INTERACTIVE JOURNAL OF MEDICAL RESEARCH

Ni et alOriginal Paper

Establishment and Evaluation of a Noninvasive
Metabolism-Related Fatty Liver Screening and Dynamic Monitoring
Model: Cross-Sectional Study
Jiali Ni1*, MD; Yong Huang2*, MS; Qiangqiang Xiang1*, MS; Qi Zheng1, MS; Xiang Xu2, MS; Zhiwen Qin2, MS;
Guoping Sheng2, MD; Lanjuan Li3, MD
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*

these authors contributed equally

Corresponding Author:
Lanjuan Li, MD
State Key Laboratory for Diagnosis and Treatment of Infectious Disease
The First Affiliated Hospital
Zhejiang University School of Medicine
79 Qingchun Rd
Hangzhou, 310058
China
Phone: 86 571 87236458
Fax: 86 571 87236459
Email: ljli@zju.edu.cn

Abstract
Background: Metabolically associated fatty liver disease (MAFLD) insidiously affects people's health, and many models havebeen 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 inthe 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 establishedbased 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

https://www.i-jmr.org/2024/1/e56035

XSL• FO
RenderX

Interact J Med Res 2024 | vol. 13 | e56035 | p. 1
(page number not for citation purposes)

INTERACTIVE JOURNAL OF MEDICAL RESEARCH

Ni et aland 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.
(Interact J Med Res 2024;13:e56035) doi: 10.2196/56035
KEYWORDS
metabolic-associated fatty liver disease; nonalcoholic fatty liver disease; nonalcoholic steatohepatitis; body fat mass; waist-heightratio; basal metabolic rate; liver

Introduction
Nonalcoholic fatty liver disease (NAFLD) is regarded as animportant cause of liver disease, affecting more than 25% of
the general population worldwide; more than 50% of patientswith 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 kidneydisease, cardiovascular disease, and other diseases that interact
and form a vicious cycle [6-14]. China has the highest incidenceof 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 serveas 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 liverbiopsy samples from 51 patients and found that 41% of thepatients were at different stages of liver fibrosis or had
nonalcoholic steatohepatitis. The uneven distribution ofhistological lesions inevitably led to sampling error when
performing biopsy. Abdominal imaging, such as B-ultrasoundimaging and the controlled attenuation parameter (CAP)
technique, can be used to diagnosis liver disease; the former isless 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 andfibrosis clinically [18,19]. The European Association for the
Study of the Liver, European Association for the Study ofDiabetes, and European Association for the Study of Obesity
updated the clinical practice guidelines that propose that the
nonalcoholic fatty liver disease fibrosis score (NFS) andfibrosis-4 (FIB-4) index can be used as prognostic markers forthe 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 ofliver-related disease and mortality but is less valuable for theprediction 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 theperformance was similar to that of liver biopsy [24]. A
cross-sectional study found that the triglyceride glucose (TyG)

https://www.i-jmr.org/2024/1/e56035

XSL• FO
RenderX

index was positively correlated with the likelihood and severityof 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 thatof 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, andbody adiposity index, are applicable for the quantification ofvisceral 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 forearly MAFLD screening. The aim of this study was to constructa 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 wasconsidered to indicate severely fatty liver [35,36].

Interact J Med Res 2024 | vol. 13 | e56035 | p. 2
(page number not for citation purposes)

INTERACTIVE JOURNAL OF MEDICAL RESEARCH

Ni et alFigure 1. Flowchart of the inclusion process for the participants in the training set and the proportion of mild, moderate, and severe fatty liver diseasein 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) atleast 2 of the following metabolic risk abnormalities: waist
https://www.i-jmr.org/2024/1/e56035

XSL• FO
RenderX

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 specificdrug treatment; fasting plasma glucose (FPG) level of 5.6
mmol/L to 6.9 mmol/L, 2-hour postload glucose level of 7.8mmol/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 theInteract J Med Res 2024 | vol. 13 | e56035 | p. 3
(page number not for citation purposes)

INTERACTIVE JOURNAL OF MEDICAL RESEARCH
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 suitabledetection point could be determined.
We collected basic information, including sex, age, height,
weight, BMI, waist circumference, hip circumference, bloodpressure, 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, aswell 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, andbody fat mass (BFM). A body fat scale (3 Pro, Huawei) wasused 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, glutamyltranspeptidase-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)

Ni et alto 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 mostvaluable indicator. The maximum Youden index (using theformula sensitivity + specificity - 1) was used to define theoptimal cutoff value. Potential confounding variables wereadded into the logistic regression equation step by step,
including age; blood pressure; and FPG, TC, TG, HDL-c, andLDL-c levels. Calibration Model I (age, blood pressure, andFPG level were added to the logistic regression equation) andModel II (age; blood pressure; and FPG, TC, TG, HDL-c, andLDL-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 weredrawn. A new prediction model, the MAFLD Screening Index(MFSI), was constructed using logistic regression analysis, andthe 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 andparticipated in the study anonymously. We ensured it was notpossible to identify individual participants in any images usedin 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, andthere were no associated adverse reactions.

TyG=ln (TG×FPG/2)

Statistical Analysis Methods

Results

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

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

All data obtained in this study were analyzed using SPSS version
26.0 (IBM Corp). The continuous variables were tested fornormality and homogeneity of variance. A t test was performed
for measurement data that followed a normal distribution, andthe results are expressed as the mean (SD). Nonnormally
distributed data were analyzed using nonparametric tests, andthe results are represented by quartiles. The chi-square test or
Fisher precision probability test was used for quantitative datasuch as sex. ANOVA was followed by post hoc analysis tests

Using ANOVA to compare the basic information andanthropometric 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).

https://www.i-jmr.org/2024/1/e56035

XSL• FO
RenderX

Interact J Med Res 2024 | vol. 13 | e56035 | p. 4
(page number not for citation purposes)

INTERACTIVE JOURNAL OF MEDICAL RESEARCH

Ni et alTable 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)

Sex, n (%)

Statistic (df)

P value

224.985a (2)

<.001

Male

326 (41.5)

668 (77.3)

375 (61.8)

Female460 (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)<.001DBPe (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

https://www.i-jmr.org/2024/1/e56035

XSL• FO
RenderX

Interact J Med Res 2024 | vol. 13 | e56035 | p. 5
(page number not for citation purposes)

INTERACTIVE JOURNAL OF MEDICAL RESEARCHa

Ni et alCharacteristics

non-FLD (n=786)

MAFLD (n=864)

NAFLD (n=607)

Statistic (df)

P value

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 (1179.701450.17)

1526.55 (1387.091655.52)

1470.15 (1276.651622.62)

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

Chi-squared test.

b

H value.

c

F value.

d

SBP: systolic blood pressure.

e

DBP: diastolic blood pressure.

f

WBC: white blood cell.

g

RBC: red blood cell.

h

Hb: hemoglobin.

i

PLT: platelet.

j

FPG: fasting plasma glucose.

k

ALT: alanine aminotransferase.

l

AST: aspartate aminotransferase.

m

ALP: alkaline phosphatase.

n

GGT: glutamyl transpeptidase.

o

TC: total cholesterol.

p

TG: triglyceride.

q

HDL-c: high-density lipoprotein cholesterol.

r

LDL-c: low-density lipoprotein cholesterol.

s

UA: uric acid.

t

CAP: controlled attenuation parameter.

u

WHtR: waist-height ratio.

v

WHR: waist-hip ratio.

w

FIB-4: fibrosis-4.

x

APRI: aspartate aminotransferase-to-platelet ratio index.

yGPR: glutamyl transpeptidase-to-platelet ratio index.

z

HSI: hepatic steatosis index.

aa

TyG: triglyceride glucose.

bb

TBW: total body water.

cc

ICW: intracellular water.

dd

BFM: body fat mass.

ee

SMM: skeletal muscle mass.

ff

BMR: basal metabolic rate.

gg

VFA: 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

https://www.i-jmr.org/2024/1/e56035

XSL• FO
RenderX

and optimal cutoff points for the selected indicators are shownin Table 2 and Figure 2. The AUCs of the WHtR, the Forns
index, the HSI, the TyG index, TBW, BFM, and BMR were0.866, 0.684, 0.873, 0.835, 0.760, 0.842, and 0.778, respectively(P<.001; Table 2).

Interact J Med Res 2024 | vol. 13 | e56035 | p. 6
(page number not for citation purposes)

INTERACTIVE JOURNAL OF MEDICAL RESEARCH

Ni et alTable 2. Cutoff points and areas under the curve (AUCs) were used to demonstrate the screening ability of the different anthropometric indicators formetabolically 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

b

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

HSI

a

WHtR: waist-to-hip ratio.

b

HSI: hepatic steatosis index.

c

TyG: triglyceride glucose.

d

TBW: total body water.

e

BFM: body fat mass.

f

BMR: basal metabolic rate.

g

N/A: not applicable.

Figure 2. Receiver operating characteristic (ROC) curves for the screening ability of different anthropometric indicators for metabolically associatedfatty 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 thestrongest 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 wereadded to the logistic regression equation; Model II: age; bloodpressure; and FPG, TC, TG, HDL-c, and LDL-c levels wereadded to the logistic regression equation). After correction forconfounding 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

https://www.i-jmr.org/2024/1/e56035

XSL• FO
RenderX

that the performance of the Forns index for MAFLD screening
was unstable and the performance of other anthropometricindicators was not easily influenced by confounders.
The HSI and WHtR showed better predictive performance thanthe other indicators. The sensitivity of the HSI was higher thanthat of the other anthropometric indicators (sensitivity=81.4%),
and the specificity of the WHtR was higher than that of theother 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).

Interact J Med Res 2024 | vol. 13 | e56035 | p. 7
(page number not for citation purposes)

INTERACTIVE JOURNAL OF MEDICAL RESEARCH

Ni et alTable 3. Confounders were corrected in the binary logistic regression analysis to compare changes in screening power for different anthropometricindicators (n=1649).
Anthropometric indicators

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 (9.0152349.524)<.001

107.825 (6.2321865.456).001

113.408 (6.7591902.900)<.001

Forns index

1.166 (1.053-1.292).003

1.043 (0.851-1.277).69

1.050 (0.854-1.293).64e1.124 (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

HSI

a

Nonadjusted model

OR: odds ratio.

b

TBW: total body water.

c

BFM: body fat mass.

d

WHtR: waist-height ratio.

e

HSI: hepatic steatosis index.

fTyG: triglyceride glucose.

Development of a New MAFLD Screening Model
The HSI, WHtR, and BFM displayed strong power in screeningfor 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 toreduce the need for invasive procedures and reduce thefrequency 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;https://www.i-jmr.org/2024/1/e56035

XSL• FO
RenderX

Model II: 95% CI 1.042-1.108; Table 3). Therefore, TBW was
included in the new model. Logistic regression analysis wasused to establish the MAFLD early screening model, which wasnamed 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, theperformance of the MFSI and the WHtR/HSI/BFM combination
models was similar.

Interact J Med Res 2024 | vol. 13 | e56035 | p. 8
(page number not for citation purposes)

INTERACTIVE JOURNAL OF MEDICAL RESEARCH

Ni et alFigure 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 themetabolically associated fatty liver disease screening index (MFSI; n=1649).

a

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

WHtR: waist-height ratio.

b

HSI: hepatic steatosis index.

c

TyG: triglyceride glucose.

d

TBW: total body water.

e

BFM: body fat mass.

f

N/A: not applicable.

https://www.i-jmr.org/2024/1/e56035

XSL• FO
RenderX

Interact J Med Res 2024 | vol. 13 | e56035 | p. 9
(page number not for citation purposes)

INTERACTIVE JOURNAL OF MEDICAL RESEARCH

Ni et alPerformance 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 thetesting 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 ofthe 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 associatedfatty 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 associatedfatty liver disease (MAFLD) screening index (MFSI) to screen for MAFLD in the testing set (n=200).

a

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

WHtR: waist-height ratio.

b

TBW: total body water.

c

BFM: body fat mass.

d

HSI: hepatic steatosis index.

https://www.i-jmr.org/2024/1/e56035

XSL• FO
RenderX

Interact J Med Res 2024 | vol. 13 | e56035 | p. 10(page number not for citation purposes)

INTERACTIVE JOURNAL OF MEDICAL RESEARCH

Ni et al

MAFLD Rating Table for Prediction of MAFLD

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).

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, andTable 6. Simple rating table to assess risk factors for metabolically associated fatty liver disease (MAFLD).
FactorsaRating
0

1

2

3

4

TBWa (kg)<33.3533.35-45.05≥45.05

N/Ab

N/A

BFMc

<17.5517.55-20.15

10.15-22.95≥22.95N/A

WHtRd<0.501

N/A

0.501-0.525

0.525-0.538≥0.538

TBW: total body water.

b

N/A: not applicable.

c

BFM: body fat mass.

d

WHtR: 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 AUCof 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 exhibitedstability while offering advantages in terms of sensitivity andspecificity 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%).

https://www.i-jmr.org/2024/1/e56035

XSL• FO
RenderX

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 areconsidered different stages of the disease course. Because oflong-term subtle inflammation and unobvious clinicalmanifestations, 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 forthe 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 distinguishpatients with MAFLD in our study population, we compared
traditional indicators and body composition between theInteract J Med Res 2024 | vol. 13 | e56035 | p. 11(page number not for citation purposes)

INTERACTIVE JOURNAL OF MEDICAL RESEARCH

Ni et al

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 screeningof MAFLD before being confirmed by histological liver
examination. Previously published work mainly focused on thepredictive ability of indicators for the detection of liver fibrosis,
while much work omitted the performance of these indicators
for the early screening of MAFLD.

predict potential morbidity [48]. However, the predictive abilityof the visceral adiposity index has not been verified. Wang etal [49] found that nonobese MAFLD patients had higher BFM
and VFA values than the healthy population, and most of themhad abnormal lipid metabolism. In addition, BFM and VFA
were valuable for distinguishing MAFLD patients fromnonobese people [49,50]. This conclusion was also confirmedin this study (BFM for the prediction of MAFLD: AUC=0.842,
sensitivity=76.2%, specificity=76.9%).

Lee et al [27] suggested a new indicator named the HSI, andthey 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 thescreening of FLD [43]. In contrast to the HSI, the FLI wasestablished 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 asensitivity of 80.8% and specificity of 79.8% for MAFLD
screening.

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

TG and FPG levels are considered 2 pivotal inducers of
metabolic syndrome. TGs are produced excessively in theprocess 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 prevalenceand 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 bevaluable for clinical practice.
A meta-analysis revealed that the visceral adiposity index was
an independent predictor of MAFLD, which could be used to

There were 2 significant advantages of our model: (1) The needfor an invasive examination and medical expenditures werereduced; (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 toobtain 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 withprevious data.
Although our study found that the new MFSI model and MRT
were valuable for MAFLD prediction, disease diagnosis stillrequires experienced clinicians, and those with the disease orat high risk should seek timely medical attention.

Acknowledgments
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.

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.

References

https://www.i-jmr.org/2024/1/e56035

XSL• FO
RenderX

Interact J Med Res 2024 | vol. 13 | e56035 | p. 12(page number not for citation purposes)

INTERACTIVE JOURNAL OF MEDICAL RESEARCH
1.

2.
3.

4.
5.
6.
7.
8.
9.

10.
11.
12.

13.
14.
15.

16.
17.

18.

19.

20.

21.
22.

23.

Younossi Z, Anstee QM, Marietti M, Hardy T, Henry L, Eslam M, et al. Global burden of NAFLD and NASH: trends,
predictions, risk factors and prevention. Nat Rev Gastroenterol Hepatol. Jan 2018;15(1):11-20. [FREE Full text] [doi:10.1038/nrgastro.2017.109] [Medline: 28930295]
Pipitone RM, Ciccioli C, Infantino G, La Mantia C, Parisi S, Tulone A, et al. MAFLD: a multisystem disease. Ther Adv
Endocrinol Metab. 2023;14:20420188221145549. [FREE Full text] [doi: 10.1177/20420188221145549] [Medline: 36726391]
Eslam M, Newsome PN, Sarin SK, Anstee QM, Targher G, Romero-Gomez M, et al. A new definition for metabolic
dysfunction-associated fatty liver disease: An international expert consensus statement. J Hepatol. Jul 2020;73(1):202-209.
[FREE Full text] [doi: 10.1016/j.jhep.2020.03.039] [Medline: 32278004]
Sun D, Jin Y, Wang T, Zheng KI, Rios RS, Zhang H, et al. MAFLD and risk of CKD. Metabolism. Feb 2021;115:154433.
[doi: 10.1016/j.metabol.2020.154433] [Medline: 33212070]
Lin S, Huang J, Wang M, Kumar R, Liu Y, Liu S, et al. Comparison of MAFLD and NAFLD diagnostic criteria in realworld. Liver Int. Sep 2020;40(9):2082-2089. [doi: 10.1111/liv.14548] [Medline: 32478487]
Sinn DH, Kang D, Chang Y, Ryu S, Cho SJ, Paik SW, et al. Non-alcoholic fatty liver disease and the incidence of myocardial
infarction: A cohort study. J Gastroenterol Hepatol. May 2020;35(5):833-839. [doi: 10.1111/jgh.14856] [Medline: 31512278]
Kim H, El-Serag HB. The epidemiology of hepatocellular carcinoma in the USA. Curr Gastroenterol Rep. Apr 11,
2019;21(4):17. [doi: 10.1007/s11894-019-0681-x] [Medline: 30976932]
Li F, Sun G, Wang Z, Wu W, Guo H, Peng L, et al. Characteristics of fecal microbiota in non-alcoholic fatty liver disease
patients. Sci China Life Sci. Jul 2018;61(7):770-778. [doi: 10.1007/s11427-017-9303-9] [Medline: 29948900]
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. Dec 2019;71(6):1229-1236. [FREE Full text] [doi:10.1016/j.jhep.2019.08.018] [Medline: 31470068]
Targher G, Chonchol MB, Byrne CD. CKD and nonalcoholic fatty liver disease. Am J Kidney Dis. Oct 2014;64(4):638-652.
[doi: 10.1053/j.ajkd.2014.05.019] [Medline: 25085644]
Targher G, Byrne CD. Non-alcoholic fatty liver disease: an emerging driving force in chronic kidney disease. Nat Rev
Nephrol. May 2017;13(5):297-310. [doi: 10.1038/nrneph.2017.16] [Medline: 28218263]
Baratta F, Pastori D, Angelico F, Balla A, Paganini AM, Cocomello N, et al. Nonalcoholic fatty liver disease and fibrosis
associated with increased risk of cardiovascular events in a prospective study. Clin Gastroenterol Hepatol. Sep
2020;18(10):2324-2331.e4. [doi: 10.1016/j.cgh.2019.12.026] [Medline: 31887443]
Deprince A, Haas JT, Staels B. Dysregulated lipid metabolism links NAFLD to cardiovascular disease. Mol Metab. Dec
2020;42:101092. [FREE Full text] [doi: 10.1016/j.molmet.2020.101092] [Medline: 33010471]
Mantovani A, Scorletti E, Mosca A, Alisi A, Byrne CD, Targher G. Complications, morbidity and mortality of nonalcoholic
fatty liver disease. Metabolism. Oct 2020;111S:154170. [doi: 10.1016/j.metabol.2020.154170] [Medline: 32006558]
Lee SJ, Kim SU. Noninvasive monitoring of hepatic steatosis: controlled attenuation parameter and magnetic resonanceimaging-proton density fat fraction in patients with nonalcoholic fatty liver disease. Expert Rev Gastroenterol Hepatol. Jun
2019;13(6):523-530. [doi: 10.1080/17474124.2019.1608820] [Medline: 31018719]
Nassir F. NAFLD: mechanisms, treatments, and biomarkers. Biomolecules. Jun 13, 2022;12(6):1. [FREE Full text] [doi:10.3390/biom12060824] [Medline: 35740949]
Ratziu V, Charlotte F, Heurtier A, Gombert S, Giral P, Bruckert E, et al. LIDO Study Group. Sampling variability of liverbiopsy in nonalcoholic fatty liver disease. Gastroenterology. Jun 2005;128(7):1898-1906. [doi: 10.1053/j.gastro.2005.03.084][Medline: 15940625]
Xu L, Lu W, Li P, Shen F, Mi Y, Fan J. A comparison of hepatic steatosis index, controlled attenuation parameter andultrasound as noninvasive diagnostic tools for steatosis in chronic hepatitis B. Dig Liver Dis. Aug 2017;49(8):910-917.
[doi: 10.1016/j.dld.2017.03.013] [Medline: 28433586]
Karlas T, Petroff D, Sasso M, Fan J, Mi Y, de Lédinghen V, et al. Individual patient data meta-analysis of controlledattenuation parameter (CAP) technology for assessing steatosis. J Hepatol. May 2017;66(5):1022-1030. [doi:10.1016/j.jhep.2016.12.022] [Medline: 28039099]
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. Jun 2016;59(6):1121-1140. [doi: 10.1007/s00125-016-3902-y] [Medline:
27053230]
Schattenberg JM, Loomba R. Refining noninvasive diagnostics in nonalcoholic fatty liver disease: closing the gap to detectadvanced fibrosis. Hepatology. Mar 2019;69(3):934-936. [doi: 10.1002/hep.30402] [Medline: 30515858]
McPherson S, Hardy T, Dufour J, Petta S, Romero-Gomez M, Allison M, et al. Age as a confounding factor for the accurate
non-invasive diagnosis of advanced NAFLD fibrosis. Am J Gastroenterol. May 2017;112(5):740-751. [FREE Full text][doi: 10.1038/ajg.2016.453] [Medline: 27725647]
Lee J, Vali Y, Boursier J, Spijker R, Anstee QM, Bossuyt PM, et al. Prognostic accuracy of FIB-4, NAFLD fibrosis scoreand APRI for NAFLD-related events: A systematic review. Liver Int. Feb 2021;41(2):261-270. [FREE Full text] [doi:10.1111/liv.14669] [Medline: 32946642]

https://www.i-jmr.org/2024/1/e56035

XSL• FO
RenderX

Ni et alInteract J Med Res 2024 | vol. 13 | e56035 | p. 13(page number not for citation purposes)

INTERACTIVE JOURNAL OF MEDICAL RESEARCH
24.

25.
26.

27.
28.

29.

30.

31.

32.

33.
34.

35.

36.

37.

38.

39.

40.

41.

42.

43.

Petta S, Wong VW, Cammà C, Hiriart J, Wong GL, Vergniol J, et al. Serial combination of non-invasive tools improves
the diagnostic accuracy of severe liver fibrosis in patients with NAFLD. Aliment Pharmacol Ther. Sep 2017;46(6):617-627.
[doi: 10.1111/apt.14219] [Medline: 28752524]
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. Aug 2021;44:483-487. [doi: 10.1016/j.clnesp.2021.04.025] [Medline: 34330512]
Fedchuk L, Nascimbeni F, Pais R, Charlotte F, Housset C, Ratziu V, et al. LIDO Study Group. Performance and limitationsof steatosis biomarkers in patients with nonalcoholic fatty liver disease. Aliment Pharmacol Ther. Nov
2014;40(10):1209-1222. [FREE Full text] [doi: 10.1111/apt.12963] [Medline: 25267215]
Lee J, Kim D, Kim HJ, Lee C, Yang JI, Kim W, et al. Hepatic steatosis index: a simple screening tool reflecting nonalcoholic
fatty liver disease. Dig Liver Dis. Jul 2010;42(7):503-508. [doi: 10.1016/j.dld.2009.08.002] [Medline: 19766548]
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.
Jan 20, 2021;18(3):1. [FREE Full text] [doi: 10.3390/ijerph18030857] [Medline: 33498329]
Rotter I, Rył A, Grzesiak K, Szylińska A, Pawlukowska W, Lubkowska A, et al. Cross-sectional inverse associations ofobesity and fat accumulation indicators with testosterone in non-diabetic aging men. Int J Environ Res Public Health. Jun
08, 2018;15(6):1. [FREE Full text] [doi: 10.3390/ijerph15061207] [Medline: 29890654]
Verma M, Rajput M, Sahoo SS, Kaur N, Rohilla R. Correlation between the percentage of body fat and surrogate indicesof obesity among adult population in rural block of Haryana. J Family Med Prim Care. 2016;5(1):154-159. [FREE Fulltext] [doi: 10.4103/2249-4863.184642] [Medline: 27453862]
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. Sep 23, 2020;370:m3324. [FREE Full text] [doi:10.1136/bmj.m3324] [Medline: 32967840]
Cai J, Lin C, Lai S, Liu Y, Liang M, Qin Y, et al. Waist-to-height ratio, an optimal anthropometric indicator for metabolic
dysfunction associated fatty liver disease in the Western Chinese male population. Lipids Health Dis. Oct 27, 2021;20(1):145.
[FREE Full text] [doi: 10.1186/s12944-021-01568-9] [Medline: 34706716]
Byrne CD, Patel J, Scorletti E, Targher G. Tests for diagnosing and monitoring non-alcoholic fatty liver disease in adults.
BMJ. Jul 12, 2018;362:k2734. [doi: 10.1136/bmj.k2734] [Medline: 30002017]
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. May
2019;17(6):1148-1156.e4. [doi: 10.1016/j.cgh.2018.11.030] [Medline: 30471458]
Sasso M, Beaugrand M, de Ledinghen V, Douvin C, Marcellin P, Poupon R, et al. Controlled attenuation parameter (CAP):a novel VCTE™ guided ultrasonic attenuation measurement for the evaluation of hepatic steatosis: preliminary study andvalidation in a cohort of patients with chronic liver disease from various causes. Ultrasound Med Biol. Nov
2010;36(11):1825-1835. [doi: 10.1016/j.ultrasmedbio.2010.07.005] [Medline: 20870345]
Mikolasevic I, Milic S, Orlic L, Stimac D, Franjic N, Targher G. Factors associated with significant liver steatosis andfibrosis as assessed by transient elastography in patients with one or more components of the metabolic syndrome. J Diabetes
Complications. 2016;30(7):1347-1353. [doi: 10.1016/j.jdiacomp.2016.05.014] [Medline: 27324703]
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. Oct 27, 2018;10(10):695-701. [FREE Full text] [doi: 10.4254/wjh.v10.i10.695][Medline: 30386462]
Motamed N, Rabiee B, Hemasi GR, Ajdarkosh H, Khonsari MR, Maadi M, et al. Body roundness index and waist-to-heightratio are strongly associated with non-alcoholic fatty liver disease: a population-based study. Hepat Mon. Sep
2016;16(9):e39575. [FREE Full text] [doi: 10.5812/hepatmon.39575] [Medline: 27822266]
Wu Y, Kumar R, Wang M, Singh M, Huang J, Zhu Y, et al. Validation of conventional non-invasive fibrosis scoring systems
in patients with metabolic associated fatty liver disease. World J Gastroenterol. Sep 14, 2021;27(34):5753-5763. [FREEFull text] [doi: 10.3748/wjg.v27.i34.5753] [Medline: 34629799]
Angulo P, Hui JM, Marchesini G, Bugianesi E, George J, Farrell GC, et al. The NAFLD fibrosis score: a noninvasive
system that identifies liver fibrosis in patients with NAFLD. Hepatology. Apr 2007;45(4):846-854. [doi: 10.1002/hep.21496][Medline: 17393509]
Shah AG, Lydecker A, Murray K, Tetri BN, Contos MJ, Sanyal AJ, et al. Nash Clinical Research Network. Comparisonof noninvasive markers of fibrosis in patients with nonalcoholic fatty liver disease. Clin Gastroenterol Hepatol. Oct
2009;7(10):1104-1112. [FREE Full text] [doi: 10.1016/j.cgh.2009.05.033] [Medline: 19523535]
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. Oct 2008;57(10):1441-1447. [doi:10.1136/gut.2007.146019] [Medline: 18390575]
Bedogni G, Bellentani S, Miglioli L, Masutti F, Passalacqua M, Castiglione A, et al. The Fatty Liver Index: a simple andaccurate predictor of hepatic steatosis in the general population. BMC Gastroenterol. Nov 02, 2006;6:33. [FREE Full text][doi: 10.1186/1471-230X-6-33] [Medline: 17081293]

https://www.i-jmr.org/2024/1/e56035

XSL• FO
RenderX

Ni et alInteract J Med Res 2024 | vol. 13 | e56035 | p. 14(page number not for citation purposes)

INTERACTIVE JOURNAL OF MEDICAL RESEARCH
44.

45.
46.

47.

48.

49.

50.

Ni et alZheng 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. [FREE Full text] [doi: 10.1155/2012/362147][Medline: 22701476]
Farrell GC. Signalling links in the liver: knitting SOCS with fat and inflammation. J Hepatol. Jul 2005;43(1):193-196. [doi:10.1016/j.jhep.2005.04.004] [Medline: 15913829]
Zhang S, Du T, Li M, Jia J, Lu H, Lin X, et al. Triglyceride glucose-body mass index is effective in identifying nonalcoholic
fatty liver disease in nonobese subjects. Medicine (Baltimore). Jun 2017;96(22):e7041. [FREE Full text] [doi:10.1097/MD.0000000000007041] [Medline: 28562560]
Zhang S, Du T, Zhang J, Lu H, Lin X, Xie J, et al. The triglyceride and glucose index (TyG) is an effective biomarker to
identify nonalcoholic fatty liver disease. Lipids Health Dis. Jan 19, 2017;16(1):15. [FREE Full text] [doi:10.1186/s12944-017-0409-6] [Medline: 28103934]
Yi X, Zhu S, Zhu L. Diagnostic accuracy of the visceral adiposity index in patients with metabolic-associated fatty liverdisease: a meta-analysis. Lipids Health Dis. Mar 06, 2022;21(1):28. [FREE Full text] [doi: 10.1186/s12944-022-01636-8][Medline: 35249545]
Wang YJ, Cheng HR, Zhou WH. Correlation of body fat composition and metabolic indicators with metabolic-associatedfatty liver disease in a non-obese population. Chinese General Practice. 2023;26(6):672-680. [doi:10.12114/j.issn.1007-9572.2022.0573]
Byrne CD, Targher G. Ectopic fat, insulin resistance, and nonalcoholic fatty liver disease: implications for cardiovasculardisease. Arterioscler Thromb Vasc Biol. Jun 2014;34(6):1155-1161. [doi: 10.1161/ATVBAHA.114.303034] [Medline:
24743428]

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

https://www.i-jmr.org/2024/1/e56035

XSL• FO
RenderX

Interact J Med Res 2024 | vol. 13 | e56035 | p. 15(page number not for citation purposes)

INTERACTIVE JOURNAL OF MEDICAL RESEARCH

Ni et alEdited by A Mavragani; submitted 03.01.24; peer-reviewed by Y Zhang, H Yu; comments to author 23.05.24; revised version received
03.06.24; accepted 03.07.24; published 22.08.24
Please cite as:
Ni J, Huang Y, Xiang Q, Zheng Q, Xu X, Qin Z, Sheng G, Li L
Establishment and Evaluation of a Noninvasive Metabolism-Related Fatty Liver Screening and Dynamic Monitoring Model:
Cross-Sectional Study
Interact J Med Res 2024;13:e56035
URL: https://www.i-jmr.org/2024/1/e56035
doi: 10.2196/56035
PMID: 39172506©Jiali Ni, Yong Huang, Qiangqiang Xiang, Qi Zheng, Xiang Xu, Zhiwen Qin, Guoping Sheng, Lanjuan Li. Originally publishedin the Interactive Journal of Medical Research (https://www.i-jmr.org/), 22.08.2024. 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.

https://www.i-jmr.org/2024/1/e56035

XSL• FO
RenderX

Interact J Med Res 2024 | vol. 13 | e56035 | p. 16(page number not for citation purposes)