INTRODUCTION

The Korean National Health Information Database (NHID) is a nationwide dataset constructed by the National Health Insurance Service (NHIS), the single public insurer that covers nearly the entire Korean population. Currently, approximately 97% of the Korean population is enrolled as subscribers in the NHIS [1,2]. The NHID consists of five component databases: an eligibility database, a healthcare utilization claims database, a national health screening database, a long-term care insurance database, and a healthcare provider database, and it can be further linked to national mortality statistics maintained by Statistics Korea. The health screening component includes detailed questionnaires on lifestyle factors, physical measurements, and laboratory test results obtained through periodic screening programs that have been progressively expanded to Korean adults beginning in early adulthood. In addition, the NHID incorporates data from the government-run Rare Incurable Disease program, in which physician-verified diagnoses are required to qualify for substantial copayment reductions, thereby providing a relatively reliable registry for these conditions. Collectively, these features make the NHID a distinctive longitudinal resource for population-based research on both common and rare endocrine and metabolic diseases in Korea.

Because the Korean NHID is generated for administrative reimbursement and billing purposes rather than for research, this design inherently introduces important constraints. As a result, diagnoses recorded in claims data may not always accurately reflect patients’ true clinical conditions. To mitigate potential misclassification, researchers must develop carefully designed operational definitions and rigorously evaluate their validity. In this review, we present commonly used operational definitions for endocrine-related diseases, including diabetes mellitus and thyroid disorders, and summarize recent research conducted using the Korean NHID.

DIABETES MELLITUS

Operational definitions of diabetes mellitus

Type 2 diabetes mellitus (T2DM) is commonly identified using NHID data based on either a fasting plasma glucose level of ≥126 mg/dL or at least one annual prescription claim for antidiabetic medications accompanied by International Classification of Diseases, 10th revision (ICD-10) codes E11–E14 (Table 1) [2,3]. The validity of this NHID-based definition was evaluated in a previous study by comparing claims-based diagnoses with data from the Korea National Health and Nutrition Examination Survey (KNHANES), which served as an external reference standard [3]. That study demonstrated that the accuracy of identifying T2DM in NHID claims data was substantially improved when prescription information was used in combination with diagnostic codes, rather than relying on diagnostic codes alone [3].

Type 1 diabetes mellitus (T1DM) was operationally defined by identifying patients with persistent insulin dependence among individuals assigned the ICD-10 code E10. To distinguish persistent insulin dependence characteristic of T1DM from transient insulin use in other clinical contexts, an additional insulin prescription occurring between 1 and 2 years after the initial prescription was required [2,4]. In a more recent study, T1DM was operationally defined using claims data as the presence of ICD-10 code E10 together with a documented history of insulin treatment, defined as more than two insulin prescription claims, with at least one claim occurring between 1 and 2 years after the first prescription [4].

Gestational diabetes mellitus (GDM) was operationally defined as cases in which pregnant women had at least two outpatient visits recorded with diagnostic codes for GDM. In Korea, diagnostic codes related to GDM are frequently applied at the time of ordering an oral glucose tolerance test, reflecting reimbursement and administrative practices rather than confirmed diagnoses. Accordingly, GDM was operationally defined as the presence of at least two outpatient visits with recorded GDM diagnostic codes to improve diagnostic specificity [5]. Pre-pregnancy body mass index (BMI), advanced maternal age, elevated fasting glucose levels before pregnancy, smoking, and fatty liver, or elevated γ-glutamyltransferase levels, were significantly associated with an increased risk of GDM, particularly insulin-requiring GDM [6].

Diabetic retinopathy was identified using ICD-10 code H36.0, and proliferative diabetic retinopathy was operationally defined by combining ICD-10 code H36.0 with procedure codes for pan-retinal photocoagulation, yielding a relatively specific definition of advanced diabetic retinopathy [2]. Diabetic foot amputation was operationally defined when procedure codes for footlevel amputation were recorded in individuals with concurrent ICD-10 diagnosis codes indicating diabetes with circulatory or multiple complications (E10.5, E11.5, E12.5, E13.5, E14.5, and E10.7–E14.7) [2,7]. A conservative definition relied on procedure codes N0572–0575, whereas a broader definition incorporating additional foot-level amputation codes (N0562, N0564–0566, and N0571–0575) was evaluated in sensitivity analyses (Table 1).

Diabetes fact sheet in Korea

Diabetes-related fact sheets for the Korean population are primarily analyzed and reported using data derived from the Korean NHID [8-10]. The diabetes fact sheet in Korea 2021 provides an updated overview of diabetes prevalence, disease management, risk-factor control, and associated comorbidities in Korea. In 2020, diabetes affected 16.7% of adults aged ≥30 years; however, only 9.7% of individuals achieved simultaneous control of glycemia, blood pressure, and lipid levels [8]. Using nationwide data from the NHID and the KNHANES, the same report comprehensively characterized the epidemiology and management status of diabetic kidney disease (DKD) in Korea [9]. DKD was present in approximately one-quarter (25.4%) of adults with diabetes. Despite a gradual increase in sodium-glucose cotransporter 2 (SGLT2) inhibitor use among patients with impaired kidney function, reaching 5.94% in 2019, overall uptake remained low compared with the widespread use of renin–angiotensin system blockers (70%), highlighting substantial gaps in DKD management [9].

Using nationwide data from the NHID and KNHANES, another study examined population-level trends in cardiovascular disease (CVD) among Korean adults with T2DM between 2010 and 2019 [10]. Although the incidence of ischemic heart disease, ischemic stroke, and peripheral artery disease declined during this period, the incidence of heart failure increased over time, with a pronounced rise observed between 2015 and 2016. This increase may reflect population aging, improved survival following major cardiac events, and changes in diagnostic coding practices coinciding with the introduction of SGLT2 inhibitors. Despite these epidemiological trends, the use of evidence-based cardioprotective therapies, including high-intensity statins, SGLT2 inhibitors, and glucagon-like peptide-1 receptor agonists, remained limited, underscoring persistent deficiencies in comprehensive cardiovascular risk management.

Accordingly, SGLT2 inhibitor use remained low in 2019, with prescription rates below 12% across major CVD subgroups.

Recent research in diabetes mellitus based on the Korean NHID

Several influential diabetes-related studies have been conducted using the Korean NHID. One nationwide investigation utilized NHID data to assess the combined and independent effects of diabetes mellitus and fatty liver disease on cardiovascular outcomes and mortality [11]. In this cohort of 7.8 million individuals, 5-year absolute risks of CVD and all-cause mortality increased in a stepwise manner with greater severity of non-alcoholic fatty liver disease (NAFLD) and were consistently higher among individuals with T2DM. The 5-year absolute risk of CVD ranged from 1.03 in individuals without NAFLD or T2DM to 4.66 in those with both T2DM and grade 2 NAFLD, with a corresponding increase in all-cause mortality from 1.25 to 5.91 [8]. Because even grade 1 NAFLD was associated with a clinically meaningful excess absolute risk among individuals with T2DM, systematic assessment of fatty liver disease should be considered in this population.

Another study investigated how longitudinal changes in income are associated with CVD risk in adults with T2DM [12,13]. Because the NHID does not directly capture household income, monthly health insurance premiums were used as a validated proxy for income status; these premiums are calculated based on wages for employee-insured individuals and on income plus property value for the self-employed and were categorized into quartiles [12,13]. Income fluctuations, income decline, and persistent low-income status over time were each independently associated with a significantly increased risk of incident CVD, even after adjustment for traditional cardiovascular risk factors. These findings indicate that socioeconomic instability represents an important and underrecognized determinant of cardiovascular risk among individuals with T2DM.

Young-onset diabetes, defined as diabetes diagnosed before 30 years of age, is associated with a markedly increased risk of cardiovascular complications, kidney failure, and all-cause mortality compared with the general population [4]. After multivariable adjustment, individuals with young-onset T1DM and T2DM exhibited substantially elevated risks of myocardial infarction, with hazard ratios of 6.76 and 5.07, respectively, as well as increased risks of kidney failure (hazard ratios, 20.92 and 2.78) and mortality (hazard ratios, 3.69 and 3.06) [4]. These findings underscore that early-onset diabetes confers a substantial long-term cardiometabolic burden, emphasizing the need for early identification, intensive risk stratification, and proactive preventive strategies.

OBESITY

Operational definitions of general and abdominal obesity

Obesity and abdominal obesity were defined using BMI and waist circumference (WC), respectively, in accordance with the 2024 Clinical Practice Guidelines for Obesity issued by the Korean Society for the Study of Obesity (KSSO) [14]. In the NHID, BMI data have been available since 2006, whereas WC measurements have been systematically collected since 2009 [15].

General obesity was defined as a BMI ≥25 kg/m², consistent with the 2024 KSSO guidelines [14]. According to these guidelines, obesity is further classified into class I (BMI 25.0–29.9 kg/m²), class II (BMI 30.0–34.9 kg/m²), and class III (BMI ≥ 35.0 kg/m²). Abdominal obesity was defined as a WC ≥90 cm in men and ≥85 cm in women.

Obesity fact sheet in Korea: a decade of data and new trends

The KSSO has published the Obesity Fact Sheet periodically since 2015 to provide national statistics on obesity based on NHID and KNHANES data [16]. The most recent 2025 Obesity Fact Sheet, marking the 10th anniversary of its publication, analyzed data spanning 2014 to 2023 and revealed notable epidemiological shifts compared with earlier reports [16].

In contrast to the steadily rising trend observed over the previous decade, the overall prevalence of obesity, defined as BMI ≥25 kg/m², among adults has stabilized at approximately 38.4% over the most recent 3-year period from 2021 to 2023 [16]. However, this apparent stabilization conceals a pronounced sex-based disparity. Obesity prevalence among men continued to increase, reaching 49.8% in 2023, whereas prevalence among women remained relatively stable at 27.5% since 2021 [16]. Similarly, the prevalence of abdominal obesity stabilized at 24.3% overall but continued to rise among men, reaching 31.3%, while declining among women to 17.7% [16].

A major public health concern identified in recent big-data analyses is the disproportionate increase in severe obesity among young adults. Although overall obesity prevalence was highest among individuals in their 30s (42.0%) and 40s (42.2%), the steepest increase over the past decade occurred among individuals in their 20s, rising from 22.7% in 2014 to 32.0% in 2023 [16]. Notably, the prevalence of class III obesity, defined as BMI ≥35.0 kg/m², was highest among young men aged 20 to 24 years at 3.22%, indicating an urgent need for early interventions targeting this young-onset high-risk population.

Recent data also provide insight into childhood obesity trends in relation to the coronavirus disease 2019 (COVID-19) pandemic. Obesity prevalence among children and adolescents peaked in 2021 during the pandemic but subsequently declined to 13.8% in 2023, returning to levels comparable to those observed before the pandemic, similar to 2019 [16]. Furthermore, for the first time, the 2025 Obesity Fact Sheet leveraged nationwide big-data analyses to quantify intergenerational transmission of obesity, demonstrating that children of parents with class II obesity or higher had more than a fivefold increased risk of developing obesity themselves (Fig. 1).

Recent research in obesity based on the Korean NHID

Paradigm shift from NAFLD to metabolic dysfunction-associated steatotic liver disease

The transition from NAFLD to metabolic dysfunction-associated steatotic liver disease (MASLD) represents a fundamental shift toward affirmative diagnostic criteria centered on metabolic dysfunction [17]. Recent landmark studies using the Korean NHID have validated the clinical relevance and applicability of this revised definition specifically within the Korean population.

A cross-sectional analysis of 476,124 Korean adults, published in 2025, provided the first large-scale evidence directly comparing the clinical characteristics of NAFLD and MASLD in Korea [18]. The study demonstrated that although the prevalence of MASLD (29.8%) was similar to that of NAFLD (30.1%), the affirmative diagnostic framework of MASLD more effectively identified individuals with a substantial metabolic burden. Importantly, individuals meeting MASLD criteria exhibited a significantly higher risk of hepatic fibrosis, a key determinant of long-term prognosis, compared with those with steatotic liver disease (SLD) alone, confirming that the revised definition preferentially captures a high-risk phenotype [18]. The study also characterized the newly defined MetALD group, defined as MASLD with moderate alcohol consumption. This subgroup, which is often overlooked in traditional binary classifications, demonstrated a distinct clinical profile with elevated fibrosis scores, suggesting that metabolic dysfunction and alcohol exposure exert a synergistic adverse effect on liver health.

Moon et al. [19] examined a nationwide screening cohort of 351,068 adults aged 47 to 86 years who were followed for a median of 9.0 years. Compared with individuals without SLD, MASLD was associated with an increased subdistribution hazard ratio for CVD (subdistribution hazard ratio, 1.19). This study validated the MASLD, MetALD, and alcohol-related liver disease categorization against hard cardiovascular outcomes and demonstrated additive cardiovascular risk attributable to alcohol exposure when combined with metabolic dysfunction. In another nationwide cohort study involving nearly 9 million Korean adults, Lee et al. [20] evaluated MASLD prevalence and its association with CVD outcomes. Over a median follow-up of 12.3 years, individuals with MASLD and related disease entities had a significantly higher risk of incident CVD, including myocardial infarction, ischemic stroke, heart failure, and cardiovascular death, compared with individuals without SLD, even after multivariable adjustment (hazard ratio, 1.38; 95% confidence interval, 1.37 to 1.39). Among disease subtypes, MASLD conferred the highest cardiovascular risk, followed by MASLD with combined etiologies and MetALD. These findings support the clinical utility of the MASLD framework in identifying individuals with metabolically complicated SLD who are at elevated cardiovascular risk.

A key limitation of defining MASLD using NHID data is the absence of direct imaging or histological confirmation of hepatic steatosis, which is a core component of the original MASLD diagnostic framework and typically requires imaging modalities such as ultrasonography, computed tomography, magnetic resonance imaging–proton density fat fraction, or histological assessment. Consequently, most NHID-based studies rely on noninvasive surrogate indices, most commonly the fatty liver index, which is derived from BMI, WC, triglyceride levels, and γ-glutamyltransferase, for epidemiological identification of fatty liver in the absence of imaging [21]. Because these indices incorporate metabolic parameters that substantially overlap with the cardiometabolic criteria used to define MASLD, they may introduce circularity and misclassification bias relative to imaging-confirmed steatosis. In addition, some NHID analyses have applied the hepatic steatosis index, a validated alternative surrogate based on the aspartate aminotransferase-to-alanine aminotransferase ratio, BMI, diabetes status, and sex, to identify fatty liver in cohort research; however, these indices likewise lack confirmation against imaging or histology [22,23]. These limitations in steatosis ascertainment should be carefully considered when interpreting MASLD prevalence estimates and associations with clinical outcomes in NHID-based studies (Fig. 1).

Obesity, cardiovascular disease, and heart failure

Large-scale analyses using the Korean NHID have provided robust evidence linking obesity to a broad spectrum of cardiovascular outcomes. In a nationwide cohort exceeding 400,000 individuals, obesity was independently associated with an increased risk of incident atrial fibrillation, with consistent findings observed across Korean NHID and UK Biobank populations, underscoring the generalizability of obesity-related arrhythmic risk [24].

Beyond conventional BMI-based definitions, recent NHID studies have emphasized the importance of body composition and fat distribution in stratifying heart failure risk. In a nationwide cohort of more than two million adults, longitudinal changes in predicted body fat and lean body mass indices were strongly associated with incident heart failure, suggesting that qualitative aspects of obesity may be as clinically relevant as absolute body weight [25]. Furthermore, among nearly one million individuals with newly diagnosed heart failure, the coexistence of abdominal obesity was associated with substantially worse cardiovascular outcomes, including all-cause mortality, myocardial infarction, atrial fibrillation, and heart failure hospitalization, even within identical BMI categories. These findings challenge the notion of a benign ‘obesity paradox’ in heart failure [26].

More recently, an integrated cardiometabolic framework has been applied to NHID data to contextualize obesity within a broader disease continuum. In a cohort of approximately 1.5 million adults, progressive stages of cardiovascular-kidney-metabolic syndrome, which incorporate obesity as a key upstream component, were associated with stepwise increases in mortality, myocardial infarction, stroke, and heart failure hospitalization [27]. Collectively, these landmark NHID-based studies establish obesity, particularly when characterized by adverse fat distribution or unfavorable body composition, as a major determinant of CVD and heart failure risk in the Korean population (Fig. 1).

Obesity and cancer

Recent large-scale analyses using the Korean NHID have consistently demonstrated that obesity is associated with an increased risk of multiple site-specific cancers. Obesity contributes to carcinogenesis through several interrelated mechanisms, including insulin resistance and enhanced insulin-like growth factor-1 signaling, chronic low-grade inflammation with elevated pro-inflammatory cytokine production, impairment of antitumor immune surveillance, and hormonal dysregulation involving sex hormones [28].

Nationwide NHID studies have consistently reported a positive association between obesity and colorectal cancer risk, particularly when excess adiposity is sustained over time. A large population-based cohort demonstrated that persistent or repeated exposure to obesity across sequential health examinations was associated with a significantly increased risk of colorectal cancer, highlighting the importance of cumulative adiposity rather than single-time-point BMI measurements [29]. Complementing these findings, another NHID-based analysis showed that longitudinal changes in BMI influenced colorectal cancer risk, with sustained or worsening obesity conferring higher risk, underscoring the dynamic nature of obesity trajectories in colorectal carcinogenesis [30]. More recently, a nationwide NHID cohort study of older adults demonstrated that overweight and obesity were associated with increased incidence of both gastric and colorectal cancers, with particularly strong associations observed for colorectal cancer, further supporting excess adiposity as a major risk factor for gastrointestinal malignancies later in life [31].

Pancreatic cancer is closely linked to obesity-related metabolic disorders, particularly T2DM and pancreatitis. In a nationwide NHID cohort of young adults aged 20 to 39 years, overweight and obesity were associated with a significantly increased risk of young-onset pancreatic cancer, whereas underweight status was not, highlighting excess adiposity as an early-life risk factor for pancreatic carcinogenesis [32]. Complementing these findings, a nationwide nested case-control study using NHID data demonstrated that T2DM and pancreatitis markedly increased pancreatic cancer risk, particularly among individuals with post-pancreatitis diabetes or diabetes of short duration, underscoring the combined roles of metabolic dysfunction and pancreatic inflammation in pancreatic cancer development [33].

Emerging NHID-based evidence further suggests that obesity and metabolic dysfunction may play important roles in cancers traditionally considered less obesity-driven. Metabolic syndrome was associated with a significantly increased risk of thyroid cancer, with risk rising in proportion to the number of metabolic syndrome components, supporting a cumulative effect of metabolic dysregulation on thyroid carcinogenesis [34]. More recently, an NHID-based analysis incorporating the metabolic score for insulin resistance demonstrated a clear dose–response relationship between worsening metabolic dysfunction and thyroid cancer incidence, highlighting insulin resistance and adverse metabolic profiles, beyond BMI alone, as key contributors to thyroid cancer risk [35]. The biological mechanisms underlying the association between obesity-related metabolic dysfunction and thyroid cancer remain to be fully elucidated.

BONE AND MINERAL METABOLISM

Research on bone and mineral metabolism using Korean nationwide data has expanded substantially over the past decade. The availability of large-scale administrative and cohort datasets, including the Korean NHID and the National Screening Program for Transitional Ages (NSPTA), has enabled population-level analyses that are not feasible using single-center or purely cross-sectional designs [36,37]. Osteoporosis-related fractures, a hallmark of skeletal aging, require long-term observation to adequately characterize their epidemiology, clinical consequences, and associated mortality [38,39]. Nationwide datasets facilitate extended follow-up and provide a platform for evaluating the cumulative effects of aging, comorbidities, medication exposure, and healthcare utilization [1]. As illustrated in Fig. 2, publications using Korean nationwide data for osteoporosis and fracture research have increased markedly since 2013, with particularly rapid growth observed in recent years. More recently, nationwide data have also been applied to parathyroid disorders, including primary hyperparathyroidism (PHPT), hypoparathyroidism, and parathyroid carcinoma, reflecting the expanding scope of mineral metabolism research [40-44].

Operational definitions of osteoporosis and osteoporosis-related fractures

Operational definitions of osteopenia, osteoporosis, and fracture outcomes require cautious application because bone mineral density (BMD) measurements are not available within the Korean NHID. Thus, osteoporosis is operationally defined using diagnostic codes in combination with medication prescription records. A commonly applied definition classifies individuals as having osteoporosis when any of the following conditions are met: (1) prescriptions for anti-osteoporosis medications (AOMs) (2) ICD-10 codes M80–M82 accompanied by prescriptions for AOMs; or (3) documentation of an osteoporosis-related fracture. In addition, individuals who visited a clinic at least once for osteoporosis-related diagnostic codes and received AOMs within 6 months before or 2 months after the baseline assessment are also classified as having osteoporosis [45-47].

Beyond claims-based definitions, additional phenotypic information is available through the NSPTA. The NSPTA incorporates BMD testing at ages 50 and 66 using dual-energy X-ray absorptiometry, quantitative computed tomography, peripheral dual-energy X-ray absorptiometry, or quantitative ultrasonography [37]. However, the NSPTA does not provide continuous T-scores; instead, each modality reports categorical outcomes, classified as normal, osteopenia, or osteoporosis, which function as standardized nominal variables [48]. When integrated with NHIS claims data, these categorical BMD results enhance operational definitions by enabling direct classification of bone health status and facilitating harmonized phenotyping across datasets.

Osteoporosis-related fractures are identified using ICD-10 diagnostic codes in combination with site-specific procedure claims to enhance diagnostic specificity. Major osteoporotic fractures are defined across six anatomical sites: vertebrae, hip, humerus, distal radius, pelvis, and ankle. A subsequent fracture is defined as a new fracture event occurring at least 6 months after the index fracture [49]. These coding strategies have been widely applied in Korean NHID-based cohorts and are summarized in Table 2. Nevertheless, operational definitions may vary across studies because investigators adapt diagnostic codes, exposure windows, and classification criteria according to specific research questions, study designs, and clinical contexts.

The NHID includes prescription information only for medications reimbursed under the national insurance system. Osteoporosis-related agents captured in the database include hormone replacement therapies, selective estrogen receptor modulators, bisphosphonates, denosumab, teriparatide, romosozumab, and calcium or vitamin D supplements.

Operational definitions for parathyroid disorders

Operational definitions for parathyroid disorders in Korean nationwide datasets vary across studies, reflecting differences in research objectives, disease subtypes, and analytic approaches. For PHPT, surgically treated cohorts typically define PHPT using repeated ICD-10 codes (E21.0, E21.2, E21.3, or D35.1), parathyroidectomy procedure codes (P4541–P4543), and at least one hospitalization record [40]. Expanded definitions that include both surgical and nonsurgical PHPT further incorporate repeated diagnostic codes while excluding individuals with chronic kidney disease, dialysis, or kidney transplantation to minimize misclassification of secondary hyperparathyroidism [41]. Nonsurgical hypoparathyroidism is identified using repeated ICD-10 codes together with at least two prescriptions for active vitamin D analogs, while excluding individuals with prior thyroid or parathyroid surgery, neck irradiation, head and neck cancer, or stage 5 chronic kidney disease [43]. Postoperative hypoparathyroidism constitutes an additional category; permanent postoperative hypoparathyroidism is defined by at least three 90-day prescriptions for active vitamin D within 1 year after total thyroidectomy [44]. Parathyroid carcinoma is consistently defined using ICD-10 code C75.0 in combination with parathyroidectomy procedure codes and a hospitalization claim [42].

These operational definitions underscore that parathyroid phenotyping in Korean nationwide databases must be tailored to the clinical context, disease subtype, and specific analytic purpose.

Osteoporosis and osteoporotic fracture fact sheets in Korea

Nationwide analyses spanning 2002 to 2022 demonstrate substantial changes in osteoporosis management in South Korea [49]. Use of AOMs increased markedly, accompanied by a clear shift from oral agents to injectable therapies. Oral prescriptions peaked in 2016 and declined thereafter, whereas injectable prescriptions surpassed oral therapy in 2020. Denosumab use increased sharply following its approval as a reimbursed first-line therapy in 2019, although anabolic agents have remained underutilized. Medication adherence also improved over time, with the 1-year medication possession ratio increasing from 35.4% in 2003 to 73.2% in 2021. Post-fracture treatment initiation rose from 31.1% to 39.9% but remained below 50%, and treatment rates varied substantially by sex and fracture type, with higher rates observed in vertebral and femoral fractures and lower rates in wrist, humerus, and ankle fractures.

Pelvic fractures increased markedly from 2006 to 2022, particularly among adults aged 80 to 89 years, while 1-year mortality declined during this period [50]. Ankle fractures also demonstrated rising incidence and refracture rates [51]. Hip fractures continued to increase with population aging, although overall incidence has plateaued in recent years; 1-year mortality declined until 2018 but increased during the COVID-19 period [52]. The growing burden of osteoporosis-related fractures in Korea, together with persistent gaps in post-fracture care, highlights the need for more precise fracture surveillance and continued epidemiological research. Because the clinical presentation and coding characteristics of fractures vary by anatomical site and treating specialty, operational definitions require periodic re-evaluation and methodological updating. Regular consensus updates involving endocrinologists, orthopedic surgeons, and other relevant experts are essential to maintain clinically accurate and reliable definitions for nationwide research.

PITUITARY AND ADRENAL DISORDERS

Operational definitions of pituitary and adrenal disorders

Nationwide big-data research using the Korean NHID has substantially advanced both epidemiological and prognostic investigations of pituitary and adrenal diseases. A critical prerequisite for these studies has been the establishment and validation of operational definitions based on ICD-10 codes, procedure codes, prescription records, and, for pituitary disorders, registration in the Rare Incurable Disease program (Table 3) [1]. Together, these methodological foundations have enabled reliable nationwide investigations of rare endocrine disorders that were previously difficult to study at the population level.

Recent research in pituitary and adrenal disorders based on the Korean NHID

Pituitary disorders

Among pituitary disorders, acromegaly has been the most extensively investigated condition using the NHID. Early nationwide studies estimated the annual incidence of acromegaly at approximately 0.36 cases per 100,000 persons and identified increased risks of malignancy and mortality, particularly among female patients [53]. Subsequent analyses confirmed sex-specific disparities in long-term outcomes, demonstrating a higher standardized mortality ratio in women, largely attributable to CVD and malignancy [54].

A growing body of NHID-based research has demonstrated that acromegaly is associated with a broad spectrum of systemic complications. Cardiovascular risks, including atrial fibrillation and congestive heart failure, were significantly increased, whereas the risks of myocardial infarction and stroke were comparable to those observed in matched control populations. Notably, the excess risk of atrial fibrillation was most pronounced within the first 4 years after diagnosis [55]. Beyond CVD, acromegaly has also been linked to neurodegenerative disorders, with increased risks of Parkinson’s disease and dementia [56]. More recent nationwide cohort studies further expanded the recognized disease burden of acromegaly, reporting elevated risks of depression [57], end-stage kidney disease [58], hip fractures [59], and malignancies, thereby reinforcing the need for long-term oncological surveillance [60]. The long-term prognosis and multisystem impact of acromegaly, based on NHID data, have been comprehensively summarized in recent review articles [61].

NHID-based research has also extended to other pituitary disorders. Nationwide epidemiological studies estimated the annual incidence of prolactinoma and Cushing’s disease at 1.6–2.4 and 0.23 cases per 100,000 person-years, respectively. Treatment was associated with reduced mortality in Cushing’s disease, whereas no significant mortality benefit was observed in prolactinoma [62,63]. In panhypopituitarism, patients exhibited increased risks of cardiovascular events, fractures, and mortality, with distinct sex-specific patterns [64,65]. During the COVID-19 pandemic, NHID analyses additionally provided important insights into infection-related outcomes among patients with pituitary diseases, highlighting altered susceptibility and prognosis in this vulnerable population [66].

Adrenal disorders

In adrenal research, NHID-based studies initially focused on defining and validating operational criteria for major adrenal disorders, including pheochromocytoma and paraganglioma (PPGL), adrenal Cushing’s syndrome (CS), primary aldosteronism (PA), and congenital adrenal hyperplasia (CAH). Using these standardized definitions, the first nationwide epidemiological estimates for these conditions in Korea were generated. The prevalence and age-standardized incidence rate of PPGL were estimated at 2.13 per 100,000 persons and 0.18 per 100,000 person-years, respectively. Similarly, adrenal CS demonstrated a prevalence of 2.3 per 100,000 persons and an incidence of 0.13 per 100,000 person-years, whereas CAH showed a point prevalence of 5.3 per 100,000 persons in 2017, with a declining annual incidence over time [67-69].

Beyond descriptive epidemiology, subsequent studies expanded the analytic scope to long-term complications and prognosis. Nationwide cohort analyses revealed that metastatic disease was present in approximately one-fifth of patients with PPGL. Importantly, extended follow-up demonstrated that a substantial proportion of tumors initially classified as non-metastatic later progressed to metastatic disease over a median follow-up period exceeding 6 years, underscoring the need for prolonged surveillance even in apparently low-risk cases [67]. Long-term outcome data further indicated that patients with adrenal CS remain at increased risk of mortality for up to a decade following adrenalectomy, suggesting that surgical remission does not fully offset the persistent adverse cardiovascular effects of chronic hypercortisolism [68].

In patients with PA, Kim et al. [70] demonstrated that the risk of new-onset atrial fibrillation remained significantly elevated for up to 3 years after adrenalectomy or medical treatment, compared with individuals with essential hypertension. These findings highlight persistent cardiovascular vulnerability despite biochemical control. Furthermore, patients with PA were shown to have a significantly increased risk of all-cause dementia, including both Alzheimer’s disease and vascular dementia, relative to individuals with essential hypertension [71]. In CAH, large-scale population-based studies demonstrated a substantially increased burden of cardiometabolic and neuropsychiatric comorbidities, including CVD, cerebrovascular events, diabetes mellitus, and psychiatric disorders, across all age groups [69].

Collectively, these studies illustrate how NHID-based research has evolved from establishing foundational disease epidemiology to uncovering previously unrecognized long-term cardiovascular, neurocognitive, and systemic complications of adrenal disorders. Overall, nationwide big-data research using the Korean NHID has transformed pituitary and adrenal disease research, progressing from descriptive epidemiology to comprehensive evaluation of long-term outcomes and multisystem involvement. These findings underscore the value of population-level data in identifying residual risks, informing long-term surveillance strategies, and guiding personalized management for rare endocrine diseases.

THYROID DISEASES

For the thyroid disease section, we systematically identified nationwide Korean big-data studies published between 2023 and 2025 that used either the Korean NHID or the Health Insurance Review and Assessment Service (HIRA) databases. Eligible studies were population-based analyses addressing thyroid cancer, functional thyroid diseases (hypothyroidism or Graves’ disease), or inflammatory thyroid disease, with outcomes derived from administrative claims, health screening databases, or linked national mortality records. To summarize the scope and volume of thyroid-related big-data research, we constructed an evidence map stratified by clinical topic and data source. Each study was assigned to a single category based on its primary research question to ensure mutually exclusive classification. Bubble size represents the number of studies within each category, with the exact count displayed in the figure, and colors indicate the predominant study design. Between 2023 and 2025, a total of 45 nationwide big-data studies on thyroid diseases were identified using NHID or HIRA databases (Fig. 3). Research volume was highest for thyroid cancer (n=17) and functional thyroid disease (n=17), followed by inflammatory thyroid disease (n=11). Although claims-only NHID remained the most frequently used data source, an increasing proportion of studies incorporated health screening databases and linked national mortality records, enabling analyses that extended beyond descriptive epidemiology toward long-term outcomes and disease trajectories.

Operational definitions of thyroid diseases and methodological convergence

Across recent nationwide studies, operational definitions of thyroid diseases have demonstrated substantial convergence, reflecting increasing methodological standardization in Korean thyroid big-data research (Table 4). Thyroid cancer was consistently defined using the ICD-10 code C73 in combination with thyroidectomy procedure codes or cancer-specific copayment reduction registration, with most studies applying a 1- to 2-year washout period to identify incident cases [1,72,73].

Similarly, functional thyroid diseases were operationally defined using diagnostic codes combined with sustained medication prescriptions, an approach widely adopted in nationwide studies evaluating long-term outcomes and treatment patterns in hypothyroidism and Graves’ disease [74-77]. Subacute thyroiditis (SAT) was identified using repeated diagnostic codes (E06.1) with supporting clinical context, a definition applied in nationwide population-based analyses, particularly in studies examining post–COVID-19 thyroiditis [78]. Collectively, this methodological convergence enhances reproducibility and comparability across NHID-based studies, thereby strengthening the reliability of evidence generated from nationwide thyroid research in Korea.

Recent research in thyroid diseases based on the Korean NHID

Survivorship and long-term complications after thyroid cancer

Recent thyroid cancer studies have increasingly focused on survivorship and long-term complications rather than incidence alone. Nationwide cohort analyses identified postoperative hypoparathyroidism as a key determinant of long-term outcomes, reporting elevated risks of skeletal complications, including fractures, as well as nonskeletal systemic complications [44,79]. This perspective reframes thyroid cancer from a condition characterized by excellent short-term prognosis to a chronic disease state associated with sustained health consequences. In addition, population-based studies evaluated mortality and healthcare burden after thyroid cancer using explicit linkage between NHID and Statistics Korea death records, enabling robust assessment of both all-cause and cancer-specific mortality at the national level [72].

Pharmaco-epidemiological analyses in thyroid diseases

Beyond disease definitions, recent nationwide studies expanded into pharmaco-epidemiological research using large-scale claims data. In Graves’ disease, nationwide cohorts systematically classified patients according to initial treatment modality, including antithyroid drugs, radioactive iodine, and surgery, enabling comparative analyses of real-world treatment pathways and long-term outcomes [73-75]. Outcome-oriented studies further evaluated long-term risks associated with radioactive iodine exposure, including malignancy and metabolic outcomes, using clearly defined exposure windows and appropriate comparator groups [73]. In contrast, pharmaco-epidemiological analyses in hypothyroidism during this period primarily relied on prescription-based exposure definitions to characterize treatment patterns and duration, with increasing attention to potential misclassification related to thyroid cancer-associated thyroid-stimulating hormone suppression therapy in nationwide datasets [77]. Collectively, these studies provide clinicians with population-level estimates of treatment-associated risks that complement evidence derived from controlled trial settings.

Metabolic axis and trajectory-based analyses using health screening data

The increasing integration of NHID-linked health screening databases has enabled large-scale analyses examining the metabolic context of thyroid disease. Recent nationwide cohort studies investigated associations between MASLD and incident thyroid cancer using baseline metabolic profiles with long-term follow-up [80]. This design permits population-level risk estimation through prospective time-to-event modeling within large thyroid cancer cohorts.

Looking ahead, incorporation of repeated metabolic measurements may further enhance these analyses by enabling trajectory-based modeling of dynamic metabolic changes over time. Such approaches, as demonstrated in large longitudinal resources including the UK Biobank [81], could be adapted to thyroid cancer susceptibility research in the Korean population.

COVID-19–associated subacute thyroiditis and post-acute outcomes

Inflammatory thyroid disease research during this period was largely driven by nationwide investigations of SAT in the context of COVID-19. Using the Korean NHID, these studies demonstrated an increased incidence of SAT following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection [78]. Although initial analyses primarily focused on incident disease, more recent studies emphasized careful definition of infection index dates and latency windows, thereby establishing a methodological framework for future investigations of postacute and long-term outcomes related to COVID-19–associated thyroiditis. While early work appropriately prioritized incident disease estimation, the increasing methodological emphasis on precise index-date and latency definitions provides a foundation for extending analyses toward post-acute and long-term outcomes [78]. This evolution illustrates how administrative data can rapidly generate hypothesis-generating evidence during public health emergencies while progressively supporting outcome-oriented investigation.

Graves’ disease as a model for RCT-adjacent real-world evidence

Recent nationwide studies of Graves’ disease exemplify the methodological maturation of thyroid big-data research beyond descriptive epidemiology. Large-scale NHID-based cohorts explicitly defined treatment pathways, including antithyroid drugs, radioactive iodine, and surgery, and evaluated long-term outcomes and treatment failure using multivariable risk stratification models [73-76]. These analyses addressed clinical questions traditionally examined in randomized controlled trials (RCTs), such as comparative effectiveness and predictors of treatment failure, thereby generating trial-adjacent real-world evidence at a national scale. Although residual confounding inherent to observational study designs remains, these investigations demonstrate that contemporary Korean thyroid big-data research can meaningfully complement randomized trials, particularly in settings where RCTs are infeasible. Nationwide cohorts further addressed comparative effectiveness questions traditionally reserved for RCTs, including prediction of treatment failure and evaluation of long-term outcomes across therapeutic modalities [73-75]. By incorporating multivariable risk stratification and longitudinal follow-up at a national scale, these studies approximate several key elements of pragmatic trials. Although residual confounding cannot be fully eliminated, such analyses meaningfully complement randomized evidence, particularly in clinical scenarios where RCTs are infeasible, underpowered for rare outcomes, or limited in follow-up duration.

RCTs and nationwide big data: complementary roles

Importantly, the advances highlighted in this review do not suggest that nationwide big-data studies should replace RCTs. Rather, they demonstrate that contemporary real-world evidence can complement and extend trial findings by addressing longterm outcomes, rare adverse events, and heterogeneous treatment effects within routine clinical practice [73-75]. When grounded in transparent operational definitions, careful exposure modeling, and appropriate data linkage, nationwide thyroid big-data research offers a robust framework for bridging the gap between efficacy demonstrated in trials and effectiveness observed in real-world settings.

CONCLUSIONS

Research utilizing the Korean NHID in the field of endocrine disorders has progressed beyond descriptive epidemiology, generating valuable real-world evidence that can complement, and in selected contexts partially substitute for, RCTs when such trials are infeasible. In addition, the nationwide scale of the NHID provides an unparalleled opportunity to generate robust evidence for rare endocrine diseases, which are often underrepresented in conventional clinical research. With continued refinement of operational disease definitions and judicious use of administrative data, NHID-based research is expected to further advance understanding of endocrine disorders and contribute meaningfully to clinical decision-making and public health policy.

Article information

CONFLICTS OF INTEREST

Sun Wook Cho, Jung Hee Kim, Beom-Jun Kim, Mee Kyoung Kim and Eun Jung Rhee are deputy editors of the journal. But they were not involved in the peer reviewer selection, evaluation, or decision process of this article. No other potential conflicts of interest relevant to this article were reported.

Fig. 1.

Overview of obesity trends, mechanistic pathways, and health outcomes based on data from the Korean National Health Information Database (NHID). NHIS, National Health Insurance Service; MASLD, metabolic dysfunction-associated steatotic liver disease.

Fig. 2.

Annual number of publications on osteoporosis and fractures using the Korean National Health Information Database (PubMed search, 2008–2025). This graph shows the annual number of publications identified through a PubMed search using the terms (osteoporosis[Title/Abstract] OR “osteoporotic fracture”[Title/Abstract] OR fracture[Title/Abstract]) AND (Korea[Title/Abstract] OR Korean[Title/Abstract]) AND (“NHIS”[All Fields] OR “National Health Insurance Service”[All Fields] OR “Health Insurance Review”[All Fields]).

Fig. 3.

Evidence map of nationwide Korean big data studies on thyroid diseases (TD) (2023–2025). This evidence map summarizes nationwide Korean big-data studies on TD published between 2023 and 2025, stratified by clinical topic (thyroid cancer, functional TD, and inflammatory TD) and data source (National Health Insurance Service [NHIS]/National Health Information Database [NHID] claims-only, NHID with health screening database, NHID-linked to Statistics Korea mortality data, and Health Insurance Review and Assessment Service [HIRA] claims). Circle size is proportional to the number of studies, and the number inside each circle indicates the exact study count (n) for each category. Circle color denotes the predominant study design, including retrospective cohort, nested case-control or matched cohort, and time-varying exposure or landmark analyses.

References

• 1. Cho SW, Kim JH, Choi HS, Ahn HY, Kim MK, Rhee EJ. Big data research in the field of endocrine diseases using the Korean National Health Information database. Endocrinol Metab (Seoul) 2023;38:10–24.ArticlePubMedPMCPDF
• 2. Kim KS, Kim B, Han K. Big data research for diabetes-related diseases using the Korean National Health Information Database. Diabetes Metab J 2025;49:13–21.ArticlePubMedPDF
• 3. Baek JH, Park YM, Han KD, Moon MK, Choi JH, Ko SH. Comparison of operational definition of type 2 diabetes mellitus based on data from Korean National Health Insurance Service and Korea National Health and Nutrition Examination Survey. Diabetes Metab J 2023;47:201–10.ArticlePubMedPMCPDF
• 4. Kim SE, Han K, Cho WK, Suh BK. Cardiovascular complications, kidney failure, and mortality in young-onset type 1 and type 2 diabetes: data from the Korean National Health Insurance Service. Diabetes Care 2025;48:422–9.ArticlePubMedPDF
• 5. Kim KS, Hong S, Han K, Park CY. The clinical characteristics of gestational diabetes mellitus in Korea: a national health information database study. Endocrinol Metab (Seoul) 2021;36:628–36.ArticlePubMedPMCPDF
• 6. Lee SH, Yu J, Han K, Lee SW, You SY, Kim HS, et al. Predicting the risk of insulin-requiring gestational diabetes before pregnancy: a model generated from a nationwide population-based cohort study in Korea. Endocrinol Metab (Seoul) 2023;38:129–38.ArticlePubMedPMCPDF
• 7. Yu J, Kim JH, Kim B, Han K, Lee SH, Kim MK. The severity of diabetes and the risk of diabetic foot amputation: a national cohort study. Endocrinol Metab (Seoul) 2025;40:574–82.ArticlePDF
• 8. Bae JH, Han KD, Ko SH, Yang YS, Choi JH, Choi KM, et al. Diabetes fact sheet in Korea 2021. Diabetes Metab J 2022;46:417–26.ArticlePubMedPMCPDF
• 9. Kim NH, Seo MH, Jung JH, Han KD, Kim MK, Kim NH. 2023 Diabetic kidney disease fact sheet in Korea. Diabetes Metab J 2024;48:463–72.ArticlePubMedPMCPDF
• 10. Kim JH, Lee J, Han K, Kim JT, Kwon HS. Cardiovascular disease & diabetes statistics in Korea: nationwide data 2010 to 2019. Diabetes Metab J 2024;48:1084–92.ArticlePubMedPMCPDF
• 11. Kim KS, Hong S, Han K, Park CY. Association of non-alcoholic fatty liver disease with cardiovascular disease and all cause death in patients with type 2 diabetes mellitus: nationwide population based study. BMJ 2024;384:e076388.ArticlePubMedPMC
• 12. Park YM, Baek JH, Lee HS, Elfassy T, Brown CC, Schootman M, et al. Income variability and incident cardiovascular disease in diabetes: a population-based cohort study. Eur Heart J 2024;45:1920–33.ArticlePubMedPDF
• 13. Lee HS, Park JC, Chung I, Liu J, Lee SS, Han K. Sustained low income, income changes, and risk of all-cause mortality in individuals with type 2 diabetes: a nationwide population-based cohort study. Diabetes Care 2023;46:92–100.ArticlePubMedPDF
• 14. Bae JH, Haam JH, Jeon E, Kang SY, Song S, Park CY, et al. 2024 Clinical practice guidelines for the diagnosis and pharmacologic treatment of overweight and obesity by the Korean Society for the Study of Obesity. J Obes Metab Syndr 2025;34:322–43.ArticlePubMedPMC
• 15. Kim YH, Han K, Son JW, Lee SS, Oh SW, Kwon HS, et al. Data analytic process of a nationwide population-based study on obesity using the National Health Information database presented by the National Health Insurance Service 2006-2015. J Obes Metab Syndr 2017;26:23–7.ArticlePubMedPMC
• 16. Korean Society for the Study of Obesity. Obesity fact sheet in Korea 2025 [Internet]. Seoul: Korean Society for the Study of Obesity; 2025 [cited 2026 Feb 6]. Available from: https://www.kosso.or.kr.
• 17. Munk Lauridsen M, Ravnskjaer K, Gluud LL, Sanyal AJ. Disease classification, diagnostic challenges, and evolving clinical trial design in MASLD. J Clin Invest 2025;135:e189953.PubMed
• 18. Lee DY, Ko JH, Jang HN, Moon SJ, Kwon HM, Park SE, et al. Characteristics of metabolic dysfunction-associated steatotic liver disease and its risk for hepatic fibrosis in 476,124 Korean adults: a cross-sectional study. Endocrinol Metab (Seoul) 2025;40:648–52.ArticlePMCPDF
• 19. Moon JH, Jeong S, Jang H, Koo BK, Kim W. Metabolic dysfunction-associated steatotic liver disease increases the risk of incident cardiovascular disease: a nationwide cohort study. EClinicalMedicine 2023;65:102292.ArticlePubMedPMC
• 20. Lee HH, Lee HA, Kim EJ, Kim HY, Kim HC, Ahn SH, et al. Metabolic dysfunction-associated steatotic liver disease and risk of cardiovascular disease. Gut 2024;73:533–40.ArticlePubMed
• 21. Biciusca T, Stan SI, Balteanu MA, Cioboata R, Ghenea AE, Danoiu S, et al. The role of the fatty liver index (FLI) in the management of non-alcoholic fatty liver disease: a systematic review. Diagnostics (Basel) 2023;13:3316.ArticlePubMedPMC
• 22. Kim GA, Oh CH, Kim JW, Jeong SJ, Oh IH, Lee JS, et al. Association between non-alcoholic fatty liver disease and the risk of dementia: a nationwide cohort study. Liver Int 2022;42:1027–36.ArticlePubMedPDF
• 23. Lee J, Kwak J, Kim MH, Lee SH, Cho JH, Lim DJ, et al. Association between metabolic dysfunction-associated steatotic liver disease and the risk of thyroid cancer: a nationwide cohort study. Korean J Intern Med 2025;40:813–22.ArticlePubMedPDF
• 24. Choi SH, Yang PS, Kim D, Sung JH, Jang E, Yu HT, et al. Association of obesity with incident atrial fibrillation in Korea and the United Kingdom. Sci Rep 2023;13:5197.ArticlePubMedPMCPDF
• 25. Woo HG, Kim DH, Lee H, Kang MK, Song TJ. Association between changes in predicted body composition and occurrence of heart failure: a nationwide population study. Front Endocrinol (Lausanne) 2023;14:1210371.ArticlePubMedPMC
• 26. Kim J, Chun S, Park JK, Lee H, Jang JY, Kim H, et al. Poor cardiovascular outcomes of underweight abdominal obesity in the entire population of newly diagnosed heart failure. ESC Heart Fail 2025;12:4242–54.ArticlePubMedPMCPDF
• 27. Kim HJ, Kim BS, Kim H, Lee J, Jo HH, Kim DW, et al. Long-term clinical outcome and risk stratification across stages of cardiovascular-kidney-metabolic syndrome in a nationwide cohort. Korean J Intern Med 2025;40:975–89.ArticlePubMedPMCPDF
• 28. Lim JS, Kim S, Cho IY, Shin DW. Obesity and cancer: mechanisms, epidemiological evidence, and potential risk reduction. J Obes Metab Syndr 2026;35:14–37.ArticlePubMedPMC
• 29. Seo JY, Jin EH, Chung GE, Kim YS, Bae JH, Yim JY, et al. The risk of colorectal cancer according to obesity status at four-year intervals: a nationwide population-based cohort study. Sci Rep 2023;13:8928.ArticlePubMedPDF
• 30. Nam SY, Jo J, Chun H, Jeon SW. BMI changes and presence of cofactors influence CRC risk in middle-aged and older Adults. Dig Liver Dis 2025;57:1838–44.ArticlePubMed
• 31. Choi J, Shin CM, Han K, Jung JH, Kim SY, Jo H, et al. Impact of overweight and obesity on gastric and colorectal cancer incidence in the older adults: a nationwide cohort study. BMC Geriatr 2026;26:184.ArticlePubMedPMCPDF
• 32. Kim HJ, Park JH, Hong JY, Han K, Shen JJ, Park JO, et al. Increased risk of young-onset pancreatic cancer among adults aged 20-39 years with overweight or obesity, but not underweight: a nationwide cohort study. Eur J Cancer 2026;232:116111.ArticlePubMed
• 33. Kim YE, Yu MH, Nam CM, Kang ES. Effects of pancreatitis and type 2 diabetes mellitus on the development of pancreatic cancer: a nationwide nested case-control study. Diabetes Metab J 2025;49:252–63.ArticlePubMedPMCPDF
• 34. Park JH, Choi M, Kim JH, Kim J, Han K, Kim B, et al. Metabolic syndrome and the risk of thyroid cancer: a nationwide population-based cohort study. Thyroid 2020;30:1496–504.ArticlePubMedPDF
• 35. Kim HR, Son M, Huh SJ, Moon SY, Moon H, Kang YW, et al. Relationship between METS-IR and thyroid cancer incidence in Korea: a nationwide population-based study. Front Oncol 2024;14:1383864.ArticlePubMedPMC
• 36. Seong SC, Kim YY, Park SK, Khang YH, Kim HC, Park JH, et al. Cohort profile: the National Health Insurance Service-National Health Screening Cohort (NHIS-HEALS) in Korea. BMJ Open 2017;7:e016640.ArticlePubMedPMC
• 37. Kim HS, Shin DW, Lee WC, Kim YT, Cho B. National screening program for transitional ages in Korea: a new screening for strengthening primary prevention and follow-up care. J Korean Med Sci 2012;27 Suppl:S70–5.ArticlePubMedPMCPDF
• 38. Clynes MA, Harvey NC, Curtis EM, Fuggle NR, Dennison EM, Cooper C. The epidemiology of osteoporosis. Br Med Bull 2020;133:105–17.ArticlePubMedPMCPDF
• 39. Morin SN, Leslie WD, Schousboe JT. Osteoporosis: a review. JAMA 2025;334:894–907.ArticlePubMed
• 40. Kong SH, Kim JH, Park MY, Kim SW, Shin CS. Residual risks of comorbidities after parathyroidectomy in a nationwide cohort of patients with primary hyperparathyroidism. Endocrine 2023;79:190–9.ArticlePubMedPDF
• 41. Kim KJ, Baek S, Yu MH, Shin S, Cho S, Rhee Y, et al. Secular trends in the incidence and treatment patterns of primary hyperparathyroidism in Korea: a nationwide cohort study. JBMR Plus 2024;8:ziae065.ArticlePubMedPMCPDF
• 42. Kong SH, Kim JH, Park MY, Kim SW, Shin CS. Epidemiology and prognosis of parathyroid carcinoma: real-world data using nationwide cohort. J Cancer Res Clin Oncol 2021;147:3091–7.ArticlePubMedPMCPDF
• 43. Kim SH, Rhee Y, Kim YM, Won YJ, Noh J, Moon H, et al. Prevalence and complications of nonsurgical hypoparathyroidism in Korea: a nationwide cohort study. PLoS One 2020;15:e0232842.ArticlePubMedPMC
• 44. Ahn SH, Lee YJ, Hong S, Park JW, Jeon YJ, Yoo BN, et al. Risk of fractures in thyroid cancer patients with postoperative hypoparathyroidism: a nationwide cohort study in Korea. J Bone Miner Res 2023;38:1268–77.PubMed
• 45. Ahn SH, Park SM, Park SY, Yoo JI, Jung HS, Nho JH, et al. Osteoporosis and osteoporotic fracture fact sheet in Korea. J Bone Metab 2020;27:281–90.ArticlePubMedPMCPDF
• 46. Ha YC, Kim HY, Jang S, Lee YK, Kim TY. Economic burden of osteoporosis in South Korea: claim data of the National Health Insurance Service from 2008 to 2011. Calcif Tissue Int 2017;101:623–30.ArticlePubMedPDF
• 47. Shim YB, Park JA, Nam JH, Hong SH, Kim JW, Jeong J, et al. Incidence and risk factors of subsequent osteoporotic fracture: a nationwide cohort study in South Korea. Arch Osteoporos 2020;15:180.ArticlePDF
• 48. Baek YH, Cho SW, Jeong HE, Kim JH, Hwang Y, Lange JL, et al. 10-Year fracture risk in postmenopausal women with osteopenia and osteoporosis in South Korea. Endocrinol Metab (Seoul) 2021;36:1178–88.ArticlePubMedPMCPDF
• 49. Kim KJ, Kim KM, Lee YK, Kim J, Jang H, Kim J, et al. Twenty-year trends in osteoporosis treatment and post-fracture care in South Korea: a nationwide study. J Bone Metab 2025;32:57–66.ArticlePubMedPMCPDF
• 50. Yoon BH, Kim TY, Chung H, Kim J, Kim J, Jang H, et al. Epidemiological (secular) trends of pelvic and acetabular fractures (2006-2022) in South Korea using a nationwide cohort data over ages 50: is it osteoporosis related? Arch Osteoporos 2025;20:67.ArticlePubMedPDF
• 51. Park JW, Kim HY, Kim KM, Kim J, Jang H, Kim J, et al. Epidemiology of osteoporotic ankle fractures in South Korea: a nationwide retrospective cohort study (2006-2022). Osteoporos Int 2025;36:801–9.ArticlePubMedPDF
• 52. Chung H, Kim TY, Kim J, Ha YC, Lee YK, Kim J, et al. Long-term trends of osteoporotic hip fractures in South Korea: analysis of National Health Insurance Data from 2006 to 2022. Osteoporos Int 2025;36:1999–2007.ArticlePubMedPDF
• 53. Park KH, Lee EJ, Seo GH, Ku CR. Risk for acromegaly-related comorbidities by sex in Korean acromegaly. J Clin Endocrinol Metab 2020;105:dgz317.ArticlePubMedPDF
• 54. Kim J, Hong N, Choi J, Moon JH, Kim EH, Hong JW, et al. Sex differences in mortality in patients with acromegaly: a nationwide cohort study in Korea. Eur J Endocrinol 2023;189:225–34.ArticlePubMedPDF
• 55. Hong S, Kim KS, Han K, Park CY. Acromegaly and cardiovascular outcomes: a cohort study. Eur Heart J 2022;43:1491–9.ArticlePDF
• 56. Hong S, Han K, Kim KS, Park CY. Risk of neurodegenerative diseases in patients with acromegaly: a cohort study. Neurology 2022;99:e1875–85.PubMed
• 57. Moon S, Hong S, Han K, Park CY. Risk of depression in patients with acromegaly in Korea (2006-2016): a nationwide population-based study. Eur J Endocrinol 2023;189:363–71.ArticlePubMedPDF
• 58. Hong S, Kim KS, Han K, Park CY. A cohort study found a high risk of end-stage kidney disease associated with acromegaly. Kidney Int 2023;104:820–7.Article
• 59. Kim J, Hong N, Choi J, Moon JH, Kim EH, Lee EJ, et al. Increased risk of hip fracture in patients with acromegaly: a nationwide cohort study in Korea. Endocrinol Metab (Seoul) 2023;38:690–700.ArticlePubMedPMCPDF
• 60. Kim YS, Yun JS, Kim H, Jeun SS, Kim B, Lee SW, et al. Acromegaly and the risk of cancer: a nationwide population-based cohort study in Korea. Eur J Endocrinol 2025;192:220–7.ArticlePubMedPDF
• 61. Hong S, Han K, Park CY. Long-term prognosis and systemic impact of acromegaly: analyses utilizing Korean National Health Insurance Data. Endocrinol Metab (Seoul) 2025;40:1–9.ArticlePubMedPMCPDF
• 62. Oh JS, Kim HJ, Hann HJ, Kang TU, Kim DS, Kang MJ, et al. Incidence, mortality, and cardiovascular diseases in pituitary adenoma in Korea: a nationwide population-based study. Pituitary 2021;24:38–47.ArticlePubMedPDF
• 63. Park JS, Yun SJ, Lee JK, Park SY, Chin SO. Descriptive epidemiology and survival analysis of prolactinomas and Cushing’s disease in Korea. Endocrinol Metab (Seoul) 2021;36:688–96.ArticlePubMedPMCPDF
• 64. Park SS, Jeong H, Ahn CH, Park MJ, Kim YH, Kim K, et al. Sex-specific cardiovascular risks and mortality in patients with panhypopituitarism: a nationwide cohort study. Endocrinol Metab (Seoul) 2025;40:469–83.ArticlePMCPDF
• 65. Park SS, Jeong H, Ahn CH, Park MJ, Kong SH, Kim SW, et al. Fracture risks in patients aged 50 years and older with panhypopituitarism: a nationwide cohort study. Osteoporos Int 2025;36:1951–63.ArticlePubMedPMCPDF
• 66. Ha J, Kim KM, Lim DJ, Song K, Seo GH. Pituitary diseases and COVID-19 outcomes in South Korea: a nationwide cohort study. J Clin Med 2023;12:4799.ArticlePubMedPMC
• 67. Kim JH, Moon H, Noh J, Lee J, Kim SG. Epidemiology and prognosis of pheochromocytoma/paraganglioma in Korea: a nationwide study based on the National Health Insurance Service. Endocrinol Metab (Seoul) 2020;35:157–64.ArticlePubMedPMCPDF
• 68. Ahn CH, Kim JH, Park MY, Kim SW. Epidemiology and comorbidity of adrenal Cushing syndrome: a nationwide cohort study. J Clin Endocrinol Metab 2021;106:e1362–72.ArticlePubMedPDF
• 69. Kim JH, Choi S, Lee YA, Lee J, Kim SG. Epidemiology and long-term adverse outcomes in Korean patients with congenital adrenal hyperplasia: a nationwide study. Endocrinol Metab (Seoul) 2022;37:138–47.ArticlePubMedPDF
• 70. Kim KJ, Hong N, Yu MH, Lee H, Lee S, Lim JS, et al. Time-dependent risk of atrial fibrillation in patients with primary aldosteronism after medical or surgical treatment initiation. Hypertension 2021;77:1964–73.ArticlePubMed
• 71. Hong N, Kim KJ, Yu MH, Jeong SH, Lee S, Lim JS, et al. Risk of dementia in primary aldosteronism compared with essential hypertension: a nationwide cohort study. Alzheimers Res Ther 2023;15:136.ArticlePubMedPMCPDF
• 72. Kim WG. Trends in thyroid cancer mortality rates in Korea: insights from National Health Database. Endocrinol Metab (Seoul) 2024;39:853–5.ArticlePubMedPMCPDF
• 73. Kim KJ, Choi J, Kim KJ, Song E, Yu JH, Kim NH, et al. Cancer risk in Graves disease with radioactive 131I treatment: a nationwide cohort study. J Nucl Med 2024;65:693–9.ArticlePubMed
• 74. Kim KJ, Choi J, Shin SM, Kim JA, Kim KJ, Kim SG. Treatment patterns and preferences for Graves’ disease in Korea: insights from a nationwide cohort study. Endocrinol Metab (Seoul) 2024;39:659–63.ArticlePubMedPMCPDF
• 75. Kim JA, Kim KJ, Choi J, Kim KJ, Song E, Yu JH, et al. Unveiling risk factors for treatment failure in patients with Graves’ disease: a nationwide cohort study in Korea. Endocrinol Metab (Seoul) 2025;40:125–34.ArticlePubMedPMCPDF
• 76. Ahn HY, Cho SW, Lee MY, Park YJ, Koo BS, Chang HS, et al. Prevalence, treatment status, and comorbidities of hyperthyroidism in Korea from 2003 to 2018: a nationwide population study. Endocrinol Metab (Seoul) 2023;38:436–44.ArticlePubMedPMCPDF
• 77. Chung CW, Ahn HY, Cho SW, Yi KH. Rising incidence and comorbidities of endogenous hypothyroidism in Republic of Korea from 2004 to 2018: a nationwide population study. Endocrinol Metab (Seoul) 2024;39:891–8.ArticlePubMedPMCPDF
• 78. Ahn HY, Choi HS, Ha S, Cho SW. Incidence of subacute thyroiditis during the COVID-19 pandemic in South Korea using the National Health Insurance Service Database. Thyroid 2022;32:1299–306.ArticlePubMedPDF
• 79. Ku EJ, Lee J, Yoo WS, Bae J, Lee EK, Ahn HY. Long-term nonskeletal complications in patients with thyroid cancer and hypoparathyroidism post total thyroidectomy. J Clin Endocrinol Metab 2025;110:3538–45.ArticlePubMedPMCPDF
• 80. Moon SY, Son M, Cho JH, Kim HI, Han JM, Bae JC, et al. Association between metabolic dysfunction-associated steatotic liver disease and thyroid cancer. Thyroid 2025;35:79–86.ArticlePubMedPDF
• 81. Lu F, Yang H, She B, Lu Q, Bao Y, Seto WK, et al. Investigating multimorbidity trajectories in people living with MASLD diagnosis: a trajectory analysis using the UK Biobank. Diabetes Obes Metab 2025;27:6968–78.PubMedPMC

Figure & Data

References

Citations

Citations to this article as recorded by  

• Age-specific loss of life expectancy after hip fracture in Asian adults: A nationwide population-based cohort study
  Kyoung Jin Kim, Su Jin Kwon, Seunghyun Lee, Seong Hee Ahn, So Young Park, Ha-Young Kim, Kyoung Min Kim
  Bone.2026; 208: 117896.     CrossRef
• Diabetes progression and its association with fracture risk in type 2 diabetes
  Bongsung Kim, Kyu-Na Lee, Kyungdo Han, Mee Kyoung Kim
  Osteoporosis International.2026;[Epub]     CrossRef