Links Between Western Diet and The Human Gut Microbiome: A Literature Review
Bibliographic record
Abstract
Introduction: The human gut microbiome plays a crucial role in maintaining health by influencing immune function and metabolic processes. Diet is a major factor in shaping microbiome composition and can be linked to a range of chronic diseases. This systematic review examines how the Western diet alters gut microbiome composition and its subsequent effects on inflammation, metabolism, and immune health. Methods: This review synthesizes findings from thirteen studies examining the impact of the Western diet on gut microbiome composition and related health outcomes. The review analyzes these studies to identify key mechanisms linking diet to microbiome changes and immune function, while highlighting gaps in current research. Results: The review found that the Western diet leads to significant alterations in gut microbiome composition, including reduced microbial diversity and an imbalance in inflammatory responses. These changes are linked to the development of metabolic and immune diseases, such as obesity, type 2 diabetes, and inflammatory bowel disease. Diets such as the Mediterranean diet promote a more diverse and stable microbiome, with associated improvements in immune function and reduced disease risk. Discussion: This review highlights the need for further research on how diet shapes gut microbiome composition and its impact on immune and metabolic health. The Western diet’s disruption of microbial diversity and its promotion of inflammation contribute significantly to the rise of chronic diseases. In contrast, diets, such as the Mediterranean diet, appear to support a healthier microbiome and reduce disease risk through their anti-inflammatory and microbiome-supporting properties. Gaps remain in understanding the causal mechanisms behind diet-microbiome interactions, and further research is needed to explore personalized dietary interventions for disease prevention and optimal gut health. Conclusion: This review underscores the importance of diet in influencing gut microbiome composition and its role in immune and metabolic health. The findings suggest that dietary interventions may offer effective strategies to mitigate the negative effects of the Western diet on health. Future research should focus on understanding the causal mechanisms of diet-microbiome interactions and developing personalized nutrition approaches to improve gut health and prevent chronic diseases.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".