Gut Microbiota: Association with Fiber Intake, Ultra-Processed Food Consumption, Sex, Body Mass Index, and Socioeconomic Status in Medical Students
Bibliographic record
Abstract
The gut microbiota plays a vital role in various physical and physiological processes, including immune system regulation, neurotransmitter production, inflammatory response modulation, and the inhibition of pathogenic organisms. An imbalance in the microbial community, known as dysbiosis, has been associated with numerous health issues. Biological influences, health behaviors, socioeconomic determinants, and nutritional status can disrupt this balance. OBJECTIVE: To evaluate the differences in the gut microbiota composition in medical students according to fiber intake, ultra-processed food (UPF) consumption, sex, body mass index, and socioeconomic status. METHODS: A cross-sectional study was conducted with 91 medical students, and 82 fecal samples were analyzed. Sociodemographic and dietary data were collected via questionnaires, UPF consumption was assessed using the NOVA classification, and trained nutritionists performed anthropometry. DNA extraction and 16S rRNA sequencing were performed for the microbial analysis. Bioinformatics and statistical tests included the Dunn and Kruskal-Wallis tests, a PCoA analysis, PERMANOVA, ANOVA, Spearman's rank correlation, and alpha and beta diversity metrics. RESULTS: was prevalent in those with higher UPF consumption. Significant differences were associated with sex and UPF consumption but not BMI or SES. Women consumed more UPF, which correlated with distinct gut microbiota profiles. CONCLUSIONS: This study highlights the significant impact of diet, particularly fiber intake and UPF, on gut microbiota composition, emphasizing the importance of dietary habits in maintaining gut health.
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 | 0.001 | 0.001 |
| 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".