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INTRODUCTION: The pandemic caused by SARS Corona Virus-2 (COVID-19) has caused widespread mortality globally. The hallmark of the disease is the "cytokine storm," which is caused due to dysregulated immune system activation. Numerous inflammatory markers are used to predict the severity and mortality of the infection. Serum Cystatin C levels are associated with immune responses to exogenous and endogenous antigens. Our study was done to assess serum cystatin C as a marker of severity and mortality among patients admitted with COVID-19 infection. METHODOLOGY: This cross-sectional study was conducted in a tertiary care center in South India. Sixty-nine patients with mild and severe COVID-19 infection admitted to the hospital were included in the study. Serum Cystatin C levels were estimated at admission. The levels were correlated with disease severity and mortality. Receiver operating characteristic curves (ROCs) was constructed for Cystatin C to predict severity and mortality. The computation of sensitivity, specificity, and positive and negative predictive values was done using optimal cut-off points. SPSS 18 was used for the statistical analysis. Version 18.0 of PASW Statistics for Windows. SPSS Inc., Chicago. RESULTS: Out of 69 patients, 28 (40.5%) had a mild illness, and 41 patients (59.4%) had severe COVID-19 illness. Mean serum Cystatin C levels measured at the time of admission among patients with mild illness was 1.83 (SD-1.53), and among patients with severe illness was 3.84 (SD- 2.59) (p<0.001). The area under receiver operating characteristic curves (ROC) for serum cystatin C for predicting COVID-19 severity and mortality was 0.904 and 0.768, respectively (p<0.001). CONCLUSION: Patients with severe COVID-19 disease had considerably higher serum levels of Cystatin C than those with mild COVID-19 illness. Cystatin C levels can be useful for predicting mortality and severity among patients admitted with COVID-19 infection.
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Background Health benefits of physical activity measured in terms of metabolic equivalent minutes (MET-minutes per week) have been established. However, factors affecting physical activity, like age, gender, body mass index, waist-hip ratio, particularly in rural communities have not been documented on a large sample. Methods Baseline physical activity data of more than 4000 subjects over 30 years of age, who were enrolled in a randomised community-based study on non-communicable diseases, were analysed. Global Physical Activity Questionnaire was used and anthropometric measurements were classified according to the MONICA study manual. Three domains of physical activity were measured as MET-minutes per week - activity at work, travel to and from places and recreational activities. Association of MET-minutes with sociodemographic variables and risk factors for cardiovascular diseases was studied. Results Mean MET-minutes per week of females were found to be significantly lower than that of males and decreased with advancing age and higher BMI in both genders. Married persons, normal BMI, normal waist-hip ratio, lower leisure time activity had demonstrated higher MET values (P = 0.000). In our study, the prevalence of inactivity (<600 MET-minutes) was 3.2% which was similar in both males and females. As high as 96.2% of the subjects had MET-minutes of > 1200. Nearly 50% of the subjects had leisure time ranging from 121 to 240 minutes per day. Conclusion A large majority of adults over 30 years of age in a rural community in Karnataka (96.2%) had very high MET-minutes per week of >1200 per day. Abnormal BMI, higher waist-hip ratio and more leisure time were associated with lower MET-minutes which are modifiable. About 50% had more than 2 hours of leisure time per day. It is recommended that health promotion for active lifestyle should be encouraged.
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INTRODUCTION: An important aspect of the assessment of cardiovascular risk for a dyslipidemic subject is the estimation of serum Low-Density Lipoprotein Cholesterol (LDL-C). There are many homogenous assays currently available for the estimation of serum LDL-C. Most clinical laboratories determine LDL-C (mg/dL) by Friedewald's formula (FF), LDL-C = (TC) - (HDL-C) - (TG/5). Recently Anandaraja and colleagues have derived a new formula for calculating LDL-C, AR-LDL-C = 0.9 TC- (0.9 TG/5)-28. AIM & OBJECTIVES: The aim of the study was: a) to determine if, and to what extent, LDL-C level was underestimated/overestimated when it was calculated using the formulae compared with direct measurement of LDL-C, and b) to determine which of the calculated formulae show maximum correlation with direct LDL cholesterol method at different TG levels. SETTING & DESIGN: A cross-sectional study. MATERIALS AND METHODS: Record analysis was done from the 370 (TG <400mg/dl) lipid profile reports of patients above 18 years. LDL-C estimation was done by homogenous assay and also calculated using the Friedewald's Formula and Anandaraja's Formula. RESULTS: The mean LDL-C levels were 105.17± 43.4, 102.98 ±42.5, and 98.20 ±43.7 mg/dl for D-LDL-C, F-LDL-C and AR-LDL-C, respectively. A good correlation was found between the calculated LDL-C methods and Direct Low-Density Lipoprotein Cholesterol method (D-LDL-C) assay, that is, F-LDL-C versus D-LDL-C (r = 0.937) and AR-LDL-C versus D-LDL-C (r= 0.918). Bland-Altman plot for FF-LDL-C & AR-LDL-C showed minimal negative bias. CONCLUSION: FF-LDL-C correlated maximally with D-LDL-C estimation at all levels of triglycerides except at TG < 100mg/dl. At TG < 100mg/dl, Anandaraja's Formula works better. FF-LDL-C can be used in place of D-LDL-C when the direct method cannot be afforded.