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Research Article ISSN: 0974-6943
Adibe M.O / Journal of Pharmacy Research 2010, 3(9),2102-2104
Available online through www.jpronline.info Prognosticators of high serum uric acid level in Nigerian type 2 diabetic patients Adibe Maxwell Ogochukwu Clinical Pharmacy and Pharmacy Management, Faculty of Pharmaceutical Sciences, University of Nigeria, Nsukka 410001, Enugu state, Nigeria.
Received on: 15-05-2010; Revised on: 18-06-2010; Accepted on:13-08-2010 ABSTRACT Aim: The aim of this study which was to determine the prognosticators of high serum uric acid (SUA) level in Nigerian type 2 diabetic patients. Methods: Ambulatory diabetic patients (202) who were attending diabetic clinics at Enugu State University of Science and Technology (ESUT) Teaching Hospital were included in this study. Information was collected on demographic factors of the participants, modifiable lifestyle related variables, body mass index (BMI), comorbidity and length of diagnosis of diabetes. Uric acid was measured by standard laboratory procedures. Four hierarchical linear regression models were built. Results: Among the demographic factors, age was the most robust correlate: being older was significantly associated with higher serum uric acid levels in all the models. Sex was negatively associated with serum uric acid level but the association was statistically insignificant (model 1:β = -0.13, p=0.09): women were more likely to have higher level of serum uric acid than their male counterparts. Body mass index made the most important contribution to our prediction model, by adding 12.6% of explained variance to the model (model 3: β = 0.33, p14 units week-1 (>5 bottles) in women (one bottle of Nigerian beer which is the most widely consumed contains about 3 units of alcohol). The threshold between occasional and regular activity was doing exercise at least 3 times per week while the threshold between low and high socio-economic class was earning monthly income >q 50,000 (q148.5 = 1 USD). Body mass index (BMI), was obtained from weight and height measured under standardized conditions16. BMI, calculated as weight in kg divided by the square of height in m, was classified into three categories: normal weight (30 kg m -2). Data were also collected on comorbidity which was diagnosed by a physician as recorded in patients’ files or as reported by the studied participants where there is no such information. Lastly, we obtained data on length of diagnosis of diabetes; this was calculated from the year the participant was diagnosed with diabetes as contained in the patient’s file, or as reported by the participants where there is no such information. There are evidences of good agreement between self-reported diseases and clinical records17,18. Statistical Analysis Data analysis was performed using SPSS for Windows 13.0 (SPSS Inc., Chicago, IL). Four hierarchical linear regression models were built. Model 1 was adjusted for age, sex. Model 2 was additionally adjusted for tobacco smoking, alcohol intake and physical activities. Model 3 was also additionally adjusted for body mass index. Lastly, model 4 was further adjusted for comorbidity and years since diabetes was first diagnosed, to asses association of these independent variables with serum uric acid. The level of serum uric acid (dependent variable) was regressed onto the 10 continuous and categorical independent variables which were significantly correlated with serum uric acid level (Table 2). The ordering of the variables was determined by the assumption that demographic factors, along with modifiable lifestyles, and BMI predispose an individual to high serum uric acid whereas diabetes related factors (comorbidity and years of diabetes diagnosis) make secondary contributions to serum uric acid level. The ten variables were fit into 4-step hierarchical regression models to
Journal of Pharmacy Research Vol.3.Issue 9.September 2010
2102-2104
Adibe M.O / Journal of Pharmacy Research 2010, 3(9),2102-2104 predict the participants’ serum uric acid level. Standardized regression coefficient (ß), incremental variance, change in F value, and significance of change in F value were reported.
Table 2: Intercorrelations for Serum Uric Acid and Predictor Variables Variablesa SUA Age
RESULTS: The subject pool consisted of 93 men and 109 women. The mean age of the participants was 53.6±7.3 and majority of them were in the age bracket of 36-60 (46.5%), with (44.1%) of them currently married, and more than half (58.4%) of them in low socioeconomic class. Results of modifiable lifestyles showed that more half of them never smoked (60%) nor drank (55%). Surprisingly, participants who engaged in regular physical exercise were only about oneeighth (13.4%) of the subjects. More than half (54%) of them were overweight with mean BMI of 27.6±2.9. Most (57.9%) of the diabetic patients had also hypertension with a mean years of diabetes diagnosis of 8.3±3.2 see Table 1.
SUA 1 Age Sex Tobacco Alc Phy. Act. BMI Comorb YD
Multiple regression analysis was conducted to determine the best linear combination of age, gender, tobacco smoking, alcohol intake, physical activities, BMI, comorbidity and years of diabetes diagnosis for predicting serum uric acid levels. The intercorrelations can be found in Table 2. The results of regression analysis were summarised in Table 3. Among the demographic factors, age was the most robust correlate: being older was significantly associated with higher serum uric acid levels in all the models. Sex was negatively associated with serum uric acid level but the association was statistically insignificant (model 1: ß = -0.13, p=0.09): women were more likely to have higher level of serum uric acid than their male counterparts. There was also significant interaction between age and sex (model 1: ß = -0.22, p
