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1000 Titel
  • Development and validation of a lifestyle-based model for colorectal cancer risk prediction: the LiFeCRC score
1000 Autor/in
  1. Aleksandrova, Krasimira |
  2. Reichmann, Robin |
  3. Kaaks, Rudolf |
  4. Jenab, Mazda |
  5. Bueno-de-Mesquita, H. Bas |
  6. Dahm, Christina C. |
  7. Eriksen, Anne Kirstine |
  8. Tjønneland, Anne |
  9. Artaud, Fanny |
  10. Boutron-Ruault, Marie-Christine |
  11. Severi, Gianluca |
  12. Hüsing, Anika |
  13. Trichopoulou, Antonia |
  14. Karakatsani, Anna |
  15. Peppa, Eleni |
  16. Panico, Salvatore |
  17. Masala, Giovanna |
  18. Grioni, Sara |
  19. Sacerdote, Carlotta |
  20. Tumino, Rosario |
  21. Elias, Sjoerd G. |
  22. May, Anne M. |
  23. Borch, Kristin B. |
  24. Sandanger, Torkjel M. |
  25. Skeie, Guri |
  26. Sánchez, Maria-Jose |
  27. Huerta, José María |
  28. Sala, Núria |
  29. Gurrea, Aurelio Barricarte |
  30. Quirós, José Ramón |
  31. Amiano, Pilar |
  32. Berntsson, Jonna |
  33. Drake, Isabel |
  34. van Guelpen, Bethany |
  35. Harlid, Sophia |
  36. Key, Tim |
  37. Weiderpass, Elisabete |
  38. Aglago, Elom K. |
  39. Cross, Amanda J. |
  40. Tsilidis, Konstantinos K. |
  41. Riboli, Elio |
  42. Gunter, Marc J. |
1000 Erscheinungsjahr 2021
1000 LeibnizOpen
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2021-01-04
1000 Erschienen in
1000 Quellenangabe
  • 19(1):1
1000 FRL-Sammlung
1000 Copyrightjahr
  • 2020
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1186/s12916-020-01826-0 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7780676/ |
1000 Ergänzendes Material
  • https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-020-01826-0#Sec28 |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • 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.
1000 Sacherschließung
lokal Risk screening
lokal Lifestyle behaviour
lokal Risk prediction
lokal Colorectal cancer
lokal Cancer prevention
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
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1000 Label
1000 Förderer
  1. Deutsche Forschungsgemeinschaft |
  2. Projekt DEAL |
1000 Fördernummer
  1. AL 1784/3-1
  2. -
1000 Förderprogramm
  1. -
  2. Open Access funding
1000 Dateien
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer Deutsche Forschungsgemeinschaft |
    1000 Förderprogramm -
    1000 Fördernummer AL 1784/3-1
  2. 1000 joinedFunding-child
    1000 Förderer Projekt DEAL |
    1000 Förderprogramm Open Access funding
    1000 Fördernummer -
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6427559.rdf
1000 Erstellt am 2021-05-18T14:15:08.880+0200
1000 Erstellt von 25
1000 beschreibt frl:6427559
1000 Bearbeitet von 25
1000 Zuletzt bearbeitet Tue May 18 14:17:02 CEST 2021
1000 Objekt bearb. Tue May 18 14:16:40 CEST 2021
1000 Vgl. frl:6427559
1000 Oai Id
  1. oai:frl.publisso.de:frl:6427559 |
1000 Sichtbarkeit Metadaten public
1000 Sichtbarkeit Daten public
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