Meta-analysis: Effect of Monotherapy and Combination Therapy with Inhibitors of the ReninAngiotensin System on Proteinuria in Renal Disease

Context Although evidence suggests that both angiotensin-receptor blockers (ARBs) and angiotensin-converting enzyme (ACE) inhibitors reduce proteinuria, the relative effects of ARBs compared with ACE inhibitors and whether the effect of these agents is greater when they are used in combination are unclear. Contribution This meta-analysis of 49 randomized trials showed that ARBs delay progression of proteinuria over the short (1 to 4 months) and longer (5 to 12 months) terms and that ARBs reduce proteinuria to a similar degree as ACE inhibitors. Although data were limited, the combination of the 2 drugs seems to reduce proteinuria more than either drug alone. Caution Data on adverse effects and long-term outcomes are limited. The Editors Proteinuria increases the risk for progression of chronic kidney disease and development of end-stage renal failure (1, 2). Experiments in animal models suggest that urinary protein excretion not only reflects the severity of the underlying renal disorder but also contributes to progressive renal scarring and therefore to progression of renal disease (1, 2). In an interventional study of diabetic nephropathy, the therapy-induced change of urinary protein excretion during the first few weeks was linearly related to renal outcome after several years (3). Inhibition of the reninangiotensin system (for example, with angiotensin-receptor blockers [ARBs]) causes a reduction in urinary protein excretion that is in part independent of the reduction in blood pressure but depends on the activity of the reninangiotensin system (1, 2). Clinical trials investigating the antiproteinuric action of ARBs have reported variable effect sizes. It is uncertain whether ARBs are equally effective antiproteinuric agents as ACE inhibitors, or whether the combination of ACE inhibitors with ARBs is preferable to either agent alone (4). We therefore conducted a systematic review and meta-analysis of the effects of ARBs on urinary protein excretion in patients with nephropathy compared with placebo and other antihypertensive agents and their combinations. Because improved renal outcomes probably require sustained reduction in proteinuria, we addressed the effects of ARB therapy over several months and up to 1 year. Methods Data Sources and Searches We searched MEDLINE and the Cochrane Library Central Register of Controlled Trials (Issue 3, 2006) for relevant articles published between January 1990 and September 2006 by using the following Medical Subject Heading terms and keywords: angiotensin-receptor-blockers*, the generic names of currently available ARBs (losartan, valsartan, irbesartan, candesartan, telmisartan, eprosartan, olmesartan), proteinuria, albuminuria, microalbuminuria, and diabetic nephropathies. We screened the reference lists of included studies and related publications and approached experts for additional studies. We restricted the search to randomized, controlled trials (RCTs) published in English. Study Selection We included full-text publications that investigated patients with microalbuminuria and proteinuria of diabetic and other causes and reported changes in urinary protein excretion during treatment with ARBs compared with placebo, ACE inhibitors, or other antihypertensive drugs. We also assessed combination therapy with an ARB plus an ACE inhibitor versus monotherapy with either drug alone. We included studies in which the investigators measured albuminuria or proteinuria by using timed quantitative measurement or spot urine specimens and calculated the albumincreatinine or proteincreatinine ratio. We included RCTs lasting at least 4 weeks with parallel-group or crossover designs. We excluded studies with patients who had renal transplantation or normal urinary protein excretion, those with fewer than 10 participants per treatment group, those that examined other combination therapies, and those that only compared various doses of the same drug. Data Extraction and Quality Assessment We extracted the following information: participants' age, sex, underlying renal disease, and baseline and follow-up urinary albumin and protein excretion rates; type and dosage of ARB and control intervention; co-interventions (concomitant use of diuretics and sodium restriction) and duration of therapy; and mean arterial blood pressure at the end of the study. When studies compared 2 or more doses of the same drug against a control drug, we used data from the group with the highest dose. We assessed the methodological quality on the basis of crossover or parallel-group design, concealment of randomization, blinding (patients and caregivers), withdrawals and dropouts, and intention-to-treat or per-protocol analysis. In crossover studies, we noted the duration of the washout period and the presence of any carryover effects. We contacted the authors of the large registration studies (58) for additional data on proteinuria. We collected information on the assessment methods for adverse drug reactions, the frequency of severe adverse drugs reactions leading to discontinuation of the study, and the frequency of moderate and mild adverse drug reactions. We investigated factors that might lead to underestimation of adverse drug reactionsstudy design, previous exposure to an ARB or ACE inhibitor, baseline potassium level, and baseline renal impairment as assessed by the National Kidney Foundation classification (9)to judge reliability and generalizability of the reported adverse drug reactions. We compared overall discontinuation rates because we lacked information to distinguish among discontinuation due to severe adverse drug reactions, discontinuation with an indirect link to drug exposure (noncompliance, withdrawal of consent), and discontinuation unrelated to drug exposure (protocol violations, ineligibility). We calculated differences between treatment groups from parallel-design trials to account for the unclear denominator in crossover studies in the presence of discontinuation. Two of the investigators independently screened titles and abstracts and retrieved all articles that either reviewer regarded as potentially relevant. Three of the investigators extracted data on population, intervention, outcomes, and methods. Disagreement was resolved by consensus or discussion with a fourth investigator. Data Synthesis and Analysis We prespecified the following explanations for heterogeneity: severity of baseline protein excretion (microalbuminuria, defined as 30 to 300 mg/d or 20 to 200 mg/g creatinine) versus proteinuria (>300 mg albumin/d or >500 mg protein/d or per gram of urinary creatinine); cause of renal disease (diabetic vs. nondiabetic); and design features, including crossover versus parallel-group design, concealment (reported vs. not reported), blinding (blinding vs. nonblinding), and type of analysis (intention-to-treat vs. per-protocol). For each comparison, we separately pooled studies with 1 to 4 months of follow-up and 5 to 12 months of follow-up to account for the short-term crossover design. To increase the power for subgroup analyses, we pooled comparisons with similar treatment effects and similar mechanisms of action (for instance, combination therapy versus monotherapy with an ARB or an ACE inhibitor). We plotted standard error against treatment effect (funnel plot) for the comparisons at 4 months to detect publication bias. The small number of studies precluded funnel plot explorations of outcomes at 5 to 12 months. To assess the blood pressureindependent effect of ARBs on proteinuria, we examined the antiproteinuric effect in studies comparing ARBs with calcium-channel blockersantihypertensive drugs without intrinsic antiproteinuric actionin which both treatment arms had similar blood pressure (for instance, difference in mean arterial blood pressure 3 mm Hg) at the end of the study. Studies reported the main outcome, albuminuria or proteinuria, in different units that cannot easily be converted (for example, mg/d and mg/mmol creatinine). Identical measurement units are necessary to statistically analyze the results by using the weighted mean difference as the measure of treatment effect. An alternative approach to pool measures of different units uses the standardized mean difference (the absolute treatment effect in pooled SD units). This approach, however, leaves clinicians with the challenge of translating the effect measure into a clinically meaningful effect. Moreover, the distribution of urinary protein excretion values is highly skewed, suggesting a logarithmic transformation of the data before the analysis. For these reasons, we chose to summarize the treatment effects on urinary protein excretion by the ratio of the average treatment effect in the intervention group relative to the control group (10). This relative effect summary is roughly comparable across different measurement units and can be directly applied in a clinical context. In accordance with the meta-analysis by Friedrich and colleagues (10), we term this relative treatment effect the ratio of means. We aggregated the log-transformed treatment effect ratios across studies with a random effects model. The pooled log-transformed treatment effect was then reverse transformed to the original scale to obtain the final overall estimate of the relative treatment effect. Several large trials reported estimates of the relative treatment effect (that is, the log-transformed data were analyzed), and these estimates were directly included in the current meta-analysis. We used the result from the reported primary analysis regardless of whether this was adjusted for baseline covariates. For the other trials, we used the following approximations: If mean values and SDs for the original data were reported, the formula of Friedrich and colleagues (10) was used. If medians were reported, they were log-transformed to obtain an average treatment effect estimate on the logarithmic scale. Similarly, quartiles or extreme value