A18 INTERKINGDOM DYNAMICS AND NUTRITION ARE ASSOCIATED WITH DIVERGENT MATURATIONAL PATTERNS OF THE INFANT BACTERIAL AND FUNGAL GUT MICROBIOME
Bibliographic record
Abstract
Abstract Background Early life has been identified as a critical window, during which time deviations from typical patterns of gut microbiome maturation have been associated with adverse health outcomes later in life. In the first 2-3 years of life, the infant gut microbiome undergoes ecological shifts characterized by increasing bacterial alpha diversity and variable changes in fungal alpha diversity. Research has shown not all infants follow these maturational trends, but our understandings of the factors linked to atypical microbiome maturation patterns are limited. Purpose We assessed bacterial and fungal gut microbiome maturation in early life to determine if atypical maturational patterns were observed in otherwise healthy infants and identify factors associated with these patterns. Method In 100 infants from the CHILD Cohort Study, we assessed the bacterial and fungal gut microbiome in stool samples collected at 3 and 12 months of age using 16S and ITS2 Illumina sequencing, respectively. We performed untargeted metabolomics on urine samples collected at 3 and 12 months using liquid chromatography-mass spectrometry/mass spectrometry. Microbiome and metabolomic measures were evaluated by ecological and multivariate analyses using RStudio. Result(s) Gut microbiome analyses revealed 24% and 20% of infants displayed atypical alpha diversity trajectories in the first year of life for bacteria or fungi, respectively. Atypical patterns were linked to reduced abundance of Bacteroides and increased Candida at 3 months. Functional analysis revealed an atypical bacterial alpha diversity trend was associated with elevated urinary trimethylamine N-oxide, creatine, indole acetic acid, and 2-furoylglycine, and an atypical fungal trend was associated with elevated urinary lactate. Using decision trees, the strongest predictors of atypical alpha diversity trends were interkingdom dynamics, breastfeeding duration, and maternal diet during pregnancy. Logistic regression revealed an atypical bacterial trend was positively associated with delivery via C-section and inversely associated with exclusive breastfeeding at 3 months, and an atypical fungal trend was positively associated with gestational consumption of artificially sweetened beverages and inversely associated with prenatal antibiotics. Interkingdom network analyses revealed the gut microbiome of infants with an atypical bacterial or fungal alpha diversity trend displayed a greater number of interkingdom interactions reflective of a less stable or immature gut microbiome. Conclusion(s) Our findings reveal a substantial proportion of infants display atypical patterns of gut microbiome maturation in the first year of life. While known microbiome-modifying factors were important determinants of maturational patterns, these factors were generally less influential than interkingdom influences. Together, this highlights the importance of interkingdom analyses at the individual level to generate more nuanced understandings of maturational trajectories in early life. Please acknowledge all funding agencies by checking the applicable boxes below CIHR, Other Please indicate your source of funding; Alberta Children's Hospital Research Institute Disclosure of Interest None Declared
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".