Systematic review and meta-analysis of the association between ABCA7 common variants and Alzheimer’s disease in non-Hispanic White and Asian cohorts
Bibliographic record
Abstract
Background and aims The relationship between the ABCA7 gene and Alzheimer’s disease (AD) has been widely studied across various populations. However, the results have been inconsistent. This meta-analysis aimed to evaluate the association of ABCA7 polymorphisms with AD risk, including specific subtypes such as late-onset Alzheimer’s disease (LOAD). Methods Relevant studies were identified through comprehensive database searches, and the quality of each study was assessed using the Newcastle-Ottawa Scale (NOS). Allele and genotype frequencies were extracted from the included studies. The pooled odds ratios (OR) with corresponding 95% confidence intervals (CI) were calculated using random-effects or fixed-effects models. Multiple testing corrections were conducted using the false discovery rate (FDR) method. The Cochran Q statistic and I2 metric were used to evaluate heterogeneity between studies, while Egger’s test and funnel plots were employed to assess publication bias. Results A total of 36 studies, covering 21 polymorphisms and involving 31,809 AD cases and 44,994 controls, were included in this meta-analysis. NOS scores ranged from 7 to 9, indicating high-quality studies. A total of 11 SNPs (rs3764650, rs3752246, rs4147929, rs3752232, rs3752243, rs3764645, rs4147934, rs200538373, rs4147914, rs4147915, and rs115550680) in ABCA7 were significantly associated with AD risk. Among these SNPs, two (rs3764650 and rs3752246) were also found to be related to the late-onset AD (LOAD) subtype. In addition, two SNPs (rs4147929 and rs4147934) were associated with the susceptibility to AD only in non-Hispanic White populations. A total of 10 SNPs (rs3764647, rs3752229, rs3752237, rs4147932, rs113809142, rs3745842, rs3752239, rs4147918, rs74176364, and rs117187003) showed no significant relationship with AD risk. Sensitivity analyses confirmed the reliability of the original results, and heterogeneity was largely attributed to deviations from Hardy–Weinberg equilibrium, ethnicity, and variations between individual studies. Conclusion The available evidence suggests that specific ABCA7 SNPs may be associated with AD risk. Future studies with larger sample sizes will be necessary to confirm these results. Systematic review registration https://www.crd.york.ac.uk/prospero/ , identifier: CRD42024540539.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.038 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".