Associations of Plasma BACE1 Level and BACE1 C786G Gene Polymorphism with Cognitive Functions in Patients with Type 2 Diabetes: A Cross- Sectional Study
Bibliographic record
Abstract
BACKGROUND: β-Site APP-cleaving enzyme 1 (BACE1) is a key enzyme involved in the pathophysiology of Type 2 Diabetes Mellitus (T2DM) and Mild Cognitive Impairment (MCI). We aimed to investigate the potential associations of plasma BACE1 levels and BACE1 gene polymorphism with different cognitive performances in T2DM patients with MCI. METHODS: The recruited 186 T2DM subjects were divided into 92 MCI group and 94 healthy-cognition controls, according to the Montreal Cognitive Assessment (MoCA) scores. Sociodemographic characteristics, clinical parameters and neuropsychological tests were assessed. BACE1 C786G gene polymorphism and plasma BACE1 level were determined. RESULTS: Compared to controls, MCI patients exhibited higher plasma BACE1 levels. Plasma BACE1 levels were negatively associated with MoCA, Clock Drawing Test and Logical Memory Test scores, whereas positively associated with Trail Making Test-B time in the MCI group (all p<0.05), after adjusting fasting blood glucose, glycosylated hemoglobin, and homeostasis model assessment of insulin resistance by C-peptide. Multivariable logistic regression analysis showed a significant trend towards increased MCI risk with high plasma BACE1 level in T2DM patients (OR = 1.492, p = 0.027). The plasma BACE1 levels of GG and GC genotypes were obviously higher than that of CC genotype in T2DM-MCI patients (p = 0.035; p = 0.026, respectively). CONCLUSION: Increased plasma BACE1 levels were associated with poor overall cognition functions, especially visuospatial abilities, visual/logical memory and executive functions in T2DM-MCI patients. Additionally, elevated plasma BACE1 level was a risk factor for MCI in T2DM patients, and might be influenced by BACE1 C786G gene mutations.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".