Adolescent pain, anxiety, and depressive problems: a twin study of their co-occurrence and the relationship to substance use
Bibliographic record
Abstract
ABSTRACT: Data on the etiological factors underlying the co-occurrence of common adolescent pain with anxiety and depression symptoms are very limited. Opioid prescriptions for adolescent pain problems are on the rise in North America and constitute a risk factor for diversion, misuse, and substance use. In this study, we aimed to investigate the phenotypic and etiological association among pain, depression, and anxiety and to test their link to substance use in adolescents. By taking advantage of the Italian National Twin Registry and of the relatively low incidence of opioid prescriptions in Italy, we applied multivariate modelling analyses to 748 Italian adolescent twins (374 pairs, mean age 16 ± 1.24 years). Twins' responses to the Achenbach Youth Self-Report questionnaire were used to build a composite adolescent pain index and to measure anxiety, depression, and substance use. All monozygotic within-pair correlations were higher than the dizygotic correlations, indicating genetic influences for adolescent pain, anxiety, and depressive problems. A common latent liability factor influenced by genetic and environmental elements shared among pain, depression, and anxiety provided the best fit to explain the co-occurrence of adolescent pain, anxiety, and depression problems. A common phenotypic factor capturing all 3 phenotypes was positively associated (β = 0.19, P < 0.001, confidence interval: 0.10-0.27) with substance use. These findings indicate that several intertwined mechanisms, including genetic factors, can explain a shared liability to common adolescent pain, anxiety, and depression problems. Their association with substance use remains traceable even in societies with relatively low prevalence of opioid prescriptions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.004 |
| 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) | 0.000 | 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 teacher head, 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".