Anti‐inflammatory effects of linagliptin in hemodialysis patients with diabetes
Bibliographic record
Abstract
Inflammation and glycemic control are important prognosis-related factors for hemodialysis (HD) patients; moreover, inflammation affects insulin secretion. Here, we evaluated the anti-inflammatory effects of monotherapy with linagliptin-a dipeptidase-4 inhibitor-in HD patients with type 2 diabetes. We examined 21 diabetic HD patients who were not receiving oral diabetes drugs or insulin therapy and with poor glycemic control (glycated albumin [GA] level, >20%). Linagliptin (5 mg) was administered to the patients daily. The levels of prostaglandin E2 (PGE2), interleukin-6 (IL-6), high-sensitivity C-reactive protein, GA, blood glucose, and active glucagon-like peptide-1 were determined before and 6 months after treatment. Body weight and serum levels of albumin, hemoglobin, total cholesterol, and low-density lipoprotein cholesterol were also recorded before and after treatment. The levels of PGE2 and GA were significantly decreased 1 month after starting linagliptin therapy, whereas the IL-6 levels were significantly decreased 6 months after starting linagliptin therapy. After 6 months of treatment, the PGE2 levels decreased from 188 ± 50 ng/mL to 26 ± 5 ng/mL; IL-6 levels, from 1.5 ± 0.4 pg/mL to 0.6 ± 0.1 pg/mL; and GA levels, from 21.3% ± 0.6% to 18.0% ± 0.6%. Glucagon-like peptide-1 levels increased 2.5-fold during the treatment. Over the 6-month treatment period, body weight and levels of high-sensitivity C-reactive protein, blood glucose, albumin, hemoglobin, and cholesterol did not change; none of the patients exhibited hypoglycemia. The anti-inflammatory effects of linagliptin monotherapy indicate that it may serve as a useful glucose control strategy for HD patients with diabetes.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".