» Articles » PMID: 38249448

Influence of Perinatal and Childhood Exposure to Tobacco and Mercury in Children's Gut Microbiota

Abstract

Background: Early life determinants of the development of gut microbiome composition in infants have been widely investigated; however, if early life pollutant exposures, such as tobacco or mercury, have a persistent influence on the gut microbial community, its stabilization at later childhood remains largely unknown.

Objective: In this exposome-wide study, we aimed at identifying the contribution of exposure to tobacco and mercury from the prenatal period to childhood, to individual differences in the fecal microbiome composition of 7-year-old children, considering co-exposure to a width of established lifestyle and clinical determinants.

Methods: Gut microbiome was studied by 16S rRNA amplicon sequencing in 151 children at the genus level. Exposure to tobacco was quantified during pregnancy through questionnaire (active tobacco consumption, second-hand smoking -SHS) and biomonitoring (urinary cotinine) at 4 years (urinary cotinine, SHS) and 7 years (SHS). Exposure to mercury was quantified during pregnancy (cord blood) and at 4 years (hair). Forty nine other potential environmental determinants (12 at pregnancy/birth/infancy, 15 at 4 years and 22 at 7 years, such as diet, demographics, quality of living/social environment, and clinical records) were registered. We used multiple models to determine microbiome associations with pollutants including multi-determinant multivariate analysis of variance and linear correlations (wUnifrac, Bray-Curtis and Aitchison ß-diversity distances), single-pollutant permutational multivariate analysis of variance adjusting for co-variates (Aitchison), and multivariable association model with single taxa (MaAsLin2; genus). Sensitivity analysis was performed including genetic data in a subset of 107 children.

Results: Active smoking in pregnancy was systematically associated with microbiome composition and ß-diversity ( 2-4%,  < 0.05, Aitchison), independently of other co-determinants. However, in the adjusted single pollutant models (PERMANOVA), we did not find any significant association. An increased relative abundance of and decreased relative abundance of were associated with smoking during pregnancy ( < 0.05).

Discussion: Our findings suggest a long-term sustainable effect of prenatal tobacco exposure on the children's gut microbiota. This effect was not found for mercury exposure or tobacco exposure during childhood. Assessing the role of these exposures on the children's microbiota, considering multiple environmental factors, should be further investigated.

Citing Articles

Biomonitoring of Mercury and Lead Levels in the Blood of Children Living near a Tropical River Impacted by Artisanal and Small-Scale Gold Mining in Colombia.

Palacios-Valoyes E, Salas-Moreno M, Marrugo-Negrete J Toxics. 2024; 12(10).

PMID: 39453164 PMC: 11511192. DOI: 10.3390/toxics12100744.


Perinatal Exposure to Tobacco Smoke and Its Association with the Maternal and Offspring Microbiome: A Systematic Review.

Falara E, Metallinou D, Nanou C, Vlachou M, Diamanti A Healthcare (Basel). 2024; 12(18).

PMID: 39337215 PMC: 11431162. DOI: 10.3390/healthcare12181874.

References
1.
Thorman A, Adkins G, Conrey S, Burrell A, Yu Y, White B . Gut Microbiome Composition and Metabolic Capacity Differ by Secretor Status in Exclusively Breastfed Infants. Nutrients. 2023; 15(2). PMC: 9866411. DOI: 10.3390/nu15020471. View

2.
Nakayama J, Watanabe K, Jiang J, Matsuda K, Chao S, Haryono P . Diversity in gut bacterial community of school-age children in Asia. Sci Rep. 2015; 5:8397. PMC: 4336934. DOI: 10.1038/srep08397. View

3.
George Markowitz R, LaBella A, Shi M, Rokas A, Capra J, Ferguson J . Microbiome-associated human genetic variants impact phenome-wide disease risk. Proc Natl Acad Sci U S A. 2022; 119(26):e2200551119. PMC: 9245617. DOI: 10.1073/pnas.2200551119. View

4.
Mesa M, Loureiro B, Iglesia I, Fernandez Gonzalez S, Olive E, Garcia Algar O . The Evolving Microbiome from Pregnancy to Early Infancy: A Comprehensive Review. Nutrients. 2020; 12(1). PMC: 7019214. DOI: 10.3390/nu12010133. View

5.
Callahan B, McMurdie P, Rosen M, Han A, Johnson A, Holmes S . DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016; 13(7):581-3. PMC: 4927377. DOI: 10.1038/nmeth.3869. View