Lean individuals with non-alcohol fatty liver disease (NAFLD) often have normal body size but abnormal visceral fat. Therefore, an alternative to body mass index should be considered for prediction of lean-NAFLD. This study aimed to use representative visceral fat links with other laboratory parameters using the least absolute shrinkage and selection operator (LASSO) method to construct a predictive model for lean-NAFLD. This retrospective cross-sectional analysis enrolled 2325 subjects with BMI < 24 kg/m² from medical records of 51,271 examinees who underwent a routine health check-up. They were randomly divided into training and validation cohorts at a ratio of 1:1. The LASSO-derived prediction model used LASSO regression to select 23 clinical and laboratory factors. The discrimination and calibration abilities were evaluated using the Hosmer-Lemeshow test and calibration curves. The performance of the LASSO model was compared with the fatty liver index (FLI) model. The LASSO-derived model included four variables-visceral fat, triglyceride levels, HDL-C-C levels, and waist hip ratio-and demonstrated superior performance in predicting lean-NAFLD with high discriminatory ability (AUC, 0.8416; 95% CI: 0.811-0.872) that was comparable with the FLI model. Using a cut-off of 0.1484, moderate sensitivity (75.69%) and specificity (79.86%), as well as high negative predictive value (95.9%), were achieved in the LASSO model. In addition, with normal WC subgroup analysis, the LASSO model exhibits a trend of higher accuracy compared to FLI (cut-off 15.45). We developed a LASSO-derived predictive model with the potential for use as an alternative tool for predicting lean-NAFLD in clinical settings.

Background: Colistin is one of the last-line antimicrobial agents against life-threatening infections. The distribution of the colistin resistance gene mcr-1 has been reported worldwide. However, most studies have focused on the distribution of mcr-1-positive bacteria in humans, animals, food, and sewage; few have focused on their distribution in natural environments. Method: We conducted a large spatial survey of mcr-1-positive Escherichia coli at 119 sites in 48 rivers, covering the entire island of Taiwan. We investigated the relationship between the livestock or poultry density in the surveyed riverine area and the number of mcr-1-positive E. coli in the river water. We then sequenced and characterized the isolated mcr-1-positive plasmids. Results: Seven mcr-1 positive E. coli were isolated from 5.9% of the sampling sites. The mcr-1-positive sites correlated with high chicken and pig stocking densities but not human population density or other river parameters. Four of the mcr-1-positive E. coli strains harbored epidemic IncX4 plasmids, and three of them exhibited identical sequences with a size of 33,309 bp. One of the plasmids contained identical 33,309 bp sequences but carried an additional 5711-bp transposon (Tn3 family). To our knowledge, this is the first demonstration that mcr-1-carrying IncX4 plasmids can contain an insertion of such transposons. All mcr-1-positive isolates belonged to phylogenetic group A and harbored few known virulence genes. Conclusion: This study showed a positive relationship between the number of mcr-1-positive sites and high livestock and poultry density. The sequencing analyses indicated that the epidemic plasmid in the mcr-1 isolates circulates not only in humans, animals, and food but also in the associated environments or natural habitats in Taiwan, suggesting that the surveillance of antibiotics-resistance genes for livestock or poultry farm quality control should include their associated environments.

Osteoporosis is a growing public health concern in aging populations such as Taiwan, where limited utilization of dual-energy X-ray absorptiometry (DXA) often leads to underdiagnosis and even delayed treatment. Therefore, we leveraged machine learning (ML) and aimed to develop a simple and easily accessible model that effectively identifies individuals at high risk of osteoporosis. This retrospective analysis enrolled 5510 men aged ≥50 years and 4720 postmenopausal women who underwent DXA at the Kaohsiung Veterans General Hospital, with another cohort of 610 men and 523 women for validation. We developed separate models for men and women using decision trees, random forests, support vector machines, k-nearest neighbors, extreme gradient boosting, and artificial neural networks (ANNs) to predict osteoporosis. Furthermore, we compared each model with the traditional Osteoporosis Self-Assessment Tool for Asians (OSTA) model. We identified age, height, weight, and BMI as variables for our prediction model and evaluated the model's performance using the area under the receiver operating characteristic curve (AUC). The ANN model significantly outperformed the OSTA model and all the other ML models for both men and women (AUC: 0.67 for men; 0.77 for women). The validation data for the ANN model showed similar AUCs for both men and women. This study developed ML models to help identify individuals at high risk of osteoporosis in postmenopausal women and men aged ≥50 years in southern Taiwan. Our ML models, especially the ANN model, surpassed the OSTA model and consistently performed well across different populations.

Hsu CL, et al. Korean J Radiol. 2023 Dec;24(12):1249-1259. https://doi.org/10.3348/kjr.2023.0555

Barrett’s esophagus (BE) is a premalignant condition with increased incidence worldwide both in old and young individuals. However, the role of certain potential risk factors remains unclear in young adults (< 50 years). We aimed to determine the risk factors of BE in young adults. A total of 4943 young adults who underwent upper gastrointestinal endoscopy at our health check-up center were enrolled. The diagnosis of BE was based on histological confirmation. We analyzed demographic factors, laboratory data, potential risk factors such as smoking, alcohol consumption, presence of gastroesophageal reflux disease (GERD) symptoms, and metabolic syndrome for the risk of BE by using binary logistic regression analysis. The prevalence of BE was 1.8% (88/4943). Male sex, the presence of GERD symptoms, and smoking were three significant risk factors related to BE. Furthermore, participants who had smoked for 10 pack-years or more had increased risk of BE with dose-dependent phenomenon (p trend < 0.001). The proportion of BE in male participants with both GERD symptoms and a smoking history of 10 pack-years or more was as high as 10.3% (16/155). Significant risk factors of BE in young adults are male sex, the presence of GERD symptoms, and smoking. The risk also increases with an increase in cumulative exposure to smoking.

Determining the target population for the screening of Barrett’s esophagus (BE), a precancerous condition of esophageal adenocarcinoma, remains a challenge in Asia. The aim of our study was to develop risk prediction models for BE using logistic regression (LR) and artificial neural network (ANN) methods. Their predictive performances were compared. We retrospectively analyzed 9646 adults aged ≥20 years undergoing upper gastrointestinal endoscopy at a health examinations center in Taiwan. Evaluated by using 10-fold cross-validation, both models exhibited good discriminative power, with comparable area under curve (AUC) for the LR and ANN models (Both AUC were 0.702). Our risk prediction models for BE were developed from individuals with or without clinical indications of upper gastrointestinal endoscopy. The models have the potential to serve as a practical tool for identifying high-risk individuals of BE among the general population for endoscopic screening.