In a metagenomic analysis of fecal samples from patients and controls, we identified virome signatures associated with CRC. These data might be used to develop tools to identify individuals with CRC or predict outcomes.

The scoring system based on age, gender, smoking, family history, Body Mass Index and self-reported diabetes is useful in predicting the risk of colorectal neoplasia.

Background and Aim: We validated a modified risk algorithm based on the Asia-Pacific Colorectal Screening (APCS) score that included body mass index (BMI) for prediction of advanced neoplasia. Methods: Among 5744 Chinese asymptomatic screening participants undergoing a colonoscopy in Hong Kong from 2008 to 2012, a random sample of 3829 participants acted as the derivation cohort. The odds ratios for significant risk factors identified by binary logistic regression analysis were used to build a scoring system ranging from 0 to 6, divided into "average risk" (AR): 0; "moderate risk" (MR): 1-2; and "high risk" (HR): 3-6. The other 1915 subjects formed a validation cohort, and the performance of the score was assessed. Results: The prevalence of advanced neoplasia in the derivation and validation cohorts was 5.4% and 6.0%, respectively (P = 0.395). Old age, male gender, family history of colorectal cancer, smoking, and BMI were significant predictors in multivariate regression analysis. A BMI cut-off at > 23 kg/m 2 had better predictive capability and lower number needed to screen than that of > 25 kg/m 2 . Utilizing the score developed, 8.4%, 57.4%, and 34.2% in the validation cohort were categorized as AR, MR, and HR, respectively. The corresponding prevalence of advanced neoplasia was 3.8%, 4.3%, and 9.3%. Subjects in the HR group had 2.48-fold increased prevalence of advanced neoplasia than the AR group. The c-statistics of the modified score had better discriminatory capability than that using predictors of APCS alone (c-statistics = 0.65 vs 0.60). Conclusions: Incorporating BMI into the predictors of APCS score was found to improve risk prediction of advanced neoplasia and reduce colonoscopy resources.

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