Association Between Metabolic Dysfunction-Associated Steatotic Liver Disease and Thyroid Cancer

Abstract

Background:

Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a hepatic manifestation of metabolic syndrome. This study investigated the association between newly developed nomenclature MASLD and the risk of thyroid cancer in the Korean population.

Methods:

After excluding individuals with a history of liver disease or malignancy, we analyzed a cohort of 214,502 Korean adults aged 40 years and above who participated in the National Health Screening Program from 2009 to 2010. Participants were categorized into four groups: no steatotic liver disease (SLD) without a cardiometabolic risk factor (CMRF), no SLD with at least one CMRF, MASLD, and metabolic and alcohol-related/associated liver disease (MetALD). SLD was diagnosed using a fatty liver index threshold of ≥30. The primary outcome was the diagnosis of new thyroid cancer during the follow-up period. We examined the relationship between CMRF/SLD and thyroid cancer incidence using the multivariable-adjusted Cox proportional hazards model.

Results:

A total of 2761 participants (1.3%) were newly diagnosed with thyroid cancer over an average follow-up of 9.61 years. Compared with participants without CMRF and SLD, those with CMRF (hazard ratio [HR] 1.33, confidence interval [CI] 1.16–1.52), those with MASLD (HR 1.36, CI 1.17–1.58), and the MetALD group (HR 1.40, CI 1.04–1.88) exhibited a significantly higher risk of thyroid cancer. In addition, MetALD is significantly associated with thyroid cancer incidence solely in men.

Conclusions:

In addition to CMRF, MASLD and MetALD were associated with a higher incidence of thyroid cancer in the Korean population. This study is the first to demonstrate the association between thyroid cancer and the CMRF-MASLD-MetALD continuum.

Introduction

Thyroid cancer, the most prevalent endocrine malignancy, has seen a dramatic global increase in incidence over the past few decades.^1,2 This increase is largely attributed to improved detection through widespread imaging and the advent of various diagnostic techniques.³ The prognosis for differentiated thyroid cancer is generally favorable compared with other malignancies.⁴ Although mortality rates for thyroid cancer in Korea peaked between 1985 and 2002, they have been steadily declining since then.¹ However, some cases exhibit aggressive behavior. Therefore, identifying modifiable risk factors for thyroid cancer is critical in an era of overdiagnosis.

Several studies have reported a significant association between obesity, metabolic syndrome (MetS), and an increased risk of thyroid cancer.^5
–7 Recently, Lee et al. demonstrated that nonalcoholic fatty liver disease (NAFLD) is linked to a higher risk of thyroid cancer in Korean young adults.⁸ NAFLD is a systemic metabolic disorder characterized by insulin resistance and metabolic dysfunction, which can lead to cardiovascular and malignant complications.^9,10 However, the previously used term “nonalcoholic” did not accurately reflect the disease’s etiology. Furthermore, the descriptor “fatty” has been perceived as stigmatizing by some.¹¹ These issues prompted a multisociety Delphi consensus to adopt the new nomenclature of metabolic dysfunction-associated steatotic liver disease (MASLD) and a new category termed metabolic and alcohol-related/associated liver disease (MetALD).¹¹ As a result, MASLD introduces a new set of diagnostic criteria that differ from those of NAFLD, with the potential to eventually replace it.

Recent evidence suggests a link between MASLD and an elevated risk of various cancers, including thyroid cancer.¹² The incidence of thyroid cancer varies significantly by geographic region and time period.¹³ Therefore, we investigated the relationship between MASLD and MetALD, a newly established diagnostic criterion, and the incidence of thyroid cancer in Korean individuals enrolled in the National Health Screening Program.

Methods

Data source

In Korea, the biennial health screening program is designed to provide comprehensive coverage for all employees, regardless of age, as well as for individuals aged 40 years and above. Using data from these health screenings, the National Health Insurance Service (NHIS) has established national health information databases, including the National Health Insurance Service-National Health Screening Cohort (NHIS-HealS) database. Created by randomly selecting 10% of approximately five million participants in the 2002–2003 screening program, the NHIS-HealS database, used in this study, contains extensive information on medical claims, as well as health screening data such as physical measurements, laboratory tests, and self-reported lifestyle questionnaires.

Study design and population

The timeline and the flow of the study population are depicted in Figure 1. In total, 514,886 individuals were included in the database following the 2002–2003 screening. Among these, 362,285 individuals were included for cohort database entry between January 1, 2009, and December 31, 2010. Although the cohort study began in 2002, key health checkup parameters such as waist circumference, creatinine, triglycerides, and HDL cholesterol were only included starting in 2009–2010. These parameters are essential for diagnosing MetS and SLD; therefore, we analyzed data from the 2009 to 2010 period. We excluded those initially ineligible for the cohort study based on claim records from one year preceding the health screening date. Individuals with a history of viral hepatitis, autoimmune hepatitis, alcoholic liver disease, toxic liver disease, Wilson’s disease, or biliary cholangitis, all of which can lead to chronic liver disease, were also excluded from the study (n = 109,737). In addition, individuals with heavy alcohol consumption (>420 g/week for men and >350 g/week for women) (n = 8364), a history of malignancy (n = 15,865), decompensated liver cirrhosis (n = 4231), incomplete health screening data (n = 6690), and extreme values of the aspartate aminotransferase/alanine aminotransferase (AST/ALT) ratio (n = 2896) were excluded. The exclusion criteria adhered to the International Classification of Diseases, Tenth Revision (ICD-10) diagnosis codes. Ultimately, the cohort included 214,502 individuals, who were monitored from the date of health checkup in 2009–2010 (time zero) until death, the diagnosis of the primary outcome, or the end of the follow-up period (December 31, 2019).

FIG. 1.

Overview of study timeline and the flow of study population.

The study cohort was divided into four groups for analysis purposes: (1) no steatotic liver disease (SLD) without cardiometabolic risk factor (CMRF), (2) no SLD with at least one CMRF, (3) MASLD, and (4) MetALD. A Fatty Liver Index (FLI) ≥30 was used to diagnose SLD, adhering to international clinical guidelines for large epidemiological studies. This FLI approach provides a practical alternative to traditional imaging methods.¹⁴ In this study, the term “CMRF” refers to the five risk factors included in the diagnostic criteria for MASLD.¹¹ These factors are defined as follows: (1) body mass index (BMI) ≥23 or waist circumference ≥90 cm for men and ≥85 cm for women (following the ethnically appropriate criteria proposed by the Korean Society for the Study of Obesity¹⁵); (2) fasting blood glucose (FBG) ≥100 mg/dL or treatment for type 2 diabetes; (3) blood pressure ≥130/85 mmHg or antihypertensive drug treatment; (4) serum triglyceride (TG) ≥150 mg/dL or lipid-lowering treatment; and (5) high-density lipoprotein cholesterol (HDL-C) ≤40 mg/dL for men or ≤50 mg/dL for women or lipid-lowering treatment. The term “with CMRF” indicates the presence of any one of the previously mentioned five conditions. MASLD is defined by the presence of SLD with at least one of these five CMRFs, excluding or including mild alcohol consumption (<210 g/week for men and <140 g/week for women). MetALD is described as the presence of SLD, one or more of these risk factors, and moderate alcohol consumption (210–420 g/week for men and 140–350 g/week for women).

Definitions of primary outcomes and covariates

The primary outcome of the study was the new diagnosis of thyroid cancer during the follow-up period. The diagnosis was based on ICD-10 code C73, as identified through NHIS claims.¹ In Korea, all cancer patients are covered under the single-payer NHIS. Following a cancer diagnosis, patients are responsible for only 5% of medical costs for the first five years. This comprehensive coverage reduces the likelihood of missed diagnoses or cases managed outside the NHIS system. Demographic data such as age, sex, income level (divided into four quartiles), and urban/rural residence were obtained from the NHIS database. The use of ICD-10 codes and prescribed medications facilitated the identification of underlying conditions, including hypertension, diabetes, and dyslipidemia. In addition, comorbidities were assessed using the Charlson Comorbidity Index (CCI).¹⁶ The health screening dataset incorporated variables such as BMI, blood pressure, FBG, total cholesterol, HDL-C, low-density lipoprotein cholesterol, AST, ALT, r-glutamyl transpeptidase, hemoglobin level, and glomerular filtration rate. Participants also self-reported their smoking status (never, former, current), alcohol consumption (never, occasional, current), the quantity of alcohol consumed (grams per week), and their exercise habits (≥5 times/week) via a self-administered questionnaire.

Statistics and data analysis

Baseline characteristics are presented as means with standard deviations for continuous variables and as counts with percentages for categorical variables. For multiple group comparisons, ANOVA was used for continuous variables, while the Chi-square test was applied for categorical variables. The association between MASLD and thyroid cancer was determined by calculating a hazard ratio (HR) with a confidence interval (CI) using the Cox proportional hazards model in four groups: No SLD without CMRF, no SLD with at least one CMRF, MASLD, and MetALD. When constructing the Cox proportional hazards models with multivariable adjustments, we accounted for several covariates, including age, sex, income, residential area, CCI, hemoglobin levels, glomerular filtration rate, smoking habits, and regular exercise status as in previous studies.^17

–20 In addition, the following analyses were conducted for subgroup and sensitivity analyses: (1) analysis by dividing male and female groups, (2) analysis using the hepatic steatosis index (HSI) instead of FLI to identify SLD, (3) analysis using metabolic syndrome (MetS) instead of CMRF, and (4) landmark analysis with the two-year lag period. For further analysis of Supplementary Table S4, subjects were diagnosed with MetS if they fulfilled at least three of the revised National Cholesterol Education Program Adult Treatment Panel III criteria.²¹ Abdominal obesity was defined as a waist circumference of ≥90 cm for males and ≥85 cm for females, in accordance with the ethnicity-specific criteria proposed by the Korean Society for the Study of Obesity.¹⁵ All statistical analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC) and R 4.3.0 (R Foundation for Statistical Computing, Vienna, Austria). Statistical significance was defined as p < 0.05.

Results

Baseline characteristics of the study population

A total of 214,502 participants were included in this study (Fig. 1). The baseline characteristics of the study population are shown in Table 1. The mean age was 58.6 ± 8.8 years, and 49.4% of the participants were women. The overall mean BMI was 23.9 ± 2.9 kg/m², and the mean waist circumference was 81.3 ± 8.1 cm. We categorized the subjects into four groups: (1) the reference group, consisting of individuals without both steatotic liver disease (SLD) and CMRF (n = 20,215, 9.4%); (2) the no-SLD but with CMRF group, which included 117,519 participants (54.8%); (3) the MASLD group, consisting of 68,800 participants (32.1%); and (4) the MetALD group, consisting of 7968 participants (3.7%). All CMRF, including BMI, waist circumference, blood pressure, FBG, TG, and HDL-C, showed a linear association across the risk factor, MASLD, and MetALD groups. Parameters of SLD, including AST, ALT, r-glutamyl transpeptidase, FLI, and HSI, demonstrated the same trend among the groups. As expected, all participants (100%) in the MetALD group consumed alcohol. Baseline characteristics of the study population, stratified by sex are presented in Supplementary Tables S1 and S2. Male participants reported higher alcohol consumption compared to female participants (289.9 vs. 195.5 g/week).

Table 1.

Baseline Characteristics of Study Population

Variables	Total (n = 214,502)	No SLD without CMRF (n = 20,215)	No SLD with CMRF (n = 117,519)	MASLD (n = 68,800)	MetALD (n = 7968)	p value
Sex (%)
Male	10,8589 (50.6)	9135 (45.2)	47,744 (40.6)	44,143 (64.2)	7567 (95.0)	<0.001
Female	105,913 (49.4)	11,080 (54.8)	69,775 (59.4)	24,657 (35.8)	401 (5.0)
Age (years)
Mean (SD)	58.6 (8.8)	55.0 (7.2)	59.1 (9.0)	59.1 (8.6)	55.6 (7.2)	<0.001
Income level (%)
1st quartile	30,328 (14.1)	2738 (13.5)	17,410 (14.8)	9287 (13.5)	893 (11.2)	<0.001
2nd quartile	44,140 (20.6)	4360 (21.6)	25,123 (21.4)	13,262 (19.3)	1395 (17.5)
3rd quartile	61,670 (28.8)	5467 (27.0)	33,614 (28.6)	20,154 (29.3)	2435 (30.6)
4th quartile	78,364 (36.5)	7650 (37.8)	41,372 (35.2)	26,097 (37.9)	3245 (40.7)
Residence (%)
Rural	72,735 (33.9)	6079 (30.1)	40,240 (34.2)	23,728 (34.5)	2688 (33.7)	<0.001
Urban	141,767 (66.1)	14,136 (69.9)	77,279 (65.8)	45,072 (65.5)	5280 (66.3)
Hypertension (%)	92,434 (43.1)	0 (0.0)	49,983 (42.5)	38,230 (55.6)	4221 (53.0)	<0.001
Diabetes (%)	26,034 (12.1)	0 (0.0)	11,871 (10.1)	12,771 (18.6)	1392 (17.5)	<0.001
Dyslipidemia (%)	79,846 (37.2)	0 (0.0)	41,189 (35.0)	35,635 (51.8)	3022 (37.9)	<0.001
Charlson Comorbidity Index (%)
0	110,828 (51.7)	13,292 (65.8)	60,039 (51.1)	32,785 (47.7)	4712 (59.1)	<0.001
1	57,689 (26.9)	5061 (25.0)	32,148 (27.4)	18,472 (26.8)	2008 (25.2)
2	25,490 (11.9)	1470 (7.3)	14,312 (12.2)	8946 (13.0)	762 (9.6)
≥3	20,495 (9.6)	392 (1.9)	11,020 (9.4)	8597 (12.5)	486 (6.1)
Body mass index (kg/m²)
Mean (SD)	23.9 (2.9)	20.9 (1.5)	23.0 (2.2)	26.2 (2.5)	25.5 (2.4)	<0.001
Waist circumference (cm)
Mean (SD)	81.3 (8.1)	73.3 (5.9)	78.3 (6.3)	88.2 (6.2)	88.0 (6.1)	<0.001
Systolic blood pressure (mmHg)
Mean (SD)	124.8 (15.2)	111.7 (9.4)	124.4 (15.0)	128.6 (14.8)	130.3 (14.6)	<0.001
Diastolic blood pressure (mmHg)
Mean (SD)	77.3 (10.0)	70.1 (7.3)	76.8 (9.8)	79.7 (9.8)	81.7 (9.8)	<0.001
Fasting blood glucose (mg/dL)
Mean (SD)	99.8 (23.5)	87.8 (7.3)	98.1 (21.2)	105.2 (27.6)	107.3 (28.3)	<0.001
Total cholesterol (mg/dL)
Mean (SD)	201.1 (36.8)	190.0 (25.4)	199.2 (36.8)	207.2 (38.5)	204.4 (36.3)	<0.001
Triglyceride (mg/dL)
Mean (SD)	133.5 (80.2)	81.2 (28.3)	106.9 (48.7)	186.8 (94.7)	199.1 (112.4)	<0.001
HDL cholesterol (mg/dL)
Mean (SD)	54.8 (23.7)	62.3 (18.8)	56.1 (23.2)	50.4 (25.5)	53.2 (18.5)	<0.001
LDL cholesterol (mg/dL)
Mean (SD)	120.5 (36.4)	111.6 (24.2)	122.3 (35.7)	121.0 (39.3)	112.7 (41.6)	<0.001
Aspartate aminotransferase (U/L)
Mean (SD)	24.8 (10.7)	23.1 (9.2)	23.5 (7.9)	27.0 (13.3)	29.5 (18.7)	<0.001
Alanine aminotransferase (U/L)
Mean (SD)	23.1 (13.9)	18.6 (10.8)	20.2 (9.8)	28.7 (17.1)	29.7 (21.2)	<0.001
r-Glutamyl transpeptidase (U/L)
Mean (SD)	30.9 (31.8)	21.1 (15.5)	21.6 (12.9)	44.6 (41.0)	74.7 (67.1)	<0.001
Hemoglobin (g/dL)
Mean (SD)	13.8 (1.5)	13.4 (1.4)	13.5 (1.4)	14.3 (1.5)	14.9 (1.2)	<0.001
Glomerular filtration rate (mL/min/1.73 m²)
Mean (SD)	78.5 (30.6)	81.7 (30.9)	78.8 (29.5)	76.8 (32.1)	80.2 (31.0)	<0.001
Smoking (%)
Non-smoker	144,781 (67.5)	14,384 (71.2)	87,960 (74.8)	40,388 (58.7)	2049 (25.7)	<0.001
Ex-smoker	36,222 (16.9)	2540 (12.6)	15,852 (13.5)	15,154 (22.0)	2676 (33.6)
Smoker	33,499 (15.6)	3291 (16.3)	13,707 (11.7)	13,258 (19.3)	3243 (40.7)
Alcohol drinking (%)	79,619 (37.1)	7216 (35.7)	35,653 (30.3)	28,782 (41.8)	7968 (100.0)	<0.001
Amount of alcohol drinking (g/week)
Mean (SD)	43.1 (91.0)	36.1 (85.4)	32.4 (87.5)	35.3 (54.7)	285.1 (69.6)	<0.001
Regular exercise (%)	9691 (4.5)	856 (4.2)	5607 (4.8)	2857 (4.2)	371 (4.7)	<0.001
Fatty liver index
Mean (SD)	27.0 (21.0)	7.5 (5.3)	14.8 (7.7)	50.1 (15.4)	56.4 (17.3)	<0.001
Hepatic steatosis index
Mean (SD)	32.5 (4.3)	28.4 (2.6)	31.2 (3.4)	35.7 (3.9)	34.0 (3.7)	<0.001

CMRF, cardiometabolic risk factor; MASLD, metabolic dysfunction—associated steatotic liver disease; MetALD, metabolic and alcohol related/associated liver disease; SLD, steatotic liver disease.

Association between steatotic liver disease and thyroid cancer

During a median follow-up period of 9.61 years, 2761 participants (1.3%) were newly diagnosed with thyroid cancer. In total, 600 (0.6%) men and 2161 (2.0%) women developed thyroid cancer. The minimally adjusted (for age and sex) hazard ratio (HR) for thyroid cancer was 1.36 (CI 1.19–1.55), 1.41 (CI 1.22–1.64), and 1.41 (CI 1.05–1.89) in participants with no SLD with CMRF, MASLD, and MetALD, respectively, compared with individuals with neither SLD nor CMRF (Table 2). After further adjusting for income level, residence, CCI, hemoglobin level, glomerular filtration rate, smoking status, and regular exercise, SLD remained significantly associated with a greater risk of thyroid cancer in both men and women (HR 1.33, CI 1.16–1.52 in the CMRF group; HR 1.36, CI 1.17–1.58 in the MASLD group; and HR 1.40, CI 1.04–1.88 in the MetALD group, respectively). Compared with the participants with no SLD and CMRF, those with risk factors had significantly higher risks of thyroid cancer in men (HR 1.66, CI 1.13–2.44) and women (HR 1.30, CI 1.12–1.50), as well as those with MASLD in men (HR 1.84, CI 1.25–2.71) and women (HR 1.31, CI 1.10–1.54). MetALD was significantly associated with thyroid cancer only in men (HR 1.86, CI 1.17–2.98). Further analysis similarly revealed a positive association between the no-SLD with MetS group, rather than CMRF alone, and thyroid cancer (HR 1.43, CI 1.22–1.67, Supplementary Table S4).

Table 2.

Multivariable Analysis of Thyroid Cancer Risk among Subjects with Cardiometabolic Risk Factors and Steatotic Liver Disease: overall and Sex-Specific Findings

Group	Number	Events	Follow-up duration(person-years)	Incidence rate (per 1000 person-years)	Model 1^a	Model 2^b
Group	Number	Events	Follow-up duration(person-years)	Incidence rate (per 1000 person-years)	Adjusted HR (CIs)	Adjusted HR (CIs)
All	214,502	2761
No SLD without CMRF	20,215	245	192,815	1.27	1(Reference)	1(Reference)
No SLD with CMRF	117,519	1719	1110,717	1.55	1.36(1.19–1.55)	1.33(1.16–1.52)
MASLD	68,800	740	650,118	1.14	1.41(1.22–1.64)	1.36(1.17–1.58)
MetALD	7968	57	75,075	0.76	1.41(1.05–1.89)	1.40(1.04–1.88)
Male	108,589	600
No SLD without CMRF	9135	29	86,274	0.34	1(Reference)	1(Reference)
No SLD with CMRF	47,744	253	443,339	0.57	1.77(1.20–2.59)	1.66(1.13–2.44)
MASLD	44,143	272	413,624	0.66	1.99(1.36–2.92)	1.84(1.25–2.71)
MetALD	7567	46	71,214	0.65	1.90(1.20–3.03)	1.86(1.17–2.98)
Female	105,913	2161
No SLD without CMRF	11,080	216	106,541	2.03	1(Reference)	1(Reference)
No SLD with CMRF	69,775	1466	667,377	2.20	1.32(1.14–1.52)	1.30(1.12–1.50)
MASLD	24,657	468	236,494	1.98	1.36(1.15–1.60)	1.31(1.10–1.54)
MetALD	401	11	3861	2.85	1.53(0.34–2.81)	1.48(0.81–2.72)

The model 1 was adjusted for age and sex.

The model 2 was adjusted for age, sex, income level, residence, Charlson Comorbidity Index, hemoglobin level, glomerular filtration rate, smoking, and regular exercise status.

CMRF, cardiometabolic risk factor; MASLD, metabolic dysfunction—associated steatotic liver disease; MetALD, metabolic and alcohol related/associated liver disease; SLD, steatotic liver disease.

Sensitivity analysis with HSI and 2-year lag period

Results of sensitivity analysis with HSI instead of FLI to identify SLD are detailed in Supplementary Table S3. After adjustments, the HRs with CIs for thyroid cancer risk were consistent with Table 2 except for the MetALD group in both men and women. To address potential survivor cohort and surveillance biases, we conducted an additional sensitivity analysis with a 2-year lag period, which confirmed consistent trends (Supplementary Table S5).

Discussion

This retrospective population-based cohort study evaluated the association between MASLD and the incidence of thyroid cancer in the Korean population. Over a median follow-up of 9.61 years, the presence of at least one CMRF was associated with a higher incidence of thyroid cancer among the non-SLD group. In addition, MASLD and MetALD were associated with an increased incidence of thyroid cancer compared with those without CMRF or SLD.

MetS encompasses a cluster of CMRFs and insulin resistance, including overweight, hyperglycemia, hypertension, and dyslipidemia, and is significantly linked to a higher risk of developing diabetes mellitus and cardiovascular disease.²¹ Reports indicate that both obesity and MetS contribute to an elevated risk of thyroid cancer.⁵ Insulin resistance can lead to elevated levels of insulin and insulin-like growth factor 1, as well as chronic low-grade inflammation accompanied by heightened oxidative stress, which may contribute to the development of thyroid cancer.^22,23 Therefore, the impact of obesity and MetS on the MASLD–thyroid cancer relationship is crucial, given their status as confirmed risk factors for both MASLD and thyroid cancer.^11,24,25

A previous Korean study⁸ identified an association between NAFLD and thyroid cancer in young adults. We aimed to investigate the connection between the newly formulated concept of MASLD and thyroid cancer, noting Korea’s high thyroid cancer prevalence.¹ MASLD focuses more on underlying metabolic abnormalities than NAFLD does. Patients solely with MASLD present with fatty liver accompanied by at least one of five CMRFs, no alternative causes of SLD, and minimal to no alcohol intake.¹¹ This updated nomenclature also refines the earlier “nonalcoholic” label, rightly linking the condition to a metabolic origin, previously acknowledged as “the hepatic manifestation of MetS.”¹¹ The pathogenesis of MASLD remains unclear. The prevailing theory suggests insulin resistance as a primary mechanism leading to liver steatosis and potentially steatohepatitis.²⁶ Additional contributing factors include a disparity between energy intake and metabolic needs, alongside systemic inflammation.²⁷ We categorized individuals solely with CMRF, lacking SLD, as a distinct group and identified a notable association with increased thyroid cancer risk, consistent with earlier findings.^6,7 Furthermore, both MASLD and MetALD showed even higher association with thyroid cancer. Analysis also confirmed a positive association between no SLD with MetS²¹ and thyroid cancer in this study (Supplementary Table S4). The results may be attributed to intersecting mechanisms, including insulin resistance and chronic low-grade inflammation accompanied by increased oxidative stress, which underlie both MetS and SLD.¹¹

MASLD, the most prevalent cause of chronic liver disease, results from metabolic dysfunction and significantly contributes to liver-related morbidity and mortality.²⁸ It is characterized by the accumulation of fat in the liver, often linked with obesity and MetS. While the mechanisms linking MASLD to carcinogenesis remain unclear, insulin resistance, a pro-inflammatory state, and elevated serum thyrotropin (TSH) levels are considered potential factors.^5,8 Cross-sectional studies have established associations between TSH and MetS components.²⁹ NAFLD patients exhibit significantly higher TSH levels compared to healthy controls across all age groups, including adults and children, and these levels increase as NAFLD progresses.³⁰ TSH is a critical regulator of thyroid function. Numerous studies indicate that higher serum TSH concentrations are associated with an elevated risk of thyroid cancer.^31,32 Furthermore, thyroid cancer incidence appears to rise with MetALD in the general population, especially among men (Table 2). Echoing our findings, a previous Korean study reported that men with alcoholic liver disease (ALD) faced a higher risk of thyroid cancer than the control group.³³ The observed sex disparity in thyroid cancer rates may be linked to variations in alcohol consumption as indicated in Supplementary Tables S1 and S2 and the impact of female sex hormones.³⁴ Our research is pioneering in demonstrating the link between thyroid cancer and the CMRF-MASLD-MetALD continuum, underscoring the importance of MASLD/MetALD as a modifiable factor that can be improved through lifestyle changes.²⁸

Besides the increased risk of thyroid cancer, previous research has indicated that obesity and MetS factors associate with greater disease aggressiveness.^25,29,35 A recent cohort study from China found a significant link between metabolic dysfunction-associated fatty liver disease (MAFLD) and higher incidences of lymph node metastasis.³⁶ However, the absence of specific clinicopathological data related to thyroid cancer prevented further analysis in this study. The indolent nature and generally favorable prognosis of papillary thyroid carcinoma have recently sparked debate over the value of active surveillance.³⁷ Therefore, it is essential to identify these high-risk individuals to both reduce thyroid cancer incidence and improve outcomes.

The use of non-invasive biochemical scores such as the FLI to diagnose SLD has occasionally led to misdiagnoses compared to liver biopsy or imaging results. However, the FLI is endorsed by international clinical practice guidelines as a viable alternative to existing imaging methods, especially for large-scale epidemiological studies.³⁸ Moreover, the sensitivity analysis using HSI corroborated the main analysis (Supplementary Table S3), supporting the robustness of our operational definition of SLD with FLI.

To our knowledge, this is the first extensive study exploring the association between the new consensus of MASLD/MetALD and thyroid cancer. There are several limitations in this study. Firstly, thyroid cancers exhibit heterogeneity in histology and genetics, with varying prognoses based on subtype,³² and information on subtypes or cancer staging was not included. Secondly, there are also concerns about survivor cohort bias and surveillance biases of receiving more medical attention during follow-up. To minimize bias, various covariates that may affect health behaviors were used as covariates, and robust results were shown even after adjustment. In addition, a sensitivity analysis with a 2-year lag period demonstrated consistent trends (Supplementary Table S5). Moreover, thyroid ultrasonography is not included in the Korean health screening program. Consequently, the detection of small, asymptomatic thyroid cancers is less likely in these patients. Lastly, due to its retrospective design, the study relied solely on data from health examinations at a single point in time, preventing the assessment of changes in these metrics and their potential influence on thyroid cancer incidence. This limitation hinders the establishment of a causal relationship. It is imperative for future studies to uncover the underlying mechanisms and evaluate how enhancements in MASLD management could mitigate thyroid cancer risk.

Our study demonstrates that, alongside CMRFs, both MASLD and MetALD are independently associated with a higher incidence of thyroid cancer in the Korean population. This insight is crucial for screening initiatives aimed at early detection of the disease. Addressing these associated factors with effective prevention strategies is vital to potentially decreasing the incidence and severity of thyroid cancer.

Footnotes

Authors’ Contributions

S.Y.M.: Conceptualization, methodology, project administration, resources, validation of the study, and writing—original draft, review, editing, and revision of the article; M.S.: Data curation and formal analysis, funding acquisition, investigation, methodology, project administration, software, supervision, validation of the study, and writing—original draft, review, editing, and revision of the article; J.H.C.: Conceptualization and design of the work, interpretation of data, writing-critical review, editing, revision, and final approval of the article; H.I.K.: Conceptualization, interpretation of data, writing-critical review, editing, revision, and final approval of the article; J.M.H.: Conceptualization and design of the work, interpretation of data, writing-critical review, editing, revision, and final approval of the article; J.C.B.: Conceptualization and design of the work, interpretation of data, writing-critical review, editing, revision, and final approval of the article; S.S.: Conceptualization, methodology, project administration, supervision, resources, validation of the study, and writing—original draft, review, editing, and revision of the article.

Ethics Statement

The study protocol was reviewed and approved by the Institutional Review Board of Dong-A University Medical Center (approval No. DAUHIRB-EXP-21–224). In Korea, the use and provision of pseudonymized data from registered databases for medical research and public interest are permitted without the consent of data subjects. Due to the retrospective design and use of previously collected, deidentified data, the requirement for written informed consent was waived.

Author Disclosure Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding Information

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. RS-2024-00342613).

Supplementary Material

Supplementary Table S1

Supplementary Table S2

Supplementary Table S3

Supplementary Table S4

Supplementary Table S5

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