Serum Insulin Degrading Enzyme Level and Other Factors in Type 2 Diabetic Patients with Mild Cognitive Impairment
Bibliographic record
Abstract
BACKGROUND AND AIMS: Insulin degrading enzyme (IDE) contributes to the degradation processes of insulin and Aβ. We aimed to investigate the role of IDE in type 2 diabetes patients with mild cognitive impairment (MCI). METHODS: A total of 146 individuals with type 2 diabetes were enrolled and divided into two groups according to the Montreal Cognitive Assessment (MoCA) score. Demographic characteristics, cognitive function and serum IDE level were examined. RESULTS: There were 75 patients with MCI and 71 patients without MCI. Diabetic patients with MCI had a higher serum level of IDE compared with the control group (p > 0.001). Among patients with MCI, serum IDE level was positively correlated with the MoCA score (r = 0.839; p > 0.001). Correlation analysis demonstrated that IDE was positively correlated with MoCA score (r = 0.815; p > 0.001) but negatively correlated with the Trail Making Test-B (r = -0.413; p > 0.001), fasting blood-glucose (r = -0.372; p > 0.001), glycosylated hemoglobin (r = -0.214; p = 0.015), homeostasis model of assessment for insulin resistance (r = -0.560; p > 0.001) and the mean amplitude of glycemic excursions (r = -0.551; p > 0.001) in all subjects. In logistic regression analysis for MCI, IDE (p = 0.010) was an independent variable, after adjusting for age, sex, education, liver function, kidney function, and lipid levels. CONCLUSION: This study demonstrated a greater likelihood of MCI with decreasing serum IDE in patients with type 2 diabetes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".