Amira H Mahmoud
Introduction & Objective: Obesity increases the risk for variety of diseases which in turn, decreases the overall lifespan in both men and women. Though the cardiovascular risks of obesity are widely acknowledged, less often identified is the relationship between obesity and renal function. The objective is to study the relationship between abdominal obesity and micro-albuminuria in elderly subjects. Method: A cross-sectional study was conducted on 200 elderly subjects, aged ≥60 years. Subjects were recruited from both Geriatrics and Gerontology department and Internal medicine at Ain Shams University Hospital, Cairo, Egypt. All patients had anthropometric measurements done including weight, height, body mass index, waist circumference, hip circumference and waist hip ratio, also assessment of blood pressure and albumin/creatinine ratio in urine. Results: Mean age of participants was 74.96±5.603 years. Mean waist circumference in whole sample measured 96.78±16.85, mean hip circumference was 106.31±19.24, mean waist hip ratio measured 0.91±0.09 and mean body mass index was 27.83±9.8. All of waist circumference, waist hip ratio, systolic blood pressure, hypertension, diabetes mellitus, ischemic heart disease, renal disease were significantly related to micro-albuminuria. Also, fasting blood sugar, serum triglycerides and renal functions were related to micro-albuminuria, meanwhile on multivariate analysis abdominal obesity as measured by waist hip ratio was the strongest variable correlated with micro-albuminuria in elderly subjects in the whole sample. The data collected were coded, tabulated, revised and analyzed statistically using the SPSS program (version 20). Descriptive statistics were produced using the mean and standard deviation for the numerical parametric data and in number and percentage for the categorical data. Statistical analysis was performed for quantitative variables using an independent t-test in the case of two independent groups, a t-test paired in samples linked with parametric data. Stepwise linear regression analysis used for significant clinical variables. The significance level was taken at a P value
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