Objectives: The brain and autonomic nervous system are tightly interconnected and may be involved in various psychiatric conditions.We explored whether electroencephalographic (EEG) coherence and vagally mediated heart rate variability (vmHRV) differ between male children with autism spectrum disorder (ASD) and typically developing (TD) children, and how these measures are associated with the clinical characteristics of ASD. Methods: We recorded resting-state EEG and vmHRV in 14 male children with ASD and 14 TD children. The Social Responsiveness Scale (SRS) was used to assess the clinical characteristics of the ASD group. Results: vmHRV measures (root mean square of successive RR intervals [RMSSD], percentage of successive RR intervals that differ by more than 50 ms [pNN50], and high-frequnecy power) did not differ between the ASD and TD groups. By contrast, the beta1 coherence between the T5 and T6 electrodes (beta1 coherence [T5–T6]) was lower in the ASD group than in the TD group, even after correction for multiple comparisons. This coherence measure demonstrated a positive correlation with RMSSD and a negative correlation with the SRS social motivation subscale score (SRS-Motivation). Moreover, pNN50 acted as a moderator of the association between beta1 coherence (T5–T6) and SRS-Motivation. Conclusion Resting-state EEG coherence may be associated with the clinical characteristics of ASD, and vmHRV may exert a moderating effect on this relationship. These preliminary findings suggest that the concurrent evaluation of brain and autonomic functions can be useful for elucidating the pathophysiology of ASD.

Objective: Many studies have explored sense of self in individuals with autism spectrum disorder (ASD); however, few have reported on their experience of “disembodiment.” This study aimed to investigate the differences in brain activity between patients with ASD and neurotypicals (NTs) under conditions causing disembodiment and to examine the correlation between their interoceptive abilities and disembodiment-related brain activity. Methods: 18 Participants with ASD and 21 NTs completed psychological evaluations, interoceptive abilities measurement, and functional magnetic resonance imaging (fMRI). The fMRI images were taken while the participants performed tasks involving ball-throwing animations. The task focused on either self-agency related to ball-throwing (Agency Task) or the spatial location of a ball (Location Task). The animations were presented from constantly changing perspective (Changing View) or fixed perspective (Fixed View). The disembodiment-related condition was the interaction between the Agency Task and Changing View. Results: Participants with ASD exhibited higher activation than NTs in regions near the left parieto-temporo-occipital junction, left precuneus, left hippocampus, and other brain areas. Furthermore, interoceptive accuracy was negatively correlated with the activity of the left superior parietal and posterior midcingulate areas, whereas interoceptive trait prediction error was positively correlated with the activity of the left hippocampus, mid-temporal area, and left posterior cingulate area in participants with ASD. Conclusion These results suggest that disembodiment-related brain activation might be easily manifested by the interaction between perspective-shifting and the experience of agency, and that interoceptive abilities might be related to disembodiment-related brain activation in individuals with ASD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Objective: Many studies have explored sense of self in individuals with autism spectrum disorder (ASD); however, few have reported on their experience of “disembodiment.” This study aimed to investigate the differences in brain activity between patients with ASD and neurotypicals (NTs) under conditions causing disembodiment and to examine the correlation between their interoceptive abilities and disembodiment-related brain activity. Methods: 18 Participants with ASD and 21 NTs completed psychological evaluations, interoceptive abilities measurement, and functional magnetic resonance imaging (fMRI). The fMRI images were taken while the participants performed tasks involving ball-throwing animations. The task focused on either self-agency related to ball-throwing (Agency Task) or the spatial location of a ball (Location Task). The animations were presented from constantly changing perspective (Changing View) or fixed perspective (Fixed View). The disembodiment-related condition was the interaction between the Agency Task and Changing View. Results: Participants with ASD exhibited higher activation than NTs in regions near the left parieto-temporo-occipital junction, left precuneus, left hippocampus, and other brain areas. Furthermore, interoceptive accuracy was negatively correlated with the activity of the left superior parietal and posterior midcingulate areas, whereas interoceptive trait prediction error was positively correlated with the activity of the left hippocampus, mid-temporal area, and left posterior cingulate area in participants with ASD. Conclusion These results suggest that disembodiment-related brain activation might be easily manifested by the interaction between perspective-shifting and the experience of agency, and that interoceptive abilities might be related to disembodiment-related brain activation in individuals with ASD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Objective: Many studies have explored sense of self in individuals with autism spectrum disorder (ASD); however, few have reported on their experience of “disembodiment.” This study aimed to investigate the differences in brain activity between patients with ASD and neurotypicals (NTs) under conditions causing disembodiment and to examine the correlation between their interoceptive abilities and disembodiment-related brain activity. Methods: 18 Participants with ASD and 21 NTs completed psychological evaluations, interoceptive abilities measurement, and functional magnetic resonance imaging (fMRI). The fMRI images were taken while the participants performed tasks involving ball-throwing animations. The task focused on either self-agency related to ball-throwing (Agency Task) or the spatial location of a ball (Location Task). The animations were presented from constantly changing perspective (Changing View) or fixed perspective (Fixed View). The disembodiment-related condition was the interaction between the Agency Task and Changing View. Results: Participants with ASD exhibited higher activation than NTs in regions near the left parieto-temporo-occipital junction, left precuneus, left hippocampus, and other brain areas. Furthermore, interoceptive accuracy was negatively correlated with the activity of the left superior parietal and posterior midcingulate areas, whereas interoceptive trait prediction error was positively correlated with the activity of the left hippocampus, mid-temporal area, and left posterior cingulate area in participants with ASD. Conclusion These results suggest that disembodiment-related brain activation might be easily manifested by the interaction between perspective-shifting and the experience of agency, and that interoceptive abilities might be related to disembodiment-related brain activation in individuals with ASD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Objective: Many studies have explored sense of self in individuals with autism spectrum disorder (ASD); however, few have reported on their experience of “disembodiment.” This study aimed to investigate the differences in brain activity between patients with ASD and neurotypicals (NTs) under conditions causing disembodiment and to examine the correlation between their interoceptive abilities and disembodiment-related brain activity. Methods: 18 Participants with ASD and 21 NTs completed psychological evaluations, interoceptive abilities measurement, and functional magnetic resonance imaging (fMRI). The fMRI images were taken while the participants performed tasks involving ball-throwing animations. The task focused on either self-agency related to ball-throwing (Agency Task) or the spatial location of a ball (Location Task). The animations were presented from constantly changing perspective (Changing View) or fixed perspective (Fixed View). The disembodiment-related condition was the interaction between the Agency Task and Changing View. Results: Participants with ASD exhibited higher activation than NTs in regions near the left parieto-temporo-occipital junction, left precuneus, left hippocampus, and other brain areas. Furthermore, interoceptive accuracy was negatively correlated with the activity of the left superior parietal and posterior midcingulate areas, whereas interoceptive trait prediction error was positively correlated with the activity of the left hippocampus, mid-temporal area, and left posterior cingulate area in participants with ASD. Conclusion These results suggest that disembodiment-related brain activation might be easily manifested by the interaction between perspective-shifting and the experience of agency, and that interoceptive abilities might be related to disembodiment-related brain activation in individuals with ASD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Purpose: This review aims to synthesize the most up-to-date evidence on effective lifestyle prescriptions for managing patients with obesity in the primary care setting. Methods: A comprehensive literature search was conducted through June 2025 using major domestic and international databases, including PubMed, Google Scholar, and RISS, as well as the websites of relevant academic societies. The search strategy employed keywords such as “obesity,” “primary care,” “lifestyle intervention,” “diet,” “exercise,” and “behavioral therapy” in both English and Korean. Results: Effective obesity management should be grounded in the new paradigm of adiposity-based chronic disease, which focuses on preventing and treating the medical complications associated with excessive fat accumulation. The essential components of a successful lifestyle prescription comprise 3 pillars: diet, exercise, and behavioral therapy. An individualized dietary plan that achieves an energy deficit of 500–750 kcal is fundamental. Exercise prescriptions should recommend a combination of aerobic and resistance training, along with strategies to minimize sedentary time. In behavioral therapy, high-intensity behavioral counseling, the 6A model (ask, assess, advise, agree, assist, arrange), self-monitoring, and cognitive-behavioral therapy techniques have all demonstrated efficacy. Conclusion Primary care physicians are not only capable but also uniquely positioned to lead long-term, patient-centered obesity management. Their comprehensive and integrative perspective makes them one of the most efficient and scalable resources in addressing the obesity epidemic.