Overview
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Key numbers
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- microbiome composition and ß-diversity (R2 2–4%, p < 0.05, Aitchison),
- and immunological functions (Ruggieri et al., 2017), with potential considered to be relatively stable over time (De Filippo et al., 2010;
- Rosenfeld, 2017; Tsiaoussis et al., 2019), there is a notable scarcity of could influence the child microbiome (Laursen et al., 2015; Nielsen
- Investigating the intricate associations between these exposures Protection Agency reference dose (5.8 μg/L of MeHg in whole
- (47.1%) based on parental reports (Aurrekoetxea et al., 2016). In
- addition, 28.2% of children, as reported by parents to have no We considered all the variables collected as part of the INMA
- this article, 24% at birth and 19% at 4 years old exhibited mercury variables to be included in the association analyses (Figure 2),
- On the other hand, the role of host genetics in determining gut Table S1.
- (participation rate in the last follow-up, 76%). As part of the 7-year validated, child-specific food frequency questionnaire (Vioque et al.,
- follow-up, 154 out of 473 children (32.6%) provided stool samples and 2019). We evaluated animal protein foods, dairy products, fruits and
- were included in the present analysis. A total of 152 children yielded vegetables, high-fiber foods, and sweet products as servings per day.
- were available for a subset of 107 children. Full description of the variables is presented in Supplementary File 1; Table S1.
- et al., 2020). In brief, both were used for pregnant mothers and The limit of quantification of the method (LOQ) was 2 μg/L for cord
- was detected in urine over the LOQ, indicating SHS exposure. gene was performed according to the 16S Metagenomic Sequencing
- questionnaires during pregnancy and summarized as the mean Illumina, San Diego, CA, USA). In brief, the V3-V4 hypervariable
- After 16S rRNA gene amplification, 2 sets of 77 amplicons were group relative to the total variation. Mean and standard deviation of
- database. Phylogenetic analysis was performed with FastTree (Price permutations) was used. Mean and standard deviation of R2 values
- 20% of samples were selected for analysis. ASVs were agglomerated to 20 imputed datasets. The mean of adjusted q-values was also
- (log), interquartile range (IQR), or log of IQR, as appropriate. Then, from the raw MaAsLin2 output table and calculated the BH-corrected
- envfit function performs multivariate analysis of variance (MANOVA) (mean ± SD: 83,143 ± 35,329 reads). Sequence data that support the
- the phylum level, defined as phyla found in more than 85% of the
- Verrucomicrobiota 11.02%, Actinobacteria 2.74%, Proteobacteria Supplementary Table S3. We found no significant associations of
- 2.29%. The core microbiota at the genus level, defined as genera found β-diversity with mercury or other tobacco exposure variables.
- genera were Bacteroides (30.9%), Akkermansia (11.2%), Alistipes the envfit analysis for all determinants and pollutant exposures are
- (11.1%), and Faecalibacterium (10.8%). We considered 207 ASVs for represented in Figure 3. All R2 and p-values are presented in
- the association study corresponding to 2 kingdoms, 6 phyla, 9 classes, Supplementary File 3: Table S3 and Supplementary File 4: Table S4.
- 20% of samples. was not significantly associated with the 12 tobacco exposure variables
- INMA birth cohort is shown in Supplementary File 2: Table S2. single pollutant model (PERMANOVA). An unadjusted model was
- interest. In the studied population, 46.6% of children were exposed to Material and methods section), considering “having siblings at birth”
- tobacco smoke in-utero, 30.3% during childhood based on the as the covariate selected by the envfit analysis. The results obtained for
- 4 year-old children data, and 17.9% along the full period (sustained each of the 20 imputations and both models are shown in
- exposure) based on questionnaires. The mean corrected cotinine Supplementary File 5: Table S5. Plots of PCA with the clr-transformed
- median cotinine levels were 6.0 μg/g for mothers and 5.5 μg/g at any time during pregnancy and having siblings at birth in
- In this study, 65% of children had mercury levels in cord blood
- pregnancy and SHS at 4 years based on detectable urinary cotinine (coef = −3.76, q-value 0.004, coef = −3.75, q-value 0.005, respectively).
- 2–4% of the variability (Aitchison distance, p-values: 0.0045 and was associated with an increased relative abundance of Dorea both in
- 0.0322, respectively, Table 1). Having siblings at birth was also the unadjusted and adjusted models (coef = 2.01, q-value 0.006,
- 5% of the β-diversity (Aitchison). model. Mercury or tobacco exposure from pregnancy to childhood
- Only the statistically significant results are shown in Table 1. All measured by other variables considered in this study were not
- TABLE 1 Pollutants and determinants significantly associated with children gut microbiome β-diversity in a multi-determinant model (envfit analysis)
- β-diversity Sample Variable Mean R2 (%, SD) Mean p-value
- Mean and standard deviation of R2 and mean of the p-value obtained for 20 imputations. Sensitivity analysis considering also genetic data was performed in a subset of 107 children. An
Methods (brief)
- (Aitchison), and multivariable association model with single taxa (MaAsLin2;
- addition, 28.2% of children, as reported by parents to have no We considered all the variables collected as part of the INMA
- influence of tobacco smoke and mercury environmental exposures education level were also collected.
- follow-up, 154 out of 473 children (32.6%) provided stool samples and 2019). We evaluated animal protein foods, dairy products, fruits and
- index, and overweight and obesity were defined following the using different biological samples; whole cord blood samples were
- normal weight individuals. Ethnic origin, based on questionnaire The recommended samples to quantify the exposure to mercury
- data, was reclassified in the two groups: white European children are blood and urine. Detection of mercury in hair samples may
- et al., 2016); children were classified as homozygous or At birth, whole cord blood samples were collected using
- polymorphism rs601338 G > A at the fucosyltransferase 2 (FUT2) cord samples were processed, separated into aliquots of 1 mL, and then
- gene. Finally, FUT2 rs601338 polymorphism was genotyped. The first frozen at −80°C until analysis. Hair samples were collected from the
- 2.2.2 Tobacco smoke exposure (Basque Country, Spain) using, for both types of samples, thermal
- Tobacco smoke exposure was assessed through two decomposition, amalgamation, and atomic absorption spectrometry.
- et al., 2020). In brief, both were used for pregnant mothers and The limit of quantification of the method (LOQ) was 2 μg/L for cord
- children of 4 years old, while only questionnaires were used for blood samples and 0.01 μg/g for hair samples.
- biomarker of tobacco smoke exposure due to its medium half-life 2.3.1 Sample processing and sequencing
- (16–18h) and its excretion in urine during the day. As urinary Fecal samples collected in sterile containers were initially kept at
- tobacco smoke. Cotinine was determined in urine samples NucliSENSⓇ EasyMAGⓇ instrument (bioMérieux). According to the
- collected from mothers at week 32 of pregnancy and children of manufacturer’s instructions, a portion of each fecal sample was
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Update history
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