Courses of depressive symptoms and diabetes incidence among middle-aged and older adults: A prospective study
Bibliographic record
Abstract
Elevated depressive symptoms are a risk factor for diabetes. Although depressive symptoms can remit or emerge over time, little work has considered if courses of depressive symptoms are associated with incident diabetes. The purpose of this study was to explore associations between courses of depressive symptoms and incident diabetes. Data came from the English Longitudinal Study of Ageing (n = 4,978), which is an ongoing, cohort study of adults aged 50 years and older residing in private households in England. Depressive symptoms were measured biennially from 2002 to 2008. Participants were categorized into one of six groups: no depressive symptoms, remitted depressive symptoms, incident depressive symptoms with remission, incident depressive symptoms without remission, chronic depressive symptoms, and variable course. Diabetes status was self-reported biennially from 2010 to 2018. After adjusting for covariates, remitted depressive symptoms (HR = 1.52, 95% CI [1.06, 2.22]) and variable course depressive symptoms (HR = 1.83, 95% CI [1.19, 2.81]) remained associated with incident diabetes. In sensitivity analyses, which lowered the cut-off score for depressive symptoms, variable course depressive symptoms (HR = 1.61, 95% CI [1.11, 2.33]) remained associated with incident diabetes. Specific courses of depressive symptoms, including variable course depressive symptoms, were associated with diabetes incidence. Continuing to examine the link between patterns of depressive symptoms over time and incident diabetes may lead to the development of more targeted interventions.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".