Changes in urinary cotinine concentrations in non-smoking adults from the Korean National Environmental Health Survey (KoNEHS) II (2012-2014) after implementation of partial smoke-free regulations

Associations of metal mixtures with metabolic-associated fatty liver disease and non-alcoholic fatty liver disease: NHANES 2003-2018

Objective

The hepatotoxicity of exposure to a single heavy metal has been examined in previous studies. However, there is limited evidence on the association between heavy metals mixture and non-alcoholic fatty liver disease (NAFLD) and metabolic-associated fatty liver disease (MAFLD). This study aims to investigate the associations of 13 urinary metals, individually and jointly, with NAFLD, MAFLD, and MAFLD components.

Methods

This study included 5,548 adults from the National Health and Nutrition Examination Survey (NHANES) 2003-2018. Binary logistic regression was used to explore the associations between individual metal exposures and MAFLD, NAFLD, and MAFLD components. Bayesian kernel machine regression (BKMR) and Quantile-based g-computation (QGC) were used to investigate the association of metal mixture exposure with these outcomes.

Results

In single metal analysis, increased levels of arsenic [OR 1.09 (95%CI 1.03-1.16)], dimethylarsinic acid [1.17 (95%CI 1.07-1.27)], barium [1.22 (95%CI 1.14-1.30)], cobalt [1.22 (95%CI 1.11-1.34)], cesium [1.35 (95%CI 1.18-1.54)], molybdenum [1.45 (95%CI 1.30-1.62)], antimony [1.18 (95%CI 1.08-1.29)], thallium [1.49 (95%CI 1.33-1.67)], and tungsten [1.23 (95%CI 1.15-1.32)] were significantly associated with MAFLD risk after adjusting for potential covariates. The results for NAFLD were similar to those for MAFLD, except for arsenic, which was insignificantly associated with NAFLD. In mixture analysis, the overall metal mixture was positively associated with MAFLD, NAFLD, and MAFLD components, including obesity/overweight, diabetes, and metabolic dysfunction. In both BKMR and QGC models, thallium, molybdenum, tungsten, and barium mainly contributed to the positive association with MAFLD.

Conclusion

Our study indicated that exposure to heavy metals, individually or cumulatively, was positively associated with NAFLD, MAFLD, and MAFLD components, including obesity/overweight, diabetes, and metabolic dysfunction. Additional research is needed to validate these findings in longitudinal settings.

İdrar kotinin ve kotinin/kreatinin oranının çevresel tütün dumanı maruziyetinin bir biyolojik belirteci olarak kullanımı

Purpose: Exposure to Environmental Tobacco Smoke (ETS) remains a worldwide public health problem. The purpose of this study was to investigate the relationship between parents' smoking habits at home and children's exposure to environmental tobacco smoke by measuring urinary cotinine levels and urine cotinine/creatinine ratios in children. Materials and Methods: This case-control typed analytical study was conducted with 357 children in the 0-18 age group. The case group consisted of 180 children exposed to environmental cigarette smoke. As the control group, it consisted of 177 healthy children and non-smoking in their family. The levels of cotinine and creatinine in spot urinary were analyzed in both groups. Results: The urinary cotinine level of the children was found to be statistically higher in those whose parents were smokers, female gender, fathers with a low educational level, and those with 3 or fewer rooms in the house. The urinary cotinine/creatinine ratio of the children was found to be statistically higher in those whose parents were smokers (15.91 pg/mg (1.54-147.54) vs 7.90 pg/mg (1.29-68.52)), female gender (13.19 pg/mg (1.79-115.07) vs 10.45 pg/mg (1.29-147.54)). Urinary cotinine levels in the ETS exposed group were affected 1042 times more than in the ETS unexposed group [OR:1042,462, 95% CI (139.821.839-7772.246)]. Conclusion: In the present study, urinary cotinine levels were found to be higher in children exposed to tobacco smoke than in children not exposed to tobacco smoke. In the light of these results, urinary cotinine can be used as a biomarker to evaluate exposure to ETS in children. Educating parents is essential to raising their awareness of exposure to ETS and teaching the right behaviors to protect children's health, especially in the home environment.

Cadmium, lead, and mercury mixtures interact with non-alcoholic fatty liver diseases

There is a scarcity of studies on the interactions between heavy metals and non-alcoholic fatty liver disease (NAFLD). Using a variety of statistical approaches, we investigated the impact of three common heavy metals on liver enzymes and NAFLD markers in a Korean adult population. We observed that cadmium, mercury, and lead all demonstrated positive correlations with liver enzymes and NAFLD indices. Our findings were mostly robust in secondary analysis, which included three novel mixture modeling approaches (WQS, qgcomp, and BKMR) as well as in silico investigation of molecular mechanisms (genes, miRNAs, biological processes, pathways, and illnesses). The 16 genes interacted with a mixture of heavy metals, which was linked to the development of NAFLD. Co-expression was discovered in nearly half of the interactions between the 18 NAFLD-linked genes. Key molecular pathways implicated in the pathogenesis of NAFLD generated by the heavy metal combination include activated oxidative stress, altered lipid metabolism, and increased cytokines and inflammatory response. Heavy metal exposure levels were related to liver enzymes and NAFLD indices, and cutoff criteria were revealed. More studies are needed to validate our findings and gain knowledge about the effects of chronic combined heavy metal exposure on adult and child liver function and the likelihood of developing NAFLD. To reduce the occurrence of NAFLD, early preventative and regulatory actions (half-yearly screening of workers at high-risk facilities; water filtration; avoiding excessive amounts of seafood, etc.) should be taken.

Effect of Second-Hand Smoke Exposure on Establishing Urinary Cotinine-Based Optimal Cut-Off Values for Smoking Status Classification in Korean Adults

Regulations for banning smoking in indoor public places and workplaces have increased worldwide in recent years. A consecutive Korean National Health and Nutrition Examination Survey (KNHANES) between 2008 and 2018 showed a trend toward significant decreases in self-reported tobacco smoke exposure and measured urinary cotinine concentrations. We established and compared each optimal cut-off value for assessing the effect of second-hand smoke (SHS) exposure on establishing urinary cotinine-based cut-off values for smoking status classification in a population setting controlled for racial and cultural diversity, using four KNHANES datasets consisting of the 2008, 2011, 2014, and 2018 surveys. A total of 18,229 Korean participants aged >19 years with measured urinary cotinine concentrations were enrolled. Self-reports of current smoking status showed that the prevalence of current smokers decreased from 22.9% to 18.2% between 2008 and 2018. During this period, the median value of urinary cotinine in nonsmokers decreased from 5.86 µg/L to 0.48 µg/L, whereas the median value showed no remarkable decrease in current smokers. The AUC-based optimal cut-off values of urinary cotinine concentration for distinguishing current smokers from nonsmokers decreased from 86.5 µg/L to 11.5 µg/L. Our study showed that decreased SHS exposure would result in decreased optimal cut-off values for distinguishing current smokers from nonsmokers. In addition, the study suggests that the range of urinary cotinine concentration to define SHS exposure for the trend monitoring of populationof SHS exposure is appropriate between 0.30 µg/L and 100 µg/L. In addition, our study showed the importance of determination of cotinine concentration, which would have allowed us to avoid mistakes in qualification to the study group in an increased use of e-cigarette setting.

The effects of chemical mixtures on lipid profiles in the Korean adult population: threshold and molecular mechanisms for dyslipidemia involved

A scarcity of research assesses the effects of exposure to a combination of chemicals on lipid profiles as well as molecular mechanisms related to dyslipidemia. A cross-sectional study of 3692 adults aims to identify the association between chemical mixtures, including blood and urine 26 chemicals, and lipid profiles among Korean adults (aged ≥ 18) using linear regression models, weighted quantile sum (WQS) regression, quantile g-computation (qgcomp), and Bayesian kernel machine regression (BKMR). In silico toxicogenomic data-mining, we assessed molecular mechanisms linked with dyslipidemia, including genes, miRNAs, pathways, biological processes, and diseases. In the linear regression models, heavy metals, volatile organic compound metabolites, and phthalate metabolites were found to be related to HDL-C, triglycerides, LDL-C, total lipids, and total cholesterol, and significant trends were observed for these chemical quartiles (p < 0.01). The WQS index was significantly linked with HDL-C, triglycerides, LDL-C, total cholesterol, and total lipids. The qgcomp index also found a significant association between chemicals and HDL-C, triglycerides, and total lipids. In BKMR analysis, the overall effect of the chemical mixture was significantly associated with HDL-C, triglycerides, total cholesterol, and total lipids. We found that mixed chemicals interacted with the PPARA gene and were linked with dyslipidemia. Several pathways ("SREBF and miR33 in cholesterol," "estrogen receptor pathway and lipid homeostasis," and "regulation of PGC-1α"), "negative regulation of hepatocyte apoptotic process," "negative regulation of sequestering of triglycerides," "regulation of hepatocyte apoptotic process," and "negative regulation of cholesterol storage," and "abdominal obesity metabolic syndrome" were identified as key molecular mechanisms that may be affected by mixed chemicals and implicated in the development of dyslipidemia. The highest interaction and expression of miRNAs involved in the process of dyslipidemia were also described. Especially, the cutoff levels for chemical exposure levels related to lipid profiles were also provided.

Mixtures modeling identifies heavy metals and pyrethroid insecticide metabolites associated with obesity

We aim to examine the association between chemical mixtures and obesity. Blood and urinary levels of tween-six chemicals were measured in adults who participated in the KoNEHS. We identified the associations of chemicals with obesity using linear regression models. Weighted quantile sum (WQS) regression, quantile g-computation (qgcomp), and Bayesian kernel machine regression (BKMR) were conducted as secondary analyses. Of the 3,692 participants included in the analysis, 18.0% had obesity. In the logistic regression model, mercury (Hg), lead (Pb), and 3PBA levels were associated with obesity, and significant trends were observed for these chemical tertiles (p < 0.001). Hg, Pb, and 3PBA levels were also associated with BMI. The WQS index was significantly associated with both obesity (OR = 2.15, 95% CI: 2.11-2.20) and BMI (β = 0.39, 95% CI: 0.37-0.51). The qgcomp index also found a significant association between chemicals and both obesity (OR = 1.70, 95% CI: 1.56-1.85) and BMI (β = 0.40, 95% CI: 0.39-0.41). Hg, Pb, and 3PBA were the most heavily weighed chemicals in these models. In BKMR analysis, the overall effect of the mixture was significantly associated with obesity. Hg, Pb, and 3PBA showed positive trends and were observed as the most important factors associated with obesity. Given increasing exposure to chemicals, there is a need to investigate the associations between chemical exposures, either separately or together, and incident obesity risk factors in well-characterized cohorts of different populations, and to identify potential approaches to chemical exposure prevention.

Association between exposure to chemical mixtures in relation to serum total IgE among adults 19-86 years old

There is a scarcity of studies on the effects of mixed chemicals on total IgE. We aim to assess whether there is a link between chemical mixtures (blood and urine of 26 chemicals including lead, mercury, cadmium, t,t-muconic acid, benzylmercapturic acid , 1-hydroxypyrene, 2-naphthol, 2-hydroxyfluorene, 1-hydroxyphenanthrene, mono-(2-ethyl-5-hydroxyhexyl) phthalate, mono-(2-ethyl-5-oxohexyl) phthalate, mono-n-butyl phthalate, mono-benzyl phthalate, mono-(2-ethyl-5-carboxypentyl) phthalate, mono-carboxyoctyl phthalate, mono-carboxy-isononly phthalate, mono (3-carboxypropyl) phthalate, bisphenol A, bisphenol F, bisphenol S, triclosan, methylparaben, ethylparaben, propylparaben, 3-phenoxybenzoic acid, and cotinine), and total IgE in 3,642 Korean adults aged ≥ 19. The effects of mixed chemical exposure on total IgE were identified using linear regression models, weighted quantile sum (WQS) regression, quantile g-computation (qgcomp), and Bayesian kernel machine regression (BKMR). The most relevant factors linked with IgE, according to the linear regression models, were blood or urine mercury and urine bisphenol A levels, with significant trends detected for these chemical tertiles (p < 0.01). The WQS index was significantly linked with ln2-transformed levels of serum total IgE (β = 0.30, 95 %CI 0.25-0.32). The qgcomp index also found a significant link between chemicals and ln2-transformed levels of serum total IgE (β = 0.52, 95 %CI 0.21-0.82), and elevated serum total IgE levels (OR = 2.55, 95 %CI 1.14-5.71). In BKMR analysis, the overall effect of the mixture was significantly associated with ln2-transformed levels of serum total IgE. The cutoff levels for exposure levels related to serum total IgE levels/elevated serum total IgE levels were reported. We discovered that whole-body exposure to 26 chemicals was associated with serum total IgE levels after assessing the findings of these four models. More research is needed in the future to gain a better understanding of the impact of mixed chemical exposure on allergic disorders and how to minimize chemical exposure, especially for people under the age of 18.

Non-linear dose-response relation between urinary levels of nicotine and its metabolites and cognitive impairment among an elderly population in China

BACKGROUND

Active smoking and exposure to environmental tobacco smoke may be related to cognitive function decline. We assessed the associations of urinary levels of nicotine and its metabolites with cognitive function.

METHODS

A total of 553 elder adults at high risk of cognitive impairment and 2212 gender- and age-matched individuals at low risk of cognitive impairment were selected at a ratio of 1: 4 from the remained individuals (n = 6771) who completed the baseline survey of the Shenzhen Ageing-Related Disorder Cohort, after excluding those with either Alzheimer's disease, Parkinson's syndrome or stroke as well as those with missing data on variables (including active and passive smoking status, Mini-Cog score). Urinary levels of nicotine and its metabolites and cognitive function for all individuals were measured by high-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS) and assessed using the Mini-Cog test, respectively. Associations of urinary levels of nicotine and its metabolites with cognitive function were analyzed by conditional logistic regression models.

RESULTS

Individuals in the highest tertile of urinary OHCotGluc (OR: 1.52, 95%CI: 1.19-1.93) or NNO (OR: 1.50, 95%CI: 1.16-1.93) levels as well as in the second tertile of urinary ∑Nic level (OR: 1.43, 95%CI: 1.13-1.82) were at higher risk of cognitive impairment compared with those in the corresponding lowest tertile. Restricted cubic spline models revealed the non-linear dose-response relationships between urinary levels of OHCotGluc, NNO or ∑Nic and the risk of cognitive impairment.

CONCLUSIONS

Urinary levels of OHCotGluc, NNO or ∑Nic exhibited a non-linear dose-response relationship with cognitive function in the urban elderly.

Characterization of urinary cotinine concentrations among non-smoking adults in smoking and smoke-free homes in the Korean national environmental health survey (KoNEHS) cycle 3 (2015-2017)

BACKGROUND

Although many indoor public places have implemented smoke-free regulations, private homes have remained sources of tobacco smoke pollutants. This study examined differences in urinary cotinine concentrations in the Korean non-smoking adult population between living in smoking and smoke-free homes, and the relationship of urinary cotinine concentrations with socio-demographic factors in smoke-free homes.

METHODS

Samples from 2575 non-smoking adults (≥19 years old) in the Korean National Environmental Health Survey cycle 3 (2015-2017), a representative Korean study, were used. Smoking and smoke-free homes were defined based on whether there were smokers at homes. Weighted linear regression models were used to determine urinary cotinine concentrations and identify factors associated with urinary cotinine.

RESULTS

The geometric mean of urinary cotinine concentrations for non-smoking adults living in smoking homes was 2.1 μg/L (95% confidence interval [CI] = 1.8-2.4), which was significantly higher than the mean of 1.3 μg/L (95% CI = 1.2-1.4) for those living in smoke-free homes. Urinary cotinine concentrations were different significantly by home smoking status in most socio-demographic subgroups. Data from smoke-free home showed urinary cotinine concentration in adults was significantly higher in those who lived in homes with ventilation duration < 30 min/day, those who spent more time indoors at home, those who spent less time outdoors, and those who worked in non-manual or manual occupations.

CONCLUSIONS

The urinary cotinine concentration in Korean non-smoking adults living in smoking homes was higher than that in adults living in smoke-free homes. Even in smoke-free homes, home-related factors, such as ventilation duration and time spent indoors, were associated with urinary cotinine concentration. Further study is warranted to examine potential sources of tobacco smoke pollution in smoke-free homes.