Serum moesin is associated with cognitive impairment and glucose fluctuations in patients with type 2 diabetes
Bibliographic record
Abstract
BACKGROUND: Cognitive impairment is a common complication of type 2 diabetes mellitus (T2DM); however, its underlying mechanisms are unclear. This study investigated the association between serum moesin levels, cognitive impairment, and glucose fluctuations in T2DM patients. METHODS: A total of 229 T2DM patients and 150 healthy controls were enrolled, and patients with T2DM were further categorized into those with mild cognitive impairment (MCI, n = 71) and without MCI (non-MCI, n = 158). An enzyme-linked immunosorbent assay (ELISA) was used to evaluate the serum levels of moesin, high-sensitivity C-reactive protein (hs-CRP), and brain-derived neurotrophic factor (BDNF) in all participants. RESULTS: Serum moesin levels were significantly elevated in T2DM patients compared to those in healthy controls (P < 0.001) and further increased in the MCI group compared to those in the non-MCI group (P < 0.001). Receiver operating characteristic (ROC) analysis identified an optimal moesin cutoff of 113.49 ng/mL (AUC = 0.866) for distinguishing MCI from T2DM, with 76.1% sensitivity and 86.7% specificity. Correlation analysis demonstrated that moesin was positively correlated with triglyceride, LDL-C, IMT, hs-CRP, and glucose variability markers (MAGE, MBG, SD, and MODD) but negatively correlated with years of education, BDNF, time in range (TIR), and Montreal Cognitive Assessment (MoCA) scores (P < 0.05). Multivariate logistic regression identified BMI, years of education, diabetes duration, FBG, hs-CRP, BDNF, MAGE, SD, and moesin as independent predictors of MCI in T2DM (P < 0.05). CONCLUSIONS: These findings suggest that elevated serum moesin levels are associated with cognitive impairment in patients with T2DM, potentially mediated by glucose fluctuations, inflammation, and vascular dysfunction.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".