Large-scale evidence of a general disease (‘<i>d</i>’) factor accounting for both mental and physical health disorders in different age groups
Bibliographic record
Abstract
Abstract Background It is unknown whether there is a general factor that accounts for the propensity for both physical and mental conditions in different age groups and how it is associated with lifestyle and well-being. Methods We analyzed health conditions data from the Millennium Cohort Study (MCS) (age = 17; N = 19,239), the National Child Development Study (NCDS) (age = 44; N = 9293), and the English Longitudinal Study of Ageing (ELSA) (age ≥ 50; N = 7585). The fit of three Confirmatory Factor models was used to select the optimal solution by Comparative Fit Index, Tucker-Lewis Index, and Root Mean Square Error of Approximation. The relationship among d factor, lifestyles, and well-being was further explored. Results Supporting the existence of the d factor, the bi-factor model showed the best model fit in 17-year-olds (MCS:CFI = 0.97, TFI = 0.96, RMSEA = 0.01), 44-year-olds (NCDS:CFI = 0.96, TFI = 0.95, RMSEA = 0.02), and 50+ year-olds (ELSA:CFI = 0.97, TFI = 0.96, RMSEA = 0.02). The d factor scores significantly correlated with lifestyle and well-being, suggesting healthier lifestyles were associated with a reduced likelihood of physical and mental health comorbidities, which in turn improved well-being. Conclusions Contrary to the traditional dichotomy between mental and physical conditions, our study showed a general factor underlying the comorbidity across mental and physical diseases, related to lifestyle and well-being. Our results inform the conceptualization of mental and physical illness as well as future research assessing risk and pathways of disease transmission, intervention, and prevention. Our results also provide a strong rationale for a systematic screening for mental disorders in individuals with physical conditions and vice versa, and for integrated services addressing multimorbidity.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".