Association Between Gut Microbiota Diversity and Body Mass Index (BMI) in Healthy Young Adults in the United States: Insights Into the Gut-Brain-Metabolic Axis Using the Curated Metagenomic Data
Bibliographic record
Abstract
BACKGROUND: Emerging evidence suggests that gut microbiota diversity plays a critical role in metabolic regulation and may influence body mass index (BMI). However, findings in healthy populations remain inconsistent. OBJECTIVE: This study aims to determine whether gut microbiota alpha-diversity is associated with BMI among healthy young adults aged 18-39 years in the United States and to explore potential implications for the gut-brain-metabolic axis. METHODS: This cross-sectional study utilized publicly available metagenomic data from the CuratedMetagenomicData repository. After preprocessing in R version 4.5.0 (R Foundation for Statistical Computing, Vienna, Austria), data were analyzed using Stata version 18 (Released 2023; StataCorp LLC, College Station, TX). Alpha-diversity indices (Shannon, Simpson, and Richness) were computed and examined across BMI categories (normal, overweight, and obese) using one-way analysis of variance (ANOVA) and chi-square tests. Linear regression models were employed to assess associations between BMI and diversity measures, adjusting for age and gender. RESULTS: Among 147 participants, BMI differed significantly across weight categories (p < 0.001), but no significant association was observed between Shannon diversity and BMI (p = 0.527). Age emerged as the only significant predictor of BMI in adjusted models (p < 0.001). CONCLUSION: Gut microbial alpha-diversity was not significantly associated with BMI among healthy young adults. Functional microbial characteristics, rather than diversity alone, may better explain variations in metabolic status.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".