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 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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".