Pharmacological interventions and telomere length in patients with schizophrenia and bipolar disorder: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Patients with schizophrenia and bipolar disorder have a life expectancy shorter than the general population. Cellular mechanisms underlying accelerated ageing, such as telomere shortening, may contribute to this premature mortality. We aimed to provide a comprehensive evaluation of the impact of pharmacological treatments for schizophrenia and bipolar disorder on telomere length. METHOD: PRISMA/MOOSE systematic review and meta-analysis from inception to June 2024. PubMed, Cochrane Library, SCOPUS, Web of Science, Embase and PsycInfo databases were searched for eligible studies. Methodological quality and risk of bias were evaluated with the Newcastle-Ottawa Scale and the Risk of Bias In Non-randomized Studies - of Exposure (ROBINS-E) respectively. RESULTS: An initial search retrieved 2133 articles. However, only 28 studies were finally included in qualitative synthesis and 19 in meta-analysis. All studies identified in the review were observational. Random-effects model analysis was used to quantify the difference in telomere length between cohorts of patients with schizophrenia or bipolar disorder and healthy control groups. The meta-analysis confirmed that telomere length was shorter in patients with schizophrenia (SMD = 0.35, 95 % CI 0.11 to 0.60; p=<0.0001) and bipolar disorder (SMD = 0.18, 95 % CI -0.04 to 0.39 p=<0.0001) than in healthy controls. This difference was not modified by predominant treatment with either lithium (SMD = 0.37, 95 % CI 0.04 to 0.69; p=<0.0001) or antipsychotics (SMD = 0.20, 95 % CI 0.02 to 0.38; p=<0.0001) at cohort level across studies. CONCLUSIONS: Patients with schizophrenia or bipolar disorder have shorter telomeres than healthy populations. Predominant treatment with lithium or antipsychotics at cohort level did not have an impact on such shortening difference. REGISTRATION: PROSPERO CRD42024598840.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.035 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".