Serum levels of neuropeptide Y in patients with chronic schizophrenia during treatment augmentation with sarcosine (results of the double‐blind randomized controlled PULSAR study)
Bibliographic record
Abstract
OBJECTIVE: Modulation of glutamatergic neurotransmission in schizophrenia by sarcosine leads to a reduction in primary negative symptoms, while its metabolic profile is safe. In order to extend research in the area, we assessed serum levels of neuropeptide Y (NPY), a hypothalamic hormone related to anxiety and depression, also involved in mechanisms inducing weight gain. Additionally, we analyzed associations between NPY concentrations and its changes with severity of symptoms and metabolic parameters. METHODS: A prospective 6-month, randomized, double-blind placebo-controlled trial was completed by 57 subjects with chronic schizophrenia with predominant negative symptoms and stable antipsychotic treatment. The participants received 2 g of sarcosine (n = 28) or placebo (n = 29) daily. We assessed serum NPY concentrations and severity of symptoms (with the Positive and Negative Syndrome Scale [PANSS] and Calgary Depression Scale for Schizophrenia) at the beginning of the study, after 6 weeks and 6 months. RESULTS: Sarcosine did not affect NPY levels in all time points. The highest decrease in NPY concentrations was observed in the subjects who were initially depressed, who became euthymic at the last visit. We noticed an improvement in the total PANSS score, and negative symptom and general psychopathology subscales in the sarcosine group, however, without any correlation with NPY levels. CONCLUSION: The use of sarcosine does not change NPY levels. Peripheral NPY concentrations may be related to depressive symptoms in schizophrenia.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".