Patterns of infant fecal metabolite concentrations and social behavioral development in toddlers
Hannah E Laue1, Julia A Bauer2, Wimal Pathmasiri3,4
1Department of Epidemiology, Geisel School of Medicine at Dartmouth College, Hanover, NH, USA. Hannah.E.Laue@Dartmouth.edu.
Insights
Infant gut bacteria metabolites may influence social behavior. A specific fecal metabolite profile in 1-year-olds, with high lactate and low short-chain fatty acids, was linked to fewer autism-related behaviors.
Area of Science:
- Microbiome research
- Neurodevelopmental science
- Metabolomics
Background:
- Gut-derived metabolites are products of microbial and host co-metabolism.
- These metabolites may offer insights into the mechanisms of children's neurodevelopment.
- Previous research on gut-brain interactions and neurodevelopmental outcomes has yielded inconsistent findings.
Purpose of the Study:
- To investigate the association between infant fecal metabolites and subsequent toddler social behavior.
- To explore potential gut microbiome contributions to neurodevelopmental trajectories.
- To identify specific metabolite profiles linked to social behavior in early childhood.
Main Methods:
- Stool samples were collected from infants at 6 weeks and 1 year of age.
- Metabolomic analysis was performed using nuclear magnetic resonance spectroscopy.
- Social behavior in 3-year-olds was assessed using the Social Responsiveness Scale (SRS-2).
- Statistical approaches included single-metabolite analysis, quantitative metabolite set enrichment (QEA), and self-organizing maps (SOMs).
Main Results:
- No individual fecal metabolite or metabolite set from 6-week-olds or 1-year-olds was associated with SRS-2 scores using single-metabolite or QEA methods.
- Self-organizing maps (SOMs) identified four fecal metabolite profiles in 6-week-olds, unrelated to SRS-2 scores.
- In 1-year-olds, one of twelve SOM-identified metabolite profiles was associated with significantly lower SRS-2 scores, indicating fewer autism-related behaviors.
- This specific profile was characterized by higher concentrations of lactate and lower concentrations of short-chain fatty acids.
Conclusions:
- Specific fecal metabolite profiles in 1-year-old infants are associated with subsequent social behavior.
- The identified profile, high in lactate and low in short-chain fatty acids, suggests a potential link between gut microbial metabolism and neurobehavioral outcomes.
- The application of SOMs, a machine learning algorithm, proved effective in uncovering novel associations between metabolite patterns and social behavior.
Background:
Gut-derived metabolites, products of microbial and host co-metabolism, may inform mechanisms underlying children's neurodevelopment. We investigated whether infant fecal metabolites were related to toddler social behavior.
Methods:
Stool samples collected from 6-week-olds (n = 86) and 1-year-olds (n = 209) in the New Hampshire Birth Cohort Study (NHBCS) were analyzed using nuclear magnetic resonance spectroscopy metabolomics. Autism-related behavior in 3-year-olds was assessed by caregivers using the Social Responsiveness Scale (SRS-2). To assess the association between metabolites and SRS-2 scores, we used a traditional single-metabolite approach, quantitative metabolite set enrichment (QEA), and self-organizing maps (SOMs).
Results:
Using a single-metabolite approach and QEA, no individual fecal metabolite or metabolite set at either age was associated with SRS-2 scores. Using the SOM method, fecal metabolites of six-week-olds organized into four profiles, which were unrelated to SRS-2 scores. In 1-year-olds, one of twelve fecal metabolite profiles was associated with fewer autism-related behaviors, with SRS-2 scores 3.4 (95%CI: -7, 0.2) points lower than the referent group. This profile had higher concentrations of lactate and lower concentrations of short chain fatty acids than the reference.
Conclusions:
We uncovered metabolic profiles in infant stool associated with subsequent social behavior, highlighting one potential mechanism by which gut bacteria may influence neurobehavior.
Impact:
Differences in host and microbial metabolism may explain variability in neurobehavioral phenotypes, but prior studies do not have consistent results. We applied three statistical techniques to explore fecal metabolite differences related to social behavior, including self-organizing maps (SOMs), a novel machine learning algorithm. A 1-year-old fecal metabolite pattern characterized by high lactate and low short-chain fatty acid concentrations, identified using SOMs, was associated with social behavior less indicative of autism spectrum disorder. Our findings suggest that social behavior may be related to metabolite profiles and that future studies may uncover novel findings by applying the SOM algorithm.
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