2026-04-17T01:43:40Zhttps://recercat.cat/oai/request
oai:recercat.cat:2445/1742162026-02-20T06:30:19Zcom_2072_1057col_2072_478916col_2072_478917
RECERCAT author Aleksandrova, Krasimira author Reichmann, Robin author Kaaks, Rudolf author Jenab, Mazda author Bueno de Mesquita, H. Bas author Dahm, Christina C. author Eriksen, Anne Kirstine author Tjønneland, Anne author Artaud, Fanny author Boutron-Ruault, Marie-Christine author Severi, Gianluca author Hüsing, Anika author Trichopoulou, Antonia author Karakatsani, Anna author Peppa, Eleni author Panico, Salvatore author Masala, Giovanna author Grioni, Sara author Sacerdote, Carlotta author Tumino, Rosario author Elias, Sjoerd G. author May, Anne M. author Borch, Kristin B. author Sandanger, Torkjel M. author Skeie, Guri author Sánchez, María José author Huerta Castaño, José María author Sala Serra, Núria author Barricarte, Aurelio author Quirós, José Ramón author Amiano, Pilar author Berntsson, Jonna author Drake, Isabel author van Guelpen, Bethany author Harlid, Sophia author Key, Tim author Weiderpass, Elisabete author Aglago, Elom K. author Cross, Amanda J. author Tsilidis, Konstantinos K. author Riboli, Elio author Gunter, Marc J. 2021-02-24T12:03:38Z2021-02-24T12:03:38Z2021-12-042021-02-08T10:34:13Z 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. cc by (c) Aleksandrova et al., 2020 http://creativecommons.org/licenses/by/3.0/es/ info:eu-repo/semantics/openAccess Càncer colorectal Estils de vida Colorectal cancer Lifestyles Development and validation of a lifestyle-based model for colorectal cancer risk prediction: the LiFeCRC score info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion