calculated using a random-effects or fixed-effects model. A Q statistic was used to evaluate homogeneity, and Egger test and funnel plot were used to assess publication bias. RESULTS: A total of 15 studies (involving 5 polymorphisms) were included and identified for the current meta-analysis. The NOS scores ranged from 7 to 8, meaning good quality of studies. It was found that the SS genotype of PON2 S311C polymorphism had an significant association with AD in the studied population (OR = 0.82, 95% CI: 0.68-0.99, P = .04), and the A allele of PON1 rs705379 polymorphism was positively related to AD in Caucasian population (OR = 1.21, 95% CI: 1.05-1.39, P = .009) as well as the GG genotype decreased AD risk significantly in Caucasians (OR = 0.7, 95% CI: 0.56-0.88, P = .002). However, there was no significant relationship between other 3 genetic variants of PON genes (L55 M, Q192 R, and -161C/T of PON1 gene) and AD. CONCLUSION: Existing evidence indicates that the S311C polymorphism (SS genotype) and the rs705379 (the A allele and GG genotype) are associated with risk of AD in studied population. Future studies with larger sample sizes will be necessary to confirm the present results.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.048 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".