Role of mtDNA Haplogroups in the Prevalence of Osteoarthritis in Different Geographic Populations: A Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Osteoarthritis (OA) is the most common form of arthritis and has become an increasingly important public-health problem. However, the pathogenesis of OA is still unclear. In recent years, its correlation with mtDNA haplogroups attracts much attention. We aimed to perform a meta-analysis to investigate the association between mtDNA haplogroups and OA. METHODS: Published English or Chinese literature from PubMed, Web of Science, SDOS, and CNKI was retrieved up until April 15, 2014. Case-control or cohort studies that detected the frequency of mtDNA haplogroups in OA patients and controls were included. The quality of the included studies was evaluated by the Newcastle-Ottawa Scale (NOS) assessment. A meta-analysis was conducted to calculate pooled odds ratio (OR) with 95% confidence interval (CI) through the random or fixed effect model, which was selected based on the between-study heterogeneity assessed by Q test and I2 test. Subgroup analysis was performed to explore the origin of heterogeneity. RESULTS: A total of 6 case-control studies (10590 cases and 7161 controls) with an average NOS score of 6.9 were involved. For the analysis between mtDNA haplogroup J and OA, random model was selected due to high heterogeneity. No significant association was found initially (OR = 0.73, 95%CI: 0.52-1.03), however, once any study from UK population was removed the association emerged. Further subgroup analysis demonstrated that there was a significant association in Spain population (OR = 0.57, 95%CI: 0.46-0.71), but not in UK population. Also, subgroup analysis revealed that there was a significant correlation between cluster TJ and OA in Spain population (OR = 0.70, 95%CI: 0.58-0.84), although not in UK population. No significant correlation was found between haplogroup T/cluster HV/cluster KU and OA. CONCLUSIONS: Our current meta-analysis suggests that mtDNA haplogroup J and cluster TJ correlate with the risk of OA in Spanish population, but the associations in other populations require further investigation.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.062 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".