Objectives: This study evaluated the intake status of key macronutrients and micronutrients (calcium, magnesium, iron, and vitamin D) among Korean older adults and investigated their associations with frailty and its individual components. Methods: Data from 1,246 participants (aged ≥ 65 years) in the 9th Korea National Health and Nutrition Examination Survey (2022–2023) were analyzed. Participants were classified into Robust, Pre-frail, and Frail groups based on a modified version of the Fried frailty phenotype (unintentional weight loss, exhaustion/fatigue, muscle weakness, slow gait speed, and low physical activity). Nutrient intake levels were categorized into tertiles. Multivariable logistic regression was used to estimate odds ratios (ORs) for frailty. Model 1 was adjusted for age, sex, and total energy intake. Model 2 included additional adjustments for socioeconomic factors (household composition, household income) and function-related factors (aerobic physical activity, chewing difficulty, and disease status). Results: Total energy intake differed significantly across frailty groups in both Model 1 (P = 0.011) and Model 2 (P = 0.043). In the fully adjusted model, participants in the highest tertile of iron intake (T3) had 35% lower odds of frailty compared to those in the lowest tertile (T1) (OR = 0.65; 95% confidence interval [CI], 0.44–0.96). Iron intake maintained the strongest independent association with reduced odds of muscle weakness (T3 vs. T1: OR = 0.45; 95% CI, 0.28–0.71). Furthermore, higher protein intake per kilogram of body weight (T3) was significantly associated with lower odds of slow gait speed (OR = 0.53; 95% CI, 0.33–0.87) in the minimally adjusted model. Vitamin D, calcium, and magnesium were not significantly associated with overall frailty after full adjustment. Conclusion Insufficient intake of protein and iron is associated with increased odds of frailty and its functional components in Korean older adults. These findings underscore the critical need for evidence-based nutritional interventions and policy development to prevent and manage frailty at the population level.

Background: The accuracy of Logical Observation Identifiers Names and Codes (LOINC) mappings is reportedly low, and the LOINC codes used for research purposes in Korea have not been validated for accuracy or usability. Our study aimed to evaluate the discrepancies and similarities in interoperability using existing LOINC mappings in actual patient care settings. Methods: We collected data on local test codes and their corresponding LOINC mappings from seven university hospitals. Our analysis focused on laboratory tests that are frequently requested, excluding clinical microbiology and molecular tests. Codes from nationwide proficiency tests served as intermediary benchmarks for comparison. A research team, comprising clinical pathologists and terminology experts, utilized the LOINC manual to reach a consensus on determining the most suitable LOINC codes. Results: A total of 235 LOINC codes were designated as optimal codes for 162 frequent tests.Among these, 51 test items, including 34 urine tests, required multiple optimal LOINC codes, primarily due to unnoted properties such as whether the test was quantitative or qualitative, or differences in measurement units. We analyzed 962 LOINC codes linked to 162 tests across seven institutions, discovering that 792 (82.3%) of these codes were consistent. Inconsistencies were most common in the analyte component (38 inconsistencies, 33.3%), followed by the method (33 inconsistencies, 28.9%), and properties (13 inconsistencies, 11.4%). Conclusion This study reveals a significant inconsistency rate of over 15% in LOINC mappings utilized for research purposes in university hospitals, underlining the necessity for expert verification to enhance interoperability in real patient care.

Background: The accuracy of Logical Observation Identifiers Names and Codes (LOINC) mappings is reportedly low, and the LOINC codes used for research purposes in Korea have not been validated for accuracy or usability. Our study aimed to evaluate the discrepancies and similarities in interoperability using existing LOINC mappings in actual patient care settings. Methods: We collected data on local test codes and their corresponding LOINC mappings from seven university hospitals. Our analysis focused on laboratory tests that are frequently requested, excluding clinical microbiology and molecular tests. Codes from nationwide proficiency tests served as intermediary benchmarks for comparison. A research team, comprising clinical pathologists and terminology experts, utilized the LOINC manual to reach a consensus on determining the most suitable LOINC codes. Results: A total of 235 LOINC codes were designated as optimal codes for 162 frequent tests.Among these, 51 test items, including 34 urine tests, required multiple optimal LOINC codes, primarily due to unnoted properties such as whether the test was quantitative or qualitative, or differences in measurement units. We analyzed 962 LOINC codes linked to 162 tests across seven institutions, discovering that 792 (82.3%) of these codes were consistent. Inconsistencies were most common in the analyte component (38 inconsistencies, 33.3%), followed by the method (33 inconsistencies, 28.9%), and properties (13 inconsistencies, 11.4%). Conclusion This study reveals a significant inconsistency rate of over 15% in LOINC mappings utilized for research purposes in university hospitals, underlining the necessity for expert verification to enhance interoperability in real patient care.

Background: The accuracy of Logical Observation Identifiers Names and Codes (LOINC) mappings is reportedly low, and the LOINC codes used for research purposes in Korea have not been validated for accuracy or usability. Our study aimed to evaluate the discrepancies and similarities in interoperability using existing LOINC mappings in actual patient care settings. Methods: We collected data on local test codes and their corresponding LOINC mappings from seven university hospitals. Our analysis focused on laboratory tests that are frequently requested, excluding clinical microbiology and molecular tests. Codes from nationwide proficiency tests served as intermediary benchmarks for comparison. A research team, comprising clinical pathologists and terminology experts, utilized the LOINC manual to reach a consensus on determining the most suitable LOINC codes. Results: A total of 235 LOINC codes were designated as optimal codes for 162 frequent tests.Among these, 51 test items, including 34 urine tests, required multiple optimal LOINC codes, primarily due to unnoted properties such as whether the test was quantitative or qualitative, or differences in measurement units. We analyzed 962 LOINC codes linked to 162 tests across seven institutions, discovering that 792 (82.3%) of these codes were consistent. Inconsistencies were most common in the analyte component (38 inconsistencies, 33.3%), followed by the method (33 inconsistencies, 28.9%), and properties (13 inconsistencies, 11.4%). Conclusion This study reveals a significant inconsistency rate of over 15% in LOINC mappings utilized for research purposes in university hospitals, underlining the necessity for expert verification to enhance interoperability in real patient care.

Background: The accuracy of Logical Observation Identifiers Names and Codes (LOINC) mappings is reportedly low, and the LOINC codes used for research purposes in Korea have not been validated for accuracy or usability. Our study aimed to evaluate the discrepancies and similarities in interoperability using existing LOINC mappings in actual patient care settings. Methods: We collected data on local test codes and their corresponding LOINC mappings from seven university hospitals. Our analysis focused on laboratory tests that are frequently requested, excluding clinical microbiology and molecular tests. Codes from nationwide proficiency tests served as intermediary benchmarks for comparison. A research team, comprising clinical pathologists and terminology experts, utilized the LOINC manual to reach a consensus on determining the most suitable LOINC codes. Results: A total of 235 LOINC codes were designated as optimal codes for 162 frequent tests.Among these, 51 test items, including 34 urine tests, required multiple optimal LOINC codes, primarily due to unnoted properties such as whether the test was quantitative or qualitative, or differences in measurement units. We analyzed 962 LOINC codes linked to 162 tests across seven institutions, discovering that 792 (82.3%) of these codes were consistent. Inconsistencies were most common in the analyte component (38 inconsistencies, 33.3%), followed by the method (33 inconsistencies, 28.9%), and properties (13 inconsistencies, 11.4%). Conclusion This study reveals a significant inconsistency rate of over 15% in LOINC mappings utilized for research purposes in university hospitals, underlining the necessity for expert verification to enhance interoperability in real patient care.

Cardiovascular disease is an important risk factor for mortality among kidney transplant recipients. In this study, we aimed to investigate the association between cardiovascular risk score at kidney transplantation and long-term outcomes of patients. Methods: In this prospective, observational cohort study, we enrolled kidney transplant recipients who participated in the Korean Organ Transplantation Registry and underwent transplantation between April 2014 and December 2019. The cardiovascular risk status of kidney transplant recipients was assessed using the Framingham risk score. All-cause mortality, major adverse cardiovascular events, allograft failure, estimated glomerular filtration rates (eGFRs), and composite outcomes were evaluated after kidney transplantation. Results: Of the 4,682 kidney transplant recipients, 96 died during 30.7 ± 19.1 months of follow-up. The Kaplan-Meier survival analysis results showed that high Framingham risk scores were associated with all-cause mortality, major adverse cardiovascular events, and composite outcomes. According to the multivariable Cox analysis, high Framingham risk scores were associated with an increased risk of mortality (hazard ratio [HR], 3.20; 95% confidence interval [CI], 1.30–7.91), major adverse cardiovascular events (HR, 8.43; 95% CI, 2.41–29.52), and composite outcomes (HR, 2.05; 95% CI, 1.19–3.46). The eGFRs after transplantation were significantly higher among patients in the low Framingham risk score group (p < 0.001). However, Framingham risk scores were not associated with graft loss or rapid decline in eGFRs. Conclusion: The Framingham risk score is a useful indicator of cardiovascular events, mortality, and kidney function after kidney transplantation.

Background: Posttransplant diabetes mellitus (PTDM) complicates kidney transplant recipients (KTRs) in morbidity and mortality. This study aimed to predict PTDM risk in KTRs using machine learning and deep learning models. Methods: Data were obtained from the Korea Organ Transplantation Registry, a nationwide cohort study of KTRs. Four machine learning algorithms, including eXtreme Gradient Boosting (XGBoost), CatBoost, light gradient boosting machine and logistic regression, and deep learning were implemented on 41 pretransplant and 31 posttransplant variables to predict PTDM. Model performance was assessed using the area under the curve (AUC) of the receiver operating characteristic curve, accuracy, precision, recall, and F1 score. Results: Among 3,213 KTRs, 497 patients (15.5%) developed PTDM within 1 year. The PTDM group had higher age, body mass index (BMI), triglyceride level, and prevalence of hypertension and cardiovascular disease, and lower total cholesterol level at baseline than the No-PTDM group. The XGBoost model showed the highest AUC (0.738) and F1 score (0.42), and modest accuracy (0.86), while the CatBoost model exhibited the highest accuracy (0.87) and precision (0.79). Feature importance in XGBoost was highest for recipient age, followed by baseline BMI, triglyceride level at posttransplant 6 months, baseline glycated hemoglobin and high-density lipoprotein cholesterol level, white blood cell (WBC) count and serum uric acid level at 6 months, baseline WBC count, and tacrolimus trough level at discharge. Conclusion The XGBoost model demonstrated the best performance for predicting PTDM within 1 year, offering an accurate tool for early identification and personalized care of high-risk KTRs for PTDM.

Objective: We intended to investigate the relationships between visual sensory impairment (VSI) or auditory sensory impairment (ASI) and brain pathological changes associated with cognitive decline in older adults. Methods: We primarily tried to examine whether each sensory impairment is related to Alzheimer’s disease (AD) pathology, specifically beta-amyloid (Aβ) deposition, through both cross-sectional and longitudinal approaches in cognitively unimpaired older adults. Self-report questionnaires on vision and hearing status were administered at the baseline.Neuroimaging scans including brain [ 11 C] Pittsburgh Compound B PET and MRI, as well as clinical assessments, were performed at baseline and 2-year follow-up. Results: Cross-sectional analyses showed that the VSI-positive group had significantly higher Aβ deposition than the VSI-negative group, whereas there was no significant association between ASI positivity and Aβ deposition. Longitudinal analyses revealed that VSI positivity at baseline was significantly associated with increased Aβ deposition over 2 years (β = 0.153, p = 0.025), although ASI positivity was not (β = 0.045, p = 0.518). VSI positivity at baseline was also significantly associated with greater atrophic changes in AD-related brain regions over the 2-year follow-up period (β = −0.207, p = 0.005), whereas ASI positivity was not (β = 0.024, p = 0.753). Neither VSI nor ASI positivity was related to cerebrovascular injury, as measured based on the white matter hyperintensity volume. Conclusion The findings suggest that VSI is probably related to AD-specific pathological changes, which possibly mediate the reported relationship between VSI and cognitive decline. In contrast, ASI appears not associated with AD pathologies but may contribute to cognitive decline via other mechanisms.

Objective: Previous studies have reported that vitamin D deficiency increased the risk of Alzheimer’s disease (AD) dementia in older adults. However, little is known about how vitamin D is involved in the pathophysiology of AD. Thus, this study aimed to examine the association and interaction of serum vitamin D levels with in vivo AD pathologies including cerebral beta-amyloid (Aβ) deposition and neurodegeneration in nondemented older adults. Methods: 428 Nondemented older adults were recruited from the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer’s Disease, a prospective cohort that began in 2014. All participants underwent comprehensive clinical assessments, measurement of serum 25-hydroxyvitamin D (25[OH]D), and multimodal brain imaging including Pittsburgh compound B (PiB) positron emission tomography and magnetic resonance imaging. Global PiB deposition was measured for the Aβ biomarker. Intracranial volume-adjusted hippocampal volume (HVa) was used as a neurodegeneration biomarker. Results: Overall, serum 25(OH)D level was not associated with either Aβ deposition or HVa after controlling for age, sex, apolipoprotein E ε4 positivity, and vascular risk factors. However, serum 25(OH)D level had a significant moderating effect on the association between Aβ and neurodegeneration, with lower serum 25(OH)D level significantly exacerbating cerebral Aβ-associated hippocampal volume loss (B = 34.612, p = 0.008). Conclusion Our findings indicate that lower serum vitamin D levels may contribute to AD by exacerbating Aβ-associated neurodegeneration in nondemented older adults. Further studies to explore the potential therapeutic effect of vitamin D supplementation on the progression of AD pathology will be necessary.

Objectives: This study was conducted to investigate the status of total sugar intake and contributing foods in Korea according to age groups. Methods: This study used 24-hour dietary recall data from the 8th Korea National Health and Nutrition Examination Survey (2019–2021) to investigate the nutritional and total sugar intake status among Koreans. A total of 18,338 research participants (≥3 years old) were included in this study. To analyze the types of foods contributing to total sugar intake, these foods were categorized into 15 types. Moreover, we examined the total sugar intake and ranked the most consumed foods by age groups (3–11 years, 12–18 years, 19–34 years, 35–49 years, 50–64 years, over 65 years). A survey procedure was employed for statistical analysis. Results: The energy intake ratio from total sugars was approximately 12%–15%, which was within the recommended range. However, the proportion of individuals consuming total sugar exceeding 20% of their total caloric intake is nearly 20%, raising concerns about excessive sugar consumption. Furthermore, the percentage of participants whose intake of sugar from processed foods exceeded 10% of their total calories was highest in the 12–18 age group at 37.1%, followed by the 3–11 age group at 35.2%, and the 19–34 age group at 34.0%. Carbonated drinks, cola, and cider were the primary foods consumed by children and adolescents (3–18 years old) and young adults (19–34 years old). For middle-aged and older adults, mixed coffee with sugar and cream was a prominent contributor to sugar intake. Conclusions This study investigated sugar consumption patterns among Koreans, finding the principal foods contributing to this intake. Identifying these contributors is pivotal, given their potential impact on public health.