Insulin Use in Type 2 Diabetes and the Risk of Dementia: A Comparative Population-Based Cohort Study
Bibliographic record
Abstract
OBJECTIVE: Evidence of an increased dementia risk with insulin use in type 2 diabetes is weakened by confounding by indication and disease severity. Herein we reassess this association, while accounting for confounding through design and analysis. RESEARCH DESIGN AND METHODS: Using administrative health care data from British Columbia, Canada, we identified patients diagnosed with type 2 diabetes in 1998-2016. To adjust for confounding by diabetes severity through design, we compared new users of insulin to new users of a noninsulin class, both from a restricted cohort of those who previously received two noninsulin antihyperglycemic classes. We further adjusted for confounding using 1) conventional multivariable adjustment and 2) inverse probability of treatment weighting (IPTW) based on the high-dimensional propensity score algorithm. The hazard ratio [HR] (95% CI) of dementia was estimated using cause-specific hazards models with death as a competing risk. RESULTS: The analytical comparative cohort included 7,863 insulin versus 25,230 noninsulin users. At baseline, insulin users were more likely to have worse health indicators. A total of 78 dementia events occurred over a median (interquartile range) follow-up of 3.9 (5.9) years among insulin users, and 179 events occurred over 4.6 (4.4) years among noninsulin users. The HR (95% CI) of dementia for insulin use versus noninsulin use was 1.68 (1.29-2.20) before adjustment and 1.39 (1.05-1.86) after multivariable adjustment, which was further attenuated to 1.14 (0.81-1.60) after IPTW. CONCLUSIONS: Among individuals with type 2 diabetes previously exposed to two noninsulin antihyperglycemic medications, no significant association was observed between insulin use and all-cause dementia.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".