From obesity to cancer: Gut microbiome mechanisms, biomarkers, and U.S. public health strategies
Bibliographic record
Abstract
BACKGROUND: Obesity, metabolic syndrome, and colorectal cancer (CRC) remain major public health challenges in the United States, collectively driving substantial morbidity, mortality, and economic burden. Beyond diet and genetics, the gut microbiome has emerged as a pivotal determinant of host metabolism, immunity, and carcinogenesis, influenced by both environmental and behavioral factors. OBJECTIVE: This review synthesizes current evidence linking gut microbial dysbiosis to obesity, metabolic syndrome, and CRC, emphasizing mechanistic pathways, environmental modifiers, and translational opportunities relevant to U.S. public health and precision medicine. METHODS: Comprehensive searches of PubMed and Scopus (2000-2025) identified large epidemiologic studies, mechanistic experiments, and clinical trials, prioritizing research from U.S. populations and nationally representative databases including NHANES, SEER, and the Nurses' Health Study. RESULTS: Microbial alterations such as enrichment of Fusobacterium nucleatum, enterotoxigenic Bacteroides fragilis, and colibactin-producing Escherichia coli contribute to CRC initiation and progression. In obesity and metabolic syndrome, shifts in Firmicutes-to-Bacteroidetes ratios, altered short-chain fatty acid metabolism, and endotoxin-mediated inflammation disrupt metabolic homeostasis. Environmental and lifestyle exposures, including air pollutants, smoking, and Westernized diets, modulate microbial ecology across the aerodigestive tract, affecting disease susceptibility. The emerging discipline of Molecular Pathological Epidemiology (MPE) integrates lifestyle, microbiome, and biomarker data to elucidate exposure-outcome relationships, enabling personalized prevention and therapeutic strategies. CONCLUSIONS: The gut microbiome functions as both a biomarker and therapeutic target across metabolic and neoplastic diseases. Integrating microbiome science with environmental epidemiology and MPE frameworks offers transformative potential for precision prevention and equitable public health strategies in the U.S.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".