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Glycaemic profile and cognitive impairment in individuals with diabetes mellitus: A cross-sectional study on HbA1c, random blood glucose, and serum insulin levels

2025· article· en· W7084137328 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2025
Typearticle
Languageen
FieldComputer Science
TopicOptimization and Variational Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsGlycated hemoglobinConfoundingDiabetes mellitusCognitionCognitive impairmentType 2 diabetesInsulin

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.020
GPT teacher head0.273
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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