A multidisciplinary approach to mental illness: do inflammation, telomere length and microbiota form a loop? A protocol for a cross-sectional study on the complex relationship between inflammation, telomere length, gut microbiota and psychiatric disorders
Bibliographic record
Abstract
INTRODUCTION: Severe psychiatric disorders are typically associated with a significant reduction in life expectancy compared with the general population. Among the different hypotheses formulated to explain this observation, accelerated ageing has been increasingly recognised as the main culprit. At the same time, telomere shortening is becoming widely accepted as a proxy molecular marker of ageing. The present study aims to fill a gap in the literature by better defining the complex interaction/s between inflammation, age-related comorbidities, telomere shortening and gut microbiota in psychiatric disorders. METHODS AND ANALYSIS: A cross-sectional study is proposed, recruiting 40 patients for each of three different diagnostic categories (bipolar disorder, schizophrenia and major depressive disorder) treated at the Section of Psychiatry and at the Unit of Clinical Pharmacology of the University Hospital Agency of Cagliari (Italy), compared with 40 age-matched and sex-matched non-psychiatric controls. Each group includes individuals suffering, or not, from age-related comorbidities, to account for the impact of these medical conditions on the biological make-up of recruited patients. The inflammatory state, microbiota composition and telomere length (TL) are assessed. ETHICS AND DISSEMINATION: The study protocol was approved by the Ethics Committee of the University Hospital Agency of Cagliari (PG/2018/11693, 5 September 2018). The study is conducted in accordance with the principles of good clinical practice and the Declaration of Helsinki, and in compliance with the relevant Italian national legislation. Written, informed consent is obtained from all participants. Participation in the study is on a voluntary basis only. Patients will be part of the dissemination phase of the study results, during which a local conference will be organised and families of patients will also be involved. Moreover, findings will be published in one or more research papers and presented at national and international conferences, in posters or oral communications.
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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 | 0.026 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.027 | 0.009 |
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".