Lead Investigator: Linong Ji, Peking University People’s Hospital
Title of Proposal Research: Risk stratification and responder identification for glucagon-like peptide-1 receptor agonists (GLP-1 RA) and sodium-glucose cotransporter 2 inhibitors (SGLT2i) in Type 2 Diabetes Mellitus (T2DM): a machine learning facilitated post-hoc analysis of clinical trials
Vivli Data Request: 7844
Funding Source: Beijing Nova Cross program (Z211100002121169) to Xiantong Zou: Precision medicine in diabetes facilitated by artificial intelligence
Potential Conflicts of Interest: None
Summary of the Proposed Research:
Type 2 diabetes mellitus (T2DM) is a chronic disease that with high blood glucose affecting approximately 537 million adults (20-79 years) people worldwide. The number of people living with T2DM are still rising rapidly in many countries, causing intensive burdens on the global health system. T2DM may also lead to kidney failure, heart attacks and stroke, which are the main cause of premature death in T2DM patients. In current diabetes management, there is a priority to prevent the development of cardio-renal complications, which are kidney disease and heart disease related to T2DM. Several prescription medicines were used in conjunction with diet and exercise in order to reduce blood sugar levels in adults with T2DM and one non-insulin medication, glucagon-like peptide-1 receptor agonists (GLP-1 RA) showed a robust effect on the cardio-renal system. As compared to GLP-1RA, sodium-glucose cotransporter 2 inhibitors (SGLT2i) appeared to have similar heart-protective benefits and enhanced kidney-protective benefits. Both drugs were now recommended for T2DM with high cardiovascular or kidney risks. However, not all T2DM patients develop heart and kidney complications, so it is important to precisely identify who might progress to these complications so these patients may require these two drugs more than others. Also, in current clinical practice, which person should use GLP-1RA usage and which person should choose SGLT2i were unknown. Machine learning (ML) is a type of artificial intelligence (AI) that allows the machine or software to become more accurate at predicting outcomes. It is essential to use multiple datasets to test the generalizability of the machine learning methods. In this study, we aimed to use machine learning methods to develop useful tools to help clinicians identify those patients with a high risk to progress to cardio-renal complications using multiple cohorts. We will develop two machine learning models, one using only baseline features and one with additional information on responses to drugs at the early phase of treatment. By using these tools, we also predicted the final responses to SGLT2i and GLP-1RA in cohorts using these drugs. By developing these models and identifying the responders to GLP-1RA and SGLT2i, we will be able to help clinicians find the most fitting patients and make a good choice on drugs.
Requested Studies:
A Long Term, Randomised, Double Blind, Placebo-controlled Study to Determine the Effect of Albiglutide, When Added to Standard Blood Glucose Lowering Therapies, on Major Cardiovascular Events in Patients With Type 2 Diabetes Mellitus
Data Contributor: GlaxoSmithKline
Study ID: NCT02465515
Sponsor ID: GLP116174
Action to Control Cardiovascular Risk in Diabetes (ACCORD)
Data Contributor: BioLINCC (a data-sharing platform funded by the National Institutes of Health)
Study ID: NCT00000620
Sponsor ID: 123
A Randomized, Double-blind, Event-driven, Placebo-controlled, Multicenter Study of the Effects of Canagliflozin on Renal and Cardiovascular Outcomes in Subjects With Type 2 Diabetes Mellitus and Diabetic Nephropathy
Data Contributor: Johnson & Johnson
Study ID: NCT02065791
Sponsor ID: CR103517
A Randomized, Multicenter, Double-Blind, Parallel, Placebo-Controlled Study of the Effects of JNJ-28431754 on Cardiovascular Outcomes in Adult Subjects With Type 2 Diabetes Mellitus
Data Contributor: Johnson & Johnson
Study ID: NCT01032629
Sponsor ID: CR016627
A Randomized, Multicenter, Double-Blind, Parallel, Placebo-Controlled Study of the Effects of Canagliflozin on Renal Endpoints in Adult Subjects With Type 2 Diabetes Mellitus
Data Contributor: Johnson & Johnson
Study ID: NCT01989754
Sponsor ID: CR102647