Prospective associations between diabetes and depressive symptoms across European regions: a secondary analysis of ELSA, TILDA, and SHARE datasets
Bibliographic record
Abstract
This article investigates predictive associations between diabetes and depressive symptoms across Ireland, the United Kingdom, and four European regions. The data were obtained by merging datasets from three large prospective cohort studies-the English Longitudinal Study on Ageing, The Irish Longitudinal study on Ageing, and the Survey on Health, Ageing and Retirement in Europe. We first applied a survival analysis design to two samples of 43 061 and 35 993 participants, investigating elevated depressive symptoms as a risk factor for diabetes, and diabetes as a risk factor for elevated depressive symptoms, respectively. We next applied a multilevel modeling approach to examine depressive symptoms before, during, and after diabetes onset across 101 799 participants. We found a bidirectional association between diabetes and depressive symptoms; however, the strength of these associations did not significantly differ between the regions (P > .01). The results also showed that individuals with newly diagnosed diabetes consistently reported higher depressive symptoms than those without diabetes, even before diagnosis. However, we observed no country-specific differences in the gradual changes in depressive symptoms regardless of participants' diabetes status. Diabetes at baseline was associated with higher risk of developing depression; and vice versa. These associations were not moderated by geographical location. Therefore, the risks of diabetes and depressive symptoms comorbidity seem to be equal across all observed geographic regions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".