Meta-Analysis: The Effect of Statins on Albuminuria

Context Albuminuria is a marker of endothelial dysfunction and is a risk factor for cardiovascular disease. We do not know whether or to what degree statins affect albuminuria. Contribution This meta-analysis of 15 randomized, placebo-controlled trials found that statins reduced albuminuria and proteinuria. Studies with greater baseline albuminuria showed greater reductions. Cautions Studies were small, showed heterogeneous effects, and were often of poor quality. Implications Statins might reduce albuminuria. We need larger, better studies to confirm these findings and to determine whether reducing albuminuria affects the incidence of end-stage renal disease or cardiovascular disease. The Editors Amarker of endothelial dysfunction, albuminuria has long been recognized as a risk factor for progression to end-stage renal disease. More recently, however, albuminuria has been recognized as an independent risk factor for cardiovascular morbidity and mortality (14). Beyond angiotensin-converting enzyme inhibitor and angiotensin II receptor blocker therapies, therapeutic options to affect the progression of albuminuria are limited. One therapeutic option may be 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors (statins). The beneficial effects of statins on cardiovascular morbidity and mortality cannot be explained solely by their effect on low-density lipoprotein (LDL) cholesterol levels (57) and may involve an independent effect on endothelial dysfunction. Some investigators have noted that the effects of statins exceed those expected from simply lowering LDL cholesterol levels and occur too early in treatment to be due to the lowering of LDL cholesterol levels (8). The nonlipid mechanisms that may be involved are called pleiotropic effects, such as lipid-independent plaque stabilization, reduced inflammation, decreased thrombogenicity, increased arterial compliance, and improved endothelial function (7, 912). We systematically reviewed the literature to determine whether and to what degree statins affect albuminuria or proteinuria. Methods Literature Search We searched the PubMed, MEDLINE, EMBASE, BIOSIS, SciSearch, PASCAL, and International Pharmaceutical Abstracts (IPA) databases, as well as the Cochrane Central Register of Controlled Trials, for all relevant articles published in any language between January 1974 and November 2005. We used the following Medical Subject Headings (MeSH) and text words: proteinuria, urinary protein excretion, albuminuria, urinary albumin excretion, pitavastatin, mevastatin, fluvastatin, pravastatin, simvastatin, atorvastatin, cerivastatin, lovastatin, and rosuvastatin. We limited our searches to randomized, placebo-controlled trials in adults (age >18 years). Study Selection Two investigators independently screened the titles and abstracts of potentially relevant studies before retrieving the full-text articles. When investigators doubted a study's eligibility for inclusion, they obtained the full-text article. We included randomized, controlled trials that studied adults and had both a statin group and a placebo group. We considered the end point to be appropriate if proteinuria or albuminuria was measured either by timed urine collections to measure 24-hour excretion or by untimed specimens to calculate albumin-to-creatinine ratios. We complemented the database searches by reviewing the a priori end points of major lipid-lowering trials and the reference lists from original research articles, review articles, and previous meta-analyses. We focused exclusively on published data and did not contact authors of trials that met selection criteria but did not have data on albuminuria or proteinuria. Validity Assessment Two reviewers independently assessed study quality by using the Jadad rating instrument (13), complemented by an assessment of the intention-to-treat analysis, loss to follow-up, and industry sponsorship. Jadad scores are based on the description of randomization, blinding, inclusion and exclusion criteria, withdrawals, and method to assess adverse events. Scores can range from 0 to 8, and higher scores indicate better methodologic quality. We calculated interrater agreement, and we resolved differences by consensus. Data Extraction We extracted characteristics of the study (author, year, country, design, duration, statin and dosage, and sample size) and the participants (age, sex, presence and type of renal disease, proportion with diabetes, proportion with hypertension, baseline and follow-up cholesterol levels, baseline and follow-up urinary albumin and protein excretion rates, angiotensin-converting enzyme inhibitor use, angiotensin II receptor blocker use, and calcium-channel blocker use). If data could not be extracted or calculated from the manuscript with confidence, no data were entered. Two reviewers independently extracted data, and we resolved disagreements by consensus. Quantitative Data Synthesis The principal measure of effect was the weighted mean difference in the proportional change from baseline to follow-up albuminuria (or proteinuria) between the statin and placebo groups. We pooled the results by using a random-effects model to obtain the summary weighted mean difference with confidence interval. To avoid bias from carryover effects, we used data from only the first phase of crossover studies for the analysis when possible. We replaced missing means with the reported medians for calculating the weighted mean difference. We imputed missing SDs on the basis of reported P values, if available. We performed these imputations conservatively to err on the side of underestimating the statistical significance of positive studies. Specifically, we approximated imputed values to just reach statistical significance (for example, if the reported P value was less than 0.050, we imputed a value that would yield a P value of 0.049). When P values were not available, we imputed the SDs by using the mean proportional SD of the other studies. Both baseline and follow-up SDs were weighted by sample size and were averaged before inclusion in the random-effects model. We conducted sensitivity analyses for the imputed values. We assessed heterogeneity by using the I2 statistic (14). The I2 statistic is an estimate of the amount of variance due to heterogeneity rather than chance and is based on the traditional measure of variance, the Cochran Q statistic. We assessed the sources of heterogeneity by performing stratified analyses (15). We considered a P value less than 0.050 to indicate statistically significant heterogeneity. We performed 2 subgroup analyses for the variables that we deemed most likely to be the potential sources of statistical heterogeneity and for which data were complete. These variables included the baseline level of urinary excretion (calculated as the weighted average of statin and placebo group data and reflecting the presence and severity of disease and the likelihood of benefit from therapy) and loss to follow-up (the quality measure exhibiting the most variation across studies). The cut-points used for urinary excretion level were less than 30 mg/d (n= 3), corresponding to nonpathologic levels; 30 to 299 mg/d (n= 6), corresponding to microalbuminuric levels; and 300 mg/d or greater (n= 6), corresponding to macroalbuminuric levels. For losses to follow-up, we used cut-points of more than 20% (n= 3) and 5% or less (n= 12), which may represent excessive and minimal bias, respectively. Publication Bias We assessed publication bias by using the Begg method with funnel plot analysis (16). Sensitivity Analyses To exclude the possibility that any one study was exerting excessive influence on the results, we conducted a sensitivity analysis by systematically excluding each study and then reanalyzing the data to assess the change in effect size. In addition, because gross proteinuria might reflect tubular dysfunction rather than endothelial glomerular dysfunction, we conducted a sensitivity analysis by excluding the 4 studies that measured only gross proteinuria. We performed all analyses with Stata software, version 8.2 (Stata Corp., College Station, Texas). We considered P values less than 0.050 to be statistically significant. We used the Quality of Reports of Meta-analyses (QUOROM) statement to guide both our reporting and our discussion of the results of our meta-analysis (17). Role of the Funding Source No funding was received in support of our study. Results Literature Search Figure 1 shows the literature search and selection flow chart. Figure 1. Study flow diagram. Study and Patient Characteristics Our final pool of eligible studies included 15 randomized, placebo-controlled trials involving 1384 participants (1832). Studies originated from 10 different countries. Most studies were performed in Europe (53%), and only 1 study was performed in the United States. All studies measured the outcome by using a 24-hour urine collection. Three studies enrolled participants with normal albumin excretion (<30 mg/d), 6 studies enrolled participants with microalbuminuria (30 to 299 mg/d), and 6 studies enrolled participants with gross albuminuria (n= 2) or proteinuria (n= 4) (300 mg/d). The median number of participants in each study was 36 (range, 18 to 864 participants). Statins were (in order of decreasing frequency) simvastatin (5 studies), pravastatin (4 studies), fluvastatin and cerivastatin (2 studies each), and atorvastatin and lovastatin (1 study each). The median reduction in LDL cholesterol level was 26% (range, 10% to 51%). Angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers were used concurrently in 7 studies and were prohibited in 4 studies. We could not determine their use for the remaining 4 studies. Except for 1 study (26), which measured albuminuria as a potential adverse event, all studies measured either albuminuria (n= 10) or proteinuria (n= 4) as an a priori efficacy outcome. The median duration of f