No Mediation Effect of Telomere Length or Mitochondrial DNA Copy Number on the Association Between Adverse Childhood Experiences (ACEs) and Central Arterial Stiffness
Bibliographic record
Abstract
Background Adverse childhood experiences (ACEs) have been linked to increased cardiovascular disease (CVD) risk. Previous reports have suggested that accelerated biological aging—indexed by telomere length (TL) and mitochondrial DNA copy number (mtDNAcn)—may contribute to associations between ACEs and cardiovascular health outcomes. Here, we examine the potential mediating effects of TL and mtDNAcn on the association between ACEs and central arterial stiffness—an intermediate cardiovascular health outcome—as a novel pathway linking ACEs to CVD risk among young adults. Methods and Results One hundred and eighty‐five (n=102 women; mean age, 22.5±1.5 years) individuals provided information on ACEs. TL (kb per diploid cell) and mtDNAcn (copies per diploid cell) were quantified using quantitative polymerase chain reaction techniques. Central arterial stiffness was measured as carotid‐femoral pulse wave velocity (cfPWV; m/s). Multiple linear regression analyses were used to examine the associations between ACEs, TL, mtDNAcn, and cfPWV. ACEs were positively associated with cfPWV ( β =0.147, P =0.035). TL ( β =−0.170, P =0.011) and mtDNAcn ( β =−0.159, P =0.019) were inversely associated with cfPWV. Neither TL ( β =−0.027, P =0.726) nor mtDNAcn ( β =0.038, P =0.620) was associated with ACEs. Neither marker mediated the association between ACEs and cfPWV. Conclusions An increasing number of ACEs were associated with a faster cfPWV and thus, a greater degree of central arterial stiffness. ACEs were not associated with either TL or mtDNAcn, suggesting that these markers do not represent a mediating pathway linking ACEs to central arterial stiffness.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".