Impact of cardiometabolic conditions on the progression from mild cognitive impairment to dementia: A large cohort study
Bibliographic record
Abstract
INTRODUCTION: This study investigates the impact of cardiometabolic conditions, including type 2 diabetes, hyperlipidemia, hypertension, and obesity, on the progression of mild cognitive impairment (MCI) to dementia. METHODS: The cohort included adults ≥ 50 years old with MCI and a cardiometabolic condition identified through electronic health records. Propensity score matching was applied to control for confounders, and Kaplan-Meier analysis was used to assess time-to-event outcomes. RESULTS: During a 7-year median follow-up, type 2 diabetes was associated with the highest risk of all-cause dementia (hazard ratio [HR] 1.18, 95% confidence interval [CI]: 1.06 to 1.31), followed by hypertension and hyperlipidemia. For vascular dementia, type 2 diabetes conferred the greatest risk (HR 1.33, 95% CI: 1.07 to 1.64). Hyperlipidemia was the sole cardiometabolic factor significantly associated with Alzheimer's disease (AD) risk (HR 1.21, 95% CI: 1.11 to 1.32). CONCLUSIONS: Hyperlipidemia is primarily associated with AD dementia risk, while type 2 diabetes is the major contributor to vascular dementia and all-cause risk in individuals with MCI. HIGHLIGHTS: Type 2 diabetes, hypertension, and hyperlipidemia are associated with a high risk of developing all-cause dementia in participants already diagnosed with mild cognitive impairment (MCI). Type 2 diabetes was shown to pose a high risk for the progression from MCI to vascular dementia. Hyperlipidemia was associated with Alzheimer's disease progression in individuals with MCI.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".