Impact of Alterations to Early Life Microbiota (Antibiotics, Prebiotics, and C-Section) on Body Weight and Brain Development
Bibliographic record
Abstract
Background: Communication between the gut microbiota, gut, and brain is now known to influence behavior, metabolic health, and immunity. Foods that improve the composition of the gut microbiota as well as factors that can disrupt it during early development are of interest to investigate the origins of and possible therapeutics for chronic diseases like obesity and mood and neurodevelopmental disorders. Objective: This dissertation examines how early life perturbations to gut microbiota, such as maternal antibiotic use, prebiotic consumption, maternal obesity, and C-section (CS) alter microbial, metabolic, behavioral, and brain outcomes. Specifically, the objectives of this thesis were to: 1) assess the impact of CS in offspring of lean and obese dams on offspring obesity risk; 2) examine the impact of CS and maternal prebiotic intake on offspring microglia and neuron morphology; 3) determine the effects of maternal antibiotic/prebiotic intake on offspring behavior and microglia reactivity; 4) investigate the effects of maternal and child antibiotic use on BMI z-score and temperament in 3-year-old children in a clinical cohort.Methods: Animal studies were conducted in Sprague-Dawley rats or NIH Swiss mice. Clinical participant data was accessed from the Alberta Pregnancy and Nutrition Outcomes (APrON) study. Body composition was measured with dual x-ray absorptiometry (DXA). Elevated Plus Maze (EPM), Novel Context Mismatch (NCM), and Sucrose Preference Tests (SPT) were used to assess behavior. Microglia were analyzed using the expression of IBA-1. Gut microbiota was assessed using 16S rRNA gene sequencing, and tissue gene expression was measured using RT-PCR. Temperament data was assessed using the Child Behavior Questionnaire (CBQ).Results: The primary findings from our study objectives were: 1) CS-associated obesity risk in offspring is dependent on maternal obesity status; 2) Maternal prebiotic intake reverses CS-induced alterations to microglia; 3) Antibiotic-induced alterations to microglia are reversed by maternal prebiotic intake; 4) Maternal and child antibiotic use is associated with increased BMI z-score in children. Conclusion: Our results provide evidence that early-life microbial perturbations alter obesity risk and microglia activation. We also demonstrated the potential for prebiotics to reduce the neuroinflammation that could modulate the risk for mood and neuropsychiatric disorders.
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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 | 0.000 |
| 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 | 0.001 |
| 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".