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Record W4403501526 · doi:10.3389/fnagi.2024.1406573

Systematic review and meta-analysis of the association between ABCA7 common variants and Alzheimer’s disease in non-Hispanic White and Asian cohorts

2024· review· en· W4403501526 on OpenAlexaboutno aff
Da Liu, Hongwei Zhang, Liu Cao, Jianyu Liu, Yan Liu, Na Bai, Qiang Zhou, Zhiyao Xu, Linyan Li, Hua Liu

Bibliographic record

VenueFrontiers in Aging Neuroscience · 2024
Typereview
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMeta-analysisSingle-nucleotide polymorphismPublication biasOdds ratioOncologyFunnel plotConfidence intervalInternal medicineMedicineGenotypeBiologyGeneticsGene

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.018
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.033
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0180.038
Bibliometrics0.0100.011
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.060
GPT teacher head0.361
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

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