Combination of the dipeptidyl peptidase-4 inhibitor linagliptin with insulin-based regimens in type 2 diabetes and chronic kidney disease
Bibliographic record
Abstract
Glucose-lowering treatment options for type 2 diabetes mellitus patients with chronic kidney disease are limited. We evaluated the potential for linagliptin in combination with insulin in type 2 diabetes mellitus patients with mild-to-severe renal impairment. Data for participants in two phase 3 trials with linagliptin who were receiving insulin were analysed separately (n = 811). Placebo-adjusted mean HbA1c changes from baseline were -0.59% (mild renal impairment) and -0.69% (moderate renal impairment) after 24 weeks and -0.43% (severe renal impairment) after 12 weeks. Drug-related adverse events with linagliptin were similar to placebo (mild renal impairment: 19.9% vs. 26.5%; moderate renal impairment: 22.0% vs. 25.0%; severe renal impairment: 46.3% vs. 43.6%, respectively). Frequencies of hypoglycaemia in patients with mild, moderate and severe renal impairment were 34.9%, 35.6% and 66.7% with linagliptin and 37.5%, 39.7% and 49.1% with placebo, respectively. Episodes of severe hypoglycaemia were low (⩽5.6%). Adding linagliptin to insulin in type 2 diabetes mellitus patients with chronic kidney disease improved glucose control and was well tolerated.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".