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Randomized Controlled Trial
. 2013 Mar 1;7(2):420-30.
doi: 10.1177/193229681300700219.

Understanding heterogeneity in response to antidiabetes treatment: a post hoc analysis using SIDES, a subgroup identification algorithm

Dana S Hardin  1 , Rachelle D Rohwer, Bradley H Curtis, Anthony Zagar, Lei Chen, Kristina S Boye, Honghua H Jiang, Ilya A Lipkovich
Affiliations
Randomized Controlled Trial

Understanding heterogeneity in response to antidiabetes treatment: a post hoc analysis using SIDES, a subgroup identification algorithm

Dana S Hardin et al. J Diabetes Sci Technol. .

Abstract

Background: Treatment response in patients with type 2 diabetes mellitus (T2DM) varies because of different genotypic and phenotypic characteristics. Results of clinical trials are based largely on aggregated estimates of treatment effect rather than individualized outcomes. This research assessed heterogeneity and differential treatment response using the subgroup identification based on differential effect search (SIDES) algorithm with data from a large multinational study.

Methods: This was a retrospective analysis of the DURABLE trial, a randomized, open-label, two-arm, parallel study. The trial enrolled 2091 insulin-naïve T2DM patients aged 30 to 80 years. Patients received twice-daily insulin lispro mix 75/25 (LM75/25) or once-daily insulin glargine (insulin glargine). The SIDES methodology was used to find subgroups where the treatment effect was substantially different from what was observed in the full population of the clinical trial. A subgroup identification tool implementing the SIDES algorithm was used to examine data for 1092 patients (584 LM75/25 and 508 insulin glargine), achieving at-goal 12- or 24-week glycated hemoglobin A1c (A1C) (≤7.0%).

Results: The overall at-goal population treatment difference (A1C reduction) was not clinically meaningful, but a clinically meaningful difference was observed (A1C reduction 2.31% ± 0.06% LM75/25 versus 2.01% ± 0.07% insulin glargine; p = .001) in patients with a baseline fasting insulin level >11.4 μIU/ml and age ≤56 years.

Conclusion: The observation that younger patients with higher levels of fasting insulin may benefit from a regimen that includes short-acting insulin targeting postprandial glycemia helps explain the heterogeneity in response and warrants further investigation.

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Figures

Figure 1
Figure 1
Relationship between change in A1C from baseline to end point and baseline A1C by treatment group of at-goal (12 or 24 weeks) patients (who provided a baseline A1C).
Figure 5
Figure 5
Relationship between age and baseline fasting insulin by treatment group of at-goal (12 or 24 weeks; who provided a baseline A1C) population.
Figure 2
Figure 2
Relationship between change in A1C from baseline to end point and baseline A1C by treatment group of at-goal (12 or 24 weeks) patients (who provided a baseline A1C): subgroup with fasting insulin > 11.4 μIU/ml and age ≤ 56 years.
Figure 3
Figure 3
Relationship between change in A1C from baseline to end point and age by treatment group of at-goal (12 or 24 weeks; who provided a baseline A1C) population.
Figure 4
Figure 4
(A) The OneTouch VerioPro+ meter. (B) Examples of onscreen QC prompts.
See this image and copyright information in PMC

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