Common Obesity-Related Genetic Variants and Papillary Thyroid Cancer Risk

Obesity has consistently been associated with increased risk of thyroid cancer in epidemiologic studies (1), but the biologic mechanisms underlying this association remain poorly understood. Evaluating genetic variation in obesity-related genes may help to identify pathways involved in thyroid cancer etiology, independent of, or mediated by, body size.

We examined associations between single-nucleotide polymorphisms (SNP) in 23 obesity-related candidate genes and papillary thyroid cancer (PTC), the most common histologic type of thyroid cancer. These genes were chosen because of their role in body energy homeostasis and metabolism or previous associations with obesity or type II diabetes (2–5).

The study population has been previously described (6). In brief, cases included individuals diagnosed with incident, histologically confirmed PTC during follow-up of the U.S. Radiologic Technologists (USRT) cohort (n = 202), and individuals diagnosed and treated for PTC at the University of Texas MD Anderson Cancer Center (UTMDACC; Houston, TX; n = 142). In USRT, controls (n = 452) were frequency matched by race, year of birth (±2 years), and sex to cases. Controls from USRT were then selected to match cases from UTMDACC. Analyses were restricted to non-Hispanic whites. Three cases and 8 controls were excluded because of missing height or weight. The Institutional Review Boards approved the use of these data, and all subjects provided written informed consent.

The 23 genes chosen for this analysis (listed in Supplementary Table S1) were selected a priori. Tag SNPs (n = 575) were selected from the common SNPs (minor allele frequency >5%) genotyped by the HapMap Project in the Caucasian population using TagZilla, part of the GLU software package, with a binning threshold of r² > 0.8. Genotyping was carried out at the National Cancer Institute (NCI) Core Genotyping Facility using a custom-designed iSelect Infinium assay. SNPs were excluded if they failed quality-control measures: less than 95% concordance, less than 90% completion, or had evidence of a departure from Hardy–Weinberg equilibrium in controls (P < 0.00001). Allele frequencies were largely similar between USRT and UTMDACC cases; thus, these groups were combined for analyses.

Data on demographics, medical history, anthropometry, and other health-related characteristics were collected by self-administered questionnaires or telephone interview in USRT and self-administered questionnaire at time of blood collection in UTMDACC.

We computed SNP-specific P_trend and ORs and 95% confidence intervals (CI) for each genotype, using logistic regression models adjusted for sex, attained age, and year of birth. Separate models additionally adjusted for body mass index (BMI). We also examined 138,605 2-way SNP–SNP interactions using allelic-based gene–gene interactions in models adjusted for sex, attained age, year of birth, and BMI (7). We combined SNP-specific P_trend into region-based P values using the adaptive rank–truncated method (8). P values less than 0.05 were considered statistically significant, and tests were 2-sided. While tables show uncorrected P values, we also conducted correction for multiple comparisons controlling the false discovery rate (FDR). Statistical analyses were conducted using Stata/SE version 11.0 and R software.

Compared with controls, PTC cases were more likely to have a family history of thyroid cancer among first-degree relatives and less likely to be current smokers (Table 1). Cases had higher BMI as compared with controls.

Of the 10 SNPs identified with the lowest SNP-level P values (Table 2), 9 were located in FTO (fat mass and obesity associated) and 1 was located in INSR (insulin receptor). However, none remained statistically significant after FDR correction. Although BMI was associated with increased PTC risk (per 5 kg/m², OR = 1.18, 95% CI: 1.02–1.37), additional adjustment for BMI did not appreciably change the SNP–PTC associations. We did not observe statistically significant SNP–SNP interactions after FDR correction. Also, at gene region level none was significantly associated with PTC risk (all region-based P values >0.2).

In general, our results do not suggest an important role of selected obesity-related genetic variants in determining PTC risk. Certain polymorphisms in the FTO and INSR genes were weakly linked to PTC risk independent of BMI, but these associations were no longer significant after multiple comparisons correction.

Genes chosen for this analysis were a priori selected on the basis of their known functions or observed associations with obesity, thereby reducing the possibility that our findings were due solely to chance. Nonetheless, there may be other obesity-related genes that were not considered in our genotyping platform but may play an important role in papillary thyroid carcinogenesis. More agnostic approaches may be needed to discover important genetic risk factors for this disease. In addition, while most individual SNPs and none of the 2-way interactions were not significantly associated with PTC risk, certain combination of SNPs may have stronger effects, although larger studies are necessary to detect SNP–SNP interactions.

As the biologic mechanisms underlying the observed obesity-thyroid cancer relationship remain unclear, the results of this study underscore the need to evaluate, directly, levels of various adipocytokines and other obesity-related biomarkers, as well as modifiable determinants of obesity, including over-nutrition and physical inactivity, as possible risk factors for this disease.

No potential conflicts of interest were disclosed. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government.

Conception and design: C.M. Kitahara, G. Neta, R.M. Pfeiffer, N.D. Freedman, E.M. Sturgis, A.J. Sigurdson, A.V. Brenner

Development of methodology: R.M. Pfeiffer, A.V. Brenner

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): A.A. Hutchinson, A.J. Sigurdson

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): C.M. Kitahara, R.M. Pfeiffer, D. Kwon, N.D. Freedman, A.J. Sigurdson, A.V. Brenner

Writing, review, and/or revision of the manuscript: C.M. Kitahara, G. Neta, R.M. Pfeiffer, L. Xu, N.D. Freedman, S.J. Chanock, E.M. Sturgis, A.J. Sigurdson, A.V. Brenner

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): G. Neta, L. Xu, E.M. Sturgis, A.J. Sigurdson, A.V. Brenner

Study supervision: A.J. Sigurdson, A.V. Brenner

Material support and database: E.M. Sturgis

This research was supported in part by the Intramural Research Program of the NCI, NIH. This project has been funded in whole or in part with federal funds from the NCI, NIH, under contract no. HHSN261200800001E.