Meta-Analysis on the Association Between the TF Gene rs1049296 and AD
Bibliographic record
Abstract
BACKGROUND: Polymorphisms of genes participating in iron transportation have been associated with Alzheimer's disease (AD) risk. The association between transferrin (TF) gene rs1049296 (P570S) polymorphism and AD is controversial. METHODS: We performed meta analysis on data from 19 studies with 6310 cases and 13661 controls to reexamine the association between the TF gene rs1049296 polymorphism and AD. We applied a fixed-effects model to combine the odds ratio (OR) and 95% confidence intervals (95% CI). Egger's test was carried out to evaluate the potential publication bias. RESULTS: The overall ORs with 95% CIs showed statistical association between the TF gene rs1049296 polymorphism and the risk of AD in the allele contrast, the recessive model and the dominant model for allele C2 (fixed-effects pooled OR 1.11; 95% CI 1.05 to 1.17, pooled OR 1.13; 95% CI 1.06 to 1.21, and pooled OR 1.23; 95% CI 1.03 to 1.47, respectively). In the contrast of C2C2+C2C1 vs C1C1, large heterogeneity among the Asian subgroup (p=0.041, I2= 68.6%) was observed but not among the overall population (p = 0.184, I2= 22.4%). No publication bias was observed. CONCLUSIONS: The present meta analysis demonstrated that TF gene rs1049296 polymorphism is a genetic determinant of AD.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.049 |
| 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) | 0.004 | 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".