Type 2 Diabetes Moderates the Association Between Amyloid and 1-Year Change in Everyday Functioning in Older Veterans
Bibliographic record
Abstract
BACKGROUND: Type 2 diabetes mellitus (T2DM) affects ∼25% of Veterans, a prevalence rate double that of the general population. T2DM is associated with greater dementia risk and has been shown to exacerbate the impact of Alzheimer's disease (AD) risk factors on declines in daily functioning; however, there are few studies that investigate these patterns in older Veterans. OBJECTIVE: This study sought to determine whether T2DM moderates the association between amyloid-β (Aβ) positron emission tomography (PET) and 1-year change in everyday functioning in older Veterans. METHODS: One-hundred-ninety-eight predominately male Vietnam-Era Veterans without dementia from the Department of Defense-Alzheimer's Disease Neuroimaging Initiative (DoD-ADNI) with (n = 74) and without (n = 124) T2DM completed Aβ PET imaging and everyday functioning measures, including the Clinical Dementia Rating-Sum of Boxes (CDR-SB) and Everyday Cognition (ECog). Linear mixed effects models tested the moderating role of T2DM on the association between Aβ PET and 1-year change in everyday functioning. RESULTS: The 3-way T2DM×Aβ PET×time interaction was significant for CDR-SB (p < 0.001) as well as the Memory (p = 0.007) and Language (p = 0.011) subscales from the ECog. Greater amyloid burden was associated with greater increases in functional difficulties, but only in Veterans with T2DM. CONCLUSIONS: Higher Aβ was only associated with declines in everyday functioning over 1 year in Veterans with T2DM. Given that people with T2DM are more likely to have co-occurring cerebrovascular disease, the combination of multiple neuropathologies may result in faster declines. Future studies should examine how diabetes duration, severity, and medications impact these associations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".