Dipeptidyl Peptidase-4 Inhibitors in Chronic Kidney Disease: A Systematic Review of Randomized Clinical Trials
Bibliographic record
Abstract
BACKGROUND: Chronic kidney disease (CKD) is common in patients with type 2 diabetes mellitus (T2DM) and limits therapeutic options. Dipeptidyl peptidase-4 (DPP-4) inhibitors represent a novel class of oral glucose-lowering agents and are known to be safe and effective in the general population. METHODS: We searched Cochrane, EMBASE, and PubMed from the time of their inception until March 2015. We included randomized controlled trials analyzing the efficacy (change in hemoglobin A1C [HbA1C]) and safety of DPP-4 agents in individuals with reduced kidney function (estimated glomerular filtration rate <60 mL/min/1.73 m2). We extracted study characteristics, participants' baseline characteristics, and safety outcomes from eligible studies. We performed a random effects meta-analysis to summarize the change in HbA1C and the relative risk of cardiovascular events in patients with T2DM and CKD. We also collected data on hypoglycemia, other serious adverse events, and mortality. RESULTS: We reviewed 12 studies with 4,403 patients with CKD and 239 on dialysis, finding a mean weighted decline in HbA1C of -0.48 (95% CI -0.61 to -0.35) with DPP-4 inhibitor therapy compared to placebo. DPP-4 inhibitors did not result in any additional adverse events, hypoglycemic episodes, or increased mortality. Restricting to studies with low risk of bias did not alter these findings. CONCLUSIONS: DPP-4 inhibitors can lower HbA1C without increasing the risk of cardiovascular or other major adverse events in patients with CKD. Few studies reported critical adverse events such as heart failure and hypersensitivity. If compared with other oral antiglycemic drugs, the effect of DPP-4 inhibitors is limited; however, their low risk of hypoglycemia may favor their use in patients with CKD. SUMMARY: This systematic review of DPP-4 inhibitors in CKD suggests that they reduce HbA1C by about 0.5%. Furthermore, there was not any increase in the risk for significant adverse events. More research is needed to determine the safety and efficacy of DPP-4 inhibitors in CKD.
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 | 0.020 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.014 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".