Internalising problems and self-reported BMI/physical health: Correlated genetic and environmental influences versus probable causal mechanisms
Bibliographic record
Abstract
Internalising problems (depressive and anxiety symptoms) are associated with poor physical health indices. This may reflect causal mechanisms or shared genetic and environmental factors but this has not been previously tested. We tested whether indirect relationships between internalising problems and physical health indices though health behaviours and poor sleep quality were better explained by genetic and environmental correlations. The sample comprised participants in the UK Twins Early Development Study cohort at ≈22 years (9697 and 8718 participants of whom 38.2 % were male, 55.6 % from low socioeconomic backgrounds and 95.5 % were white). Participants were assessed for internalising symptoms, health behaviours, sleep quality, BMI and self-rated health. We compared three twin genetic models to determine whether genetic and environmental correlations versus mediation were a better explanation for phenotypic relationships; and for the best genetic model, we tested differences by sex, socioeconomic status and high versus normal BMI. Although, health behaviours and sleep quality appeared to mediate the phenotypic associations between internalising problems and physical health, genetic and environmental correlations emerged a better explanation for observed associations; and these correlations were stronger in those with high BMI. We concluded that poor health behaviours and sleep quality are relevant to understanding the aetiological links between internalising problems and elevated BMI, especially among those who are overweight or obese. Causal mechanisms alone appear insufficient to explain the links between internalising problems and physical health outcomes. Future research should incorporate genetic information in investigating these relationships.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".