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doi: 10.1111/1753-0407.12218
Journal of Diabetes 7 (2015) 1–2
EDITORIAL
Individualizing therapy: Do we have the tools to do it? In this edition of The Journal of Diabetes, Paul et al. report an interesting analysis of the risk factors for hypoglycemia. Their novel analysis of trial data from studies of once-weekly exenatide and insulin glargine show potentially important insights into the on-treatment interactions of glycemic control and body weight with risk of hypoglycemia. The analysis is one of an all too small number of publications designed to identify how individuals might respond to specific drugs. Two phenomena have been prominent in the management of type 2 diabetes in the last decade. First, several new classes of glucose-lowering agents have emerged, and, second, there has been an emphasis on individualizing therapy. The challenge lies in combining the two. Choosing the right drug from among at least 10 classes for an individual patient requires knowledge of contraindications, how to predict response and side-effects, and what range of responses are likely. How does the published literature help clinicians and patients to deal with these issues? The answer appears to be – not very well. On the assumption that metformin is the drug of first choice, the available information with which to choose second, third and fourth line agents is very limited. For glucose-lowering drugs, we would ideally like to see information on predicting responses in relation to hard outcomes – i.e. which drugs prevent microvascular and mavcrovascular outcomes in which patients. That goal seems very far away, but a useful first step would be to be able to understand and predict glycemic responses. There are myriads of clinical trials reporting mean reductions in HbA1c in both placebo-controlled and activecomparator trials. How much information do they provide on identification of responder phenotypes, on description of the range of expected effects on HbA1c, and on side-effects?
goal level.2 However, two other systematic reviews, which focused specifically on DPP4 inhibitors, reported the opposite effect, with greater HbA1c lowering occurring in trials with lower baseline HbA1c levels.3,4 These latter two reviews also reported greater HbA1c lowering in Asians than non-Asians,3 in individuals with a lower body mass index,3 and in older patients.4 The significant limitation of all of these reviews was that the authors did not have access to the individual participant data. The analyses drew only from the published information from each trial, and therefore linked group means of baseline descriptors with group mean changes in HbA1c. Perhaps this explains some of the counter-intuitive and conflicting findings, such as greater HbA1c lowering being associated with a lower baseline HbA1c. Rosenstock et al., in a trial of exenatide as add-on to insulin, provide one of the few explorations of predictions of HbA1c lowering.5 They found that longer diabetes duration and lower baseline BMI were associated with better HbA1c lowering with exenatide. However, the active titration of insulin doses in this trial may have obscured some of the effects. It might seem that this issue could be easily addressed by simply encouraging trialists to report these data. However, we would first need some consensus on what we mean by a good response. Is a good response a large fall in the HbA1c or the achievement of an HbA1c target? The former is likely to be predicted by a high baseline HbA1c, the latter by a lower baseline HbA1c. Is a reduction in HbA1c from 11% to 10% the sign of a better response than a fall from 7.5% to 6.8%? There are no simple answers here, and analyses of patterns of HbA1c lowering, with and without accounting for placebo effects, will need to be undertaken to properly define what is meant by a good response. Only when response is better defined can we hope to identify responders.
Predicting who will respond best Knowing the phenotype of patients who will respond best to a drug is probably the most valuable piece of information for drug selection, and with which to individualize therapy. However, little is known and little is published on this. A number of individual trials report that HbA1c lowering is greater in those with higher baseline HbA1c levels. This is supported by two systematic reviews,1,2 one of which also showed that those with lower baseline HbA1c are more likely to reach a given
Range of expected responses Received wisdom tells us that a single drug typically lowers HbA1c by a little less than 1 percentage point. On the basis of this knowledge, we might decide that it is not possible for a patient with an HbA1c of 8.5% to achieve a target of
doi: 10.1111/1753-0407.12218
Journal of Diabetes 7 (2015) 1–2
EDITORIAL
Individualizing therapy: Do we have the tools to do it? In this edition of The Journal of Diabetes, Paul et al. report an interesting analysis of the risk factors for hypoglycemia. Their novel analysis of trial data from studies of once-weekly exenatide and insulin glargine show potentially important insights into the on-treatment interactions of glycemic control and body weight with risk of hypoglycemia. The analysis is one of an all too small number of publications designed to identify how individuals might respond to specific drugs. Two phenomena have been prominent in the management of type 2 diabetes in the last decade. First, several new classes of glucose-lowering agents have emerged, and, second, there has been an emphasis on individualizing therapy. The challenge lies in combining the two. Choosing the right drug from among at least 10 classes for an individual patient requires knowledge of contraindications, how to predict response and side-effects, and what range of responses are likely. How does the published literature help clinicians and patients to deal with these issues? The answer appears to be – not very well. On the assumption that metformin is the drug of first choice, the available information with which to choose second, third and fourth line agents is very limited. For glucose-lowering drugs, we would ideally like to see information on predicting responses in relation to hard outcomes – i.e. which drugs prevent microvascular and mavcrovascular outcomes in which patients. That goal seems very far away, but a useful first step would be to be able to understand and predict glycemic responses. There are myriads of clinical trials reporting mean reductions in HbA1c in both placebo-controlled and activecomparator trials. How much information do they provide on identification of responder phenotypes, on description of the range of expected effects on HbA1c, and on side-effects?
goal level.2 However, two other systematic reviews, which focused specifically on DPP4 inhibitors, reported the opposite effect, with greater HbA1c lowering occurring in trials with lower baseline HbA1c levels.3,4 These latter two reviews also reported greater HbA1c lowering in Asians than non-Asians,3 in individuals with a lower body mass index,3 and in older patients.4 The significant limitation of all of these reviews was that the authors did not have access to the individual participant data. The analyses drew only from the published information from each trial, and therefore linked group means of baseline descriptors with group mean changes in HbA1c. Perhaps this explains some of the counter-intuitive and conflicting findings, such as greater HbA1c lowering being associated with a lower baseline HbA1c. Rosenstock et al., in a trial of exenatide as add-on to insulin, provide one of the few explorations of predictions of HbA1c lowering.5 They found that longer diabetes duration and lower baseline BMI were associated with better HbA1c lowering with exenatide. However, the active titration of insulin doses in this trial may have obscured some of the effects. It might seem that this issue could be easily addressed by simply encouraging trialists to report these data. However, we would first need some consensus on what we mean by a good response. Is a good response a large fall in the HbA1c or the achievement of an HbA1c target? The former is likely to be predicted by a high baseline HbA1c, the latter by a lower baseline HbA1c. Is a reduction in HbA1c from 11% to 10% the sign of a better response than a fall from 7.5% to 6.8%? There are no simple answers here, and analyses of patterns of HbA1c lowering, with and without accounting for placebo effects, will need to be undertaken to properly define what is meant by a good response. Only when response is better defined can we hope to identify responders.
Predicting who will respond best Knowing the phenotype of patients who will respond best to a drug is probably the most valuable piece of information for drug selection, and with which to individualize therapy. However, little is known and little is published on this. A number of individual trials report that HbA1c lowering is greater in those with higher baseline HbA1c levels. This is supported by two systematic reviews,1,2 one of which also showed that those with lower baseline HbA1c are more likely to reach a given
Range of expected responses Received wisdom tells us that a single drug typically lowers HbA1c by a little less than 1 percentage point. On the basis of this knowledge, we might decide that it is not possible for a patient with an HbA1c of 8.5% to achieve a target of
