Relationship Between the Dynamics of Telomere Loss in Peripheral Blood Leukocytes From Knee Osteoarthritis Patients and Mitochondrial DNA Haplogroups
Bibliographic record
Abstract
OBJECTIVE: To evaluate the evolution of telomere length from peripheral blood leukocytes (PBLs) in subjects from the Osteoarthritis Initiative (OAI) cohort in relation to the incidence of osteoarthritis (OA), and to explore its possible interactive influence with the mitochondrial DNA (mtDNA) haplogroup. METHODS: Dynamics of telomere sequence loss were quantified in PBLs from initially healthy individuals (without symptoms or radiological signs), 78 carrying the mtDNA cluster HV, and 47 with cluster JT, from the OAI, during a 72-month follow-up period. The incidence of knee OA during this period (n = 39) was radiographically established when Kellgren-Lawrence (KL) score increased from < 2 at recruitment, to ≥ 2 at the end of 72 months of follow-up. Multivariate analysis using binary logistic regression was performed to assess PBL telomere loss and mtDNA haplogroups as associated risk factors of incidence of knee OA. RESULTS: Carriers of cluster HV showed knee OA incidence twice that of the JT carriers (n = 30 vs 9). The rate of PBL telomere loss was higher in cluster HV carriers and in individuals with incident knee OA. Multivariate analysis showed that the dynamics of PBL telomere shortening can be a consistent risk marker of knee OA incidence. Subjects with nonincident knee OA showed a slower telomere loss than those with incident knee OA; the difference was more significant in carriers of cluster JT than in HV. CONCLUSION: An increased rate of telomere loss in PBLs may reflect a systemic accelerated senescence phenotype that could be potentiated by the mitochondrial function, increasing the susceptibility of developing knee OA.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".