Multigenerational adversity impacts on human gut microbiome composition and socioemotional functioning in early childhood
Bibliographic record
Abstract
Adversity exposures in the prenatal and postnatal period are associated with an increased risk for psychopathology, which can be perpetuated across generations. Nonhuman animal research highlights the gut microbiome as a putative biological mechanism underlying such generational risks. In a sample of 450 mother-child dyads living in Singapore, we examined associations between three distinct adversity exposures experienced across two generations-maternal childhood maltreatment, maternal prenatal anxiety, and second-generation children's exposure to stressful life events-and the gut microbiome composition of second-generation children at 2 y of age. We found distinct differences in gut microbiome profiles linked to each adversity exposure, as well as some nonaffected microbiome features (e.g., beta diversity). Remarkably, some of the microbial taxa associated with concurrent and prospective child socioemotional functioning shared overlapping putative functions with those affected by adversity, suggesting that the intergenerational transmission of adversity may have a lasting impact on children's mental health via alterations to gut microbiome functions. Our findings open up a new avenue of research into the underlying mechanisms of intergenerational transmission of mental health risks and the potential of the gut microbiome as a target for intervention.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".