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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)

Murphy, Neil; Cross, Amanda J; Abubakar, Mustapha; Jenab, Mazda; Aleksandrova, Krasimira; Boutron-Ruault, Marie-Christine; Dossus, Laure; Racine, Antoine; Kühn, Tilman and Katzke, Verena A, et al. (2016) In PLoS Medicine 13(4).
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... (More)

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.

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4
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  • scopus:84964777727
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1549-1676
DOI
10.1371/journal.pmed.1001988
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English
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28db184e-4f0b-4623-a979-76ff84cfc563
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2017-03-26 04:41:51
@article{28db184e-4f0b-4623-a979-76ff84cfc563,
  abstract     = {<p>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.</p><p>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 &lt; 25 kg/m2), (2) metabolically healthy/overweight (BMI ≥ 25 kg/m2), (3) metabolically unhealthy/normal weight (BMI &lt; 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 &lt;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.</p><p>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.</p>},
  articleno    = {e1001988},
  author       = {Murphy, Neil and Cross, Amanda J and Abubakar, Mustapha and Jenab, Mazda and Aleksandrova, Krasimira and Boutron-Ruault, Marie-Christine and Dossus, Laure and Racine, Antoine and Kühn, Tilman and Katzke, Verena A and Tjønneland, Anne and Petersen, Kristina E N and Overvad, Kim and Quirós, J Ramón and Jakszyn, Paula and Molina-Montes, Esther and Dorronsoro, Miren and Huerta, José-María and Barricarte, Aurelio and Khaw, Kay-Tee and Wareham, Nick and Travis, Ruth C and Trichopoulou, Antonia and Lagiou, Pagona and Trichopoulos, Dimitrios and Masala, Giovanna and Krogh, Vittorio and Tumino, Rosario and Vineis, Paolo and Panico, Salvatore and Bueno-de-Mesquita, H Bas and Siersema, Peter D and Peeters, Petra H and Ohlsson, Bodil and Ericson, Ulrika and Palmqvist, Richard and Nyström, Hanna and Weiderpass, Elisabete and Skeie, Guri and Freisling, Heinz and Kong, So Yeon and Tsilidis, Kostas and Muller, David C and Riboli, Elio and Gunter, Marc J},
  issn         = {1549-1676},
  language     = {eng},
  number       = {4},
  publisher    = {Public Library of Science},
  series       = {PLoS Medicine},
  title        = {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)},
  url          = {http://dx.doi.org/10.1371/journal.pmed.1001988},
  volume       = {13},
  year         = {2016},
}