Diabetes in a Large Dementia Cohort: Clinical Characteristics and Treatment From the Swedish Dementia Registry
Bibliographic record
Abstract
OBJECTIVE: We aimed to investigate the differences in clinical characteristics and pharmacological treatment associated with the presence of diabetes in a large cohort of patients with dementia. RESEARCH DESIGN AND METHODS: A cross-sectional registry-based study was conducted using data from the Swedish Dementia Registry (SveDem). Data on dementia diagnosis, dementia type, and demographic determinants were extracted from SveDem. Data from the Swedish Patient Register and Prescribed Drug Register were combined for the diagnosis of diabetes. Data on antidiabetic, dementia, cardiovascular, and psychotropic medications were extracted from the Swedish Prescribed Drug Register. Logistic regression was used to determine whether the variables were associated with diabetes after adjustment for confounders. In total, 29,630 patients were included in the study, and 4,881 (16.5%) of them received a diagnosis of diabetes. RESULTS: In the fully adjusted model, diabetes was associated with lower age at dementia diagnosis (odds ratio [OR] 0.97 [99% CI 0.97-0.98]), male sex (1.41 [1.27-1.55]), vascular dementia (1.17 [1.01-1.36]), and mixed dementia (1.21 [1.06-1.39]). Dementia with Lewy bodies (0.64 [0.44-0.94]), Parkinson disease dementia (0.46 [0.28-0.75]), and treatment with antidepressants (0.85 [0.77-0.95]) were less common among patients with diabetes. Patients with diabetes who had Alzheimer disease obtained significantly less treatment with cholinesterase inhibitors (0.78 [0.63-0.95]) and memantine (0.68 [0.54-0.85]). CONCLUSIONS: Patients with diabetes were younger at dementia diagnosis and obtained less dementia medication for Alzheimer disease, suggesting less optimal dementia treatment. Future research should evaluate survival and differences in metabolic profile in patients with diabetes and different dementia disorders.
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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 | 0.000 |
| 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".