# Obesity and antibiotic exposure: an overhyped theory

Valérie Lannoy

► To cite this version:

Valérie Lannoy. Obesity and antibiotic exposure: an overhyped theory. 2025. hal-05378965

HAL Id: hal-05378965

https://hal.science/hal-05378965v1

Preprint submitted on 24 Nov 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Obesity and antibiotic exposure: an overhyped theory

Valérie Lannoy*¹

¹ Centre d'Immunologie et des Maladies Infectieuses (CIMI), INSERM U1135, Sorbonne Université – Paris, France

*Corresponding author

Correspondence: valerie.lannoy@inserm.fr

## ABSTRACT

The notion that antibiotic use during infancy predisposes individuals to obesity has enjoyed over a decade of enthusiastic attention in both the scientific literature and the media. The hypothesis appears intuitively sound and culturally satisfying: antibiotics disrupt the developing gut microbiota, which, in turn, deranges metabolic homeostasis and increases long-term adiposity. Yet the empirical evidence underpinning this idea is remarkably fragile. Most epidemiological studies report odds ratios (ORs) between 1.1 and 1.3—values indistinguishable from statistical background noise. When familial, socioeconomic, and dietary factors are rigorously accounted for, the association disappears. Moreover, global epidemiological patterns contradict the hypothesis: obesity flourishes where antibiotic stewardship is strict, not where antibiotics are freely available. This review dissects the origins, evidence, and persistence of the antibiotic-obesity narrative, arguing that it exemplifies how mechanistic plausibility and cultural appeal can inflate weak associations into scientific dogma. Despite its methodological fragility, the hypothesis has nevertheless had positive side effects: promoting rational antibiotic stewardship and catalyzing a new wave of research on the interplay between the microbiota and metabolism.

**Keywords:** obesity, antibiotics, early exposure, microbiota, metabolism, adiposity, odds ratios
36

# Introduction

In the early 2010s, a series of observational studies ignited a striking claim: that antibiotic exposure during infancy could increase the risk of obesity in later life (Azad et al., 2014; Bailey et al., 2014; Podolsky, 2017). The idea quickly gained traction, both because it resonated with the zeitgeist of microbiome science and because it carried a moral undertone—suggesting that the life-saving tools of modern medicine might, in excess, turn against us. The hypothesis rested on a simple chain of logic: antibiotics disrupt the gut microbiota; the gut microbiota regulates metabolism; therefore, antibiotics must disturb metabolic homeostasis. This syllogism was elegant, emotionally charged, and almost impossible to resist. But elegance, as history repeatedly shows, is no guarantee of truth (Podolsky, 2017). Between antibiotic exposure and obesity lies a tangle of confounders—genetics, socioeconomic status, maternal weight, infant feeding practices, and healthcare behaviours. To conflate the correlation with causation in this context is to overlook the complexity of human development and the multiplicity of pathways that lead to adiposity.

Nonetheless, the concept became a powerful narrative. It echoed long-standing cultural anxieties about overmedication, the “unnaturalness” of modern life, and the loss of microbial balance. The idea that a brief course of antibiotics could permanently alter one’s metabolism offered a story both tragic and moralizing. It makes obesity not a product of diet or the environment, but of modern medicine’s microbial recklessness. The problem, as this review will argue, is that the data never justified the drama. The association, though statistically reproducible, is quantitatively negligible. Once familial confoundings are properly controlled, the signal vanishes. Yet the hypothesis persists—fuelled by mechanistic enthusiasm, media amplification, and the seductive power of the microbiome narrative.

59

# Origins of the hypothesis

60

## From animal husbandry to human epidemiology

The conceptual seed of the antibiotic-obesity hypothesis was planted decades before its epidemiological flowering. Since the 1950s, low-dose antibiotics have been used in agriculture as growth promoters. Subtherapeutic antibiotic feed supplements reliably increase weight gain and feed efficiency in livestock (Cho et al., 2012). The extrapolation to humans seemed natural. To put it provocatively, if antibiotics make pigs and chickens fatter, why not children? The analogy was rhetorically compelling—but biologically naive. Livestock growth promotion involves chronic exposure to low-dose antibiotics over months or years, typically in feed, under carefully controlled nutritional and environmental conditions. By contrast, pediatric antibiotic use in immunocompetent patients is rather short and episodic, usually five to ten days per course. To equate the two is to conflate chronic ecological conditioning with transient therapeutic intervention. The cumulative antibiotic exposure of a farm animal outweighs that of a human child by several orders of magnitude. Moreover, the diets, housing, genetics, and microbial ecology of livestock differ radically from those of human infants. The agricultural analogy, while rhetorically powerful, thus collapses upon quantitative scrutiny.

75

## Antibiotic mechanisms

Antibiotics encompass diverse structural classes whose antibacterial activity derives from the selective inhibition of essential microbial processes. β-lactams block peptidoglycan transpeptidation by binding penicillin-binding proteins, leading to cell lysis (Brunton et al., 2018). Glycopeptides such as vancomycin bind termini of cell-wall precursors, preventing cross-linking in Gram-positive bacteria (Levine, 2006). Protein synthesis inhibitors include aminoglycosides, which induce misreading at the 30S ribosome (Mingeot-Leclercq et al., 1999); tetracyclines, which prevent aminoacyl-tRNA binding at the 30S site (Nelson & Levy, 2011); macrolides and
lincosamides, which bind the 50S subunit to inhibit peptide elongation (Tenson et al., 2003); and oxazolidinones, which disrupt initiation-complex formation (Bozdogan & Appelbaum, 2004).

Other agents target nucleic-acid metabolism: fluoroquinolones inhibit DNA gyrase and topoisomerase IV (Aldred et al., 2014), while rifamycins block RNA polymerase (Wehrli, 1983). Metabolic inhibitors such as trimethoprim–sulfamethoxazole interfere with folate synthesis (Huovinen, 2001), whereas nitroimidazoles generate reactive intermediates causing DNA strand breaks in anaerobes (Edwards, 1993). All these mechanisms correlate with characteristic toxicities. However, a Canadian clinical study has revealed that the most prevalent side effect of antibiotics is gastrointestinal disorders, including nausea, vomiting, diarrhea, abdominal pain, loss of appetite, and bloating, because they are known to perturb the intestinal microbiota (Mohsen et al., 2020).

## Early epidemiological evidence

The modern version of the hypothesis began with a handful of retrospective cohort studies using electronic health records. Trasande et al., analyzing 11,532 children from the Avon Longitudinal Study of Parents and Children, reported that antibiotic exposure before six months of age was associated with an increased body mass index (BMI) at 38 months (Trasande et al., 2013). The adjusted Odds Ratio (OR) is 1.22; 95% Confidence Interval (CI) 1.06–1.40. Bailey et al., using a United States of America cohort of 64,580 infants, had the highest Rate Ratio (RR) = 1.16 (95% CI 1.06–1.29) for obesity by age five (Bailey et al., 2014). The effect sizes were small, but consistent, and thus appeared persuasive. Yet beneath the surface of statistical consistency lay a striking fragility: none of these associations exceeded the conventional threshold of epidemiological credibility (OR or RR > 1.5).

In any other research domain, such small effects would be treated as hypothesis-generating at best, artefactual at worst. Actually, the theory fitted the cultural moment. The 2010s marked the rise of the microbiome as the grand unifying theory of chronic diseases. A mechanistic explanation linking antibiotic exposure, microbial disruption, and obesity fit seamlessly into that paradigm. The story was simple, plausible, and optimistic—it suggested that by preserving or “restoring” our microbes, we might reverse a global epidemic. But the very simplicity that made the idea compelling also made it misleading. When public fascination meets statistical ambiguity, the narrative tends to triumph over nuance.

## When small numbers make big headlines

An OR of 1.1 implies a 10% increase in relative risk. If 10% of unexposed children become obese, the prevalence rises to 11% among the exposed. Such a change is clinically trivial, easily overwhelmed by measurement errors or uncontrolled lifestyle variables. Nevertheless, these small differences generated major headlines. The Journal of the American Medical Association (JAMA) Pediatrics and International Journal of Obesity published studies with enormous samples and minuscule ORs and RR s. In the hierarchy of epidemiological credibility, these values sit barely above random noise. As Grimes and Schulz mentioned, an RR that does not exceed 2 in a cohort study should not be considered credible, tending to residual confounding and reporting bias (Grimes & Schulz, 2012). According to clinician epidemiologists, OR greater than 4 in case-control studies are those providing strong support for causation (Sackett et al., 1985). When effect sizes are small, any residual confounding—no matter how minor—can fully explain the observed association. The power of big data lies in its ability to detect small effects—but that is also its peril. With hundreds of thousands of participants, even trivial differences yield p-values far below 0.001. Statistical significance thus becomes a function of sample size, not effect magnitude. The result is what Ioannidis called “the tyranny of the p-value”: large datasets producing significant but meaningless results (Ioannidis, 2005).

Meta-analyses soon followed the disclosure of the link between early antibiotic exposure and obesity. A systematic review reported as highest OR = 1.24 (95% CI 1.09–1.43) (Rasmussen et al., 2018). Miller et al. in 2018 confirmed an almost identical estimate (pooled OR = 1.05; 95% CI
133

1.00–1.11) across over 500,000 children (Miller et al., 2018). Children prone to infections or with delayed immune maturation are more likely to receive antibiotics and to develop later metabolic irregularities (Brustad et al., 2025). Furthermore, infants who receive more antibiotics often do so because they suffer from recurrent respiratory or ear infections. It is these infections themselves—rather than antibiotic treatments—that may alter feeding patterns, appetite, or growth (Brustad et al., 2025). If studies do not carefully control for the frequency and severity of infections, it is not methodologically sound to attribute weight changes to antibiotics. For example, overweight and obese children exhibit low-grade inflammation (Khanna et al.), which makes them more susceptible to urinary tract infections (Hsu & Chen, 2018). In this context, antibiotic exposure is better interpreted as a marker of underlying vulnerability than as a causal factor. In other words, we are not observing causation, but confounding disguised as robust statistics.

134

135

136

137

138

139

140

141

142

143

144

145

# When families explain more than microflora

146

## The sibling paradox

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

Familial factors are potent predictors of both antibiotic exposure and obesity. Parents with a higher Body Mass Index (BMI) are more likely to have obese children and to seek medical care frequently, leading to more antibiotic prescriptions. Sibling-comparison studies offer a rare window into causality because they automatically control for shared genetics, upbringing, and socioeconomic context. In a nationwide New Zealand cohort of 132,852 mother–child pairs, the crude association between early-life antibiotic exposure and obesity at 4 years (including an apparent dose-response) vanished in sibling and twin fixed-effect models, indicating that shared familial factors, rather than antibiotics *per se*, explained the association (Leong et al., 2020). In a large paediatric network cohort, early antibiotic exposure within the first 6 months of life was not associated with excess weight gain up to age 7 years; in twin pairs discordant for exposure, there was no measurable difference in body weight between the exposed and unexposed twin (Gerber et al., 2016). Such null results are not trivial: they suggest that the entire effect seen in conventional analyses arises from familial clustering rather than biological causality. In other words, antibiotics “predict” obesity because families that use more antibiotics also share other obesity risk factors—genetic, dietary, or behavioural.

162

163

164

165

166

167

168

169

170

171

172

173

One invoked limitation in the nationwide New Zealand cohort analysis of sibling-controlled studies is an ecological mechanism of intra-household microbial exchange (Azad & Owora, 2020). According to this view, the gut microbiota of cohabiting siblings is so intertwined that any antibiotic-induced disruption in one child would be rapidly normalized through microbial transfer from the other. This concept is supported by evidence from animal studies. Ridaura et al. demonstrated that when mice colonized with twin human “obese” or “lean” microbiota were cohoused, the transfer of microbes between cages prevented the increase in adiposity normally seen in recipients of the obese microbiota (Ridaura et al., 2013). The juxtaposition, however, is misplaced. Co-housed mice engage in coprophagy—a direct, continuous, and bidirectional transfer of intestinal contents that rapidly homogenizes their gut communities within days. Human siblings, by contrast, share only intermittent and environmentally mediated exposure. The microbial overlap observed in families reflects shared ecology rather than fecal exchange.

174

175

176

177

178

179

180

181

182

183

In a comparison of 94 spouse and 83 sibling pairs from the Wisconsin Longitudinal Study, spouses displayed higher microbiota similarities and shared more bacterial taxa than siblings. No difference was detected between siblings and unrelated pairs, and these findings remained after controlling for dietary intake. Interestingly, the effect was specific to couples reporting very close relationships; those with less intimacy showed microbiota profiles indistinguishable from unrelated individuals (Dill-McFarland et al., 2019). It was confirmed in a cohort from the Fiji Islands, where common barriers to bacterial transmission are absent (Brito et al., 2019). In this study, the household members who showed the highest levels of strain similarities in their gut microbiomes were the mother-child pairs and the spouses (Brito et al., 2019). If early-life antibiotics truly exerted a profound and lasting metabolic disruption, such an insult would not be
184
185
186
187
188

erased by the mere sharing of toys, towels, and intestinal bacteria between siblings. The claim that a perturbation vanishes through casual microbial exchange tacitly concedes that the effect was never devastating in the first place. The microbiota may be shared, but so is the diet, the parental genome, and the socioeconomic context—each infinitely more influential on weight gain than a bout of amoxicillin.

189

## Maternal BMI and healthcare behavior

190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209

Ajslev et al., analysing 28,354 Danish mother-child dyads, found the highest association between early antibiotics and later BMI in children of obese mothers (adjusted OR = 2.59; 95% CI 1.28–5.23), even slightly lower than children of obese mothers who did not receive antibiotics (adjusted OR = 2.85; 95% CI 2.25–3.62) (Ajslev et al., 2011). Therefore, maternal weight before pregnancy is among the strongest predictors of offspring BMI. Mothers with higher BMI are more likely to develop gestational diabetes, undergo caesarean delivery, and request antibiotic treatment during infancy—all pathways that inflate the apparent link between antibiotics and obesity. Furthermore, perinatal factors—such as caesarean birth, formula feeding, and early weaning—alter the microbiota and metabolic development independently of antibiotics. Any model ignoring these factors is bound to overestimate antibiotic effects.

Antibiotic exposure is also a social phenomenon. Families with higher healthcare engagement, insurance coverage, or anxiety about infection are more inclined to obtain antibiotic prescriptions (Morgan et al., 2011). These same families may exhibit different feeding habits, screen time, and dietary environments—all powerful confounders; families who use more antibiotics tend to have other, more relevant risk factors for obesity. Crucially, studies that adjust only crudely for demographic variables tend to report a small association between early-life antibiotic exposure and later obesity (relative risks in the ~1.2 range) (Bailey et al., 2014). Childhood obesity is a profoundly familial condition. Parental BMI, household diet, and social environment shape a child's metabolic trajectory far more powerfully than transient antibiotic exposures.

210

## Mechanistic enthusiasm: the microbiota as metabolic oracle

211

### A story too good to doubt?

212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228

Mechanistic plausibility is the hypothesis's strongest and weakest pillar. The idea that antibiotics disrupt the gut microbiota, reducing diversity and altering energy harvest, seems consistent with our understanding of host–microbe symbiosis. But evidence supporting a sustained metabolic impact in humans is scarce. Early mouse studies such as Cho et al. showed that chronic antibiotic exposure in neonatal mice increased adiposity (Cho et al., 2012). More precisely, the authors developed a murine experimental model of Sub-Theraeutic Antibiotic Treatment (STAT), where mice are exposed at weaning in their drinking water for 7 weeks. STAT did not alter the microbiota richness (Cho et al., 2012). Nevertheless, its composition was affected in favor of the *Firmicutes* phylum in STAT mice, as measured both in faecal and caecal samples. Accordingly, STAT exposure substantially increased the gut Short-Chain Fatty Acids (SCFA), supporting mechanisms for the STAT-induced adiposity phenomenon (Figure 1). The same team then elaborated a murine model of Low-Dose Penicillin (LDP), delivered from birth (Cox et al., 2014). These mice significantly displayed a higher adiposity than those exposed to LDP at weaning. LDP also aggravated the effect of high-fat diet-induced obesity. Growth promotion is transferrable to germ-free mice by LDP-selected microbiota, showing its causal role (Cox et al., 2014). These findings provided the mechanistic scaffolding for human extrapolation.
<figure><img src="image_2.png" /><figcaption>**Figure 1** – Metabolic mechanisms by which SubTherapeutic Antibiotic Treatment favors adiposity through intestinal microbiota reshaping.</figcaption></figure>

Yet these models use antibiotic exposures incomparable to clinical practice—weeks to months of continuous treatment, in genetically uniform animals on fixed diets. A child's five-day amoxicillin course bears little resemblance to a mouse's chronic exposure to antibiotic cocktails. Some links in the evidential chain suffer from methodological fragility—from exposure definition to outcome measurement. Most studies rely on prescription records, not verified intake. Parents may not fill prescriptions or may discontinue treatment early. Over-the-counter antibiotic use, common even in regulated countries, is rarely recorded (Morgan et al., 2011). Such misclassification biases effect estimates toward null—or creates spurious associations when correlated with family variables. One of the highest relevant criteria for causality is the presence of a dose-response gradient. Leong et al. (Leong et al., 2020) conducted a meta-analysis stratifying by number of antibiotic courses. Children receiving 1–2, 3–5, or > 6 courses showed nearly identical pooled ORs for obesity in sibling and twin fixed-effects models. No gradient emerged, and without dose dependency, the hypothesis lacks a cornerstone of biological credibility.

## The resilient infant microbiota

Early exposure may refer to antibiotics before 6, 12, or 24 months, depending on the study. The resulting heterogeneity inflates statistical noise. Trasande's 2013 analysis defined obesity at 38 months (Trasande et al., 2013) and Bailey's 2014 study used obesity at age five (Bailey et al., 2014). Such variability undermines comparability across cohorts and exaggerates apparent reproducibility. Most studies treat antibiotic exposure in the first two years as a uniform variable. Nonetheless, the developing gut microbiome progresses through three distinct stages: a developmental phase (3–14 months), a transitional phase (15–30 months), and a stable phase (31–46 months) (Stewart et al., 2018). Among all variables, the intake of breast milk—whether exclusively or in combination with other foods—emerged as the strongest determinant of microbiome composition (Stewart et al., 2018). Lumping these intervals erases temporal resolution, while antibiotic use often reflects prior infection burden, blurring causality altogether.

Longitudinal metagenomic analyses reveal that the infant microbiota, though sensitive to perturbation, is remarkably resilient. Yassour et al. followed 39 newborns across two years and found that antibiotic-induced gut disruptions in microbial diversity normalized within weeks (Yassour et al., 2016). Their results shed light on the resilient infant microbiota, related to the

229

230

231

232

233

234

235

236

237

238

239

240

241

242

243

244

245

246

247

248

249

250

251

252

253

254

255

256

257

258

259

260

261
ability of an ecosystem to equilibrate following perturbations (Relman, 2012). Also, Yassour et al. did not observe any increase in BMI at the age of three in children repeatedly exposed to antibiotics (Yassour et al., 2016). Bokulich et al. confirmed similar findings: even after repeated exposures, microbiota composition converged toward a stable adult-like configuration, diet-dominated profile by age three, parallelizing the dietary transition to solid foods (Bokulich et al., 2016), an important driver for childhood microflora maturation (Bäckhed et al., 2015). α-diversity was not decreased in individual children immediately following antibiotic administration. Rather, exposures altered the α-diversity trajectory during the first year of life, but transient effects are indistinguishable between exposed and non-exposed infants at 12 months of age at the Operational Taxonomic Unit (OTU) level (Bokulich et al., 2016).

The rhetoric of “microbial loss” exaggerates the causal power of diversity metrics. The microbiota is not a fragile relic of premodern life but a dynamic ecosystem that reorganizes in response to diet and environment. Diet-induced changes in microbiota composition far exceed those induced by short antibiotic courses (David et al., 2014). A meta-analysis has shown that the difference between the Shannon diversity of obese and nonobese individuals, while significant, is at best around 2% (Sze & Schloss, 2016). To elucidate the link between obesity and early antibiotic exposure, Nobel et al. designed an appropriate murine model of early life Pulsed Antibiotic Treatment (PAT), shortly after weaning to mimic early life use in human children (Nobel et al., 2015). The amoxicillin PAT showed no effect on body composition (Nobel et al., 2015), which is substantial, knowing that this is the most pediatric prescribed antibiotic (Lehrer et al., 2024).

# Reassessing the mechanisms

## Exposure misclassifications

In a prospective birth cohort study, Chelimo et al. enrolled 6,853 children before birth between 2009 and 2010 (Chelimo et al., 2020). Anthropometric measurements, including height and weight, were longitudinally collected until 4.5 years of age. Data on systemic antibiotic dispensations were retrieved from the New Zealand Pharmaceutical Collection. Of the 5,128 children retained for analysis, 95% received at least one antibiotic prescription before the age of 4, and 9% met the criteria for obesity at 4.5 years of age. Exposure to more than nine antibiotic treatments was associated with a 2.4-fold increase in the OR of obesity (95% CI 1.07-5.41), compared with non-exposed children. However, the near-universal exposure rate in this cohort (95%) highlights the need for cautious interpretation when extrapolating microbiota-mediated mechanisms from observational associations.

In 2014, provincial healthcare records were linked to clinical data from a Canadian longitudinal birth cohort study (Azad et al., 2014). Infants exposed to systemic antibiotics during the first year of life exhibited a higher prevalence of subsequent overweight compared with non-exposed peers. After multivariate adjustment for birth weight, breastfeeding status, maternal overweight, and additional covariates, this association remained statistically robust in boys (adjusted OR = 5.35; 95% CI 1.94-14.72), but not in girls (adjusted OR = 1.13; 95% CI 0.46-2.81). Sex-stratified analyses revealed a consistent dimorphism across adiposity-related outcomes. At 9 years of age, early antibiotic exposure was associated with increased odds of overweight in boys (adjusted OR = 2.19; 95% CI 1.06-4.54), whereas no significant association was detected in girls (adjusted OR = 1.20; 95% CI 0.53-2.70). Importantly, the unusually broad confidence interval surrounding the strongest association in boys (95% CI 1.94-14.72) signals substantial statistical imprecision, likely reflecting model instability. In the cohort, boys were significantly more exposed to antibiotics during the first year of life (p = 0.03) than girls (Azad et al., 2014), which was not taken into consideration by the authors.

Preterm neonates are subjected to repeated, often prolonged prophylactic antibiotic courses in neonatal intensive care units (Simeoli et al., 2022). If antibiotics were obesogenic, this group would display clear long-term weight gain. Yet follow-ups show the opposite: preterm children often have lower BMI in later childhood (Vinther et al., 2023). The most heavily antibiotic-
exposed population on Earth thus directly contradicts the hypothesis. Very recently, some stool gut microbiota were collected from 700 children in the Copenhagen Prospective Studies on Asthma in Childhood 2010 (COPSAC 2010) cohort (Poulsen et al., 2025). In this deeply characterized longitudinal birth cohort, no association between early-life microbiota and BMI in later childhood was observed. It suggests that if such associations exist, they may be influenced by later factors, including lifestyle changes. Finally, from the same COPSAC 2010 cohort, children exposed to antibiotics had similar BMI and body composition between 1 and 6 years of life, in comparison with the unexposed group (Sejersen et al., 2019).

## An emerging hypothesis

Early mouse studies such as the STAT model, used since 1976 in livestock (Moore & Evenson, 1946), showed that chronic antibiotic exposure in neonatal mice increased adiposity (Cho et al., 2012). Seminal experiments conducted over half a century ago, demonstrating that the growth-promoting effect of STAT vanishes in germ-free chicks, established an early principle: the ability of antibiotics to modulate host growth is contingent upon the presence of a gut microbiota (Coates et al., 1963). Recently, a Chinese study has reported that recruited children presented associations between overweight or obese status and veterinary antibiotics in their morning urine samples (Sun et al., 2024). It advocates that exposure to certain types of antibiotics, mainly through food and drinking water, is correlated with an increased risk of pediatric obesity.

In line with these observations, a recent cross-sectional case-control study, including 3,730 primary school students, in which 10 antibiotics in urine samples were measured by liquid chromatography-tandem mass spectrometry (Wang et al., 2024). Elevated OR of obesity were observed in children exposed to low sulfa-monomethoxine (OR = 1.91; 95% CI 1.05-3.47) or middle levels of tetracycline (OR = 1.96; 95% CI 1.05-3.66), with no association found in any high-level antibiotic exposure groups (Wang et al., 2024). While the microbiota was not investigated, these new data suggest that low- to middle-level contacts with certain environmental antibiotics may contribute to the risk of childhood obesity. This outline appears closer to the situation in animal husbandry, namely chronic exposure to low doses, and may represent the missing link in the antibiotic-obesity hypothesis, deserving further exploration.

## The missing geography

If consumption of antibiotics promoted obesity, global epidemiology should reflect it, but it does not. Antibiotic overuse is rampant in Low- and Middle-Income Countries (LMICs), where over-the-counter sales are common and regulation minimal. A systematic review has divulged that in many LMICs, 60-80% of antibiotics are obtained without prescription (Morgan et al., 2011). Yet obesity remains low in these same regions, with childhood obesity rates often below 5% (Ng et al., 2014) (Figure 2B). If antibiotics were obesogenic, one would expect obesity epidemics where antibiotic access is unregulated. This contradiction suggests that the antibiotic-obesity hypothesis captures not a biological mechanism but a socioeconomic pattern. The theory thus fails the ecological test: its predicted patterns contradict global data.

This incoherence arises from “epidemiological parochialism”—the tendency to generalize findings from high-income nations to the world at large (Steinke & Lang, 2011). Epidemiology, when disconnected from geography, becomes tautological: it explains local patterns to itself. Several analyses show that non-prescription antibiotic use is common where primary care access, regulation and surveillance are frail (Cabral et al., 2024) (Figure 2A); therefore, weak health systems are associated with higher antibiotic self-medication (over-the-counter, leftovers) (Ocan et al., 2015). In many LMICs, antibiotics are not only easily obtainable without prescription, they are actively administered by caregivers to infants and young children for common, mostly self-limiting respiratory and febrile illnesses (Zewdie et al., 2024). Recent meta-analyses estimate that roughly one third to two fifths of under-five children in such settings receive antibiotics without medical oversight (Zewdie et al., 2025).
## Share of out-of-pocket spending on healthcare, 2023

Out-of-pocket spending on healthcare as percent of total current healthcare expenditure.

A

<figure><img src="image_3.png" /></figure>

Data source: World Health Organization Global Health Expenditure Database, via World Bank (2025)

Note: "Out-of-pocket" refers to direct payments made by households to healthcare providers.

OurWorldinData.org/financing-healthcare | CC BY

## Share of children who are overweight or obese, 2023

Share of children under five years old that are defined as overweight or obese. A child is classified as overweight if their weight-for-height is more than two standard deviations from the median of the World Health Organization (WHO) Child Growth Standards.

B

<figure><img src="image_4.png" /></figure>

Data source: World Health Organization - Global Health Observatory (2025)

<caption>**Figure 2** – Comparison between the world's repartitions of poor healthcare systems (A) (Global Health Expenditure Database, 2025) and childhood overweight or obesity (B) (Global Health Observatory, 2025).</caption>

In countries with massive antibiotic consumption, the true public-health crisis is antimicrobial resistance, not obesity (Antimicrobial Resistance Collaborators, 2022). In sub-Saharan Africa and South Asia, widespread antibiotic access has fueled resistant pathogens (**Figure 3**).

363

364

365

366

367

368

369
<figure><img src="image_5.png" /><figcaption>**Figure 3** – Percentages of Escherichia coli isolates resistant to fluoroquinolones by country. Adapted from (Antimicrobial Resistance Collaborators, 2022).</figcaption></figure>

When the public meets the p-value

The 2010s witnessed a transformation in biomedical discourse: the gut microbiota became the new genetic code—a master key promising to explain every chronic condition. Within this intellectual climate, the antibiotic–obesity hypothesis required little evidence to be embraced. It satisfied an emotional need to locate modern pathology in modern disruption. The narrative resonated with cultural archetypes: a fall from microbial grace brought about by technological excess. Antibiotics, once symbols of medical triumph, were reimagined as ecological villains. This reversal appealed to both public skepticism and scientific curiosity. Once a few statistically significant studies appeared, the feedback loop between science and media took over.

Headlines such as “Antibiotics can make young children heavier” (British Broadcasting Corporation, 2012) or “Giving antibiotics to babies may lead to obesity” (Sample, 2014) transformed subtle associations into deterministic proclamations. Popular reporting rarely mentions OR values or confidence intervals. A weak increase in risk, framed as “a link to obesity”, becomes irresistible clickbait. Scientists, wary of sounding uncertain, rarely correct them. Thus was born a moralized narrative: the overuse of antibiotics as a symbol of modern hubris. The microbiome boom brought new funding streams, new journals, and new career incentives. Projects linking microbiota to noncommunicable diseases multiplied exponentially after 2010 (Sheng et al., 2022).

The silver lining: lessons worth keeping

The unintended triumph of antibiotic stewardship

Despite its empirical frailty, the antibiotic–obesity episode left behind constructive legacies—scientifically valuable, and perhaps morally instructive. The hypothesis, though exaggerated, helped renew public and professional attention to the overuse of antibiotics in early childhood. The message resonated even if the mechanism did not. Physicians became more judicious in prescribing, parents more cautious in demanding, and public-health agencies more explicit in advocating narrow-spectrum stewardship. In this sense, a flawed hypothesis served a noble function: it contributed to a cultural shift toward restraint. By associating antibiotics with
401
402

unintended harm—even mistakenly—it reinforced the broader imperative to preserve their efficacy. Antibiotic stewardship, not antibiotic guilt, should be the lasting legacy of this debate.

403

## The burst of the microbiota-metabolism field

404
405
406
407
408
409
410
411
412
413
414
415

More constructively, the controversy catalyzed a wave of legitimate research into the metabolic functions of the microbiota. Studies exploring microbial metabolites, bile acid signaling, and SCFA pathways have since deepened our understanding of host-microbe metabolic dialogue (Nicholson et al., 2012). Even if antibiotics are not causal agents of obesity, the broader question—how microbial ecosystems influence host energy balance—remains scientifically fertile. The field that emerged from this flawed premise has matured into one of the most interdisciplinary areas of modern biomedicine. Major experimental demonstrations, like neonatal low-dose antibiotic models producing lasting metabolic shifts (Cox et al., 2014), together with syntheses by leading groups (Cox & Blaser, 2015), helped to focalize attention on the microbiome as a mechanistic axis for noncommunicable diseases. Bibliometric analyses confirm a steep rise in microbiome-obesity publications after 2010, consistent with these papers acting as important catalysts for the field (Yao et al., 2018).

416

## Conclusion

417
418
419
420
421
422
423
424
425
426
427

The antibiotic-obesity hypothesis thus embodies science's paradoxical virtue: even wrong ideas can be productive. It reminds us that research progress depends not on the infallibility of hypotheses, but on our willingness to let evidence, not fashion, decide their fate. In the end, antibiotics do not make babies fat. But the idea that they might has made the scientific community leaner in its reasoning, and healthier in its habits of skepticism. New findings raise the possibility that sustained, low-to-moderate exposure to environmental antibiotics—rather than short therapeutic courses—could contribute to childhood obesity risk. This exposure profile more closely mirrors the growth-promoting antibiotic regimens historically used in animal husbandry, characterized by chronic subtherapeutic dosing. Such parallelism offers a biologically plausible, yet underexplored, framework that could help reconcile inconsistencies in the antibiotic-obesity literature and warrants systematic investigation.

428

## Acknowledgements

429
430
431
432
433

I warmly thank Dr. Daria Augustyniak (Department of Pathogen Biology and Immunology, University of Wroclaw, Poland) for her thoughtful reading of the manuscript and for her friendly suggestions that improved its clarity. I apologize to my colleagues, whose relevant work could not be cited due to space constraints. The figures have been created with miMind 8.39 (CrytoBees™, Parker, Colorado, United States of America) and Our World in Data.

434

## Funding

435

The author declares that she has received no specific funding for this study.

436

## Conflict of interest disclosure

437
438
439
440
441
442
443
444

The author declares that she complies with the PCI rule of having no financial conflicts of interest in relation to the content of the article.
Ajslev TA, Andersen CS, Gamborg M, Sørensen TIA, Jess T (2011) Childhood overweight after establishment of the gut microbiota: the role of delivery mode, pre-pregnancy weight and early administration of antibiotics. *International Journal of Obesity*, **35**, 522–529.
https://doi.org/10.1038/ijo.2011.27

Aldred KJ, Kerns RJ, Osheroff N (2014) Mechanism of quinolone action and resistance. *Biochemistry*, **53**, 1565–1574. https://doi.org/10.1021/bi5000564

Antimicrobial Resistance Collaborators (2022) Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. *Lancet (London, England)*, **399**, 629–655.
https://doi.org/10.1016/S0140-6736(21)02724-0

Azad MB, Bridgman SL, Becker AB, Kozyrskyj AL (2014) Infant antibiotic exposure and the development of childhood overweight and central adiposity. *International Journal of Obesity (2005)*, **38**, 1290–1298. https://doi.org/10.1038/ijo.2014.119

Azad MB, Owora A (2020) Is Early-Life Antibiotic Exposure Associated With Obesity in Children? *JAMA Network Open*, **3**, e1919694.
https://doi.org/10.1001/jamanetworkopen.2019.19694

Bäckhed F, Roswall J, Peng Y, Feng Q, Jia H, Kovatcheva-Datchary P, Li Y, Xia Y, Xie H, Zhong H, Khan MT, Zhang J, Li J, Xiao L, Al-Aama J, Zhang D, Lee YS, Kotowska D, Colding C, Tremaroli V, Yin Y, Bergman S, Xu X, Madsen L, Kristiansen K, Dahlgren J, Wang J (2015) Dynamics and Stabilization of the Human Gut Microbiome during the First Year of Life. *Cell Host & Microbe*, **17**, 690–703. https://doi.org/10.1016/j.chom.2015.04.004

Bailey LC, Forrest CB, Zhang P, Richards TM, Livshits A, DeRusso PA (2014) Association of antibiotics in infancy with early childhood obesity. *JAMA pediatrics*, **168**, 1063–1069.
https://doi.org/10.1001/jamapediatrics.2014.1539

BBC News (2012) Antibiotics can make young children heavier, says study. https://www.bbc.com/news/health-19341639

Bokulich NA, Chung J, Battaglia T, Henderson N, Jay M, Li H, D Lieber A, Wu F, Perez-Perez GI, Chen Y, Schweizer W, Zheng X, Contreras M, Dominguez-Bello MG, Blaser MJ (2016) Antibiotics, birth mode, and diet shape microbiome maturation during early life. *Science Translational Medicine*, **8**, 343ra82.
https://doi.org/10.1126/scitranslmed.aad7121

Bozdogan B, Appelbaum PC (2004) Oxazolidinones: activity, mode of action, and mechanism of resistance. *International Journal of Antimicrobial Agents*, **23**, 113–119.
https://doi.org/10.1016/j.ijantimicag.2003.11.003

Brito IL, Gurry T, Zhao S, Huang K, Young SK, Shea TP, Naisilisili W, Jenkins AP, Jupiter SD, Gevers D, Alm EJ (2019) Transmission of human-associated microbiota along family and social networks. *Nature Microbiology*, **4**, 964–971. https://doi.org/10.1038/s41564-019-0409-6

Brunton LL, Knollmann BC, Hilal-Dandan R (Eds.) (2018) *Goodman & Gilman's: the Pharmacological Basis of Therapeutics*, 13e. McGraw-Hill Education LLC., New York, N.Y.

Brustad N, Buchvald F, Jensen SK, Kyvsgaard JN, Vahman N, Thorsen J, Schoos A-MM, Nygaard U, Vissing N, Stokholm J, Bønnelykke K, Chawes B (2025) Burden of Infections in Early Life and Risk of Infections and Systemic Antibiotics Use in Childhood. *JAMA network open*, **8**, e2453284. https://doi.org/10.1001/jamanetworkopen.2024.53284

Cabral C, Zhang T, Oliver I, Little P, Yardley L, Lambert H (2024) Influences on use of antibiotics without prescription by the public in low- and middle-income countries: a systematic review and synthesis of qualitative evidence. *JAC-Antimicrobial Resistance*, **6**, dlae165.
https://doi.org/10.1093/jacamr/dlae165
494

Chelimo C, Camargo CA, Morton SMB, Grant CC (2020) Association of Repeated Antibiotic Exposure Up to Age 4 Years With Body Mass at Age 4.5 Years. *JAMA Network Open, 3*, e1917577. https://doi.org/10.1001/jamanetworkopen.2019.17577

495

496

497

Cho I, Yamanishi S, Cox L, Methé BA, Zavadil J, Li K, Gao Z, Mahana D, Raju K, Teitler I, Li H, Alekseyenko AV, Blaser MJ (2012) Antibiotics in early life alter the murine colonic microbiome and adiposity. *Nature, 488*, 621–626. https://doi.org/10.1038/nature11400

498

499

500

Coates ME, Fuller R, Harrison GF, Lev M, Suffolk SF (1963) A comparison of the growth of chicks in the Gustafsson germ-free apparatus and in a conventional environment, with and without dietary supplements of penicillin. *The British Journal of Nutrition, 17*, 141–150. https://doi.org/10.1079/bjn19630015

501

502

503

504

Cox LM, Blaser MJ (2015) Antibiotics in early life and obesity. *Nature reviews. Endocrinology, 11*, 182–190. https://doi.org/10.1038/nrendo.2014.210

505

506

Cox LM, Yamanishi S, Sohn J, Alekseyenko AV, Leung JM, Cho I, Kim SG, Li H, Gao Z, Mahana D, Zárate Rodriguez JG, Rogers AB, Robine N, Loke P, Blaser MJ (2014) Altering the Intestinal Microbiota during a Critical Developmental Window Has Lasting Metabolic Consequences. *Cell, 158*, 705–721. https://doi.org/10.1016/j.cell.2014.05.052

507

508

509

510

David LA, Maurice CF, Carmody RN, Gootenberg DB, Button JE, Wolfe BE, Ling AV, Devlin AS, Varma Y, Fischbach MA, Biddinger SB, Dutton RJ, Turnbaugh PJ (2014) Diet rapidly and reproducibly alters the human gut microbiome. *Nature, 505*, 559–563. https://doi.org/10.1038/nature12820

511

512

513

514

Dill-McFarland KA, Tang Z-Z, Kemis JH, Kerby RL, Chen G, Palloni A, Sorenson T, Rey FE, Herd P (2019) Close social relationships correlate with human gut microbiota composition. *Scientific Reports, 9*, 703. https://doi.org/10.1038/s41598-018-37298-9

515

516

517

Edwards DI (1993) Nitroimidazole drugs--action and resistance mechanisms. I. Mechanisms of action. *The Journal of Antimicrobial Chemotherapy, 31*, 9–20. https://doi.org/10.1093/jac/31.1.9

518

519

520

Gerber JS, Bryan M, Ross RK, Daymont C, Parks EP, Localio AR, Grundmeier RW, Stallings VA, Zaoutis TE (2016) Antibiotic Exposure During the First 6 Months of Life and Weight Gain During Childhood. *JAMA, 315*, 1258–1265. https://doi.org/10.1001/jama.2016.2395

521

522

523

Global Health Expenditure Database, via World Bank, World Health Organization (2025) Share of out-of-pocket spending on healthcare. Processed by Our World in Data.

524

525

Global Health Observatory, World Health Organization (2025) Share of children who are overweight or obese. Processed by Our World in Data.

526

527

Grimes DA, Schulz KF (2012) False alarms and pseudo-epidemics: the limitations of observational epidemiology. *Obstetrics and Gynecology, 120*, 920–927. https://doi.org/10.1097/AOG.0b013e31826af61a

528

529

530

Hsu P-C, Chen S-J (2018) Obesity and risk of urinary tract infection in young children presenting with fever. *Medicine, 97*, e13006. https://doi.org/10.1097/MD.0000000000013006

531

532

Huovinen P (2001) Resistance to trimethoprim-sulfamethoxazole. *Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America, 32*, 1608–1614. https://doi.org/10.1086/320532

533

534

535

Ioannidis JPA (2005) Why most published research findings are false. *PLoS medicine, 2*, e124. https://doi.org/10.1371/journal.pmed.0020124

536

537

Khanna D, Khanna S, Khanna P, Kahar P, Patel BM (2009) Obesity: A Chronic Low-Grade Inflammation and Its Markers. *Cureus, 14*, e22711. https://doi.org/10.7759/cureus.22711

538

539

Lehrer BJ, Mutamba G, Thure KA, Evans CD, Hersh AL, Banerjee R, Katz SE (2024) Optimal Pediatric Outpatient Antibiotic Prescribing. *JAMA Network Open, 7*, e2437409. https://doi.org/10.1001/jamanetworkopen.2024.37409

540

541

542

Leong KSW, McLay J, Derraik JGB, Gibb S, Shackleton N, Taylor RW, Glover M, Audas R, Taylor B, Milne BJ, Cutfield WS (2020) Associations of Prenatal and Childhood Antibiotic

543
544

Exposure With Obesity at Age 4 Years. *JAMA network open*, **3**, e1919681.

545

https://doi.org/10.1001/jamanetworkopen.2019.19681

546

Levine DP (2006) Vancomycin: a history. *Clinical Infectious Diseases: An Official Publication of*

547

the *Infectious Diseases Society of America*, **42** **Suppl 1**, S5-12.

548

https://doi.org/10.1086/491709

549

Miller SA, Wu RKS, Oremus M (2018) The association between antibiotic use in infancy and

550

childhood overweight or obesity: a systematic review and meta-analysis. *Obesity*

551

Reviews: An Official Journal of the International Association for the Study of Obesity, **19**,

552

1463-1475. https://doi.org/10.1111/obr.12717

553

Mingeot-Leclercq M-P, Glupczynski Y, Tulkens PM (1999) Aminoglycosides: Activity and

554

Resistance. *Antimicrobial Agents and Chemotherapy*, **43**, 727-737.

555

https://doi.org/10.1128/aac.43.4.727

556

Mohsen S, Dickinson JA, Somayaji R (2020) Mise à jour sur les effets indésirables des

557

traitements antimicrobiens en pratique communautaire. *Canadian Family Physician*, **66**,

558

e228-e237.

559

Moore PR, Evenson A (1946) Use of sulfasuxidine, streptothricin, and streptomycin in nutritional

560

studies with the chick. *The Journal of Biological Chemistry*, **165**, 437-441.

561

Morgan DJ, Okeke IN, Laxminarayan R, Perencevich EN, Weisenberg S (2011) Non-prescription

562

antimicrobial use worldwide: a systematic review. *The Lancet. Infectious Diseases*, **11**,

563

692-701. https://doi.org/10.1016/S1473-3099(11)70054-8

564

Nelson ML, Levy SB (2011) The history of the tetracyclines. *Annals of the New York Academy of*

565

Sciences, **1241**, 17-32. https://doi.org/10.1111/j.1749-6632.2011.06354.x

566

Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C, Mullany EC, Biryukov S,

567

Abbafati C, Abera SF, Abraham JP, Abu-Rmeileh NME, Achoki T, AlBuhairan FS, Alemu

568

ZA, Alfonso R, Ali MK, Ali R, Guzman NA, Ammar W, Anwari P, Banerjee A, Barquera S,

569

Basu S, Bennett DA, Bhutta Z, Blore J, Cabral N, Nonato IC, Chang J-C, Chowdhury R,

570

Courville KJ, Criqui MH, Cundiff DK, Dabhadkar KC, Dandona L, Davis A, Dayama A,

571

Dharmaratne SD, Ding EL, Durrani AM, Esteghamati A, Farzadfar F, Fay DFJ, Feigin VL,

572

Flaxman A, Forouzanfar MH, Goto A, Green MA, Gupta R, Hafezi-Nejad N, Hankey GJ,

573

Harewood HC, Havmoeller R, Hay S, Hernandez L, Husseini A, Idrisov BT, Ikeda N,

574

Islami F, Jahangir E, Jassal SK, Jee SH, Jeffreys M, Jonas JB, Kabagambe EK, Khalifa

575

SEAH, Kengne AP, Khader YS, Khang Y-H, Kim D, Kimokoti RW, Kinge JM, Kokubo Y,

576

Kosen S, Kwan G, Lai T, Leinsalu M, Li Y, Liang X, Liu S, Logroscino G, Lotufo PA, Lu Y,

577

Ma J, Mainoo NK, Mensah GA, Merriman TR, Mokdad AH, Moschandreas J, Naghavi M,

578

Naheed A, Nand D, Narayan KMV, Nelson EL, Neuhouser ML, Nisar MI, Ohkubo T, Oti

579

SO, Pedroza A, Prabhakaran D, Roy N, Sampson U, Seo H, Sepanlou SG, Shibuya K,

580

Shiri R, Shiue I, Singh GM, Singh JA, Skirbekk V, Stapelberg NJC, Sturua L, Sykes BL,

581

Tobias M, Tran BX, Trasande L, Toyoshima H, van de Vijver S, Vasankari TJ, Veerman

582

JL, Velasquez-Melendez G, Vlassov VV, Vollset SE, Vos T, Wang C, Wang X,

583

Weiderpass E, Werdecker A, Wright JL, Yang YC, Yatsuya H, Yoon J, Yoon S-J, Zhao Y,

584

Zhou M, Zhu S, Lopez AD, Murray CJL, Gakidou E (2014) Global, regional, and national

585

prevalence of overweight and obesity in children and adults during 1980-2013: a

586

systematic analysis for the Global Burden of Disease Study 2013. *Lancet (London,*

587

England), **384**, 766-781. https://doi.org/10.1016/S0140-6736(14)60460-8

588

Nicholson JK, Holmes E, Kinross J, Burcelin R, Gibson G, Jia W, Pettersson S (2012) Host-gut

589

microbiota metabolic interactions. *Science (New York, N.Y.)*, **336**, 1262-1267.

590

https://doi.org/10.1126/science.1223813

591

Nobel YR, Cox LM, Kirigin FF, Bokulich NA, Yamanishi S, Teitler I, Chung J, Sohn J, Barber CM,

592

Goldfarb DS, Raju K, Abubucker S, Zhou Y, Ruiz VE, Li H, Mitreva M, Alekseyenko AV,

593

Weinstock GM, Sodergren E, Blaser MJ (2015) Metabolic and metagenomic outcomes
594

from early-life pulsed antibiotic treatment. *Nature Communications*, **6**, 7486.

595

https://doi.org/10.1038/ncomms8486

596

Ocan M, Obuku EA, Bwanga F, Akena D, Richard S, Ogwal-Okeng J, Obua C (2015) Household

597

antimicrobial self-medication: a systematic review and meta-analysis of the burden, risk

598

factors and outcomes in developing countries. *BMC Public Health*, **15**, 742.

599

https://doi.org/10.1186/s12889-015-2109-3

600

Podolsky SH (2017) Historical Perspective on the Rise and Fall and Rise of Antibiotics and

601

Human Weight Gain. *Annals of Internal Medicine*, **166**, 133-138.

602

https://doi.org/10.7326/M16-1855

603

Poulsen CE, Vinding R, Rasmussen MA, Shah S, Trivedi U, Rodriguez CL, Widdowson ML,

604

Jiang J, Poulsen CS, Eliasen A, Chawes B, Bønnelykke K, Hansen CHF, Sørensen SJ,

605

Thorsen J, Stokholm J (2025) No association between the early-life gut microbiota and

606

childhood body mass index and body composition. *Med (New York, N.Y.)*, **6**, 100538.

607

https://doi.org/10.1016/j.medj.2024.10.015

608

Rasmussen SH, Shrestha S, Bjerregaard LG, Ängquist LH, Baker JL, Jess T, Allin KH (2018)

609

Antibiotic exposure in early life and childhood overweight and obesity: A systematic

610

review and meta-analysis. *Diabetes, Obesity & Metabolism*, **20**, 1508-1514.

611

https://doi.org/10.1111/dom.13230

612

Relman DA (2012) The human microbiome: ecosystem resilience and health. *Nutrition Reviews*,

613

**70 Suppl 1**, S2-9. https://doi.org/10.1111/j.1753-4887.2012.00489.x

614

Ridaura VK, Faith JJ, Rey FE, Cheng J, Duncan AE, Kau AL, Griffin NW, Lombard V, Henrissat

615

B, Bain JR, Muehlbauer MJ, Ilkayeva O, Semenkovich CF, Funai K, Hayashi DK, Lyle

616

BJ, Martini MC, Ursell LK, Clemente JC, Van Treuren W, Walters WA, Knight R,

617

Newgard CB, Heath AC, Gordon JI (2013) Gut microbiota from twins discordant for

618

obesity modulate metabolism in mice. *Science (New York, N.Y.)*, **341**, 1241214.

619

https://doi.org/10.1126/science.1241214

620

Sackett DL, Haynes RB, Tugwell P (1985) *Clinical epidemiology: a basic science for clinical*

621

medicine. Boston, Etats-Unis d'Amérique.

622

Sample I, editor science (2014) Giving antibiotics to babies may lead to obesity, researchers

623

claim. *The Guardian*.

624

Sejersen TS, Vinding RK, Stokholm J, Chawes B, Bønnelykke K, Krakauer M, Bisgaard H (2019)

625

Antibiotic exposure in infancy and development of BMI and body composition in

626

childhood. *EClinicalMedicine*, **17**, 100209. https://doi.org/10.1016/j.eclinm.2019.10.020

627

Sheng M, Xu S, Chen W-W, Li F-Q, Zhong Y-M, Ouyang Y-X, Liao Y-L, Lai P (2022) A

628

bibliometric analysis of studies on the gut microbiota in cardiovascular disease from 2004

629

to 2022. *Frontiers in Cellular and Infection Microbiology*, **12**, 1083995.

630

https://doi.org/10.3389/fcimb.2022.1083995

631

Simeoli R, Cairoli S, Decembrino N, Campi F, Dionisi Vici C, Corona A, Goffredo BM (2022) Use

632

of Antibiotics in Preterm Newborns. *Antibiotics*, **11**, 1142.

633

https://doi.org/10.3390/antibiotics11091142

634

Steinke H, Lang Y (2011) Parochialism or self-consciousness? Internationality in medical history

635

journals 1997-2006. *Medical History*, **55**, 523-538.

636

https://doi.org/10.1017/s002572730000497x

637

Stewart CJ, Ajami NJ, O'Brien JL, Hutchinson DS, Smith DP, Wong MC, Ross MC, Lloyd RE,

638

Doddapaneni H, Metcalf GA, Muzny D, Gibbs RA, Vatanen T, Huttenhower C, Xavier RJ,

639

Rewers M, Hagopian W, Toppari J, Ziegler A-G, She J-X, Akolkar B, Lernmark A, Hyoty

640

H, Vehik K, Krischer JP, Petrosino JF (2018) Temporal development of the gut

641

microbiome in early childhood from the TEDDY study. *Nature*, **562**, 583-588.

642

https://doi.org/10.1038/s41586-018-0617-x
643

Sun H, Huang J, Zhou Y, Guo X, Jiao M, Zhu X, Tan W, Zhang W (2024) Antibiotic exposure and risk of overweight/obesity in children: a biomonitoring-based study from eastern Jiangsu, China. *Frontiers in Public Health*, **12**, 1494511. https://doi.org/10.3389/fpubh.2024.1494511

644

645

646

647

Sze MA, Schloss PD (2016) Looking for a Signal in the Noise: Revisiting Obesity and the Microbiome. *mBio*, **7**, 10.1128/mbio.01018-16. https://doi.org/10.1128/mbio.01018-16

648

649

Tenson T, Lovmar M, Ehrenberg M (2003) The mechanism of action of macrolides, lincosamides and streptogramin B reveals the nascent peptide exit path in the ribosome. *Journal of Molecular Biology*, **330**, 1005-1014. https://doi.org/10.1016/s0022-2836(03)00662-4

650

651

652

Trasande L, Blustein J, Liu M, Corwin E, Cox LM, Blaser MJ (2013) Infant antibiotic exposures and early-life body mass. *International Journal of Obesity* (2005), **37**, 16-23. https://doi.org/10.1038/ijo.2012.132

653

654

655

Vinther JL, Cadman T, Avraam D, Ekstrøm CT, Sørensen TIA, Elhakeem A, Santos AC, Pinot de Moira A, Heude B, Iñiguez C, Pizzi C, Simons E, Voerman E, Corpeleijn E, Zariouh F, Santorelli G, Inskip HM, Barros H, Carson J, Harris JR, Nader JL, Ronkainen J, Strandberg-Larsen K, Santa-Marina L, Calas L, Cederkvist L, Popovic M, Charles M-A, Welten M, Vrijheid M, Azad M, Subbarao P, Burton P, Mandhane PJ, Huang R-C, Wilson RC, Haakma S, Fernández-Barrés S, Survey S, Santos S, Tough SC, Sebert S, Moraes TJ, Salika T, Jaddoe VWV, Lawlor DA, Nybo Andersen A-M (2023) Gestational age at birth and body size from infancy through adolescence: An individual participant data meta-analysis on 253,810 singletons in 16 birth cohort studies. *PLoS medicine*, **20**, e1004036. https://doi.org/10.1371/journal.pmed.1004036

656

657

658

659

660

661

662

663

664

665

Wang Y-Q, Zhang Y, Tang W-F, Luo Z-C, Zhang Y-T, Yan C-H, Zhang J, Chen Q (2024) Environmental antibiotics exposure and childhood obesity: A cross-sectional case-control study. *Ecotoxicology and Environmental Safety*, **277**, 116339. https://doi.org/10.1016/j.ecoenv.2024.116339

666

667

668

669

Wehrli W (1983) Rifampin: mechanisms of action and resistance. *Reviews of Infectious Diseases*, **5 Suppl 3**, S407-411. https://doi.org/10.1093/clinids/5.supplement_3.s407

670

671

Yao H, Wan J-Y, Wang C-Z, Li L, Wang J, Li Y, Huang W-H, Zeng J, Wang Q, Yuan C-S (2018) Bibliometric analysis of research on the role of intestinal microbiota in obesity. *PeerJ*, **6**, e5091. https://doi.org/10.7717/peerj.5091

672

673

674

Yassour M, Vatanen T, Siljander H, Hämäläinen A-M, Härkönen T, Ryhänen SJ, Franzosa EA, Vlamakis H, Huttenhower C, Gevers D, Lander ES, Knip M, DIABIMMUNE Study Group, Xavier RJ (2016) Natural history of the infant gut microbiome and impact of antibiotic treatment on bacterial strain diversity and stability. *Science Translational Medicine*, **8**, 343ra81. https://doi.org/10.1126/scitranslmed.aad0917

675

676

677

678

679

Zewdie S, Kassa AA, Bizuneh MM, Tesfaye TC, Yayehrad AT (2025) Antibiotic use without prescription among children aged under 5 years in low- and middle-income countries: a systematic review and meta-analysis. *JAC-Antimicrobial Resistance*, **7**, dlaf093. https://doi.org/10.1093/jacamr/dlaf093

680

681

682

683

Zewdie S, Kassa AA, Yayehrad AT, Bizuneh MM, Ayenew W, Zewudie M, Mulat S, Bitew BE, Zewudie S, Meharie BG, Tesfaye TC, Zerga AA, Ayele FY, Toleha HN, Workineh BD, Bayked EM (2024) Non-prescription antibiotic use and its predictors among children in low- and middle-income countries: a systematic review and meta-analysis. *Italian Journal of Pediatrics*, **50**, 260. https://doi.org/10.1186/s13052-024-01808-5

684

685

686

687

688