Glycaemic profile and cognitive impairment in individuals with diabetes mellitus: A cross-sectional study on HbA1c, random blood glucose, and serum insulin levels
Bibliographic record
Abstract
This study aimed to evaluate the association between glycaemic profile, evaluated by glycated hemoglobin (HbA1c), random blood glucose, and serum insulin, and the cognitive impairment in individuals with DM. This cross-sectional study was conducted in a clinical school at a University in Southern Brazil, between March and August 2023. Individuals (≥18 years) with a medical diagnosis of DM were studied. The outcome was cognitive impairment assessed by the Montreal Cognitive Assessment, and the exposures were random blood glucose, HbA1c, and serum insulin. In total, 365 individuals were studied. Cognitive impairment was identified in 67.9% of the participants, and high levels of blood glucose, HbA1c, and blood insulin were found in 41.9%, 55.1%, and 48.2% of the individuals, respectively. There was no association between the glycaemic profile (random blood glucose, HbA1c, serum insulin) and cognitive impairment, before and after adjustment for confounding factors. Sensitivity analyses also showed no association. In conclusion, although there was no association between glycaemic profile and cognitive function, a high prevalence of both cognitive impairment and uncontrolled glycemia was found in individuals with DM. These findings raise questions about the mechanisms involved in DM-related cognitive impairments, highlighting the need for broader investigations to guide public health strategies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".