In patients with differentiated thyroid cancer (DTC), initial recurrence risk stratification based on clinical, histopathological, and perioperative data remains the key determinant for guiding management strategies during the first 1-2 years post-treatment. However, the adoption of ongoing risk stratification (ORS), which dynamically reassesses risk by integrating longitudinal clinical data and treatment response, enables more precise long-term prognostic assessment and facilitates highly individualized management. Building upon recent guidelines, the 2026 KTA guideline has been further refined by incorporating robust evidence from large-scale national cohorts and comprehensive systematic reviews. These updated recommendations outline contemporary concepts of ORS, risk-adapted TSH suppression targets, optimized surveillance modalities for recurrence detection, and disease-specific long-term follow-up strategies. Reflecting the paradigm shift toward de-escalated treatment, this revision integrates evolved perspectives on TSH suppression intensity, the clinical interpretation of thyroglobulin levels, and tailored follow-up intervals. These evidence-based recommendations aim to minimize unnecessary treatment and excessive surveillance in the large proportion of patients with excellent prognosis after initial therapy, while ensuring that each patient receives appropriately tailored and effective long-term management.

Objective: Depression involves mood-related behavioral changes typically monitored through subjective reports, which are limited by recall bias and low temporal resolution. Digital mental health tools offer objective, continuous monitoring, but prior studies have focused on outpatients subject to environmental variability. In this preliminary feasibility study, we examined psychiatric inpatients in a controlled setting to assess associations between behavioral regularity and depression severity, highlighting the clinical potential of digital phenotyping. Methods: Thirty-five adults from a closed psychiatric ward were recruited, and data from 10 inpatients with ≥7 days of valid monitoring were analyzed. Depression severity was assessed weekly using the Hamilton Depression Rating Scale (HAMD) and Dysfunctional Self-focus Attributes Scale, yielding 18 samples. Hourly accelerometer and location data from wearable devices and ward sensors were processed to generate digital phenotypes—interdaily stability (IS), intradaily variability (IV), ratio of IS to IV (ISV), entropy (EN), and normalized entropy (NE)—segmented into daytime and nighttime. Linear mixed models assessed group differences, and correlation and multiple regression examined associations with depression. Results: Patients with asymptomatic/mild depression showed significantly higher IS_day and ISV_day, and lower EN_night, and NE_night (all p<0.05). These four features correlated with HAMD after false discovery rate (all p<0.05) correction. A regression model including IS_day and NE_night explained 60.6% of HAMD variance (p<0.05). Conclusion Digital monitoring provides an objective and continuous method to assess depression severity. By capturing macro- and micro-level movement regularity across day and night in an inpatient environment, this approach offers practical relevance for psychiatric care. However, results should be considered preliminary due to the limited sample size.

Background/Aims: Adiponectin, a hormone primarily produced by adipocytes, typically shows an inverse relationship with body mass index (BMI). However, some studies have reported a positive correlation between the two. Thus, this study aimed to examine the relationship between adiponectin level and BMI in diabetic patients, focusing on the impact of past obesity on current adiponectin levels. Methods: We conducted an observational study analyzing data from 323 diabetic patients at Kyungpook National University Hospital. Based on past and current BMIs, participants were categorized into never-obese (nn, n = 106), previously obese (on, n = 43), and persistently obese (oo, n = 73) groups based on a BMI threshold of 25 kg/m2. Adiponectin level and BMI were key variables. Kaplan–Meier analysis assessed their impact on all-cause mortality up to August 2023, with survival differences based on adiponectin quartiles and follow-up starting from patient enrollment (2010–2015). Results: The analysis revealed a significant inverse correlation between adiponectin level and past maximum BMI. The on group exhibited approximately 10% lower adiponectin levels compared to the nn group. This association remained significant after adjusting for current BMI, age, and sex, highlighting the lasting influence of previous obesity on adiponectin levels. Furthermore, survival analysis indicated that patients in the lowest adiponectin quartile had reduced survival, with a statistically significant trend (p = 0.062). Conclusions Findings of this study suggest that lower adiponectin levels, potentially reflecting past obesity, are associated with decreased survival in diabetic patients, underscoring a critical role of adiponectin in long-term health outcomes.

Purpose: The classification of asthma phenotypes frequently depends on the age of onset. However, the rationale for specific age cutoffs remains unclear. This study aimed to explore the distribution of asthma onset age, to define subgroups based on onset age, and to examine their characteristics within a broad Korean population. Methods: An analysis of cross-sectional data involving 56,632 participants from the Korean National Health and Nutrition Examination Survey (2010–2016) was conducted. Data on asthma history, including diagnosis, self-reported age of asthma onset, and current disease status, were collected using structured questionnaires. Results: The distribution of asthma onset age showed a distinct peak in early childhood, with a decline between the ages 15 and 20.Based on this distribution, asthma was categorized into childhood-onset ( ≤ 18 years) and adult-onset ( > 18 years) for further analysis.Multivariate analyses indicated that adult-onset asthma was associated with older age, female sex, obesity, and a history of smoking, whereas childhood-onset asthma was linked to younger age, male sex, allergic rhinitis, and atopic dermatitis. Among the adultonset group, current asthma had a later onset age, increased history of smoking history, and atopic dermatitis compared to past asthma. Conclusion This analysis of nationwide general population data suggests that an age threshold around 18 years may be relevant for defining adult-onset asthma.

Purpose: The classification of asthma phenotypes frequently depends on the age of onset. However, the rationale for specific age cutoffs remains unclear. This study aimed to explore the distribution of asthma onset age, to define subgroups based on onset age, and to examine their characteristics within a broad Korean population. Methods: An analysis of cross-sectional data involving 56,632 participants from the Korean National Health and Nutrition Examination Survey (2010–2016) was conducted. Data on asthma history, including diagnosis, self-reported age of asthma onset, and current disease status, were collected using structured questionnaires. Results: The distribution of asthma onset age showed a distinct peak in early childhood, with a decline between the ages 15 and 20.Based on this distribution, asthma was categorized into childhood-onset ( ≤ 18 years) and adult-onset ( > 18 years) for further analysis.Multivariate analyses indicated that adult-onset asthma was associated with older age, female sex, obesity, and a history of smoking, whereas childhood-onset asthma was linked to younger age, male sex, allergic rhinitis, and atopic dermatitis. Among the adultonset group, current asthma had a later onset age, increased history of smoking history, and atopic dermatitis compared to past asthma. Conclusion This analysis of nationwide general population data suggests that an age threshold around 18 years may be relevant for defining adult-onset asthma.

Purpose: The classification of asthma phenotypes frequently depends on the age of onset. However, the rationale for specific age cutoffs remains unclear. This study aimed to explore the distribution of asthma onset age, to define subgroups based on onset age, and to examine their characteristics within a broad Korean population. Methods: An analysis of cross-sectional data involving 56,632 participants from the Korean National Health and Nutrition Examination Survey (2010–2016) was conducted. Data on asthma history, including diagnosis, self-reported age of asthma onset, and current disease status, were collected using structured questionnaires. Results: The distribution of asthma onset age showed a distinct peak in early childhood, with a decline between the ages 15 and 20.Based on this distribution, asthma was categorized into childhood-onset ( ≤ 18 years) and adult-onset ( > 18 years) for further analysis.Multivariate analyses indicated that adult-onset asthma was associated with older age, female sex, obesity, and a history of smoking, whereas childhood-onset asthma was linked to younger age, male sex, allergic rhinitis, and atopic dermatitis. Among the adultonset group, current asthma had a later onset age, increased history of smoking history, and atopic dermatitis compared to past asthma. Conclusion This analysis of nationwide general population data suggests that an age threshold around 18 years may be relevant for defining adult-onset asthma.

Purpose: The classification of asthma phenotypes frequently depends on the age of onset. However, the rationale for specific age cutoffs remains unclear. This study aimed to explore the distribution of asthma onset age, to define subgroups based on onset age, and to examine their characteristics within a broad Korean population. Methods: An analysis of cross-sectional data involving 56,632 participants from the Korean National Health and Nutrition Examination Survey (2010–2016) was conducted. Data on asthma history, including diagnosis, self-reported age of asthma onset, and current disease status, were collected using structured questionnaires. Results: The distribution of asthma onset age showed a distinct peak in early childhood, with a decline between the ages 15 and 20.Based on this distribution, asthma was categorized into childhood-onset ( ≤ 18 years) and adult-onset ( > 18 years) for further analysis.Multivariate analyses indicated that adult-onset asthma was associated with older age, female sex, obesity, and a history of smoking, whereas childhood-onset asthma was linked to younger age, male sex, allergic rhinitis, and atopic dermatitis. Among the adultonset group, current asthma had a later onset age, increased history of smoking history, and atopic dermatitis compared to past asthma. Conclusion This analysis of nationwide general population data suggests that an age threshold around 18 years may be relevant for defining adult-onset asthma.

Background/Aims: Adiponectin, a hormone primarily produced by adipocytes, typically shows an inverse relationship with body mass index (BMI). However, some studies have reported a positive correlation between the two. Thus, this study aimed to examine the relationship between adiponectin level and BMI in diabetic patients, focusing on the impact of past obesity on current adiponectin levels. Methods: We conducted an observational study analyzing data from 323 diabetic patients at Kyungpook National University Hospital. Based on past and current BMIs, participants were categorized into never-obese (nn, n = 106), previously obese (on, n = 43), and persistently obese (oo, n = 73) groups based on a BMI threshold of 25 kg/m2. Adiponectin level and BMI were key variables. Kaplan–Meier analysis assessed their impact on all-cause mortality up to August 2023, with survival differences based on adiponectin quartiles and follow-up starting from patient enrollment (2010–2015). Results: The analysis revealed a significant inverse correlation between adiponectin level and past maximum BMI. The on group exhibited approximately 10% lower adiponectin levels compared to the nn group. This association remained significant after adjusting for current BMI, age, and sex, highlighting the lasting influence of previous obesity on adiponectin levels. Furthermore, survival analysis indicated that patients in the lowest adiponectin quartile had reduced survival, with a statistically significant trend (p = 0.062). Conclusions Findings of this study suggest that lower adiponectin levels, potentially reflecting past obesity, are associated with decreased survival in diabetic patients, underscoring a critical role of adiponectin in long-term health outcomes.

Purpose: The classification of asthma phenotypes frequently depends on the age of onset. However, the rationale for specific age cutoffs remains unclear. This study aimed to explore the distribution of asthma onset age, to define subgroups based on onset age, and to examine their characteristics within a broad Korean population. Methods: An analysis of cross-sectional data involving 56,632 participants from the Korean National Health and Nutrition Examination Survey (2010–2016) was conducted. Data on asthma history, including diagnosis, self-reported age of asthma onset, and current disease status, were collected using structured questionnaires. Results: The distribution of asthma onset age showed a distinct peak in early childhood, with a decline between the ages 15 and 20.Based on this distribution, asthma was categorized into childhood-onset ( ≤ 18 years) and adult-onset ( > 18 years) for further analysis.Multivariate analyses indicated that adult-onset asthma was associated with older age, female sex, obesity, and a history of smoking, whereas childhood-onset asthma was linked to younger age, male sex, allergic rhinitis, and atopic dermatitis. Among the adultonset group, current asthma had a later onset age, increased history of smoking history, and atopic dermatitis compared to past asthma. Conclusion This analysis of nationwide general population data suggests that an age threshold around 18 years may be relevant for defining adult-onset asthma.