Premature Aging Among Trauma Survivors—The Longitudinal Implications of Sleep Disruptions on Telomere Length and Cognitive Performance
Bibliographic record
Abstract
OBJECTIVES: Sleep is necessary for brain function as well as physical and cognitive processes. Sleep disruptions, common with aging, intensify among trauma survivors. Moreover, former prisoners-of-war (ex-POWs) often experience premature aging. This study investigates the longitudinal effects of sleep disruptions for ex-POWs in relation to cognitive performance and telomere length as well as between cognition and telomeres. METHOD: This study included Israeli veterans from the 1973 Yom Kippur War who participated in four assessments (1991, 2003, 2008, 2015): (a) ex-POWs (n = 99), and (b) veterans who not were captured (controls) (n = 101). Among both groups, sleep disruptions were assessed using a self-report item in all four assessments. Cognitive performance was assessed using the Montreal Cognitive Assessment (MOCA) and telomere length was assessed via total white blood cells (leukocytes) from whole blood samples using Southern blot, both were measured only among ex-POWs in 2015. We conducted descriptive statistics, repeated measures, correlations, and path analyses. RESULTS: Sleep disruptions were related to lower cognitive performance but not to shorter telomeres. Moreover, cognitive performance and telomere length were found to be related when sleep disruptions were taken into consideration. CONCLUSION: Interpersonal trauma was shown to be a unique experience resulting in sleep disruptions over time, leading to cognitive impairment. These findings highlight the importance of viewing trauma survivors at high-risk for sleep disruptions. Therefore, it is imperative to inquire about sleep and diagnose cognitive disorders to help identify and treat premature aging.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".