
==== Front
Interact J Med Res
Interact J Med Res
IJMR
Interactive Journal of Medical Research
1929-073X
JMIR Publications Toronto, Canada

v13i1e56035
39172506
10.2196/56035
Original Paper
Original Paper
Establishment and Evaluation of a Noninvasive Metabolism-Related Fatty Liver Screening and Dynamic Monitoring Model: Cross-Sectional Study
Mavragani Amaryllis
Zhang Yijue
Yu Hairui
Ni Jiali MD 1https://orcid.org/0000-0002-3239-9330

Huang Yong MS 2https://orcid.org/0009-0006-8880-8849

Xiang Qiangqiang MS 1https://orcid.org/0009-0009-2484-5255

Zheng Qi MS 1https://orcid.org/0009-0009-1848-7519

Xu Xiang MS 2https://orcid.org/0009-0005-7849-376X

Qin Zhiwen MS 2https://orcid.org/0009-0000-1573-3440

Sheng Guoping MD 2https://orcid.org/0000-0002-6152-2967

Li Lanjuan MD https://orcid.org/0000-0001-6945-0593
3State Key Laboratory for Diagnosis and Treatment of Infectious Disease The First Affiliated Hospital Zhejiang University School of Medicine 79 Qingchun Rd Hangzhou, 310058 China 86 571 87236459 86 571 87236458 ljli@zju.edu.cn

1 The First Affiliated Hospital Zhejiang University School of Medicine Hangzhou China
2 Shulan (Hangzhou) Hospital Affiliated to Zhejiang Shuren University Shulan International Medical College Hangzhou China
3 State Key Laboratory for Diagnosis and Treatment of Infectious Disease The First Affiliated Hospital Zhejiang University School of Medicine Hangzhou China
Corresponding Author: Lanjuan Li ljli@zju.edu.cn
2024
22 8 2024
13 e560353 1 2024
23 5 2024
3 6 2024
3 7 2024
©Jiali Ni, Yong Huang, Qiangqiang Xiang, Qi Zheng, Xiang Xu, Zhiwen Qin, Guoping Sheng, Lanjuan Li. Originally published in the Interactive Journal of Medical Research (https://www.i-jmr.org/), 22.08.2024.
2024
https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Interactive Journal of Medical Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.i-jmr.org/, as well as this copyright and license information must be included.

Background

Metabolically associated fatty liver disease (MAFLD) insidiously affects people's health, and many models have been proposed for the evaluation of liver fibrosis. However, there is still a lack of noninvasive and sensitive models to screen MAFLD in high-risk populations.

Objective

The purpose of this study was to explore a new method for early screening of the public and establish a home-based tool for regular self-assessment and monitoring of MAFLD.

Methods

In this cross-sectional study, there were 1758 eligible participants in the training set and 200 eligible participants in the testing set. Routine blood, blood biochemistry, and FibroScan tests were performed, and body composition was analyzed using a body composition instrument. Additionally, we recorded multiple factors including disease-related risk factors, the Forns index score, the hepatic steatosis index (HSI), the triglyceride glucose index, total body water (TBW), body fat mass (BFM), visceral fat area, waist-height ratio (WHtR), and basal metabolic rate. Binary logistic regression analysis was performed to explore the potential anthropometric indicators that have a predictive ability to screen for MAFLD. A new model, named the MAFLD Screening Index (MFSI), was established using binary logistic regression analysis, and BFM, WHtR, and TBW were included. A simple rating table, named the MAFLD Rating Table (MRT), was also established using these indicators.

Results

The performance of the HSI (area under the curve [AUC]=0.873, specificity=76.8%, sensitivity=81.4%), WHtR (AUC=0.866, specificity=79.8%, sensitivity=80.8%), and BFM (AUC=0.842, specificity=76.9%, sensitivity=76.2%) in discriminating between the MAFLD group and non-fatty liver group was evaluated (P<.001). The AUC of the combined model including WHtR, HSI, and BFM values was 0.900 (specificity=81.8%, sensitivity=85.6%; P<.001). The MFSI was established based on better performance at screening MAFLD patients in the training set (AUC=0.896, specificity=83.8%, sensitivity=82.1%) and was confirmed in the testing set (AUC=0.917, specificity=89.8%, sensitivity=84.4%; P<.001).

Conclusions

The novel MFSI model was built using WHtR, BFM, and TBW to screen for early MAFLD. These body parameters can be easily obtained using a body fat scale at home, and the mobile device software can record specific values and perform calculations. MFSI had better performance than other models for early MAFLD screening. The new model showed strong power and stability and shows promise in the area of MAFLD detection and self-assessment. The MRT was a practical tool to assess disease alterations in real time.

metabolic-associated fatty liver disease
nonalcoholic fatty liver disease
nonalcoholic steatohepatitis
body fat mass
waist-height ratio
basal metabolic rate
liver
==== Body
pmcIntroduction

Nonalcoholic fatty liver disease (NAFLD) is regarded as an important cause of liver disease, affecting more than 25% of the general population worldwide; more than 50% of patients with NAFLD also have dysmetabolism [1,2]. In 2020, experts redefined NAFLD as metabolically associated fatty liver disease (MAFLD), and much emphasis was placed on the presence of metabolic-related diseases or dysfunction [3-5]. Researchers have found that MAFLD is a multisystem disease, and liver steatosis is associated with type 2 diabetes, chronic kidney disease, cardiovascular disease, and other diseases that interact and form a vicious cycle [6-14]. China has the highest incidence of NAFLD or MAFLD morbidity in Asia [3,15,16]. Therefore, much attention should be given to MAFLD by enhancing awareness of MAFLD and optimizing its management.

To date, guidelines have suggested that liver biopsy could serve as the gold standard to diagnose histological liver damage, but noninvasive, quantitative assessment of liver fibrosis may also have prognostic implications. Ratziu et al [17] collected liver biopsy samples from 51 patients and found that 41% of the patients were at different stages of liver fibrosis or had nonalcoholic steatohepatitis. The uneven distribution of histological lesions inevitably led to sampling error when performing biopsy. Abdominal imaging, such as B-ultrasound imaging and the controlled attenuation parameter (CAP) technique, can be used to diagnosis liver disease; the former is less sensitive to mild steatosis, while the latter can detect steatosis of more than 5% and is one of the most common noninvasive methods for quantifying hepatic steatosis and fibrosis clinically [18,19]. The European Association for the Study of the Liver, European Association for the Study of Diabetes, and European Association for the Study of Obesity updated the clinical practice guidelines that propose that the nonalcoholic fatty liver disease fibrosis score (NFS) and fibrosis-4 (FIB-4) index can be used as prognostic markers for the progression of liver disease [20]. The NFS has higher specificity in the older adult population (individuals aged >65 years old) [21,22]. The predictive performance of the NFS, FIB-4 index, and aspartate aminotransferase-to-platelet ratio index (APRI) has been consistent in relation to rates of liver-related disease and mortality but is less valuable for the prediction of liver fibrosis [23]. One study found that the combination of the NFS, FIB-4 index, and liver stiffness measurement greatly improved the diagnostic accuracy, and the performance was similar to that of liver biopsy [24]. A cross-sectional study found that the triglyceride glucose (TyG) index was positively correlated with the likelihood and severity of NAFLD. The TyG index is generally considered a biomarker of steatosis, while its causal role in the judgement of fibrosis progression remains unclear [25,26]. In addition, the hepatic steatosis index (HSI) is more accurate in discriminating between MAFLD and nonfatty liver disease (non-FLD) than ultrasound. The predictive ability of the CAP for steatosis is superior to that of the HSI, and the HSI is more effective at discriminating patients with moderate-to-severe disease [18,27].

Studies have shown that numerous anthropometric indicators, such as BMI, waist-height ratio (WHtR), waist-hip ratio, and body adiposity index, are applicable for the quantification of visceral steatosis [28-32]. Body fat scales, a new popular domestic tool for health analysis, can be used to analyze basic parameters of body conditions such as the basal metabolic rate (BMR), body water distribution, and fat distribution. Reputable experts in the field have conducted extensive long-term studies on NAFLD and MAFLD, yet few noninvasive scoring models that accurately reflect disease activity or progression have been identified [33,34].

Therefore, there is an urgent need to identify more accurate predictive indicators and develop new screening methods for early MAFLD screening. The aim of this study was to construct a noninvasive prediction system for MAFLD, explore this new system for early screening in public, and establish a home-based tool for regular self-assessment and monitoring of MAFLD.

Methods

Study Population

The participants came from Hangzhou, Shaoxing, and Quzhou from March 2021 to November 2021, and a total of 2097 participants were enrolled (Figure 1). All participants signed the informed consent form and completed the examination as required. There were 1758 eligible participants in the training set who truthfully and completely answered the questionnaire, which contained items regarding height, weight, drinking history, past medical history, and other basic information.

To validate the results of the training set, there were 200 eligible participants grouped into the testing set.

All participants were diagnosed using the liver stiffness measurement and classified according to the CAP. CAP values <238 was considered to indicate a healthy liver, ≥238 and <259 was considered to indicate mildly fatty liver, ≥259 and <292 was considered to indicate moderately fatty liver, and ≥292 was considered to indicate severely fatty liver [35,36].

Figure 1 Flowchart of the inclusion process for the participants in the training set and the proportion of mild, moderate, and severe fatty liver disease in the nonalcoholic fatty liver disease (NAFLD) group and metabolically associated fatty liver disease (MAFLD) group. CAP: controlled attenuation parameter; FLD: fatty liver disease.

Exclusion Criteria

Patients who met one or more of the following criteria could not participate in this study: (1) <18 years old; (2) long-term use of various health products and drugs; (3) presence of cirrhosis or liver cancer; (4) previous organ transplantation; and (5) patients for whom B-ultrasound imaging indicated FLD but who could not be diagnosed with MAFLD.

Diagnostic Criteria

The researchers in this study entered and organized the data, and the following diagnostic criteria were used to distinguish MAFLD patients [3]: (1) overweight or obese (BMI ≥23 kg/m2 in Asians), (2) presence of type 2 diabetes mellitus, and (3) at least 2 of the following metabolic risk abnormalities: waist circumference ≥90 cm in Asian men and ≥80 cm in Asian women; blood pressure ≥130/80 mm Hg or specific drug treatment; triglyceride (TG) level ≥1.7 mmol/L or specific drug treatment; high-density lipoprotein cholesterol (HDL-c) level <1.0 mmol/L for men and <1.3 mmol/L for women or specific drug treatment; fasting plasma glucose (FPG) level of 5.6 mmol/L to 6.9 mmol/L, 2-hour postload glucose level of 7.8 mmol/L to 11.0 mmol/L, or glycosylated hemoglobin level of 5.7% to 6.4%; and homeostasis model assessment of insulin resistance score ≥2.5.

Data Collection and Model Selection

All items were completed under the guidance of the researchers. The participants underwent fasting blood tests. Waist circumference and hip circumference were measured with the participants wearing thin clothes. Body composition analysis was performed with bare feet. The patients were in a supine position during the FibroScan exam, and the right upper limb was held high and flat close to the ear. The probe was moved a small distance from the anchor point so that the most suitable detection point could be determined.

We collected basic information, including sex, age, height, weight, BMI, waist circumference, hip circumference, blood pressure, heart rate, and alcohol consumption history. The following laboratory results were included: alanine aminotransferase (ALT), aspartate aminotransferase (AST), glutamyl transpeptidase (GGT), alkaline phosphatase, hemoglobin, total cholesterol (TC), TG, HDL-c, low-density lipoprotein cholesterol (LDL-c), uric acid, and FPG levels, as well as white blood cell, red blood cell, and platelet (PLT) counts.

A body composition analyzer (InBody770, Biospace) was used to measure body composition and determine total body water (TBW), intracellular water, skeletal muscle mass, protein, and body fat mass (BFM). A body fat scale (3 Pro, Huawei) was used to determine the BMR, fat%, and visceral fat area (VFA).

The models or formulas involved in this study, including BMI, FIB-4 index, Forns index score, APRI, glutamyl transpeptidase-to-platelet ratio index (GPR), HSI, and TyG index, were developed using the following standard equations:

BMI=weight/height2

FIB-4=age×AST/(PLT×√ALT)

Forns index score=7.811-3.131×In PLT(109/L)+0.781×In GGT+3.467×In age-0.014×TC

APRI=(AST/upper limit of normal)/PLT×100

glutamyl transpeptidase-to-platelet ratio index=(GGT/upper limit of normal)/PLT×100

HSI=8×(ALT/AST)+BMI (female+2, diabetes+2)

TyG=ln (TG×FPG/2)

Statistical Analysis Methods

Participants were divided into the non-FLD group, which was the healthy group; MAFLD group; and NAFLD group.

All data obtained in this study were analyzed using SPSS version 26.0 (IBM Corp). The continuous variables were tested for normality and homogeneity of variance. A t test was performed for measurement data that followed a normal distribution, and the results are expressed as the mean (SD). Nonnormally distributed data were analyzed using nonparametric tests, and the results are represented by quartiles. The chi-square test or Fisher precision probability test was used for quantitative data such as sex. ANOVA was followed by post hoc analysis tests to compare numerical data among the 3 groups (MAFLD, NAFLD, and non-FLD). A P<.01 indicated that the difference was statistically significant.

Binary logistic regression analysis was performed to explore the potential anthropometric indicators with predictive ability to screen for MAFLD. A receiver operating characteristic (ROC) curve was drawn based on the selected indicators, and the area under the ROC curve (AUC) was calculated correspondingly. The indicator with the highest AUC was considered the most valuable indicator. The maximum Youden index (using the formula sensitivity + specificity - 1) was used to define the optimal cutoff value. Potential confounding variables were added into the logistic regression equation step by step, including age; blood pressure; and FPG, TC, TG, HDL-c, and LDL-c levels. Calibration Model I (age, blood pressure, and FPG level were added to the logistic regression equation) and Model II (age; blood pressure; and FPG, TC, TG, HDL-c, and LDL-c levels were added to the logistic regression equation) were established, and the predictive ability was evaluated before and after calibration. All significant indicators were included for the combination of diagnostic tests, and ROC curves were drawn. A new prediction model, the MAFLD Screening Index (MFSI), was constructed using logistic regression analysis, and the model was validated with the testing set. All tests were 2-tailed, and P<.01 was considered statistically significant.

Ethical Considerations

Every participant signed a written informed consent form and participated in the study anonymously. We ensured it was not possible to identify individual participants in any images used in manuscripts or other materials.

Every participant was given an allowance of ¥300 (US $0.14) upon completion of the research project. The study protocol was approved by the Ethics Committee of Shulan Hangzhou Hospital (approval number KY2021001).

The study did not involve additional invasive procedures, and there were no associated adverse reactions.

Results

Comparing Numerical Data Among the 3 Groups (MAFLD, NAFLD, and Non-FLD)

Using ANOVA to compare the basic information and anthropometric indicators among the 3 groups, the results showed that all parameters were significantly different among the MAFLD, NAFLD, and non-FLD groups. After the post hoc analysis, PLT count (P=.10) was not significantly different between the MAFLD and non-FLD groups, and white blood cell count (P=.26), TC (P=.35), LDL-c (P=.11), VFA (P=.07), and Fat% (P=.38) were not significantly different between the MAFLD and NAFLD groups (Table 1).

Table 1 Baseline characteristics and anthropometric indicators compared among 3 groups: metabolically associated fatty liver disease (MAFLD), nonalcoholic fatty liver disease (NAFLD), and non-fatty liver disease (non-FLD)

Characteristics	non-FLD (n=786)	MAFLD (n=864)	NAFLD (n=607)	Statistic (df)	P value	
Sex, n (%)	224.985a (2)	<.001	

	Male	326 (41.5)	668 (77.3)	375 (61.8)	
	
	

	Female	460 (58.5)	196 (22.7)	232 (38.2)	
	
	
Age (years), mean (range)	36 (28-48)	45 (34-55)	40 (31-53)	107.212b (2)	<.001	
Height (cm), mean (SD)	164.17 (7.820)	168.23 (7.844)	166.81 (8.358)	54.873c	<.001	
Weight (kg), mean (range)	57.30 (51.60-64.30)	72.9 (66.2-80.7)	69.7 (61.1-78.1)	664.463b (2)	<.001	
BMI (kg/m2), mean (range)	21.49 (20.06-23.18)	25.66 (24.04-27.79)	25.04 (22.99-2t7.12)	798.113b (2)	<.001	
SBPd (mm Hg), mean (range)	119 (110-135)	132 (121-144)	128 (118-140)	218.758b (2)	<.001	
DBPe (mm Hg), mean (SD)	74.89 (11.216)	82.88 (11.863)	79.72 (11.611)	91.652c (2,2249)	<.001	
WBCf (109/L), mean (range)	5.80 (4.95-6.80)	6.5 (5.5-7.6)	6.3 (5.4-7.5)	91.944b (2)	<.001	
RBCg (1012/L), mean (range)	4.69 (4.39-5.11)	5.12 (4.81-5.42)	5.02 (4.65-5.38)	210.018b (2)	<.001	
Hbh (g/L), mean (range)	140 (131-153)	155 (145-163)	150 (137-160)	213.919b (2)	<.001	
PLTi (109/L), mean (SD)	234.66 (55.331)	238.18 (58.518)	244.67 (58.671)	5.041c (2,2091)	.006	
FPGj (mmol/L), mean (range)	4.60 (4.36-4.89)	4.88 (4.55-5.34)	4.80 (4.50-5.19)	128.314b (2)	<.001	
ALTk (U/L), mean (range)	14 (10-20)	27.00 (19.00-34.00)	24.00 (17.75-40.00)	475.850b (2)	<.001	
ASTl (U/L), mean (range)	20 (17-24)	25.00 (21.00-32.00)	24.00 (19.00-29.00)	245.274b (2)	<.001	
ALPm (U/L), mean (range)	57 (47-70)	69.00 (57.00-83.00)	66.00 (55.00-81.00)	146.960b (2)	<.001	
GGTn (U/L), mean (range)	15 (12-21)	31.00 (20.00-52.00)	24.00 (17.00-38.00)	496.679b (2)	<.001	
TCo (mmol/L), mean (range)	4.71 (4.13-5.23)	4.99 (4.38-5.33)	4.96 (4.33-5.55)	47.740b (2)	<.001	
TGp (mmol/L), mean (range)	0.93 (0.71-1.26)	1.76 (1.21-2.45)	1.49 (1.07-2.24)	529.457b (2)	<.001	
HDL-cq (mmol/L), mean (range)	1.48 (1.28-1.68)	1.21 (1.07-1.40)	1.26 (1.07-1.45)	284.363b (2)	<.001	
LDL-cr (mmol/L), mean (range)	2.64 (2.23-3.16)	3.10 (2.64-3.64)	3.04 (2.56-3.55)	146.926b (2)	<.001	
UAs (μmol/L), mean (SD)	295.99 (80.86)	378.82 (85.081)	358.39 (87.676)	206.430c (2,2234)	<.001	
E-value (kPa), mean (range)	4.40 (3.70-5.20)	5.2 (4.3-6.3)	5.0 (4.2-6.0)	164.275b (2)	<.001	
CAPt (dB/m), mean (range)	200 (179-218)	284 (257-320)	278 (255-315)	1538.285b (2)	<.001	
WHtRu, mean (SD)	0.459 (0.0656)	0.524 (0.5117)	0.512 (0.0984)	113.769c	<.001	
WHRv, mean (range)	0.829 (0.783-0.880)	0.918 (0.872-0.950)	0.892 (0.840-0.939)	284.420b (2)	<.001	
FIB-4w index, mean (range)	0.813 (0.588-1.234)	0.954 (0.608-1.362)	0.782 (0.521-1.235)	15.698b (2)	<.001	
Forns index, mean (SD)	5.471 (1.6797)	6.536 (1.5977)	6.005 (1.6630)	166.437c (2,2190)	<.001	
APRIx, mean (range)	0.240 (0.187-0.301)	0.282 (0.215-0.376)	0.263 (0.201-0.351)	72.070b (2)	<.001	
GPRy, mean (range)	0.133 (0.103-0.192)	0.230 (0.159-0.403)	0.186 (0.135-0.292)	325.345b (2)	<.001	
HSIz, mean (rang)	28.57 (26.31-30.84)	35.28 (32.02-39.04)	34.47 (30.87-38.63)	772.797b (2)	<.001	
TyGaa index, mean (range)	8.137 (7.863-8.463)	8.855 (8.463-9.234)	8.674 (8.322-9.092)	585.116b (2)	<.001	
TBWbb (kg), mean (range)	30.30 (27.2-036.40)	38.65 (33.80-42.90)	36.85 (30.23-42.88)	341.198b (2)	<.001	
ICWcc, mean (range)	18.80 (16.70-22.60)	24.10 (20.90-26.80)	22.80 (18.70-26.10)	340.014b (2)	<.001	
Protein, mean (range)	8.10 (7.20-9.80)	10.40 (9.08-11.60)	9.90 (8.10-11.30)	339.536b (2)	<.001	
BFMdd, mean (range)	14.90 (12.00-17.40)	20.80 (17.60-24.43)	20.10 (16.80-24.20)	638.768b (2)	<.001	
SMMee, mean (range)	22.50 (19.80-27.53)	29.40 (25.30-32.93)	27.80 (22.35-32.10)	336.593b (2)	<.001	
BMRff (kcal), mean (range)	1283.95 (1179.70-1450.17)	1526.55 (1387.09-1655.52)	1470.15 (1276.65-1622.62)	358.763b (2)	<.001	
VFAgg, mean (range)	6.552 (5.235-7.762)	9.013 (7.598-10.813)	8.753 (7.294-10.671)	518.559b (2)	<.001	
Fat (%), mean (SD)	25.319 (5.9566)	28.255 (5.2256)	28.524 (5.7358)	68.099c (2,2018)	<.001	
aChi-squared test.

bH value.

cF value.

dSBP: systolic blood pressure.

eDBP: diastolic blood pressure.

fWBC: white blood cell.

gRBC: red blood cell.

hHb: hemoglobin.

iPLT: platelet.

jFPG: fasting plasma glucose.

kALT: alanine aminotransferase.

lAST: aspartate aminotransferase.

mALP: alkaline phosphatase.

nGGT: glutamyl transpeptidase.

oTC: total cholesterol.

pTG: triglyceride.

qHDL-c: high-density lipoprotein cholesterol.

rLDL-c: low-density lipoprotein cholesterol.

sUA: uric acid.

tCAP: controlled attenuation parameter.

uWHtR: waist-height ratio.

vWHR: waist-hip ratio.

wFIB-4: fibrosis-4.

xAPRI: aspartate aminotransferase-to-platelet ratio index.

yGPR: glutamyl transpeptidase-to-platelet ratio index.

zHSI: hepatic steatosis index.

aaTyG: triglyceride glucose.

bbTBW: total body water.

ccICW: intracellular water.

ddBFM: body fat mass.

eeSMM: skeletal muscle mass.

ffBMR: basal metabolic rate.

ggVFA: visceral fat area.

Predictive Performance of Different Anthropometric Indicators

The variables in the previous section with a P<.01 were further included in the logistic regression analysis. The ROC curves and optimal cutoff points for the selected indicators are shown in Table 2 and Figure 2. The AUCs of the WHtR, the Forns index, the HSI, the TyG index, TBW, BFM, and BMR were 0.866, 0.684, 0.873, 0.835, 0.760, 0.842, and 0.778, respectively (P<.001; Table 2).

Table 2 Cutoff points and areas under the curve (AUCs) were used to demonstrate the screening ability of the different anthropometric indicators for metabolically associated fatty liver disease (n=1649).

Anthropometric indicators	AUC (95% CI)	P value	Cutoff point	Specificity (%)	Sensitivity (%)	
WHtRa	0.866	<.001	0.501449713	79.8	80.8	
Forns index	0.684	<.001	6.160599276	67.8	61.3	
HSIb	0.873	<.001	31.15061285	76.8	81.4	
TyGc index	0.835	<.001	8.450341708	74	76.5	
TBWd (kg)	0.760	<.001	36.55	75.3	65.3	
BFMe	0.842	<.001	17.55	76.9	76.2	
BMRf	0.778	<.001	1434.263124	73.1	70.1	
Combination (WHtR/HSI)	0.885	<.001	N/Ag	76	85.6	
Combination (WHtR/BFM)	0.881	<.001	N/A	81.7	80	
Combination (BFM/HSI)	0.889	<.001	N/A	81	82.9	
Combination (WHtR/HSI/BFM)	0.900	<.001	N/A	81.8	85.6	
aWHtR: waist-to-hip ratio.

bHSI: hepatic steatosis index.

cTyG: triglyceride glucose.

dTBW: total body water.

eBFM: body fat mass.

fBMR: basal metabolic rate.

gN/A: not applicable.

Figure 2 Receiver operating characteristic (ROC) curves for the screening ability of different anthropometric indicators for metabolically associated fatty liver disease (MAFLD): (A) screening ability of the waist-height ratio (WHtR), Frons index, hepatic steatosis index (HSI), triglyceride glucose (TyG) index, total body water (TBW), body fat mass (BFM), basal metabolic rate (BMR) and (B) screening ability of combinations of WHtR, HSI, and BFM. Diagonal segments were produced by ties.

According to the ROC curve and AUC (Figure 2), HSI had the strongest predictive performance for MAFLD in the training set, and the performance ranking was as follows: HSI, WHtR, BFM, TyG index, TBW, and Forns index. The confounding factors were further corrected for in the logistic regression analysis (Model I: age, blood pressure, and FPG level were added to the logistic regression equation; Model II: age; blood pressure; and FPG, TC, TG, HDL-c, and LDL-c levels were added to the logistic regression equation). After correction for confounding factors, the odds ratio (OR) of the Forns index in Model I was 1.043 (95% CI 0.851-1.277), and that in Model II was 1.050 (95% CI 0.854-1.293; Table 3). The results showed that the performance of the Forns index for MAFLD screening was unstable and the performance of other anthropometric indicators was not easily influenced by confounders.

The HSI and WHtR showed better predictive performance than the other indicators. The sensitivity of the HSI was higher than that of the other anthropometric indicators (sensitivity=81.4%), and the specificity of the WHtR was higher than that of the other anthropometric indicators (specificity=79.8%). The combination of WHtR, HSI, and BFM increased the predictive ability for MAFLD, and the AUC was 0.900 (specificity=81.8%, sensitivity=85.6%; P<.001; Table 2).

Table 3 Confounders were corrected in the binary logistic regression analysis to compare changes in screening power for different anthropometric indicators (n=1649).

Anthropometric indicators	Nonadjusted model	Model I	Model II	

	ORa (95% CI)	P value	OR (95% CI)	P value	OR (95% CI)	P value	
TBWb (kg)	1.079 (1.051-1.109)	<.001	1.086 (1.055-1.118)	<.001	1.075 (1.042-1.108)	<.001	
BFMc	1.255 (1.194-1.319)	<.001	1.250 (1.186-1.317)	<.001	1.257 (1.192-1.326)	<.001	
WHtRd	145.540 (9.015-2349.524)	<.001	107.825 (6.232-1865.456)	.001	113.408 (6.759-1902.900)	<.001	
Forns index	1.166 (1.053-1.292)	.003	1.043 (0.851-1.277)	.69	1.050 (0.854-1.293)	.64	
HSIe	1.124 (1.070-1.182)	<.001	1.128 (1.070-1.190)	<.001	1.110 (1.052-1.172)	<.001	
TyGf index	6.557 (4.560-9.427)	<.001	5.832 (3.972-8.562)	<.001	6.005 (3.764-9.579)	<.001	
aOR: odds ratio.

bTBW: total body water.

cBFM: body fat mass.

dWHtR: waist-height ratio.

eHSI: hepatic steatosis index.

fTyG: triglyceride glucose.

Development of a New MAFLD Screening Model

The HSI, WHtR, and BFM displayed strong power in screening for MAFLD. The HSI was calculated based on BMI, ALT levels, and AST levels and was not suitable for early screening for MAFLD. The purpose of establishing a new model was to reduce the need for invasive procedures and reduce the frequency of medical visits, as well as to screen for MAFLD in high-risk populations. The predictive ability of TBW was stable after correcting for confounders (Model I: 95% CI 1.055-1.118; Model II: 95% CI 1.042-1.108; Table 3). Therefore, TBW was included in the new model. Logistic regression analysis was used to establish the MAFLD early screening model, which was named the MFSI. The formula was as follows: MFSI=–13.968+0.120×TBW+0.254×BFM+10.793×WHtR (Figure 3). The AUC of the MFSI was 0.896 (specificity: 83.8%, sensitivity: 82.1%; P<.001; Table 4). Collectively, the performance of the MFSI and the WHtR/HSI/BFM combination models was similar.

Figure 3 Receiver operator characteristic (ROC) curves showing the screening ability of different combinations of anthropometric indicators and a new metabolically associated fatty liver disease (MAFLD) screening model named the MAFLD screening index (MFSI=–13.968+0.120×total body water [TBW]+0.254×body fat mass [BFM]+10.793×waist-height ratio [WHtR]). Diagonal segments were produced by ties. BMR: basal metabolic rate; HSI: hepatic steatosis index; TyG: triglyceride glucose.

Table 4 Cutoff points and areas under the curve (AUCs) were used to compare the screening ability of different anthropometric indicators and the metabolically associated fatty liver disease screening index (MFSI; n=1649).

Anthropometric indicators	AUC (95% CI)	P value	Cutoff point	Specificity (%)	Sensitivity (%)	
WHtRa	0.866	<.001	0.501449713	79.8	80.8	
Forns index	0.684	<.001	6.160599276	67.8	61.3	
HSIb	0.873	<.001	31.15061285	76.8	81.4	
TyGc index	0.835	<.001	8.450341708	74	76.5	
TBWd (kg)	0.760	<.001	36.55	75.3	65.3	
BFMe	0.842	<.001	17.55	76.9	76.2	
Combination (WHtR/HSI)	0.885	<.001	N/Af	76	85.6	
Combination (WHtR/BFM)	0.881	<.001	N/A	81.7	80	
Combination (BFM/HSI)	0.889	<.001	N/A	81	82.9	
Combination (WHtR/HSI/BFM)	0.900	<.001	N/A	81.8	85.6	
MFSI	0.896	<.001	0.5146795	83.8	82.1	
aWHtR: waist-height ratio.

bHSI: hepatic steatosis index.

cTyG: triglyceride glucose.

dTBW: total body water.

eBFM: body fat mass.

fN/A: not applicable.

Performance of the MFSI in the Testing Set

There were a further 200 participants enrolled in the testing set, including 51 non-FLD patients and 149 MAFLD patients. To evaluate the predictive ability of the MFSI for screening for MAFLD in a high-risk population, the MFSI was used with the testing set, and ROC curves were drawn based on the MFSI; BFM; WHtR; HSI; TBW; and the combined model with WHtR, HSI, and BFM (Figure 4). The AUC (testing set) of the MFSI was 0.917, the specificity was 89.8%, and the sensitivity was 84.4%. The AUC (testing set) of the combined model with WHtR, HSI, and BFM was 0.920, the specificity was 89.8%, and the sensitivity was 81.6% (Table 5). The performance of the MFSI was similar to that of the combined model with WHtR, HSI, and BFM in the testing set.

Figure 4 Receiver operating characteristic (ROC) curves for the screening ability of different anthropometric indicators and metabolically associated fatty liver disease screening index (MFSI) in the testing set. Diagonal segments were produced by ties. BFM: body fat mass; HSI: hepatic steatosis index; TBW: total body water; WHtR: waist-height ratio.

Table 5 Areas under the curve (AUCs) were used to compare the ability of the different anthropometric indicators and the metabolically associated fatty liver disease (MAFLD) screening index (MFSI) to screen for MAFLD in the testing set (n=200).

Anthropometric indicators	AUC (95% CI)	P value	Specificity (%)	Sensitivity (%)	
WHtRa	0.886	<.001	83.7	78.7	
TBWb (kg)	0.767	<.001	81.6	63.8	
BFMc	0.858	<.001	81.6	78.7	
HSId	0.877	<.001	73.5	87.2	
Combination (WHtR/HSI/BFM)	0.920	<.001	89.8	81.6	
MFSI	0.917	<.001	89.8	84.4	
aWHtR: waist-height ratio.

bTBW: total body water.

cBFM: body fat mass.

dHSI: hepatic steatosis index.

MAFLD Rating Table for Prediction of MAFLD

The scoring system based on the MFSI and the application program was more practical for patient self-assessment. The MAFLD Rating Table (MRT) also included TBW, BFM, and WHtR. An MRT score ranging from 0 to 2 indicated a healthy individual, and a score ≥3 indicated MAFLD (Table 6). The AUC of the MRT for MAFLD prediction was 0.876 (P<.001; Figure 5).

Table 6 Simple rating table to assess risk factors for metabolically associated fatty liver disease (MAFLD).

Factors	Rating	

	0	1	2	3	4	
TBWa (kg)	<33.35	33.35-45.05	≥45.05	N/Ab	N/A	
BFMc	<17.55	17.55-20.15	10.15-22.95	≥22.95	N/A	
WHtRd	<0.501	N/A	0.501-0.525	0.525-0.538	≥0.538	
aTBW: total body water.

bN/A: not applicable.

cBFM: body fat mass.

dWHtR: waist-to-height ratio.

Figure 5 Receiver operating characteristic (ROC) curves of the correlation between the metabolically associated fatty liver disease (MAFLD) Rating Table (MRT) and MAFLD. Diagonal segments were produced by ties.

Discussion

WHtR, BFM, and TBW were predictors of MAFLD. The AUC of the WHtR was 0.866 (specificity=79.8%, sensitivity=80.8%), the AUC of BFM was 0.842 (specificity=76.9%, sensitivity=76.2%), and the AUC of TBW was 0.760 (specificity=75.3%, sensitivity=65.3%). The novel MFSI model, derived through logistic regression analysis, included the WHtR, BFM, and TBW. Notably, the MFSI demonstrated independence from laboratory findings. Upon validation, the MFSI exhibited stability while offering advantages in terms of sensitivity and specificity for MAFLD screening (training set: AUC=0.896, specificity=83.8%, sensitivity=82.1%; testing set: AUC=0.917, specificity=89.8%, sensitivity=84.4%).

Researchers found that the measurement of visceral fat can predict the occurrence of chronic diseases, such as diabetes, hyperuricemia, and metabolic syndrome [37,38]. NAFLD and MAFLD affect more than 25% of the global population and are considered different stages of the disease course. Because of long-term subtle inflammation and unobvious clinical manifestations, some patients gradually develop liver fibrosis and cirrhosis [1]. It is important to raise awareness within the population and optimize the management of this disease.

In recent decades, researchers have considered that the APRI, FIB-4 index, BMI, HSI, and TyG index have high accuracy for the diagnosis of liver fibrosis. Nonfibrosis scores were higher in patients with MAFLD than in those with NAFLD [11,39-42]. Similar conclusions were drawn in this work. To distinguish patients with MAFLD in our study population, we compared traditional indicators and body composition between the MAFLD and non-FLD groups and found there was a significant difference between the 2 groups. Although traditional indicators had efficient performance for the prediction of liver fibrosis, it was doubtful that these indicators were robust for the screening of MAFLD before being confirmed by histological liver examination. Previously published work mainly focused on the predictive ability of indicators for the detection of liver fibrosis, while much work omitted the performance of these indicators for the early screening of MAFLD.

Lee et al [27] suggested a new indicator named the HSI, and they found that NAFLD cannot be diagnosed when the HSI was <30.0, with a sensitivity of 92.5% (95% CI 91.4-93.5) The HSI showed similar performance for MAFLD diagnosis in this study. Italian researchers proposed another new indicator, the fatty liver index (FLI), which is calculated based on waist circumference, BMI, TG levels, and GGT levels. When the FLI is <30, a diagnosis of FLD can be ruled out, and when the FLI is ≥60, patients can be diagnosed with FLD. Waist circumference and BMI are the most robust predictors for the screening of FLD [43]. In contrast to the HSI, the FLI was established by incorporating waist circumference. However, BMI and waist circumference are totally different for people with varied dietary habits, and the study did not take this into account. Zheng et al [44] found that the WHtR had great performance for MAFLD screening, with a sensitivity of 96% and specificity of 64%. In our study, the WHtR showed a sensitivity of 80.8% and specificity of 79.8% for MAFLD screening.

TG and FPG levels are considered 2 pivotal inducers of metabolic syndrome. TGs are produced excessively in the process of fat accumulation, and insulin resistance accelerates hepatic steatosis. The TyG index can be used as a simple alternative marker for the detection of insulin resistance in the diagnostic test combining TG and FPG levels. The prevalence and severity of MAFLD are positively correlated with the TyG index [25,45-47]. The AUC of the TyG index for predicting MAFLD was 0.835 (95% CI 4.560-9.427), which might be valuable for clinical practice.

A meta-analysis revealed that the visceral adiposity index was an independent predictor of MAFLD, which could be used to predict potential morbidity [48]. However, the predictive ability of the visceral adiposity index has not been verified. Wang et al [49] found that nonobese MAFLD patients had higher BFM and VFA values than the healthy population, and most of them had abnormal lipid metabolism. In addition, BFM and VFA were valuable for distinguishing MAFLD patients from nonobese people [49,50]. This conclusion was also confirmed in this study (BFM for the prediction of MAFLD: AUC=0.842, sensitivity=76.2%, specificity=76.9%).

This study aimed to establish a home-based model for early screening of MAFLD to promote disease self-assessment and management. Compared with previously published models that rely heavily on laboratory indicators, our model combined body composition and the WHtR to screen for MAFLD, and the body parameters that were used to build the screening model can be easily obtained using a body fat scale at home. The mobile device software can record specific values and perform calculations.

There were 2 significant advantages of our model: (1) The need for an invasive examination and medical expenditures were reduced; (2) early screening models can provide early warning signs of disease, prompting people to modify diet and exercise or seek medical treatment if necessary; (3) patient-physician interactions were enhanced.

There were also some limitations of our work. First, this study was limited by geographical factors, and regional bias existed. Second, due to ethical considerations, the results in this study cannot be confirmed by histological liver examination. Third, in some villages we went to for recruitment, we were unable to obtain a radiological diagnosis due to manpower, transportation, and other constraints. In addition, it was difficult to follow participants who underwent physical examination in different areas, and reexamination data could not be compared with previous data.

Although our study found that the new MFSI model and MRT were valuable for MAFLD prediction, disease diagnosis still requires experienced clinicians, and those with the disease or at high risk should seek timely medical attention.

This study was funded by the National Key Research and Development Program (NO. 2018YFC2000500) and the HUAWEI (Huawei Terminal Co, Ltd) Liver Health Research Technical Cooperation Project.

Abbreviations

ALT alanine aminotransferase

APRI aspartate aminotransferase-to-platelet ratio index

AST aspartate aminotransferase

AUC area under the curve

BFM body fat mass

BMR basal metabolic rate

CAP controlled attenuation parameter

FIB-4 fibrosis-4

FLD fatty liver disease

FLI fatty liver index

FPG fasting plasma glucose

GGT glutamyl transpeptidase

GPR glutamyl transpeptidase-to-platelet ratio index

HDL-c high-density lipoprotein cholesterol)

HSI hepatic steatosis index

LDL-c low-density lipoprotein cholesterol

MAFLD metabolically associated fatty liver disease

MFSI MAFLD screening index

MRT MAFLD Rating Table

NAFLD nonalcoholic fatty liver disease

NFS nonalcoholic fatty liver disease fibrosis score

OR odds ratio

PLT platelet

ROC receiver operating characteristic

TBW total body water

TC total cholesterol

TG triglyceride

TyG triglyceride glucose

VFA visceral fat area

WHtR waist-height ratio

Data Availability

The data are not publicly available due to cooperative project clauses. Please contact the author to inquire if the data in this study are available for other studies.

Conflicts of Interest: None declared.
==== Refs
1 Younossi Z Anstee QM Marietti M Hardy T Henry L Eslam M George J Bugianesi E Global burden of NAFLD and NASH: trends, predictions, risk factors and prevention Nat Rev Gastroenterol Hepatol 2018 01 15 1 11 20 10.1038/nrgastro.2017.109 28930295 nrgastro.2017.109 28930295
2 Pipitone RM Ciccioli C Infantino G La Mantia C Parisi S Tulone A Pennisi G Grimaudo S Petta S MAFLD: a multisystem disease Ther Adv Endocrinol Metab 2023 14 20420188221145549 10.1177/20420188221145549 36726391 10.1177_20420188221145549 36726391
3 Eslam M Newsome PN Sarin SK Anstee QM Targher G Romero-Gomez M Zelber-Sagi S Wai-Sun Wong V Dufour J Schattenberg JM Kawaguchi T Arrese M Valenti L Shiha G Tiribelli C Yki-Järvinen H Fan J Grønbæk H Yilmaz Y Cortez-Pinto H Oliveira CP Bedossa P Adams LA Zheng M Fouad Y Chan W Mendez-Sanchez N Ahn SH Castera L Bugianesi E Ratziu V George J A new definition for metabolic dysfunction-associated fatty liver disease: An international expert consensus statement J Hepatol 2020 07 73 1 202 209 10.1016/j.jhep.2020.03.039 32278004 S0168-8278(20)30201-4 32278004
4 Sun D Jin Y Wang T Zheng KI Rios RS Zhang H Targher G Byrne CD Yuan W Zheng M MAFLD and risk of CKD Metabolism 2021 02 115 154433 10.1016/j.metabol.2020.154433 33212070 S0026-0495(20)30297-3 33212070
5 Lin S Huang J Wang M Kumar R Liu Y Liu S Wu Y Wang X Zhu Y Comparison of MAFLD and NAFLD diagnostic criteria in real world Liver Int 2020 09 40 9 2082 2089 10.1111/liv.14548 32478487 32478487
6 Sinn DH Kang D Chang Y Ryu S Cho SJ Paik SW Song YB Pastor-Barriuso R Guallar E Cho J Gwak G Non-alcoholic fatty liver disease and the incidence of myocardial infarction: A cohort study J Gastroenterol Hepatol 2020 05 35 5 833 839 10.1111/jgh.14856 31512278 31512278
7 Kim H El-Serag HB The epidemiology of hepatocellular carcinoma in the USA Curr Gastroenterol Rep 2019 04 11 21 4 17 10.1007/s11894-019-0681-x 30976932 10.1007/s11894-019-0681-x 30976932
8 Li F Sun G Wang Z Wu W Guo H Peng L Wu L Guo X Yang Y Characteristics of fecal microbiota in non-alcoholic fatty liver disease patients Sci China Life Sci 2018 07 61 7 770 778 10.1007/s11427-017-9303-9 29948900 10.1007/s11427-017-9303-9 29948900
9 Allen AM Hicks SB Mara KC Larson JJ Therneau TM The risk of incident extrahepatic cancers is higher in non-alcoholic fatty liver disease than obesity - A longitudinal cohort study J Hepatol 2019 12 71 6 1229 1236 10.1016/j.jhep.2019.08.018 31470068 S0168-8278(19)30485-4 31470068
10 Targher G Chonchol MB Byrne CD CKD and nonalcoholic fatty liver disease Am J Kidney Dis 2014 10 64 4 638 52 10.1053/j.ajkd.2014.05.019 25085644 S0272-6386(14)00971-8 25085644
11 Targher G Byrne CD Non-alcoholic fatty liver disease: an emerging driving force in chronic kidney disease Nat Rev Nephrol 2017 05 13 5 297 310 10.1038/nrneph.2017.16 28218263 nrneph.2017.16 28218263
12 Baratta F Pastori D Angelico F Balla A Paganini AM Cocomello N Ferro D Violi F Sanyal AJ Del Ben M Nonalcoholic fatty liver disease and fibrosis associated with increased risk of cardiovascular events in a prospective study Clin Gastroenterol Hepatol 2020 09 18 10 2324 2331.e4 10.1016/j.cgh.2019.12.026 31887443 S1542-3565(19)31506-X 31887443
13 Deprince A Haas JT Staels B Dysregulated lipid metabolism links NAFLD to cardiovascular disease Mol Metab 2020 12 42 101092 10.1016/j.molmet.2020.101092 33010471 S2212-8778(20)30166-6 33010471
14 Mantovani A Scorletti E Mosca A Alisi A Byrne CD Targher G Complications, morbidity and mortality of nonalcoholic fatty liver disease Metabolism 2020 10 111S 154170 10.1016/j.metabol.2020.154170 32006558 S0026-0495(20)30034-2 32006558
15 Lee SJ Kim SU Noninvasive monitoring of hepatic steatosis: controlled attenuation parameter and magnetic resonance imaging-proton density fat fraction in patients with nonalcoholic fatty liver disease Expert Rev Gastroenterol Hepatol 2019 06 13 6 523 530 10.1080/17474124.2019.1608820 31018719 31018719
16 Nassir F NAFLD: mechanisms, treatments, and biomarkers Biomolecules 2022 06 13 12 6 1 10.3390/biom12060824 35740949 biom12060824
17 Ratziu V Charlotte F Heurtier A Gombert S Giral P Bruckert E Grimaldi A Capron F Poynard T LIDO Study Group Sampling variability of liver biopsy in nonalcoholic fatty liver disease Gastroenterology 2005 06 128 7 1898 906 10.1053/j.gastro.2005.03.084 15940625 S001650850500630X 15940625
18 Xu L Lu W Li P Shen F Mi Y Fan J A comparison of hepatic steatosis index, controlled attenuation parameter and ultrasound as noninvasive diagnostic tools for steatosis in chronic hepatitis B Dig Liver Dis 2017 08 49 8 910 917 10.1016/j.dld.2017.03.013 28433586 S1590-8658(17)30793-4 28433586
19 Karlas T Petroff D Sasso M Fan J Mi Y de Lédinghen V Kumar M Lupsor-Platon M Han K Cardoso AC Ferraioli G Chan W Wong VW Myers RP Chayama K Friedrich-Rust M Beaugrand M Shen F Hiriart J Sarin SK Badea R Jung KS Marcellin P Filice C Mahadeva S Wong GL Crotty P Masaki K Bojunga J Bedossa P Keim V Wiegand J Individual patient data meta-analysis of controlled attenuation parameter (CAP) technology for assessing steatosis J Hepatol 2017 05 66 5 1022 1030 10.1016/j.jhep.2016.12.022 28039099 S0168-8278(16)30755-3 28039099
20 European Association for the Study of the Liver (EASL)European Association for the Study of Diabetes (EASD)European Association for the Study of Obesity (EASO) EASL-EASD-EASO Clinical Practice Guidelines for the management of non-alcoholic fatty liver disease Diabetologia 2016 06 59 6 1121 40 10.1007/s00125-016-3902-y 27053230 10.1007/s00125-016-3902-y 27053230
21 Schattenberg JM Loomba R Refining noninvasive diagnostics in nonalcoholic fatty liver disease: closing the gap to detect advanced fibrosis Hepatology 2019 03 69 3 934 936 10.1002/hep.30402 30515858 30515858
22 McPherson S Hardy T Dufour J Petta S Romero-Gomez M Allison M Oliveira CP Francque S Van Gaal L Schattenberg JM Tiniakos D Burt A Bugianesi E Ratziu V Day CP Anstee QM Age as a confounding factor for the accurate non-invasive diagnosis of advanced NAFLD fibrosis Am J Gastroenterol 2017 05 112 5 740 751 10.1038/ajg.2016.453 27725647 ajg2016453 27725647
23 Lee J Vali Y Boursier J Spijker R Anstee QM Bossuyt PM Zafarmand MH Prognostic accuracy of FIB-4, NAFLD fibrosis score and APRI for NAFLD-related events: A systematic review Liver Int 2021 02 41 2 261 270 10.1111/liv.14669 32946642 32946642
24 Petta S Wong VW Cammà C Hiriart J Wong GL Vergniol J Chan AW Di Marco V Merrouche W Chan HL Marra F Le-Bail B Arena U Craxì A de Ledinghen V Serial combination of non-invasive tools improves the diagnostic accuracy of severe liver fibrosis in patients with NAFLD Aliment Pharmacol Ther 2017 09 46 6 617 627 10.1111/apt.14219 28752524 28752524
25 Tutunchi H Naeini F Mobasseri M Ostadrahimi A Triglyceride glucose (TyG) index and the progression of liver fibrosis: A cross-sectional study Clin Nutr ESPEN 2021 08 44 483 487 10.1016/j.clnesp.2021.04.025 34330512 S2405-4577(21)00167-4 34330512
26 Fedchuk L Nascimbeni F Pais R Charlotte F Housset C Ratziu V LIDO Study Group Performance and limitations of steatosis biomarkers in patients with nonalcoholic fatty liver disease Aliment Pharmacol Ther 2014 11 40 10 1209 22 10.1111/apt.12963 25267215 25267215
27 Lee J Kim D Kim HJ Lee C Yang JI Kim W Kim YJ Yoon J Cho S Sung M Lee H Hepatic steatosis index: a simple screening tool reflecting nonalcoholic fatty liver disease Dig Liver Dis 2010 07 42 7 503 8 10.1016/j.dld.2009.08.002 19766548 S1590-8658(09)00336-3 19766548
28 Lin I Lee M Wang C Wu D Chen S Gender differences in the relationships among metabolic syndrome and various obesity-related indices with nonalcoholic fatty liver disease in a Taiwanese population Int J Environ Res Public Health 2021 01 20 18 3 1 10.3390/ijerph18030857 33498329 ijerph18030857
29 Rotter I Rył A Grzesiak K Szylińska A Pawlukowska W Lubkowska A Sipak-Szmigiel O Pabisiak K Laszczyńska M Cross-sectional inverse associations of obesity and fat accumulation indicators with testosterone in non-diabetic aging men Int J Environ Res Public Health 2018 06 08 15 6 1 10.3390/ijerph15061207 29890654 ijerph15061207
30 Verma M Rajput M Sahoo SS Kaur N Rohilla R Correlation between the percentage of body fat and surrogate indices of obesity among adult population in rural block of Haryana J Family Med Prim Care 2016 5 1 154 9 10.4103/2249-4863.184642 27453862 JFMPC-5-154 27453862
31 Jayedi A Soltani S Zargar MS Khan TA Shab-Bidar S Central fatness and risk of all cause mortality: systematic review and dose-response meta-analysis of 72 prospective cohort studies BMJ 2020 09 23 370 m3324 10.1136/bmj.m3324 32967840 32967840
32 Cai J Lin C Lai S Liu Y Liang M Qin Y Liang X Tan A Gao Y Lu Z Wu C Huang S Yang X Zhang H Kuang J Mo Z Waist-to-height ratio, an optimal anthropometric indicator for metabolic dysfunction associated fatty liver disease in the Western Chinese male population Lipids Health Dis 2021 10 27 20 1 145 10.1186/s12944-021-01568-9 34706716 10.1186/s12944-021-01568-9 34706716
33 Byrne CD Patel J Scorletti E Targher G Tests for diagnosing and monitoring non-alcoholic fatty liver disease in adults BMJ 2018 07 12 362 k2734 10.1136/bmj.k2734 30002017 30002017
34 Hagström H Nasr P Ekstedt M Stål P Hultcrantz R Kechagias S Accuracy of noninvasive scoring systems in assessing risk of death and liver-related endpoints in patients with nonalcoholic fatty liver disease Clin Gastroenterol Hepatol 2019 05 17 6 1148 1156.e4 10.1016/j.cgh.2018.11.030 30471458 S1542-3565(18)31273-4 30471458
35 Sasso M Beaugrand M de Ledinghen V Douvin C Marcellin P Poupon R Sandrin L Miette V Controlled attenuation parameter (CAP): a novel VCTE™ guided ultrasonic attenuation measurement for the evaluation of hepatic steatosis: preliminary study and validation in a cohort of patients with chronic liver disease from various causes Ultrasound Med Biol 2010 11 36 11 1825 35 10.1016/j.ultrasmedbio.2010.07.005 20870345 S0301-5629(10)00354-6 20870345
36 Mikolasevic I Milic S Orlic L Stimac D Franjic N Targher G Factors associated with significant liver steatosis and fibrosis as assessed by transient elastography in patients with one or more components of the metabolic syndrome J Diabetes Complications 2016 30 7 1347 53 10.1016/j.jdiacomp.2016.05.014 27324703 S1056-8727(16)30167-2 27324703
37 Almeida NS Rocha R Cotrim HP Daltro C Anthropometric indicators of visceral adiposity as predictors of non-alcoholic fatty liver disease: A review World J Hepatol 2018 10 27 10 10 695 701 10.4254/wjh.v10.i10.695 30386462 30386462
38 Motamed N Rabiee B Hemasi GR Ajdarkosh H Khonsari MR Maadi M Keyvani H Zamani F Body roundness index and waist-to-height ratio are strongly associated with non-alcoholic fatty liver disease: a population-based study Hepat Mon 2016 09 16 9 e39575 10.5812/hepatmon.39575 27822266 27822266
39 Wu Y Kumar R Wang M Singh M Huang J Zhu Y Lin S Validation of conventional non-invasive fibrosis scoring systems in patients with metabolic associated fatty liver disease World J Gastroenterol 2021 09 14 27 34 5753 5763 10.3748/wjg.v27.i34.5753 34629799 34629799
40 Angulo P Hui JM Marchesini G Bugianesi E George J Farrell GC Enders F Saksena S Burt AD Bida JP Lindor K Sanderson SO Lenzi M Adams LA Kench J Therneau TM Day CP The NAFLD fibrosis score: a noninvasive system that identifies liver fibrosis in patients with NAFLD Hepatology 2007 04 45 4 846 54 10.1002/hep.21496 17393509 17393509
41 Shah AG Lydecker A Murray K Tetri BN Contos MJ Sanyal AJ Nash Clinical Research Network Comparison of noninvasive markers of fibrosis in patients with nonalcoholic fatty liver disease Clin Gastroenterol Hepatol 2009 10 7 10 1104 12 10.1016/j.cgh.2009.05.033 19523535 S1542-3565(09)00533-3 19523535
42 Harrison SA Oliver D Arnold HL Gogia S Neuschwander-Tetri BA Development and validation of a simple NAFLD clinical scoring system for identifying patients without advanced disease Gut 2008 10 57 10 1441 7 10.1136/gut.2007.146019 18390575 gut.2007.146019 18390575
43 Bedogni G Bellentani S Miglioli L Masutti F Passalacqua M Castiglione A Tiribelli C The Fatty Liver Index: a simple and accurate predictor of hepatic steatosis in the general population BMC Gastroenterol 2006 11 02 6 33 10.1186/1471-230X-6-33 17081293 1471-230X-6-33 17081293
44 Zheng R Chen Z Chen J Lu Y Chen J Role of body mass index, waist-to-height and waist-to-hip ratio in prediction of nonalcoholic fatty liver disease Gastroenterol Res Pract 2012 2012 362147 10.1155/2012/362147 10.1155/2012/362147 22701476 22701476
45 Farrell GC Signalling links in the liver: knitting SOCS with fat and inflammation J Hepatol 2005 07 43 1 193 6 10.1016/j.jhep.2005.04.004 15913829 S0168-8278(05)00270-9 15913829
46 Zhang S Du T Li M Jia J Lu H Lin X Yu X Triglyceride glucose-body mass index is effective in identifying nonalcoholic fatty liver disease in nonobese subjects Medicine (Baltimore) 2017 06 96 22 e7041 10.1097/MD.0000000000007041 28562560 00005792-201706020-00036 28562560
47 Zhang S Du T Zhang J Lu H Lin X Xie J Yang Y Yu X The triglyceride and glucose index (TyG) is an effective biomarker to identify nonalcoholic fatty liver disease Lipids Health Dis 2017 01 19 16 1 15 10.1186/s12944-017-0409-6 28103934 10.1186/s12944-017-0409-6 28103934
48 Yi X Zhu S Zhu L Diagnostic accuracy of the visceral adiposity index in patients with metabolic-associated fatty liver disease: a meta-analysis Lipids Health Dis 2022 03 06 21 1 28 10.1186/s12944-022-01636-8 35249545 10.1186/s12944-022-01636-8 35249545
49 Wang YJ Cheng HR Zhou WH Correlation of body fat composition and metabolic indicators with metabolic-associated fatty liver disease in a non-obese population Chinese General Practice 2023 26 6 672 80 10.12114/j.issn.1007-9572.2022.0573
50 Byrne CD Targher G Ectopic fat, insulin resistance, and nonalcoholic fatty liver disease: implications for cardiovascular disease Arterioscler Thromb Vasc Biol 2014 06 34 6 1155 61 10.1161/ATVBAHA.114.303034 24743428 ATVBAHA.114.303034 24743428
