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The impact of mass drug administration of antibiotics on the gut microbiota of target populations

Abstract

Antibiotics have become a mainstay of healthcare in the past century due to their activity against pathogens. This manuscript reviews the impact of antibiotic use on the intestinal microbiota in the context of mass drug administration (MDA). The importance of the gut microbiota to human metabolism and physiology is now well established, and antibiotic exposure may impact host health via collateral effects on the microbiota and its functions. To gain further insight into how gut microbiota respond to antibiotic perturbation and the implications for public health, factors that influence the impact of antibiotic exposure on the microbiota, potential health outcomes of antibiotic-induced microbiota alterations, and strategies that have the potential to ameliorate these wider antibiotic-associated microbiota perturbations are also reviewed.

Graphical Abstract

Background

Greater accessibility to antimicrobials has contributed to an estimated 65% increase in human antibiotic consumption from 2000 to 2015 [1]. The clearest consequence is greater selection for antibiotic resistance, which has made common infections difficult to treat in recent years [2]. Antibiotic use can also have off-target effects, meaning that the concentrations at which antibiotics are administered elicit antimicrobial effects beyond elimination of pathogenic bacteria that impact the composition and function of the microbiome [3, 4]. Next-generation and targeted sequencing have demonstrated that antibiotics induce a reduction in gut bacterial biomass, taxonomic diversity, and functional diversity [5].

Virtually every niche of the human body harbors diverse microbial communities [6], even sites once thought as sterile, such as the bladder [7] and lungs [8]. The gastrointestinal tract is home to the greatest microbial biomass and biodiversity in the human body. Advances in DNA and RNA sequencing technology have revolutionized our ability to quantify and explore the dynamics of the gut microbiome, which elicits wide reaching effects on physiologic development, immune function, homeostasis, nutrient acquisition, and protection against infections [9, 10]. A causal role of the gut microbiome in development of celiac disease [11], inflammatory bowel disease [12], obesity [13, 14], undernutrition [15, 16], metabolic syndrome [17], asthma [18], and brain and nervous system development [19], have been clearly demonstrated using animal models. However, microbiome signatures associated with disease in clinical studies have been inconsistent across studies and outcomes [20]. Absence of clear evidence in human studies has even been demonstrated for disease conditions where the role of the microbiome is widely accepted, such as celiac disease [21] and IBD [22], due to heterogeneity in study designs [21], study procedures [21] or disease subtypes [22].

The prevailing view holds that microbial colonization of the gastrointestinal tract begins at birth in full-term infants, after which the microbiome progresses through a succession of identifiable shifts in composition [23]. Immediately after birth, the infant gut is predominantly colonized by facultative anaerobic Enterobacteriaceae [24]. Over the next few days, microbiota composition shifts to predominantly anaerobic bacteria [24]. In preterm neonates, however, gut colonization by anaerobes is delayed, likely due to delayed gut and immune system maturation, greater exposure to the hospital environment, and greater exposure to antibiotics [25].

The major initial sources of bacteria for colonization of the infant gut are the mother’s vaginal, fecal, and skin microbiota and the immediate environment [24, 26, 27]. In vaginally delivered newborns, the gut microbiota is more similar to the mothers’ vaginal microbiota [28]. The maternal gut microbiota is also transferred to the infant during vaginal delivery [29,30,31]. By contrast to vaginal delivery, cesarean delivered newborns’ gut microbiota more closely resembles the mothers’ skin and the environmental microbiota the infant is exposed to.

After birth, infant diet largely determines the composition of the gut microbiota. In breastfed infants, Bifidobacterium spp. are the most abundant bacteria [23, 32]. Human breast milk is rich in oligosaccharides, which Bifidobacterium spp. preferentially consume [33]. Weaning induces the next major shift in composition. A wider variety of nutrients in solid foods and reduced availability of milk oligosaccharides correspond with an increase in diversity and a decrease in Bifidobacterium. In the subsequent period, the infant microbiota develops a more adult-like composition, with greater bacterial diversity and evenness by 2–3 years of age [24, 26, 27, 34]. Composition and function may continue to develop through childhood and early adolescence [35, 36]. Geography also plays an important role. Infant microbiota composition differs between high-income versus low- and middle-income populations [23, 37, 38], likely driven by differences in diet, sanitation, and human genetics [25, 39]. Variations in the genetic potential for human lactase production, for example, explain Bifidobacterium abundance [40]. Diet and antibiotic exposure are also key factors that continue to influence this ongoing process [41, 42].

While the on-target effects of antibiotic exposure on pathogen clearance are beneficial to host health, the off-target effects of antibiotic exposure on gut microbiome composition and function can result in either detrimental or beneficial effects on host health, depending on the initial composition and function of the autochthonous microbial community. This is particularly relevant given recent increased interest in mass drug administration (MDA) with antibiotics—empirically administering drugs to an entire at-risk community or population to suppress pathogen growth—in view of, for example, global targets to reduce neonatal and under-5 mortality by 2030 [43], global targets for elimination of trachoma [44] and yaws [45], guidelines for cotrimoxazole prophylaxis among persons living with HIV [46], and recommendations for prevention of maternal and newborn peripartum infections by Group B Streptococcus (GBS) [47, 48]. Recent interest in MDA with antibiotics has also gained traction due to concerns about emergence of more difficult to treat enteric [49] and non-enteric [49, 50] infections and potential transmission of resistant pathogens to more vulnerable populations [49, 51]. The need to balance these risks with the demonstrated benefits of MDA with antibiotics [52, 53] is of particular relevance.

This narrative review describes studies that used high-throughput 16S rRNA gene amplicon sequencing or next generation whole genome shotgun sequencing (WGS) to investigate the impact of antibiotic use on gut microbiome composition and function in the context of MDA.

Main text

Methods

Data for this review were initially identified through a search of PubMed up to March 2021 using the search terms “mass drug administration”, “antibiotic*”, and “microbiome” or “microbiota”. This search provided an initial set of studies relevant to the topic. References from the initially identified studies were also searched and included in this review if relevant. Publications were included if they (i) reported the impact of antibiotic use on gut microbiome composition or function in the context of MDA and (ii) used high-throughput methods to survey the gut microbiome because those methods provide a more comprehensive catalogue of microbiome composition and antibiotic-associated alterations. Only articles published in English were included. The search period was not restricted. Included studies are summarized in Tables 1, 2, 3. To gain further insight into the findings of these MDA studies, other studies that investigated the factors which determine the impact of antibiotic use on the gut microbiome and its recovery, strategies to ameliorate these effects, and potential health outcomes are also discussed.

Table 1 Characteristics of studies included in this review of the impact of mass drug administration with antibiotics for trachoma, yaws elimination or child mortality reduction on the human gut microbiome

Impact of MDA on gut microbiota

Prophylaxis in the general population

Azithromycin is an integral part of the World Health Organization (WHO) strategy to eliminate trachoma [54] and yaws [45]. Five randomized controlled trials (RCT) investigated the impact on the gut microbiota of MDA with azithromycin for prophylaxis in the general population. The gut microbiome was characterized primarily in terms of α-diversity, which quantifies the number of different bacterial taxa in an individual microbiota and the uniformity of their abundances (Table 1). One RCT investigated impacts on the microbiota after repeated biannual treatment over a follow-up period of 48-months (long-term use), and four RCTs investigated impacts in the period immediately following a single dose or course of treatment (short-term use). All five RCTs were conducted in low- and middle-income countries (LMICs).

The impact of long-term use was reported in three studies representing data from a single cluster-RCT. MDA comprised of one dose of azithromycin given biannually to every child 1–60 months of age in participating communities in Niger. A 16%–22% decrease in microbiota α-diversity was reported after 12 months [55]. However, longer follow-up at 24 months found reduced abundance of Campylobacter upsaliensis and Campylobacter hominis with azithromycin treatment, as well as reduced abundance of bacterial metabolic pathways predominantly geared toward microbial survival, growth and inflammation, which may partly explain the reported benefits to child mortality [56]. C. upsaliensis is a well-recognized food-borne enteric pathogen in high-income settings, particularly among children in rural populations [57]; while C. hominis has been associated with gut inflammation and bacteremia [58]. By contrast, after 48 months of treatment, there was greater abundance of bacterial genetic determinants for macrolide resistance in the gut [59]. Azithromycin also selected for determinants of non-macrolide resistance, including beta-lactam, tetracycline and fluoroquinolone antibiotic resistance genes [59]. Other RCTs of MDA with azithromycin have not reported on changes in antibiotic resistance gene carriage by the fecal microbiome.

The impact of short-term use was reported in four RCTs, in three countries (Table 1). Two RCTs (India [60] and Burkina Faso [61]) allocated children up to 60 months of age to a single multi-day course of antibiotics. Azithromycin treatment reduced microbiota α-diversity by up to 32% relative to placebo. Reductions in Akkermansia muciniphila and Proteobacteria were also reported [60]. The latter group of bacteria includes common pathogens such as E. coli and Campylobacter spp. [60] Furthermore, acquisition of enteric infections by E. coli pathotypes and Campylobacter spp. during the 2 week study period was reduced, and clearance of baseline infections with these enteric pathogens was increased among azithromycin-treated infants, as confirmed by polymerase chain reaction (PCR). [60] Two RCTs of single dose azithromycin (Niger [62] and Burkina Faso [63]) conducted in a comparable age group, also found a 27%–31% reduction in α-diversity [62], or a decrease in C. jejuni, C. ureolyticus, and C. hominis with azithromycin treatment [63]. However, α-diversity in children treated with amoxicillin or cotrimoxazole was not significantly altered [61].

Together, these studies show a clear reduction in α-diversity caused by azithromycin alongside a reduction in pathogens of the Enterobacteriaceae family. MDA trials of amoxicillin or cotrimoxazole did not report any such effects.

Prophylaxis in persons-living with HIV

For immunocompromised individuals living with HIV who have limited access to rapid bedside diagnostic testing, the widespread use of prophylactic antibiotics has become essential to treat and prevent infections. Cotrimoxazole is recommended long-term for people living with HIV in areas where malaria or severe bacterial infections are highly prevalent [64], due to demonstrated reductions in morbidity and mortality among HIV-positive adults and children [65,66,67]. The WHO also recommends cotrimoxazole for all HIV-exposed infants from 4 to 6 weeks of age until determination of the infant’s HIV-negative status [64]. Two RCTs investigated the impact on the gut microbiota of MDA with cotrimoxazole for prophylaxis in persons-living with HIV (Table 2). These RCTs were conducted in children or newborns, both in sub-Saharan Africa. One reported the impact of daily cotrimoxazole after four years of use (long-term use), and one reported the impact over the first 6 months of after birth (short-term use).

Table 2 Characteristics of studies included in this review of the impact of mass drug administration with antibiotics for prophylaxis in persons-living with HIV on the human gut microbiome

One RCT conducted in Zimbabwe, reported the impact of long-term cotrimoxazole use on the gut microbiome. HIV-positive children on ART who were taking once daily cotrimoxazole for an average of two years were randomized to continue or stop cotrimoxazole prophylaxis and followed-up for an additional two year period [67]. In accordance with the short-term 5-day course tested in children in the general population of Burkina Faso [61], long-term daily cotrimoxazole did not alter gut microbiota diversity. By contrast, cotrimoxazole suppressed gut-resident viridans group streptococci species that were associated with gut inflammation, but did not impact clinical illness, HIV progression, nutritional status, or abundance of Enterobacteriaceae [68]. These changes corresponded with reductions in systemic and gut inflammatory markers of mortality risk, providing a possible explanation for the beneficial effects of cotrimoxazole on mortality in persons living with HIV. However, continued use resulted in increased carriage of the dfrA1 genetic determinant of resistance to trimethoprim after 2 years [69]. In contrast, other genes that confer resistance to cotrimoxazole were not affected [69].

Another RCT investigated the impact of short-term once daily cotrimoxazole use on the microbiota of HIV-exposed uninfected newborns in South Africa [70]. Consistent with long-term use in HIV-infected children in Zimbabwe, cotrimoxazole use in this HIV-exposed infant population did not significantly affect taxonomic or functional diversity [70]. However, the abundance and α-diversity of total antibiotic and cotrimoxazole resistance genes were greater in the cotrimoxazole-treated group, and these differences grew larger after 4 months of use [70]. Furthermore, there were significant differences in microbiota composition between groups as measured by β-diversity indices, which quantify the loss or gain (i.e. turnover) of bacterial taxa across an exposure gradient. Within the treated group, microbiome taxonomic, functional and resistance gene compositions became more similar over one year (i.e. reduced β-diversity), indicating that the selective pressure of cotrimoxazole shaped the microbiomes of the treated infants in a consistent way [70].

From these RCTs, cotrimoxazole does not seem to influence α-diversity but is associated with profound microbiome changes and an increase in antibiotic resistance genes during the initial period of antibiotic exposure.

Intrapartum antibiotic prophylaxis

Globally, the use of intrapartum antibiotic prophylaxis (IAP) is the most common strategy for preventing peripartum infections and associated adverse pregnancy outcomes [47]. IAP involves administering broad spectrum antibiotics that are effective against the microorganisms most likely to cause infections in at risk mothers, predominantly GBS [71]. IAP is recommended before incision in caesarean sections; during labor when the mother is culture positive for GBS or at risk of invasive GBS infection; or immediately after birth to reduce risk of infections associated with manual removal of the placenta, excessive intrauterine manipulations or lacerations of the genital tract [47, 48, 72, 73]. Seven observational studies and one RCT investigated the association between MDA for IAP and the gut microbiota, primarily in terms of α-diversity and β-diversity (Table 3). Three observational studies reported on infant gut microbiota composition and diversity during the neonatal period, while five observational studies and the RCT reported post-neonatal gut microbiota outcomes.

Table 3 Characteristics of studies included in this review of the impact of mass drug administration with antibiotics for intrapartum antibiotic prophylaxis on the human gut microbiome

Two cross-sectional studies and one prospective cohort study conducted in three high-income countries reported the association between IAP and gut microbiota composition during the neonatal period (Table 3). Microbiota α-diversity at 6–7 days of age was reduced in infants born to IAP-treated mothers [63]. Exposure to IAP was also associated with greater similarity in microbiota composition (i.e. reduced β-diversity), suggesting that the selective pressure of maternal antibiotic exposure had a congenerous effect on early infant microbiota composition across infants [62]. The IAP group was also dominated by Enterobacteriaceae, and depleted in Bifidobacterium spp. [63]. Finally, a prospective cohort study found a significant reduction in the relative abundance of the Actinobacteria phylum at 10 days of age (which includes Bifidobacterium) and a significant increase in the Firmicutes phylum (which includes some notable pathogenic groups, including Staphylococcus, Listeria and Streptococcus, as well as probiotic groups, including Lactobacillus) [64].

Five prospective cohort studies conducted in four high-income countries reported the association of IAP and the post-neonatal gut microbiota (Table 3). A reduction in microbiota α-diversity at age 2–3 months in IAP-exposed infants was reported in three North American cohorts [74,75,76]. Infants exposed to IAP had a more phylogenetically similar microbiota composition at 6 weeks of age (reduced β-diversity) as measured by UniFrac indices in one study [66], suggesting a consistent effect of maternal antibiotic exposure on the loss or gain of specific, phylogenetically-related subsets of gut bacteria. Changes in the relative abundance of bacterial taxa associated with IAP-exposure included increased Proteobacteria (a phylum that includes several enteric pathogens) [74], Escherichia spp. [75] and Enterobacteriaceae [77] at 1–3 months of age in three countries. IAP-exposure was also associated at age 3 months with decreased Bifidobacterium spp. [75, 77] in two cohorts, and an increase in the Firmicutes phylum [78]. Longer term changes included a reduction in the Bacteroidetes phylum [74, 76] and Bifidobacterium spp. at 1 year of age [74].

In spite of this evidence, it is challenging to separate the impact of maternal antibiotic exposure on the infant gut microbiota from the impact of the indications for IAP, such as cesarean delivery or GBS infection, in observational studies. One RCT tested the impact of IAP for cesarean section on infant microbiota α-diversity in Denmark (Table 3) [79]. Forty-four mothers were allocated to receive a single intravenous dose of cefuroxime either before or after umbilical cord clamping. Microbiota α-diversity was greater at 9 months in infants born to mothers who received IAP after cord-clamping, suggesting that later administration may ameliorate the duration of diminished infant microbiota α-diversity [79]. IAP after cord-clamping was also associated with greater abundance of Bifidobacterium spp. at 10 days. Although this difference was not statistically significant, this association may still hold biological significance since Bifidobacterium are essential members of the breastfed infant gut microbiota that may drive ongoing microbiota development [80] through symbiotic cross-feeding interactions with other important gut bacteria such as Lactobacilli [81].

Overall, these studies suggest reduced α-diversity in infant microbiota associated with maternal exposure to IAP and consistent compositional changes, including a higher relative abundance of Enterobacteriaceae and lower abundance of Bifidobacterium.

Drivers of antibiotic effects on the gut microbiota

Recent reviews reported few studies that investigated factors which determine the impact of antibiotic exposure on microbiota composition [3, 82, 83]. However, to gain deeper insight into potential determinants of how gut microbiota respond to perturbation by antibiotics, studies that aimed to explore these factors are discussed in this section around the following common themes: antibiotic class, bacterial clades, starting microbiota composition and long-term recovery dynamics (Fig. 1).

Fig. 1
figure 1

Antibiotic impacts on human gut microbiota and associated health impacts. The horizontal, black arrow represents the time axis along which key life events associated with gut microbiome development occur. Horizontal, gray, solid lines represent antibiotic exposure periods. Vertical, gray dashed lines link exposure periods to associated health outcomes. The position and length of each box relative to the time axis represents the timing and duration of microbiome changes and health effects

Antibiotic class and bacterial clade

A recent review of 55 human studies summarized changes in microbiota composition that were significantly associated with 68 different antibiotics grouped into 22 antibiotic classes [5]. It revealed that each antibiotic most strongly affects bacteria belonging to a restricted set of genera. Furthermore, the number of affected genera varied both between and within antibiotic classes [5]. For example, azithromycin was associated with changes in the abundance of 3 genera (Bacteroides, Bifidobacterium, Clostridium), while other macrolides were associated with altered abundance of 2–6 genera (including the former three, as well as Faecalibacterium, Prevotella, Ruminococcus, Streptococcus, Enterococcus, Haemophilus or Pseudomonas). Beta-lactams, such as ampicillin and cephalosporin for example, were associated with changes in abundance of 8 and 12 genera, respectively, spanning a phylogenetically diverse range of phyla (Actinobacteria, Firmicutes, Proteobacteria, Verrucomicrobia) that differed from other beta-lactams [5].

These taxon-specific effects of different antibiotics are corroborated by culture experiments. For example, metronidazole strongly reduced the growth of Bacteroides spp. and Bifidobacterium spp. grown in anaerobic culture [84], while ampicillin significantly reduced Bacteroides spp. and Clostridium perfringens, but its effect on Bifidobacterium spp. depended on bacterial growth rates [84]. A more comprehensive assessment of a larger number of antibiotics tested on single species in anaerobic culture confirmed the wide ranging effects on habitual colonic bacteria [85]. For instance, aminoglycosides and sulfonamides had little effect on the bacteria tested [85]. Macrolides uniformly had bacteriostatic effects (but did not kill) all bacterial species tested, except for Clostridium difficile, on which they had no impact [85]. However, the effect of beta-lactams was strain specific and differed by the specific antibiotic used [85]. The impact of antibiotics on the microbiota, therefore, depends on the antibiotic used, as well as which and how bacterial clades respond.

Nevertheless, suppression of microbes that are susceptible to an antibiotic’s mechanism of action can disrupt colonization resistance and open an ecological niche for opportunistic pathogenic bacteria, increasing the host’s susceptibility to post-antibiotic infection [86, 87]. Alternatively, antibiotic use can also produce an increase in antibiotic-tolerant commensal bacteria, that have potential benefits to the host [84, 88]. Factors that influence these effects include the pharmacokinetics of different antibiotics [89, 90], the degradation and resistance mechanisms different bacteria utilize against each antibiotic [91,92,93,94], the indirect effects of these degradation and resistance mechanisms on other bacteria that do not actively perform these functions [95], and the local or regional prevalence of specific resistance determinants [96, 97]. Overall, these studies suggest that both antibiotic class and bacterial clade specificities determine the impact of antibiotics on the microbiota.

Starting composition of gut microbiota

Considering the variation in antibiotic effects by antibiotic class and bacterial clade, the initial composition of the microbiota may also determine the short and long-term effects of antibiotics. Analyses of data from five adult cohorts determined that the abundance of specific species with an increased functional capacity to degrade complex carbohydrates, particularly host mucins, accurately predicted microbiota recovery from antibiotic-associated reductions in α-diversity [98]. The baseline abundance of these bacteria could identify participants whose diminished α-diversity recovered after antibiotic use with 70% accuracy [98]. They proposed that capacity for mucin degradation may confer an advantage for these bacteria to repopulate the gut. Furthermore, degradation of these complex carbohydrates may produce secondary metabolites that can in turn be utilized as a nutrient source by other bacteria to support their regrowth. Recovery-associated bacteria included several species from the genus Bacteroides.

This finding in humans is supported by evidence from germ-free mice. Two groups were colonized with either a Prevotella and Faecalibacterium enriched or a Bacteroides and Parabacteroides enriched human fecal microbiome, and both were treated with a 7-day course of amoxicillin-clavulanate [99]. α-Diversity in the Bacteroides group was more resilient to antibiotic exposure after 18 days [99]. However, mucin foraging by Bacteroides spp. can facilitate enteric infection, which has been demonstrated for C. difficile, C. rodentium, and S. typhimurium in mouse models, and further community-level investigation in different age groups is required to translate the findings to human populations [86, 87, 100].

Long-term recovery dynamics

Two comprehensive analyses provide further insights into the temporal dynamics of microbiota response and recovery from antibiotic exposure [101, 102]. Both analyses used data from adults collected longitudinally during a 10-month period that included two 5-day courses of ciprofloxacin [101, 102]. One analysis determined that the time needed for the microbiota to reach a new stable configuration varied from 2 to 42 days for different subsets of bacteria. One bacterial subset in the genus Bacteroides established a new equilibrium at the same or higher relative abundance within 2 weeks [102]. However, other bacteria in the Bacteroides genus and a subset of bacteria belonging to the family Lachnospiraceae increased during treatment and required 42 days to return to pre-antibiotic levels. By contrast, a group of bacteria in the family Ruminococcaceae was reduced to undetectable levels after a single treatment with no recovery after ten months [102]. This analysis is consistent with the aforementioned review which found bacterial clade specificities determine the impact of antibiotics on the microbiota [5]. A second analysis of the same data suggested that shorter recovery time may result from greater exposure to environmental sources of bacteria that can recolonize the perturbed microbiome [101].

Although these studies included very few individuals, and may represent inter-individual heterogeneity at best, they explored a richly sampled time-series of microbiota abundance data, and their findings are supported by experimental work. In one such experiment, two groups of mice were assigned to either specific-pathogen free conditions or general animal conditions with exposure to soil, and both were treated with a 2-week course of vancomycin and streptomycin [103]. Soil-exposed mice showed greater α-diversity and stability after treatment and recovered their pre-treatment α-diversity more quickly after antibiotic withdrawal [103]. These studies suggest that a more controlled exogenous source of bacteria, such as a complex probiotic mixture, may mitigate the impact of antibiotic exposure on the microbiome.

A third analysis used a separate dataset from a small, randomized perturbation experiment of adults to quantify the recovery time of the gut microbiota over a one-year period after exposure to different antibiotics [104]. Recovery was defined as the time from exposure to a stable microbiota configuration. The recovery time varied with the antibiotic used. The gut microbiota transitioned to a new stable configuration 4–6 months after treatment with clindamycin and ciprofloxacin [104]. By contrast, the composition of microbiota exposed to minocycline or amoxicillin was unchanged [104].

Long-term effects of antibiotic exposure have also been investigated in infants [105, 106]. Reduced α-diversity was associated with treatment immediately after birth, but recovered during the first postpartum year [106]. The reduction in α-diversity was driven by suppression of Lachnospiraceae and Enterobacteriaceae [106]. While the clinical significance of Lachnospiraceae is not clear, the presence of members of this family in the early gut microbiota has been associated with reduced risk of asthma [18], and Enterobacteriaceae are the first colonizers of the infant gut. Although microbiota α-diversity did largely recover by one year of age, the impact on host health of reductions in these early colonizers due to neonatal exposure to antibiotics requires careful investigation.

In another study, infants treated with antibiotics from birth to three years had lower species α-diversity compared to infants who had no antibiotic-exposure during that time, but the reduction in α-diversity was modest and was only evident for year one [105]. At the bacterial strain level, however, differences between antibiotic exposed and unexposed infants were greater [105]. Furthermore, bacterial strains were identified that only colonized the gut once. Such strains were eliminated by antibiotic-exposure but persisted in the untreated infants. By contrast, other strains were identified that recolonized the gut multiple times during follow-up. Such strains were more likely to persist in both exposed and unexposed infants, providing further support for the notion that recolonization is a driving force in microbiota recovery after antibiotic use [105].

Altogether, these analyses suggest that microbiota recovery from antibiotic-exposure may depend on the antibiotic used, the initial microbiota composition and the specific bacterial taxa affected by the drug. Furthermore, longitudinal observational studies provide additional evidence that recolonization of the gut may potentially play a critical role in microbiome recovery from antibiotic-exposure. However, more studies are needed which are designed to test the specific hypotheses suggested by these few studies.

Associated health impacts

It is important to note that recovery alone may not protect the host from potential long-term effects of antibiotic perturbations to the microbiome (Fig. 1). Epidemiologic studies have established associations between antibiotic use and obesity; asthma, allergy, atopy; and colorectal cancer, among other health disorders. Antibiotic disruption of the gut microbiome has been proposed as an important mechanism underlying these associations [4, 83, 107]. However, most studies have investigated the relationships between antibiotic use, gut microbiome composition or function, and health outcomes separately. Recent reviews have reported little to no studies that assessed and reported antibiotic use, microbiota composition and host health outcomes in the same study population to demonstrate a direct link between antibiotic modulation of the gut microbiota and effects on health, or the specific antibiotic-induced changes to the microbiome that may be involved [4, 83, 107]. To gain further insight into the health implications of microbiome perturbation by MDA with antibiotics, I discuss such studies in this section, with reference to the findings of the MDA studies.

Infectious morbidity

Fifty percent or more of infants in LMICS carry at least one enteric pathogen by age 3 months and experience at least one diarrheal episode by age 24 months [108]. The most common causes of moderate-to-severe diarrhea are viral (rotavirus, sapovirus, norovirus) or bacterial (E. coli pathotypes, Shigella, Campylobacter spp.), but the prevalence of specific pathogens varies by country [109, 110]. Mortality [110] and severity [109] of diarrhea episodes also vary by country as well as by pathogen. The broad goal of MDA with antibiotics is to reduce the burden and impact of pathogenic bacteria on host health. The results of RCTs of MDA with azithromycin in LMICS suggest that suppression of Campylobacter spp. and pro-inflammatory microbiome metabolic pathways may partly explain the sustained reductions in child mortality due to diarrhea and dysentery observed in those settings [111,112,113]. Gut microbiota composition is associated with both increased and reduced severity of enteric infections [114]. Limited evidence suggest that better or worse enteric infection outcomes may vary by microbiota composition and pathogen [114]. For example, mixed diarrheagenic E. coli and viral infections in children were associated with increased Bifidobacterium spp. and more severe illness [115], but Bifidobacterium longum inhibited rotavirus isolates from pediatric cases and reduced the duration of viral gastroenteritis in another study [116].

Additional evidence comes from murine models. For example, mucin foraging by Bacteroides spp. after antibiotic perturbation of the gut microbiome can facilitate enteric infection, which has been demonstrated for C. difficile, C. rodentium, and S. typhimurium. [86, 87] Alternatively, gut microbiome perturbation with antibiotics has been shown to delay rotavirus infection and prevent norovirus infection in mice [117, 118]. Nevertheless, more careful elucidation of the specific microbiome components that may moderate enteric pathogen virulence is still required, particularly in human populations using well-designed, adequately powered, longitudinal studies.

Furthermore, the increase in antibiotic resistance gene carriage by the gut microbiome that can result from MDA suggests the potential for the beneficial effects to become limited by decreased susceptibility of pathogenic bacteria. Once resistance in pathogenic organisms develop, efficacy may reverse, as has been observed for other antibiotics in high-income settings [119]. In contrast, long-term use of Cotrimoxazole among children living with HIV still suppressed inflammatory viridans Streptococci after 4 years and the associated systemic inflammation that increases morality risk in this population [68]. More evidence is needed from LMICs where the availability of antibiotics [120], and treatment needs may be context specific.

Overweight and obesity

Antibiotic use is associated with an increased risk of obesity. The association is greater with earlier (prior to 6 months of age) and with greater frequency of exposure [121,122,123]. Lower gut microbiota α-diversity is also associated with obesity [14, 124]. By contrast, antibiotics can promote weight gain in undernourished children, although effects are heterogenous and are likely driven by antibiotic class as well as variable prevalence of growth-restricting comorbidities such as HIV and severe acute malnutrition in different populations [125]. Causal effects of microbiome composition on weight gain have also been demonstrated in germ free mice [13].

Without a clear understanding of the causal mechanisms that link changes in microbiota composition to health outcomes, microbiota composition is often defined as “dysbiotic” based on associations with disease relative to healthy controls [20]. In high-income adult populations, a decrease in α-diversity is a common feature of “dysbiosis” defined in terms of associations with disease. In this setting, reduced α-diversity is indicative of increased risk for obesity [126] and associated metabolic disorders such as type I [127] and II [128] diabetes. This may be particularly relevant to reduced α-diversity associated with a high-fat, low-fiber diet rich in refined-sugar [129]. By contrast, in exclusively breastfed infants the gut microbiome is dominated by Bifidobacterium spp. [32] due to the high concentrations of oligosaccharides present in human milk, resulting in lower α-diversity compared to mixed [32] or formula fed [32, 76] infants. Greater α-diversity is associated with earlier introduction of complementary foods [130] and increased risk of overweight in adolescence [131]. As such, greater microbiota α-diversity in early infancy may be indicative of “dysbiosis” due to inadequate early infant nutrition, thus the health implications of antibiotic-associated reductions α-diversity may be context-specific as well.

The timing of antibiotic exposure may provide further insight into context-specific effects. In a cohort of 12,422 full-term newborns in Finland, neonatal antibiotic-exposure (predominantly intravenous benzylpenicillin and gentamicin) was associated with impaired length growth among boys in the first 6 years of life. The association with growth in boys was confirmed in an independent cohort of 1707 newborns from Germany [132]. Conversely, the number of antibiotic courses received after the neonatal period, was associated with increased body mass index (BMI) at age 6 in both sexes [132]. In a subset of the Finish cohort, neonatal antibiotic-exposure was associated with decreased abundance of the genus Bifidobacterium, as well as reduced α-diversity at 1 month, but greater α-diversity beyond age 12 months [132]. Notwithstanding the later increase in overall α-diversity, diversity within the Bifidobacterium genus remained decreased at 24 months [132].

In the same study, when germ-free mice were colonized with fecal microbiomes from 1-month old antibiotic-exposed infants, male mice experienced reduced weight gain compared to mice colonized with an antibiotic-unexposed infant microbiome. However, female mice did not, corroborating the observational findings in the human cohorts [132]. The results of this study show that the timing of antibiotic exposure in infancy, and sex-related host differences, can have a profound impact on the early microbiota and infant development.

While a causal effect of early-life antibiotic exposure on obesity is yet to be confirmed [122], and the mechanism is unclear, some Bacteroides spp. associated with microbiota recovery (B. caccae, B. intestinalis, B. uniformis) are also associated with reduced BMI. This may be due to their increased capacity to degrade complex carbohydrates [98]. By contrast, upregulation of microbiome metabolic pathways involved in biosynthesis of simpler sugars via degradation of more complex, host-derived polysaccharides is also associated with increased BMI and insulin resistance [133]. Forty percent of the BMI-associated and insulin resistance-associated sugar metabolism pathways in this study were upregulated after beta-lactam treatment. [133]

A recent study in mice demonstrated that low-dose penicillin delivered from birth elicited an increase in adiposity and exacerbated the effect of a high fat diet on increased body weight. The growth phenotype was induced in germ-free inoculated with fecal microbiota from antibiotic treated mice, showing that the altered microbiota plays a causal role [134]. Antibiotic exposed gut microbiota were characterized by reduced abundance of Lactobacillus spp., Allobaculum spp., Rikenellaceae spp., and Candidatus arthromitus [134]. The increased adiposity induced by neonatal antibiotic exposure in these mice is consistent with the increased BMI associated with post-neonatal exposure previously described [132]. However, more research is needed to characterize and confirm the components of the microbiome that promote recovery from antibiotic exposure, those that may be required for healthy metabolism, and to translate these mechanism to human health.

Asthma and atopy

Earlier [135, 136] and more frequent antibiotic prescription is also associated with increased risk of childhood asthma [137,138,139]. Lower gut microbiota α-diversity is associated with eczema and allergic sensitization in infancy and childhood as well [18, 140, 141]. A direct link between microbiota alterations by infant antibiotic use and asthma risk at age 5 years was recently demonstrated [142]. Infants exposed to antibiotics in the first year of life were two times as likely to develop asthma by age 5. An estimated 25% of this association was attributable to antibiotic-induced reductions in α-diversity or antibiotic-associated changes in the abundance of specific taxa [142]. Although there is tremendous variability in the gut microbiome through infancy, a meaningful fraction of the infancy microbiome is acquired just after birth from the maternal gut [31] and is retained up to 3 years [127]. Maternal exposure to antibiotics, such as with IAP, may, therefore, also alter microbial species transmission to the infant during delivery and impact early colonization. Antibiotic exposures that impact neonatal microbiome composition may have long-term consequences for child development. Several studies using animal models have shown that the neonatal period is critical for immune system maturation, a process that is dependent on microbiome colonization of the neonatal gut [18, 138, 139, 143,144,145] and disrupted by antibiotic ablation of the gut microbiome [143, 145].

Promising preventive measures to protect the microbiota

A few promising approaches have been suggested to mitigate the impacts of antibiotic use on the microbiome (Fig. 1). Co-administration of prebiotics to promote the growth of commensal bacteria is one such strategy. In infants, human milk is rich in complex oligosaccharides that serve as substrates for the growth of Bifidobacterium spp. and facilitate cross-feeding by other species [81]. Breastfeeding is associated with quicker recovery of microbiome α-diversity in IAP-exposed infants in one observational cohort [77]. The benefits of breastfeeding may also extend beyond its prebiotic content [146]. In children, co-administration of lactulose with azithromycin helped to restore the relative abundance of Lactobacillus, Enterococcus, Anaerostipes, Blautia and Roseburia within 18 days of treatment, while azithromycin alone caused an increase in the abundance of proinflammatory Streptococci up to 60 days after treatment [147].

Co-administration of probiotics with antibiotics in adults has also shown some potential to mitigate antibiotic selection for genetic determinants of resistance [148]. However, successful gut colonization by orally administered probiotic bacteria has shown considerable variability by person, gut-region and probiotic strain, explained by host and autochthonous microbiome features [149]. Also, absence of resistance to the administered antibiotic among the probiotic strains could exacerbate selection for antibiotic resistance in the microbiome [150], further limiting the potential protective effects of probiotics. Fecal microbiota transplantation using a self-provided, healthy fecal specimen collected prior to antibiotic administration may be a more effective approach to microbiome restoration [151], although this may be a less practical option for LMICs.

Another strategy involves use of antibiotic-drug combinations to achieve more targeted species-specific effects than single antibiotic treatments. For example, one investigation screened > 1000 drugs to identify candidates that reduce the broad-spectrum activity of antibiotics without impairing their activity against relevant pathogens [85]. The anticoagulant drug dicumarol, and two non-steroidal anti-inflammatory drugs, tolfenamic acid and diflunisal, emerged as strong inhibitors of the effect of erythromycin on commensal gut bacteria (e.g. Bacteroides vulgatus and Bacteroides uniformis). However, erythromycin’s impact on pathogenic bacteria (e.g. Staphylococcus aureus, Streptococcus pneumoniae and Enterococcus faecium) was not impacted [85]. Another investigation profiled almost 3,000 combinations of antibiotics, drugs, and food additives to identify candidate compounds that could mitigate the collateral impacts on the microbiome without reducing the effect on the pathogens (e.g. Escherichia coli, Salmonella enterica serovar Typhimurium and Pseudomonas aeruginosa) [152]. More than 70% of the drug combinations had narrow species-specific effects and 20% showed strain-specific effects [152].

Identifying narrow-spectrum antibiotic alternatives is another approach. The microbiome itself may be a source of such narrow-spectrum compounds. For example, investigation of 752 bacterial genomes from the human microbiome project identified a cluster of genes carried by commensal bacteria that encode thiopeptides [153]. Lactocillin, a thiopeptide encoded by Lactobacillus gasseri showed strong inhibitory activity against common pathogens such as Streptococcus aureus and Gardnerella vaginalis, but not against commensals [153]. Thuricin-CD is another such antimicrobial that has been identified, which is produced by Bacillus thuringiensis [154]. It has been shown to be effective against Clostridium difficile without impacting microbiota composition in a fecal-culture system that models the human colon [154]. Development of such interventions will require deeper understanding of the mechanisms by which MDA with antibiotics produce health benefits, or undesirable side-effects, so that narrow-spectrum alternatives can achieve the desired outcomes while minimizing unwanted risks.

Finally, there may also be important microbial components in the environment that may help to prevent dysbiosis or restore physiologically important subsets of the gut microbiome during critical developmental periods [155, 156]. Identification of such components could guide development and testing of mitigating interventions [157].

Limitations

This is a narrative review restricted to studies of antibiotics given as part of a mass drug administration program or policy to reduce morbidity and mortality in specific at-risk target populations, rather than treatment for a specific infection. Due to current public health interest in MDA with antibiotics to reduce childhood morbidity and mortality in view of global targets, and longer-standing interest in trachoma and yaws elimination programs, I focused on antibiotics administered in these contexts. I also focused on antibiotic prophylaxis in persons-living with HIV and intrapartum antibiotic prophylaxis due to the existence of recommendations and guidelines for those uses. There are few such publications and they are limited to infants, children, and pregnant women, reducing generalizability to populations outside these risk groups or to other indications for treatment. Most of these studies only reported antibiotic impacts on α- and β-diversity metrics, precluding more in-depth analysis of antibiotic-associated taxonomic changes. To gain further insight into the results of these MDA studies, I also discussed studies that reported the factors which determine how gut microbiota respond to antibiotic perturbation and studies which investigated antibiotic use, gut microbiota, and health outcomes in the same study population. The number of such studies was also small, limiting the conclusions that can be drawn. Finally, most studies reviewed here used 16S amplicon sequencing, and did not provide any information regarding the functional capability of the microbiome, which could provide more insight into the effects of antibiotic-induced alterations on health and the mechanisms involved.

Conclusions

The most consistent reported impact of MDA with antibiotics has been a reduction in microbiota α-diversity in infants and children, in addition to a reduction in Bifidobacterium spp. and an increase in Enterobacteriaceae with prenatal or neonatal exposure. The microbiome may not fully return to its initial composition, depending on the class of antibiotic used, the initial composition of the microbiome, or the sources and frequency of recolonization. The potential impact on host health may depend on the timing of antibiotic exposure and the specific context. Interventions to mitigate or monitor the impacts of antibiotic use on the microbiome show promise but require further testing. Microbiome alteration by antibiotics is a complex process, and more comprehensive studies are required to fully characterize the potential mechanisms of antibiotic-induced changes in the human gut microbiome and consequences for host health, and to inform their translation into alternative treatments and public health strategies.

Availability of data and materials

All data generated or analyzed during this study are included in this published article.

Abbreviations

ART:

Antiretroviral Therapy

BMI:

Body Mass Index

DNA:

Deoxyribonucleic acid

GBS:

Group B Streptococcus

HIV:

Human Immunodeficiency Virus

IAP:

Intrapartum Antibiotic Prophylaxis

LMIC:

Low- and Middle-Income Country

MDA:

Mass Drug Administration

PCR:

Polymerase Chain Reaction

RCT:

Randomized Controlled Trial

RNA:

Ribonucleic acid

rRNA:

Ribosomal Ribonucleic acid

WGS:

Whole Genome Sequencing

WHO:

World Health Organization

References

  1. Klein EY, Van Boeckel TP, Martinez EM, Pant S, Gandra S, Levin SA, et al. Global increase and geographic convergence in antibiotic consumption between 2000 and 2015. Proc Natl Acad Sci USA. 2018;115(15):E3463–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Cosgrove SE. The relationship between antimicrobial resistance and patient outcomes: mortality, length of hospital stay, and health care costs. Clin Infect Dis. 2006;42(Suppl 2):S82–9.

    Article  PubMed  Google Scholar 

  3. Duan H, Yu L, Tian F, Zhai Q, Fan L, Chen W. Antibiotic-induced gut dysbiosis and barrier disruption and the potential protective strategies. Crit Rev Food Sci Nutr. 2022;62(6):1427–52.

    Article  CAS  PubMed  Google Scholar 

  4. Shah T, Baloch Z, Shah Z, Cui X, Xia X. The intestinal microbiota: Impacts of antibiotics therapy, colonization resistance, and diseases. Int J Mol Sci. 2021;22(12):6597.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Ferrer M, Méndez-García C, Rojo D, Barbas C, Moya A. Antibiotic use and microbiome function. Biochem Pharmacol. 2017;134:114–26.

    Article  CAS  PubMed  Google Scholar 

  6. Huttenhower C, Gevers D, Knight R, Abubucker S, Badger JH, Chinwalla AT, et al. Structure, function and diversity of the healthy human microbiome. Nature. 2012;486(7402):207–14.

    Article  CAS  Google Scholar 

  7. Shoemaker R, Kim J. Urobiome: An outlook on the metagenome of urological diseases. Investig Clin Urol. 2021;62:6.

    Article  Google Scholar 

  8. Santacroce L, Charitos IA, Ballini A, Inchingolog F, Luperto P, De Nitto E, et al. The human respiratory system and its microbiome at a glimpse. Biology (Basel). 2020;9(10):318.

    CAS  Google Scholar 

  9. Hooper LV. Do symbiotic bacteria subvert host immunity? Nat Rev Microbiol. 2009;7(5):367–74. https://doi.org/10.1038/nrmicro2114.

    Article  CAS  PubMed  Google Scholar 

  10. Chung H, Kasper DL. Microbiota-stimulated immune mechanisms to maintain gut homeostasis. Curr Opin Immunol. 2010;22(4):455–60.

    Article  CAS  PubMed  Google Scholar 

  11. Verdu EF, Galipeau HJ, Jabri B. Novel players in coeliac disease pathogenesis: role of the gut microbiota. Nat Rev Gastroenterol Hepatol. 2015;12(9):497–506.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Gkouskou KK, Deligianni C, Tsatsanis C, Eliopoulos AG. The gut microbiota in mouse models of inflammatory bowel disease. Front Cell Infect Microbiol. 2014;4:28.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Ridaura VK, Faith JJ, Rey FE, Cheng J, Duncan AE, Kau AL, et al. Gut Microbiota from Twins discordant for obesity modulate metabolism in mice. Science (80-). 2013;341(6150):1241214. https://doi.org/10.1126/science.1241214.

    Article  CAS  Google Scholar 

  14. Goodrich JK, Waters JL, Poole AC, Sutter JL, Koren O, Blekhman R, et al. Human genetics shape the gut microbiome. Cell. 2014;159(4):789–99.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Smith MI, Yatsunenko T, Manary MJ, Trehan I, Mkakosya R, Cheng J, et al. Gut microbiomes of Malawian twin pairs discordant for Kwashiorkor. Science. 2013;339(6119):548–54.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Charbonneau MR, O’Donnell D, Blanton LV, Totten SM, Davis JCC, Barratt MJ, et al. Sialylated milk oligosaccharides promote microbiota-dependent growth in models of infant undernutrition. Cell. 2016;164(5):859–71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Vijay-Kumar M, Aitken JD, Carvalho FA, Cullender TC, Mwangi S, Srinivasan S, et al. Metabolite syndrome and altered gut microbiota in mice lacking toll-like receptor 5. Science. 2010;328(5975):228–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Arrieta MC, Stiemsma LT, Dimitriu PA, Thorson L, Russell S, Yurist-Doustch S, et al. Early infancy microbial and metabolic alterations affect risk of childhood asthma. Sci Transl Med. 2015;7(307):307.

    Article  CAS  Google Scholar 

  19. Vuong HE, Pronovost GN, Williams DW, et al. The maternal microbiome modulates fetal neurodevelopment in mice. Nature. 2020;586(7828):281–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. McBurney MI, Davis C, Fraser CM, Coley EJL, Siegler EL, Qiu A, et al. Establishing what constitutes a healthy human gut microbiome: state of the science, regulatory considerations, and future directions. J Nutr. 2019;149(11):1882–95.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Abdukhakimova D, Dossybayeva K, Poddighe D. Fecal and duodenal microbiota in pediatric celiac disease. Front Pediatr. 2021;9: 652208.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Amos GCA, Sergaki C, Logan A, Iriarte R, Bannaga A, Chandrapalan S, et al. Exploring how microbiome signatures change across inflammatory bowel disease conditions and disease locations. Sci Rep. 2021;11(1):18699.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Yatsunenko T, Rey FE, Manary MJ, Trehan I, Dominguez-Bello MG, Contreras M, et al. Human gut microbiome viewed across age and geography. Nature. 2012;486:222–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Arrieta M-C, Stiemsma LT, Amenyogbe N, Brown EM, Finlay B. The intestinal microbiome in early life: health and disease. Front Immunol. 2014;5:427.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Milani C, Duranti S, Bottacini F, Casey E, Turroni F, Mahony J, et al. The first microbial colonizers of the human gut: composition, activities, and health implications of the infant gut microbiota. Microbiol Mol Biol Rev. 2017;81(4):1–67.

    Article  Google Scholar 

  26. Collado MC, Cernada M, Baüerl C, Vento M, Pérez-Martínez G. Microbial ecology and host-microbiota interactions during early life stages. Gut Microbes. 2012;3(4):352–65.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Matamoros S, Gras-Leguen C, Le Vacon F, Potel G, de La Cochetiere MF. Development of intestinal microbiota in infants and its impact on health. Trends Microbiol. 2013;21(4):167–73.

    Article  CAS  PubMed  Google Scholar 

  28. Dominguez-Bello MG, Costello EK, Contreras M, Magris M, Hidalgo G, Fierer N, et al. Delivery mode shapes the acquisition and structure of the initial microbiota across multiple body habitats in newborns. Proc Natl Acad Sci USA. 2010;107(26):11971–5.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Tannock GW, Fuller R, Smith SL, Hall MA. Plasmid profiling of members of the family Enterobacteriaceae, lactobacilli, and bifidobacteria to study the transmission of bacteria from mother to infant. J Clin Microbiol. 1990;28(6):1225–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Vaishampayan PA, Kuehl JV, Froula JL, Morgan JL, Ochman H, Francino MP. Comparative metagenomics and population dynamics of the gut microbiota in mother and infant. Genome Biol Evol. 2010;2:53–66.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Ferretti P, Pasolli E, Tett A, Asnicar F, Gorfer V, Fedi S, et al. Mother-to-infant microbial transmission from different body sites shapes the developing infant gut microbiome. Cell Host Microbe. 2018;24:133–45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Stewart CJ, Ajami NJ, Brien JL, Hutchinson DS, Smith DP, Wong MC, et al. Temporal development of the gut microbiome in early childhood from the TEDDY study. Nature. 2018;562:583–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Marcobal A, Sonnenburg JL. Human milk oligosaccharide consumption by intestinal microbiota: Human milk oligosaccharide consumption. Clin Microbiol Infect. 2012;18:12–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Laursen MF. Gut microbiota development: influence of diet from infancy to toddlerhood. Ann Nutr Metab. 2021;8:78.

    Google Scholar 

  35. Agans R, Rigsbee L, Kenche H, Michail S, Khamis HJ, Paliy O. Distal gut microbiota of adolescent children is different from that of adults. FEMS Microbiol Ecol. 2011;77(2):404–12.

    Article  CAS  PubMed  Google Scholar 

  36. Hollister EB, Riehle K, Luna RA, Weidler EM, Rubio-Gonzales M, Mistretta TA, et al. Structure and function of the healthy pre-adolescent pediatric gut microbiome. Microbiome. 2015;3:36.

    Article  PubMed  PubMed Central  Google Scholar 

  37. De Filippo C, Cavalieri D, Di Paola M, Ramazzotti M, Poullet JB, Massart S, et al. Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa. Proc Natl Acad Sci USA. 2010;107(33):14691–6.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Vatanen T, Plichta DR, Somani J, Münch PC, Arthur TD, Hall AB, et al. Genomic variation and strain-specific functional adaptation in the human gut microbiome during early life. Nat Microbiol. 2019;4(3):470–9.

    Article  CAS  PubMed  Google Scholar 

  39. Chang CS, Kao CY. Current understanding of the gut microbiota shaping mechanisms. J Biomed Sci. 2019;26:1.

    Article  Google Scholar 

  40. Bonder MJ, Kurilshikov A, Tigchelaar EF, Mujagic Z, Imhann F, Vila AV, et al. The effect of host genetics on the gut microbiome. Nat Genet. 2016;48(11):1407–12.

    Article  CAS  PubMed  Google Scholar 

  41. Becattini S, Taur Y, Pamer EG. Antibiotic-induced changes in the intestinal microbiota and disease. Trends Mol Med. 2016;22(6):458–78.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Leeming ER, Johnson AJ, Spector TD, Roy CIL. Effect of diet on the gut microbiota: rethinking intervention duration. Nutrients. 2019;11(12):1–28.

    Article  Google Scholar 

  43. United Nations. The Sustainable Development Goals Report 2020. United Nations. 2020. https://sdgs.un.org/publications/sustainable-development-goals-report-2020-24686 Accessed 1 Mar 2021.

  44. World Health Organization. Report of the 21st Meeting of the WHO Alliance for the Global Elimination of Trachoma by 2020. WHO. 2019. https://www.who.int/publications/i/item/who-cds-ntd-pct-2019.02. Accessed 1 Mar 2021.

  45. Asiedu K, Fitzpatrick C, Jannin J. Eradication of Yaws: Historical Efforts and Achieving WHO’s 2020 Target. PLoS Negl Trop Dis. 2014;8(9): e3016.

    Article  PubMed  PubMed Central  Google Scholar 

  46. World Health Organization. WHO Guidelines on post-exposure prophylaxis for HIV and the use of co-trimoxazole prophylaxis for HIV-related infections among adults, adolescents and children: Recommendations for a public health approach-December 2014 supplement to the 2013 consolidated ARV guidelines. WHO: WHO Press; 2015. p. 2014.

  47. World Health Organization. WHO recommendations for prevention and treatment of maternal peripartum infections. WHO: WHO Press; 2015.

    Google Scholar 

  48. Verani JR, McGee L, Schrag SJ. Morbidity and mortality weekly report prevention of perinatal group b streptococcal disease. Morb Mortal Wkly Rep. 2010;59(10):1–31.

    Google Scholar 

  49. Doan T, Arzika AM, Hinterwirth A, Maliki R, Zhong L, Cummings S, et al. Macrolide Resistance in MORDOR I– A Cluster-Randomized Trial in Niger. N Engl J Med. 2019;380(23):2271–3.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Hema-Ouangraoua S, Tranchot-Diallo J, Zongo I, Kabore NF, Nikièma F, Yerbanga RS, et al. Impact of mass administration of azithromycin as a preventive treatment on the prevalence and resistance of nasopharyngeal carriage of Staphylococcus aureus. PLoS ONE. 2021;16(10): e0257190.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Poddighe D. Macrolide resistance and longer-term assessment of azithromycin in MORDOR I. N Engl J Med. 2019;381(22):2184.

    Article  PubMed  Google Scholar 

  52. Xiong T, Yue Y, Li WX, Choonara I, Qazi S, Chen HJ, et al. Effectiveness of azithromycin mass drug administration on trachoma: a systematic review. Chin Med J (Engl). 2021;134(24):2944–53.

    Article  Google Scholar 

  53. Mahmud H, Landskroner E, Amza A, Aragie S, Godwin WW, de Hostos BA, et al. Stopping azithromycin mass drug administration for trachoma: A systematic review. PLoS Negl Trop Dis. 2021;15(7): e0009491.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Mpyet C, Kello AB, Solomon AW. Global Elimination of Trachoma by 2020: A Work in Progress. Ophthalmic Epidemiol. 2015;22(3):148–50.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Doan T, Hinterwirth A, Arzika AM, Cotter SY, Ray KJ, O’Brien KS, et al. Mass azithromycin distribution and community microbiome: A cluster-randomized trial. Open Forum Infect Dis. 2018;5:8.

    Article  CAS  Google Scholar 

  56. Doan T, Hinterwirth A, Worden L, Arzika AM, Maliki R, Abdou A, et al. Gut microbiome alteration in MORDOR I: a community-randomized trial of mass azithromycin distribution. Nat Med. 2019;25(9):1370–6.

    Article  CAS  PubMed  Google Scholar 

  57. Patrick ME, Henao OL, Robinson T, Geissler AL, Cronquist A, Hanna S, et al. Features of illnesses caused by five species of Campylobacter, Foodborne Diseases Active Surveillance Network (FoodNet) - 2010–2015. Epidemiol Infect. 2018;146(1):1–10.

    Article  CAS  PubMed  Google Scholar 

  58. Igwaran A, Okoh AI. Human campylobacteriosis: A public health concern of global importance. Heliyon. 2019;5(11): e02814.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Doan T, Worden L, Hinterwirth A, Arzika AM, Maliki R, Abdou A, et al. Macrolide and nonmacrolide resistance with mass azithromycin distribution. N Engl J Med. 2020;383(20):1941–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Parker EPK, Praharaj I, John J, Kaliappan SP, Kampmann B, Kang G, et al. 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):1–9.

    Article  CAS  Google Scholar 

  61. Oldenburg CE, Sié A, Coulibaly B, Ouermi L, Dah C, Tapsoba C, et al. Effect of commonly used pediatric antibiotics on gut microbial diversity in preschool children in Burkina Faso: A randomized clinical trial. Open Forum Infect Dis. 2018;5:11.

    Article  CAS  Google Scholar 

  62. Doan T, Arzika AM, Ray KJ, Cotter SY, Kim J, Maliki R, et al. Gut microbial diversity in antibiotic-naive children after systemic antibiotic exposure: A randomized controlled trial. Clin Infect Dis. 2017;64(9):1147–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Hinterwirth A, Sié A, Coulibaly B, Ouermi L, Dah C, Tapsoba C, et al. Rapid reduction of campylobacter species in the gut microbiome of preschool children after oral azithromycin: A randomized controlled trial. Am J Trop Med Hyg. 2020;103(3):1266–9.

    Article  PubMed  PubMed Central  Google Scholar 

  64. World Health Organization. WHO Guidelines on Post-Exposure Prophylaxis for HIV and the Use of Co-Trimoxazole Prophylaxis for HIV-Related Infections among Adults, Adolescents and Children: Recommendations for a Public Health Approach-December 2014 Supplement to the 2013 Consolidated A. WHO: WHO Press; 2015. p. 2014.

  65. Chintu C, Bhat GJ, Walker AS, Mulenga V, Sinyinza F, Lishimpi K, et al. Co-trimoxazole as prophylaxis against opportunistic infections in HIV-infected Zambian children (CHAP): a double-blind randomised placebo-controlled trial. Lancet. 2004;364(9448):1865–71.

    Article  CAS  PubMed  Google Scholar 

  66. Anglaret X, Chêne G, Attia A, Toure S, Lafont S, Combe P, et al. Early chemoprophylaxis with trimethoprim-sulphamethoxazole for HIV-1-infected adults in Abidjan Cote d’Ivoire: A randomised trial. Lancet. 1999;353(9163):1463–8.

    Article  CAS  PubMed  Google Scholar 

  67. Bwakura-Dangarembizi M, Kendall L, Bakeera-Kitaka S, Nahirya-Ntege P, Keishanyu R, Nathoo K, et al. A randomized trial of prolonged co-trimoxazole in HIV-infected children in Africa. N Engl J Med. 2014;370(1):41–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Bourke CD, Gough EK, Pimundu G, Shonhai A, Berejena C, Terry L, et al. Cotrimoxazole reduces systemic inflammation in HIV infection by altering the gut microbiome and immune activation. Sci Transl Med. 2019;11(486):1–14.

    Article  CAS  Google Scholar 

  69. Francis F, Gough EK, Edens TJ, Berejena C, Bwakura-Dangarembizi M, Shonhai A, et al. Brief Report: Cessation of Long-Term Cotrimoxazole Prophylaxis in HIV-infected children does not alter the carriage of antimicrobial resistance genes. JAIDS J Acquir Immune Defic Syndr. 2020;85(5):601–5.

    Article  CAS  PubMed  Google Scholar 

  70. D’Souza AW, Moodley-Govender E, Berla B, Kelkar T, Wang B, Sun X, et al. Cotrimoxazole prophylaxis increases resistance gene prevalence and α-diversity but decreases β-diversity in the gut microbiome of human immunodeficiency virus–exposed, uninfected infants. Clin Infect Dis. 2020;71(11):2858–68.

    Article  PubMed  CAS  Google Scholar 

  71. Lawn JE, Bianchi-Jassir F, Russell NJ, Kohli-Lynch M, Tann CJ, Hall J, et al. Group B streptococcal disease worldwide for pregnant women, stillbirths, and children: why, what, and how to undertake estimates? Clin Infect Dis. 2017;65(2):S89–99.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Aronoff DM, Blaser MJ. Disturbing the neonatal microbiome is a small price to pay for preventing early-onset neonatal group B streptococcus disease: AGAINST: against relying on antibiotics to prevent early-onset neonatal group B streptococcus disease. BJOG An Int J Obstet Gynaecol. 2020;127(2):229.

    Article  Google Scholar 

  73. Padilha M, Iaucci JM, Cabral VP, Diniz EMA, Taddei CR, Saad SMI. Maternal antibiotic prophylaxis affects Bifidobacterium spp. Counts in the human milk, during the first week after delivery. Benef Microbes. 2019;10(2):155–63.

    Article  CAS  PubMed  Google Scholar 

  74. Coker MO, Hoen AG, Dade E, Lundgren S, Li Z, Wong AD, et al. Specific class of intrapartum antibiotics relates to maturation of the infant gut microbiota: a prospective cohort study. BJOG An Int J Obstet Gynaecol. 2020;127(2):217–27.

    Article  CAS  Google Scholar 

  75. Stearns JC, Simioni J, Gunn E, McDonald H, Holloway AC, Thabane L, et al. Intrapartum antibiotics for GBS prophylaxis alter colonization patterns in the early infant gut microbiome of low risk infants. Sci Rep. 2017;7(1):1–9.

    Article  CAS  Google Scholar 

  76. Azad MB, Konya T, Persaud RR, Guttman DS, Chari RS, Field CJ, et al. Impact of maternal intrapartum antibiotics, method of birth and breastfeeding on gut microbiota during the first year of life: A prospective cohort study. BJOG An Int J Obstet Gynaecol. 2016;123(6):983–93.

    Article  CAS  Google Scholar 

  77. Mazzola G, Murphy K, Ross RP, Di Gioia D, Biavati B, Corvaglia LT, et al. Early gut microbiota perturbations following intrapartum antibiotic prophylaxis to prevent group B streptococcal disease. PLoS ONE. 2016;11(6): e0157527.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  78. Nogacka A, Salazar N, Suárez M, Milani C, Arboleya S, Solís G, et al. Impact of intrapartum antimicrobial prophylaxis upon the intestinal microbiota and the prevalence of antibiotic resistance genes in vaginally delivered full-term neonates. Microbiome. 2017;5(1):93.

    Article  PubMed  PubMed Central  Google Scholar 

  79. Kamal SS, Hyldig N, Krych Ł, Greisen G, Krogfelt KA, Zachariassen G, et al. Impact of early exposure to cefuroxime on the composition of the gut microbiota in infants following cesarean delivery. J Pediatr. 2019;210:99-105.e2.

    Article  CAS  PubMed  Google Scholar 

  80. Kumar H, Collado MC, Wopereis H, Salminen S, Knol J, Roeselers G. The bifidogenic effect revisited—ecology and health perspectives of bifidobacterial colonization in early life. Microorganisms. 2020;8:12.

    Article  Google Scholar 

  81. Michelini S, Balakrishnan B, Parolo S, Matone A, Mullaney JA, Young W, et al. A reverse metabolic approach to weaning: In silico identification of immune-beneficial infant gut bacteria, mining their metabolism for prebiotic feeds and sourcing these feeds in the natural product space. Microbiome. 2018;6:1.

    Article  Google Scholar 

  82. Zimmermann P, Curtis N. The effect of antibiotics on the composition of the intestinal microbiota—a systematic review. J Infect. 2019;79(6):471–89.

    Article  PubMed  Google Scholar 

  83. Ramirez J, Guarner F, Bustos Fernandez L, Maruy A, Sdepanian VL, Cohen H. Antibiotics as major disruptors of gut microbiota. Front Cell Infect Microbiol. 2020;10: 572912.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Newton DF, Macfarlane S, Macfarlane GT. Effects of antibiotics on bacterial species composition and metabolic activities in chemostats containing defined populations of human gut microorganisms. Antimicrob Agents Chemother. 2013;57(5):2016–25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Maier L, Goemans C, Pruteanu M, Wirbel J, Kuhn M, Cacace E, et al. Dissecting the collateral damage of antibiotics on gut microbes. Gut. 2020. https://doi.org/10.1101/2020.01.09.893560v1.

    Article  Google Scholar 

  86. Ferreyra JA, Wu KJ, Hryckowian AJ, Bouley DM, Weimer BC, Sonnenburg JL. Gut microbiota-produced succinate promotes C Difficile infection after antibiotic treatment or motility disturbance. Cell Host Microbe. 2014;16(6):770–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Ng KM, Ferreyra JA, Higginbottom SK, Lynch JB, Kashyap PC, Gopinath S, et al. Microbiota-liberated host sugars facilitate post-antibiotic expansion of enteric pathogens. Nature. 2013;502(7469):96–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Ponziani FR, Scaldaferri F, Petito V, Sterbini FP, Pecere S, Lopetuso LR, et al. The role of antibiotics in gut microbiota modulation: the eubiotic effects of rifaximin. Dig Dis. 2016;34(3):269–78.

    Article  PubMed  Google Scholar 

  89. Levison ME, Levison JH. Pharmacokinetics and pharmacodynamics of antibacterial agents. Infect Dis Clin North Am. 2009;23(4):791–815.

    Article  PubMed  PubMed Central  Google Scholar 

  90. Tsai D, Jamal JA, Davis JS, Lipman J, Roberts JA. Interethnic differences in pharmacokinetics of antibacterials. Clin Pharmacokinet. 2015;54(3):243–60.

    Article  CAS  PubMed  Google Scholar 

  91. Kim D-H. Gut microbiota-mediated drug-antibiotic interactions. Drug Metab Dispos. 2015;43(10):1581–9.

    Article  CAS  PubMed  Google Scholar 

  92. Redgrave LS, Sutton SB, Webber MA, Piddock LJV. Fluoroquinolone resistance: mechanisms, impact on bacteria, and role in evolutionary success. Trends Microbiol. 2014;22(8):438–45.

    Article  CAS  PubMed  Google Scholar 

  93. Liao X, Li B, Zou R, Dai Y, Xie S, Yuan B. Biodegradation of antibiotic ciprofloxacin: pathways, influential factors, and bacterial community structure. Environ Sci Pollut Res. 2016;23(8):7911–8.

    Article  CAS  Google Scholar 

  94. Sköld O. Resistance to trimethoprim and sulfonamides. Vet Res. 2001;32(3–4):261–73.

    Article  PubMed  Google Scholar 

  95. Stiefel U, Tima MA, Nerzic MM. Metallo-β-lactamase-producing Bacteroides species can shield other members of the gut microbiota from antibiotics. Antimicrob Agents Chemother. 2015;59(1):650–3.

    Article  PubMed  CAS  Google Scholar 

  96. Ben-Ami R, Rodríguez-Baño J, Arslan H, Pitout JDD, Quentin C, Calbo ES, et al. A multinational survey of risk factors for infection with extended-spectrum β-lactamase-producing enterobacteriaceae in nonhospitalized patients. Clin Infect Dis. 2009;49:5.

    Article  Google Scholar 

  97. Forslund K, Sunagawa S, Kultima JR, Mende DR, Arumugam M, Typas A, et al. Country-specific antibiotic use practices impact the human gut resistome. Genome Res. 2013;23:7.

    Article  CAS  Google Scholar 

  98. Chng KR, Ghosh TS, Tan YH, Nandi T, Lee IR, Qi Ng Ah, et al. Metagenome-wide association analysis identifies microbial determinants of post-antibiotic ecological recovery in the gut. Nat Ecol Evol. 2020;4(9):1256–67.

    Article  PubMed  Google Scholar 

  99. Lavelle A, Hoffmann TW, Pham HP, Langella P, Guédon E, Sokol H. Baseline microbiota composition modulates antibiotic-mediated effects on the gut microbiota and host. Microbiome. 2019;7:1.

    Article  Google Scholar 

  100. Curtis MM, Hu Z, Klimko C, Narayanan S, Deberardinis R, Sperandio V. The gut commensal bacteroides thetaiotaomicron exacerbates enteric infection through modification of the metabolic landscape. Cell Host Microbe. 2014;16(6):759–69.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Bucci V, Bradde S, Biroli G, Xavier JB. Social interaction, noise and antibiotic-mediated switches in the intestinal microbiota. PLoS Comput Biol. 2012;8(4): e1002497.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Gerber GK, Onderdonk AB, Bry L. Inferring dynamic signatures of microbes in complex host ecosystems. PLoS Comput Biol. 2012;8(8): e1002624.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Li N, Zhang H, Bai Z, Jiang H, Yang F, Sun X, et al. Soil exposure accelerates recovery of the gut microbiota in antibiotic-treated mice. Environ Microbiol Rep. 2021;100:89.

    Google Scholar 

  104. Shaw LP, Bassam H, Barnes CP, Walker AS, Klein N, Balloux F. Modelling microbiome recovery after antibiotics using a stability landscape framework. ISME J. 2019;13(7):1845–56.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Yassour M, Jason E, Hogstrom LJ, Arthur TD, Tripathi S, Siljander J, et al. Strain-level analysis of mother-to-child bacterial transmission during the first few months of life. Cell Host Microbe. 2018;24(1):146-154.e4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Bokulich NA, Chung J, Battaglia T, Henderson N, Jay M, Li H, et al. Antibiotics, birth mode, and diet shape microbiome maturation during early life. Sci Trans Med. 2016;8(343):343ra82.

    Article  CAS  Google Scholar 

  107. Vallianou N, Dalamaga M, Stratigou T, Karampela I, Tsigalou C. Do antibiotics cause obesity through long-term alterations in the gut microbiome? a review of current evidence. Curr Obes Rep. 2021;10(3):244–62.

    Article  PubMed  PubMed Central  Google Scholar 

  108. Lang D. Investigators MAL-ED Network Investigators. Opportunities to assess factors contributing to the development of the intestinal microbiota in infants living in developing countries. Microb Ecol Health Dis. 2015;26:28316.

    PubMed  Google Scholar 

  109. Platts-Mills JA, Liu J, Rogawski ET, Kabir F, Lertsethtakarn P, Siguas M, et al. Use of quantitative molecular diagnostic methods to assess the aetiology, burden, and clinical characteristics of diarrhoea in children in low-resource settings: a reanalysis of the MAL-ED cohort study. Lancet Glob Heal. 2018;6(12):e1309–18.

    Article  Google Scholar 

  110. Kotloff KL, Nataro JP, Blackwelder WC, Nasrin D, Farag TH, Panchalingam S, et al. Burden and aetiology of diarrhoeal disease in infants and young children in developing countries (the Global Enteric Multicenter Study, GEMS): A prospective, case-control study. Lancet. 2013;382(9888):209–22.

    Article  PubMed  Google Scholar 

  111. Hart JD, Kalua K, Keenan JD, Lietman TM, Bailey RL. Effect of Mass Treatment with Azithromycin on Causes of Death in Children in Malawi: Secondary Analysis from the MORDOR Trial. Am J Trop Med Hyg. 2020;103(3):1319–28.

    Article  PubMed  PubMed Central  Google Scholar 

  112. Keenan JD, Arzika AM, Maliki R, Adamou SE, Ibrahim F, Kiemago M, et al. Cause-specific mortality of children younger than 5 years in communities receiving biannual mass azithromycin treatment in Niger: verbal autopsy results from a cluster-randomised controlled trial. Lancet Glob Heal. 2020;8(2):e288–95.

    Article  Google Scholar 

  113. Keenan JD, Arzika AM, Maliki R, Boubacar N, Adamou SE, Ali MM, et al. Longer-term assessment of azithromycin for reducing childhood mortality in Africa. N Engl J Med. 2019;380(23):2207–14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. George S, Aguilera X, Gallardo P, Farfán M, Lucero Y, Torres JP, et al. Bacterial gut microbiota and infections during early childhood. Front Microbiol. 2022;12: 793050.

    Article  PubMed  PubMed Central  Google Scholar 

  115. Mathew S, Smatti MK, Al Ansari K, Nasrallah GK, Al Thani AA, Yassine HM. Mixed viral-bacterial infections and their effects on gut microbiota and clinical illnesses in children. Sci Rep. 2019;9(1):865.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  116. Lee DK, Park JE, Kim MJ, Seo JG, Lee JH, Ha NJ. Probiotic bacteria, B. longum and L. acidophilus inhibit infection by rotavirus in vitro and decrease the duration of diarrhea in pediatric patients. Clin Res Hepatol Gastroenterol. 2015;39(2):237–44.

    Article  PubMed  Google Scholar 

  117. Baldridge MT, Nice TJ, McCune BT, Yokoyama CC, Kambal A, Wheadon M, et al. Commensal microbes and interferon-λ determine persistence of enteric murine norovirus infection. Science. 2015;347(6219):266–9.

    Article  CAS  PubMed  Google Scholar 

  118. Uchiyama R, Chassaing B, Zhang B, Gewirtz AT. Antibiotic treatment suppresses rotavirus infection and enhances specific humoral immunity. J Infect Dis. 2014;210(2):171–82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Koningstein M, Simonsen J, Helms M, Hald T, Mølbak K. Antimicrobial use: A risk factor or a protective factor for acquiring campylobacteriosis? Clin Infect Dis. 2011;53:7.

    Article  Google Scholar 

  120. Knowles R, Sharland M, Hsia Y, Magrini N, Moja L, Siyam A, et al. Measuring antibiotic availability and use in 20 low-and middle-income countries. Bull World Health Organ. 2020;98(3):177–87.

    Article  PubMed  PubMed Central  Google Scholar 

  121. Saari A, Virta LJ, Sankilampi U, Dunkel L, Saxen H. Antibiotic exposure in infancy and risk of being overweight in the first 24 months of life. Pediatrics. 2015;135(4):617–26.

    Article  PubMed  Google Scholar 

  122. Leong KSW, McLay J, Derraik JGB, Gibb S, Shackleton N, Taylor RW, et al. Associations of prenatal and childhood antibiotic exposure with obesity at age 4 years. JAMA Netw Open. 2020;3(1):1–13.

    Article  Google Scholar 

  123. Chelimo C, Camargo CA, Morton SMB, Grant CC. Association of repeated antibiotic exposure up to age 4 years with body mass at age 4.5 years. JAMA Netw Open. 2020;3(1):1–14.

    Article  Google Scholar 

  124. Stanislawski MA, Dabelea D, Lange LA, Wagner BD, Lozupone CA. Gut microbiota phenotypes of obesity. Biofilms Microbiomes. 2019;5:1.

    CAS  Google Scholar 

  125. Gough EK, Moodie EEM, Prendergast AJ, Johnson SMA, Humphrey JH, Stoltzfus RJ, et al. The impact of antibiotics on growth in children in low and middle income countries: Systematic review and meta-analysis of randomised controlled trials. BMJ. 2014;348: g2267.

    Article  PubMed  PubMed Central  Google Scholar 

  126. Turnbaugh PJ, Hamady M, Yatsunenko T, Cantarel BL, Duncan A, Ley RE, et al. A core gut microbiome in obese and lean twins. Nature. 2009;457(7228):480–4.

    Article  CAS  PubMed  Google Scholar 

  127. Kostic AD, Gevers D, Siljander H, Vatanen T, Hyötyläinen T, Hämäläinen AM, et al. The dynamics of the human infant gut microbiome in development and in progression toward type 1 diabetes. Cell Host Microbe. 2015;17(2):P260-273.

    Article  CAS  Google Scholar 

  128. Wang J, Qin J, Li Y, et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature. 2012;490(7418):55–60.

    Article  PubMed  CAS  Google Scholar 

  129. Sonnenburg ED, Smits SA, Tikhonov M, Higginbottom SK, Wingreen NS, Sonnenburg JL. Diet-induced extinctions in the gut microbiota compound over generations. Nature. 2016;529(7585):212–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. Differding MK, Benjamin-Neelon SE, Hoyo C, Østbye T, Mueller NT. Timing of complementary feeding is associated with gut microbiota diversity and composition and short chain fatty acid concentrations over the first year of life. BMC Microbiol. 2020;20:1.

    Article  CAS  Google Scholar 

  131. Stanislawski MA, Dabelea D, Wagner BD, Iszatt N, Dahl C, Sontag MK, et al. Gut Microbiota in the First 2 Years of Life and the Association with Body Mass Index at Age 12 in a Norwegian Birth Cohort. MBio. 2018;9:5.

    Article  Google Scholar 

  132. Uzan-Yulzari A, Turta O, Belogolovski A, Ziv O, Kunz C, Perschbacher S, et al. Neonatal antibiotic exposure impairs child growth during the first six years of life by perturbing intestinal microbial colonization. Nat Commun. 2021;12(1):1–12.

    Article  CAS  Google Scholar 

  133. Hernández E, Bargiela R, Diez MS, Friedrichs A, Pérez-Cobas AE, Gosalbes MJ, et al. Functional consequences of microbial shifts in the human gastrointestinal tract linked to antibiotic treatment and obesity. Gut Microbes. 2013;4(4):306–15.

    Article  PubMed  PubMed Central  Google Scholar 

  134. Cox LM, Yamanishi S, Sohn J, Alekseyenko AV, Leung JM, Cho I, et al. Altering the intestinal microbiota during a critical developmental window has lasting metabolic consequences. Cell. 2014;158(4):705–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  135. Droste JHJ, Wieringa MH, Weyler JJ, Nelen VJ, Vermeire PA, Van Bever HP. Does the use of antibiotics in early childhood increase the risk of asthma and allergic disease? Clin Exp Allergy. 2000;30:11.

    Article  Google Scholar 

  136. Ong MS, Umetsu DT, Mandl KD. Consequences of antibiotics and infections in infancy: Bugs, drugs, and wheezing. Ann Allergy, Asthma Immunol. 2014;112:5.

    Article  CAS  Google Scholar 

  137. McKeever TM, Lewis SA, Smith C, Collins J, Heatlie H, Frischer M, et al. Early exposure to infections and antibiotics and the incidence of allergic disease: A birth cohort study with the West Midlands General Practice Research Database. J Allergy Clin Immunol. 2002;109:1.

    Article  Google Scholar 

  138. Cahenzli J, Köller Y, Wyss M, Geuking MB, McCoy KD. Intestinal microbial diversity during early-life colonization shapes long-term IgE levels. Cell Host Microbe. 2013;14:559–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  139. Olszak T, An D, Zeissig S, Vera MP, Richter J, Franke A, et al. Microbial exposure during early life has persistent effects on natural killer T cell function. Science. 2012;336(6080):489–93. https://doi.org/10.1126/science.1219328.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  140. Bisgaard H, Li N, Bonnelykke K, Chawes BLK, Skov T, Paludan-Müller G, et al. Reduced diversity of the intestinal microbiota during infancy is associated with increased risk of allergic disease at school age. J Allergy Clin Immunol. 2011;128:3.

    Article  Google Scholar 

  141. Abrahamsson TR, Jakobsson HE, Andersson AF, Björkstén B, Engstrand L, Jenmalm MC. Low diversity of the gut microbiota in infants with atopic eczema. J Allergy Clin Immunol. 2012;129:2.

    Article  Google Scholar 

  142. Patrick DM, Sbihi H, Dai DLY, Al Mamun A, Rasali D, Rose C, et al. Decreasing antibiotic use, the gut microbiota, and asthma incidence in children: evidence from population-based and prospective cohort studies. Lancet Respir Med. 2020;8:11.

    Article  Google Scholar 

  143. Russell SL, Gold MJ, Hartmann M, Willing BP, Thorson L, Wlodarska M, et al. Early life antibiotic-driven changes in microbiota enhance susceptibility to allergic asthma. EMBO Rep. 2012;13:440–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  144. Levan SR, Stamnes KA, Lin DL, Panzer AR, Fukui E, McCauley K, et al. Elevated faecal 12,13-diHOME concentration in neonates at high risk for asthma is produced by gut bacteria and impedes immune tolerance. Nat Microbiol. 2019;4:1851–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  145. De Agüero MG, Ganal-Vonarburg SC, Fuhrer T, Rupp S, Uchimura Y, Li H, et al. The maternal microbiota drives early postnatal innate immune development. Science. 2016;351(6279):1296–302.

    Article  CAS  Google Scholar 

  146. Nadimpalli ML, Bourke CD, Robertson RC, Delarocque-Astagneau E, Manges AR, Pickering AJ. Can breastfeeding protect against antimicrobial resistance? BMC Med. 2020;18:1.

    Article  CAS  Google Scholar 

  147. Nikolaou E, Kamilari E, Savkov D, Sergeev A, Zakharova I, Vogazianos P, et al. Intestinal microbiome analysis demonstrates azithromycin post-treatment effects improve when combined with lactulose. World J Pediatr. 2020;16(2):168–76.

    Article  CAS  PubMed  Google Scholar 

  148. Wieërs G, Verbelen V, Van Den Driessche M, Melnik E, Vanheule G, Marot JC, et al. Do probiotics during in-hospital antibiotic treatment prevent colonization of gut microbiota with multi-drug-resistant bacteria? a randomized placebo-controlled trial comparing saccharomyces to a mixture of lactobacillus, bifidobacterium, and saccharomyce. Front Public Heal. 2021;8:1–10.

    Google Scholar 

  149. Zmora N, Zilberman-Schapira G, Suez J, Mor U, Dori-Bachash M, Bashiardes S, et al. Personalized gut mucosal colonization resistance to empiric probiotics is associated with unique host and microbiome features. Cell. 2018;174(6):1388–405.

    Article  CAS  PubMed  Google Scholar 

  150. Montassier E, Batard E, Zmora N, Dori-bachash M, Suez J, Elinav E. Probiotics impact the antibiotic resistance gene reservoir along the human GI tract in a person-specific and antibiotic-dependent manner. Nat Microbiol. 2021;6:1043.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  151. Suez J, Zmora N, Zilberman-Schapira G, Mor U, Dori-Bachash M, Bashiardes S, et al. Post-antibiotic gut mucosal microbiome reconstitution is impaired by probiotics and improved by autologous FMT. Cell. 2018;174(6):1406–23.

    Article  CAS  PubMed  Google Scholar 

  152. Brochado AR, Telzerow A, Bobonis J, Banzhaf M, Mateus A, Selkrig J, et al. Species-specific activity of antibacterial drug combinations. Nature. 2018;559(7713):259–63.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  153. Donia MS, Cimermancic P, Schulze CJ, Brown LCW, Martin J, Mitreva M, et al. A systematic analysis of biosynthetic gene clusters in the human microbiome reveals a common family of antibiotics. Cell. 2014;158(6):1402–14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  154. Rea MC, Dobson A, O’Sullivan O, Crispie F, Fouhy F, Cotter PD, et al. Effect of broad- and narrow-spectrum antimicrobials on Clostridium difficile and microbial diversity in a model of the distal colon. Proc Natl Acad Sci USA. 2011;108(1):4639–44.

    Article  CAS  PubMed  Google Scholar 

  155. Zhou D, Zhang H, Bai Z, Zhang A, Bai F, Luo X, et al. Exposure to soil, house dust and decaying plants increases gut microbial diversity and decreases serum immunoglobulin E levels in BALB/c mice. Environ Microbiol. 2016;18(5):1326–37.

    Article  CAS  PubMed  Google Scholar 

  156. Zhou D, Bai Z, Zhang H, Li N, Zhiu Bai1 Z, Cheng F, et al: Soil is a key factor influencing gut microbiota and its effect is comparable to that exerted by diet for mice. https://f1000research.com/articles/7-1588. Accessed 1 Mar 2021

  157. Wold AE. The hygiene hypothesisi revised: Is the rising frequency of allergy due to changes in rising the intestinal flora? Allergy Eur J Allergy Clin Immunol. 1998;53(46):20–5.

    Article  CAS  Google Scholar 

  158. Aloisio I, Quagliariello A, De Fanti S, Luiselli D, De Filippo C, Albanese D, et al. Evaluation of the effects of intrapartum antibiotic prophylaxis on newborn intestinal microbiota using a sequencing approach targeted to multi hypervariable 16S rDNA regions. Appl Microbiol Biotechnol. 2016;100(12):5537–46.

    Article  CAS  PubMed  Google Scholar 

  159. Zhou P, Zhou Y, Liu B, Jin Z, Zhuang X, Dai W, et al. Perinatal antibiotic exposure affects the transmission between maternal and neonatal microbiota and is associated with early-onset sepsis. Sphere. 2020;5:1.

    CAS  Google Scholar 

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Acknowledgements

We thank the following persons for their expert commentary on the initial draft of this manuscript: Dr. Cynthia Sears, Dr. Pasacale Vonaesch, Dr. Clifford McDonald, Dr. Meghan Azad, Dr. Anthony So, Alex Kong, Matthew DeCamp, and Dr. Dawn Sievert.

Funding

This work was supported under a Greenwall Foundation grant on the “Mass Administration of Antibiotics: Reaching Clinical and Community Equipoise” as part of the Making a Difference in Real-World Bioethics Dilemmas program. The Greenwall Foundation did not have a role in the design of the study, collection, analysis, and interpretation of data or in writing the manuscript.

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EKG conducted the literature review, performed data abstraction, drafted, finalized the final manuscript. The author read and approved the final manuscript.

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Gough, E.K. The impact of mass drug administration of antibiotics on the gut microbiota of target populations. Infect Dis Poverty 11, 76 (2022). https://doi.org/10.1186/s40249-022-00999-5

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