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. 2015 Jul 1;107(9):djv178.
doi: 10.1093/jnci/djv178. Print 2015 Sep.

Evidence of a Causal Association Between Insulinemia and Endometrial Cancer: A Mendelian Randomization Analysis

Kevin T Nead  1 , Stephen J Sharp  1 , Deborah J Thompson  1 , Jodie N Painter  1 , David B Savage  1 , Robert K Semple  1 , Adam Barker  1 ; Australian National Endometrial Cancer Study Group (ANECS); John R B Perry  1 , John Attia  1 , Alison M Dunning  1 , Douglas F Easton  1 , Elizabeth Holliday  1 , Luca A Lotta  1 , Tracy O'Mara  1 , Mark McEvoy  1 , Paul D P Pharoah  1 , Rodney J Scott  1 , Amanda B Spurdle  1 , Claudia Langenberg  1 , Nicholas J Wareham  1 , Robert A Scott  2
Affiliations

Evidence of a Causal Association Between Insulinemia and Endometrial Cancer: A Mendelian Randomization Analysis

Kevin T Nead et al. J Natl Cancer Inst. .

Abstract

Background: Insulinemia and type 2 diabetes (T2D) have been associated with endometrial cancer risk in numerous observational studies. However, the causality of these associations is uncertain. Here we use a Mendelian randomization (MR) approach to assess whether insulinemia and T2D are causally associated with endometrial cancer.

Methods: We used single nucleotide polymorphisms (SNPs) associated with T2D (49 variants), fasting glucose (36 variants), fasting insulin (18 variants), early insulin secretion (17 variants), and body mass index (BMI) (32 variants) as instrumental variables in MR analyses. We calculated MR estimates for each risk factor with endometrial cancer using an inverse-variance weighted method with SNP-endometrial cancer associations from 1287 case patients and 8273 control participants.

Results: Genetically predicted higher fasting insulin levels were associated with greater risk of endometrial cancer (odds ratio [OR] per standard deviation = 2.34, 95% confidence internal [CI] = 1.06 to 5.14, P = .03). Consistently, genetically predicted higher 30-minute postchallenge insulin levels were also associated with endometrial cancer risk (OR = 1.40, 95% CI = 1.12 to 1.76, P = .003). We observed no associations between genetic risk of type 2 diabetes (OR = 0.91, 95% CI = 0.79 to 1.04, P = .16) or higher fasting glucose (OR = 1.00, 95% CI = 0.67 to 1.50, P = .99) and endometrial cancer. In contrast, endometrial cancer risk was higher in individuals with genetically predicted higher BMI (OR = 3.86, 95% CI = 2.24 to 6.64, P = 1.2x10(-6)).

Conclusion: This study provides evidence to support a causal association of higher insulin levels, independently of BMI, with endometrial cancer risk.

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Figures

Figure 1.
Figure 1.
Association of a genetic score of body mass index (BMI)–associated single nucleotide polymorphisms with BMI and a range of potential confounders in the Fenland study (20). Thirty-minute insulin was only available in the Ely study (21), so the sample size is smaller. Given 55 tests (α = 9.1x10-4), the score was associated with BMI and with age at menarche (29). Associations with quantitative traits were tested by linear regression and with binary traits by logistic regression. All statistical tests were two-sided.
Figure 2.
Figure 2.
Association of a genetic score of fasting insulin–associated single nucleotide polymorphisms with fasting insulin and a range of potential confounders in the Fenland study (20). Thirty-minute insulin was only available in the Ely study (21), so the sample size is smaller. Given 55 tests (α = 9.1x10-4), the score was only associated with fasting insulin. Associations with quantitative traits were tested by linear regression and with binary traits by logistic regression. All statistical tests were two-sided.
Figure 3.
Figure 3.
Association of a genetic score of insulin secretion–associated single nucleotide polymorphisms with early insulin secretion and a range of potential confounders in the Fenland study (20). Thirty-minute insulin was only available in the Ely study (21), so the sample size is smaller. Given 55 tests (α = 9.1x10-4), the score was only associated with early insulin secretion and with fasting glucose. Associations with quantitative traits were tested by linear regression and with binary traits by logistic regression. All statistical tests were two-sided.
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