Type 2 diabetes mellitus might be a risk factor for mild cognitive impairment progressing to Alzheimer’s disease
Bibliographic record
Abstract
Background: Mild cognitive impairment (MCI) is the prodromal stage of Alzheimer’s disease (AD), so identification of the related risk factors can be helpful. Although the association between type 2 diabetes mellitus (T2DM) and these modest changes in cognition is well established, whether T2DM will promote the transformation of MCI into AD is not a unified conclusion. Objective: This study aims to explore the relationship between T2DM and MCI in the elderly population living in the community in Shanghai, People’s Republic of China. Methods: A total of 197 participants were included in the study. They were screened for T2DM, hyperlipidemia, traumatic brain injury, and family history of dementia. The Mini-Mental State Examination and the Montreal Cognitive Assessment were used to assess cognitive function. The diagnosis of AD was made according to Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition , whereas the diagnosis of MCI was made according to Petersen’s criteria. Then, we investigated the relation between T2DM and MCI. Results: A total of 82 (41.6%) participants had no cognitive impairment, 82 (41.6%) participants had MCI, and 33 (16.8%) participants had AD. Multivariate logistic regression models demonstrated that T2DM was a risk factor for AD (odds ratio =49.723, 95% CI =21.173–111.987). Conclusion: T2DM might be a risk factor for MCI progressing into AD. Keywords: T2DM, AD, MCI, risk factor
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".