Metabolic signatures and potential biomarkers in the progression of type 2 diabetes mellitus with cognitive impairment patients: a cross-sectional study
Bibliographic record
Abstract
Abstract Background: Type 2 diabetes mellitus (T2DM), a growing global chronic disease, can increase the risk of cognitive impairment. The microbiota-gut-brain axis has a crucial role in the development of neurological pathologies. Therefore, it is necessary to examine host-gut microbiota metabolites associated with diabetic cognitive impairment (DCI) progression. Objective: This study aimed to describe metabolic signatures, identify potential biomarkers in the progression from T2DM to DCI, and analyze the correlation between the potential biomarkers and clinical characteristics. Methods: A cross-sectional study involving 8 patients with T2DM and 8 with DCI was carried out between May 2018 and May 2020. The characteristic clinical data of the patients, such as demographics, hematological parameters, Mini-Mental State Examination, and Montreal Cognitive Assessment, were collected. Metabolomics profiling measured the host-gut microbiota metabolites in the serum. The potential biomarkers were found by getting intersection of the differential host-gut microbiota metabolites from multidimensional statistics (Orthogonal Partial Least Squares-Discriminant Analysis and permutation plot) and univariate statistics (independent-sample t test and Mann-Whitney U test). In addition, we examined the relationship between potential biomarkers and characteristic clinical data using the Spearman correlation coefficient test. Results: A total of 22 potential biomarkers were identified in the T2DM and DCI groups, including 15 upregulated potential biomarkers (such as gluconolactone, 4-hydroxybenzoic acid, and 3-hydroxyphenylacetic acid) and 7 downregulated potential biomarkers (such as benzoic acid, oxoglutaric acid, and rhamnose) in DCI group. Most of the potential biomarkers were associated with clinical characteristics, such as Mini-Mental State Examination, Montreal Cognitive Assessment, and glycated hemoglobin A1c. Conclusion: This study showed that metabolic signatures in the serum were associated with DCI development and clinical severity, providing new ideas for extensive screening and targeted treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".