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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:
© 2021 The Author(s). Published with
license by Taylor & Francis Group, LLC. View supplementary material
Published online: 02 Mar 2021. Submit your article to this journal
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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, and Patrick White
School of Population Health and Environmental Sciences, King’s College London, London, UK
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
Received 2 June 2020
Revised 30 November 2020
Accepted 18 December 2020
Gut dysbiosis; Antibiotics;
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
and fecal microbial transplant (FMT) is
being used successfully to treat patients with allergic
colitis or Clostridium dicile infection.
On the other
hand, damage to the microbiome has been linked with
conditions such as asthma,
juvenile idio-
pathic arthritis,
type 1 diabetes,
celiac disease,
mental illness,
Crohn’s disease,
and impaired neurocognitive outcomes.
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,
appear to disrupt the normal
maturation of the microbiome and destabilize it, alter-
ing basic physiological equilibria.
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.
Antibiotic exposure has
already been shown to alter the gut microbiome in
adults and in neonates.
This review sought to
systematically examine the research into the associa-
CONTACT Patrick White School of Population Health and Environmental Science, King’s College London, 3
Floor, Addison
House, Great Maze Pond, London SE1 1UL, UK.
Supplemental data for this article can be accessed on the publisher’s website.
2021, VOL. 13, NO. 1, e1870402 (18 pages)
© 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 (, 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.
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).
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.
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.
Included studies’ design and participant
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,
and reference
group Aim
Study partici-
pants (n), age
Antibiotic expo-
(route; duration)
Exposure to
outcome inter-
val (sampling
Key findings:
Key findings:
Key findings:
Taxonomical change
1.Bai et al.
Added impact of
antibiotics on
changes in
33 healthy
10 received
1–12 years
(oral + IV:
10 days)
1–4 weeks
n/a Reduction in α-
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,
in first 2 years
of life
43 infants, 25
0–2 years
duration not
3–139 days
PCR, 16s RNA
n/a No change in α-divers
ity after antibiotic
exposure for median
52 days (13–139)
Reduced β–
diversity: UniFrac
< 0.01,
p < .001
No effect on Bifidobacterium
et al.
Impact on
of prebiotic
130 infants,
before and
1–2 years
(oral; 7 days)
1–3 weeks
FISH* and flow
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
GUT MICROBES e1870402-3
Table 1. (Continued).
Author, year,
and reference
group Aim
Study partici-
pants (n), age
Antibiotic expo-
(route; duration)
Exposure to
outcome inter-
val (sampling
Key findings:
Key findings:
Key findings:
Taxonomical change
4.Doan et al.
Effects of
on gut
80 children, 40
1–5 years
(oral; single
5 days
16S rRNA
n/a Reduction in α-
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-
Decrease in
Simpson’s community
level γ diversity with
azithromycin (10.10
95% CI 7.80–11.40) vs
(p < .001)
et al.
Cohort study
Effect of
antibiotics on
18 children, 9
Ampicillin and
(IV:2–9 days)
4 and 8 weeks
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
Increased Clostridium in treated
infants than controls at week 8 (7%
vs 2%, p < .035)
e1870402-4 L. MCDONNELL ET AL.
Table 1. (Continued).
Author, year,
and reference
group Aim
Study partici-
pants (n), age
Antibiotic expo-
(route; duration)
Exposure to
outcome inter-
val (sampling
Key findings:
Key findings:
Key findings:
Taxonomical change
et al.
changes in
142 children, 99
2–7 years
duration not
(<6 months
to 2 years)
DNA extraction,
16s rRNA
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).
Decreased Lactobacillus (0.09 fold;
p < .004) with exposure in previous
12 months
et al.
Impact of
amoxicillin on
31 infants, all
12–24 months
(oral: 7 days)
0 days
Total DNA
n/a n/a No change in total Bifidobacteria
Disappearance of Bifidobacterium
adolescentis species (0% vs 36.4%
(p < .001)
8. Oldenburg
et al.
effect of 3
antibiotics on
124 children, 93
6–59 months
(oral: 5 days)
5 days
DNA extraction,
deep gene
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
et al.
114 infants,
56 received
6–11 months
(oral: 3 days)
12 days
16s rRNA gene
Lower OTU with
(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
(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,
GUT MICROBES e1870402-5
Table 1. (Continued).
Author, year,
and reference
group Aim
Study partici-
pants (n), age
Antibiotic expo-
(route; duration)
Exposure to
outcome inter-
val (sampling
Key findings:
Key findings:
Key findings:
Taxonomical change
et al.
of external
influences to
1032 infants,
28 received
1 month
(oral: duration
not specified)
<1 month
DNA isolation,
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.
Examine short-
and long-
term impacts
treatment on
microbiota in
72 children, 33
episodes of
12–36 months
(oral: 3 days)
14 days and up
to 4 years
DNA extraction
Decrease in richness at
14 days: Observed
richness: 23%
(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–
UniFrac distance,
treatment accounted
for variance
= 3.8%, p = .027
(weighted) and
F2 = 4.2% p < .001
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
et al.
of infant gut
and effect of
39 children,
20 received
2–36 months
Amoxicillin and
clavulanic acid
G Netilmicin
(oral: duration
not specified)
<1 month
16S rRNA gene
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)
(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.
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.
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,
and ≤ 6 months in one study.
The reduction in richness reported by Wei et al.
had resolved by the time of a second analysis
(mean of 223 days following exposure).
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.
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
Three other authors also com-
mented on richness but did not report raw data
and hence are not included in Table 2.
Microbiome diversity
Microbiome species diversity was reported by 8
Data were available for
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.
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.
examined the impact of exposure to several different
classes of antibiotics: cephalosporins, beta-lactams,
macrolides, quinolones, and nitrofurantoin.
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,
< 0.01 p < .001).
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
group Country Antibiotic
of treat-
Time from
to analysis
Index of
Placebo or
(mean ± SD)
(mean ± SD)
difference Significance
et al.
RCT 1–3 years Denmark Azithromycin 3 days 14 days Observed
230.6 ± 61.2 177.8 ± 56.0 −25.9% p < .001
et al.
RCT 6–11 months India Azithromycin 3 days 12 days OTU count 73.6 ± 13.6 68.1 ± 15.4 −7.5% p = .027
et al.
2–7 years Finland Macrolides
n/a <6 months OTU count 230
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
Time between exposure and
Indices of alpha-diversity
mean (95% CI)
mean (95% CI or
difference Significance
Doan et al.
1–5 years Azithromycin Single dose 5 days Shannon
Inverse Simpson
6.91 (5.82–8.21)
p = .004
p = .03
Oldenburg et al.
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)
p < .001
p < .001
Wei et al.
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.
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.
1–12 years Cephalosporin,
10 days 1–4 weeks Shannon
3.25* 2.75* −15.38% P < .05
Fouhy et al.
New-born Ampicillin;
2–9 days 4 weeks Shannon 3.8
−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
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.
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
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.
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
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.
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.
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.
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.
One study examining
only amoxicillin exposure reported a decrease of
the species Bacteroides fragilis.
In 3 studies there
was no change (studies examining amoxicillin,
ampicillin/gentamicin and azithromycin).
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).
A statistically sig-
nificant decrease was seen in 4 studies following anti-
biotic exposure.
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.
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).
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.
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.
One study reported
a decrease in Proteobacteria following azithromycin
exposure only.
At species level, a statistically signifi-
cant increase in E.coli was reported following amox-
icillin exposure in children aged 1–2 years,
a statistically significant decrease in E.coli was
reported following azithromycin exposure in children
aged 6–11 months.
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.
Author, year,
type of study,
(days of treatment
where given)
Time from
to analysis
(includes genus Bifidobacteria) Bacteroidetes
(gram positive)
(gram negative)
Bai et al.
1–12 years
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.
0–2 years
Beta lactams
3–139 days No change in Bifidobacteria Increased (p < .05) * Clostridiales decreased from 3–9 months of age* Increased (p < .05) *
Brunser et al.
0–2 years
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.
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
(50% vs 32%; p = .006)
at 8 weeks vs control
Korpela et al.
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.
6–59 months
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.
6–11 months
3 days
12 days No change in Actinobacteria or
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.
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.
e1870402-10 L. MCDONNELL ET AL.
Table 4. (Continued).
Author, year,
type of study,
(days of treatment
where given)
Time from
to analysis
(includes genus Bifidobacteria) Bacteroidetes
(gram positive)
(gram negative)
Wei et al.
1–3 years
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.
2–36 months
Amoxicillin and
clavulanic acid
Trimethoprim and
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)
(p = .037)
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
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).
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
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
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
Next Generation
Sequencing (NGS)
Sequencing of DNA and RNA with different
Whole genome shotgun
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
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
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
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.
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.
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
A second found that Azithromycin was
statistically significantly associated with reduced num-
bers of Akkermansia Mucinophilia.
This species has
previously been recognized as having anti-
inflammatory and immunostimulant properties, and
improving intestinal barrier function, endotoxinaemia
and insulin sensitivity.
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
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.
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.
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,
depending on the antibiotic studied. In this
interval some children will receive a further
course of antibiotics potentially disrupting
microbiome recovery.
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.
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.
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
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.
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
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
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.
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
statistic. Degrees of heterogeneity were defined as
none (I
0–20%), low (I
25–49%), moderate (I
50–74.9%) and high (I
> 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.
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.
LM and PW independently
applied the risk of bias assessments to each
RCT. Disagreement was resolved through
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.
Secondary outcome measures were beta- and
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
Gamma-diversity measures diver-
sity across many samples taking into account the
different species found in each sample.
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.
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
Disclosure of potential conicts of interest
The authors report no conflicts of interest
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.
Lucy McDonnell
Alexander Gilkes
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.
1. Sawh SC, Deshpande S, Jansen S, Reynaert CJ,
Jones PM. Prevention of necrotizing enterocolitis with
probiotics: a systematic review and meta-analysis. PeerJ.
2016;4:e2429. doi:10.7717/peerj.2429.
2. Liu SX, Li YH, Dai WK, Li X-S, Qiu C-Z, Ruan M-L,
Zou B, Dong C, Liu Y-H, He J-Y, et al. Fecal microbiota
transplantation induces remission of infantile allergic
colitis through gut microbiota re-establishment. World
J Gastroenterol. 2017;23(48):8570–8578. doi:10.3748/
3. Khan MY, Dirweesh A, Khurshid T, Siddiqui W.
Comparing fecal microbiota transplantation to
standard-of-care treatment for recurrent clostridium
difficile infection: a systematic review and
meta-analysis. Eur J Gastroenterol Hepatol. 2018;30
(11):1309–1317. doi:10.1097/MEG.000000000000124.
4. Marra F, Marra CA, Richardson K, Lynd LD,
Kozyrskyj A, Patrick DM, Bowie WR, Fitzgerald JM.
Antibiotic use in children is associated with increased
risk of asthma. Pediatrics. 2009;123(3):1003–1010.
5. Stromberg Celind F, Wennergren G, Vasileiadou S, Alm B,
Goksor E. Antibiotics in the first week of life were asso-
ciated with atopic asthma at 12 years of age. Acta Paediatr.
2018;107(10):1798–1804. doi:10.1111/apa.14332.
6. Yamamoto-Hanada K, Yang L, Narita M, Saito H,
Ohya Y. Influence of antibiotic use in early childhood
on asthma and allergic diseases at age 5. Ann Allergy
Asthma Immunol. 2017;119(1):54–58. doi:10.1016/j.
7. Ahmadizar F, Vijverberg SJH, Arets HGM, de Boer A,
Turner S, Devereux G, Arabkhazaeli A, Soares P,
Mukhopadhyay S, Garssen J, et al. Early life antibiotic
use and the risk of asthma and asthma exacerbations in
children. Pediatr Allergy Immunol. 2017;28(5):430–437.
8. Kim DH, Han K, Kim SW. Effects of antibiotics on the
development of asthma and other allergic diseases in
children and adolescents. Allergy Asthma Immunol
Res. 2018;10(5):457–465. doi:10.4168/aair.2018.10.5.
9. Horton DB, Scott FI, Haynes K, Putt ME, Rose CD,
Lewis JD, Strom BL. Antibiotic exposure and juvenile
idiopathic arthritis: a case-control study. Pediatrics.
2015;136(2):e333–43. doi:10.1542/peds.2015-0036.
10. Arvonen M, Virta LJ, Pokka T, Kröger L, Vähäsalo P.
Repeated exposure to antibiotics in infancy:
a predisposing factor for juvenile idiopathic arthritis
or a sign of this group’s greater susceptibility to infec-
tions? J Rheumatol. 2015;42(3):521–526. doi:10.1093/
11. Clausen TD, Bergholt T, Bouaziz O, Arpi M, Eriksson F,
Rasmussen S, Keiding N, Løkkegaard EC. Broad-
spectrum antibiotic treatment and subsequent child-
hood type 1 diabetes: a nationwide danish cohort
study. PLoS One. 2016;11(8):e0161654. doi:10.1371/
12. Kelly D, Kelly A, O’Dowd T, Hayes C. Antibiotic use in
early childhood and risk of obesity: longitudinal analysis
of a national cohort. World J Pediatr. 2019;15
(4):390–397. doi:10.1007/s12519-018-00223-1.
13. Block JP, Bailey LC, Gillman MW, Lunsford D,
Daley MF, Eneli I, Finkelstein J, Heerman W,
Horgan CE, Hsia DS. Early antibiotic exposure
and weight outcomes in young children.
Pediatrics. 2018;142(6):e20180290. doi:10.1542/
14. Stark CM, Susi A, Emerick J, Nylund CM. Antibiotic
and acid-suppression medications during early
e1870402-16 L. MCDONNELL ET AL.
childhood are associated with obesity. Gut. 2019;68
(1):62–69. doi:10.1136/gutjnl-2017-314971.
15. Miller SA, Wu RKS, Oremus M. The association between
antibiotic use in infancy and childhood overweight or
obesity: a systematic review and meta-analysis. Obes Rev.
2018;19(11):1463–1475. doi:10.1111/obr.12717.
16. Korpela K, Zijlmans MA, Kuitunen M, Kukkonen K,
Savilahti E, Salonen A, de Weerth C, de Vos WM.
Childhood BMI in relation to microbiota in infancy
and lifetime antibiotic use. Microbiome. 2017;5(1):26.
17. Scott FI, Horton DB, Mamtani R, Haynes K,
Goldberg DS, Lee DY, Lewis JD. Administration of
antibiotics to children before age 2 years increases risk
for childhood obesity. Gastroenterology. 2016;151
(1):120–9.e5. doi:10.1053/j.gastro.2016.03.006.
18. Dydensborg Sander S, Nybo Andersen AM, Murray JA,
Karlstad Ø, Husby S, Størdal K. Association between
antibiotics in the first year of life and celiac disease.
Gastroenterology. 2019;156(8):2217–2229. doi:10.1053/
19. Köhler-Forsberg O, Gasse C, Petersen L, Nierenberg AA,
Mors O, Østergaard SD, Nationwide A. Study in Denmark
of the association between treated infections and the sub-
sequent risk of treated mental disorders in children and
adolescents. JAMA Psychiatry. 2018;76(3):271–279.
20. Hviid A, Svanstrom H, Frisch M. Antibiotic use and
inflammatory bowel diseases in childhood. Gut. 2011;60
(1):49–54. doi:10.1136/gut.2010.219683.
21. Slykerman RF, Coomarasamy C, Wickens K,
Thompson JMD, Stanley TV, Barthow C, Kang J,
Crane J, Mitchell EA. Exposure to antibiotics in the
first 24 months of life and neurocognitive outcomes at
11 years of age. Psychopharmacology (Ber). 2019;236
(5):1573–1582. doi:10.1007/s00213-019-05216-0.
22. Hales CM, Kit BK, Gu Q, Ogden C. Trends in prescrip-
tion medication use among children and adolescents-
United States, 1999–2014. JAMA. 2018;319
(19):2009–2020. doi:10.1001/jama.2018.569.
23. Bhalodi AA, van Engelen TSR, Virk HS, Wiersinga WJ.
Impact of antimicrobial therapy on the gut microbiome.
J Antimicrob Chemother. 2019;74(Supplement_1):i6–
i15. doi:10.1093/jac/dky530.
24. Francino MP. Antibiotics and the human gut micro-
biome: dysbioses and accumulation of resistances. Front
Microbiol. 2015;6:1543. doi:10.3389/fmicb.2015.01543.
25. Pérez-Cobas AE, Gosalbes MJ, Friedrichs A, Knecht H,
Artacho A, Eismann K, Otto W, Rojo D, Bargiela R, von
Bergen M, et al. Gut microbiota disturbance during anti-
biotic therapy: a multi-omic approach. Gut. 2013;62
(11):1591–1601. doi:10.1136/gutjnl-2012-303184.
26. Palleja A, Mikkelsen KH, Forslund SK, Kashani A,
Allin KH, Nielsen T, Hansen TH, Liang S, Feng Q,
Zhang C. Recovery of gut microbiota of healthy adults
following antibiotic exposure. Nat Microbiol. 2018;3
(11):1255–1265. doi:10.1038/s41564-018-0257-9.
27. Fjalstad JW, Esaiassen E, Juvet LK, van den Anker JN,
Klingenberg C. Antibiotic therapy in neonates and impact
on gut microbiota and antibiotic resistance development:
a systematic review. J Antimicrob Chemother. 2018;73
(3):569–580. doi:10.1093/jac/dkx088.
28. Higgins JPT, Altman DG, Gøtzsche PC, Jüni P,
Moher D, Oxman AD, Savovic J, Schulz KF, Weeks L,
Sterne JA, et al. The cochrane collaboration’s tool for
assessing risk of bias in randomised trials. BMJ.
2011;343:d5928. doi:10.1136/bmj.d5928.
29. Wei S, Mortensen MS, Stokholm J, Brejnrod AD,
Thorsen J, Rasmussen MA, Trivedi U, Bisgaard H,
Sørensen SJ. Short- and long-term impacts of azithro-
mycin treatment on the gut microbiota in children: a
double-blind, randomized, placebo-controlled trial.
EBioMedicine. 2018;38:265–272. doi:10.1016/j.
30. Wells G, Shea B, O’Connell D, Peterson J, Welch V,
Losos M, Tugwell P. The Newcastle-Ottawa Scale (NOS)
for assessing the quality of nonrandomised studies in
meta-analyses. 2013. Accessed 06 January 2021. http://
31. Margulis AV, Pladevall M, Rierz-Guardia N, Varas-Lorenzo
C, Hazell L, Berkman ND, Viswanathan M, Perez-
Gutthann S. Quality assessment of observational studies in
a drug-safety systematic review, comparison of two tools: the
newcastle-ottawa scale and the RTI item bank. Clin
Epidemiol. 2014;6:359–368. doi:10.2147/CLEP.S66677.
32. Bai L, Zhou P, Li D, Ju X. Changes in the gastrointest-
inal microbiota of children with acute lymphoblastic
leukaema and its association with antibiotics in the
short term. J Med Microbiol. 2017;66(9):1297–1307.
33. Bokulich NA, Chung J, Battaglia T, Henderson N, Jay M,
Li H, Lieber A D, Wu F, Perez-Perez GI, Chen Y, et al.
Antibiotics, birth mode, and diet shape microbiome
maturation during early life. Sci Transl Med. 2016;8
(343):343ra82. doi:10.1126/scitranslmed.aad7121.
34. Brunser O, Gotteland M, Cruchet S, Figueroa G,
Garrido D, Steenhout P. Effect of a milk formula with
prebiotics on the intestinal microbiota of infants after
an antibiotic treatment. Pediatr Res. 2006;59
(3):451–456. doi:10.1203/01.pdr.0000198773.40937.61.
35. Doan T, Arzika AM, Ray KJ, Cotter SY, Kim J, Maliki R,
Zhong L, Zhou Z, Porco TC, Vanderschelden B, et al.
Gut microbial diversity in antibiotic-naive children
after systemic antibiotic exposure: a randomized con-
trolled trial. Clin Infect Dis. 2017;64(9):1147–1153.
36. Fouhy F, Guinane CM, Hussey S, Wall R, Ryan CA,
Dempsey EM, Murphy B, Ross RP, Fitzgerald GF,
Stanton C, et al. High-throughput sequencing reveals
the incomplete, short-term recovery of infant gut
microbiota following parenteral antibiotic treatment
with ampicillin and gentamicin. Antimicrob Agents
Chemother. 2012;56(11):5811–5820. doi:10.1128/
GUT MICROBES e1870402-17
37. Korpela K, Salonen A, Virta LJ, Kekkonen RA,
Forslund K, Bork P, de Vos WM. Intestinal microbiome
is related to lifetime antibiotic use in Finnish pre-school
children. Nat Commun. 2016;7:10410. doi:10.1038/
38. Mangin I, Suau A, Gotteland M, Brunser O, Pochart P.
Amoxicillin treatment modifies the composition of
Bifidobacterium species in infant intestinal microbiota.
Anaerobe. 2010;16(4):433–438. doi:10.1016/j.
39. Oldenburg CE, Sie A, Coulibaly B, Ouermi L, Dah C,
Tapsoba C, Bärnighausen T, Ray KJ, Zhong L,
Cummings S, et al. Effect of commonly used pediatric
antibiotics on gut microbial diversity in preschool chil-
dren in burkina faso: a randomized clinical trial. Open
Forum Infect Dis. 2018;5(11). doi:10.1093/ofid/ofz061.
40. Parker EPK, Praharaj I, John J, Kaliappan SP,
Kampmann B, Kang G, Grassly NC. Changes in the
intestinal microbiota following the administration of
azithromycin in a randomised placebo-controlled trial
among infants in south India. Sci Rep. 2017;7(1):9168.
41. Penders J, Thijs C, Vink C, Stelma FF, Snijders B,
Kummeling I, van den Brandt PA, Stobberingh EE.
Factors influencing the composition of the intestinal
microbiota in early infancy. Pediatrics. 2006;118
(2):511–521. doi:10.1542/peds.2005-2824.
42. Yassour M, Vatanen T, Siljander H, Hämäläinen AM,
Härkönen T, Ryhänen SJ, Franzosa EA, Vlamakis H,
Huttenhower C, Gevers D, et al. Natural history of the
infant gut microbiome and impact of antibiotic treat-
ment on bacterial strain diversity and stability. Sci
Transl Med. 2016;8(343):343ra81. doi:10.1126/sci-
43. Kim BR, Shin J, Guevarra R, Lee JH, Kim DW, Seol KH,
Lee J-H, Kim HB, Isaacson RE. Deciphering diversity
indices for a better understanding of microbial
communities. J Microbiol Biotechnol. 2017;27
(12):2089–2093. doi:10.4014/jmb.1709.09027.
44. Lozupone C, Lladser ME, Knights D, Stombaugh J,
Knight R. UniFrac: an effective distance metric for
microbial community comparison. Isme J. 2011;5
(2):169–172. doi:10.1038/ismej.2010.133.
45. King S, Tancredi D, Lenoir-Wijnkoop I, Gould K,
Vann H, Connors G, Sanders ME, Linder JA,
Shane AL, Merenstein D. Does probiotic consumption
reduce antibiotic utilization for common acute infec-
tions? A systematic review and meta-analysis. Eur
J Public Health. 2018;29(3):494–499. doi:10.1093/eur-
46. Fujio-Vejar S, Vasquez Y, Morales P, Magne F, Vera-
Wolf P, Ugalde JA, Navarrete P, Gotteland M. The gut
microbiota of healthy chilean subjects reveals a high
abundance of the phylum verrucomicrobia. Front
Microbiol. 2017;8:1221. doi:10.3389/fmicb.2017.01221.
47. Wexler HM. Bacteroides: the good, the bad, and the
nitty-gritty. Clin Microbiol Rev. 2007;20(4):593–621.
48. Vihta KD, Stoesser N, Llewelyn MJ, Quan TP, Davies T,
Fawcett NJ, Dunn L, Jeffery K, Butler CC, Hayward G.
Trends over time in Escherichia coli bloodstream infec-
tions, urinary tract infections, and antibiotic suscept-
ibilities in Oxfordshire, UK, 1998-2016: a study of
electronic health records. Lancet Infect Dis. 2018;18
(10):1138–1149. doi:10.1016/S1473-3099(18)30353-0.
49. Hicks LA, Bartoces MG, Roberts RM, Suda KJ,
Hunkler RJ, Taylor TH Jr, Schrag SJ. US outpatient anti-
biotic prescribing variation according to geography,
patient population, and provider specialty in 2011. Clin
Infect Dis. 2015;60(9):1308–1316. doi:10.1093/cid/
50. Nobel YR, Cox LM, Kirigin FF, Bokulich NA,
Yamanishi S, Teitler I, Chung J, Sohn J,
Barber CM, Goldfarb DS. Metabolic and metage-
nomic outcomes from early-life pulsed antibiotic
treatment. Nat Commun. 2015;6:7486. doi:10.1038/
51. Yao J, Carter RA, Vuagniaux G, Barbier M, Rosch JW,
Rock CO. A pathogen-selective antibiotic minimizes
disturbance to the microbiome. Antimicrob Agents
Chemother. 2016;60(7):4264–4273. doi:10.1128/
e1870402-18 L. MCDONNELL ET AL.
... 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]. ...
... An adequate level of physical activity increases the synaptic transmission of monoamines, releases endorphins, and improves positive emotions experienced after the exercise [68]. A recent systematic review has shown that combined resistance and aerobic training or aerobic training alone may 4 Oxidative Medicine and Cellular Longevity have positive effect on the microbiota, incrementing some bacteria phyla (i.e., Bacteroidetes, Firmicutes, and Proteobacteria) although further research with higher methodological rigor is needed to better understand such a relationship [9]. Studies on physical activity in clinical samples pointed out that it can normalize reduced levels of brain-derived neurotrophic factor (i.e., BDFN), with neuroprotective effects on the brain while other investigations have documented anxiolytic effects of aerobic exercise for induced-panic symptoms [69]. ...
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What benefit might emerge from connecting clinical neuroscience with microbiology and exercise science? What about the influence of the muscle-gut-brain (MGB) axis on mental health? The gut microbiota colonizes the intestinal tract and plays a pivotal role in digestion, production of vitamins and immune system development, but it is also able to exert a particular effect on psychological well-being and appears to play a critical role in regulating several muscle metabolic pathways. Endogenous and exogenous factors may cause dysbiosis, with relevant consequences on the composition and function of the gut microbiota that may also modulate muscle responses to exercise. The capacity of specific psychobiotics in ameliorating mental health as complementary strategies has been recently suggested as a novel treatment for some neuropsychiatric diseases. Moreover, physical exercise can modify qualitative and quantitative composition of the gut microbiota and alleviate certain psychopathological symptoms. In this minireview, we documented evidence about the impact of the MGB axis on mental health, which currently appears to be a possible target in the context of a multidimensional intervention mainly including pharmacological and psychotherapeutic treatments, especially for depressive mood.
... Microbiome dysbiosis may therefore play a part in susceptibility to varicella zoster virus reactivation, a process that may occur when a patient is given broad spectrum antibiotics for some unrelated condition. It is now well established that antibiotics create some degree of dysbiosis in the microbiome [11][12][13] and there is also some evidence that, in its turn, alterations of the gut microbiome may cause dysregulated mucosal immune responses leading, for example, to the onset of inflammatory bowel diseases [14]. To explore the effect of antibiotics on the reactivation of varicella zoster virus we therefore carried out a case control study comparing the antibiotic prescriptions of patients with an HZ diagnosis and those without. ...
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Background: The effect of antibiotics on the human microbiome is now well established, but their indirect effect on the related immune response is less clear. The possible association of Herpes zoster, which involves a reactivation of a previous varicella zoster virus infection, with prior antibiotic exposure might indicate a potential link with the immune response. Methods: A case-control study was carried out using a clinical database, the UK's Clinical Practice Research Datalink. A total of 163,754 patients with varicella zoster virus infection and 331,559 age/sex matched controls were identified and their antibiotic exposure over the previous 10 years, and longer when data permitted, was identified. Conditional logistic regression was used to identify the association between antibiotic exposure and subsequent infection in terms of volume and timing. Results: The study found an association of antibiotic prescription and subsequent risk of varicella zoster virus infection (adjusted odds ratio of 1.50; 95%CIs: 1.42-1.58). The strongest association was with a first antibiotic over 10 years ago (aOR: 1.92; 95%CIs: 1.88-1.96) which was particularly pronounced in the younger age group of 18 to 50 (aOR 2.77; 95%CIs: 1.95-3.92). Conclusions: By finding an association between prior antibiotics and Herpes zoster this study has shown that antibiotics may be involved in the reactivation of the varicella zoster virus. That effect, moreover, may be relatively long term. This indirect effect of antibiotics on viruses, possibly mediated through their effect on the microbiome and immune system, merits further study.
... From that cohort, one patient presented severe NASH and auto-brewing syndrome; that is, he developed ultra-high alcohol blood concentration after an alcohol-free, high-carbohydrate diet [172] . Transplantation of isolates of high alcohol producing Klebsiella pneumonia from that patient into a rodent recipient induced hepatic steatosis [171,173] . An increased relative abundance of Klebsiella was corroborated by other groups [147] . ...
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Obesity, the metabolic syndrome, and metabolic dysfunction-associated fatty liver disease (MAFLD) can be portrayed as transmissible diseases. Indeed, they can be induced, in animal models, by cohabitation or by transplantation of fecal microbiota from other animals or humans with those diseases. As such, to get a 10,000-foot view, we need to see under the lens the microbes that populate our gut. Gut microbiota participates in the harvesting of energy from nutrients, it allows the digestion of otherwise indigestible nutrients such as fibers, and it also produces short chain fatty acids and some vitamins while emitting different compounds that can regulate whole-body metabolism and elicit proinflammatory responses. The metabolic syndrome and MAFLD share physiopathology and also patterns of gut dysbiota. Moreover, MAFLD also correlates with dysbiota patterns that are associated with direct steatogenic or fibrogenic effects. In the last decade, a tremendous effort has allowed a fair understanding of the dysbiota patterns associated with MAFLD. More recently, research is moving towards the delineation of microbiota-targeted therapies to manage metabolic dysfunction and MAFLD. This review provides in-depth insight into the state-of-the-art of gut dysbiosis in MAFLD, targeting clinical hepatologists.
... Studies have confirmed that the body's homeostasis and the intestinal immune function depend on the balance of bacteria in different degrees, and once this balance is destroyed, it will lead to the occurrence of related diseases (Bruellman and Llorente, 2021;Han et al., 2021;Khan et al., 2021;Shute et al., 2021;Sun et al., 2021;Deng et al., 2022b;Hu et al., 2022). The dysregulation of gut microbiota can be caused by many factors, such as age (Du et al., 2021;Janiak et al., 2021), lifestyle (Pauer et al., 2021;Shin et al., 2021), dietary habits (Neumann et al., 2021;Tap et al., 2021;Yu et al., 2021), immunity (Foley et al., 2021;Hosseinkhani et al., 2021) and the use of antibiotics Mcdonnell et al., 2021;Strati et al., 2021;Vicentini et al., 2021). ...
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Renal ischemia-reperfusion injury (IRI) is the main cause of acute kidney injury and the cause of rapid renal dysfunction and high mortality. In recent years, with the gradual deepening of the understanding of the intestinal flora, exploring renal IRI from the perspective of the intestinal flora has become a research hotspot. It is well known that the intestinal flora plays an important role in maintaining human health, and dysbiosis is the change in the composition and function of the intestinal tract, which in turn causes intestinal barrier dysfunction. Studies have shown that there are significant differences in the composition of intestinal flora before and after renal IRI, and this difference is closely related to the occurrence and development of renal IRI and affects prognosis. In addition, toxins produced by dysregulated gut microbes enter the bloodstream, which in turn exacerbates kidney damage. This article reviews the research progress of intestinal flora and renal IRI, in order to provide new treatment ideas and strategies for renal IRI.
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The literature of human and other host-associated microbiome studies is expanding rapidly, but systematic comparisons among published results of host-associated microbiome signatures of differential abundance remain difficult. We present BugSigDB, a community-editable database of manually curated microbial signatures from published differential abundance studies, accompanied by information on study geography, health outcomes, host body site, and experimental, epidemiological, and statistical methods using controlled vocabulary. BugSigDB is seeded for initial release with >2,500 manually curated signatures from >600 published studies on three host species, enabling high-throughput analysis of signature similarity, taxon enrichment, co-occurrence and co-exclusion, and consensus signatures, allowing assessment of microbiome differential abundance within and across experimental conditions, environments, or body sites. Database-wide analysis revealed experimental conditions with the highest level of consistency in signatures reported by independent studies and identified commonalities among disease-associated signatures including frequent introgression of oral pathobionts into the gut.
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Antimicrobial resistance is a growing public health burden, but little is known about the effects of antibiotic exposure on the gut resistome. As childhood (0–5 years) represents a sensitive window of microbiome development and a time of relatively high antibiotic use, the aims of this systematic review were to evaluate the effects of antibiotic exposure on the gut resistome of young children and identify knowledge gaps. We searched PubMed, Scopus, Web of Science, and the Cochrane Central Register of Controlled Trials. A PICO framework was developed to determine eligibility criteria. Our main outcomes were the mean or median difference in overall resistance gene load and resistome alpha diversity by antibiotic exposure groups. Bias assessment was completed using RoB 2 and ROBINS-I with quality of evidence assessed via the GRADE criteria. From 4885 records identified, 14 studies (3 randomized controlled trials and 11 observational studies) were included in the qualitative review. Eight studies that included information on antibiotic exposure and overall resistance gene load reported no or positive associations. Inconsistent associations were identified for the nine studies that assessed resistome alpha diversity. We identified three main groups of studies based on study design, location, participants, antibiotic exposures, and indication for antibiotics. Overall, the quality of evidence for our main outcomes was rated low or very low, mainly due to potential bias from the selective of reporting results and confounding. We found evidence that antibiotic exposure is associated with changes to the overall gut resistance gene load of children and may influence the diversity of antimicrobial resistance genes. Given the overall quality of the studies, more research is needed to assess how antibiotics impact the resistome of other populations. Nonetheless, this evidence indicates that the gut resistome is worthwhile to consider for antibiotic prescribing practices.
Emerging evidence is highlighting the microbiome as a key regulator of the effect of nutrition on gut-brain axis signaling. Nevertheless, it is not yet clear whether the impact of nutrition is moderating the microbiota-gut-brain interaction or if diet has a mediating role on microbiota composition and function to influence central nervous system function, brain phenotypes and behavior. Mechanistic evidence from cell-based in vitro studies, animal models and preclinical intervention studies are linking the gut microbiota to the effects of diet on brain function, but they have had limited translation to human intervention studies. While increasing evidence demonstrates the triangulating relationship between diet, microbiota, and brain function across the lifespan, future mechanistic and translational studies in the field of microbiota and nutritional neuroscience are warranted to inform potential strategies for prevention and management of several neurological, neurodevelopmental, neurodegenerative, and psychiatric disorders. This brief primer provides an overview of the most recent advances in the nutritional neuroscience - microbiome field, highlighting significant opportunities for future research.
Antibiotic are the most common type of medication prescribed to children, including infants, in western world. Antibiotics alter the gut microbial composition. Since the gut microbiota plays crucial role in immunity, metabolism and endocrinology the effects of antibiotics on the microbiota may lead to further health complications. Antibiotic in childhood have been linked with disease including asthma, juvenile, arthritis, type 1diabets, chronic disease and mental illness. In COVID-19 probiotics plays a therapeutic role for GI, IBD, colitis, and even in viral infection. So, we assume that the inclusion of studies to investigate gut microbiome and subsequent therapies such as probiotic might help decrease the inflammatory response of viral pathogenesis and respiratory symptoms by strengthening the host immune system, amelioration of gut microbiome, and improvement of gut barrier function. Focused on four types of dysbiosis loss of keystone taxa, loss of diversity. Establishment of large and diverse baseline healthy infant microbiome development is essential to advancing diagnosis interpretation and eventual treatment pediatric dysbiosis. In this review we present an overview of effects of antibiotics on microbiome in children and correlate them to long lasting complications. Objectives: • To review on antibiotics are alter the gut microbial composition in children, adult. • To review on gut microbiota plays crucial roles in immunity, metabolism and endocrinology, the effects of antibiotics on microbiota may lead to further health..
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Background: Early life antibiotic treatment is one likely exposure influencing allergy risk. The objective was to investigate associations between pre- and postnatal antibiotic exposures and the development of allergic manifestations until age 18 months. Methods: We included 1387 mother-child dyads from the prospective, population-based NorthPop birth cohort study. Data on antibiotic exposures in pregnancy and childhood were collected by web-based questionnaires. Until the child turned 18 months old, parents (n = 1219) reported symptoms of wheeze, eczema, and physician-diagnosed asthma; parents (n = 1025) reported physician-diagnosed food allergy. At age 18 months, serum immunoglobulin E levels to inhalant (Phadiatop) and food (Food mix fx5) allergens were determined. Associations were estimated using bivariable and multivariable logistic regressions. Results: Prenatal antibiotic exposure was positively associated with food sensitization in the crude (OR 1.82, 95% CI 1.01-3.26) but not in the adjusted analyses (aOR 1.58, 0.82-3.05). A borderline significant association was found between prenatal exposure and wheeze (aOR 1.56, 0.95-2.57). Postnatal antibiotics were positively associated with wheeze (aOR 2.14, 1.47-3.11), asthma (aOR 2.35, 1.32-4.19), and eczema (aOR 1.49, 1.07-2.06). Postnatal antibiotics were negatively associated with food sensitization (aOR 0.46, 95% CI 0.25-0.83) but not with food allergy nor sensitization to inhalants. Conclusion: Pre- and postnatal antibiotic exposure demonstrated positive associations with allergic manifestations and the former also with food sensitization. In contrast, there was a negative association between postnatal antibiotics and food sensitization. Food sensitization is often transient but may precede respiratory allergies. Future studies should investigate the relationship between antibiotic exposure and food sensitization later in childhood.
The human gastrointestinal tract is home to a complex and dynamic community of microorganisms known as gut microbiota, which provide the host with important metabolic, signaling, and immunomodulatory functions. Both the commensal and pathogenic members of the gut microbiome serve as reservoirs of antimicrobial-resistance genes (ARG), which can cause potential health threats to the host and can transfer the ARGs to the susceptible microbes and into the environment. Antimicrobial resistance is becoming a major burden on human health and is widely recognized as a global challenge. The diversity and abundance of ARGs in the gut microbiome are variable and depend on the exposure to healthcare-associated antibiotics, usage of antibiotics in veterinary and agriculture, and the migration of the population. The transfer frequency of the ARGs through horizontal gene transfer (HGT) with the help of mobile genetic elements (MGEs) like plasmids, transposons, or phages is much higher among bacteria living in the GI tract compared to other microbial ecosystems. HGT in gut bacteria is facilitated through multiple gene transfer mechanisms, including transformation, conjugation, transduction, and vesicle fusion. It is the need of the hour to implement strict policies to limit indiscriminate antibiotic usage when needed. Developing rapid diagnostic tests for resistance determination and alternatives to antibiotics like vaccination, probiotics, and bacteriophage therapy should have the highest priority in the research and development sectors. Collective actions for sustainable development against resistant pathogens by promoting endogenous gut microbial growth and diversity through interdisciplinary research and findings are key to overcoming the current antimicrobial resistance crisis.
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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.
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.
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).
: 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.