Older adults at‐risk for type 2 diabetes exhibit decreased performance on spatial and working memory tasks using classic and novel cognitive testing
Bibliographic record
Abstract
BACKGROUND: Type 2 diabetes (T2D) and older age are well-known risk factors for dementia. Indeed, there is evidence that older adults not diagnosed, but at-risk for T2D can show early signs of cognitive decline, further exacerbated by excessive body weight or high blood glucose levels. Such a finding would have implications for early treatment strategies; however, the evidence is still sparse. We examined the correlation of risk factors for diabetes with cognitive function in older adults at-risk for T2D using a battery of touchscreen tasks translated from their rodent versions, as well as traditional pen-to-paper cognitive tests. METHOD: Sixty-five older adults (69.39 ± 10.31 years old, 68% female) at-risk for T2D (BMI ≥ 25 kg/m2, hemoglobin A1c ≥ 6.0%, CANRISK score ≥ 21) completed 3 novel touchscreen tasks: paired associative learning (PAL) (learning and object-in-location memory), progressive ratio (motivation), and trial unique, non-matching to location (TUNL) (spatial pattern separation and working memory). They were also tested on pen-to-paper cognitive tests: trail-making (task switching), Stroop (selective inhibition), and digit span (working memory). A correlation analysis was performed between BMI or HbA1c and cognitive performance. Performance on touchscreen tasks was analyzed using a repeated measures one-way ANOVA. RESULT: Higher HbA1c levels were correlated with lower digit span scores (r2 = 0.12, p = 0.011). There was no correlation between breakpoint (motivation level) and BMI or HbA1c during the progressive ratio task (r2 = 0.0, p = 0.38). Interestingly, this population performed at chance level (59.7 ± 5.3% accuracy) on the PAL task, indicating they were unable to learn object-location paired associates. When manipulating the spatial similarity in distance between stimuli during the TUNL task, older adults at risk for diabetes were 10% lower in accuracy when stimuli were close together compared to further apart (p<0001). Participants also responded more slowly to stimuli at choice during the TUNL task during the heaviest working memory load condition (p = 0.003). CONCLUSION: Older adults at-risk for T2D exhibit decreased performance on tasks with higher demands on spatial and working memory. Future research will compare performance on all tasks to healthy age-matched controls and reassess performance after a six-month exercise intervention.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".