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A Nested Case-Control Study of Metabolically Defined Body Size Phenotypes and Risk of Colorectal Cancer in the European Prospective Investigation into Cancer and Nutrition (EPIC).
PLoS Med 2016; 13(4):e1001988PM

Abstract

BACKGROUND

Obesity is positively associated with colorectal cancer. Recently, body size subtypes categorised by the prevalence of hyperinsulinaemia have been defined, and metabolically healthy overweight/obese individuals (without hyperinsulinaemia) have been suggested to be at lower risk of cardiovascular disease than their metabolically unhealthy (hyperinsulinaemic) overweight/obese counterparts. Whether similarly variable relationships exist for metabolically defined body size phenotypes and colorectal cancer risk is unknown.

METHODS AND FINDINGS

The association of metabolically defined body size phenotypes with colorectal cancer was investigated in a case-control study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Metabolic health/body size phenotypes were defined according to hyperinsulinaemia status using serum concentrations of C-peptide, a marker of insulin secretion. A total of 737 incident colorectal cancer cases and 737 matched controls were divided into tertiles based on the distribution of C-peptide concentration amongst the control population, and participants were classified as metabolically healthy if below the first tertile of C-peptide and metabolically unhealthy if above the first tertile. These metabolic health definitions were then combined with body mass index (BMI) measurements to create four metabolic health/body size phenotype categories: (1) metabolically healthy/normal weight (BMI < 25 kg/m2), (2) metabolically healthy/overweight (BMI ≥ 25 kg/m2), (3) metabolically unhealthy/normal weight (BMI < 25 kg/m2), and (4) metabolically unhealthy/overweight (BMI ≥ 25 kg/m2). Additionally, in separate models, waist circumference measurements (using the International Diabetes Federation cut-points [≥80 cm for women and ≥94 cm for men]) were used (instead of BMI) to create the four metabolic health/body size phenotype categories. Statistical tests used in the analysis were all two-sided, and a p-value of <0.05 was considered statistically significant. In multivariable-adjusted conditional logistic regression models with BMI used to define adiposity, compared with metabolically healthy/normal weight individuals, we observed a higher colorectal cancer risk among metabolically unhealthy/normal weight (odds ratio [OR] = 1.59, 95% CI 1.10-2.28) and metabolically unhealthy/overweight (OR = 1.40, 95% CI 1.01-1.94) participants, but not among metabolically healthy/overweight individuals (OR = 0.96, 95% CI 0.65-1.42). Among the overweight individuals, lower colorectal cancer risk was observed for metabolically healthy/overweight individuals compared with metabolically unhealthy/overweight individuals (OR = 0.69, 95% CI 0.49-0.96). These associations were generally consistent when waist circumference was used as the measure of adiposity. To our knowledge, there is no universally accepted clinical definition for using C-peptide level as an indication of hyperinsulinaemia. Therefore, a possible limitation of our analysis was that the classification of individuals as being hyperinsulinaemic-based on their C-peptide level-was arbitrary. However, when we used quartiles or the median of C-peptide, instead of tertiles, as the cut-point of hyperinsulinaemia, a similar pattern of associations was observed.

CONCLUSIONS

These results support the idea that individuals with the metabolically healthy/overweight phenotype (with normal insulin levels) are at lower colorectal cancer risk than those with hyperinsulinaemia. The combination of anthropometric measures with metabolic parameters, such as C-peptide, may be useful for defining strata of the population at greater risk of colorectal cancer.

Authors+Show Affiliations

Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom.Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom.Division of Genetics and Epidemiology, Institute of Cancer Research, Sutton, United Kingdom.International Agency for Research on Cancer, World Health Organization, Lyon, France.Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Potsdam, Germany.Inserm, Nutrition, Hormones and Women's Health, Centre for Research in Epidemiology and Population Health (CESP), U1018, Villejuif, France. Université Paris Sud, UMRS 1018, Villejuif, France. Institut Gustave Roussy, Villejuif, France.Inserm, Nutrition, Hormones and Women's Health, Centre for Research in Epidemiology and Population Health (CESP), U1018, Villejuif, France. Université Paris Sud, UMRS 1018, Villejuif, France. Institut Gustave Roussy, Villejuif, France.Inserm, Nutrition, Hormones and Women's Health, Centre for Research in Epidemiology and Population Health (CESP), U1018, Villejuif, France. Université Paris Sud, UMRS 1018, Villejuif, France. Institut Gustave Roussy, Villejuif, France.Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.Danish Cancer Society Research Center, Copenhagen, Denmark.Danish Cancer Society Research Center, Copenhagen, Denmark.Section for Epidemiology, Department of Public Health, Aarhus University, Aarhus, Denmark.Public Health Directorate, Asturias, Spain.Unit of Nutrition, Environment and Cancer, Catalan Institute of Oncology, Barcelona, Spain.Andalusian School of Public Health, Granada, Spain. Biomedical Research Centre Network for Epidemiology and Public Health (CIBERESP), Madrid, Spain.Public Health Direction and Biodonostia-CIBERESP, Basque Regional Health Department, Vitoria, Spain.Biomedical Research Centre Network for Epidemiology and Public Health (CIBERESP), Madrid, Spain. Department of Epidemiology, Murcia Regional Health Council, Murcia, Spain.Biomedical Research Centre Network for Epidemiology and Public Health (CIBERESP), Madrid, Spain. Navarre Public Health Institute, Pamplona, Spain.University of Cambridge, Cambridge, United Kingdom.MRC Epidemiology Unit, Cambridge, United Kingdom.Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.Hellenic Health Foundation, Athens, Greece. Department of Hygiene, Epidemiology and Medical Statistics, University of Athens Medical School, Athens, Greece. Bureau of Epidemiologic Research, Academy of Athens, Athens, Greece.Department of Hygiene, Epidemiology and Medical Statistics, University of Athens Medical School, Athens, Greece. Bureau of Epidemiologic Research, Academy of Athens, Athens, Greece. Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America.Hellenic Health Foundation, Athens, Greece. Bureau of Epidemiologic Research, Academy of Athens, Athens, Greece. Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America.Molecular and Nutritional Epidemiology Unit, Cancer Research and Prevention Institute (ISPO), Florence, Italy.Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.Cancer Registry and Histopathology Unit, Civic-M.P.Arezzo Hospital, Azienda Sanitaria Provinciale di Ragusa, Italy.Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom. HuGeF Foundation, Torino, Italy.Dipartimento di Medicina Clinica e Sperimentale, Federico II University, Naples, Italy.Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom. Department of Determinants of Chronic Diseases, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands. Department of Gastroenterology and Hepatology, University Medical Centre Utrecht, Utrecht, The Netherlands. Department of Social & Preventive Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.Department of Gastroenterology and Hepatology, University Medical Centre Utrecht, Utrecht, The Netherlands.Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.Division of Internal Medicine, Department of Clinical Sciences, Skåne University Hospital, Lund University, Malmö, Sweden.Diabetes and Cardiovascular Disease-Genetic Epidemiology, Department of Clinical Sciences, Skåne University Hospital, Lund University, Malmö, Lund University, Sweden.Medical Bioscience, Umeå University, Umeå, Sweden.Medical Bioscience, Umeå University, Umeå, Sweden.Department of Community Medicine, Faculty of Health Sciences, University of Tromsø-The Arctic University of Norway, Tromsø, Norway. Cancer Registry of Norway, Oslo, Norway. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. Department of Genetic Epidemiology, Folkhälsan Research Center, Helsinki, Finland.Department of Community Medicine, Faculty of Health Sciences, University of Tromsø-The Arctic University of Norway, Tromsø, Norway.International Agency for Research on Cancer, World Health Organization, Lyon, France.International Agency for Research on Cancer, World Health Organization, Lyon, France.Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom. Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece.International Agency for Research on Cancer, World Health Organization, Lyon, France.Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom.Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom. International Agency for Research on Cancer, World Health Organization, Lyon, France.

Pub Type(s)

Comparative Study
Journal Article
Multicenter Study
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

27046222

Citation

Murphy, Neil, et al. "A Nested Case-Control Study of Metabolically Defined Body Size Phenotypes and Risk of Colorectal Cancer in the European Prospective Investigation Into Cancer and Nutrition (EPIC)." PLoS Medicine, vol. 13, no. 4, 2016, pp. e1001988.
Murphy N, Cross AJ, Abubakar M, et al. A Nested Case-Control Study of Metabolically Defined Body Size Phenotypes and Risk of Colorectal Cancer in the European Prospective Investigation into Cancer and Nutrition (EPIC). PLoS Med. 2016;13(4):e1001988.
Murphy, N., Cross, A. J., Abubakar, M., Jenab, M., Aleksandrova, K., Boutron-Ruault, M. C., ... Gunter, M. J. (2016). A Nested Case-Control Study of Metabolically Defined Body Size Phenotypes and Risk of Colorectal Cancer in the European Prospective Investigation into Cancer and Nutrition (EPIC). PLoS Medicine, 13(4), pp. e1001988. doi:10.1371/journal.pmed.1001988.
Murphy N, et al. A Nested Case-Control Study of Metabolically Defined Body Size Phenotypes and Risk of Colorectal Cancer in the European Prospective Investigation Into Cancer and Nutrition (EPIC). PLoS Med. 2016;13(4):e1001988. PubMed PMID: 27046222.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - A Nested Case-Control Study of Metabolically Defined Body Size Phenotypes and Risk of Colorectal Cancer in the European Prospective Investigation into Cancer and Nutrition (EPIC). AU - Murphy,Neil, AU - Cross,Amanda J, AU - Abubakar,Mustapha, AU - Jenab,Mazda, AU - Aleksandrova,Krasimira, AU - Boutron-Ruault,Marie-Christine, AU - Dossus,Laure, AU - Racine,Antoine, AU - Kühn,Tilman, AU - Katzke,Verena A, AU - Tjønneland,Anne, AU - Petersen,Kristina E N, AU - Overvad,Kim, AU - Quirós,J Ramón, AU - Jakszyn,Paula, AU - Molina-Montes,Esther, AU - Dorronsoro,Miren, AU - Huerta,José-María, AU - Barricarte,Aurelio, AU - Khaw,Kay-Tee, AU - Wareham,Nick, AU - Travis,Ruth C, AU - Trichopoulou,Antonia, AU - Lagiou,Pagona, AU - Trichopoulos,Dimitrios, AU - Masala,Giovanna, AU - Krogh,Vittorio, AU - Tumino,Rosario, AU - Vineis,Paolo, AU - Panico,Salvatore, AU - Bueno-de-Mesquita,H Bas, AU - Siersema,Peter D, AU - Peeters,Petra H, AU - Ohlsson,Bodil, AU - Ericson,Ulrika, AU - Palmqvist,Richard, AU - Nyström,Hanna, AU - Weiderpass,Elisabete, AU - Skeie,Guri, AU - Freisling,Heinz, AU - Kong,So Yeon, AU - Tsilidis,Kostas, AU - Muller,David C, AU - Riboli,Elio, AU - Gunter,Marc J, Y1 - 2016/04/05/ PY - 2015/03/25/received PY - 2016/02/23/accepted PY - 2016/4/6/entrez PY - 2016/4/6/pubmed PY - 2016/8/24/medline SP - e1001988 EP - e1001988 JF - PLoS medicine JO - PLoS Med. VL - 13 IS - 4 N2 - BACKGROUND: Obesity is positively associated with colorectal cancer. Recently, body size subtypes categorised by the prevalence of hyperinsulinaemia have been defined, and metabolically healthy overweight/obese individuals (without hyperinsulinaemia) have been suggested to be at lower risk of cardiovascular disease than their metabolically unhealthy (hyperinsulinaemic) overweight/obese counterparts. Whether similarly variable relationships exist for metabolically defined body size phenotypes and colorectal cancer risk is unknown. METHODS AND FINDINGS: The association of metabolically defined body size phenotypes with colorectal cancer was investigated in a case-control study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Metabolic health/body size phenotypes were defined according to hyperinsulinaemia status using serum concentrations of C-peptide, a marker of insulin secretion. A total of 737 incident colorectal cancer cases and 737 matched controls were divided into tertiles based on the distribution of C-peptide concentration amongst the control population, and participants were classified as metabolically healthy if below the first tertile of C-peptide and metabolically unhealthy if above the first tertile. These metabolic health definitions were then combined with body mass index (BMI) measurements to create four metabolic health/body size phenotype categories: (1) metabolically healthy/normal weight (BMI < 25 kg/m2), (2) metabolically healthy/overweight (BMI ≥ 25 kg/m2), (3) metabolically unhealthy/normal weight (BMI < 25 kg/m2), and (4) metabolically unhealthy/overweight (BMI ≥ 25 kg/m2). Additionally, in separate models, waist circumference measurements (using the International Diabetes Federation cut-points [≥80 cm for women and ≥94 cm for men]) were used (instead of BMI) to create the four metabolic health/body size phenotype categories. Statistical tests used in the analysis were all two-sided, and a p-value of <0.05 was considered statistically significant. In multivariable-adjusted conditional logistic regression models with BMI used to define adiposity, compared with metabolically healthy/normal weight individuals, we observed a higher colorectal cancer risk among metabolically unhealthy/normal weight (odds ratio [OR] = 1.59, 95% CI 1.10-2.28) and metabolically unhealthy/overweight (OR = 1.40, 95% CI 1.01-1.94) participants, but not among metabolically healthy/overweight individuals (OR = 0.96, 95% CI 0.65-1.42). Among the overweight individuals, lower colorectal cancer risk was observed for metabolically healthy/overweight individuals compared with metabolically unhealthy/overweight individuals (OR = 0.69, 95% CI 0.49-0.96). These associations were generally consistent when waist circumference was used as the measure of adiposity. To our knowledge, there is no universally accepted clinical definition for using C-peptide level as an indication of hyperinsulinaemia. Therefore, a possible limitation of our analysis was that the classification of individuals as being hyperinsulinaemic-based on their C-peptide level-was arbitrary. However, when we used quartiles or the median of C-peptide, instead of tertiles, as the cut-point of hyperinsulinaemia, a similar pattern of associations was observed. CONCLUSIONS: These results support the idea that individuals with the metabolically healthy/overweight phenotype (with normal insulin levels) are at lower colorectal cancer risk than those with hyperinsulinaemia. The combination of anthropometric measures with metabolic parameters, such as C-peptide, may be useful for defining strata of the population at greater risk of colorectal cancer. SN - 1549-1676 UR - https://www.unboundmedicine.com/medline/citation/27046222/A_Nested_Case_Control_Study_of_Metabolically_Defined_Body_Size_Phenotypes_and_Risk_of_Colorectal_Cancer_in_the_European_Prospective_Investigation_into_Cancer_and_Nutrition__EPIC__ L2 - http://dx.plos.org/10.1371/journal.pmed.1001988 DB - PRIME DP - Unbound Medicine ER -