Background Nutrition and lifestyle have been long established as risk factors for colorectal cancer (CRC). Modifiable lifestyle behaviours bear potential to minimize long-term CRC risk; however, translation of lifestyle information into individualized CRC risk assessment has not been implemented. Lifestyle-based risk models may aid the identification of high-risk individuals, guide referral to screening and motivate behaviour change. We therefore developed and validated a lifestyle-based CRC risk prediction algorithm in an asymptomatic European population. Methods The model was based on data from 255,482 participants in the European Prospective Investigation into Cancer and Nutrition (EPIC) study aged 19 to 70 years who were free of cancer at study baseline (1992–2000) and were followed up to 31 September 2010. The model was validated in a sample comprising 74,403 participants selected among five EPIC centres. Over a median follow-up time of 15 years, there were 3645 and 981 colorectal cancer cases in the derivation and validation samples, respectively. Variable selection algorithms in Cox proportional hazard regression and random survival forest (RSF) were used to identify the best predictors among plausible predictor variables. Measures of discrimination and calibration were calculated in derivation and validation samples. To facilitate model communication, a nomogram and a web-based application were developed. Results The final selection model included age, waist circumference, height, smoking, alcohol consumption, physical activity, vegetables, dairy products, processed meat, and sugar and confectionary. The risk score demonstrated good discrimination overall and in sex-specific models. Harrell’s C-index was 0.710 in the derivation cohort and 0.714 in the validation cohort. The model was well calibrated and showed strong agreement between predicted and observed risk. Random survival forest analysis suggested high model robustness. Beyond age, lifestyle data led to improved model performance overall (continuous net reclassification improvement = 0.307 (95% CI 0.264–0.352)), and especially for young individuals below 45 years (continuous net reclassification improvement = 0.364 (95% CI 0.084–0.575)). Conclusions LiFeCRC score based on age and lifestyle data accurately identifies individuals at risk for incident colorectal cancer in European populations and could contribute to improved prevention through motivating lifestyle change at an individual level.

Resistin is a polypeptide implicated in inflammatory processes, and as such could be linked to colorectal carcinogenesis. In case-control studies, higher resistin levels have been found in colorectal cancer (CRC) patients compared to healthy individuals. However, evidence for the association between pre-diagnostic resistin and CRC risk is scarce. We investigated pre-diagnostic resistin concentrations and CRC risk within the European Prospective Investigation into Cancer and Nutrition using a nested case-control study among 1293 incident CRC-diagnosed cases and 1293 incidence density-matched controls. Conditional logistic regression models controlled for matching factors (age, sex, study center, fasting status, and women-related factors in women) and potential confounders (education, dietary and lifestyle factors, body mass index (BMI), BMI-adjusted waist circumference residuals) were used to estimate relative risks (RRs) and 95% confidence intervals (CIs) for CRC. Higher circulating resistin concentrations were not associated with CRC (RR per doubling resistin, 1.11; 95% CI 0.94–1.30; p = 0.22). There were also no associations with CRC subgroups defined by tumor subsite or sex. However, resistin was marginally associated with a higher CRC risk among participants followed-up maximally two years, but not among those followed-up after more than two years. We observed no substantial correlation between baseline circulating resistin concentrations and adiposity measures (BMI, waist circumference), adipokines (adiponectin, leptin), or metabolic and inflammatory biomarkers (C-reactive protein, C-peptide, high-density lipoprotein cholesterol, reactive oxygen metabolites) among controls. In this large-scale prospective cohort, there was little evidence of an association between baseline circulating resistin concentrations and CRC risk in European men and women.

Objective: Chemerin is a novel inflammatory biomarker suggested to play a role in the development of metabolic disorders, providing new avenues for treatment and prevention. Little is known about the factors that predispose elevated chemerin concentrations. We therefore aimed to explore a range of lifestyle-associated, dietary, and metabolic factors as potential determinants of elevated chemerin concentrations in asymptomatic adults. Design: We used cross-sectional data from a random subsample of 2,433 participants (1,494 women, 939 men) aged 42-58 years of the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort. Methods: Random forest regression (RFR) was applied to explore the relative importance of 32 variables as statistical predictors of elevated chemerin concentrations overall and by sex. Multivariable-adjusted linear regression was applied to evaluate associations between selected predictors and chemerin concentrations. Results: Results from RFR suggested BMI, waist circumference, C-reactive protein, fatty liver index, and estimated glomerular filtration rate as the strongest predictors of chemerin concentrations. Additional predictors included sleeping duration, alcohol, red and processed meat, fruits, sugar sweetened beverages (SSB), vegetables, dairy and refined grains. Collectively, these factors explained 32.9% variation of circulating chemerin. Multivariable-adjusted analyses revealed linear associations of elevated chemerin with metabolic parameters, obesity, longer sleep, higher intakes of red meat and SSB, and lower intakes of dairy. Conclusions: These findings come in support of the role of chemerin as a biomarker characterizing inflammatory and metabolic phenotypes in asymptomatic adults. Modifiable dietary and lifestyle-associated determinants of elevated chemerin concentrations require further evaluation in a prospective study setting.