Comparison of renal arterial resistive index in type 2 diabetic nephropathy stage 0-4

Main Article Content

Sharareh Sanei Sistani *
Ali Alidadi
Alireza Ansari Moghadam
Fatemeh Mohamadnezhad
Bahareh Heshmat Ghahderijani
(*) Corresponding Author:
Sharareh Sanei Sistani | sanei1345@yahoo.com

Abstract

Chronic kidney disease (CKD) is one of world health problems and its prevalence and incidence is increasing. Chronic Kidney Failure involves a range of pathophysiologic processes that are associated with impaired renal function, leading to cardiovascular morbidity and mortality. Renal artery resistive index (RI) is indicator of atherosclerotic change in small vessels. The current study was aimed to assess RI in diabetic nephropathy patients at stage 0-4 and to compare RRI with HbA1c, systolic blood pressure, diastolic blood pressure, albuminuria and glomerular filtration rate (GFR). In this cross sectional study,100 diabetic nephropathy patients who attend to nephrology clinic of Ali-ibn Abi Talib Hospital were entered to the study. Ultrasound Doppler renal resistive index was measured and other information was recorded from their last lab data that was recorded in their medical records. Variable included: systolic blood pressure, diastolic blood pressure, albuminuria, GFR, HbA1c. All data was analyzed by Pearson's Correlation Coefficient. The findings indicated a significant correlation of RI with systolic BP (p=0.04 R=0.75), microalbuminuria (P=0.001 R=0.67), and GFR (P=0.001 R=0.76), while diastolic BP (P=0/45 R=0/32), HbA1c (P=0/56 R=0/43) were not found to be associated with RI. The findings indicated that increased systolic blood pressure, albumin excretion (microalbuminuria) and severity of disease were capable of increasing RI values in diabetic nephropathy patients. In addition, decreased GFR.

Downloads

Download data is not yet available.

PUBLICATION METRICS

PlumX Metrics provide insights into the ways people interact with individual pieces of research output (articles, conference proceedings, book chapters, and many more) in the online environment. Examples include, when research is mentioned in the news or is tweeted about. Collectively known as PlumX Metrics, these metrics are divided into five categories to help make sense of the huge amounts of data involved and to enable analysis by comparing like with like.


Article Details

Most read articles by the same author(s)