Correlation between long-term glycemic variability and cognitive function in middle-aged and elderly patients with type 2 diabetes mellitus
Bibliographic record
Abstract
Abstract Objective To investigate the correlation associated with long-term glycemic variability on cognitive function in middle-aged and elderly patients with T2DM. Methods This study was a single-institution, retrospective analysis of data. A total of 138 patients who were hospitalized in the Department of Endocrinology, the Second Affiliated Hospital of Anhui Medical University from June 2021 to November 2022 were recruited. The Montreal Cognitive Assessment (MoCA) was applied to assess the cognitive function of the patients, which were divided into MCI and non-MCI. Glycated hemoglobin A1c standard deviation (HbAlc-SD) and fasting plasma glucose standard deviation (FPG-SD) were used to measure long-term blood glucose fluctuations. General clinical data, blood biochemical indicators, and glycemic variability indicators were compared between the two groups of patients. The differences between the groups were compared using t-test, x2 test, ornonparametric test. Correlation and diagnostic power were further analyzed using multiple logistic regression analysis and ROC curve analysis. Results The differences in age, BMI, HbA1c-M, HbA1c-SD, FPG-M, FPG-SD, GFR, 24h urinary protein, and UACR were statistically significant between the two groups (P<0.05). In a multiple logistic regression analysis, HbA1c-SD and FPG-SD were found to be risk factors for cognitive dysfunction and eGFR to be a protective factor. The area under the curve (AUC) of HbA1c-SD for predicting MCI prevalence was 0.828 (95% CI 0.754～0.887, P<0.001), with a sensitivity of 62.69%, a specificity of 94.29%, and an optimal diagnostic value 1.01. The area under the curve (AUC) of FPG-SD for predicting MCI prevalence was 0.737 (95% CI 0.655～0.808, P<0.001), with a sensitivity of 76.12%, a specificity of 61.43%, and an best diagnostic value 0.94. The area under the curve (AUC) of eGFR for prediction of MCI prevalence was 0.712 (95% CI 0.628～0.786, P<0.01), with a sensitivity of 70.15 %, a specificity of 64.29 %, and an optimal diagnostic value 79.82 ml/min/1.73m2. Conclusions Long-term blood glucose variability affects cognitive function in middle-aged and elderly T2DM patients, and cognitive function is poorer in those with high blood glucose variability, for whom renal function is a protective 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".