Metagenomic Insights into the Impact of Nutrition on Human Gut Microbiota and Associated Disease Risk
Bibliographic record
Abstract
Metagenomic investigation of gut microbiome is a comprehensive and rapid technique for the analysis and diagnosis of numerous diseases. The gut microbiome is an intricate ecosystem, coordinated by the interaction of various microbes and the metabolites produced by them, which helps in developing and sustaining immunity and homeostasis. A healthy gut microbiome is driven by different factors, such as nutrition, lifestyle, etc. The current study examines the association of diet to gut microbiome dysbiosis and its role in various disease conditions. Gut microbiome data was collected from 73 patients and tested at BioAro Inc. lab, using shotgun metagenomics through next generation sequencing. It was then analyzed and compared with data from 20 healthy subjects from HMP database. An in-house bioinformatics pipeline (PanOmiQ) and Pathogen Fast Identifier were utilized for secondary analysis, while tertiary analysis was accomplished using R software. Results showed a higher number of opportunistic pathogen microorganisms in the gut microbiome of subjects consuming a meat diet, as compared to those consuming a plant diet. These opportunistic pathogens included Ruminococcus torques (>3.34%), Ruminococcus gnavus (>2.22%), and Clostridium symbiosum (>1.87%). The study also found a higher relative abundance of these pathogens in cancer patients, as compared to healthy subjects. We also observed a highly significant (p < 0.0001) correlation of a meat diet with obesity in comparison to the subjects on a plant diet and the healthy subjects. Our findings suggest that patients following a plant diet have a lower relative abundance of pathogens that are associated with cancer and obesity. These findings provide critical insight into how we can use shotgun metagenomics to study the composition and diversity of the gut microbiome and the effects of a diet on the gut microbiome and its role in metabolic diseases. This is the first report investigating gut microbiota using shotgun metagenomics, correlating with different diseases and diet followed, which might impact the presence of opportunistic pathogens or keystones species. Additionally, it can provide valuable insights to physicians and dietetic practitioners for providing personalized treatment or customizing a diet plan.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".