Neural–genetic–environmental evidence for a disease factor in mental and physical health multimorbidity
Bibliographic record
Abstract
Increasing evidence reveals the presence of multimorbidity across physical and mental disorders. A general disease factor (d factor) has been recently identified to capture the shared liability across these conditions, yet its biological basis remains poorly understood. Here, using data from the UK Biobank, we reveal the d factor’s neural, genetic, and environmental underpinnings. We show that the d factor is associated with extreme negative deviations in grey matter volume and white matter microstructure. A genome-wide association study identifies its genetic loci and correlations with unhealthy lifestyle, anthropometric measures, and mood-related phenotypes. Furthermore, Mendelian randomization suggests a causal effect of living environmental deprivation on the d factor. Mediation analysis further reveals that the d factor links this environmental adversity to individual differences in brain structure. Our findings establish a multi-level biological characterization of general disease liability, connecting environmental, genetic, and neural factors and inform transdiagnostic approaches to prevention and treatment. Many physical and mental disorders share common risk factors captured by a general disease factor. Here, the authors identify the neural, genetic, and environmental bases of this factor, linking environmental deprivation to brain structure and multimorbidity risk.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".