Cognitive Impairment and its Association with Glycemic Control in Type 2 Diabetes Mellitus Patients
Bibliographic record
Abstract
INTRODUCTION: Type 2 diabetes mellitus is one of the major causes of increasing morbidity worldwide. Effective screening is carried out routinely for diabetic retinopathy, neuropathy, and nephropathy. Of late, studies have reported that cognitive decline can occur in people with diabetes, which could go undetected for a long period, and hence routine screening could be warranted. METHODOLOGY: Our objective was to study the prevalence of previously unknown mild cognitive impairment (MCI) in type 2 diabetic patients visiting a tertiary care center with the Montreal Cognitive Assessment (MoCA) test and to study the correlations of HbA1c, fasting blood sugar (FBS), postprandial blood sugar (PPBS), age, and duration of diabetes with the MoCA scores. Seventy patients with type 2 diabetes mellitus were included in the study. Patients with MoCA scores ≥26 were considered to have normal cognition (NC) and those with <26 MCI. RESULTS: MCI was noted in 38 (54.29%) type 2 diabetes mellitus patients and NC in 32 (45.71%). Those with MCI had higher HbA1c (8.79 ± 1.85 vs. 7.78 ± 1.60), higher FBS (177.05 ± 62.48 vs. 149.38 ± 54.38), and PPBS (282.03 ± 85.61 vs. 214.50 ± 82.43), which were statistically significant. The cognitive domains of executive function, naming, attention, language, and memory showed a statistically significant difference between those with MCI and NC. There were no differences in the mean age, duration of diabetes, and educational status between the groups. CONCLUSION: The high prevalence of MCI in type 2 diabetic patients highlights the importance of implementing routine cognitive testing. The correlation of cognitive impairment with poor glucose control needs further studies to find out whether improving glycemic control will help improve cognition.
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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 | 0.000 | 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".