Association of maternal postpartum depression symptoms with infant neurodevelopment and gut microbiota
Bibliographic record
Abstract
Introduction Understanding the mechanisms underlying maternal postpartum depression (PPD) and its effects on offspring development is crucial. However, research on the association between maternal PPD, gut microbiota, and offspring neurodevelopment remains limited. This study aimed to examine the association of maternal PPD symptoms with early gut microbiome, gut metabolome, and neurodevelopment in infants at 6 months. Methods Maternal PPD symptoms were assessed using the Edinburgh Postpartum Depression Scale (EPDS) at 42 days postpartum. Infants stool samples collected at 42 days after birth were analyzed using 16S rRNA sequencing and liquid chromatography–mass spectrometry (LC–MS) detection. Infant neurodevelopment was measured at 6 months using the Ages and Stages Questionnaire, Third Edition (ASQ-3). Correlations between gut microbiota, metabolites and neurodevelopment were identified through co-occurrence network analysis. Finally, mediation analyses were conducted to determine potential causal pathways. Results A total of 101 mother-infant dyads were included in the final analysis. Infants born to mothers with PPD symptoms at 42 days postpartum had lower neurodevelopmental scores at 6 months. These infants also had increased alpha diversity of gut microbiota and were abundant in Veillonella and Finegoldia, while depleted abundance of Bifidobacterium, Dialister, Cronobacter and Megasphaera. Furthermore, alterations were observed in metabolite levels linked to the Alanine, aspartate, and glutamate metabolic pathway, primarily characterized by decreases in N-Acetyl-L-aspartic acid, L-Aspartic acid, and L-Asparagine. Co-occurrence network and mediation analyses revealed that N-Acetyl-L-aspartic acid and L-Aspartic acid levels mediated the relationship between maternal PPD symptoms and the development of infant problem-solving skills. Conclusions Maternal PPD symptoms are associated with alterations in the gut microbiota and neurodevelopment in infants. This study provides new insights into potential early intervention for infants whose mother experienced PPD. Further research is warranted to elucidate the biological mechanisms underlying these associations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".