ali khoshdel

INTRODUCTION Evidence demonstrates that cardiovascular risk reduces after kidney transplantation, but is still a major cause of death. With increasing inclusion of diabetic patients for kidney transplantation, the evaluation of cardiovascular disease in this population becomes more important. We compared arterial stiffness and pulse wave reflection as well as other cardiovascular risk factors in kidney transplant patients with and without diabetes mellitus. MATERIALS AND METHODS One hundred kidney transplant recipients, including 33 diabetic patients, were evaluated for their renal-cardiovascular risk factors, including blood pressure, lipids, glucose control, homocysteine, and arterial stiffness indexes. The tests were repeated after 1 year in 47 individuals. RESULTS There was no significant difference in pulse wave velocity (PWV) between the diabetic and nondiabetic groups, despite a greater augmentation index (AI) in the diabetic group (20.5 +/- 2.3 versus 13.1 +/- 2.2). Multivar...

Abstract: Background: In modern epidemiology, risk assessment is a crucial step in diabetes care. Clinic blood pressure reading though is not a good measurement for this purpose since both uncontrolled hypertension and white coat hypertension (WCH) are frequent ...

Objective The marked variations in urinary stone disease prevalence by age, gender, race, and geographic location may provide clues to their etiology and prevention. We investigated the demographic profile of urolithiasis across Iran to draw out implications for national healthcare policies. Materials and methods In a nationwide multi-center epidemiologic study from September 2006 to August 2007, a multi-stage stratified sampling was taken in 12 ecologic zones across Iran during four seasons. 6,089 imaging-proven cases were identified out of 117,956 referrals to the radiologic centers. The demographic characteristics of positive cases were determined by a detailed questionnaire. Target population characteristics were derived from the most recent formal national census (2006). Results Male-to-female ratio was 1.38 (male: 58%, female: 42%). Uneducated people ratio was significantly greater among stone formers. (Mean difference = 10.4%, 95% CI = 9–12%).Mean age at presentation was 41.5 years ± 16.3 and the peak incidence range was between 55 and 65 years. The most frequent co-existence diseases were hypertension (15.8%) and diabetes (11.4%). It ranged from 2.8 to 21.3 for diabetes and 6.1 to 30.4 for hypertension compared to 1 to 4.2% and 4 to 7.7, respectively, in the general population. Surprisingly, the number of current smokers among stone formers was significantly less than the general population. BMI failed to show a significant correlation. Conclusions The putative risk factors for urinary stone disease were male gender, hypertension, diabetes mellitus, and lack of education. Preventive strategies based on indigenous demographic data may have a role in public healthcare policies.

With the expanding volume of medical literature, meta-analysis, a form of systematic review, has become indispensable for clinicians for evidence-based decision making. While the number of meta-analyses has substantially increased during recent years, there are still controversial issues regarding their methodology, interpretation and clinical application. In this review, the basic concepts of meta-analysis have been discussed from a clinician's perspective in order to facilitate its understanding, appraising and applicability in clinical practice. Although randomised controlled trials are the usual source for meta-analysis, observational studies are also being increasingly considered for meta-analysis. Like every other research design, meta-analysis starts with formulating a question, followed by searching for related data, based on predefined criteria and strategies. Inclusion of studies must be carried out with careful consideration of their quality and assessment of homogeneity using graphical means as well as statistical tools such as Q statistics, I2 statistics and meta-regression. The pooled effect size is commonly calculated using either a ‘fixed effect model’ or ‘random effect model’. Publication bias and other source of bias should be investigated and the impact of potential confounders should be eliminated as required. Given the above-mentioned considerations, meta-analysis can provide a more precise estimate of an effect size to be used in clinical decision making.