Association of Brain‐derived Neurotrophic Factor Polymorphisms With Alcohol Use Disorder: An Updated Meta‐Analysis of Genetic Association Studies
Bibliographic record
Abstract
ABSTRACT Background Brain‐derived neurotrophic factor (BDNF) has been proposed to play a role in chronic alcohol consumption. However, studies investigating the association of single nucleotide polymorphisms (SNPs) in the BDNF gene with alcohol use disorder (AUD), including alcohol dependence, have obtained inconsistent results. This meta‐analysis aims to examine the role of BDNF SNPs (rs6265, rs16917204, rs7103411, and rs11030104) in the risk of AUD. Materials and Methods A multidatabase search identified 17 articles (20 studies) for inclusion. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to estimate associations using standard genetic models (homozygous, recessive, dominant, and codominant). Significant associations were defined as those with a p ‐value ≤ 0.05 after applying the Bonferroni correction ( p BC ). Subgroup analysis was conducted based on ethnicity (Caucasian and Asian populations). Sources of heterogeneity were investigated through outlier treatment and meta‐regression analysis. Only significant outcomes were further subjected to sensitivity analysis and assessment of publication bias. Results This meta‐analysis generated four significant pooled ORs, representing the core outcomes, all of which indicated reduced risks. Overall, the results indicated a significant association between the BDNF polymorphism and the risk of AUD in homozygous (OR = 0.72, 95% CIs = 0.60–0.85, p BC = 0.0038) and codominant (OR = 0.84, 95% CIs = 0.78–0.91, p BC = 0.0019) model. In subgroup analysis by ethnicity, homozygous (OR = 0.59, 95% CIs = 0.44–0.78, p BC = 0.0057) and recessive (OR = 0.61, 95% CIs = 0.46–0.81, p BC = 0.0133) models of BDNF polymorphisms were significantly associated with a reduced risk of AUD in Caucasians. However, no significant associations were found in Asians. Meta‐regression analysis did not identify any covariates that significantly contributed to the observed heterogeneity. The core significant associations were robust and showed no evidence of publication bias. Conclusion The current meta‐analysis suggests that the examined BDNF SNPs have a protective effect in the overall analysis (homozygous and codominant) and in the Caucasians subgroup (homozygous and recessive) while the Asians exhibited no effects of BDNF SNPs on AUD. BDNF polymorphisms might serve as a protective factor against the risk of AUD and could be useful markers in the clinical genetics of AUD.
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 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 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.044 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".