Doctors lack clear evidence on which blood pressure drug works best for people with both diabetes and kidney disease, because the drugs have never been tested directly against each other in trials. This matters because diabetes and high blood pressure together are the leading causes of kidney failure. Lowering blood pressure is one of the few things that can slow the damage, but the growing number of drug classes—ACE inhibitors, ARBs, calcium channel blockers, diuretics, and others—makes choosing one a gamble. Without head-to-head trials, clinicians rely on indirect comparisons that may be misleading. This project will use a statistical method called component network meta-analysis to compare all available drugs at once, using data from hundreds of existing trials. The result will be a clear, clinically useful ranking of which therapies best reduce deaths and prevent progression to end-stage kidney disease. If successful, the findings could directly change prescribing guidelines in the UK and globally. The team will also produce an animated video for patients, so that people living with diabetes and kidney disease understand what the evidence says about their treatment options.
View original technical description
Research Question: which blood pressure lowering therapies in adults with diabetes who have or are at risk of kidney disease are most efficacious in improving survival and reducing the risk of end-stage kidney disease (ESKD)? Background: Diabetes and high blood pressure are the leading causes of chronic kidney disease (CKD), and are the most common causes of ESKD. Together they markedly increase the risk of both cardiovascular disease and mortality. One of the most common modifiable factors to reduce these risks in people with diabetes and CKD is through the management of blood pressure. However, in these individuals the pharmacology of blood pressure lowering therapies is increasingly complex, and the efficacy and safety of available drugs is unknown due to the lack of head-to-head trials. Aims and Objectives: The research question is: which blood pressure lowering therapies in adults with diabetes who have or are at risk of kidney disease are most efficacious in improving survival and reducing the risk of ESKD. The research has four aims: 1. To update a previous systematic review using the same search strategies and databases to the previously published review. 2. To use the novel approach of component network meta-analysis with the updated dataset to compare the efficacy and acceptability of blood pressure lowering therapies in adults with diabetes who have or are at risk of CKD. 3. To use the data generated from the component network meta-analysis to produce a clinically meaningful hierarchy of antihypertensive interventions according to their efficacy and safety. 4. To feedback the results of this research to patients and patient groups (alongside other key stakholders) through an animated video which will be developed alongside our public co-applicant and our PPIE panel. Methods: This project will conduct a systematic review and component network meta-analysis comparing blood pressure lowering therapies in adults with diabetic kidney disease. Electronic databases will be searched systematically for trials in adults with diabetes and kidney disease comparing orally administered blood pressure drugs. Primary outcomes will be total mortality and progression to ESKD. We will initially perform a pairwise meta-analysis. We will then perform component network meta-analysis using three models; additive effects model, pairwise interactions model, and standard network meta-analysis model. A clinically meaningful hierarchy of blood pressure lowering therapies will be produced. Timelines for delivery: Months 1-8 formulate review protocol, perform searches, data extraction, data synthesis and interpretation. Months 9-12 development of animated video, and other knowledge mobilisation strategies. Anticipated Impact and Dissemination: As well as having a direct impact upon patients, this research has the capacity to lead to a direct change and adoption within UK and global clinical practice.
Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.
Is something wrong? Let us know