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

被引：22

作者：

Aleksandrova, Krasimira ^{[1
,2
,3
]}

Reichmann, Robin ^{[1
,2
]}

Kaaks, Rudolf ^{[4
]}

Jenab, Mazda ^{[5
]}

Bueno-de-Mesquita, H. Bas ^{[6
,7
]}

Dahm, Christina C. ^{[8
]}

Eriksen, Anne Kirstine ^{[9
]}

Tjonneland, Anne ^{[9
]}

Artaud, Fanny ^{[10
,11
]}

Boutron-Ruault, Marie-Christine ^{[10
,11
]}

Severi, Gianluca ^{[10
,11
,12
]}

Husing, Anika ^{[4
]}

Trichopoulou, Antonia ^{[13
]}

Karakatsani, Anna ^{[13
,14
]}

Peppa, Eleni ^{[13
]}

Panico, Salvatore ^{[15
]}

Masala, Giovanna ^{[16
]}

Grioni, Sara ^{[17
]}

Sacerdote, Carlotta ^{[18
,19
]}

Tumino, Rosario ^{[20
]}

Elias, Sjoerd G. ^{[21
]}

May, Anne M. ^{[21
]}

Borch, Kristin B. ^{[22
]}

Sandanger, Torkjel M. ^{[22
]}

Skeie, Guri ^{[22
]}

Sanchez, Maria-Jose ^{[23
,24
,25
,26
]}

Huerta, Jose Maria ^{[25
,27
]}

Sala, Nuria ^{[28
,29
]}

Gurrea, Aurelio Barricarte ^{[25
,30
,31
]}

Quiros, Jose Ramon ^{[32
]}

Amiano, Pilar ^{[25
,33
]}

Berntsson, Jonna ^{[34
]}

Drake, Isabel ^{[35
]}

van Guelpen, Bethany ^{[36
,37
]}

Harlid, Sophia ^{[36
]}

Key, Tim ^{[38
]}

Weiderpass, Elisabete ^{[5
]}

Aglago, Elom K. ^{[5
]}

Cross, Amanda J. ^{[7
]}

Tsilidis, Konstantinos K. ^{[7
,39
]}

Riboli, Elio ^{[7
]}

Gunter, Marc J. ^{[5
]}

机构：

[1] German Inst Human Nutr Potsdam Rehbruecke DIfE, Nutr Immun & Metab Senior Scientist Grp, Dept Nutr & Gerontol, Nuthetal, Germany

[2] Univ Potsdam, Inst Nutr Sci, Potsdam, Germany

[3] Leibniz Inst Prevent Res & Epidmiol, Dept Epidmiol Methods & Etiol Res BIPS, Bremen, Germany

[4] German Canc Res Ctr, Div Canc Epidmiol, Heidelberg, Germany

[5] World Hlth Org, Int Agcy Res Canc, Lyon, France

[6] Natl Inst Publ Hlth & Environm, RIVM, Bilthoven, Netherlands

[7] Imperial Coll London, Sch Publ Hlth, Dept Epidemiol & Biostat, London, England

[8] Aarhus Univ, Dept Publ Hlth, Aarhus, Denmark

[9] Danish Canc Soc Res Ctr, Copenhagen, Denmark

[10] Univ Paris Saclay, CESP, Fac Med, Villejuif, France

[11] Inst Gustave Roussy, Villejuif, France

[12] Univ Florence, Dipartimento Stat Informat & Applicaz G Parenti D, Florence, Italy

[13] Hellenic Hlth Fdn, Athens, Greece

[14] Natl & Kapodistrian Univ Athens, ATTIKON Univ Hosp, Sch Med, Pulm Med Dept 2, Haidari, Greece

[15] Univ Naples Federico II, Ep Ctr Naples, Dipartimento Med Clin & Chirurg, Naples, Italy

[16] Inst Canc Res, Canc Risk Factors & Lifestyle Epidmiol Unit, Prevent & Clin Network, ISPRO, Florence, Italy

[17] Fdn IRCCS Ist Nazl Tumori Milano, Epidmiol & Prevent Unit, Milan, Italy

[18] Citta Salute & Sci Univ, Unit Canc Epidemiol, Turin, Italy

[19] Ctr Canc Prevent CPO, Turin, Italy

[20] Prov Hlth Author ASP, Canc Registry & Histopathol Dept, Ragusa, Italy

[21] Univ Utrecht, Univ Med Ctr Utrecht, Julius Ctr Hlth Sci & Primary Care, Utrecht, Netherlands

[22] Arctic Univ Norway UiT, Hlth Fac, Dept Community Med, Tromso, Norway

[23] Escuela Andaluza Salud Publ EASP, Granada, Spain

[24] Inst Invest Biosanitaria ibs, Granada, Spain

[25] CIBERESP, Madrid, Spain

[26] Univ Granada, Granada, Spain

[27] IMIBArrixaca, Dept Epidmiol, Murcia Reg Hlth Council, Murcia, Spain

[28] Catalan Inst Oncol, Canc Epidmiol Res Program, Unit Nutr & Canc, Translat Res Lab, Barcelona, Spain

[29] IDIBELL, Bellvitge BioMed Res Inst, Barcelona, Spain

[30] Navarra Publ Hlth Inst, Pamplona, Spain

[31] Navarra Inst Hlth Res, IdiSNA, Pamplona, Spain

[32] Publ Hlth Directorate, Asturias, Asturias, Spain

[33] Minist Hlth Basque Govt, Biodonostia Hlth Res Inst, Publ Hlth Div Gipuzkoa, Donostia San Sebastian, Spain

[34] Lund Univ, Dept Clin Sci, Div Oncol & Pathol, Lund, Sweden

[35] Lund Univ, Dept Clin Sci Malmo, Lund, Sweden

[36] Umea Univ, Dept Radiat Sci, Oncol, Umea, Sweden

[37] Umea Univ, Wallenberg Ctr Mol Med, Umea, Sweden

[38] Univ Oxford, Nuffield Dept Populat Hlth, Canc Epidemiol Unit, Oxford, England

[39] Univ Ioannina, Dept Hyg & Epidmiol, Sch Med, Ioannina, Greece

来源：

BMC MEDICINE | 2021年 / 19卷 / 01期

基金：

英国医学研究理事会; 瑞典研究理事会;

关键词：

Colorectal cancer; Risk prediction; Lifestyle behaviour; Risk screening; Cancer prevention; INDIVIDUAL PROGNOSIS; PHYSICAL-ACTIVITY; DIAGNOSIS TRIPOD; COLON; PROJECT; INTERVENTION; ASSOCIATION; SELECTION; HEALTH; FIBER;

D O I：

10.1186/s12916-020-01826-0

中图分类号：

R5 [内科学];

学科分类号：

1002 ; 100201 ;

摘要：

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.

引用

页数：19

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