Development and validation of a lifestyle-based model for colorectal cancer risk prediction: the LiFeCRC score
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 implem...
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Online Access: | https://doi.org/10.1186/s12916-020-01826-0 |
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author |
Krasimira Aleksandrova Robin Reichmann Rudolf Kaaks Mazda Jenab H. Bas Bueno-de-Mesquita Christina C. Dahm Anne Kirstine Eriksen Anne Tjønneland Fanny Artaud Marie-Christine Boutron-Ruault Gianluca Severi Anika Hüsing Antonia Trichopoulou Anna Karakatsani Eleni Peppa Salvatore Panico Giovanna Masala Sara Grioni Carlotta Sacerdote Rosario Tumino Sjoerd G. Elias Anne M. May Kristin B. Borch Torkjel M. Sandanger Guri Skeie Maria-Jose Sánchez José María Huerta Núria Sala Aurelio Barricarte Gurrea José Ramón Quirós Pilar Amiano Jonna Berntsson Isabel Drake Bethany van Guelpen Sophia Harlid Tim Key Elisabete Weiderpass Elom K. Aglago Amanda J. Cross Konstantinos K. Tsilidis Elio Riboli Marc J. Gunter |
spellingShingle |
Krasimira Aleksandrova Robin Reichmann Rudolf Kaaks Mazda Jenab H. Bas Bueno-de-Mesquita Christina C. Dahm Anne Kirstine Eriksen Anne Tjønneland Fanny Artaud Marie-Christine Boutron-Ruault Gianluca Severi Anika Hüsing Antonia Trichopoulou Anna Karakatsani Eleni Peppa Salvatore Panico Giovanna Masala Sara Grioni Carlotta Sacerdote Rosario Tumino Sjoerd G. Elias Anne M. May Kristin B. Borch Torkjel M. Sandanger Guri Skeie Maria-Jose Sánchez José María Huerta Núria Sala Aurelio Barricarte Gurrea José Ramón Quirós Pilar Amiano Jonna Berntsson Isabel Drake Bethany van Guelpen Sophia Harlid Tim Key Elisabete Weiderpass Elom K. Aglago Amanda J. Cross Konstantinos K. Tsilidis Elio Riboli Marc J. Gunter Development and validation of a lifestyle-based model for colorectal cancer risk prediction: the LiFeCRC score BMC Medicine Colorectal cancer Risk prediction Lifestyle behaviour Risk screening Cancer prevention |
author_facet |
Krasimira Aleksandrova Robin Reichmann Rudolf Kaaks Mazda Jenab H. Bas Bueno-de-Mesquita Christina C. Dahm Anne Kirstine Eriksen Anne Tjønneland Fanny Artaud Marie-Christine Boutron-Ruault Gianluca Severi Anika Hüsing Antonia Trichopoulou Anna Karakatsani Eleni Peppa Salvatore Panico Giovanna Masala Sara Grioni Carlotta Sacerdote Rosario Tumino Sjoerd G. Elias Anne M. May Kristin B. Borch Torkjel M. Sandanger Guri Skeie Maria-Jose Sánchez José María Huerta Núria Sala Aurelio Barricarte Gurrea José Ramón Quirós Pilar Amiano Jonna Berntsson Isabel Drake Bethany van Guelpen Sophia Harlid Tim Key Elisabete Weiderpass Elom K. Aglago Amanda J. Cross Konstantinos K. Tsilidis Elio Riboli Marc J. Gunter |
author_sort |
Krasimira Aleksandrova |
title |
Development and validation of a lifestyle-based model for colorectal cancer risk prediction: the LiFeCRC score |
title_short |
Development and validation of a lifestyle-based model for colorectal cancer risk prediction: the LiFeCRC score |
title_full |
Development and validation of a lifestyle-based model for colorectal cancer risk prediction: the LiFeCRC score |
title_fullStr |
Development and validation of a lifestyle-based model for colorectal cancer risk prediction: the LiFeCRC score |
title_full_unstemmed |
Development and validation of a lifestyle-based model for colorectal cancer risk prediction: the LiFeCRC score |
title_sort |
development and validation of a lifestyle-based model for colorectal cancer risk prediction: the lifecrc score |
publisher |
BMC |
series |
BMC Medicine |
issn |
1741-7015 |
publishDate |
2021-01-01 |
description |
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. |
topic |
Colorectal cancer Risk prediction Lifestyle behaviour Risk screening Cancer prevention |
url |
https://doi.org/10.1186/s12916-020-01826-0 |
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doaj-fbbdb2a532a54f3a9e7bbb1302b38baa2021-01-10T12:43:24ZengBMCBMC Medicine1741-70152021-01-0119111910.1186/s12916-020-01826-0Development and validation of a lifestyle-based model for colorectal cancer risk prediction: the LiFeCRC scoreKrasimira Aleksandrova0Robin Reichmann1Rudolf Kaaks2Mazda Jenab3H. Bas Bueno-de-Mesquita4Christina C. Dahm5Anne Kirstine Eriksen6Anne Tjønneland7Fanny Artaud8Marie-Christine Boutron-Ruault9Gianluca Severi10Anika Hüsing11Antonia Trichopoulou12Anna Karakatsani13Eleni Peppa14Salvatore Panico15Giovanna Masala16Sara Grioni17Carlotta Sacerdote18Rosario Tumino19Sjoerd G. Elias20Anne M. May21Kristin B. Borch22Torkjel M. Sandanger23Guri Skeie24Maria-Jose Sánchez25José María Huerta26Núria Sala27Aurelio Barricarte Gurrea28José Ramón Quirós29Pilar Amiano30Jonna Berntsson31Isabel Drake32Bethany van Guelpen33Sophia Harlid34Tim Key35Elisabete Weiderpass36Elom K. Aglago37Amanda J. Cross38Konstantinos K. Tsilidis39Elio Riboli40Marc J. Gunter41Nutrition, Immunity and Metabolism Senior Scientist Group, Department of Nutrition and Gerontology, German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE)Nutrition, Immunity and Metabolism Senior Scientist Group, Department of Nutrition and Gerontology, German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE)Division of Cancer Epidemiology, German Cancer Research Center (DKFZ)International Agency for Research on Cancer, World Health OrganizationNational Institute for Public Health and the Environment (RIVM)Department of Public Health, Aarhus UniversityDanish Cancer Society Research CenterDanish Cancer Society Research CenterCESP, Faculté de Medicine, Université Paris-SaclayCESP, Faculté de Medicine, Université Paris-SaclayCESP, Faculté de Medicine, Université Paris-SaclayDivision of Cancer Epidemiology, German Cancer Research Center (DKFZ)Hellenic Health FoundationHellenic Health FoundationHellenic Health FoundationEPIC Centre of Naples, Dipartimento di Medicina Clinica e Chirurgia, University of Naples Federico IICancer Risk Factors and Lifestyle Epidemiology Unit, Institute for Cancer Research, Prevention and Clinical Network – ISPROEpidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori di MilanoUnit of Cancer Epidemiology, Città della Salute e della Scienza University-Hospital and Center for Cancer Prevention (CPO)Cancer Registry and Histopathology Department, Provincial Health Authority (ASP)Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht UniversityJulius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht UniversityDepartment of Community Medicine, Health Faculty, UiT-the Arctic university of NorwayDepartment of Community Medicine, Health Faculty, UiT-the Arctic university of NorwayDepartment of Community Medicine, Health Faculty, UiT-the Arctic university of NorwayEscuela Andaluza de Salud Pública (EASP)Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP)Unit of Nutrition and Cancer, Cancer Epidemiology Research Program, Translational Research Laboratory, Catalan Institute of Oncology (ICO)Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP)Public Health DirectorateCentro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP)Department of Clinical Sciences, Division of Oncology and Pathology, Lund UniversityDepartment of Clinical Sciences in Malmö, Lund UniversityDepartment of Radiation Sciences, Oncology, Umeå UniversityDepartment of Radiation Sciences, Oncology, Umeå UniversityCancer Epidemiology Unit, Nuffield Department of Population Health, University of OxfordInternational Agency for Research on Cancer, World Health OrganizationInternational Agency for Research on Cancer, World Health OrganizationDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College LondonDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College LondonDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College LondonInternational Agency for Research on Cancer, World Health OrganizationAbstract 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.https://doi.org/10.1186/s12916-020-01826-0Colorectal cancerRisk predictionLifestyle behaviourRisk screeningCancer prevention |