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Updated: May 17, 2025
A Method to Define the Effects of Environmental Enrichment on Colon Microbiome Biodiversity in a Mouse Colon Tumor Model
Published on: February 28, 2018
Derivation and validation of lifestyle-based and microbiota-based models for colorectal adenoma risk evaluation and
Yi-Lu Zhou1, Jia-Wen Deng1, Zhu-Hui Liu1
1Division of Gastroenterology and Hepatology, NHC Key Laboratory of Digestive Diseases, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Objective:
Early warning and screening of colorectal adenoma (CRA) is important for colorectal cancer (CRC) prevention. This study aimed to construct a non-invasive prediction model to improve CRA screening efficacy.
Methods:
This study incorporated three cohorts, comprising 9747 participants who underwent colonoscopy. In cohort 1, 683 participants were prospectively recruited with comprehensive lifestyle information and faecal samples. CRA-associated bacteria were identified through 16S rRNA sequencing and quantitative real-time PCR. CRA prediction models were established using lifestyle and gut microbiota information. Cohort 2 prospectively enrolled 1529 participants to validate the lifestyle-based model, while cohort 3 retrospectively analysed 7535 individuals to determine the recommended initial colonoscopy screening ages for different risk groups based on age-specific CRA incidence rates.
Results:
Multivariable logistic regression yielded a prediction model incorporating 14 variables, demonstrating robust discrimination (c-statistic=0.79, 95% CI 0.75, 0.82). Other machine learning approaches showed comparable performance (random forest: 0.78, 95% CI 0.73, 0.81; gradient boosting: 0.78, 95% CI 0.76, 0.83). The ages for starting colonoscopy screening were established at 42 years for the high-risk group vs 53 years for the low-risk group. The inclusion of Fusobacterium nucleatum and pks+ Escherichia coli enhanced the model's performance (c-statistic=0.84-0.86).
Conclusion:
Integrated mathematical modelling incorporating lifestyle parameters and gut microbial signatures provides an effective non-invasive strategy for CRA risk stratification, while the accompanying machine learning-assisted prediction application enables cost-effective, population-level screening implementation to optimise CRC prevention protocols.
