Biomarkers of tobacco smoke exposure in racial/ethnic groups at high risk for lung cancer

Plasma nicotine and its metabolite as biomarkers of tobacco exposure and their relevance to pulmonary nodule

Aim: Explore the optimal cut-off values for plasma nicotine and its metabolites in assessing smoking status and quantify the association between individual tobacco exposure and pulmonary nodules (PNs).Materials & methods: A total of 2245 plasma samples were included for the determination of nicotine (Nic), cotinine (Cot) and trans-3'-hydroxycotinine (OHCot) concentrations. The receiver operating characteristic curve was used to determine the optimal biomarkers reflecting smoking status. Binary logistic regression, restricted cubic spline and generalized linear model were used to analyze the association of nicotine and its metabolites with PNs. Quantile g-computation was used to investigate the mixed effects between them.Results: Cot was found to be the best biomarker of self-reported active-passive smoking, with optimal thresholds of 9.06 and 1.26 ng/ml, respectively. Except for OHCot, increased concentrations of Cot, Nic, total nicotine equivalent (TNE2) and TNE3 were significantly positively associated with the risk of PNs, whereas nicotine metabolite ratio presented a negative association. The mixed effects of OHCot, Cot and Nic were associated with PNs, with an odds ratio of 1.17 and a 95% CI of 1.05-1.30.Conclusion: Nicotine and its metabolites as potential biomarkers of tobacco exposure were significantly associated with PNs.

Racial and ethnic disparities in biomarkers of exposure and potential harm among U.S. adult exclusive e-cigarette users: 2013-2019

OBJECTIVE

Provide evidence on racial and ethnic differences in biomarkers of exposure from rising e-cigarette use among U.S. adults.

METHODS

Data were drawn from Waves 1-5 of the Population Assessment of Tobacco and Health study (September 2013-November 2019). Differences in biomarkers of exposure and potential harm (BOE/BoPH) across non-Hispanic (NH)-Whites, NH Blacks, Hispanic/Latinos, and NH others were examined using generalized estimation equations.

RESULTS

Among exclusive e-cigarette users, mean concentrations of BOEs/BoPHs were not significantly different across NH Blacks (n=97), NH others (n=122), and NH Whites (n=1062), after adjustment by wave, age, sex, education, exposure to the secondhand smoke, and the number of recent puffs. Compared to NH Whites, Hispanics (n=151) had lower concentrations of nicotine equivalents (0.5[0.2-1.7] vs. 15.5 [12.5-19.1] nmol/mg creatinine, p<.0001), cotinine (33.4[9.7-114.7] vs. 1008.3 [808.3-1257.9] ng/mg creatinine, p<.0001), and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) (2.6[1.5-4.4] vs. 5.7 [4.9-6.6] pg/mg creatinine, p=.004), but similar concentrations of BOEs for heavy metals, polycyclic aromatic hydrocarbons, volatile organic compounds, and oxidative stress. Differences between Hispanics and NH Whites are expected, given different e-cigarette use profiles. Specifically, Hispanics were less likely to be daily vapers (49.4[35.1-63.8]% vs 81.3[77.7-84.5]%, p<.0001) and nicotine e-cigarette users (72.7 [64.0-79.9]% vs. 89.2 [86.4-91.5]%, p=.0002] and reported a lower number of recent puffs (mean[standard error]=16.7[3.6] vs. 28.6[2.0], p=.02] than their NH-White counterparts. Hispanic vapers were also less likely than NH Whites to have previously smoked cigarettes (49.7 [37.2-62.3]% vs. 88.5 [84.7-91.5]%, p<.0001]).

CONCLUSIONS

Hispanic vapers exhibited lower exposure to nicotine metabolites and carcinogens than their non-Hispanic White counterparts. The harm reduction potential from e-cigarette use are likely to be realized across diverse racial and ethnic groups.

Increased Surgical Complications in Smokers Undergoing Radical Cystectomy

Background:

Not only is smoking a risk factor for the development of bladder cancer, it has also been implicated in increasing surgical morbidity and mortality. In general, the demographic and clinical characteristics of smokers are different to non-smokers which can bias the results of the impact of smoking.

Objective:

To evaluate the impact of smoking on radical cystectomy outcomes.

Methods:

Radical cystectomy cases were identified in the National Surgical Quality Improvement Program database from 2007–2015. Smokers were matched with non-smokers using propensity scores in a 1:1 ratio. Multivariate logistic regression was performed to evaluate the overall incidence of Clavien III-V complications. Secondary analysis was performed for the incidence of each complication recorded in NSQIP.

Results:

A total of 850 smokers undergoing radical cystectomy were matched to 850 non-smokers. The matching process improved the balance of covariates between smokers and non-smokers. The overall incidence of Clavien III-V complications was higher in smokers (13.1% vs 7.4%, p < 0.001). This corresponded to an adjusted odds ratio of 1.9 [95% CI 1.4–2.6, p = 0.028]. Other comorbid conditions worsened post-operative complications amongst smokers. When evaluating each complication recorded in the database, smokers had a higher incidence of wound dehiscence, pneumonia and myocardial infarction.

Conclusion:

Current smokers have a greater risk of morbidity following radical cystectomy. This should be considered when evaluating safety of surgery and patients should be counselled accordingly. Furthermore, even a short period of pre-operative smoking cessation can improve surgical outcomes.

Biosignature Discovery for Substance Use Disorders Using Statistical Learning

There are limited biomarkers for substance use disorders (SUDs). Traditional statistical approaches are identifying simple biomarkers in large samples, but clinical use cases are still being established. High-throughput clinical, imaging, and 'omic' technologies are generating data from SUD studies and may lead to more sophisticated and clinically useful models. However, analytic strategies suited for high-dimensional data are not regularly used. We review strategies for identifying biomarkers and biosignatures from high-dimensional data types. Focusing on penalized regression and Bayesian approaches, we address how to leverage evidence from existing studies and knowledge bases, using nicotine metabolism as an example. We posit that big data and machine learning approaches will considerably advance SUD biomarker discovery. However, translation to clinical practice, will require integrated scientific efforts.

Research Article. Relationship Between High Levels of Salivary Cotinine Test and Demographic Characteristics of Pregnant Smokers from Mures County

Objectives: To evaluate the relationship between the frequency of self-declared status regarding smoking in a group of pregnant women from Mures county, Romania and the high levels of Salivary Cotinine (SC) like biomarkers. Material and methods: It was conducted a retrospective study among 230 pregnant women presented for prenatal care at 50 General Practitioners cabinets in Mures county, Romania, in 2015. Data were collected with a validated questionnaire which included age, level of education, socioeconomic status and ethnicity, also the self-reported smoking status. The Salivary Cotinine level was evaluated using NicAlert Saliva test kits. Results: Using salivary test we identified a high prevalence of involuntary exposure to cigarette smoke among both non-smokers and those who quit smoking before pregnancy. Also we registered pregnant women that although declared smoking cessation before pregnancy their salivary Cotinine levels were high, almost like to an active smoker, probably because of second-hand exposure or because they didn’t say the truth about their habit. Conclusions: We underline the importance of implementing more efficient community interventions among this vulnerable group in order to reduce the frequency of smoking and sustain quitting.

Biomarker development in the precision medicine era: lung cancer as a case study

Precision medicine relies on validated biomarkers with which to better classify patients by their probable disease risk, prognosis and/or response to treatment. Although affordable 'omics'-based technology has enabled faster identification of putative biomarkers, the validation of biomarkers is still stymied by low statistical power and poor reproducibility of results. This Review summarizes the successes and challenges of using different types of molecule as biomarkers, using lung cancer as a key illustrative example. Efforts at the national level of several countries to tie molecular measurement of samples to patient data via electronic medical records are the future of precision medicine research.