Is the Gly82Ser polymorphism in the <i>RAGE</i> gene relevant to schizophrenia and the personality trait psychoticism?
Bibliographic record
Abstract
BACKGROUND: The receptor for advanced glycation end products (RAGE) is the main receptor for S100B, an astrogial proinflammatory mediator that has been suggested to be involved in the pathophysiology of schizophrenia. To further elucidate the possible relevance of inflammation for mental functions, we investigated a functional polymorphism in the gene coding for RAGE in relation to personality traits and susceptibility to schizophrenia. METHODS: We studied the Gly82Ser polymorphism (rs2070600, 244G>A) in 2 population-based cohorts of middle-aged participants assessed using the Karolinska Scales of Personality. In addition, we compared genotype frequencies between patients with schizophrenia and controls. RESULTS: The population-based cohorts included 270 women and 247 men, and the case-control study involved 138 patients with schizophrenia and 258 controls. In the population-based cohorts, 82Ser carriers were found to have significantly higher scores for the psychoticism personality trait comprising the detachment and suspicion subscales. The case-control study revealed that the 82Ser allele was significantly more frequent among patients than controls. LIMITATIONS: This study was limited by the modest sample size and the use of a self-report measure to assess personality traits. CONCLUSION: Our findings suggest that the proven relation between certain personality traits and schizophrenia can at least to some extent be explained on a genetic level. Also, the activated S100B-RAGE axis may be an underlying cause, not only a consequence, of the disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".