Telomere biology and its maintenance in schizophrenia spectrum disorders: Exploring links to cognition
Bibliographic record
Abstract
OBJECTIVE: Contemporary research suggests reduced telomere length in schizophrenia spectrum disorders (SZ) compared to age-adjusted non-affected individuals. However, the role of telomere maintenance and telomere repair in SZ is poorly understood as well as the involvement of telomere biology in cognitive abnormalities in SZ. METHODS: The study consisted of 758 participants (SZ [n = 357] and healthy controls, HC [n = 401]) collected as part of the Norwegian TOP study. Participants were assessed with standardized neuropsychological tests measuring five cognitive domains. Leucocyte telomere length (TL) was measured via blood and determined by quantitative real-time Polymerase Chain Reaction (qPCR) providing a telomere to single copy ratio (T/S ratio), used to estimate the mean telomere length. Telomerase activity was assessed by the expression levels of the Telomerase Reverse Transcriptase (TERT) and Telomerase RNA Component (TERC) genes. To assess telomere maintenance and telomere repair we calculated the telomerase expression to TL ratio (TERT/TL and TERC/TL respectively). RESULTS: Patients had reduced TERT (F = 5.03, p = 0.03), but not TERC expression (F = 1.04, p = 0.31), and higher TERT/TL (F = 6.68, p = 0.01) and TERC/TL (F = 6.71, p = 0.01), adjusted for age, sex, and ethnicity. No statistically significant association was observed between any of the telomere biology markers and the cognitive domains (p > 0.05). CONCLUSION: Our study shows changes in TERT expression and telomere maintenance and telomere repair in SZ compared HC. However, the role of telomere biology in the mechanism underlying cognitive impairment in psychosis seems limited.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".