Insulin resistance is associated with reductions in specific cognitive domains and increases in CSF tau in cognitively normal adults
Bibliographic record
Abstract
Growing evidence supports the hypothesis that type 2 diabetes (T2D) increases the risk of developing dementia. Experimental evidence from mouse models demonstrates that the induction of T2D/insulin resistance (IR) can promote the accumulation of Alzheimer's disease (AD) pathological features. However, the association of T2D with pathological and clinical phenotypes in humans is unclear. Here we investigate the relationship of indices of IR (HOMA-IR) and pancreatic β-cell function (HOMA-B) with cognitive performance across several domains (Verbal/Visual Episodic Memory, Executive Function, Language and a measure of Global cognition) and AD biomarkers (CSF Aβ42, T-tau/P-tau, hippocampal volume and neocortical Aβ-amyloid burden). We reveal that HOMA-IR (p < 0.001) incrementally increases across diagnostic groups, becoming significantly elevated in the AD group compared with cognitively normal (CN) adults. In CN adults, higher HOMA-IR was associated with poorer performance on measures of verbal episodic memory (p = 0.010), executive function (p = 0.046) and global cognition (p = 0.007), as well as with higher CSF T-tau (p = 0.008) and P-tau (p = 0.014) levels. No association was observed with CSF Aβ or imaging modalities. Together our data suggest that IR may contribute to reduced cognitive performance and the accumulation of CSF tau biomarkers in cognitively normal adults.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".