Sex differences in clinical risk factors in obese ischemic stroke patients with a history of smoking.

Clinical risk factors associated obesity and smoking, as well as their combined effect, are not fully understood. This study aims to determine sex differences in risk factors in a population of acute ischemic stroke (AIS) patients who are obese and with a history of previous or current smoking. METHODS: A retrospective analysis of risk factors in male and female AIS patients with baseline data of obesity and current or previous history of smoking, smoking, and obesity alone was determined. The primary predictor and outcome are risk factors associated with male and female AIS patients. Baseline risk factors were analyzed using a multivariate regression analysis to determine specific risk factors linked with the combined effect of obesity and current or previous history of smoking''. RESULTS: Male obese AIS patients who are current or previous smokers were more likely to be older patients(OR = 1.024, 95% CI, 1.022-1.047, P = 0.033) that present with coronary artery disease (OR = 1.806, 95% CI, 1.028-3.174, P = 0.040), a history of alcohol use (OR = 2.873, 95% CI, 1.349-6.166, P = 0.006), elevated serum creatinine (OR = 4.724, 95% CI, 2.171-10.281, P < 0.001) and systolic blood pressure (OR = 1.029, 95% CI, 1.011-1.047, P < 0.002). Females were more associated with depression (OR = 0.432, 95% CI, 0.244-0.764, P = 0.004), previous TIA (OR = 0.319, 95% CI, 0.142-0.714, P < 0.005), and higher levels of HDL (OR = 0.938, 95% CI, 0.915-0.962, P < 0.001). CONCLUSION: Our results reveal sex differences in risk factors in obese AIS patients with a current or past history of smoking. This finding emphasizes the need to develop management strategies to improve the care of obese AIS patients who are either current or former smokers.

Association Between Changes in Smoking Habits and Incident Fracture After Acute Ischemic Stroke.

BACKGROUND: Survivors of stroke, particularly the older population, are at an increased risk of falls and incident fractures. Smoking is a widely recognized risk factor for fractures. However, the association between changes in smoking habits before and after an index stroke and increased risk of fracture remains unelucidated. METHODS AND RESULTS: Using the Korean National Health Insurance program, patients with ischemic stroke between 2010 and 2016 were enrolled. Individuals were classified by smoking habits: "never smoker," "former smoker," "smoking quitter," "new smoker," and "sustained smoker." The primary outcome was the composite outcome of the vertebral, hip, and any fractures. Multivariable Cox proportional hazards regression analysis was conducted, using the never-smoker group as the reference. Among 177 787 patients with health screening data within 2 years before and after ischemic stroke, 14 991 (8.43%) patients had any fractures. After multivariable adjustment, the sustained smokers had a significantly increased risk of composite primary outcomes of any, vertebral, and hip fractures (adjusted HR [aHR], 1.222 [95% CI, 1.124-1.329]; aHR, 1.27 [95% CI, 1.13-1.428]; aHR, 1.502 [95% CI, 1.218-1.853], respectively). Additionally, the new smoker group exhibited a similar or higher risk of any fractures and hip fractures (aHR, 1.218 [95% CI, 1.062-1.397]; aHR, 1.772 [95% CI, 1.291-2.431], respectively). CONCLUSIONS: Sustained smokers had a significantly increased risk of vertebral and hip fractures after an ischemic stroke. The risk of any hip fractures was higher in new smokers after ischemic stroke. As poststroke fractures are detrimental to the rehabilitation process of patients with stroke, physicians should actively advise patients to stop smoking.

Quantifying uptake and completion of pulmonary rehabilitation programs in people with chronic obstructive pulmonary disease known to tertiary care.

BACKGROUND: People with symptomatic chronic obstructive pulmonary disease (COPD) benefit from pulmonary rehabilitation programs (PRPs), but program attrition is common. METHODS: For people with COPD who presented to tertiary care and appeared appropriate for a PRP, we prospectively mapped their PRP journey, explored factors influencing attendance to pre-program assessment and captured program attrition. RESULTS: Of the 391 participants, 31% (95% CI 27 to 36) were referred to a PRP (n = 123; age 68 ± 10years, 62 males [50%], FEV1 45 ± 19%predicted). Of those referred, 94 (76% [69 to 84]) attended a pre-program assessment. Ex-smokers and those who had a healthcare professional (HCP) explain they would be referred were more likely to attend a pre-program assessment (odds ratio [95%CI]; 2.6 [1.1 to 6.1]; and 4.7 [1.9 to 11.7], respectively). Of the 94 who attended, 63 (67% [58 to 77]) commenced; and of those who commenced, 35 (56% [43 to 68]) completed a PRP. All who completed (n = 35, 100%) were provided at least one strategy to maintain training-related gains. CONCLUSION: Attrition occurs throughout the PRP journey. Interactions with HCPs about PRPs positively influenced attendance. Understanding how HCPs can best contextualise PRPs to encourage referral acceptance and uptake is an important area for further work.

Profiles of tobacco smokers and ex-smokers in a large-scale random sample survey across Wales: an unsupervised machine-learning cluster analysis.

BACKGROUND: The Welsh government recently set a target to be smoke-free by 2030, which means reducing the prevalence of tobacco smoking in adults to 5% by then. The goal is to improve health and population life expectancy. To support this strategy, we identified profile groups with different sets of socioeconomic and demographic characteristics within the population of smokers. We compared these profiles to those identified in the ex-smoker population to provide a broader understanding of smokers and inform targeting of interventions and policy. METHODS: We did a cross-sectional study using data from the National Survey for Wales. This survey is a random sample telephone survey of individuals aged 16 years and older across Wales carried out from Sept 1, 2021 to Jan 31, 2022, weighted to be representative of the Welsh population. For the smoking subgroup, we did a weighted hierarchical cluster analysis with multiple imputation to impute missing data and repeated it for ex-smokers. In total, 63 survey variables were used in the analysis. These variables included smoking history, e-cigarette use, sociodemographics, lifestyle factors, individual-level deprivation, general health and long-term conditions, mental health, and wellbeing. FINDINGS: Among the 6407 respondents (weighted proportions: 49% male, 51% female; 28% aged 16-34 years, 46% aged 35-44 years, 26% aged ≥65 years; 95% white, 5% other ethnicity), 841 (13%) smoked and 2136 (33%) were ex-smokers. Four distinctive profiles of smokers were identified, the groups were of relatively comparable size and characterised by similarities described as (1) high-risk alcohol drinkers and without children; (2) single, mostly in social housing, and poor health and mental health; (3) mostly single, younger, tried e-cigarettes, and poor mental health; (4) older couples and poor health; when comparing the groups with each other. Cluster quality and validation statistics were considered fair: silhouette coefficient=0·09, Dunn index (Dunn2)=1·06. Generally, ex-smoker clusters differed from smoking clusters because of themes related to increased sickness, better affluence, employment, and older age (≥75 years). INTERPRETATION: This study suggests that not all smokers are the same, and they do not fall into one coherent group. Smoking cessation interventions to improve the health of ageing populations might need a different approach to consider a wider context or motivations to inform targeted quitting. It is acknowledged that smoking might be underreported because of perceived social unacceptability. FUNDING: Public Health Wales.

Use of the Smoking Cessation App Ex-Smokers iCoach and Associations With Smoking-Related Outcomes Over Time in a Large Sample of European Smokers: Retrospective Observational Study.

BACKGROUND: Digital interventions are increasingly used to support smoking cessation. Ex-smokers iCoach was a widely available app for smoking cessation used by 404,551 European smokers between June 15, 2011, and June 21, 2013. This provides a unique opportunity to investigate the uptake of a freely available digital smoking cessation intervention and its effects on smoking-related outcomes. OBJECTIVE: We aimed to investigate whether there were distinct trajectories of iCoach use, examine which baseline characteristics were associated with user groups (based on the intensity of use), and assess if and how these groups were associated with smoking-related outcomes. METHODS: Analyses were performed using data from iCoach users registered between June 15, 2011, and June 21, 2013. Smoking-related data were collected at baseline and every 3 months thereafter, with a maximum of 8 follow-ups. First, group-based modeling was applied to detect distinct trajectories of app use. This was performed in a subset of steady users who had completed at least 1 follow-up measurement. Second, ordinal logistic regression was used to assess the baseline characteristics that were associated with user group membership. Finally, generalized estimating equations were used to examine the association between the user groups and smoking status, quitting stage, and self-efficacy over time. RESULTS: Of the 311,567 iCoach users, a subset of 26,785 (8.6%) steady iCoach users were identified and categorized into 4 distinct user groups: low (n=17,422, 65.04%), mild (n=4088, 15.26%), moderate (n=4415, 16.48%), and intensive (n=860, 3.21%) users. Older users and users who found it important to quit smoking had higher odds of more intensive app use, whereas men, employed users, heavy smokers, and users with higher self-efficacy scores had lower odds of more intensive app use. User groups were significantly associated with subsequent smoking status, quitting stage, and self-efficacy over time. For all groups, over time, the probability of being a smoker decreased, whereas the probability of being in an improved quitting stage increased, as did the self-efficacy to quit smoking. For all outcomes, the greatest change was observed between baseline and the first follow-up at 3 months. In the intensive user group, the greatest change was seen between baseline and the 9-month follow-up, with the observed change declining gradually in moderate, mild, and low users. CONCLUSIONS: In the subset of steady iCoach users, more intensive app use was associated with higher smoking cessation rates, increased quitting stage, and higher self-efficacy to quit smoking over time. These users seemed to benefit most from the app in the first 3 months of use. Women and older users were more likely to use the app more intensively. Additionally, users who found quitting difficult used the iCoach app more intensively and grew more confident in their ability to quit over time.

Reduced Total Airway Count and Airway Wall Tapering after Three-Years in Ex-Smokers.

Computed tomography (CT) total-airway-count (TAC) and airway wall-thickness differ across chronic obstructive pulmonary disease (COPD) severities, but longitudinal insights are lacking. The aim of this study was to evaluate longitudinal CT airway measurements over three-years in ex-smokers. In this prospective convenience sample study, ex-smokers with (n = 50; 13 female; age = 70 ± 9 years; pack-years = 43 ± 26) and without (n = 40; 17 female; age = 69 ± 10 years; pack-years = 31 ± 17) COPD completed CT, 3He magnetic resonance imaging (MRI), and pulmonary function tests at baseline and three-year follow-up. CT TAC, airway wall-area (WA), lumen-area (LA), and wall-area percent (WA%) were generated. Emphysema was quantified as the relative-area-of-the-lung with attenuation < -950 Hounsfield-units (RA950). MRI ventilation-defect-percent (VDP) was also quantified. Differences over time were evaluated using paired-samples t tests. Multivariable prediction models using the backwards approach were generated. After three-years, forced-expiratory-volume in 1-second (FEV1) was not different in ex-smokers with (p = 0.4) and without (p = 0.5) COPD, whereas RA950 was (p < 0.001, p = 0.02, respectively). In ex-smokers without COPD, there was no change in TAC (p = 0.2); however, LA (p = 0.009) and WA% (p = 0.01) were significantly different. In ex-smokers with COPD, TAC (p < 0.001), WA (p = 0.04), LA (p < 0.001), and WA% (p < 0.001) were significantly different. In all ex-smokers, TAC was related to VDP (baseline: ρ = -0.30, p = 0.005; follow-up: ρ = -0.33, p = 0.002). In significant multivariable models, baseline airway wall-thickness was predictive of TAC worsening. After three-years, in the absence of FEV1 worsening, TAC diminished only in ex-smokers with COPD and airway walls were thinner in all ex-smokers. These longitudinal findings suggest that the evaluation of CT airway remodeling may be a useful clinical tool for predicting disease progression and managing COPD.Clinical trial registration: www.clinicaltrials.gov NCT02279329.

Recovery From Nicotine Addiction: A Diffusion Model Decomposition of Value-Based Decision-Making in Current Smokers and Ex-smokers.

INTRODUCTION: A considerable number of people successfully give up tobacco smoking. In nicotine-dependent individuals, tobacco choice is determined by greater expected drug value; however, less is known about the underlying mechanisms through which people quit smoking. AIMS AND METHODS: This study aimed to explore whether computational parameters of value-based decision-making (VBDM) characterize recovery from nicotine addiction. Using a preregistered, between-subject design, current daily smokers (n = 51) and ex-smokers who used to smoke daily (n = 51) were recruited from the local community. Participants completed a two-alternative forced choice task in which they chose between either two tobacco-related images (in one block) or tobacco-unrelated images (in a different block). During each trial, participants pressed a computer key to select the image they rated most positively during a previous task block. To estimate evidence accumulation (EA) processes and response thresholds during the different blocks, a drift-diffusion model was fitted to the reaction time and error data. RESULTS: Ex-smokers had significantly higher response thresholds when making tobacco-related decisions (p = .01, d = 0.45) compared to current smokers, although there were no significant group differences during tobacco-unrelated decisions. Furthermore, there were no significant group differences in EA rates when making tobacco or tobacco-unrelated decisions. CONCLUSIONS: Greater cautiousness when making value-based decisions about tobacco-related cues characterized recovery from nicotine addiction. IMPLICATIONS: The number of people dependent on nicotine has decreased steadily during the past decade; however, the mechanisms that underlie recovery are currently less well understood. The present study applied advances in the measurement of value-based choice. The aim was to explore whether the internal processes that underpin VBDM discriminate current daily tobacco smokers from ex-tobacco smokers who used to smoke daily. Findings revealed that recovery from nicotine addiction was characterized by higher response thresholds when making value-based decisions about tobacco-related cues; this may serve as a novel target for treatment interventions that focus on helping people to stop smoking.

The impact of smoking status on anticipated stigma and experience of care among smokers and ex-smokers with chronic illness in general practice.

OBJECTIVES: To compare self-reported levels of 'anticipated' stigma and experience of care in general practice between current and ex-smokers living with COPD, other chronic illnesses, or those with no chronic conditions. METHODS: Participants completed an online survey, advertised through social media, about their experience of care from general practitioners (GPs) in the past 12 months. Respondents self-reported doctor-diagnosed chronic illnesses. Experience of care and anticipated stigma was assessed using validated questions. Multi-nominal regressions were used to determine independent effect of smoking status on anticipated stigma and other indicators of patient experience in primary care. RESULTS: Patients with COPD (n = 161) reported significantly higher anticipated stigma scores compared to those with other chronic conditions (n = 225) and this was strongly related to delayed or avoidance in seeking help from a GP when needed. This relationship remained irrespective of current smoking status. There was no difference between groups for relational components of experience of care. DISCUSSION: Primary care patients living with COPD reported worse experience of care across several domains and were more likely to anticipate experiencing stigma in the GP setting irrespective of their current smoking status compared to those with other chronic illnesses or no chronic illnesses.

Perceived Health and Capacity to Cope With Stress in Recent Ex-smokers: Impact of Vaping Versus Quitting All Nicotine.

INTRODUCTION: Little is known about the continued use of nicotine following smoking cessation on perceived well-being in comparison to complete cessation of nicotine use. AIMS AND METHODS: To explore aspects of perceived well-being and coping among recent ex-smokers as a function of vaping status. Ever-daily smokers in the International Tobacco Control 4 country smoking and vaping surveys in 2016 (w1 N = 883) and 2018 (w2 N = 1088). Cross-sectional associations and longitudinal samples for those who quit between waves and those who quit at w1 and maintained abstinence to w2. Main outcome measures were: Past 30 days of depression symptoms, perceived stress, stress management since quitting, and change in perceived day-to-day health. RESULTS: In the cross-sectional analyses vapers were more likely to report both improved stress management (aOR = 1.71, 95% CI 1.23-2.36) and perceived day-to-day health (aOR = 1.65, 95% CI 1.26-2.16) than nicotine abstainers. In the longitudinal analyses, smokers who switched to vaping between waves (n = 372) were more likely to report depression symptoms at w2 (aOR = 2.00, 95% CI 1.09-3.65) but reported improved perceived health (aOR = 1.92, 95% CI 1.16-3.20). For the past daily smokers who remained quit between waves (n = 382), vapers were more likely to report improved stress management relative to abstainers (RRR = 5.05. 95% CI 1.19-21.40). There were no other significant differences between vapers and nicotine abstainers. CONCLUSIONS: There is little evidence to support the view that perceptions of well-being deteriorate in vapers compared to complete nicotine abstainers in the immediate years after smoking cessation. IMPLICATIONS: This study could find no conclusive evidence that the continued use of nicotine via e-cigarettes was detrimental to health compared to completely stopping nicotine intake altogether. Our results would suggest that continuing to use nicotine may even result in some benefits in the short term such as improved stress management, however further longitudinal studies are required to examine if these effects are restricted to the early post-quitting phase and whether other positive or negative effects on psychosocial health emerge in the future.

Induced sputum eosinophilia and pulmonary function tests in ex-smokers with asthma controlled with an inhaled steroid and long-acting beta-agonist therapy.

Introduction: This study aims to investigate the relationship between induced sputum eosinophilia and pulmonary functions in ex-smoker asthma patients controlled with an ICS/LABA therapy. Materials and Methods: Asthma patients who are known to use ICS/LABA regularly for at least three months, without an attack in the last month, quit smoking (5-20 pack-years) and have asthma under control (ACT> 20), and concurrent with induced sputum cytology who had spirometry and lung volume measurements were included in the study. Cytology results, induced sputum eosinophil and neutrophil counts, FEV1 (L), FEV1 (%), FVC (L), FVC (%), RV (L), RV (%) and RV/TLC (%) values of all patients were recorded. The relationship between sputum neutrophil and eosinophil count and pulmonary function test parameters was evaluated. Result: Seventeen (68%) of the patients were female, eight (32%) were male, and the mean age was 49.7 ± 13.6 years. The mean sputum eosinophil percentage was 9.4 ± 16.7, and the neutrophil percentage was 71.4 ± 20.5. A positive correlation was found between induced sputum eosinophil percentage values and FEV1 (L) (r= +0.472; p= 0.01) and FVC (L) (r= +0.502; p= 0.01). No correlation was found between the FEV1/FVC%, FEV1%, FVC%, RV (L), RV%, and RV/TLC% values and the percentage of induced sputum eosinophils (p> 0.05). Conclusions: It was observed that controlled asthmatic patients with induced sputum eosinophilia treated with ICS/LABA and who quit smoking had high FVC (L) and FEV1 (L) levels.

Do Current Smokers and Ex-Smokers Who Use Nicotine Vaping Products Daily Versus Weekly Differ on Their Reasons for Vaping? Findings from the 2020 ITC Four Country Smoking and Vaping Survey.

This study examined reasons why adults who currently smoke or formerly smoked cigarettes use nicotine vaping products (NVPs) by vaping frequency (daily vs. weekly) stratified by smoking status. This cross-sectional study included 3070 adults from the 2020 ITC Four Country Smoking and Vaping Survey (Australia, Canada, England, United States) who reported using a NVP (vaping) at least weekly and who either currently smoke (n = 2467) or formerly smoked (n = 603). Respondents were asked to select the reason(s) they use NVPs, including to manage their smoking (reduce/quit or remain quit) and/or for reasons unrelated to managing smoking (e.g., to save money, enjoyment, flavours). We found that both current and former smokers endorsed an average of six reasons for vaping, with those vaping daily reporting significantly more reasons than those vaping weekly. Among current smokers, 72.8% reported vaping may help them quit smoking, 13.0% reported vaping to reduce smoking but not to quit, and 14.2% reported vaping only for reasons other than to reduce or quit smoking. The most common reason for vaping among current smokers was to reduce smoking (81.3%). Current smokers vaping daily were significantly more likely than those vaping weekly to report using a NVP to reduce smoking, for enjoyment, to reduce harm to themselves and others, to quit smoking, likeable flavours, and to save money. The most common reason cited for vaping by respondents who formerly smoked was enjoyment, with those who vaped daily more likely than those who vaped weekly to report vaping for enjoyment and to reduce harm to themselves. Nearly all reported vaping to help stay abstinent from smoking (92.3%), with no significant difference by vaping frequency. In conclusion, a majority of respondents reported using NVPs to manage their smoking (reduce/quit smoking or remain quit), particularly those vaping daily. Those who were vaping daily also endorsed a greater number of reasons other than managing smoking relative to those who were vaping weekly.

Household expenditure of smokers and ex-smokers across socioeconomic groups: results from a large nationwide Australian longitudinal survey.

BACKGROUND: Countries with best practice tobacco control measures have experienced significant reductions in smoking prevalence, but socioeconomic inequalities remain. Spending on tobacco products, particularly by low-income groups can negatively affect expenditure on other goods and services. This study aims to compare the household expenditure of adults who smoke tobacco products and those who formerly smoked across socioeconomic groups. METHODS: Daily smokers and ex-smokers were compared using the Household, Income and Labour Dynamics in Australia Survey, over 7 waves. Adults who never smoked were not included. Participants were continuing sample members across waves. Mean number of participants per wave was 2505, 25% were smokers and 75% ex-smokers. The expenditure variables investigated included tobacco products, alcohol, motor vehicle fuel, health practitioners, insurance, education, and meals eaten out. Regression models using the generalized estimating equation technique were employed to compare expenditure data aggregated across the waves by Socioeconomic Index for Areas (SEIFA) quintiles of relative socio-economic advantage/disadvantage while accounting for within-participant autocorrelation. Quintiles are ranked by information such as the income, occupation and access to material and social resources of the residents. RESULTS: Smokers from all quintiles spent significantly less per year on meals out, education and insurance than ex-smokers (p < 0.001). Smokers from quintiles 2-5 spent less on groceries, medicines, and health practitioners (p < 0.01). Smokers from quintiles 1 and 2 (most disadvantaged), spent less on motor vehicle fuel than ex-smokers ($280;95%CI: $126-$434), ($213;95%CI: $82-$344). Smokers from quintiles 2 and 3 spent more on alcohol ($212;95%CI: $86-$339), ($231.8;95%CI: $94-$370) than ex-smokers. Smokers from the least disadvantaged groups spent less on clothing than ex-smokers ($348;95%CI: $476-$221), ($501; 95%CI: $743-$258). Across the whole sample, smokers spent more than ex-smokers on alcohol ($230;95%CI:$95-$365) and less on meals out ($361;95%CI:$216-$379), groceries ($529;95%CI:$277-$781), education ($456;95%CI:$288-$624), medicine ($71;95%CI:$38-$104), health practitioners ($345;95%CI:$245-$444) and insurance ($318;95%CI:$229-$407). CONCLUSIONS: Smoking cessation leads to reallocation of spending across all socioeconomic groups, which could have positive impacts on households and their local communities. Less spending on alcohol by ex-smokers across the whole sample could indicate a joint health improvement associated with smoking cessation.

Aberrant callosal morphology in ex-smokers.

BACKGROUND: Cigarette smoking is associated with widespread structural alterations in both brain hemispheres as well as of the corpus callosum (i.e., the brain's main interhemispheric white matter pathway). While similar hemispheric alterations have also been reported in ex-smokers, no study has yet examined the corpus callosum in ex-smokers. METHODS: We compared callosal morphology in a sample of 107 ex-smokers (57 males/50 females) and 193 non-smokers (73 males/120 females), aged between 42 and 97 years. More specifically, we measured the total callosal area as well as seven callosal subregions using the Witelson parcellation scheme. RESULTS: At uncorrected levels, we detected significantly smaller callosal areas in ex-smokers than in non-smokers within the posterior midbody, genu, and isthmus (albeit the latter only on a trend level). When applying corrections for multiple comparisons, only the effect within the posterior midbody remained significant. CONCLUSIONS: Our findings suggest a weaker interhemispheric connectivity in ex-smokers compared to non-smokers, specifically between frontal and temporal areas.

Metabolic Differences between Ex-Smokers and Nonsmokers: A Metabolomic Analysis.

The aim of this study was to compare changes in the metabolite levels of ex-smokers and nonsmokers using a metabolomics approach, accounting for the weight gain in ex-smokers. Volunteer ex-smokers and nonsmokers were recruited from two cohorts Shijingshan (174) and Xishan (78), respectively, at a 1 : 1 ratio for age and sex. Nontargeted metabolomics was performed on the volunteers' blood samples using liquid chromatography-mass spectrometry, and multivariate statistical analysis was performed using principal component analysis and orthogonal partial least squares discriminant analysis. Enrichment analysis was used to identify Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways associated with differential metabolites and weighted gene co-expression network analysis and maximal correlation coefficient (MCC) algorithms were used to identify key metabolites. The results revealed no significant differences between the distribution of blood metabolite levels in the ex-smokers and nonsmokers. The biosynthesis of valine, leucine, and isoleucine was determined to be associated with differential metabolites, and five key metabolites were identified. Further analysis revealed differences in weight gain and regained metabolite levels in ex-smokers, and 10 differential metabolites were identified that may be associated with weight gain in ex-smokers. These findings suggest that quitting smoking restores metabolites to almost normal levels and results in weight gain. The identified key metabolites and metabolic pathways may also provide a basis for clinical studies.

The Microbiome in Bronchial Biopsies from Smokers and Ex-Smokers with Stable COPD - A Metatranscriptomic Approach.

Current knowledge about the respiratory microbiome is mainly based on 16S ribosomal RNA gene sequencing. Newer sequencing approaches, such as metatranscriptomics, offer the technical ability to measure the viable microbiome response to environmental conditions such as smoking as well as to explore its functional role by investigating host-microbiome interactions. However, knowledge about its feasibility in respiratory microbiome research, especially in lung biopsies, is still very limited. RNA sequencing was performed in bronchial biopsies from clinically stable smokers (n = 5) and ex-smokers (n = 6) with COPD not using (inhaled) steroids. The Trinity assembler was used to assemble non-human reads in order to allow unbiased taxonomical and microbial transcriptional analyses. Subsequently, host-microbiome interactions were analyzed based on associations with host transcriptomic data. Ultra-low levels of microbial mass (0.009%) were identified in the RNA-seq data. Overall, no differences were identified in microbiome diversity or transcriptional profiles of microbial communities or individual microbes between COPD smokers and ex-smokers in the initial test dataset as well as a larger replication dataset. We identified an upregulated host gene set, related to the simultaneous presence of Bradyrhizobium, Roseomonas, Brevibacterium.spp., which were related to PERK-mediated unfolded protein response (UPR) and expression of the microRNA-155-5p. Our results show that metatranscriptomic profiling in bronchial biopsy samples from stable COPD patients yields ultra-low levels of microbial mass. Further, this study illustrates the potential of using transcriptional profiling of the host and microbiome to gain more insight into their interaction in the airways.

Interaction between smoking and diabetes in relation to subsequent risk of cardiovascular events.

BACKGROUND: Whether smoking modifies the associations of diabetes and risk factor management with subsequent risk of cardiovascular disease (CVD), and whether the smoking related CVD risk differs among people with and without diabetes are unclear. This study aimed to examine the associations and interactions of smoking, diabetes, and risk factor management in relation to incident CVD. METHODS: This nationwide, population-based, prospective cohort study of 20 communities from various geographic regions recruited adults aged 40 years or older during 2011-2012. The follow-up survey was conducted between 2014 and 2016. This study included 126,181 participants who were free from CVD at baseline. RESULTS: Study participants included 19,397 current smokers (15.4%), 6,049 former smokers (4.8%), and 100,735 never smokers (79.8%). Mean (SD) age ranged from 55.8 (8.6) years to 60.7 (9.1) years. Compared with never smokers, heavy smokers exhibited a greater risk of CVD events among participants with diabetes (multivariable-adjusted hazard ratio [HR], 1.45; 95% CI, 1.17-1.78) than among participants without diabetes (HR, 1.20; 95% CI, 1.01-1.42; P for interaction = 0.006). Compared with participants without diabetes, participants with diabetes who were never smokers and had 5 or more controlled risk factors showed no significantly excess CVD risk (HR, 0.93; 95% CI, 0.71-1.22), but the cardiovascular benefits from risk factor management were counteracted among participants with diabetes who were current smokers (HR, 1.28; 95% CI, 0.77-2.14) or former smokers (HR, 1.22; 95% CI, 0.66-2.28). CONCLUSIONS: Smoking and diabetes interacted with each other in relation to increased risk of CVD events, and the beneficial effect of risk factor management on CVD risk among participants with diabetes was attenuated by current or former smoking.

Cost-effectiveness of screening smokers and ex-smokers for lung cancer in the Netherlands in different age groups.

OBJECTIVE: We aimed to assess the cost-effectiveness of screening smokers and ex-smokers for lung cancer in the Netherlands. METHODS: A Markov model was used to evaluate the health effects and costs of lung cancer screening from the healthcare perspective. The effects and costs of ten screening scenarios with different start and stop ages of screening were examined across a lifetime horizon in a cohort of 100,000 smokers and ex- smokers 50 years and older. RESULTS: The incremental cost-effectiveness ratios (ICERs) of screening smokers and ex-smokers aged 50-60 years, 50-70 years, and 50 years and older are below the cost-effectiveness threshold of € 20,000 per quality adjusted life year (QALY) gained. Screening 50-60-year-old smokers and ex-smokers was the most cost-effective scenario with an ICER of € 14,094 per QALY gained. However, screening smokers and ex-smokers 50 years and older yielded the highest QALYs and resulted in an ICER of € 16,594 per QALY, which is below the threshold of € 20,000 per QALY. All screening scenarios compared to no screening resulted in CERs between the € 14,000 and € 16,000 per QALY gained. The efficiency frontier showed that screening smokers and ex-smokers in the age groups 70 years and older, 60-70 years, 60 years and older are excluded by extended dominance by no screening, screening smokers and ex-smokers aged 50-60 years and 50-70 years. CONCLUSION: This study showed that lung cancer screening is cost-effective in the Netherlands.

Inclusion of Smoking Data in Cardiovascular Disease Risk Estimation.

Importance: Former heavy smokers (ie, those with ≥20 pack-years of smoking) may have higher atherosclerotic cardiovascular disease (ASCVD) risk than never smokers for up to 16 years after smoking cessation. However, the 2013 pooled cohort equations (PCE) do not account for pack-years of smoking and only consider current vs noncurrent smoking status without distinguishing former smokers from never smokers. Objective: To assess the predictive utility of smoking history when added to the PCE using data from 18 400 person examinations among Framingham offspring participants. Design, Setting, and Participants: This is a retrospective analysis of prospectively collected data from the Framingham Heart Study, a community-based cohort. Framingham Heart Study offspring cohort participants attending their first examination (1971-1975) who were followed-up through December 2016 were included. Exposures: Self-reported current/former/never smoking status, pack-years smoked, and years since quitting. Main Outcomes and Measures: Incident ASCVD (myocardial infarction, fatal/nonfatal ischemic stroke, coronary heart disease death). Results: Of 3908 patients, there were 358 and 197 events among 1895 men and 2013 women, respectively, with a mean (SD) age of 55 (9.5) years. Ever smoking prevalence was high (6474 men [77%] and 7760 women [78%]), as were median pack-years (men: 39; women: 32 overall person examinations). Four sex-specific ASCVD risk prediction models were built using pooled-repeated Cox proportional hazards regression. The PCEs were was fit in this sample with continuous predictors on their natural scale (ie, not logarithmically transformed) as well as polynomials accounting for nonlinearity and then cumulatively adjusted for former smoking, pack-years, and years since quitting. Models were compared via change in C statistic, continuous net reclassification improvement (NRI[>0]), and relative integrated discrimination improvement (rIDI). Including former smoking status, pack-years, and years since quitting had significant but modest NRI(>0) and rIDI values compared with the PCE with continuous variables on their natural scale in both sexes (men: NRI[>0] = 0.23; rIDI = 0.19; women: NRI[>0] = 0.34, rIDI = 0.11; change in C statistic = 0.01 for both). Conclusions and Relevance: Former smoking, pack-years, and years since quitting significantly improved ASCVD risk prediction in this sample. The Framingham Heart Study offspring cohort is largely composed of non-Hispanic White participants of European ancestry. If results are validated in cohorts of race and ethnicity groups other than White, these variables could be considered for inclusion in future ASCVD risk prediction models.

Indicadores referentes à cessação do comportamento de fumar no Brasil, Pesquisa Nacional de Saúde, edições 2013 e 2019 / Indicadores relacionados con el abandono del comportamiento de fumar en Brasil, Encuesta Nacional de Salud, ediciones 2013 y 2019 / Indicators related to smoking cessation in Brazil, National Health Survey, 2013 and 2019 editions

Objetivo: Descrever os indicadores de abandono do uso de tabaco, em 2013 e 2019, para o Brasil e as Unidades da Federação, segundo variáveis sociodemográficas, coletadas na Pesquisa Nacional de Saúde (PNS). Métodos: Estudo transversal, populacional e descritivo realizado com dados da PNS 2013 e 2019, uma pesquisa domiciliar coletada por entrevistadores treinados. Foram calculadas a prevalência de ex-fumantes e a proporção de fumantes que tentaram parar de fumar nos últimos 12 meses imediatamente anteriores à data da entrevista, e os respectivos intervalos de confiança (IC95%), segundo as variáveis sociodemográficas. Ademais, calculou-se a variação percentual entre os anos estudados. Resultados: Em 2013, a prevalência de ex-fumantes foi 17,5% (IC95% 16,9;18,0) e, em 2019, 26,6% (IC95% 26,1;27,2). Tentaram parar de fumar 51,1% (IC95% 49,3;52,9), em 2013, e 46,6% (IC95% 45,0;48,3) em 2019. Conclusão: É importante o fortalecimento e manutenção de estratégias para enfrentamento do uso de tabaco no país, de forma a aumentar a disposição e a capacidade do fumante atual de parar de fumar.

Objetivo: Describir los indicadores de abandono del hábito tabáquico en 2013 y 2019 para Brasil y Unidades Federadas, según variables sociodemográficas, recogidas en la Encuesta Nacional de Salud (PNS). Métodos: Estudio transversal, poblacional y descriptivo con datos de las PNS, 2013 y 2019, una encuesta de hogares recolectada por entrevistadores capacitados. Se calculó la prevalencia de exfumadores y proporción de fumadores que intentaron dejar de fumar en los últimos 12 meses y respectivos intervalos de confianza (IC95%), según variables sociodemográficas. Además, se calculó la variación porcentual entre los años. Resultados: En 2013, la prevalencia de exfumadores fue de 17,5% (IC95% 16,9;18,0), en 2019, 26,6% (IC95% 26,1;27,2). En 2013, el 51,1% intentó dejar de fumar (IC95% 49,3;52,9), y, en 2019, el 46,6% (IC95% 45,0;48,3). Conclusión: Es importante fortalecer y mantener las estrategias de afrontamiento del tabaquismo, para incrementar la disposición y capacidad del fumador actual para dejar de fumar.

Objective: To describe the indicators of smoking cessation in 2013 and 2019 for Brazil and federative units, according to sociodemographic variables, collected in the National Health Survey (PNS). Methods: Cross-sectional, population-based and descriptive study with data from the 2013 and 2019 PNS, a household survey collected by trained interviewers. The prevalence of ex-smokers and the proportion of smokers who tried to quit smoking in the 12 months prior to the interview, and respective confidence intervals (95%CI) were calculated, according to sociodemographic variables. Additionally, the percentage variation between the years was calculated. Results: In 2013, the prevalence of ex-smokers was 17.5% (95%CI 16.9;18.0) and, in 2019, 26.6% (95%CI 26.1;27.2). In 2013, 51.1% tried to quit smoking (95%CI 49.3;52.9) and, in 2019, 46.6% (95%CI 45.0;48.3). Conclusion: It is important to strengthen and maintain strategies for coping with tobacco use in Brazil, to increase the current smoker's willingness and ability to quit smoking.

Lung Cancer in Non-smokers in Czech Republic: Data from LUCAS Lung Cancer Clinical Registry.

BACKGROUND/AIM: LUCAS is a clinical lung cancer registry (ClinicalTrials.gov identifier is NCT04228237), prospectively collecting data from newly diagnosed lung cancer patients in seven pneumooncology centers in the Czech Republic, since June 1, 2018. The aim of the study was to assess the stage of the disease at the time of diagnosis, percentage of morphological types, survival, percentage of driving mutations, eligibility for radical surgery, and percentage of patients who undergo radical surgery, in the non-smoking population in comparison with smokers and former smokers. PATIENTS AND METHODS: The total number of patients in the registry at the time of the analysis was 2,743. Only 2,439 patients with complete records (smoking status, stage, and type of tumor) were included in this study. RESULTS: The analysis indicated that non-smokers are diagnosed at a later stage of the disease but they have a better survival rate than smokers. Fewer smokers with stage III disease who are eligible for radical surgery will undergo surgery compared to non-smokers with the same clinical stage. Driving mutations are more common in non-smokers, even after adjustment for the more frequent occurrence of adenocarcinoma in the group of non-smokers. CONCLUSION: The data from LUCAS registry are consistent with already known facts, suggesting that the LUCAS registry is a useful clinical tool.