Abstract

Background: No cohort studies have evaluated the effect of obesity on the incidence of thyroid cancer according to metabolic health status. Therefore, this study examined the association of body mass index (BMI) and metabolic health status with thyroid cancer risk.
Methods: A cohort study was performed involving 255,051 metabolically healthy (MH) and metabolically unhealthy (MUH) adults free of thyroid cancer at baseline who were followed for a median of 5.3 years. A parametric proportional hazard model was used to estimate the adjusted hazard ratio (aHR) and confidence interval (CI).
Results: During 1,402,426.3 person-years of follow-up, 2927 incident thyroid cancers were identified. Among men, the multivariable aHR for thyroid cancer comparing obesity, defined as a BMI ≥25 kg/m2, with a BMI of 18.5–22.9 kg/m2 was 1.47 [CI 1.12–1.93] in MH individuals, whereas the corresponding HR in MUH individuals was 1.26 [CI 1.03–1.53]. Among women, the corresponding HR in MH individuals was 1.05 [CI 0.80–1.36], whereas the corresponding HR in MUH individuals was 1.43 [CI 1.22–1.69]. Increasing quartiles of waist circumference were positively associated with risk of thyroid cancer in MUH men and women (p for trend <0.005) but not in MH individuals.
Conclusions: In both MH and MUH men, obesity was associated with an increased risk of incident thyroid cancer, indicating excessive adiposity per se as an independent risk factor for thyroid cancer. Conversely, women with MUH obesity but not MH obesity were found to have an increased risk of thyroid cancer, indicating that obesity with accompanying metabolic abnormalities may affect thyroid cancer risk in women.

Introduction

The incidence of thyroid cancer has been increasing worldwide during the last few decades (1–4). This pattern appears to be attributed to enhanced detection through increasingly widespread use of sensitive screening and diagnostic procedures (4–6). However, this increasing incidence includes larger tumors as well as small tumors <1 cm, which are detectable by sensitive diagnostic procedures (4,7,8). Thus, the possibility has been raised that a true increase in thyroid cancer incidence may have occurred and may be attributable to changes in life-style and environmental factors (4,6). During the same time period, the prevalence of obesity has dramatically increased and has become a major public-health issue throughout the world (9). Obesity is a known risk factor for several cancers, such as breast cancer, endometrial cancer, colon cancer, and prostate cancer, suggesting that obesity can affect cancer incidence (8,10–12). Previous epidemiological studies and meta-analyses of prospective cohort studies have also demonstrated an association between obesity, measured by body mass index (BMI), and thyroid cancer risk (12–17). The mechanisms underlying the association between obesity and thyroid cancer have not yet been elucidated, but obesity-related metabolic abnormalities have been proposed to mediate their associations (8,18–23).
However, the prevalence of obesity-related metabolic disturbances varies among individuals with obesity. Indeed, a proportion of subjects with obesity, referred to as metabolically healthy (MH) individuals with obesity, have received attention for appearing to have a favorable metabolic profile with no metabolic abnormalities (24–27). Different phenotypes of obesity may help in the understanding of whether obesity per se or the presence of co-existing metabolic abnormalities can increase thyroid cancer risk. Until now, no cohort studies have evaluated the effect of obesity on the incidence of thyroid cancer according to metabolic health status.
Therefore, a longitudinal cohort study was performed to evaluate for associations of BMI with the development of thyroid cancer in MH and metabolically unhealthy (MUH) adults free of thyroid cancer at baseline who underwent a health screening examination program while using a stricter criterion with zero metabolic abnormalities and with no insulin resistance to define the MH phenotype, as previously applied (28,29).

Methods

Study population

The Kangbuk Samsung Health Study is a cohort study of men and women aged ≥18 years who underwent a comprehensive annual or biennial health examination at the clinics of the Kangbuk Samsung Hospital Total Healthcare Screening Center in Seoul and Suwon, South Korea, from 2002 to present (30). Most examinees (>80%) were employees of various companies and local governmental organizations and their spouses. In South Korea, the Industrial Safety and Health Law requires annual or biennial health screening examinations of all employees, free of charge. Other examinees voluntarily had health checkups at the same healthcare centers.
The present analysis included all study participants with at least one follow-up visit who underwent a comprehensive health examination between 2002 and 2014 and who were followed annually or biannually until December 2017 (N = 284,180). Participants with missing data on BMI, glucose, high-density lipoprotein cholesterol (HDL-C), triglycerides, blood pressure (BP), high sensitivity C-reactive protein (hsCRP), and HOMA-IR (n = 4843), a history of cancer (n = 4755), a history of thyroid disease or current use of medication for thyroid diseases (n = 12,211), and overt hypothyroidism or hyperthyroidism (n = 10,866) were excluded. Because some individuals met more than one exclusion criterion, the total number of patients eligible for the study was 255,051. This study was approved by the Institutional Review Board of Kangbuk Samsung Hospital, and the requirement for informed consent was waived because de-identified retrospective data routinely collected during health screening processes were used.

Data collection

Data on socioeconomic status, life-style factors, medical history, and medication use were collected by standardized, self-administered questionnaires. The weekly frequency of moderate- or vigorous-intensity physical activity was evaluated and categorized into at least three times per week or fewer than times per week. Smoking status was categorized into never, former, and current smokers. Alcohol consumption was categorized into none, <20 g of ethanol/day, and ≥20 g of ethanol/day.
Weight, height, and sitting BP were assessed by trained nurses. Height was measured to the nearest 0.1 cm using a stadiometer with the examinee standing without shoes. Weight was measured in a light gown while barefoot to the nearest 0.1 kg using a bioimpedance analyzer (InBody 3.0 and InBody 720; Biospace Co., Seoul, Korea), which was calibrated every morning prior to testing. BMI was calculated as weight in kilograms divided by height in meters squared, and was categorized according to Asian-specific criteria (31). BMI categories included underweight (BMI <18.5 kg/m2), normal weight (BMI 18.5–22.9 kg/m2), overweight (BMI 23–24.9 kg/m2), and obese (BMI ≥25 kg/m2) (31–33). Waist circumference was measured by trained personnel to the nearest 0.1 cm at the midpoint between the bottom of the rib cage and the top of the iliac crest with the subjects standing with their weight equally distributed on both feet, their arms at their sides, and head facing straight forward. Abdominal obesity was defined as waist circumference ≥90 cm for men and ≥85 cm for women, which are specific for the Korean population (34–36). Hypertension was defined as a systolic blood pressure ≥140 mmHg, diastolic blood pressure ≥90 mmHg, or current use of antihypertensive medication.

Laboratory analyses

Blood specimens were taken from the antecubital vein of subjects following a fast of at least 10 hours. Serum levels of glucose, total cholesterol, low-density lipoprotein cholesterol (LDL-C), triglycerides, HDL-C, insulin, hsCRP, and thyroid hormones were measured at the Laboratory Medicine Department of Kangbuk Samsung Hospital, as previously described (30). Insulin resistance was assessed using the homeostatic model assessment of insulin resistance (HOMA-IR) equation: fasting blood insulin (μIU/mL) × fasting blood glucose (mmol/L)/22.5. Diabetes mellitus (DM) was defined as a fasting serum glucose of ≥126 mg/dL, or current use of antidiabetic medication. To assess thyroid function, serum free thyroxine and thyrotropin (TSH) levels were measured by radioimmunoassay (RIA) using a commercial kit (RIA-gnost® VRhTSH; Schering-Cis Bio International, Gif-sur-Yvette, France) between 2002 and 2009, and thereafter were measured by an electrochemiluminescent immunoassay (Roche, Tokyo, Japan). The Laboratory Medicine Department at Kangbuk Samsung Hospital, Korea, has been accredited by the Korean Society of Laboratory Medicine and the Korean Association of Quality Assurance for Clinical Laboratories. The laboratory participates in the College of American Pathologists survey proficiency testing.
MUH persons were defined as those having at least one of the following metabolic abnormalities (37,38): (i) fasting glucose level ≥100 mg/dL or current use of glucose-lowering agents; (ii) BP ≥130/85 mmHg or current use of BP-lowering agents; (iii) elevated triglyceride level (≥150 mg/dL) or current use of lipid-lowering agents; (iv) low HDL-C (<40 mg/dL in men or <50 mg/dL in women); or (v) insulin resistance, defined as a HOMA-IR score ≥2.5 (39). Otherwise, being MH was defined as having none of the metabolic abnormalities described above, as previously applied (28,29).

Incident thyroid cancer cases

Information on physician-diagnosed thyroid cancer and age at diagnosis was obtained using a standardized, self-administered, structured questionnaire at baseline and at each follow-up visit. When self-reports of physician-diagnosed thyroid cancer were compared with confirmed thyroid cancer by linking the data to national cancer registry data among a subsample of 169,260 participants who visited between 2011 and 2014 and gave informed consent for the linkage because informed consent was only requested from 2011, the sensitivity and specificity for thyroid cancer were 98.3% and 99.8%, respectively. Specific types of thyroid cancer included papillary (97.8%), follicular (1.5%), medullary (0.1%), mixed medullary-papillary (0.3%), and unclassified cancer (0.2%).

Statistical analysis

Subject characteristics are presented according to the presence of incident thyroid cancer separately in men and women, and were compared according to the presence of incident thyroid cancer using t-tests for continuous variables or chi-square tests for categorical variables. The distribution of continuous variables was evaluated, and right-skewed variables (triglycerides, hsCRP, TSH, and HOMA-IR) were log transformed for t-tests.
The study endpoint was the development of self-reports of physician-diagnosed thyroid cancer. Follow-up for each participant extended from the baseline exam until either the development of thyroid cancer or the last health exam conducted prior to December 31, 2017. Incidence rate was calculated as the number of incident cases divided by person-years of follow-up. Because it was known that thyroid cancer had occurred at some point between the two visits but the precise timing of cancer development were not known, a parametric proportional hazards model was used to take into account this type of interval censoring (stpm command in Stata) (40). In these models, the baseline hazard function was parameterized with restricted cubic splines in log time with four degrees of freedom. The adjusted hazard ratio (aHR) with a confidence interval (CI) for incident thyroid cancer comparing BMI category to the normal weight category was estimated separately in MH and MUH groups of men and women. Then, the association between waist circumference and risk of thyroid cancer was also evaluated. For this analysis, waist circumference was categorized into the following quartiles: <80.1, 80.1–84.8, 84.9–90, and ≥90.1 cm for men; and <69.1, 69.1–73.9, 74.0–79.1, and ≥79.2 cm for women.
The model was initially adjusted for age and then further adjusted for center of treatment (Seoul and Suwon), year of screening exam, alcohol intake (≥20 vs. <20 g of ethanol/day), regular exercise (≥3 vs. <3 times/week), and level of education (college graduate or above vs. college graduate or below) in model 1. To evaluate whether the association between BMI categories and incident thyroid cancer was mediated by total cholesterol, HDL-C, triglycerides, glucose, systolic blood pressure, hsCRP, HOMA-IR, and TSH, these variables were included in multivariable adjusted models (model 2). The number of categories was used as a continuous variable and tested on each model to determine linear trends of incidence of thyroid cancer. Sensitivity analysis was performed using different definitions of metabolic health status that included elevated hsCRP level (>0.1 mg/L) as a criterion.
All statistical analyses were performed using STATA v15.0 (StataCorp LP, College Station, TX).

Results

Baseline characteristics of study participants

At baseline, the mean age of participants was 38.0 years (SD = 8.0 years), and 57% were male. The prevalence of the MUH phenotype was 63.8% in men and 36.8% in women. Baseline characteristics of the subjects are shown according to the presence of incident thyroid cancer separately in men and women (Table 1). Among men, incident thyroid cancer cases were more likely to have higher levels of BMI, blood pressure, triglycerides, hsCRP, and HOMA-IR and lower levels of HDL-C, and were less likely to be current smokers. Among women, incident thyroid cancer cases were more likely to be older and to have higher levels of BMI, blood pressure, triglycerides, hsCRP, and HOMA-IR and lower levels of HDL-C, and were less likely to be current smokers and alcohol drinkers.
Table 1. Baseline Characteristics of Study Participants According to Incident Thyroid Cancer by Sex
CharacteristicMenp-ValueWomenp-Value
No cancerCancerNo cancerCancer
N144,3311037 107,7931890 
Age (years)a38.5 (7.9)38.1 (6.4)0.08137.3 (8.0)37.9 (7.6)<0.001
Height (m)173.1 (5.8)173.3 (5.7)0.192160.5 (5.3)160.6 (5.2)0.480
Weight (kg)73.4 (10.0)75.1 (10.0)<0.00155.8 (7.9)57.5 (8.8)<0.001
BMI (kg/m2)24.5 (2.9)25.0 (2.9)<0.00121.7 (3.0)22.3 (3.2)<0.001
Current smoker (%)39.936.40.0303.02.20.054
Alcohol intake (%)b28.025.50.0794.21.8<0.001
Vigorous exercise (%)c18.518.90.73514.918.8<0.001
Higher education (%)d87.185.70.40476.074.80.471
Systolic BP (mmHg)a116.6 (12.1)118.3 (11.5)<0.001104.7 (12.3)107.4 (12.7)<0.001
Diastolic BP (mmHg)a75.1 (9.0)76.3 (8.6)<0.00166.9 (8.8)68.4 (8.9)<0.001
Glucose (mg/dL)a96.8 (15.3)96.7 (14.6)0.72191.2 (11.3)92.2 (13.5)<0.001
Total cholesterol (mg/dL)a197.2 (33.7)195.6 (32.2)0.117184.9 (31.7)184.1(31.7)0.274
LDL-C (mg/dL)a120.8 (30.5)118.5 (28.4)0.016104.3 (28.0)104.4 (27.8)0.961
HDL-C (mg/dL)a51.5 (11.7)49.1 (10.9)<0.00162.7 (14.2)59.5 (13.5)<0.001
Triglycerides (mg/dL)e117 (83–168)125 (88–183)<0.00172 (55–98)75 (58–103)<0.001
hsCRP (mg/L)e0.5 (0.3–1.1)0.6 (0.3–1.2)0.0020.3 (0.2–0.7)0.3 (0.1–0.7)0.596
HOMA-IRe1.60 (1.02–2.27)1.72 (1.12–2.39)<0.0011.34 (0.83–1.93)1.49 (0.95–2.10)<0.001
fT3 (pg/mL)3.34 (0.32)3.34 (0.30)0.6103.05 (0.31)3.08 (0.30)<0.001
fT4 (ng/dl)1.33 (0.15)1.33 (0.15)0.9331.23 (0.14)1.24 (0.14)<0.001
TSH (IU/mL)1.76 (1.23–2.50)1.70 (1.16–2.49)0.1761.98 (1.35–2.86)1.95 (1.33–2.81)0.001
Data shown are amean (standard deviation), emedian (interquartile range), or percentage.
b
≥20 g of ethanol per day; c≥3 times per week; d≥college graduate.
BMI, body mass index; BP, blood pressure; HDL-C, high-density lipoprotein cholesterol; hsCRP, high sensitivity C-reactive protein; HOMA-IR, homeostasis model assessment of insulin resistance; LDL-C, low-density lipoprotein cholesterol; fT3, free triiodothyronine; fT4, free thyroxine; TSH, thyrotropin.

Development of thyroid cancer by BMI category in MH and MUH obesity

Table 2 shows the development of thyroid cancer according to BMI categories in MH and MUH groups separately by sex. The median follow-up period for participants was 5.3 years (interquartile range [IQR] 3.3–7.8 years). For men, during 815,830 person-years of follow-up, 1037 participants developed thyroid cancer (incidence rate 1.3/1000 person-years). For women, during 586,596.3 person-years of follow-up, 1890 participants developed thyroid cancer (incidence rate 3.2/1000 person-years).
Table 2. Development of Thyroid Cancer by Body Mass Index Category in MH and MUH Phenotypes
BMI category (kg/m2)Person-yearsIncident casesIncidence rate (cases per 1000 person-years)Age-adjusted HR [CI]Multivariable adjusted HRa [CI]
Model 1Model 2
Men (n = 145,368)      
MH phenotype (n = 52,727)      
 <18.57632.640.50.52 [0.19–1.41]0.56 [0.21–1.52]0.57 [0.21–1.55]
 18.5–22.9130,956.31281.01.00 [reference]1.00 [reference]1.00 [reference]
 23.0–24.985,215.8951.11.15 [0.88–1.50]1.12 [0.86–1.46]1.09 [0.83–1.43]
 ≥25.062,012.1911.51.54 [1.18–2.02]1.47 [1.12–1.93]1.39 [1.04–1.86]
p for trend   <0.0010.0020.015
MUH phenotype (n = 92,641)      
 <18.52665.300
 18.5–22.9113,442.71381.21.00 [reference]1.00 [reference]1.00 [reference]
 23.0–24.9152,533.51871.21.01 [0.81–1.26]1.02 [0.82–1.27]0.98 [0.78–1.22]
 ≥25.0261,371.63941.51.25 [1.03–1.52]1.26 [1.03–1.53]1.17 [0.95–1.43]
p for trend   0.0030.0030.036
Women (n = 109,683)      
MH phenotype (n = 69,327)      
 <18.548,861.91132.30.83 [0.68–1.02]0.83 [0.68–1.01]0.85 [0.70–1.04]
 18.5–22.9250,998.57252.91.00 [reference]1.00 [reference]1.00 [reference]
 23.0–24.941,324.81363.31.10 [0.92–1.33]1.10 [0.91–1.32]1.07 [0.89–1.29]
 ≥25.019,619.2613.11.05 [0.81–1.37]1.05 [0.80–1.36]1.00 [0.77–1.31]
p for trend   0.0600.0670.207
MUH phenotype (n = 40,356)      
 <18.511,268.6292.60.79 [0.54–1.15]0.80 [0.55–1.16]0.82 [0.56–1.20]
 18.5–22.9117,309.13933.41.00 [reference]1.00 [reference]1.00 [reference]
 23.0–24.946,497.31884.01.19 [1.00–1.41]1.18 [0.99–1.41]1.17 [0.98–1.40]
 ≥25.050,717.12454.81.44 [1.23–1.70]1.43 [1.22–1.69]1.42 [1.20–1.69]
p for trend   <0.001<0.001<0.001
p = 0.717 for the overall interaction between metabolic health status and BMI category for incident thyroid cancer (adjusted model 1) among men. p = 0.278 for the overall interaction between metabolic health status and BMI category for incident thyroid cancer (adjusted model 1) among women.
a
Estimated from parametric Cox models. Multivariable model 1 was adjusted for age, center, year of screening exam, smoking status, alcohol intake, regular exercise, and educational level. Multivariate model 2 was the same as model 1 but with adjustment for total cholesterol, HDL-C, triglycerides, glucose, systolic blood pressure, hsCRP, HOMA-IR, and TSH.
MH, metabolically healthy; MUH, metabolically unhealthy; CI, confidence interval; HR, hazard ratio.
In MH and MUH states, increased BMI categories were positively associated with an increased risk of incident thyroid cancer, and these associations between BMI categories and incident thyroid cancer did not differ by metabolic health status (p for interaction = 0.717 among men). Among men, after adjusting for age, examination center, year of screening exam, smoking status, regular exercise, and education level, the multivariable aHRs for incident thyroid cancer comparing BMIs of 23–24.9 and ≥25 kg/m2 with a BMI of 18.5–22.9 kg/m2 as the reference were 1.12 [CI 0.86–1.46] and 1.47 [CI 1.12–1.93], respectively, in MH individuals (p for trend = 0.002). In contrast, corresponding aHRs in MUH male individuals were 1.02 [CI 0.82–1.27] and 1.26 [CI 1.03–1.53], respectively.
The association between increased BMI and thyroid cancer tended to be stronger in MUH women than in MH ones, but these association did not statistically differ by metabolic health status (p for interaction = 0.278). Among women, the multivariable aHRs for thyroid cancer comparing BMIs of 23–24.9 and ≥25 kg/m2 with a BMI of 18.5–22.9 kg/m2 as the reference were 1.10 [CI 0.91–1.32] and 1.05 [CI 0.80–1.36], respectively, in MH individuals (p for trend = 0.067). Corresponding aHRs in MUH female individuals were 1.18 [CI 0.99–1.41] and 1.43 [CI 1.22–1.69], respectively (p for trend <0.001).
To explore whether the association between BMI categories and development of thyroid cancer was mediated by metabolic parameters and levels of TSH, additional analyses were performed adjusting for total cholesterol, HDL-C, triglycerides, glucose, systolic blood pressure, hsCRP, HOMA-IR, and TSH (Table 2, model 2). The association between BMI categories and incident thyroid cancer was slightly attenuated but remained statistically significant in MH men and women.
In sensitivity analysis using a different definition of MUH status when high hsCRP level (>0.1 mg/L) was added to the criteria, the association between BMI category and risk of thyroid cancer was similar to the original analyses (Supplementary Table S1).
Additional analyses were performed using the MH normal weight category as a single reference in MH and MUH phenotypes (Supplementary Table S2). In the same BMI categories, the risk of incident thyroid cancer was higher than in MUH individuals compared to MH individuals, especially in women. A significantly higher risk of thyroid cancer was observed in MUH overweight and obese women.

Development of thyroid cancer by waist circumference in MH and MUH obesity

Table 3 shows the development of thyroid cancer according to the waist circumference category in MH and MUH individuals separated by sex. Among men, after adjusting for age, examination center, year of screening examination, smoking status, regular exercise, and education level, the multivariable aHRs for incident thyroid cancer comparing waist circumference quartiles 2, 3, and 4 to the lowest quartile were 0.99 [CI 0.69–1.41], 1.42 [CI 1.00–2.02], and 1.15 [CI 0.71–1.87], respectively, in MH individuals (p for trend =0.134). Corresponding aHRs in MUH male individuals were 1.28 [CI 0.93–1.77], 1.43 [1.05–1.95], and 1.60 [CI 1.17–2.17], respectively (p for trend = 0.002).
Table 3. Development of Thyroid Cancer by Waist Circumference Category in MH and MUH Phenotypes (n = 183,907)
Waist circumferencePerson-yearsIncident casesIncidence rate (cases per 1000 person-years)Age adjusted HR [CI]Multivariable adjusted HRa [CI]
Model 1Model 2
Men (n = 104,118)      
MH phenotype (n = 38,940)      
 Abdominal obesity      
  No (<90 cm)165,490.31781.11.00 [reference]1.00 [reference]1.00 [reference]
  Yes (≥90 cm)21,564.8271.31.30 [0.86–1.95]1.21 [0.80–1.82]1.16 [0.76–1.78]
 Waist circumference quartiles      
  Q1 (<80.1 cm)7632.640.51.00 [reference]1.00 [reference]1.00 [reference]
  Q2 (80.1–84.8 cm)130,930.51281.01.03 [0.72–1.48]0.99 [0.69–1.41]0.96 [0.66–1.38]
  Q3 (84.9–90.0 cm)85,203.3951.11.52 [1.07–2.14]1.42 [1.00–2.02]1.38 [0.96–1.99]
  Q4 (≥90.1 cm)59,864.1901.51.27 [0.79–2.03]1.15 [0.71–1.87]1.11 [0.67–1.84]
  p for trend   0.0460.1340.221
 MUH phenotype (n = 65,178)      
 Abdominal obesity      
  No (<90 cm)232,726.22731.21.00 [reference]1.00 [reference]1.00 [reference]
  Yes (≥90 cm)101,999.31401.41.26 [1.03–1.55]1.21 [0.98–1.49]1.14 [0.92–1.41]
 Waist circumference quartiles      
  Q1 (<80.1 cm)7632.640.51.00 [reference]1.00 [reference]1.00 [reference]
  Q2 (80.1–84.8 cm)130,930.51281.01.31 [0.94–1.80]1.28 [0.93–1.77]1.23 [0.89–1.70]
  Q3 (84.9–90.0 cm)85,203.3951.11.49 [1.10–2.03]1.43 [1.05–1.95]1.35 [0.98–1.85]
  Q4 (≥90.1 cm)59,864.1901.51.69 [1.25–2.28]1.60 [1.17–2.17]1.46 [1.06–2.02]
  p for trend   <0.0010.0020.019
Women (n = 79,789)      
 MH phenotype (n = 51,151)      
 Abdominal obesity      
  No (<85 cm)223,248.35902.61.00 [reference]1.00 [reference]1.00 [reference]
  Yes (≥85 cm)10,976.1343.11.20 [0.85–1.69]1.19 [0.84–1.69]1.15 [0.81–1.64]
 Waist circumference quartiles      
  Q1 (<69.1 cm)7632.640.51.00 [reference]1.00 [reference]1.00 [reference]
  Q2 (69.1–73.9 cm)130,930.51281.01.06 [0.86–1.31]1.06 [0.87–1.31]1.05 [0.85–1.29]
  Q3 (74.0–79.1 cm)85,203.3951.11.15 [0.93–1.42]1.15 [0.93–1.43]1.13 [0.90–1.40]
  Q4 (≥79.2 cm)59,864.1901.51.21 [0.95–1.54]1.21 [0.95–1.55]1.17 [0.90–1.51]
  p for trend   0.0840.0840.177
 MUH phenotype (n = 28,638)      
 Abdominal obesity      
  No (<85 cm)119,077.84133.51.00 [reference]1.00 [reference]1.00 [reference]
  Yes (≥85 cm)25,290.8983.91.17 [0.94–1.46]1.18 [0.94–1.49]1.13 [0.89–1.44]
 Waist circumference quartiles      
  Q1 (<69.1 cm)7632.640.51.00 [reference]1.00 [reference]1.00 [reference]
  Q2 (69.1–73.9 cm)130,930.51281.01.27 [0.91–1.76]1.27 [0.92–1.77]1.27 [0.91–1.77]
  Q3 (74.0–79.1 cm)85,203.3951.11.55 [1.13–2.11]1.54 [1.13–2.10]1.54 [1.12–2.11]
  Q4 (≥79.2 cm)59,864.1901.51.78 [1.32–2.39]1.80 [1.33–2.44]1.79 [1.31–2.45]
  p for trend   <0.001<0.001<0.001
p = 0.981 for the overall interaction between metabolic health status and abdominal obesity for incident thyroid cancer (adjusted model 1) among men. p = 0.521 for the overall interaction between metabolic health status and waist circumference quartiles for incident thyroid cancer (adjusted model 1) among men. p = 0.815 for the overall interaction between metabolic health status and abdominal obesity for incident thyroid cancer (adjusted model 1) among women. p = 0.314 for the overall interaction between metabolic health status and waist circumference quartile for incident thyroid cancer (adjusted model 1) among women.
a
Estimated from parametric proportional hazard models. Multivariable model 1 was adjusted for age, center, year of screening exam, smoking status, alcohol intake, regular exercise, and educational level. Model 2 was the same as model 1 but adjusted for total cholesterol, HDL-C, triglycerides, glucose, systolic blood pressure, hsCRP, HOMA-IR, and TSH.
Among women, the multivariable aHRs for thyroid cancer comparing waist circumference quartiles 2, 3, and 4 to the lowest quartile were 1.06 [CI 0.87–1.31], 1.15 [0.93–1.43], and 1.21 [CI 0.95–1.55], respectively, in MH individuals (p for trend = 0.084). Corresponding aHRs in MUH female individuals were 1.27 [CI 0.92–1.77], 1.54 [CI 1.13–2.10], and 1.80 [CI 1.33–2.44], respectively (p for trend <0.001). In the mediation analyses (model 2), the association between waist circumference and incident thyroid cancer was slightly attenuated but remained statistically significant in MUH individuals of both sexes.
Abdominal obesity tended to be associated with increased risk for incident thyroid cancer without statistical significance among MH and MUH men and women.

Discussion

In a large cohort of young and middle-aged Korean adults, the association between BMI and the development of thyroid cancer was evaluated in MH and MUH adults free of thyroid cancer at baseline. In men, increased BMI categories were positively associated with an increased risk of incident thyroid cancer in both MH and MUH individuals (p for trend <0.05). In women, a positive association between increased BMI category and incident thyroid cancer was observed in MUH individuals. After further adjustment for metabolic parameters and levels of TSH as potential mediators, these associations were slightly attenuated but remained similar. On the other hand, increasing quartiles of waist circumference were positively associated with the risk of thyroid cancer in MUH individuals of both sex but not in MH individuals. The findings indicate that obesity, abdominal obesity, and metabolic health may affect the development of thyroid cancer differently by sex.
Previous studies have also demonstrated an association between obesity based on BMI and thyroid cancer risk (12–17). A large pooled analysis of 22 prospective studies showed a positive association of BMI with thyroid cancer incidence, with a HR for BMI (per 5 kg/m2) of 1.06 [CI 1.02–1.10] (17). In that study, the multivariable aHRs for thyroid cancer risk comparing BMI 25–29.9 and ≥30 kg/m2 to 18.5–24.9 kg/m2 as the reference were 1.23 [CI 1.02–1.47] and 1.35 [CI 1.07–1.71] for men, respectively, and 1.02 [CI 0.93–1.14] and 1.05 [CI 0.92–1.19] for women, respectively. Therein, however, height and weight were self-reported (as opposed to measured) in most cohorts, and the HRs for BMI were attenuated following the restriction of analysis to subjects with measured values: HRs for BMI (per 5 kg/m2) in men and women were 0.76 [CI 0.57–1.03] and 1.03 [CI 0.95–1.13], respectively (17). Furthermore, none of the previous studies evaluated the effect of obesity on the incidence of thyroid cancer according to metabolic health status.
Obesity is characterized by an excess of adipose tissue and commonly accompanies metabolic deterioration, such as hyperglycemia, hypertension, dyslipidemia, and insulin resistance (27). Abnormal metabolic status is associated with increased risk of thyroid cancer (20,22,23). A population-based cohort study reports that blood glucose levels were inversely associated with the incidence of thyroid cancer among women (22). Another case-control study reported that 20 women with differentiated thyroid cancer had a high prevalence of insulin resistance compared to control subjects matched for age, sex, and BMI (23). However, it is unclear whether the increased risk of thyroid cancer is associated with obesity per se or the presence of co-existing metabolic abnormalities (20,22,23) because most of the previous studies evaluated the association between BMI and the risk of thyroid cancer without consideration of the metabolic status associated with obesity (12–17,41).
In the current study of relatively healthy, young, middle-aged adults, the risks of incident thyroid cancer according to BMI categories were evaluated separately in MH and MUH phenotypes. Increased BMI was found to be associated with an increased risk of thyroid cancer in men, even without accompanying metabolic abnormalities. This finding suggests that excessive adiposity per se is an independent risk factor for thyroid cancer, regardless of accompanying metabolic abnormalities. However, in women, obesity was significantly associated with increased risk of thyroid cancer in MUH individuals but not in MH individuals. The findings indicate that both obesity and metabolic health may play an important role in the development of thyroid cancer among women. Sex differences in fat distribution can affect the development of thyroid cancer. Men predominantly store fat in the visceral area, while women tend to store fat predominantly in the gluteal-femoral region (42). Additionally, recent studies addressing the different roles of upper- and lower-body fat in metabolism have suggested disease-protective effects of lower-body fat (43,44).
In this study, increased waist circumference was positively associated with thyroid cancer risk in MUH men and women. This result suggests that abdominal obesity is associated with the development of thyroid cancer, especially when accompanied by metabolic abnormalities. Further studies with larger samples and measures of body composition and distribution are warranted to elucidate the mechanism of how BMI, waist circumference, and metabolic health differently affect risk of thyroid cancer by sex.
Possible mechanisms have been reported to explain the association between obesity and thyroid carcinogenesis through metabolic dysregulation. Chronic low-grade inflammation increases the formation of reactive oxygen species and cell cycle rates, and decreases tumor suppressor function (8,21). Inflammatory cells, such as macrophages and lymphocytes, accumulate in the tumoral stroma of thyroid cancers (21). In addition to local thyroid inflammation, systemic inflammation may contribute to thyroid cancer development and/or progression (21). Several cytokines have been studied as potential mediators, including tumor-necrosis factor, interleukin (IL)-6, IL-1β, IL-10, and transforming growth factor beta (8,10,11). Hyperinsulinemia and increased insulin-like growth factor 1 (IGF-1), which are the results of insulin resistance, are other hypotheses for thyroid carcinogenesis (8,19,21). After binding to the insulin receptor, insulin activates downstream AKT/mTOR/PI3K and ERK/RAS/MAPK pathways, which are involved in cancer proliferation and survival (21). When additional analyses were performed using MH normal weight category as a single reference, the risk of incident thyroid cancer was higher in MUH individuals than in MH individuals for a given BMI category especially in women. This result suggests that metabolic abnormalities could contribute to the development of thyroid cancer in addition to obesity per se.
Excessive adiposity itself without metabolic deterioration might affect thyroid carcinogenesis. Adipose tissue is an active endocrine organ that produces and releases adipokines, and expanding adipose tissue in obesity could contribute to the development of cancer via dysregulated secretion of adipokines (21,27,45,46). Adiponectin is an adipokine that has antitumor effects through an AMP-activated protein kinase pathway, as well as insulin-sensitizing and anti-inflammatory effects (47,48) Decreased levels of adiponectin in obesity are reported to be an independent risk factor for obesity-related cancers (10,11,21,48). On the other hand, leptin has been shown to stimulate cell proliferation, inhibit apoptosis, and enhance the migratory activity of thyroid cell lines via activation of the PI3K/Akt signaling pathway (21). Considering the increased expression of leptin and its receptors in thyroid cancer cells, the autocrine/paracrine effect of leptin is a possible mechanism (49,50). Leptin may have a direct effect on cancer initiation or tumor progression without systemic effects (49,50). Increased serum TSH levels may stimulate the proliferation and growth of thyroid cells, increased mutation, and the development of thyroid cancer (51). The TSH receptor-mediated increase in intracellular cAMP levels is considered a major stimulus for thyroid cell proliferation and interacts with other growth factors, such as insulin, IGF-1, and other pathways, including the RAS-BRAF and the PI3K/AKT pathway (19). Circulating estradiol is a possible mechanism for thyroid carcinogenesis because adipocytes produce estrogens via aromatase activity, and obesity is associated with higher level of estradiol (52,53). In particular, the conversion of androstenedione to estrone in adipose tissue is the main source of estrogen in men and postmenopausal women (54). Estrogen is a potent growth factor for malignant thyroid cells (55,56). It exerts its growth-promoting effect through a classical genomic and non-genomic pathway mediated via a membrane-bound estrogen receptor (55,56). This receptor is linked to the tyrosine kinase signaling pathways MAPK and PI3K (55,56). However, estrogen levels were not available in the current study. The mechanisms through which excessive adiposity per se without metabolic deterioration contributes to the development of thyroid cancer are not fully understood. A better understanding of the association between obesity and thyroid cancer is warranted.
This study has several limitations. First, the incident cases of thyroid cancer were obtained via self-reported questionnaires. When self-reported thyroid cancer was compared to confirmed thyroid cancer through a link to national cancer registry data, the sensitivity and specificity rates for thyroid cancer were 98.3% and 99.8%, respectively. Information on baseline and follow-up ultrasound/cytology was not available in this study, even though it is acknowledged that it is the gold standard for the diagnosis of thyroid cancer. If individuals with thyroid cancer were misclassified as being without thyroid cancer, the association between BMI category and thyroid cancer could have been underestimated. Furthermore, it was not possible to evaluate the types of thyroid cancer and clinicopathologic features, including primary tumor size or nodal metastasis.
Second, the incidence of thyroid cancer in this study may differ from the incidence of thyroid cancer in the general population because the study subjects regularly participated in health screening examinations.
Third, the information on incidentally detected thyroid cancer versus symptomatic thyroid cancer can help control the differential detection-related bias by BMI category. Unfortunately, this was not available in this study. The median frequency of follow-up visits was similar across the BMI categories: 3 (IQR 2–5) in the BMI category <18.5 kg/m2, 4 (IQR 3–6) in the BMI category 18.5–22.9 kg/m2, 4 (IQR 3–7) in the BMI category 23.0–24.9 kg/m2, and 4 (IQR 3–7) in the BMI category ≥25 kg/m2. Thus, the differences in follow-up characteristics by group might not have affected the association between BMI category and incident thyroid cancer seen in this study.
Finally, the study data included seemingly healthy young and middle-aged, highly educated Koreans, with great access to healthcare resources. Accordingly, the findings might not be generalizable to other populations with different characteristics of age and race/ethnicity.
In conclusion, increased BMI was shown to be associated with an increased risk of incident thyroid cancer in both MH and MUH men. This result indicates that excessive adiposity per se is an independent risk factor for thyroid cancer, regardless of accompanying metabolic abnormalities. On the other hand, a positive association between obesity and thyroid cancer risk was observed in MUH women, supporting that both obesity and metabolic health contribute to the development of thyroid cancer in women. Further research is required to elucidate the possible mechanisms underlying the association between obesity and thyroid cancer. Maintaining a normal healthy weight may help reduce the risk of thyroid cancer, as well as other chronic diseases, in both men and women.

Supplementary Material

File (supp_table1.pdf)
File (supp_table2.pdf)

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cover image Thyroid®
Thyroid
Volume 29Issue Number 3March 2019
Pages: 349 - 358
PubMed: 30648486

History

Published in print: March 2019
Published online: 15 March 2019
Published ahead of print: 6 February 2019
Published ahead of production: 16 January 2019

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Hyemi Kwon*
Division of Endocrinology and Metabolism, Department of Internal Medicine; Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Yoosoo Chang*
Center for Cohort Studies, Total Healthcare Center; Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Department of Occupational and Environmental Medicine; Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Department of Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea.
Ara Cho
Center for Cohort Studies, Total Healthcare Center; Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Jiin Ahn
Center for Cohort Studies, Total Healthcare Center; Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Se Eun Park
Division of Endocrinology and Metabolism, Department of Internal Medicine; Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Cheol-Young Park
Division of Endocrinology and Metabolism, Department of Internal Medicine; Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Won-Young Lee
Division of Endocrinology and Metabolism, Department of Internal Medicine; Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Ki-Won Oh
Division of Endocrinology and Metabolism, Department of Internal Medicine; Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Sung-Woo Park
Division of Endocrinology and Metabolism, Department of Internal Medicine; Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Hocheol Shin
Center for Cohort Studies, Total Healthcare Center; Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Department of Family Medicine; Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Seungho Ryu [email protected]
Center for Cohort Studies, Total Healthcare Center; Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Department of Occupational and Environmental Medicine; Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Department of Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea.
Eun-Jung Rhee [email protected]
Division of Endocrinology and Metabolism, Department of Internal Medicine; Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.

Notes

*
These authors contributed equally to this work and are co-first authors.
Address correspondence to: Seungho Ryu, MD, PhD, Department of Occupational and Environmental Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Samsung Main Building B2, 250, Taepyung-ro 2ga, Jung-gu, Seoul 04514, Republic of Korea [email protected]
Eun-Jung Rhee, MD, PhD, Division of Endocrinology and Metabolism, Department of Internal Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29 Saemunan-ro, Jongno-gu, Seoul 03181, Republic of Korea [email protected]

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