Alexithymia and Tissue Inflammation
Bibliographic record
Abstract
BACKGROUND: Altered immune responses are seen in depression, and recent data suggest that similar changes could also be observable in alexithymia. We examined whether the inflammatory markers high-sensitivity C-reactive protein (hs-CRP) and interleukin (IL)-6 are independently related to alexithymia or its factors in a population-based sample. METHODS: This study formed a clinical part of the Kuopio Depression (KUDEP) general population study focusing on the mental health of a general population of adults aged 25-64 years (n = 308). Alexithymia was measured using the Toronto Alexithymia Scale (TAS-20), and depressive symptoms were assessed using the Beck Depression Inventory (BDI-21). RESULTS: The levels of IL-6 (in picograms per milliliter) and hs-CRP (in milligrams per liter) were significantly higher in alexithymic than in nonalexithymic subjects (IL-6 effect size, ES: 0.50; hs-CRP ES: 0.27). The BDI scores, hs-CRP and IL-6 explained 33.5% of the variation in TAS scores in the whole study population. According to logistic regression analysis, hs-CRP but not IL-6 increased the likelihood of belonging to the alexithymic group. This observation remained unaltered after additional adjustments for chronic inflammation-related disorders, the use of inflammation-modulating medications and depressive symptoms. CONCLUSIONS: Our findings suggest that the association between hs-CRP and alexithymia resembles that observed in depressed patients. It is, however, independent of depressive symptoms. These findings widen our view on the stress-alexithymia concept.
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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".