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

The use of machine learning in paediatric nutrition

PURPOSE OF REVIEW

In recent years, there has been a burgeoning interest in using machine learning methods. This has been accompanied by an expansion in the availability and ease of use of machine learning tools and an increase in the number of large, complex datasets which are suited to machine learning approaches. This review summarizes recent work in the field and sets expectations for its impact in the future.

RECENT FINDINGS

Much work has focused on establishing good practices and ethical frameworks to guide the use of machine learning in research. Machine learning has an established role in identifying features in 'omics' research and is emerging as a tool to generate predictive models to identify people at risk of disease and patients at risk of complications. They have been used to identify risks for malnutrition and obesity. Machine learning techniques have also been used to develop smartphone apps to track behaviour and provide healthcare advice.

SUMMARY

Machine learning techniques are reaching maturity and their impact on observational data analysis and behaviour change will come to fruition in the next 5 years. A set of standards and best practices are emerging and should be implemented by researchers and publishers.

Prenatal metal exposures and childhood gut microbial signatures are associated with depression score in late childhood

BACKGROUND

Childhood depression is a major public health issue worldwide. Previous studies have linked both prenatal metal exposures and the gut microbiome to depression in children. However, few, if any, have studied their interacting effect in specific subgroups of children.

OBJECTIVES

Using an interpretable machine-learning method, this study investigates whether children with specific combinations of prenatal metals and childhood microbial signatures (cliques or groups of metals and microbes) were more likely to have higher depression scores at 9-11 years of age.

METHODS

We leveraged data from a well-characterized pediatric longitudinal birth cohort in Mexico City and its microbiome substudy (n = 112). Eleven metal exposures were measured in maternal whole blood samples in the second and third trimesters of pregnancy. The gut microbial abundances were measured at 9-11-year-olds using shotgun metagenomic sequencing. Depression symptoms were assessed using the Child Depression Index (CDI) t-scores at 9-11 years of age. We used Microbial and Chemical Exposure Analysis (MiCxA), which combines interpretable machine-learning into a regression framework to identify and estimate joint associations of metal-microbial cliques in specific subgroups. Analyses were adjusted for relevant covariates.

RESULTS

We identified a subgroup of children (11.6 % of the sample) characterized by a four-component metal-microbial clique that had a significantly high depression score (15.4 % higher than the rest) in late childhood. This metal-microbial clique consisted of high Zinc in the second trimester, low Cobalt in the third trimester, a high abundance of Bacteroides fragilis, a high abundance of Faecalibacterium prausnitzii. All combinations of cliques (two-, three-, and four-components) were significantly associated with increased log-transformed t-scored CDI (β = 0.14, 95%CI = [0.05,0.23], P < 0.01 for the four-component clique).

SIGNIFICANCE

This study offers a new approach to chemical-microbial analysis and a novel demonstration that children with specific gut microbiome cliques and metal exposures during pregnancy may have a higher likelihood of elevated depression scores.

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

Emerging research suggests that exposures to metals during pregnancy and gut microbiome (GM) disruptions are associated with depressive disorders in childhood. Akkermansia muciniphila, a GM bacteria, has been studied for its potential antidepressant effects. However, its role in the influence of prenatal metal exposures on depressive symptoms during childhood is unknown. Leveraging a well-characterized pediatric longitudinal birth cohort and its microbiome substudy (n=112) and using a state-of-the-art machine-learning model, we investigated whether the presence of A.muciniphila in GM of 9-11-year-olds modifies the associations between exposure to a specific group of metals (or metal-clique) during pregnancy and concurrent childhood depressive symptoms. Among children with no A.muciniphila, a metal-clique of Zinc-Chromium-Cobalt was strongly associated with increased depression score (P<0.0001), whereas, for children with A.muciniphila, this same metal-clique was weakly associated with decreased depression score(P<0.4). Our analysis provides the first exploratory evidence hypothesizing A. muciniphila as a probiotic intervention attenuating the effect of prenatal metal-exposures-associated depressive disorders in late childhood.