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Mertens E, Keuchkarian M, Vasquez MS, Vandevijvere S, Peñalvo JL. Lifestyle predictors of colorectal cancer in European populations: a systematic review. BMJ Nutr Prev Health 2024; 7:183-190. [PMID: 38966096 PMCID: PMC11221299 DOI: 10.1136/bmjnph-2022-000554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 10/10/2023] [Indexed: 07/06/2024] Open
Abstract
Background Colorectal cancer (CRC) is the second most prevalent cancer in Europe, with one-fifth of cases attributable to unhealthy lifestyles. Risk prediction models for quantifying CRC risk and identifying high-risk groups have been developed or validated across European populations, some considering lifestyle as a predictor. Purpose To identify lifestyle predictors considered in existing risk prediction models applicable for European populations and characterise their corresponding parameter values for an improved understanding of their relative contribution to prediction across different models. Methods A systematic review was conducted in PubMed and Web of Science from January 2000 to August 2021. Risk prediction models were included if (1) developed and/or validated in an adult asymptomatic European population, (2) based on non-invasively measured predictors and (3) reported mean estimates and uncertainty for predictors included. To facilitate comparison, model-specific lifestyle predictors were visualised using forest plots. Results A total of 21 risk prediction models for CRC (reported in 16 studies) were eligible, of which 11 were validated in a European adult population but developed elsewhere, mostly USA. All models but two reported at least one lifestyle factor as predictor. Of the lifestyle factors, the most common predictors were body mass index (BMI) and smoking (each present in 13 models), followed by alcohol (11), and physical activity (7), while diet-related factors were less considered with the most commonly present meat (9), vegetables (5) or dairy (2). The independent predictive contribution was generally greater when they were collected with greater detail, although a noticeable variation in effect size estimates for BMI, smoking and alcohol. Conclusions Early identification of high-risk groups based on lifestyle data offers the potential to encourage participation in lifestyle change and screening programmes, hence reduce CRC burden. We propose the commonly shared lifestyle predictors to be further used in public health prediction modelling for improved uptake of the model.
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Affiliation(s)
- Elly Mertens
- Unit of Non-Comunicable Diseases, Department of Public Health, Institute of Tropical Medicine, Antwerp, Belgium
| | - Maria Keuchkarian
- Unit of Non-Comunicable Diseases, Department of Public Health, Institute of Tropical Medicine, Antwerp, Belgium
- Faculty of Bioscience Engineering, Ghent University, Gent, Belgium
| | | | | | - José L Peñalvo
- Unit of Non-Comunicable Diseases, Department of Public Health, Institute of Tropical Medicine, Antwerp, Belgium
- Global Health Institute, University of Antwerp, Wilrijk, Belgium
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Campi R, Rebez G, Klatte T, Roussel E, Ouizad I, Ingels A, Pavan N, Kara O, Erdem S, Bertolo R, Capitanio U, Mir MC. Effect of smoking, hypertension and lifestyle factors on kidney cancer - perspectives for prevention and screening programmes. Nat Rev Urol 2023; 20:669-681. [PMID: 37328546 DOI: 10.1038/s41585-023-00781-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/12/2023] [Indexed: 06/18/2023]
Abstract
Renal cell carcinoma (RCC) incidence has doubled over the past few decades. However, death rates have remained stable as the number of incidental renal mass diagnoses peaked. RCC has been recognized as a European health care issue, but to date, no screening programmes have been introduced. Well-known modifiable risk factors for RCC are smoking, obesity and hypertension. A direct association between cigarette consumption and increased RCC incidence and RCC-related death has been reported, but the underlying mechanistic pathways for this association are still unclear. Obesity is associated with an increased risk of RCC, but interestingly, improved survival outcomes have been reported in obese patients, a phenomenon known as the obesity paradox. Data on the association between other modifiable risk factors such as diet, dyslipidaemia and physical activity with RCC incidence are conflicting, and potential mechanisms underlying these associations remain to be elucidated.
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Affiliation(s)
- Riccardo Campi
- Department of Urology, University of Florence, Careggi Hospital, Florence, Italy
- Young Academic Urologists (YAU) Renal Cancer Working Group, Arnhem, Netherlands
| | - Giacomo Rebez
- Young Academic Urologists (YAU) Renal Cancer Working Group, Arnhem, Netherlands
- Department of Urology, Cattinara Hospital, University of Trieste, Trieste, Italy
| | - Tobias Klatte
- Young Academic Urologists (YAU) Renal Cancer Working Group, Arnhem, Netherlands
- Department of Urology, Royal Bournemouth Hospital, Bournemouth, UK
| | - Eduard Roussel
- Young Academic Urologists (YAU) Renal Cancer Working Group, Arnhem, Netherlands
- Department of Urology, KU Leuven, Leuven, Belgium
| | - Idir Ouizad
- Young Academic Urologists (YAU) Renal Cancer Working Group, Arnhem, Netherlands
- Department of Urology, Bichat-Claude Bernard Hospital, Paris, France
| | - Alexander Ingels
- Young Academic Urologists (YAU) Renal Cancer Working Group, Arnhem, Netherlands
- Department of Urology, Henri Mondor Hospital, Créteil, France
| | - Nicola Pavan
- Young Academic Urologists (YAU) Renal Cancer Working Group, Arnhem, Netherlands
- Department of Urology, Cattinara Hospital, University of Trieste, Trieste, Italy
| | - Onder Kara
- Young Academic Urologists (YAU) Renal Cancer Working Group, Arnhem, Netherlands
- Faculty of Medicine, Kocaeli University, İzmit, Turkey
| | - Selcuk Erdem
- Young Academic Urologists (YAU) Renal Cancer Working Group, Arnhem, Netherlands
- Department of Urology, Istanbul University, Istanbul, Turkey
| | - Riccardo Bertolo
- Young Academic Urologists (YAU) Renal Cancer Working Group, Arnhem, Netherlands
- Urology Unit, Department of Surgery, Tor Vergata University of Rome, Rome, Italy
| | - Umberto Capitanio
- Young Academic Urologists (YAU) Renal Cancer Working Group, Arnhem, Netherlands
- Department of Urology, San Raffaele Scientific Institute, Milan, Italy
- Division of Experimental Oncology/Unit of Urology, IRCCS San Raffaele Hospital, Milan, Italy
| | - Maria Carmen Mir
- Young Academic Urologists (YAU) Renal Cancer Working Group, Arnhem, Netherlands.
- Department of Urology, Hospital Universitario La Ribera, Valencia, Spain.
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Mertens E, Barrenechea-Pulache A, Sagastume D, Vasquez MS, Vandevijvere S, Peñalvo JL. Understanding the contribution of lifestyle in breast cancer risk prediction: a systematic review of models applicable to Europe. BMC Cancer 2023; 23:687. [PMID: 37480028 PMCID: PMC10360320 DOI: 10.1186/s12885-023-11174-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 07/12/2023] [Indexed: 07/23/2023] Open
Abstract
BACKGROUND Breast cancer (BC) is a significant health concern among European women, with the highest prevalence rates among all cancers. Existing BC prediction models account for major risks such as hereditary, hormonal and reproductive factors, but research suggests that adherence to a healthy lifestyle can reduce the risk of developing BC to some extent. Understanding the influence and predictive role of lifestyle variables in current risk prediction models could help identify actionable, modifiable, targets among high-risk population groups. PURPOSE To systematically review population-based BC risk prediction models applicable to European populations and identify lifestyle predictors and their corresponding parameter values for a better understanding of their relative contribution to the prediction of incident BC. METHODS A systematic review was conducted in PubMed, Embase and Web of Science from January 2000 to August 2021. Risk prediction models were included if (i) developed and/or validated in adult cancer-free women in Europe, (ii) based on easily ascertained information, and (iii) reported models' final predictors. To investigate further the comparability of lifestyle predictors across models, estimates were standardised into risk ratios and visualised using forest plots. RESULTS From a total of 49 studies, 33 models were developed and 22 different existing models, mostly from Gail (22 studies) and Tyrer-Cuzick and co-workers (12 studies) were validated or modified for European populations. Family history of BC was the most frequently included predictor (31 models), while body mass index (BMI) and alcohol consumption (26 and 21 models, respectively) were the lifestyle predictors most often included, followed by smoking and physical activity (7 and 6 models respectively). Overall, for lifestyle predictors, their modest predictive contribution was greater for riskier lifestyle levels, though highly variable model estimates across different models. CONCLUSIONS Given the increasing BC incidence rates in Europe, risk models utilising readily available risk factors could greatly aid in widening the population coverage of screening efforts, while the addition of lifestyle factors could help improving model performance and serve as intervention targets of prevention programmes.
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Affiliation(s)
- Elly Mertens
- Unit of Non-Communicable Diseases, Department of Public Health, Institute of Tropical Medicine, Nationalestraat 155, 2000, Antwerp, Belgium.
| | - Antonio Barrenechea-Pulache
- Unit of Non-Communicable Diseases, Department of Public Health, Institute of Tropical Medicine, Nationalestraat 155, 2000, Antwerp, Belgium
| | - Diana Sagastume
- Unit of Non-Communicable Diseases, Department of Public Health, Institute of Tropical Medicine, Nationalestraat 155, 2000, Antwerp, Belgium
| | - Maria Salve Vasquez
- Health Information, Scientific Institute of Public Health (Sciensano), Brussels, Belgium
| | - Stefanie Vandevijvere
- Health Information, Scientific Institute of Public Health (Sciensano), Brussels, Belgium
| | - José L Peñalvo
- Unit of Non-Communicable Diseases, Department of Public Health, Institute of Tropical Medicine, Nationalestraat 155, 2000, Antwerp, Belgium
- Global Health Institute, University of Antwerp, Antwerp, Belgium
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4
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Biziaev T, Aktary ML, Wang Q, Chekouo T, Bhatti P, Shack L, Robson PJ, Kopciuk KA. Development and External Validation of Partial Proportional Odds Risk Prediction Models for Cancer Stage at Diagnosis among Males and Females in Canada. Cancers (Basel) 2023; 15:3545. [PMID: 37509208 PMCID: PMC10377619 DOI: 10.3390/cancers15143545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 07/04/2023] [Accepted: 07/05/2023] [Indexed: 07/30/2023] Open
Abstract
Risk prediction models for cancer stage at diagnosis may identify individuals at higher risk of late-stage cancer diagnoses. Partial proportional odds risk prediction models for cancer stage at diagnosis for males and females were developed using data from Alberta's Tomorrow Project (ATP). Prediction models were validated on the British Columbia Generations Project (BCGP) cohort using discrimination and calibration measures. Among ATP males, older age at diagnosis was associated with an earlier stage at diagnosis, while full- or part-time employment, prostate-specific antigen testing, and former/current smoking were associated with a later stage at diagnosis. Among ATP females, mammogram and sigmoidoscopy or colonoscopy were associated with an earlier stage at diagnosis, while older age at diagnosis, number of pregnancies, and hysterectomy were associated with a later stage at diagnosis. On external validation, discrimination results were poor for both males and females while calibration results indicated that the models did not over- or under-fit to derivation data or over- or under-predict risk. Multiple factors associated with cancer stage at diagnosis were identified among ATP participants. While the prediction model calibration was acceptable, discrimination was poor when applied to BCGP data. Updating our models with additional predictors may help improve predictive performance.
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Affiliation(s)
- Timofei Biziaev
- Department of Mathematics and Statistics, University of Calgary, Calgary, AB T2N 4N2, Canada
| | - Michelle L Aktary
- Faculty of Kinesiology, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Qinggang Wang
- Cancer Epidemiology and Prevention Research, Cancer Care Alberta, Alberta Health Services, Calgary, AB T2S 3C3, Canada
| | - Thierry Chekouo
- Department of Mathematics and Statistics, University of Calgary, Calgary, AB T2N 4N2, Canada
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
| | - Parveen Bhatti
- Cancer Control Research, BC Cancer, Vancouver, BC V5Z 1L3, Canada
- School of Population and Public Health, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - Lorraine Shack
- Cancer Surveillance and Reporting, Alberta Health Services, Calgary, AB T2S 3C3, Canada
| | - Paula J Robson
- Department of Agricultural, Food and Nutritional Science and School of Public Health, University of Alberta, Edmonton, AB T6G 2P5, Canada
- Cancer Care Alberta and Cancer Strategic Clinical Network, Alberta Health Services, Edmonton, AB T5J 3H1, Canada
| | - Karen A Kopciuk
- Department of Mathematics and Statistics, University of Calgary, Calgary, AB T2N 4N2, Canada
- Cancer Epidemiology and Prevention Research, Cancer Care Alberta, Alberta Health Services, Calgary, AB T2S 3C3, Canada
- Departments of Oncology, Community Health Sciences, University of Calgary, Calgary, AB T2N 4N2, Canada
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Harrison H, Thompson RE, Lin Z, Rossi SH, Stewart GD, Griffin SJ, Usher-Smith JA. Risk Prediction Models for Kidney Cancer: A Systematic Review. Eur Urol Focus 2021; 7:1380-1390. [PMID: 32680829 PMCID: PMC8642244 DOI: 10.1016/j.euf.2020.06.024] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 06/18/2020] [Accepted: 06/29/2020] [Indexed: 12/24/2022]
Abstract
CONTEXT Early detection of kidney cancer improves survival; however, low prevalence means that population-wide screening may be inefficient. Stratification of the population into risk categories could allow for the introduction of a screening programme tailored to individuals. OBJECTIVE This review will identify and compare published models that predict the risk of developing kidney cancer in the general population. EVIDENCE ACQUISITION A search identified primary research reporting or validating models predicting the risk of kidney cancer in Medline and EMBASE. After screening identified studies for inclusion, we extracted data onto a standardised form. The risk models were classified using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines and evaluated using the PROBAST assessment tool. EVIDENCE SYNTHESIS The search identified 15 281 articles. Sixty-two satisfied the inclusion criteria; performance measures were provided for 11 models. Some models predicted the risk of prevalent undiagnosed disease and others future incident disease. Six of the models had been validated, two using external populations. The most commonly included risk factors were age, smoking status, and body mass index. Most of the models had acceptable-to-good discrimination (area under the receiver-operating curve >0.7) in development and validation. Many models also had high specificity; however, several had low sensitivity. The highest performance was seen for the models using only biomarkers to detect kidney cancer; however, these were developed and validated in small case-control studies. CONCLUSIONS We identified a small number of risk models that could be used to stratify the population according to the risk of kidney cancer. Most exhibit reasonable discrimination, but a few have been validated externally in population-based studies. PATIENT SUMMARY In this review, we looked at mathematical models predicting the likelihood of an individual developing kidney cancer. We found several suitable models, using a range of risk factors (such as age and smoking) to predict the risk for individuals. Most of the models identified require further testing in the general population to confirm their usefulness.
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Affiliation(s)
- Hannah Harrison
- The Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
| | - Rachel E Thompson
- University of Cambridge School of Clinical Medicine, Addenbrooke's Hospital, Cambridge, UK
| | - Zhiyuan Lin
- University of Cambridge School of Clinical Medicine, Addenbrooke's Hospital, Cambridge, UK
| | - Sabrina H Rossi
- Department of Surgery, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
| | - Grant D Stewart
- Department of Surgery, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
| | - Simon J Griffin
- The Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Juliet A Usher-Smith
- The Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
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6
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Singleton RK, Heath AK, Clasen JL, Scelo G, Johansson M, Calvez-Kelm FL, Weiderpass E, Liedberg F, Ljungberg B, Harbs J, Olsen A, Tjønneland A, Dahm CC, Kaaks R, Fortner RT, Panico S, Tagliabue G, Masala G, Tumino R, Ricceri F, Gram IT, Santiuste C, Bonet C, Rodriguez-Barranco M, Schulze MB, Bergmann MM, Travis RC, Tzoulaki I, Riboli E, Muller DC. Risk Prediction for Renal Cell Carcinoma: Results from the European Prospective Investigation into Cancer and Nutrition (EPIC) Prospective Cohort Study. Cancer Epidemiol Biomarkers Prev 2021; 30:507-512. [PMID: 33335022 DOI: 10.1158/1055-9965.epi-20-1438] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 11/11/2020] [Accepted: 12/14/2020] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Early detection of renal cell carcinoma (RCC) has the potential to improve disease outcomes. No screening program for sporadic RCC is in place. Given relatively low incidence, screening would need to focus on people at high risk of clinically meaningful disease so as to limit overdiagnosis and screen-detected false positives. METHODS Among 192,172 participants from the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort (including 588 incident RCC cases), we evaluated a published RCC risk prediction model (including age, sex, BMI, and smoking status) in terms of discrimination (C-statistic) and calibration (observed probability as a function of predicted probability). We used a flexible parametric survival model to develop an expanded model including age, sex, BMI, and smoking status, with the addition of self-reported history of hypertension and measured blood pressure. RESULTS The previously published model yielded well-calibrated probabilities and good discrimination (C-statistic [95% CI]: 0.699 [0.679-0.721]). Our model had slightly improved discrimination (0.714 [0.694-0.735], bootstrap optimism-corrected C-statistic: 0.709). Despite this good performance, predicted risk was low for the vast majority of participants, with 70% of participants having 10-year risk less than 0.0025. CONCLUSIONS Although the models performed well for the prediction of incident RCC, they are currently insufficiently powerful to identify individuals at substantial risk of RCC in a general population. IMPACT Despite the promising performance of the EPIC RCC risk prediction model, further development of the model, possibly including biomarkers of risk, is required to enable risk stratification of RCC.
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Affiliation(s)
- Rosie K Singleton
- School of Public Health, Imperial College London, London, United Kingdom
| | - Alicia K Heath
- School of Public Health, Imperial College London, London, United Kingdom
| | - Joanna L Clasen
- School of Public Health, Imperial College London, London, United Kingdom
| | | | | | | | | | - Fredrik Liedberg
- Institution of Translational Medicine, Lund University, Malmö, Sweden
| | - Börje Ljungberg
- Department of Surgical and Perioperative Sciences, Urology and Andrology, Umeå University, Umea, Sweden
| | - Justin Harbs
- Department of Radiation Sciences, Umeå University, Umea, Sweden
| | - Anja Olsen
- Danish Cancer Society Research Center, Copenhagen, Denmark
- Department of Public Health, University of Århus, Århus, Denmark
| | - Anne Tjønneland
- Danish Cancer Society Research Center, Copenhagen, Denmark
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | | | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Renée T Fortner
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Salvatore Panico
- Department of Clinical and Surgical Medicine, Federico II University, Naples, Italy
| | - Giovanna Tagliabue
- Lombardy Cancer Registry Unit, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy
| | - Giovanna Masala
- Cancer Risk Factors and Life-Style Epidemiology Unit, Institute for Cancer Research, Prevention and Clinical Network-ISPRO, Florence, Italy
| | - Rosario Tumino
- Cancer Registry and Histopathology Department, Provincial Health Authority (ASP 7), Ragusa, Italy
| | - Fulvio Ricceri
- Department of Clinical and Biological Sciences, University of Turin, Orbassano (TO), Italy
- Unit of Epidemiology Regional Health Service ASL TO3, Grugliasco (TO), Italy
| | - Inger T Gram
- Faculty of Health Sciences, Department of Community Medicine, University of Tromsø, The Arctic University of Norway, Tromsø, Norway
| | - Carmen Santiuste
- Department of Epidemiology, Murcia Regional Health Council, IMIB-Arrixaca, Murcia, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| | - Catalina Bonet
- Unit of Nutrition, Environment, and Cancer, Catalan Institute of Oncology, Barcelona, Spain
| | - Miguel Rodriguez-Barranco
- Escuela Andaluza de Salud Pública (EASP), Granada, Madrid, Spain
- Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Mattias B Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke (DifE), Potsdam, Germany
- Institute of Nutrition Science, University of Potsdam, Nuthetal, Germany
| | - Manuela M Bergmann
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke (DifE), Potsdam, Germany
| | - Ruth C Travis
- Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Ioanna Tzoulaki
- School of Public Health, Imperial College London, London, United Kingdom
- MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
- University of Ioannina Medical School, Ioannina, Greece
| | - Elio Riboli
- School of Public Health, Imperial College London, London, United Kingdom
| | - David C Muller
- School of Public Health, Imperial College London, London, United Kingdom.
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7
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Aleksandrova K, Reichmann R, Kaaks R, Jenab M, Bueno-de-Mesquita HB, Dahm CC, Eriksen AK, Tjønneland A, Artaud F, Boutron-Ruault MC, Severi G, Hüsing A, Trichopoulou A, Karakatsani A, Peppa E, Panico S, Masala G, Grioni S, Sacerdote C, Tumino R, Elias SG, May AM, Borch KB, Sandanger TM, Skeie G, Sánchez MJ, Huerta JM, Sala N, Gurrea AB, Quirós JR, Amiano P, Berntsson J, Drake I, van Guelpen B, Harlid S, Key T, Weiderpass E, Aglago EK, Cross AJ, Tsilidis KK, Riboli E, Gunter MJ. Development and validation of a lifestyle-based model for colorectal cancer risk prediction: the LiFeCRC score. BMC Med 2021; 19:1. [PMID: 33390155 PMCID: PMC7780676 DOI: 10.1186/s12916-020-01826-0] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 10/23/2020] [Indexed: 12/24/2022] Open
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.
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Affiliation(s)
- Krasimira Aleksandrova
- Nutrition, Immunity and Metabolism Senior Scientist Group, Department of Nutrition and Gerontology, German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Nuthetal, Germany.
- Institute of Nutritional Science, University of Potsdam, Potsdam, Germany.
- Department of Epidemiological Methods and Etiological Research, Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany.
| | - Robin Reichmann
- Nutrition, Immunity and Metabolism Senior Scientist Group, Department of Nutrition and Gerontology, German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Nuthetal, Germany
- Institute of Nutritional Science, University of Potsdam, Potsdam, Germany
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Mazda Jenab
- International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - H Bas Bueno-de-Mesquita
- National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | | | | | | | - Fanny Artaud
- CESP, Faculté de Medicine, Université Paris-Saclay, Villejuif, France
- Institut Gustave Roussy, Villejuif, France
| | | | - Gianluca Severi
- CESP, Faculté de Medicine, Université Paris-Saclay, Villejuif, France
- Institut Gustave Roussy, Villejuif, France
- Dipartimento di Statistica, Informatica e Applicazioni "G. Parenti" (DISIA), University of Florence, Florence, Italy
| | - Anika Hüsing
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Anna Karakatsani
- Hellenic Health Foundation, Athens, Greece
- 2nd Pulmonary Medicine Department, School of Medicine, National and Kapodistrian University of Athens, "ATTIKON" University Hospital, Haidari, Greece
| | | | - Salvatore Panico
- EPIC Centre of Naples, Dipartimento di Medicina Clinica e Chirurgia, University of Naples Federico II, Naples, Italy
| | - Giovanna Masala
- Cancer Risk Factors and Lifestyle Epidemiology Unit, Institute for Cancer Research, Prevention and Clinical Network - ISPRO, Florence, Italy
| | - Sara Grioni
- Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy
| | - Carlotta Sacerdote
- Unit of Cancer Epidemiology, Città della Salute e della Scienza University-Hospital and Center for Cancer Prevention (CPO), Turin, Italy
| | - Rosario Tumino
- Cancer Registry and Histopathology Department, Provincial Health Authority (ASP), Ragusa, Italy
| | - Sjoerd G Elias
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Anne M May
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Kristin B Borch
- Department of Community Medicine, Health Faculty, UiT-the Arctic university of Norway, Tromsø, Norway
| | - Torkjel M Sandanger
- Department of Community Medicine, Health Faculty, UiT-the Arctic university of Norway, Tromsø, Norway
| | - Guri Skeie
- Department of Community Medicine, Health Faculty, UiT-the Arctic university of Norway, Tromsø, Norway
| | - Maria-Jose Sánchez
- Escuela Andaluza de Salud Pública (EASP), Granada, Spain
- Instituto de Investigación Biosanitaria ibs. GRANADA, Granada, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Universidad de Granada, Granada, Spain
| | - José María Huerta
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Department of Epidemiology, Murcia Regional Health Council, IMIB-Arrixaca, Murcia, Spain
| | - Núria Sala
- Unit of Nutrition and Cancer, Cancer Epidemiology Research Program, Translational Research Laboratory, Catalan Institute of Oncology (ICO), Barcelona, Spain
- Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain
| | - Aurelio Barricarte Gurrea
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Navarra Public Health Institute, Pamplona, Spain
- Navarra Institute for Health Research (IdiSNA), Pamplona, Spain
| | | | - Pilar Amiano
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Ministry of Health of the Basque Government, Public Health Division of Gipuzkoa, Biodonostia Health Research Institute, Donostia-San Sebastian, Spain
| | - Jonna Berntsson
- Department of Clinical Sciences, Division of Oncology and Pathology, Lund University, Lund, Sweden
| | - Isabel Drake
- Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden
| | - Bethany van Guelpen
- Department of Radiation Sciences, Oncology, Umeå University, Umeå, Sweden
- Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden
| | - Sophia Harlid
- Department of Radiation Sciences, Oncology, Umeå University, Umeå, Sweden
| | - Tim Key
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Elisabete Weiderpass
- International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Elom K Aglago
- International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Amanda J Cross
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Konstantinos K Tsilidis
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - Elio Riboli
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Marc J Gunter
- International Agency for Research on Cancer, World Health Organization, Lyon, France
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8
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Kachuri L, Graff RE, Smith-Byrne K, Meyers TJ, Rashkin SR, Ziv E, Witte JS, Johansson M. Pan-cancer analysis demonstrates that integrating polygenic risk scores with modifiable risk factors improves risk prediction. Nat Commun 2020; 11:6084. [PMID: 33247094 PMCID: PMC7695829 DOI: 10.1038/s41467-020-19600-4] [Citation(s) in RCA: 77] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 10/05/2020] [Indexed: 12/28/2022] Open
Abstract
Cancer risk is determined by a complex interplay of environmental and heritable factors. Polygenic risk scores (PRS) provide a personalized genetic susceptibility profile that may be leveraged for disease prediction. Using data from the UK Biobank (413,753 individuals; 22,755 incident cancer cases), we quantify the added predictive value of integrating cancer-specific PRS with family history and modifiable risk factors for 16 cancers. We show that incorporating PRS measurably improves prediction accuracy for most cancers, but the magnitude of this improvement varies substantially. We also demonstrate that stratifying on levels of PRS identifies significantly divergent 5-year risk trajectories after accounting for family history and modifiable risk factors. At the population level, the top 20% of the PRS distribution accounts for 4.0% to 30.3% of incident cancer cases, exceeding the impact of many lifestyle-related factors. In summary, this study illustrates the potential for improving cancer risk assessment by integrating genetic risk scores.
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Affiliation(s)
- Linda Kachuri
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Rebecca E Graff
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Karl Smith-Byrne
- Genetic Epidemiology Group, Section of Genetics, International Agency for Research on Cancer, Lyon, France
| | - Travis J Meyers
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Sara R Rashkin
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Elad Ziv
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
| | - John S Witte
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA.
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA.
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA.
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA.
| | - Mattias Johansson
- Genetic Epidemiology Group, Section of Genetics, International Agency for Research on Cancer, Lyon, France.
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9
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Usher-Smith J, Simmons RK, Rossi SH, Stewart GD. Current evidence on screening for renal cancer. Nat Rev Urol 2020; 17:637-642. [PMID: 32860009 PMCID: PMC7610655 DOI: 10.1038/s41585-020-0363-3] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/22/2020] [Indexed: 02/07/2023]
Abstract
Renal cell carcinoma (RCC) incidence is increasing worldwide. A high proportion of individuals are asymptomatic at diagnosis, but RCC has a high mortality rate. These facts suggest that RCC meets some of the criteria for screening, and a new analysis shows that screening for RCC could potentially be cost-effective. Targeted screening of high-risk individuals is likely to be the most cost-effective strategy to maximize the benefits and reduce the harms of screening. However, the size of the benefit of earlier initiation of treatment and the overall cost-effectiveness of screening remains uncertain. The optimal screening modality and target population is also unclear, and uncertainties exist regarding the specification and implementation of a screening programme. Before moving to a fully powered trial of screening, future work should focus on the following: developing and validating accurate risk prediction models; developing non-invasive methods of early RCC detection; establishing the feasibility, public acceptability and potential uptake of screening; establishing the prevalence of RCC and stage distribution of RCC detected by screening; and evaluating the potential harms of screening, including the impact on quality of life, overdiagnosis and over-treatment.
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Affiliation(s)
- Juliet Usher-Smith
- The Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Rebecca K Simmons
- Department of Public Health, Bartolins Allé 2, University of Aarhus, Aarhus C, Denmark
| | - Sabrina H Rossi
- Department of Surgery, University of Cambridge, Addenbrooke's Hospital, Cambridge Biomedical Campus, Cambridge, UK
| | - Grant D Stewart
- Department of Surgery, University of Cambridge, Addenbrooke's Hospital, Cambridge Biomedical Campus, Cambridge, UK.
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10
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Masson G, Mills K, Griffin SJ, Sharp SJ, Klein WMP, Sutton S, Usher-Smith JA. A randomised controlled trial of the effect of providing online risk information and lifestyle advice for the most common preventable cancers. Prev Med 2020; 138:106154. [PMID: 32473959 PMCID: PMC7378571 DOI: 10.1016/j.ypmed.2020.106154] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 05/20/2020] [Accepted: 05/22/2020] [Indexed: 11/01/2022]
Abstract
Few trial data are available concerning the impact of personalised cancer risk information on behaviour. This study assessed the short-term effects of providing personalised cancer risk information on cancer risk beliefs and self-reported behaviour. We randomised 1018 participants, recruited through the online platform Prolific, to either a control group receiving cancer-specific lifestyle advice or one of three intervention groups receiving their computed 10-year risk of developing one of the five most common preventable cancers either as a bar chart, a pictograph or a qualitative scale alongside the same lifestyle advice. The primary outcome was change from baseline in computed risk relative to an individual with a recommended lifestyle (RRI)1 at three months. Secondary outcomes included: health-related behaviours, risk perception, anxiety, worry, intention to change behaviour, and a newly defined concept, risk conviction. After three months there were no between-group differences in change in RRI (p = 0.71). At immediate follow-up, accuracy of absolute risk perception (p < 0.001), absolute and comparative risk conviction (p < 0.001) and intention to increase fruit and vegetables (p = 0.026) and decrease processed meat (p = 0.033) were higher in all intervention groups relative to the control group. The increases in accuracy and conviction were only seen in individuals with high numeracy and low baseline conviction, respectively. These findings suggest that personalised cancer risk information alongside lifestyle advice can increase short-term risk accuracy and conviction without increasing worry or anxiety but has little impact on health-related behaviour. Trial registration: ISRCTN17450583. Registered 30 January 2018.
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Affiliation(s)
- Golnessa Masson
- The Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge School of Clinical Medicine, Box 113, Cambridge Biomedical Campus, Cambridge CB2 0SR, UK.
| | - Katie Mills
- The Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge School of Clinical Medicine, Box 113, Cambridge Biomedical Campus, Cambridge CB2 0SR, UK.
| | - Simon J Griffin
- The Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge School of Clinical Medicine, Box 113, Cambridge Biomedical Campus, Cambridge CB2 0SR, UK.
| | - Stephen J Sharp
- MRC Epidemiology Unit, University of Cambridge, Institute of Metabolic Science, Cambridge CB2 0QQ, UK.
| | | | - Stephen Sutton
- The Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge School of Clinical Medicine, Box 113, Cambridge Biomedical Campus, Cambridge CB2 0SR, UK.
| | - Juliet A Usher-Smith
- The Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge School of Clinical Medicine, Box 113, Cambridge Biomedical Campus, Cambridge CB2 0SR, UK.
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11
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Mills K, Griffin SJ, Sutton S, Usher-Smith JA. Development and usability testing of a very brief intervention for personalised cancer risk assessment to promote behaviour change in primary care using normalisation process theory. Prim Health Care Res Dev 2020; 21:e1. [PMID: 31934843 PMCID: PMC7005588 DOI: 10.1017/s146342361900080x] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Accepted: 09/13/2019] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Cancer is the second leading cause of death worldwide. Lifestyle choices play an important role in the aetiology of cancer with up to 4 in 10 cases potentially preventable. Interventions delivered by healthcare professionals (HCPs) that incorporate risk information have the potential to promote behaviour change. Our aim was to develop a very brief intervention incorporating cancer risk, which could be implemented within primary care. METHODS Guided by normalisation process theory (NPT), we developed a prototype intervention using literature reviews, consultation with patient and public representatives and pilot work with patients and HCPs. We conducted focus groups and interviews with 65 HCPs involved in delivering prevention activities. Findings were used to refine the intervention before 22 HCPs completed an online usability test and provided further feedback via a questionnaire incorporating a modified version of the NoMAD checklist. RESULTS The intervention included a website where individuals could provide information on lifestyle risk factors view their estimated 10-year risk of developing one or more of the five most common preventable cancers and access lifestyle advice incorporating behaviour change techniques. Changes incorporated from feedback from the focus groups and interviews included signposting to local services and websites, simplified wording and labelling of risk information. In the usability testing, all participants felt it would be easy to collect the risk information. Ninety-one percent felt the intervention would enable discussion about cancer risk and believed it had potential to be easily integrated into National Health Service (NHS) Health Checks. However, only 36% agreed it could be delivered within 5 min. CONCLUSIONS With the use of NPT, we developed a very brief intervention that is acceptable to HCPs in primary care and could be potentially integrated into NHS Health Checks. However, further work is needed to assess its feasibility and potential effectiveness.
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Affiliation(s)
- Katie Mills
- Research Associate, The Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Simon J. Griffin
- Professor of General Practice, The Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Stephen Sutton
- Professor of Behavioural Science, The Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Juliet A. Usher-Smith
- Clinical Senior Research Associate, The Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge School of Clinical Medicine, Cambridge, UK
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