1) The study examined 310 children with autism spectrum disorder (ASD) and 1240 typical children as controls to assess the prevalence of small intestinal bacterial overgrowth (SIBO) using hydrogen breath tests.
2) The results found that 31.0% of children with ASD had SIBO, compared to 9.3% of typical children, which was a statistically significant difference.
3) Children with both ASD and SIBO had significantly higher median Autism Treatment Evaluation Checklist (ATEC) scores than children without ASD or SIBO, indicating that SIBO may contribute to worse autism symptoms.
2. 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-
3. 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
http://crossmark.crossref.org/dialog/?doi=10.1007/s00787-017-
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
4. 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
5. 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-
6. 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
7. 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 parents, NBS results, whether ASD diagnosis had been
confirmed or expected before follow-up, and family history
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
8. 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
9. 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
236 Eur Child Adolesc Psychiatry (2018) 27:233–240
1 3
in 60 (62.5%) of the 96 children with bacterial overgrowth
10. (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 parents, NBS results, whether ASD diagnosis had been
confirmed or expected before follow-up, and family history
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
11. 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
237Eur Child Adolesc Psychiatry (2018) 27:233–240
1 3
with autism and with SIBO was significantly high when
12. 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
13. 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,
14. 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
238 Eur Child Adolesc Psychiatry (2018) 27:233–240
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
15. 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
16. 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
17. 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
239Eur Child Adolesc Psychiatry (2018) 27:233–240
1 3
18. 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
19. 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 …
Course outcomes: This assessment enables the student to meet
the following course outcomes:
1. Explore the DNP role and advanced practice issues within
collaborative teams across diverse healthcare systems. (PO 1, 6,
8, 9)
2. Examine nursing ways of knowing and the development of
nursing science. (PO 3, 5, 9)
3. Analyze the concepts and principles used in theory
development in nursing. (PO 3, 5. 9)
4. Differentiate research, quality improvement, and evidence
based practice as it relates to the role of the DNP. (PO 3, 5, 6)
5. Demonstrate evidence search techniques to support
translation science (PO 3, 5)
6. Critically appraise level and quality of evidence (PO 3, 5)
7. Translate evidence at the microsystem, mesosystem, and
macrosystem levels of healthcare systems (PO 2, 3, 4, 5, 6, 9)
Due date: Sunday 11:59 p.m. MT at the end of Week 7. The
Late Assignment Policy applies to this assignment.
Total points possible: 100 points
20. Preparing the Assignment
Follow these guidelines when completing each component of
this assignment. Contact your course faculty if you have
questions. It is each student's responsibility to save and
maintain all artifacts required in the e-Portfolio (Links to an
external site.).
1. Write a brief 1-2 paragraph weekly reflection addressing the
questions posed in the Reflect section of each weekly module.
Edit your ePorfolio Reflection to include each weekly
reflection.
2. Include the following sections in your ePorfolio Reflection.
Week 1
· What were the most important concepts you learned in week
1?
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· How will they prepare you for your future role as a DNP-
prepared nurse?
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ways do you feel unprepared?
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objectives.
· What do you value most about your learning this week?
· What else do you need to explore to further grow as a DNP
practice scholar?
Week 3
· Provide one specific example of how you achieved the weekly
objectives.
· What personal values, if any, were challenged this week?
· What values can you reaffirm or want to reconsider after this
learning?
Week 4
· Provide one specific example of how you achieved the weekly
objectives.
21. · What concepts, theories, models, tools, techniques, and
resources in this week did you find most valuable?
· What issues had you not considered before?
Week 5
· Provide one specific example of how you achieved the weekly
objectives.
· How might you use this information in your future role as a
DNP practice scholar?
· What topics, if any, had you not considered before?
Week 6
· Provide one specific example of how you achieved the weekly
objectives.
· What knowledge do you still need to demonstrate competency
in the weekly concepts?
· What skills do you need to continue developing to apply these
concepts?
Week 7
· Provide one specific example of how you achieved the weekly
objectives.
· What are the core values and best practices that define the
roles of the DNP practice scholar?
· How will this knowledge improve your effectiveness as a DNP
practice scholar?
· What skills do you need to implement these best practices and
how will you develop them?
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
22. 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
23. 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
24. 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
26. 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
28. 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
29. 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
30. 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
31. 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.
H. Ezaki, et al. Clinica Chimica Acta 500 (2020) 20–27
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
32. 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-
33. 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
34. 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-
35. 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-
36. 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
37. 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.
H. Ezaki, et al. Clinica Chimica Acta 500 (2020) 20–27
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
38. 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
39. 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),
40. 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
41. 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
43. 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
parametrically distributed.
Logarithmic transformation was conducted if needed.
H. Ezaki, et al. Clinica Chimica Acta 500 (2020) 20–27
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
44. 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
45. 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 …
46. 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.
47. 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
48. 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].
49. 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,
50. Pendik, Istanbul, Turkey
123
Ann Nucl Med (2015) 29:786–791
DOI 10.1007/s12149-015-1005-3
http://crossmark.crossref.org/dialog/?doi=10.1007/s12149-015-
1005-3&domain=pdf
http://crossmark.crossref.org/dialog/?doi=10.1007/s12149-015-
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.
51. 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
52. 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-
53. 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
54. 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-
55. 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
56. 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.
57. 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
58. 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
59. 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-
60. 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
61. 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
62. radioactivity count threshold [50 CPM) [16–18]. Asking
the patient to stay in left lateral decubitus or supine posi-
tion during urease reaction, giving citric acid or anti-
motility drugs, direct spraying of the C-13 urea over the
gastric mucosa endoscopically, collecting breath samples
in later periods and decreasing the DOB (‘‘delta over
baseline’’) threshold were recommended in order to
increase the performance of UBT [17, 19, 20, 23, 24]. In
this study, obtaining unsatisfactory results with standard
C-14 UBT despite placing the patient in left lateral decu-
bitus position in the first 10 minutes showed that the
patient position itself might not be enough for improving
the test performance. However, the extension of the reac-
tion period after oral intake of C-14 labeled urea (i.e.,
collecting breath samples at 20th or 30th min) gave rise to
more than a twofold increase in sensitivity (28.6 % at 10th
min vs. 57.1 % at 20th min and 71.4 % at 30th min) as
reported in previous studies with C-13 UBT. We noticed
63. that the sensitivity of the standard radioactivity threshold
([50 CPM) was very low for PGR patients. In accordance
with the results of the C-13 UBT studies, decreasing this
threshold might improve the sensitivity even with 10th min
breath samples without deteriorating the specificity
significantly.
The liquid forms of the test were not preferred anymore
due to the false-positive results caused by the urease pos-
itive oral bacterial flora [25]. In our study, rapidly disin-
tegrating capsules containing labeled urea were used
instead and no false-positive result was encountered.
Although not statistically significant, this study also
revealed an inverse relation between the prevalence of H.
pylori and the elapsed time after surgery in partially gas-
trectomized patients. In early postoperative period, the
prevalence is as high as that in the normal population [1].
However, due to the improper microenvironment for bac-
terial colonization in the residual stomach, this declines by
64. the time [1].
Combined use of at least two diagnostic tests was rec-
ommended for the detection of H. pylori infection [26]. For
this reason, C-14 UBT and HpSAT were compared with
invasive reference test. The sensitivity of the standard C-14
UBT was significantly lower than that of the HpSAT (29
vs. 71 %). However, when C-14 UBT’s criteria were
modified, results were changed in favor of C-14 UBT. It is
noteworthy that HpSAT gave false-positive result in one
patient and false negative in another one in whom C-14
UBT was strongly positive even with standard diagnostic
criteria. As a qualitative diagnostic method, HpSAT is
advantageous since it does not require adjusting a thresh-
old. However, the two frequently experienced complaints
in this group of patients, diarrhea and constipation, can
cause false-negative results [27, 28]. Giving false-positive
results in the presence of gastrointestinal bleeding is the
another disadvantage of this method [27, 29]. None of
65. these factors are anticipated to affect the performance of
UBT.
Relatively small sample size was the main limitation of
the study. Additionally, all of the participants suffered from
gastric cancer and most of them underwent Billroth type II
reconstruction. Because of these we could not perform
subgroup analysis according to type and cause of the sur-
gery. The relatively long time interval (max. 4 weeks)
between UBT and endoscopy and the variable post-op
periods was the other potential limitations of our study.
In conclusion, the sensitivity of the standard C-14 UBT
was very poor for patients with PGR, and the results of
HpSAT were superior in this patient population. As in C-13
UBT, promising results could be obtained with collecting
breath samples at a later time point and decreasing the
radioactivity cut-off level.
Conflict of interest All the authors state that there were no
conflicts
of interests when the manuscript was written.
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Ann Nucl Med (2015) 29:786–791 791
123
Carbon-14 urea breath test: does it work in patients with partial
gastric
resection?AbstractObjectiveMethodsResultsConclusionsIntrodu
ctionMaterials and methodsPatientsHistopathological
analysisCarbon-14 Urea breath testH. pylori stool antigen test
(HpSAT)StatisticsResultsUBT resultsUBT radioactivity counts
(CPM)HpSAT resultsElapsed time after
surgeryDiscussionConflict of interestReferences
73. The final assignment for this course will be a research paper on
a specific medical laboratory test or emerging technology of the
student’s choice. Students should indicate their chosen area of
focus for the research paper in the Discussion Board for Week 3
of the course so that it may be approved by the course instructor
and students have ample time to conduct research, write the
paper, and prepare an abstract and presentation summarizing
their findings.
The paper must be in APA format, double-spaced, 12 pt font
with 1 inch margins, and 3-5 pages in length, not including title
page, abstract, and references, and address the following
elements:
Describe the test or technology in terms understandable to your
classmates (e.g., undergraduate students who may not have
significant experience in a clinical or research laboratory or
have not yet taken a college genetics course).
Provide history/development/current use of the technology. Is it
currently being used in clinical settings? Research only? Still
under development?
What is the primary purpose of the test or technology? For
whom (target population) is the test or technology intended
(screening of general population, specific at risk groups)?
Discuss other potential stakeholders (e.g., those potentially
impacted by results, including insurance companies, healthcare
providers, employers, extended family members, offspring)
What are the intended benefits of the test or technology?
What are the potential ethical issues that may arise through the
use of the test or technology? Be sure to frame your answer in
terms of the four main principles of bioethics: maleficence,
beneficence, autonomy, and justice.
Abstract:
The abstract serves as a concise yet complete summary of your
74. paper. It should be no more than 200 words long.
References:
Include at least 3 references. Websites should be used sparingly
but still cited. At least one reference must be a peer-reviewed
research article describing the use of the test or technology.
Be sure to incorporate appropriate citations from course
readings and research throughout the paper in proper APA
format.