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Models to predict non-alcoholic fatty liver disease linked to obesity in Morocco
1Laboratory of Biotechnology, Biochemistry & Nutrition, Training and Research Unit on Nutrition & Food Sciences, Chouaib Doukkali University, Faculty of Sciences, El Jadida, Morocco.
Background:
The prevalence, risk factors and screening for the problem of non-alcoholic fatty liver disease linked to obesity are not well known in Morocco. The diagnosis of this disease by biopsy is invasive and the assessment of its severity by ultrasound shows variability in observation.
Objective:
The aim of this retrospective study is to estimate the prevalence of NAFLD linked to obesity, to determine the risk factors associated with it and to develop a non-invasive procedure as a method of diagnosing this disease in Morocco.
Material And Methods:
It's a retrospective study. The collection of anthropometric, clinical, biochemical, and radiological data over a period from 2014 to 2018 were captured from registers of patients at the Med VI University Hospital in Marrakech. Data were analyzed using SPSS version 26 software. Descriptive statistics were presented using frequencies and means +/- standard deviation to describe categorical and numeric data respectively. Pearson's chi-square test was used to test the association between categories of two independent samples. Multinomial logistic regression is used to find disease risk factors and models to predict non-alcoholic fatty liver disease (NAFLD) linked to obesity in Morocco.
Results:
Gender, increased age, body mass index, alanine aminotransferase, triglycerides, C-reactive protein, alkaline phosphatase, gamma-glutamyl transferase were significantly correlated with NAFLD and its evolvement.
Conclusion:
The prevalence of NAFLD linked to obesity is an alarming problem in Morocco. It was 83.5%. Age, gender, body mass index, alanine aminotransferase, triglycerides, C-reactive protein, alkaline phosphatase and gamma-glutamyl transferase are risk factors for NAFLD and its severity. It were used to develop two algorithms that can be used, as a more objective and non-invasive screening method for NAFLD.
