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.

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.

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.

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.

Primary cilia function as critical sensory organelles that mediate multiple signaling pathways, including the Hedgehog (Hh) pathway, which is essential for organ patterning and morphogenesis. Disruptions in Hh signaling have been implicated in supernumerary tooth formation and molar fusion in mutant mice. Cilk1, a highly conserved serine/threonine-protein kinase localized within primary cilia, plays a critical role in ciliary transport. Loss of Cilk1 results in severe ciliopathy phenotypes, including polydactyly, edema, and cleft palate. However, the role of Cilk1 in tooth development remains unexplored. In this study, we investigated the role of Cilk1 in tooth development. Cilk1 was found to be expressed in both the epithelial and mesenchymal compartments of developing molars. Cilk1 deficiency resulted in altered ciliary dynamics, characterized by reduced frequency and increased length, accompanied by downregulation of Hh target genes, such as Ptch1 and Sostdc1, leading to the formation of diastemal supernumerary teeth. Furthermore, in Cilk1^-/-;PCS1-MRCS1^△/△ mice, which exhibit a compounded suppression of Hh signaling, we uncovered a novel phenomenon: diastemal supernumerary teeth can be larger than first molars. Based on these findings, we propose a progressive model linking Hh signaling levels to sequential changes in tooth patterning: initially inducing diastemal supernumerary teeth, then enlarging them, and ultimately leading to molar fusion. This study reveals a previously unrecognized role of Cilk1 in controlling tooth morphology via Hh signaling and highlights how Hh signaling levels shape tooth patterning in a gradient-dependent manner.
Animals ; Hedgehog Proteins/physiology* ; Mice ; Signal Transduction/physiology* ; Tooth, Supernumerary ; Molar ; Cilia/physiology* ; Odontogenesis/physiology* ; Patched-1 Receptor ; Protein Serine-Threonine Kinases/physiology* ; Mice, Knockout ; Adaptor Proteins, Signal Transducing

Glycemic control is particularly important in hemodialysis (HD) patients with diabetes mellitus (DM). Although fasting blood glucose (FBG) level is an important indicator of glycemic control, a clear target for reducing mortality in HD patients with DM is lacking. Methods: A total of 26,162 maintenance HD patients with DM were recruited from the National Health Insurance Database of Korea between 2002 and 2018. We analyzed the association of FBG levels at the baseline health examination with the risk of all-cause and cause-specific mortality. Results: Patients with FBG 80–100 mg/dL showed a higher survival rate compared with that of other FBG categories (p < 0.001). The risk of all-cause mortality increased with the increase in FBG levels, and adjusted hazard ratios (HRs) were 1.10 (95% confidence interval [CI], 1.04–1.17), 1.21 (95% CI, 1.13–1.29), 1.36 (95% CI, 1.26–1.46), and 1.61 (95% CI, 1.51–1.72) for patients with FBG 100–125, 125–150, 150–180, and ≥180 mg/dL, respectively. The HR for mortality was also significantly increased in patients with FBG <80 mg/dL (adjusted HR, 1.14; 95% CI, 1.05–1.23). The analysis of cause-specific mortality also revealed a J-shaped curve between FBG levels and the risk of cardiovascular deaths. However, the risk of infection or malignancy-related deaths was not linearly increased as FBG levels increased. Conclusion: A J-shaped association was observed between FBG levels and the risk of all-cause mortality, with the lowest risk at FBG 80–100 mg/dL in HD patients with DM.

Glycemic control is particularly important in hemodialysis (HD) patients with diabetes mellitus (DM). Although fasting blood glucose (FBG) level is an important indicator of glycemic control, a clear target for reducing mortality in HD patients with DM is lacking. Methods: A total of 26,162 maintenance HD patients with DM were recruited from the National Health Insurance Database of Korea between 2002 and 2018. We analyzed the association of FBG levels at the baseline health examination with the risk of all-cause and cause-specific mortality. Results: Patients with FBG 80–100 mg/dL showed a higher survival rate compared with that of other FBG categories (p < 0.001). The risk of all-cause mortality increased with the increase in FBG levels, and adjusted hazard ratios (HRs) were 1.10 (95% confidence interval [CI], 1.04–1.17), 1.21 (95% CI, 1.13–1.29), 1.36 (95% CI, 1.26–1.46), and 1.61 (95% CI, 1.51–1.72) for patients with FBG 100–125, 125–150, 150–180, and ≥180 mg/dL, respectively. The HR for mortality was also significantly increased in patients with FBG <80 mg/dL (adjusted HR, 1.14; 95% CI, 1.05–1.23). The analysis of cause-specific mortality also revealed a J-shaped curve between FBG levels and the risk of cardiovascular deaths. However, the risk of infection or malignancy-related deaths was not linearly increased as FBG levels increased. Conclusion: A J-shaped association was observed between FBG levels and the risk of all-cause mortality, with the lowest risk at FBG 80–100 mg/dL in HD patients with DM.

Glycemic control is particularly important in hemodialysis (HD) patients with diabetes mellitus (DM). Although fasting blood glucose (FBG) level is an important indicator of glycemic control, a clear target for reducing mortality in HD patients with DM is lacking. Methods: A total of 26,162 maintenance HD patients with DM were recruited from the National Health Insurance Database of Korea between 2002 and 2018. We analyzed the association of FBG levels at the baseline health examination with the risk of all-cause and cause-specific mortality. Results: Patients with FBG 80–100 mg/dL showed a higher survival rate compared with that of other FBG categories (p < 0.001). The risk of all-cause mortality increased with the increase in FBG levels, and adjusted hazard ratios (HRs) were 1.10 (95% confidence interval [CI], 1.04–1.17), 1.21 (95% CI, 1.13–1.29), 1.36 (95% CI, 1.26–1.46), and 1.61 (95% CI, 1.51–1.72) for patients with FBG 100–125, 125–150, 150–180, and ≥180 mg/dL, respectively. The HR for mortality was also significantly increased in patients with FBG <80 mg/dL (adjusted HR, 1.14; 95% CI, 1.05–1.23). The analysis of cause-specific mortality also revealed a J-shaped curve between FBG levels and the risk of cardiovascular deaths. However, the risk of infection or malignancy-related deaths was not linearly increased as FBG levels increased. Conclusion: A J-shaped association was observed between FBG levels and the risk of all-cause mortality, with the lowest risk at FBG 80–100 mg/dL in HD patients with DM.

Glycemic control is particularly important in hemodialysis (HD) patients with diabetes mellitus (DM). Although fasting blood glucose (FBG) level is an important indicator of glycemic control, a clear target for reducing mortality in HD patients with DM is lacking. Methods: A total of 26,162 maintenance HD patients with DM were recruited from the National Health Insurance Database of Korea between 2002 and 2018. We analyzed the association of FBG levels at the baseline health examination with the risk of all-cause and cause-specific mortality. Results: Patients with FBG 80–100 mg/dL showed a higher survival rate compared with that of other FBG categories (p < 0.001). The risk of all-cause mortality increased with the increase in FBG levels, and adjusted hazard ratios (HRs) were 1.10 (95% confidence interval [CI], 1.04–1.17), 1.21 (95% CI, 1.13–1.29), 1.36 (95% CI, 1.26–1.46), and 1.61 (95% CI, 1.51–1.72) for patients with FBG 100–125, 125–150, 150–180, and ≥180 mg/dL, respectively. The HR for mortality was also significantly increased in patients with FBG <80 mg/dL (adjusted HR, 1.14; 95% CI, 1.05–1.23). The analysis of cause-specific mortality also revealed a J-shaped curve between FBG levels and the risk of cardiovascular deaths. However, the risk of infection or malignancy-related deaths was not linearly increased as FBG levels increased. Conclusion: A J-shaped association was observed between FBG levels and the risk of all-cause mortality, with the lowest risk at FBG 80–100 mg/dL in HD patients with DM.