Podocyturia mRNAs and Risk of Adverse Cardiovascular Outcomes from the Multi-Ethnic Study of Atherosclerosis (MESA): Preliminary Findings

Abstract

Prior work established albumin excretion rates(AER) as an independent predictor of CVD. However, AER is affected not only by endothelial injury, but also by the proportion of filtered albumin retrieve by the proximal tubule. For that reason, a part of the albumin excreted, as a marker of endothelial injury, is reabsorbed and therefore cannot be measured in the urine. As there is no retrieve phenomenon on the podocyturia rate, we could assume that podocyturia is maybe a more relevant and earlier predictor of CVD than AER. To investigate the association of podocyturia mRNA level and the risk of CVD. Among the 6814 participants asymptomatic men and women aged 45-84 years, with baseline urine samples available for analysis in the Multi-Ethnic Study of Atherosclerosis, we analysed only a random sample of 480 patients. First examination took place from July 2000-July 2002 with assay podocyturia mRNA in urine samples collected at baseline in the MESA cohort. Podocyturia mRNAs quantification will be performed according to a previously described protocol: total urine pellet RNA will be isolated using the manufacturer protocol. Quantitation of the absolute nephrin and podocin, mRNA abundance will be performed. In the 480 patients, mean podocyturia mRNA level was 2.3 ± 1.9 10 6 copies. These 480 patients were younger, more often diabetic and with hypertension than the total MESA population (for all P < 0.0001). The median follow-up was 15.6(12.7-16.3) years. The prevalence of cardiovascular disease was similar in the two groups ( P = 0.64). The level of podocyturia mRNA was significantly higher in patients with diabetes mellitus than others ( P < 0.01). There is no difference between the podocyturia mRNA terciles to predict CV events occurrence ( Fig. 1 ). The podocyturia mRNA was not associated with CVD. However, additional investigations should be performed from MESA with more statistical power and specific analysis in diabetics.