Is there a mitochondrial DNA haplogroup connection between osteoarthritis and elite athletes? A narrative review
Bibliographic record
Abstract
Elite athletes are at greater risk of joint injuries linked to the subsequent risk of developing osteoarthritis (OA). Genetic factors such as mitochondrial (mt) DNA haplogroups have been associated with the incidence/progression of OA and athletic performance. This review highlights an area not yet addressed: is there a common pattern in the mtDNA haplogroups for OA occurrence in individuals and elite athletes of populations of the same descent? Haplotypes J and T confer a decreased risk of OA in Caucasian/European descent, while H and U increase this risk. Both J and T haplogroups are under-represented in Caucasian/European individuals and endurance athletes with OA, but power athletes showed a greater percentage of the J haplogroup. Caucasian/European endurance athletes had a higher percentage of haplogroup H, which is associated with increased athletic performance. In a Chinese population, haplogroup G appears to increase OA susceptibility and is over-represented in Japanese endurance athletes. In contrast, in Koreans, haplogroup B had a higher frequency of individuals with OA but was under-represented in the endurance athlete population. For Caucasian endurance athletes, it would be interesting to evaluate if those carrying haplotype H would be at an increased risk of accelerated OA, as well as the haplogroup G in Chinese and Japanese endurance athletes. The reverse might be studied for the Korean descent for haplogroup B. Knowledge of such genetic data could be used as a preliminary diagnosis to identify individuals at high risk of OA, adding prognostic information and assisting in personalising the early management of both populations.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".