Cytokine Gene Polymorphisms and Parkinson's Disease: A Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Cytokines, which are involved in immunological responses, play and important role in the development and progression of Parkinson's disease (PD). The functional polymorphisms identified in cytokine genes are thought to influence PD risk. However the findings of studies investigating the association between cytokine gene polymorphisms and PD risk are still controversial. Therefore, we conducted a meta-analysis, in order to investigate the potential associations between cytokine gene polymorphisms and PD. METHODS: Studies of PD and cytokine polymorphisms were identified by searches of PubMed and PDGene. Pooled analyses were performed to assess the association between cytokine gene polymorphisms and PD. RESULTS: Our results indicated a positive association of TNFα -1031 CC genotype in overall analysis(CC vs. TT: OR=3.146; 95%CI: 1.631-6.070, p=0.008; CC vs. CT+TT: OR=3.187: 95%CI: 1.657-6.128,p=0.008), and an Asian subgroup, C variant(OR=1.328; 95%CI: 1.053-1.675, p=0.034) also conveyed an increased PD risk as well as CC genotype ( CC vs. TT: OR=3.207; 95%CI: 1.614-6.373, p=0.004; CC vs. CT+TT: OR=3.238; 95%CI: 1.636-6.410, p=0.004). A decreased risk for PD was associated with IL-6-174C allele (OR=0.761; 95%CI: 0.641-0.903, p=0.008) and IL-1RA VNTR 2 allele(OR=0.641; 95%CI: 0.456-0.826 p=0.004). For the polymorphisms of IL-1β C[-511]T, IL-1α C[-889]T , TNFα G[-308]A, and IL-10 G[-1082]A no significant association was found between the gene polymorphisms and PD risk. CONCLUSIONS: Our meta-analysis suggested that gene polymorphisms of TNFα -1031, IL-6-174 and IL-1RA VNTR may be associated with PD risk. However, more large well-designed studies will be necessary to validate our findings.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.038 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 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".