IL-18 Serum Levels and Variants of the Serotonin Transporter Gene Are Related to Awareness of Emotions in Healthy Subjects: A Preliminary Study
Bibliographic record
Abstract
OBJECTIVE: Interaction between the nervous and immune systems may influence emotions, ultimately affecting human health. Cytokines may play a role in developing emotional dysregulation as in alexithymia, a personality construct characterized by the subclinical inability to identify and describe emotions, often associated with several psychiatric and psychosomatic disorders. The proinflammatory cytokine IL-18, with a recognized role in brain functions, may influence serotonin metabolism and appears to be associated with alexithymia. Healthy individuals carrying the long allele (L) of the serotonin transporter gene polymorphic region (5-HTTLPR), and thus having lower concentrations of serotonin in the synaptic cleft, show a greater tendency toward alexithymia, with some gender differences. To explore a potential physiological interaction between IL-18, serotonin neurotransmission, and alexithymia, we investigated whether IL-18 serum levels and 5-HTTLPR are linked to alexithymic traits in healthy subjects. METHODS: We measured IL-18 serum levels in 115 Italian-Caucasian healthy subjects genotyped for 5-HTTLPR allele variants, divided by gender and assessed for alexithymia scores using the 20-item Toronto Alexithymia Scale. RESULTS: IL-18 levels are significantly more elevated in individuals with the LL genotype (n = 25) than in carriers of the short allele (n = 90, p = 0.0073). Specifically, in LL males (n = 11), i.e., the group with the most relevant increase in IL-18, cytokine values positively correlated with difficulty identifying feelings, which is a component of alexithymia (r = 0.634, p = 0.036). CONCLUSIONS: These results indicate a possible novel interaction between IL-18 and the serotoninergic system to mediate emotional unawareness, suggesting putative biological predictors of emotional dysregulation, which in turn can act as a risk factor for a variety of medical conditions in susceptible subjects.
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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 | 0.000 |
| 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) | 0.002 | 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".