Multigenerational adversity impacts on gut microbiome composition and socioemotional functioning in early childhood
Bibliographic record
Abstract
Both prenatal and postnatal exposure to early life adversity is associated with increased risk for psychopathology. The impacts of those adversity exposures can also persist across generations, although the mechanisms for intergenerational transmission remain unclear. Emerging evidence from non-human animal models suggests that the gut microbiome (the collection of microorganisms living in the gastrointestinal tract) may be a biological mechanism underlying those increased transmitted risks, but this hypothesis has not been directly tested in humans. In a sample of 450 mother-child dyads, we examined how three adversity exposures experienced across two generations: maternal childhood maltreatment (generation 1), maternal prenatal anxiety (generation 1 and 2), and children’s exposure to stressful life events (generation 2), as well as the accumulation of adversity exposure across these time points, is associated with children’s gut microbiome composition at 2 years of age (generation 2). We then explored associations between second generation children’s gut microbiome at 2 years of age and socioemotional functioning at 2 and 4 years of age. We found distinct differences in gut microbiome composition as a function of each adversity exposure, some of which overlapped with microbiome profiles associated with concurrent and prospective child socioemotional functioning. These results highlight an intergenerational impact of adversity on the microbiome of children that may increase their vulnerability to future mental illness. That several of these taxonomic differences may reflect shared functional impacts, e.g. on immune system regulation, across adversity exposures, motivates future research characterizing microbiome functional potential in the context of intergenerational adversity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".