Prenatal Lead Exposure Is Associated with Reduced Abundance of Beneficial Gut Microbial Cliques in Late Childhood: An Investigation Using Microbial Co-Occurrence Analysis (MiCA)

Abstract

Many analytical methods used in gut microbiome research focus on either single bacterial taxa or the whole microbiome, ignoring multibacteria relationships (microbial cliques). We present a novel analytical approach to identify microbial cliques within the gut microbiome of children at 9−11 years associated with prenatal lead (Pb) exposure. Data came from a subset of participants (n = 123) in the Programming Research in Obesity, Growth, Environment and Social Stressors cohort. Pb concentrations were measured in maternal whole blood from the second and third trimesters of pregnancy. Stool samples collected at 9−11 years old underwent metagenomic sequencing to assess the gut microbiome. Using a novel analytical approach, Microbial Co-occurrence Analysis (MiCA), we paired a machine learning algorithm with randomization-based inference to first identify microbial cliques that were predictive of prenatal Pb exposure and then estimate the association between prenatal Pb exposure and microbial clique abundance. With second-trimester Pb exposure, we identified a two-taxa microbial clique that included Bif idobacterium adolescentis and Ruminococcus callidus and a three-taxa clique that also included Prevotella clara. Increasing second-trimester Pb exposure was associated with significantly increased odds of having the two-taxa microbial clique below the median relative abundance (odds ratio (OR) = 1.03, 95% confidence interval (CI) [1.01−1.05]). Using a novel combination of machine learning and causal inference, MiCA identified a significant association between second-trimester Pb exposure and the reduced abundance of a probiotic microbial clique within the gut microbiome in late childhood.

Akkermansia muciniphila modifies the association between metal exposure during pregnancy and depressive symptoms in late childhood

“…Although the details can be found in previous works, we present and discuss this method comprehensively for interpretability and further replicability. 27,29,30 A metal-clique is an indicator of a multi-ordered combination of metals. For example, consider a three-metal combination consisting of metals A, B, and C, denoted as A+B+C-.…”

“…Details on concentrations of prenatal metals at both trimesters (with strati cation by the presence of A. muciniphila) are shown in Supplemental Tables 1 and 2. We used an interpretable machine learning model called repeated holdout signed-iterated Random Forest (rh-SiRF), 27,29,30 along with a regression framework to identify metal cliques that are both predictive and associated with depression score (see Methods). We obtained a total of 123 unique two-component cliques from rh-SiRF, with only 3% of cliques having stability (frequency of occurrence) of more than 5% (see Supplemental Table 3 for the list of top 10 combinations).…”

“…We followed a similar set of minimal covariates in line with our previous work. 27,29,30 Potential confounding variables used in this analysis encompassed a range of factors identi ed in previous literature and prioritized using DAGs. These included the child's reported sex at birth, age at the time of stool sample collection, the socioeconomic status (SES) of the mother during pregnancy, the mother's age at childbirth, the mother's 2 nd -trimester body mass index (BMI) while pregnant, and the batch of microbiome analysis (two batches).…”

“…Exposure to toxic metals is also associated with the composition, diversity, and functional activities of the GM. [22][23][24][25] We previously found that higher prenatal lead exposure reduced the diversity and abundance of bene cial taxa within the GM in children during mid-childhood (9-11 years) 26, 27 Although metals have been linked to both alterations of the GM and increased depression symptoms, the speci c role of A. muciniphila in modifying the association between metal exposure and depression has yet to be examined. This study, therefore, investigates the role of A. muciniphila in modifying the association between prenatal metal clique and depressive symptoms in late childhood.…”

The use of machine learning in paediatric nutrition

“…Midya et al . [6 ▪ ] used a novel method based on random forests to identify groups of bacteria (‘cliques’ as they were described in the study) which were associated with prenatal lead exposure in prepubertal children. As with much of the current research into the microbiome, the study focused on developing methods to understand complex interactions between different bacteria.…”