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Association between antibiotics and gut microbiome dysbiosis in children: systematic review and meta-analysis

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Antibiotics in childhood have been linked with diseases including asthma, juvenile arthritis, type 1 diabetes, Crohn’s disease and mental illness. The underlying mechanisms are thought related to dysbiosis of the gut microbiome. We conducted a systematic review of the association between antibiotics and disruption of the pediatric gut microbiome. Searches used MEDLINE, EMBASE and Web of Science. Eligible studies: association between antibiotics and gut microbiome dysbiosis; children 0–18 years; molecular techniques of assessment; outcomes of microbiome richness, diversity or composition. Quality assessed by Newcastle–Ottawa Scale or Cochrane Risk of Bias Tool. Meta-analysis where possible. A total of 4,668 publications identified: 12 in final analysis (5 randomized controlled trials (RCTs), 5 cohort studies, 2 cross-sectional studies). Microbiome richness was measured in 3 studies, species diversity in 6, and species composition in 10. Quality of evidence was good or fair. 5 studies found a significant reduction in diversity and 3 a significant reduction in richness. Macrolide exposure was associated with reduced richness for twice as long as penicillin. Significant reductions were seen in Bifidobacteria (5 studies) and Lactobacillus (2 studies), and significant increases in Proteobacteria such as E. coli (4 studies). A meta-analysis of RCTs of the effect of macrolide (azithromycin) exposure on the gut microbiome found a significant reduction in alpha-diversity (Shannon index: mean difference −0.86 (95% CI −1.59, −0.13). Antibiotic exposure was associated with reduced microbiome diversity and richness, and with changes in bacterial abundance. The potential for dysbiosis in the microbiome should be taken into account when prescribing antibiotics for children. Systematic review registration number: CRD42018094188
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Gut Microbes
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Association between antibiotics and gut
microbiome dysbiosis in children: systematic
review and meta-analysis
Lucy McDonnell, Alexander Gilkes, Mark Ashworth, Victoria Rowland,
Timothy Hugh Harries, David Armstrong & Patrick White
To cite this article: Lucy McDonnell, Alexander Gilkes, Mark Ashworth, Victoria Rowland, Timothy
Hugh Harries, David Armstrong & Patrick White (2021) Association between antibiotics and gut
microbiome dysbiosis in children: systematic review and meta-analysis, Gut Microbes, 13:1, 1-18,
DOI: 10.1080/19490976.2020.1870402
To link to this article: https://doi.org/10.1080/19490976.2020.1870402
© 2021 The Author(s). Published with
license by Taylor & Francis Group, LLC. View supplementary material
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RESEARCH PAPER
Association between antibiotics and gut microbiome dysbiosis in children:
systematic review and meta-analysis
Lucy McDonnell , Alexander Gilkes , Mark Ashworth, Victoria Rowland, Timothy Hugh Harries,
David Armstrong, and Patrick White
School of Population Health and Environmental Sciences, King’s College London, London, UK
ABSTRACT
Antibiotics in childhood have been linked with diseases including asthma, juvenile arthritis, type 1
diabetes, Crohn’s disease and mental illness. The underlying mechanisms are thought related to
dysbiosis of the gut microbiome. We conducted a systematic review of the association between
antibiotics and disruption of the pediatric gut microbiome. Searches used MEDLINE, EMBASE and
Web of Science. Eligible studies: association between antibiotics and gut microbiome dysbiosis;
children 0–18 years; molecular techniques of assessment; outcomes of microbiome richness,
diversity or composition. Quality assessed by Newcastle–Ottawa Scale or Cochrane Risk of Bias
Tool. Meta-analysis where possible. A total of 4,668 publications identied: 12 in nal analysis (5
randomized controlled trials (RCTs), 5 cohort studies, 2 cross-sectional studies). Microbiome rich-
ness was measured in 3 studies, species diversity in 6, and species composition in 10. Quality of
evidence was good or fair. 5 studies found a signicant reduction in diversity and 3 a signicant
reduction in richness. Macrolide exposure was associated with reduced richness for twice as long as
penicillin. Signicant reductions were seen in Bidobacteria (5 studies) and Lactobacillus (2 studies),
and signicant increases in Proteobacteria such as E. coli (4 studies). A meta-analysis of RCTs of the
eect of macrolide (azithromycin) exposure on the gut microbiome found a signicant reduction in
alpha-diversity (Shannon index: mean dierence −0.86 (95% CI −1.59, −0.13). Antibiotic exposure
was associated with reduced microbiome diversity and richness, and with changes in bacterial
abundance. The potential for dysbiosis in the microbiome should be taken into account when
prescribing antibiotics for children.
Systematic review registration number: CRD42018094188
ARTICLE HISTORY
Received 2 June 2020
Revised 30 November 2020
Accepted 18 December 2020
KEYWORDS
Gut dysbiosis; Antibiotics;
Children
Introduction
Research over recent years has emphasized the impor-
tance of the gut microbiome, and its association with
health and the immune system. On the one hand,
methods of enhancing the microbiome have proved
effective. For example, probiotics have been used to
reduce the incidence of severe necrotizing enterocoli-
tis in preterm neonates as the gut microbiome is
insufficiently developed to regulate the intestinal
mucosa,
1
and fecal microbial transplant (FMT) is
being used successfully to treat patients with allergic
colitis or Clostridium dicile infection.
2,3
On the other
hand, damage to the microbiome has been linked with
conditions such as asthma,
4–7
allergy,
8
juvenile idio-
pathic arthritis,
9,10
type 1 diabetes,
11
obesity,
12–17
celiac disease,
18
mental illness,
19
Crohn’s disease,
20
and impaired neurocognitive outcomes.
21
Although the mechanism of association for these
diseases has not been fully explored, antibiotics, one of
the most commonly prescribed drugs in children in
western populations,
22
appear to disrupt the normal
maturation of the microbiome and destabilize it, alter-
ing basic physiological equilibria.
23,24
Antibiotics also
seem to affect gene expression, protein activity and
overall metabolism of the gut microbiota which may
directly influence major organ development and
immune functioning.
25
Antibiotic exposure has
already been shown to alter the gut microbiome in
adults and in neonates.
26,27
This review sought to
systematically examine the research into the associa-
CONTACT Patrick White patrick.white@kcl.ac.uk School of Population Health and Environmental Science, King’s College London, 3
rd
Floor, Addison
House, Great Maze Pond, London SE1 1UL, UK.
Supplemental data for this article can be accessed on the publisher’s website.
GUT MICROBES
2021, VOL. 13, NO. 1, e1870402 (18 pages)
https://doi.org/10.1080/19490976.2020.1870402
© 2021 The Author(s). Published with license by Taylor & Francis Group, LLC.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided the original work is properly cited.
tion between antibiotic exposure and pediatric gut
microbiome disruption.
Results
Study selection
The literature search identified 4,688 publications.
The process of publication selection is described in
Figure 1. Twelve studies met the eligibility criteria,
were deemed good (nine studies) or fair (3 studies)
in quality and were included in the final analysis.
Meta-analysis was carried out on four RCTs that
shared the Shannon Index as their outcome mea-
sure of the impact of antibiotics up to 14 days after
administration. Quality assessments of RCTs are
presented in Supplementary Data Figure S1
(Cochrane Risk of Bias Tool).
28
A high risk of bias
was found in Wei et al.’s trial with respect to blind-
ing of the outcome in the analysis done at 4 years
but there was no such risk with respect to the
analysis done at 14 days.
29
Quality assessments
(Newcastle–Ottawa Scale) of observational cohort
studies are presented in Supplementary Data Table
S1 and of cross-sectional studies in Supplementary
Data Table S2.
30
,
31
Included studies’ design and participant
characteristics
The main characteristics of the included studies
are summarized in Table 1. There were five
randomized controlled trials (RCTs), five cohort
studies and two cross-sectional studies. All stu-
dies detected changes in composition of the
microbiome following antibiotic exposure in 3
main outcomes: reduction in microbiome species
richness; reduction in species diversity; and
change in taxonomic composition (change in
a specific phylum, genus or species). The pri-
mary aim varied between studies. The age of
participants ranged from new-born to 12 years
Figure 1. PRISMA flow chart. Preferred reporting items for systematic reviews and meta-analyses 2009.
e1870402-2 L. MCDONNELL ET AL.
Table 1. Summary of included studies: author, year, location, design, aim, participants, antibiotic, duration, molecular techniques used, and outcomes.
Author, year,
location
Design
and reference
group Aim
Study partici-
pants (n), age
Antibiotic expo-
sure
(route; duration)
Exposure to
outcome inter-
val (sampling
frequency)
Molecular
technique
Key findings:
Richness
Key findings:
Diversity
Key findings:
Taxonomical change
1.Bai et al.
32
China
Cross-
sectional
with
(control
group)
Added impact of
antibiotics on
microbiota
changes in
ALL
33 healthy
children;
10 received
antibiotics
1–12 years
Cephalosporin
Penicillin
(oral + IV:
10 days)
1–4 weeks
(x1)
Next
generation
sequencing
(NGS)
n/a Reduction in α-
diversity
Shannon index, ~2.75
(antibiotics) vs ~3.25
(control), p < .05
#
Simpson index ~0.15
(antibiotics) vs ~0.09
(control) p < .05
# a
Decreased Firmicutes/Bacteroidetes
ratio by approximately one third
(p < .05)
No difference in abundances of
Actinobacteria and Proteobacteria
2. Bokulich
et al,
33
USA
Cohort
(baseline
assessment)
Microbiome
development
in first 2 years
of life
43 infants, 25
received
antibiotics
0–2 years
Cephalosporin
Beta-lactams
Macrolides
Quinolones
Nitrofurantoin
(route/
duration not
specified)
3–139 days
(x25)
PCR, 16s RNA
gene
amplification
n/a No change in α-divers
ity after antibiotic
exposure for median
52 days (13–139)
Reduced β–
diversity: UniFrac
distance,
Permutational
MANOVA, R
2
< 0.01,
p < .001
No effect on Bifidobacterium
abundance
3.Brunser
et al.
34
Chile
RCT
(baseline
assessment)
Impact on
microbiome
of prebiotic
supplement
following
antibiotic
130 infants,
before and
after
antibiotics
1–2 years
Amoxicillin
(oral; 7 days)
1–3 weeks
(x3)
FISH* and flow
cytometry
n/a n/a Decrease in amoxicillin-associated
fecal bacteria by 30% (p < .001)
Increase in amoxicillin associated
E. Coli Log count 4.77 ± 0.96
(baseline) vs 5.10 ± 1.39 after
treatment (p = .015)
No change in total counts of
Bifidobacterium and Bacteroides
(Continued)
GUT MICROBES e1870402-3
Table 1. (Continued).
Author, year,
location
Design
and reference
group Aim
Study partici-
pants (n), age
Antibiotic expo-
sure
(route; duration)
Exposure to
outcome inter-
val (sampling
frequency)
Molecular
technique
Key findings:
Richness
Key findings:
Diversity
Key findings:
Taxonomical change
4.Doan et al.
35
Niger
RCT
(control
group)
Effects of
azithromycin
on gut
microbiome
diversity
80 children, 40
received
antibiotics
1–5 years
Azithromycin
(oral; single
dose)
5 days
(x1)
16S rRNA
sequencing
n/a Reduction in α-
diversity:
Inverse Simpson’s α-
diversity decreased
(5.03 95% CI
4.08–6.14) vs placebo
(6.91; 5.82 − 8.21)
p = .03
Shannon’s α-diversity
decreased (10.60;
95% CI 8.82–12.36) vs
placebo (15.42;
13.24–17.80) p = .004
No change in β-
diversity
Decrease in
Simpson’s community
level γ diversity with
azithromycin (10.10
95% CI 7.80–11.40) vs
placebo
(17.72;13.80–20.21)
(p < .001)
n/a
5.Fouhy
et al.
36
Ireland
Cohort study
(control
group)
Effect of
antibiotics on
gut
microbiome
18 children, 9
received
antibiotics
Newborn
Ampicillin and
gentamicin
(IV:2–9 days)
4 and 8 weeks
(x2)
High
throughput
sequencing
of 16S rRNA
n/a No change in α-
diversity (Shannon
Index) at 4 weeks (3.6)
vs control (3.6)
(p = .575)
Decreased Bifidobacterium (5% vs
25%; p = .013) and Lactobacillus
(1% vs 4%; p < .009) in treated
group at 4 weeks vs control; no
difference at 8 weeks
Increased Proteobacteria (44% vs
23%; p < .005)
and Enterobacteriaceae (50% vs
32%; p = .006) at 8 weeks vs
control.
Increased Clostridium in treated
infants than controls at week 8 (7%
vs 2%, p < .035)
(Continued)
e1870402-4 L. MCDONNELL ET AL.
Table 1. (Continued).
Author, year,
location
Design
and reference
group Aim
Study partici-
pants (n), age
Antibiotic expo-
sure
(route; duration)
Exposure to
outcome inter-
val (sampling
frequency)
Molecular
technique
Key findings:
Richness
Key findings:
Diversity
Key findings:
Taxonomical change
6.Korpela
et al.
37
Finland
Retro-spective
controlled
cohort
study
Antibiotic
induced
changes in
microbiota
composition
142 children, 99
received
antibiotics
2–7 years
Macrolides
Penicillins
(route/
duration not
specified)
Variable
(<6 months
to 2 years)
(x1-2)
DNA extraction,
16s rRNA
gene
sequencing
Macrolides:
Reduced richness up
to 2 years (p < .05)
#
Penicillins: Reduced
richness over
6 months
#
(p < .001)
resolved by
12 months
n/a Macrolides: Exposure over 6 months
Reduced Bifidobacterium (0.23-fold
change p < .004) and Lactobacillus
(0.12-fold change p < .004)
Increased Bacteroides (2.04-fold
change p < .004) and
Proteobacteria (1.96-fold change
(p < .02).
Penicillins:
Decreased Lactobacillus (0.09 fold;
p < .004) with exposure in previous
12 months
7.Mangin
et al.
38
Chile
Cohort
(baseline
assessment)
Impact of
amoxicillin on
fecal
bifidobacteria
31 infants, all
received
antibiotics
12–24 months
Amoxicillin
(oral: 7 days)
0 days
(x2)
Total DNA
extraction,
PCR
n/a n/a No change in total Bifidobacteria
Disappearance of Bifidobacterium
adolescentis species (0% vs 36.4%
(p < .001)
8. Oldenburg
et al.
39
Burkina
Faso
RCT
(baseline
assessment)
Investigate
effect of 3
antibiotics on
microbial
diversity
124 children, 93
received
antibiotics
6–59 months
Amoxicillin
Azithromycin
Cotrimoxazole
(oral: 5 days)
5 days
(x2)
DNA extraction,
deep gene
sequencing
n/a Reduced α- diversity
with Azithromycin:
Inverse Simpsons’
index decreased (6.6
95% CI 5.5–7.8) vs
baseline (8.8 95% CI
7.5–10.1) (p < .001)
Shannon index
decreased (11.0 95%
CI 9.3–12.7) vs
baseline (14.6 95% CI
13.0–16.2) (p < .001)
No reduction with
Amoxicillin or
Cotrimoxazole
n/a
9.Parker
et al.
40
India
RCT
(baseline
assessment)
Assess
microbiota
changes
following
azithromycin
114 infants,
56 received
antibiotics
6–11 months
Azithromycin
(oral: 3 days)
12 days
(x2)
16s rRNA gene
sequencing,
DNA
extraction
PCR
Lower OTU with
azithromycin:
(68.1 ± 15.4) vs
placebo (73.6 ± 13.7)
(linear regression
p = .027) c. 7% less
No significant change
in α-diversity
(Shannon index)
azithromycin (2.6 95%
CI 2.47–2.73) vs
placebo (2.8 95%CI
2.8(2.67–2.93)
(p = .087)
Decreased relative abundance of
Proteobacteria (mainly Escherichia)
mean % ± SD: 15.9 ± 13.2 vs
10.2 ± 15.4 FDR (p < .001) and
Verrucomicrobia (genus
Akkermansia muciniphilia) 0.5 ± 3.1
vs 0.0 ± 0.0 FDR (p < .012)
No change in Actinobacteria,
Bacteroides and Firmicutes,
Bifidobacterium
(Continued)
GUT MICROBES e1870402-5
Table 1. (Continued).
Author, year,
location
Design
and reference
group Aim
Study partici-
pants (n), age
Antibiotic expo-
sure
(route; duration)
Exposure to
outcome inter-
val (sampling
frequency)
Molecular
technique
Key findings:
Richness
Key findings:
Diversity
Key findings:
Taxonomical change
10.Penders
et al.
41
The
Netherlands
Cross
Sectional
(control
group)
Examine
contribution
of external
influences to
gut
microbiota
composition
1032 infants,
28 received
antibiotics
1 month
Mainly
Amoxicillin
(oral: duration
not specified)
<1 month
(x1)
DNA isolation,
PCR
n/a n/a Decrease with antibiotics in
Bifidobacteria. Antibiotics (10.29
CFU/g log10) vs Control (10.7 CFU/g
log10 (p < .01)
Decrease in Bacteroides fragilis.
Antibiotics (6.39 CFU/g log10) vs
Control (9.31 CFU/g log10) (p < .01)
No change in Lactobacilli,
Escherichia coli, Clostridium difficile
11. Wei et al.
29
Denmark
RCT
(control
group)
Examine short-
and long-
term impacts
of
azithromycin
treatment on
gut
microbiota in
children
72 children, 33
episodes of
asthma-like
symptoms
received
antibiotics
12–36 months
Azithromycin
(oral: 3 days)
14 days and up
to 4 years
(x3)
DNA extraction
and
sequencing
Decrease in richness at
14 days: Observed
richness: 23%
reduction
(177.8 ± 56.0 vs.
230.6 ± 61.2,
p < .001); no
difference by mean
233 days
Reduced α-diversity:
at 14 days: Shannon
index: 13% reduction;
2.96 ± 0.80 (mean ±
SD) vs control
3.41 ± 0.58, p = .009)
Reduced β–
diversity:
UniFrac distance,
treatment accounted
for variance
(R
2
= 3.8%, p = .027
(weighted) and
F2 = 4.2% p < .001
(un-weighted)
Reduction, 50-fold, in genus
Bifidobacterium at 14 days (p
adjusted <0.011 (FDR p < .05)
Long term (13–39 months) no
differences seen between
azithromycin and placebo groups
12.Yassour
et al.
42
Finland
Cohort
(control
group)
Study
development
of infant gut
microbiome
and effect of
antibiotics
39 children,
20 received
antibiotics
2–36 months
Amoxicillin
Cefalexin
Clarithromycin
Amoxicillin and
clavulanic acid
Trimethoprim
and
sulfadiazine
Azithromycin
Cefaclor
Penicillin
G Netilmicin
(oral: duration
not specified)
<1 month
(x28)
16S rRNA gene
and
whole
genome
shotgun
sequencing
n/a Reduced microbiome
strain (subspecies)
diversity (diversity
index 0.0003 vs 0.55
(control) (p < .001)
Decreased abundance of species
from clostridium clusters IV and
XIVa (T regulatory immune cells) at
aged 3 (median abundance ~9% vs
~15% control)
b
(p = .037)
Less stable gut microbiome
following antibiotic treatment
(Jaccard Index P = <0.001)
Key: # – Approximate mean values taken from a Box and Whisker plot; – statistical significance testing, confidence intervals, or standard deviations not given; a – higher the Simpson index, the lower the diversity; b -raw
data not given, approximate values taken from graph
Abbreviations: n/a- data not available. ALL: acute lymphoblastic leukemia. CFU/g = Colony forming units per gram of sample. FISH: Fluorescent in-situ hybridization. FDR – False Discovery Rate correction. OTU count:
operational taxonomical unit.
e1870402-6 L. MCDONNELL ET AL.
old. Nearly all studies reported the short-term
associations between antibiotic exposure (up to
1 month) and microbiome composition; some
also reported longer-term outcomes up to
2 years and 4 years.
33,37,29,42
Microbiome richness
Microbiome richness (Table 6) data were avail-
able for 3 studies and are shown in Table 2.
Microbiota richness in children exposed to
antibiotics was statistically significantly reduced
compared to that of children not exposed to
antibiotics in all three studies.
29,37,40
Measures
of richness included Operational Taxonomic
Unit (OTU) count (see Table 2) and a generic
measure of ‘observed richness’. The time
between exposure and analysis was ≤ 14 days
in 2 studies,
29,40
and ≤ 6 months in one study.
37
The reduction in richness reported by Wei et al.
had resolved by the time of a second analysis
(mean of 223 days following exposure).
29
Korpela et al., found that microbiome richness
was reduced for up to 1 year following penicil-
lin exposure and for up to 2 years following
macrolide exposure.
37
Parker reported that the
significant reduction in species richness was
driven through depletion of Proteobacteria
(mainly the species Akkermansia mucinophilia)
which were particularly susceptible to
azithromycin.
40
Three other authors also com-
mented on richness but did not report raw data
and hence are not included in Table 2.
32,36,42
Microbiome diversity
Microbiome species diversity was reported by 8
studies.
32,33,35,36,39,40,29,42
Data were available for
6.
29,32,35,36,39,39,40
The main diversity outcome mea-
sure was alpha-diversity (Table 3). Antibiotic use
was associated with a reduction in alpha-diversity
(measured by Shannon or Simpson/Inverse
Simpson indices) in 4 studies.
29,32,35,39
Initial
Shannon diversity indices varied substantially by
geographical location (approximate index value of
‘3ʹ in studies in China, Denmark, India, and Ireland
to approximate index value of ‘15ʹ in Burkina Faso
and Niger). We carried out a meta-analysis of 4
RCTs examining the effect of azithromycin on the
microbiome measured by the Shannon Index. We
found a statistically significant overall reduction in
alpha-diversity (mean difference −0.86 (−1.59 to
−0.13, p < .001) (Figure 2).
Beta-diversity was reported in 3 studies and sig-
nificantly reduced in 2 of those.
29,33,35
Bokulich
examined the impact of exposure to several different
classes of antibiotics: cephalosporins, beta-lactams,
macrolides, quinolones, and nitrofurantoin.
33
They
found that although microbiome alpha-diversity
was unchanged following antibiotic exposure, beta-
diversity differed significantly between children
exposed to antibiotics and those unexposed
(UniFrac distance, permutational MANOVA,
R
2
< 0.01 p < .001).
33
This means that although the
individual diversity index did not change (i.e. wide
species variety and abundance) there was
a significant change in the types of species found.
With regards to azithromycin exposure alone, Wei
Table 2. Associations between antibiotic use and changes in microbiome richness in children up to 7 years.
Study Type
Age
group Country Antibiotic
Duration
of treat-
ment
Time from
exposure
to analysis
Index of
richness
used
Placebo or
Control
(mean ± SD)
Intervention
(mean ± SD)
Percentage
difference Significance
Wei
et al.
29
RCT 1–3 years Denmark Azithromycin 3 days 14 days Observed
richness
230.6 ± 61.2 177.8 ± 56.0 −25.9% p < .001
Parker
et al.
40
RCT 6–11 months India Azithromycin 3 days 12 days OTU count 73.6 ± 13.6 68.1 ± 15.4 −7.5% p = .027
Korpela
et al.
37
Retro-
spective
cohort
2–7 years Finland Macrolides
(M)
Penicillins
(P)
n/a <6 months OTU count 230
#
175
#
(M)
180
#
(P)
−23.91%
(M)
-21.74%
(P)
p < .001
p < .001
# Approximate value taken from bar chart. Confidence intervals or standard deviations not available. n/a = not available.
SD = Standard Deviation.
GUT MICROBES e1870402-7
Table 3. Association between antibiotic use and changes in microbiome alpha-diversity (species level) in children up to 12 years.
Study authors, year, type and
setting Age group Antibiotic
Days of
therapy
Time between exposure and
analysis
Indices of alpha-diversity
used
Placebo/Control
Group
mean (95% CI)
Intervention
group
mean (95% CI or
SD)
Percentage
difference Significance
Doan et al.
35
RCT
Niger
1–5 years Azithromycin Single dose 5 days Shannon
Inverse Simpson
15.42
(13.24–17.80)
6.91 (5.82–8.21)
10.60
(8.82–12.36)
5.03
(4.08–6.14)
−31.25%
-27.21%
p = .004
p = .03
Oldenburg et al.
39
RCT
Burkino Faso
6–59 months Azithromycin 5 days 5 days Shannon
Inverse Simpson
14.6 (13.0–16.2)
8.80 (7.5–10.1)
11.0 (9.3–12.7)
6.6 (5.5–7.8)
−24.65%
-25.0%
p < .001
p < .001
Wei et al.
29
RCT
Denmark
1–3 years Azithromycin 3 days 14 days Shannon 3.41 (3.23–3.59) 2.96 (2.69–3.23) −13.19% P = .009
Parker et al.
40
RCT
India
6–11 months Azithromycin 3 days 12 days Shannon 2.8 (2.67–2.93) 2.6 (2.47–2.73) −7.14% P = .087
Bai et al.
32
Cross-sectional
China
1–12 years Cephalosporin,
Penicillin
10 days 1–4 weeks Shannon
Simpson
3.25* 2.75* −15.38% P < .05
Fouhy et al.
36
Cohort
Ireland
New-born Ampicillin;
gentamicin
2–9 days 4 weeks Shannon 3.8
3.6
−5.26% P = .575
† Confidence intervals or standard deviation not available*approximate values taken from box and whisker plot.
e1870402-8 L. MCDONNELL ET AL.
reported associations with reduced alpha and beta
diversity.
29
Doan however found that beta diversity
was unaffected (i.e., similar types of species in the
two groups) following azithromycin exposure. But
there was a 43% decrease in Simpson’s community-
level gamma diversity (p < .001) which reflected the
overall reduction in bacterial diversity of the treat-
ment group compared to the placebo group.
35
Six of
eight studies reporting on species diversity found
a significant association between antibiotic use and
a reduction in species diversity.
Taxonomic composition
The major phyla reported in all studies were
Actinobacteria, Bacteroidetes, Firmicutes, and
Proteobacteria. One study reported the phylum
Veruccomicrobia.
40
A significant increase or
decrease in the abundance of a particular phylum,
genus or species was reported in 10 studies. These
results are summarized in Table 4.
Actinobacteria
The association between antibiotics and the abun-
dance of genus Bifidobacterium (phylum
Actinobacteria) was examined in 9 studies (Table
4). In five studies, antibiotics were significantly
associated with reduced abundance of
Bifidobacteria.
29,36–38,41
Both penicillins and
macrolides were associated with a decrease in
Bifidobacteria although in some studies there was
no change. Comparing macrolides with penicillins,
Korpela et al. found that exposure to macrolides
was associated with a fourfold decrease in
Bifidobacteria but that exposure to penicillins was
not associated with Bifidobacteria levels.
37
Fouhy
et al. found that a combination of ampicillin and
gentamicin was associated with reduced
Bifidobacteria initially, but that by 8 weeks levels
had returned to that of the control group.
36
At
species level, Mangin et al. found amoxicillin expo-
sure was associated with complete disappearance of
Bifidobacterium adolescentis but that overall con-
centrations of Bifidobacteria were not altered.
38
Bacteroidetes
The association between antibiotics and the abun-
dance of Bacteroidetes phylum (which includes the
genus Bacteroides) was examined in seven studies
(Table 4). There was a statistically significant
change in 4 studies. The 3 studies that reported an
increase in Bacteroidetes examined exposure to
a combination of antibiotics including cephalos-
porins and macrolides.
32,33,37
One study examining
only amoxicillin exposure reported a decrease of
the species Bacteroides fragilis.
41
In 3 studies there
was no change (studies examining amoxicillin,
ampicillin/gentamicin and azithromycin).
34,36,40
Firmicutes
The association between antibiotics and the abun-
dance of Firmicutes phylum (which includes the gen-
era Lactobacillus and Clostridium) was examined in
seven studies (Table 4).
32,33,36,40,41,42
A statistically sig-
nificant decrease was seen in 4 studies following anti-
biotic exposure.
32,36,37,42
Korpela et al. reported that
Lactobacillus levels were reduced for up to 12 months
following penicillin use and for up to 24 months
following macrolide use.
37
The same study found
a nearly 3-fold increase in Clostridium within
6 months of exposure to macrolides only (details of
specific species not given).
37
Yassour et al reported
a 40% decrease in Clostridium spp. belonging to clus-
ters IV and XIVa (inducers of T regulatory immune
cells) in children aged 3 who had had antibiotics.
42
Proteobacteria
The abundance of Proteobacteria following antibiotic
exposure was examined in six studies (Table 4). In 5
studies there was a statistically significant change in
Proteobacteria following exposure to a variety of anti-
biotics, however the direction of association was not
consistent. At phylum level, 4 studies reported an
increase in Proteobacteria following exposure to dif-
ferent antibiotics including penicillins, cephalospor-
ins and macrolides.
33,34,36,37
One study reported
a decrease in Proteobacteria following azithromycin
exposure only.
40
At species level, a statistically signifi-
cant increase in E.coli was reported following amox-
icillin exposure in children aged 1–2 years,
34
but
a statistically significant decrease in E.coli was
reported following azithromycin exposure in children
aged 6–11 months.
40
Verrucomicrobia
The association between azithromycin and a reduction
in the abundance of phylum Verrucomicrobia was
examined in one study (Table 4). This phylum has
GUT MICROBES e1870402-9
Table 4. Associations between antibiotic use and changes in taxonomic composition of the microbiome in children up to 12 years.
Study
Author, year,
type of study,
country,
duration.
Antibiotic
(days of treatment
where given)
Time from
exposure
to analysis
Actinobacteria
(includes genus Bifidobacteria) Bacteroidetes
Firmicutes
(gram positive)
Proteobacteria
(gram negative)
Bai et al.
32
Cross-
Sectional
China
1–12 years
Cephalosporin,
Penicillin
10 days
1–4 weeks No change in Actinobacteria F/B ratio decreased by approx.
1/3(p < .05) *
(increase in Bacteroidetes)
F/B ratio decreased by approx. 1/3 (p < .05) *
(decrease in Firmicutes)
No change
Bokulich et al.
33
Cohort
USA
0–2 years
Cephalosporin
Beta lactams
Macrolides
Quinolones
Nitrofurantoin
3–139 days No change in Bifidobacteria Increased (p < .05) * Clostridiales decreased from 3–9 months of age* Increased (p < .05) *
Brunser et al.
34
Cohort
Chile
0–2 years
Amoxicillin
7 days
1–3 weeks No change in Bifidobacteria No change n/a E. coli increased vs baseline
(Log 4.77 ± 0.96 vs Log 5.10 ± 1.39
p = .015)
Fouhy et al.
36
Cohort
Ireland
Neonates
Ampicillin; gentamicin
2–9 days
4 weeks Lower levels of Bifidobacteria at
4 weeks vs control
(5% vs 25%; p = .013);
no difference at 8 weeks
No change Lower levels of Lactobacillus at 4 weeks vs control
(1% vs 4%; p < .009);
no difference at 8 weeks
Higher proportions of Proteobacteria
(44% vs 23%; p < .005) and
Enterobacteriaceae
(50% vs 32%; p = .006)
at 8 weeks vs control
Korpela et al.
37
Retrospective
cohort
Finland
2–7 years
Macrolides (M)
Penicillins (P)
<6 months Exposure in previous 6 months
M: Bifidobacterium (0.23-fold
decrease p < .004)
P: Bifidobacterium: no change
Exposure in previous 6 months
M: Bacteroides increased
(2.04-fold change p < .004)
P: Bacteroides: no change
Exposure in previous 6 months
M: Lactobacillus decreased (0.12-fold change
p < .004), Clostridium increased (2.68-fold
change p < .004)
P: Lactobacillus decreased (0.11-fold change
p < .004), Clostridium: no change
M: Proteobacteria increased (1.96-fold
change p < .02) with exposure in
previous 6 months
P: no change
Mangin et al.
38
Cohort
Chile
6–59 months
Amoxicillin
7 days
0 days Total Bifidobacterium
concentrations not
significantly altered but
complete disappearance of
Bifidobacterium adolescentis
species (0% vs 36.4%
(p < .001)
n/a n/a n/a
Parker et al.
40
RCT
India
6–11 months
Azithromycin
3 days
12 days No change in Actinobacteria or
Bifidobacterium
No change No change Proteobacteria reduced (relative
abundance mean % ± SD: 15.889
±13.207 (placebo) vs 10.200± 15.401
(azithromycin), FDR p < .001)
E.coli reduced (relative abundance
mean % ± SD:12.087± 12.457
(placebo) vs 7.309 ±13.258
(azithromycin), FDR p < .013)
Penders et al.
41
Cross
sectional
Netherlands
1 month
Amoxicillin** < 1 month Bifidobacteria median count
placebo log10 CFU/g feces
10.70 vs amoxicillin log CFU/
g feces 10.29 (p < .01)
Bacteroides fragilis median
count placebo log10 CFU/g
feces 9.31 vs amoxicillin
log10 CFU/g 6.39 (P < .01)
No change (lactobacilli) No change in Clostridium difficile and E.
Coli
(Continued)
e1870402-10 L. MCDONNELL ET AL.
Table 4. (Continued).
Study
Author, year,
type of study,
country,
duration.
Antibiotic
(days of treatment
where given)
Time from
exposure
to analysis
Actinobacteria
(includes genus Bifidobacteria) Bacteroidetes
Firmicutes
(gram positive)
Proteobacteria
(gram negative)
Wei et al.
29
RCT
Denmark
1–3 years
Azithromycin
3 days
14 days 50 x reduction (fold change) in
genus Bifidobacterium at
14 days (p adjusted < 0.011
(FDR p < .05)
n/a n/a n/a
Yassour et al.
42
Cohort
Finland
2–36 months
Amoxicillin
Cefalexin
Clarithromycin
Amoxicillin and
clavulanic acid
Trimethoprim and
sulfadiazine
Azithromycin
Cefaclor
Penicillin
G Netilmicin
< 1 month n/a n/a Decrease in abundance of species from
clostridium clusters and 14XIVa IV (T regulatory
immune cells) at aged 3 (median abundance
~9% vs ~15% control)
b
(p = .037)
n/a
Key: †Confidence intervals or Standard Deviation not available. # Approximate value taken from bar chart. No confidence intervals or SD available. *no raw data. ** authors state that the antibiotics taken were ‘mainly
amoxicillin’.
b
raw data not given; approximate values taken from graph.
Abbreviations: CFU/g = Colony forming unit/gram; F/B ratio = Firmicutes/Bacteroidetes ratio. FDR – false discovery rate correction. M = macrolides . P = penicillins. n/a = Data not available.
GUT MICROBES e1870402-11
relatively few species described. Parker et al. examined
the association between azithromycin and the species
Akkermansia mucinophila which completely disap-
peared with azithromycin use (p < .003).
40
Discussion
Key ndings
As far as we are aware this is the first systematic
review to synthesize the evidence of the association
between antibiotic exposure and changes in the
microbiome specifically in children. We found evi-
dence of microbiome disruption characterized by
changes in richness, diversity, and taxonomic com-
position. We cannot be sure of the duration of these
changes from the data presented as most studies
only presented short-term data. The studies were
heterogeneous, with variation between studies in
participant age, setting, duration of antibiotic expo-
sure, type of antibiotic given, mode of delivery,
outcome measures and time between exposure
and analysis. These factors may influence the asso-
ciation between antibiotic use and microbiome
composition. Evidence of change in a wide range
of microbiome characteristics associated with anti-
biotic exposure requires further investigation and
explanation.
We found evidence that antibiotic exposure was
associated with a reduction in both richness and
diversity. In particular azithromycin exposure reduced
microbiome alpha-diversity by a mean reduction in
Shannon index of 0.86. The studies looked at a variety
of antibiotics covering narrow to broad-spectrum
antibiotics, with macrolides and penicillins represent-
ing the antibiotics most commonly studied. Although
Figure 2. Meta-analysis of trials of azithromycin that used Shannon Index of microbiome alpha diversity as the outcome.
Table 5. Definition of molecular techniques used by studies in
the review.
Technique name Definition
Fluorescent in-situ
hybridization (FISH)
Molecular cytogenic analysis using fluorescent
probes to detect, quantify and map genetic
material.
Flow cytometry Analysis of the frequency and other properties
of cells stained with specific fluorochrome
conjugated antibodies to identify bacteria,
their viability, and their DNA content.
16s rRNA sequencing Amplification of a piece of RNA (amplicon) and
sequencing to identify and compare bacteria
within a sample.
DNA extraction Purification of DNA using physical and chemical
methods
Next Generation
Sequencing (NGS)
Sequencing of DNA and RNA with different
technologies
Whole genome shotgun
sequencing
Comprehensive sampling of all genes in all
organisms present to evaluate diversity and
study ‘difficult to culture’ microorganisms.
Table 6. Definitions and examples of indices measuring micro-
biome richness and diversity.
Measures Definition and example indices
Species
Richness
43
Total number of bacterial species in sample
Example indices:
Operational Taxonomic Unit (OTU) count
OTUs are organisms defined by similarity in DNA
sequences, usually 97%
Observed Richness/Richness score
Chao 1 score
Alpha
Diversity
The number of individual bacteria from each bacterial
species present in sample
Example indices:
Shannon Index
Simpson Index*
Inverse Simpson index*
Beta diversity Difference in microbial composition between two samples
Example index:
Weighted and unweighted UniFrac distances (a dis-
tance metric used for comparing microbial
communities)
44
Gamma
diversity
The overall total species diversity of a range of samples
(incorporating the range of different species found in
each sample)
Example index
Simpson’s community-level gamma diversity
*Simpson’s Index is an inverse scale i.e. the higher the score the lower the
diversity. It is therefore often reported as the Inverse Simpson Index so that
higher scores indicate higher diversity.
e1870402-12 L. MCDONNELL ET AL.
no specific change in richness or diversity emerged
according to antibiotic class, we found evidence that
macrolides were associated with more changes in the
microbiome than penicillins and with effects that per-
sisted for longer.
37,39
We also found evidence that antibiotic use was
associated with a reduced number of gut bacteria
thought to be beneficial. Bifidobacteria (phylum
Actinobacteria) and Lactobacilli (phylum Firmicutes)
are producers of short-chain fatty acids which have
positive effects on mammalian energy metabolism
and form the basis of probiotic supplements.
45
The
majority of studies, however, did not report changes in
these genera at species level which limits our apprecia-
tion of the changes in specific species associated with
antibiotics. We also found evidence that changes in
other beneficial bacteria were associated with antibio-
tic use. One study reported a decrease in Clostridium
clusters IV and XIVa which are inducers of
T regulatory immune cells which have a role in reg-
ulating or suppressing other cells in the immune
system.
42
A second found that Azithromycin was
statistically significantly associated with reduced num-
bers of Akkermansia Mucinophilia.
40
This species has
previously been recognized as having anti-
inflammatory and immunostimulant properties, and
improving intestinal barrier function, endotoxinaemia
and insulin sensitivity.
46
We found evidence that antibiotics were asso-
ciated with a rise in Bacteroidetes and
Proteobacteria following antibiotic exposure.
These phyla include species which have been
implicated in serious infection. Although
Bacteroides spp. may provide some level of pro-
tection from invasive pathogens as a gut com-
mensal, Bacteroides have also been associated
with bloodstream infections and abscess
formation.
47
However, it cannot be assumed
that higher levels of Bacteroides in the gut are
the source of these infections. E.coli
(Proteobacteria) is a common cause of urinary
tract infections and sepsis and a major source of
antimicrobial resistance.
48
Study strengths and limitations
Our review highlights important findings regard-
ing the relationship between antibiotic exposure
and microbiome disruption in children.
A strength of our study is that we only included
studies with named antibiotics which included
specific details of antibiotic administration,
rather than exposure to ‘antibiotics’ in general.
However, several studies included more than one
named antibiotic, so in these cases it was not
possible to associate a particular change with
a specific antibiotic or class. In the majority of
studies, the indication for antibiotic use was
infection. In one study there was no clinical
indication for antibiotic use but associations
with changes in the microbiome were still pre-
sent. This supports the independent association
between antibiotic exposure and microbiome
disruption, although further studies of this rela-
tionship are required.
40
The use of different outcome measures lim-
ited our ability to make comparisons between
studies. Although the primary outcomes
reported in the RCTs were similar, the applic-
ability of the meta-analysis result may be lim-
ited by variation in initial Shannon index
scores which in turn might reflect microbiome
diversity by geographical location. We could
find no evidence of agreement in the literature
on the definition of a normal Shannon Index.
This substantial difference in variation by geo-
graphical location does not seem to have been
highlighted in the literature previously and
may be worth further investigation. Outcomes
in observational studies covered a number of
indices of richness, diversity, and taxonomical
changes which precluded meta-analysis of all
studies. This variation is likely to reflect
a lack of consensus among researchers about
the most suitable outcome measures in addi-
tion to the complexity of the microbiome itself.
The majority of the studies included in the
review focussed on microbiome changes over
a short time following antibiotic prescription,
i.e. less than 1 month. There was limited evi-
dence therefore of the duration of the changes
following exposure. Studies that examined
effects over time, found that microbiome dis-
ruption lasted between 1 and 2 years,
29,37
depending on the antibiotic studied. In this
interval some children will receive a further
course of antibiotics potentially disrupting
microbiome recovery.
37,49
Further studies are
GUT MICROBES e1870402-13
necessary to determine the duration of micro-
biome disruption.
Comparison/relation to existing literature
A systematic review of antibiotic prescribing in
neonates (up to 44 weeks gestational age) looked
at the effects of antibiotics on the neonatal
microbiome and similarly found that antibiotic
exposure was associated with reduced gut micro-
bial diversity and reduced colonization rates of
protective commensal bacteria, although the
quality of evidence was low.
27
A study looking
at the gut microbiota of adults also found that
antibiotic exposure was associated with
a decrease in beneficial bacteria such as
Bifidobacterium and butyrate producers and an
increase in Enterobactericae (phylum
Proteobacteria). The majority of the changes
lasted for approximately 45 days, but the micro-
biome had not fully recovered by 180 days.
26
Studies in mice support the findings of more
reduced diversity following macrolide exposure
compared to amoxicillin exposure. Cumulative
effects on the microbiome of multiple antibiotic
courses, delayed microbiome maturation follow-
ing antibiotics and fewer changes associated with
narrow-spectrum antibiotics have all been
observed.
50,51
Conclusion
In conclusion this review has gathered compel-
ling evidence that antibiotic exposure in chil-
dren is associated with a reduction in richness
and/or diversity, and a change in the balance of
species in the microbiome with reductions in
the numbers of commensal bacteria thought to
be beneficial. Studies that looked at the impact
on the microbiome for more than 1 month
were limited but there is evidence that antibio-
tics are associated with disruption to the micro-
biome for up to 2 years. Macrolide antibiotics
cause immediate and longer term damage.
More detailed understanding of the strength
and duration of antibiotic-specific associations
with microbiome dysbiosis in children is
needed. Evidence should be sought of a causal
relationship between antibiotic use in children,
gut dysbiosis and subsequent risk of local or
systemic pathological changes with repeated
courses of antibiotics. In the meantime, health-
care practitioners should consider the potential
for damage to the gut microbiome when pre-
scribing antibiotics for children.
Methods
Procedures used in this review were consistent with
Preferred Reporting Items for Systematic Reviews
and Meta-Analyses (PRISMA) guidelines.
Protocol and registration
A review protocol was submitted in advance to
PROSPERO, a database of systematic review pro-
tocols (registration ID: CRD42018094188).
Eligibility criteria
Our inclusion criteria were: studies of any
design-assessing change in the microbiome
associated with named antibiotic exposure; par-
ticipants aged from 0 to <18 years (excluding
pre-term babies); assessment of composition
and diversity of the microbiome using
a genetic analysis technique; comparable refer-
ence group or baseline assessment and adequate
statistical analysis. Our exclusion criteria were
non-original research; studies investigating the
impact of antibiotics in labor on neonates; stu-
dies investigating exposure to any intervention
which was not a named antibiotic; studies asses-
sing the impact of antibiotics on other systemic
microflora only, e.g. skin, nasal; conference
abstracts where insufficient data were given
and where the study authors did not reply to
further enquiries; and non-English language
articles.
Information sources and search strategy
The literature search was performed in
February 2019. The databases searched were
MEDLINE, EMBASE and Web of Science. No
restrictions were placed on the publication per-
iod. Search terms included both text words and
e1870402-14 L. MCDONNELL ET AL.
MESH terms. The full search strategy can be
viewed in Supplementary data Table S3.
Study Selection and data collection process
Papers were screened using Covidence software
(Melbourne, Australia) to efficiently identify the
most relevant and appropriate papers. The first
reviewer (LM) conducted the literature search and
imported the references. Duplicate articles were
removed. Two reviewers (LM and AG) screened titles
and abstracts with respect to eligibility criteria. Full-
text articles of potentially relevant studies were inde-
pendently assessed for eligibility by two reviewers
(LM and VR). Any disagreements were reviewed by
another reviewer (PW) and resolved through
discussion.
Data extraction
Information was extracted from included studies on
the study type, purpose, characteristics of study par-
ticipants (age, co-morbidities), details of the antibiotic
exposure (name, route of administration), time
between exposure and microbiome analysis, molecu-
lar technique used and study outcomes. Molecular
techniques used included Fluorescent in-situ hybridi-
zation (FISH) and flow cytometry, 16s RNA sequen-
cing, DNA extraction, Next Generation Sequencing
(NGS) and whole-genome shotgun sequencing (see
Table 5). We excluded papers that did not name the
antibiotic as we could not guarantee that the partici-
pants had been exposed to antibiotics.
Meta-analysis
eta-analysis was performed where studies shared
the same outcome and where output data were
available to include in the analysis. We performed
a meta-analysis of four RCTs including 390 patients
looking at the mean difference in Shannon Index
before and after antibiotic exposure. Continuous
outcomes were analyzed using an inverse variance
model with a 95% CI. Values were reported as
mean differences. P-values were two-tailed and sta-
tistically significant if p < .05. Statistical heteroge-
neity quantification was performed using the I
2
statistic. Degrees of heterogeneity were defined as
none (I
2
0–20%), low (I
2
25–49%), moderate (I
2
50–74.9%) and high (I
2
> 75%). When heterogene-
ity was quantified as low or above, a random-effects
model was used. The meta-analysis was performed
using review manager (Revman) for MAC (Version
5.3. Copenhagen: The Nordic Cochrane Center.
The Cochrane Collaboration, 2014).
Quality assessment and risk of bias
Observational study quality (cohort and cross-
sectional studies) was assessed using a modified
version of the Newcastle–Ottawa scale.
30,31
The
Newcastle–Ottawa scale is used to assess qual-
ity and biases. Points are assigned on a nine-
point scale. LM and PW independently
assessed quality factors including: i) compar-
ability of exposed and non-exposed groups; ii)
evidence of microbiome assessment prior to
exposure; iii) record of antibiotic exposure; iv)
confounding factors; and v) statistical analysis.
RCT quality was assessed using the Cochrane
Risk of Bias Tool.
28
LM and PW independently
applied the risk of bias assessments to each
RCT. Disagreement was resolved through
discussion.
Additional quality features for RCTs included
clear description of inclusion/exclusion criteria
and of withdrawals/dropouts.
Summary measures
The primary outcome measure was the change in
bacterial composition of the microbiome. This was
measured as the changes in microbiome richness,
alpha-diversity or taxonomic composition.
29,33,35
Secondary outcome measures were beta- and
gamma-diversity.
29,33,35
Microbiome richness
score measures the total number of species found
in a single sample. Microbiome alpha-diversity
score measures the number of individual bacteria
from each of the bacterial species isolated from
a single sample. Beta-diversity examines the differ-
ences in species composition between 2
samples.
29,33,35
Gamma-diversity measures diver-
sity across many samples taking into account the
different species found in each sample.
35
With
regards to change in taxonomic composition, the
four main phyla reported were Actinobacteria,
GUT MICROBES e1870402-15
Bacteroidetes, Firmicutes and Proteobacteria. The
various different indices used by authors to quan-
tify these measures are summarized in Table 6.
Acknowledgment
We are grateful to the library staff at King’s College London
for their advice on the literature search strategy, and to Dr Iain
Marshall for his advice on the quality assessment of included
studies.
Disclosure of potential conicts of interest
The authors report no conflicts of interest
Funding
LM is supported by a National Institute for Health Research
(NIHR) In-Practice Fellowship. THH is supported by
a National Institute for Health Research (NIHR) Doctoral
Research Fellowship. This paper presents independent
research. The views expressed are those of the authors and
not necessarily those of the NHS, the NIHR or the Department
of Health.
ORCID
Lucy McDonnell http://orcid.org/0000-0002-8299-0612
Alexander Gilkes http://orcid.org/0000-0001-8957-3216
Author contributions
LM conceived the research. LM, DA, MA and PW all con-
tributed to the design of the study. LM, AG, VR and TH
reviewed the research papers identified at each stage. LM
and PW reviewed the quality criteria. LM drafted the paper
and all authors contributed to the writing of the paper.
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... Alterations that affect the commensal flora impair microbial homeostasis and generate a condition called "dysbiosis"; particularly, gut dysbiosis is characterized by a significant decrease of Bacteroidetes and Lactobacilli [4]. In a similar way, Lactobacillus abundance is predominant in other body districts, including vagina and endometrium [5], and even in the latter, eubiosis exists if the percentage of endometrial Lactobacilli is greater than 90% [6]. ...
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Rationale Antibiotics are commonly prescribed for infants. In addition to increasing concern about antibiotic resistance, there is a concern about the potential negative impact of antibiotics on the gut microbiota and health and development outcomes. Objective The aim of this study was to investigate the association between early life antibiotic exposure and later neurocognitive outcomes. Methods Participants were infants born to mothers enrolled in the probiotics study. The initial study was designed to evaluate the effect of two different probiotics on allergy outcomes in childhood. Antibiotic exposure was based on parent report and categorised according to the following timing of the first exposure: 0–6 months, 6–12 months, 12–24 months or not at all. At 11 years of age, children’s neurocognitive outcomes were assessed using psychologist-administered, parent-report and self-report measures. The relationship between the timing of antibiotic exposure and neurocognitive outcomes was examined using regression models. Results Of the 474 participants initially enrolled, 342 (72%) children had a neurocognitive assessment at 11 years of age. After adjustment for mode of delivery, probiotic treatment group assignment, income and breastfeeding, children who had received antibiotics in the first 6 months of life had significantly lower overall cognitive and verbal comprehension abilities, increased risk of problems with metacognition, executive function, impulsivity, hyperactivity, attention-deficit hyperactivity disorder, anxiety and emotional problems. Conclusions These results provide further evidence that early exposure to antibiotics may be associated with detrimental neurodevelopmental outcomes.
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Cohabiting children may share components of their intestinal microbiome. We evaluated whether receipt of azithromycin in one sibling confers changes to the intestinal microbiome in an untreated sibling compared with placebo in a randomized controlled trial. We found no evidence of an indirect effect of antibiotic use in cohabiting children. Clinical Trials Registrations: NCT03187834.
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The gut microbiome is now considered an organ unto itself and plays an important role in health maintenance and recovery from critical illness. The commensal organisms responsible for the framework of the gut microbiome are valuable in protection against disease and various physiological tasks. Critical illness and the associated interventions have a detrimental impact on the microbiome. While antimicrobials are one of the fundamental and often life-saving modalities in septic patients, they can also pave the way for subsequent harm because of the resulting damage to the gut microbiome. Contributing to many of the non-specific signs and symptoms of sepsis, the balance between the overuse of antimicrobials and the clinical need in these situations is often difficult to delineate. Given the potency of antimicrobials utilized to treat septic patients, the effects on the gut microbiome are often rapid and long-lasting, in which case full recovery may never be observed. The overgrowth of opportunistic pathogens is of significant concern as they can lead to infections that become increasingly difficult to treat. Continued research to understand the disturbances within the gut microbiome of critically ill patients and their outcomes is essential to help develop future therapies to circumvent damage to, or restore, the microbiome. In this review, we discuss the impact of the antimicrobials often used for the treatment of sepsis on the gut microbiota.
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Background Taking oral antibiotics during childhood has been linked with an increased risk of childhood obesity. This study assessed any potential association in number of courses of antibiotics taken between 2–3 and 4–5 years of age and body mass trajectory up to age 5. Methods The study was a secondary analysis of 8186 children and their parents from the infant cohort of the Irish National Longitudinal Study of Children. Antibiotic use was measured by parental recall between ages 2–3 and 4–5. Longitudinal models described the relationship between antibiotic exposure and body mass index (BMI) standard deviation scores and binary outcomes, and examined interactions between covariates, which included socioeconomic status, diet assessed by food frequency questionnaires and maternal BMI. Results Any antibiotic usage between 2 and 3 years did not predict risk of overweight or obesity at age 5. Four or more courses of antibiotics between 2 and 3 years were independently associated with obesity at age 5 (odds ratio 1.6, 95% confidence interval 1.11–2.31). Effect size was modest (coefficient + 0.09 body mass SD units, standard error 0.04, P = 0.037). Maternal BMI modified the relationship: ≥ 4 courses of antibiotics between 2 and 3 years were associated with a + 0.12 body mass SD units increase in weight at age 5 among children of normal-weight mothers (P = 0.035), but not in children of overweight mothers. Conclusions Number of antibiotic courses, rather than antibiotic use, may be an important factor in any link between early antibiotic exposure and subsequent childhood obesity. Research is needed to confirm differential effects on babies of normal versus overweight/obese mothers independent of socioeconomic factors.
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Importance Infections have been associated with increased risks for mental disorders, such as schizophrenia and depression. However, the association between all infections requiring treatment and the wide range of mental disorders is unknown to date. Objective To investigate the association between all treated infections since birth and the subsequent risk of development of any treated mental disorder during childhood and adolescence. Design, Setting, and Participants Population-based cohort study using Danish nationwide registers. Participants were all individuals born in Denmark between January 1, 1995, and June 30, 2012 (N = 1 098 930). Dates of analysis were November 2017 to February 2018. Exposures All treated infections were identified in a time-varying manner from birth until June 30, 2013, including severe infections requiring hospitalizations and less severe infection treated with anti-infective agents in the primary care sector. Main Outcomes and Measures This study identified all mental disorders diagnosed in a hospital setting and any redeemed prescription for psychotropic medication. Cox proportional hazards regression was performed reporting hazard rate ratios (HRRs), including 95% CIs, adjusted for age, sex, somatic comorbidity, parental education, and parental mental disorders. Results A total of 1 098 930 individuals (51.3% male) were followed up for 9 620 807.7 person-years until a mean (SD) age of 9.76 (4.91) years. Infections requiring hospitalizations were associated with subsequent increased risk of having a diagnosis of any mental disorder (n = 42 462) by an HRR of 1.84 (95% CI, 1.69-1.99) and with increased risk of redeeming a prescription for psychotropic medication (n = 56 847) by an HRR of 1.42 (95% CI, 1.37-1.46). Infection treated with anti-infective agents was associated with increased risk of having a diagnosis of any mental disorder (HRR, 1.40; 95% CI, 1.29-1.51) and with increased risk of redeeming a prescription for psychotropic medication (HRR, 1.22; 95% CI, 1.18-1.26). Antibiotic use was associated with particularly increased risk estimates. The risk of mental disorders after infections increased in a dose-response association and with the temporal proximity of the last infection. In particular, schizophrenia spectrum disorders, obsessive-compulsive disorder, personality and behavior disorders, mental retardation, autistic spectrum disorder, attention-deficit/hyperactivity disorder, oppositional defiant disorder and conduct disorder, and tic disorders were associated with the highest risks after infections. Conclusions and Relevance Although the results cannot prove causality, these findings provide evidence for the involvement of infections and the immune system in the etiology of a wide range of mental disorders in children and adolescents.
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Background Macrolides are commonly prescribed for respiratory infections and asthma-like episodes in children. While their clinical benefits have been proved, concerns regarding the side-effects of their therapeutic use have been raised. Here we assess the short- and long-term impacts of azithromycin on the gut microbiota of young children. Methods We performed a randomized, double-blind, placebo-controlled trial in a group of children aged 12–36 months, diagnosed with recurrent asthma-like symptoms from the COPSAC2010 cohort. Each acute asthma-like episode was randomized to a 3-day course of azithromycin oral solution of 10 mg/kg per day or placebo. Azithromycin reduced episode duration by half, which was the primary end-point and reported previously. The assessment of gut microbiota after treatment was the secondary end-point and reported in this study. Fecal samples were collected 14 days after randomization (N = 59, short-term) and again at age 4 years (N = 49, long-term, of whom N = 18 were placebo treated) and investigated by 16S rRNA gene amplicon sequencing. Findings Short-term, azithromycin caused a 23% reduction in observed richness and 13% reduction in Shannon diversity. Microbiota composition was shifted primarily in the Actinobacteria phylum, especially a reduction of abundance in the genus Bifidobacterium. Long-term (13–39 months after treatment), we did not observe any differences between the azithromycin and placebo recipients in their gut microbiota composition. Interpretation Azithromycin treatment induced a perturbation in the gut microbiota 14 days after randomization but did not have long-lasting effects on the gut microbiota composition. However, it should be noted that our analyses included a limited number of fecal samples for the placebo treated group at age 4 years. Fund Lundbeck Foundation, Danish Ministry of Health, Danish Council for Strategic Research, Capital Region Research Foundation, China Scholarship Council.
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Background Exposure to antibiotics may result in alterations to the composition of intestinal microbiota, however few trials have been conducted and observational studies are subject to confounding by indication. We conducted a randomized controlled trial to determine the effect of three commonly-used pediatric antibiotics on the intestinal microbiome in healthy preschool children. Methods Children aged 6–59 months were randomized (1:1:1:1) to a 5-day course of one of three antibiotics, including included amoxicillin (25 mg/kg/day twice-daily doses), azithromycin (10 mg/kg dose on day one and then 5 mg/kg once daily for four days), or co-trimoxazole (240 mg once daily), or placebo. Rectal swabs were obtained at baseline and five days after the last dose, and processed for 16S rRNA gene sequencing. The pre-specified primary outcome was inverse Simpson’s -diversity index. Results Post-treatment Simpson’s diversity was significantly different across the four arms (P=0.003). Mean Simpson’s -diversity among azithromycin-treated children was significantly lower than placebo-treated children (6.6, 95% CI 5.5 to 7.8 versus 9.8, 95% CI 8.7 to 10.9, P=0.0001). Diversity in children treated with amoxicillin (8.3, 95% CI 7.0 to 9.6; P=0.09) or cotrimoxazole (8.3, 95% CI 8.2 to 9.7; P=0.08) was not significantly different than placebo. Conclusions Azithromycin affects the composition of the pediatric intestinal microbiome. The effect of amoxicillin and cotrimoxazole on microbiome composition was less clear.
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Background & Aims: The intestinal microbiota is believed to be involved in the pathogenesis of celiac disease, in addition to genetic variants and dietary gluten. The gut microbiota is strongly influenced by systemic antibiotics—especially in early life. We explored the association between exposure to a systemic antibiotic in the first year of life and risk of diagnosed celiac disease. Methods: We performed an observational nationwide register-based cohort study. We included all children born in Denmark from 1995 through 2012 or Norway from 2004 through 2012. Children born in Denmark were followed until May 8, 2015 (age at end of follow-up was 2.3–20.3 years) and children born in Norway were followed until December 31, 2013 (age at end of follow-up was 1–10 years). We collected medical information from more than 1.7 million children, including 3346 with a diagnosis of celiac disease. Exposure to systemic antibiotics was defined as a dispensed systemic antibiotic in the first year of life. Results: Exposure to systemic antibiotics in the first year of life was positively associated with diagnosed celiac disease in the Danish and Norwegian cohorts (pooled odds ratio 1.26, 95% confidence interval 1.16–1.36). We found a dose-dependent relation between an increasing number of dispensed antibiotics and the risk of celiac disease (pooled odds ratio for each additional dispensed antibiotic 1.08, 95% confidence interval 1.05–1.11). No specific type of antibiotic or age period within the first year of life was prominent. Adjustment for hospital admissions with an infectious disease in the first year of life did not change the estimates; adjustment for the number of maternally reported infections in the child in 2 large sub-cohorts decreased the association slightly (pooled odds ratio 1.18, 95% confidence interval 0.98–1.39). Conclusion: In a nationwide study of children in Denmark and Norway, we found exposure to systemic antibiotics in the first year of life to be associated with a later diagnosis of celiac disease. These findings indicate that childhood exposure to systemic antibiotics could be a risk factor for celiac disease.
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Background : The effect of statin treatment on the risk of developing depression remains unclear. Therefore, we assessed the association between statin treatment and depression in a nationwide register-based cohort study with up to 20 years of follow up. Methods : We identified all statin users in the period from 1996 to 2013 among individuals born in Denmark between 1920 and 1983. One non-user was matched to each statin user based on age, sex and a propensity score taking several potential confounders into account. Using Cox regression we investigated the association between statin use and: (I) redemption of prescriptions for antidepressants, (II) redemption of prescriptions for any other drug, (III) depression diagnosed at psychiatric hospitals, (IV) cardiovascular mortality and (V) all-cause mortality. Results : A total of 193,977 statin users and 193,977 non-users were followed for 2,621,282 person-years. Statin use was associated with (I) increased risk of antidepressant use (hazard rate ratio (HRR) = 1.33; 95% confidence interval (95%-CI) = 1.31–1.36), (II) increased risk of any other prescription drug use (HRR = 1.33; 95%-CI = 1.31–1.35), (III) increased risk of receiving a depression diagnosis (HRR = 1.22, 95%-CI = 1.12–1.32) - but not after adjusting for antidepressant use (HRR = 1.07, 95%-CI = 0.99–1.15), (IV) reduced cardiovascular mortality (HRR = 0.92, 95%-CI = 0.87–0.97) and (V) reduced all-cause mortality (HRR = 0.90, 95%-CI = 0.88–0.92). Conclusions : These results suggest that the association between statin treatment and antidepressant use is unspecific (equivalent association between statins and most other drugs) and that the association between statin use and depression diagnoses is mediated by residual confounding, bias or by downstream effects of the statin prescription (seeing a physician more often).
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: media-1vid110.1542/5839981580001PEDS-VA_2018-0290Video Abstract OBJECTIVES: To determine the association of antibiotic use with weight outcomes in a large cohort of children. Methods: Health care data were available from 2009 to 2016 for 35 institutions participating in the National Patient-Centered Clinical Research Network. Participant inclusion required same-day height and weight measurements at 0 to <12, 12 to <30, and 48 to <72 months of age. We assessed the association between any antibiotic use at <24 months of age with BMI z score and overweight or obesity prevalence at 48 to <72 months (5 years) of age, with secondary assessments of antibiotic spectrum and age-period exposures. We included children with and without complex chronic conditions. Results: Among 1 792 849 children with a same-day height and weight measurement at <12 months of age, 362 550 were eligible for the cohort. One-half of children (52%) were boys, 27% were African American, 18% were Hispanic, and 58% received ≥1 antibiotic prescription at <24 months of age. At 5 years, the mean BMI z score was 0.40 (SD 1.19), and 28% of children had overweight or obesity. In adjusted models for children without a complex chronic condition at 5 years, we estimated a higher mean BMI z score by 0.04 (95% confidence interval [CI] 0.03 to 0.05) and higher odds of overweight or obesity (odds ratio 1.05; 95% CI 1.03 to 1.07) associated with obtaining any (versus no) antibiotics at <24 months. Conclusions: Antibiotic use at <24 months of age was associated with a slightly higher body weight at 5 years of age.