Predicting individual treatment response in diabetes
Authors: Hiddo J L Heerspink
Journal: The Lancet Diabetes & Endocrinology (2019)

Diabetes is a heterogeneous disease associated with a large variation among patients in the types of complications and rates of disease progression. Despite the recognition of such heterogeneity, the rather simple classification into type 1 and type 2 diabetes does not reflect this. Much effort has been devoted to improving the classification of diabetes with the rationale that improved classification would help in the earlier identification of patients at risk for complications and that it would enable individualisation of therapy. Against this background, in 2018, Ahlqvist and colleagues proposed a new classification of diabetes consisting of five clusters of patients with different risks of complications of diabetes and underlying disease mechanisms.1Ahlqvist E Storm P Käräjämäki A et al.Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables.Lancet Diabetes Endocrinol. 2018; 6: 361-369Summary Full Text Full Text PDF PubMed Scopus (983) Google Scholar Although this study was an important effort towards an improved approach to individualised therapy, the important question as to whether the five proposed clusters differed in the way patients respond to therapies, so-called clinical utility, was not directly examined. In The Lancet Diabetes & Endocrinology, John M Dennis and colleagues2Dennis JM Shields BM Henley WE Jones AG Hattersley AT Disease progression and treatment response in data-driven subgroups of type 2 diabetes compared with models based on simple clinical features: an analysis using clinical trial data.Lancet Diabetes Endocrinol. 2019; (published online April 29.)http://dx.doi.org/10.1016/S2213-8587(19)30087-7Summary Full Text Full Text PDF PubMed Scopus (169) Google Scholar assess the clinical utility of the newly proposed diabetes clusters. The authors analysed two large randomised controlled trials, ADOPT3Kahn SE Haffner SM Heise MA et al.Glycemic durability of rosiglitazone, metformin, or glyburide monotherapy.N Engl J Med. 2006; 355: 2427-2443Crossref PubMed Scopus (2569) Google Scholar and RECORD,4Home PD Pocock SJ Beck-Nielsen H et al.Rosiglitazone evaluated for cardiovascular outcomes in oral agent combination therapy for type 2 diabetes (RECORD): a multicentre, randomised, open-label trial.Lancet. 2009; 373: 2125-2135Summary Full Text Full Text PDF PubMed Scopus (1187) Google Scholar that randomly assigned patients with newly diagnosed diabetes (ADOPT) or established diabetes (RECORD) to metformin (only in ADOPT), sulfonylurea, and thiazolidinedione therapy.3Kahn SE Haffner SM Heise MA et al.Glycemic durability of rosiglitazone, metformin, or glyburide monotherapy.N Engl J Med. 2006; 355: 2427-2443Crossref PubMed Scopus (2569) Google Scholar, 4Home PD Pocock SJ Beck-Nielsen H et al.Rosiglitazone evaluated for cardiovascular outcomes in oral agent combination therapy for type 2 diabetes (RECORD): a multicentre, randomised, open-label trial.Lancet. 2009; 373: 2125-2135Summary Full Text Full Text PDF PubMed Scopus (1187) Google Scholar Dennis and colleagues compared the utility of the diabetes clusters with routinely available clinical variables by assigning patients to a cluster and assuming equal risk and treatment response in each cluster, or using specific clinical variables of individual patients to predict risk of diabetes complications and response to treatment. The authors compared how well each approach predicted the rate of HbA1c progression and new onset chronic kidney disease as well as the effectiveness of both approaches to predict the most effective therapy for each individual. The authors found similar clusters in the ADOPT and RECORD trials as in the original Scandinavian diabetes cohorts. As in the original study, the authors found differences in the rates of worsening of glycaemia and risk of chronic kidney disease across the clusters. However, the clusters did not outperform routine clinical variables in explaining the proportion of variation in disease progression. With respect to treatment response, a greater HbA1c response was observed with thiazolidinedione in patients who had severe insulin resistant diabetes (cluster 3) and with sulfonylurea therapy in patients who had mild age-related diabetes (cluster 5). Models that incorporated simple clinical features were better in selecting and predicting therapy than was a cluster approach that assumed that all individuals within the subgroup responded similarly. These findings are clinically relevant because they suggest that incorporation of known clinical characteristics into a prediction model to estimate an individual's risk and response to treatment provides a better estimate than does assigning patients to a specific subgroup to estimate risk and treatment at the subgroup level. A strength of this study is the use of data from randomised controlled trials with protocolised study visits, central laboratory assessments minimising variability in clinical chemistry measurements, and randomised treatment assignment, which enabled a systematic comparison of the performance of a cluster-based strategy with a strategy based on individual variables in selecting optimal therapy. Additionally, the framework that was used to assess the effectiveness of the clusters strategy or clinical features strategy is novel and is applicable to future studies. One issue that merits further attention is whether the treatment response in renal or cardiovascular outcomes varies across clusters or clinical variables. The authors analysed glycaemic responses and progression to new onset chronic kidney disease but these responses are surrogate outcomes. Further studies are needed to assess whether clinical variables will also outperform a cluster-based approach when clinical renal or cardiovascular outcomes are analysed. Although models directly incorporating precise clinical features outperformed clusters in the study by Dennis and colleagues, the explained variation of the clinical features was low. Alternative strategies using clinical features that add to predicting individual therapy response are thus needed. The study used clinical features measured before the individual was exposed to the selected therapy. Another strategy would be to prescribe a drug for several weeks or months and use the observed change in clinical features to predict the drug's efficacy to reduce the risk of long-term clinical outcomes such as end-stage kidney disease or cardiovascular events. Development and validation of such models is ongoing and studies show that these models accurately predict who will benefit from treatment.5Smink PA Miao Y Eijkemans MJ et al.The importance of short-term off-target effects in estimating the long-term renal and cardiovascular protection of angiotensin receptor blockers.Clin Pharmacol Ther. 2014; 95: 208-215Crossref PubMed Scopus (21) Google Scholar, 6Schievink B de Zeeuw D Parving HH Rossing P Lambers Heerspink HJ The renal protective effect of angiotensin receptor blockers depends on intra-individual response variation in multiple risk markers.Br J Clin Pharmacol. 2015; 80: 678-686Crossref PubMed Scopus (37) Google Scholar Thus, perhaps an optimal approach would be to use models that incorporate clinical variables to make an initial selection of the most effective therapy, followed by monitoring of early changes in clinical features directly after therapy initiation to predict long-term efficacy on clinical outcomes. The study adds to emerging data that illustrate that routinely measured patient characteristics can be used to explain, at least in part, the variation in risk of diabetic complications and treatment response among individual patients. Ongoing research in this area will be important to implement personalised medicine in daily diabetes care. I was a member of the Study of Diabetic Nephropathy with Atrasentan steering committee and serve as a consultant for AbbVie, Astellas, AstraZeneca, Boehringer Ingelheim, Fresenius, Gilead, Janssen, Merck, Mundi Pharma, and Mitsubishi Tanabe. I am supported by a Vidi grant from the Netherlands Organisation for Scientific Research (917.15.306). Disease progression and treatment response in data-driven subgroups of type 2 diabetes compared with models based on simple clinical features: an analysis using clinical trial dataThe proposed data-driven clusters differ in diabetes progression and treatment response, but models that are based on simple continuous clinical features are more useful to stratify patients. This finding suggests that precision medicine in type 2 diabetes is likely to have most clinical utility if it is based on an approach of using specific phenotypic measures to predict specific outcomes, rather than assigning patients to subgroups. Full-Text PDF Open Access