Differential Relationships among Facets of Alexithymia and BDNF- and Dopamine-Related Polymorphisms
Bibliographic record
Abstract
Alexithymia refers to a cluster of emotion-related deficits such as difficulty attending to and identifying one’s feelings. Although not a diagnosable psychiatric condition, alexithymia is considered a personality risk factor for multiple pathologies, including somatoform, substance use, eating, and mood disorders. Evidence suggests heritability, but few studies have examined the influence of specific genes on alexithymic traits. Candidate genes explored thus far include those involved in modulation of brain-derived neurotrophic factor (BDNF) and dopamine, two neurotransmitters whose functions have been implicated in human emotion processing. This study investigated the relationship between the C270T polymorphism of the BDNF gene, facets of alexithymia, and possible interactions with the COMT, DAT1, and ANKK1 genes in a sample of 130 healthy adults. Given the multidimensionality of the alexithymia construct and its overlap with the related constructs of emotional intelligence and mood awareness, we used principal components analysis to derive Clarity of Emotion and Attention to Emotion as specific facets of alexithymia. Results showed that the C270T C/C genotype group had lower Clarity of Emotion scores relative to the C/T genotype group, even after covarying for COMT, DAT1, and ANKK1 genotypes. Dopamine-related genes had no association with alexithymia dimensions, nor did they interact with the C270T polymorphism to predict Clarity of Emotion. Although the molecular mechanisms by which this polymorphism influences BDNF are unknown, this study suggests a role for BDNF in modulating aspects of alexithymia. We discuss these results in the context of BDNF’s trophic effects in the nervous system.
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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 | 0.000 | 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) | 0.002 | 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".