P02-310 - Alexithymia Predicts Triglyceride Level, Systolic Blood Pressure, and Diabetic Status in Metabolic Syndrome
Bibliographic record
Abstract
Objectives The current study elucidates the relations between alexithymia and several biological components of the Metabolic Syndrome (MS). We hypothesized that various facets of the alexithymia construct are differentially related to single components characterizing the MS. Methods Investigated were N = 101 patients with MS. To establish the diagnosis of MS according to IDFC criteria, both laboratory (lipid profile, fasting plasma glucose, type 2 diabetes) and non-laboratory (blood pressure, waist circumference, BMI) tests were included. Alexithymia was measured using the Toronto Alexithymia Scale (TAS-20). Results Based on alexithymia scores, patients were classified as low- (TAS-20 score ≤ 51; N = 31), moderate- (51 < TAS-20 score < 61; N = 33), or high-alexithymic (TAS-20 score ≥ 61; N = 37). The amount of moderate and high alexithymic patients proved highly significant amongst patients with MS with readily established diabetes type 2. The alexithymia score showed overall correlations with diabetes type 2 (r = 0.380, p < 0.001) and triglyceride levels (r = 0.214, p < 0.016). Correlations with non-laboratory measures were significant for high blood pressure levels (r = 0.233, p < 0.010). Linear regression models confirmed the existence of linear causal relationships for the observed correlations. Conclusions The present results suggest that the alexithymia trait is related to specific biological marker variables in the metabolic syndrome. Alexithymia may, according to our study, contribute to the aggravation of the MS.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".