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A review of treatment response in type 2 diabetes: assessing the role of patient heterogeneity

Authors


Ronald A. Cantrell, PhD, Epidemiology and Health Services Research, Global Health Outcomes, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN 46285, USA.
E-mail: ronald.cantrell@lilly.com

Abstract

The response to treatment for type 2 diabetes typically varies among individuals within a study population. This variation is known as heterogeneity of treatment response. We conducted a comprehensive literature review to identify factors that account for heterogeneity of treatment response in patients treated for type 2 diabetes. Three databases (PubMed, EMBASE and Cochrane Library) were searched for articles published in the last 10 years describing investigations of factors associated with treatment response and outcomes among people with type 2 diabetes receiving pharmacological treatment. Of the 43 articles extracted and summarized, 35 (81%) discussed clinical factors, 31 (72%) described sociodemographic factors and 17 (40%) reported on comorbidity or behavioural factors. Clinical factors identified included baseline glycated hemoglobin A1c or fasting plasma glucose (FPG) levels, insulin response or sensitivity, C-peptide, body composition, adipose tissue proteins, lipid profile, plasma albumin levels and duration of disease or insulin treatment. Other factors identified included age, sex, race, socioeconomic status and comorbidities. This review identified the following research gaps: use of multiple definitions for response, few patient-reported measures and lack of evidence regarding whether factors were associated with treatment response for only specific medications or across pharmacological therapies. Furthermore, identification of factors associated with type 2 diabetes treatment response was generally a secondary objective in the research reviewed. Understanding which patient subgroups are more likely to respond to treatment and identifying factors associated with response may result in targeted treatment decisions and alter the interpretation of efficacy or effectiveness of results. In conclusion, accounting for these factors in clinical trials and when making clinical treatment decisions may improve therapy selection and individual patient outcomes.

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