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. 2014 Aug 12;141(1):93–98. doi: 10.1007/s00432-014-1792-2

Associations between body mass and papillary thyroid cancer stage and tumor size: a population-based study

Peter Dieringer 1, Evan M Klass 2, Brenna Caine 3, Julie Smith-Gagen 1,✉
PMCID: PMC11823684  PMID: 25113832

Abstract

Purpose

The incidence of large thyroid tumors has increased over the past decades, suggesting that improved diagnosis is not the only driver of increased thyroid cancer incidence. Obesity has recently been implicated as an independent risk factor for thyroid cancer in specific populations. We aimed to investigate whether thyroid tumor size and advanced stage of diagnosis is associated with the obesity epidemic, for the first time, in a US population-based cohort.

Methods

We leveraged existing data and linked 1,077 papillary thyroid cancer patients from the Nevada Central Cancer Registry to the Department of Motor Vehicle dataset. Tumor size and cancer stage were assessed from cancer registry records, and obesity was obtained using height and weight in the Department of Motor Vehicle records and measured by a body mass index greater than 25 kg/m2.

Results

Crude analysis showed obesity as was associated with tumors larger than 2 cm [odds ratio (OR) 1.50, p = 0.0423] and advanced cancer stage (stage III and IV) (OR 1.40, p = 0.0111). After adjusting for confounders, a significant association was still observed between obesity and tumor larger than 2 cm (OR 1.53, p = 0.0339). A marginally significant association was shown between obesity and advanced cancer stage (OR 1.29, p = 0.0649).

Conclusion

As thyroid cancer incidence continues to increase, this study’s finding that obesity was significantly associated with larger tumor size and marginally significantly associated with advanced tumor stage can help establish new preventative actions and identify new target populations for interventions.

Keywords: Obesity, Thyroid cancer, Tumor size, Stage at diagnosis

Introduction

Thyroid cancer incidence has risen in the past few decades, both in the United States (Cramer et al. 2010; Davies and Welch 2006) and worldwide (Colonna et al. 2002; Fahey et al. 1995; Kilfoy et al. 2009; Leenhardt et al. 2004). Ionizing radiation is the most significant risk factor for thyroid cancer. However, recently, the greatest increase has been observed among patients with papillary thyroid tumors, a treatable type of thyroid cancer. A 58.1 % average increase was observed between 1973–1997 and 1998–2002 Kilfoy et al. 2009. The use of thyroid ultrasonography, ultrasound-guided biopsy, and unrelated imaging of the neck, such as CT or MRI of the spine and vascular ultrasound, has likely contributed to the detection of small and early-stage tumors (Davies and Welch 2006), accounting for some of the increased incidence. However, increases have been seen in tumors of all sizes and stages of thyroid cancer (Enewold et al. 2009), indicating the increased incidence cannot completely be explained by improved detection of small cancers and may be related to some other factor.

As thyroid cancer incidence has increased so to have obesity rates. In the USA and worldwide, adult obesity rates have nearly doubled in the past 30 years (Flegal et al. 1998, 2012). Associations between obesity and increased cancer risk have been shown for a number of cancers with strong evidence of increased risk for postmenopausal female breast, esophageal, kidney, colon and rectal, and endometrial cancers (Vucenik and Stains 2012). The association between obesity and diagnosis of thyroid cancer risk has been explored (Han et al. 2013; Kitahara et al. 2011; Marcello et al. 2012; Mijovic et al. 2011; Rinaldi et al. 2012; Zhao et al. 2012), and relationships between obesity and extent of diagnosis are beginning to be explored with mixed results (Brindel et al. 2009; Cléro et al. 2010; Kim et al. 2011, 2013; Paes et al. 2010).

Obesity has been shown to be associated with worse prognostic factors in both prostate and female breast cancer (Allott et al. 2013; Cui et al. 2002; De Nunzio et al. 2013; Deglise et al. 2010; Haakinson et al. 2012). A recent review by Marcello et al. (2014) documented the relationship between thyroid cancer and obesity, but less is known about the relationship between obesity and extent of thyroid cancer at diagnosis. Paes et al. found an inverse association between obesity and tumor invasion and nodal metastasis through analysis of a US population, although this study was limited by small sample size and represented data from a single institution (Paes et al. 2010). Researchers have investigated the associations between obesity and the extent of diagnosis of thyroid tumors in the South Korean population, with inconsistent results (Kim et al. 2011, 2013). Significant associations were not found in one study, though it was limited by a small sample size (Kim et al. 2011). In a larger sample, increased BMI was significantly associated with larger tumor size, microscopic extrathyroidal invasion, and advanced TNM stage (Kim et al. 2013).

This study aims to clarify the relationship between obesity and extent of cancer at diagnosis, through the use of a population-based sample. This study intends to expand upon previous studies through the use of a population-based US cohort. This study sets out to examine whether thyroid tumor size and stage is associated with obesity in a US population.

Methods

Study population

Data from the Nevada Central Cancer Registry (NCCR), a population-based registry that maintains data on all cancer patients within the state of Nevada, were used to gather information on cancer from incident years 2001–2010. As height and weight, or other body mass measurements are not recorded in NCCR, height and weight data were derived from Nevada Department of Motor Vehicles (DMV) license and identification records. DMV records were obtained from 2005, 2008, 2010, 2011, and 2013 data files; these years were the only years available for review. The most recent DMV record prior to cancer diagnosis was used to assess BMI. NCCR staff matched NCCR and DMV records using probabilistic matching with Centers for Disease Control and Prevention’s Registry Plus™ Link Plus software. The study was considered exempt by the Research Integrity Office at the University of Nevada, Reno.

This analysis was limited to papillary thyroid cancer (PTC) patients over the age of 20. Only patients with a first primary PTC were included, because their mechanism of cancer development may be different from patients with secondary PTC.

Patient and tumor characteristics

Insurance status at time of diagnosis was classified into four groups: uninsured, insured, government payers (Medicaid, Medicare and Tricare), and missing insurance information. Race was categorized as white versus other. Age was classified as <45 versus 45 and older. Gender was male or female. Body mass index (BMI) was used as a measurement of body size and was calculated as weight divided by the square meter of height. BMI was dichotomized using World Health Organization (WHO) cutoff for normal weight: underweight/normal weight (<25 kg/m2), overweight/obese (≥25 kg/m2).

Cancer stage was determined through the Collaborative Stage (CS) Data Collection system, version 1, which provided CS-derived AJCC TNM-stage groups (AJCC 2004). Due to small sample size for stage 0 and stage IV PTC, cancer stage was dichotomized as in situ/localized (stages 0, I, and II), and regional/distant (stages III and IV). Tumor size was classified as <2 cm versus 2 cm and greater.

Statistical analysis

SAS version 9.3 for Windows was used to conduct all statistical analyses. Chi-square test was used to describe the characteristics of the study participants by BMI category. Logistic regression was used to calculate odds ratios for cancer stage and tumor size. Multiple logistic and linear regression models were then used to adjust for confounding variables. Model building was used to find the most parsimonious model; cancer stage was controlled for gender, and tumor size was adjusted for age. Models were also assessed for interactions between BMI and age, BMI and race, and age and insurance status.

Results

A total of 1,502 PTC patient records were available for review. Patients younger than 20 (n = 21), with a non-primary PTC (n = 59), and those with no DMV record prior to diagnosis (n = 245) were excluded, for a total sample size of 1,177. Cancer-staging data were not available for 93 patients, and tumor size data were not available for 631; therefore, a total of 1084 records were used for analysis of cancer stage and 453 for tumor size.

Patient characteristics are shown in Table 1. Among the 1177 papillary thyroid cancer patients available for the stage at diagnosis analysis, there was a 3:1 ratio of women to men. The average age was 48.6 years. The average BMI was 26.4 kg/m2. The prevalence of overweight and obesity was 32.3 and 21 %, respectively; 1.2 % were underweight. The majority were white (71.5 %), and over half were 45 years of age or older (60.8 %). Most patients had private insurance as the primary payer at diagnosis (72.6 %), 12.7 % reported a government payer, 1.4 % reported no insurance, and 13.3 % had missing insurance information. The majority of patients had in situ or localized cancers (67.6 %), while the rest were staged as regional/distant cancers (32.4 %). Large tumors (>2 cm) were seen in 35.6 % of subjects. Chi-square analysis showed significant differences between BMI and gender and cancer stage categories.

Table 1.

Number of patients diagnosed with papillary thyroid cancer over age 20 with linkage to DMV prior to diagnosis, Nevada Central Cancer Registry 2001–2010

Clinicopathological features (total) BMIa p valueb
High (%) Low (%)
Age
 <45 (461) 231 (50.11) 230 (49.89) 0.081
 ≥45 (716) 396 (55.31) 320 (44.69)
Primary payer
 Private insurance (855) 452 (52.87) 403 (47.13) 0.7947
 Government (149) 83 (55.7) 66 (44.3)
 No insurance (16) 10 (62.5) 6 (37.5)
 Unknown (157) 82 (52.23) 75 (47.77)
Race
 Other (335) 178 (53.13) 157 (46.87) 0.9527
 White (842) 449 (53.33) 393 (46.67)
Gender
 Male (293) 222 (75.77) 71 (24.23) <0.0001
 Female (844) 405 (45.81) 479 (54.19)
Stagec
 In situ/localized (733) 374 (51.02) 359 (48.98) 0.0109
 Regional/distant (351) 208 (59.26) 143 (40.74)
Sized
 Small (758) 388 (51.19) 370 (48.81) 0.054
 Large (419) 239 (57.04) 180 (42.96)
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aBody mass index: high = ≥25 kg/m2, low = <25 kg/m2

bChi-square

cIn situ/localized = stages 0, I, II; regional/distant = stages III, IV

dSmall = <2 cm; large ≥2 cm

On crude analysis, overweight and obese patients (BMI ≥25 kg/m2) were 40 % more likely to be diagnosed with later stage cancer (stages III or IV) relative to under and normal weight patients (BMI <25 kg/m2). After adjusting for gender, overweight and obese patients were shown to have marginally significant higher odds of late stage relative to under and normal weight patients, OR and 95 % CI 1.286 (0.985–1.678). Results are shown in Table 2. Relative to females, males had approximately 40 % higher odds of late stage cancer, OR and 95 % CI 1.406 (1.061–1.863).

Table 2.

Odds ratios (OR) (with 95 % CI) of overweight or obese patients relative to normal weight by extent of papillary thyroid cancer at diagnosis, Nevada Central Cancer Registry 2001–2010

Crude Adjusteda
OR (95 % CI) p value OR (95 % CI) p value
Cancer stage 1.396 (1.079–1.806) 0.0111 1.286 (0.985–1.678) 0.0649
Tumor size 1.497 (1.014–2.21) 0.0423 1.53 (1.033–2.267) 0.0339
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aCancer stage adjusted for gender. Tumor size adjusted for age

On crude analysis, overweight and obese patients were 50 % more likely to be diagnosed with large PTC tumor (≥2 cm) than under and normal weight patients. After adjusting for age, a similar association was seen, OR and 95 % CI 1.53 (1.033–2.267). Older patients (≥45 years) were 40 % less likely to be diagnosed with large tumor, OR and 95 % CI 0.593 (0.4–0.879). No interactions (synergistic relationships between overweight and obesity and other covariates) were found.

Missing data are assessed in Table 3. Comparison of those patients excluded for not having a DMV record prior to cancer diagnosis and those with a DMV record prior to diagnosis showed a significant difference in age grouping (chi-square p value = 0.0014). A higher proportion of patients with a DMV record prior to diagnosis were aged 45 years or older. Chi-square analysis did not show any other significant differences. In comparison with patients with available tumor size data and those without, chi-square analysis showed significant differences in the distribution of primary payer at diagnosis (p = 0.0004). A larger proportion of patients without tumor size data also did not have insurance data, and a lower proportion had private insurance. There were no other significant differences found.

Table 3.

Comparison of included and excluded records due to missing linkage with DMV and missing tumor size: papillary thyroid cancer: Nevada Central Cancer Registry, 2001–2010

Prior DMV record (%) No DMV prior record (%) p valuea Tumor size (%) No tumor size (%) p valuea
Age
 <45 461 (39.17) 123 (50.20) 0.0014 181 (39.96) 249 (39.46) 0.8696
 ≥45 716 (60.83) 122 (49.80) 272 (60.04) 382 (60.54)
Gender
 Male 293 (24.89) 53 (21.63) 0.2791 102 (22.52) 169 (26.78) 0.1096
 Female 884 (75.11) 192 (78.37) 351 (77.48) 462 (73.22)
Race
 White 842 (71.54) 181 (73.88) 0.4583 333 (73.51) 452 (71.63) 0.4951
 Nonwhite 335 (28.46) 64 (26.12) 120 (26.49) 179 (28.37)
Insurance
 Private insurance 855 (72.64) 183 (74.69) 0.3658 368 (81.24) 474 (75.12) 0.0004
 Government 149 (12.66) 22 (8.98) 56 (12.36) 92 (14.58)
 No insurance 16 (1.36) 5 (2.04) 10 (2.21) 4 (0.63)
 Unknown 157 (13.34) 35 (14.29) 19 (4.19) 61 (9.67)
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aChi-square

Discussion

Obesity has been shown to be a risk factor for many different cancers including thyroid cancer (Brindel et al. 2009; Cléro et al. 2010; Han et al. 2013; Kitahara et al. 2011; Marcello et al. 2014; Mijovic et al. 2011; Rinaldi et al. 2012; Zhao et al. 2012). The impact of obesity on greater extent of cancer at diagnosis, such as advanced cancer stage, larger tumor size, and greater lymph node involvement, has been widely studied in female breast cancer, with strong evidence of increased risk of greater extent for obese individuals (Cui et al. 2002; Deglise et al. 2010; Haakinson et al. 2012; Majed et al. 2008). We found that overweight and obese patients are more likely to be diagnosed with a later stage of thyroid cancer and larger thyroid tumors. Although the main risk factor for thyroid cancer is radiation exposure (Ron et al. 1995), it is more common in women and diagnosed at earlier ages in women (Rahbari et al. 2010). We did not find a synergistic relationship between female thyroid cancer and either stage or tumor size.

There is limited and conflicting research on the relationship between the extent of thyroid cancer and body mass index. Researchers found increased body mass was significantly associated with larger tumor size, microscopic extrathyroidal invasion, and advanced TNM stage in a Korean population (Kim et al. 2013). Another Korean study did not find a significant association between obesity and T stage, which considers tumor size in staging (Kim et al. 2011; Paes et al. 2010). Paes et al. (2010) did not find such an association in an American population; in fact, obesity was associated with a lower risk of advanced tumor invasion and nodal metastasis. Clero et al. (Cléro et al. 2010) also did not find an association on tumor size in a French Polynesian study sample. Brindel et al. (2009) stratified case–control data in a French Polynesian study sample by size of tumor and found patient height was associated with very small tumor size. The present study does not support genetic or cultural factors as potentially being responsible for the differing results.

In comparison with previous studies of the relationship between thyroid cancer and obesity, this study investigates the extent of disease as an end point and represents the first US population-based analysis, as all others analyses investigating stage and tumor size were hospital or clinic based. Through the use of NCCR records, this study intended to gather a representative sample of the entire population of Nevada. This study also benefited from a larger sample size and cohort design.

These findings indicate the need for further study of the effects obesity has on thyroid cancer progression. Determining whether biological mechanisms, such as thyroid or sex hormones, are responsible may prove useful in screening or treatment. It is possible increased adipose tissue in the neck may decrease early detection as a growth cannot easily be identified, leading to larger tumor sizes and later stage at diagnosis in the overweight/obese population. Other potential biological mechanisms include insulin resistance, adipokines, and inflammation (Marcello et al. 2012).

This study has limitations. DMV records are all self-reported, and it has been shown people tend to over-report height and obese individuals underreport their weight. Men are typically more likely to over-report height and women more likely to underreport weight (Gorber et al. 2007; Stommel and Schoenborn, 2009). This could bias results toward the null, that is, the OR might be smaller than observed if we did not have this potential bias. In a comparison of obesity rates in Nevada DMV and Behavior Risk Factor Surveillance System (BRFSS) populations, no overall difference in the prevalence of obesity was found between the two populations. However, the obesity rate for women in DMV records was significantly lower than that found in BRFSS (Mburia-Mwalili and Whitehill 2013). Our study population was approximately 75 % female, and this potential underreporting of weight could bias results toward the null. This is exemplified by the significant association between increased body mass and cancer stage found on crude analysis that became marginally significant after adjustment for gender. Although we assessed BMI from the closest DMV record prior to diagnosis, it is possible changes in weight may have occurred. It is unknown whether these changes occurred equally among groups or differed, which could potentially bias the results.

Only basic demographic data were available, and no information regarding family history, cancer risk factors, or frequency or utilization of care was available. Therefore, these factors could not be controlled for and may have biased the results. For example, individuals who smoke may be thinner and have greater risk of late stage diagnosis, which could bias results toward the null. In addition, other tumor characteristics were unavailable, limiting analysis solely to tumor size and cancer stage. Records were limited to those with a DMV record prior to diagnosis. Those excluded for not having a DMV record prior to diagnosis were shown to be younger than those included in analysis. Though it was used as a covariate and not a predictor variable, our analysis showed older individuals to have lower odds of larger tumor size. Older individuals could be more likely to have regular care appointments or imaging studies, such as MRI of the spine or vascular ultrasound, which could lead to earlier or incidental detection of a tumor. A stronger association may have been seen if the younger patients who were excluded from analysis could have been included.

This study aimed to improve on previous research through the use of a larger sample; though a larger sample was used, missing data limited the available cases for analysis. Of the 1,502 records available, 1,084 were used for analysis of cancer stage. Documentation of tumor size is a subset of those used to assess stage and was missing for 59 % of the cases. Our results are similar to the 63 % of cases missing pathological information in a New South Wales Cancer registry (Kahn et al. 2012). These authors noted patients with missing data had significantly smaller tumors or localized spread. The missing data decreased the statistical power. A significant difference between those with tumor size and those missing tumor size data was only seen in the distribution of primary payer at diagnosis. Primary payer was not shown to be a significant predictor of either tumor size or cancer stage on crude analysis; therefore, it does not appear this would have biased the results. Though limitations in data were present, this study benefited from a larger sample than other studies and the use of a population-based registry. The findings of this study indicate obesity may be a risk factor for late stage of PTC and larger tumor size. Continued, and more systematic, investigation of obesity’s impact on extent of PTC at diagnosis is warranted.

Conflict of interest

None.

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