American Diabetes Association
DC21-1765R1_Supplement_-_Clean_Copy.pdf (1.09 MB)

Phenomapping-Derived Tool to Individualize the Effect of Canagliflozin on Cardiovascular Risk in Type 2 Diabetes

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posted on 2022-02-04, 18:44 authored by Evangelos K Oikonomou, Marc A Suchard, Darren K. McGuire, Rohan Khera
Objective: Sodium-glucose cotransporter-2 (SGLT2) inhibitors have well-documented cardioprotective effects but are underused, partly due to high cost. We aimed to develop a machine learning support tool to individualize the atherosclerotic cardiovascular disease (ASCVD) benefit of canagliflozin in type 2 diabetes.

Research Design and Methods: We constructed a topological representation of the Canagliflozin Cardiovascular Assessment Study (CANVAS) using 75 baseline variables collected from 4327 patients with type 2 diabetes randomly assigned 1:1:1 to one of two canagliflozin doses (n=2886) or placebo (n=1441). Within each patient’s 5% neighborhood, we calculated age- and sex-adjusted risk estimates for major adverse cardiovascular events (MACE). An extreme gradient boosting algorithm was trained to predict the personalized ASCVD effect of canagliflozin using features most predictive of topological benefit. For validation, this algorithm was applied to the CANVAS-Renal (CANVAS-R) trial, comprising 5808 patients with type 2 diabetes randomized 1:1 to canagliflozin or placebo.

Results: In CANVAS (mean age 60.9±8.1 years, 33.9% women) 1605 (37.1%) patients had a neighborhood hazard ratio more protective than the effect estimate of 0.86 reported for MACE in the original trial. A 15-variable tool, INSIGHT, trained to predict the personalized ASCVD effects of canagliflozin in CANVAS, was tested in CANVAS-R (mean age 62.4±8.4 years, 2164 (37.3%) women) where it identified patient phenotypes with higher ASCVD effect of canagliflozin (adj. HR 0.60 [95%CI:0.41-0.89] versus 0.99 [95%CI:0.76-1.29]; P for interaction=0.04).

Conclusions: We present an evidence-based, machine learning-guided algorithm to personalize the prescription of SGLT2 inhibitors for patients with type 2 diabetes for ASCVD effect.


This study was supported by research funding awarded by the Yale School of Medicine to Dr. Khera. Dr. Khera also receives support from the National Heart, Lung, and Blood Institute of the National Institutes of Health under the award K23HL153775-01A1, outside of the submitted work. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.