Effect and its mechanism of Linagliptin on mild cognitive impairment in elderly type 2 diabetes mellitus patients
Bibliographic record
Abstract
Objective To investigate the effect and its underlying mechanism of Linagliptin on mild cognitive impairment(MCI)in elderly type 2 diabetes mellitus (T2DM) patients. Methods Montreal Cognitive Assessment(MoCA)scale was used to prospectively screen T2DM patients for MCI in our hospital from December 2016 to June 2017, and a total of 98 elderly T2DM patients with MCI were recruited.They were randomly divided into the linagliptin group(Linagliptin + metformin, n=50)and the non-linagliptin group(gliclazide + metformin, n=48). Serum fasting plasma glucose(FPG), glycosylated hemoglobin(HbAlc), blood lipids and amyloid β-protein 1-42(Aβ1-42)levels were determined, and MoCA score and homeostasis model assessment of insulin resistance(HOMA-IR)were calculated, and were compared between the two groups before and after 24 weeks of treatment. Results In the linagliptin group, serum FPG, HbA1c, HOMA-IR, Aβ1-42 levels were significantly decreased and MoCA score was increased after 24 weeks of treatment as compared with pre-treatment[(7.29±1.00)mmol/L vs.(9.16±1.60)mmol/L, (7.19±0.99)% vs.(9.36±1.07)%, (3.05±1.12) vs.(4.05±1.30), (0.463±0.093)g/L vs.(0.528±0.110)g/L, (24.48±1.18) vs.(23.22±1.37), all P 0.05]. It is worth to pay close attention to the key discovery of this paper that HOMA-IR and Aβ1-42 levels were significantly lower and MoCA score was significantly higher in the linagliptin group than in the non-linagliptin group after 24 weeks of treatment(all P<0.05). Conclusions Linagliptin as one of DPP-4 enzyme inhibitors can improve the cognitive function in elderly patients with T2DM, which might be relevant to reducing serum Aβ level and improving HOMA-IR.DPP-4 enzyme inhibitor may be a good option for treatment of mild cognitive dysfunction in T2DM patients in the future. Key words: Diabetes mellitus, type 2; Cognitive impairment; Linagliptin; Amyloid β-protein
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".