Brain glucose and ketone metabolism in first-episode psychosis: Neuroimaging and brain metabolism before and after antipsychotic treatment: The protocol for the CAST-ATP study
Bibliographic record
Abstract
First episode of psychosis (FEP) has an early onset and is associated with significant functional impairment, loss of productivity and premature cardiovascular disease. Antipsychotics (AP) remain the cornerstone treatment of FEP yet they fail to improve key symptom domains and contribute to the metabolic burden of this disorder. A growing body of evidence suggests that a metabolic deficit in the brain, specifically of glucose, at the earliest stages of illness could represent an etiopathological phenotype of FEP. Correcting this metabolic deficit could improve outcomes and disease course. The acronym for this study is CAST-ATP for the collaboration between our clinical research sites in Copenhagen, Aarhus, Sherbrooke and Toronto, on the subject of Antipsychotic (AP) treatment, PET and Psychosis. The main aims of CAST-ATP are to evaluate the effect of 1) a diagnosis of FEP, and, 2) 4-6 weeks of AP treatment on brain energy metabolism measured by PET scans (uptake of ketones and glucose). The hypothesis is that (i) glucose metabolism will be impaired in AP-naïve patients as compared to healthy controls, and (ii) this defect will be worsened by AP. In contrast, across the two aims, brain ketone metabolism is predicted to not be significantly influenced by FEP or AP treatment. Participants on both sites will undergo an imaging protocol (PET scans + MRI) in addition to measures of psychopathology and related peripheral metabolic, inflammatory and hormonal markers. If our hypothesis is confirmed, it will reinforce the strategy to leverage ketone supplementation to improve symptoms, functioning and quality of life by bypassing the brain glucose deficit in FEP. As such, this should be a significant therapeutic development. To this last point, the pharmaceutical treatment of schizophrenia spectrum disorders has not progressed beyond currently available AP for over seven decades.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 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".