Glucose Fluctuations Are Linked to Disrupted Brain Functional Architecture and Cognitive Impairment
Bibliographic record
Abstract
BACKGROUND: Type 2 diabetes mellitus (T2DM) accelerates cognitive decline, which is believed to be triggered by aberrant neural activity. OBJECTIVE: To explore how glucose fluctuations impact brain functional architecture and cognition in T2DM patients. METHODS: T2DM patients were divided according to glycemic variability, forming two categories: patients with fluctuating glucose levels and patients with stable glucose levels. Degree centrality (DC) was calculated within the cerebral gray matter of each participant and was compared among the two patient groups and a healthy control group. The relationships between glucose fluctuations and aberrant DC and cognitive performance, as well as the relationship between aberrant DC and cognitive performance, were further explored. RESULTS: Compared with T2DM patients with stable glucose levels, T2DM patients with fluctuating glucose levels exhibited significantly worse performance on the Montreal Cognitive Assessment, Trail Making Test-B (TMT-B), and verbal fluency test (VFT), as well as significant decreases in DC in certain regions, most of which were within the default mode network. In the combined T2DM group, the mean amplitude of glycemic excursions (MAGE) was positively correlated with TMT-B scores and negatively correlated with VFT scores. Moreover, the MAGE was negatively correlated with DC in the left medial prefrontal cortex (mPFC). In addition, TMT-B scores were negatively correlated with reduced DC in the left mPFC. CONCLUSION: These findings further contribute to the mounting evidence of the effects of glycemic variability on the diabetic brain. Tightened control of glucose fluctuations might prevent cognitive decline and changes in brain functional architecture in T2DM individuals.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".