The prevalence of obesity in Korea has steadily increased over the past decade, reaching 38.4% in 2021. Notably, the rate of class II– III obesity, defined as a body mass index (BMI) of 30 kg/m2 or higher, exceeded 7% in the same year. Since January 2019, the National Health Insurance Service (NHIS) has provided coverage for bariatric surgery (BS) for eligible patients. Coverage is available for individuals with a BMI of 35 kg/m2 or higher, or those with a BMI of 30 kg/m2 or higher who also have obesity-related comorbidities. Additionally, partial reimbursement is offered for BS in patients with type 2 diabetes mellitus who have BMI values between 27.5 and 30 kg/m2. From 2019 to 2022, the NHIS recorded 9,080 BS procedures, with sleeve gastrectomy being the most commonly performed. The average percentage of weight loss 198±99.7 days post-surgery was 17.9%, with 80.0% of patients losing more than 10% of their body weight. This article presents the trends in obesity and BS in Korea.
Background The effects of dipeptidyl peptidase 4 (DPP-4) inhibitors over the course of long-term treatment remain unclear, and concerns have been raised regarding the role of DPP-4 inhibitors in carcinogenesis in the pancreas. Earlier studies of pancreatic adverse events have reported conflicting results.
Methods This study analyzed Korean National Health Insurance Service data from January 2009 to December 2012. Patients who had type 2 diabetes mellitus and took two or more oral glucose-lowering drugs (GLDs) were included. Patients prescribed DPP-4 inhibitors (n=51,482) or other GLDs (n=51,482) were matched at a 1:1 ratio using propensity score matching. The risk of pancreatic cancer was calculated using Kaplan-Meier curves and Cox proportional-hazards regression analysis.
Results During a median follow-up period of 7.95 years, 1,051 new cases of pancreatic cancer were identified. The adjusted hazard ratio (HR) for DPP-4 inhibitor use was 0.99 (95% confidence interval [CI], 0.88 to 1.12) compared with the other GLD group. In an analysis limited to cases diagnosed with pancreatic cancer during hospitalization, the adjusted HR for the use of DPP-4 inhibitors was 1.00 (95% CI, 0.86 to 1.17) compared with patients who took other GLDs. Using the other GLD group as the reference group, no trend was observed for elevated pancreatic cancer risk with increased DPP-4 inhibitor exposure.
Conclusion In this population-based cohort study, DPP-4 inhibitor use over the course of relatively long-term follow-up showed no significant association with an elevated risk of pancreatic cancer.
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Endocrinol Metab. 2023;38(2):260-268. Published online April 27, 2023
Background Persistence with denosumab in male patients has not been adequately investigated, although poor denosumab persistence is associated with a significant risk of rebound vertebral fractures.
Methods We retrospectively evaluated 294 Korean male osteoporosis patients treated with denosumab at three medical centers and examined their persistence with four doses of denosumab injection over 24 months of treatment. Persistence was defined as the extent to which a patient adhered to denosumab treatment in terms of the prescribed interval and dose, with a permissible gap of 8 weeks. For patients who missed their scheduled treatment appointment(s) during the follow-up period (i.e., no-shows), Cox proportional regression analysis was conducted to explore the factors associated with poor adherence. Several factors were considered, such as age, prior anti-osteoporotic drug use, the treatment provider’s medical specialty, the proximity to the medical center, and financial burdens of treatment.
Results Out of 294 male patients, 77 (26.2%) completed all four sequential rounds of the denosumab treatment. Out of 217 patients who did not complete the denosumab treatment, 138 (63.6%) missed the scheduled treatment(s). Missing treatment was significantly associated with age (odds ratio [OR], 1.03), prior bisphosphonate use (OR, 0.76), and prescription by non-endocrinologists (OR, 2.24). Denosumab was stopped in 44 (20.3%) patients due to medical errors, in 24 (11.1%) patients due to a T-score improvement over –2.5, and in five (2.3%) patients due to expected dental procedures.
Conclusion Our study showed that only one-fourth of Korean male osteoporosis patients were fully adherent to 24 months of denosumab treatment.
The Korean National Health Information Database (NHID) contains big data combining information obtained from the National Health Insurance Service and health examinations. Data are provided in the form of a cohort, and the NHID can be used to conduct longitudinal studies and research on rare diseases. Moreover, data on the cause and date of death are provided by Statistics Korea. Research and publications based on the NHID have increased explosively in the field of endocrine disorders. However, because the data were not collected for research purposes, studies using the NHID have limitations, particularly the need for the operational definition of diseases. In this review, we describe the characteristics of the Korean NHID, operational definitions of endocrine diseases used for research, and an overview of recent studies in endocrinology using the Korean NHID.
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Original Articles
Diabetes, Obesity and Metabolism Big Data Articles (National Health Insurance Service Database)
Background The severity of gestational diabetes mellitus (GDM) is associated with adverse pregnancy outcomes. We aimed to generate a risk model for predicting insulin-requiring GDM before pregnancy in Korean women.
Methods A total of 417,210 women who received a health examination within 52 weeks before pregnancy and delivered between 2011 and 2015 were recruited from the Korean National Health Insurance database. The risk prediction model was created using a sample of 70% of the participants, while the remaining 30% were used for internal validation. Risk scores were assigned based on the hazard ratios for each risk factor in the multivariable Cox proportional hazards regression model. Six risk variables were selected, and a risk nomogram was created to estimate the risk of insulin-requiring GDM.
Results A total of 2,891 (0.69%) women developed insulin-requiring GDM. Age, body mass index (BMI), current smoking, fasting blood glucose (FBG), total cholesterol, and γ-glutamyl transferase were significant risk factors for insulin-requiring GDM and were incorporated into the risk model. Among the variables, old age, high BMI, and high FBG level were the main contributors to an increased risk of insulin-requiring GDM. The concordance index of the risk model for predicting insulin-requiring GDM was 0.783 (95% confidence interval, 0.766 to 0.799). The validation cohort’s incidence rates for insulin-requiring GDM were consistent with the risk model’s predictions.
Conclusion A novel risk engine was generated to predict insulin-requiring GDM among Korean women. This model may provide helpful information for identifying high-risk women and enhancing prepregnancy care.
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Diabetes, Obesity and Metabolism Big Data Articles (National Health Insurance Service Database)
Background Elevated γ-glutamyl transferase (γ-GTP) level is associated with metabolic syndrome, impaired glucose tolerance, and insulin resistance, which are risk factors for type 2 diabetes. We aimed to investigate the association of cumulative exposure to high γ-GTP level with risk of diabetes.
Methods Using nationally representative data from the Korean National Health Insurance system, 346,206 people who were free of diabetes and who underwent 5 consecutive health examinations from 2005 to 2009 were followed to the end of 2018. High γ-GTP level was defined as those in the highest quartile, and the number of exposures to high γ-GTP level ranged from 0 to 5. Hazard ratio (HR) and 95% confidence interval (CI) for diabetes were analyzed using the multivariable Cox proportional-hazards model.
Results The mean follow-up duration was 9.2±1.0 years, during which 15,183 (4.4%) patients developed diabetes. There was a linear increase in the incidence rate and the risk of diabetes with cumulative exposure to high γ-GTP level. After adjusting for possible confounders, the HR of diabetes in subjects with five consecutive high γ-GTP levels were 2.60 (95% CI, 2.47 to 2.73) in men and 3.05 (95% CI, 2.73 to 3.41) in women compared with those who never had a high γ-GTP level. Similar results were observed in various subgroup and sensitivity analyses.
Conclusion There was a linear relationship between cumulative exposure to high γ-GTP level and risk of diabetes. Monitoring and lowering γ-GTP level should be considered for prevention of diabetes in the general population.
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Thyroid Big Data Articles (National Health Insurance Service Database)
Background High-density lipoprotein cholesterol (HDL-C) plays an important role in the reverse cholesterol transport pathway and prevents atherosclerosis-mediated disease. It has also been suggested that HDL-C may be a protective factor against cancer. However, an inverse correlation between HDL-C and cancer has not been established, and few studies have explored thyroid cancer.
Methods The study participants received health checkups provided by the Korean National Health Insurance Service from 2009 to 2013 and were followed until 2019. Considering the variability of serum HDL-C level, low HDL-C level was analyzed by grouping based on four consecutive health checkups. The data analysis was performed using univariate and multivariate Cox proportional hazard regression models.
Results A total of 3,134,278 total study participants, thyroid cancer occurred in 16,129. In the crude model, the hazard ratios for the association between repeatedly measured low HDL-C levels and thyroid cancer were 1.243, 1.404, 1.486, and 1.680 (P for trend <0.01), respectively, which were significant even after adjusting for age, sex, lifestyle factors, and metabolic diseases. The subgroup analysis revealed that low HDL-C levels likely had a greater impact on the group of patients with central obesity (P for interaction= 0.062), high blood pressure (P for interaction=0.057), impaired fasting glucose (P for interaction=0.051), and hyperlipidemia (P for interaction=0.126).
Conclusion Repeatedly measured low HDL-C levels can be considered a risk factor for cancer as well as vascular disease. Low HDL-C levels were associated with the risk of thyroid cancer, and this correlation was stronger in a metabolically unhealthy population.
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Original Articles
Diabetes, Obesity and Metabolism Big Data Articles (National Health Insurance Service Database)
Background Metabolic abnormalities, such as impaired fasting glucose (IFG), are dynamic phenomena; however, it is unclear whether the timing of IFG exposure and cumulative exposure to IFG are related to cardiovascular disease (CVD) and mortality risk.
Methods Data were extracted from a nationwide population-based cohort in South Korea for adults (n=2,206,679) who were free of diabetes and had 4 years of consecutive health examination data. Fasting blood glucose levels of 100 to 125 mg/dL were defined as IFG, and the number of IFG diagnoses for each adult in the 4-year period was tabulated as the IFG exposure score (range, 0 to 4). Adults with persistent IFG for the 4-year period received a score of 4.
Results The median follow-up was 8.2 years. There were 24,820 deaths, 13,502 cases of stroke, and 13,057 cases of myocardial infarction (MI). IFG exposure scores of 1, 2, 3, and 4 were associated with all-cause mortality (multivariable-adjusted hazard ratio [aHR], 1.11; 95% confidence interval [CI], 1.08 to 1.15; aHR, 1.16; 95% CI, 1.12 to 1.20; aHR, 1.20; 95% CI, 1.15 to 1.25; aHR, 1.18; 95% CI, 1.11 to 1.25, respectively) compared with an IFG exposure score of 0. Adjusting for hypertension and dyslipidemia attenuated the slightly increased risk of MI or stroke associated with high IFG exposure scores, but significant associations for allcause mortality remained.
Conclusion The intensity of IFG exposure was associated with an elevated risk of all-cause mortality, independent of cardiovascular risk factors. The association between IFG exposure and CVD risk was largely mediated by the coexistence of dyslipidemia and hypertension.
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Background Prospective comparative studies on the effects of various antidiabetic agents on bone metabolism are limited. This study aimed to assess changes in bone mass and biochemical bone markers in postmenopausal patients with type 2 diabetes mellitus (T2DM).
Methods This prospective, multicenter, open-label, comparative trial included 264 patients with T2DM. Patients who had received a metformin, or sulfonylurea/metformin combination (Group 1); a thiazolidinedione combination (Group 2); a dipeptidyl peptidase-4 inhibitor (gemigliptin) combination (Group 3); or an sodium-glucose cotransporter 2 inhibitor (empagliflozin) combination (Group 4) were prospectively treated for 12 months; bone mineral density (BMD) and bone turnover marker (BTM) changes were evaluated.
Results The femoral neck BMD percentage changes were −0.79%±2.86% (Group 1), −2.50%±3.08% (Group 2), −1.05%±2.74% (Group 3), and −1.24%±2.91% (Group 4) (P<0.05). The total hip BMD percentage changes were −0.57%±1.79% (Group 1), −1.74%±1.48% (Group 2), −0.75%±1.87% (Group 3), and −1.27%±1.72% (Group 4) (P<0.05). Mean serum BTM (C-terminal type 1 collagen telopeptide and procollagen type 1 amino-terminal propeptide) levels measured during the study period did not change over time or differ between groups.
Conclusion Significant bone loss in the femoral neck and total hip was associated with thiazolidinedione combination regimens. However, bone loss was not significantly associated with combination regimens including gemigliptin or empagliflozin. Caution should be exercised during treatment with antidiabetic medications that adversely affect the bone in patients with diabetes at a high risk of bone loss.
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Clinical Study Big Data Articles (National Health Insurance Service Database)
Background Metabolic disturbances are modifiable risk factors for dementia. Because the status of metabolic syndrome (MetS) and its components changes over time, we aimed to investigate the association of the cumulative exposure to MetS and its components with the risk of dementia.
Methods Adults (n=1,492,776; ≥45-years-old) who received health examinations for 4 consecutive years were identified from a nationwide population-based cohort in Korea. Two exposure-weighted scores were calculated: cumulative number of MetS diagnoses (MetS exposure score, range of 0 to 4) and the composite of its five components (MetS component exposure score, range of 0 to 20). Hazard ratio (HR) and 95% confidence interval (CI) values for dementia were analyzed using the multivariable Cox proportional-hazards model.
Results Overall, 47.1% of subjects were diagnosed with MetS at least once, and 11.5% had persistent MetS. During the mean 5.2 years of follow-up, there were 7,341 cases (0.5%) of incident dementia. There was a stepwise increase in the risk of all-cause dementia, Alzheimer’s disease, and vascular dementia with increasing MetS exposure score and MetS component exposure score (each P for trend <0.0001). The HR of all-cause dementia was 2.62 (95% CI, 1.87 to 3.68) in subjects with a MetS component exposure score of 20 compared with those with a score of 0. People fulfilling only one MetS component out of 20 already had an approximately 40% increased risk of all-cause dementia and Alzheimer’s disease.
Conclusion More cumulative exposure to metabolic disturbances was associated with a higher risk of dementia. Of note, even minimal exposure to MetS components had a significant effect on the risk of dementia.
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Background Most of the widely used prediction models for cardiovascular disease are known to overestimate the risk of this disease in Asians. We aimed to generate a risk model for predicting myocardial infarction (MI) in middle-aged Korean subjects with type 2 diabetes.
Methods A total of 1,272,992 subjects with type 2 diabetes aged 40 to 64 who received health examinations from 2009 to 2012 were recruited from the Korean National Health Insurance database. Seventy percent of the subjects (n=891,095) were sampled to develop the risk prediction model, and the remaining 30% (n=381,897) were used for internal validation. A Cox proportional hazards regression model and Cox coefficients were used to derive a risk scoring system. Twelve risk variables were selected, and a risk nomogram was created to estimate the 5-year risk of MI.
Results During 7.1 years of follow-up, 24,809 cases of MI (1.9%) were observed. Age, sex, smoking status, regular exercise, body mass index, chronic kidney disease, duration of diabetes, number of anti-diabetic medications, fasting blood glucose, systolic blood pressure, total cholesterol, and atrial fibrillation were significant risk factors for the development of MI and were incorporated into the risk model. The concordance index for MI prediction was 0.682 (95% confidence interval [CI], 0.678 to 0.686) in the development cohort and 0.669 (95% CI, 0.663 to 0.675) in the validation cohort.
Conclusion A novel risk engine was generated for predicting the development of MI among middle-aged Korean adults with type 2 diabetes. This model may provide useful information for identifying high-risk patients and improving quality of care.
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Background Discordances between glycated hemoglobin (HbA1c) levels and glycemic control are common in clinical practice. We aimed to investigate the consistency of the glycation gap with the hemoglobin glycation index (HGI).
Methods From 2016 to 2019, 36 patients with type 2 diabetes were enrolled. HbA1c, glycated albumin (GA), and fasting blood glucose levels were simultaneously measured and 72-hour continuous glucose monitoring (CGM) was performed on the same day. Repeated tests were performed at baseline and 1 month later, without changing patients’ diabetes management. The HGI was calculated as the difference between the measured HbA1c and the predicted HbA1c that was derived from CGM. The glycation gap was calculated as the difference between the measured and GA-based predicted HbA1c levels.
Results Strong correlations were found between the mean blood glucose (MBG)-based HGI and the prebreakfast glucose-based HGI (r=0.867, P<0.001) and between the glycation gap and the MBG-based HGI (r=0.810, P<0.001). A close correlation was found between the MBG-based HGI at baseline and that after 1 month (r=0.729, P<0.001), with a y-intercept of 0 and a positive slope.
Conclusion The HGI and glycation gap were highly reproducible, and the magnitudes of repeated determinations were closely correlated. Patients with similar mean glucose levels may have significantly different HbA1c levels.
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