C reactive protein concentrations can predict metabolic syndrome in adults

This study analysedthe correlations between C reactive protein [CRP] serum concentrations and demographics and anthropometric aspects of developing metabolic syndrome components in adults. A cross-sectional study. This study was carried out in Azad Kashmir Sheik Khalifa Bin Zyad Hospital Muzaffarabad from March 2012 to April 2013. Demographic, anthropometic parameters of MS such as body mass index [BMI] and waist hips ratio [WEE], biochemical and clinical data were collected from 115 adults of age ranged between 22- 55yearsold. Adults BMI had direct correlation with CRP concentrations. In our study CRP concentrations were statistical significant correlated with age [r = 0.282, p = <0.002], BMI [r = 0.787, p = <0.001], waist hips ratio [r = 0.850, p =< 0.001] and weight [r = 0.662, p =<0.001]. The height had poor correlation with CRP [r = 0.101, p = 0.825].Thecorrected CRP [r = 0.101, p =0.825] was also poorly correlated to CRP concentrations. Statistical analysis has shown there is direct correlation between BMI, WHR and CRP concentrations which suggests that inflammation might be an important event in the development of metabolic disorders in adults

Factors associated with tuberculosis treatment default

This study was carried out at Azad Kashmir Combined Military Hospital [AK CMH]/Sheik Khalifa Bin Zyad [SKBZ] Muzaffarabad [MZD] designed for patients defaulting from tuberculosis treatment from 1.1.2013 to 31.12.2013. This study included 110 adults with diagnosis of TB treatment default. The study protocol incorporated structured questionnaire, physical examinations, radiological, laboratory investigations and potential factors for TB treatment defaults. The statistical analysis was performed using SPSS-20, the chi square test was done and p<0.05 was considered as statistical significance. PLUM-Ordinal regression analysis revealed that many clinical variables have statistical significant association with tuberculosis treatment defaults. Factors identified to be associated with treatment default were male gender [p<0.007], distance from the health post [p<0.007], displacement [p<0.024], financial Constraints [p<0.00 1], no body at home to bring medicine or take patients to hospital [p = 0.001], route closed in winters [p = 0.001], improvement from symptoms [0.009] and went abroad [0.001]. Determinants of treatment defaults and associated factors should he considered in treatments plan and Policy actions to tuberculosis control programs. Information on disease, treatment plan and education of the individual along with population should be done in order to minimize treatment default and spread of multi drug resistance to anti-TB drugs

Correlation of BMI with cholesterol and sugar in adults

This study was aimed to find the correlation of BMI with cholesterol and sugar level in adult. This study was carried out in Department of Medicine, Combined Military Hospital [CMH] Quita from 2006 to 2009. Prospective observational cross sectional studies. Individuals with different ages, sex were selected as study population. The cholesterol and fasting blood sugar were measured according to standard protocol. Height in centimeter and weight in kilograms of each individual was recorded and BMI calculated as kg/m[2]. Physical examination was done for everybody. The SPSS-20 was used for statistical significant analysis. The frequencies of variable and correlation between BMI, heights, weight, sugar and cholesterol were comprehensively analyzed. A total of 2,174 adults, 1,947 [89.56%] male and 227 [10.44%] female were included in study. Age range was between 20 and 55years.The mean age was 38.47 +/- 12.66. Mean BMI was 23.57 +/- 2.58.Mean cholesterol was4.57 +/- .60. Mean fasting blood sugar [FBS] was 4.67 +/- .75.Mean weight 70.32 +/- 9.1 Mean height 172.73 +/- .7.85.The correlation analysis revealed that weight, fasting blood sugar [FBS] and cholesterol had positive correlation with BMI [correlation coefficient of 0.734 [p<0.000], 0.167 [p<0.000], 0.164 [p<0.000] respectively and height had negative correlation with BMI [-0.123 [p<0.000]. BMI is positively correlated with weight, RBS and cholesterol. The effect of age, sex, exercise and current medical status, this correlation is reduced