Browsing by Author "Khakurel, G"
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Publication Assessment of Obesity Indices in Predicting Hyperglycemia in Adults of Duwakot, Bhaktapur(Kathmandu University, 2025) Khakurel, G; Gautam, K; Karki, PK; Chalise, SABSTRACT Background Obesity is a major risk factor for metabolic disorders, including hyperglycemia, which is a precursor to diabetes. Various obesity indices, such as Body Mass Index (BMI), Waist Circumference (WC) and Waist-to-Height Ratio (WHtR), are used to assess adiposity. Objective To evaluate and compare the effectiveness of obesity indices in predicting hyperglycemia among adults in Duwakot, Bhaktapur. Method This was a cross-sectional study conducted among 128 adults visiting Kathmandu Medical College Public Limited, Duwakot from August 2024 to January 2025. Anthropometric measurements (BMI, WC and WHtR) were recorded, and fasting blood glucose (FBG) levels were measured to define hyperglycemia (FBG ≥ 126 mg/ dL). Pearson correlation was used to evaluate the relationship between obesity indices and FBG levels, while an independent t-test compared the mean values between males and females. The predictive ability of obesity indices was assessed using Receiver Operating Characteristic (ROC) curve analysis, and the area under the curve (AUC) and optimal cut-off values were determined. Values of p≤0.05 were considered statistically significant. Result The prevalence of hyperglycemia among the participants was 17.2 %. Pearson correlation analysis showed that FBG was significantly correlated with WC (Male: r = 0.233, p < 0.05; Female: r = 0.459, p < 0.05), and WHtR (Male: r = 0.227, p < 0.05; Female: r = 0.482, p < 0.05). Independent t-test analysis revealed a statistically significant difference in WC (p = 0.025) and WHtR (p = 0.014), with males having higher WC and females having higher WHtR. However, BMI (p = 0.179) did not show a significant difference between the two groups. ROC curve analysis revealed that WHtR had the highest AUC (Male:0.607, Female:0.721), followed by WC and BMI. Conclusion This study found that WHtR was the strongest predictor of hyperglycemia, followed by WC, and BMI. WHtR could be an effective screening tool for early hyperglycemia detection in community settings. KEY WORDS Hyperglycemia, Predictive value, Obesity indicesPublication Association of Subjective Sleep Quality and Glycemic Level in Patients with Type 2 Diabetes Mellitus: A cross sectional study(Kathmandu University, 2020) Khakurel, G; Shakya, D; Chalise, P; Chalise, SABSTRACT Background Sleep disorders are considered as one of the important risk factor which have a great impact on patients with type 2 diabetes mellitus. Objective The objectives of this study was to determine the effect of sleep quality on the glycemic level among type 2 diabetic patients. Method This was a cross sectional study done in 208 type 2 diabetic patients visiting Kathmandu Medical College Public Limited from July 2019 to December 2019. Data regarding sleep quality was collected by using Pittsburgh Sleep Quality Index taking global cut off score ≥ 8 as poor sleeper. Glycated hemoglobin level ≥ 7 was considered as poor glycemic control. Chi square test was used to compare parameters between good sleeper and poor sleeper. Independent sample t test compared the means of Pittsburgh Sleep Quality Index factors and glycemic contol. A logistic regression analysis of Pittsburgh Sleep Quality Index factors and glycated hemoglobin was done. Values of p ≤ 0.05 were considered statistically significant. Result The study findings revealed that 62 % had poor glycemic control and 58.7 % were poor quality sleeper. There was a significant association of sleep quality with glycemic control and duration of diabetes. Logistic regression analyses showed that subjective sleep quality was risk factor for poor glycemic control. The odds ratio for subjective sleep quality was found to be 4.59 (2.13-9.91). Conclusion Poor sleep quality was common in type 2 diabetic patients. This study showed that the risk factors for poor subjective sleep quality include poor glycemic control and longer duration of diabetes mellitus. KEY WORDS Diabetes mellitus, Glycemic level, Sleep