Interaction of Smoking and Lead Exposure among Carriers of Genetic Variants Associated with a Higher Level of Oxidative Stress Indicators

Ho, Kuen‐Yao; Chen, Tzu-Hua; Yang, Chen-Cheng; Chuang, Yao-Chung; Chuang, Hung-Yi

doi:10.3390/ijerph18168325

Cited by 3 publications

(3 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Non-smoking might obviously reduce the association between Pb and OA as well as the correlation between Cd and OA. The results of Ho study showed that smoking and lead exposure had a synergistic effect, which increased the oxidative stress in the human body ( 27 ). People with some gene variants might be sensitive to Pb and smoking through oxidative stress, suggesting that smoking cessation was an important issue in the Pb-exposed working environment.…”

Section: Discussionmentioning

confidence: 99%

Identification for heavy metals exposure on osteoarthritis among aging people and Machine learning for prediction: A study based on NHANES 2011-2020

Xia

Luo

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

ObjectiveHeavy metals are present in many environmental pollutants, and have cumulative effects on the human body through water or food, which can lead to several diseases, including osteoarthritis (OA). In this research, we aimed to explore the association between heavy metals and OA.MethodsWe extracted 18 variables including age, gender, race, education level, marital status, smoking status, body mass index (BMI), physical activity, diabetes mellitus, hypertension, poverty level index (PLI), Lead (Pb), cadmium (Cd), mercury (Hg), selenium (Se), manganese (Mn), and OA status from National Health and Nutrition Examination Survey (NHANES) 2011-2020 datasets.ResultsIn the baseline data, the t test and Chi-square test were conducted. For heavy metals, quartile description and limit of detection (LOD) were adopted. To analyze the association between heavy metals and OA among elderly subjects, multivariable logistic regression was conducted and subgroup logistic by gender was also carried out. Furthermore, to make predictions based on heavy metals for OA, we compared eight machine learning algorithms, and XGBoost (AUC of 0.8, accuracy value of 0.773, and kappa value of 0.358) was the best machine learning model for prediction. For interactive use, a shiny application was made (https://alanwu.shinyapps.io/NHANES-OA/).ConclusionThe overall and gender subgroup logistic regressions all showed that Pb and Cd promoted the prevalence of OA while Mn could be a protective factor of OA prevalence among the elderly population of the United States. Furthermore, XGBoost model was trained for OA prediction.

show abstract

Section: Discussionmentioning

confidence: 99%

Identification for heavy metals exposure on osteoarthritis among aging people and Machine learning for prediction: A study based on NHANES 2011-2020

Xia

Luo

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

show abstract

“…Such alterations in lipid profile may contribute to the development of dyslipidemia and elevate the risk of cardiovascular disease ( Girish & Harish, 2018 ). Previous researches inferred that smoking has the potential to influence abnormal blood LDL levels by affecting oxidative stress ( Ho et al, 2021 ). Further investigation is required to gain a comprehensive understanding of the underlying mechanisms.…”

Section: Discussionmentioning

confidence: 99%

Association Between Smoking and Lipid Profile in Men Aged 35 to 70 Years: Dose–Response Analysis

Moosazadeh,

Ebrahimnejad,

Kheradmand

et al. 2024

Am J Mens Health

View full text Add to dashboard Cite

Dyslipidemia is linked to various health complications, including cardiovascular disease and inflammation. This study aimed to assess the association between smoking and lipid profile in the Tabari cohort population. Data from the Tabari Cohort Study involving 4,149 men were analyzed. A standardized questionnaire collected smoking history, while blood samples measured lipid levels and anthropometric measurements were recorded. Statistical analysis utilized chi-square tests and logistic regression, adjusting for potential confounders. The prevalence of smoking was 893 (21.52%; urban: 20.6%, mountainous: 23.8%, significant level: .024). The adjusted odds ratio (OR) of low high-density lipoprotein (HDL) among smokers 1.48 (95% confidence interval [CI]: 1.25–1.77, p < .001) was the same as non-smokers. The adjusted OR of high low-density lipoprotein (LDL) in men with 1 to 10, 11 to 20, and more than 20 cigarettes per day was 0.95 (95% CI: 0.73–1.25), 1.30 (95% CI: 0.99–1.71), and 2.64 (95% CI: 1.32–5.27) and low HDL was equal to 1.34 (95% CI: 1.06–1.68), 1.61 (95% CI: 1.26–2.05), and 2.24 (95% CI: 1.13–4.42) compared with non-smokers, respectively. The study findings indicate that smoking is associated with lower HDL levels, even after adjusting for potential confounders. The odds of low HDL and high LDL increases with higher smoking intensity. The low HDL and high LDL levels in individuals smoking over 20 cigarettes/day, respectively, show a 2.24-fold and a 2.64-fold increased odds compared to non-smokers. These findings highlight the importance of smoking cessation in relation to lipid profiles and related health risks.

show abstract

“…The results showed that smokers’ blood levels of lead (Pb), alanine transaminase (ALT), thiobarbituric acid reactive substances, and oxidized LDL were considerably greater than those of nonsmokers. [ 17 ]…”

Section: Introductionmentioning

confidence: 99%

Genetic Polymorphisms of GSTM1 and GPX1 Genes and Smoking Susceptibility in the Saudi Population

Al-Zahrani,

Almutairi

2023

Journal of Pharmacy and Bioallied Sciences

View full text Add to dashboard Cite

Background/Objective/Methods: Glutathione-S-transferase Mu1 (GSTM1) and glutathione peroxidase 1 (GPX1) are known antioxidant enzymes that help protect cells from the oxidative damage that occurs from smoking. This study explored the correlation between GSTM1 and GPX1 levels between a group of smokers with the GSTM1 and GPX1 genes in the Saudi population and a control group and investigated the genetic risk factors in the group of smokers. Results: The control and smokers’ group (n = 50; aged 22.3 ± 3.1 years; BMI 24.6 ± 5.9 kg/m 2 ) were genotyped using quantitative polymerase chain reaction (qPCR). In comparison with the control group, the smokers’ group displayed a different genotype disruption of GSTM1 and GPX1. Carriers of the homozygous (TT) genotype of GSTM1 had more than a twofold (OR = 2.71, 95% CI = 0.10–70.79, P = 1.000) smoking risk than the carriers of the heterozygous (CT) genotype. Those with the GPX1 gene showed no risk in the control and smokers’ groups. Smokers with the TT/GG combination (homozygous for GPX1 and normal for GPX1) were identified as high risk (OR = 2.58, 95% CI = 0.096–69.341). Conclusion: The main outcomes showed no significant association between genetic polymorphism of the GSTM1 and GPX1 genes and cigarette smoking in the Saudi Arabian population. However, the results showed a slight decrease in the number of GSTM1 and GPX1 gene modifications among smokers.

show abstract

Interaction of Smoking and Lead Exposure among Carriers of Genetic Variants Associated with a Higher Level of Oxidative Stress Indicators

Cited by 3 publications

References 65 publications

Identification for heavy metals exposure on osteoarthritis among aging people and Machine learning for prediction: A study based on NHANES 2011-2020

Identification for heavy metals exposure on osteoarthritis among aging people and Machine learning for prediction: A study based on NHANES 2011-2020

Association Between Smoking and Lipid Profile in Men Aged 35 to 70 Years: Dose–Response Analysis

Genetic Polymorphisms of GSTM1 and GPX1 Genes and Smoking Susceptibility in the Saudi Population

Contact Info

Product

Resources

About