Relationship Between Baseline Glycemic Control and Cognitive Function in Individuals With Type 2 Diabetes and Other Cardiovascular Risk Factors
Bibliographic record
Abstract
OBJECTIVE: Diabetes is associated with cognitive decline and dementia. However, the relationship between the degree of hyperglycemia and cognitive status remains unclear. This was explored using baseline cognitive measures collected in the ongoing Memory in Diabetes (MIND) substudy of the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial. RESEARCH DESIGN AND METHODS: The relationship of A1C and fasting plasma glucose (FPG) levels to performance on four cognitive tests was assessed, adjusting for age and other determinants of cognitive status. The tests were the Digit Symbol Substitution Test (DSST), Mini Mental Status Examination (MMSE), Rey Auditory Verbal Learning Test, and Stroop Test. RESULTS: A statistically significant age-adjusted association was observed between the A1C level and the score on all four cognitive tests. Specifically, a 1% higher A1C value was associated with a significant 1.75-point lower DSST score (95% CI -1.22 to -2.28; P < 0.0001), a 0.20-point lower MMSE score (-0.11 to -0.28; P < 0.0001), a 0.11-point lower memory score (-0.02 to -0.19, P = 0.0142), and a worse score (i.e., 0.75 s more) on the Stroop Test (1.31-0.19, P = 0.0094). The association between the DSST score and A1C persisted in all multiple linear regression models. FPG was not associated with test performance. CONCLUSIONS: Higher A1C levels are associated with lower cognitive function in individuals with diabetes. The effect of glucose lowering on cognitive function will be determined by the ongoing ACCORD-MIND trial.
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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) | 0.001 | 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 | 0.001 | 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".