Tobacco Induced Diseases最新文献

Introduction: Maternal smoking during pregnancy is associated with placental DNA methylation and RNA expression, offspring DNA methylation, and affects the decline of mature neurons and the prenatal human brain development trajectory.

Methods: This study is a secondary analysis of data from the National Health and Nutrition Examination Survey (NHANES) spanning 2003 to 2008, comprising 10111 children and adolescents. Inclusion criteria required participants to have complete questionnaire responses regarding maternal smoking during pregnancy and receipt of special education or early intervention services. The risk of developmental disabilities was assessed using a multifactor logistic regression model.

Results: In the cohort of 10111 children and adolescents, 727 (7.2%) received special education or early intervention services. Of these participants, 1504 (14.9%) were exposed to maternal smoking during pregnancy. The prevalence of maternal smoking was higher (12.3%) in the group receiving special education or early intervention compared to those who did not (6.3%). After adjusting for other relevant factors in a multifactorial logistic regression model, maternal smoking during pregnancy was significantly associated with an increased likelihood of requiring special education or early intervention services (adjusted odds ratio, AOR=1.51; 95% CI: 1.24-1.83, p<0.001).

Conclusions: This cross-sectional analysis found an association between maternal smoking during pregnancy and the need for special education or early intervention services among US children and adolescents, after adjusting for confounding variables. Our findings suggest that maternal smoking during pregnancy may increase the odds of developmental disabilities.

Introduction: This study aims to investigate whether non-rotating, specific health risk messages on cigarette packaging could be a practical alternative to rotating health warnings to improve smokers' health risk perceptions and intentions to quit smoking.

Methods: The study employs a cross-sectional randomized survey experiment conducted among 1700 adult smokers (aged ≥18 years) in China, with data collected using a snowball sampling method. Participants were randomly assigned to one of three groups: the control group (viewed standard packaging with a general health warning), the rotating health risk text group (exposed to four rotating disease-related warnings), and the reproductive health risk text group (focused on smoking's impact on sexual health). After viewing the corresponding health warnings, participants reported their health risk perceptions and intentions to quit smoking, and responses to additional control variables.

Results: Rotating health risk text warnings on cigarette packaging significantly increased participants' perceptions of cardiovascular (β=0.20; 95% CI: 0.05-0.35), digestive (β=0.22; 95% CI: 0.07-0.37), respiratory (β=0.17; 95% CI: 0.07-0.26), and reproductive system risks (β=0.21; 95% CI: 0.06-0.37), while the non-rotating reproductive health risk text warnings only significantly improved perceptions of reproductive system risks (β=0.18; 95% CI: 0.10-0.25). Both types of text warnings significantly increased smokers' intentions to quit smoking (p≤0.001), indicating that non-rotating specific health risk warnings can be equally effective in promoting quitting intentions.

Conclusions: This study demonstrates that rotating health risk text on cigarette packaging offers comprehensive advantages in enhancing health risk perceptions. However, its effects on intentions to quit smoking are similar to those of non-rotating reproductive health risk warnings. These findings suggest that in contexts where implementing rotating warnings is challenging, non-rotating, specific health risk messages can serve as a feasible alternative to support the effective implementation of tobacco health warning policies.

Introduction: It remains unclear whether shared decision-making (SDM) can help smoking cessation. This study aims to determine whether the SDM model increases the 24-week point abstinence rate and medication adherence rate for adult smokers.

Methods: This prospective cohort study, conducted between January 2019 and June 2021, enrolled 1268 adult smokers at the outpatient cessation clinic of a national medical center. SDM-integrated counseling was provided to those opting for the SDM cessation model, involving cessation educators and decision aids. Patients who declined the model received cessation medication. The self-reported 7-day point prevalence abstinence rate at week 24, medication adherence rate, and the proportion of participants agreeing to receive pharmacotherapy were measured.

Results: Out of the 1268 participants, 1187 (93.6%) were included in the primary analysis. Of these, 610 (48%) opted for the SDM model. Participants in the SDM group used cessation medication more frequently (83.4% vs 71.9%, p<0.001) and exhibited higher medication adherence (39.1% vs 28.6%, p=0.04). Logistic regression analysis revealed that the SDM group did not demonstrate a significantly higher 7-day point abstinence rate at week 24 (OR=0.89; 95% CI: 0.68-1.15; p=0.37).

Conclusions: The SDM cessation model was positively associated with medication adherence and the proportion of participants using pharmacotherapies. However, the association of SDM with the 7-day point prevalence of abstinence at week 24 was not statistically significant. Longer follow-up studies are needed to understand the association of the SDM intervention with absolute abstinence.

Introduction: Smoking poses a serious threat to cardiovascular health. The aim of the study is to evaluate the global, regional, and national burden of smoking-attributable cardiovascular disease (CVD) and set priorities for future tobacco control.

Methods: Data on deaths and years lived with disability (YLDs) for smoking-related CVD from 1990 to 2021, including age-standardized rates (ASRs), were sourced from the Global Burden of Disease (GBD) 2021. This study is a secondary descriptive analysis of the GBD 2021 data. We used the estimated annual percentage change (EAPC) to reflect temporal trends in disease burden and conducted a sensitivity analysis using the average annual percentage change (AAPC) to corroborate the findings. We also analyzed the relationship between disease burden and the Sociodemographic Index (SDI).

Results: In 2021, smoking-related CVD caused 2.25 million deaths and 3.09 million YLDs globally, marking increases of 26.16% and 59.73% from 1990, respectively. However, from 1990 to 2021, the global age-standardized mortality rate (ASMR) and age-standardized years lived with disability rate (ASYR) for smoking-related CVDs decreased, with EAPCs of -1.94 and -0.92, respectively. Between 1990 and 2021, Australasia and Tropical Latin America experienced the largest declines in ASMR and ASYR, with EAPCs of -5.54 and -2.63, respectively, while Lesotho and Mali had the largest increases, with EAPCs of 2.68 and 1.67, respectively. Throughout the period, the burden of smoking-related CVD was consistently higher in men. Moreover, the trend of decline in disease burden was slower in men compared to women (EAPC for ASMR: -1.78 for men vs -3.25 for women). In 2021, countries with higher SDI also had higher ASYR.

Conclusions: Although the global burden of smoking-related CVD has declined over the past three decades, the challenge remains severe, particularly in less developed countries and regions. More proactive and effective tobacco control measures should be urgently implemented in countries where the burden of smoking-related CVD is severe.

Introduction: It is currently uncertain whether smoking is a risk factor for carpal tunnel syndrome (CTS). This study aims to elucidate association between smoking and CTS using Mendelian randomization (MR) analysis.

Methods: This study was a secondary analysis of publicly available GWAS data, using four smoking phenotypes (smoking initiation, smoking status, lifetime smoking, and never smoking) as exposures, and two CTS datasets (discovery and validation sets) as outcomes for MR analysis. The discovery set (n=480201) was used to explore the causal relationship between smoking and CTS, while the validation set (n=385304) was used to confirm the results. The effects of smoking on CTS were assessed using inverse variance weighted (IVW), MR-Egger, and weighted median methods. Cochran's Q test was used to detect heterogeneity, and MREgger to test for pleiotropy. Finally, a meta-analysis was performed on the IVW results from both the discovery and validation sets.

Results: IVW results showed that in both the discovery and validation sets, smoking initiation, smoking status, and lifetime smoking are risk factors for CTS. The summary results from the meta-analysis are as follows: smoking initiation (OR=1.17; 95% CI: 1.08-1.27, p<0.001), smoking status (OR=1.87; 95% CI: 1.56-2.24, p<0.001), and lifetime smoking (OR=2.46; 95% CI: 2.03-3.00, p<0.001). Conversely, never smoking is a protective factor against CTS, with the summary result of the meta-analysis being: OR=0.55; 95% CI: 0.42-0.71, p<0.001.

Conclusions: Based on genetic evidence, smoking may be a risk factor for CTS. Further clinical trials are needed to confirm this causal relationship.

Introduction: Cigarette smoking is an important risk factor in the development of dyspnea. Programs designed to strengthen the respiratory muscles can improve dyspnea in people with or without lung disease. As a first step in understanding the feasibility of offering a respiratory muscle training (RMT) program to people who are seeking help to try to quit smoking, we asked callers who contacted the New York State Quitline about their dyspnea and potential interest in a home-based RMT program.

Methods: Consecutive callers who contacted the New York State Quitline (n=1019) between 19 May and 9 June 2023 completed the Modified Medical Research Council (mMRC) dyspnea scale and reported their level of interest in RMT. Participants were categorized as: high breathlessness (HB: 0-1), or low breathlessness (LB: 2-4). We examined characteristic differences between participants who reported HB versus LB and examined differences in level of interest in home-based RMT.

Results: Those with HB were older [mean (SD): 61.3 (12.5) vs 53.6 (15.0) years, p<0.001], had more cumulative years of smoking [38.8 (15.1) vs 28.8 (15.4) years, p<0.001], smoked more cigarettes per day [19.3 (10.5) vs 17.3 (8.8), p<0.01], reported more disability (p<0.001) and chronic health conditions (78.5% vs 53.9%, p<0.001). Those with HB also expressed greater interest in RMT [7.8 (3.3) vs 6.2 (4.1), p<0.001].

Conclusions: These preliminary findings suggest that about 20% of quitline callers report clinically significant levels of breathlessness and most respondents, regardless of their level of breathlessness, report interest in a home-based RMT program, underscoring a potential opportunity to offer this program along with cessation support.

Introduction: Ischemic heart disease (IHD) remains a leading cause of death and disability worldwide. This study evaluates the trends in IHD burden attributable to smoking, utilizing Global Burden of Disease Study 2021 (GBD 2021) data, across 204 countries and territories from 1990 to 2021. By examining age-standardized death rates (ASDR) and disability-adjusted life years (ASRDALYs), the study provides insights into the spatiotemporal variations associated with smokingattributable IHD in different sociodemographic regions.

Methods: Data on smoking-attributable IHD mortality and DALYs were obtained from the GBD 2021 database. This secondary analysis examined ASDR and ASRDALYs for IHD as primary outcomes, with active smoking as the primary exposure. Temporal trends were analyzed using estimated annual percentage changes (EAPCs). The burden was stratified by age, sex, and sociodemographic index (SDI) to identify disparities across regions.

Results: Over the last three decades, global ASDR and ASRDALYs for smokingrelated IHD have generally declined. High-SDI regions had the largest reductions, with EAPCs of -4.31 (95% CI: -5.73 - -2.87) and -4.02 (95% CI: -5.40 - -2.62), respectively. In contrast, low-SDI regions experienced slower declines, with EAPCs of -0.54 (95% CI: -1.41-0.33) and -0.80 (95% CI: -1.52 - -0.08), respectively. Older age groups and males consistently had a higher burden across all regions. Global death rates for populations aged 15-49, 50-74, and ≥75 years were 4.31, 46.57, and 142.52 per 100000, respectively. The global ASDR for males (30.24) was 8.54 times higher than that for females (3.54). Regional disparities were most pronounced in low- and middle-income areas, particularly in Eastern Europe and Central Asia, where smoking related IHD burden remains high.

Conclusions: While global reductions in smoking-related IHD burden are encouraging, sustained disparities remain, particularly in low-SDI regions. Males and older populations continue to have a disproportionately higher burden, emphasizing the need for targeted interventions and sustained efforts to address these inequities.

Introduction: Smoking behaviors can be quantified using various indices. Previous studies have shown that these indices measure and predict health risks differently. Additionally, the choice of measure differs depending on the health outcome of interest. We compared how each smoking index predicted all-cause mortality and assessed the goodness-of-fit of each model.

Methods: A population-based retrospective cohort, the Korea National Health Examination Baseline Cohort, was used (N=6001607). Data from 2009 were utilized, and the participants were followed until 2021. Cox proportional hazards regression analyses were performed among all participants and ever smokers, respectively, to estimate all-cause mortality. Model fit was assessed by the Akaike Information Criterion.

Results: For men, smoking intensity showed the strongest effect size (hazard ratio HR=1.16; 95% CI: 1.14-1.18), while pack-years provided the best model fit for all-cause mortality. Among women, smoking intensity showed both the strongest effect size (HR=1.49; 95% CI: 1.28-1.74) and the best model fit. Smoking status (never/former/current) also showed comparable effect sizes (men, HR=1.14; 95% CI: 1.13-1.15; women, HR=1.14; 95% CI: 1.11- 1.18) with fair model fit. Analyses of people who ever smoked indicated that a model incorporating smoking status, duration, and intensity best described the mortality data.

Conclusions: The smoking indices showed varying effect sizes and model fits by sex, making it challenging to recommend a single optimal measure. Smoking intensity may be preferred for capturing cumulative exposure, whereas smoking status is notable for its simplicity, comparable effect size, and model fit. Further research that includes biochemical measurements, additional health outcomes, and longer follow-up periods is needed to refine these findings.

Introduction: There is substantial interest in the association of vaping e-cigarettes with the risk of cancer. We analyzed this risk in different populations by updating the Kings College London (KCL) review to include the period between July 2021 and December 2023.

Methods: We searched six databases and included peer-reviewed human, animal, and cell/in vitro original studies examining the association between e-cigarettes and cancer risk, but we excluded qualitative studies. We summarized findings on three types of e-cigarette exposure: acute, short- to medium-term, and long-term. Additionally, we assessed whether the health effects differ between subgroup populations based on various sociodemographic factors, for which we also screened the previously included studies in the KCL review. Different risk-of-bias tools were used to assess the quality of the included human studies.

Results: We included 39 studies in the main analysis and 12 in the subgroup analysis. Of these, 2 were longitudinal observational studies, 9 were cross-sectional studies, 1 case report and 27 were cell/in vitro and animal studies. All human studies were conducted in adults, and about half of them had a low risk of bias. No significant incident or prevalent risk of lung cancer or other types of cancer was found in the never smoker current vapers population. However, there was substantial biomarker-based evidence of a significant association between e-cigarette exposure and oxidative stress, cellular apoptosis, DNA damage, genotoxicity, and tumor growth, particularly following acute exposure. We did not find any age or sex-based differences in cancer risk, and findings on race and education-based differences were insufficient.

Conclusions: There is substantial evidence that e-cigarette exposure is associated with biomarkers reflective of cancer disease risk. However, the overall evidence on cancer risk is still limited and should be further investigated by future research, particularly rigorously designed clinical trials and population-based research.

Introduction: Electronic cigarette (e-cigarette) use has increased globally among adolescents. However, data on its use among adolescents in Saudi Arabia remain limited. Therefore, this study describes the characteristics and factors associated with e-cigarette use in this population.

Methods: Data from the Saudi Arabia 2022 Global Youth Tobacco Survey targeting students aged 13-15 years were analyzed. This study examined current use and ever use of e-cigarettes as dependent variables, and sociodemographic characteristics, smoking behaviors, and exposure to tobacco-related messages as independent variables.

Results: Among the adolescents, 14.2% had ever used e-cigarettes, while 5.3% were current users. Older adolescents exhibited a higher prevalence of ever use (12%, 13%, and 19% among those aged 13, 14, and 15 years, respectively). Of those who ever smoked cigarettes, 44% reported ever using e-cigarettes, and 23% were current users. Older adolescents (15 years) were 35% more likely to be ever e-cigarette users (AOR=1.35; 95% CI: 1.32-1.38) and 39% more likely to be current e-cigarette users (AOR=1.39; 95% CI: 1.34-1.43) than those aged 13 years. Exposure to pro-tobacco marketing was associated with twice the odds of ever using e-cigarettes (AOR=2.23; 95% CI: 2.21-2.26) and 2.76 times the odds of becoming current e-cigarette users (AOR=2.64; 95% CI: 2.58-2.69) compared with non-exposed adolescents.

Conclusions: E-cigarette use is more prevalent among older adolescents, males, and those with higher weekly allowances. Additionally, e-cigarette use is associated with adolescents who use tobacco cigarettes, are exposed to secondhand smoke, or encounter tobacco promotional marketing.