Carcinogenic and tobacco smoke-derived particulate matter biomarker uptake and associated healthcare patterns among children

Distinguishing Exposure to Secondhand and Thirdhand Tobacco Smoke among U.S. Children Using Machine Learning: NHANES 2013-2016

While the thirdhand smoke (THS) residue from tobacco smoke has been recognized as a distinct public health hazard, there are currently no gold standard biomarkers to differentiate THS from secondhand smoke (SHS) exposure. This study used machine learning algorithms to assess which combinations of biomarkers and reported tobacco smoke exposure measures best differentiate children into three groups: no/minimal tobacco smoke exposure (NEG); predominant THS exposure (TEG); and mixed SHS and THS exposure (MEG). Participants were 4485 nonsmoking 3-17-year-olds from the National Health and Nutrition Examination Survey 2013-2016. We fitted and tested random forest models, and the majority (76%) of children were classified in NEG, 16% were classified in TEG, and 8% were classified in MEG. The final classification model based on reported exposure, biomarker, and biomarker ratio variables had a prediction accuracy of 95%. This final model had prediction accuracies of 100% for NEG, 88% for TEG, followed by 71% for MEG. The most important predictors were the reported number of household smokers, serum cotinine, serum hydroxycotinine, and urinary 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL). In the absence of validated biomarkers specific to THS, comprehensive biomarker and questionnaire data for tobacco smoke exposure can distinguish children exposed to SHS and THS with high accuracy.

Hand nicotine as an independent marker of thirdhand smoke pollution in children's environments

BACKGROUND

Hand nicotine (HN) levels measure children's exposure to tobacco smoke pollutants from thirdhand and secondhand smoke. HN is associated with urinary and salivary cotinine, but the associations of HN with other tobacco smoke exposure (TSE) markers remain unknown.

OBJECTIVES

We compared levels of HN and four urinary TSE biomarkers: cotinine, trans-3'-hydroxycotinine (3HC), nicotelline N-oxides, and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL), and children's sociodemographic and TSE patterns. We also examined if HN is a plausible pathway for children's exposure to active smoking.

METHODS

Data were collected from 175 non-smoking patients (Mean (SD) age = 5.4 (3.4) years) who lived with ≥1 cigarette smoker(s). HN and TSE biomarker levels were determined using LC-MS/MS. Multivariate and multivariable regression analyses were conducted to examine associations between TSE markers and parent-reported measures, controlling for sociodemographics.

RESULTS

Of the five markers of TSE, cotinine (R² = 0.221; p = 0.003) and HN (R² = 0.247; p = 0.001) showed the strongest overall associations. Of the five markers, only cotinine showed significantly higher levels among Black children (β^=0.307,p<0.05) independent of age, reported exposure, and home smoking bans. Cotinine (β^=0.010,p<0.05), NNAL (β^=0.012,p<0.05), and HN (β^=0.011,p<0.05) showed significant positive associations with reported exposure independent of race, age, and home smoking bans. NNAL (β^=-0.285,p<0.05) and HN (β^=-0.336,p<0.05), but not cotinine, 3HC, and N-oxides, showed significantly lower levels among children who lived in homes with smoking bans. Child age, hand surface area, home smoking ban, and reported exposure independently accounted for 21 % of the variance in HN levels (p = 0.002). HN accounted for 30 % of the variance in cotinine independent of child race and child age.

DISCUSSION

HN levels were associated with modifiable tobacco-related behaviors and shows promise as a marker of sources of THS pollution in a child's environment not captured by measurement of urinary cotinine alone. HN levels provide additional information about TSE, complementing other biomarkers when assessing children's overall TSE.