Dear Editor,

We read with great interest the article by Mohamed et al,1 recently published in the Oman Medical Journal. The article has highlighted the prevalence and risk factors of non-alcoholic fatty liver disease (NAFLD) among patients with type 2 diabetes mellitus in Bahrain. We agree that the authors have included an impressive number of covariates in determining the risk factors of NAFLD. However, we notice that in the case of metabolic syndrome, the reference used was Adult Treatment Panel III guidelines instead of the harmonized criteria which is ethnic-specific in waist circumference measures with a lower cut-off for fasting blood glucose.2 In future studies, we suggest that the authors may consider using the harmonized criteria to ensure all possible causes of metabolic syndrome are detected. In addition, the demographic data in Table 1 mentioned cardiovascular risks as one of the variables; however, we could not elicit it in the univariate analysis.1 We are wondering whether it is associated with physical activity or other variables.

The authors also considered obesity as measured through body mass index (BMI) as a risk factor for NAFLD. This was confirmed by the findings of the study as an independent risk factor for NAFLD. Although BMI is robust and simple to use as an indicator of obesity, similar BMI does not necessarily mean similar proportion of body fat. Thus, the measurement of body fat percentage through bioimpedance analysis may be a better indicator of obesity.3 Future studies may consider various body composition measures to accurately define obesity.

For the statistical analysis, researchers did a good job by performing all the univariate analyses and then selected the statistically significant variables to be included in the binomial logistic regression. However, there is another way to analyze the data by performing a full multiple logistic regression.4 In multiple logistic regression, the univariate analysis called simple logistic regression is required to select the possible variables that could be included in the preliminary model.4 Performing multiple logistic regression (multivariate analysis) can increase the power of statistical analysis.5 We notice some missing data for most of the measured variables, which could lead to potential bias and compromise the inferences from the research if not handled and treated appropriately. We would like to know how the researchers handled or treated the missing data.

references

1. 1. Mohamed AM, Isa HM, Ali MS, Dadi A, Kadhim Z. Prevalence of non-alcoholic fatty liver disease among patients with diabetes mellitus attending primary health care centers in Bahrain. Oman Med J 2022 Mar;37(2):e350.
2. 2. Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al; International Diabetes Federation Task Force on Epidemiology and Prevention; Hational Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; International Association for the Study of Obesity. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 2009 Oct;120(16):1640-1645.
3. 3. Yajnik CS, Yudkin JS. The Y-Y paradox. Lancet 2004 Jan;363(9403):163.
4. 4. Naing NN, D’Este C, Isa AR, Salleh R, Bakar N, Mahmod MR. Factors contributing to poor compliance with anti-TB treatment among tuberculosis patients. Southeast Asian J Trop Med Public Health 2001 Jun;32(2):369-382.
5. 5. Tabachnick BG, Fidell LS. Using multivariate statistics. 7th ed. United States of America: Pearson; 2019.