Genetic and Environmental Factors in Alexithymia: A Population-Based Study of 8,785 Danish Twin Pairs
Bibliographic record
Abstract
BACKGROUND: The role of genetic and environmental factors for developing alexithymia is still unclear, and the aim of this study was to examine these factors in a large population-based sample of twins. METHODS: The Toronto Alexithymia Scale-20 (TAS-20) was included in a mail survey of 46,418 individuals born between 1931 and 1982 and registered with the Danish Twin Registry. The response rate was 75.3%. A total of 8,785 twin pairs, where both cotwins had completed all items of the TAS-20, were selected for this study. Analyses were conducted for total TAS-20 scores and the subscales of (1) difficulties in identifying feelings, (2) difficulties in describing feelings, and (3) externally oriented thinking. The phenotypes were analyzed both as categorical and continuous data. RESULTS: All measures of similarity suggested that genetic factors added to all facets of alexithymia. Structural equation modeling of the noncategorical data, an ACE model including additive genetic, shared environmental and nonshared environmental effects, provided the best fit for all three facets of alexithymia as well as total alexithymia scores, with heritabilities of 30-33% and the remaining variance being explained by shared (12-20%) and nonshared environmental effects (50-56%). CONCLUSION: The results from this large population-based sample suggest that genetic factors have a noticeable and similar impact on all facets of alexithymia. While the results suggested a moderate influence of shared environmental factors, our results are in concordance with the general finding that environmental influences on most psychological traits are primarily of the nonshared rather than the shared type.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".