Risk Factors and Metabolomics of Mild Cognitive Impairment in Type 2 Diabetes Mellitus
Bibliographic record
Abstract
Abstract Objective: This study aimed to explore the controllable risk factors, metabolic characteristics and potential biomarkers of T2DM-MCI and to provide potential proof for the diagnosis, prevention and treatment of cognitive impairment in T2DM patients. Methods: 103 T2DM patients who were hospitalized in the Endocrinology Department of the Second Hospital of Dalian Medical University were included. MoCA was used to evaluate the cognitive function of all subjects. 50 patients were included in T2DM-MCI group (MoCA score <26 points), and 53 subjects were in T2DM-NCI group (MoCA score ≥ 26 points). Serum samples of subjects were collected, and metabolomics data was collected by UHPLC-MS. The serum samples of 12 subjects were excluded due to poor quality, 47 subjects were in the T2DM-MCI group, 44 cases in T2DM-NCI group were used to analyze the differentially expressed small molecule metabolites, metabolics pathway and the potential specific biomarkers. Results: Comparison between T2DM-MCI group and T2DM-NCI group, years of education, history of insulin application, and insulin resistance index, insulin-like growth factor-binding protein-3, and creatinine were significantly different between groups. Further binary logistic regression analysis of the variables showed low educational level and low serum insulin-like growth factor-binding protein-3 was an independent risk factor for T2DM-MCI. Metabolomics analysis showed that 10 metabolites were differentially expressed between T2DM-MCI group and T2DM-NCI group (P<0.05 and FDR<0.05, VIP>1.5). KEGG enrichment pathway reveals that fatty acid degradation is the most significant. ROC shows that LPC 18:0 has a greater diagnostic efficiency. Conclusion: This study suggests that T2DM patients with low education levels, history of insulin use, high insulin resistance, low serum insulin-like growth factor binding protein-3, and low creatinine values are more susceptible to MCI. Short years of education and low serum insulin-like growth factor binding protein-3 levels are independent risk factors for MCI in combination with T2DM. There were significant differences in serum metabolites between T2DM-MCI and T2DM-NCI. Abnormal lipid metabolism plays an important role in the development of cognitive impairment in T2DM patients. LPC 18:0 can effectively differentiate T2DM-MCI and T2DM-NCI, expected to identify cognitive impairment in T2DM patients at an early stage.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| 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".