ANTHROPOMETRIC AND BIOCHEMICAL CHARACTERISTICS OF ADULT PATIENTS WITH NON-ALCOHOLIC FATTY LIVER DISEASE
DOI:
https://doi.org/10.22159/ajpcr.2019.v12i1.29278Keywords:
Non-alcoholic fatty liver disease, Fatty liver, Steatohepatitis, Metabolic syndrome, Fibrosis scoreAbstract
Objective: The objective of this study was to analyze the distribution of non-alcoholic fatty liver disease (NAFLD) with reference to age, gender, and socioeconomic status, to look for clinical features, anthropometric measurements, and biochemical parameters in patients of NAFLD, and to analyze statistically significant differences in biochemical parameters of lean, overweight, and obese NAFLD patients, in a medical college hospital of Durgapur.
Methods: This was a prospective, cross-sectional hospital-based study carried out over 2 months. Patients diagnosed as NAFLD based on clinical findings and ultrasonography (USG) were evaluated. Patients were classified on the basis of body mass index (BMI) and their fibrosis score calculated.
Results: The authors report several interesting findings that warrant further exploration. In this study, average age was 42 years and there was a male preponderance. 31.25% among patients who had undergone USG evaluation were diagnosed as NAFLD. Glucose, glycosylated hemoglobin, thyroid-stimulating hormone (TSH), BMI, and lipid profile were significantly different in controls and cases. In NAFLD patient group, 48% were lean, 40% overweight, and 12% obese. Lean patients had significantly different alanine transaminase/aspartate transaminase, TSH, and fibrosis score compared to overweight and obese patients.
Conclusion: Authors stress on the importance of early identification of patients and stratifying them using less invasive, highly accurate, and affordable screening tools such as USG, biochemical tests, and fibrosis scores. More large-scale prospective studies can validate our observations, help physicians in early identification of patients who may benefit from therapeutic interventions, and even help them formulate more effective treatment algorithms.
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