Available via license: CC BY-NC 4.0
Content may be subject to copyright.
1
XiaoY, etal. BMJ Open 2024;14:e081131. doi:10.1136/bmjopen-2023-081131
Open access
Predictive value of anthropometric and
biochemical indices in non- alcoholic
fatty pancreas disease: a cross-
sectional study
Yang Xiao,1 Han Wang,1 Lina Han,1 Zhibin Huang,1 Guorong Lyu,1,2 Shilin Li 1
To cite: XiaoY, WangH, HanL,
etal. Predictive value of
anthropometric and biochemical
indices in non- alcoholic fatty
pancreas disease: a cross-
sectional study. BMJ Open
2024;14:e081131. doi:10.1136/
bmjopen-2023-081131
►Prepublication history
and additional supplemental
material for this paper are
available online. To view these
les, please visit the journal
online (https://doi.org/10.1136/
bmjopen-2023-081131 ).
Received 19 October 2023
Accepted 22 March 2024
1Department of Ultrasonography,
Second Afliated Hospital
of Fujian Medical University,
Quanzhou, China
2Department of Medicine,
Quanzhou Medical College,
Quanzhou, China
Correspondence to
Dr Shilin Li; lslqz@ fjmu. edu. cn
Original research
© Author(s) (or their
employer(s)) 2024. Re- use
permitted under CC BY- NC. No
commercial re- use. See rights
and permissions. Published by
BMJ.
ABSTRACT
Objectives Triglyceride (TG), triglyceride- glucose index
(TyG), body mass index (BMI), TyG- BMI and triglyceride to
high- density lipoprotein ratio (TG/HDL) have been reported
to be reliable predictors of non- alcoholic fatty liver disease.
However, there are few studies on potential predictors of
non- alcoholic fatty pancreas disease (NAFPD). Our aim
was to evaluate these and other parameters for predicting
NAFPD.
Design Cross- sectional study design.
Setting Physical examination centre of a tertiary hospital
in China.
Participants This study involved 1774 subjects who
underwent physical examinations from January 2016 to
September 2016.
Primary and secondary outcome measures From
each subject, data were collected for 13 basic physical
examination and blood biochemical parameters: age,
weight, height, BMI, TyG, TyG- BMI, high- density lipoprotein
(HDL), low- density lipoprotein, total cholesterol, TG,
fasting plasma glucose, TG/HDL and uric acid. NAFPD
was diagnosed by abdominal ultrasonography. A logistic
regression model with a restricted cubic spline was used
to evaluate the relationship between each parameter and
NAFPD. The receiver operating characteristic (ROC) curve
was used to calculate the area under the curve for each
parameter.
Results HDL was negatively correlated with NAFPD,
height was almost uncorrelated with NAFPD and the
remaining 11 parameters were positively correlated with
NAFPD. ROC curve showed that weight- related parameters
(weight, BMI and TyG- BMI) and TG- related parameters
(TyG, TG and TG/HDL) had high predictive values for the
identication of NAFPD. The combinations of multiple
parameters had a better prediction effect than a single
parameter. All the predictive effects did not differ by sex.
Conclusions Weight- related and TG- related parameters
are good predictors of NAFPD in all populations. BMI
showed the greatest predictive potential. Multiparameter
combinations appear to be a good way to predict NAFPD.
INTRODUCTION
In recent decades, diet structure, working
style and exercise status have changed, and
the population obesity rate shows a contin-
uously increasing trend.1 When the body
fat content exceeds the storage capacity of
the adipose tissue, it will be stored in non-
fatty tissues, such as the liver and pancreas.2
Non- alcoholic fatty liver disease (NAFLD)
is the most common liver metabolic disease
worldwide, affecting approximately one-
quarter of adults and children.3 The relation-
ship between non- alcoholic fatty pancreas
disease (NAFPD) and NAFLD is complex.
Some studies have shown that the former
is a predictor of the latter,4 whereas others
have shown that the latter is a risk factor for
the former.5 However, all studies agree that
obesity is a key factor leading to ectopic fat
deposition.
Increasing evidence suggests that NAFPD
is associated with diabetes, pancreatitis and
pancreatic cancer.6–9 Some studies have
further shown the difference in adipose tissue
in the pancreas between chronic pancreatitis
and acute pancreatitis.10 11 The adipose tissue
in chronic pancreatitis is replaced by more
fibrous tissue, which is like a compartment,
separating adipocytes from the pancreatic
parenchyma, and reducing the damage to
the pancreas. Acute pancreatitis lacks fibrous
tissue, so more adipose tissue has a greater
impact on the severity of acute pancreatitis.
STRENGTHS AND LIMITATIONS OF THIS STUDY
⇒Risk factors of non- alcoholic fatty pancreas disease
(NAPFD) were analysed on a large scale of partic-
ipants to obtain the single predictive power and
combined predictive power of each factor in pre-
dicting NAPFD.
⇒The lack of important predictors such as waist cir-
cumference and hip circumference in this study
may reduce the completeness of NAFPD prediction.
⇒In this study, only ultrasound was used as a diag-
nostic method for NAFPD, which has a good effect
but may still lead to missed diagnosis.
⇒This study included only the Chinese population, and
the ndings may not apply to other ethnic groups.
on April 6, 2024 by guest. Protected by copyright.http://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2023-081131 on 5 April 2024. Downloaded from
2XiaoY, etal. BMJ Open 2024;14:e081131. doi:10.1136/bmjopen-2023-081131
Open access
Moreover, studies show that the prevalence of NAFPD is
high, from 30% to 33%.12 13 Therefore, early identifica-
tion of NAFPD is essential to reducing the various related
risks. Tissue biopsy is the gold standard for diagnosing
NAFPD, as is NAFLD. However, pancreas biopsy is rarely
performed in clinical practice because of its invasive
nature, poor acceptability,14 and the procedural difficulty
of the retroperitoneal position of the pancreas.
Some anthropometric and biochemical indicators,
combinations of plain arithmetic and complicated
numerical structures have been used to appraise the
risk of NAFLD.15 16 Parameters previously used to assess
NAFPD risk include triglyceride (TG) level, triglyceride-
glucose index (TyG), body mass index (BMI), TyG- BMI
and triglyceride to high- density lipoprotein ratio (TG/
HDL).17–19 The present study was aimed to evaluate the
best parameters to predict NAFPD through an epide-
miological survey of 1774 general public subjects who
underwent health checkups, including easy- collected
anthropometric and biochemical indicators, combina-
tions of plain arithmetic and complicated numerical
structures.
METHODS
Case data
1774 healthy subjects were selected from the Physical
Examination Center of the Second Affiliated Hospital of
Fujian Medical University. The baseline characteristics of
the study participants are presented in table 1.
Data collection and calculations
Haematological samples were collected in the morning
after at least 8 hours of fasting. Basic parameters such as
age, height and weight and blood biochemical parameters
such as high- density lipoprotein (HDL), low- density lipo-
protein (LDL), total cholesterol (TC), TG, fasting plasma
glucose (FPG), and uric acid (UA) were obtained. The
composite parameters were calculated as follows: BMI=-
weight (kg)/height2 (m2), TyG=ln (fasting triglyceride
(mg/dL) × fasting glucose (mg/dL)/2), TyG- BMI=TyG ×
BMI and TG/HDL=TG (mmol/L)/HDL (mmol/L).
Diagnosis and grading of NAFPD
NAFPD was diagnosed by ultrasound detection of pancre-
atic steatosis, ruling out medications, infections or
alcohol as potential causes. Pancreatic ultrasonography
was performed by two skilled technicians on the same
morning blood was drawn. Patients were fasting until the
completion of the ultrasound examination. At the time
of the ultrasound examination, the patient was placed
in the supine position with both arms raised above his
head to fully expose the upper abdomen. The ultrasound
machine and probe used were as follows: HI VISION
Preirus (Hitachi (Japan)) with a probe C715 and SonoS-
cape (China) with a probe C1- 6A. Experienced senior
physicians judged the ultrasound images without knowl-
edge of the subjects’ medical information or biochem-
istry results. The diagnostic criterion for NAFPD was
that the echo of the pancreas was enhanced and higher
than that of the kidney cortex. Since the pancreas and
Table 1 Baseline characteristics of the study subjects with and without NAFPD
Female (n=928) Male (n=846)
Non- NAFPD NAFPD P value Non- NAFPD NAFPD P value
No. 511 (55.06) 417 (44.94) 423 (50.00) 423 (50.00)
Age, years 37.44 (12.64) 45.99 (15.54) <0.001 39.64 (13.60) 48.75 (15.41) <0.001
Weight, kg 55.16 (9.18) 66.43 (11.18) <0.001 55.09 (8.48) 66.71 (12.00) <0.001
Height, m 1.64 (0.08) 1.64 (0.08) 0.631 1.63 (0.08) 1.64 (0.08) 0.203
BMI, kg/m220.55 (2.61) 24.64 (3.06) <0.001 20.66 (2.37) 24.78 (3.50) <0.001
TyG 2.27 (0.59) 2.78 (0.63) <0.001 2.24 (0.58) 2.76 (0.67) <0.001
TyG- BMI 47.18 (15.26) 68.85 (19.19) <0.001 46.49 (13.95) 69.04 (22.05) <0.001
HDL, mmol/L 1.36 (0.40) 1.19 (0.36) <0.001 1.37 (0.39) 1.19 (0.35) <0.001
LDL, mmol/L 2.84 (0.82) 3.32 (0.90) <0.001 2.83 (0.84) 3.26 (0.90) <0.001
TC, mmol/L 4.61 (0.88) 5.19 (1.01) <0.001 4.62 (0.91) 5.16 (0.99) <0.001
TG, mmol/L 0.72 (0.52) 1.20 (1.00) <0.001* 0.75 (0.46) 1.60 (0.95) <0.001*
FPG, mmol/L 4.90 (0.70) 5.30 (0.90) <0.001* 5.00 (0.60) 5.30 (0.90) <0.001*
TG/HDL 0.53 (0.56) 1.13 (1.24) <0.001* 0.55 (0.45) 1.07 (1.24) <0.001*
UA, μmol/L 306.32 (79.89) 357.84 (93.26) <0.001 307.37 (80.29) 344.87 (95.84) <0.001
Values are expressed as mean (SD), median (quartile interval) or n (%).
*These data are presented as median (quartile interval) using the Mann- Whitney U test due to non- normally distributed data.
BMI, body mass index; FPG, fasting plasma glucose; HDL, high- density lipoprotein; LDL, low- density lipoprotein; NAFPD, non- alcoholic
fatty pancreas disease; TC, total cholesterol; TG, triglyceride; TG/HDL, triglyceride- to- high- density lipoprotein ratio; TyG, triglyceride- glucose
index; TyG- BMI, triglyceride glucose- body mass index; UA, uric acid.
on April 6, 2024 by guest. Protected by copyright.http://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2023-081131 on 5 April 2024. Downloaded from
3
XiaoY, etal. BMJ Open 2024;14:e081131. doi:10.1136/bmjopen-2023-081131
Open access
kidney are often difficult to display on the same screen,
the echo differences between the liver and kidney were
compared first, followed by the comparison of the liver
and pancreas.13 20 A schematic of this process is shown in
figure 1.
On the basis that NAFPD had already been diagnosed,
we used the following criteria to grade NAFPD.21–23 Grade
1 (mild): The echo intensity was slightly greater than that
of the right renal cortex, and the pancreatic borders and
splenic vein were clearly visible. Grade 2 (moderate): The
echo intensity was significantly higher than that of the
right renal cortex, but lower than that of the retroperito-
neal fat, and the pancreatic borders were vague. Grade 3
(severe): The echo intensity was the same or higher than
that of retroperitoneal fat, the pancreatic borders could
not be evaluated and the splenic vein was not seen.
Statistical analysis
Data were analysed using SPSS V.26.0 software and R soft-
ware (V.4.3.1). The distribution patterns of continuous
variables were examined using QQ plots and Shapiro-
Wilk tests. Continuous variables with a normal or approx-
imately normal distribution were expressed as mean (SD)
and compared using Student’s t- test, whereas continuous
variables with skewed distribution were expressed as the
median (quartile range) and compared using the Mann-
Whitney U test. Categorical variables were expressed as
numbers (%) and compared using Pearson’s χ2 test. A
logistic regression model was established using restricted
cubic splines (RCS), and the ORs and 95% CIs of NAFPD
under different parameters were plotted. Four knots
were placed at the 5th, 35th, 65th and 95th percentiles.
In addition, to compare the predictive ability of the 13
parameters for NAFPD, receiver operating characteristic
(ROC) curve analysis was used to calculate the largest
sum of sensitivity and specificity and to determine the
best threshold value for each parameter. The area under
the curve (AUC) was handled according to the following
criteria: <0.5, invalid; 0.5–0.65, poor; 0.65–0.75, moderate;
0.75–0.85, good; 0.85–1.0, very good. All tests were two-
sided, and statistical significance was set at p<0.05.
Patient and public involvement
None.
RESULTS
Characteristics of the subjects
A total of 928 female and 846 male subjects were included
in this study. Among them, 417 women (44.94%) and
423 men (50.0%) were diagnosed with NAFPD. Table 1
describes the basic physical examination parameters,
blood biochemical parameters and related composite
parameters for the subjects diagnosed with and without
NAFPD. Age (t=9.059 for women, t=9.127 for men,
p<0.001), weight (t=16.539 for women, t=16.265 for men,
p<0.001), BMI (t=21.953 for women, t=20.032 for men,
p<0.001), TyG (t=12.506 for women, t=12.038 for men,
p<0.001), TyG- BMI (t=18.732 for women, t=17.770 for
men, p<0.001), LDL (t=8.392 for women, t=7.323 for
men, p<0.001), TC (t=9.369 for women, t=8.172 for men,
p<0.001), TG (u=11.991 for women, u=11.848 for men,
p<0.001), FPG (u=7.824 for women, u=7.950 for men,
p<0.001), TG/HDL (u=11.967 for women, u=11.794
for men, p<0.001) and UA (t=8.920 for women, t=6.169
for men, p<0.001) in patients with NAFPD were signifi-
cantly higher than those in patients without NAFPD in
both women and men. However, HDLs in patients with
NAFPD were significantly lower than those in patients
without NAFPD in both women and men (t=6.759 for
women, t=7.417 for men, p<0.001). There was no signif-
icant difference in height between patients with NAFPD
and without NAFPD in either women or men (t=0.472,
p=0.631 for women; t=1.275, p=0.203 for men).
Figure 1 Schematic diagrams of the pancreas under ultrasound. (A)Liver (blue circle), pancreas (red circle) and right renal
cortex (orange circle) of a normal subject. The echo intensity of the pancreas was comparable to that of the right renal cortex.
Liver echo was used as an intermediate bridge to better distinguish the difference between the pancreas and the right renal
cortex. (B)Liver (blue circle), pancreas (red circle) and right renal cortex (orange circle) of an NAFPD subject. The echo intensity
of the pancreas was signicantly higher than that of the right renal cortex. NAFPD, non- alcoholic fatty pancreas disease.
on April 6, 2024 by guest. Protected by copyright.http://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2023-081131 on 5 April 2024. Downloaded from
4XiaoY, etal. BMJ Open 2024;14:e081131. doi:10.1136/bmjopen-2023-081131
Open access
Associations between parameters and NAFPD
Figure 2 shows the associations between 12 parame-
ters (except for height) and NAFPD risk in women
(figure 2A1–L1) and men (figure 2A2–L2). Age, weight,
BMI, TyG, TyG- BMI, LDL, TC, TG, FPG, TG/HDL and UA
were positively correlated with NAFPD, whereas HDL was
negatively correlated with NAFPD (all p<0.001). There
was little difference between women and men. The differ-
ence lies only in the linear (p for non- linear >0.05) or
non- linear (p for non- linear<0.05) correlations between
each parameter and NAFPD. Data for height, which did
not show any significant difference between patients with
NAFPD and without NAFPD, are shown in online supple-
mental figure 1.
Correlation between parameters and grading of NAFPD
Table 2 describes the correlation between parameters and
grading of NAFPD. Using grade 0 as the reference, the
higher the grading of NAFPD, the higher the correlation
(age, weight, BMI, TyG, TyG- BMI, TC, TG, FPG, TG/HDL
and UA) (all p<0.001). Besides, using grade 0 as the refer-
ence, the higher the grading of NAFPD, the lower the
correlation (HDL and LDL) (all p<0.001). Furthermore,
there was no correlation between height and grading of
NAFPD (all p>0.05).
Accuracy of each parameter in predicting NAFPD in the
general public
ROC curve analysis was used to evaluate the accuracy of
the 13 parameters for predicting NAFPD in the general
public. The ability of the weight- related parameters
(weight, BMI and TyG- BMI) to predict NAFPD was rela-
tively good (table 3). The AUC for BMI was the largest
(0.8526), with a sensitivity of 0.7774, specificity of 0.7752,
best threshold of 22.3457, positive likelihood ratio of
3.4575 and negative likelihood ratio of 0.2872. The
predictive power of the TG- related parameters (TyG, TG
and TG/HDL) was slightly lower than that of the weight-
related parameters; however, the AUC was greater than
0.70. The AUCs for age, HDL, LDL, TC, FPG and UA
were all greater than 0.60. However, the AUC for height
was only 0.5258.
Accuracy of 13 parameters in predicting NAFPD by sex
Figure 3A,B and online supplemental table 1 show the
results of the ROC curve analyses and AUCs for the 13
parameters used to predict NAFPD in women and men.
The AUCs for all 13 parameters were greater than 0.5,
indicating that all parameters had a predictive value
for NAFPD. Additionally, the AUC for each parameter
was similar between women and men, with no signifi-
cant differences. Among all parameters, weight- related
parameters had the largest AUCs. For women, AUCs
were 0.8497 (95% CI: 0.8249 to 0.8746) for BMI, 0.8158
(95% CI: 0.7882 to 0.8434) for TyG- BMI and 0.7893 (95%
CI: 0.7598 to 0.8188) for weight. The best thresholds
for BMI, TyG- BMI and weight in women for predicting
NAFPD were 23.2405, 52.9662 and 59.7500, respectively.
For men, AUCs were 0.8580 (95% CI: 0.8330 to 0.8831)
for BMI, 0.8186 (95% CI: 0.7907 to 0.8466) for TyG- BMI
and 0.7946 (95% CI: 0.7649 to 0.8243) for weight. The
best thresholds for BMI, TyG- BMI and weight in men for
predicting NAFPD were 22.5014, 54.5370 and 62.2500,
respectively. TG- related parameters also showed high
NAFPD prediction performance, with AUCs greater than
0.70. The AUCs of the other parameters were greater than
0.60, except for that of height, which was close to 0.50.
Accuracy of multiple parameters in predicting NAFPD
Age, weight, BMI, TyG, TyG- BMI, HDL, LDL, TC, TG,
FPG, TG/HDL and UA were numbered from 1 to 12. The
combinations of multiple parameters were partly listed in
online supplemental table 2. The AUC was larger when
multiple parameters were combined (figure 3C). The
AUC of the best model should be 0.8773, with a Youden
index of 0.6109, positive likelihood ratio of 5.0469.
DISCUSSION
In this study, 13 non- invasive parameters were evaluated
for their associations with NAFPD and their ability to
predict NAFPD. Age, weight, BMI, TyG, TyG- BMI, HDL,
LDL, TC, TG, FPG, TG/HDL and UA had certain predic-
tive effects on NAFPD, whereas the predictive effect of
height on NAFPD was very limited. Notably, weight-
related parameters had the best predictive performance,
followed by TG- related parameters. Moreover, the predic-
tive effects of these parameters did not differ between the
sexes.
BMI is an indicator of obesity and is linked to an elevated
risk of insulin resistance (IR), NAFLD or other metabolic
diseases.24 25 The BMI cut- off in our study was <25 kg/m2,
which is lower than the 30 kg/m2 cut- off in most Western
countries, probably because of the higher intake of
unsaturated fats in the Chinese population.26 The TyG,
based on fasting blood glucose and triglyceride levels, is
extensively used as an important indicator of cardiovas-
cular events and IR.27–29 The above weight- related and
TG- related parameters all had good predictive effects
on NAFPD, with AUCs greater than 0.70. Although the
accurate diagnosis of NAFPD requires a tissue biopsy, this
is difficult to perform and runs counter to the Declara-
tion of Helsinki. Therefore, the diagnosis often relies
on indicators that are easy to collect, low- risk and highly
accurate in practical clinical work. The weight- related
and TG- related parameters in this study only required
simple measurement and drawing of a small amount of
peripheral blood to achieve a high NAFPD prediction
effect. Therefore, the utility of these parameters should
be promoted for surveillance in the general public to
reduce the occurrence of major related diseases.
A further six parameters in this study (age, HDL, LDL,
TC, FPG and UA) were also predictive of NAFPD, but to
a lesser extent than the weight- related and TG- related
parameters. Height has no obvious effect on the predic-
tion of NAFPD, but the composite index formed after
on April 6, 2024 by guest. Protected by copyright.http://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2023-081131 on 5 April 2024. Downloaded from
5
XiaoY, etal. BMJ Open 2024;14:e081131. doi:10.1136/bmjopen-2023-081131
Open access
Figure 2 Dose- response relationship between NAFPD and 12 parameters (A)age, (B)weight, (C)BMI, (D)TyG, (E)TyG- BMI,
(F)HDL, (G)LDL, (H)TC, (I)TG, (J)FPG, (K)TG/HDL and (L)UA. A1–L1 represent women, while A2–L2 represent men. BMI,
body mass index; FPG, fasting plasma glucose; HDL, high- density lipoprotein; LDL, low- density lipoprotein; NAFPD, non-
alcoholic fatty pancreas disease; TC, total cholesterol; TG, triglyceride; TG/HDL, triglyceride- to- high- density lipoprotein ratio;
TyG, triglyceride- glucose index; TyG- BMI, triglyceride glucose- body mass index; UA, uric acid.
on April 6, 2024 by guest. Protected by copyright.http://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2023-081131 on 5 April 2024. Downloaded from
6XiaoY, etal. BMJ Open 2024;14:e081131. doi:10.1136/bmjopen-2023-081131
Open access
combining height with other indicators has a fairly
broad predictive ability and excellent predictive perfor-
mance,30–33 so it cannot be ignored. Additionally, the
combinations of multiple parameters had higher predic-
tion power and positive likelihood ratio than a single
parameter. Multiparameter combination may be a good
way to predict NAFPD.
In addition, we explored the correlation of each param-
eter with the grading of NAFPD. Some parameters, including
age, weight, BMI, TyG, TyG- BMI, TC, TG, FPG, TG/HDL
and UA, became more and more important as the grading
of NAFPD increased. This means that in moderate- to- severe
NAFPD, the above parameters are more worthy of detection.
In contrast, the effect of HDL and LDL was more obvious
only in the prediction of mild NAFPD, while it was not very
satisfactory in the prediction of moderate- to- severe NAFPD.
Our study has several limitations. First, the study
lacked information on some measurements, such as waist
Table 2 Correlation between each parameter and grading of NAFPD
OR (95% CI) P value OR (95% CI) P value
Age, years LDL, mmol/L
Grade 0 1 (reference) Grade 0 1 (reference)
Grade 1 1.03 (1.02 to 1.04) <0.001 Grade 1 1.98 (1.73 to 2.27) <0.001
Grade 2 1.06 (1.05 to 1.07) <0.001 Grade 2 1.78 (1.52 to 2.09) <0.001
Grade 3 1.07 (1.05 to 1.08) <0.001 Grade 3 1.65 (1.27 to 2.14) <0.001
Weight, kg TC, mmol/L
Grade 0 1 (reference) Grade 0 1 (reference)
Grade 1 1.10 (1.09 to 1.11) <0.001 Grade 1 1.86 (1.64 to 2.11) <0.001
Grade 2 1.15 (1.14 to 1.17) <0.001 Grade 2 1.97 (1.71 to 2.28) <0.001
Grade 3 1.19 (1.16 to 1.21) <0.001 Grade 3 2.03 (1.62 to 2.55) <0.001
Height, m TG, mmol/L
Grade 0 1 (reference) Grade 0 1 (reference)
Grade 1 1.66 (0.43 to 6.43) 0.466 Grade 1 2.66 (2.19 to 3.23) <0.001
Grade 2 2.32 (0.46 to 11.68) 0.309 Grade 2 4.33 (3.54 to 5.30) <0.001
Grade 3 5.19 (0.34 to 78.19) 0.234 Grade 3 4.63 (3.74 to 5.73) <0.001
BMI, kg/m2FPG, mmol/L
Grade 0 1 (reference) Grade 0 1 (reference)
Grade 1 1.56 (1.48 to 1.64) <0.001 Grade 1 1.72 (1.49 to 1.99) <0.001
Grade 2 1.99 (1.86 to 2.13) <0.001 Grade 2 1.96 (1.69 to 2.27) <0.001
Grade 3 2.25 (2.05 to 2.46) <0.001 Grade 3 2.10 (1.79 to 2.46) <0.001
TyG TG/HDL
Grade 0 1 (reference) Grade 0 1 (reference)
Grade 1 2.62 (2.14 to 3.20) <0.001 Grade 1 2.24 (1.90 to 2.64) <0.001
Grade 2 7.09 (5.51 to 9.12) <0.001 Grade 2 3.35 (2.83 to 3.97) <0.001
Grade 3 8.68 (6.02 to 12.50) <0.001 Grade 3 3.38 (2.85 to 4.01) <0.001
TyG- BMI UA, μmol/L
Grade 0 1 (reference) Grade 0 1 (reference)
Grade 1 1.06 (1.05 to 1.07) <0.001 Grade 1 1.004 (1.003 to 1.005) <0.001
Grade 2 1.11 (1.10 to 1.12) <0.001 Grade 2 1.008 (1.006 to 1.010) <0.001
Grade 3 1.13 (1.11 to 1.14) <0.001 Grade 3 1.010 (1.008 to 1.013) <0.001
HDL, mmol/L
Grade 0 1 (reference)
Grade 1 0.43 (0.32 to 0.59) <0.001
Grade 2 0.12 (0.08 to 0.19) <0.001
Grade 3 0.11 (0.05 to 0.24) <0.001
Grade 0: no NAFPD; Grade 1: mild NAFPD; Grade 2: moderate NAFPD; Grade 3: severe NAFPD.
Abbreviations are dened in table1.
on April 6, 2024 by guest. Protected by copyright.http://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2023-081131 on 5 April 2024. Downloaded from
7
XiaoY, etal. BMJ Open 2024;14:e081131. doi:10.1136/bmjopen-2023-081131
Open access
circumference (WC) and hip circumference. Thus, waist-
to- height ratio (WHtR), TyG- WC, TyG- WHtR, conicity
index, body roundness index, body- shape index, lipid
accumulation product, visceral adiposity index, abdom-
inal volume index and body adiposity index could not
be calculated.34 35 The inclusion of these parameters may
lead to a more accurate prediction of NAFPD. Second,
the predictive effect of IR on NAFPD is missing in this
study, which needs to be supplemented in the future.
Third, the diagnosis of NAFPD was based solely on ultra-
sonographic findings. Many studies have proven the
feasibility of ultrasound in the diagnosis of NAFPD.13 20 36
However, further research is required to confirm its accu-
racy. Furthermore, the data used in this study were from
a Chinese population; therefore, the conclusions may not
apply to other racial groups. Finally, the cross- sectional
Table 3 The best threshold, LR+, LR−, sensitivities, specicities and area under the curve of related indices for the screening
of NAFPD in the general public
AUC 95% CI lower 95% CI upper Best threshold LR+ LR− Specicity Sensitivity
Age, years 0.6683 0.6431 0.6935 47.5000 2.1141 0.6506 0.7612 0.5048
Weight, kg 0.7919 0.7710 0.8128 59.1500 2.4323 0.3615 0.6916 0.7500
Height, m 0.5258 0.4988 0.5528 1.6425 1.2161 0.8491 0.5889 0.5000
BMI, kg/m20.8526 0.8349 0.8703 22.3457 3.4575 0.2872 0.7752 0.7774
TyG 0.7270 0.7036 0.7504 2.4449 2.0794 0.4689 0.6702 0.6857
TyG- BMI 0.8168 0.7972 0.8364 53.9929 2.7478 0.3222 0.7206 0.7679
HDL, mmol/L 0.6452 0.6197 0.6707 1.2850 1.4931 0.5971 0.5503 0.6714
LDL, mmol/L 0.6602 0.6348 0.6856 3.2650 1.9904 0.6517 0.7398 0.5179
TC, mmol/L 0.6715 0.6465 0.6965 4.9250 1.7705 0.6098 0.6638 0.5952
TG, mmol/L 0.7315 0.7082 0.7548 0.8650 2.0762 0.4284 0.6531 0.7202
FPG, mmol/L 0.6538 0.6284 0.6793 5.2500 1.9312 0.6652 0.7355 0.5107
TG/HDL 0.7304 0.7070 0.7538 0.6728 2.0374 0.4385 0.6488 0.7155
UA, μmol/L 0.6418 0.6161 0.6675 318.5000 1.5747 0.6230 0.6039 0.6238
Other abbreviations are dened in table1.
AUC, area under the curve; LR+, positive likelihood ratio; LR−, negative likelihood ratio.
Figure 3 ROC curve analysis of NAFPD- related indicators. (A)ROC curve analysis for women, (B)ROC curve analysis for
men, (C)ROC curve analysis of multiple parameters. AUC, area under the curve; BMI, body mass index; FPG, fasting plasma
glucose; HDL, high- density lipoprotein; LDL, low- density lipoprotein; M1, multiple parameters of age and weight for predicting
NAFPD; M2, multiple parameters of age, weight and BMI; M3, multiple parameters of age, weight, BMI and TyG; M4, multiple
parameters of age, weight, BMI, TyG and TyG- BMI; M5, multiple parameters of age, weight, BMI, TyG, TyG- BMI and HDL; M6,
multiple parameters of age, weight, BMI, TyG, TyG- BMI, HDL and LDL; M7, multiple parameters of age, weight, BMI, TyG,
TyG- BMI, HDL, LDL and TC; M8, multiple parameters of age, weight, BMI, TyG, TyG- BMI, HDL, LDL, TC and TG; M9, multiple
parameters of age, weight, BMI, TyG, TyG- BMI, HDL, LDL, TC, TG and FPG; M10, multiple parameters of age, weight, BMI,
TyG, TyG- BMI, HDL, LDL, TC, TG, FPG and TG/HDL; M11, multiple parameters of age, weight, BMI, TyG, TyG- BMI, HDL, LDL,
TC, TG, FPG, TG/HDL and UA; NAFPD, non- alcoholic fatty pancreas disease; ROC, receiver operating characteristic; TC, total
cholesterol; TG, triglyceride; TG/HDL, triglyceride- to- high- density lipoprotein ratio; TyG, triglyceride- glucose index; TyG- BMI,
triglyceride glucose- body mass index; UA, uric acid.
on April 6, 2024 by guest. Protected by copyright.http://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2023-081131 on 5 April 2024. Downloaded from
8XiaoY, etal. BMJ Open 2024;14:e081131. doi:10.1136/bmjopen-2023-081131
Open access
design employed in this study limits our interpretation of
the causality between parameters.
CONCLUSIONS
In conclusion, our study showed that weight- related and
TG- related parameters were good predictors of NAFPD,
with BMI having the greatest predictive potential. Besides,
multiparameter combinations may be a good way for
better prediction for NAFPD.
Acknowledgements We would like to thank Editage (www.editage.cn) for English
language editing.
Contributors The original draft of the manuscript was written by YX. The data
were collected by HW, LH and ZH. The data curation and formal analysis were
performed by YX and SL. The revision of the manuscript was done by YX, GL and SL.
The funding acquisition was performed by SL. The responsibility for the accuracy
of the data in this article and taking full responsibility for the work and/or conduct
of the study, having access to the data, and controlling publication decisions are
guaranteed by SL. All authors read and approved the nal manuscript.
Funding This study was supported by the Quanzhou High- level Talents Innovation
and Entrepreneurship Project under Grant number 2021C045R and the Joint funds
for the innovation of science and technology, Fujian province under Grant number
2023Y9231.
Competing interests None declared.
Patient and public involvement Patients and/or the public were not involved in
the design, or conduct, or reporting, or dissemination plans of this research.
Patient consent for publication Not applicable.
Ethics approval This study was conducted in full conformance with the principles
of the Declaration of Helsinki, and the animal study protocol was approved by the
Ethics Committee of the Second Afliated Hospital of Fujian Medical University
(protocol code 231 and date of approval 2022).
Provenance and peer review Not commissioned; externally peer reviewed.
Data availability statement Data are available upon reasonable request.
Supplemental material This content has been supplied by the author(s). It has
not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been
peer- reviewed. Any opinions or recommendations discussed are solely those
of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and
responsibility arising from any reliance placed on the content. Where the content
includes any translated material, BMJ does not warrant the accuracy and reliability
of the translations (including but not limited to local regulations, clinical guidelines,
terminology, drug names and drug dosages), and is not responsible for any error
and/or omissions arising from translation and adaptation or otherwise.
Open access This is an open access article distributed in accordance with the
Creative Commons Attribution Non Commercial (CC BY- NC 4.0) license, which
permits others to distribute, remix, adapt, build upon this work non- commercially,
and license their derivative works on different terms, provided the original work is
properly cited, appropriate credit is given, any changes made indicated, and the use
is non- commercial. See:http://creativecommons.org/licenses/by-nc/4.0/.
ORCID iD
ShilinLi http://orcid.org/0000-0003-1195-2245
REFERENCES
1 GBD 2015 Obesity Collaborators, Afshin A, Forouzanfar MH, etal.
Health effects of overweight and obesity in 195 countries over 25
years. N Engl J Med 2017;377:13–27.
2 Cao M- J, Wu W- J, Chen J- W, etal. Quantication of ectopic fat
storage in the liver and Pancreas using six- point Dixon MRI and its
association with insulin sensitivity and Β-cell function in patients with
central obesity. Eur Radiol 2023;33:9213–22.
3 Younossi Z, Anstee QM, Marietti M, etal. Global burden of NAFLD
and NASH: trends, predictions, risk factors and prevention. Nat Rev
Gastroenterol Hepatol 2018;15:11–20.
4 Catanzaro R, Cuffari B, Italia A, etal. Exploring the metabolic
syndrome: Nonalcoholic fatty Pancreas disease. World J
Gastroenterol 2016;22:7660–75.
5 Yamazaki H, Tauchi S, Kimachi M, etal. Association between
Pancreatic fat and incidence of metabolic syndrome: a 5- year
Japanese cohort study. J Gastroenterol Hepatol 2018;33:2048–54.
6 Skudder- Hill L, Sequeira IR, Cho J, etal. Fat distribution within the
Pancreas according to diabetes status and insulin traits. Diabetes
2022;71:1182–92.
7 Alempijevic T, Dragasevic S, Zec S, etal. Non- alcoholic fatty
Pancreas disease. Postgrad Med J 2017;93:226–30.
8 Ji J, Petropavlovskaia M, Khatchadourian A, etal. Type 2 diabetes is
associated with suppression of Autophagy and lipid accumulation in
Β-cells. J Cell Mol Med 2019;23:2890–900.
9 Petrov MS. Post- Pancreatitis diabetes mellitus and excess intra-
Pancreatic fat deposition as Harbingers of Pancreatic cancer. World
J Gastroenterol 2021;27:1936–42.
10 Acharya C, Cline RA, Jaligama D, etal. Fibrosis reduces severity
of acute- on- chronic Pancreatitis in humans. Gastroenterology
2013;145:466–75.
11 Acharya C, Navina S, Singh VP. Role of Pancreatic fat in the
outcomes of Pancreatitis. Pancreatology 2014;14:403–8.
12 Singh RG, Yoon HD, Poppitt SD, etal. Ectopic fat accumulation in
the Pancreas and its biomarkers: A systematic review and meta-
analysis. Diabetes Metab Res Rev 2017;33.
13 Zhou J, Li M- L, Zhang D- D, etal. The correlation between Pancreatic
steatosis and metabolic syndrome in a Chinese population.
Pancreatology 2016;16:578–83.
14 Castera L, Friedrich- Rust M, Loomba R. Noninvasive assessment
of liver disease in patients with Nonalcoholic fatty liver disease.
Gastroenterology 2019;156:1264–81.
15 Loomba R, Adams LA. Advances in non- invasive assessment of
hepatic brosis. Gut 2020;69:1343–52.
16 Wong VW- S, Adams LA, de Lédinghen V, etal. Noninvasive
biomarkers in NAFLD and NASH - current progress and future
promise. Nat Rev Gastroenterol Hepatol 2018;15:461–78.
17 Zheng R, Du Z, Wang M, etal. A longitudinal Epidemiological study
on the Triglyceride and glucose index and the incident Nonalcoholic
fatty liver disease. Lipids Health Dis 2018;17:262.
18 Fan N, Peng L, Xia Z, etal. Triglycerides to high- density lipoprotein
cholesterol ratio as a Surrogate for Nonalcoholic fatty liver disease: a
cross- sectional study. Lipids Health Dis 2019;18:39.
19 Li Y, Zheng R, Li J, etal. Association between Triglyceride glucose-
body mass index and non- alcoholic fatty liver disease in the non-
obese Chinese population with normal blood lipid levels: a secondary
analysis based on a prospective cohort study. Lipids Health Dis
2020;19:229.
20 Wang C- Y, Ou H- Y, Chen M- F, etal. Enigmatic ectopic fat:
prevalence of Nonalcoholic fatty Pancreas disease and its
associated factors in a Chinese population. J Am Heart Assoc
2014;3:e000297.
21 Sezgin O, Yaraş S, Özdoğan O. Pancreatic steatosis is associated
with both metabolic syndrome and Pancreatic stiffness detected by
ultrasound Elastography. Dig Dis Sci 2022;67:293–304.
22 Ramkissoon R, Gardner TB. Pancreatic steatosis: an emerging
clinical entity. Am J Gastroenterol 2019;114:1726–34.
23 van Geenen E- JM, Smits MM, Schreuder TCMA, etal. Nonalcoholic
fatty liver disease is related to Nonalcoholic fatty Pancreas disease.
Pancreas 2010;39:1185–90.
24 Juonala M, Magnussen CG, Berenson GS, etal. Childhood Adiposity,
adult Adiposity, and cardiovascular risk factors. N Engl J Med
2011;365:1876–85.
25 Brody GH, Yu T, Chen E, etal. Racial discrimination, body mass
index, and insulin resistance: A longitudinal analysis. Health Psychol
2018;37:1107–14.
26 Khatua B, El- Kurdi B, Patel K, etal. Adipose saturation reduces
Lipotoxic systemic inammation and explains the obesity paradox.
Sci Adv 2021;7:eabd6449.
27 Ding X, Wang X, Wu J, etal. Triglyceride- glucose index and the
incidence of Atherosclerotic cardiovascular diseases: a meta-
analysis of cohort studies. Cardiovasc Diabetol 2021;20:76.
28 Hong S, Han K, Park CY. The Triglyceride glucose index is a simple
and low- cost marker associated with Atherosclerotic cardiovascular
disease: a population- based study. BMC Med 2020;18:361.
29 Tao L- C, Xu J- N, Wang T- T, etal. Triglyceride- glucose index as
a marker in cardiovascular diseases: landscape and limitations.
Cardiovasc Diabetol 2022;21:68.
30 Ren Z, Li Y, Li X, etal. Associations of body mass index, waist
circumference and waist- to- height ratio with cognitive impairment
among Chinese older adults: based on the CLHLS. J Affect Disord
2021;295:463–70.
on April 6, 2024 by guest. Protected by copyright.http://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2023-081131 on 5 April 2024. Downloaded from
9
XiaoY, etal. BMJ Open 2024;14:e081131. doi:10.1136/bmjopen-2023-081131
Open access
31 Mahmoud I, Al- Wandi AS, Gharaibeh SS, etal. Concordances and
correlations between Anthropometric indices of obesity: a systematic
review. Public Health 2021;198:301–6.
32 Merianos AL, Jandarov RA, Khoury JC, etal. Tobacco smoke
exposure association with lipid proles and Adiposity among U.S. J
Adolesc Health 2018;62:463–70.
33 Ezzatvar Y, Izquierdo M, Ramírez- Vélez R, etal. Accuracy of
different cutoffs of the waist- to- height ratio as a screening tool for
Cardiometabolic risk in children and adolescents: A systematic
review and meta- analysis of diagnostic test accuracy studies. Obes
Rev 2022;23:e13375.
34 Sheng G, Lu S, Xie Q, etal. The usefulness of obesity and lipid-
related indices to predict the presence of non- alcoholic fatty liver
disease. Lipids Health Dis 2021;20:134.
35 Perona JS, Schmidt Rio- Valle J, Ramírez- Vélez R, etal. Waist
circumference and abdominal volume index are the strongest
Anthropometric Discriminators of metabolic syndrome in Spanish
adolescents. Eur J Clin Invest 2019;49:e13060.
36 Sotoudehmanesh R, Tahmasbi A, Sadeghi A, etal. The prevalence
of Nonalcoholic fatty Pancreas by endoscopic Ultrasonography.
Pancreas 2019;48:1220–4.
on April 6, 2024 by guest. Protected by copyright.http://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2023-081131 on 5 April 2024. Downloaded from
