Association of Hyperlipidemia and Hyperglycemia With Cognitive Function in Type 2 Diabetes: A Cross-Sectional Analysis
Bibliographic record
Abstract
Introduction Cognitive impairment is increasingly recognized as a significant health concern, particularly in the early stages of diabetes. The type and severity of cognitive deficits may vary with the duration of diabetes and the effectiveness of glucose management. Complications associated with metabolic syndrome may exacerbate these cognitive declines. This study investigates the association of hyperlipidemia and hyperglycemia with cognitive function in patients with type 2 diabetes (T2D). Methods We conducted a cross-sectional study on type 2 diabetic patients aged between 30 and 40 years of age and having the disease for less than 10 years duration. We collected anthropometric measurements, tested glycated hemoglobin (HbA1c) and fasting lipid profiles, and assessed cognitive function using the Montreal Cognitive Assessment (MoCA). Results All participants exhibited elevated HbA1c levels and abnormal lipid profiles. We observed weak positive correlations between the duration of diabetes and low-density lipoprotein cholesterol (LDL; 0.448), very low-density lipoprotein cholesterol (VLDL; 0.398), total cholesterol (0.526), and HbA1c (0.360). There were moderately negative correlations between the duration of diabetes and MoCA scores (-0.522) and weak negative correlations between LDL and MoCA (-0.304), VLDL and MoCA (-0.259), and total cholesterol and MoCA (-0.409). The correlation between HbA1c and MoCA was also moderately negative (-0.779). Regression analysis revealed statistically significant associations of MoCA with the duration of diabetes, HbA1c, and lipid parameters, with HbA1c being the largest contributor to cognitive decline at 60.66%, while the contributions of various lipid parameters were considerably lower (LDL: R² = 0.092, VLDL: R² = 0.067, total cholesterol: R² = 0.167). The contribution of the duration of diabetes (R² = 0.272) to cognitive decline was less than that of HbA1c but more than the lipid parameters. Conclusions The findings suggest that hyperglycemia and the duration of diabetes are the major factors contributing to cognitive decline in patients with T2D. Patients should be advised to maintain optimal glycemic control and engage in activities that enhance cognitive function to prevent cognitive impairment. Regular cognitive screening for diabetic patients is also recommended.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".