The Proposed 5 Subgroups of Diabetes Have Less Clinical Utility Than Models Using Simple Clinical Features: An Evaluation in Randomised Trial Data

Abstract

Background: Recent research using data-driven cluster analysis has proposed five subgroups of diabetes with different diabetes progression and complication risk. We aimed to compare the clinical utility of this subgroup-based approach with an alternative strategy using specific outcome models that use routine clinical features. Methods: We identified clusters in the ADOPT (n=4,351) trial cohort using the cluster analysis reported by Ahlqvist and colleagues (Lancet Diabetes Endocrinology 2018;6:361-69). Differences between clusters in glycaemic and renal progression were evaluated, and contrasted with stratification using routine measures (age at diagnosis and baseline renal function). We tested the performance of a strategy of selecting glucose lowering therapy using clusters with one using simple clinical features (sex, BMI, age at diagnosis, baseline HbA1c) in an independent trial cohort (RECORD (n=4,447)). Findings: Clusters identified in trial data were similar to those described in the original study. Clusters showed differences in glycaemic progression, but a model with age at diagnosis alone had similar predictive ability. We found differences in CKD incidence between clusters however baseline eGFR was a better predictor of time to CKD. Clusters differed in glycaemic response, with a particular benefit for cluster 3 (insulin-resistant) with thiazolidinediones and cluster 5 (older) with sulfonylureas. However simple clinical features outperformed clusters to select therapy for individual patients. Interpretation: Precision medicine in type 2 diabetes is likely to have most clinical utility if based on an approach of using specific continuous clinical measures to predict specific outcomes, rather than stratifying patients into subgroups. Funding Statement: This work was supported by the Medical Research Council (UK) (MR/N00633X/1). Data for both ADOPT and RECORD trials were accessed through the Clinical Trial Data Transparency Portal under approval from GSK (Proposal 930). ATH is a NIHR Senior Investigator and a Wellcome Trust Senior Investigator (098395/Z/12/Z). AGJ is supported by an NIHR Clinician Scientist award (CS-2015-15- 018). JMD, ATH and BMS are supported by the NIHR Exeter Clinical Research Facility. The views expressed are those of the authors and not necessarily those of the MRC, the NIHR or the Wellcome Trust Declaration of Interests: WEH declares a grant from IQVIA. All other authors declare no competing interests. Ethics Approval Statement: Individual-level participant data from the ADOPT and RECORD trials were accessed through the Clinical Trial Data Transparency Portal, with study approval from GlaxoSmithKline (Proposal 930). Both trials were conducted according to Good Clinical (Research) Practice guidelines and the Declaration of Helsinki (1996).