Serum metabolic profiling using small molecular water‐soluble metabolites in individuals with schizophrenia: A longitudinal study using a pre–post‐treatment design
Bibliographic record
Abstract
AIM: We sought to compare alterations in serum bioenergetic markers within a well-characterized sample of adults with schizophrenia at baseline and after 8 weeks of pharmacological treatment with the hypothesis that treatment would be associated with significant changes in bioenergetic markers given the role of bioenergetic dysfunction in schizophrenia. METHODS: We recruited adults with schizophrenia (n = 122) who had not received pharmacological treatment for at least 1 month prior to enrollment, including drug-naïve (i.e., first-episode) participants and treatment non-adherent participants. Pre- and post-treatment serum samples were analyzed using liquid chromatography-tandem mass spectrometry. RESULTS: Metabolites with the greatest change, when comparing pre- and post-treatment levels, were identified revealing 14 water-soluble metabolites of interest. The composition of these metabolites was: amino acids (n = 6), carnitines (n = 4), polar lipids (n = 3), and organic acid (n = 1). All amino acids and lysophosphatidylcholines (LysoPC) were increased, while the four carnitines - oleoylcarnitine, L-palmitoylcarnitine, linoleyl carnitine, and L-acetylcarnitine - were decreased post-treatment. Of these metabolite biomarkers, six - oleoylcarnitine, linoleyl carnitine, L-acetylcarnitine, LysoPC(15:0), D-glutamic acid, and L-arginine - were identified as having most consistently and predictably changed after 8 weeks of treatment. CONCLUSION: The current study identified several bioenergetic markers that consistently change with pharmacological treatment. These bioenergetic changes may provide further insights into the pathophysiology of schizophrenia along with furthering our understanding of the mechanisms subserving both the effects (e.g., antipsychotic effects) and side-effects (e.g., metabolic syndrome) of antipsychotics.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".