Should All Patients with Type 1 Diabetes Mellitus and Microalbuminuria Receive Angiotensin-Converting Enzyme Inhibitors?

Angiotensin-converting enzyme (ACE) inhibitors are recommended for all patients with type 1 diabetes and microalbuminuria, regardless of blood pressure (1). These recommendations are based on well-conducted clinical trials (2), but several questions remain unanswered. First, the range of renal disease included in studies of patients with microalbuminuria is broad, from relatively high albumin excretion rates associated with advanced renal disease to mild elevations of albumin excretion rate and relatively normal glomerular structure. Because microalbuminuria often occurs in the first 5 years after diagnosis (3), relatively young people may begin therapy with ACE inhibitors on the basis of this indication alone and would be expected to continue taking these drugs indefinitely. Furthermore, in several of these trials, change in albumin excretion rate is used as a surrogate end point, with the implicit assumption that it indicates progressive renal disease. A demonstration that ACE inhibitors reduced the risk for progression to a more definite end point, such as clinical proteinuria, would add support to intervention strategies. Although some studies have reported a statistically significant effect of ACE inhibitors on progression to macroalbuminuria (4, 5), most were not adequately powered to do so. For example, if the risk for progression to macroalbuminuria in the placebo group is assumed to be 20% over a 2-year period, 147 patients in each group would be required to detect a relative risk of 0.33 with a power of 0.9 power and a significance of 0.05. No study has yet reported the risk for regression to normoalbuminuria, and published individual studies are not powered to do so. Some researchers claim that among patients who have nondiabetic renal disease, ACE inhibitors are particularly beneficial in men compared with women (6). These claims have not been explored in type 1 diabetes mellitus, and it is not clear whether the beneficial effects of ACE inhibitors are related to age or duration of diabetes or are affected by different levels of blood pressure and glycemic control. Some researchers have suggested that the effect of ACE inhibitors on renal disease is independent of blood pressure (2); however, this hypothesis remains controversial, and it is unclear how it applies to patients with type 1 diabetes mellitus who are in relatively early stages of renal disease and do not have systemic hypertension. Finally, it is still not clear whether ACE inhibitors reverse, stop, or merely slow the progression of renal disease with time. These questions, which are of importance in guiding the most cost-effective treatment, have not been addressed by any single published study. With few exceptions, each individual study is underpowered for such subgroup analyses. We performed a meta-analysis of studies of ACE inhibitors in patients with type 1 diabetes mellitus and microalbuminuria using raw patient-level data obtained from all investigators. Unlike conventional meta-analyses, this study did not aim to demonstrate the general effectiveness of the drug in decreasing the albumin excretion rate; this has already been shown. Rather, we intended to examine a more refined series of questions about which subgroup of patients, if any, is most likely to benefit from therapy and to explore the effect of covariates, such as blood pressure changes, on treatment effect. Furthermore, the analysis of individual patient data was necessary because published studies used different measures of renal outcome that may not be strictly comparable (for example, progression to macroalbuminuria or a percentage or absolute change in albumin excretion rate [5, 7]). Moreover, results for the subgroups we wished to examine were often not published in original articles. Reanalysis of the individual patient data allowed us to use a consistent approach for definition of the end point, inclusion of covariates, and subgroup analysis. Methods Inclusion and Exclusion Criteria Only patients with type 1 diabetes mellitus, as defined by the individual trials, were included. We restricted our analysis to nonhypertensive patients. Because hypertension is an indication for antihypertensive therapy, the control group would be receiving other antihypertensive agents, which would complicate comparisons with placebo-controlled trials. The definition of normotension was taken from the individual trial. We acknowledge that the definition of normotension used in early trials would not be acceptable now, but one objective of our analysis was to explore treatment effect over a range of blood pressure values. Approximately two thirds of the patients included in this meta-analysis had normal blood pressure according to current standards (8). Patients could only have microalbuminuria, defined as an albumin excretion rate of 20 to 200 g/min, at baseline. Because we were using patient-based data, we were able to impose a standard definition when examining end points. To be included, a trial had to be placebo-controlled or include a nonintervention group for comparison (9). It was not the aim of our meta-analysis to compare ACE inhibitors with other agents. Each trial had to include at least 10 patients, and at least 1 year of follow-up was required to provide meaningful outcome data. These requirements reduced the potential of bias from small, short-duration trials. Albumin excretion rate had to be measured at baseline and at one or more follow-up visits. Search Strategies and Data Assembly A MEDLINE search was performed to find all suitable studies. Bibliographies of recent studies and a previous meta-analysis were also scrutinized (2). Key investigators were approached and asked to identify other studies that may have met our inclusion criteria but were not currently included. A total of 12 studies that met our inclusion criteria were identified (4, 5, 7, 9-17). We requested data from investigators on the following items for each patient included in the trial: age, duration of diabetes, sex, and treatment assignment at baseline, and baseline and follow-up data on albumin excretion rate, blood pressure, and glycemic control. All of these data items were the minimum collected by all identified studies. These data were converted to a similar format and analyzed. Original protocols and manuals of operation were requested to check entry criteria. Statistical Analysis The primary outcome measure was the rate of change in log albumin excretion rate. Mean albumin excretion rate at each time point was calculated for each patient, and these values were log-transformed for analysis. To calculate a summary measure of albumin excretion rate for a given length of follow-up in each patient, we used regression through the origin estimator, which gave the gradient or rate of change of albumin excretion rate per 6 months for the placebo and treatment groups (12, 18). We used analysis of covariance to calculate the difference in slopes of log albumin excretion rate, adjusted for the baseline rate, between treatment and control groups. The absolute differences are related to the log values of albumin excretion rate and are therefore the relative differences for the actual albumin excretion rate. The relative change in albumin excretion rate in treatment groups compared with placebo groups over a given period was calculated and expressed as a percentage. The meta-analysis was performed in two ways. First, a trial-level analysis was performed in which the treatment differences in summary measure, adjusted for baseline albumin excretion rate, were calculated separately for each trial. The estimate and the associated standard errors were then used to obtain an overall estimate of treatment effect. Both fixed-effects and random-effects methods (19, 20) were used, and estimates of the between-trial variance were obtained (21). A patient-level analysis was also performed by using random-effects regression models in which the summary outcome measure for each patient was adjusted for baseline albumin excretion rate (22). Dummy variables representing each trial were also included in all models. The estimate for the treatment effect was expressed as a percentage difference, as in the trial-level analysis. Absolute rates of progression to macroalbuminuria and regression to normoalbuminuria were considered as outcomes. Progression to macroalbuminuria was defined as an albumin excretion rate that was greater than 200 g/min on two successive occasions at least 3 months apart or at the final visit for that patient. Similarly, regression to normoalbuminuria was defined as an albumin excretion rate less than 20 g/min at two consecutive visits at least 3 months apart or at the final visit for that patient. Estimated odds ratios from fixed-effects and random-effects models were calculated, using empirical logits because some trials had zero events in a given treatment group. Given that there were zero events in some treatment groups and small numbers of events in some studies, the assumptions of normality underlying the methods may also be violated. A novel method for binary outcome data using the exact distribution of the treatment effect was therefore used (23), and results are reported for comparison. The treatment effect was adjusted by age, diabetes duration, blood pressure (categorized into tertiles on the basis of the whole data set), sex, and glycemic control in the patient-level analysis. Because the measure used for glycemic control differed by trial, it was dichotomized at the median for each trial and then analyzed. Interactions between these baseline risk factors and treatment were also explored, as was the interaction between baseline albumin excretion rate, both as a continuous and a categorical variable. Treatment effects for the whole data set were examined at different cut-points of duration of follow-up. Finally, the role of change in blood pressure was explored by including a term for the p