U.S. patent application number 17/626632 was filed with the patent office on 2022-09-01 for kit for predicting or diagnosing nonalcoholic fatty liver disease, and method for diagnosing nonalcoholic fatty liver disease.
The applicant listed for this patent is KoBioLabs, Inc.. Invention is credited to Won KIM, Gwang Pyo KO, Giljae LEE, Hyun Ju YOU.
Application Number | 20220276246 17/626632 |
Document ID | / |
Family ID | 1000006373190 |
Filed Date | 2022-09-01 |
United States Patent
Application |
20220276246 |
Kind Code |
A1 |
KO; Gwang Pyo ; et
al. |
September 1, 2022 |
KIT FOR PREDICTING OR DIAGNOSING NONALCOHOLIC FATTY LIVER DISEASE,
AND METHOD FOR DIAGNOSING NONALCOHOLIC FATTY LIVER DISEASE
Abstract
The present invention relates to a kit for predicting or
diagnosing the degree of risk of disease, a method for providing
information for predicting or diagnosing the degree of risk of
disease, a method for screening a therapeutic agent of disease, and
a pharmaceutical composition for prevention or treatment of
disease, for nonalcoholic fatty liver disease. Specifically,
through the kit for predicting or diagnosing of the present
invention, the degree of risk of nonalcoholic fatty liver disease
can be effectively predicted or diagnosed, and in particular, the
predictive value and significance of information provision in
non-obese subjects are excellent. Therefore, by providing effective
information on nonalcoholic fatty liver disease through this, it
can be effectively used to prevent or treat the corresponding
disease. In addition, the pharmaceutical composition for prevention
or treatment of the present invention may be effectively used for
treatment of nonalcoholic fatty liver disease.
Inventors: |
KO; Gwang Pyo; (Seoul,
KR) ; KIM; Won; (Seoul, KR) ; YOU; Hyun
Ju; (Incheon, KR) ; LEE; Giljae; (Seoul,
KR) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
KoBioLabs, Inc. |
Seoul |
|
KR |
|
|
Family ID: |
1000006373190 |
Appl. No.: |
17/626632 |
Filed: |
July 30, 2020 |
PCT Filed: |
July 30, 2020 |
PCT NO: |
PCT/KR2020/010092 |
371 Date: |
January 12, 2022 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01N 2333/26 20130101;
G01N 2333/36 20130101; G01N 33/56916 20130101 |
International
Class: |
G01N 33/569 20060101
G01N033/569 |
Foreign Application Data
Date |
Code |
Application Number |
Jul 30, 2019 |
KR |
10-2019-0092689 |
Jul 14, 2020 |
KR |
10-2020-0087105 |
Jul 30, 2020 |
KR |
10-2020-0094922 |
Jul 30, 2020 |
KR |
10-2020-0095361 |
Claims
1. A kit for predicting or diagnosing a degree of risk of
nonalcoholic fatty liver disease comprising a detection means
detecting one or more detection markers selected from the group
consisting of (a) one or more detection markers selected from the
group consisting of microbial biomarkers of nonalcoholic fatty
liver disease; (b) one or more detection markers selected from the
group consisting of total bile acid and components of bile acid;
and (c) intestinal short chain fatty acid.
2. The kit according to claim 1, wherein the (a) microbial
biomarkers of nonalcoholic fatty liver disease are one or more
selected from the group consisting of Enterobacteriaceae,
Veillonellaceae, Rikenellaceae, Fusobacteriaceae, Ruminococcaceae,
Lachnospiraceae, Actinomycetaceae, Desulfovibrioceae and
Desulfovibrionaceae.
3. The kit according to claim 1, wherein the (a) microbial
biomarkers of nonalcoholic fatty liver disease are one or more
selected from the group consisting of Citrobacter, Klebsiella,
Veillonella, Megamonas, Fusobacterium, Ruminococcus,
Faecalibacterium, Oscillospira, Coprococcus, Lachnospira,
Actinomyces and Desulfovibrio, wherein the (b) one or more
detection markers selected from the group consisting of total bile
acid and components of bile acid are one or more selected from the
group consisting of total bile acid, cholic acid, chenodeoxycholic
acid, ursodeoxycholic acid, lithocholic acid and deoxycholic acid,
or wherein the (c) intestinal short chain fatty acid is one or more
selected from the group consisting of acetate, propionate and
butyrate.
4-5. (canceled)
6. The kit according to claim 1, wherein the kit comprises one or
more selected from combinations of (a) to (p) below: (a) one or
more detection means capable of detecting one or more selected from
the group consisting of Enterobacteriaceae, Veillonellaceae,
Lachnospiraceae and Ruminococcaceae, respectively; (b) one or more
detection means capable of detecting one or more selected from the
group consisting of total bile acid, cholic acid, chenodeoxycholic
acid, ursodeoxycholic acid, lithocholic acid and deoxycholic acid,
respectively; (c) one or more detection means capable of detecting
one or more selected from the group consisting of short chain fatty
acid, acetate, propionate and butyrate, respectively; (d) one or
more detection means capable of detecting one or more selected from
the group consisting of Enterobacteriaceae, Veillonellaceae,
Lachnospiraceae, Ruminococcaceae, cholic acid, chenodeoxycholic
acid, ursodeoxycholic acid and propionate, respectively; (e) one or
more detection means capable of detecting one or more selected from
the group consisting of Ruminococcus, Faecalibacterium,
Oscillospira, Coprococcus, Lachnospira, total bile acid, cholic
acid, chenodeoxycholic acid, ursodeoxycholic acid, lithocholic acid
and deoxycholic acid, respectively; (f) one or more detection means
capable of detecting one or more selected from the group consisting
of Ruminococcus, Faecalibacterium, Oscillospira, Coprococcus,
Lachnospira and fecal propionate, respectively; (g) one or more
detection means capable of detecting one or more selected from the
group consisting of Veillonella, Megamonas, total bile acid, cholic
acid, chenodeoxycholic acid, ursodeoxycholic acid, lithocholic acid
and deoxycholic acid, respectively; (h) one or more detection means
capable of detecting one or more selected from the group consisting
of Veillonella, Megamonas and fecal propionate, respectively, (i)
one or more detection means capable of detecting one or more
selected from the group consisting of Enterobacteriaceae,
Veillonellaceae, Ruminococcaceae, Citrobacter, Klebsiella,
Veillonella, Megamonas, Ruminococcus, Faecalibacterium and
Oscillospira, respectively; (j) one or more detection means capable
of detecting one or more selected from the group consisting of
cholic acid, chenodeoxycholic acid, ursodeoxycholic acid and
metabolites thereof, respectively; (k) one or more detection means
capable of detecting one or more selected from the group consisting
of intestinal short chain fatty acid and propionate, respectively,
(l) a detection means capable of detecting Enterobacteriaceae,
Veillonellaceae, and Ruminococcaceae, (m) a detection means capable
of detecting Megamonas and Ruminococcus, (n) a detection means
capable of detecting cholic acid, chenodeoxycholic acid,
ursodeoxycholic acid, and propionate, (o) combination of (l) and
(n), or (p) combination of (m) and (n).
7-11. (canceled)
12. The kit according to claim 1, wherein the nonalcoholic fatty
liver disease is nonalcoholic fatty liver, nonalcoholic
steatohepatitis, liver fibrosis or cirrhosis.
13. The kit according to claim 1, wherein the nonalcoholic fatty
liver disease is non-obese nonalcoholic fatty liver disease, or
wherein the kit is for a non-obese patient.
14. The kit according to claim 1, wherein the predicting the degree
of risk is predicting the severity of fibrosis comprising F=0 to
F=4.
15. (canceled)
16. A method for providing information for predicting or diagnosing
a degree of risk of nonalcoholic fatty liver disease comprising
detecting one or more detection markers selected from the group
consisting of (a) one or more detection markers selected from the
group consisting of microbial biomarkers of nonalcoholic fatty
liver disease; (b) one or more detection markers selected from the
group consisting of total bile acid and components of bile acid;
and (c) intestinal short chain fatty acid.
17. The method according to claim 16, wherein the (a) microbial
biomarkers of nonalcoholic fatty liver disease are one or more
selected from the group consisting of Enterobacteriaceae,
Veillonellaceae, Rikenellaceae, Fusobacteriaceae, Ruminococcaceae,
Lachnospiraceae, Actinomycetaceae, Desulfovibrioceae and
Desulfovibrionaceae.
18. The method according to claim 16, wherein the (a) microbial
biomarkers of nonalcoholic fatty liver disease are one or more
selected from the group consisting of Citrobacter, Klebsiella,
Veillonella, Megamonas, Fusobacterium, Ruminococcus,
Faecalibacterium, Oscillospira, Coprococcus, Lachnospira,
Actinomyces, and Desulfovibrio, wherein the (b) one or more
detection markers selected from the group consisting of total bile
acid and components of bile acid are one or more selected from the
group consisting of total bile acid, cholic acid, chenodeoxycholic
acid, ursodeoxycholic acid, lithocholic acid and deoxycholic acid,
or wherein the (c) intestinal short chain fatty acid is one or more
selected from the group consisting of acetate, propionate and
butyrate.
19-20. (canceled)
21. The method according to claim 16, wherein the method comprises
one or more steps selected from combinations of (a) to (p) below:
(a) comparing abundances of one or more selected from the group
consisting of Enterobacteriaceae, Veillonellaceae, Lachnospiraceae
and Ruminococcaceae measured in a subject, with a reference value
of a healthy individual; (b) comparing the contents in feces of one
or more selected from the group consisting of total bile acid,
cholic acid, chenodeoxycholic acid, ursodeoxycholic acid,
lithocholic acid and deoxycholic acid measured in a subject, with a
reference value of a healthy individual; (c) comparing the contents
in feces of one or more selected from the group consisting of short
chain fatty acid, acetate, propionate and butyrate measured in a
subject, with a reference value of a healthy individual; (d)
comparing abundances or the contents in feces of one or more
selected from the group consisting of Enterobacteriaceae,
Veillonellaceae, Lachnospiraceae, Ruminococcaceae, cholic acid,
chenodeoxycholic acid, ursodeoxycholic acid and propionate measured
in a subject, with a reference value of a healthy individual; (e)
comparing abundances or the contents in feces of one or more
selected from the group consisting of Ruminococcus,
Faecalibacterium, Oscillospira, Coprococcus, Lachnospira, total
bile acid, cholic acid, chenodeoxycholic acid, ursodeoxycholic
acid, lithocholic acid and deoxycholic acid measured in a subject,
with a reference value of a healthy individual; (f) comparing
abundances or the contents in feces of one or more selected from
the group consisting of Ruminococcus, Faecalibacterium,
Oscillospira, Coprococcus, Lachnospira and feces propionate
measured in a subject, with a reference value of a healthy
individual; (g) comparing abundances or the contents in feces of
one or more selected from the group consisting of Veillonella,
Megamonas, total bile acid, cholic acid, chenodeoxycholic acid,
ursodeoxycholic acid, lithocholic acid and deoxycholic acid
measured in a subject, with a reference value of a healthy
individual; (h) comparing abundances or the contents in feces of
one or more selected from the group consisting of Veillonella,
Megamonas and feces propionate measured in a subject, with a
reference value of a healthy individual; (i) comparing abundances
of one or more selected from the group consisting of
Enterobacteriaceae, Veillonellaceae, Ruminococcaceae, Citrobacter,
Klebsiella, Veillonella, Megamonas, Ruminococcus, Faecalibacterium
and Oscillospira measured in a subject, with a reference value of a
healthy individual; (j) comparing the contents in feces of one or
more selected from the group consisting of cholic acid,
chenodeoxycholic acid, ursodeoxycholic acid and metabolites thereof
measured in a subject, with a reference value of a healthy
individual; (k) comparing the contents in feces of one or more
selected from the group consisting of intestinal short chain fatty
acid and propionate measured in a subject, with a reference value
of a healthy individual, (l) comparing abundances of
Enterobacteriaceae, Veillonellaceae, and Ruminococcaceae measured
in a subject, with a reference value of a healthy individual; (m)
comparing abundances of Megamonas and Ruminococcus measured in a
subject, with a reference value of a healthy individual; (n)
comparing the contents in feces of cholic acid, chenodeoxycholic
acid, ursodeoxycholic acid and propionate measured in a subject,
with a reference value of a healthy individual; (o) comprising (l)
and (n); and (p) comprising (m) and (n).
22-25. (canceled)
26. The method according to claim 16, wherein the nonalcoholic
fatty liver disease is nonalcoholic fatty liver, nonalcoholic
steatohepatitis, liver fibrosis or cirrhosis.
27. The method according to claim 16, wherein the nonalcoholic
fatty liver disease is non-obese nonalcoholic fatty liver disease,
or wherein the method is for a non-obese patient.
28. The method according to claim 16, wherein the method further
comprises treating the subject determined to have risk of
nonalcoholic fatty liver disease.
29. A method for screening a therapeutic agent for nonalcoholic
fatty liver disease, comprising (1) administering a test substance
to an experimental animal having nonalcoholic fatty liver disease;
(2) measuring one or more of detection markers selected from the
group consisting of (a) one or more detection markers selected from
the group consisting of microbial biomarkers of nonalcoholic fatty
liver disease; (b) one or more detection markers selected from the
group consisting of total bile acid and components of bile acid;
and (c) one or more detection markers selected from the group
consisting of intestinal short chain fatty acids, in an
experimental animal untreated with the test substance and the
experimental animal administered with the test substance; and (3)
comparing the measured results in a control group untreated with
the test substance and the experimental animal administered with
the test substance.
30. The method according to claim 29, wherein the (a) microbial
biomarkers of nonalcoholic fatty liver disease are one or more
selected from the group consisting of Enterobacteriaceae,
Veillonellaceae, Rikenellaceae, Fusobacteriaceae, Ruminococcaceae,
Lachnospiraceae, Actinomycetaceae, Desulfovibrioceae and
Desulfovibrionaceae.
31. The method according to claim 29, wherein the (a) microbial
biomarkers of nonalcoholic fatty liver disease are one or more
selected from the group consisting of Citrobacter, Klebsiella,
Veillonella, Megamonas, Fusobacterium, Ruminococcus,
Faecalibacterium, Oscillospira, Coprococcus, Lachnospira,
Actinomyces, and Desulfovibrio, wherein the (b) one or more
detection markers selected from the group consisting of total bile
acid and components of bile acid are total bile acid, cholic acid,
chenodeoxycholic acid, ursodeoxycholic acid, lithocholic acid, and
deoxycholic acid, or wherein the (c) intestinal short chain fatty
acid is one or more selected from the group consisting of acetate,
propionate and butyrate.
32-33. (canceled)
34. The method for screening a therapeutic agent of nonalcoholic
fatty liver according to claim 25, wherein the method comprises one
or more steps selected from combinations of (a) to (p) below: (a)
comparing abundances of one or more selected from the group
consisting of Enterobacteriaceae, Veillonellaceae, Lachnospiraceae
and Ruminococcaceae, measured before and after administration of a
candidate substance of therapeutic agent; (b) comparing the
contents in feces of one or more selected from the group consisting
of total bile acid, cholic acid, chenodeoxycholic acid,
ursodeoxycholic acid, lithocholic acid and deoxycholic acid,
measured before and after administration of a candidate substance
of therapeutic agent; (c) comparing the contents in feces of one or
more selected from the group consisting of short chain fatty acid,
acetate, propionate and butyrate, measured before and after
administration of a candidate substance of therapeutic agent; (d)
comparing abundances or the contents in feces of one or more
selected from the group consisting of Enterobacteriaceae,
Veillonellaceae, Lachnospiraceae, Ruminococcaceae, cholic acid,
chenodeoxycholic acid, ursodeoxycholic acid and propionate,
measured before and after administration of a candidate substance
of therapeutic agent; (e) comparing abundances or the contents in
feces of one or more selected from the group consisting of
Ruminococcus, Faecalibacterium, Oscillospira, Coprococcus,
Lachnospira, total bile acid, cholic acid, chenodeoxycholic acid,
ursodeoxycholic acid, lithocholic acid and deoxycholic acid,
measured before and after administration of a candidate substance
of therapeutic agent; (f) comparing abundances or the contents in
feces of one or more selected from the group consisting of
Ruminococcus, Faecalibacterium, Oscillospira, Coprococcus,
Lachnospira and feces propionate, measured before and after
administration of a candidate substance of therapeutic agent; (g)
comparing abundances or the contents in feces of one or more
selected from the group consisting of Veillonella, Megamonas, total
bile acid, cholic acid, chenodeoxycholic acid, ursodeoxycholic
acid, lithocholic acid and deoxycholic acid, measured before and
after administration of a candidate substance of therapeutic agent;
(h) comparing abundances or the contents in feces of one or more
selected from the group consisting of Veillonella, Megamonas and
feces propionate, measured before and after administration of a
candidate substance of therapeutic agent; (i) comparing abundances
of one or more selected from the group consisting of
Enterobacteriaceae, Veillonellaceae, Ruminococcaceae, Citrobacter,
Klebsiella, Veillonella, Megamonas, Ruminococcus, Faecalibacterium
and Oscillospira, measured before and after administration of a
candidate substance of therapeutic agent; (j) comparing the
contents in feces of one or more selected from the group consisting
of cholic acid, chenodeoxycholic acid, ursodeoxycholic acid and
metabolites thereof, measured before and after administration of a
candidate substance of therapeutic agent; (k) comparing the
contents in feces of one or more selected from the group consisting
of intestinal short chain fatty acids and propionate, measured
before and after administration of a candidate substance of
therapeutic agent; (l) comparing abundances of Enterobacteriaceae,
Veillonellaceae, and Ruminococcaceae, measured before and after
administration of a candidate substance of therapeutic agent; (m)
comparing abundances of Megamonas and Ruminococcus, measured before
and after administration of a candidate substance of therapeutic
agent; (n) comparing the contents in feces of cholic acid,
chenodeoxycholic acid, ursodeoxycholic acid and propionate,
measured before and after administration of a candidate substance
of therapeutic agent; (o) comprising the (l) and (n); and (p)
comprising the (m) and (n).
35. (canceled)
36. The method for screening a therapeutic agent for nonalcoholic
fatty liver disease according to claim 29, wherein the nonalcoholic
fatty liver disease is non-obese nonalcoholic fatty liver
disease.
37-42. (canceled)
43. The method according to claim 16, wherein the method further
comprises administering a therapeutic agent of nonalcoholic fatty
to the subject determined to have risk of nonalcoholic fatty liver
disease.
Description
TECHNICAL FIELD
[0001] The present invention relates to a kit for predicting or
diagnosing nonalcoholic fatty liver disease, and a method for
diagnosing thereof.
BACKGROUND ART
[0002] Nonalcoholic fatty liver disease (NAFLD) is characterized by
liver disease of metabolic disorders ranging from simple steatosis,
to nonalcoholic steatohepatitis (NASH) which is an aggressive
tissue form that ultimately leads to advanced fibrosis and
cirrhosis. The global prevalence of NAFLD is estimated to be 24-30%
in most epidemiological studies, and is increasing in parallel with
obesity and metabolic syndrome. Although NAFLD is commonly
associated with obesity, clinical symptoms and pathological
severity similar to those observed in obese NAFLD patients may
occur in non-obese subjects. Without considering the cut-off of the
different body mass index (BMI) defining obesity (Asian .gtoreq.25,
other races .gtoreq.30), it is consistently reported that 3-30% of
the non-obese population has NAFLD in both the West and the East.
Although visceral fat, food composition and genetic factors may be
associated with non-obese NAFLD, additional studies considering
other environmental factors are needed to elucidate the
pathogenesis of non-obese NAFLD.
[0003] Recently, increased interests have focused on identifying
and understanding specific roles of the gut microbiota in various
metabolic diseases. Gut dysbiosis, which refers to abnormal changes
in the gut microbiota compared to the normal microbiota, is
associated with a decrease in bacteria producing beneficial short
chain fatty acid (SCFA), changes in bile acid composition,
activation of immune response against lipopolysaccharide (LPS), an
increase of ethanol production by hyperplasia of ethanol producing
bacteria, and conversion of phosphatidylcholine into choline and
trimethylamine. Changes in the gut microbiome that affects the
gut-liver axis contribute to the progression of chronic liver
disease such as NAFLD and cirrhosis and advanced fibrosis.
[0004] Boursier et al. compared microbiome changes between patients
with mild and severs fibrosis, and observed significant intestinal
bacterial imbalance and functional changes in patients with severe
fibrosis (Non-patent Document 1). Loomba et al. used metagenomic
data to identify 37 bacteria that were significantly enriched or
significantly reduced in NAFLD patients with advanced fibrosis, and
proposed a microbiome-based biomarker to predict fibrosis
(Non-patent Document 2). Bajaj et al. defined the gut microbiome
profile during the progression of cirrhosis (Non-patent Document
3). A Chinese cohort study observed changes in the gut microbiome
of cirrhosis patients (Non-patent Document 4). However, the
microbial taxa associated with disease severity and fibrosis stage
were not consistent with previous NAFLD studies. This discrepancy
may be due to the influence of regional differences (Non-patent
Document 5). However, differences in basic BMI status may explain
these inconsistent results. Moreover, specific changes in gut
microbiome and related metabolites in the non-obese NAFLD group
were rarely defined.
[0005] Therefore, there is a need for a method for preventing,
treating and diagnosing non-obese NAFLD, which determines the
histological severity of NAFLD, well-characterizes the gut
microbiome changes, and is effective.
DISCLOSURE
Technical Problem
[0006] The present invention is to solve the above problem, and its
purpose is to provide a detection marker of nonalcoholic fatty
liver disease comprising one or more of detection markers selected
from the group consisting of a microbial biomarker, bile acid and
components thereof, and intestinal short chain fatty acid, a kit
for predicting or diagnosing a degree of risk of nonalcoholic fatty
liver disease using the detection marker, a method for predicting
or diagnosing, or a method for providing information for predicting
or diagnosing the degree of risk of nonalcoholic fatty liver
disease using the detection marker, and a method for screening a
therapeutic agent of nonalcoholic fatty liver disease.
Technical Solution
[0007] In order to achieve the above purpose, the present invention
provides a detection marker of nonalcoholic fatty liver disease and
a kit for predicting or diagnosing a degree of risk of nonalcoholic
fatty liver disease comprising one or more detection means
detecting the detection marker.
[0008] The kit may be used for predicting or diagnosing a degree of
risk of nonalcoholic fatty liver disease by comprising the
aforementioned means of detecting a specific subject and specifying
the amount, activity, population, and the like of the specific
detection subject, or comparing results with other detection
subject.
[0009] In the present invention, diagnosis comprises confirming the
presence or absence of disease, degree of risk of disease, state of
disease and prognosis of disease, and comprises all types of
analysis used to derive disease state and decision.
[0010] In an embodiment of the present invention, the nonalcoholic
fatty liver disease may be nonalcoholic fatty liver, nonalcoholic
steatohepatitis or cirrhosis.
[0011] In an embodiment of the present invention, predicting the
degree of risk of the disease may predict the severity of fibrosis.
The severity of fibrosis may include F=0 to F=4, and F=0 means no
liver fibrosis; F=1 means mild liver fibrosis; F=2 means
significant liver fibrosis; F=3 means advanced liver fibrosis; and
F=4 means cirrhosis.
[0012] In an embodiment of the present invention, the kit may be
for a non-obese patient, for example, a non-obese patient with BMI
of 25 kg/m.sup.2 or less.
[0013] The detection marker of nonalcoholic fatty liver disease may
comprise one or more kinds among microbial biomarkers, total bile
acid and components, and intestinal short chain fatty acid.
[0014] In an embodiment of the present invention, the kit may
comprise a detection means capable of detecting one or more of
detection markers selected from the following:
[0015] (a) one or more detection means respectively detecting one
or more selected from the group consisting of microbial biomarkers
of nonalcoholic fatty liver disease;
[0016] (b) one or more detection means respectively detecting one
or more selected from the group consisting of total bile acid and
components of bile acid; and
[0017] (c) one or more detection means respectively detecting one
or more selected from the group consisting of intestinal short
chain fatty acids.
[0018] The kit may comprise for example, (a); (b); (c); (a) and
(b); (a) and (c); (b) and (c); or (a), (b), and (c).
[0019] In the present specification, the total bile acid and
components of bile acid, the short chain fatty acid, and the like
are used as a meaning comprising all metabolites thereof.
[0020] The microbial biomarkers of nonalcoholic fatty liver
disease, at the Family level, may be one or more selected from the
group consisting of Enterobacteriaceae, Veillonellaceae,
Rikenellaceae, Fusobacteriaceae, Ruminococcaceae, Lachnospiraceae,
Actinomycetaceae, Desulfovibrioceae, and Desulfovibrionaceaeat. For
example, it may be one or more selected from the group consisting
of Enterobacteriaceae, Veillonellaceae, Ruminococcaceae,
Lachnospiraceae and Actinomycetaceae, one or more selected from the
group consisting of Enterobacteriaceae, Veillonellaceae,
Lachnospiraceae and Ruminococcaceae, or one or more selected from
the group consisting of Enterobacteriaceae, Veillonellaceae, and
Ruminococcaceae.
[0021] One example of the Enterobacteriaceae microorganism may be
one or more of Citrobacter and Klebsiella, one example of the
Veillonellaceae microorganism may be one or more of Veillonella and
Megamonas, one example of the Fusobacteriaceae microorganism may be
Fusobacterium, one example of the Desulfovibrionaceae microorganism
may be Desulfovibrio, the Ruminococcaceae microorganism may be one
or more of Ruminococcus, Faecalibacterium and Oscillospira, the
Lachnospiraceae microorganism may be one or more of Coprococcus and
Lachnospira, and the Actinomycetaceae microorganism may be
Actinomyces.
[0022] As one example, the (a) may be one or more of detection
means each capable of detecting one or more selected from the group
consisting of Enterobacteriaceae, Veillonellaceae, Lachnospiraceae
and Ruminococcaceae.
[0023] The microbial biomarker according to the present invention,
at the genus level, may be one or more kinds selected from the
group consisting of Citrobacter, Klebsiella, Ruminococcus,
Faecalibacterium, Oscillospira, Coprococcus, and Lachnospira, and
specifically, it may be one or more kinds selected from the group
consisting of Ruminococcus, Faecalibacterium, Oscillospira,
Coprococcus, Veillonella, Megamonas, and Lachnospira, and more
specifically, it may be one or more kinds selected from the group
consisting of Ruminococcus, Faecalibacterium, Oscillospira,
Coprococcus, and Lachnospira, or one or more kinds selected from
the group consisting of Veillonella and Megamona.
[0024] The total bile acid and components of bile acid may be one
or more selected from the group consisting of primary bile acid
comprising total bile acid, cholic acid and chenodeoxycholic acid;
and secondary bile acid comprising ursodeoxycholic acid,
lithocholic acid and deoxycholic acid.
[0025] As one example, the (b) may be one or more of detection
means each capable of detecting one or more selected from the group
consisting of total bile acid, cholic acid, chenodeoxycholic acid,
ursodeoxycholic acid, lithocholic acid and deoxycholic acid.
[0026] As one example, the (c) may be one or more of detection
means each capable of detecting one or more selected from the group
consisting of acetate, propionate and butyrate.
[0027] In an embodiment of the present invention, the kit may
comprise one or more selected from combinations of (a) to (h) as
follows:
[0028] detection means of the (a),
[0029] detection means of the (b),
[0030] detection means of the (c),
[0031] (d) one or more detection means each capable of detecting
one or more selected from the group consisting of
Enterobacteriaceae, Veillonellaceae, Lachnospiraceae,
Ruminococcaceae, cholic acid, chenodeoxycholic acid,
ursodeoxycholic acid and propionate;
[0032] (e) one or more detection means each capable of detecting
one or more selected from the group consisting of Ruminococcus,
Faecalibacterium, Oscillospira, Coprococcus, Lachnospira, total
bile acid, cholic acid, chenodeoxycholic acid, ursodeoxycholic
acid, lithocholic acid and deoxycholic acid;
[0033] (f) one or more detection means each capable of detecting
one or more selected from the group consisting of Ruminococcus,
Faecalibacterium, Oscillospira, Coprococcus, Lachnospira and fecal
propionate;
[0034] (g) one or more detection means each capable of detecting
one or more selected from the group consisting of Veillonella,
Megamonas, total bile acid, cholic acid, chenodeoxycholic acid,
ursodeoxycholic acid, lithocholic acid and deoxycholic acid;
and
[0035] (h) one or more detection means each capable of detecting
one or more selected from the group consisting of Veillonella,
Megamonas and fecal propionate.
[0036] The detection means of the (a) may be, for example, one or
more detection means each capable of detecting one or more selected
from the group consisting of Enterobacteriaceae, Veillonellaceae,
Lachnospiraceae and Ruminococcaceae.
[0037] The detection means of the (b) may be, for example, one or
more detection means each capable of detecting one or more selected
from the group consisting of total bile acid, cholic acid,
chenodeoxycholic acid, ursodeoxycholic acid, lithocholic acid and
deoxycholic acid.
[0038] The detection means of the (c) may be, for example, one or
more detection means each capable of detecting one or more selected
from the group consisting of short chain fatty acid, acetate,
propionate and butyrate.
[0039] In an embodiment of the present invention, the kit may
comprise one or more selected from combinations of (i) to (k) as
follows:
[0040] (i) one or more detection means each capable of detecting
one or more selected from the group consisting of
Enterobacteriaceae, Veillonellaceae, Ruminococcaceae, Citrobacter,
Klebsiella, Veillonella, Megamonas, Ruminococcus, Faecalibacterium
and Oscillospira;
[0041] (j) one or more detection means each capable of detecting
one or more selected from the group consisting of cholic acid,
chenodeoxycholic acid, ursodeoxycholic acid and metabolites
thereof; and
[0042] (k) one or more detection means each capable of detecting
one or more selected from the group consisting of intestinal short
chain fatty acids and propionate.
[0043] As one example, the detection marker of nonalcoholic fatty
liver disease according to the present invention may comprise one
or more kinds selected from the group consisting of
Enterobacteriaceae, Veillonellaceae, Rikenellaceae,
Fusobacteriaceae, Ruminococcaceae, Lachnospiraceae,
Actinomycetaceae, Desulfovibrioceae, Desulfovibrionaceae,
Citrobacter, Klebsiella, Veillonella, Megamonas, Fusobacterium,
Ruminococcus, Faecalibacterium, Oscillospira, Coprococcus,
Lachnospira, Actinomyces, Desulfovibrio, total bile acid, cholic
acid, chenodeoxycholic acid, ursodeoxycholic acid, lithocholic
acid, deoxycholic acid, short chain fatty acid, acetate, propionate
and butyrate.
[0044] As one specific example, the detection marker of
nonalcoholic fatty liver disease according to the present invention
may comprise Enterobacteriaceae, Veillonellaceae, and
Ruminococcaceae. According to the Examples of the present
application, in case of using the three bacterial markers in
combination, non-alcoholic fatty liver can be predicted with high
accuracy of AUROC 0.8 or higher in a non-obese subject (FIG.
5a).
[0045] As one specific example, the detection marker of
nonalcoholic fatty liver disease according to the present invention
may comprise Megamonas and Ruminococcus. According to the Examples
of the present application, in case of using the two bacterial
markers in combination, non-alcoholic fatty liver could be
predicted with high accuracy of AUROC 0.7 or higher in a non-obese
subject (FIG. 5b).
[0046] As one specific example, the detection marker of
nonalcoholic fatty liver disease according to the present invention
may comprise cholic acid, chenodeoxycholic acid, ursodeoxycholic
acid, and propionate. According to the Examples of the present
application, in case of using 4 metabolite markers in combination,
non-alcoholic fatty liver could be predicted with high accuracy of
AUROC 0.7 or higher in a non-obese subject.
[0047] As one specific example, the detection marker of
nonalcoholic fatty liver disease according to the present invention
may comprise Enterobacteriaceae, Veillonellaceae, Ruminococcaceae,
cholic acid, chenodeoxycholic acid, ursodeoxycholic acid, and
propionate. According to the Examples of the present application,
in case of using the 3 bacterial markers and 4 metabolite markers
in combination, non-alcoholic fatty liver could be predicted with
high accuracy of AUROC 0.9 or higher in a non-obese subject.
Otherwise, the detection marker of nonalcoholic fatty liver disease
according to the present invention may comprise Megamonas,
Ruminococcus, cholic acid, chenodeoxycholic acid, ursodeoxycholic
acid, and propionate. According to the Examples of the present
application, in case of using the 2 bacterial markers and 4
metabolite markers in combination, non-alcoholic fatty liver could
be predicted with high accuracy of AUROC 0.9 or higher in a
non-obese subject. This was significantly higher accuracy than the
biomarker of non-alcoholic fatty liver used conventionally, and
thus, it could be seen that the biomarker for predicting
non-alcoholic fatty liver according to the present invention could
predict non-alcoholic fatty liver accurately, and in particular, it
could predict non-alcoholic fatty liver of a non-obese subject more
accurately.
[0048] The present invention provides a method for predicting or
diagnosing, or a method for providing information for predicting or
diagnosing a degree of risk of nonalcoholic fatty liver disease
comprising detecting one or more of detection markers selected from
the group consisting of (a) one or more detection markers selected
from the group consisting of microbial biomarkers of nonalcoholic
fatty liver disease; (b) one or more detection markers selected
from the group consisting of total bile acid and components of bile
acid; and (c) one or more detection markers selected from the group
consisting of intestinal short chain fatty acids.
[0049] In an embodiment of the present invention, the nonalcoholic
fatty liver disease may be nonalcoholic fatty liver, nonalcoholic
steatohepatitis or cirrhosis. In an embodiment of the present
invention, predicting the degree of risk may be predicting the
severity of fibrosis. In an embodiment of the present invention,
the method for diagnosing or method for providing information for
diagnosing may be for a non-obese patient with BMI<25
kg/m.sup.2.
[0050] In an embodiment of the present invention, the method may
comprise one or more steps selected from the following:
[0051] (i) measuring abundance of microbial biomarkers as one or
more detection markers selected from the group consisting of
microbial biomarkers of nonalcoholic fatty liver disease;
[0052] (ii) measuring the content in feces of one or more detection
markers selected from the group consisting of total bile acid and
components of bile acid; and
[0053] (iii) measuring the content in feces of one or more
detection markers selected from the group consisting of intestinal
short chain fatty acid, for example, acetate, propionate and
butyrate.
[0054] In an embodiment of the present invention, the method may
comprise comparing the detected values of a subject individual,
with a reference value of a healthy individual corresponding
thereto, for detection markers (a) to (c), (a) to (h), (i) to (k),
or (a) to (k) which can be comprised in the kit.
[0055] In an embodiment of the present invention, the method may
comprise determining that the severity of fibrosis is high, the
detected value of the subject individual compared to the reference
value of the healthy individual is increased or decreased according
to an increase or decrease of the following detection markers, as
the result of comparing the detected values of the subject and
reference value of the healthy individual corresponding
thereto:
[0056] (a) with respect to one or more detection markers selected
from the group consisting of microbial biomarkers of nonalcoholic
fatty liver disease, the abundance of Enterobacteriaceae is
increased, the abundance of Veillonellaceae is increased, or the
abundance of Ruminococcaceae is decreased,
[0057] (b) with respect to one or more detection markers selected
from the group consisting of total bile acid and components of bile
acid, the content of cholic acid in feces is increased, the content
of chenodeoxycholic acid in feces is increased, or the content of
ursodeoxycholic acid in feces is increased, or
[0058] (c) with respect to one or more detection markers selected
from the group consisting of intestinal short chain fatty acids,
for example, in case that the content of one or more selected from
the group consisting of acetate, propionate and butyrate in feces
is increased, as one example, in case that the content of
propionate in feces is increased, it may comprise determining that
the severity of fibrosis is high.
[0059] The present invention provides a method for screening a
therapeutic agent for nonalcoholic fatty liver disease comprising
the following steps:
[0060] (1) administering a test substance to an experimental animal
having nonalcoholic fatty liver disease;
[0061] (2) measuring one or more detection markers selected from
the group consisting of (a) one or more detection markers selected
from the group consisting of microbial biomarkers of nonalcoholic
fatty liver disease; (b) one or more detection markers selected
from the group consisting of total bile acid and components of bile
acid; and (c) one or more detection markers selected from the group
consisting of intestinal short chain fatty acids, in the
experimental animal untreated with the test substance and the
experimental animal treated with the test substance; and
[0062] (3) comparing the measured results in a control group
untreated with the test substance and an experimental group
administered with the test substance.
[0063] In an embodiment of the present invention, the nonalcoholic
fatty liver disease may be nonalcoholic fatty liver, nonalcoholic
steatohepatitis or cirrhosis.
[0064] In an embodiment of the present invention, the experimental
animal of the step (1) may have one or more characteristics of the
following (1) to (5):
[0065] (1) A condition in which the blood ALT concentration is
increased, for example, a condition in which it is over 1 time, 1.1
times or more, 1.2 times or more, 1.3 times or more, 1.4 times or
more, 1.5 times or more, 1.6 times or more, 1.7 times or more, 1.8
times or more, 1.9 times or more, 2 times or more, 2.1 times or
more, 2.2 times or more, 2.3 times or more, 2.4 times or more, 2.5
times or more, 2.6 times or more, 2.7 times or more, 2.8 times or
more, 2.9 times or more, 3 times or more, 3.5 times or more, 4
times or more, 4.5 times or more, 5 times or more, 5.5 times or
more, 6 times or more, 6.5 times or more, 7 times or more, 7.5
times or more, 8 times or more, 8.5 times or more, 9 times or more,
9.5 times or more, or 10 times or more, of the blood ALT
concentration of a normal control group.
[0066] (2) A condition in which the blood AST concentration is
increased, for example, a condition in which it is over 1 time, 1.1
times or more, 1.2 times or more, 1.3 times or more, 1.4 times or
more, 1.5 times or more, 1.6 times or more, 1.7 times or more, 1.8
times or more, 1.9 times or more, 2 times or more, 2.1 times or
more, 2.2 times or more, 2.3 times or more, 2.4 times or more, 2.5
times or more, 2.6 times or more, 2.7 times or more, 2.8 times or
more, 2.9 times or more, 3 times or more, 3.5 times or more, 4
times or more, 4.5 times or more, 5 times or more, 5.5 times or
more, 6 times or more, 6.5 times or more, 7 times or more, 7.5
times or more, 8 times or more, 8.5 times or more, 9 times or more,
9.5 times or more, or 10 times or more, of the blood AST
concentration of a normal control group.
[0067] (3) A condition in which the secondary bile acid
concentration in cecum is decreased, for example, a condition in
which it is less than 1 time, 0.9 times or less, 0.8 times or less,
0.7 times or less, 0.6 times or less, 0.5 times or less, 0.4 times
or less, 0.3 times or less, 0.2 times or less, or 0.1 time or less,
of the secondary bile acid concentration in cecum of a normal
control group.
[0068] (4) A condition in which the fibrosis marker gene expression
is increased, for example, a condition in which it is overexpressed
more than 1 time, 1.1 times or more, 1.2 times or more, 1.3 times
or more, 1.4 times or more, 1.5 times or more, 1.6 times or more,
1.7 times or more, 1.8 times or more, 1.9 times or more, 2 times or
more, 2.1 times or more, 2.2 times or more, 2.3 times or more, 2.4
times or more, 2.5 times or more, 2.6 times or more, 2.7 times or
more, 2.8 times or more, 2.9 times or more, 3 times or more, 3.5
times or more, 4 times or more, 4.5 times or more, 5 times or more,
5.5 times or more, 6 times or more, 6.5 times or more, 7 times or
more, 7.5 times or more, 8 times or more, 8.5 times or more, 9
times or more, 9.5 times or more, 10 times or more, 11 times or
more, 12 times or more, 13 times or more, 14 times or more, 15
times or more, 16 times or more, 17 times or more, 18 times or
more, 19 times or more, or 20 times or more, of the fibrosis marker
gene expression of a normal control group. The fibrosis marker gene
may be one or more kinds selected from the group consisting of
Col1a1, Timp1, and .alpha.-SMA.
[0069] (5) A condition in which the liver weight ratio to body
weight is increased, for example, a condition in which it is over 1
time, 1.1 times or more, 1.2 times or more, 1.3 times or more, 1.4
times or more, 1.5 times or more, 1.6 times or more, 1.7 times or
more, 1.8 times or more, 1.9 times or more, 2 times or more, 2.1
times or more, 2.2 times or more, 2.3 times or more, 2.4 times or
more, 2.5 times or more, 2.6 times or more, 2.7 times or more, 2.8
times or more, 2.9 times or more, or 3 times or more, of the liver
weight ratio to body weight of a normal control group.
[0070] In an embodiment of the present invention, the test
substance may comprise a candidate substance of a therapeutic agent
for nonalcoholic fatty liver disease, and for example, it may be
one or more selected from the group consisting of peptide, protein,
nonpeptide compound, active compound, fermented product, cell
extract, plant extract, animal tissue extract and plasma.
[0071] In an embodiment of the present invention, the method may
comprise one or more steps selected in the following:
[0072] measuring the content in feces of one or more detection
markers selected from the group consisting of
[0073] (i) measuring abundance of microbial biomarkers as one or
more detection markers selected from the group consisting of
microbial biomarkers of nonalcoholic fatty liver disease;
[0074] (ii) measuring the content in feces of one or more detection
markers selected from the group consisting of total bile acid and
components of bile acid; and
[0075] (iii) measuring the content in feces of one or more
detection markers selected from the group consisting of intestinal
short chain fatty acid, for example, acetate, propionate and
butyrate.
[0076] In an embodiment of the present invention, the method may
comprise comparing detection values of an experimental group
administered with a test substance and a control group not
administered with the test substance for an experimental animal
having nonalcoholic fatty liver disease, for detection markers (a)
to (c), (a) to (h), (i) to (k), or (a) to (k).
[0077] In an embodiment of the present invention, the method may
comprise selecting a test substance as a therapeutic agent of
nonalcoholic fatty liver disease, according to an increase or
decrease result for each detection marker of the detected value of
the experimental group compared to a reference value of the control
group, as the result of comparing the detected values of an
experimental group and the detected value of a control group:
[0078] (a) with respect to one or more detection markers selected
from the group consisting of microbial biomarkers of nonalcoholic
fatty liver disease, the abundance of Enterobacteriaceae is
increased, the abundance of Veillonellaceae is increased, or the
abundance of Ruminococcaceae is decreased,
[0079] (b) with respect to one or more detection markers selected
from the group consisting of total bile acid and components of bile
acid, the content in feces of cholic acid is increased, the content
in feces of chenodeoxycholic acid is increased, or the content in
feces of ursodeoxycholic acid is increased, or
[0080] (c) with respect to one or more detection markers selected
from the group consisting of intestinal short chain fatty acids,
for example, in case that the content in feces of one or more
selected from the group consisting of acetate, propionate and
butyrate is increased, or as one example, the content in feces of
propionate is increased, a step of determining that the severity of
fibrosis is high may be comprised.
[0081] In an embodiment of the present invention, the method may
comprise selecting a test substance in which the abundance of
Enterobacteriaceae is decreased, the abundance of Veillonellaceae
is decreased, the abundance of Ruminococcaceae is increased, the
content in feces of cholic acid is decreased, the content in feces
of chenodeoxycholic acid is decreased, the content in feces of
ursodeoxycholic acid is decreased, or the content in feces of
propionate is decreased, compared to the control group untreated
with the test substance.
[0082] The method for predicting a degree of risk of nonalcoholic
fatty liver disease, the method for providing information for
prediction, the method for diagnosis, or the method for providing
information for diagnosis, according to one example of the present
invention may further comprise administering a therapeutic agent of
nonalcoholic fatty liver disease to a subject.
[0083] Other embodiment of the present invention relates to a
method for treatment of nonalcoholic fatty liver disease,
comprising administering a therapeutic agent of nonalcoholic fatty
liver disease to a subject determined as having risk of
nonalcoholic fatty liver disease by the kit for predicting or
diagnosing a degree of risk of nonalcoholic fatty liver disease,
the method for predicting a degree of risk of nonalcoholic fatty
liver disease, the method for providing information for prediction,
the method for diagnosis, or the method for providing information
for diagnosis, according to the present invention.
Advantageous Effects
[0084] The degree of risk of nonalcoholic fatty liver disease can
be effectively predicted or diagnosed using the kit for predicting
or diagnosing of the present invention, and in particular, the
predictability and significance of information provision in
non-obese subjects are excellent. Therefore, it can be effectively
used to prevent or treat the corresponding disease by providing
effective information on nonalcoholic fatty liver disease through
this.
BRIEF DESCRIPTION OF THE DRAWINGS
[0085] FIG. 1a to 1l are results of comparing the diversities of
the gut microbiome. Values dividing the alpha and beta diversity by
histological spectrum of NAFLD or fibrosis severity of all subjects
are shown in FIG. 1a to 1d, of non-obese subjects are shown in FIG.
1e to 1h, and of obese subjects are shown in FIG. 1i to FIG. 1l.
Rarefaction curves were generated using the Shannon index with
12,000 sequences per sample. Statistical analysis was performed
using nonparametric Kruskal-Wallis test. NMDS plots were generated
using relative OTU abundance data according to Bray-Curtis
distance, and statistical significance was determined using Adonis
analysis. **P<0.01
[0086] FIG. 2a to 2d are univariate analysis results for
differences in specific microbial taxa according to the severity of
fibrosis. For clarity, 13 family- and 14 genus-level taxa are shown
along with their upper relative abundance. The box plot shows the
interquartile range (IQR) between the first and third quartiles
with Tukey whiskers. The color in the box indicates the severity of
fibrosis. For statistical significance, a nonparametric
Kruskal-Wallis test was used. *P<0.05, **P<0.01,
***P<0.001
[0087] FIG. 2e to 2h are multivariate analysis results for
differences in specific microbial taxa according to the severity of
fibrosis. Arcsine root-modified abundance of four bacteria was
regressed for age, gender and BMI according to the severity of
fibrosis, and the standard residual was indicated as a box plot.
*P<0.05, **P<0.01, ***P<0.001
[0088] FIG. 2i to 2k are results of co-expression analysis of
specific gut microbiota elements in total, non-obese and obese
subjects. Solid lines (orange) and dotted lines (grey) indicate
positive and negative correlations, respectively. The size of the
node indicates the relative amount of bacteria, and the color
indicates the degree of correlation according to the severity of
fibrosis.
[0089] FIG. 3a to 3j are evaluation results of fecal metabolites
mainly related to the gut microbiota. FIG. 3a is the bile acid
profile result in various clinical environments, and stacked plots
were generated using the average abundance of five bile acids. In
FIG. 3b to 3g, the box plots show stratified fecal bile acid levels
according to fibrosis severity and obesity status. The
concentrations of the five fecal bile acids were stratified by
fibrosis severity and obesity status. In FIG. 3h to FIG. 3j, the
box plots show the most abundant fecal SCFA (acetate, propionate
and butyrate) stratified by fibrosis severity and obesity status.
The interquartile ranges (IQRs) between the first and third
quartiles are described as Tukey whiskers. For statistical
significance, a nonparametric Kruskal-Wallis test was used.
*P<0.05, **P<0.01, ***P<0.001
[0090] FIG. 4 is the network profile result between microbial taxa
and fecal metabolite components in the non-obese (a) and obese (b)
subjects. Co-expression coefficients between family-level
microbiota elements and fecal metabolites were calculated using
SparCC and described using Cytoscape. Solid lines (orange) and
dotted lines (grey) indicate positive and negative correlations,
respectively. The shape of the node indicates the components used
in the present study (oval: microbiota, diamond: fecal bile acid,
and round rectangle: SCFA), and the color indicates the degree of
correlation according to the severity of fibrosis.
[0091] FIG. 5a and FIG. 5b are receiver operating characteristic
curves (ROC) for prediction of significant fibrosis in total,
non-obese and obese subjects. FIG. 5a is the ROC curve using the
combination of three selected bacteria (Veillonellaceae,
Ruminococcacea, and Enterobacteriaceae) and four fecal metabolites
(CD, CDCA, UDCA, and propionate) drawn for prediction of
significant fibrosis in all the non-obese subjects and obese
subjects, and the areas under the curve (AUC) was calculated. FIG.
5b is the ROC curve using the combination of two selected bacteria
(Megamonas and Ruminococcus) and four fecal metabolites (CD, CDCA,
UDCA, and propionate) drawn for prediction of significant fibrosis
in all the non-obese subjects and obese subjects, and the areas
under the curve (AUC) was calculated.
[0092] FIG. 6 is a schematic diagram that comprehensively
summarizes the contents corresponding to the differences in the gut
microbiome, changes in microorganisms and metabolites, and the
prediction result of fibrosis through the combination of
representative microorganisms and metabolites, shown in the
non-obese subjects and obese subjects.
[0093] FIG. 7 is a diagram showing the histological distribution of
study subjects stratified by fibrosis severity.
[0094] FIG. 8 is a diagram showing the correlation between the
microbial taxa and metabolic indexes in the non-obese and obese
patients.
[0095] FIG. 9a to 9e are diagrams showing the correlation between
the microbial taxa and metabolic indexes in the non-obese and obese
patients.
[0096] FIG. 10a to 10c are diagrams showing the relationship
between the relative abundance of specific gut microbial taxa and
the severity of fibrosis at the genus level stratified by the
degree of obesity.
[0097] FIG. 11 is a diagram showing the relationship between the
relative abundance of Actiomyces stratified by the degree of
obesity and the TM6SF2 (rs58542926) variant.
[0098] FIG. 12a to 12d are diagrams showing the relationship
between the relative abundance of specific gut microbial components
and the presence or absence of diabetes mellitus.
[0099] FIG. 13a to 13e are diagrams showing the relative abundance
of fecal bile acid stratified by the severity of fibrosis and the
degree of obesity.
MODE FOR INVENTION
[0100] Hereinafter, specific Examples are provided to help the
understanding of the present invention, but the following Examples
are only illustrative of the present invention, and it is apparent
to those skilled in the art that various changes and modifications
are possible within the scope and spirit of the present invention,
and it is also obvious that these changes and modifications fall
within the scope of the appended claims. In the following Examples
and comparative Examples, "%" and "part" indicating the content are
by weight unless otherwise specified.
[0101] The values presented in the following experimental Example
are expressed as means.+-.standard deviation (S.D.), and the
statistical significance of the difference between each treatment
group was determined by one-way ANOVA using Graph Pad Prism 4.0
(San Diego. Calif.).
Experimental Example 1
[0102] 1. Material and Method
[0103] 1) Experimental Subject
[0104] 171 subjects demonstrated by biopsy to have NAFLD and 31
subjects without NAFLD were included. When NAFLD was confirmed
histologically and BMI was BMI<25 kg/m.sup.2, it was classified
as the non-obese NAFLD group.
[0105] 2) Subject Inclusion and Exclusion Criteria
[0106] Subjects were enrolled long-term from January 2013 to
February 2017, and the inclusion criteria were as follows:
[0107] 1. An adult at least 18 years of age,
[0108] 2. Ultrasonic findings confirming fatty infiltration of
liver, and
[0109] 3. An increase of alanine aminotransferase (ALT) level of
unknown etiology within the past 6 months.
[0110] On the other hand, subjects who met any of the following
criteria were excluded:
[0111] 1. Hepatitis B or C infection,
[0112] 2. Autoimmune hepatitis, primary biliary cholangitis, or
primary sclerosing cholangitis,
[0113] 3. Gastrointestinal cancers or hepatocellular carcinoma,
[0114] 4. Drug-induced steatosis or liver damage,
[0115] 5. Wilson disease or hemochromatosis,
[0116] 6. Excessive alcohol consumption (male: >210 g/week,
female: >140 g/week),
[0117] 7. Antibiotic use within the previous month,
[0118] 8. Diagnosis of malignancy in the past year,
[0119] 9. Human immunodeficiency virus infection, and
[0120] 10. Chronic disorders related to lipodystrophy or
immunosuppression.
[0121] Non-obese and obese control groups included subjects without
any suspicion of NFALD (a) during evaluation of living donor liver
transplantation or (b) during liver biopsy for characterization of
solid liver mass suspected for hepatic adenoma or focal nodular
hyperplasia based on imaging studies (Koo B K, Joo S K, Kim D, Bae
J M, Park J H, Kim J H, et al. Additive effects of PNPLA3 and
TM6SF2 on the histological severity of non-alcoholic fatty liver
disease. J Gastroenterol Hepatol 2018; 33:1277-1285.).
[0122] 3) Liver Histology
[0123] Liver histology was evaluated by a single liver pathologist
using the NASH CRN histological scoring system. NAFLD was defined
as the presence of .gtoreq.5% macrovesicular steatosis based on
histological examination. NASH was defined based on the overall
pattern of liver damage consisting of steatosis, lobular
inflammation or ballooning of hepatocytes according to the criteria
of Brunt et al. (Brunt E M, Janney C G, Di Bisceglie A M,
Neuschwander-Tetri B A, Bacon B R. Nonalcoholic steatohepatitis: a
proposal for grading and staging the histological lesions. Am J
Gastroenterol 1999; 94:2467-2474; Brunt E M, Kleiner D E, Wilson L
A, Belt P, Neuschwander-Tetri B A. Nonalcoholic fatty liver disease
(NAFLD) activity score and the histopathologic diagnosis in NAFLD:
distinct clinicopathologic meanings. Hepatology 2011; 53:810-820).
In addition, steatosis, hepatic lobular inflammation and swelling
were scored according to the NAFLD activity scoring system, and the
severity of fibrosis was evaluated according to the criteria of
Kleiner et al. (Kleiner D E, Brunt E M, Van Natta M, Behling C,
Contos M J, Cummings O W, et al. Design and validation of a
histological scoring system for nonalcoholic fatty liver disease.
Hepatology 2005; 41:1313-1321).
[0124] 4) Microbiome Analysis Using 16S rRNA Sequencing
[0125] DNA of the fecal sample was extracted using QIAamp DNA Stool
Mini Kit (Qiagen, Hilden, Germany). V4 region sequencing targeting
of 16S rRNA was performed using MiSeq platform (Illumina, San
Diego, Calif., USA), and additional treatment of raw sequencing
data was performed using QIIME pipeline (v 1.8.0) (Caporaso J G,
Kuczynski J, Stombaugh J, Bittinger K, Bushman F D, Costello E K,
et al. QIIME allows analysis of high-throughput community
sequencing data. Nat Methods 2010; 7:335-336).
[0126] 5) Measurement of Fecal Metabolites Using GC-FID and Q-TOP
System
[0127] Fecal SCFA was measured using Agilent Technologies 7890A GC
system (Agilent Technologies, Santa Clara, Calif., USA) according
to the method of David (David L A, Maurice C F, Carmody R N,
Gootenberg D B, Button J E, Wolfe B E, et al. Diet rapidly and
reproducibly alters the human gut microbiome. Nature 2014;
505:559-563), and the bile acid profile was evaluated using Q-TOF
mass spectrometer (Waters Micromass Technologies, Manchester,
UK).
[0128] 6) Bioinformatics Analysis and Statistical Test
[0129] Statistical comparison was performed with Kruskal-Wallis
test using GraphPad Prism software Ver. 7.0d (GraphPad Software,
San Diego, Calif., USA). For rarefaction curves, the OUT table was
selected by 12,000 sequences per sample, and Shannon index was
measured by QIIME. Nonparametric multi-dimensional scaling (NMDS)
plots were represented using Vergan package of R (Oksanen J, Kindt
R, Legendre P, O'Hara B, Stevens M H H, Oksanen M J, et al. The
vegan package. Community ecology package 2007; 10.), and the
distance was measured using Bray-Curtis method. The statistical
significance between groups was estimated using Adonis function.
Multivariate association analysis using microbiome data was
performed using multivariate association using a linear model
(MaAsLin) for identification of specific taxa related to the host
phenotype without being affected by other metadata (Morgan X C,
Tickle T L, Sokol H, Gevers D, Devaney K L, Ward D V, et al.
Dysfunction of the gut microbiome in inflammatory bowel disease and
treatment. Genome Biol 2012; 13:R79.). In addition, age, gender and
BMI or diabetes were designated as fixed variables, and when the
p-value adjusted by Benjamini and Hochberg's false discovery rate
(FDR) was lower than 0.20, the association rate was considered as
significant.
[0130] 7) Significant Prediction of Fibrosis by ROC Curves
[0131] In order to demonstrate the prediction ability of fibrosis
of the microbiome-based biomarkers, the area under the receiver
operating characteristic curve (AUROC) method was used. The three
family-level bacteria, basic characteristics of subjects (age,
gender and BMI) and relative abundance of FIB-4 confirmed in the
present experiment were used as inputs for AUROC, and the
combination of their factors was calculated using binary logistic
regression in SPSS Ver. 25.0 (SPSS Inc., Armonk, N.Y., USA). AUROC
comparison was performed by DeLong test using MedCalc software Ver.
18.2.1 (MedCalc Software BVBA, Ostend, Belgium).
[0132] 2. Experimental Result
[0133] 1) Basic Characteristics
[0134] 171 subjects demonstrated as NAFLD (NAFL, n=88; NASH, n=83)
by biopsy and 31 non-NAFLD subjects were included, and all subjects
were divided into two groups (non-obese, BMI<25; obese,
BMI.gtoreq.25), and each subject was divided into three subgroups
according to the histological spectrum of NAFLD or fibrosis. In
Table 1 and Table 2, the result of detailed characteristics of each
group including clinical, metabolic, biochemical and histological
profiles was shown.
TABLE-US-00001 TABLE 1 Baseline characteristics of study subjects
stratified by obesity status and histological spectrum of NAFLD.
Non-obese (n = 64) Obese (n = 138) No No NAFLD NAFL NASH P-value
NAFLD NAFL NASH P-value N 7/14 13/11 7/12 4/6 37/27 24/40
(male/female) Age 58.7 .+-. 10.7 58.3 .+-. 10.2 60.2 .+-. 8.84
0.8601 .sup.ns 58 .+-. 12.6 52.7 .+-. 14.8 53.6 .+-. 16.7 0.6463
.sup.ns (years) BMI 22.8 .+-. 1.67 23.6 .+-. 1.34 23.6 .+-. 0.83
0.0871 .sup.ns 27 .+-. 2.09 28.8 .+-. 3.25 28.8 .+-. 3.02 0.1374
.sup.ns (kg/m.sup.2) WC (cm) 80 .+-. 6.53.sup.a 82.7 .+-.
3.45.sup.ab 85.4 .+-. 4.sup.b 0.0141 * 92.7 .+-. 5.93 94.3 .+-.
8.04 96.6 .+-. 7.81 0.2328 .sup.ns AST 31.6 .+-. 24.4.sup.a 28.4
.+-. 9.05.sup.b 54.7 .+-. 45.7.sup.bc 0.002 ** 25.6 .+-. 7.5.sup.a
42.3 .+-. 26.5.sup.b 62 .+-. 32.3.sup.bc <0.0001 *** (IU/L) ALT
32.5 .+-. 32.6.sup.a 32.8 .+-. 17.3.sup.b 59 .+-. 52.9.sup.c
<0.0001 *** 27.8 .+-. 25.8.sup.a 56.9 .+-. 48.5.sup.b 79.1 .+-.
57.6.sup.c <0.0001 *** (IU/L) GGT 44.6 .+-. 54.sup.a 31.8 .+-.
34.1.sup.a 66.7 .+-. 55.2.sup.b 0.0016 ** 44.7 .+-. 51.8.sup.a 49.2
.+-. 57.4.sup.a 78.5 .+-. 79.2.sup.b <0.0001 *** (IU/L) HDL 54
.+-. 14 46.3 .+-. 10.8 43.8 .+-. 11.7 0.0527 .sup.ns 58.9 .+-.
14.3.sup.a 45.5 .+-. 11.6.sup.b 45.5 .+-. 11.2.sup.bc 0.0196 *
cholesterol (mg/dL) LDL 93.5 .+-. 25.7 111 .+-. 39.8 97.3 .+-. 32.1
0.5322 .sup.ns 123 .+-. 35.8 103 .+-. 32.3 107 .+-. 32.7 0.1782
.sup.ns cholesterol (mg/dL) Albumin 4.1 .+-. 0.285 4.24 .+-. 0.257
4.03 .+-. 0.413 0.1128 .sup.ns 4.12 .+-. 0.312 4.2 .+-. 0.254 4.15
.+-. 0.279 0.516 .sup.ns (g/dL) Platelet 223 .+-. 72.2.sup.ab 259
.+-. 54.4.sup.a 179 .+-. 79.sup.bc 0.0023 ** 232 .+-. 53 234 .+-.
59.9 215 .+-. 69 0.3027 .sup.ns (.times.10.sup.3/.mu.L) Ferritin
103 .+-. 57.2 109 .+-. 80.2 173 .+-. 114 0.1285 .sup.ns 63.8 .+-.
26.9.sup.a 137 .+-. 88.4.sup.b 159 .+-. 95.1.sup.bc 0.0026 **
(ng/mL) HA 66.6 .+-. 70.2.sup.ab 37.1 .+-. 30.2.sup.a 93.9 .+-.
64.9.sup.bc 0.0048 ** 76.8 .+-. 99.2.sup.ab 62.1 .+-. 95.4.sup.a
95.7 .+-. 112.sup.bc 0.0344 * (ng/mL) Insulin 9.45 .+-. 3.77.sup.a
11.2 .+-. 5.96.sup.ab 13.6 .+-. 5.85.sup.b 0.037 * 11.2 .+-.
5.55.sup.a 18.1 .+-. 17.1.sup.ab 18.2 .+-. 11.1.sup.b 0.0227 *
(.mu.IU/mL) HbA1c (%) 5.71 .+-. 0.481.sup.a 6.06 .+-. 0.676.sup.a
7.12 .+-. 1.96.sup.b <0.0001 *** 5.72 .+-. 0.326.sup.a 6.1 .+-.
0.814.sup.ab 6.6 .+-. 1.27.sup.b 0.0099 ** C-peptide 1.91 .+-.
0.61.sup.a 2.43 .+-. 0.832.sup.ab 2.88 .+-. 1.09.sup.b 0.0005 ***
2.42 .+-. 0.916.sup.a 4.42 .+-. 3.4.sup.b 4.19 .+-. 2.39.sup.bc
0.0087 ** (ng/mL) HOMA-IR 2.56 .+-. 1.17.sup.a 3.12 .+-.
1.72.sup.ab 4.39 .+-. 2.19.sup.bc 0.0085 ** 2.92 .+-. 1.77.sup.a
4.96 .+-. 4.36.sup.ab 5.77 .+-. 4.42.sup.bc 0.0131 * Adipo-IR 4.68
.+-. 2.85.sup.a 6.6 .+-. 3.59.sup.ab 9.63 .+-. 5.39.sup.bc 0.0027
** 6.42 .+-. 3.28.sup.a 10.1 .+-. 10.1.sup.ab 12.8 .+-. 9.51.sup.bc
0.0096 ** FFA (.mu.Eq/L) 493 .+-. 166.sup.ab 620 .+-. 240.sup.a 720
.+-. 268.sup.bc 0.0121 * 605 .+-. 289a 603 .+-. 259.sup.ab 712 .+-.
238.sup.bc 0.0089 ** hsCRP 0.249 .+-. 0.428.sup.a 0.0896 .+-.
0.066.sup.a 0.354 .+-. 0.549.sup.b 0119 * 0.152 .+-. 0.154.sup.ab
0.206 .+-. 0.403.sup.a 0.278 .+-. 0.336.sup.bc 0.0294 * (mg/dL)
Cholesterol 167 .+-. 28.2 185 .+-. 41.8 167 .+-. 42.9 0.3932
.sup.ns 200 .+-. 43.2 183 .+-. 34.8 181 .+-. 40.7 0.4122 .sup.ns
(mg/dL) TG 102 .+-. 47.sup.a 140 .+-. 44.7.sup.b 141 .+-.
70.9.sup.ab 0.0105 * 87 .+-. 34.sup.a 161 .+-. 82.2.sup.b 151 .+-.
61.9.sup.bc 0.0024 ** (mg/dL) FPG 110 .+-. 25.6 113 .+-. 28.8 132
.+-. 42.5 0.0986 .sup.ns 102 .+-. 14.1 113 .+-. 27.4 128 .+-. 55.8
0.2074 .sup.ns (mg/dL) HTN, 8 (38.1) 8 (33.3) 9 (47.4) 0.641
.sup.ns 4 .sup.ns 24 (37.5) 32 (50.0) 0.352 n (%) Diabetes, 1
(4.76) 8 (33.3) 13 (68.4) 0.0001 *** 1 * 19 (29.7) 30 (46.9) 0.026
n (%) Abbreviations: BMI, body mass index; WC, waist circumference;
AST, aspartatetransaminase; ALT, alanine transaminase; GGT,
gamma-glutamyl transferase; NAS, nonalcoholic fatty liver disease
activity score; HDL, high-density lipoprotein; LDL, low-density
lipoprotein; HA, hyaluronic acid; HbAlc, glycosylated hemoglobin;
HOMA-IR, homeostasis model assessment of insulin resistance;
Adipo-IR, adipose tissue insulin resistance; PM, free fatty acid;
hsCRP, high-sensity C-reactive protein; TG, triglycerides; FBG,
fasting blood glucose; HTN, hypertension. Data are expressed as the
mean .+-. SD or n (%). Mean .+-. SD or n (%) with defferent
superscript letters indicates significant differences by the
nonparametric Kruskal-Wallis test or the chi-square test. *P <
0.05, **P < 0.01, ***P < 0.001
TABLE-US-00002 TABLE 2 Baseline characteristics of study subjects
stratified by obesity status and fibrosis severity. Non-obese (n =
64) Fibrosis stage 0 1 .gtoreq.2 P-value N (male/female) 27 (11/16)
20 (9/11) 17 (7/10) Age (years) 57.67 .+-. 9.01 57.85 .+-. 11.80
62.47 .+-. 8.32 0.2371 .sup.ns BMI (kg/m.sup.2) 22.81 .+-.
1.42.sup.a 23.76 .+-. 1.46.sup.b 23.71 .+-. 0.92.sup.ab 0.0084 **
WC (cm) 79.95 .+-. 5.2.sup.a 83.22 .+-. 3.53.sup.ab 86.33 .+-.
4.22.sup.b 0.0010 ** AST (IU/L) 29.11 .+-. 21.64.sup.a 32.20 .+-.
10.63.sup.ab 56.06 .+-. 48.13.sup.b 0.0017 ** ALT (IU/L) 31.89 .+-.
27.83 39.10 .+-. 30.99 55.71 .+-. 52.04 0.0886 .sup.ns GGT (IU/L)
33.6 .+-. 41.4.sup.a 44 .+-. 46.2.sup.ab 69.7 .+-. 58.3.sup.b
0.0046 ** HDL-cholesterol 51.4 .+-. 13.4 47.9 .+-. 11.1 43.1 .+-.
12.3 0.1701 .sup.ns (mg/dL) LDL-cholesterol 104 .+-. 29.9 108 .+-.
38.6 90.8 .+-. 32.3 0.3250 .sup.ns (mg/dL) Albumin (g/dL) 4.14 .+-.
0.25 4.22 .+-. 0.29 3.99 .+-. 0.43 0.1781 .sup.ns Platelet
(.times.10.sup.3/.mu.L) 230.19 .+-. 48.76.sup.a 247.75 .+-.
74.84.sup.a 183.88 .+-. 95.35.sup.b 0.0255 * Ferritin (ng/mL)
117.75 .+-. 73.97 100.94 .+-. 74.93 282.19 .+-. 386.91 0.053
.sup.ns HA (ng/mL) 33.08 .+-. 19.62.sup.a 64.2 .+-. 67.47.sup.a
109.59 .+-. 65.56.sup.b 0.0002 *** Insulin (.mu.IU/mL) 10.76 .+-.
5.57 10.05 .+-. 4.15 13.71 .+-. 6.19 0.1103 .sup.ns HbAlc (%) 5.86
.+-. 0.69.sup.a 5.98 .+-. 0.44.sup.ab 7.23 .+-. 2.05.sup.c 0.0007
*** C-peptide (ng/mL) 2.23 .+-. 0.87.sup.a 2.23 .+-. 0.64.sup.ab
2.85 .+-. 1.17.sup.bc 0.0256 * HOMA-IR 2.94 .+-. 1.61.sup.a 2.81
.+-. 1.33.sup.ab 4.50 .+-. 2.28.sup.b 0.0207 * Adipo-IR 5.72 .+-.
3.01 6.33 .+-. 3.72 9.49 .+-. 5.95 0.0645 .sup.ns FFA (.mu.Eq/L)
553.96 .+-. 186.66 615.65 .+-. 238.13 684.88 .+-. 308.55 0.2678
.sup.ns hsCRP (mg/dL) 0.17 .+-. 0.33.sup.a 0.23 .+-. 0.41.sup.ab
0.29 .+-. 0.48.sup.bc 0.0186 * Cholesterol (mg/dL) 177.7 .+-. 28
180.75 .+-. 43.68 158.53 .+-. 44.94 0.1860 .sup.ns TG (mg/dL)
120.70 .+-. 45.23 128.42 .+-. 51.84 137.12 .+-. 77.04 0.9889
.sup.ns FPG (mg/dL) 111.15 .+-. 31.51.sup.a 111.85 .+-.
19.58.sup.ab 134.47 .+-. 43.79.sup.b 0.0402 * HTN, n (%) 7 (25.9) 9
(45.0) 9 (52.9) 0.163 .sup.ns Diabetes, n (%) 4 (14.8) 5 (25.0) 13
(76.5) 0.0001 *** Obese (n = 138) Fibrosis stage 0 1 .gtoreq.2
P-value N (male/female) 25 (17/8) 73 (38/35) 40 (10/30) Age (years)
57.08 .+-. 12.41 48.36 .+-. 15.70.sup.a 60.63 .+-. 13.56.sup.b
0.0001 *** BMI (kg/m.sup.2) 27.48 .+-. 2.58.sup.a 29.30 .+-.
3.20.sup.b 28.27 .+-. 2.97.sup.ab 0.0119 * WC (cm) 91.82 .+-. 6.56
96.16 .+-. 7.35 95.73 .+-. 8.98 0.0074 .sup.ns AST (IU/L) 28.40
.+-. 13.03.sup.a 48.99 .+-. 26.74.sup.b 66.13 .+-. 36.43.sup.c
<0.0001 *** ALT (IU/L) 38.88 .+-. 43.67.sup.a 70.85 .+-.
53.02.sup.bc 70.85 .+-. 56.63.sup.c 0.0003 *** GGT (IU/L) 39.6 .+-.
41.1.sup.a 57.5 .+-. 56.1.sup.ab 85.9 .+-. 95.2.sup.b 0.0016 **
HDL-cholesterol 48.4 .+-. 13.1 47.2 .+-. 12 46.3 .+-. 11.5 0.9175
.sup.ns (mg/dL) LDL-cholesterol 105 .+-. 35.7 109 .+-. 31.7 102
.+-. 33.8 0.5024 .sup.ns (mg/dL) Albumin (g/dL) 4.12 .+-.
0.24.sup.ab 4.24 .+-. 0.26.sup.a 4.08 .+-. 027.sup.b 0.0027 **
Platelet (.times.10.sup.3/.mu.L) 238.2 .+-. 55.38.sup.a 241.44 .+-.
62.49.sup.a 188.33 .+-. 58.05.sup.b 0.0001 *** Ferritin (ng/mL)
145.55 .+-. 89.51 219.37 .+-. 255.97 169.26 .+-. 133.32 0.8403
.sup.ns HA (ng/mL) 51.32 .+-. 66.4.sup.a 61.58 .+-. 90.48.sup.a
127.28 .+-. 129.4.sup.b 0.0001 *** Insulin (.mu.IU/mL) 14.22 .+-.
11.57.sup.a 17.83 .+-. 15.46.sup.ab 19.50 .+-. 12.58.sup.b 0.0148 *
HbAlc (%) 5.87 .+-. 0.54.sup.a 6.15 .+-. 0.85.sup.ab 6.87 .+-.
1.42.sup.c 0.0007 *** C-peptide (ng/mL) 3.22 .+-. 1.57.sup.a 4.43
.+-. 3.43.sup.ab 4.29 .+-. 2.28.sup.bc 0.0498 * HOMA-IR 3.84 .+-.
3.35.sup.a 4.82 .+-. 4.18.sup.ac 6.69 .+-. 4.70.sup.b 0.0006 ***
Adipo-IR 7.48 .+-. 6.2.sup.a 10.76 .+-. 9.7.sup.ab 13.86 .+-.
10.55.sup.bc 0.0043 ** FFA (.mu.Eq/L) 556.08 .+-. 209.52.sup.a
642.76 .+-. 257.61.sup.ab 737.1 .+-. 259.42.sup.bc 0.0059 ** hsCRP
(mg/dL) 0.14 .+-. 0.17.sup.a 0.23 .+-. 0.39.sup.b 0.3 .+-.
0.39.sup.bc 0.0121 * Cholesterol (mg/dL) 180.96 .+-. 38.04 188.58
.+-. 34.01 175.33 .+-. 44.67 0.2143 .sup.ns TG (mg/dL) 127.36 .+-.
51.42 156.23 .+-. 80.68 155.43 .+-. 67.19 0.2363 .sup.ns FPG
(mg/dL) 110.96 .+-. 34.36.sup.a 107.82 .+-. 21.43.sup.a 144.53 .+-.
63.53.sup.b 0.0001 *** HTN, n (%) 9 (36.0) 30 (41.1) 21 (52.5)
0.357 .sup.ns Diabetes, n (%) 3 (12.0) 23 (31.5) 24 (60.0) 0.0002
***
[0135] As a result of confirmation, subjects with NASH or
significant fibrosis (F2-4) had high levels of aspartate
aminotransferase (AST), alanine aminotransferase (ALT) and diabetic
markers in all obese and non-obese groups. The subjects with
significant fibrosis had higher NAFLD activity scores, and showed
more severe liver histology in terms of histological classification
of NAFLD (Table 3 and FIG. 7). More detailed standard
characteristics of each fibrosis stage, comprising well-known
NAFLD-related genetic variations such as PNPLA3, TM6SF2,
MBOAT7-TMC4, and SREBF-2 were shown in Table 4.
TABLE-US-00003 TABLE 3 Histological characteristics of study
subjects stratified by obesity status and fibrosis severity.
Non-obese (n = 64) Obese (n = 138) Fibrosis stage 0 1 2 0 1 2
Steatosis, n (%) 0 (<5%) 15 (55.6) 5 (25.0) 1 (5.9) 7 (28.0) 2
(2.7) 1 (2.5) 1 (5-33%) 7 (25.9) 6 (30.0) 8 (47.1) 12 (48.0) 9
(12.3) 13 (32.5) 2 (34-66%) 4 (14.8) 7 (35.0) 2 (11.8) 3 (12.0) 29
(39.7) 12 (30.0) 3 (>66%) 1 (3.7) 2 (10.0) 6 (35.3) 3 (12.0) 33
(45.2) 14 (35.0) Lobular inflammation, n (%) 0 15 (55.6) 3 (15.0) 1
(5.9) 13 (52.0) 5 (6.9) 3 (7.5) 1 12 (44.4) 14 (70.0) 11 (64.7) 12
(48.0) 60 (82.2) 30 (75.0) 2-3 0 3 (15.0) 5 (29.4) 0 8 (11.0) 7
(17.5) Ballooning, n (%) 0 22 (81.5) 7 (35.0) 0 22 (88.0) 18 (24.7)
5 (12.5) 1-2 5 (18.5) 13 (65.0) 17 (100.0) 3 (12.0) 55 (75.3) 35
(87.5) Histological classification, n (%) No NAFLD 15 (55.6) 5
(25.0) 1 (5.9) 7 (28.0) 2 (2.7) 0 NAFL 11 (40.7) 13 (65.0) 0 18
(72.0) 40 (54.8) 7 (17.5) NASH 1 (3.7) 2 (10.0) 16 (94.1) 0 31
(42.5) 33 (82.5) NAS 1.30 .+-. 1.46 2.95 .+-. 1.61 4.00 .+-. 1.32
1.68 .+-. 1.22 4.11 .+-. 1.23 4.08 .+-. 1.10 Abbreviations: NAFLD,
nonalcoholic fatty liver disease; NAFL, nonalcoholic fatty liver;
NASH, nonalcoholic steatohepatitis; NAS, NAFLD activity score. Date
are expressed as the mean .+-. SD or n (%).
TABLE-US-00004 TABLE 4 Baseline clinical, metabolic, histological,
and genetic characteristics of study subjects stratified by obesity
status and fibrosis stage. Non-obese (n = 64) Fibrosis stage 0 1 2
3 4 N (male/female) 27 (11/16) 20 (9/11) 9 (5/4) 4 (1/3) 4 (1/3)
Age (years) 57.7 .+-. 9.01 57.8 .+-. 11.8 58.6 .+-. 9.9 67.2 .+-.
2.5 66.5 .+-. 1.73 BMI 22.8 .+-. 1.42 23.8 .+-. 1.46 23.5 .+-.
0.862 23.9 .+-. 1.33 24 .+-. 0.66 WC (cm) 79.9 .+-. 5.2 83.2 .+-.
3.53 85.5 .+-. 2.27 88.1 .+-. 4.3 86.1 .+-. 8.37 SBP (mm Hg) 128
.+-. 16.9 127 .+-. 14.3 132 .+-. 17.1 158 .+-. 37.4 124 .+-. 21.7
DBP (mm Hg) 76.8 .+-. 12.8 77.4 .+-. 8.26 80.2 .+-. 12.9 86.8 .+-.
19.1 74.5 .+-. 10.8 AST (IU/L) 29.1 .+-. 21.6 32.2 .+-. 10.6 55.1
.+-. 55.8 80.5 .+-. 50.1 33.8 .+-. 8.34 ALT (IU/L) 31.9 .+-. 27.8
39.1 .+-. 31 52.6 .+-. 53.2 93.5 .+-. 60.5 25 .+-. 7.53 GGT (IU/L)
33.6 .+-. 41.4 44 .+-. 46.2 69.7 .+-. 64 373 .+-. 592 63 .+-. 43.8
Insulin (.mu.IU/mL) 10.8 .+-. 5.57 10 .+-. 4.15 15 .+-. 6.14 15.3
.+-. 7.18 9.32 .+-. 4.29 HbAlc (%) 5.86 .+-. 0.688 5.98 .+-. 0.445
6.8 .+-. 0.689 6.05 .+-. 0.557 9.38 .+-. 3.52 HOMA-IR 2.94 .+-.
1.61 2.81 .+-. 1.33 4.57 .+-. 2.05 4.56 .+-. 2.04 4.27 .+-. 3.5
Adipo-IR 5.72 .+-. 3.01 6.33 .+-. 3.72 11.1 .+-. 6.82 8.94 .+-.
5.18 6.44 .+-. 4.2 Diabetes, n (%) 4 (14.8) 5 (25.0) 7 (77.8) 2
(50.0) 4 (100) Albumin (g/dL) 4.15 .+-. 0.259 4.22 .+-. 0.291 4.19
.+-. 0.285 3.92 .+-. 0.45 3.62 .+-. 0.499 Platelet
(.times.10.sup.3/.mu.L) 230 .+-. 48.8 248 .+-. 74.8 222 .+-. 67.2
206 .+-. 123 76.8 .+-. 32.1 TG (mg/dL) 121 .+-. 45.2 128 .+-. 51.8
180 .+-. 82.9 82.5 .+-. 26.6 96 .+-. 31.1 FPG (mg/dL) 111 .+-. 31.5
112 .+-. 19.6 123 .+-. 26.7 123 .+-. 37 172 .+-. 66.6 Histological
classification No NAFLD 15 (55.6) 5 (25.0) 0 1 (25.0) 0 NAFL 11
(40.7) 13 (65.0) 0 0 0 NASH 1 (3.7) 2 (10.0) 9 (100) 3 (75.0) 4
(100) Genetic variants PNPLA3 G/G 6 (22.2) 4 (20.0) 1 (11.1) 0 2
(50.0) (rs738409) C/G 13 (38.1) 13 (65.0) 5 (55.6) 1 (25.0) 2
(50.0) C/C 7 (25.9) 3 (15.0) 2 (22.2) 3 (75.0) 0 TM6SF2 C/C 21
(77.8) 18 (90.0) 6 (66.7) 2 (50.0) 3 (75.0) (rs58542926) C/T 5
(18.5) 2 (10.0) 2 (22.2) 2 (50.0) 1 (25.0) T/T 0 0 0 0 0
MBOAT7-TMC4 C/C 17 (63.0) 13 (65.0) 5 (55.6) 1 (25.0) 4 (100)
(rs641738) C/T 9 (33.3) 5 (25.0) 3 (33.3) 3 (75.0) 0 T/T 0 2 0 0 0
SREBF-2 C/C 7 (25.9) 6 (30.0) 1 (11.1) 2 (50.0) 2 (50.0) (rs133291)
C/T 12 (44.4) 8 (40.0) 6 (66.7) 1 (25.0) 2 (50.0) T/T 3 (11.1) 6
(30.0) 1 (11.1) 1 (25.0) 0 Obese (n = 138) Fibrosis stage 0 1 2 3 4
N (male/female) 25 (17/8) 73 (38/35) 20 (5/15) 7 (2/5) 13 (3/10)
Age (years) 57.1 .+-. 12.4 48.4 .+-. 15.7 55.6 .+-. 16.4 65.6 .+-.
8.89 65.8 .+-. 6.92 BMI 27.5 .+-. 2.58 29.3 .+-. 3.2 28.5 .+-. 3.26
28 .+-. 3.43 28 .+-. 2.39 WC (cm) 91.8 .+-. 6.55 96.2 .+-. 7.35
95.8 .+-. 9.02 95.8 .+-. 13.2 95.6 .+-. 7.76 SBP (mm Hg) 130 .+-.
14.8 136 .+-. 18.4 133 .+-. 17 132 .+-. 11.3 126 .+-.17.9 DBP (mm
Hg) 80 .+-. 9.78 84.1 .+-. 11.8 77.4 .+-. 13.4 77 .+-. 10.8 74.3
.+-. 9.55 AST (IU/L) 28.4 .+-. 13 49 .+-. 26.7 66.6 .+-. 46.5 70.4
.+-. 27.9 63 .+-. 21.7 ALT (IU/L) 38.9 .+-. 43.7 70.8 .+-. 53 87.2
.+-. 74.3 58 .+-. 19.3 52.6 .+-. 2 4.6 GGT (IU/L) 39.6 .+-. 41 57.5
.+-. 56.1 79.4 .+-. 86.1 86.6 .+-. 68.5 95.6 .+-. 123 Insulin
(.mu.IU/mL) 14.2 .+-. 11.6 17.8 .+-. 15.5 21.3 .+-. 16.1 13.5 .+-.
4.73 20 .+-. 8.1 HbAlc (%) 5.87 .+-. 0.538 6.15 .+-. 0.851 7.01
.+-. 1.45 6.79 .+-. 1.15 6.74 .+-. 1.59 HOMA-IR 3.84 .+-. 3.36 4.82
.+-. 4.18 7.28 .+-. 5.79 4.4 .+-. 1.42 7.01 .+-. 3.71 Adipo-IR 7.48
.+-. 6.2 10.8 .+-. 9.7 14.3 .+-. 12.9 9.29 .+-. 7.57 15.1 .+-. 7.42
Diabetes, n (%) 3 (12.0) 23 (31.5) 12 (60.0) 4 (57.1) 8 (61.5)
Albumin (g/dL) 4.12 .+-. 0.243 4.24 .+-. 0.26 4.11 .+-. 0.192 4.06
.+-. 0.207 4.04 .+-. 0.393 Platelet (.times.10.sup.3/.mu.L) 238
.+-. 55.4 241 .+-. 62.5 206 .+-. 50.6 195 .+-. 48.8 157 .+-. 63.7
TG (mg/dL) 127 .+-. 51.4 156 .+-. 80.7 175 .+-. 71.5 140 .+-. 48.6
134 .+-. 64.5 FPG (mg/dL) 111 .+-. 34.4 108 .+-. 21.4 148 .+-. 78.3
138 .+-. 30.6 143 .+-. 53.9 Histological classification No NAFLD 7
(28.0) 2 (2.7) 0 0 0 NAFL 18 (72.0) 40 (54.8) 6 (30.0) 1 (14.3) 0
NASH 0 31 (42.5) 14 (70.0) 6 (85.7) 13 (100) Genetic variants
PNPLA3 G/G 4 (27.4) 20 (27.4) 11 (55.0) 2 (28.6) 6 (46.2)
(rs738409) C/G 11 (44.0) 35 (47.9) 4 (20.0) 4 (57.1) 4 (30.8) C/C 7
(17.8) 13 (17.8) 4 (20.0) 1 (14.3) 1 (7.7) TM6SF2 C/C 18 (72.0) 56
(76.7) 16 (80.0) 4 (57.1) 10 (76.9) (rs58542926) C/T 4 (16.0) 11
(15.1) 3 (15.0) 2 (28.6) 1 (7.7) T/T 0 1 (1.4) 0 1 0 MBOAT7-TMC4
C/C 15 (60.0) 42 (57.5) 11 (55.0) 3 (42.9) 7 (53.8) (rs641738) C/T
4 (16.0) 21 (28.8) 8 (40.0) 4 (57.1) 4 (30.8) T/T 3 (12.0) 5 (6.8)
0 0 0 SREBF-2 C/C 6 (24.0) 23 (31.5) 8 (40.0) 1 (14.3) 3 (23.1)
(rs133291) C/T 7 (28.0) 27 (37.0) 5 (25.0) 5 (71.4) 5 (38.5) T/T 3
(12.0) 8 (11.0) 4 (20.0) 1 (14.3) 2 (15.4) Abbreviations: BMI, body
mass index; WC, waist circumference; SBP, systolic bloodpressure;
DBP, diastolic blood pressure; AST, aspartate transaminase; ALT,
alaninetransaminase; GGT, gamma-glutamyl transferase; HbAlc,
glycosylated hemoglobin;HOMA-IR, homeostasis model assessment of
insulin resistance; Adipo-IR, adiposetissue insulin resistance; TG,
triglycerides; FBG, fasting blood glucose; NAFLD,nonalcoholic fatty
liver disease; NASH, nonalcoholic steatohepatitis; PNPLA3,
patatin-like phospholipase domain-containing protein 3; TM6SF2,
transmembrane 6 superfamily 2; MBOAT7-TMC4, membrane bound
O-acyltransferase domain-containing 7 gene and transmembrane
channel-like 4 gene; SREBF-2, sterolregulatory element binding
transcription factor 2. Data are expressed .+-. SD or nas
mean(%).
[0136] 2) Observation of Changes in Microbiome According to
Fibrosis Severity
[0137] Depending on the fibrosis severity, changes of the
microbiome were shown differently in the non-obese NAFLD subjects
and obese NAFLD subjects.
[0138] Specifically, the microbial diversity was compared according
to the histological spectrum of NAFLD or fibrosis severity (FIG.
1). For comparison of alpha diversity, rarefaction curves based on
Shannon metric were plotted, and NMDS plots based on Bray-Curtis
distance were plotted for beta diversity. As a result of
confirmation, any significant changes between groups stratified by
the histological spectrum of NAFLD or fibrosis severity were not
found in the merged subjects (FIG. 1a to 1d).
[0139] The subjects were classified into two groups according to
their BMI status. In the non-obese group, a significant decrease in
microbial diversity was observed between F1 and F0 (p=0.0074), as
well as between F2-4 and F0 (p=0.0084) (FIG. 1e to 1h). Moreover,
clear clustering between F0 and F2-4 was observed (p=0.038). In the
obese group, there was no significant change in diversity between
groups stratified by the histological classification of NAFLD or
fibrosis severity (FIG. 1i to 1l).
[0140] The result indicates that the fibrosis severity is more
related to gut microbiome change than necroinflammatory activity,
and basic BMI status may also be an important factor contributing
to gut microbiome change.
[0141] 3) Observation of Proliferation of Fibrosis-Related
Microbial Taxa
[0142] Proliferation of the fibrosis-related microbial taxa was
remarkably shown in the non-obese NAFLD subjects. Specifically, in
the non-obese and obese subjects, the differences of the specific
microbial taxa according to the fibrosis severity were compared
using univariate and multivariate analyses (FIGS. 2a to 2d and 2e
to 2h).
[0143] In the univariate analysis, not only gradual proliferation
of Veillonellaceae mostly found in the oral cavity and small
intestine and large intestine, but also Enterobacteriaceae were
observed according to the fibrosis severity of the non-obese
subjects. In the obese subjects, Rikenellaceae became gradually
enriched. On the contrary, the abundance of Ruminococcaceae was
significantly reduced as fibrosis became more severe, and this was
found only in the non-obese subjects. This result could be
confirmed in correlation plots (FIG. 8), and Enterobacteriaceae and
Veillonellaceae showed a positive correlation with the fibrosis
severity (p=1.09.times.10.sup.-4, p=2.44.times.10.sup.-3,
respectively), but Ruminococcaceae showed an inverse
correlation.
[0144] At the genus level, Faecalibacterium (Ruminococcaceae),
Ruminococcus (Ruminococcaceae), Coprococcus (Lachnospiraceae), and
Lachnospira (Lachnospiraceae) were significantly drastically
reduced in the significant fibrosis group, but the abundance of
Enterobacteriaceae_Other (Enterobacteriaceae) and Citrobacter was
gradually increased according to the fibrosis severity. This change
was observed only in the non-obese subjects.
[0145] For multivariate analysis, the age, gender and BMI were
adjusted using MaAsLin. Enterobacteriaceae was an abundant family
significantly related to the fibrosis severity in the non-obese
subjects (p=0.0108, q=0.214) (FIG. 2e to 2h). In phylum Firmicutes,
Veillonellaceae showed a steep increase of the relative abundance
in the non-obese subjects than the obese subjects (non-obese,
p=0.0002, q=0.0195), but the abundance of Ruminococcaceae showed an
inverse correlation with the fibrosis severity in the non-obese
subjects (p=0.0019, q=0.0908). A representative genus of
Ruminococcaceae, Ruminococcus also showed a significant inverse
correlation according to the fibrosis severity (p=0.0009, q=0.135)
(FIG. 10a to 10c). In addition, Veillonellaceae and
Enterobacteriaceae showed a significant positive correlation with
the serum free fatty acid (FFA) level in the non-obese subjects
(q=0.178, q=0.118, respectively), but it did not in the obese
subjects (FIG. 9a to 9e).
[0146] Adipo-IR and glycosylated hemoglobin (HbA1c) also showed a
positive correlation according to the abundance of Veillonellaceae
(adipo-IR, q=0.142; HbA1c, q=0.157). On the contrary, the serum FFA
level showed an inverse correlation with the abundance of
Ruminococcus in all subjects (q=0.0838) and non-obese subjects
(q=0.0838), but it did not in the obese subjects (q=1.00).
[0147] In order to elucidate whether these remarkable microbiome
changes in the non-obese subjects are related to the host gene
effect, the association between bacteria and genetic mutations of
PNPL3, TM6SF2, MBOAT7-TMC4, and SREBF-2 using MaAsLin was analyzed.
However, significant association of the four genetic mutations with
three bacteria was not observed. Only Actinomyces enriched the
minor allele of TM6SF2 (C/T) (q=0.169) in the non-obese subjects
(FIG. 1l).
[0148] In addition to the three variables of age, gender and BMI,
the presence of type 2 diabetes mellitus (DM) was well known to
affect the general changes in the microbiome (Qin J, Li Y, Cai Z,
Li S, Zhu J, Zhang F, et al. A metagenome-wide association study of
gut microbiota in type 2 diabetes. Nature 2012; 490:55-60.). After
additional adjustment for DM, it was found that Enterobacteriaceae
(p=0.00197, q=0.0616) and Faecalibacterium (p=0.00242, q=0.0707)
were related to the presence of DM in all the subjects (FIG. 12a to
12d). In the non-obese subjects, not only depletion of Lachnospira
(p=5.26.times.10.sup.-4, q=0.0676), but also the proliferation of
Klebsiella (p=0.00339, q=0.141) belonging to Enterobacteriaceae in
the obese subjects were also observed in the DM subjects.
[0149] In order to understand the interaction between the microbial
components and gut microbiota network characteristics in the obese
and non-obese subjects, co-expression of the taxa related to the
fibrosis severity was measured, and the relative abundance was
shown (FIG. 2i to 2k).
[0150] As a result, in the non-obese subjects, Veillonellaceae and
Enterobacteriaceae had an inverse correlation with Ruminococcaceae
(rho=-0.275 and -0.333, respectively), and Prevotellaceae showed an
inverse correlation with Bacteroidaceae (rho=-0.391). However, the
strong interaction between Veillonellaceae/Enterobacteriaceae and
Ruminococcaceae was not observed in the obese and all subjects. In
particular, the correlation of Veillonellaceae and fibrosis
severity was not significant in the obese subjects and all
subjects, and this suggests its specific role in progression of
fibrosis in the non-obese subjects.
[0151] In summary, the proliferation of the specific taxa according
to the fibrosis severity was more pronounced in the non-obese group
than in the obese group.
[0152] 4) Observation of Fecal Metabolite Level According to
Fibrosis Severity of Non-Obese and Obese NAFLD Subjects
[0153] The non-obese and obese NAFLD subjects had different fecal
metabolite levels according to the fibrosis severity. Specifically,
fecal metabolites mainly related to the gut microbiota were
evaluated.
[0154] The composition of the total bile acid pool between the
non-obese and obese subjects was various, and the non-obese
subjects had an increased primary bile acid level according to the
increased fibrosis stage (FIG. 3a).
[0155] The total fecal bile acid level was 3 times higher in the
non-obese subjects having significant fibrosis (F2-4) than the
subjects without fibrosis (F0) (FIG. 3b to FIG. 3g). In particular,
the cholic acid (CA), chenodeoxycholic acid (CDCA), and
ursodeoxycholic acid (UDCA) levels were increased according to the
fibrosis severity increased in the non-obese subjects (FIG. 3b to
FIG. 3g and FIG. 13a to 13e). The lithocholic acid (LCA) and
deoxycholic acid (DCA) levels were significantly high in the obese
subjects having significant fibrosis, and only lithocholic acid
showed significant improvement after percentage display.
[0156] Among three SCFA, the fecal propionate level was gradually
increased as fibrosis became severe in the non-obese subjects
(non-obese; p=0.0032, obese; p=0.7979), and showed a significantly
positive correlation with the amount of Veillonellaceae known as
propionate-producing bacteria (p=0.0155) (FIG. 3h to 3j).
[0157] On the contrary to the bile acid profile, the correlation
between the significant change of fecal SCFA and its bacterial taxa
was observed only in the non-obese subjects (FIG. 4b). Ruminococcus
(p=0.0189), Oscillospira (p=1.57.times.10.sup.-4) and Desulfovibrio
(p=9.76.times.10.sup.-4) well-known as SCFA-producing bacteria
showed an inverse correlation with the fecal propionate level. The
change in the fecal butyrate level according to the fibrosis
severity was not found in all the non-obese and obese subjects, and
the reduction of Ruminococcaceae did not affect the fecal butyrate
level (non-obese, p=0.597; obese, p=0.109).
[0158] 5) Observation of Bacterial Taxa-Metabolite Network Pattern
in Non-Obese and Obese NAFLD
[0159] A bacterial taxa-metabolite network showed a unique pattern
in the non-obese and obese NAFLD. Specifically, when comparing the
gut microbiota elements according to the fibrosis severity and
obesity status, a clear change in the microbiome was observed only
in the non-obese subjects. To investigate its core cause,
NAFLD-associated genetic variant and intestinal metabolite analysis
was performed.
[0160] Based on the result, co-expression of the taxa and
metabolites was evaluated, and the interaction network was shown in
FIG. 4. Strong interaction between bile acids was observed in all
the non-obese and obese subjects. However, the bacterial taxa and
metabolite co-expression pattern according to the fibrosis severity
was different in the non-obese subjects and obese subjects: The
non-obese subjects showed a more significant co-expression pattern
than the obese subjects.
[0161] Interestingly, primary bile acid had an inverse correlation
with Ruminococcaceae and Rikenellaceae known as indexes of healthy
intestine in all the non-obese and obese subjects. Veillonellaceae
exhibited a positive correlation with propionate, as well as
primary bile acid. Bile acid usually has the potential to regulate
growth of susceptible bacteria or to propagate relatively resistant
bacteria regardless of obesity status. Nevertheless, the
correlation of the intestinal bacterial taxa and fecal metabolites
with the sever fibrosis was more remarkable in the non-obese NAFLD
subjects than the obese subjects.
[0162] 6) NAFLD Prediction of Non-Obese Subjects by Microbiota and
Metabolite Combination
[0163] The microbiota-metabolite combination accurately predicted
significant fibrosis in the non-obese NAFLD subjects. Specifically,
in order to evaluate the usefulness as a fibrosis-predicting
biomarker of the gut microbiota and related fecal metabolites,
AUROC for predicting significant fibrosis was compared (FIG. 5a and
FIG. 5b).
[0164] Enterobacteriaceae, Veillonellaceae, and Ruminococcaceae
were selected as most representative, and significant
fibrosis-related bacterial taxa. As shown in FIG. 5a, the combined
bacterial marker to predict significant fibrosis yielded an AUROC
of 0.824 in the non-obese subjects (0.661 for all subjects; 0.584
for obese subjects).
[0165] In addition, Megamonas belonging to Veillonellaceae family
and Ruminococcus belonging to Ruminococcaceae were selected. As
shown in FIG. 5b, AUROC to predict significant fibrosis was yielded
as 0.718 (0.673 for all subjects; 0.648 for obese subjects).
[0166] As fibrosis-related metabolites, four fecal metabolites
(cholic acid, chenodeoxycholic acid, ursodeoxycholic acid, and
propionate) were selected, and the combination of the four
metabolites predicted significant fibrosis as AUROC of 0.758 in the
non-obese subjects (0.505 for all subjects; 0.520 for obese
subjects).
[0167] In case of addition of the intestinal metabolites to the
bacterial marker at a family level, as shown in FIG. 5a, the
predicting ability was significantly enhanced as improved AUROC of
0.977 (0.786 for all subjects; 0.609 for obese subjects). In
addition, in case of addition of the intestinal metabolites to the
bacterial marker at a genus level, as shown in FIG. 5b, it was
enhanced as improved AUROC of 0.955 (0.590 for all subjects; 0.636
for obese subjects). The predictive ability of the novel
microbiota-metabolite biomarker was significantly higher than FIB-4
widely used as a non-invasive biomarker of NAFLD.
[0168] The result demonstrated that the diagnosis accuracy of the
combination of the identified intestinal bacterial taxa and fecal
metabolite, for predicting significant fibrosis in NAFLD subjects
was significantly higher in the non-obese subjects than the obese
subjects, and clear differences of specific bacterial taxa and
large intestine metabolite between the obese and non-obese NAFLD
groups could be confirmed. This result emphasizes not only the
importance of the gut microbiome as a risk factor explaining the
pathogenesis of non-obese NAFLD, but also the importance of
application in diagnosis of the novel microbiome-metabolite
combination as a non-invasive biomarker for significant fibrosis in
non-obese NAFLD.
* * * * *