Blood and urine multi-omics analysis of the impact of e-vaping, smoking, and cessation: from exposome to molecular responses.

Cigarette smoking is a major preventable cause of morbidity and mortality. While quitting smoking is the best option, switching from cigarettes to non-combustible alternatives (NCAs) such as e-vapor products is a viable harm reduction approach for smokers who would otherwise continue to smoke. A key challenge for the clinical assessment of NCAs is that self-reported product use can be unreliable, compromising the proper evaluation of their risk reduction potential. In this cross-sectional study of 205 healthy volunteers, we combined comprehensive exposure characterization with in-depth multi-omics profiling to compare effects across four study groups: cigarette smokers (CS), e-vapor users (EV), former smokers (FS), and never smokers (NS). Multi-omics analyses included metabolomics, transcriptomics, DNA methylomics, proteomics, and lipidomics. Comparison of the molecular effects between CS and NS recapitulated several previous observations, such as increased inflammatory markers in CS. Generally, FS and EV demonstrated intermediate molecular effects between the NS and CS groups. Stratification of the FS and EV by combustion exposure markers suggested that this position on the spectrum between CS and NS was partially driven by non-compliance/dual use. Overall, this study highlights the importance of in-depth exposure characterization before biological effect characterization for any NCA assessment study.

Analysis of chemical deposits on tooth enamel exposed to total particulate matter from cigarette smoke and tobacco heating system 2.2 aerosol by novel GC-MS deconvolution procedures.

Tobacco smoking contributes to tooth discoloration. Pigmented compounds in the smoke generated by combustion of tobacco can cause discoloration of dental hard tissues. However, aerosols from heated tobacco products cause less discoloration than cigarette smoke (CS) in vitro. The objective of the present study was to optimize a method for extracting the colored chemical compounds deposited on tooth enamel following exposure to total particulate matter (TPM) from CS or a heated tobacco product (Tobacco Heating System [THS] 2.2), analyze the extracts by gas chromatography coupled to time-of-flight mass spectrometry, and identify the key chemicals associated with tooth discoloration. Sixty bovine enamel blocks were exposed for 2 weeks to TPM from CS or THS 2.2 aerosol or to artificial saliva as a control. Brushing without toothpaste and color measurements were performed each week. Noticeable discoloration of enamel was observed following exposure to CS TPM. The discoloration following exposure to THS 2.2 aerosol TPM or artificial saliva was not distinguishable to the eye (ΔE < 3.3). Carbon disulfide was used to extract surface-deposited chemicals. Untargeted analyses were followed by partial least squares correlation against discoloration scores (R2 = 0.96). Eleven compounds had variable importance in projection scores greater than 2. Discriminant autocorrelation matrix calculation of their mass spectral information identified eight of the eleven compounds as terpenoids. None of the compounds were related to nicotine. Several of these compounds were also detected in THS 2.2 aerosol TPM-exposed enamel, but at lower levels, in line with our findings showing less discoloration. Compared with CS TPM exposure, THS 2.2 aerosol TPM exposure resulted in lower deposition of color-related compounds on enamel surface, consistent with minimal discoloration of dental enamel.

Correlation of porous and functional properties of food materials by NMR relaxometry and multivariate analysis.

The porous properties of food materials are known to determine important macroscopic parameters such as water-holding capacity and texture. In conventional approaches, understanding is built from a long process of establishing macrostructure-property relations in a rational manner. Only recently, multivariate approaches were introduced for the same purpose. The model systems used here are oil-in-water emulsions, stabilised by protein, and form complex structures, consisting of fat droplets dispersed in a porous protein phase. NMR time-domain decay curves were recorded for emulsions with varied levels of fat, protein and water. Hardness, dry matter content and water drainage were determined by classical means and analysed for correlation with the NMR data with multivariate techniques. Partial least squares can calibrate and predict these properties directly from the continuous NMR exponential decays and yields regression coefficients higher than 82%. However, the calibration coefficients themselves belong to the continuous exponential domain and do little to explain the connection between NMR data and emulsion properties. Transformation of the NMR decays into a discreet domain with non-negative least squares permits the use of multilinear regression (MLR) on the resulting amplitudes as predictors and hardness or water drainage as responses. The MLR coefficients show that hardness is highly correlated with the components that have T2 distributions of about 20 and 200 ms whereas water drainage is correlated with components that have T2 distributions around 400 and 1800 ms. These T2 distributions very likely correlate with water populations present in pores with different sizes and/or wall mobility. The results for the emulsions studied demonstrate that NMR time-domain decays can be employed to predict properties and to provide insight in the underlying microstructural features.