APOE E4 is associated with hyperlipidemia and obesity in elderly schizophrenic patients
Bibliographic record
Abstract
Obesity is a critical issue in patients with schizophrenia, which is considered to be brought about by both environmental and genetic factors. Apolipoprotein E (APOE) gene polymorphisms might be involved in the pathogenesis of schizophrenia, however, the effect of APOE gene polymorphism on obesity has never been investigated in Chinese aging with schizophrenia. This cross-sectional study was to investigate the effect of obesity on cognitive and psychiatric symptoms in elderly participants with schizophrenia. At the same time, we also discussed the inner link between APOE E4 and obesity. 301 elderly participants with schizophrenia and 156 normal controls were included in the study. Their cognitive function was assessed using the Montreal Cognitive Assessment (MoCA), psychiatric symptoms were assessed using the Positive and Negative Syndrome Scale (PANSS), and APOE gene polymorphism was determined by polymerase chain reaction (PCR). The prevalence of obesity in elderly schizophrenic patients and healthy controls accounted for 15.9% (48/301) and 10.3% (16/156), respectively, with no statistically significant difference. By using stepwise linear regression analysis, we found that elevated fasting blood glucose, hypertension, and hyperlipidemia were risk factors for obesity in elderly schizophrenic patients. Although there was no direct correlation between APOE E4 and obesity in patients with schizophrenia, it was significantly correlated with hyperlipemia(r = - 0.154, p = 0.008), suggesting that APOE E4 may induce obesity in elderly patients with schizophrenia through hyperlipemia, However, the above conclusions do not apply to the normal elderly. What's more, we did not find a link between obesity and cognitive function or mental symptoms for both patients with schizophrenia and normal controls. APOE E4 is associated with hyperlipidemia in elderly schizophrenic patients, which may be a risk factor for obesity, however, the above conclusion does not apply to the normal elderly.
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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 | 0.001 | 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) | 0.002 | 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".