Fasting Insulin Levels and Cognitive Decline in Older Women without Diabetes
Bibliographic record
Abstract
BACKGROUND: Type 2 diabetes has been associated with an increased risk of dementia. To assess possible independent effects of insulin, we investigated the relation of insulin levels to cognitive decline in nondiabetic women. METHODS: Fasting plasma insulin levels were measured in mid-life in 1,416 nondiabetic Nurses' Health Study participants, who also completed cognitive testing that began 10 years later (current age: 70-75 years). Over 4 years, 3 assessments of general cognition, verbal memory, category fluency and attention were administered. Primary outcomes were the Telephone Interview for Cognitive Status (TICS) performance, the global score (average of all tests) and verbal memory (average of verbal recall tests). Linear mixed-effects models were used to calculate the association between insulin and cognitive decline. RESULTS: Higher insulin levels were associated with a faster decline on the TICS and verbal memory. For analysis, batch-specific quartiles of insulin levels were constructed. Compared to the lowest quartile, adjusted differences in the annual rates of decline (with 95% CI values in parentheses) for the second, third and fourth quartiles were: TICS, -0.06 (-0.16, 0.03), -0.14 (-0.24, -0.04), and -0.09 (-0.19, 0.01) points (p trend = 0.04); verbal memory, -0.01 (-0.04, 0.02), -0.05 (-0.08, -0.02), and -0.02 (-0.05, 0.01) units (p trend = 0.02). These associations remained after multivariable adjustment. CONCLUSIONS: Our study provides evidence for a potential role of higher fasting insulin levels in cognitive decline, possibly independent of diabetes.
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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 | 0.002 |
| 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 | 0.000 |
| 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".