Joint effect of multiple air pollutants on cardiometabolic health in normal-weight and obese adults: A novel insight into the role of circulating free fatty acids.

The cardiometabolic effects of air pollution in the context of mixtures and the underlying mechanisms remain not fully understood. This study aims to investigate the joint effect of air pollutant mixtures on a broad range of cardiometabolic parameters, examine the susceptibility of obese individuals, and determine the role of circulating fatty acids. In this panel study, metabolically healthy normal-weight (MH-NW, n = 49) and obese (MHO, n = 39) adults completed three longitudinal visits (257 person-visits in total). Personal exposure levels of PM2.5, PM10, O3, NO2, SO2, CO and BC were estimated based on fixed-site monitoring data, time-activity logs and infiltration factor method. Blood pressure, glycemic homeostasis, lipid profiles, systematic inflammation and coagulation biomarkers were measured. Targeted metabolomics was used to quantify twenty-eight plasma free fatty acids (FFAs). Bayesian kernel machine regression models were applied to establish the exposure-response relationships and identify key pollutants. Significant joint effects of measured air pollutants on systematic inflammation and coagulation biomarkers were observed in the MHO group, instead of the MH-NW group. Lipid profiles showed the most significant changes in both groups and O3 contributed the most to the total effect. Specific FFA patterns were identified, and de novo lipogenesis (DNL)-related pattern was most closely related to blood lipid profiles. In particular, interaction analysis suggested that DNL-related FFA pattern augmented the effects of O3 on triglyceride (TG, Pinteraction = 0.040), high-density lipoprotein cholesterol (HDL-C, Pinteraction = 0.106) and TG/HDL-C (Pinteraction = 0.020) in the MHO group but not MH-NW group. This modification was further confirmed by interaction analysis with estimated activity of SCD1, a key enzyme in the DNL pathway. Therefore, despite being metabolically healthy, obese subjects have a higher cardiometabolic susceptibility to air pollution, especially O3, and the DNL pathway may represent an intrinsic driver of lipid susceptibility. This study provides new insights into the cardiometabolic susceptibility of obese individuals to air pollution.

Short-term ozone exposure and metabolic status in metabolically healthy obese and normal-weight young adults: A viewpoint of inflammatory pathways.

Unhealthy metabolic status increases risks of cardiovascular and other diseases. This study aims to explore whether there is a link between O3 and metabolic health indicators through a viewpoint of inflammatory pathways. 49 metabolically healthy normal-weight (MH-NW) and 39 metabolically healthy obese (MHO) young adults aged 18-26 years were recruited from a panel study with three visits. O3 exposure were estimated based on fixed-site environmental monitoring data and time-activity diary for each participant. Compared to MH-NW people, MHO people were more susceptible to the adverse effects on metabolic status, including blood pressure, glucose, and lipid indicators when exposed to O3. For instance, O3 exposure was associated with significant decreases in high-density lipoprotein cholesterol (HDL-C), and increases in C-peptide and low-density lipoprotein cholesterol (LDL-C) among MHO people, while only weaker changes in HDL-C and LDL-C among MH-NW people. Mediation analyses indicated that leptin mediated the metabolic health effects in both groups, while eosinophils and MCP-1 were also important mediating factors for the MHO people. Although both with a metabolically healthy status, compared to normal-weight people, obese people might be more susceptible to the negative effects of O3 on metabolic status, possibly through inflammatory indicators such as leptin, eosinophils, and MCP-1.

Loan default prediction of Chinese P2P market: a machine learning methodology.

Repayment failures of borrowers have greatly affected the sustainable development of the peer-to-peer (P2P) lending industry. The latest literature reveals that existing risk evaluation systems may ignore important signals and risk factors affecting P2P repayment. In our study, we applied four machine learning methods (random forest (RF), extreme gradient boosting tree (XGBT), gradient boosting model (GBM), and neural network (NN)) to predict important factors affecting repayment by utilizing data from Renrendai.com in China from Thursday, January 1, 2015, to Tuesday, June 30, 2015. The results showed that borrowers who have passed video, mobile phone, job, residence or education level verification are more likely to default on loan repayment, whereas those who have passed identity and asset certification are less likely to default on loans. The accuracy and kappa value of the four methods all exceed 90%, and RF is superior to the other classification models. Our findings demonstrate important techniques for borrower screening by P2P companies and risk regulation by regulatory agencies. Our methodology and findings will help regulators, banks and creditors combat current financial disasters caused by the coronavirus disease 2019 (COVID-19) pandemic by addressing various financial risks and translating credit scoring improvements.

An efficient method to simultaneously analyze multi-class organic pollutants in human serum.

The degree of population exposure to various organic pollutants (OPs), including polycyclic aromatic hydrocarbons, organochlorinated pesticides, polychlorinated biphenyls, and polybrominated diphenyl ethers, can be determined by measuring their concentrations in human serum. However, performing large-scale measurements with such a variety of compounds in serum is challenging in terms of efficiency and cost. We describe herein the development of a high-efficiency extraction and sample cleanup protocol for simultaneous and quantitative analyses of OPs using gas chromatography-mass spectrometry. OPs, together with crude lipid impurities, were extracted from human serum with a mixture of n-hexane and methyl tert-butyl ether. A disperse sorbent composed of primary secondary amine and C18 (PSA/C18) was used to roughly remove co-extracted impurities. A combined column of neutral silica gel and neutral alumina oxide (AlO/SiG) was then used for deep cleanup. For the removal of impurities, the overall performance of our protocol for the analysis of OPs in serum was comparable to that of traditional gel permeation chromatography (GPC) and dramatically better than that of PSA/C18, which is a frequently used QuEChERS (quick, easy, cheap, effective, rugged, safe) based method. While both the proposed protocol and GPC yielded recoveries of 80%-110% for four classes of OPs, our protocol consumed about 10 times less solvent, resulting in lower experimental expenses and a lower risk of contamination from residual OPs in the solvent and other supplies. In contrast to GPC, our protocol also permits efficient batch processing of serum samples, allowing for large sample sizes such as those encountered in epidemiological studies.