Stress, Cortisol and NR3C1 in At-Risk Individuals for Psychosis: A Mendelian Randomization Study
Bibliographic record
Abstract
Introduction The emergence of psychosis in at-risk individuals results from interactions between genetic vulnerability and environmental factors, possibly involving dysregulation of the hypothalamic-pituitary-adrenal axis. Hypercorticism was indeed described in schizophrenia and ultra-high-risk states, but its association with clinical outcome has yet to be demonstrated. The impact of stress through cortisol may vary depending on the expression level of genes related to the stress pathway. Methods To test this hypothesis, we selected NR3C1, the gene encoding the glucocorticoid receptor, and modeled through logistic regression how its peripheral expression could explain some of the risk of psychosis, independently of peripheral cortisol levels, in a French longitudinal prospective cohort of 133 at-risk individuals, adjusted for sex, age, cannabis, and antipsychotic medication intake. We then performed a genome-wide association analysis, stratified by sex (55 females and 78 males), to identify NR3C1 expression quantitative trait loci to be used as instrumental variables in a Mendelian randomization framework. Results NR3C1 expression was significantly associated with a higher risk of conversion to psychosis (OR = 2.03, p = 0.03), independently of any other factor. Cortisol was not associated with outcome nor correlated with NR3C1. In the female subgroup, rs6849528 was associated both with NR3C1 mRNA levels (p = 0.015, Effect-Size = 2.7) and conversion (OR = 8.24, p = 0.03). Conclusions For the same level of cortisol, NR3C1 expression increases psychotic risk, independently of sex, age, cannabis, and antipsychotic intake. In females, Mendelian randomization confirmed NR3C1’s effect on outcome to be unbiased by any environmental confounder.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".