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.

Objectives: The study aim was to analyze the regional differences in dietary protein intake and protein sources of Korean older adults and their association with metabolic syndrome. Methods: Study participants were 1,721 older adults aged 65 and over who participated in 2016–2019 Korea National Health and Nutrition Examination Survey. Using 24-hour recall dietary intake data, protein intake and their food sources were examined. The association between protein intake and metabolic syndrome, obesity, and abdominal obesity were analyzed by multiple logistic regression. Results: Total protein and animal protein intakes were higher in urban area (60.0 g, 24.4 g, respectively) than in rural area (54.6 g, 19.6 g, respectively). With increase of protein intake level, animal to total protein proportion was increased in both areas. Total protein and plant protein intake was negatively associated with the risk of obesity, abdominal obesity in both areas. Animal protein intake was negatively associated with the risk of obesity in both areas, and with abdominal obesity only in urban area. In urban area, plant protein intake was also negatively associated with the risks of metabolic syndrome, elevated triglyceride, and reduced high density lipoprotein-cholesterol. In urban area, the risk of metabolic syndrome was decreased when their protein intake was more than 0.91 g/kg and was lowest when their protein intake was more than 1.5 g/kg (P for trend < 0.001). Conclusions Korean older adults showed inadequate protein intake and those in rural area showed lower animal protein intake than in urban area. The risk of obesity and metabolic syndrome was decreased with the increase of protein intake level. These findings may help develop effective nutrition support strategy for older adults to reduce regional health disparity.

Objectives: The study aim was to identify changes in the nutritional status of older adults during the COVID-19 pandemic according to household income and demographic characteristics. Methods: Study participants were 2,408 adults aged 65 and over who participated in the 2019–2020 Korea National Health and Nutrition Examination Survey (KNHANES). To examine changes in nutrient intake levels resulting from COVID-19, data of 2019 and of 2020 were compared. Study participants were divided into three groups based on household income level to compare these changes. The changes were compared according to household income level, age group, and household type. Results: Percentages of recommended intakes for energy, protein, and most micronutrients were the lowest for the low-income group of both males and females in 2020. The Mean Adequacy Ratio (MAR) score was the lowest for the low-income group in both years. When comparing nutrient density for 2019 and 2020 by income group, the male low-income group experienced a decrease in nutrient densities of vitamin A, thiamine, calcium, and iron. For the same group, a decreased percentage for energy intake from protein was noted. Fruit intake was lowest in the low-income group for both males and females. Low-income males had the lowest intake levels for meat, fish, eggs, and legumes in both 2019 and 2020 and the lowest milk and milk product intake levels in 2020. Older adults living alone or single older adults with children had lower MAR scores than those living with a spouse. Older adults living alone experienced decreases in energy and thiamine and iron intake levels in 2020 compared to their intake levels in 2019. Conclusions Because of the COVID-19 pandemic, nutrition intake levels worsened for older adult males in the low-income group and older adults living alone. This finding shows the need for a more systematic nutritional support strategy for the vulnerable older adults population in national disaster situations.

Objectives: With an increase in the population of the elderly in Korea, their nutritional status has become a cause for concern. This study was designed to compare the nutritional intake and health status of the Korean elderly according to their body mass index. Methods: The subjects were 3,274 elderly people aged 65 and above who had participated in the 2016-2018 Korea National Health and Nutrition Examination Survey. The subjects were divided into four groups: underweight, normal, overweight, and obese, based on their BMI. The general characteristics, daily energy, and nutrient intakes, nutrient intakes compared to the recommended nutrient intake, percentage of participants whose nutrient intake was lower than the estimated average requirement (EAR), index of nutrient quality, the mean adequacy ratio (MAR), intakes by food group, and health status of the four groups were compared. Results: Underweight elderly people showed lower energy, lipids, dietary fiber, vitamin C, riboflavin, niacin, phosphorus, sodium, and potassium intake and MAR score (P < 0.001) compared to the normal or obese elderly. The mean protein, riboflavin, niacin, vitamin C, phosphorus, and iron intake of the underweight elderly was lower than the EAR (P < 0.05). Underweight elderly people also had a lower intake of vegetables and fats, oil and sweets food groups than the other groups (P < 0.001). The prevalence of diabetes and dyslipidemia was higher in the obese group, but the percentage of anemia was higher in the underweight group. Conclusions Underweight elderly people were vulnerable to undernutrition and were at a higher risk of anemia.

The age- and sex-related differences on the impacts of body composition on diabetes mellitus (DM) remain uncertain.

The age- and sex-related differences on the impacts of body composition on diabetes mellitus (DM) remain uncertain.