Diabetes status and cognitive function in middle-aged and older adults in the Canadian longitudinal study on aging
Bibliographic record
Abstract
Objectives: Diabetes is recognized as a significant risk factor for cognitive impairment. However, this association has not been thoroughly examined using large-scale population-based datasets in the Canadian context. The objective of this study was to investigate the potential association between cognitive function and diabetes in a large population-based sample of middle-aged and older Canadians. Methods: We utilized baseline data from the Canadian Longitudinal Study on Aging (N=30,097) to test our hypotheses, using five indicators of cognitive function (animal fluency, Stroop interference, reaction time, immediate and delayed memory recall). We conducted multivariate multivariable linear regression and subsequently performed tests for moderation analysis with lifestyle factors and health status. Results: The analysis revealed that type 2 diabetes (T2DM) was associated with lower performance on most cognitive tasks, including those assessing executive function (b=0.60, 95% CI 0.31 to 0.90), reaction time (b=16.94, 95% CI 9.18 to 24.70), immediate memory recall (b=-0.10, 95% CI -0.18 to -0.02), and delayed memory recall (b=-0.12, 95% CI -0.21 to -0.02). However, no significant association was observed between other types of diabetes and cognitive performance. Moderation effects were largely null for T2DM, with the exception of alcohol intake for reaction time, and physical activity for animal fluency. Conclusions: The study showed that individuals with T2DM exhibit poor performance on tasks that assess executive function, reaction time, and memory. Therefore, optimizing cognitive health among individuals with T2DM should be a priority in primary care. Additionally, further studies should examine this association using longitudinal data.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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".