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Comparison of cardiometabolic risk factors between obese and non-obese patients with nonalcoholic fatty liver disease – Scientific Reports


Subjects and study design

From 2019 to 2021, 452 Fibroscan-proven NAFLD patients were enrolled in the present study and their clinical data were collected prospectively. The inclusion criteria for this cross-sectional study were as follows: (1) adults 18–65-year-old; (2) Fibroscan findings confirming fatty liver grade ≥ 2, and (3) willingness to participate in the study. In this study, we excluded the subjects with (1) significant alcohol consumption (> 30 g/day); (2) history of treatment for viral hepatitis; (3) diagnosis renal failure, malignancies, infectious disease, chronic liver disease other than NAFLD; and (4) receiving effective drug treatment for NAFLD and/or bariatric surgery over the past 6 months.

This study was conducted in accordance with the ethical guidelines of the Helsinki Declaration. The written informed consent form was signed and dated by all participants. The study protocol has been approved by the Ethical Committees of National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences.

Clinical and laboratory evaluations

After recording general information about medical history, medications, alcohol consumption, and smoking habits, all patients underwent laboratory testing, physical examination and liver assessment. Height and body weight were measured without shoes and in light clothing by a well-trained nutritionist. BMI was calculated as body weight (kg) divided by height squared (m2). Waist circumference was measured in centimeters at the minimum circumference between the lower rib and the iliac crest to the nearest 0.1 cm using an inextensible metric tape. Blood pressure was measured in seated position, after 15 min rest, by using a standard mercury sphygmomanometer.

Venous blood samples were collected after a 12-h overnight fast to measure serum levels of aspartate aminotransferase (AST), alanine aminotransferase (ALT), gamma-glutamyl transpeptidase (GGT), total cholesterol (TC), triglycerides (TG), high density lipoprotein-cholesterol (HDL-C), low density lipoprotein-cholesterol (LDL-C), fasting blood sugar (FBS) and insulin. All laboratory parameters were assessed by commercial kits (Pars Azmoon, Tehran, Iran) using standard methods.

Enrolled individuals were classified based on BMI as normal weight (< 25 kg/m2), overweight (25–30 kg/m2) or obese (≥ 30 kg/m2)13. Participants who met at least three abnormalities of the following metabolic syndrome criteria recommended by National Cholesterol Education Program’s Adult Treatment Panel III (NCEP: ATP III)14 were categorized as unhealthy metabolic phenotype: (1) abdominal obesity, defined as waist circumference (> 102 cm in men and > 88 cm in women), (2) high systolic/diastolic blood pressure ≥ 130/85 mmHg, and/or the current use of anti-hypertensive medication, (3) FBS ≥ 100 mg/dl and/or current treatment with anti-diabetic medication, (4) low HDL-C concentration (< 40 mg/dl in men and < 50 mg/dl in women), and (5) triglycerides ≥ 150 mg/dl and/or the current use of lipid-lowering drugs.

Liver assessment

NAFLD was diagnosed through FibroScan® (Echosens, Paris, France) equipped with XL probe, after exclusion of heavy alcohol consumption, viral, or other chronic liver disease. This examination was carried out by experienced hepatologist according to manufacturer’s protocol. Accordingly, fibrosis was measured in kilopascals and scored with a 6-grade scale, from normal to cirrhosis and severe fibrosis. Steatosis was reported in decibels per meter (dB/m) and graded from 0 to 3.

Moreover, the following formulas were applied to estimate the fatty liver in the study:

Lipid Accumulation Product (LAP)15: :

$$\begin{aligned} & \left( {Waist\; \, circumference \, \left( {cm} \right) \, {-} \, 65} \right) \times \left( {TG \, \;concentration \, \left( {mmol/l} \right)} \right) \, for \, \;men \\ & \left( {Waist \, \;circumference \, \left( {cm} \right) \, {-} \, 58} \right) \times \left( {TG\; \, concentration \, \left( {mmol/l} \right)} \right) \, for\; \, women \\ \end{aligned}$$

Fatty Liver Index (FLI)16:

$$\frac{\left(\mathrm{e}0.953\times \mathrm{loge}\left(\mathrm{triglycerides}\right)+0.139\times \mathrm{BMI}+0.718\times \mathrm{loge}\left(\mathrm{GGT}\right)+0.053\times \mathrm{waistcircumference}-15.745\right)}{\left(1 + \mathrm{e}0.953\times \mathrm{loge}\left(\mathrm{triglycerides}\right)+0.139\times \mathrm{BMI}+0.718\times \mathrm{loge}\left(\mathrm{GGT}\right)+0.053\times \mathrm{waistcircumference}-15.745\right)}\times 100$$

Hepatic Steatosis Index (HSI)17:

$$8 \times \left( {ALT/AST\; \, ratio} \right) + BMI \, \left( { + 2, \, if\; \, female; \, + 2, \, if\; \, diabetes \, \;mellitus} \right)$$

Fatty Liver Score (FLS)18:

$$1.18 \times \, metabolic \, \;syndrome \, + 0.45 \times \, diabetes \, \left( {2, \, if\; \, yes; \, 0, \, if\; \, no} \right) \, + 0.15 \times \, FSI \, \left( {\text{mU/L}} \right) \, + 0.04 \times \, AST \, \left( {\text{U/L}} \right) \, – 0.94 \times \, \left( {AST/ALT} \right) \, – 2.89$$

BMI, Age, ALT, TG score (BAAT) = was calculated as the sum of the following categorical variables19:

$$BMI \, \left( { \ge 28 \, = \, 1, \, < 28 = \, 0} \right), \, age \, at \, liver \, biopsy \, \left( { \ge \, 50 \, years \, = ; \, < \, 50 \, = \, 0} \right), \, ALT \, \left( { \ge \, 2N \, = \, 1, \, < 2N \, = \, 0} \right), \, and \, \;serum\; \, triglycerides \, \left( { \ge \, 1.7 \, \;{\text{mmol/l }} = \, 1, \, < \, 1.7 \, = \, 0} \right) \, thus\; \, ranging\; \, from \, 0 \, to \, 4.$$

(All patients had triglycerides < 400 mg/dl).

Statistical analysis

All the statistical analyses were carried out using SPSS software, version 20.0 (SPSS Inc., Chicago, IL, USA). Two-sided P value less than 0.05 was considered statistically significant. Smirnov–Kolmogorov test was used to check the normality of our data. Continuous variables are presented as mean ± standard deviation (SD), and compared by one-way analysis of variance (ANOVA). Categorical variables are presented as frequency and percentage, and compared by Chi-square test.

Both univariate and multivariate logistic regression models were applied to calculate odds ratios (ORs) and 95% confidence interval for occurrence of metabolic syndrome in each category of BMI. In multivariate logistic regression models, we adjusted potential confounding factors, including age, sex, steatosis and fibrosis score.

Ethics approval and consent to participate

The Ethical Committee of Shahid Beheshti University of Medical Sciences approved the study protocol in accordance with the Declaration of Helsinki. All patients signed an informed consent form and the aims and procedures were explained to them.



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