Gut Microbiome and Childhood Asthma: a Mendelian Randomization Study

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Antibiotic resistome of gut microbiomes may also influence childhood asthma risk. However, the underlying causal effect remains undefined. We attempted to explore the causal association of these conditions through Mendelian randomization (MR) analysis. First, we review literatures to identify core gut microbiomes potentially associated with childhood asthma. The instrumental variables (IVs) for gut microbiome and gut microbiomes antibiotic resistome were obtained from MiBioGen consortium and a multiomics study respectively. And the genetic instruments for childhood asthma in East Asian populations and European were selected from genome-wide association studies (GWAS). We implemented Two-sample MR analysis to elucidate the effect of gut microbiome and gut microbiome antibiotic resistome on childhood asthma risk. The inverse variance weighted (IVW) was employed as the primary analysis, followed by heterogeneity and pleiotropy analysis. In the European population, within the core gut microbiomes, genus Dialister was significantly positively associated with childhood asthma risk by IVW ( OR = 1.251, 95% CI :1.016–1.539, P = 0.035). Moreover, there was a positive correlation between genus Eubacterium nodatum group ( OR = 1.12, 95% CI :1.002–1.251, P = 0.047), genus Bilophila ( OR = 1.29, 95% CI :1.046–1.581, P = 0.017) and childhood asthma risk. Conversely, genus Holdemanella ( OR = 0.82, 95% CI :0.706–0.951, P = 0.009), genus Oxalobacter ( OR = 0.84, 95% CI:0.747–0.955, P = 0.007) and genus Slackia ( OR = 0.81, 95% CI:0.655–0.996, P = 0.046) exhibited a significant negative correlation with childhood asthma risk. In the East Asian population, our analysis revealed correlations between decreased childhood asthma risk and the order Actinomycetales ( OR = 0.390, 95% CI :0.173–0.882, P = 0.024), family Actinomycetaceae ( OR = 0.391, 95% CI :0.173–0.883, P = 0.224), genus Actinomyces ( OR = 0.528, 95% CI :0.289–0.965, P = 0.038), and genus Fusicatenibacter ( OR = 0.465, 95% CI :0.230–0.938, P = 0.019). Conversely, genus Coprobacter showed a significant positive correlation with childhood asthma risk ( OR = 1.826, 95% CI :1.106–3.016, P = 0.032). Finally, there was a negative correlation between Evenness, an index representing the α-diversity of the gut antibiotic resistome, and childhood asthma risk ( OR = 0.825, 95% CI:0.684–0.994, P = 0.043). Conclusions : This study is the first to employ MR analysis to validate the association between gut microbiomes identified in literature and childhood asthma risk. We try to explore additional bacterial taxes that may be associated with childhood asthma risk. Furthermore, the present study innovatively explores the effect of the gut microbiome antibiotic resistome on the risk of pediatric asthma using MR analysis. These findings provide opportunities for early intervention on childhood asthma and offer new insights into the underlying mechanisms of childhood asthma. However, further studies are required to validate and generalize the results in future research. Gut Microbiome Gut Antibiotic Resistome Childhood Asthma Mendelian Randomization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 What is Known • Early antibiotic exposure and the subsequent disruption of the gut microbiome are linked to an increased risk of childhood asthma. It is assumed that specific gut microbiomes play a role in the development of childhood asthma. What is New: • The causal association between gut microbiome and childhood asthma risk was explored through Mendelian randomization. The effect of both the gut microbiome and its antibiotic resistome on the risk of pediatric asthma were confirmed. Introduction Asthma is an inflammation-associated chronic disease of low airways found in 5–10% of children worldwide[1; 2],which has always been a leading cause of physical and mental morbidity in the children. At present, the therapies can slow the progression of asthma to a degree, but not permanently halt. Therefore, the identification and mitigation of risk factors during the early stages of pediatric asthma bear significant clinical implications. The prospective studies focusing on childhood asthma have revealed that asthma is linked to an immature gut microbiome in early life[ 3 ]. In addition, the risk of asthma in childhood can be elevated by early antibiotic exposure and the subsequent destroyed gut microbiome of infant[4; 5]. Several observational studies have proposed a potential association between gut microbiota dysbiosis and the development of pediatric asthma. These studies have explored disparities in gut microbiome between children with asthma and control group[6; 7; 8; 9; 10; 11; 12; 13; 14]. The findings collectively suggest that gut microbiome is associated with the onset of childhood asthma. Nevertheless, partial studies have presented inconclusive outcomes concerning alterations in individual gut microbiota species among children with asthma in comparison to the non-patients. Earlier experimental studies have also indicated that the colonization of human gut microbiota can affect immune responses as observed in the humanized mouse model[13; 15]. However, establishing causal inferences is challenging in observational studies due to the inevitable presence of confounding factors. Therefore, the causal impact of gut microbiota on pediatric asthma remains unclear. On the other hand, antibiotic-resistant genes enrichment of infant gut microbiome is recognized to undergo changes after early-life antibiotic exposure[16; 17; 18]. Therefore, we propose a hypothesis that the antibiotic resistome within the gut microbiome may be associated with an elevated risk of pediatric asthma. Recent advancements made in Mendelian randomization (MR) analysis have enabled the examination of potential causal association between exposure and interest outcome using genetic variants. Analogous to randomized experiments, genetic variants are assigned randomly during the inception phase in MR methodology, so the affection from reverse causality and confounding can be reduced[ 19 ]. Large-sample single nucleotide polymorphisms (SNPs) associated with childhood asthma, gut microbiota and gut microbiomes antibiotic resistome have been identified respectively in genome-wide association studies (GWAS). This affords us an opportunity to conduct an in-depth exploration. We therefore conducted an MR study to evaluate the causal association of gut microbiota and gut microbiomes antibiotic resistome on childhood asthma. Methods Study design Our study design is shown in Fig. 1 . We first conducted a comprehensive literature review to validate core gut microbiomes potentially associated with pediatric asthma and then performed a two-sample MR to examine their association. Subsequently, we also performed mediation MR analysis to investigated additional gut microbiomes that may be associated with childhood asthma, which had not been previously addressed in the existing literature. Finally, we initiated a preliminary exploration into the causal relationship between gut microbiomes antibiotic resistome and the pediatric asthma. Our findings revealed a potential causal association between specific gut microbiota and childhood asthma. Furthermore, our research introduces novel insights into the impact of gut microbiomes antibiotic resistome on pediatric asthma. As shown in the Fig. 2 , a two-sample MR design was employed in the present study in accordance with the STROBE-MR guidelines[ 20 ]. The following three fundamental assumptions should be satisfied in order to ensure the validity of potential causal effects in MR analyses. First, genetic variants must exhibit a robust association with the exposure. Secondly, genetic variants show independence from confounding variables. Additionally, genetic variants impact the outcome solely via the exposure variable. Determining candidate gut microbiomes First, we searched for the childhood asthma-associated human gut microbiomes based on literature databases including Google Scholar and PubMed. Our search terms included “child”, “childhood”, “pediatric”, “asthma”, “gut microbiome” and “gut microbiota”. Following manual screening of the studies, candidate gut microbiomes associated with pediatric asthma, as documented in the literature, were extracted from qualifying articles. Consequently, the intersection between candidate gut microbiomes and gut microbiomes in the MiBioGen consortium ( https://www.mibiogen.org ) were termed as core gut microbiomes. Genetic instruments for exposures The exposure factors considered in the present study encompass gut microbiomes and gut microbiomes antibiotic resistome. The GWAS summary data for gut microbiota were derived from the most extensive genome-wide meta-analysis on gut microbiota composition, which conducted by MiBioGen consortium. The study comprised 18,340 individuals across 24 cohorts, with the majority having European ancestry. In the study, the lowest taxonomic level was genus, identifying 131 genera with an average abundance exceeding 1%. And the host genetic variants, which correlated with genetic loci linked to the abundance levels of bacterial taxa in the gut microbiota, were identified by Microbiota quantitative trait loci mapping analysis[21; 22]. Excluding 15 taxa with unidentified name, a total of 196 bacterial taxa, including 9 phylum, 16 class, 20 order, 32 family and 119 genus, were incorporated into the present study for further analysis. The GWAS summary data for the gut microbiomes antibiotic resistome were obtained from a multiomics study which extensively profiled the metagenomic landscape of gut antibiotic resistome in a sizable human cohort (n = 1210)[ 21 ]. A total of 4 gut antibiotic resistance genes (ARG) types including MLS_ermX, Multidrug_emrE, Quinolone_norB, and Vancomycin_vanX were extracted from this study. Additionally, three indices characterizing the α-diversity of gut antibiotic resistome, including Shannon, Evenness, and Richness were also obtained from the research. Genetic instruments for Childhood Asthma First, the genetic variant dataset related to East Asian childhood asthma was sourced from the GWAS summary statistics of the BioBank Japan Project (BBJ, https://pheweb.jp/ ). BBJ is a prospective genome biobank that gathered DNA and serum samples from around 260,000 participants, primarily of Japanese descent[ 23 ]. In addition, the data on the European childhood asthma was obtained from the FinnGen study ( https://r8.finngen.fi ) by ICD-10 codes J45 and J46, and only cases with an age below 16 were included in the diagnosis of childhood asthma. FinnGen is a personalized medicine projects encompassing genome and health data derived from 377,277 Finnish biobank participants, including 210,870 females and 166,407 males[ 24 ]. Details of all genetic instruments in this study are summarized in Table 1 . Table 1 Characteristics of Datasets for Analyses Phenotype Cases Controls Population Data source Gut microbial 18,340 Cross-population (including United States, South Korea, Canada, Israel, Germany, Denmark, the Netherlands, Belgium, Sweden, Finland and the United Kingdom) MiBioGen Gut microbiomes antibiotic resistome 1210 East Asian Multiomics study[ 21 ] Childhood Asthma 547 161803 East Asian BBJ 6010 51577 European FinnGen Instrument selection The instrumental variables (IVs) for gut microbiota and gut microbiomes antibiotic resistome utilizing a less stringent significance cutoff at p < 1×10 − 5 [25; 26], which was deemed optimal due to the higher average variance observed for the same microbiome features within the 500FG cohort[ 25 ].Further, the linkage disequilibrium (LD) between the SNPs was calculated using data from the European samples within the 1000 Genomes project as the reference panel, and only the independent SNPs exhibiting r2 10,000 kb) were retained. Subsequently, palindromic SNPs were excluded for their potential ambiguity in targeted alleles. The comprehensive details regarding the included IVs are listed in supplementary Table 1. Statistical analysis Two-sample MR analysis was conducted separately to estimate the potential effects of gut microbiomes and gut microbiomes antibiotic resistome on childhood asthma based on at least 4 SNPs with TwoSampleMR package in R 4.3.0 ( https://www.r-project.org/ ). The inverse-variant weight (IVW) MR method, which can provide the greatest statistical power based on a random effect model when all genetic variants are valid instruments, was used as the main analysis[ 27 ]. In addition, heterogeneity among the instruments were evaluated the Cochrane’s Q statistics for IVW. And the intercept term from MR-Egger method was used to directional pleiotropy assess the presence of directional pleiotropy. A non-zero value indicates the existence of directional pleiotropy and potential bias in the IVW estimate. A nominal p-value of 0.05 was regarded as statistically significant here. Results Identification of core gut microbiomes 33 candidate gut microbiomes associated with the onset of childhood asthma were extracted from literature (Table 2 ). There are 20 overlapping microbiomes, namely core gut microbiomes, including Lactobacillus, Faecalibacterium, Akkermansia, Lachnospira, Veillonella, Roseburia, Blautia, Parabacteroides, Clostridiales, Clostridiaceae1, Firmicutes, Streptococcus, Oscillospira, Lachnospiraceae, Alistipes, Flavonifractor, Rikenellaceae, Dialister, Collinsella and Dorea between candidate microbiomes and Mibiogen (Fig. 3 ). Previous researches show that there is a positive correlation between Firmicutes and pediatric asthma, and there is also a negative correlation between Lactobacillus, Akkermansia, Ruminococcus 1, Ruminococcus 2, Clostridiales, Clostridiaceae 1, Alistipes, Rikenellaceae, Dialister, Collinsella, Dorea and childhood asthma. However, various studies in the literature exploring the relationship between Bifidobacterium, Faecalibacterium, Veillonella, Roseburia, Lachnospiraceae, Flavonifractor and childhood asthma have yielded different results. Table 2 Core Gut Microbiomes Associated with Childhood Asthma Gut microbiome Correlation Contributor Bifidobacterium Negative Cristina Garcia-Maurino Alcazar[ 6 ], Kei E Fujimura[ 11 ] Positive Cristina Garcia-Maurino Alcazar[ 6 ], Marie-Claire Arrieta[7; 8], Jakob Stokholm[ 14 ] Lactobacillus Negative Cristina Garcia-Maurino Alcazar[ 6 ], Kei E Fujimura[ 11 ] Faecalibacterium Negative Cristina Garcia-Maurino Alcazar[ 6 ], Kei E Fujimura[ 11 ], Jakob Stokholm[ 14 ] Positive Cristina Garcia-Maurino Alcazar[ 6 ], Marie-Claire Arrieta[ 8 ], Rozlyn C T Boutin[ 10 ], David M Patrick[ 12 ] Akkermansia Negative Cristina Garcia-Maurino Alcazar[ 6 ], Kei E Fujimura[ 11 ] Malassezia Negative Cristina Garcia-Maurino Alcazar[ 6 ], Kei E Fujimura[ 11 ] Candida Negative Cristina Garcia-Maurino Alcazar[ 6 ], Kei E Fujimura[ 11 ] Rhodotorula Negative Cristina Garcia-Maurino Alcazar[ 6 ], Kei E Fujimura[ 11 ] Lachnospira Positive Cristina Garcia-Maurino Alcazar[ 6 ], Marie-Claire Arrieta[ 8 ], Rozlyn C T Boutin[ 10 ] Negative Cristina Garcia-Maurino Alcazar[ 6 ], Marie-Claire Arrieta[ 8 ], Leah T Stiemsma[ 13 ] Rothia Negative Cristina Garcia-Maurino Alcazar[ 6 ], Marie-Claire Arrieta[ 8 ], Leah T Stiemsma[ 13 ] Veillonella Negative Cristina Garcia-Maurino Alcazar[ 6 ], Marie-Claire Arrieta[ 8 ], Jakob Stokholm[ 14 ] Positive Cristina Garcia-Maurino Alcazar[ 6 ], Marie-Claire Arrieta[ 7 ], Marie-Claire Arrieta[ 8 ], Leah T Stiemsma[ 13 ] Peptostreptococcus Negative Cristina Garcia-Maurino Alcazar[ 6 ], Marie-Claire Arrieta[ 8 ] Coprococcus Negative Cristina Garcia-Maurino Alcazar[ 6 ], Rozlyn C T Boutin[ 10 ] Roseburia Negative Cristina Garcia-Maurino Alcazar[ 6 ], Jakob Stokholm[ 14 ], Rozlyn C T Boutin[ 10 ], David M Patrick[ 12 ], Leah T Stiemsma[ 13 ] Positive Jakob Stokholm[ 14 ] Blautia Negative Cristina Garcia-Maurino Alcazar[ 6 ], Rozlyn C T Boutin[ 10 ] Parabacteroides Positive Cristina Garcia-Maurino Alcazar[ 6 ], Rozlyn C T Boutin[ 10 ] Ruminococcus 1 Negative Cristina Garcia-Maurino Alcazar[ 6 ], Jakob Stokholm[ 14 ], Rozlyn C T Boutin[ 10 ], David M Patrick[ 12 ] Ruminococcus 2 Negative Cristina Garcia-Maurino Alcazar[ 6 ], Jakob Stokholm[ 14 ], Rozlyn C T Boutin[ 10 ], David M Patrick[ 12 ] Clostridiales Negative Cristina Garcia-Maurino Alcazar[ 6 ], Leah T Stiemsma[ 13 ] Clostridium neonatale Negative Cristina Garcia-Maurino Alcazar[ 6 ], Leah T Stiemsma[ 13 ] Clostridiaceae 1 Negative Cristina Garcia-Maurino Alcazar[ 6 ], Leah T Stiemsma[ 13 ] Firmicutes Positive Cristina Garcia-Maurino Alcazar[ 6 ], Leah T Stiemsma[ 13 ] Streptococcus Negative Cristina Garcia-Maurino Alcazar[ 6 ], Marie-Claire Arrieta[ 7 ] Pichia kudriavzevii Negative Cristina Garcia-Maurino Alcazar[ 6 ], Marie-Claire Arrieta[ 7 ] Oscillospira Negative Cristina Garcia-Maurino Alcazar[ 6 ], Marie-Claire Arrieta[ 7 ] Lachnospiraceae Negative Cristina Garcia-Maurino Alcazar[ 6 ], Leah T Stiemsma[ 13 ] Positive Jakob Stokholm[ 14 ] Alistipes Negative Cristina Garcia-Maurino Alcazar[ 6 ], Jakob Stokholm[ 14 ], Leah T Stiemsma[ 13 ] Flavonifractor Positive Cristina Garcia-Maurino Alcazar[ 6 ], Leah T Stiemsma[ 13 ] Negative Jakob Stokholm[ 14 ] Faecalibacterium prausnitzii Positive Cristina Garcia-Maurino Alcazar[ 6 ], David M Patrick[ 12 ] Ruminococcus bromii Negative Cristina Garcia-Maurino Alcazar[ 6 ], David M Patrick[ 12 ] Rikenellaceae Negative Cristina Garcia-Maurino Alcazar[ 6 ], David M Patrick[ 12 ] Dialister Negative Cristina Garcia-Maurino Alcazar[ 6 ], Jakob Stokholm[ 14 ], David M Patrick[ 12 ] Gemmiger Negative Michiel A. G. E. Bannier[ 9 ] Escherichia Negative Michiel A. G. E. Bannier[ 9 ] Collinsella Negative Michiel A. G. E. Bannier[ 9 ] Dorea Negative Michiel A. G. E. Bannier[ 9 ] Effect of gut microbiomes on childhood asthma risk in East Asians Regrettably, we did not detect significant causal associations between core gut microbiomes and the risk of childhood asthma (Fig. 4 A, B). However, among non-core gut microbiomes, the result of IVW displayed the possible causal effects of order Actinomycetales ( OR = 0.390, 95% CI :0.173–0.882, P = 0.024) (Fig. 4 E), family Actinomycetaceae ( OR = 0.391, 95% CI :0.173–0.883, P = 0.224) (Fig. 4 F), genus Actinomyces ( OR = 0.528, 95% CI :0.289–0.965, P = 0.038) and genus Fusicatenibacter ( OR = 0.465, 95% CI :0.230–0.938, P = 0.019) (Fig. 4 G) on decreased childhood asthma risk (Fig. 4 B). Although results of supplementary models including MR-Egger, Weighted median, Simple mode and Weighted mode, were not statistically significant, the direction of effect remained consistent with IVW models (OR < 1). On the contrary, the genus Coprobacter had a significant positive correlation with the risk of childhood asthma ( OR = 1.826, 95% CI :1.106–3.016, P = 0.032) (Fig. 4 G). The results of other models were not significant, but they consistently indicated a positive association between the genus Coprobacter and childhood asthma (OR > 1). The aforementioned results were unaffected by heterogeneity or horizontal pleiotropy. No additional significant causal associations between other gut microbiomes and the risk of childhood asthma were found (Fig. 4 C-G). All results of tests for heterogeneity and horizontal pleiotropy are reported in the Supplementary Table 2. Effect of gut microbiomes on childhood asthma risk in European population Unlike previous studies[6; 12; 14], the results of the IVW analysis showed that the genus Dialister significantly increased the risk of childhood asthma ( OR = 1.251, 95% CI :1.016–1.539, P = 0.035). According to the results of weighted median model, the genus Dialister exhibited significant positive causal association with childhood asthma risk ( OR = 1.325, 95% CI :1.004–1.749, P = 0.046), aligning with the findings of the IVW analysis. The results of simple mode ( OR = 1.396, 95% CI :0.882–2.229, P = 0.185), and weighted mode models ( OR = 1.402, 95% CI :0.857–2.293, P = 0.209) were not significant, but they still indicated a positive association between the genus Dialister and childhood asthma (OR > 1). We did not detect that other core gut microbiomes were significantly causally associated with the childhood asthma risk (Fig. 5 A). At the genus biological level of non-core gut microbiota, based on the MR analysis results between non-core gut microbiomes and pediatric asthma, the IVW models indicated that genus Eubacterium nodatum group ( OR = 1.12, 95% CI :1.002–1.251, P = 0.047) and genus Bilophila ( OR = 1.29, 95% CI :1.046–1.581, P = 0.017) had a significant positive correlation with childhood asthma. On the other hand, there was a negative correlation between genus Holdemanella ( OR = 0.82, 95% CI :0.706–0.951, P = 0.009), genus Oxalobacter ( OR = 0.84, 95% CI:0.747–0.955, P = 0.007) and genus Slackia ( OR = 0.81, 95% CI:0.655–0.996, P = 0.046) and childhood asthma. The MR estimates from supplementary models consistently supported their negative effect on childhood asthma (Fig. 5 B). The aforementioned results were unaffected by heterogeneity or horizontal pleiotropy. No further significant causal associations between the remaining non-core gut microbiota and the risk of childhood asthma could be identified (Fig. 5 C-G). All results of tests for heterogeneity and horizontal pleiotropy are reported in the Supplementary Table 3. Effect of gut microbiomes antibiotic resistome on childhood asthma risk To further delve into the effect of gut microbiome resistome on childhood asthma, MR analyses were conducted in East Asians. The results of the IVW analysis showed that the Pielou's index Evenness, one of the diversity indices, exhibited significant causal associations with the reduced risk of childhood asthma ( OR = 0.825, 95% CI:0.684–0.994, P = 0.043). The results of the weighted median ( OR = 0.866, 95% CI :0.670–1.120, P = 0.274), simple mode ( OR = 0.892, 95% CI :0.608–1.307, P = 0.571), and weighted mode models ( OR = 0.889, 95% CI :0.603–1.309, P = 0.564) were not statistically significant. However, their effect directions were consistent with the IVW results (OR < 1). This result was unaffected by heterogeneity or horizontal pleiotropy. Ultimately, we did not detect that the other diversity indices (including Richness and Shannon) and 4 ARGs (including MLS_ermX, Multidrug_emrE, Quinolone_norB and Vancomycin_vanX) were significantly causally associated with the childhood asthma risk (Fig. 6 ). All results of tests for heterogeneity and horizontal pleiotropy are reported in the Supplementary Table 4. Discussion Asthma is one of the most significant and prevalent chronic diseases to address during childhood, given its potential impact on learning, growth, and psychology. In research, the aberrant gut microbiota composition and antibiotic use in early life have been proposed to be the potential risk factors for asthma. However, only a very limited number of gut bacterial taxa associated with childhood asthma were identified, and there was no evidence for a link between the antibiotic resistome of gut microbiomes and pediatric asthma. Therefore, we have sought to uncover the causal effect of gut microbiomes and their antibiotic resistome on risk of childhood asthma by MR analyses. Here, we provide insight into the role of gut microbiomes in the risk of childhood asthma from the perspective of bacterial entities and resistome. First, the total 33 gut microbiomes that have been previously reported to be associated with childhood asthma were identified on the basis of the outcome of comprehensive literature search. Furthermore, among 33 candidate gut microbiomes identified, the single nucleotide polymorphisms of the 20 were retrieved from the MiBioGen consortium. And then, MR methods were employed to identify the causal relationship between gut microbiota and the childhood asthma risk in both European and East Asian populations utilizing GWAS summary statistics. The study findings indicated that gut microbiota might be causally associated with the risk of childhood asthma in the European population and East Asians. However, the specific gut microbiota influencing the risk of childhood asthma differs between European and Asian populations. For East Asian populations, unlike previous studies, no significant causal association was observed between any core gut microbiomes and the risk of childhood asthma. However, the study results identified that the order Actinomycetales, family Actinomycetaceae, genus Actinomyces and genus Fusicatenibacter might be genetically predicted to be causally associated with a lower risk of childhood asthma. Conversely, genus Coprobacter showed a positive correlation with the childhood asthma risk. Previous research has suggested a potential correlation between the order Actinomycetales of infants' homes and a reduced risk of asthma. The findings of this study indicated an association between the abundance of 12 bacterial genera and a decreased risk of asthma, notably with 7 bacterial genera originating from the order Actinomycetales [ 28 ]. And in contrast to the outcomes of our study, a clinical research revealed a positive association between the abundance of Actinomycetaceae and asthma[ 29 ]. Actinomyces has been reported as a significant genus with negative associations with adverse correlations to serum IgE levels and genes associated with bronchial inflammatory responses[ 30 ]. Therefore, genus Actinomyces may be a protective factor against asthma. In a murine model of ulcerative colitis, the genus Fusicatenibacter exerted anti-inflammatory effects by stimulating the production of the IL-10 in lamina propria cells[ 31 ]. However, the relationship between Coprobacter and asthma requires further investigation. In European populations, contrary to previous findings, the results of MR analyses indicate that a positive relationship exists between the genus Dialister and pediatric asthma possibly. A longitudinal flux balance analysis reveals that the inflammatory responses may be triggered by Dialister genus through the releasing of L-serine and formate and cooperating with pathogenic strains[ 32 ]. And by production of lipopolysaccharide, the genus Dialister can also aggravate host inflammatory response [ 33 ]. Furthermore, some negative health outcomes, such as type 2 diabetes mellitus[ 34 ], inflammatory bowel disease[ 35 ] and high risk of colorectal cancer progression[ 36 ], have been linked to the genera Dialister in previous research. Additionally, exposure to ambient air pollutants from birth to 6-months is positively associated with the Dialister in infancy[ 37 ]. It has been widely proven that exposure to air pollution early in life was positive associated with an increased risk of asthma[38; 39]. Hence, we speculate that the genus Dialister appears to be influenced by air pollution and may in turn have influence on the risk of childhood asthma. In general, the genus Dialister was suggested as one of the impact factors for pediatric asthma, but further study is certainly warranted. The results of the further MR analyses showed that genus Eubacterium nodatum group and genus Bilophila were positively correlated with the risk of childhood asthma. On the other hand, genus Holdemanella, genus Oxalobacter and genus Slackia were associated with the decreased risk of childhood asthma. Bilophila can degrade of inflammation inhibitory factor such as butyrate, and facilitate the expression of inflammatory components such as microbe-associated molecular pattern and pathogen-associated molecular pattern factors [40; 41]. Although Holdemanella has not been associated with asthma previously, it was recently reported to exhibit significant positive correlations with IL-6 and TNF-α[ 42 ]. Hence, the Bilophila and Holdemanella may exert pro-inflammatory effects potentially.In addition, the Slackia can modulate bile acid and lipid metabolism, which may influence host homeostasis. And Oxalobacter carries out important functions such as metabolizing oxalate in the intestinal tract and is thought to be the center of the oxalate-metabolizing microbial network[43; 44]. Nevertheless, the potential contribution of Eubacterium nodatum group, Oxalobacter and Slackia on asthma remains unclear. The relationship between the antibiotic resistome of gut microbiomes and childhood asthma warrants further investigation. The antibiotic usage might impact the diversity of the gut antibiotic resistome, potentially leading to a lower maturity of the gut microbiota[18; 45]. Low gut maturity in early life may elevate the risk of pediatric asthma[ 14 ]. As an index characterizing the α-diversity of the gut antibiotic resistome, Evenness showed a negative correlation with childhood asthma risk in the present study, but further research is needed to validate this finding. There is no conclusive evidence regarding the association between ARG and the risk of childhood asthma from MR analyses. However, this research direction is intriguing and requires future research using more comprehensive genome-wide association analysis data about antibiotic resistance of human gut microbiota. However, the present study has certain limitations and drawbacks. Participants of the GWAS about gut microbiomes and their antibiotic resistome may include not just the children of the same race, which can lead to unstable results. We acknowledge there is a risk of participants discrepancy between exposure GWAS and outcome GWAS leading to consequence with limited extrapolability. Indeed, we have observed significant associations between several gut microbiomes and childhood asthma, but further studies based on larger GWAS are need for the generalizability of the results in the future. Conclusion In conclusion, consistent with previous research, our study has confirmed that genus Dialister may participate in onset of childhood asthma. Meanwhile, several potential pediatric asthma-related gut microbiomes have been proposed. However, European and East Asian populations exhibit different gut microbiota. Additionally, the α-diversity of the gut antibiotic resistome was also found to be associated with the risk of childhood asthma. Special attention should be paid to these gut microbiomes and antibiotic usage in the following research targeting prevention and treatment of childhood asthma. However, further studies are need for the generalizability of the results in the future. Abbreviations MR Mendelian randomization IVs instrumental variables GWAS genome-wide association studies IVW inverse variance weighted SNPs single nucleotide polymorphisms ARG antibiotic resistance genes BBJ BioBank Japan Project Declarations Human Ethics, Animal Ethics, and Consent to Participate declarations: not applicable. Author Contribution Zhiwei Zheng contributed to conceptualization and design of the study. Yongmao Zhou and Pan Chen downloaded and organized the data. Zhiwei Zheng, Ganghua Huang and Baofei Li performed the Mendelian Randomization analysis and visualizationZhiwei Zheng wrote the original draft of the manuscript. Yongmao Zhou, Pan Chen, Qinhai Huang and Baofei Li wrote sections of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version. References van Beveren GJ, Said H, van Houten MA, Bogaert D. The respiratory microbiome in childhood asthma. J Allergy Clin Immunol (2023). Pedersen M, Liu S, Zhang J, Jovanovic Andersen Z, Brandt J, Budtz-Jørgensen E, Bønnelykke K, Frohn LM, Nybo Andersen AM, Ketzel M, Khan J, Stayner L, Brunekreef B, Loft S. Early-Life Exposure to Ambient Air Pollution from Multiple Sources and Asthma Incidence in Children: A Nationwide Birth Cohort Study from Denmark. Environ Health Perspect. 2023;131:57003. Dai H, Hou T, Wang Q, Hou Y, Zhu Z, Zhu Y, Zhao Z, Li M, Lin H, Wang S, Zheng R, Xu Y, Lu J, Wang T, Ning G, Wang W, Zheng J, Bi Y, Xu M. Roles of gut microbiota in atrial fibrillation: insights from Mendelian randomization analysis and genetic data from over 430,000 cohort study participants. Cardiovasc Diabetol. 2023;22:306. Dai DLY, Petersen C, Hoskinson C, Del Bel KL, Becker AB, Moraes TJ, Mandhane PJ, Finlay BB, Simons E, Kozyrskyj AL, Patrick DM, Subbarao P, Bode L, Azad MB, Turvey SE. Breastfeeding enrichment of B. longum subsp. infantis mitigates the effect of antibiotics on the microbiota and childhood asthma risk. Med (New York, N.Y.) 4 (2023) 92–112e5. Rantala AK, Tapia G, Magnus MC, Stene LC, Jaakkola JJK, Størdal K, Karlstad Ø, Nystad W. Maternal antibiotic use and infections during pregnancy and offspring asthma: the Norwegian Mother, Father and Child Cohort Study and a nationwide register cohort. Eur J Epidemiol. 2022;37:983–92. Alcazar CG, Paes VM, Shao Y, Oesser C, Miltz A, Lawley TD, Brocklehurst P, Rodger A, Field N. The association between early-life gut microbiota and childhood respiratory diseases: a systematic review. Lancet Microbe. 2022;3:e867–80. Arrieta MC, Arévalo A, Stiemsma L, Dimitriu P, Chico ME, Loor S, Vaca M, Boutin RCT, Morien E, Jin M, Turvey SE, Walter J, Parfrey LW, Cooper PJ, Finlay B. Associations between infant fungal and bacterial dysbiosis and childhood atopic wheeze in a nonindustrialized setting. J Allergy Clin Immunol. 2018;142:424–434e10. Arrieta MC, Stiemsma LT, Dimitriu PA, Thorson L, Russell S, Yurist-Doutsch S, Kuzeljevic B, Gold MJ, Britton HM, Lefebvre DL, Subbarao P, Mandhane P, Becker A, McNagny KM, Sears MR, Kollmann T, Mohn WW, Turvey SE, Finlay BB. Early infancy microbial and metabolic alterations affect risk of childhood asthma. Sci Transl Med. 2015;7:307ra152. Bannier M, van Best N, Bervoets L, Savelkoul PHM, Hornef MW, van de Kant KDG, Jöbsis Q, Dompeling E, Penders J. Gut microbiota in wheezing preschool children and the association with childhood asthma. Allergy. 2020;75:1473–6. Boutin RCT, Sbihi H, Dsouza M, Malhotra R, Petersen C, Dai D, Sears MR, Moraes TJ, Becker AB, Azad MB, Mandhane PJ, Subbarao P, Finlay BB, Turvey SE. Mining the infant gut microbiota for therapeutic targets against atopic disease. Allergy. 2020;75:2065–8. Fujimura KE, Sitarik AR, Havstad S, Lin DL, Levan S, Fadrosh D, Panzer AR, LaMere B, Rackaityte E, Lukacs NW, Wegienka G, Boushey HA, Ownby DR, Zoratti EM, Levin AM, Johnson CC, Lynch SV. Neonatal gut microbiota associates with childhood multisensitized atopy and T cell differentiation. Nat Med. 2016;22:1187–91. Patrick DM, Sbihi H, Dai DLY, Al Mamun A, Rasali D, Rose C, Marra F, Boutin RCT, Petersen C, Stiemsma LT, Winsor GL, Brinkman FSL, Kozyrskyj AL, Azad MB, Becker AB, Mandhane PJ, Moraes TJ, Sears MR, Subbarao P, Finlay BB, Turvey SE. Decreasing antibiotic use, the gut microbiota, and asthma incidence in children: evidence from population-based and prospective cohort studies. Lancet Respir Med. 2020;8:1094–105. Stiemsma LT, Arrieta MC, Dimitriu PA, Cheng J, Thorson L, Lefebvre DL, Azad MB, Subbarao P, Mandhane P, Becker A, Sears MR, Kollmann TR, Mohn WW, Finlay BB, Turvey SE. Shifts in Lachnospira and Clostridium sp. in the 3-month stool microbiome are associated with preschool age asthma. Clin Sci (Lond). 2016;130:2199–207. Stokholm J, Blaser MJ, Thorsen J, Rasmussen MA, Waage J, Vinding RK, Schoos AM, Kunøe A, Fink NR, Chawes BL, Bønnelykke K, Brejnrod AD, Mortensen MS, Al-Soud WA, Sørensen SJ, Bisgaard H. Maturation of the gut microbiome and risk of asthma in childhood. Nat Commun. 2018;9:141. Wilson NG, Hernandez-Leyva A, Rosen AL, Jaeger N, McDonough RT, Santiago-Borges J, Lint MA, Rosen TR, Tomera CP, Bacharier LB, Swamidass SJ, Kau AL. The gut microbiota of people with asthma influences lung inflammation in gnotobiotic mice. iScience. 2023;26:105991. Samarra A, Esteban-Torres M, Cabrera-Rubio R, Bernabeu M, Arboleya S, Gueimonde M, Collado MC. Maternal-infant antibiotic resistance genes transference: what do we know? Gut Microbes. 2023;15:2194797. Nogacka AM, Salazar N, Arboleya S, Suárez M, Fernández N, Solís G, de Reyes-Gavilán CG, Gueimonde M. Early microbiota, antibiotics and health. Cell Mol Life Sci. 2018;75:83–91. Li X, Stokholm J, Brejnrod A, Vestergaard GA, Russel J, Trivedi U, Thorsen J, Gupta S, Hjelmsø MH, Shah SA, Rasmussen MA, Bisgaard H, Sørensen SJ. The infant gut resistome associates with E. coli, environmental exposures, gut microbiome maturity, and asthma-associated bacterial composition. Cell Host Microbe. 2021;29:975–987e4. Burgess S, Timpson NJ, Ebrahim S, Davey Smith G. Mendelian randomization: where are we now and where are we going? Int J Epidemiol. 2015;44:379–88. Skrivankova VW, Richmond RC, Woolf BAR, Yarmolinsky J, Davies NM, Swanson SA, VanderWeele TJ, Higgins JPT, Timpson NJ, Dimou N, Langenberg C, Golub RM, Loder EW, Gallo V, Tybjaerg-Hansen A, Davey Smith G, Egger M, Richards JB. Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement. JAMA. 2021;326:1614–21. Shuai M, Zhang G, Zeng FF, Fu Y, Liang X, Yuan L, Xu F, Gou W, Miao Z, Jiang Z, Wang JT, Zhuo LB, Chen YM, Ju F, Zheng JS. Human Gut Antibiotic Resistome and Progression of Diabetes. Advanced science (Weinheim, Baden-Wurttemberg, Germany) 9 (2022) e2104965. Xiang Y, Zhang C, Wang J, Cheng Y, Wang L, Tong Y, Yan D. Identification of host gene-microbiome associations in colorectal cancer patients using mendelian randomization. J translational Med. 2023;21:535. Sakaue S, Kanai M, Tanigawa Y, Karjalainen J, Kurki M, Koshiba S, Narita A, Konuma T, Yamamoto K, Akiyama M, Ishigaki K, Suzuki A, Suzuki K, Obara W, Yamaji K, Takahashi K, Asai S, Takahashi Y, Suzuki T, Shinozaki N, Yamaguchi H, Minami S, Murayama S, Yoshimori K, Nagayama S, Obata D, Higashiyama M, Masumoto A, Koretsune Y, Ito K, Terao C, Yamauchi T, Komuro I, Kadowaki T, Tamiya G, Yamamoto M, Nakamura Y, Kubo M, Murakami Y, Yamamoto K, Kamatani Y, Palotie A, Rivas MA, Daly MJ, Matsuda K, Okada Y. A cross-population atlas of genetic associations for 220 human phenotypes. Nat Genet. 2021;53:1415–24. Kurki MI, Karjalainen J, Palta P, Sipilä TP, Kristiansson K, Donner KM, Reeve MP, Laivuori H, Aavikko M, Kaunisto MA, Loukola A, Lahtela E, Mattsson H, Laiho P, Della Briotta Parolo P, Lehisto AA, Kanai M, Mars N, Rämö J, Kiiskinen T, Heyne HO, Veerapen K, Rüeger S, Lemmelä S, Zhou W, Ruotsalainen S, Pärn K, Hiekkalinna T, Koskelainen S, Paajanen T, Llorens V, Gracia-Tabuenca J, Siirtola H, Reis K, Elnahas AG, Sun B, Foley CN, Aalto-Setälä K, Alasoo K, Arvas M, Auro K, Biswas S, Bizaki-Vallaskangas A, Carpen O, Chen CY, Dada OA, Ding Z, Ehm MG, Eklund K, Färkkilä M, Finucane H, Ganna A, Ghazal A, Graham RR, Green EM, Hakanen A, Hautalahti M, Hedman K, Hiltunen ÅM, Hinttala R, Hovatta I, Hu X, Huertas-Vazquez A, Huilaja L, Hunkapiller J, Jacob H, Jensen JN, Joensuu H, John S, Julkunen V, Jung M, Junttila J, Kaarniranta K, Kähönen M, Kajanne R, Kallio L, Kälviäinen R, Kaprio J, Kerimov N, Kettunen J, Kilpeläinen E, Kilpi T, Klinger K, Kosma VM, Kuopio T, Kurra V, Laisk T, Laukkanen J, Lawless N, Liu A, Longerich S, Mägi R, Mäkelä J, Mäkitie A, Malarstig A, Mannermaa A et al. J. Maranville, A. Matakidou, T. Meretoja, S.V. Mozaffari, FinnGen provides genetic insights from a well-phenotyped isolated population. Nature 613 (2023) 508–518. Sanna S, van Zuydam NR, Mahajan A, Kurilshikov A, Vich Vila A, Võsa U, Mujagic Z, Masclee AAM, Jonkers D, Oosting M, Joosten LAB, Netea MG, Franke L, Zhernakova A, Fu J, Wijmenga C, McCarthy MI. Causal relationships among the gut microbiome, short-chain fatty acids and metabolic diseases. Nat Genet. 2019;51:600–5. Xiao L, Liu S, Wu Y, Huang Y, Tao S, Liu Y, Tang Y, Xie M, Ma Q, Yin Y, Dai M, Zhang M, Llamocca E, Gui H, Wang Q. The interactions between host genome and gut microbiome increase the risk of psychiatric disorders: Mendelian randomization and biological annotation. Brain Behav Immun. 2023;113:389–400. Ruan X, Huang D, Zhan Y, Huang J, Huang J, Ng AT, Tsu JH, Na R. Risk of second primary cancers after a diagnosis of first primary cancer: A pan-cancer analysis and Mendelian randomization study. eLife 12 (2023). Karvonen AM, Kirjavainen PV, Täubel M, Jayaprakash B, Adams RI, Sordillo JE, Gold DR, Hyvärinen A, Remes S, von Mutius E, Pekkanen J. Indoor bacterial microbiota and development of asthma by 10.5 years of age. J Allergy Clin Immunol. 2019;144:1402–10. Li N, Qiu R, Yang Z, Li J, Chung KF, Zhong N, Zhang Q. Sputum microbiota in severe asthma patients: Relationship to eosinophilic inflammation. Respir Med. 2017;131:192–8. Chun Y, Do A, Grishina G, Grishin A, Fang G, Rose S, Spencer C, Vicencio A, Schadt E, Bunyavanich S. Integrative study of the upper and lower airway microbiome and transcriptome in asthma. JCI insight 5 (2020). Essex M, Rios Rodriguez V, Rademacher J, Proft F, Löber U, Markó L, Pleyer U, Strowig T, Marchand J, Kirwan JA, Siegmund B, Forslund SK. and D. Poddubnyy, Shared and Distinct Gut Microbiota in Spondyloarthritis, Acute Anterior Uveitis, and Crohn's Disease. Arthritis Rheumatol (2023). Basile A, Heinken A, Hertel J, Smarr L, Li W, Treu L, Valle G, Campanaro S, Thiele I. Longitudinal flux balance analyses of a patient with episodic colonic inflammation reveals microbiome metabolic dynamics. Gut Microbes. 2023;15:2226921. Mena-Vázquez N, Ruiz-Limón P, Moreno-Indias I, Manrique-Arija S, Lisbona-Montañez JM, Rioja J, Mucientes A, Martin-Núñez GM, Cano-García L, Tinahones FJ. Fernández-Nebro, Adiposity is associated with expansion of the genus Dialister in rheumatoid arthritis patients. Biomed pharmacotherapy = Biomedecine pharmacotherapie. 2023;160:114388. Gradisteanu Pircalabioru G, Chifiriuc MC, Picu A, Petcu LM, Trandafir M, Savu O. Snapshot into the Type-2-Diabetes-Associated Microbiome of a Romanian Cohort. Int J Mol Sci 23 (2022). Čipčić Paljetak H, Barešić A, Panek M, Perić M, Matijašić M, Lojkić I, Barišić A, Vranešić Bender D, Ljubas Kelečić D, Brinar M, Kalauz M, Miličević M, Grgić D, Turk N, Karas I, Čuković-Čavka S, Ž., Krznarić, Verbanac D. Gut microbiota in mucosa and feces of newly diagnosed, treatment-naïve adult inflammatory bowel disease and irritable bowel syndrome patients. Gut microbes 14 (2022) 2083419. Huh JW, Kim MJ, Kim J, Lee HG, Ryoo SB, Ku JL, Jeong SY, Park KJ, Kim D, Kim JF, Park JW. Enterotypical Prevotella and three novel bacterial biomarkers in preoperative stool predict the clinical outcome of colorectal cancer. Microbiome. 2022;10:203. Bailey MJ, Holzhausen EA, Morgan ZEM, Naik N, Shaffer JP, Liang D, Chang HH, Sarnat J, Sun S, Berger PK, Schmidt KA, Lurmann F, Goran MI, Alderete TL. Postnatal exposure to ambient air pollutants is associated with the composition of the infant gut microbiota at 6-months of age. Gut Microbes. 2022;14:2105096. Holst GJ, Pedersen CB, Thygesen M, Brandt J, Geels C, Bønløkke JH, Sigsgaard T. Air pollution and family related determinants of asthma onset and persistent wheezing in children: nationwide case-control study. BMJ (Clinical research ed). 2020;370:m2791. Gehring U, Wijga AH, Koppelman GH, Vonk JM, Smit HA, Brunekreef B. Air pollution and the development of asthma from birth until young adulthood. Eur Respir J 56 (2020). Gupta VK, Cunningham KY, Hur B, Bakshi U, Huang H, Warrington KJ, Taneja V, Myasoedova E, Davis JM 3rd, and, Sung J. Gut microbial determinants of clinically important improvement in patients with rheumatoid arthritis. Genome Med. 2021;13:149. Ye Z, Zhang N, Wu C, Zhang X, Wang Q, Huang X, Du L, Cao Q, Tang J, Zhou C, Hou S, He Y, Xu Q, Xiong X, Kijlstra A, Qin N, Yang P. A metagenomic study of the gut microbiome in Behcet's disease. Microbiome. 2018;6:135. Tang W, Yao X, Xia F, Yang M, Chen Z, Zhou B, Liu Q. Modulation of the Gut Microbiota in Rats by Hugan Qingzhi Tablets during the Treatment of High-Fat-Diet-Induced Nonalcoholic Fatty Liver Disease. Oxidative medicine and cellular longevity 2018 (2018) 7261619. Miller AW, Choy D, Penniston KL, Lange D. Inhibition of urinary stone disease by a multi-species bacterial network ensures healthy oxalate homeostasis. Kidney Int. 2019;96:180–8. Siener R, Bangen U, Sidhu H, Hönow R, von Unruh G, Hesse A. The role of Oxalobacter formigenes colonization in calcium oxalate stone disease. Kidney Int. 2013;83:1144–9. Lee K, Raguideau S, Sirén K, Asnicar F, Cumbo F, Hildebrand F, Segata N, Cha CJ, Quince C. Population-level impacts of antibiotic usage on the human gut microbiome. Nat Commun. 2023;14:1191. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1ThecomprehensivedetailsoftheincludedIVs.xlsx SupplementaryTable2TestsforheterogeneityandhorizontalpleiotropyintheMRanalysisofgutmicrobiomesandchildhoodasthmariskinEastAsians.xlsx SupplementaryTable3TestsforheterogeneityandhorizontalpleiotropyintheMRanalysisofgutmicrobiomesandchildhoodasthmariskinEuropeanpopulation.xlsx SupplementaryTable4TestsforheterogeneityandhorizontalpleiotropyintheMRanalysisofgutmicrobiomesantibioticresistomeandchildhoodasthmarisk.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3856245","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":268348317,"identity":"2578e8bc-7255-4f07-934a-e690a0e6ac3d","order_by":0,"name":"zhiwei 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1","display":"","copyAsset":false,"role":"figure","size":670122,"visible":true,"origin":"","legend":"\u003cp\u003eThe flowchart of whole process of data analysis.\u003c/p\u003e","description":"","filename":"Fig.1Theflowchartofwholeprocessofdataanalysis.png","url":"https://assets-eu.researchsquare.com/files/rs-3856245/v1/6a06e978b8a3ee17d0df4661.png"},{"id":49946841,"identity":"be760119-6b6b-4670-9332-41a4b8935b1b","added_by":"auto","created_at":"2024-01-22 04:49:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":583472,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of the MR study.\u003c/p\u003e","description":"","filename":"Fig.2SchematicdiagramoftheMRstudy.png","url":"https://assets-eu.researchsquare.com/files/rs-3856245/v1/c52d3314a4d9a997381159b1.png"},{"id":49946171,"identity":"79815bcf-0a58-4ef3-8534-51cdadee1f0d","added_by":"auto","created_at":"2024-01-22 04:41:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":537124,"visible":true,"origin":"","legend":"\u003cp\u003e20 intersections between candidate gut microbiomes and MiBioGen.\u003c/p\u003e","description":"","filename":"Fig.3intersectionsbetweencandidategutmicrobiomesandMiBioGen.png","url":"https://assets-eu.researchsquare.com/files/rs-3856245/v1/9f43a06fa906edcdb727f73b.png"},{"id":49946173,"identity":"fe764a36-7235-449c-b291-7524baf874ec","added_by":"auto","created_at":"2024-01-22 04:41:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":9599232,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCausal effects of gut microbiomes on childhood asthma risk in East Asians. \u003c/strong\u003eForest plot displayed causal effects of core gut microbiomes on childhood asthma risk using IVW (A). Chord diagram of the causal effects of core gut microbiomes on childhood asthma risk (B). Chord diagram of the causal effects of non-core gut microbiomes on childhood asthma risk at the phylum (C), class (D), order (E), family (F), and genus (G) levels.\u003c/p\u003e","description":"","filename":"Fig.4CausaleffectsofgutmicrobiomesonchildhoodasthmariskinEastAsians.png","url":"https://assets-eu.researchsquare.com/files/rs-3856245/v1/45c763ca65fc873dcfafb60b.png"},{"id":49946844,"identity":"9dc5ee18-01c6-440c-884e-de6d673031d6","added_by":"auto","created_at":"2024-01-22 04:49:29","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":8729170,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCausal effects of gut microbiomes on childhood asthma risk in European population.\u003c/strong\u003e Forest plot displayed causal effects of core gut microbiomes on childhood asthma risk using IVW (A). Chord diagram of the causal effects of core gut microbiomes on childhood asthma risk (B). Chord diagram of the causal effects of non-core gut microbiomes on childhood asthma risk at the phylum (C), class (D), order (E), family (F), and genus (G) levels.\u003c/p\u003e","description":"","filename":"Fig.5CausaleffectsofgutmicrobiomesonchildhoodasthmariskinEuropeanpopulation..png","url":"https://assets-eu.researchsquare.com/files/rs-3856245/v1/10746829a0ad33353550f06a.png"},{"id":49946169,"identity":"1c6d5409-0e01-4151-b014-68573a02c07f","added_by":"auto","created_at":"2024-01-22 04:41:28","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":651795,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCausal effects of gut microbiomes antibiotic resistome on childhood asthma risk. \u003c/strong\u003eForest plot of causal effects of gut microbiomes antibiotic resistome on childhood asthma risk.\u003c/p\u003e","description":"","filename":"Fig.6Causaleffectsofgutmicrobiomesantibioticresistomeonchildhoodasthmarisk..png","url":"https://assets-eu.researchsquare.com/files/rs-3856245/v1/2b8b7998377fad24fb3d184a.png"},{"id":56679649,"identity":"9b13ae24-10cd-454d-984c-f481d16af40c","added_by":"auto","created_at":"2024-05-17 16:50:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":18717956,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3856245/v1/263ae28c-cd81-4efc-a105-61582f0ce956.pdf"},{"id":49947242,"identity":"367a6162-4d30-4dfa-833d-54daf01bc015","added_by":"auto","created_at":"2024-01-22 04:57:29","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":270263,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1ThecomprehensivedetailsoftheincludedIVs.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3856245/v1/422bfed9dababc772be2735f.xlsx"},{"id":49946178,"identity":"6e5bc62f-3613-42d8-aca8-7688261e5df3","added_by":"auto","created_at":"2024-01-22 04:41:29","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":51712,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2TestsforheterogeneityandhorizontalpleiotropyintheMRanalysisofgutmicrobiomesandchildhoodasthmariskinEastAsians.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3856245/v1/5cccc2e29ed2556c2b0ae4ad.xlsx"},{"id":49946842,"identity":"d0523b71-bbbf-44b4-b5c1-918b1b00fe68","added_by":"auto","created_at":"2024-01-22 04:49:29","extension":"xlsx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":49537,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable3TestsforheterogeneityandhorizontalpleiotropyintheMRanalysisofgutmicrobiomesandchildhoodasthmariskinEuropeanpopulation.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3856245/v1/79ed4f68b1cbd0b2b9dea442.xlsx"},{"id":49946175,"identity":"71925c90-0d74-438c-afda-d9a706bd5d42","added_by":"auto","created_at":"2024-01-22 04:41:29","extension":"xlsx","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":12647,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable4TestsforheterogeneityandhorizontalpleiotropyintheMRanalysisofgutmicrobiomesantibioticresistomeandchildhoodasthmarisk.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3856245/v1/c73fa21064e7102ef991f9c5.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Gut Microbiome and Childhood Asthma: a Mendelian Randomization Study","fulltext":[{"header":"What is Known","content":"\u003cp\u003e\u0026bull; Early antibiotic exposure and the subsequent disruption of the gut microbiome are linked to an increased risk of childhood asthma. It is assumed that specific gut microbiomes play a role in the development of childhood asthma.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat is New:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026bull; The causal association between gut microbiome and childhood asthma risk was explored through Mendelian randomization. The effect of both the gut microbiome and its antibiotic resistome on the risk of pediatric asthma were confirmed.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eAsthma is an inflammation-associated chronic disease of low airways found in 5–10% of children worldwide[1; 2],which has always been a leading cause of physical and mental morbidity in the children. At present, the therapies can slow the progression of asthma to a degree, but not permanently halt. Therefore, the identification and mitigation of risk factors during the early stages of pediatric asthma bear significant clinical implications.\u003c/p\u003e \u003cp\u003eThe prospective studies focusing on childhood asthma have revealed that asthma is linked to an immature gut microbiome in early life[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In addition, the risk of asthma in childhood can be elevated by early antibiotic exposure and the subsequent destroyed gut microbiome of infant[4; 5]. Several observational studies have proposed a potential association between gut microbiota dysbiosis and the development of pediatric asthma. These studies have explored disparities in gut microbiome between children with asthma and control group[6; 7; 8; 9; 10; 11; 12; 13; 14]. The findings collectively suggest that gut microbiome is associated with the onset of childhood asthma. Nevertheless, partial studies have presented inconclusive outcomes concerning alterations in individual gut microbiota species among children with asthma in comparison to the non-patients. Earlier experimental studies have also indicated that the colonization of human gut microbiota can affect immune responses as observed in the humanized mouse model[13; 15]. However, establishing causal inferences is challenging in observational studies due to the inevitable presence of confounding factors. Therefore, the causal impact of gut microbiota on pediatric asthma remains unclear. On the other hand, antibiotic-resistant genes enrichment of infant gut microbiome is recognized to undergo changes after early-life antibiotic exposure[16; 17; 18]. Therefore, we propose a hypothesis that the antibiotic resistome within the gut microbiome may be associated with an elevated risk of pediatric asthma.\u003c/p\u003e \u003cp\u003eRecent advancements made in Mendelian randomization (MR) analysis have enabled the examination of potential causal association between exposure and interest outcome using genetic variants. Analogous to randomized experiments, genetic variants are assigned randomly during the inception phase in MR methodology, so the affection from reverse causality and confounding can be reduced[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Large-sample single nucleotide polymorphisms (SNPs) associated with childhood asthma, gut microbiota and gut microbiomes antibiotic resistome have been identified respectively in genome-wide association studies (GWAS). This affords us an opportunity to conduct an in-depth exploration. We therefore conducted an MR study to evaluate the causal association of gut microbiota and gut microbiomes antibiotic resistome on childhood asthma.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003eStudy design\u003c/p\u003e\u003cp\u003eOur study design is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. We first conducted a comprehensive literature review to validate core gut microbiomes potentially associated with pediatric asthma and then performed a two-sample MR to examine their association. Subsequently, we also performed mediation MR analysis to investigated additional gut microbiomes that may be associated with childhood asthma, which had not been previously addressed in the existing literature. Finally, we initiated a preliminary exploration into the causal relationship between gut microbiomes antibiotic resistome and the pediatric asthma. Our findings revealed a potential causal association between specific gut microbiota and childhood asthma. Furthermore, our research introduces novel insights into the impact of gut microbiomes antibiotic resistome on pediatric asthma. As shown in the Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, a two-sample MR design was employed in the present study in accordance with the STROBE-MR guidelines[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The following three fundamental assumptions should be satisfied in order to ensure the validity of potential causal effects in MR analyses. First, genetic variants must exhibit a robust association with the exposure. Secondly, genetic variants show independence from confounding variables. Additionally, genetic variants impact the outcome solely via the exposure variable.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cp\u003eDetermining candidate gut microbiomes\u003c/p\u003e\u003cp\u003eFirst, we searched for the childhood asthma-associated human gut microbiomes based on literature databases including Google Scholar and PubMed. Our search terms included “child”, “childhood”, “pediatric”, “asthma”, “gut microbiome” and “gut microbiota”. Following manual screening of the studies, candidate gut microbiomes associated with pediatric asthma, as documented in the literature, were extracted from qualifying articles. Consequently, the intersection between candidate gut microbiomes and gut microbiomes in the MiBioGen consortium (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mibiogen.org\u003c/span\u003e\u003cspan address=\"https://www.mibiogen.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were termed as core gut microbiomes.\u003c/p\u003e\u003cp\u003eGenetic instruments for exposures\u003c/p\u003e\u003cp\u003eThe exposure factors considered in the present study encompass gut microbiomes and gut microbiomes antibiotic resistome. The GWAS summary data for gut microbiota were derived from the most extensive genome-wide meta-analysis on gut microbiota composition, which conducted by MiBioGen consortium. The study comprised 18,340 individuals across 24 cohorts, with the majority having European ancestry. In the study, the lowest taxonomic level was genus, identifying 131 genera with an average abundance exceeding 1%. And the host genetic variants, which correlated with genetic loci linked to the abundance levels of bacterial taxa in the gut microbiota, were identified by Microbiota quantitative trait loci mapping analysis[21; 22]. Excluding 15 taxa with unidentified name, a total of 196 bacterial taxa, including 9 phylum, 16 class, 20 order, 32 family and 119 genus, were incorporated into the present study for further analysis. The GWAS summary data for the gut microbiomes antibiotic resistome were obtained from a multiomics study which extensively profiled the metagenomic landscape of gut antibiotic resistome in a sizable human cohort (n = 1210)[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. A total of 4 gut antibiotic resistance genes (ARG) types including MLS_ermX, Multidrug_emrE, Quinolone_norB, and Vancomycin_vanX were extracted from this study. Additionally, three indices characterizing the α-diversity of gut antibiotic resistome, including Shannon, Evenness, and Richness were also obtained from the research.\u003c/p\u003e\u003cp\u003eGenetic instruments for Childhood Asthma\u003c/p\u003e\u003cp\u003eFirst, the genetic variant dataset related to East Asian childhood asthma was sourced from the GWAS summary statistics of the BioBank Japan Project (BBJ, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pheweb.jp/\u003c/span\u003e\u003cspan address=\"https://pheweb.jp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). BBJ is a prospective genome biobank that gathered DNA and serum samples from around 260,000 participants, primarily of Japanese descent[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In addition, the data on the European childhood asthma was obtained from the FinnGen study (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://r8.finngen.fi\u003c/span\u003e\u003cspan address=\"https://r8.finngen.fi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) by ICD-10 codes J45 and J46, and only cases with an age below 16 were included in the diagnosis of childhood asthma. FinnGen is a personalized medicine projects encompassing genome and health data derived from 377,277 Finnish biobank participants, including 210,870 females and 166,407 males[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Details of all genetic instruments in this study are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of Datasets for Analyses\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhenotype\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCases\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eData source\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGut microbial\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18,340\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-population (including United States, South Korea, Canada, Israel, Germany, Denmark, the Netherlands, Belgium, Sweden, Finland and the United Kingdom)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMiBioGen\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGut microbiomes antibiotic resistome\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1210\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEast Asian\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMultiomics study[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eChildhood Asthma\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e547\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e161803\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEast Asian\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBBJ\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6010\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51577\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFinnGen\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eInstrument selection\u003c/p\u003e\u003cp\u003eThe instrumental variables (IVs) for gut microbiota and gut microbiomes antibiotic resistome utilizing a less stringent significance cutoff at p \u0026lt; 1×10\u003csup\u003e− 5\u003c/sup\u003e[25; 26], which was deemed optimal due to the higher average variance observed for the same microbiome features within the 500FG cohort[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].Further, the linkage disequilibrium (LD) between the SNPs was calculated using data from the European samples within the 1000 Genomes project as the reference panel, and only the independent SNPs exhibiting r2 \u0026lt; 0.001(distance \u0026gt; 10,000 kb) were retained. Subsequently, palindromic SNPs were excluded for their potential ambiguity in targeted alleles. The comprehensive details regarding the included IVs are listed in supplementary Table\u0026nbsp;1.\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eTwo-sample MR analysis was conducted separately to estimate the potential effects of gut microbiomes and gut microbiomes antibiotic resistome on childhood asthma based on at least 4 SNPs with TwoSampleMR package in R 4.3.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The inverse-variant weight (IVW) MR method, which can provide the greatest statistical power based on a random effect model when all genetic variants are valid instruments, was used as the main analysis[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In addition, heterogeneity among the instruments were evaluated the Cochrane’s Q statistics for IVW. And the intercept term from MR-Egger method was used to directional pleiotropy assess the presence of directional pleiotropy. A non-zero value indicates the existence of directional pleiotropy and potential bias in the IVW estimate. A nominal p-value of 0.05 was regarded as statistically significant here.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eIdentification of core gut microbiomes\u003c/p\u003e \u003cp\u003e33 candidate gut microbiomes associated with the onset of childhood asthma were extracted from literature (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). There are 20 overlapping microbiomes, namely core gut microbiomes, including \u003cem\u003eLactobacillus, Faecalibacterium, Akkermansia, Lachnospira, Veillonella, Roseburia, Blautia, Parabacteroides, Clostridiales, Clostridiaceae1, Firmicutes, Streptococcus, Oscillospira, Lachnospiraceae, Alistipes, Flavonifractor, Rikenellaceae, Dialister, Collinsella\u003c/em\u003e and \u003cem\u003eDorea\u003c/em\u003e between candidate microbiomes and Mibiogen (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Previous researches show that there is a positive correlation between Firmicutes and pediatric asthma, and there is also a negative correlation between \u003cem\u003eLactobacillus, Akkermansia, Ruminococcus 1, Ruminococcus 2, Clostridiales, Clostridiaceae 1, Alistipes, Rikenellaceae, Dialister, Collinsella, Dorea\u003c/em\u003e and childhood asthma. However, various studies in the literature exploring the relationship between \u003cem\u003eBifidobacterium, Faecalibacterium, Veillonella, Roseburia, Lachnospiraceae, Flavonifractor\u003c/em\u003e and childhood asthma have yielded different results.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCore Gut Microbiomes Associated with Childhood Asthma\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGut\u0026nbsp;microbiome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorrelation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContributor\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBifidobacterium\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCristina Garcia-Maurino Alcazar[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], Kei E Fujimura[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCristina Garcia-Maurino Alcazar[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], Marie-Claire Arrieta[7; 8], Jakob Stokholm[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLactobacillus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCristina Garcia-Maurino Alcazar[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], Kei E Fujimura[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFaecalibacterium\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCristina Garcia-Maurino Alcazar[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], Kei E Fujimura[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], Jakob Stokholm[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCristina Garcia-Maurino Alcazar[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], Marie-Claire Arrieta[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], Rozlyn C T Boutin[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], David M Patrick[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAkkermansia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCristina Garcia-Maurino Alcazar[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], Kei E Fujimura[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMalassezia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCristina Garcia-Maurino Alcazar[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], Kei E Fujimura[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCandida\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCristina Garcia-Maurino Alcazar[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], Kei E Fujimura[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRhodotorula\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCristina Garcia-Maurino Alcazar[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], Kei E Fujimura[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLachnospira\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCristina Garcia-Maurino Alcazar[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], Marie-Claire Arrieta[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], Rozlyn C T Boutin[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCristina Garcia-Maurino Alcazar[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], Marie-Claire Arrieta[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], Leah T Stiemsma[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRothia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCristina Garcia-Maurino Alcazar[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], Marie-Claire Arrieta[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], Leah T Stiemsma[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVeillonella\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCristina Garcia-Maurino Alcazar[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], Marie-Claire Arrieta[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], Jakob Stokholm[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCristina Garcia-Maurino Alcazar[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], Marie-Claire Arrieta[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], Marie-Claire Arrieta[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], Leah T Stiemsma[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePeptostreptococcus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCristina Garcia-Maurino Alcazar[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], Marie-Claire Arrieta[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCoprococcus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCristina Garcia-Maurino Alcazar[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], Rozlyn C T Boutin[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRoseburia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCristina Garcia-Maurino Alcazar[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], Jakob Stokholm[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], Rozlyn C T Boutin[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], David M Patrick[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], Leah T Stiemsma[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJakob Stokholm[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlautia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCristina Garcia-Maurino Alcazar[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], Rozlyn C T Boutin[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParabacteroides\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCristina Garcia-Maurino Alcazar[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], Rozlyn C T Boutin[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRuminococcus 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCristina Garcia-Maurino Alcazar[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], Jakob Stokholm[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], Rozlyn C T Boutin[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], David M Patrick[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRuminococcus 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCristina Garcia-Maurino Alcazar[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], Jakob Stokholm[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], Rozlyn C T Boutin[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], David M Patrick[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClostridiales\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCristina Garcia-Maurino Alcazar[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], Leah T Stiemsma[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClostridium neonatale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCristina Garcia-Maurino Alcazar[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], Leah T Stiemsma[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClostridiaceae 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCristina Garcia-Maurino Alcazar[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], Leah T Stiemsma[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFirmicutes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCristina Garcia-Maurino Alcazar[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], Leah T Stiemsma[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStreptococcus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCristina Garcia-Maurino Alcazar[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], Marie-Claire Arrieta[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePichia kudriavzevii\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCristina Garcia-Maurino Alcazar[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], Marie-Claire Arrieta[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOscillospira\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCristina Garcia-Maurino Alcazar[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], Marie-Claire Arrieta[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLachnospiraceae\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCristina Garcia-Maurino Alcazar[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], Leah T Stiemsma[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJakob Stokholm[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlistipes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCristina Garcia-Maurino Alcazar[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], Jakob Stokholm[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], Leah T Stiemsma[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFlavonifractor\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCristina Garcia-Maurino Alcazar[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], Leah T Stiemsma[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJakob Stokholm[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFaecalibacterium prausnitzii\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCristina Garcia-Maurino Alcazar[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], David M Patrick[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRuminococcus bromii\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCristina Garcia-Maurino Alcazar[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], David M Patrick[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRikenellaceae\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCristina Garcia-Maurino Alcazar[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], David M Patrick[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDialister\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCristina Garcia-Maurino Alcazar[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], Jakob Stokholm[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], David M Patrick[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGemmiger\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMichiel A. G. E. Bannier[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEscherichia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMichiel A. G. E. Bannier[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCollinsella\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMichiel A. G. E. Bannier[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDorea\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMichiel A. G. E. Bannier[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEffect of gut microbiomes on childhood asthma risk in East Asians\u003c/p\u003e \u003cp\u003eRegrettably, we did not detect significant causal associations between core gut microbiomes and the risk of childhood asthma (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B). However, among non-core gut microbiomes, the result of IVW displayed the possible causal effects of \u003cem\u003eorder Actinomycetales\u003c/em\u003e (\u003cem\u003eOR =\u003c/em\u003e 0.390, 95% \u003cem\u003eCI \u003c/em\u003e:0.173\u0026ndash;0.882, \u003cem\u003eP =\u003c/em\u003e 0.024) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE), \u003cem\u003efamily Actinomycetaceae\u003c/em\u003e (\u003cem\u003eOR =\u003c/em\u003e 0.391, 95% \u003cem\u003eCI \u003c/em\u003e:0.173\u0026ndash;0.883, \u003cem\u003eP =\u003c/em\u003e 0.224) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF), \u003cem\u003egenus Actinomyces\u003c/em\u003e (\u003cem\u003eOR =\u003c/em\u003e 0.528, 95% \u003cem\u003eCI \u003c/em\u003e:0.289\u0026ndash;0.965, \u003cem\u003eP =\u003c/em\u003e 0.038) and \u003cem\u003egenus Fusicatenibacter\u003c/em\u003e (\u003cem\u003eOR =\u003c/em\u003e 0.465, 95% \u003cem\u003eCI \u003c/em\u003e:0.230\u0026ndash;0.938, \u003cem\u003eP =\u003c/em\u003e 0.019) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG) on decreased childhood asthma risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Although results of supplementary models including MR-Egger, Weighted median, Simple mode and Weighted mode, were not statistically significant, the direction of effect remained consistent with IVW models (OR\u0026thinsp;\u0026lt;\u0026thinsp;1). On the contrary, the \u003cem\u003egenus Coprobacter\u003c/em\u003e had a significant positive correlation with the risk of childhood asthma (\u003cem\u003eOR =\u003c/em\u003e 1.826, 95% \u003cem\u003eCI \u003c/em\u003e:1.106\u0026ndash;3.016, \u003cem\u003eP =\u003c/em\u003e 0.032) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG). The results of other models were not significant, but they consistently indicated a positive association between the \u003cem\u003egenus Coprobacter\u003c/em\u003e and childhood asthma (OR\u0026thinsp;\u0026gt;\u0026thinsp;1). The aforementioned results were unaffected by heterogeneity or horizontal pleiotropy. No additional significant causal associations between other gut microbiomes and the risk of childhood asthma were found (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC-G). All results of tests for heterogeneity and horizontal pleiotropy are reported in the Supplementary Table\u0026nbsp;2.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEffect of gut microbiomes on childhood asthma risk in European population\u003c/p\u003e \u003cp\u003eUnlike previous studies[6; 12; 14], the results of the IVW analysis showed that the \u003cem\u003egenus Dialister\u003c/em\u003e significantly increased the risk of childhood asthma (\u003cem\u003eOR =\u003c/em\u003e 1.251, 95% \u003cem\u003eCI \u003c/em\u003e:1.016\u0026ndash;1.539, \u003cem\u003eP =\u003c/em\u003e 0.035). According to the results of weighted median model, the \u003cem\u003egenus Dialister\u003c/em\u003e exhibited significant positive causal association with childhood asthma risk (\u003cem\u003eOR =\u003c/em\u003e 1.325, 95% \u003cem\u003eCI \u003c/em\u003e:1.004\u0026ndash;1.749, \u003cem\u003eP =\u003c/em\u003e 0.046), aligning with the findings of the IVW analysis. The results of simple mode (\u003cem\u003eOR =\u003c/em\u003e 1.396, 95% \u003cem\u003eCI \u003c/em\u003e:0.882\u0026ndash;2.229, \u003cem\u003eP =\u003c/em\u003e 0.185), and weighted mode models (\u003cem\u003eOR =\u003c/em\u003e 1.402, 95% \u003cem\u003eCI \u003c/em\u003e:0.857\u0026ndash;2.293, \u003cem\u003eP =\u003c/em\u003e 0.209) were not significant, but they still indicated a positive association between the \u003cem\u003egenus Dialister\u003c/em\u003e and childhood asthma (OR\u0026thinsp;\u0026gt;\u0026thinsp;1). We did not detect that other core gut microbiomes were significantly causally associated with the childhood asthma risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). At the genus biological level of non-core gut microbiota, based on the MR analysis results between non-core gut microbiomes and pediatric asthma, the IVW models indicated that \u003cem\u003egenus Eubacterium nodatum group\u003c/em\u003e (\u003cem\u003eOR\u0026thinsp;=\u003c/em\u003e\u0026thinsp;1.12, 95% \u003cem\u003eCI\u003c/em\u003e:1.002\u0026ndash;1.251, \u003cem\u003eP =\u003c/em\u003e 0.047) and \u003cem\u003egenus Bilophila\u003c/em\u003e (\u003cem\u003eOR\u0026thinsp;=\u003c/em\u003e\u0026thinsp;1.29, 95% \u003cem\u003eCI\u003c/em\u003e:1.046\u0026ndash;1.581, \u003cem\u003eP =\u003c/em\u003e 0.017) had a significant positive correlation with childhood asthma. On the other hand, there was a negative correlation between \u003cem\u003egenus Holdemanella\u003c/em\u003e (\u003cem\u003eOR\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.82, 95% \u003cem\u003eCI\u003c/em\u003e:0.706\u0026ndash;0.951, \u003cem\u003eP =\u003c/em\u003e 0.009), \u003cem\u003egenus Oxalobacter\u003c/em\u003e (\u003cem\u003eOR\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.84, 95% CI:0.747\u0026ndash;0.955, \u003cem\u003eP =\u003c/em\u003e0.007) and \u003cem\u003egenus Slackia\u003c/em\u003e (\u003cem\u003eOR\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.81, 95% CI:0.655\u0026ndash;0.996, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.046) and childhood asthma. The MR estimates from supplementary models consistently supported their negative effect on childhood asthma (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The aforementioned results were unaffected by heterogeneity or horizontal pleiotropy. No further significant causal associations between the remaining non-core gut microbiota and the risk of childhood asthma could be identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC-G). All results of tests for heterogeneity and horizontal pleiotropy are reported in the Supplementary Table\u0026nbsp;3.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEffect of gut microbiomes antibiotic resistome on childhood asthma risk\u003c/p\u003e \u003cp\u003eTo further delve into the effect of gut microbiome resistome on childhood asthma, MR analyses were conducted in East Asians. The results of the IVW analysis showed that the Pielou's index Evenness, one of the diversity indices, exhibited significant causal associations with the reduced risk of childhood asthma (\u003cem\u003eOR\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.825, 95% CI:0.684\u0026ndash;0.994, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.043). The results of the weighted median (\u003cem\u003eOR =\u003c/em\u003e 0.866, 95% \u003cem\u003eCI \u003c/em\u003e:0.670\u0026ndash;1.120, \u003cem\u003eP =\u003c/em\u003e 0.274), simple mode (\u003cem\u003eOR =\u003c/em\u003e 0.892, 95% \u003cem\u003eCI \u003c/em\u003e:0.608\u0026ndash;1.307, \u003cem\u003eP =\u003c/em\u003e 0.571), and weighted mode models (\u003cem\u003eOR =\u003c/em\u003e 0.889, 95% \u003cem\u003eCI \u003c/em\u003e:0.603\u0026ndash;1.309, \u003cem\u003eP =\u003c/em\u003e 0.564) were not statistically significant. However, their effect directions were consistent with the IVW results (OR\u0026thinsp;\u0026lt;\u0026thinsp;1). This result was unaffected by heterogeneity or horizontal pleiotropy. Ultimately, we did not detect that the other diversity indices (including Richness and Shannon) and 4 ARGs (including MLS_ermX, Multidrug_emrE, Quinolone_norB and Vancomycin_vanX) were significantly causally associated with the childhood asthma risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). All results of tests for heterogeneity and horizontal pleiotropy are reported in the Supplementary Table\u0026nbsp;4.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAsthma is one of the most significant and prevalent chronic diseases to address during childhood, given its potential impact on learning, growth, and psychology. In research, the aberrant gut microbiota composition and antibiotic use in early life have been proposed to be the potential risk factors for asthma. However, only a very limited number of gut bacterial taxa associated with childhood asthma were identified, and there was no evidence for a link between the antibiotic resistome of gut microbiomes and pediatric asthma. Therefore, we have sought to uncover the causal effect of gut microbiomes and their antibiotic resistome on risk of childhood asthma by MR analyses.\u003c/p\u003e \u003cp\u003eHere, we provide insight into the role of gut microbiomes in the risk of childhood asthma from the perspective of bacterial entities and resistome. First, the total 33 gut microbiomes that have been previously reported to be associated with childhood asthma were identified on the basis of the outcome of comprehensive literature search. Furthermore, among 33 candidate gut microbiomes identified, the single nucleotide polymorphisms of the 20 were retrieved from the MiBioGen consortium. And then, MR methods were employed to identify the causal relationship between gut microbiota and the childhood asthma risk in both European and East Asian populations utilizing GWAS summary statistics. The study findings indicated that gut microbiota might be causally associated with the risk of childhood asthma in the European population and East Asians. However, the specific gut microbiota influencing the risk of childhood asthma differs between European and Asian populations. For East Asian populations, unlike previous studies, no significant causal association was observed between any core gut microbiomes and the risk of childhood asthma. However, the study results identified that the \u003cem\u003eorder Actinomycetales, family Actinomycetaceae, genus Actinomyces and genus Fusicatenibacter\u003c/em\u003e might be genetically predicted to be causally associated with a lower risk of childhood asthma. Conversely, \u003cem\u003egenus Coprobacter\u003c/em\u003e showed a positive correlation with the childhood asthma risk. Previous research has suggested a potential correlation between the \u003cem\u003eorder Actinomycetales\u003c/em\u003e of infants' homes and a reduced risk of asthma. The findings of this study indicated an association between the abundance of 12 bacterial genera and a decreased risk of asthma, notably with 7 bacterial genera originating from the \u003cem\u003eorder Actinomycetales\u003c/em\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. And in contrast to the outcomes of our study, a clinical research revealed a positive association between the abundance of \u003cem\u003eActinomycetaceae\u003c/em\u003e and asthma[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Actinomyces has been reported as a significant genus with negative associations with adverse correlations to serum IgE levels and genes associated with bronchial inflammatory responses[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Therefore, \u003cem\u003egenus Actinomyces\u003c/em\u003e may be a protective factor against asthma. In a murine model of ulcerative colitis, the \u003cem\u003egenus Fusicatenibacter\u003c/em\u003e exerted anti-inflammatory effects by stimulating the production of the IL-10 in lamina propria cells[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. However, the relationship between \u003cem\u003eCoprobacter\u003c/em\u003e and asthma requires further investigation. In European populations, contrary to previous findings, the results of MR analyses indicate that a positive relationship exists between the \u003cem\u003egenus Dialister\u003c/em\u003e and pediatric asthma possibly. A longitudinal flux balance analysis reveals that the inflammatory responses may be triggered by \u003cem\u003eDialister genus\u003c/em\u003e through the releasing of L-serine and formate and cooperating with pathogenic strains[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. And by production of lipopolysaccharide, the \u003cem\u003egenus Dialister\u003c/em\u003e can also aggravate host inflammatory response [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Furthermore, some negative health outcomes, such as type 2 diabetes mellitus[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], inflammatory bowel disease[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] and high risk of colorectal cancer progression[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], have been linked to the \u003cem\u003egenera Dialister\u003c/em\u003e in previous research. Additionally, exposure to ambient air pollutants from birth to 6-months is positively associated with the \u003cem\u003eDialister\u003c/em\u003e in infancy[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. It has been widely proven that exposure to air pollution early in life was positive associated with an increased risk of asthma[38; 39]. Hence, we speculate that the \u003cem\u003egenus Dialister\u003c/em\u003e appears to be influenced by air pollution and may in turn have influence on the risk of childhood asthma. In general, the \u003cem\u003egenus Dialister\u003c/em\u003e was suggested as one of the impact factors for pediatric asthma, but further study is certainly warranted. The results of the further MR analyses showed that \u003cem\u003egenus Eubacterium nodatum group\u003c/em\u003e and \u003cem\u003egenus Bilophila\u003c/em\u003e were positively correlated with the risk of childhood asthma. On the other hand, \u003cem\u003egenus Holdemanella, genus Oxalobacter and genus Slackia\u003c/em\u003e were associated with the decreased risk of childhood asthma. \u003cem\u003eBilophila\u003c/em\u003e can degrade of inflammation inhibitory factor such as butyrate, and facilitate the expression of inflammatory components such as microbe-associated molecular pattern and pathogen-associated molecular pattern factors [40; 41]. Although \u003cem\u003eHoldemanella\u003c/em\u003e has not been associated with asthma previously, it was recently reported to exhibit significant positive correlations with IL-6 and TNF-α[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Hence, the \u003cem\u003eBilophila and Holdemanella\u003c/em\u003e may exert pro-inflammatory effects potentially.In addition, the \u003cem\u003eSlackia\u003c/em\u003e can modulate bile acid and lipid metabolism, which may influence host homeostasis. And \u003cem\u003eOxalobacter\u003c/em\u003e carries out important functions such as metabolizing oxalate in the intestinal tract and is thought to be the center of the oxalate-metabolizing microbial network[43; 44]. Nevertheless, the potential contribution of \u003cem\u003eEubacterium nodatum group, Oxalobacter and Slackia\u003c/em\u003e on asthma remains unclear.\u003c/p\u003e \u003cp\u003eThe relationship between the antibiotic resistome of gut microbiomes and childhood asthma warrants further investigation. The antibiotic usage might impact the diversity of the gut antibiotic resistome, potentially leading to a lower maturity of the gut microbiota[18; 45]. Low gut maturity in early life may elevate the risk of pediatric asthma[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. As an index characterizing the α-diversity of the gut antibiotic resistome, Evenness showed a negative correlation with childhood asthma risk in the present study, but further research is needed to validate this finding. There is no conclusive evidence regarding the association between ARG and the risk of childhood asthma from MR analyses. However, this research direction is intriguing and requires future research using more comprehensive genome-wide association analysis data about antibiotic resistance of human gut microbiota.\u003c/p\u003e \u003cp\u003eHowever, the present study has certain limitations and drawbacks. Participants of the GWAS about gut microbiomes and their antibiotic resistome may include not just the children of the same race, which can lead to unstable results. We acknowledge there is a risk of participants discrepancy between exposure GWAS and outcome GWAS leading to consequence with limited extrapolability. Indeed, we have observed significant associations between several gut microbiomes and childhood asthma, but further studies based on larger GWAS are need for the generalizability of the results in the future.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, consistent with previous research, our study has confirmed that genus Dialister may participate in onset of childhood asthma. Meanwhile, several potential pediatric asthma-related gut microbiomes have been proposed. However, European and East Asian populations exhibit different gut microbiota. Additionally, the α-diversity of the gut antibiotic resistome was also found to be associated with the risk of childhood asthma. Special attention should be paid to these gut microbiomes and antibiotic usage in the following research targeting prevention and treatment of childhood asthma. However, further studies are need for the generalizability of the results in the future.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMR \u0026nbsp; \u0026nbsp; \u0026nbsp;Mendelian randomization\u003c/p\u003e\n\u003cp\u003eIVs\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp;instrumental variables\u003c/p\u003e\n\u003cp\u003eGWAS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;genome-wide association studies\u003c/p\u003e\n\u003cp\u003eIVW \u0026nbsp; \u0026nbsp;inverse variance weighted\u003c/p\u003e\n\u003cp\u003eSNPs \u0026nbsp;\u0026nbsp;single nucleotide polymorphisms\u003c/p\u003e\n\u003cp\u003eARG \u0026nbsp; \u0026nbsp;antibiotic resistance genes\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBBJ \u0026nbsp; \u0026nbsp; BioBank Japan Project\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eHuman Ethics, Animal Ethics, and Consent to Participate declarations: not applicable.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZhiwei Zheng contributed to conceptualization and design of the study. Yongmao Zhou and Pan Chen downloaded and organized the data. Zhiwei Zheng, Ganghua Huang and Baofei Li performed the Mendelian Randomization analysis and visualizationZhiwei Zheng wrote the original draft of the manuscript. Yongmao Zhou, Pan Chen, Qinhai Huang and Baofei Li wrote sections of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003evan Beveren GJ, Said H, van Houten MA, Bogaert D. The respiratory microbiome in childhood asthma. J Allergy Clin Immunol (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePedersen M, Liu S, Zhang J, Jovanovic Andersen Z, Brandt J, Budtz-J\u0026oslash;rgensen E, B\u0026oslash;nnelykke K, Frohn LM, Nybo Andersen AM, Ketzel M, Khan J, Stayner L, Brunekreef B, Loft S. Early-Life Exposure to Ambient Air Pollution from Multiple Sources and Asthma Incidence in Children: A Nationwide Birth Cohort Study from Denmark. Environ Health Perspect. 2023;131:57003.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDai H, Hou T, Wang Q, Hou Y, Zhu Z, Zhu Y, Zhao Z, Li M, Lin H, Wang S, Zheng R, Xu Y, Lu J, Wang T, Ning G, Wang W, Zheng J, Bi Y, Xu M. Roles of gut microbiota in atrial fibrillation: insights from Mendelian randomization analysis and genetic data from over 430,000 cohort study participants. Cardiovasc Diabetol. 2023;22:306.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDai DLY, Petersen C, Hoskinson C, Del Bel KL, Becker AB, Moraes TJ, Mandhane PJ, Finlay BB, Simons E, Kozyrskyj AL, Patrick DM, Subbarao P, Bode L, Azad MB, Turvey SE. Breastfeeding enrichment of B. longum subsp. infantis mitigates the effect of antibiotics on the microbiota and childhood asthma risk. Med (New York, N.Y.) 4 (2023) 92\u0026ndash;112e5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRantala AK, Tapia G, Magnus MC, Stene LC, Jaakkola JJK, St\u0026oslash;rdal K, Karlstad \u0026Oslash;, Nystad W. Maternal antibiotic use and infections during pregnancy and offspring asthma: the Norwegian Mother, Father and Child Cohort Study and a nationwide register cohort. Eur J Epidemiol. 2022;37:983\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlcazar CG, Paes VM, Shao Y, Oesser C, Miltz A, Lawley TD, Brocklehurst P, Rodger A, Field N. The association between early-life gut microbiota and childhood respiratory diseases: a systematic review. Lancet Microbe. 2022;3:e867\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArrieta MC, Ar\u0026eacute;valo A, Stiemsma L, Dimitriu P, Chico ME, Loor S, Vaca M, Boutin RCT, Morien E, Jin M, Turvey SE, Walter J, Parfrey LW, Cooper PJ, Finlay B. Associations between infant fungal and bacterial dysbiosis and childhood atopic wheeze in a nonindustrialized setting. J Allergy Clin Immunol. 2018;142:424\u0026ndash;434e10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArrieta MC, Stiemsma LT, Dimitriu PA, Thorson L, Russell S, Yurist-Doutsch S, Kuzeljevic B, Gold MJ, Britton HM, Lefebvre DL, Subbarao P, Mandhane P, Becker A, McNagny KM, Sears MR, Kollmann T, Mohn WW, Turvey SE, Finlay BB. Early infancy microbial and metabolic alterations affect risk of childhood asthma. Sci Transl Med. 2015;7:307ra152.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBannier M, van Best N, Bervoets L, Savelkoul PHM, Hornef MW, van de Kant KDG, J\u0026ouml;bsis Q, Dompeling E, Penders J. Gut microbiota in wheezing preschool children and the association with childhood asthma. Allergy. 2020;75:1473\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoutin RCT, Sbihi H, Dsouza M, Malhotra R, Petersen C, Dai D, Sears MR, Moraes TJ, Becker AB, Azad MB, Mandhane PJ, Subbarao P, Finlay BB, Turvey SE. Mining the infant gut microbiota for therapeutic targets against atopic disease. Allergy. 2020;75:2065\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFujimura KE, Sitarik AR, Havstad S, Lin DL, Levan S, Fadrosh D, Panzer AR, LaMere B, Rackaityte E, Lukacs NW, Wegienka G, Boushey HA, Ownby DR, Zoratti EM, Levin AM, Johnson CC, Lynch SV. Neonatal gut microbiota associates with childhood multisensitized atopy and T cell differentiation. Nat Med. 2016;22:1187\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatrick DM, Sbihi H, Dai DLY, Al Mamun A, Rasali D, Rose C, Marra F, Boutin RCT, Petersen C, Stiemsma LT, Winsor GL, Brinkman FSL, Kozyrskyj AL, Azad MB, Becker AB, Mandhane PJ, Moraes TJ, Sears MR, Subbarao P, Finlay BB, Turvey SE. Decreasing antibiotic use, the gut microbiota, and asthma incidence in children: evidence from population-based and prospective cohort studies. Lancet Respir Med. 2020;8:1094\u0026ndash;105.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStiemsma LT, Arrieta MC, Dimitriu PA, Cheng J, Thorson L, Lefebvre DL, Azad MB, Subbarao P, Mandhane P, Becker A, Sears MR, Kollmann TR, Mohn WW, Finlay BB, Turvey SE. Shifts in Lachnospira and Clostridium sp. in the 3-month stool microbiome are associated with preschool age asthma. Clin Sci (Lond). 2016;130:2199\u0026ndash;207.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStokholm J, Blaser MJ, Thorsen J, Rasmussen MA, Waage J, Vinding RK, Schoos AM, Kun\u0026oslash;e A, Fink NR, Chawes BL, B\u0026oslash;nnelykke K, Brejnrod AD, Mortensen MS, Al-Soud WA, S\u0026oslash;rensen SJ, Bisgaard H. Maturation of the gut microbiome and risk of asthma in childhood. Nat Commun. 2018;9:141.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilson NG, Hernandez-Leyva A, Rosen AL, Jaeger N, McDonough RT, Santiago-Borges J, Lint MA, Rosen TR, Tomera CP, Bacharier LB, Swamidass SJ, Kau AL. The gut microbiota of people with asthma influences lung inflammation in gnotobiotic mice. iScience. 2023;26:105991.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSamarra A, Esteban-Torres M, Cabrera-Rubio R, Bernabeu M, Arboleya S, Gueimonde M, Collado MC. Maternal-infant antibiotic resistance genes transference: what do we know? Gut Microbes. 2023;15:2194797.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNogacka AM, Salazar N, Arboleya S, Su\u0026aacute;rez M, Fern\u0026aacute;ndez N, Sol\u0026iacute;s G, de Reyes-Gavil\u0026aacute;n CG, Gueimonde M. Early microbiota, antibiotics and health. Cell Mol Life Sci. 2018;75:83\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi X, Stokholm J, Brejnrod A, Vestergaard GA, Russel J, Trivedi U, Thorsen J, Gupta S, Hjelms\u0026oslash; MH, Shah SA, Rasmussen MA, Bisgaard H, S\u0026oslash;rensen SJ. The infant gut resistome associates with E. coli, environmental exposures, gut microbiome maturity, and asthma-associated bacterial composition. Cell Host Microbe. 2021;29:975\u0026ndash;987e4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurgess S, Timpson NJ, Ebrahim S, Davey Smith G. Mendelian randomization: where are we now and where are we going? Int J Epidemiol. 2015;44:379\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSkrivankova VW, Richmond RC, Woolf BAR, Yarmolinsky J, Davies NM, Swanson SA, VanderWeele TJ, Higgins JPT, Timpson NJ, Dimou N, Langenberg C, Golub RM, Loder EW, Gallo V, Tybjaerg-Hansen A, Davey Smith G, Egger M, Richards JB. Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement. JAMA. 2021;326:1614\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShuai M, Zhang G, Zeng FF, Fu Y, Liang X, Yuan L, Xu F, Gou W, Miao Z, Jiang Z, Wang JT, Zhuo LB, Chen YM, Ju F, Zheng JS. Human Gut Antibiotic Resistome and Progression of Diabetes. Advanced science (Weinheim, Baden-Wurttemberg, Germany) 9 (2022) e2104965.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiang Y, Zhang C, Wang J, Cheng Y, Wang L, Tong Y, Yan D. Identification of host gene-microbiome associations in colorectal cancer patients using mendelian randomization. J translational Med. 2023;21:535.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSakaue S, Kanai M, Tanigawa Y, Karjalainen J, Kurki M, Koshiba S, Narita A, Konuma T, Yamamoto K, Akiyama M, Ishigaki K, Suzuki A, Suzuki K, Obara W, Yamaji K, Takahashi K, Asai S, Takahashi Y, Suzuki T, Shinozaki N, Yamaguchi H, Minami S, Murayama S, Yoshimori K, Nagayama S, Obata D, Higashiyama M, Masumoto A, Koretsune Y, Ito K, Terao C, Yamauchi T, Komuro I, Kadowaki T, Tamiya G, Yamamoto M, Nakamura Y, Kubo M, Murakami Y, Yamamoto K, Kamatani Y, Palotie A, Rivas MA, Daly MJ, Matsuda K, Okada Y. A cross-population atlas of genetic associations for 220 human phenotypes. Nat Genet. 2021;53:1415\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKurki MI, Karjalainen J, Palta P, Sipil\u0026auml; TP, Kristiansson K, Donner KM, Reeve MP, Laivuori H, Aavikko M, Kaunisto MA, Loukola A, Lahtela E, Mattsson H, Laiho P, Della Briotta Parolo P, Lehisto AA, Kanai M, Mars N, R\u0026auml;m\u0026ouml; J, Kiiskinen T, Heyne HO, Veerapen K, R\u0026uuml;eger S, Lemmel\u0026auml; S, Zhou W, Ruotsalainen S, P\u0026auml;rn K, Hiekkalinna T, Koskelainen S, Paajanen T, Llorens V, Gracia-Tabuenca J, Siirtola H, Reis K, Elnahas AG, Sun B, Foley CN, Aalto-Set\u0026auml;l\u0026auml; K, Alasoo K, Arvas M, Auro K, Biswas S, Bizaki-Vallaskangas A, Carpen O, Chen CY, Dada OA, Ding Z, Ehm MG, Eklund K, F\u0026auml;rkkil\u0026auml; M, Finucane H, Ganna A, Ghazal A, Graham RR, Green EM, Hakanen A, Hautalahti M, Hedman K, Hiltunen \u0026Aring;M, Hinttala R, Hovatta I, Hu X, Huertas-Vazquez A, Huilaja L, Hunkapiller J, Jacob H, Jensen JN, Joensuu H, John S, Julkunen V, Jung M, Junttila J, Kaarniranta K, K\u0026auml;h\u0026ouml;nen M, Kajanne R, Kallio L, K\u0026auml;lvi\u0026auml;inen R, Kaprio J, Kerimov N, Kettunen J, Kilpel\u0026auml;inen E, Kilpi T, Klinger K, Kosma VM, Kuopio T, Kurra V, Laisk T, Laukkanen J, Lawless N, Liu A, Longerich S, M\u0026auml;gi R, M\u0026auml;kel\u0026auml; J, M\u0026auml;kitie A, Malarstig A, Mannermaa A et al. J. Maranville, A. Matakidou, T. Meretoja, S.V. Mozaffari, FinnGen provides genetic insights from a well-phenotyped isolated population. Nature 613 (2023) 508\u0026ndash;518.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanna S, van Zuydam NR, Mahajan A, Kurilshikov A, Vich Vila A, V\u0026otilde;sa U, Mujagic Z, Masclee AAM, Jonkers D, Oosting M, Joosten LAB, Netea MG, Franke L, Zhernakova A, Fu J, Wijmenga C, McCarthy MI. Causal relationships among the gut microbiome, short-chain fatty acids and metabolic diseases. Nat Genet. 2019;51:600\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiao L, Liu S, Wu Y, Huang Y, Tao S, Liu Y, Tang Y, Xie M, Ma Q, Yin Y, Dai M, Zhang M, Llamocca E, Gui H, Wang Q. The interactions between host genome and gut microbiome increase the risk of psychiatric disorders: Mendelian randomization and biological annotation. Brain Behav Immun. 2023;113:389\u0026ndash;400.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuan X, Huang D, Zhan Y, Huang J, Huang J, Ng AT, Tsu JH, Na R. Risk of second primary cancers after a diagnosis of first primary cancer: A pan-cancer analysis and Mendelian randomization study. eLife 12 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarvonen AM, Kirjavainen PV, T\u0026auml;ubel M, Jayaprakash B, Adams RI, Sordillo JE, Gold DR, Hyv\u0026auml;rinen A, Remes S, von Mutius E, Pekkanen J. Indoor bacterial microbiota and development of asthma by 10.5 years of age. J Allergy Clin Immunol. 2019;144:1402\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi N, Qiu R, Yang Z, Li J, Chung KF, Zhong N, Zhang Q. Sputum microbiota in severe asthma patients: Relationship to eosinophilic inflammation. Respir Med. 2017;131:192\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChun Y, Do A, Grishina G, Grishin A, Fang G, Rose S, Spencer C, Vicencio A, Schadt E, Bunyavanich S. Integrative study of the upper and lower airway microbiome and transcriptome in asthma. JCI insight 5 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEssex M, Rios Rodriguez V, Rademacher J, Proft F, L\u0026ouml;ber U, Mark\u0026oacute; L, Pleyer U, Strowig T, Marchand J, Kirwan JA, Siegmund B, Forslund SK. and D. Poddubnyy, Shared and Distinct Gut Microbiota in Spondyloarthritis, Acute Anterior Uveitis, and Crohn's Disease. Arthritis Rheumatol (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBasile A, Heinken A, Hertel J, Smarr L, Li W, Treu L, Valle G, Campanaro S, Thiele I. Longitudinal flux balance analyses of a patient with episodic colonic inflammation reveals microbiome metabolic dynamics. Gut Microbes. 2023;15:2226921.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMena-V\u0026aacute;zquez N, Ruiz-Lim\u0026oacute;n P, Moreno-Indias I, Manrique-Arija S, Lisbona-Monta\u0026ntilde;ez JM, Rioja J, Mucientes A, Martin-N\u0026uacute;\u0026ntilde;ez GM, Cano-Garc\u0026iacute;a L, Tinahones FJ. Fern\u0026aacute;ndez-Nebro, Adiposity is associated with expansion of the genus Dialister in rheumatoid arthritis patients. Biomed pharmacotherapy = Biomedecine pharmacotherapie. 2023;160:114388.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGradisteanu Pircalabioru G, Chifiriuc MC, Picu A, Petcu LM, Trandafir M, Savu O. Snapshot into the Type-2-Diabetes-Associated Microbiome of a Romanian Cohort. Int J Mol Sci 23 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eČipčić Paljetak H, Barešić A, Panek M, Perić M, Matijašić M, Lojkić I, Barišić A, Vranešić Bender D, Ljubas Kelečić D, Brinar M, Kalauz M, Miličević M, Grgić D, Turk N, Karas I, Čuković-Čavka S, Ž., Krznarić, Verbanac D. Gut microbiota in mucosa and feces of newly diagnosed, treatment-na\u0026iuml;ve adult inflammatory bowel disease and irritable bowel syndrome patients. Gut microbes 14 (2022) 2083419.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuh JW, Kim MJ, Kim J, Lee HG, Ryoo SB, Ku JL, Jeong SY, Park KJ, Kim D, Kim JF, Park JW. Enterotypical Prevotella and three novel bacterial biomarkers in preoperative stool predict the clinical outcome of colorectal cancer. Microbiome. 2022;10:203.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBailey MJ, Holzhausen EA, Morgan ZEM, Naik N, Shaffer JP, Liang D, Chang HH, Sarnat J, Sun S, Berger PK, Schmidt KA, Lurmann F, Goran MI, Alderete TL. Postnatal exposure to ambient air pollutants is associated with the composition of the infant gut microbiota at 6-months of age. Gut Microbes. 2022;14:2105096.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHolst GJ, Pedersen CB, Thygesen M, Brandt J, Geels C, B\u0026oslash;nl\u0026oslash;kke JH, Sigsgaard T. Air pollution and family related determinants of asthma onset and persistent wheezing in children: nationwide case-control study. BMJ (Clinical research ed). 2020;370:m2791.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGehring U, Wijga AH, Koppelman GH, Vonk JM, Smit HA, Brunekreef B. Air pollution and the development of asthma from birth until young adulthood. Eur Respir J 56 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGupta VK, Cunningham KY, Hur B, Bakshi U, Huang H, Warrington KJ, Taneja V, Myasoedova E, Davis JM 3rd, and, Sung J. Gut microbial determinants of clinically important improvement in patients with rheumatoid arthritis. Genome Med. 2021;13:149.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYe Z, Zhang N, Wu C, Zhang X, Wang Q, Huang X, Du L, Cao Q, Tang J, Zhou C, Hou S, He Y, Xu Q, Xiong X, Kijlstra A, Qin N, Yang P. A metagenomic study of the gut microbiome in Behcet's disease. Microbiome. 2018;6:135.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang W, Yao X, Xia F, Yang M, Chen Z, Zhou B, Liu Q. Modulation of the Gut Microbiota in Rats by Hugan Qingzhi Tablets during the Treatment of High-Fat-Diet-Induced Nonalcoholic Fatty Liver Disease. Oxidative medicine and cellular longevity 2018 (2018) 7261619.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiller AW, Choy D, Penniston KL, Lange D. Inhibition of urinary stone disease by a multi-species bacterial network ensures healthy oxalate homeostasis. Kidney Int. 2019;96:180\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiener R, Bangen U, Sidhu H, H\u0026ouml;now R, von Unruh G, Hesse A. The role of Oxalobacter formigenes colonization in calcium oxalate stone disease. Kidney Int. 2013;83:1144\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee K, Raguideau S, Sir\u0026eacute;n K, Asnicar F, Cumbo F, Hildebrand F, Segata N, Cha CJ, Quince C. Population-level impacts of antibiotic usage on the human gut microbiome. Nat Commun. 2023;14:1191.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Gut Microbiome, Gut Antibiotic Resistome, Childhood Asthma, Mendelian Randomization","lastPublishedDoi":"10.21203/rs.3.rs-3856245/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3856245/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eA few gut microbiomes have been reported in observational studies to be associated with childhood asthma. Antibiotic resistome of gut microbiomes may also influence childhood asthma risk. However, the underlying causal effect remains undefined. We attempted to explore the causal association of these conditions through Mendelian randomization (MR) analysis. First, we review literatures to identify core gut microbiomes potentially associated with childhood asthma. The instrumental variables (IVs) for gut microbiome and gut microbiomes antibiotic resistome were obtained from MiBioGen consortium and a multiomics study respectively. And the genetic instruments for childhood asthma in East Asian populations and European were selected from genome-wide association studies (GWAS). We implemented Two-sample MR analysis to elucidate the effect of gut microbiome and gut microbiome antibiotic resistome on childhood asthma risk. The inverse variance weighted (IVW) was employed as the primary analysis, followed by heterogeneity and pleiotropy analysis. In the European population, within the core gut microbiomes, \u003cem\u003egenus Dialister\u003c/em\u003e was significantly positively associated with childhood asthma risk by IVW (\u003cem\u003eOR =\u003c/em\u003e 1.251, 95% \u003cem\u003eCI \u003c/em\u003e:1.016–1.539, \u003cem\u003eP = \u003c/em\u003e0.035). Moreover, there was a positive correlation between \u003cem\u003egenus Eubacterium nodatum group\u003c/em\u003e (\u003cem\u003eOR =\u003c/em\u003e 1.12, 95% \u003cem\u003eCI\u003c/em\u003e:1.002–1.251, \u003cem\u003eP =\u003c/em\u003e 0.047), \u003cem\u003egenus Bilophila\u003c/em\u003e (\u003cem\u003eOR =\u003c/em\u003e 1.29, 95% \u003cem\u003eCI\u003c/em\u003e:1.046–1.581, \u003cem\u003eP =\u003c/em\u003e 0.017) and childhood asthma risk. Conversely, \u003cem\u003egenus Holdemanella\u003c/em\u003e (\u003cem\u003eOR =\u003c/em\u003e 0.82, 95% \u003cem\u003eCI\u003c/em\u003e:0.706–0.951, \u003cem\u003eP =\u003c/em\u003e 0.009), \u003cem\u003egenus Oxalobacter\u003c/em\u003e (\u003cem\u003eOR =\u003c/em\u003e 0.84, 95% CI:0.747–0.955, \u003cem\u003eP =\u003c/em\u003e0.007) and \u003cem\u003egenus Slackia\u003c/em\u003e (\u003cem\u003eOR =\u003c/em\u003e 0.81, 95% CI:0.655–0.996, \u003cem\u003eP =\u003c/em\u003e 0.046) exhibited a significant negative correlation with childhood asthma risk. In the East Asian population, our analysis revealed correlations between decreased childhood asthma risk and the \u003cem\u003eorder Actinomycetales\u003c/em\u003e (\u003cem\u003eOR =\u003c/em\u003e 0.390, 95% \u003cem\u003eCI \u003c/em\u003e:0.173–0.882, \u003cem\u003eP =\u003c/em\u003e 0.024), \u003cem\u003efamily Actinomycetaceae\u003c/em\u003e (\u003cem\u003eOR =\u003c/em\u003e 0.391, 95% \u003cem\u003eCI \u003c/em\u003e:0.173–0.883, \u003cem\u003eP =\u003c/em\u003e 0.224), \u003cem\u003egenus Actinomyces\u003c/em\u003e (\u003cem\u003eOR =\u003c/em\u003e 0.528, 95% \u003cem\u003eCI \u003c/em\u003e:0.289–0.965, \u003cem\u003eP =\u003c/em\u003e 0.038), and \u003cem\u003egenus Fusicatenibacter\u003c/em\u003e (\u003cem\u003eOR =\u003c/em\u003e 0.465, 95% \u003cem\u003eCI \u003c/em\u003e:0.230–0.938, \u003cem\u003eP =\u003c/em\u003e 0.019). Conversely, \u003cem\u003egenus Coprobacter\u003c/em\u003e showed a significant positive correlation with childhood asthma risk (\u003cem\u003eOR =\u003c/em\u003e 1.826, 95% \u003cem\u003eCI \u003c/em\u003e:1.106–3.016, \u003cem\u003eP =\u003c/em\u003e 0.032). Finally, there was a negative correlation between Evenness, an index representing the α-diversity of the gut antibiotic resistome, and childhood asthma risk (\u003cem\u003eOR =\u003c/em\u003e 0.825, 95% CI:0.684–0.994, \u003cem\u003eP =\u003c/em\u003e 0.043).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: This study is the first to employ MR analysis to validate the association between gut microbiomes identified in literature and childhood asthma risk. We try to explore additional bacterial taxes that may be associated with childhood asthma risk. Furthermore, the present study innovatively explores the effect of the gut microbiome antibiotic resistome on the risk of pediatric asthma using MR analysis. These findings provide opportunities for early intervention on childhood asthma and offer new insights into the underlying mechanisms of childhood asthma. However, further studies are required to validate and generalize the results in future research.\u003c/p\u003e","manuscriptTitle":"Gut Microbiome and Childhood Asthma: a Mendelian Randomization Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-22 04:41:24","doi":"10.21203/rs.3.rs-3856245/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"79455ff9-6d75-4abc-a7cd-0adc9a89f0aa","owner":[],"postedDate":"January 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-17T16:42:02+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-22 04:41:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3856245","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3856245","identity":"rs-3856245","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}