Metabolomic Analysis Identified Reduced Levels of Xenobiotics, Oxidative Stress, and Improved Vitamin Metabolism in Smokers Switched to Vuse Electronic Nicotine Delivery System

Development of a Flavor Ingredient Wheel Linking E-Liquid Additives to the Labeled Flavor of Vaping Products

E-liquids contain combinations of chemicals, with many enhancing the sensory attractiveness of the product. Studies are needed to understand and characterize e-liquid ingredients, particularly flavorings, to inform future research and regulations of these products. We identified common flavor ingredients in a convenience sample of commercial e-liquids using gas chromatography-mass spectrometry. E-liquid flavors were categorized by flavor descriptors provided on the product packaging. A Flavor Ingredient Wheel was developed to link e-liquid flavor ingredients with flavor categories. An analysis of 109 samples identified 48 flavor ingredients. Consistency between the labeled flavor and ingredients used to produce such flavor was found. Our novel Flavor Ingredient Wheel organizes e-liquids by flavor and ingredients, enabling efficient analysis of the link between ingredients and their flavor profiles and allowing for quick assessment of an e-liquid ingredient's flavor profile. Investigating ingredient profiles and identifying and classifying commonly used chemicals in e-liquids may assist with future studies and improve the ability to regulate these products.

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.

Metabolomics Provides Novel Insights into the Potential Toxicity Associated with Heated Tobacco Products, Electronic Cigarettes, and Tobacco Cigarettes on Human Bronchial Epithelial BEAS-2B Cells

Smoking is an established risk factor for various pathologies including lung cancer. Electronic cigarettes (e-cigs) and heated tobacco products (HTPs) have appeared on the market in recent years, but their safety or, conversely, their toxicity has not yet been demonstrated. This study aimed to compare the metabolome of human lung epithelial cells exposed to emissions of e-cigs, HTPs, or 3R4F cigarettes in order to highlight potential early markers of toxicity. BEAS-2B cells were cultured at the air-liquid interface and exposed to short-term emissions from e-cigs set up at low or medium power, HTPs, or 3R4F cigarettes. Untargeted metabolomic analyses were performed using liquid chromatography coupled with mass spectrometry. Compared to unexposed cells, both 3R4F cigarette and HTP emissions affected the profiles of exogenous compounds, one of which is carcinogenic, as well as those of endogenous metabolites from various pathways including oxidative stress, energy metabolism, and lipid metabolism. However, these effects were observed at lower doses for cigarettes (2 and 4 puffs) than for HTPs (60 and 120 puffs). No difference was observed after e-cig exposure, regardless of the power conditions. These results suggest a lower acute toxicity of e-cig emissions compared to cigarettes and HTPs in BEAS-2B cells. The pathways deregulated by HTP emissions are also described to be altered in respiratory diseases, emphasizing that the toxicity of HTPs should not be underestimated.

Role of untargeted omics biomarkers of exposure and effect for tobacco research

Tobacco research remains a clear priority to improve individual and population health, and has recently become more complex with emerging combustible and noncombustible tobacco products. The use of omics methods in prevention and cessation studies are intended to identify new biomarkers for risk, compared risks related to other products and never use, and compliance for cessation and reinitation. to assess the relative effects of tobacco products to each other. They are important for the prediction of reinitiation of tobacco use and relapse prevention. In the research setting, both technical and clinical validation is required, which presents a number of complexities in the omics methodologies from biospecimen collection and sample preparation to data collection and analysis. When the results identify differences in omics features, networks or pathways, it is unclear if the results are toxic effects, a healthy response to a toxic exposure or neither. The use of surrogate biospecimens (e.g., urine, blood, sputum or nasal) may or may not reflect target organs such as the lung or bladder. This review describes the approaches for the use of omics in tobacco research and provides examples of prior studies, along with the strengths and limitations of the various methods. To date, there is little consistency in results, likely due to small number of studies, limitations in study size, the variability in the analytic platforms and bioinformatic pipelines, differences in biospecimen collection and/or human subject study design. Given the demonstrated value for the use of omics in clinical medicine, it is anticipated that the use in tobacco research will be similarly productive.

Changes in Biomarkers of Exposure and Potential Harm in Smokers Switched to Vuse Vibe or Vuse Ciro Electronic Nicotine Delivery Systems

Electronic nicotine delivery systems (ENDS) have the potential to provide nicotine to tobacco consumers while reducing exposure to combustion-related toxicants. Here, we report changes in biomarkers of exposure (BoE) and biomarkers of potential harm (BoPH) in smokers who completely switched to Vuse Vibe and Vuse Ciro ENDS products, or to smoking abstinence in a randomized, controlled clinical study. Thirteen BoE (12 urinary and one blood) that indicate exposure to harmful and potentially harmful toxicants (HPHCs) were evaluated at baseline on day 5. Urinary BoPH linked to oxidative stress, platelet activation, and inflammation were also assessed at baseline, and on day 5 and day 7. Nicotine exposure was lower in Vuse Vibe and Vuse Ciro groups compared to baseline values. Urinary non-nicotine BoE decreased significantly (52.3-96.7%) in the Vuse ENDS groups, and the reductions were similar in magnitude to those observed in the abstinence group. Blood carboxyhemoglobin decreased 52.8-55.0% in all study groups. Decreases (10-50%) in BoPH were observed in all study groups. Thus, smokers who switch exclusively to Vuse Vibe or Vuse Ciro products or completely abstain from smoking are exposed to substantially lower levels of HPHCs, and experience improvements in BoPH of oxidative stress and inflammation pathways.

Untargeted Metabolomic Analysis of Sjögren-Larsson Syndrome Reveals a Distinctive Pattern of Multiple Disrupted Biochemical Pathways

Sjögren-Larsson syndrome (SLS) is a rare inherited neurocutaneous disease characterized by ichthyosis, spastic diplegia or tetraplegia, intellectual disability and a distinctive retinopathy. SLS is caused by bi-allelic mutations in ALDH3A2, which codes for fatty aldehyde dehydrogenase (FALDH) and results in abnormal lipid metabolism. The biochemical abnormalities in SLS are not completely known, and the pathogenic mechanisms leading to symptoms are still unclear. To search for pathways that are perturbed in SLS, we performed untargeted metabolomic screening in 20 SLS subjects along with age- and sex-matched controls. Of 823 identified metabolites in plasma, 121 (14.7%) quantitatively differed in the overall SLS cohort from controls; 77 metabolites were decreased and 44 increased. Pathway analysis pointed to disrupted metabolism of sphingolipids, sterols, bile acids, glycogen, purines and certain amino acids such as tryptophan, aspartate and phenylalanine. Random forest analysis identified a unique metabolomic profile that had a predictive accuracy of 100% for discriminating SLS from controls. These results provide new insight into the abnormal biochemical pathways that likely contribute to disease in SLS and may constitute a biomarker panel for diagnosis and future therapeutic studies.

Diagnosis and prognosis of COVID-19 employing analysis of patients' plasma and serum via LC-MS and machine learning

OBJECTIVE

To implement and evaluate machine learning (ML) algorithms for the prediction of COVID-19 diagnosis, severity, and fatality and to assess biomarkers potentially associated with these outcomes.

MATERIAL AND METHODS

Serum (n = 96) and plasma (n = 96) samples from patients with COVID-19 (acute, severe and fatal illness) from two independent hospitals in China were analyzed by LC-MS. Samples from healthy volunteers and from patients with pneumonia caused by other viruses (i.e. negative RT-PCR for COVID-19) were used as controls. Seven different ML-based models were built: PLS-DA, ANNDA, XGBoostDA, SIMCA, SVM, LREG and KNN.

RESULTS

The PLS-DA model presented the best performance for both datasets, with accuracy rates to predict the diagnosis, severity and fatality of COVID-19 of 93%, 94% and 97%, respectively. Low levels of the metabolites ribothymidine, 4-hydroxyphenylacetoylcarnitine and uridine were associated with COVID-19 positivity, whereas high levels of N-acetyl-glucosamine-1-phosphate, cysteinylglycine, methyl isobutyrate, l-ornithine and 5,6-dihydro-5-methyluracil were significantly related to greater severity and fatality from COVID-19.

CONCLUSION

The PLS-DA model can help to predict SARS-CoV-2 diagnosis, severity and fatality in daily practice. Some biomarkers typically increased in COVID-19 patients' serum or plasma (i.e. ribothymidine, N-acetyl-glucosamine-1-phosphate, l-ornithine, 5,6-dihydro-5-methyluracil) should be further evaluated as prognostic indicators of the disease.

Exposure to Toxicants Associated With Use and Transitions Between Cigarettes, e-Cigarettes, and No Tobacco

IMPORTANCE

Transitions between e-cigarettes and cigarettes are common among tobacco users, but empirical evidence on the health outcomes of switching tobacco products is scarce.

OBJECTIVES

To examine changes in urinary biomarkers between baseline and 1-year follow-up among adult tobacco users switching between e-cigarettes and cigarettes.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study used data from wave 1 (baseline, September 2013 to December 2014) and wave 2 (1-year follow-up, October 2014 to October 2015) of the Population Assessment of Tobacco and Health Study. A subset of the probability sample of US adults who voluntarily provided biospecimens at 2 waves was analyzed. Participants were divided into 3 mutually exclusive groups at baseline: exclusive cigarette smokers, exclusive e-cigarette users, and dual users. Data analysis was performed in 2021.

EXPOSURES

Harmful and potentially harmful constituents included nicotine metabolites, tobacco-specific nitrosamines (TSNAs; including 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol [NNAL]), metals, polycyclic aromatic hydrocarbons (PAHs), and volatile organic compounds (VOCs).

MAIN OUTCOMES AND MEASURES

Within-participant changes in 55 urinary biomarkers of exposure (BOEs) to harmful and potentially harmful constituents were examined using multivariable regression models.

RESULTS

Among 3211 participants (55.6% women, 68.3% White, 13.2% Black, and 11.8% Hispanic) at baseline, 21.9% of exclusive cigarette users, 42.8% of exclusive e-cigarette users, and 62.1% of dual users changed product use at follow-up (all percentages are weighted). There was a significant reduction in urine concentrations of TSNAs, PAHs, and VOCs when users transitioned from exclusive cigarette to exclusive e-cigarette use, with a 92% decrease in NNAL, from a mean of 168.4 pg/mg creatinine (95% CI, 102.3-277.1 pg/mg creatinine) to 12.9 pg/mg creatinine (95% CI, 6.4-25.7 pg/mg creatinine; P < .001). A similar panel of BOEs decreased when dual users transitioned to exclusive e-cigarette use; NNAL levels decreased by 96%, from a mean of 143.4 pg/mg creatinine (95% CI, 86.7-237.0 pg/mg creatinine) to 6.3 pg/mg creatinine (95% CI, 3.5-11.4 pg/mg creatinine; P < .001). Nicotine metabolites, TSNAs, PAHs, and VOCs significantly increased when baseline exclusive e-cigarette users transitioned to exclusive cigarette use or dual use. Switching from exclusive cigarette use to dual use was not associated with significant decreases in BOEs.

CONCLUSIONS AND RELEVANCE

This national cohort study provides evidence on the potential harm reduction associated with transitioning from exclusive cigarette use or dual use to exclusive e-cigarette use. e-Cigarettes tend to supplement cigarettes through dual use instead of cessation at the population level. Continuous monitoring of BOE at the population level and assessment of BOE change by product transition are warranted, as well as defined adverse health outcomes.