A total of 2894 adult patients who visited the department of Health Care, Qilu Hospital (Qingdao), Shandong University between January 2015 and December 2019 were sequentially enrolled in this registry. Information on smoking habit, alcohol drinking habit, and personal medical history were obtained from the subjects. A flow chart of the study participants is shown in Figure 1. Exclusion criteria were subjects with hepatitis B surface antigen positive, hepatitis C antibody positive and excessive alcohol consumption (alcohol equivalent: ≥30 g/day for men, ≥20 g/day for women), and subjects who lacked data on abdominal ultrasonography, sex hormones, and fatty liver assessment formula components. In order to study the role of SHBG in fatty liver, we also excluded subjects with acute/chronic infection, viral/toxic mediated liver diseases, chronic kidney disease, hypo/hyperthyroidism, hypopituitarism, and history of taking corticosteroids or sex hormone replacement.
After prespecified exclusion, a total of 1417 Chinese subjects (men/women: 869/548; mean age: 62.12 ± 13.9 years) were finally recruited in the present study. The original dataset was randomly divided into training dataset and validation dataset according to 7:3 ratio.
The study was in accordance with the principles outlined in the Declaration of Helsinki and was approved by the Ethics Committee of Qilu Hospital (Qingdao), Shandong University. Written informed consent was obtained from all participants.
Clinical and laboratory measurements
Height and body weight were measured to the nearest millimeter and kilogram. BMI was calculated as weight in kilograms divided by height in meters squared. WC was measured at an intermediate level between the lowest rib and the iliac crest. Blood pressure was measured using standard method as in a seated position after rest for 15 more minutes.
After fasting for 12 hours, venous blood samples were drawn from all subjects before 10:00 a.m. All biochemical assays were performed in the same laboratory using standard methods. Laboratory tests included: alanine aminotransferase (ALT), aspartate aminotransferase (AST), GGT, serum fasting plasma glucose (FPG), haemoglobin A1c (HbA1c), total cholesterol (TC), TG, high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C), fasting insulin (FINS), fasting C-peptide(FCP), and SHBG. The following equation was used for the homeostasis model assessment of insulin resistance: HOMA-IR= FPG(mmol/L)*FINS(mU/L)/2217.
US assessments for NAFLD
Two well-experienced ultrasonographers, who were blinded to the clinical data, performed the abdominal US examination to diagnose NAFLD. The diagnostic criteria for fatty liver by ultrasonography included: increased echogenicity in the hepatic parenchyma than in the kidney, blurred intrahepatic vessel structure, and abnormal visualization of diaphragm and posterior right hepatic lobe18.
Definition of Variables
The diagnosis of MetS was considered based on the International Diabetes criteria as we described before19. Diagnosis of diabetes mellitus was based on the guideline of the American Diabetes Association in 2014: FPG ≥7.0mmol/L or HbA1c≥6.5%, or a previous diagnosis made by a healthcare professional.
NAFLD prediction algorithms
FLI = (e0.953*loge (triglycerides) + 0.139*BMI + 0.718*loge (GGT) + 0.053*WC - 15.745) / (1 + e 0.953*loge (triglycerides) + 0.139*BMI + 0.718*loge (GGT) + 0.053*WC - 15.745) *100.
LFS = -2.89+1.18*MetS (yes=1/no=0) + 0.45*type 2 diabetes (yes=2/no=0) +0.15*fS-insulin(mU/L) +0.04*fS-AST-0.94*AST/ALT.
LAP = (waist circumference - 65) × (triglycerides) in men and (waist circumference - 58) × (triglycerides) in women.
HSI = 8 x (ALT/AST ratio) + BMI (+2, if female; +2, if diabetes mellitus).
Continuous and categorical variables were expressed as means ± standard deviation (SD) and percentage (%), respectively. Comparison of continuous variables was done with Student’s t-test or Mann-Whitney’s U-test, and categorical parameters were compared using the chi-square test. Variables that were statistically significant by univariate analyses were added to a multiple logistic regression model to identify independent predictors for the presence of NAFLD after adjusting for age and sex. Based on the result of multiple logistic regression analysis, we formulated a nomogram by proportionally converting regression coefficient to a 0- to 100-point scale. The variable with the highest β coefficient was assigned 100 points. Accordingly, the points across the respective variables were added to obtain total points, which were converted into prediction probabilities. The area under the receiver-operating characteristic (AUROC) as with sensitivity and specificity were constructed and compared to evaluate the predictive power of indices for diagnosing NAFLD. Effective cutoff values were obtained by calculating the Youden’s index from ROC curves20. The predictive performance of nomogram was measured by calibration using 1000 bootstrap samples to reduce the over fitting bias. By quantifying the probability of net benefits at a threshold from 0.0 to 1.0, we conducted DCA curve to evaluate the clinical utility of SFI. All analyses were performed using SPSS version 22.0 (Chicago, IL, USA) and R version 4.0.2 (College Station, Texas, USA). P value <0.05 was considered statistically significant.