ABSTRACT

Objectives: To evaluate the frequency of different risk profiles and associated clinical parameters in patients with nonalcoholic fatty liver disease (NAFLD).

Methodology: This cross-sectional study was conducted at the Gambat Institute Of Medical Sciences,Gambat District, Khairpur, between March 2019 to January 2020. A total of  345 patients participated in the study. Demographics, clinical features, investigations, and causative agents of NAFLD were noted in a document. Patients with raised ALT, fatty liver on imaging, aged between 18-75 years were a part of the study. Exclusion criteria included patients with overconsumption of alcohol, positive HBsAg, positive anti-HCV, and other underlying liver diseases with known origin. Patients’ blood samples were also tested for fasting blood sugar, random blood sugar, fasting cholesterol and fasting triglyceride levels.Levels of glucose, triglycerides and cholesterol were measured with an autoanalyzer; Photometer 4010; Beohrnger Mannheim; using the enzymatic-calorimetric methods. All data was analyzed using SPSS version 24.

Results: The mean age of the patient was 48.4 ± 12.2 years and a mean body mass index of 30.2 The mean cholesterol was 199.4 ± 54.3 mg/dl. The majority i.e. > 60 percent were women with only 128 (37.1%) males (Table 1). The body mass index (BMI) was significantly higher in female patients as compared to males (p<0.001). The males had a significantly greater frequency of traits for metabolic syndrome as compared to women i.e. 111 (86.7%) vs. 145 (66.8%) (p=0.02). Obesity in patients was also significantly associated with female gender. We found a significant relationship of hypercholesterolemia in patients with DMT2 (p=0.04). Similarly, the majority of the patients i.e. 55 (47%) with DMT2 also had hypertriglyceridemia (p=0.002).

Conclusion: The present study indicated that female gender, obesity, and diabetes mellitus were significantly associated with NAFLD. NAFLD places a significant burden on the healthcare system and is associated with poor quality of life of patients. Metabolic syndrome is another leading association that needs to be explored in further detail. Recognition of high-risk profile patients can help establish early diagnosis and hence treatment plans can be implemented at an early stage of disease.