Krasimira Aleksandrova1,2,3, Robin Reichmann4,5, Rudolf Kaaks6, Mazda Jenab7, H Bas Bueno-de-Mesquita8,9, Christina C Dahm10, Anne Kirstine Eriksen11, Anne Tjønneland11, Fanny Artaud12,13, Marie-Christine Boutron-Ruault12,13, Gianluca Severi12,13,14, Anika Hüsing6, Antonia Trichopoulou15, Anna Karakatsani15,16, Eleni Peppa15, Salvatore Panico17, Giovanna Masala18, Sara Grioni19, Carlotta Sacerdote20, Rosario Tumino21, Sjoerd G Elias22, Anne M May22, Kristin B Borch23, Torkjel M Sandanger23, Guri Skeie23, Maria-Jose Sánchez24,25,26,27, José María Huerta26,28, Núria Sala29,30, Aurelio Barricarte Gurrea26,31,32, José Ramón Quirós33, Pilar Amiano26,34, Jonna Berntsson35, Isabel Drake36, Bethany van Guelpen37,38, Sophia Harlid37, Tim Key39, Elisabete Weiderpass7, Elom K Aglago7, Amanda J Cross9, Konstantinos K Tsilidis9,40, Elio Riboli9, Marc J Gunter7. 1. Nutrition, Immunity and Metabolism Senior Scientist Group, Department of Nutrition and Gerontology, German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Nuthetal, Germany. aleksandrova@leibniz-bips.de. 2. Institute of Nutritional Science, University of Potsdam, Potsdam, Germany. aleksandrova@leibniz-bips.de. 3. Department of Epidemiological Methods and Etiological Research, Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany. aleksandrova@leibniz-bips.de. 4. Nutrition, Immunity and Metabolism Senior Scientist Group, Department of Nutrition and Gerontology, German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Nuthetal, Germany. 5. Institute of Nutritional Science, University of Potsdam, Potsdam, Germany. 6. Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany. 7. International Agency for Research on Cancer, World Health Organization, Lyon, France. 8. National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands. 9. Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK. 10. Department of Public Health, Aarhus University, Aarhus, Denmark. 11. Danish Cancer Society Research Center, Copenhagen, Denmark. 12. CESP, Faculté de Medicine, Université Paris-Saclay, Villejuif, France. 13. Institut Gustave Roussy, Villejuif, France. 14. Dipartimento di Statistica, Informatica e Applicazioni "G. Parenti" (DISIA), University of Florence, Florence, Italy. 15. Hellenic Health Foundation, Athens, Greece. 16. 2nd Pulmonary Medicine Department, School of Medicine, National and Kapodistrian University of Athens, "ATTIKON" University Hospital, Haidari, Greece. 17. EPIC Centre of Naples, Dipartimento di Medicina Clinica e Chirurgia, University of Naples Federico II, Naples, Italy. 18. Cancer Risk Factors and Lifestyle Epidemiology Unit, Institute for Cancer Research, Prevention and Clinical Network - ISPRO, Florence, Italy. 19. Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy. 20. Unit of Cancer Epidemiology, Città della Salute e della Scienza University-Hospital and Center for Cancer Prevention (CPO), Turin, Italy. 21. Cancer Registry and Histopathology Department, Provincial Health Authority (ASP), Ragusa, Italy. 22. Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands. 23. Department of Community Medicine, Health Faculty, UiT-the Arctic university of Norway, Tromsø, Norway. 24. Escuela Andaluza de Salud Pública (EASP), Granada, Spain. 25. Instituto de Investigación Biosanitaria ibs. GRANADA, Granada, Spain. 26. Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain. 27. Universidad de Granada, Granada, Spain. 28. Department of Epidemiology, Murcia Regional Health Council, IMIB-Arrixaca, Murcia, Spain. 29. Unit of Nutrition and Cancer, Cancer Epidemiology Research Program, Translational Research Laboratory, Catalan Institute of Oncology (ICO), Barcelona, Spain. 30. Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain. 31. Navarra Public Health Institute, Pamplona, Spain. 32. Navarra Institute for Health Research (IdiSNA), Pamplona, Spain. 33. Public Health Directorate, Asturias, Spain. 34. Ministry of Health of the Basque Government, Public Health Division of Gipuzkoa, Biodonostia Health Research Institute, Donostia-San Sebastian, Spain. 35. Department of Clinical Sciences, Division of Oncology and Pathology, Lund University, Lund, Sweden. 36. Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden. 37. Department of Radiation Sciences, Oncology, Umeå University, Umeå, Sweden. 38. Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden. 39. Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK. 40. Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece.
Abstract
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.
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.
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