Association of Plasma DPP4 Activity With Mild Cognitive Impairment in Elderly Patients With Type 2 Diabetes: Results From the GDMD Study in China
Bibliographic record
Abstract
OBJECTIVE: Hyperglycemia, inflammation, and oxidative stress are thought to be involved in the pathogenesis of cognitive decline. Dipeptidyl peptidase-4 (DPP4) is a newly identified adipokine related to these risk factors. Hence, we aimed to investigate the association between plasma DPP4 activities and mild cognitive impairment (MCI) in elderly patients with type 2 diabetes. RESEARCH DESIGN AND METHODS: We evaluated plasma DPP4 activity, inflammatory markers, and oxidative stress parameters in a cross-sectional sample of 1,160 patients with type 2 diabetes aged 60 years or older in China. MCI was diagnosed based on criteria established by the National Institute on Aging-Alzheimer's Association workgroups RESULTS: Patients in the highest quartile of DPP4 activity had higher HbA1c, interleukin 6 (IL-6), CRP, nitrotyrosine, 8-iso-PGF2a, and lower Montreal Cognitive Assessment (MoCA) scores compared with subjects in the lowest quartile (P < 0.001). In the highest DPP4 quartile, MCI risk was higher (odds ratio 3.49; 95% CI 1.97-4.57) than in the lowest quartile after adjustment for potential confounders. The risk for MCI increased more with higher levels of DPP4 activity, IL-6, CRP, nitrotyrosine, and 8-iso-PGF2a (P < 0.05), but not with higher levels of HbA1c. CONCLUSIONS: This study shows that increased DPP4 activities are independently associated with MCI in elderly patients with type 2 diabetes. The mechanisms might be partly explained by the effect of DPP4 on inflammation and oxidative stress. These observations raise further interest in DPP4 activity for its potential effect on these MCI-related risk factors as a biological marker or even a possible therapeutic target for MCI.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".