Exploring the association between brain-derived neurotrophic factor (BDNF) levels and longitudinal psychopathological and cognitive changes in Sardinian psychotic patients
Bibliographic record
Abstract
Introduction Schizophrenia spectrum disorders are among the most debilitating mental disorders and evidence on its pathophysiological underpinnings is scant. The brain-derived neurotrophic factor (BDNF) appears to be involved in the pathophysiology of these complex psychiatric disorders. Objectives The present study investigates the longitudinal variation of serum BDNF levels in a 24-month observational cohort study of Sardinian psychotic patients (LABSP). This study assessed the variation in BDNF serum levels and its relationship with psychopathological and cognitive changes. Further, we also examined if genetic variations within the BDNF gene could moderate these relationships. Methods Every six months 105 LABSP patients were assessed for their BDNF serum levels, as well as for a series of psychopathological, cognitive, and drug-related measures. Four tag single nucleotide polymorphisms (SNPs) within the BDNF gene were selected and analyzed using Polymerase Chain Reaction (PCR). Longitudinal data were analyzed using mixed-effects linear regression models (MLRM). Results Analysis showed significantly lower peripheral BDNF levels in psychotic patients with depressive and negative symptoms. BDNF levels were also decreased in patients scoring lower in cognitive measures such as symbol coding and semantic fluency. In addition, Val66Met polymorphism within the BDNF gene significantly moderated the relationship between the severity of negative symptoms and BDNF levels. Conclusions Our findings are consistent with previous literature suggesting that peripheral BDNF levels are associated with some cognitive domains and mood disruption in major psychosis. The results also suggest the lack of association between most BDNF genetic variants, except Val66Met polymorphism, with the severity of negative symptoms. Disclosure No significant relationships.
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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 | 0.000 |
| 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) | 0.001 | 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".