Individual and joint associations of per- and polyfluoroalkyl substances (PFAS) with gallstone disease in adults: A cross-sectional study.

Disturbances in the enterohepatic circulation are important biological mechanisms for causing gallstones and also have important effects on the metabolism of Per- and polyfluoroalkyl substances (PFAS). Moreover, PFAS is associated with sex hormone disorder which is another important cause of gallstones. However, it remains unclear whether PFAS is associated with gallstones. In this study, we used logistic regression, restricted cubic spline (RCS), quantile g-computation (qg-comp), Bayesian kernel machine regression (BKMR), and subgroup analysis to assess the individual and joint associations of PFAS with gallstones and effect modifiers. We observed that the individual associations of perfluorodecanoic acid (PFDeA) (OR: 0.600, 95% CI: 0.444 to 0.811), perfluoroundecanoic acid (PFUA) (OR: 0.630, 95% CI: 0.453 to 0.877), n-perfluorooctane sulfonic acid (n-PFOS) (OR: 0.719, 95% CI: 0.571 to 0.906), and perfluoromethylheptane sulfonic acid isomers (Sm-PFOS) (OR: 0.768, 95% CI: 0.602 to 0.981) with gallstones were linearly negative. Qg-comp showed that the PFAS mixture (OR: 0.777, 95% CI: 0.514 to 1.175) was negatively associated with gallstones, but the difference was not statistically significant, and PFDeA had the highest negative association. Moreover, smoking modified the association of perfluorononanoic acid (PFNA) with gallstones. BKMR showed that PFDeA, PFNA, and PFUA had the highest groupPIP (groupPIP = 0.93); PFDeA (condPIP = 0.82), n-perfluorooctanoic acid (n-PFOA) (condPIP = 0.68), and n-PFOS (condPIP = 0.56) also had high condPIPs. Compared with the median level, the joint association of the PFAS mixture with gallstones showed a negative trend; when the PFAS mixture level was at the 70th percentile or higher, they were negatively associated with gallstones. Meanwhile, when other PFAS were fixed at the 25th, 50th, and 75th percentiles, PFDeA had negative associations with gallstones. Our evidence emphasizes that PFAS is negatively associated with gallstones, and more studies are needed in the future to definite the associations of PFAS with gallstones and explore the underlying biological mechanisms.

Machine Learning for Early Prediction of Major Adverse Cardiovascular Events After First Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Retrospective Cohort Study.

BACKGROUND: The incidence of major adverse cardiovascular events (MACEs) remains high in patients with acute myocardial infarction (AMI) who undergo percutaneous coronary intervention (PCI), and early prediction models to guide their clinical management are lacking. OBJECTIVE: This study aimed to develop machine learning-based early prediction models for MACEs in patients with newly diagnosed AMI who underwent PCI. METHODS: A total of 1531 patients with AMI who underwent PCI from January 2018 to December 2019 were enrolled in this consecutive cohort. The data comprised demographic characteristics, clinical investigations, laboratory tests, and disease-related events. Four machine learning models-artificial neural network (ANN), k-nearest neighbors, support vector machine, and random forest-were developed and compared with the logistic regression model. Our primary outcome was the model performance that predicted the MACEs, which was determined by accuracy, area under the receiver operating characteristic curve, and F1-score. RESULTS: In total, 1362 patients were successfully followed up. With a median follow-up of 25.9 months, the incidence of MACEs was 18.5% (252/1362). The area under the receiver operating characteristic curve of the ANN, random forest, k-nearest neighbors, support vector machine, and logistic regression models were 80.49%, 72.67%, 79.80%, 77.20%, and 71.77%, respectively. The top 5 predictors in the ANN model were left ventricular ejection fraction, the number of implanted stents, age, diabetes, and the number of vessels with coronary artery disease. CONCLUSIONS: The ANN model showed good MACE prediction after PCI for patients with AMI. The use of machine learning-based prediction models may improve patient management and outcomes in clinical practice.

Prediction of transition from mild cognitive impairment to Alzheimer's disease based on a logistic regression-artificial neural network-decision tree model.

AIM: To develop a logistic regression model, artificial neural network (ANN) model and decision tree (DT) model for the progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) to compare the performance of the three models. METHODS: A total of 425 patients with MCI were screened from the original cohort. The actual follow up included 361 patients, with AD as the outcome variable. Three kinds of prediction models were developed: a logistic regression model, ANN model and DT model. The performance of all three models was measured with accuracy, sensitivity, positive predictive value and area under the receiver operating characteristic curve. RESULTS: A total of 121 patients with MCI developed AD, and the average conversion rate was 9.49% per year. The ANN model had higher accuracy (89.52 ± 0.36%), area under the receiver operating characteristic curve (92.08 ± 0.12), sensitivity (82.11 ± 0.42%) and positive predictive value (75.26 ± 0.86%) than the other two models. The first five important predictors of the ANN model were, in order, ADL score, age, urine AD-associated neuronal thread protein, alcohol consumption and smoking. For the DT model, they were age, activities of daily living score, family history of dementia, urine AD-associated neuronal thread protein and alcohol consumption. For the logistic regression model, they were age, sex, activities of daily living score, alcohol consumption and smoking. CONCLUSION: The logistic regression, ANN and DT models performed well at predicting the transition from MCI to AD with ideal stability. However, the ANN model had the best predictive value. Increased age, activities of daily living score, urine AD-associated neuronal thread protein, alcohol consumption, smoking and sex were important factors. Geriatr Gerontol Int 2021; 21: 43-47.