BackgroundAtherogenic dyslipidemia is an important modifiable risk factor for cardiovascular disease among patients of type 2 diabetes mellitus. Timely detection and characterization of this condition help clinicians estimate future risk of cardiovascular disease and take appropriate preventive measures. The aim of this study was to determine the prevalence, pattern and predictors of dyslipidemia in a cohort of Nepalese patients with type 2 diabetes.ResultsWe found mixed dyslipidemia as the most prevalent (88.1%) and isolated dyslipidemia (10.1%) as the least prevalent forms of dyslipidemia in our patients. The most prevalent form of single dyslipidemia was high LDL-C (73.8%) and combined dyslipidemia was high TG, high LDL-C and low HDL-C (44.7%). Prevalence of all single and mixed dyslipidemia was higher in patients with poor glycemic control and hypertension. The glycemic status of patients correlated with their fasting serum lipid profile. Dyslipidemia was associated mainly with male gender, poor glycemic control and hypertension.ConclusionsAtherogenic dyslipidemia is associated mainly with male gender, poor glycemic control and hypertension. It is highly prevalent in Nepalese patients with type 2 diabetes. Urgent lifestyle modification, sustained glycemic control and aggressive lipid lowering treatment plans are necessary to minimize the future risk of cardiovascular disease in this population.Electronic supplementary materialThe online version of this article (doi:10.1186/s13104-017-2465-4) contains supplementary material, which is available to authorized users.
BackgroundMetabolic syndrome (MetS) present in type 2 diabetic patients greatly increases the risk of strokes and cardiovascular diseases. Timely detection and mapping of MetS facilitates appropriate preventive and therapeutic approaches to minimize these risks. Our study aimed to determine the prevalence of MetS among Nepalese type 2 diabetic patients using WHO (1999), NCEP ATP III (2001), IDF (2005) and Harmonized (2009) definitions and identify the diagnostic concordance and disparity resulting from these four definitions.MethodsClinical and biochemical data were collected for 1061 type 2 diabetic patients at Manipal Teaching Hospital, Pokhara, Nepal. The data was analyzed in order to identify prevalence of MetS in these patients. Statistical analysis included usage of Student’s t- and Chi-square tests, kappa statistics and 95% confidence intervals.ResultsThe total age adjusted prevalence rates of MetS were 80.3%, 73.9%, 69.9% and 66.8% according to Harmonized, NCEP ATP III, WHO and IDF definitions, respectively. Prevalence increased with the age and was higher in females (p <0.001) according to WHO, NCEP ATP III and Harmonized definitions. Patients of Dalit community had the highest prevalence (p<0.05) according to NCEP ATP III and Harmonized definitions while Mongoloid and Newar patients had the highest prevalence (p <0.05) according to WHO and IDF definitions, respectively. Prevalence was also highest among patient engaged in agriculture occupation. Central obesity and hypertension were respectively the most and the least prevalent components of MetS. The highest overall agreement was between Harmonized and NCEP ATP III definitions (κ =0.62, substantial) and the lowest between WHO & IDF definitions (κ=0.26, slight). The Harmonized definition had the highest sensitivity (99.9%) and negative predictive value (98.9%) while NCEP ATP III definition had the highest specificity (98.9%) and positive predictive values (99.9%) in identifying the cases of MetS.ConclusionsThe prevalence of MetS among Nepalese type 2 diabetic patients was very high suggesting that these patients were at increased risk of strokes, cardiovascular diseases and premature death. The Harmonized definition was the most sensitive while NCEP ATP III and IDF definitions were the most specific in detecting the presence of MetS in Nepalese type 2 diabetic patients.
Various synthetic progestogens that are used as contraceptives have been reported to influence lipid and lipoprotein fractions differently. Depo-medroxyprogesterone acetate (DMPA), a synthetic progestogen, is used by Nepalese women as a contraceptive agent. Our study aims to determine the effects of long-term use of DMPA on lipid metabolism. We performed this study on 60 healthy Nepalese women who had been using DMPA for more than 2 yr and age- and weight-matched control subjects who were not using hormonal contraceptives. Fasting blood samples were collected from the subjects for the estimation of total cholesterol (TC) and triglyceride (TG) levels, and the levels of high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) were estimated using the Friedewald's equation. TC and LDL-C levels in DMPA users were significantly higher than those in non-users. Our study concluded that DMPA use induces lipid metabolism changes that can increase the risk of cardiovascular diseases.
Background:Predicting future coronary heart disease (CHD) risk with the help of a validated risk prediction function helps clinicians identify diabetic patients at high risk and provide them with appropriate preventive medicine.Aim:The aim of this study is to estimate and compare 10-year CHD risks of Nepalese diabetic patients using two most common risk prediction functions: The Framingham risk equation and United Kingdom Prospective Diabetes Study (UKPDS) risk engine that are yet to be validated for Nepalese population.Patients and Methods:We conducted a hospital-based, cross-sectional study on 524 patients with type 2 diabetes. Baseline and biochemical variables of individual patients were recorded and CHD risks were estimated by the Framingham and UKPDS risk prediction functions. Estimated risks were categorized as low, medium, and high. The estimated CHD risks were compared using kappa statistics, Pearson's bivariate correlation, Bland-Altman plots, and multiple regression analysis.Results:The mean 10-year CHD risks estimated by the Framingham and UKPDS risk functions were 17.7 ± 12.1 and 16.8 ± 15 (bias: 0.88, P > 0.05), respectively, and were always higher in males and older age groups (P < 0.001). The two risk functions showed moderate convergent validity in predicting CHD risks, but differed in stratifying them and explaining the patients' risk profile. The Framingham equation predicted higher risk for patients usually below 70 years and showed better association with their current risk profile than the UKPDS risk engine.Conclusions:Based on the predicted risk, Nepalese diabetic patients, particularly those associated with increased numbers of risk factors, bear higher risk of future CHDs. Since this study is a cross-sectional one and uses externally validated risk functions, Nepalese clinicians should use them with caution, and preferably in combination with other guidelines, while making important medical decisions in preventive therapy of CHD.
Background and Objectives: High sensitivity C-reactive protein (hsCRP) has been associated with metabolic syndrome (MetS) and its components. Several studies have suggested hsCRP to be used as a marker for the primary prevention of cardiovascular diseases. So, we aimed to evaluate the association between hsCRP levels and the components of MetS in diabetic and non-diabetic population.
BackgroundSerum total cholesterol (TC) and LDL cholesterol (LDL-C) have been used as major laboratory measures in clinical practice to assess cardiovascular risk in the general population and disease management as well as prognosis in patients. However, some studies have also reported the use of non-HDL cholesterol (non-HDL-C). As non-HDL-C can be calculated by subtracting HDL-C from TC, both of which do not require fasting blood sample in contrast to LDL-C which requires fasting blood sample, we aimed to compare non-HDL-C with LDL-C as a predictor of myocardial infarction (MI).MethodsThis hospital based cross sectional study was undertaken among 51 cases of MI and equal number of controls. MI was diagnosed based on the clinical history, ECG changes and biochemical parameters. 5 mL of fasting blood sample was collected from each research participant for the analysis of lipid profile. Non-HDL-C was calculated by using the equation; Non-HDL-C = TC – HDL-C. Statistical analysis was performed using SPSS 14.0.Results42 MI cases were dyslipidemic in contrast to 20 dyslipidemic subjects under control group. The differences in the median values of each lipid parameter were statistically significant between MI cases and controls. The lipid risk factors most strongly associated with MI were HDL-C (OR 5.85, 95% CI 2.41-14.23, P value = 0.000) followed by non-HDL-C (OR 3.77, 95% CI 1.64-8.66, P value = 0.002), LDL-C/HDL-C (OR 3.38, 95% CI 1.44-7.89, P value = 0.005), TC/HDL-C (OR 2.93, 95% CI 1.36-7.56, P value = 0.026), LDL-C (OR 2.70, 95% CI 1.20-6.10, P value = 0.017), TC (OR 2.68, 95% CI 1.04-6.97, P value = 0.042) and Tg (OR 2.54, 95% CI 1.01-6.39, P value = 0.047). Area under the receiver operating curve was greater for non-HDL-C than for LDL-C. Non-HDL-C was also found to be more sensitive and specific than LDL-C for MI.ConclusionsHDL-C and non-HDL-C are better discriminating parameters than LDL-C for MI. Thus, we can simply perform test for HDL-C and non-HDL-C both of which do not require fasting blood sample rather than waiting for fasting blood sample to measure LDL-C.
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