Pesticides, polycyclic aromatic hydrocarbons (PAHs), and phthalates are recognized as potential contributors to metabolic disorders. Nevertheless, the combined effect of simultaneous exposure to these chemicals on the metabolic syndrome (MetS) remains elusive. To explore the impacts of simultaneous exposure to pesticides, PAHs and phthalates and identify critical chemicals on MetS. Based on the National Health and Nutrition Examination Survey (NHANES) database from 2007—2012, our study included 4030 non-pregnant individuals aged 20 years or older. We used the weighted linear regression model, variable selection models (including LASSO regression and BMA models), as well as a mixture exposure model (WQS model) to investigate the correlation between chemicals and MetS. Additionally, stratified analyses were performed based on gender and age. The weighted generalized linear regression model revealed a positive correlation of 2-hydroxyphenanthrene (2-PHEN) with MetS (OR: 1.37, 95% CI: 1.19–1.59, P < 0.001). Both the LASSO regression and BMA models identified 2-PHEN as a significant chemical positively associated with MetS. Additionally, the WQS model showed a positive association between overall exposure to the three chemical categories and MetS, with the highest weighted chemicals being 2-PHEN. Stratified analyses demonstrated a significant correlation between 2-PHEN and MetS between different subgroups. Notably, the WQS regression model revealed a significant association in the subgroup of female (OR = 1.40, 95% CI: 1.08–1.83, P < 0.05), with 2-PHEN, 2,5-dichlorophenol (2,5-DCP), 2-hydroxynaphthalene (2-NAP), and mono-ethyl phthalate (MEP) identified as the primary contributions to MetS. Combined exposure to the three chemical groups was associated with an increased risk of MetS, with the PAHs group exhibiting the most pronounced effect and 2-PHEN emerging as a key chemical, underscoring significant public health concerns regarding the potential health risks of endocrine-disrupting chemicals (EDCs) exposure to metabolic diseases.

There is limited evidence about the gender- and obesity-specific effects of personal care product and plasticizing chemicals (PCPPCs) on short sleep duration in adults. We evaluated the gender- and obesity-specific association of co-exposure to PCPPCs and short sleep duration among adults aged 20–60 years using the National Health and Nutrition Examination Survey (NHANES) 2011–2016, a secondary data source from the United States. Seventeen PCPPCs, including five phenols, two parabens, and ten phthalates, were detected, and sleep duration was assessed among 3012 adults. Logistic regression, weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR) were employed. We found that bisphenol A (BPA), mono (caboxy-isooctyl) phthalate (MCOP), and mono (3-carboxypropyl) phthalate (MCPP) were consistently positively associated with short sleep duration in both females and males regardless of obesity status, except for BPA with general obesity. In particular, mono benzyl phthalate (MBzP) revealed a positive association in females, mono (2-ethyl-5-oxohexyl) phthalate (MEOHP) revealed a positive association in males, and MiBP revealed a positive association in abdominal obesity. Similar associations were observed in the mixture. Our study highlights that PCPPCs are independently associated with an increasing risk of short sleep duration in adults both individually and as a mixture; however, gender- and obesity-specific differences may have little effect on certain individual PCPPCs on short sleep duration.

Coronavirus Disease 2019 (COVID-19) is currently a global pandemic, and the early screening of COVID-19 is one of the key factors for COVID-19 control and treatment. Here, we developed and validated chest CT-based imaging biomarkers for COVID-19 patient screening. We identified the vasculature-like signals from CT images and found that, compared to healthy and community acquired pneumonia (CAP) patients, the COVID-19 patients revealed significantly higher abundance of these signals. Furthermore, unsupervised feature learning leads to the discovery of clinical-relevant imaging biomarkers from the vasculature-like signals for accurate and sensitive COVID-19 screening that has been double-blindly validated in an independent hospital (sensitivity: 0.941, specificity: 0.904, AUC: 0.952). Our findings could open a new avenue to assist screening of COVID-19 patients.