The efficacy of canagliflozin in diabetes subgroups stratified by data-driven clustering or a supervised machine learning method: a post hoc analysis of canagliflozin clinical trial data

Conclusions/interpretationData-driven clusters of individuals with diabetes showed different responses to canagliflozin in glucose lowering but not renal outcome prevention. Canagliflozin reduced the risk of albumin progression in high-risk individuals identified by supervised machine learning. Further studies with larger sample sizes for external replication and subtype-specific clinical trials are necessary to determine the clinical utility of these stratification strategies in sodium –glucose cotransporter 2 inhibitor treatment.Data availabilityThe application for the clinical trial data source is available on the YODA website (http://yoda.yale.edu/).Graphical abstract
Source: Diabetologia - Category: Endocrinology Source Type: research