East Asian populations exhibit a genetic predisposition to obesity, yet comprehensive research on these traits is limited. We conducted a genome-wide association study (GWAS) with 93,673 Korean subjects to uncover novel genetic loci linked to obesity, examining metrics such as body mass index, waist circumference, body fat ratio, and abdominal fat ratio. Participants were categorized into non-obese, metabolically healthy obese (MHO), and metabolically unhealthy obese (MUO) groups. Using advanced computational methods, we developed a multifaceted polygenic risk scores (PRS) model to predict obesity. Our GWAS identified significant genetic effects with distinct sizes and directions within the MHO and MUO groups compared with the non-obese group. Gene-based and gene-set analyses, along with cluster analysis, revealed heterogeneous patterns of significant genes on chromosomes 3 (MUO group) and 11 (MHO group). In analyses targeting genetic predisposition differences based on metabolic health, odds ratios of high PRS compared with medium PRS showed significant differences between non-obese and MUO, and non-obese and MHO. Similar patterns were seen for low PRS compared with medium PRS. These findings were supported by the estimated genetic correlation (0.89 from bivariate GREML). Regional analyses highlighted significant local genetic correlations on chromosome 11, while single variant approaches suggested widespread pleiotropic effects, especially on chromosome 11. In conclusion, our study identifies specific genetic loci and risks associated with obesity in the Korean population, emphasizing the heterogeneous genetic factors contributing to MHO and MUO.

We conducted a comprehensive genetic investigation of obesity in a cohort of 93,673 Korean individuals, categorized by body mass index and waist circumference using Korean-specific and international criteria. To explore the genetic architecture of obesity and its related comorbidities, we performed genome-wide association studies and constructed polygenic risk scores (PRSs) using both conventional single-trait and advanced multiple-trait models, including the PRSsum approach. Our analyses identified genome-wide significant loci and demonstrated their higher heritability for general obesity than for abdominal obesity, and for moderate obesity than for severe obesity. East Asian populations showed stronger genetic correlations between abdominal obesity and obesity-related diseases. Both single-trait and multiple-trait PRSs stratified individuals by risk, with low-PRS individuals exhibiting reduced risk for obesity, hypertension, and type 2 diabetes, while high-PRS individuals displayed elevated risk, particularly under the multiple-trait model. Interaction and mediation analyses revealed distinct genetic pathways through which obesity contributes to disease development. Collectively, our findings revealed key loci and shared genetic mechanisms linking obesity and its comorbidities in the Korean population. These insights highlight the value of multiple-trait PRS models and underscore the importance of ancestry-specific genetic research for addressing the obesity epidemic.

Background/Objectives: Appropriate postoperative management, especially in pediatric patients, can be challenging for anesthesiologists. This retrospective study used machine learning to investigate the effects and complications of patient-controlled analgesia (PCA) in children undergoing orthopedic surgery. Methods: The medical records of children who underwent orthopedic surgery in a single tertiary hospital and received intravenous and epidural PCA were analyzed. Predictive models were developed using machine learning, and various demographic, anesthetic, and surgical factors were investigated to predict postoperative pain and complications associated with PCA. Results: Data from 1968 children were analyzed. Extreme gradient boosting effectively predicted moderate postoperative pain for the 6–24-h (area under curve (AUC): 0.85, accuracy (ACC): 0.79) and 24–48-h (AUC: 0.89, ACC: 0.87) periods after surgery. The factors that predicted moderate postoperative pain included the pain score immediately before the measurement period, the total amount of opioid infused, and age. For predicting side effects during the 6–24-h period after surgery, a least absolute shrinkage and selection operator model (AUC: 0.75, ACC: 0.64) was selected, while a random forest model (AUC: 0.91, ACC: 0.87) was chosen for the 24–48-h period post-surgery. The factors that predicted complications included the occurrence of side effects immediately before the measurement period, the total amount of opioid infused before the measurement period, and age. Conclusions: This retrospective study introduces machine-learning-based models and factors aimed at forecasting moderate postoperative pain and complications of PCA in children undergoing orthopedic surgery. This research has the potential to enhance postoperative pain management strategies for children.

We conducted a comprehensive genetic investigation of obesity in a cohort of 93,673 Korean individuals, categorized by both body mass index and waist circumference using Korean-specific and international criteria. To explore the genetic architecture of obesity and its comorbidities, we performed genome-wide association studies and constructed polygenic risk scores (PRSs) using both conventional single trait and advanced multiple-trait models, including the PRSsum approach. Our analyses identified genome-wide significant loci and demonstrated higher heritability for general obesity than abdominal obesity, and for moderate compared to severe obesity. Notably, East Asian populations showed stronger genetic correlations between abdominal obesity and obesity-related diseases. Both single trait and multiple trait PRSs stratified individuals by risk, with low PRS individuals exhibiting reduced risk for obesity, hypertension, and type 2 diabetes, while high PRS individuals displayed elevated risk, particularly under the multiple trait model. Additionally, interaction and mediation analyses revealed distinct genetic pathways through which obesity contributes to disease development. Collectively, our findings uncover key loci and shared genetic mechanisms linking obesity and its comorbidities in the Korean population. These insights highlight the value of multiple trait PRS models and underscore the importance of ancestry-specific genetic research for addressing the obesity epidemic.