Peripheral Lipid Signatures, Metabolic Dysfunction, and Pathophysiology in Schizophrenia Spectrum Disorders
Bibliographic record
Abstract
Metabolic dysfunction is commonly observed in schizophrenia spectrum disorders (SSDs). The causes of metabolic comorbidity in SSDs are complex and include intrinsic or biological factors linked to the disorder, which are compounded by antipsychotic (AP) medications. The exact mechanisms underlying SSD pathophysiology and AP-induced metabolic dysfunction are unknown, but dysregulated lipid metabolism may play a role. Lipidomics, which detects lipid metabolites in a biological sample, represents an analytical tool to examine lipid metabolism. This systematic review aims to determine peripheral lipid signatures that are dysregulated among individuals with SSDs (1) with minimal exposure to APs and (2) during AP treatment. To accomplish this goal, we searched MEDLINE, Embase, and PsychINFO databases in February 2024 to identify all full-text articles written in English where the authors conducted lipidomics in SSDs. Lipid signatures reported to significantly differ in SSDs compared to controls or in relation to AP treatment and the direction of dysregulation were extracted as outcomes. We identified 46 studies that met our inclusion criteria. Most of the lipid metabolites that significantly differed in minimally AP-treated patients vs. controls comprised glycerophospholipids, which were mostly downregulated. In the AP-treated group vs. controls, the significantly different metabolites were primarily fatty acyls, which were dysregulated in conflicting directions between studies. In the pre-to-post AP-treated patients, the most impacted metabolites were glycerophospholipids and fatty acyls, which were found to be primarily upregulated and conflicting, respectively. These lipid metabolites may contribute to SSD pathophysiology and metabolic dysfunction through various mechanisms, including the modulation of inflammation, cellular membrane permeability, and metabolic signaling pathways.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".