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Changes in Indices of Steatosis and Fibrosis in Liver Grafts of Living Donors After Weight Reduction

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BackgroundA short-term weight reduction program for potential living donors was introduced to reduce the extent of hepatic steatosis prior to liver transplantation. We aimed to investigate changes in non-invasive hepatic steatosis and fibrosis indices among those who completed the program.Methods Among 1,950 potential living liver donors between January 2011 and May 2019, 160 living donors joined the weight reduction program. The prospectively collected clinical data of these potential liver donors were analyzed retrospectively. Hepatic steatosis and fibrosis scores were determined using the fatty liver index (FLI), hepatic steatosis index (HSI), and NAFLD fibrosis score (NFS) and compared to MR spectroscopy (MRS) fat fraction results before and after weight reduction.ResultsThirty-nine potential living donors who had undergone MRS both before and after weight reduction were included in the analysis. Their body weight decreased from 78.02 ± 10.89 kg to 72.36 ± 10.38 kg over a mean of 71.74 ± 58.11 days. FLI, HSI, and MRS values decreased significantly from 41.52 ± 19.05 to 24.53 ± 15.93, 39.64 ± 3.74 to 35.06 ± 3.82, and 12.20 ± 4.05 to 6.24 ± 3.36, respectively. No significant decreases in NFS were observed. There was a significant correlation between the extent of HSI change and the extent of MRS change (R2 value = 0.69, P < 0.001), although there was no correlation between MRS and FLI.Conclusion The weight reduction program significantly improved non-invasive indices of hepatic steatosis over a short period. HSI may allow for prediction of simple decreases in hepatic steatosis.
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ORIGINAL RESEARCH
published: 03 May 2022
doi: 10.3389/fsurg.2022.827526
Frontiers in Surgery | www.frontiersin.org 1May 2022 | Volume 9 | Article 827526
Edited by:
Peter Schemmer,
Medical University of Graz, Austria
Reviewed by:
Behzad Hatami,
Shahid Beheshti University of Medical
Sciences, Iran
Martin Krššák,
Medical University of Vienna, Austria
*Correspondence:
YoungRok Choi
choiyoungrok@gmail.com
Specialty section:
This article was submitted to
Visceral Surgery,
a section of the journal
Frontiers in Surgery
Received: 02 December 2021
Accepted: 28 March 2022
Published: 03 May 2022
Citation:
Choi J, Choi Y, Hong SY, Suh S,
Hong K, Han ES, Lee JM, Hong SK,
Yi NJ, Lee KW and Suh KS (2022)
Changes in Indices of Steatosis and
Fibrosis in Liver Grafts of Living
Donors After Weight Reduction.
Front. Surg. 9:827526.
doi: 10.3389/fsurg.2022.827526
Changes in Indices of Steatosis and
Fibrosis in Liver Grafts of Living
Donors After Weight Reduction
Jaehyuk Choi, YoungRok Choi*, Su young Hong, Sanggyun Suh, Kwangpyo Hong,
Eui Soo Han, Jeong-Moo Lee, Suk Kyun Hong, Nam-Joon Yi, Kwang-Woong Lee and
Kyung-Suk Suh
Department of Surgery, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, South Korea
Background: A short-term weight reduction program for potential living donors was
introduced to reduce the extent of hepatic steatosis prior to liver transplantation. We
aimed to investigate changes in non-invasive hepatic steatosis and fibrosis indices
among those who completed the program.
Methods: Among 1,950 potential living liver donors between January 2011 and
May 2019, 160 living donors joined the weight reduction program. The prospectively
collected clinical data of these potential liver donors were analyzed retrospectively.
Hepatic steatosis and fibrosis scores were determined using the fatty liver index (FLI),
hepatic steatosis index (HSI), and NAFLD fibrosis score (NFS) and compared to MR
spectroscopy (MRS) fat fraction results before and after weight reduction.
Results: Thirty-nine potential living donors who had undergone MRS both before and
after weight reduction were included in the analysis. Their body weight decreased from
78.02 ±10.89 kg to 72.36 ±10.38 kg over a mean of 71.74 ±58.11 days. FLI, HSI,
and MRS values decreased significantly from 41.52 ±19.05 to 24.53 ±15.93, 39.64
±3.74 to 35.06 ±3.82, and 12.20 ±4.05 to 6.24 ±3.36, respectively. No significant
decreases in NFS were observed. There was a significant correlation between the extent
of HSI change and the extent of MRS change (R2value =0.69, P<0.001), although
there was no correlation between MRS and FLI.
Conclusion: The weight reduction program significantly improved non-invasive indices
of hepatic steatosis over a short period. HSI may allow for prediction of simple decreases
in hepatic steatosis.
Keywords: hepatic steatosis, hepatic fibrosis, fatty liver, living donor, NAFLD, weight loss
INTRODUCTION
The prevalence of non-alcoholic fatty liver disease (NAFLD) in the general population ranges from
20 to 30% in Europe (1,2), and is as high as 46% in the United States (3). In Korea, the prevalence
of NAFLD among potential donors is also increasing due to increasing adoption of a sedentary
and Westernized lifestyle (46). However, NAFLD—which is related to metabolic syndrome—has
an adverse effect on liver transplantation. Indeed, NAFLD has been associated with postoperative
morbidity and mortality in patients with liver grafts (7,8).
Choi et al. Hepatic Steatosis and Fibrosis Indices After Weight Loss
Although NAFLD in the donor is an exclusion criterion
for living donor liver transplantation (LDLT) (9), donors with
NAFLD are considered marginal donors because the supply of
deceased donors has not met the demand for liver transplantation
in Korea. Previous studies have indicated that weight reduction
and lifestyle modification can reduce hepatic steatosis in such
marginal donors (1014).
Education on weight reduction and lifestyle modification
is being provided to potential living donors with NAFLD
to improve outcomes. Potential donors who have successfully
reduced weight prior to liver transplantation exhibit comparable
outcomes to donors from whom liver transplantation is
completed using liver grafts with no NAFLD (11,15,16).
Hepatic steatosis can be assessed either via liver biopsy or MR
spectroscopy (MRS). However, liver biopsy can cause procedure-
related complications, and MRS is expensive. Therefore, there is
a need for a simple index that can predict the degree of tissue
change following weight reduction.
In this retrospective study, we investigated whether indices of
hepatic steatosis and fibrosis, which are already in use and can be
calculated simply from the donor’s data, can predict the extent of
NAFLD after weight loss.
MATERIALS AND METHODS
Study Design
This study involved a single-center retrospective analysis of
the electronic medical records of 1,950 potential donors
between January 2011 and May 2019. Of the 1,950 potential
donors, 160 potential donors with hepatic steatosis received
recommendations for weight reduction and lifestyle modification
programs. Ninety-three of 160 potential donors lost body weight
and underwent LDLT. In this study, the fat fraction of MRS was
used as the reference value for the extent of hepatic steatosis
(1719). MRS acquired as previous described (20).
The study protocol conformed to the ethical guidelines of
the 1975 Declaration of Helsinki as reflected in a prior approval
by the appropriate Institutional Review Committee (2011-121-
1173) at Seoul National University Hospital.
Variable Definitions and Data Collection
Baseline body weight prior to weight loss and final weight
after weight loss were measured during the first and last MRI
sessions, respectively. Alanine transaminase (ALT), aspartate
transaminase (AST), gamma-glutamyl-transpeptidase (GGT),
platelet count, albumin, waist circumference, weight loss
duration, fat fraction on MRS, and body weight were compared
between the two measurement sessions.
Abbreviations: NAFLD, non-alcoholic fatty liver disease; LDLT, living donor
liver transplantation; MRS, magnetic resonance spectroscopy; ALT, alanine
transaminase; AST, aspartate transaminase; GGT,gamma-glutamyl transpeptidase;
FLI, fatty liver index; HSI, hepatic steatosis index; LAP, lipid accumulation product;
ION, Index of Nash; NAFLD-LFS, NAFLD Liver Fat Score; APRI, AST to platelet
ratio index; FIB-4, Fibrosis-4; ELF, Enhanced Liver Fibrosis; NFS, NAFLD Fibrosis
Score; BMI, body mass index; TG, triglycerides; DM, diabetes mellitus.
Indices
We noninvasively reviewed indices for NAFLD assessment,
including six indices [fatty liver index (FLI), hepatic steatosis
index (HSI), SteatoTest, lipid accumulation product (LAP), Index
of Nash (ION), NAFLD-Liver Fat Score (LFS)] for liver steatosis
and eight indices [AST to platelet ratio index (APRI), Fibrosis-4
(FIB-4), FibroTest, Fibrometer NAFLD, Enhanced Liver Fibrosis
(ELF), Hepacore, BARD score, NAFLD fibrosis score (NFS)]
for liver fibrosis (21,22). The FLI, HIS, and NFS were selected
for this study because these indices can be calculated with only
simple laboratory findings, body weight, and circumference using
retrospective data.
The FLI is used to predict the degree of hepatic steatosis
(23). Scores are derived based on body mass index (BMI), GGT,
triglycerides (TG), and waist circumference, as follows: FLI =
logistic (0.953 ×ln (TG) +0.139 ×BMI +0.718 ×ln (GGT) +
0.053 ×waist circumference 15.745) ×100, where logistic(x) =
1/(1 +ex) and ln denotes the natural logarithm. Its lower cutoff
is 30, while its upper cutoff is 60 (24,25).
The HSI predicts the degree of hepatic steatosis and is derived
using BMI, diabetes mellitus (DM) status, and AST/ALT ratio,
as follows: HSI =8×ALT/AST +BMI (+2, if DM) (+2, if
female). Its lower cutoff is 30, while its upper cutoff is 36. A prior
ultrasonography study revealed that HIS is correlated with the
extent of hepatic steatosis (26,27).
The NFS is used to predict the degree of hepatic fibrosis and
is derived based on age, sex, DM status, platelet count, AST/ALT
ratio, and albumin levels, as follows. NFS = 1.675 +0.037 ×age
+0.094 ×BMI +1.13 ×DM +0.99 ×AST/ALT ratio 0.013
×platelet (109) 0.66 ×ALB (g/dl). Its lower cutoff is 1.445,
while its upper cutoff is 0.676 (2830).
Statistical Analysis
Statistical analyses were performed using SPSS 26.0 (IBM
Corp., Armonk, NY, USA). Continuous variables such as body
weight, BMI, AST, ALT, GGT, platelet count, albumin, waist
circumference, and indices (NFS, FLI, HSI, MRS) before and
after weight loss were compared using Student’s t-tests. Variable
distributions were subjected to a normality test prior to the
Student’s t-test. All p-values were one-sided, and P-values
<0.05 were considered statistically significant. Quantitative
variables are expressed as the mean ±standard deviation. Linear
regression analyses were performed to investigate correlations
between each index and the fat fraction obtained via MRS. R2>
0.6 with a p-value <0.05 was considered statistically significant.
Arrow graphs were drawn using the R ggplot 2 package.
RESULTS
Study Population
We analyzed data for 39 of 93 living donors who had undergone
assessments of fat fraction before and after weight loss via MRS
(Figure 1).
Patient Characteristics
The mean patient age was 35.03 ±10.45 years (range: 20–59
years), and 29 patients were male (29/39, 74.4%). No patients had
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Choi et al. Hepatic Steatosis and Fibrosis Indices After Weight Loss
FIGURE 1 | Flow diagram of participant selection.
TABLE 1 | Baseline characteristics of the 39 living donors.
Variables Before After P-value
Sex (male), n(%) 29 (74.4%) - NA
Age (years) 35.03 ±10.45 - NA
Weight (kg) 78.02 ±10.89 72.36 ±10.38 <0.001
Height (cm) 169.53 - NA
BMI (kg/m2) 27.10 ±2.83 25.11 ±2.60 <0.001
Waist circumference (mm) 909.35 ±59.58 879.92 ±61.16 <0.001
DM 0 - NA
HT, n(%) 2 (5.1%) - NA
Weight loss duration (days) 71.74 ±58.11 - NA
BMI, body mass index; DM, diabetes mellitus; HT, hypertension.
DM, although two donors (5.1%) had hypertension. The mean
duration of weight reduction was 71.74 ±58.11 days (range: 11–
298 days). Six donors (15.4%) had a body weight reduction period
of over 100 days (Table 1).
Participation in the program was associated with a significant
decrease in weight from 78.02 ±10.89 kg to 72.36 ±10.38 kg
(P<0.001), as well as a significant decrease in waist
circumference from 909.35 ±59.58 mm to 879.92 ±61.16 mm
(P<0.001). Moreover, BMI decreased from 27.10 ±2.83 kg/m2
to 25.11 ±2.60 kg/m2(P<0.001), and the distribution of
BMI changed. Thirty living donors (76.9%) had obesity (BMI
>25 kg/m2), eight donors (20.5%) were overweight (25 kg/m2
>BMI >23 kg/m2), and one donor’ s BMI was 22.89 kg/m2,
which is nearly borderline. After completing the weight reduction
program, 19 donors (48.8%) had obesity (BMI >25 kg/m2), 10
donors (25.6%) were overweight (25 kg/m2>BMI >23 kg/m2),
and 10 donors (25.6%) had normal weight (Table 1).
TABLE 2 | Laboratory findings.
Variables Before After P-value
AST (U/L) 21.28 ±5.88 19.23 ±6.58 0.167
ALT (U/L) 33.08 ±15.80 23.10 ±11.35 0.007
GGT (U/L) 33.13 ±18.41 21.69 ±10.79 <0.001
Platelet (×109/L) 256.36 ±57.66 249.44 ±58.28 1
Albumin (g/dL) 4.52 ±0.25 4.41 ±0.34 0.004
AST, aspartate aminotransferase; ALT, alanine aminotransferase; GGT, gamma-
glutamyl transferase.
TABLE 3 | Hepatic steatosis & fibrosis indices.
Variables Before After P-value
NFS 3.45 ±0.89 3.22 ±0.94 1
FLI 41.52 ±19.05 24.53 ±15.93 <0.001
HIS 39.64 ±3.74 35.06 ±3.82 <0.001
MRS 12.20 ±4.05 6.24 ±3.36 <0.001
NFS, NAFLD fibrosis score; FLI, fatty liver index; HSI, hepatic steatosis index; MRS, MR
spectroscopy liver fat fraction; NAFLD, non-alcoholic fatty liver disease.
There was no significant difference in AST levels between
the baseline and final assessments (21.28 ±5.88 U/L vs. 19.23
±6.58 U/L, P-value =0.167); however, there were significant
decreases in ALT (33.08 ±15.80 U/L vs. 23.10 ±11.35 U/L, P=
0.007) and GGT levels (33.13 ±18.41 U/L vs. 21.69 ±10.79 U/L,
P<0.001) between the two time points. The number of patients
with ALT levels above the normal range (0–40 U/L) changed
from 9/39 to 3/39, while the number of patients with GGT levels
above the normal range changed from 19/39 to 6/39. There was
a significant decrease in albumin levels between the baseline and
final assessments (4.52 ±0.25 g/dl vs. 4.41 ±0.34 g/dl, P=0.004).
However, platelet count did not significantly differ between the
two time points (P=1.00) (Table 2).
Index Analysis
There was a significant decrease in HSI between the baseline and
final assessments (39.64 ±3.74 vs. 35.06 ±3.82, P<0.001).
Before weight loss, there were 37 donors (94.9%) in the high HSI
group (HSI >36) and two donors (5.1%) in the intermediate HSI
group (36 >HSI >30). After weight loss, there were 16 donors
(41.0%) in the high HSI group (HSI >36), 21 donors (53.8%) in
the intermediate HSI group (36 >HSI >30), and two donors
(5.2%) in the low HSI group (HSI <30) (Table 3).
There was a significant decrease in FLI between the baseline
and final assessments (41.52 ±19.05 vs. 24.53 ±15.93, P<
0.001). Before weight loss, there were seven donors (17.9%) in the
high FLI group (FLI >60), 27 donors (69.2%) in the intermediate
FLI group (60 >FLI >20), and five donors (12.9%) in the low FLI
group (FLI <20). After weight loss, there was one donor (2.6%)
in the high FLI group (FLI >60), while there were 17 donors
(43.6%) in the intermediate FLI group (60 >FLI >20) and 21
donors (53.8%) in the low FLI group (FLI <20).
No significant changes in NFS were observed (P=1).
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Choi et al. Hepatic Steatosis and Fibrosis Indices After Weight Loss
FIGURE 2 | Trend line for identifying the correlation between the change in
MRS fat fraction and the change in HSI. MRS, magnetic resonance
spectroscopy; HSI, hepatic stenosis index.
Measurements of fat fraction obtained via MRS significantly
decreased from 12.20 ±4.05% to 6.24 ±3.36% (P<0.001)
between the two periods. Before weight loss, the fat fraction was
10% or higher in 26 donors, and the maximum value was 22.73%.
After weight loss, only four donors had fat fractions of 10% or
higher (Table 3).
Correlation in the Value of Delta Between
HSI and MRS
There were no significant correlations between the extent of
change on MRS and indices, except HSI (R2=0.69 and P<
0.001) (Figure 2).
The gradient trend of the delta HSI/delta MRS graph was
similar to that of the trend line, but there were some exceptional
cases (Figure 3). No correlations were observed in the other two
graphs (Figures 4,5).
DISCUSSION
Previous studies have reported that MRS can accurately predict
steatosis, although it remains controversial to what extent it can
predict the degree of fibrosis (25,31,32). MRS is also non-
invasive and more objective than biopsy assessments. In the
present study, we investigated whether indices of hepatic steatosis
and fibrosis can predict the extent of NAFLD after weight loss.
Our findings indicated that there was no strong correlation
between either of the two indices and the degree of steatosis,
suggesting that the extent of change in hepatic steatosis due
to weight reduction can only be roughly determined based on
changes in HSI and FLI values.
Kahl et al. and Cuthbertson et al. reported the similar result
that these HSI and FLI would be surrogate parameters for hepatic
steatosis clinically, but could not replace MRS (33,34).
Previous studies have suggested a correlation between HSI and
fat fraction on ultrasonography; however, the R2value was 0.334,
and ultrasonography is a user-dependent method. Thus, the
results were not clinically significant. Furthermore, the authors
did not validate the correlation using liver histology findings
FIGURE 3 | The changes of HIS according to MRI fat before and after body
weight reduction. The values of HSI and MRS tend to decrease with weight
loss. The line slope representing the change is the ratio of the MRS decrease
and the HSI decrease. Therefore, the HSI value changes appropriately
according to the change amount of the MRI fat fraction. MRS, magnetic
resonance spectroscopy; HSI, hepatic stenosis index. Blue circle, before
weight reduction; red circle and arrows, after weight reduction.
FIGURE 4 | The changes of FLI according to MRI fat before and after body
weight reduction. The slope of each line appears in various ways without a
particular pattern. Since the amount of change in the FLI value according to
the amount of change in the MRS fat fraction is not constant, it appears that
the FLI cannot consistently reflect the MRS fat fraction. MRS, magnetic
resonance spectroscopy; FLI, fatty liver index. Blue circle, before weight
reduction; red circle and arrows, after weight reduction.
(26). Although they attempted to determine the correlation
between the index and MRS, no indices exhibited a quantitative
correlation. Because we used the objective value for the fat
fraction obtained via MRS as the reference value, our trends
observed for HSI and FLI may be more reliable. Our regression
analysis revealed a correlation for HSI only. Given that one can
infer the extent of change on MRS based on the extent of change
Frontiers in Surgery | www.frontiersin.org 4May 2022 | Volume 9 | Article 827526
Choi et al. Hepatic Steatosis and Fibrosis Indices After Weight Loss
FIGURE 5 | The changes of NFS according to MRI fat before and after body
weight reduction. Regardless of the change in the MRS fat fraction according
to the change in body weight, there was little change in the NFS value. MRS,
magnetic resonance spectroscopy; NFS, NAFLD fibrosis score. Blue circle,
before weight reduction; red circle and arrows, after weight reduction.
in HSI, this tool may allow clinicians to predict improvements in
liver fat fraction without additional MRI or biopsy.
Liu et al. reported that FLI had a high diagnostic predictive
rate for metabolic-associated fatty liver disease, but this may be
due to triglyceride, gamma GT, which is highly correlated with
metabolic diseases (35).
While NFS values increased, the change was not statistically
significant, likely because we observed unexpected decreases
in platelet levels, which are used in the calculation of NFS.
Therefore, the results may have been clinically insignificant
because the extent of change was not large. In addition, the NFS
value was significantly smaller than the lower cutoff of 1.455
(3.45 ±0.89 before weight loss and 3.22 ±0.94 after weight
loss), and there was no donor with an index value exceeding the
lower cutoff at either time point. Such findings indicate a degree
of fibrosis without clinical significance. Since the study involved
only those patients who had completed liver transplantation,
our results were likely influenced by selection bias due to the
inclusion of people with relatively healthy livers. For the same
reason, most patients may have had low NFS values caused by
simple steatosis. However, since this is also an index that suggests
the probability of fibrosis, it is limited in that the true extent of
fibrosis cannot be determined without a direct biopsy.
Our findings indicate that improvements in NAFLD can
be predicted based only on routine laboratory examination
results, which may aid in improving outcomes among donors
with NAFLD. However, the arrow graph shows cases outside
the normal gradient range. For clinical use, an additional
understanding of the characteristics of exceptional cases will be
required. In this study, we did not perform feature analysis of
exceptional cases due to the small number of cases, although such
analyses will be possible in larger-scale studies.
Our results also indicated that the fibrosis index was clinically
and statistically insignificant; therefore, only steatosis could be
identified. Pathological analyses should be performed before and
after weight reduction to identify other factors that contribute to
NAFLD, such as inflammation, ballooning injury, and fibrosis.
The results of this study should also be validated based on
pathological results.
Previous studies that have examined pathological findings
at 1-year post-operatively have reported that recipient-related
factors can influence the risk of recipient NAFLD after liver
transplantation (36). To exclude these factors, future studies
should examine pathology findings 7 days post-operatively, as
this will help to ascertain the effect of weight reduction on liver
engraftment in a linear fashion.
DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be
made available by the authors, with permission of IRB.
ETHICS STATEMENT
The studies involving human participants were reviewed and
approved by Seoul National University Hospital. Written
informed consent for participation was not required for this
study in accordance with the national legislation and the
institutional requirements.
AUTHOR CONTRIBUTIONS
JC: data collection, draft, and the final manuscript. YC: initial
idea, study conceptualization, critical review, draft, and the final
manuscript. SYH, SS, KH, and EH: data collection and draft
review. JML: data collection, study conceptualization, critical
review, draft, and review. SKH, NJY, KWL, and KSS: study
conceptualization, critical review, and draft review. All authors
contributed to the article and approved the submitted version.
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Frontiers in Surgery | www.frontiersin.org 6May 2022 | Volume 9 | Article 827526
... While preoperative volumetric assessment shows high accuracy when comparing it to the actual graft weight, there are still cases where the estimated volume differs from the graft weight. Additionally, some donors undergo weight reduction before surgery to reduce steatosis of the graft, which can lead to the graft being reduced compared to the initial estimated size [7][8][9]. Therefore, we designed this study to build a multivariable linear regression model for predicting graft weight based on CT volumetry combined with other potential factors that can increase the accuracy of prediction. ...
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Purpose The purpose of this study is to build a prediction model for estimating graft weight about different graft volumetry methods combined with other variables. Methods Donors who underwent living-donor right hepatectomy from March 2021 to March 2023 were included. Estimated graft volume measured by conventional method and 3-dimensional (3D) software were collected as well as the actual graft weight. Linear regression was used to build a prediction model. Donor groups were divided according to the 3D volumetry of <700 cm³, 700–899 cm³, and ≥900 cm³ to compare the performance of different models. Results A total of 119 donors were included. Conventional volumetry showed R² of 0.656 (P < 0.001) while 3D software showed R² of 0.776 (P < 0.001). The R² of the multivariable model was 0.842 (P < 0.001) including for 3D volume (β = 0.623, P < 0.001), body mass index (β = 7.648, P < 0.001), and amount of weight loss (β = -7.252, P < 0.001). The median errors between different models and actual graft weight did not differ in donor groups (<700 and 700–899 cm³), while the median error of univariable linear model using 3D software (122.5; interquartile range [IQR], 61.5–179.8) was significantly higher than multivariable-adjusted linear model (41.5; IQR, 24.8–69.8; P = 0.003) in donors with estimated graft weight ≥900 cm³. Conclusion The univariable 3D volumetry model showed an acceptable outcome for donors with an estimated graft volume <900 cm³. For donors with an estimated graft volume ≥900 cm³, the multivariable-adjusted linear model showed higher accuracy.
... Additional studies also examined the effect of comorbid conditions on the utility of the HSI in detecting NAFLD. In the context of weight loss, the HSI showed a significant correlation with changes in fat fraction measured by magnetic resonance spectroscopy (R 2 = 0.69; p < 0.001), while the FLI did not show any correlation in a study of 39 living liver donors [40]. While the HSI was developed using an adult Korean population, a study showed that the index could also be useful to determine NAFLD in pediatric patients. ...
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Nonalcoholic fatty liver disease (NAFLD) is currently the most common form of chronic liver disease. The growing prevalence of NAFLD is strongly associated with the high incidence of metabolic syndrome. NAFLD affects as much as 19% of the US population with a disproportionate impact on minority racial groups such as Asian Americans. If not promptly managed, NAFLD may progress to more feared complications. Liver indices for NAFLD screening have been proposed but were often developed using study populations with different anthropometrics than patients of East Asian descent. This review compares the accuracy of five indices for NAFLD screening in Asian cohorts. The Fatty Liver Index performed well in multiple large-scale community studies, although other indices may be more suited for specific patient cohorts. This is important, as the utilization of liver indices could accelerate screening for NAFLD for early management and to reduce liver disease-related health disparities among Asian Americans.
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Aims: Non-invasive hepatic steatosis algorithms are recommended in detecting metabolic-associated fatty liver disease (MAFLD) in epidemiological studies. However, the diagnostic accuracy of these models is unclear. This study aimed to evaluate the diagnostic efficiency of five common models in a national survey population. Materials and methods: The Third National Health and Nutrition Examination Survey (NHANES III) datasets were used in this study. The fatty liver index (FLI), hepatic steatosis index (HSI), non-alcoholic liver disease-liver fat score (NAFLD-LFS), the SteatoText (ST), and visceral adiposity index (VAI) were evaluated. Results: The prevalence of MAFLD in the general population was 31.2%. The proportion of MAFLD estimated using the NAFLD-LFS (30.8%) was the closest to the real number, whereas the ST model (66.1%) significantly overestimated the prevalence of MAFLD in this cohort. The FLI (36.9%) and HSI models (38.5%) also slightly overestimated the prevalence of MAFLD in the study population. The FLI had the highest area under the receiver operating characteristic (AUROC) value (0.793) among all models, with a sensitivity of 57.0%, a specificity of 83.8%, a positive predictive value (PPV) of 67.3%, and a negative predictive value (NPV) of 77.0%. The combination of the original algorithm with additional metabolic dysfunction criteria did not improve the diagnostic efficiency. The discriminative ability for MAFLD in all models was lower in participants with a normal body mass index (BMI). Conclusions: Non-invasive models, especially the FLI, have satisfactory diagnostic performance in detecting MAFLD. However, models in people with normal BMIs require further development.
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Background and aim: Little is known about the diagnostic value of hepatic steatosis index (HSI) and fatty liver index (FLI), as well as their link to metabolic syndrome in type 1 diabetes mellitus. We have screened the effectiveness of FLI and HSI in an observational pilot study of 40 patients with type 1 diabetes. Methods: FLI and HSI indices were calculated for 201 patients with type 1 diabetes. 40 patients with FLI/HSI values corresponding to different risk of liver steatosis were invited for liver magnetic resonance study. In-phase/opposed-phase technique of magnetic resonance was used. Accuracy of indices was assessed from the area under the receiver operating characteristic curve (AUROC). Results: 12 (30.0%) patients had liver steatosis. For FLI, sensitivity was 90%, specificity was 74%, positive likelihood ratio was 3.46, negative likelihood ratio - 0.14, positive predictive value - 0.64; negative predictive value - 0.93. For HSI, sensitivity was 86%, specificity was 66%, positive likelihood ratio was 1.95, negative likelihood ratio - 0.21, positive predictive value - 0.50; negative predictive value - 0.92. AUROC for FLI was 0.86 (95% confidence interval [0.72; 0.99]); for HSI - 0.75 [0.58; 0.91]. Liver fat correlated with liver enzymes, waist circumference, triglycerides and C-reactive protein. FLI correlated with C-reactive protein, liver enzymes, blood pressure. HSI correlated with waist circumference and C-reactive protein. FLI ≥ 60 and HSI ≥ 36 were significantly associated with metabolic syndrome and nephropathy. Conclusions: the tested indices, especially FLI, can serve as surrogate markers for liver fat content and metabolic syndrome in type 1 diabetes.
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Purpose: We evaluated the heterogeneity of steatosis in living donor livers to determine its regional differences. Methods: Between June 2011 and February 2012, 81 liver donors were selected. Fat fraction was estimated using magnetic resonance triple-echo chemical shifting gradient imaging in 13 different regions: segment 1 (S1), S2, S3, and each peripheral and deep region of S4, S5, S6, S7, and S8. Results: There were differences (range, 3.2%–5.3%) in fat fractions between each peripheral and deep region of S4, S6, S7, and S8 (P < 0.001, P = 0.004, P < 0.001, and P = 0.006). Fat deposit amount in S1, S2, S3 and deep regions of S4–S8 were significantly different from one another (F [4.003, 58.032] = 8.684, P < 0.001), while there were no differences among the peripheral regions of S4-S8 (F [2.9, 5.3] = 1.3, P = 0.272) by repeated measure analysis of variance method. And regional differences of the amount of fat deposit in the whole liver increased as a peripheral fat fraction of S5 increased (R2 = 0.428, P < 0.001). Conclusion: Multifocal fat measurements for the whole liver are needed because a small regional evaluation might not represent the remaining liver completely, especially in patients with severe hepatic steatosis.
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Nonalcoholic fatty liver disease (NAFLD) is estimated to afflict approximately 1 billion individuals worldwide. In a subset of NAFLD patients, who have the progressive form of NAFLD termed nonalcoholic steatohepatitis (NASH), it can progress to advanced fibrosis, cirrhosis, hepatocellular carcinoma, and liver-related morbidity and mortality. NASH is typically characterized by a specific pattern on liver histology, including steatosis, lobular inflammation, and ballooning with or without peri-sinusoidal fibrosis. Thus, key issues in NAFLD patients are the differentiation of NASH from simple steatosis and identification of advanced hepatic fibrosis. Until now, liver biopsy has been the gold standard for identifying these 2 critical end points, but has well-known limitations, including invasiveness; rare but potentially life-threatening complications; poor acceptability; sampling variability; and cost. Furthermore, due to the epidemic proportion of individuals with NAFLD worldwide, liver biopsy evaluation is impractical, and noninvasive assessment for the diagnosis of NASH and fibrosis is needed. Although much of the work remains to be done in establishing cost-effective strategies for screening for NASH, advanced fibrosis, and cirrhosis, in this review, we summarize the current state of the noninvasive assessment of liver disease in NAFLD, and we provide an expert synthesis of how these noninvasive tools could be utilized in clinical practice. Finally, we also list the key areas of research priorities in this area to move forward clinical practice.
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The process of evaluating potential donors in liver transplantation is important to ensure donor safety and provide optimal recipient outcome. However, there has been no report about donor exclusion rates and reasons for such exclusion in Korea. In this study, we aimed to elucidate the outcomes of potential live liver donor evaluation in a major LDLT center. From July 2011 to June 2015, prospectively collected data of 726 potential donors for 588 matched recipients were subsequently evaluated. Among 726 potential donors, 374 potential donors (51.5%) finally reached donation. Three hundred and fifty two potential donors (48.5%) were excluded for various reasons. Donor reasons were 29.8%, including medical problems, withdrawal of consent, graft volume issue and identification of a better suitable donor. Recipient reasons were 20.7%, including recipient death or recovery, allocation to deceased donor, and progressions of HCC. Thirty eight (5.2%) potential donors had a fatty liver. Among them, 15 (39.5%) potential donors tried short-term weight reduction, and eventually were able to donate. In conclusion, the main reasons for donor exclusion were due to medical problems and withdrawal of consent. Therefore, thorough medical screening and careful examination for donor voluntarism are important in the donor evaluation process. This article is protected by copyright. All rights reserved.
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Background: The degree of liver fibrosis in non-alcoholic fatty liver disease (NAFLD) is a critical predictive factor for patient prognosis. This study was intended to perform external validation of the various fibrosis prediction models for assessing advanced fibrosis in Korean NAFLD patients. Methods: A retrospective study of 412 patients with NAFLD confirmed by liver biopsy in hospitals affiliated with the Koran NAFLD study group was conducted and the predictive ability of existing liver fibrosis prediction models including NAFLD fibrosis score (NFS), BARD, and FIB-4 were compared. Results: Among 412 samples, 328 liver slides were suitable for evaluation. Advanced fibrosis was present in 60 (18.3%) of the patient samples. Univariate analysis found that the group with advanced fibrosis showed low ALT values and high AST/ALT ratios as well as a high incidence of diabetes. However, multivariate analysis showed that only the presence of diabetes and triglycerides were independent risk factors. The receiver operating characteristic was 0.64 in NFS, 0.58 in FIB-4, and 0.594 in the BARD model. The NFS was found to be the best at predicting advanced fibrosis among the three prediction models. The negative predictive value (NPV) which predicts advanced fibrosis using the low cutoff (<-1.455) was high (86.6%). However, the positive predictive value (PPV) which predicts advanced fibrosis using the high cutoff (>0.676) was 50.0% when we applied the NFS. Conclusion: NPV using the low cutoff value was high, but PPV using the high cutoff value was low in a Korean NAFLD cohort using NFS.