Muscle Mitochondrial DNA Copy Number, Deletion Mutation Frequency, and Physical Performance in Older Adults
Bibliographic record
Abstract
Abstract Mitochondrial DNA (mtDNA) quantity and quality influence hallmarks of aging – mitochondrial dysfunction and genomic instability. The interactions between mtDNA quantity and quality and physical performance have not been extensively examined in humans. The aim of this study was to test the interactions between skeletal muscle mtDNA copy number, mtDNA deletion mutation frequency, and physical performance measures in older adults. Total DNA was isolated from muscle biopsies and used for quantitation of mtDNA copy number and mutation frequency by digital PCR. The biopsies were obtained from a cross-sectional cohort of 53 adults aged 50 to 86 years. Before the biopsy, physical performance measures were collected. MtDNA deletions increased exponentially with advancing age. On average, mtDNA deletion frequency increased 18-fold between 50 and 80, with a trend toward lower deletion frequency in females. MtDNA deletion frequency predicted declines in VO2 max, where 4.7% of the variation in VO2 max was explained by mtDNA deletion frequency. MtDNA copy number was negatively correlated with age and mtDNA deletion frequency, but positively correlated with lean mass. There was a trend to lower mtDNA deletion frequency in females, consistent with increased longevity in females. Larger studies may better delineate sex effects. These data are consistent with a role for mitochondrial function and genome integrity in the maintenance of physical performance with age. Analyses of mtDNA quality and quantity in longitudinal studies could extend our understanding of the importance of mitochondria in human aging and longevity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".