High Serum-Induced AhRL Is Associated with Prevalent Metabolic Syndrome and Future Impairment of Glucose Tolerance in the Elderly

A Novel Aryl Hydrocarbon Receptor Antagonist HBU651 Ameliorates Peripheral and Hypothalamic Inflammation in High-Fat Diet-Induced Obese Mice

Obesity is a chronic peripheral inflammation condition that is strongly correlated with neurodegenerative diseases and associated with exposure to environmental chemicals. The aryl hydrocarbon receptor (AhR) is a ligand-activated nuclear receptor activated by environmental chemical, such as dioxins, and also is a regulator of inflammation through interacting with nuclear factor (NF)-κB. In this study, we evaluated the anti-obesity and anti-inflammatory activity of HBU651, a novel AhR antagonist. In BV2 microglia cells, HBU651 successfully inhibited lipopolysaccharide (LPS)-mediated nuclear localization of NF-κB and production of NF-κB-dependent proinflammatory cytokines, such as tumor necrosis factor (TNF)-α, interleukin (IL)-1β, and IL-6. It also restored LPS-induced mitochondrial dysfunction. While mice being fed a high-fat diet (HFD) induced peripheral and central inflammation and obesity, HBU651 alleviated HFD-induced obesity, insulin resistance, glucose intolerance, dyslipidemia, and liver enzyme activity, without hepatic and renal damage. HBU651 ameliorated the production of inflammatory cytokines and chemokines, proinflammatory Ly6c^high monocytes, and macrophage infiltration in the blood, liver, and adipose tissue. HBU651 also decreased microglial activation in the arcuate nucleus in the hypothalamus. These findings suggest that HBU651 may be a potential candidate for the treatment of obesity-related metabolic diseases.

An Interactive Online App for Predicting Diabetes via Machine Learning from Environment-Polluting Chemical Exposure Data

The early prediction and identification of risk factors for diabetes may prevent or delay diabetes progression. In this study, we developed an interactive online application that provides the predictive probabilities of prediabetes and diabetes in 4 years based on a Bayesian network (BN) classifier, which is an interpretable machine learning technique. The BN was trained using a dataset from the Ansung cohort of the Korean Genome and Epidemiological Study (KoGES) in 2008, with a follow-up in 2012. The dataset contained not only traditional risk factors (current diabetes status, sex, age, etc.) for future diabetes, but it also contained serum biomarkers, which quantified the individual level of exposure to environment-polluting chemicals (EPC). Based on accuracy and the area under the curve (AUC), a tree-augmented BN with 11 variables derived from feature selection was used as our prediction model. The online application that implemented our BN prediction system provided a tool that performs customized diabetes prediction and allows users to simulate the effects of controlling risk factors for the future development of diabetes. The prediction results of our method demonstrated that the EPC biomarkers had interactive effects on diabetes progression and that the use of the EPC biomarkers contributed to a substantial improvement in prediction performance.