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Development and validation of a lifestyle-based model for colorectal cancer risk prediction : the LiFeCRC score

Aleksandrova, Krasimira ; Reichmann, Robin ; Kaaks, Rudolf ; Jenab, Mazda ; Bueno-de-Mesquita, H. Bas ; Dahm, Christina C. ; Eriksen, Anne Kirstine ; Tjønneland, Anne ; Artaud, Fanny and Boutron-Ruault, Marie Christine , et al. (2021) In BMC Medicine 19(1).
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... (More)

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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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Cancer prevention, Colorectal cancer, Lifestyle behaviour, Risk prediction, Risk screening
in
BMC Medicine
volume
19
issue
1
article number
1
publisher
BioMed Central (BMC)
external identifiers
  • scopus:85098547972
  • pmid:33390155
ISSN
1741-7015
DOI
10.1186/s12916-020-01826-0
language
English
LU publication?
yes
id
c74fe4ee-ccb6-4073-98cd-6ec6e0579091
date added to LUP
2021-01-12 13:33:10
date last changed
2021-06-08 04:39:45
@article{c74fe4ee-ccb6-4073-98cd-6ec6e0579091,
  abstract     = {<p>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.</p>},
  author       = {Aleksandrova, Krasimira and Reichmann, Robin and Kaaks, Rudolf and Jenab, Mazda and Bueno-de-Mesquita, H. Bas and Dahm, Christina C. and Eriksen, Anne Kirstine and Tjønneland, Anne and Artaud, Fanny and Boutron-Ruault, Marie Christine and Severi, Gianluca and Hüsing, Anika and Trichopoulou, Antonia and Karakatsani, Anna and Peppa, Eleni and Panico, Salvatore and Masala, Giovanna and Grioni, Sara and Sacerdote, Carlotta and Tumino, Rosario and Elias, Sjoerd G. and May, Anne M. and Borch, Kristin B. and Sandanger, Torkjel M. and Skeie, Guri and Sánchez, Maria Jose and Huerta, José María and Sala, Núria and Gurrea, Aurelio Barricarte and Quirós, José Ramón and Amiano, Pilar and Berntsson, Jonna and Drake, Isabel and van Guelpen, Bethany and Harlid, Sophia and Key, Tim and Weiderpass, Elisabete and Aglago, Elom K. and Cross, Amanda J. and Tsilidis, Konstantinos K. and Riboli, Elio and Gunter, Marc J.},
  issn         = {1741-7015},
  language     = {eng},
  number       = {1},
  publisher    = {BioMed Central (BMC)},
  series       = {BMC Medicine},
  title        = {Development and validation of a lifestyle-based model for colorectal cancer risk prediction : the LiFeCRC score},
  url          = {http://dx.doi.org/10.1186/s12916-020-01826-0},
  doi          = {10.1186/s12916-020-01826-0},
  volume       = {19},
  year         = {2021},
}