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.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 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".