ApoE and cholesterol in schizophrenia and bipolar disorder: comparison of grey and white matter and relation with APOE genotype
Bibliographic record
Abstract
BACKGROUND: Apolipoprotein E (apoE) and cholesterol play a critical role in synapse and myelin maintenance and integrity and are thus appealing candidates in the pathogenesis of schizophrenia and bipolar disorder. To explore the role of these 2 molecules, we quantified cholesterol and apoE levels in prefrontal grey and white matter in patients with schizophrenia, bipolar disorder and healthy controls. Furthermore, we investigated the relations between apoE and cholesterol levels and the APOE genotype. METHODS: We obtained dorsolateral prefrontal grey and white matter from the Stanley Medical Research Institute Brain Collection (schizophrenia n = 35, bipolar disorder n = 35 and controls n = 35). Cholesterol levels were quantified using high-pressure liquid chromatography, whereas apoE was measured by enzyme-linked immunosorbent assay. RESULTS: We found no significant differences in cholesterol or apoE levels among the groups. ApoE levels were higher in grey matter than in white matter in all groups; conversely, levels of cholesterol were higher in white matter than in grey matter. We observed a significant inverse correlation between apoE and cholesterol levels in both grey and white matter. Furthermore, in grey matter, apoE levels were significantly higher in APOE ε2 carriers compared with APOE ε3 or APOE ε4 carriers, with cholesterol levels following the opposite trend. LIMITATIONS: LIMITATIONS of our study include our inability to control for potential confounding variables and the small numbers of APOE ε2 and ε4 carriers in each group. CONCLUSION: Although large amounts of cholesterol are present in white matter, apoE expression is limited. The APOE genotype may play a role in the regulation of both cholesterol and apoE levels in grey matter. The impact of APOE polymorphisms on lipid homeostasis in people with psychiatric disorders warrants further investigation.
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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 | 0.001 |
| 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".