Purpose: Rare diseases occur in <50 per 100000 people and require lifelong management. However, essential epidemiological data on such diseases are lacking, and a consecutive monitoring system across time and regions remains to be established. Standardized digital phenotypes are required to leverage an international data network for research on rare endocrine diseases. We developed digital phenotypes for rare endocrine diseases using the observational medical outcome partnership common data model. Materials and Methods: Digital phenotypes of three rare endocrine diseases (medullary thyroid cancer, hypoparathyroidism, pheochromocytoma/paraganglioma) were validated across three databases that use different vocabularies: Severance Hospital’s electronic health record from South Korea; IQVIA’s United Kingdom (UK) database for general practitioners; and IQVIA’s United States (US) hospital database for general hospitals. We estimated the performance of different digital phenotyping methods based on International Classification of Diseases (ICD)-10 in the UK and the US or systematized nomenclature of medicine clinical terms (SNOMED CT) in Korea. Results: The positive predictive value of digital phenotyping was higher using SNOMED CT-based phenotyping than ICD-10-based phenotyping for all three diseases in Korea (e.g., pheochromocytoma/paraganglioma: ICD-10, 58%–62%; SNOMED CT, 89%). Estimated incidence rates by digital phenotyping were as follows: medullary thyroid cancer, 0.34–2.07 (Korea), 0.13–0.30 (US); hypoparathyroidism, 0.40–1.20 (Korea), 0.59–1.01 (US), 0.00–1.78 (UK); and pheochromocytoma/paraganglioma, 0.95–1.67 (Korea), 0.35–0.77 (US), 0.00–0.49 (UK). Conclusion Our findings demonstrate the feasibility of developing digital phenotyping of rare endocrine diseases and highlight the importance of implementing SNOMED CT in routine clinical practice to provide granularity for research.

Purpose: Rare diseases occur in <50 per 100000 people and require lifelong management. However, essential epidemiological data on such diseases are lacking, and a consecutive monitoring system across time and regions remains to be established. Standardized digital phenotypes are required to leverage an international data network for research on rare endocrine diseases. We developed digital phenotypes for rare endocrine diseases using the observational medical outcome partnership common data model. Materials and Methods: Digital phenotypes of three rare endocrine diseases (medullary thyroid cancer, hypoparathyroidism, pheochromocytoma/paraganglioma) were validated across three databases that use different vocabularies: Severance Hospital’s electronic health record from South Korea; IQVIA’s United Kingdom (UK) database for general practitioners; and IQVIA’s United States (US) hospital database for general hospitals. We estimated the performance of different digital phenotyping methods based on International Classification of Diseases (ICD)-10 in the UK and the US or systematized nomenclature of medicine clinical terms (SNOMED CT) in Korea. Results: The positive predictive value of digital phenotyping was higher using SNOMED CT-based phenotyping than ICD-10-based phenotyping for all three diseases in Korea (e.g., pheochromocytoma/paraganglioma: ICD-10, 58%–62%; SNOMED CT, 89%). Estimated incidence rates by digital phenotyping were as follows: medullary thyroid cancer, 0.34–2.07 (Korea), 0.13–0.30 (US); hypoparathyroidism, 0.40–1.20 (Korea), 0.59–1.01 (US), 0.00–1.78 (UK); and pheochromocytoma/paraganglioma, 0.95–1.67 (Korea), 0.35–0.77 (US), 0.00–0.49 (UK). Conclusion Our findings demonstrate the feasibility of developing digital phenotyping of rare endocrine diseases and highlight the importance of implementing SNOMED CT in routine clinical practice to provide granularity for research.

Purpose: Rare diseases occur in <50 per 100000 people and require lifelong management. However, essential epidemiological data on such diseases are lacking, and a consecutive monitoring system across time and regions remains to be established. Standardized digital phenotypes are required to leverage an international data network for research on rare endocrine diseases. We developed digital phenotypes for rare endocrine diseases using the observational medical outcome partnership common data model. Materials and Methods: Digital phenotypes of three rare endocrine diseases (medullary thyroid cancer, hypoparathyroidism, pheochromocytoma/paraganglioma) were validated across three databases that use different vocabularies: Severance Hospital’s electronic health record from South Korea; IQVIA’s United Kingdom (UK) database for general practitioners; and IQVIA’s United States (US) hospital database for general hospitals. We estimated the performance of different digital phenotyping methods based on International Classification of Diseases (ICD)-10 in the UK and the US or systematized nomenclature of medicine clinical terms (SNOMED CT) in Korea. Results: The positive predictive value of digital phenotyping was higher using SNOMED CT-based phenotyping than ICD-10-based phenotyping for all three diseases in Korea (e.g., pheochromocytoma/paraganglioma: ICD-10, 58%–62%; SNOMED CT, 89%). Estimated incidence rates by digital phenotyping were as follows: medullary thyroid cancer, 0.34–2.07 (Korea), 0.13–0.30 (US); hypoparathyroidism, 0.40–1.20 (Korea), 0.59–1.01 (US), 0.00–1.78 (UK); and pheochromocytoma/paraganglioma, 0.95–1.67 (Korea), 0.35–0.77 (US), 0.00–0.49 (UK). Conclusion Our findings demonstrate the feasibility of developing digital phenotyping of rare endocrine diseases and highlight the importance of implementing SNOMED CT in routine clinical practice to provide granularity for research.

Purpose: Rare diseases occur in <50 per 100000 people and require lifelong management. However, essential epidemiological data on such diseases are lacking, and a consecutive monitoring system across time and regions remains to be established. Standardized digital phenotypes are required to leverage an international data network for research on rare endocrine diseases. We developed digital phenotypes for rare endocrine diseases using the observational medical outcome partnership common data model. Materials and Methods: Digital phenotypes of three rare endocrine diseases (medullary thyroid cancer, hypoparathyroidism, pheochromocytoma/paraganglioma) were validated across three databases that use different vocabularies: Severance Hospital’s electronic health record from South Korea; IQVIA’s United Kingdom (UK) database for general practitioners; and IQVIA’s United States (US) hospital database for general hospitals. We estimated the performance of different digital phenotyping methods based on International Classification of Diseases (ICD)-10 in the UK and the US or systematized nomenclature of medicine clinical terms (SNOMED CT) in Korea. Results: The positive predictive value of digital phenotyping was higher using SNOMED CT-based phenotyping than ICD-10-based phenotyping for all three diseases in Korea (e.g., pheochromocytoma/paraganglioma: ICD-10, 58%–62%; SNOMED CT, 89%). Estimated incidence rates by digital phenotyping were as follows: medullary thyroid cancer, 0.34–2.07 (Korea), 0.13–0.30 (US); hypoparathyroidism, 0.40–1.20 (Korea), 0.59–1.01 (US), 0.00–1.78 (UK); and pheochromocytoma/paraganglioma, 0.95–1.67 (Korea), 0.35–0.77 (US), 0.00–0.49 (UK). Conclusion Our findings demonstrate the feasibility of developing digital phenotyping of rare endocrine diseases and highlight the importance of implementing SNOMED CT in routine clinical practice to provide granularity for research.

Purpose: Rare diseases occur in <50 per 100000 people and require lifelong management. However, essential epidemiological data on such diseases are lacking, and a consecutive monitoring system across time and regions remains to be established. Standardized digital phenotypes are required to leverage an international data network for research on rare endocrine diseases. We developed digital phenotypes for rare endocrine diseases using the observational medical outcome partnership common data model. Materials and Methods: Digital phenotypes of three rare endocrine diseases (medullary thyroid cancer, hypoparathyroidism, pheochromocytoma/paraganglioma) were validated across three databases that use different vocabularies: Severance Hospital’s electronic health record from South Korea; IQVIA’s United Kingdom (UK) database for general practitioners; and IQVIA’s United States (US) hospital database for general hospitals. We estimated the performance of different digital phenotyping methods based on International Classification of Diseases (ICD)-10 in the UK and the US or systematized nomenclature of medicine clinical terms (SNOMED CT) in Korea. Results: The positive predictive value of digital phenotyping was higher using SNOMED CT-based phenotyping than ICD-10-based phenotyping for all three diseases in Korea (e.g., pheochromocytoma/paraganglioma: ICD-10, 58%–62%; SNOMED CT, 89%). Estimated incidence rates by digital phenotyping were as follows: medullary thyroid cancer, 0.34–2.07 (Korea), 0.13–0.30 (US); hypoparathyroidism, 0.40–1.20 (Korea), 0.59–1.01 (US), 0.00–1.78 (UK); and pheochromocytoma/paraganglioma, 0.95–1.67 (Korea), 0.35–0.77 (US), 0.00–0.49 (UK). Conclusion Our findings demonstrate the feasibility of developing digital phenotyping of rare endocrine diseases and highlight the importance of implementing SNOMED CT in routine clinical practice to provide granularity for research.

As extensive as the concept of and the resources required for ‘Health for Korean Unification’ are, and due to the limited access to information on the state of health and medical care in North Korea, discussion on ‘Health for Korean Unification’ has tended to be intermittent and lacked concrete action plans. In this article, we specifically distinguished areas of cooperation and selected five executable agenda that meet the goals of international development cooperation: 1) Health security; 2) Easing the burden of major diseases; 3) Resilient healthcare system; 4) R&D cooperation; 5) Sustainable cooperation system. Then we provided corresponding strategic priorities and operative directions, in consideration of future military and political sanctions against North Korea. The strategies we outline are sustainable, preemptive for problems that might affect lives of South and North Korean citizens, and satisfy the unmet needs of the North Korean health system. Throughout the process, we utilized a special platform, the ‘Korean Peninsula Healthcare Cooperation Platform,’ designed to enable continual communication across sectors engaged in public health and medical care. By doing so, we take the first step to actually carry out the 'Health for Korean Unification,’ which tended to have remained on the discussion agenda.

Purpose: Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with a poor prognosis and a lack of targeted therapy. Overexpression of FRAT1 is thought to be associated with this aggressive subtype of cancer. Here, we performed a comprehensive analysis and assessed the association between overexpression of FRAT1 and TNBC. Methods: First, using different web-based bioinformatics platforms (TIMER 2.0, UALCAN, and GEPIA 2), the expression of FRAT1 was assessed. Then, the expression of the FRAT1 protein and hormone receptors and HER2 status were assessed by immunohistochemical analysis. For samples of tumors with equivocal immunoreactivity, we performed silver in situ hybridization of the HER2 gene to determine an accurate HER2 status. Next, we used the R package and bc-GenExMiner 4.8 to analyze the relationship between FRAT1 expression and clinicopathological parameters in breast cancer patients.Finally, we determined the relationship between FRAT1 overexpression and prognosis in patients. Results: The expression of FRAT1 in breast cancer tissues is significantly higher than in normal tissue. FRAT1 expression was significantly related to worse overall survival (P < 0.05) and was correlated with these clinicopathological features:T stage, N stage, age, high histologic grade, estrogen receptor status, progesterone receptor status, Her-2 status, TNBC status, basal-like status, CK5/6 status, and Ki67 status. Conclusion FRAT1 was overexpressed in breast cancer compared to normal tissue, and it may be involved in the progression of breast cancer malignancy. This study provides suggestive evidence of the prognostic role of FRAT1 in breast cancer and the therapeutic target for TNBC.

BACKGROUND: To estimate and compare the trends of all-cause and cause-specific mortality rates for subjects with and without diabetes in South Korea, from 2003 to 2013. METHODS: Using a population-based cohort (2003 to 2013), we evaluated annual mortality rates in adults (≥30 years) with and without diabetes. The number of subjects in this analysis ranged from 585,795 in 2003 to 670,020 in 2013. RESULTS: Age- and sex-adjusted all-cause mortality rates decreased consistently in both groups from 2003 to 2013 (from 14.4 to 9.3/1,000 persons in subjects with diabetes and from 7.9 to 4.4/1,000 persons in those without diabetes). The difference in mortality rates between groups also decreased (6.61 per 1,000 persons in 2003 to 4.98 per 1,000 persons in 2013). The slope associated with the mortality rate exhibited a steeper decrease in subjects with diabetes than those without diabetes (regression coefficients of time: −0.50 and −0.33, respectively; P=0.004). In subjects with diabetes, the mortality rate from cardiovascular disease decreased by 53.5% (from 2.73 to 1.27 per 1,000 persons, P for trend < 0.001). Notably, the decrease in mortality from ischemic stroke (79.2%, from 1.20 to 0.25 per 1,000 persowns) was more profound than that from ischemic heart disease (28.3%, from 0.60 to 0.43 per 1,000 persons). CONCLUSION: All-cause and cardiovascular mortality rates decreased substantially from 2003 to 2013, and the decline in ischemic stroke mortality mainly contributed to the decreased cardiovascular mortality in Korean people with diabetes.
Adult* ; Cardiovascular Diseases ; Cohort Studies ; Diabetes Mellitus* ; Humans ; Korea* ; Mortality* ; Myocardial Ischemia ; Stroke

BACKGROUND: The aim of this multicenter, randomized, double-blind study was to examine the effect of lobeglitazone, a novel thiazolidinedione, on the changes in bone mineral density (BMD) in patients with type 2 diabetes mellitus. METHODS: A 24-week, double-blinded phase was followed by a 28-week, open-label phase, in which the placebo group also started to receive lobeglitazone. A total of 170 patients aged 34 to 76 years were randomly assigned in a 2:1 ratio to receive lobeglitazone 0.5 mg or a matching placebo orally, once daily. BMD was assessed using dual-energy X-ray absorptiometry at week 24 and at the end of the study (week 52). RESULTS: During the double-blinded phase, the femur neck BMD showed decreasing patterns in both groups, without statistical significance (−0.85%±0.36% and −0.78%±0.46% in the lobeglitazone and placebo groups, respectively). The treatment difference between the groups was 0.07%, which was also not statistically significant. Further, minimal, nonsignificant decreases were observed in both groups in the total hip BMD compared to values at baseline, and these differences also did not significantly differ between the groups. During the open-label phase, the BMD was further decreased, but not significantly, by −0.32% at the femur neck and by −0.60% at the total hip in the lobeglitazone group, and these changes did not significantly differ compared with the original placebo group switched to lobeglitazone. CONCLUSION: Our results indicate that treatment with lobeglitazone 0.5 mg over 52 weeks showed no detrimental effect on the BMD compared to the placebo.
Absorptiometry, Photon ; Bone Density* ; Diabetes Mellitus, Type 2* ; Double-Blind Method ; Femur Neck ; Hip ; Humans ; Thiazolidinediones

PURPOSE: This study aimed to investigate the relationships between body mass index (BMI) and prostate-specific antigen (PSA) levels, international prostate symptom score (IPSS), quality of life (QoL), and prostate volume (PV). MATERIALS AND METHODS: Height, weight, PSA levels, PV, and IPSS were analyzed in 15,435 patients who underwent a prostate examination between 2001 and 2014. Patients aged <50 years or with a PSA level ≥10 ng/mL were excluded. The relationships between BMI and PSA, IPSS, QoL, and PV were analyzed by a scatter plot, one-way analysis of variance, and the Pearson correlation coefficient. RESULTS: The mean age was 71.95±7.63 years, the mean BMI was 23.59±3.08 kg/m2, the mean PSA level was 1.45±1.45 ng/mL, the mean IPSS was 15.53±8.31, the mean QoL score was 3.48±1.25, and the mean PV was 29.72±14.02 mL. PSA, IPSS, and QoL showed a tendency to decrease with increasing BMI, and there were statistically significant differences for each parameter (p≤0.001). PV showed a significant tendency to increase with BMI (p < 0.001). In the correlation analysis, BMI showed a statistically significant correlation (p < 0.001) with PSA, IPSS, and QoL, although the correlations were very weak. In contrast, BMI showed a significant correlation with PV (p < 0.001), with a meaningful Pearson correlation coefficient of 0.124. CONCLUSIONS: Higher BMI was associated with lower PSA levels and higher IPSS and QoL scores. Meanwhile, PV increased with BMI. Although obese individuals had a greater PV, obesity did not aggravate lower urinary tract symptoms.
Body Mass Index* ; Humans ; Lower Urinary Tract Symptoms ; Male ; Obesity ; Prostate ; Prostate-Specific Antigen ; Prostatic Hyperplasia ; Quality of Life