Mood Disorders, Accelerated Aging, and Inflammation: Is the Link Hidden in Telomeres?
Bibliographic record
Abstract
Mood disorders are associated with an increased risk of aging-related diseases, which greatly contribute to the excess morbidity and mortality observed in affected individuals. Clinical and molecular findings also suggest that mood disorders might be characterized by a permanent state of low-grade inflammation. At the cellular level, aging translates into telomeres shortening. Intriguingly, inflammation and telomere shortening show a bidirectional association: a pro-inflammatory state seems to contribute to aging and telomere dysfunction, and telomere attrition is able to induce low-grade inflammation. Several independent studies have reported shorter telomere length and increased levels of circulating inflammatory cytokines in mood disorders, suggesting a complex interplay between altered inflammatory⁻immune responses and telomere dynamics in the etiopathogenesis of these disorders. In this review, we critically discuss studies investigating the role of telomere attrition and inflammation in the pathogenesis and course of mood disorders, and in pharmacological treatments with psychotropic medications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".