An epidemiological model for prediction of endometrial cancer risk in Europe

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• 作者：Anika Hüsing ; Laure Dossus ; Pietro Ferrari…
• 关键词：Endometrial cancer ; Risk model ; Epidemiology ; Prevention
• 刊名：European Journal of Epidemiology
• 出版年：2016
• 出版时间：January 2016
• 年：2016
• 卷：31
• 期：1
• 页码：51-60
• 全文大小：559 KB
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• 作者单位：Anika Hüsing (1)
  Laure Dossus (2) (3) (4)
  Pietro Ferrari (5)
  Anne Tjønneland (6)
  Louise Hansen (6)
  Guy Fagherazzi (2) (3) (4)
  Laura Baglietto (7) (8)
  Helena Schock (1)
  Jenny Chang-Claude (9)
  Heiner Boeing (10)
  Annika Steffen (10)
  Antonia Trichopoulou (11) (12)
  Christina Bamia (13)
  Michalis Katsoulis (11)
  Vittorio Krogh (14)
  Domenico Palli (15)
  Salvatore Panico (16)
  N. Charlotte Onland-Moret (17) (18)
  Petra H. Peeters (17) (18)
  H. Bas Bueno-de-Mesquita (18) (19) (20)
  Elisabete Weiderpass (21) (22) (23) (24)
  Inger T. Gram (21)
  Eva Ardanaz (25) (26)
  Mireia Obón-Santacana (27)
  Carmen Navarro (26) (28) (29)
  Emilio Sánchez-Cantalejo (26) (30)
  Nerea Etxezarreta (26) (31)
  Naomi E. Allen (32)
  Kay Tee Khaw (33)
  Nick Wareham (34)
  Sabina Rinaldi (5)
  Isabelle Romieu (5)
  Melissa A. Merritt (18)
  Marc Gunter (18)
  Elio Riboli (18)
  Rudolf Kaaks (1)
  
  1. Division of Cancer Epidemiology (C020), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, 69120, Heidelberg, Germany
  2. Inserm, Centre for Research in Epidemiology and Population Health (CESP), U1018, Nutrition, Hormones and Women’s Health Team, 94805, Villejuif, France
  3. UnivParis Sud, UMRS 1018, 94805, Villejuif, France
  4. IGR, 94805, Villejuif, France
  5. International Agency for Research on Cancer, Lyon, France
  6. Danish Cancer Society Research Center, Copenhagen, Denmark
  7. Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, 3053, Australia
  8. Centre for Molecular, Environmental, Genetic, and Analytic Epidemiology, The University of Melbourne, Melbourne, 3010, Australia
  9. Unit of Genetic Epidemiology, Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
  10. German Institute of Human Nutrition in Potsdam-Rehbruecke, Potsdam, Germany
  11. Hellenic Health Foundation, 13 Kaisareias Street, 115 27, Athens, Greece
  12. Bureau of Epidemiologic Research, Academy of Athens, 23 Alexandroupoleos Street, 115 27, Athens, Greece
  13. Department of Hygiene, Epidemiology and Medical Statistics, University of Athens Medical School, 75 M. Asias Street, Goudi, 115 27, Athens, Greece
  14. Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Via Venezian, 1, 20133, Milan, Italy
  15. Molecular and Nutritional Epidemiology Unit, Cancer Research and Prevention Institute – ISPO, Florence, Italy
  16. Dipartimento Di Medicina Clinica E Chirurgia, Federico II University, Naples, Italy
  17. Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
  18. Department of Epidemiology and Biostatistics, School of Public Health, Imperial College, London, UK
  19. National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
  20. Department of Gastroenterology and Hepatology, University Medical Centre, Utrecht, The Netherlands
  21. Department of Community Medicine, Faculty of Health Sciences, University of Tromsø, UiT The Arctic University of Norway, Tromsø, Norway
  22. Department of Research, Cancer Registry of Norway, Oslo, Norway
  23. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
  24. Samfundet Folkhälsan, Helsinki, Finland
  25. Navarre Public Health Institute, Pamplona, Spain
  26. CIBER Epidemiology and Public Health CIBERESP, Madrid, Spain
  27. Unit of Nutrition, Environment and Cancer. Cancer Epidemiology Research Program, Catalan Institute of Oncology (ICO-IDIBELL), Barcelona, Spain
  28. Department of Epidemiology, Murcia Regional Health Council, Murcia, Spain
  29. Department of Health and Social Sciences, Universidad de Murcia, Murcia, Spain
  30. Escuela Andaluza de Salud Pública, Instituto de Investigación Biosanitaria de Granada (Granada.ibs), Granada, Spain
  31. Public Health Division of Gipuzkoa-BIODONOSTIA, Basque Regional Health Department, San Sebastián, Spain
  32. CTSU, Nuffield Department of Population Health, University of Oxford, Oxford, UK
  33. School of Clinical Medicine, University of Cambridge, Cambridge, UK
  34. MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
  
• 刊物类别：Medicine
• 刊物主题：Medicine & Public Health
  Epidemiology
  Public Health
  Infectious Diseases
  Cardiology
  Oncology
  
• 出版者：Springer Netherlands
• ISSN：1573-7284

文摘

Endometrial cancer (EC) is the fourth most frequent cancer in women in Europe, and as its incidence is increasing, prevention strategies gain further pertinence. Risk prediction models can be a useful tool for identifying women likely to benefit from targeted prevention measures. On the basis of data from 201,811 women (mostly aged 30–65 years) including 855 incident EC cases from eight countries in the European Prospective Investigation into Cancer and Nutrition cohort, a model to predict EC was developed. A step-wise model selection process was used to select confirmed predictive epidemiologic risk factors. Piece-wise constant hazard rates in 5-year age-intervals were estimated in a cause-specific competing risks model, five-fold-cross-validation was applied for internal validation. Risk factors included in the risk prediction model were body-mass index (BMI), menopausal status, age at menarche and at menopause, oral contraceptive use, overall and by different BMI categories and overall duration of use, parity, age at first full-term pregnancy, duration of menopausal hormone therapy and smoking status (specific for pre, peri- and post-menopausal women). These variables improved the discriminating capacity to predict risk over 5 years from 71 % for a model based on age alone to 77 % (overall C statistic), and the model was well-calibrated (ratio of expected to observed cases = 0.99). Our model could be used for the identification of women at increased risk of EC in Western Europe. To achieve an EC-risk model with general validity, a large-scale cohort-consortium approach would be needed to assess and adjust for population variation.