Vol.(0123456789)1 3Eur Child Adolesc Psychiatry (2018) 27.docx

Vol.:(0123456789)1 3
Eur Child Adolesc Psychiatry (2018) 27:233–240
https://doi.org/10.1007/s00787-017-1039-2
ORIGINAL CONTRIBUTION
Hydrogen breath test to detect small intestinal bacterial
overgrowth: a prevalence case–control study in autism
Li Wang1 · Yu‑Mei Yu1 · You‑qi Zhang1 · Jie Zhang1 ·
Na Lu1 · Na Liu2
Received: 9 July 2016 / Accepted: 4 August 2017 / Published
online: 10 August 2017
© Springer-Verlag GmbH Germany 2017
Keywords Small intestinal bacterial overgrowth · Autism
spectrum disorders · Hydrogen glucose breath test ·
Chinese
Abbreviations
ASD Autism spectrum disorder
GI Gastrointestinal
IBD Inflammatory bowel disease
VTE Venous thromboembolism
NBS Expanded newborn screening
ADI-R Autism Diagnostic Interview-Revised
SIBO Small intestine bacterial overgrowth (SIBO)
ATEC Autism Treatment Evaluation Checklist
BMI Body mass index
NICU Neonatal intensive care unit

6-GSI 6-Gastrointestinal Severity Index
ID Intellectual disability
IQ Intelligence quotient
H2 Hydrogen
CH4 Methane
SD Standard deviation
IQR Interquartile ranges
LPS Lipopolysaccharide
Introduction
The prevalence of autism spectrum disorders (ASDs) has
increased over recent years in much of the Western world
[1]. In China, relatively little is known about the prevalence
of ASDs. The underlying causes remain unclear, but both
genetic and environmental factors appear to play a role [2].
High proportions of children with autism suffer from gas-
trointestinal (GI) symptoms, implying a link between autism
and abnormalities in gut microbial functions [3]. A pilot
Abstract The aim of this study is to assess the prevalence
of small intestinal bacterial overgrowth (SIBO) by hydro-
gen breath test in patients with autism spectrum disorders
(ASD) with respect to a consistent control group. From
2011 to 2013, 310 children with ASD and 1240 sex- and
age-matched typical children were enrolled in this study
to undergo glucose breath test. The study participants
were considered to exhibit SIBO when an increase in H2
of ≥20 ppm or CH4 of ≥10 ppm with respect to the fast-
ing value was observed up to 60 min after the ingestion
of glucose. Ninety-six children with autism suffered from
SIBO, giving a prevalence rate of SIBO was 31.0% (95%
CI 25.8–36.1%). In contrast, 9.3% of the typical children
acknowledged SIBO. The difference between groups was
statistically significant (P < 0.0001). The median Autism
Treatment Evaluation Checklist (ATEC) score in the chil-

dren with autism and with SIBO was significantly high when
compared with the children without autism and without
SIBO [98 (IQR, 45–120) vs. 63 (32–94), P < 0.001]. For
the autism group, the 6-GI Severity Index (6-GSI) score
was found to be strongly and significantly correlated with
the total ATEC score (r = 0.639, P < 0.0001). SIBO was
significantly associated with worse symptoms of autism,
demonstrating that children with SIBO may significantly
contribute to symptoms of autism.
Li Wang and Yu-Mei Yu are co-first authors.
* Li Wang
[email protected]
1 Department of Pediatrics, Cangzhou Central Hospital, No.
16, Xinhua West Road, Cangzhou 061000, Hebei, China
2 Department of Pediatrics, The Second Hospital of Hebei
Medical University, Shijiazhuang, China
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1039-2&domain=pdf
234 Eur Child Adolesc Psychiatry (2018) 27:233–240
1 3
study suggested the role of gut microbiota in autism as a part
of the “gut-brain” axis [4]. Interestingly, de Theije et al. [5]
showed that autism-like behavior and its intestinal pheno-
type are associated with altered microbial colonization and
activity in a murine model for ASD, with preponderance in
male offspring. However, the data from another study did not
support the hypothesis that the gastrointestinal microbiota
of children with ASD plays a role in the symptomatology

of ASD [6].
Small intestinal bacterial overgrowth (SIBO) occurs
when colonic quantities of commensal bacteria are present
in the small bowel. SIBO leads to impaired micronutrient
absorption and increased GI permeability, both of which
may contribute to celiac disease and stunting in children [7].
SIBO has been associated with irritable bowel syndrome [8],
Parkinson’s disease [9], inflammatory bowel disease (IBD)
[10], and venous thromboembolism (VTE) [11]. Interest-
ingly, there is no study yet on the interaction between SIBO
and children with ASD. The aim of this study was to assess
the prevalence of SIBO by hydrogen breath test in patients
with ASD with respect to a consistent control group.
Patients and methods
From 2011 to 2013, 310 children with ASD were enrolled
in this study. Those children were all native and singleton
live births who had taken part in expanded newborn screen-
ing (NBS) in Beijing, China from 2008 to 2010, with out-
patient follow-up when the children were 36 months old.
All children will autism analysis. The Autism Diagnostic
Interview-Revised (ADI-R) and DSM-5 criteria were used to
confirm a diagnosis of ASD in outpatient follow-up. Those
protocols were translated into Chinese Mandarin. Chinese-
translated materials along with English materials were
provided in advance. For each child with ASD diagnosis,
we selected 4-to-1 sex- and age-matched controls in those
typical children without ASD diagnosis. To exclude the pos-
sibility that the controls could have any sub-clinical autistic
features, each control subject was also clinically examined
by the pediatrician. The Cangzhou Central Hospital Institu-
tional Review Board for the Protection of Human Subjects
approved this study. Neither data nor specimens were col-
lected until written informed consents were obtained from

the parents.
Sociodemographic factors [age, sex, body mass index
(BMI), and ethnicity], infant characteristics [chronic com-
plication before pregnancy, pregnancy-induced complica-
tion, mode of delivery, and those transferred to neonatal
intensive care unit (NICU) after delivery], family structure,
area of residence (urban or rural), educational background
of parents, NBS results, whether ASD diagnosis had been
confirmed or expected before follow-up, and family history
of mental illness subdivided hierarchically as ASD were
obtained from outpatient follow-up.
Autism severity was assessed with the Autism Treat-
ment Evaluation Checklist (ATEC), which is an instrument
designed to provide a quantitative assessment of autism
severity [12]. ASD severity was divided into three groups,
according to the ATEC score (mild <50; moderate 50–104;
and severe 104–180). GI symptoms were assessed using a
modified version of the GI Severity Index [13]. Specifically,
we included only the first six items (constipation, diarrhea,
stool consistency, stool smell, flatulence, and abdominal
pain), but did not include “unexplained daytime irritabil-
ity”, “nighttime awakening,” or “abdominal tenderness.” We
call this shortened version the 6-Gastrointestinal Severity
Index (6-GSI). Intellectual disability (ID) status was con-
firmed using DSM-5 in the children in those processes [ID
was defined as the IQ (Intelligence Quotient) <80]. IQ was
assessed using the Combined Raven’s Test and then con-
verted to a standard IQ score according to Chinese children’s
norm.
Hydrogen and methane breath test
Glucose breath testing was performed under standard condi-

tions. The glucose breath test was performed in the morn-
ing following oral hygiene using 0.05% chlorhexidine. The
children fasted for a period of 12 h prior to the test. Breath
samples were collected using a non-rebreathing valve setup
(QuinTron Instrument Co. Inc., Menomonee Falls, WI, US).
After collection of the fasting breath, a dose of 50 g of glu-
cose in the form of iso-osmotic solution was administered
and samples were collected 15, 30, 45, 60, 90, 120, and
180 min after the ingestion of glucose. The levels of hydro-
gen (H2) and methane (CH4) in the samples were simultane-
ously measured by gas chromatography using a 12i model
QuinTron MicroLyzer unit (QuinTron Instrument Company,
Milwaukee, WI, US). The results were expressed in parts
per million (ppm). Study participants were considered to
exhibit SIBO when an increase in H2 of ≥20 ppm or CH4
of ≥10 ppm with respect to the fasting value was observed
up to 60 min after the ingestion of glucose [14]. Determina-
tions were performed in an independent laboratory blinded
to clinical and sociodemographic data.
Data analysis
Results are expressed as percentages for categorical vari-
ables and as means (standard deviation, SD) and medians
(interquartile ranges, IQRs) for the continuous variables,
depending on the normal or non-normal distribution of
data. Shapiro–Wilk tests were used for normal distribution
235Eur Child Adolesc Psychiatry (2018) 27:233–240
1 3
test. Proportions were compared using the Chi-square test.
Two-group comparison of not normally distributed data was

performed using the Mann–Whitney U test, and a two-tailed
Student’s unpaired t test was used for normally distributed
continuous variables.
Correlations among continuous variables were assessed
by the Spearman rank-correlation coefficient. In addition,
associations between the ATEC and 6-GSI scores were also
assessed using logistic regression models in multivariate
adjustment with possible confounders, such as sociode-
mographic factors (age, sex, BMI, and ethnicity), infant
characteristics (chronic complication before pregnancy,
pregnancy-induced complication, mode of delivery, and
those transferred to NICU after delivery), family structure,
area of residence (urban or rural), educational background
of mental illness. All statistical analyses were performed
with SPSS for Windows, version 20.0 (SPSS Inc., Chicago,
IL, US). Statistical significance was defined as P < 0.05.
Results
In our study, 310 children with ASD were included. The
male-to-female ratio was 3.43:1, and the ratios of autistic
disorders to other ASD subtypes were 2.44:1. The rate of
children who had ID was significantly lower in controls as
compared with ASD groups (2.3 vs. 32.6%, P < 0.001).
Ninety-six children with autism suffered from SIBO, giv-
ing a prevalence rate of 31.0% (95% CI 25.8–36.1%). In con-
trast, 9.3% (115/1240; 95% CI 7.7–0.9%) of the typical chil-
dren acknowledged SIBO (Table 1). The difference between
groups was statistically significant (odds ratio 3.33; 95% CI
2.28–7.75; P < 0.0001). In the typical children group, we
found that the rates of SIBO in rural and urban areas were

14.8 and 5.8%, respectively. In the group with autism, it was
50.5 and 21.9%, respectively.
The hydrogen concentrations (ppm) obtained by the glu-
cose breath test were analyzed for the areas under the indi-
vidual curves. The children with autism exhibited greater
(Student’s t test, P < 0.001) mean hydrogen production dur-
ing the first hour of the test, which presumably originated
from the small intestine when compared with the typical
children group (505.32 ± 315.46 vs. 302.14 ± 244.57 ppm
per min, respectively). Between 60 min and 180 min of
the test, the period during which hydrogen production
occurs predominantly in the large intestine, the concentra-
tion of hydrogen in the breath of the children in the ASD
and control groups was similar (4576.77 ± 1012.55 vs.
4365.15 ± 997.52 ppm per min, respectively, P = 0.582)
(Fig. 1a). Figure 1b shows the mean hydrogen concen-
trations (ppm) obtained from the breath tests of children
with and without bacterial overgrowth in the ASD group.
A greater area under the curve for the small intestine was
observed among the 96 children with SIBO compared with
the 214 children without SIBO up to 60 min after the inges-
tion of glucose (893.6 ± 505.56 ppm vs. 375.2 ± 177.1 ppm
per min; Student’s t test, P < 0.001). A similar response was
observed for the colon during the 60–180 min of the test
(5128.9 ± 1262.5 vs. 4021.1 ± 711.8 ppm per min; Student’s
t test, P < 0.001). Figure 2 shows the mean methane con-
centrations (ppm) obtained from the breath tests of children
with and without bacterial overgrowth in the ASD group. In
the small intestine, differences in the area under the curve
were not observed among the 96 children with SIBO in rela-
tion to the 214 children without SIBO up to 60 min after the
ingestion of glucose (P = 0.12). In addition, no significant
difference was observed in the area under the curve for the
colon during the 60 min to 180 min of the test (P = 0.08).
Methane production was observed in 138 (64.5%) of the

214 children who did not exhibit bacterial overgrowth and
Table 1 The prevalence of
SIBO in different groups
ASD autism spectrum disorders, ATEC Autism Treatment
Evaluation Checklist, SIBO small intestinal bac-
terial overgrowth
a ASD was divided into three group, according to ATEC score
(mild <50; moderate 50–104; severe 104–
180). Proportions were compared using the Chi-square test
b P < 0.01 vs. typical children
c P < 0.05 vs. typical children
Cohort No. of children No. of with
SIBO
Prevalence (%) 95% CI (%) OR (95% CI)
ASDa 310 96 31.0 25.8–36.1 3.33 (2.28–7.75)b
Mild 99 17 17.2 9.7–24.6 1.85 (1.29–3.05)c
Moderate 130 39 30.0 22.1–37.8 3.23 (2.10–6.93)b
Severe 81 40 49.4 38.5–62.3 5.31 (2.76–12.63)b
Typical children 1240 115 9.3 7.7–10.9 Reference
1 3
in 60 (62.5%) of the 96 children with bacterial overgrowth

(X2 test, P = 0.74).
Diarrhea was the most common SIBO symptom (71.0%
of ASD patients), followed by abdominal pain (37.1%), and
abnormal feces (30.0%). Children with autism and with
SIBO were more likely from the rural area (50.0 vs. 21.9%;
P < 0.001), NBS positive (4.2 vs. 0.9%; P = 0.046), ID
(45.8 vs. 26.6%; P = 0.001), and transferred to NICU after
delivery (16.7 vs. 2.4%; P < 0.001), Table 2. Table 3 shown
that transferred to NICU, place of residence, NBS screening
result, ATEC score and 6-GSI were associated with hydro-
gen concentrations (P < 0.05). Associations were not found
between SIBO and sex, age, BMI, ethnicity, chronic compli-
cation before pregnancy, pregnancy-induced complication,
mode of delivery, family structure, educational background
of mental illness (P > 0.05; Table 2).
The median ATEC score in the children with autism was
75(IQR, 38-106). The median ATEC score in the children
Fig. 1 Mean concentrations
of hydrogen (ppm) in breath
samples in different groups. a
Mean concentrations of hydro-
gen (ppm) in breath samples
collected after fasting and at 15,
30, 45, 60, 90, 150 and 180 min
after glucose ingestion from
children in the ASD (n = 310)
and typical children (n = 1240)
groups. Student’s t test,
comparison between groups
for collection time during the

breath test (complementation
of the analysis of area under
the curve). aP < 0.05 vs. typical
children group. b Mean concen-
trations of hydrogen (ppm) in
breath samples collected after
fasting and at 15, 30, 60, 90,
120, 150 and 180 min after glu-
cose ingestion from children in
the autistic group with (n = 96)
and without (n = 214) small
intestinal bacterial overgrowth
(SIBO). aP < 0.05 vs. the
autistic group without SIBO in
relation of the hydrogen produc-
tion in breath test
Fig. 2 Mean concentrations of
methane (ppm) in breath sam-
ples collected after fasting and
at 15, 30, 60, 90, 120, 150 and
180 min after glucose ingestion
from children in the autistic
group with (n = 96) and without
(n = 214) SIBO. bP < 0.05
vs. the autistic group without
SIBO in relation of the methane
production in breath test
1 3
with autism and with SIBO was significantly high when

compared with the children with autism but without SIBO
[98 (IQR, 45–120) vs. 63 (32–94), P < 0.001]. In addition,
the prevalence of SIBO in the mild group was 17.2% (95%
CI 9.7–24.6%). The corresponding prevalence rates for the
moderate and severe groups were 30.0 and 49.4%, respec-
tively (Table 2). For the autism group, the median 6-GSI
score was 4 (2–7), and the 6-GSI was found to be strongly
and significantly correlated with the total ATEC score
(r = 0.639, P < 0.0001). A significant positive correlation
was also found between the 6-GSI and ATEC scores, using
ordered logistic regression after multivariate adjustment for
the abovementioned possible confounders (P < 0.001).
Discussion
Recent studies have correlated gut dysfunction with the ASD
group and suggested a possible role of the GI microflora in
the symptomatology and/or severity of symptoms in children
with autism [15]. However, the evidence is rather specula-
tive. To date, no study has directly examined the association
between SIBO detected by hydrogen glucose breath test and
ASD. We had the opportunity to examine this relationship
in a large, population-based Chinese case–control study. We
found that a higher proportion of ASD patients suffered from
Table 2 Baseline characteristics of the enrolled ASD with SIBO
and without SIBOa
SIBO small intestinal bacterial overgrowth ASDs, autism
spectrum disorders, NBS newborn screening, NICU neonatal
intensive care unit, ID
intellectual disability, ATEC Autism Treatment Evaluation
Checklist, 6-GSI 6-GI Severity Index
a Two-group comparison was performed using the Chi-square
test and Mann–Whitney U test or Student’s unpaired t test

SIBO(+) SIBO(−)
N 96 214
Han Chinese (%) 93.8 93.4
Preterm birth (<37 weeks), (%) 11.5 11.2
Assisted delivery (%) 37.5 39.2
Parental depression (%) 12.5 10.8
Chronic complication before pregnancy (%) 10.4 10.2
Pregnancy-induced complication (%) 13.5 11.7
Transfer to NICU (%) 16.7 2.4
Sex of child, male (%) 81.3 75.7
Autistic disorders (%) 75.0 69.1
ID (%) 45.8 26.6
ASDs diagnosis had been confirmed or expected before follow-
up (%) 27.1 22.9
NBS positive (%) 4.2 0.9
Live (rural, %) 50.0 21.9
Marital status, single (%) 9.4 8.4
Family’s socio-professional category, high (%) 11.5 9.8
Family history of ASDs (%) 8.3 9.9
ATEC score, IQR 98 (45–120) 63 (32–94)
6-GSI, IQR 6 (4–9) 2 (1–3)
Hydrogen concentrations 60 min after glucose ingestion, mean
(SD) 893.6 (505.5) 375.2 (177.1)
Hydrogen concentrations 60–180 min after glucose ingestion,
mean (SD) 5128.9 (1262.5) 4021.1 (711.8)
Methane concentrations 60 min after glucose ingestion, mean
(SD) 558.3 (332.2) 528.2 (324.1)
Methane concentrations 60–180 min after glucose ingestion,
mean (SD) 3045.2 (1102.3) 2905 (1054.5)
Table 3 The associations of hydrogen concentration with demo-
graphic and health characteristics of the ASD group
NBS newborn screening, NICU neonatal intensive care unit,

ATEC
Autism Treatment Evaluation Checklist, 6-GSI 6-GI Severity
Index
Characteristics r (Spearman) P
Race 0.08 >0.05
Mode of delivery 0.06 >0.05
Transferred to NICU 0.37 <0.001
Place of residence 0.28 <0.01
NBS screening result 0.32 <0.01
Marital status 0.09 >0.05
Family’s socio-professional
category
0.13 >0.05
Family history of ASDs 0.12 >0.05
ATEC score 0.48 <0.001
6-GSI 0.59 <0.001
1 3
SIBO than in controls (31.0 vs. 9.3%, P < 0.001). A recent
study of children with autism and their first-degree relatives
found that 37 and 21%, respectively, had increased intestinal
permeability based on a lactulose/mannitol test, compared to
5% of normal subjects [16]. Finegold et al. [17] used a high
throughput sequencing technique, that is, pyrosequencing to
investigate gut bacteria in children with autism vs. controls,
and found several differences at the phylum level, includ-
ing higher levels of Bacteroidetes in the group with severe

autism, and higher levels of Firmicutes in the control group.
Another study indicated that autistic subjects with gastroin-
testinal disease harbor statistically significantly (P = 0.031)
higher counts of C. perfringens in their gut compared to
control children [18]. Furthermore, we found that SIBO was
associated with worse symptoms of autism, demonstrating
that children with more severe autism are more likely to suf-
fer from SIBO. Similarly, Adams et al. [19] reported that the
strong correlation of gastrointestinal symptoms with autism
severity indicated that children with more severe autism are
likely to have more severe gastrointestinal symptoms and
vice versa. Therefore, it is possible that symptoms of autism
are exacerbated or may even be partially due to the underly-
ing gastrointestinal problems.
SIBO had been studied in different groups of children.
Mello et al. [20] found that 21.6% of the children suffered
from SIBO (those from slum areas vs. those from private
schools: 30.9 vs. 2.4%). These values are similar to those
previously reported in Brazil [21] for children living in
slums and those attending private health clinics (37.5 and
2.1% SIBO in each group, respectively). In Australia, SIBO
was found in 27.2% of aboriginal children fewer than 5 years
old [22]. In our study, we found that the rates of SIBO in
the rural and urban areas were 14.8 and 5.8%, respectively.
Interestingly, we also found that 50.5% of the children with
autism live in rural areas and suffered from SIBO, while
21.9% live in urban areas.
In a study of 137 children with ASD, 24% had a history
of at least one gastrointestinal symptom, with diarrhea being
the most prevalent one-occurring in 17% of individuals [23].
Similarly, a study of 172 children with autism spectrum dis-
order found that 22.7% were positive for GI distress, primar-
ily with diarrhea and constipation [24]. A characterization
study of 160 children with ASD found that 59% had GI

dysfunction with diarrhea or unformed stools, constipation,
bloating, and/or gastroesophageal reflux (GERD)[25]. The
exact percentage suffering from SIBO or GI problems varies
from study to study, depending on the age of the study popu-
lation and the different methodologies employed, but there is
a general consensus that GI problems are common in autism.
The cause of these SIBO problems in autism is unclear,
but it appears to partly relate to abnormal gut flora and
possibly to the excessive use of oral antibiotics which can
alter gut flora. Several studies have reported significantly
higher oral antibiotic use in children with autism vs. typi-
cal children [19, 26]. In this study, we also found that
children with autism and with SIBO were more frequently
transferred to NICU after delivery (P = 0.028). In addition,
Luna et al. [27] identified distinctive mucosal microbial
signatures in ASD children with functional gastrointesti-
nal disorders that correlate with cytokine and tryptophan
homeostasis. However, Gondalia et al. [6] found no dif-
ference between GI microbiota of children with autism
and their neurotypical siblings. They suggested that other
explanations for the gastrointestinal dysfunction in this
population should be considered including elevated anxi-
ety and self-restricted diets. More work should be carried
out to assess the relationship between SIBO problems and
autism.
Furthermore, we found that SIBO was associated with
worse symptoms of autism. However, it is difficult to estab-
lish whether the changes seen play a causative role or are
merely a consequence of the disease. Interestingly, the
effectiveness of oral, non-absorbable antibiotics in tem-
porarily reducing symptoms of autism [28] suggests that
the relationship may be causal, that is, we hypothesize that
SIBO may significantly contribute to symptoms of autism in

some children. Several possible mechanisms can be inferred.
First, propionate has severe neurological effects in rats and
Clostridia species are propionate producers. Studies by Mac-
Fabe et al. [29] have demonstrated that injecting propionate
directly into specific regions of rat brains in vivo can cause
significant behavioral problems. Second, differences in the
microbiota may also result in altered microbial metabolism
of aromatic amino acids, with consequent changes in sys-
temic metabolites (as reflected in urinary metabolite pro-
files), which could lead to neurological symptoms [30].
Third, the microbiota could also be involved in the disease
etiology via interactions with the immune system [31]. Some
of the possible mechanisms outlined above are more likely
to involve changes within the overall balance of the whole
microbial community, while others may be exerted by spe-
cific bacteria. Fourth, SIBO leads to steatorrhoea, vitamin
B12 absorptive impairment and also injury to the small
intestinal microvilli which itself causes malabsorption [32].
Zhang et al. [33] suggested that decrease in brain vitamin
B12 status across the lifespan that may reflect an adaptation
to increasing antioxidant demand, while accelerated deficits
due to GSH deficiency may contribute to neurodevelopmen-
tal and neuropsychiatric disorders. Finally, many pathogenic
Gram-negative bacteria contain lipopolysaccharide (LPS)
in their cell walls, which can cause damage in various tis-
sues including the brain [3]. LPS-induced inflammation in
the brain increases permeability of the blood–brain barrier
and facilitates an accumulation of high levels of mercury in
the cerebrum, which may aggravate ASD symptoms [34].
A test in rats showed that prenatal LPS exposure decreased
1 3

levels of glutathione [35], which is an important antioxidant
involved in heavy metal detoxification in the brain.
The strengths of our study include the fact that it is a
prospective study with a relatively larger sample, making
the results robust and generalizable. Furthermore, we col-
lected data on a wide range of potentially confounding risk
factors, allowing us to estimate the independent effect of
SIBO on ASD. Finally, this is the first study which chose
hydrogen glucose breath test to detect SIBO in children with
ASD. This method is simple and efficient, and has a broad
application prospects.
The following limitations of our study must also be
considered. First, our sample is still relatively young, and
the relationship between SIBO and later-onset ASDs will
require revisiting in future years. Second, the results from
even well-designed observational studies can be influenced
by residual confounding. For example, socioeconomic status
and dietary history would seem important, especially given
that the ASD group with SIBO were more likely to require
the NICU and show more severe symptoms of autism. How-
ever, in this study, the information about socioeconomic sta-
tus and dietary history were not obtained. Third, this was
a prospective observational study; we could not determine
whether SIBO treatment improves clinical manifestations
in children with autism. Properly designed treatment tri-
als are needed to confirm a causal link between SIBO and
ASD. Interestingly, Kang et al. [36] suggested that alter the
gut microbiome and virome can improve GI and behavio-
ral symptoms of ASD. Furthermore, probiotics are hypoth-
esized to positively impact gut microbial communities and
alter the levels of specific potentially harmful metabolites
in children with ASD [37]. Fourth, in addition, a decreased
value of vitamin B12 is an important clinical problem of

SIBO with potential neurological consequences [32, 33].
However, in this study we did not obtain the information
about the dosage of vitamin B12 in the autistic children.
Thus, we cannot determine the association of vitamin B12s
with SIBO and autism. Finally, one of weakness of this study
is in the assessment of SIBO itself, since there is no gold
standard way to diagnose SIBO and the accuracy of all cur-
rent tests, remains limited [38].
Conclusion
Children with ASD tend to suffer from severe SIBO prob-
lems. SIBO was significantly associated with worse symp-
toms of autism, demonstrating that children with SIBO
may also significantly contribute to symptoms of autism.
Strategies to treat SIBO or to improve gut microflora profile
through dietary modulation may help to alleviate gut disor-
ders common in children with autism.
Acknowledgements This work was supported by the National
Natu-
ral Science Foundation of China (30950031). The funding
organiza-
tions had no role in the design and concept of the study; the
collection,
management, analysis, and interpretation of the data; or the
prepara-
tion, review, or approval of the manuscript.
Compliance with ethical standards
Conflict of interest All authors have no conflicts of interest to
dis-
close.
References

1. Rai D, Lee BK, Dalman C, Golding J, Lewis G, Magnusson
C
(2013) Parental depression, maternal antidepressant use during
pregnancy, and risk of autism spectrum disorders: population
based case-control study. BMJ 346:f2059
2. Tu W, Yin C, Guo Y, Li SO, Chen H, Zhang Y, Feng YL,
Long
BH (2013) Serum homocysteine concentrations in Chinese chil-
dren with autism. Clin Chem Lab Med 51(2):e19–e22
3. Kang DW, Park JG, Ilhan ZE, Wallstrom G, Labaer J, Adams
JB,
Krajmalnik-Brown R (2013) Reduced incidence of Prevotella
and
other fermenters in intestinal microflora of autistic children.
PLoS
One 8(7):e68322
4. Tomova A, Husarova V, Lakatosova S, Bakos J, Vlkova B,
Babinska K, Ostatnikova D (2015) Gastrointestinal microbiota
in
children with autism in Slovakia. Physiol Behav 138:179–187
5. de Theije CGM, Wopereis H, Ramadan M, van Eijndthoven
T,
Lambert J, Knol J, Garssen J, Kraneveld AD, Oozeer R (2014)
Altered gut microbiota and activity in a murine model of autism
spectrum disorders. Brain Behav Immun 37:197–206
6. Gondalia SV, Palombo EA, Knowles SR, Cox SB, Meyer D,
Austin DW (2012) Molecular characterisation of gastrointestinal
microbiota of children with autism (with and without gastroin-
testinal dysfunction) and their neurotypical siblings. Autism
Res

5(6):419–427
7. Donowitz JR, Petri WA (2015) Pediatric small intestine
bacterial
overgrowth in low-income countries. Trends Mol Med 21(1):6–
15
8. Lupascu A, Gabrielli M, Lauritano EC, Scarpellini E, San-
toliquido A, Cammarota G, Flore R, Tondi P, Pola P, Gasbarrini
G, Gasbarrini A (2005) Hydrogen glucose breath test to detect
small intestinal bacterial overgrowth: a prevalence case–control
study in irritable bowel syndrome. Aliment Pharmacol Ther
22(11–12):1157–1160
9. Tan AH, Mahadeva S, Thalha AM, Gibson PR, Kiew CK,
Yeat
CM, Ng SW, Ang SP, Chow SK, Tan CT, Yong HS, Marras C,
Fox SH, Lim SY (2014) Small intestinal bacterial overgrowth in
Parkinson’s disease. Parkinsonism Relat Disord 20(5):535–540
10. Sartor RB (2008) Microbial influences in inflammatory
bowel
diseases. Gastroenterology 134(2):577–594
11. Fialho A, Fialho A, Schenone A, Thota P, McCullough A,
Shen B
(2016) Association between small intestinal bacterial
overgrowth
and deep vein thrombosis. Gastroenterol Rep 4(4):299–303
12. Geier DA, Kern JK, Geier MR (2013) A comparison of the
Autism Treatment Evaluation Checklist (ATEC) and the Child-
hood Autism Rating Scale (CARS) for the quantitative
evaluation
of autism. J Mental Health Res Intell Disabil 6(4):255–267

13. Cryan JF, Dinan TG (2012) Mind-altering microorganisms:
the
impact of the gut microbiota on brain and behaviour. Nat Rev
Neurosci 13(10):701–712
14. Leiby A, Mehta D, Gopalareddy V, Jackson-Walker S,
Horvath K
(2010) Bacterial overgrowth and methane production in children
with encopresis. J Pediatr 156:766–770
1 3
15. Parracho HMRT, Bingham MO, Gibson GR, McCartney AL
(2005) Differences between the gut microflora of children with
autistic spectrum disorders and that of healthy children. J Med
Microbiol 54(10):987–991
16. de Magistris L, Familiari V, Pascotto A, Sapone A, Frolli
A, Iar-
dino P, Carteni M, De Rosa M, Francavilla R, Riegler G, Milit-
erni R, Bravaccio C (2010) Alterations of the intestinal barrier
in
patients with autism spectrum disorders and in their first-degree
relatives. J Pediatr Gastroenterol Nutr 51(4):418–424
17. Finegold SM, Dowd SE, Gontcharova V, Liu C, Henley KE,
Wolcott RD, Youn E, Summanen PH, Granpeesheh D, Dixon D,
Liu M, Molitoris DR, Green JA (2010) Pyrosequencing study
of fecal microflora of autistic and control children. Anaerobe
16(4):444–453
18. Finegold SM, Summanen PH, Downes J (2017) Detection of

Clostridium perfringens toxin genes in the gut microbiota of
autistic children. Anaerobe 45:133–137
19. Adams JB, Johansen LJ, Powell LD, Quig D, Rubin RA
(2011)
Gastrointestinal flora and gastrointestinal status in children
with
autism—comparisons to typical children and correlation with
autism severity. BMC gastroenterology 11(1):1
20. Mello CS, Tahan S, Melli LC, Rodrigues MS, de Mello RM,
Sca-
letsky IC, de Morais MB (2012) Methane production and small
intestinal bacterial overgrowth in children living in a slum.
World
J Gastroenterol 18:5932–5939
21. dos Reis JC, de Morais MB, Oliva CA, Fagundes-Neto U
(2007)
Breath hydrogen test in the diagnosis of environmental
enteropa-
thy in children living in an urban slum. Dig Dis Sci 52:1253–
1258
22. Pereira SP, Khin-Maung-U TD, Duncombe VM, Nyunt-
Nyunt-
Wai JM (1991) A pattern of breath hydrogen excretion
suggesting
small bowel bacterial overgrowth in Burmese village children. J
Pediatr Gastroenterol Nutr 13:32–38
23. Molloy CA, Manning-Courtney P (2003) Prevalence of
chronic
gastrointestinal symptoms in children with autism and autistic
spectrum disorders. Autism 7(2):165–171

24. Nikolov RN, Bearss KE, Lettinga J, Erickson C, Rodowski
M,
Aman MG, McCracken JT, McDougle CJ, Tierney E, Vitiello
B, Arnold EL, Shah B, Posey DJ, Ritz L, Scahill L (2009) Gas-
trointestinal symptoms in a sample of children with pervasive
developmental disorders. J Autism Dev Disord 39:405–413
25. Ming X, Brimacombe M, Chaaban J, Zimmerman-Bier B,
Wagner
GC (2008) Autism spectrum disorders: concurrent clinical
disor-
ders. J Child Neurol 23(1):6–13
26. Adams JB, Holloway CE, George F, Quig D (2006)
Analyses of
toxic metals and essential minerals in the hair of arizona
children
with autism and their mothers. Biol Tr El Res 110:193–209
27. Luna RA, Oezguen N, Balderas M, Venkatachalam A,
Runge JK,
Versalovic J, Veenstra-VanderWeele J, Anderson GM, Savidge
T, Williams KC (2017) Distinct microbiome-neuroimmune
signatures correlate with functional abdominal pain in children
with autism spectrum disorder. Cell Mol Gastroenterol Hepatol
3(2):218–230
28. Sandler RH, Finegold SM, Bolte ER, Buchanan CP,
Maxwell AP,
Väisänen ML, Nelson MN, Wexler HM (2000) Short-term ben-
efit from oral vancomycin treatment of regressive-onset autism.
J
Child Neurol 15(7):429–435
29. MacFabe DF, Cain DP, Rodriguez-Capote K, Franklin AE,

Hoff-
man JE, Boon F, Taylor AR, Kavaliers M, Ossenkopp KP (2007)
Neurobiological effects of intraventricular propionic acid in
rats:
possible role of short chain fatty acids on the pathogenesis and
characteristics of autism spectrum disorders. Behav Brain Res
176(1):149–169
30. Louis P (2012) Does the human gut microbiota contrib-
ute to the etiology of autism spectrum disorders? Dig Dis Sci
57(8):1987–1989
31. Critchfield JW, Van Hemert S, Ash M, Mulder L, Ashwood
P
(2011) The potential role of probiotics in the management of
childhood autism spectrum disorders. Gastroenterol Res Pract
2011(2011):161358-1–161358-8. doi:10.1155/2011/161358
32. Zhang Y, Hodgson NW, Trivedi MS, Abdolmaleky HM,
Fournier
M, Cuenod M, Do KQ, Deth RC (2016) Decreased brain levels
of vitamin B12 in aging, autism and schizophrenia. PLoS One
11(1):e0146797
33. Khalighi AR, Khalighi MR, Behdani R, Jamali J, Khosravi
A,
Kouhestani Sh, Radmanesh H, Esmaeelzadeh S, Khalighi N
(2014) Evaluating the efficacy of probiotic on treatment in
patients
with small intestinal bacterial overgrowth (SIBO)—a pilot
study.
Indian J Med Res 140(5):604–608
34. Adams JB, Romdalvik J, Levine KE, Hu LW (2008)
Mercury
in first-cut baby hair of children with autism versus typically-

developing children. Toxicol Environ Chem 90:739–753
35. Zhu YG, Carvey PM, Ling ZD (2007) Altered glutathione
homeo-
stasis in animals prenatally exposed to lipopolysaccharide. Neu-
rochem Int 50:671–680
36. Kang DW, Adams JB, Gregory AC, Borody T, Chittick L,
Fas-
ano A, Khoruts A, Geis E, Maldonado J, McDonough-Means S,
Pollard EL, Roux S, Sadowsky MJ, Lipson KS, Sullivan MB,
Caporaso JG, Krajmalnik-Brown R (2017) Microbiota transfer
therapy alters gut ecosystem and improves gastrointestinal and
autism symptoms: an open-label study. Microbiome 5(1):10
37. Navarro F, Liu Y, Rhoads JM (2016) Can probiotics benefit
children with autism spectrum disorders? World J Gastroenterol
22(46):10093–10102
38. Niu XL, Liu L, Song ZX, Li Q, Wang ZH, Zhang JL, Li HH
(2016) Prevalence of small intestinal bacterial overgrowth in
Chinese patients with Parkinson’s disease. J Neural Transm
123(12):1381–1386
https://doi.org/10.1155/2011/161358Hydrogen breath test
to detect small intestinal bacterial overgrowth: a prevalence
case–control study in autismAbstract IntroductionPatients
and methodsHydrogen and methane breath testData
analysisResultsDiscussionConclusionAcknowledgements
References
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Contents lists available at ScienceDirect
Clinica Chimica Acta
journal homepage: www.elsevier.com/locate/cca
The fasting 13C-glucose breath test is a more sensitive
evaluation method for
diagnosing hepatic insulin resistance as a cardiovascular risk
factor than
HOMA-IR
Hirotaka Ezakia,b, Tomokazu Matsuuraa,⁎, Makoto Ayaorib,
Sae Ochia, Yoshihiro Mezakia,
Takahiro Masakia, Masanori Taniwakib, Takayuki Miyakeb,
Masami Sakuradab,
Katsunori Ikewakic
a Department of Laboratory Medicine, The Jikei University
School of Medicine, 3-25-8 Nishi-shimbashi, Minato-ku, Tokyo
105-8461, Japan
b Department of Cardiology, Tokorozawa Heart Center, 2-61-11
Kamiarai, Tokorozawa, Saitama 359-1142, Japan
c Division of Anti-aging and Vascular Medicine, National
Defense Medical College, 3-2 Namiki, Tokorozawa, Saitama
359-8513, Japan
A R T I C L E I N F O
Keywords:

Fasting 13C-glucose breath test
Homeostatic model assessment insulin
resistance
Hepatic insulin resistance
Cardiovascular risk factors
A B S T R A C T
Background: Although we previously reported the fasting 13C-
glucose breath test (FGBT) was useful for the
diagnosis of hepatic insulin resistance (IR), there has been no
report in an actual clinical setting. We therefore
performed the FGBT in patients with heart disease to assess the
difference in the diagnostic ability of HIR
between the FGBT and HOMA-IR; we also assessed the
relationship between the FGBT and known cardiovascular
risk factors.
Methods: Two hundred patients (100 with ischemic heart
disease [IHD], 50 with non-ischemic heart disease
[NIHD], and 50 with non-cardiac lifestyle-related disease
[NCD]) participated in this study. The data of 40
healthy volunteers [HV] was obtained in our previous study. We
evaluated the 13C excretion rate at 120 min
(C120) as the indicator of hepatic IR in the FGBT.
Results: The value of C120 in each disease group was
significantly lower than in HV, but the HOMA-IR in the IHD
and NCD groups was not significantly different from that in
HV. The value of C120 significantly correlated with
known cardiovascular risk factors.
Conclusions: These results indicated the FGBT is more sensitive
than HOMA-IR for evaluating hepatic IR as a
cardiovascular risk factor and is likely useful for managing
patients to prevent cardiovascular disease.
1. Introduction

Despite accumulating evidence showing that statins reduce the
risk
of coronary heart disease in both primary and secondary
prevention, a
residual risk of roughly 70% still remains [1]. This residual risk
pre-
sumably includes low high-density-lipoprotein (HDL)
cholesterolemia
and glucose intolerance based on insulin resistance (IR) [2].
Cardiac
diseases have been reported to progress under a glucose
intolerant state
with low hemoglobin A1C (HbA1C) levels [2], therefore
evaluating
hepatic IR is important to manage various cardiovascular risk
factors.
Glucose clamp tests are recognized as the gold-standard tests
for
diagnosing IR but are invasive and complicated to use for IR
screening.
Although the 75-g oral glucose tolerance test (OGTT) is widely
used for
diagnosing glucose intolerance, this test takes a long time to
perform
and is stressful for patients, requiring frequent blood sampling.
We
previously reported that the fasting 13C-glucose breath test
(FGBT) is
useful for diagnosing hepatic IR and diabetes mellitus (DM)
among
healthy volunteers and mild glucose intolerance patients [3].
The result
of FGBT was calculated from the concentration of the 13CO2 in
a pa-

tient’s expired gas. In a fasting state after taking 100 mg of
13C-glucose,
the rate of 13CO2/
12CO2 in the expired gas of a patient with hepatic IR
decreases compared to that of a healthy volunteer. This is
because the
glycolytic system pathway is suppressed and the
gluconeogenesis
pathway is activated in a fasting state when a patient develops a
hepatic
resistant state with impaired glucose tolerance [4].
Homeostatic model assessment insulin resistance (HOMA-IR) is
https://doi.org/10.1016/j.cca.2019.09.014
Received 31 May 2019; Received in revised form 13 September
2019; Accepted 28 September 2019
⁎ Corresponding author at: 3-25-8 Nishi-shimbashi, Minato-ku,
Tokyo 105-8461, Japan.
E-mail addresses: [email protected] (H. Ezaki),
[email protected] (T. Matsuura), [email protected] (M. Ayaori),
[email protected] (S. Ochi), [email protected] (Y. Mezaki),
[email protected] (T. Masaki), [email protected] (M. Taniwaki),
[email protected] (T. Miyake), [email protected] (M. Sakurada),
[email protected] (K. Ikewaki).
Clinica Chimica Acta 500 (2020) 20–27
Available online 10 October 2019
0009-8981/ © 2019 Elsevier B.V. All rights reserved.
T

http://www.sciencedirect.com/science/journal/00098981
https://www.elsevier.com/locate/cca
mailto:[email protected]
http://crossmark.crossref.org/dialog/?doi=10.1016/j.cca.2019.09
.014&domain=pdf
widely used as an indicator of IR. However, the reliability of
HOMA-IR
is reduced in patients with a high fasting blood glucose (FBG)
level
(> 140 mg/dl) or impaired insulin secretion; such conditions are
not an
issue with the FGBT. The values of HOMA-IR also reportedly
differ
among races [5], so whether or not the reference range of
HOMA-IR for
Caucasoids is applicable to Japanese populations for diagnosing
IR
remains unclear. Again, this issue does not affect the utility of
the FGBT.
The FGBT is a non-invasive and simple test. Furthermore, if the
results

of the FGBT are found to correlate with residual risk factors,
this test may
be useful for managing risk factors in the early pathologic
stage. To address
this issue, we investigated the relationship between the results
of the FGBT
and the disease profile and biochemical parameters by
performing the
Fig. 1. Study design. HV = healthy volunteer; IHD = ischemic
heart disease; NIHD = non-ischemic heart disease; NCD = non-
cardiac disease.
Table 1
Patient characteristics.
IHD (n = 100) NIHD (n = 50) NCD (n = 50) P value
Age (years old) 68.3 ± 8.9 66.0 ± 9.7 66.0 ± 12.3 0.265
Male gender (n, (%)) 82 (82.0%) 39 (78.0%) 34 (68.0%) 0.153
Hypertension (n, (%)) 47 (47.0%) 20 (40.0%) 34 (68.0%) 0.012
Dyslipidemia (n, (%)) 66 (66.0%) 30 (60.%) 33 (66.0%) 0.745
Diabetes (n, (%)) 41 (41.0%) 9 (18.0%) 8 (16.0%) 0.001
ischemic heart diseas (n, (%)) 100 (100%) 0 (0%) 0 (0%) –
non ischemic heart disease (n, (%)) 11 (11.0%) 50 (100%) 0
(0%) –
C120 (mmol/h) 0.245 ± 0.064 0.244 ± 0.055 0.255 ± 0.060
0.531
BMI (kg/m2) 24.1 ± 3.1 24.4 ± 2.4 24.3 ± 3.3 0.793
WBC (/mm3) 5920 ± 1570 5575 ± 1634 5535 ± 1467 0.256
hemoglobin (g/dl) 14.1 ± 1.6 14.0 ± 1.6 14.4 ± 1.4 0.429
platelet (104/mm3) 22.6 ± 5.1 21.7 ± 5.9 24.0 ± 5.8 0.107
TP (g/dl) 7.1 ± 0.4 7.1 ± 0.4 7.2 ± 0.4 0.317
Alb (g/dl) 4.2 ± 0.3 4.2 ± 0.2 4.3 ± 0.2 0.191
T-Bil (mg/dl) 0.7 ± 0.3 0.8 ± 0.3 0.8 ± 0.3 0.348

AST (U/L) 25.5 ± 10.0 23.8 ± 5.9 26.6 ± 8.6 0.274
ALT (U/L) 25.3 ± 12.9 21.3 ± 10.4 26.3 ± 12.8 0.084
ALP (U/L) 235 ± 81.8 204 ± 51.7 212 ± 59.3 0.022
γ-GTP (U/L) 37.3 ± 25.5 45.6 ± 31.1 41.5 ± 32.8 0.244
LDH (U/L) 194 ± 36.9 200 ± 37.4 196 ± 29.8 0.627
CPK (U/L) 127 ± 94.7 138 ± 99.7 149 ± 76.2 0.368
HDL-C (mg/dl) 50.7 ± 12.4 55.7 ± 13.5 59.6 ± 18.8 0.002
TG (mg/dl) 120 ± 58.7 114 ± 53.9 134 ± 71.5 0.247
LDL-C (mg/dl) 83.4 ± 24.7 107.7 ± 26.3 109.3 ± 27.6 < 0.001
UA (mg/dl) 5.7 ± 1.0 6.2 ± 1.5 5.8 ± 1.3 0.121
BUN (mg/dl) 16.9 ± 4.4 16.5 ± 4.9 16.1 ± 4.0 0.564
Cr (mg/dl) 0.86 ± 0.21 0.90 ± 0.27 0.85 ± 0.22 0.581
Na (mEq/L) 142 ± 2.0 142 ± 1.8 141 ± 1.9 0.014
K (mEq/L) 4.3 ± 0.4 4.3 ± 0.4 4.2 ± 0.3 0.922
CRP (ng/ml) 0.23 ± 0.5 0.15 ± 0.2 0.12 ± 0.2 0.175
BNP (pg/ml) 30.6 ± 53.4 58.9 ± 61.1 16.0 ± 15.9 < 0.001
FBG (mg/dl) 109 ± 25.4 101 ± 12.3 99 ± 15.8 0.007
HbA1C (%) 6.1 ± 0.7 5.7 ± 0.5 5.7 ± 0.5 < 0.001
IRI (µU/ml) 7.9 ± 7.8 9.9 ± 13.8 6.7 ± 4.0 0.199
HOMA-IR 2.1 ± 2.2 2.7 ± 4.5 1.7 ± 1.2 0.242
eGFR (ml/min/1.73 m2) 68 ± 15.6 67 ± 17.6 68 ± 15.6 0.873
Abbreviations: Alb, albumin; ALP, alkaline phosphatase; ALT
alanine amino transferase; AST, aspartate amino tranferase;
BMI, body mass index; BNP, brain na-
triuretic peptide; BUN, blood urea nitrogen; CPK, creatine
phosphokinase; Cr, creatinine; CRP, C-reactive protein; eGFR,
estimate glomerular filtration rate; FBG,
fasting blood glucose; HbA1C, hemogrobin A1C; HDL-C, high-
density lipoprotein cholesterol; HOMA-IR, homeostatic model
assessment insulin resistance; IHD,
ischemic heart disease; IRI, immunoreactive insulin; K,
potassium; LDH, lactate dehydrogenase; LDL-C, low-density
lipoprotein cholesterol; Na, sodium; NCD, non-
cardiac heart disease; NIHD, non-ischemic heart disease; T-Bil,
total bilirubin; TG, triglyceride; TP, total protein; UA, uric

acid; WBC, white blood cell; γ-GTP, gamma-
glutamyl transpeptidase
Values are presented as mean ± SD except for categorical
variables.
P value was calculated using the chi-squared test for categorical
values and using the one-way ANOVA for continuous values.
H. Ezaki, et al. Clinica Chimica Acta 500 (2020) 20–27
21
FGBT in 200 patients who regularly attended Tokorozawa Heart
Center, a
cardiovascular center in Saitama, Japan. We also assessed the
difference in
the results of the FGBT between patients with disease and
healthy volun-
teers, compared with HOMA-IR, a widely used indicator for IR,
as the
primary outcome, and we evaluated the relationship between the
results of
the FGBT and known residual risk factors for cardiovascular
disease as the
secondary outcome.
2. Materials and methods
2.1. Study population
Two hundred patients who regularly attended Tokorozawa Heart
Center, a cardiovascular center in Saitama, Japan, were
included.
Tokorozawa Heart Center is a regional secondary emergency

medical
facility with 30 beds that specializes in treating cardiovascular
disease
and primary prevention of cardiovascular disease.
The 200 patients included 100 ischemic heart disease (IHD) pa-
tients, 50 non-ischemic heart disease (NIHD) patients, and 50
non-
cardiac lifestyle-related disease (NCD) patients (see Fig. 1).
The NIHD
patients mainly had arrhythmia or non-ischemic heart failure;
they
were confirmed to have no coronary diseases using coronary
angio-
graphy or computed tomography before their inclusion in this
study.
The NCD patients were those with lifestyle-related diseases,
such as
hypertension, dyslipidemia, and DM, who regularly attended our
hos-
pital to manage their risk factors; they were confirmed to have
no or-
ganic heart disease using echocardiography before their
inclusion in
this study.
Fig. 2. The difference in the value of C120 and HOMA-IR
between the HV group and disease group. A: The difference in
the mean value of C120 (mmol/h) between the
HV group and disease group. There was a significant difference
between the 2 groups (p < 0.001). The P value was calculated
using Student’s t-test. B: The difference
in the mean value of HOMA-IR between the HV group and
disease group. There was a significant difference between the 2
groups (p = 0.020). The P value was
calculated using Student's t-test. Logarithmic transformation

was conducted before analyzing HOMA-IR using Student's t-
test. C: The difference in the mean value of
C120 (mmol/h) between the HV group and each disease profile.
The value of C120 was significantly higher in the HV group
than in any disease profile (IHD group,
NIHD group, and NCD group: p < 0.001, p < 0.001, p < 0.001
respectively). The P value was calculated using a one-way
analysis of variance. The P value
between 2 groups was calculated using Scheffe's method to
analyze C120. D: The difference in the mean value of HOMA-
IR between the HV group and each disease
profile. There were no significant differences between the HV
group and IHD group or between the HV group and NCD group
(p = 0.122, p = 1.000 respectively).
The value of HOMA-IR was significantly lower in the HV group
than in the NIHD group (p = 0.018). The P value was calculated
using a one-way analysis of variance.
The P value between 2 groups was calculated using Bonferroni's
method for analyzing HOMA-IR. HV = healthy volunteer; IHD
= ischemic heart disease;
NIHD = non-ischemic heart disease; NCD = non-cardiac
disease; C120 =
13C excretion rate at 120 min; HOMA-IR = homeostatic model
assessment insulin re-
sistance.
22
The exclusion criteria were (1) < 20 years old or ≥85 years old,
(2)
acute coronary syndrome, (3) end-stage renal disease (including

pa-
tients receiving hemodialysis), (4) type 1 DM, (5) pregnant or
may
become pregnant, (6) shock vitals, (7) scheduled to undergo
surgery or
endoscopic therapy within three months and required to stop
anti-
platelet therapy, and 8) doctor in charge objected to the
patient’s par-
ticipation.
We used the data of 40 healthy volunteers (HV group) for a
com-
parison with the disease group (combined IHD group, NIHD
group, and
NCD group). These data had been obtained in our previous
study [3].
2.2. Outcome evaluation and ethical considerations
The FGBT and fasting blood collection were performed in every
patient. The primary outcome was the difference in the value of
C120
(see details below) using the FGBT and HOMA-IR between the
disease
groups and HV group. The secondary outcomes were the
relationship
between the known coronary risk factors and the value of C120.
This study was registered with the University Hospital Medical
Information Network-Clinical Trials registry (UMIN-CTR
number:
UMIN000025662). The Ethics Committee of Tokorozawa Heart
Center
(Registration Number: 1504) and The Jikei University School of
Medicine (Registration Number: 18–188 [4850], 28–105 [8348])

ap-
proved this study protocol, which was in accordance with the
Declaration of Helsinki, and all patients gave their written
informed
consent to participate.
2.3. FGBT
The FGBT was performed at 6:00 a.m. in an overnight fasting
state
(last meal: 21:00). First, patients took 100 mg of glucose
labeled with
13C orally after having a control breath sample collected. Two
hours
later, at rest, patients had their breath sample taken again. 13C-
glucose
was created by replacing all carbon atoms with 13C. The 13C-
glucose
used in this study was D-Dlucose-U-13C6 (13C: 99 atom%;
Chlorella
Industry Co., Ltd., Tokyo, Japan). Breath samples were mailed
to the
Department of Laboratory Medicine, The Jikei University
School of
Medicine. The 13CO2-to-
12CO2 ratio was measured using a carbon di-
oxide carbon isotope ratio analyzer/spectral analyzer POC one
(Otsuka
Electronics Co., Ltd., Osaka, Japan.). We then calculated the
13C ex-
cretion rate (mmol/h) using the 13CO2-to
12CO2 ratio and patient’s body
surface area.

Our previous study demonstrated that the area under the curve
until
360 min (AUC360) of the
13C excretion kinetic curve after the ingestion
of labeled glucose reflected the efficiency of glucose
metabolism in the
liver [3]. The 13C excretion rate reached a maximum at 120 min
after
the start of FGBT and the 13C excretion rate at 120 min (C120)
showed a
strong correlation with the AUC360 value [3]. Furthermore, in
addition
to the AUC360 value [3], the C120 value showed high
diagnostic accu-
racy in the detection of hepatic IR. Because an AUC360 study is
time
consuming and difficult to perform for large numbers of
patients, we
used the C120 value to evaluate the hepatic IR of patients in
this study.
2.4. Biochemical parameters
Venous blood was collected in a fasting state. A complete blood
count, parameters reflecting the liver and renal function, serum
lipid
profile, FBG, fasting immunoreactive insulin levels,
hemoglobin A1C
(HbA1C), C-reactive protein, and brain natriuretic peptide
(BNP) were
analyzed. HOMA-IR was calculated by the following equation:
HOMA-
IR = (FBG × immunoreactive insulin levels)/405.

2.5. Statistical analyses
Categorical variables are presented as the frequency (%). A chi-
squared test was used to compare the distribution of categorical
vari-
ables among groups. Differences in C120 values among groups
were
compared using Student’s t-test, while differences in the
HOMA-IR
value were compared using Student’s t-test, after logarithmic
transfor-
mation. Quantitative variables were presented as the mean and
stan-
dard deviation. A parametric analysis was performed when
nonpara-
metric parameters showed a parametric distribution after
logarithmic
transformation. Nonparametric analyses were performed for
nonpara-
metric parameters after logarithmic transformation. Differences
in the
distribution of quantitative variables among three groups were
assessed
using a one-way analysis of variance. When a significant
difference was
identified among three groups, Bartlett’s test was used to test
the
homogeneity of variance. Differences between two groups were
com-
pared using the Scheffe test if the variables had equal variance
or
Bonferroni’s correction if the variables did not have equal
variance. The
correlation between C120 and quantitative variables was
assessed by
Pearson’s correlation coefficient if a variable was

parametrically dis-
tributed and by Spearman’s correlation coefficient if a variable
was not
parametrically distributed.
A multiple regression analysis was performed to analyze
variables
that had a significant correlation with C120. We calculated the
variance
inflation factor (VIF) to measure the degree of multi-
collinearity in the
multiple regression analysis. VIFs were calculated by taking a
predictor
and regressing it against all other predictors in the model. A
high cor-
relation with other predictors was represented by a VIF value of
> 5,
Table 2
Differences in glucose metabolism parameters between HV
group and disease group.
HV group (n = 62) Disease group (n = 200) P value vs. HV
group P value between groups
IHD (n = 100) P value vs. HV group NIHD (n = 50) P value vs.
HV group NCD (n = 50) P value vs. HV group
C120 (mmol/h) 0.345 ± 0.05 0.247 ± 0.06
*P < 0.001
0.245 ± 0.06
P < 0.001
0.244 ± 0.06
P < 0.001

0.255 ± 0.06
P < 0.001
P < 0.001
HOMA-IR 1.0 ± 0.4 2.2 ± 2.8
*P < 0.001
2.1 ± 2.2
P = 0.122
2.7 ± 4.5
P = 0.018
1.7 ± 1.2
P = 1.000
P = 0.020
Abbrevations: HbA1C, hemogrobin A1C; HOMA-IR,
homeostatic model assessment insulin resistance; HV, healthy
volunteer; IHD, ischemic heart disease; NCD, non-
cardiac heart disease; NIHD, non-ischemic heart disease.
Values are presented as mean ± SD.
P value was calculated using oneway ANOVA.
P value between 2 groups was calculated using the Scheffe's
method for analyzing C120.
P value between 2 groups was calculated using the Bonferroni's
method for analyzing HOMA-IR.
* P value was calculated using Student's t test. Logarithmic
transformation was conducted before analyzing HOMA-IR using
Student's t test.
23

while no correlation with other predictors was represented as a
VIF
value of 1. The correlations between HOMA-IR and quantitative
vari-
ables were analyzed in the same way as C120 after logarithmic
trans-
formation of HOMA-IR. Two-sided P values of < 0.05 were
considered
to indicate statistical significance. The descriptive assessments
and
statistical analyses were performed using STATA/IC 15.1
(StataCorp
LLC, College Station, TX, USA).
3. Results
We were able to obtain FGBT data and biochemical parameters
from
all participants. The patient characteristics are shown in Table
1. The
value of C120 in the disease group was significantly lower than
in the
HV group (0.245 ± 0.06 vs. 0.345 ± 0.05p < 0.001, Fig. 2A). Al-
though there were no significant differences in the value of
C120 among
the IHD, NIHD, and NCD groups (Table 1), the value of C120
in each
disease group (IHD group, NIHD group, and NCD group) was
sig-
nificantly lower than in the HV group (0.245 ± 0.06 vs.
0.345 ± 0.05p < 0.0001, 0.244 ± 0.05 vs. 0.345 ± 0.05p <
0.0001, 0.255 ± 0.06 vs. 0.345 ± 0.05p = 0.0008, respectively;
Table 2, Fig. 2C). Although the value of HOMA-IR in the

overall disease
group was significantly higher than in the HV group (2.2 ± 2.8
vs.
1.0 ± 0.4p = 0.020, Fig. 2B), there were no significant
differences
between the values in the IHD and NCD groups and the HV
group
(1.0 ± 0.4 vs. 2.1 ± 2.2, p = 0.122, 1.0 ± 0.4 vs. 1.7 ± 1.2,
p = 1.000, respectively; Table 2, Fig. 2D).
The value of C120 was significantly lower in men (p = 0.024,
Table 3) and DM patients (p < 0.001, Table 3) than female and
non-
DM patients, respectively. The value of C120 significantly
correlated
with the body mass index (BMI) (r = −0.205 p < 0.001), white
blood
cell (r = −0.209 p = 0.004), hemoglobin (r = −0.139 p = 0.049),
gamma-glutamyl transpeptidase (r = −0.201 p < 0.001), HDL-C
(r = 0.144 p = 0.042), C-reactive protein (r = −0.195 p = 0.006),
FBG (r = −0.360 p < 0.001), HbA1C (r = −0.323 p < 0.001), and
HOMA-IR (r = −0.145 p = 0.040) (Table 3). We performed a
multiple
regression analysis of these parameters, and only HbA1C was an
in-
dependently significant predictor of C120, as shown in Table 4.
We also
examined the relationship between HOMA-IR and these
parameters.
The HOMA-IR value was significantly higher in dyslipidemia
patients
than patients without dyslipidemia (p = 0.020), but there was no
sig-
nificant difference between DM and non-DM patients (p =
0.304;
Table 5). The HOMA-IR significantly correlated with the age

(r = −0.184 p = 0.009), BMI (r = 0.447 p < 0.001), white blood
cell
(r = 0.213 p = 0.003), hemoglobin (r = 0.231 p = 0.001), total
bilir-
ubin (r = −0.140 p = 0.049), alanine amino transferase (r =
0.324
p < 0.001), gamma-glutamyl transpeptidase (r = 0.197 p =
0.005),
lactate dehydrogenase (r = −0.177 p = 0.012), HDL-C (r =
−0.432
p < 0.001), triglyceride (r = 0.410 p < 0.001), BNP (r = −0.190
p = 0.007), and HbA1C (r = 0.185 p = 0.009) (Table 5). The
results of
the multiple regression analysis showed that the BMI (p <
0.001),
HDL-C (p = 0.004), and triglyceride (p = 0.007) were
independently
significant predictors of the HOMA-IR (Table 6).
4. Discussion
4.1. Discussion
In this study, we performed the FGBT in patients who had
cardio-
vascular disease or lifestyle-related disease requiring
medication in an
actual clinical setting. The FGBT results (value of C120) in
these patients
was significantly lower than in HVs. There were no significant
differ-
ences in the value of C120 among the three disease groups,
suggesting
that the value of C120 was already low in the patients with
lifestyle-
related diseases who had not yet developed cardiovascular

disease.
Regarding HOMA-IR, there was no significant difference in the
value
between the IHD group and HV group or between the NCD
group and
HV group. Although the value of C120 in the patients receiving
medical
intervention with lifestyle-related disease (i.e. the NCD group)
was si-
milarly low in the NIHD and IHD groups, the HOMA-IR in the
NCD and
IHD group did not differ significantly from that in the HV
group. These
findings suggested that C120 is a more sensitive indicator for
risk
management than HOMA-IR in the early clinical stage.
The value of C120 was significantly related to the gender,
prevalence
of DM, BMI, WBC, hemoglobin, gamma-glutamyl
transpeptidase, HDL-
C, C-reactive protein, FBG, HbA1C, and HOMA-IR. This
suggested that
Table 3
Differences in C120 about categorical variables and correlation
between C120
and quantitative variables.
P value
categorical variables
(+) (−)
Male gender 0.242 ± 0.057 0.265 ± 0.071 0.024

Hypertension 0.246 ± 0.051 0.249 ± 0.070 0.732
Dyslipidemia 0.242 ± 0.062 0.256 ± 0.058 0.113
Diabetes 0.224 ± 0.057 0.256 ± 0.060 < 0.001
ischemic heart disease 0.245 ± 0.064 0.249 ± 0.058 0.574
non-ischemic heart disease 0.243 ± 0.055 0.249 ± 0.063 0.535
quantitative variables
correlation coefficient
Age −0.008 0.911
BMI −0.205 0.004
WBC −0.209 0.003
hemoglobin −0.139 0.005
platelet 0.106 0.135
TP −0.058 0.412
Alb 0.038 0.594
T-Bil 0.049 0.488
AST −0.002 0.982
ALT −0.070 0.327
ALP −0.075 0.293
γ-GTP −0.201 0.004
LDH 0.080 0.263
CPK 0.024 0.737
HDL-C 0.144 0.042
TG −0.036 0.614
LDL-C −0.060 0.403
UA −0.009 0.896
BUN −0.079 0.269
Cr −0.014 0.850
Na −0.030 0.676
K 0.132 0.063
CRP −0.195 0.006
BNP −0.037 0.601
FBG −0.360 < 0.001
HbA1C −0.323 < 0.001
IRI −0.101 0.155

HOMA-IR −0.145 0.040
eGFR −0.053 0.454
Abbrevations: Alb, albumin; ALP, alkaline phosphatase; ALT
alanine amino
transferase; AST, aspartate amino tranferase; BMI, body mass
index; BNP, brain
natriuretic peptide; BUN, blood urea nitrogen; CPK, creatine
phosphokinase; Cr,
creatinine; CRP, C-reactive protein; eGFR, estimate glomerular
filtration rate;
FBG, fasting blood glucose; HbA1C, hemogrobin A1C; HDL-C,
high-density li-
poprotein cholesterol; HOMA-IR, homeostatic model
sistance; IHD, ischemic heart disease; IRI, immunoreactive
insulin; K, po-
tassium; LDH, lactate dehydrogenase; LDL-C, low-density
lipoprotein
cholesterol; Na, sodium; NCD, non-cardiac heart disease;
NIHD, non-ischemic
heart disease; T-Bil, total bilirubin; TG, triglyceride; TP, total
protein; UA, uric
acid; WBC, white blood cell; γ-GTP, gamma-glutamyl
transpeptidase.
Vaalues are presented as mean ± SD of C120 in the colums of
categorical
variables.
P value was calculated using the Student's t test in categorical
variables.
Correlation coefficient and p value were calculated using
Pearson's product
moment correlation coefficient if parameters were
parametrically distributed
and using Spearman's rank correlation coefficient if parameters
were not

Logarithmic transformation was conducted if needed.
24
the results of the FGBT were related to the residual risk factors
based on
the IR. A multivariate analysis showed that HbA1C was the
independent
predictor of C120. That meant that DM was the factor most
influential on
the value of C120. Therefore, to identify the predictors of C120
in the
non-DM state, we performed a multivariate analysis in the
patients
whose HbA1C were less than 6.2% (Table S7). The multiple
regression
analysis showed that the gender and BMI were independent
predictors
for C120. In contrast, HOMA-IR, which is widely used as an
indicator of
IR, showed no significant relationship with HbA1C according to
a
multiple regression analysis, but it was shown to be
significantly related
to the BMI, HDL-C, and triglyceride. This result was unchanged
in the
setting of non-DM patients (Table S8). These results suggested
that both
the FGBT and HOMA-IR were correlated with the residual risk
factors of
ischemic heart disease, but the FGBT was presumably related to

glucose
metabolism disorders based on IR, whereas HOMA-IR was
related to
dyslipidemia based on IR.
Although HOMA-IR is widely used for diagnosing IR and DM
[6],
the value of HOMA-IR in the Japanese population is reportedly
lower
than that in Caucasian populations, both in a healthy state and
in an
insulin-resistant state [5]. Therefore, false negative cases are
more
frequent in Japanese patients using global standard reference
values of
HOMA-IR. In addition, the reliability of HOMA-IR was
reported to be
reduced when the FBG level was > 140 mg/dl [7]. Using
HOMA-IR to
diagnose glucose metabolism disorders for Japanese patients
requires
close attention and care because of these problems. HOMA-IR
was re-
ported to have an inverse correlation with BNP [8]. Although
the same
inverse correlation was seen in this study (n = 200 r = −0.190
p = 0.007), HOMA-IR was significantly higher than in the HV
group
only in the NIHD group (Fig. 2D). Many patients with IR were
pre-
sumably included, even among heart failure patients, although
only the
relationship between BNP and HOMA-IR was an inverse
correlation.
BNP itself may reduce the value of HOMA-IR through several
proposed

mechanisms [8]. According to this theory, the IR may be under-
estimated in the NIHD group when evaluated by HOMA-IR
because the
BNP was significantly higher in the NIHD group than in the
other
groups. On the other hand, the value of C120 did not correlate
with the
BNP, so an underestimation of hepatic IR might not occur in the
NIHD
group when they are evaluated by the FGBT.
The cut-off values of C120 for diagnosing IR and DM differed
be-
tween genders in our previous study. The cut-off value of C120
for di-
agnosing IR in men was 0.285 mmol/h (sensitivity 84.6%,
specificity
84.2%) whereas that in women was 0.323 mmol/h (sensitivity
88.9%,
specificity 85.7%). The cut-off value of C120 for diagnosing
DM in men
was 0.261 mmol/h (sensitivity 100%, specificity 94.7%)
whereas that
in women was 0.308 mmol/h (sensitivity 100%, specificity
95.2%). In
this study, the average value of C120 in women was low
(0.265 ± 0.071), as was that in non-DM women (0.277 ± 0.075),
compared to our previous study. This result seems to suggest
that the
value of C120 was low in patients with cardiac disease or
lifestyle-re-
lated disease. The multivariate analysis showed that gender was
not a
significant predictor of the value of C120 in DM patients who
required

medical treatment in this study. Given this finding, the FGBT
might not
be suitable for diagnosing patients receiving medical
intervention, al-
though it may be suitable for evaluating the effects of lifestyle
im-
provement or exercise. To clarify this issue, chronological data
are
needed. A cohort study rather than a non-cross-sectional study
should
be performed.
Mizrahi, et al. reported that the breath test using 13C-glucose
re-
liably assessed the changes in the liver glucose metabolism, and
the
degree of IR evaluated using the HOMA-IR and the OGTT [9].
Hussain,
et al. reported that the 13CO2 appearance in exhaled breath
following a
standard OGTT with 13C-glucose provided a valid surrogate
index of the
whole-body glucose disposal rate as measured by the golden
standard
hyperinsulinemic euglycemic clamp, with good accuracy and
precision
[10]. Maldonado-Hernandez, et al. also reported that the breath
test
using 13C-glucose for adolescents was a suitable method for IR
screening with a reasonable sensitivity and specificity [11].
In those studies, 13C-glucose was used to perform the 75-g
OGTT,
and frequent breath sampling was needed in order to measure
the area
under the curve of the 13C excretion rate. In contrast, our

method (i.e.
FGBT) requires only a small amount of glucose (100 mg) and 2
breath
samples (baseline and 2 h after taking glucose), making it easy
and
simple for patients to perform. We previously reported that the
diag-
nostic ability of the FGBT using C120 was equivalent to that of
the FGBT
using the AUC360 required 10 breath samples [3]. In actual
clinical
settings, the FGBT using C120 is far easier on patients than that
using the
AUC360. The reports mentioned above using the OGTT
involved eva-
luations in a small number of HVs, and there have been no
reports
involving the breath test using glucose in patients with
cardiovascular
disease or lifestyle-related disease in actual clinical settings.
This study
showed that patients with lifestyle-related diseases already had
a low
value of C120 before developing cardiac disease, suggesting
that the
FGBT is feasible for the management of risk factors.
Several methods for evaluating IR exist, but most require a
blood
sample and are relatively invasive. The FGBT is a noninvasive
and
simple method that is correlated with residual risk factors of
cardio-
vascular disease, including glucose metabolism disorders, BMI,
dysli-
pidemia (low-HDL cholesterolemia), and inflammation. The

FGBT is
presumably useful for managing the risk factors in patients with
car-
diovascular disease and lifestyle-related disease.
4.2. Limitations
Several limitations associated with the present study warrant
men-
tion. Our present study was a single-center study, which might
have
caused selection bias. In addition, the study periods differed
between
the disease group (present study) and HV group (previous
study). This
difference in study period may have affected the results.
However, the
FGBT is still a simple test, and we used the same method and
machine
to measure the value of C120 in the same place using
13C-glucose pro-
duced by the same company. We therefore believe that there
was no
issue with comparing the data obtained in the present study to
those
Table 4
Results of the multiple regression analysis of C120.
Coefficient Standard error P value 95% confidential interval
VIF
Male gender −0.020 0.012 0.093 −0.044 to 0.003 1.53
BMI −0.002 0.002 0.186 −0.005 to 0.001 1.35
WBC −3.3exp(−6) 2.9exp(−6) 0.256 −9.0exp(−6) to 2.4exp(−6)

1.26
γ-GTP −0.005 0.008 0.481 −0.020 to 0.010 1.26
HbA1C −0.027 0.007 < 0.001 −0.041 to −0.013 1.18
hemogrobin −0.001 0.003 0.862 −0.007 to 0.006 1.58
HOMA-IR −0.0002 0.006 0.981 −0.013 to 0.012 1.48
HDL-C −0.005 0.020 0.815 −0.034 to 0.043 1.53
Abbreviations: BMI, body mass index; exp, exponential
function; HbA1C, hemogrobin A1C; HDL-C, high-density
lipoprotein cholesterol; HOMA-IR, homeostatic
model assessment insulin resistance; WBC, white blood cell;
VIF, variance inflation factor; γ-GTP, gamma-glutamyl
transpeptidase
Logarithmic transformation was conducted before analyzing if
needed.
25
from our previous study.
This study was a cross-sectional study, so longitudinal studies
may
be needed in order to clarify whether or not the FGBT can
predict the
cardiovascular disease onset risk.
5. Conclusions
The value of C120 was significantly lower in the IHD group,
NIHD
group, and NCD group than in the HV group, in contrast to
findings

concerning HOMA-IR. The value of C120 significantly
correlated with
the glucose metabolism, BMI, dyslipidemia, and inflammation.
Our
observations suggest that the FGBT is a useful test for
managing car-
diovascular risk factors.
Declaration of Competing Interest
The authors have read the journal’s policy on conflicts of
interest
and have none to declare in association with this manuscript.
All au-
thors have read the journal’s authorship agreement and have
reviewed
and approved this manuscript.
Acknowledgements
We are grateful to Ms. Ristuko Nakayama, a technician in the
Department of Laboratory Medicine of The Jikei University
School of
Medicine, for measuring all of the FGBT samples and for her
fast and
accurate work. We also thank the outpatient medical clerks of
Tokorozawa Heart Center, especially Ms. Yuki Kusama the
chief out-
patient medical clerk, for their kind support.
This research was supported by The Jikei University Research
Fund
for Graduate Students and supported in part by the Grant-in-Aid
for
Scientific Research from the Japan Society for the Promotion of

Science
(JSPS KAKENHI Grant Number JP16H03044) and a research
grant from
the Uehara Foundation and the Research Program on Hepatitis
of the
Japan Agency for Medical Research and Development, AMED
(Grant
Numbers JP18fk0210009 and JP18fk0310112).
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://
doi.org/10.1016/j.cca.2019.09.014.
References
[1] P. Libby, The forgotten majority: unfinished business in
cardiovascular risk re-
duction, J. Am. Coll. Cardiol. 46 (7) (2005) 1225–1228.
[2] I. Saito, Y. Kokubo, K. Yamagishi, H. Iso, M. Inoue, S.
Tsugane, Diabetes and the risk
of coronary heart disease in the general Japanese population:
the Japan Public
Health Center-based prospective (JPHC) study, Atherosclerosis
216 (1) (2011)
187–191.
[3] K. Tanaka, T. Matsuura, D. Shindo, Y. Aida, Y. Matsumoto,
K. Nagatsuma, M. Saito,
H. Ishii, H. Abe, F. Tanaka, T. Shimada, K. Nakada, K.
Ikewaki, Y. Aizawa, H. Tajiri,
M. Suzuki, Noninvasive assessment of insulin resistance in the
liver using the fasting
(13)C-glucose breath test, Transl. Res. 162 (3) (2013) 191–200.

[4] P. Staehr, O. Hother-Nielsen, H. Beck-Nielsen, M. Roden,
H. Stingl, J.J. Holst,
Table 5
Differences in HOMA-IR about categorical variables and
correlation between
HOMA-IR and quantitative variables.
P value
categorical variables
(+) (−)
Male gender 2.1 ± 3.1 2.0 ± 1.5 0.404
Hypertension 2.3 ± 3.2 2.0 ± 2.3 0.254
Dyslipidemia 2.5 ± 3.7 1.6 ± 1.1 0.042
Diabetes 2.1 ± 1.5 2.2 ± 3.2 0.304
ischemic heart disease 2.1 ± 2.2 2.2 ± 3.3 0.214
non-ischemic heart disease 2.5 ± 4.1 2.0 ± 2.0 0.957
quantitative variables
correlation coefficient
Age −0.184 0.009
BMI 0.447 < 0.001
WBC 0.213 0.003
hemoglobin 0.231 0.001
platelet −0.076 0.282
TP −0.012 0.867
Alb −0.032 0.652
T-Bil −0.140 0.049
AST 0.022 0.760
ALT 0.324 < 0.001
ALP 0.061 0.392
γ-GTP 0,1968 0.005
LDH −0.177 0.012

CPK −0.104 0.144
HDL-C −0.432 < 0.001
TG 0.410 < 0.001
LDL-C 0.096 0.175
UA 0.105 0.138
BUN −0.014 0.848
Cr 0.115 0.106
Na −0.013 0.857
K −0.061 0.394
CRP 0.136 0.055
BNP −0.190 0.007
FBG 0.381 < 0.01
HbA1C 0.186 0.009
IRI 0.968 < 0.001
eGFR −0.049 0.493
Abbreviations: Alb, albumin; ALP, alkaline phosphatase; ALT
alanine amino
transferase; AST, aspartate amino tranferase; BMI, body mass
index; BNP, brain
natriuretic peptide; BUN, blood urea nitrogen; CPK, creatine
phosphokinase; Cr,
creatinine; CRP, C-reactive protein; eGFR, estimate glomerular
filtration rate;
FBG, fasting blood glucose; HbA1C, hemogrobin A1C; HDL-C,
high-density li-
poprotein cholesterol; HOMA-IR, homeostatic model
sistance; IHD, ischemic heart disease; IRI, immunoreactive
insulin; K, po-
tassium; LDH, lactate dehydrogenase; LDL-C, low-density
lipoprotein
cholesterol; Na, sodium; NCD, non-cardiac heart disease;
NIHD, non-ischemic
heart disease; T-Bil, total bilirubin; TG, triglyceride; TP, total
protein; UA, uric

acid; WBC, white blood cell; γ-GTP, gamma-glutamyl
transpeptidase.
Vaalues are presented as mean ± SD of HOMA-IR in the colums
of categorical
variables.
P value was calculated using the Student's t test in categorical
variables.
Correlation coefficient and p value were calculated using
Pearson's product
moment correlation coefficient if parameters were
parametrically distributed
and using Spearman's rank correlation coefficient if parameters
were not
Logarithmic transformation was conducted if needed.
Table 6
Results of the multiple regression analysis of HOMA-IR.
Coefficient Standard
error
P value 95% confidential
interval
VIF
Age −0.007 0.005 0.194 −0.017 to 0.003 1.37
BMI 0.065 0.017 < 0.001 0.031 to 0.098 1.31
WBC 8.3exp(−6) 0.00003 0.795 −0.00005 to
0.00007
1.26
hemogrobin 0.005 0.036 0.884 −0.066 to 0.076 1.57
ALT 0.179 0.120 0.137 −0.058 to 0.416 1.65

LDH −0.002 0.001 0.166 −0.005 to 0.001 1.19
HDL-C −0.612 0.210 0.004 −1.027 to −0.198 1.47
TG 0.293 0.107 0.007 0.081 to 0.505 1.48
γ-GTP 0.032 0.088 0.713 −0.141 to 0.206 1.43
BNP −0.061 0.048 0.202 −0.156 to 0.033 1.45
HbA1C 0.073 0.077 0.347 −0.080 to 0.226 1.20
Abbreviations: ALT alanine amino transferase; BMI, body mass
index; BNP,
brain natriuretic peptide; exp, exponential function; HbA1C,
hemogrobin A1C;
HDL-C, high-density lipoprotein cholesterol; HOMA-IR,
homeostatic model as-
sessment insulin resistance; LDH, lactate dehydrogenase; TG,
triglyceride; WBC,
white blood cell; VIF, variance inflation factor; γ-GTP, gamma-
glutamyl trans-
peptidase.
Logarithmic transformation was used before analyzing if
needed.
26
http://refhub.elsevier.com/S0009-8981(19)32061-3/h0005

P.K. Jones, V. Chandramouli, B.R. Landau, Hepatic
autoregulation: response of
glucose production and gluconeogenesis to increased
glycogenolysis, Am. J.
Physiol. Endocrinol. Metab. 292 (5) (2007) E1265–E1269.
[5] M. Fukushima, H. Suzuki, Y. Seino, Insulin secretion
capacity in the development
from normal glucose tolerance to type 2 diabetes, Diab. Res.
Clin. Pract. 66 (Suppl
1) (2004) S37–S43.
[6] A. Morimoto, Y. Tatsumi, F. Soyano, N. Miyamatsu, N.
Sonoda, K. Godai, Y. Ohno,
M. Noda, K. Deura, Increase in homeostasis model assessment
of insulin resistance
(HOMA-IR) had a strong impact on the development of type 2
diabetes in Japanese
individuals with impaired insulin secretion: the Saku study,
PLoS One 9 (8) (2014)
e105827.
[7] R. Muniyappa, S. Lee, H. Chen, M.J. Quon, Current
approaches for assessing insulin
sensitivity and resistance in vivo: advantages, limitations, and
appropriate usage,
Am. J. Physiol. Endocrinol. Metab. 294 (1) (2008) E15–E26.
[8] Y. Inoue, M. Kawai, K. Minai, K. Ogawa, T. Nagoshi, T.
Ogawa, M. Yoshimura, The
impact of an inverse correlation between plasma B-type

natriuretic peptide levels
and insulin resistance on the diabetic condition in patients with
heart failure,
Metabolism 65 (3) (2016) 38–47.
[9] M. Mizrahi, G. Lalazar, T. Adar, I. Raz, Y. Ilan, Assessment
of insulin resistance by a
13C glucose breath test: a new tool for early diagnosis and
follow-up of high-risk
patients, Nutr. J. 9 (2010) 25.
[10] M. Hussain, M. Jangorbhani, S. Schuette, R.V. Considine,
R.L. Chisholm,
K.J. Mather, 13C-glucose breath testing provides a noninvasive
measure of insulin
resistance: calibration analyses against clamp studies, Diab.
Technol. Ther. 16 (2)
(2014) 102–112.
[11] J. Maldonado-Hernandez, A. Martinez-Basila, A. Salas-
Fernandez, J.R. Navarro-
Betancourt, M.I. Pina-Aguero, M. Bernabe-Garcia, The 13C-
Glucose breath test for
insulin resistance assessment in adolescents: comparison with
fasting and post-
glucose stimulus surrogate markers of insulin resistance, J.
Clin. Res. Pediatr.
Endocrinol. 8 (4) (2016) 419–424.
27

http://refhub.elsevier.com/S0009-8981(19)32061-3/h0055The
fasting 13C-glucose breath test is a more sensitive evaluation
method for diagnosing hepatic insulin resistance as a
cardiovascular risk factor than HOMA-IRIntroductionMaterials
and methodsStudy populationOutcome evaluation and ethical
considerationsFGBTBiochemical parametersStatistical
analysesResultsDiscussionDiscussionLimitationsConclusionsmk
:H1_13Acknowledgementsmk:H1_16Supplementary
dataReferences

ORIGINAL ARTICLE
Carbon-14 urea breath test: does it work in patients with partial
gastric resection?
Fuat Dede1,5 • Hüseyin Civen2 • Faysal Dane3 • Mehmet
Aliustaoglu4 •
Serdar Turhal3 • Halil Turgut Turoglu1 • Sabahat Inanir1
Received: 3 March 2015 / Accepted: 8 July 2015 / Published
online: 18 July 2015
� The Japanese Society of Nuclear Medicine 2015
Abstract
Objective The diagnostic value of Carbon-14 urea breath
test (C-14 UBT) in the detection of Helicobacter pylori (H.
pylori) infection in non-operated patients has been proved.
However, the efficacy of C-14 UBT in patients with partial
gastric resection (PGR) has not been evaluated yet. Herein,
the results of the C-14 UBT and H. pylori stool antigen test
(HpSAT) in this patient group were compared with the
endoscopic findings.

Methods Multi-breath samples C-14 UBT and HpSAT
were performed in all patients on the same day. Histology
was used as a gold standard for testing C-14 UBT and
HpSAT diagnostic efficacies.
Results 30 patients (mean age: 54.6 ± 11 year) with
PGR were included. The sensitivity and specificity of
standard C-14 UBT were 29 and 100 %, respectively.
When breath samples were collected at 20th min, and[35
CPM was selected as radioactivity threshold, the sensitivity
raised to 86 % without any loss of specificity. The
specificity and sensitivity of the HpSAT were 71 and 96 %,
respectively.
Conclusions The sensitivity of the standard C-14 UBT
was very poor for patients with PGR, and results of HpSAT
were superior in this population. Certain modifications are
needed if C-14 UBT is to be used in PGR patients.
Keywords Carbon-14 urea breath test � Stool antigen
test � Partial gastrectomy � Gastric cancer � Helicobacter
pylori
Introduction

After the discovery of Helicobacter pylori (H. pylori) in
1982, epidemiologic studies have revealed that it is a very
common pathogen in the society that has infected nearly
half of the world’s population [1, 2]. Geographic area, age,
race, and socioeconomic status determine the prevalence of
H. pylori infection [3]. H. pylori plays an important role in
the development of duodenal ulcer (responsible for
90–95 % of all duodenal ulcers), atrophic gastritis and
gastric cancer (represents nearly 5.5 % of all cancers and
25 % of all infection-related cancers) [4–6]. Because of
this, diagnosing this bacteria and thereafter starting multi-
drug eradication therapy is important. Invasive (endoscopy,
histology, rapid urease test, and culture) and non-invasive
[serology, urea breath test (UBT), and H. pylori stool
antigen test (HpSAT)] methods are used to diagnose this
microorganism [7].
UBT is a very successful method for both initial diag-
nosis of H. pylori and monitoring response to treatment [7].

The principles and mechanisms of the test are as follows:
The labeled urea [with either non-radioactive carbon-13
(C-13) or radioactive carbon-14 (C-14)] in the test material
& Fuat Dede
[email protected]
1
Department of Nuclear Medicine, Marmara University
School of Medicine, Istanbul, Turkey
2
Nuclear Medicine Clinic, Kocaeli State Hospital, Kocaeli,
Turkey
3
Department of Medical Oncology, Marmara University
School of Medicine, Istanbul, Turkey
4
Internal Medicine Clinic, Kartal Dr. Lutfi Kirdar Research
and Training Hospital, Istanbul, Turkey
5
Nukleer Tip Anabilim Dali, S.B. Marmara Universitesi
Pendik Egitim ve Arastirma Hastanesi, -1 kat A1 Blok Fevzi
Cakmak Mahallesi Mimar Sinan Caddesi No:41 Ustkaynarca,

Pendik, Istanbul, Turkey
123
Ann Nucl Med (2015) 29:786–791
DOI 10.1007/s12149-015-1005-3
1005-3&domain=pdf
1005-3&domain=pdf
is degraded to carbon dioxide (CO2) and ammonia with the
presence of urease, an enzyme that is synthesized by H.
pylori. The labeled CO2 is then absorbed from gastric
mucosa and exhaled. The detection of the labeled CO2 in
the exhaled breath confirms the diagnosis of H. pylori.
C-14 UBT is a cheap and rapid test that does not require a
test meal [8]. On the other hand, the need for authorized
centers for handling radioactive material and transport
problems limit its usage. Since C-13 is a stable isotope, it
can be safely used in children and childbearing women.
The major disadvantage of C-13 UBT is its higher cost.

The risk of developing cancer in the residual stomach is
increased in patients who underwent partial gastric resec-
tion (PGR) due to benign (ulcer, etc.) or malignant causes
[9–11]. The incidence of gastric stump cancer reached up
to 2 % in this group [12]. As in non-operated patients,
atrophic gastritis, intestinal metaplasia, dysplasia, and
finally gastric cancer are closely related with the H. pylori
infection in PGR patients [1]. Therefore, these high-risk
cases should be screened for H. pylori, and eradication
therapy should be given when the infection is detected [9–
11].
The importance of UBT in patients without history of
gastric surgery is undebatable [13, 14]. However, studies
with C-13 UBT showed that with its relatively low sensi-
tivity and specificity rates (77 and 89 %, respectively), the
test failed to meet the expectations after PGR [15–18].
Decreased gastric volume, decreased bacterial load, rapid
gastric emptying, and increased gastric pH could be

responsible for the failure of the C-14 UBT in partially
gastrectomized patients [17, 19, 20]. Certain modifications
were recommended in order to suppress the effects of these
factors. On the other hand, the diagnostic performance of
C-14 UBT in this patient group has not been fully inves-
tigated. The aim of this study was to evaluate the perfor-
mance of the standard and modified C-14 UBT and to
compare results with HpSAT and endoscopy in patients
with PGR.
Materials and methods
Patients
A total of 30 gastric cancer patients (F/M = 8/22; mean
age = 54.6 ± 11 years; range = 30–74 years) with PGR
were included in this prospective study. Billroth II pro-
cedure was performed in 26 patients (87 %), Roux-en-Y
anastomosis in 3 patients (10 %), and wedge resection
was done in one case (3 %). The pathology was reported
as adenocarcinoma in all but two patients (94 %). Gas-

trointestinal stromal tumor was the diagnosis in one
patient (3 %) and maltoma in the other one (3 %).
Patients who received eradication therapy for H. pylori
after surgery and patients who were found to have taken
medication (bismuth, antibiotics, proton pump inhibitors,
H2 blockers, and antacids) 4 weeks before the diagnostic
tests were excluded from the study. This study was
approved by the institutional ethics committee and all
patients gave informed consent for participation in the
study.
Histopathological analysis
All patients underwent postoperative routine fiber-optic
esophagogastroscopy 15 days to 4 weeks (median
2.5 weeks) before the non-invasive tests (C-14 UBT and
HpSAT). During procedure, multiple mucosal biopsy
samples were obtained and stained with hematoxylin and
eosin and modified Giemsa. Specimens were examined for
the presence of H. pylori. Histology was used as a gold

standard for testing C-14 UBT and HpSAT diagnostic
efficacies.
Carbon-14 Urea breath test
After an overnight fast, C-14 UBT (Heliprobe
�
System,
Kibion AB, Uppsala, Sweden) was performed for all
enrolled patients. After ingestion of 37 kBq (1 lCi) C-14
Urea capsule with 50 mL water, the breath samples were
collected at 10th, 20th, and 30th min after ingestion.
During this 30-minute C-14 UBT urea reaction period,
patients lay on the left side horizontally. The results were
expressed as both counts per minute (CPM), and grading
[0–1 (negative for H. pylori infection, CPM B 50), and 2
(positive for H. pylori infection, CPM [ 50)] as suggested
by the manufacturer.
H. pylori stool antigen test (HpSAT)
For all cases, HpSAT was performed simultaneously with
14
C-UBT on the same day. H. pylori antigens in feces were
investigated by monoclonal antibodies with one-step col-

ored immunochromatography (ImmunoCard STAT!
HpSA, Meridian Diagnostics Inc., Cincinnati, Ohio, USA)
technique according to the standard manufacturer
recommendations.
Statistics
Statistical analysis was performed by GraphPad InStat
Version 3.00 (GraphPad Software Inc, Sandiego, Califor-
nia, USA) and MedCalc Version 11.6.1.0 (MedCalc Soft-
ware, Mariakerke, Belgium). Based on analyzed data,
Kruskal–Wallis test (non-parametric ANOVA), the Mann–
Whitney U-test, Spearman non-parametric correlation,
Ann Nucl Med (2015) 29:786–791 787
123
ROC curve analysis, and comparison of ROC curves were
used. A p value less than 0.05 was considered significant.
Results
The interval between the surgery and UBT ranged from

27 days to 21 years (mean = 27.7 ± 47 months; med-
ian = 14 months). H. pylori was detected in 7 patients
(23.3 %) at follow-up endoscopy. The mean ages of H.
pylori positive and negative patients were 53 ± 7 and
55 ± 23 years, respectively. No statistically significant
difference in terms of age was detected between these two
groups (Mann–Whitney U-test).
UBT results
With standard C-14 UBT criteria, H. pylori was detected in
2 patients at 10th min, 4 patients at 20th min, and 5 patients
at 30th min. The sensitivity rates for each time point were
29, 57, and 71 %, respectively, while the specificity was
100 % for all (Table 1).
UBT radioactivity counts (CPM)
The C-14 UBT radioactivity counts in non-operated H.
pylori (?) patients were reported to change between 69
CPM and 770 CPM (median and mean 269 and 300 CPM,
respectively) [21]. When the radioactivity counts in H.

pylori positive and negative patients were analyzed sepa-
rately, we found that the breath sampling time did not have
a statistically significant impact on radioactivity count rates
(Kruskal–Wallis test, P [ 0.05, Table 2). When endoscopy
was assumed to be the gold standard, the radioactivity
thresholds for 10th, 20th, and 30th min were found as
[23, [35, and [29 CPM, respectively, by ROC curve
analysis (Fig. 1). The sensitivity, specificity, negative
predictive value (NPV), positive predictive value (PPV),
and accuracy are presented in Table 1. Although no sta-
tistically significant difference was found between three
ROC curves, the best performance was achieved with 20th
min breath sampling and radioactivity threshold[35 CPM.
HpSAT results
It gave true positive results in 5 patients and false-positive
result in one patient. The sensitivity and specificity of
HpSAT in our study population were 71.4 and 95.7 %,
respectively (Table 1).
Elapsed time after surgery

Although not statistically significant, inverse relation
between the prevalence of H. pylori and the elapsed time
after surgery was found in partially gastrectomized patients
(Spearman non-parametric correlation, r: -0.69, p [ 0.05,
Fig. 2).
Discussion
Regardless of the type and cause of the surgery, patients
with PGR are prone to developing gastric cancer [9–11].
Enterogastric reflux and H. pylori colonization in the
residual stomach are the main risk factors for the occur-
rence of malignancy [16]. In this group of subjects,
Table 1 Results of C-14 UBT
and HpSAT
t
a
Test Sensitivity Specificity NPV PPV Accuracy
Threshold
b
C-14 UBT
10 [50c 28.6 100 82.1 100 83.3

20 [50c 57.1 100 88.5 100 90
30 [50c 71.4 100 92 100 93
10 [23 85.7 86.9 95.2 66.7 87
20 [35 85.7 100 95.8 100 97
30 [29 71.4 100 92 100 93
HpSAT 71.4 95.7 91.7 83 90
C-14 UBT
d
and HpSAT 85.7 95.7 95.7 85.7 93.3
Bold values indicate better results
NPV negative predictive value, PPV positive predictive value,
UBT urea breath test, HpSAT H. pylori stool
antigen test
a
breath sampling time (minute)
b
counts per minute (CPM)
c
standard radioactivity threshold
d
Standard C-14 UBT
788 Ann Nucl Med (2015) 29:786–791
123
screening for H. pylori is important in order to start multi-

drug eradication therapy after diagnosis for preventing
undesirable consequences. The utility of UBT in normal
population has been proved. However, its usage in PGR is
controversial due to the reported relatively low sensitivity
and specificity rates (77 and 89 %, respectively) [15, 16].
Up to now, all of the studies investigating the performance
of UBT in partially gastrectomized patients have used C-13
non-radioactive isotope as screening tool. Although no
statistically significant difference in terms of sensitivity
and specificity was found between C-13 and C-14 UBT in
normal population, the exact results of C-14 UBT in this
patient group have not been revealed yet [22].
Herein, we studied multi-breath sample C-14 UBT in
patients who underwent distal subtotal gastrectomy due to
gastric malign tumors. Similar to previous results obtained
with C-13 UBT, very low sensitivity rate (28.6 %) was
found for standard C-14 UBT (10th min breath sample and
Table 2 Comparison of

radioactivity counts at 10th,
20th, and 30th min in H. Pylori
(?) and (-) patients
10th min 20th min 30th min p:
H. pylori (?) (n:7) 65.1 ± 72 CPM 77 ± 66 CPM 72 ± 58 CPM
0.84*
H. pylori (-) (n:23) 15.3 ± 10 CPM 14.3 ± 9 CPM 15.4 ± 8 CPM
0.80*
p: 0.0011** <0.0001** 0.0174**
p values in bold and italics are statistically significant
CPM counts per minute
* Kruskal–Wallis test (non-parametric ANOVA)
** Mann–Whitney U-test
Fig. 1 ROC curves generated from radioactivity counts
belonging to
10th, 20th, and 30th min breath samples
Fig. 2 Statistically insignificant inverse relation between the
prevalence of H. pylori and the elapsed time after surgery (r: -
0.69, p [ 0.05)
Ann Nucl Med (2015) 29:786–791 789
123

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