Childhood maltreatment and telomere length in men and women with and without CAD : mediating and moderating role of psychological distress and cardiac autonomic responses
Bibliographic record
Abstract
Childhood maltreatment has long been recognized as a significant risk factor for various psychological and physical health issues throughout the lifespan, including coronary artery disease (CAD). Mechanisms leading to increased risk of CAD or other physical conditions are likely diverse and may involve changes at the cellular level, such as altered telomere length (TL). Telomeres refers to the protective caps at the ends of chromosomes, which shorten with each cell division. Shorter telomeres are often associated with cellular aging and an increased risk of age related diseases. While certain factors contribute to maintaining telomere length, other factors, such as childhood maltreatment have been associated with shorter TL. Prior to this thesis, little was known about the factors underlying or influencing this relationship. This thesis specifically investigated the association between childhood maltreatment and leukocyte telomere length (LTL), along with the mediating effects of psychological and psychophysiological correlates of emotional dysregulation. Additionally, it explored potential moderating effects of these factors, as well as the moderating effects of sex and health status. In addition to confirming that the association between childhood maltreatment and LTL was present among older individuals with chronic health conditions, results suggest that the experience of greater perceived chronic stress among individuals having experienced maltreatment in childhood may contribute to the LTL attrition. Further, the relation between childhood maltreatment and LTL may differ as a function of the integrity of the participants’ parasympathetic nervous system. More specifically, childhood maltreatment predicted shorter LTL only among those individuals who showed less vagal control of the heart in resting state, as well as in those who showed blunted or conversely increased vagal activation in response to stress. Finally, there was some limited evidence to suggest that sex and health status may influence the results. The theoretical and clinical implications of these findings are discussed in detail in the final section of this thesis, Future directions are also provided.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".