Risk of Dementia in Seniors With Newly Diagnosed Diabetes: A Population-Based Study
Bibliographic record
Abstract
OBJECTIVE: To study whether diabetes onset in late life is a risk factor for dementia. RESEARCH DESIGN AND METHODS: We conducted a population-based matched cohort study using provincial health data from Ontario, Canada. Seniors with (n = 225,045) and without newly diagnosed diabetes (n = 668,070) between April 1995 and March 2007 were followed until March 2012 for a new diagnosis of dementia. Cox proportional hazards modeling was used to compare the risk of dementia between groups after adjusting for baseline cardiovascular disease, chronic kidney disease (CKD), hypertension, and other risk factors. RESULTS: Over this period, we observed 169,114 new cases of dementia. Individuals with diabetes had a modestly higher incidence of dementia (2.68 vs. 2.62 per 100 person-years) than those without diabetes. In the fully adjusted Cox model, the risk of dementia was 16% higher among our subgroup with diabetes (hazard ratio [HR] 1.16 [95% CI 1.15-1.18]). Adjusted HRs for dementia were 1.20 (95% CI 1.17-1.22) and 1.14 (95% CI 1.12-1.16) among men and women, respectively. Among seniors with diabetes, the risk of dementia was greatest in those with prior cerebrovascular disease (HR 2.03; 95% CI 1.88-2.19), peripheral vascular disease (HR 1.47; 95% CI 1.19-1.82), and CKD (HR 1.44; 95% CI 1.38-1.51), and those with one or more hospital visits for hypoglycemia (HR 1.73; 95% CI 1.62-1.84). CONCLUSIONS: In this population-based study, newly diagnosed diabetes was associated with a 16% increase in the risk of dementia among seniors. Preexisting vascular disease and severe hypoglycemia were the greatest risk factors for dementia in seniors with diabetes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".