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.

This study was conducted to investigate potential differences in vaccine efficacy between patients undergoing palliative chemotherapy and receiving adjuvant chemotherapy. Additionally, the study proved the influence of vaccination timing on vaccine efficacy during active chemotherapy. Anti-receptor-binding domain (RBD) IgG binding antibody assays and surrogate neutralizing antibody assays were performed after BNT162b2 or mRNA-1273 vaccination in 45 solid cancer patients (23 adjuvant and 22 palliative chemotherapy) and in 24 healthy controls before vaccination (baseline), at every two to four weeks after the first (post-dose 1) and the second vaccination (post-dose 2). The levels of anti-RBD IgG and neutralizing antibodies increased significantly from baseline through post-dose 1 to post-dose 2 in all three groups. At the post-dose 1, the anti-RBD IgG and neutralizing antibody levels were significantly lower in cancer patients than in healthy controls. However, by post-dose 2, the seropositivity of anti-RBD IgG and neutralizing antibodies uniformly reached 100% across all groups, with no significant disparity in antibody levels among the three groups. Moreover, the antibody titers were not significantly different between patients with a vaccine and chemotherapy interval of more than 14 days or those with less than 14 days. This study demonstrated that after second doses of mRNA COVID-19 vaccines, humoral immune responses in patients receiving chemotherapy were comparable to those of healthy controls, regardless of whether the purpose of the anti-cancer treatment was palliative or adjuvant. Furthermore, the timing of vaccination did not affect the level of humoral immunity after the second vaccination.

Metabolic (dysfunction) associated fatty liver disease (MAFLD), previously known as non-alcoholic fatty liver disease, is the most common cause of chronic liver disease worldwide. Many risk factors contribute to the pathogenesis of MAFLD with metabolic dysregulation being the final arbiter of its development and progression. MAFLD poses a substantial economic burden to societies, which based on current trends is expected to increase over time. Numerous studies have addressed various aspects of MAFLD from its risk associations to its economic and social burden and clinical diagnosis and management, as well as the molecular mechanisms linking MAFLD to end-stage liver disease and hepatocellular carcinoma. This review summarizes current understanding of the pathogenesis of MAFLD and related diseases, particularly liver cancer. Potential therapeutic agents for MAFLD and diagnostic biomarkers are discussed.
Carcinoma, Hepatocellular/complications* ; Humans ; Liver Neoplasms/complications* ; Non-alcoholic Fatty Liver Disease/complications* ; Risk Factors

Background: Problem drinking increases the incidence of all-cause mortality and specific cancers, and persistent drinking is associated with cardiovascular disease in certain cancer survivors. This study analyzed the cardiovascular risk factors before and after diagnosis in Korean cancer survivors. Methods: Data for the period between 2002 and 2013 were collected from the National Health Insurance Service Health-Examinee Cohort Database. Among the 27,835 patients included, those with moderate alcohol consumption before and after cancer diagnosis were excluded. Problem drinking was defined as males under 65 years consuming over 14 glasses a week, and males over 65 years or females consuming over seven glasses a week. A t-test, chi-square test, and linear regression analysis were performed for differences in cardiovascular risk factors and differences according to cancer types. Results: There was a difference in the body mass index, systolic and diastolic blood pressure, and total cholesterol among patients who became moderate drinkers after diagnosis, but fasting blood glucose did not show any significant changes. Risk factors for cardiovascular disease were analyzed in patients with liver, stomach, rectal, and breast cancer with improved drinking behavior, and there were significant differences in body mass index, systolic and diastolic blood pressure, fasting blood glucose, and total cholesterol in stomach cancer patients. Conclusion Moderate drinking can lower cardiovascular risk in cancer survivors, and among the many drinking-related cancers, stomach cancer patients demonstrated significantly reduced cardiovascular risk factors.

PURPOSE: BioPATH is a non-interventional study evaluating the relationship of molecular biomarkers (PTEN deletion/downregulation, PIK3CA mutation, truncated HER2 receptor [p95HER2], and tumor HER2 mRNA levels) to treatment responses in Asian patients with HER2+ advanced breast cancer treated with lapatinib and other HER2-targeted agents. MATERIALS AND METHODS: Female Asian HER2+ breast cancer patients (n=154) who were candidates for lapatinib-based treatment following metastasis and having an available primary tumor biopsy specimen were included. The primary endpoint was progression-free survival (PFS). Secondary endpoints were response rate, overall survival on lapatinib, correlation between biomarker status and PFS for any previous trastuzumab-based treatment, and conversion/conservation rates of the biomarker status between tissue samples collected at primary diagnosis and at recurrence/metastasis. Potential relationships between tumor mRNA levels of HER2 and response to lapatinib-based therapy were also explored. RESULTS: p95HER2, PTEN deletion/downregulation, and PIK3CA mutation did not demonstrate any significant co-occurrence pattern and were not predictive of clinical outcomes on either lapatinib-based treatment or any previous trastuzumab-based therapy in the metastatic setting. Proportions of tumors positive for p95HER2 expression, PIK3CA mutation, and PTEN deletion/down-regulation at primary diagnosis were 32%, 31.2%, and 56.2%, respectively. Despite limited availability of paired samples, biomarker status patterns were conserved in most samples. HER2 mRNA levels were not predictive of PFS on lapatinib. CONCLUSION: The prevalence of p95HER2 expression, PIK3CA mutation, and PTEN deletion/downregulation at primary diagnosis were similar to previous reports. Importantly, no difference was observed in clinical outcome based on the status of these biomarkers, consistent with reports from other studies.
Asian Continental Ancestry Group ; Biomarkers ; Biopsy ; Breast Neoplasms ; Breast ; Diagnosis ; Disease-Free Survival ; Female ; Humans ; Neoplasm Metastasis ; Prevalence ; RNA, Messenger ; Trastuzumab

BACKGROUND: Dietary patterns have a significant impact on prognosis, recurrence, and survival in patients with cancer. This study investigated dietary patterns using the Diet Quality Index-International (DQI-I) in cancer survivors compared to those in the general population without cancer.METHODS: Using the Korea National Health and Nutrition Examination Survey VI data, cancer survivors were defined as those diagnosed with cancer more than 1 year before the survey. The associations between possible predictors and the DQI-I score were examined using t-tests and analysis of variance. Adjusted multiple linear regression analysis was performed to compare the differences in DQI-I scores between cancer survivors and controls.RESULTS: In univariate analysis of 9,351 subjects (433 cancer survivors and 8,918 controls), age, sex, body mass index, marital status, education level, income, residential area, smoking status, and alcohol consumption were associated with the DQI-I score (each P-value < 0.05). After adjustment, cancer survivors showed higher DQI-I scores than the controls (67.40±8.90 vs. 65.50±9.40, P-value=0.007). In subgroup analysis, cancer survivors within 5 years after cancer diagnosis showed higher DQI-I scores than the controls (68.70±8.30 vs. 65.50±9.40, P-value=0.034), whereas those who survived beyond 5 years post-diagnosis did not show significant differences from the controls (66.70±9.20 vs. 65.50±9.40, P-value=0.063).CONCLUSION: Cancer survivors within 5 years of cancer diagnosis showed better dietary patterns than those in the general population. However, the differences were not observed after 5 years post-diagnosis. To reduce the risks of second primary cancer and mortality, targeted inventions for dietary habits are necessary for long-term survivors of cancer.
Alcohol Drinking ; Body Mass Index ; Diagnosis ; Diet ; Education ; Food Habits ; Humans ; Inventions ; Korea ; Linear Models ; Marital Status ; Mortality ; Neoplasms, Second Primary ; Nutrition Surveys ; Prognosis ; Recurrence ; Smoke ; Smoking ; Survivors