Reduced Serum Adiponectin Levels in Alexithymia
Bibliographic record
Abstract
OBJECTIVES: Clinical studies have demonstrated that circulating cytokine profiles may differ between alexithymic and non-alexithymic subjects. We examined whether the levels of adiponectin (μg/ml) and resistin (ng/ml) are independently related to alexithymic features in a population-based sample. METHODS: In 2005, clinical data including laboratory assessments were obtained from a sub-sample (n = 308) of the Kuopio Depression Study general population study including subjects aged 25-64 years. Based on the Toronto Alexithymia Scale score in 1998, 1999, 2001 and 2005, a group of subjects with high alexithymic features (n = 85) was formed and compared with non-alexithymic controls (n = 206). RESULTS: Serum adiponectin levels were significantly lower in subjects with alexithymic features than in non-alexithymic control subjects. No difference was found in resistin levels. Similarly, in a logistic regression model adjusted for age, gender and body mass index (BMI), lowered levels of adiponectin, but not resistin, were associated with an increased likelihood of belonging to the group with alexithymic features. Further adjustments for cardiovascular risk factors (i.e. smoking, BMI, metabolic syndrome, alcohol use, and coronary heart disease), depressive symptoms (Hamilton Depression Rating Scale with 17 items) and the use of antidepressants in addition to age and gender did not change these patterns. CONCLUSIONS: Our findings suggest that a disturbed anti-inflammatory balance may characterize alexithymia. In addition, our results widen the concept of alexithymia and highlight the role of immune system alterations and stress in alexithymic individuals.
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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 | 0.000 |
| 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".