Baseline characteristics of the study cohort

The COPSAC 2010 cohort has been followed prospectively with deep clinical phenotyping and structured interviews at 11 scheduled visits during the first 5 years of life, with asthma as primary outcome. The characteristics of the entire cohort of 700 children have been described in detail30. Of these children, 51% were boys, 57% had at least one older child in the home at birth, 22% were delivered by cesarean section, and 46% were treated with antibiotics during the first year of life. The mean maternal age at delivery was 32.2 years and 26% of the mothers had physician-diagnosed asthma.

Microbial composition changes during first year of life

The composition of the gut microbiome changes extensively in early life, with regard to diversity, complexity, and dominant bacterial taxa5,6,7,8,9. Therefore, we examined the compositional changes that occurred in the first year of life, before asthma onset. A total of 1696 fecal samples from 690 children arrived in the lab within 24 h of being produced and were characterized by 16S rRNA gene amplicon sequencing of the V4 region. With a median sequencing depth of 44,827 reads (interquartile range (IQR): 2358–78,208) increasing with age of sample, we identified 3651 unique operational taxonomic units (OTUs), demonstrating a median richness of 116 OTUs per sample, with the dominating genera Bacteroides, Bifidobacterium, and Veillonella. Alpha-diversity, assessed by the Shannon diversity index, was not different between 1 week (median (IQR), 2.0 (1.5–2.5)) and 1 month (1.9 (1.4–2.5)), but substantially increased at age 1 year (2.8 (2.4–3.3)), (P < 0.001). Similarly, the Chao1 index showed a minor decrease between 1 week (131 (96–175)) and 1 month (121 (78–168)) (P < 0.001) and a substantial increase at age 1 year (295 (203–366)) (P < 0.001). Although significant differences in the population structure (β-diversity), as determined by weighted UniFrac distances, were found between ages 1 week and 1 month (F = 26.5, R2 = 2.4%, P < 0.001), the greatest change in structure was between ages 1 month and 1 year (F = 558.5, R2 = 33.2%, P < 0.001, Fig. 1a). To evaluate which bacterial genera were involved in these observed temporal differences in α- and β-diversity, we examined the relative abundances of the most prevalent taxa. Each of the 20 most abundant genera changed significantly in relative abundance during the first year of life (Kruskal–Wallis test, all P-values < 0.01) with the largest differences observed from age 1 month to 1 year (Supplementary Fig. 1).

Fig. 1 Microbial compositions change in the gut over the first year of life. PCoA plots of weighted UniFrac distances. a Separation of the composition by sampling time point. b Separation of the bacterial populations by Partitioning around medoids (PAM) clusters (optimal number of clusters is 2; silhouette width = 0.30). The five most distinctive indicator OTUs for each of the two clusters were: PAM cluster 1 (N = 1019): Enterobacteriaceae, Staphylococcus, Streptococcus, Bifidobacterium and Enterococcus, and PAM cluster 2 (N = 677): Faecalibacterium, Bacteroides(x3), and Anaerostipes. Ellipses demonstrate the mean ± 2 SD in a and b. c Relationship between sampling time point and PAM cluster Full size image

Microbial community types and maturation are age-determined

To further describe the compositional differences in the microbial populations, we applied a clustering method (Partitioning around medoids (PAM) clustering)31,32 to separate all the fecal samples by their weighted UniFrac distances taking microbial phylogeny and abundances into account, but without including the time point in the computational model (Fig. 1b). This approach divided the samples into two distinct microbial community types (PAM clusters) (silhouette width, 0.30), largely representing the child’s age at sampling, with PAM cluster 1 (N = 1019) mainly composed of the 1-week and 1-month samples and PAM cluster 2 (N = 677) chiefly composed of the 1-year samples (Fig. 1c). The five most discriminating indicator OTUs for each cluster were identified for PAM cluster 1 as Enterobacteriaceae, Staphylococcus, Streptococcus, Bifidobacterium and Enterococcus, and for PAM cluster 2 as Faecalibacterium, Bacteroides(x3), and Anaerostipes. Using the Shannon diversity index, we identified higher α-diversity, as well as a microbiome dominated by the phylum Bacteroidetes in PAM cluster 2 compared to PAM cluster 1, which was dominated by Proteobacteria (Supplementary Fig. 2). As such, these PAM clusters likely represent the age-related maturation of the intestinal microbial populations7,33,34. To determine whether the PAM cluster transition at age 1 year was dependent on environmental factors, a wide range of exposures (including delivery method and antibiotics in the first year of life) were examined. Among these, only the presence of older children in the home from birth was significantly associated with the clusters; of the 34 children with a PAM cluster 1 composition at age 1 year, only 24% (N = 8) had older children in the home compared to 57% (N = 334) of the 589 children in PAM cluster 2 (χ2 test P < 0.001). The microbial populations by means of β-diversity at age 1 month was not associated with PAM cluster at age 1 year.

To further explore the microbial maturation process over time, we calculated microbiota-by-age z-scores (MAZ)7 for all samples. The model is trained from the microbial composition on a data set with known sample age, and afterward predicts the microbial age for each sample based solely on the microbial composition. Microbiota age increased significantly for each adjacent time point (Wilcoxon test, P-values < 0.001) (Supplementary Fig. 3).

Microbial population structure associates with later asthma

Next, we sought to examine whether associations existed between the overall microbial composition at the different time points and the later development of asthma. Current asthma at age 5 years was used as the primary end-point, as it represents a strong persistent clinical phenotype; many children experience episodes of asthma-like symptoms in the first years of life, but outgrow their asthma symptoms before school-age35. Among the 648 children with follow-up to age 5 years, the prevalence of ongoing asthma at age 5 was 9% (N = 60). There were no significant associations between α-diversity (Shannon diversity and Chao1 indices) at any time point and asthma risk, also after adjustment for potential confounders (older siblings, duration of exclusive breastfeeding, hospitalization after birth, antibiotic use, and delivery mode). There were no significant associations between β-diversity at the two earliest time points; however, the microbial populations were significantly different at 1 year in children who had asthma at age 5 compared to non-asthmatics (F = 3.4, R2 = 0.6%, P = 0.003). To further explore this finding, we stratified the samples at age 1 year according to maternal asthma status. The microbiome-asthma association was found only in the 147 children born to asthmatic mothers (F = 6.3, R2 = 4.2%, P < 0.001) but not in children of non-asthmatic mothers (F = 0.7, R2 = 0.2%, P = 0.65), demonstrating a significant interaction (P = 0.003) (Fig. 2). In a test of sensitivity, we adjusted these analyses for potential confounders (older siblings, duration of exclusive breastfeeding, hospitalization after birth, antibiotic use, and delivery mode). The significance levels remained essentially unchanged (All children: F = 3.5, R2 = 0.6%, P < 0.001; asthmatic mother: F = 6.2, R2 = 4.1%, P < 0.001; non-asthmatic mother: F = 0.7, R2 = 0.2%, P = 0.647). There were no differences in relation to asthmatic status of the father. Neither maternal nor paternal asthma status was associated with the α- or β-diversity at any time point (maternal asthma vs. β-diversity: Supplementary Fig. 4). However, that the microbial composition was not affected by maternal asthma status suggests that only susceptible children, exposed to inappropriate microbial stimulation during the first year of life, may express their inherited asthma risk. In a test of sensitivity, we also examined associations between population structure at age 1 year in children born by asthmatic mothers and asthma risk using other β-diversity indices that are not biased by the dominant taxa (unweighted UniFrac and Jensen–Shannon divergence), which yielded similar results (P < 0.001).

Fig. 2 β-diversity in the 1-year fecal samples associates with later asthma. PCoA plots of weighted UniFrac distances. Microbial compositions are assessed in relation to a child’s asthma status at age 5 years, and stratified by maternal asthma. P-values correspond to Adonis PERMANOVA tests. Ellipses demonstrate the mean ± 1 SD of children, who at age 5 years were asthmatic (orange) (N = 58) or non-asthmatic (green) (N = 531). Subsets include: asthmatic mother, asthmatic (orange) (N = 25), and non-asthmatic (green) (N = 122); non-asthmatic mother, asthmatic (orange) (N = 33), and non-asthmatic (green) (N = 409) Full size image

Relative abundance at age 1 year associates with later asthma

To understand whether the later asthma risk was affected by specific bacterial genera present earlier, we examined the relative abundances of the most common genera. For the 20 most abundant genera present in the 1-year samples, we observed increased risk of asthma at age 5 years associated with higher abundance of Veillonella (asthma vs. non-asthma; median relative abundance, 0.94 vs. 0.29%; Wilcoxon rank-sum test, P = 0.035) and with lower abundance of Roseburia (0.27 vs. 0.66%; P = 0.042), Alistipes (0.04 vs. 0.35%; P = 0.002), and Flavonifractor (0.05 vs. 0.07%; P = 0.002). If the child was born to an asthmatic mother, 8 of these 20 genera were significantly associated with their development of asthma, whereas there were no significant associations if the mother did not have asthma (Fig. 3). In children born to asthmatic mothers, asthma at age 5 years was negatively associated with relative abundance in the age 1-year sample of the genera Faecalibacterium (0.59 vs. 3.27%; P = 0.010), Bifidobacterium (0.47 vs. 2.27%; P = 0.006), Roseburia (0.01 vs. 0.76%; P < 0.001), Alistipes (0.01 vs. 0.39%; P = 0.003), Lachnospiraceae incertae sedis (0.05 vs. 0.19%; P = 0.018), Ruminococcus (<0.01 vs. 0.13%; P = 0.004) and Dialister (<0.01 vs. 0.15%; P = 0.007) and positively correlated only with Veillonella (1.41 vs. 0.23%; P = 0.039) (Fig. 3). The genera that were found in lower abundances at age 1 year among children who later became asthmatics have been considered as determinants of a healthy mature gut composition36. The majority of these genera were highly correlated at 1 year, while being negatively correlated with the genus Veillonella (Supplementary Fig. 5). None of the 20 most abundant genera at 1 week or 1 month were associated with asthma development. To further examine the associations between the microbial composition at age 1 year and asthma at age 5 in children born to asthmatic mothers, a cross-validated sparse PLS model was constructed to identify jointly contributing taxa at age 1 year that would predict later asthma in these children. This resulted in a one-component model based on the relative abundances of 60 genera (Supplementary Fig. 6). The model demonstrated a high predictive capacity for asthma (cross-validated AUC 0.76) and the many contributing taxa suggest a global delayed microbial maturation at age 1 year in children with asthma at age 5.

Fig. 3 Relative abundances in the 1-year fecal samples associate with later asthma. Comparison among the 20 most abundant bacterial genera. Relative abundance of each genus is shown with respect to asthma at age 5 years in all children, and stratified by maternal asthma. P-values correspond to Wilcoxon rank-sum tests of the relative abundances, with significant values (P < 0.05) bolded. FDR limits were calculated for the comparisons: Bonferroni (all: P < 0.0025), Benjamini & Hochberg (all children: P < 0.0024, asthmatic mother: P < 0.0102, non-asthmatic mother: P < 0.05). A pseudocount (+1e−06) was added to all abundances for the log-scale presentation. The black dots indicate median values and the abundances are colored according to the asthmatic (orange) (N = 58) or non-asthmatic (green) (N = 531) status of the child at age 5 years Full size image

Community types at age 1 year associates with later asthma

To formally test whether the maturation of the microbiome was a determinant for asthma development, we used our microbial community types (PAM clusters) as indicators of microbial maturation. After excluding children without full 5-year follow-up, transient asthma and diagnosis before the 1-year sample (N = 105), the risk of developing persistent asthma if the child’s microbiome remained in PAM cluster 1 at 1 year of age (N = 28) was compared to children with transition to PAM cluster 2 (N = 490), and all analyses were adjusted for the presence of older children (Fig. 4). During the first 5 years of life, the risk of developing persistent asthma was increased (adjusted hazard ratio (aHR) 2.87 (1.25−6.55), P = 0.013) if the microbiome remained in PAM cluster 1 at age 1. This effect was driven solely by the 120 children born to asthmatic mothers (PAM1, N = 7 vs. PAM2, N = 113) (aHR 12.99 (4.17−40.51), P < 0.001), whereas there was no microbial effect on asthma development for the 398 children of non-asthmatic mothers (PAM1, N = 21 vs. PAM2, N = 377) (aHR 0.56 (0.07−4.16), P = 0.57), demonstrating significant interaction, P = 0.011. In a test of sensitivity, we adjusted these analyses further by including other potential confounders (older siblings, duration of exclusive breastfeeding, hospitalization after birth, antibiotic use, and delivery mode). All estimates and significance levels remained essentially unchanged (all children: aHR 2.91 (1.25−6.79), P = 0.013; asthmatic mother: aHR 10.92 (3.44−34.67), P < 0.001; non-asthmatic mother: aHR 0.56 (0.07−4.23), P = 0.572). We found no associations of PAM clusters with the transient asthma phenotype. These analyses provide further evidence that having the more immature gut microbial composition at age 1 year may be a trigger for asthma development in susceptible children.

Fig. 4 Kaplan–Meier plots illustrate risk of asthma in the first 5 years of life according to Partitioning Around Medoids (PAM) cluster at 1 year. Comparisons shown of all children and stratified by maternal asthma, demonstrating interaction between PAM cluster and maternal asthma (P = 0.01). Associations are quantified by Cox proportional hazards regression, adjusted for presence of older children in the home (aHR, adjusted Hazard Ratio). Children with a transient asthmatic phenotype (remission before age 5 years) are excluded. Curves are colored according to cluster; All: PAM cluster 1 (red) (N = 28), PAM cluster 2 (blue) (N = 490). Subsets include: asthmatic mother, PAM cluster 1 (red) (N = 7), PAM cluster 2 (blue) (N = 113); non-asthmatic mother: PAM cluster 1 (red) (N = 21), PAM cluster 2 (blue) (N = 377) Full size image

Low 1-year microbial maturity associates with later asthma

We then applied another validated method for determining the early life maturation of the intestinal microbial populations using the MAZ score. The microbial maturity (predicted microbiota age−median microbiota age) and MAZ (microbial maturity/SD) were used as metrics7,33,34. We sought to determine whether a lower MAZ score at age 1 year was associated with later asthma. During the first 5 years of life, the risk of developing persistent asthma was increased with low maturity (below median) in MAZ at 1 year (low maturity, N = 257 vs. high maturity, N = 261) (HR 1.77 (1.02−3.07), P = 0.043). Low microbial maturity was only associated with later asthma in children born to asthmatic mothers (low maturity, N = 63 vs. high maturity, N = 57) (HR 6.53 (1.93−22.06), P = 0.003), and not in children of non-asthmatic mothers (low maturity, N = 194 vs. high maturity, N = 204) (HR 0.92 (0.46−1.84), P = 0.81), demonstrating significant interaction, P = 0.006 (Supplementary Fig. 7). In total, adequate maturation of the gut microbiome in this period appears critical for healthy (non-asthmatic) development and may protect children with pre-dispositions.

Low microbial maturity associates with number of asthma-like episodes

We then examined a secondary asthmatic end-point, the total number of asthma-like episodes in the first 3 years of life, which is a marker of disease burden. Children with microbiome composition in PAM cluster 1 at 1 year experienced more episodes of troublesome lung symptoms compared with PAM cluster 2 (median (IQR); PAM1 vs. PAM2, 6 (4–18) vs. 5 (2–10); incidence risk ratio (IRR) 1.54 (1.12–2.12), P = 0.008). This was more pronounced in children born to asthmatic mothers (interaction P = 0.040, median (IQR); PAM1 vs. PAM2, 19 (12–27) vs. 6 (3–12); IRR 2.35 (1.48-3.74), P < 0.001). Likewise, lower microbial maturity as assessed by MAZ score at age 1 year was associated with more episodes (IRR 1.13 (1.06–1.20), P < 0.001), again with an enhanced effect in children of asthmatic mothers IRR 1.26 (1.05–2.91), P = 0.016. Thus, consistent with the findings for asthma development, low maturity of the microbiome at age 1 year was also associated with more asthma-like episodes.

The associations remain robust at higher sequencing depth

To test the robustness of these findings, the analyses were stratified by sequencing depth to only include samples with more than 10,000 reads. The PAM clustering, MAZ, and association results with asthma and episodes of troublesome lung symptoms all were robust with reference to this more stringent inclusion criterion. Furthermore, we found no significant differences in sequencing depth at 1 year between children, who did or did not develop asthma (median (IQR); asthma vs. non-asthma, 54,260 (12,018–74,971) vs. 51,940 (12,010–73,050), P = 0.8); adjustment of the results for sequencing depth resulted in essentially no difference.

Microbial populations describe asthma with sensitization

To explore whether the results were characterizing a specific asthmatic phenotype, the children were further subdivided by allergic sensitization in childhood. The microbial populations by means of β-diversity at age 1 year were significantly different whether the child had asthma or not with or without sensitization (four categories of comparison) (F = 1.7, R2 = 0.9%, P = 0.020). Again, this was mainly apparent among children born to asthmatic mothers (F = 2.9, R2 = 5.9%, P < 0.001) with no significant effects among children born to non-asthmatic mothers F = 0.7, R2 = 0.5%, (P = 0.887). Especially, the phenotype of asthma with allergic sensitization was associated with skewed β-diversity (Fig. 5).