Shared and unique risk factors for depression and diabetes mellitus in a longitudinal study, implications for prevention: an analysis of a longitudinal population sample aged ⩾45 years
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to examine shared and unique risk factors for incident depression and diabetes mellitus in a national longitudinal population-based survey. METHODS: = 4845), free of depression or diabetes mellitus at baseline was tracked over a 10-year period. Univariate and multivariate modified Poisson regression models were used to estimate the relative risk (RR). Stratified analyses by sex were conducted to measure its moderating role. The goodness-of-fit of the various models was tested. RESULTS: The cumulative incidence rate of major depressive disorder and incident diabetes mellitus at 10-year follow-up were 4.1% and 10.1% respectively. Hypertension, daily smoking, physical inactivity and being overweight or obese were shared risk factors for major depressive episode and diabetes mellitus. Being female, family stress, traumatic events, having any chronic disease or heart disease were uniquely associated with depression while increasing age and ethnicity (non-white) were unique risk factors for diabetes mellitus. Also, underweight, family stress, chronic disease, and heart disease were risk factors for major depressive disorder in both sexes. Six risk factors, age, ethnicity (non-white), high blood pressure, daily smoking, physical inactivity, and body mass index were associated with incident diabetes mellitus in both sexes. CONCLUSION: We found common risk behaviors/conditions not specific to either diabetes mellitus or depression. These risks have also been implicated in the development of a variety of chronic diseases. These findings underline the importance of public health prevention programs targeting generic risk behaviors.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".