Genetic and neurodevelopmental markers in schizophrenia-spectrum disorders: analysis of the combined role of the Cannabinoid Receptor 1 gene ( <i>CNR1</i> ) and dermatoglyphics
Bibliographic record
Abstract
ABSTRACT The aetiology of schizophrenia-spectrum disorders (SSD) involves genetic and environmental factors impacting neurodevelopmental trajectories. Dermatoglyphic pattern deviances have been associated with SSD and considered vulnerability markers for these disorders based on the shared ectodermal origin of the epidermis and the central nervous system. The endocannabinoid system participates in epidermal differentiation, is sensitive to the prenatal environment and is associated with SSD. We assessed whether the Cannabinoid receptor 1 ( CNR1 ) gene is a common denominator in dermatoglyphic pattern configurations and SSD risk and whether it modulates the dermatoglyphics-SSD association. In a sample of 112 controls and 97 SSD patients, three dermatoglyphic markers were assessed: the total palmar a-b ridge count (TABRC), the a-b ridge count fluctuating asymmetry (ABRC-FA), and the pattern intensity index (PII). Two CNR1 polymorphisms were genotyped: rs2023239-A/G and rs806379-A/T. We tested the CNR1 association with SSD and with the dermatoglyphic variability within diagnostic groups. Secondly, we assessed the CNR1 x dermatoglyphic measures interaction on SSD susceptibility. Both polymorphisms were associated with the risk for SSD, and within controls, rs2023239 and rs806379 modulated the PII and TABRC, respectively. Lastly, our data showed that rs2023239 modulated the relationship between PII and SSD: a high PII score was associated with a lower SSD risk within G-allele-carriers and a higher SSD risk within AA-homozygotes. These novel results highlight the endocannabinoid system’s role in the development and variability of dermatoglyphic patterns. The identified interaction encourages combining genetic and dermatoglyphics to assess neurodevelopmental alterations predisposing to SSD.
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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 | 0.001 | 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) | 0.003 | 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".