Depression, diabetes and change in cognitive functioning: results from the Canadian Longitudinal study on Aging
Bibliographic record
Abstract
Abstract Background Individuals who ultimately receive a diagnosis of dementia typically have an observable accelerated cognitive decline (ACD) many years prior to diagnosis. Depression in combination with diabetes is an emerging risk factor that is associated with cognitive problems. Using data from the Canadian Longitudinal Study on Aging, the objective of the present study was to investigate the longitudinal association between depression, diabetes, and cognitive decline in an elderly cohort. Methods Baseline and follow-up data from a population-based study in Canada were used. The sample consisted of 18161 adults between 45 and 85 years of age without diabetes. Cognitive functioning was assessed at baseline and after 4 years using six measures: the Rey Auditory Verbal Learning Test (RAVLT), the Mental Alternation Test (MAT), the Animal Fluency Test (AF), the Controlled Oral Word Association Test (COWAT), the Stroop Test, and the Prospective Memory Test. Depression was assessed using the CES-D10. Regression analysis was conducted to evaluate interactions between depression, diabetes and cognitive decline. Results The mean age of participants was 61 years. Participants with a comorbidity of depression and diabetes had an accelerated cognitive decline (g-factor) compared to those with depression without diabetes and those with diabetes without depression (regression coefficients ß=-0.145 (0.036), ß=-0.076 (0.011), and ß=-0.053 (0.021), respectively). Conclusions This study suggests that depression and diabetes might increase the risk of cognitive decline in a synergistic way.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".