Estimating the reduction in US mortality if cigarettes were largely replaced by e-cigarettes.

BACKGROUND: Recent estimates indicated substantially replacing cigarettes by e-cigarettes would, during 2016-2100, reduce US deaths and life-years lost (millions) by 6.6 and 86.7 (Optimistic Scenario) and 1.6 and 20.8 (Pessimistic). To provide additional insight we use alternative modelling based on a shorter period (1991-2040), four main smoking-associated diseases, deaths aged 30-79 years, and a full product history. We consider variations in: assumed effective dose of e-cigarettes versus cigarettes (F); their relative quitting rate (Q); proportions smoking after 10 years (X); and initiation rate (I) of vaping, relative to smoking. METHODS: We set F = 0.05, X = 5%, Q = 1.0 and I = 1.0 (Main Scenario) and F = 0.4, X = 10%, Q = 0.5 and I = 1.5 (Pessimistic Scenario). Sensitivity Analyses varied Main Scenario parameters singly; F from 0 to 0.4, X 0.01% to 15%, and Q and I 0.5 to 1.5. To allow comparison with prior work, individuals cannot be dual users, re-initiate, or switch except from cigarettes to e-cigarettes. RESULTS: Main Scenario reductions were 2.52 and 26.23 million deaths and life-years lost; Pessimistic Scenario reductions were 0.76 and 8.31 million. These were less than previously, due to the more limited age-range and follow-up, and restriction to four diseases. Reductions in deaths (millions) varied most for X, from 3.22 (X = 0.01%) to 1.31 (X = 15%), and F, 2.74 (F = 0) to 1.35 (F = 0.4). Varying Q or I had little effect. CONCLUSIONS: Substantial reductions in deaths and life-years lost were observed even under pessimistic assumptions. Estimates varied most for X and F. These findings supplement literature indicating e-cigarettes can importantly impact health challenges from smoking.

Estimating the public health impact had tobacco-free nicotine pouches been introduced into the US in 2000.

BACKGROUND: For smokers not intending to quit, switching to a reduced-risk nicotine product should be healthier than continuing smoking. We estimate the health impact, over the period 2000-2050, had the nicotine pouch ZYN hypothetically been introduced into the US in 2000. ZYN's toxicant profile and method of use is like that for Swedish snus, a product with known health effects much less than smoking. METHODS: Our modelling approach is similar to others developed for estimating potential effects of new tobacco products. It starts with a simulated cohort of 100,000 individuals in the year 2000 subdivided by age, sex, and smoking status (including years since quitting). They are followed annually accounting for births, net immigrations, deaths and product use changes, with follow-up carried out in the Base Case (ZYN not introduced) and Modified Case (ZYN introduced). Using informed assumptions about initiation, quitting and switching rates, distributions of the population over time are then constructed for each Case, and used to estimate product mortality based on assumptions about the relative risk according to product use. RESULTS: Whereas in both Base and Modified Cases, the prevalence of any current product use is predicted to decline from about 22% to 10% during follow-up, in the Modified Case about 25% of current users use ZYN by 2050, about a quarter being dual users and the rest ZYN-only users. Over the 50 years, deaths at ages 35-84 from product use among the 100,000 are estimated as 249 less in the Modified than the Base Case, equivalent to about 700,000 less in the whole US. Sensitivity analyses varying individual parameter values confirm the benefits of switching to ZYN, which increase as either the switching rate to ZYN increases or the initiation rate of ZYN relative to smoking increases. Even assuming the reduction in excess mortality risk using ZYN use is 20% of that from smoking rather than the 3.5% assumed in the main analyses, the reduction in product-related deaths would still be 213, or about 600,000 in the US. CONCLUSIONS: Although such model-based estimates involve uncertainties, the results suggest that introducing ZYN could substantially reduce product-related deaths.

Updating the evidence relating smoking bans to incidence of heart disease.

In our latest update of the evidence on smoking bans and heart disease we summarize 59 studies. We take account of the underlying trends in incidence rates as far as possible by using control data in eight studies, and by adjustment based on observed trends in cases pre- and post-ban in 40 studies, being unable to make an adjustment in the remaining 11 studies. Overall, based on 62 independent estimates from the 59 studies, we estimate that bans reduce incidence by 5.0% (95% CI 3.2-6.8%), though this estimate reduces to 2.9% (0.01-5.6%) when we exclude regional estimates where national estimates are available, and studies where trend adjustment is not possible. For 25 of the studies, quadratic rather than linear adjustment is possible, but this hardly affects the overall estimates. Ban effects are somewhat greater when the pre-ban period studied is relatively short, and in smaller studies. We compare our findings with those in other recent reviews, one of which totally ignored underlying trends and results from control populations. We discuss reasons why we believe there is likely to be a true small effect of smoking bans, and weaknesses in the data which preclude reaching any very confident conclusion.

Investigation into the risk of ultra-low tar cigarettes and lung cancer.

We present analyses relating cigarette type to lung cancer based on a case-control study in five European countries. The analyses involved 3561 cases and 2301 controls with diseases not associated with smoking. Subjects completed a detailed questionnaire, including a lifetime smoking history. Analyses included never smokers, and those who smoked for at least 80% of the "critical period" from 2 to 20 years before diagnosis, ignoring those who ever smoked pipes or cigars, or chewed tobacco. The main analysis compares risk in those who, in the critical period, smoked ultra-low tar (ULT) cigarettes (machine yield ≤3 mg tar/cigarette) for 8 + years, with those who only smoked full flavour (FF) cigarettes (≥10 mg tar/cigarette). After adjustment for sex, age, country, education, age of starting smoking, mean cigarette consumption and mean tar level 21-50 years before interview, the odds ratio (OR) was 0.73 (95% confidence interval (CI) 0.50-1.06). Other analyses showed a modest, not statistically significant, reduction in risk with tar reduction. Risk in ULT smokers for 8 + years was substantially higher than in never smokers (OR 16.27, 95% CI 10.14-26.09). The study was prematurely terminated due to cost overrun, limiting the power to detect an association. More evidence is needed, particularly on lifetime ULT smoking.

Estimating the effect of differing assumptions on the population health impact of introducing a Reduced Risk Tobacco Product in the USA.

We use Population Health Impact Modelling to assess effects on tobacco prevalence and mortality of introducing a Reduced Risk Tobacco Product (RRP). Simulated samples start in 1990 with a US-representative smoking prevalence. Individual tobacco histories are updated annually until 2010 using estimated probabilities of switching between never/current/former smoking where the RRP is not introduced, with current users subdivided into cigarette/RRP/dual users where it is. RRP-related mortality reductions from lung cancer, IHD, stroke and COPD are derived from the histories and the assumed relative risks of the RRP. A basic analysis assumes a hypothetical RRP reduces effective dose 80% in users and 40% in dual users, with an uptake rate generating ∼10% RRP and ∼6% dual users among current users after 10 years. Sensitivity study changes in tobacco prevalence and mortality from varying effective doses, current smoking risks, quitting half-lives and rates of initiation, switching, re-initiation and cessation. They also study extreme situations (e.g. everyone using RRP), and investigate assumptions which might eliminate the RRP-related mortality reduction. The mortality reduction is proportional to the dose reduction, increasing rapidly with time of follow-up. Plausible increases in re-initiation or dual users' consumption, or decreased quitting by smokers would not eliminate the drop.

Is the shape of the decline in risk following quitting smoking similar for squamous cell carcinoma and adenocarcinoma of the lung? A quantitative review using the negative exponential model.

One possible contributor to the reported rise in the ratio of adenocarcinoma to squamous cell carcinoma of the lung may be differences in the pattern of decline in risk following quitting for the two lung cancer types. Earlier, using data from 85 studies comparing overall lung cancer risks in current smokers, quitters (by time quit) and never smokers, we fitted the negative exponential model, deriving an estimate of 9.93years for the half-life - the time when the excess risk for quitters compared to never smokers becomes half that for continuing smokers. Here we applied the same techniques to data from 16 studies providing RRs specific for lung cancer type. From the 13 studies where the half-life was estimable for each type, we derived estimates of 11.68 (95% CI 10.22-13.34) for squamous cell carcinoma and 14.45 (11.92-17.52) for adenocarcinoma. The ratio of the half-lives was estimated as 1.32 (95% CI 1.20-1.46, p<0.001). The slower decline in quitters for adenocarcinoma, evident in subgroups by sex, age and other factors, may be one of the factors contributing to the reported rise in the ratio of adenocarcinoma to squamous cell carcinoma. Others include changes in the diagnosis and classification of lung cancer.

A review of the evidence on smoking bans and incidence of heart disease.

We update an earlier review of smoking bans and heart disease, restricting attention to admissions for acute myocardial infarction. Forty-five studies are considered. New features of our update include consideration of non-linear trends in the underlying rate, a modified trend adjustment method where there are multiple time periods post-ban, comparison of estimates based on changes in rates and numbers of cases, and comparison of effect estimates according to post-ban changes in smoking restrictiveness. Using a consistent approach to derive ban effect estimates, taking account of linear time trends and control data, the reduction in risk following a ban was estimated as 4.2% (95% confidence interval 1.8-6.5%). Excluding regional estimates where national estimates are available, and studies where trend adjustment was not possible, the estimate reduced to 2.6% (1.1-4.0%). Estimates were little affected by non-linear trend adjustment, where possible, or by basing estimates on changes in rates. Ban effect estimates tended to be greater in smaller studies, and studies with greater post-ban changes in smoking restrictiveness. Though the findings suggest a true effect of smoking bans, uncertainties remain, due to the weakness of much of the evidence, the small estimated effect, and various possibilities of bias.

Estimating the decline in excess risk of chronic obstructive pulmonary disease following quitting smoking - a systematic review based on the negative exponential model.

We quantified the decline in COPD risk following quitting using the negative exponential model, as previously carried out for other smoking-related diseases. We identified 14 blocks of RRs (from 11 studies) comparing current smokers, former smokers (by time quit) and never smokers, some studies providing sex-specific blocks. Corresponding pseudo-numbers of cases and controls/at risk formed the data for model-fitting. We estimated the half-life (H, time since quit when the excess risk becomes half that for a continuing smoker) for each block, except for one where no decline with quitting was evident, and H was not estimable. For the remaining 13 blocks, goodness-of-fit to the model was generally adequate, the combined estimate of H being 13.32 (95% CI 11.86-14.96) years. There was no heterogeneity in H, overall or by various studied sources. Sensitivity analyses allowing for reverse causation or different assumed times for the final quitting period little affected the results. The model summarizes quitting data well. The estimate of 13.32years is substantially larger than recent estimates of 4.40years for ischaemic heart disease and 4.78years for stroke, and also larger than the 9.93years for lung cancer. Heterogeneity was unimportant for COPD, unlike for the other three diseases.

Estimating the decline in excess risk of cerebrovascular disease following quitting smoking--a systematic review based on the negative exponential model.

We attempted to quantify the decline in stroke risk following quitting using the negative exponential model, with methodology previously employed for IHD. We identified 22 blocks of RRs (from 13 studies) comparing current smokers, former smokers (by time quit) and never smokers. Corresponding pseudo-numbers of cases and controls/at risk formed the data for model-fitting. We tried to estimate the half-life (H, time since quit when the excess risk becomes half that for a continuing smoker) for each block. The method failed to converge or produced very variable estimates of H in nine blocks with a current smoker RR <1.40. Rejecting these, and combining blocks by amount smoked in one study where problems arose in model-fitting, the final analyses used 11 blocks. Goodness-of-fit was adequate for each block, the combined estimate of H being 4.78(95%CI 2.17-10.50) years. However, considerable heterogeneity existed, unexplained by any factor studied, with the random-effects estimate 3.08(1.32-7.16). Sensitivity analyses allowing for reverse causation or differing assumed times for the final quitting period gave similar results. The estimates of H are similar for stroke and IHD, and the individual estimates similarly heterogeneous. Fitting the model is harder for stroke, due to its weaker association with smoking.

How rapidly does the excess risk of lung cancer decline following quitting smoking? A quantitative review using the negative exponential model.

The excess lung cancer risk from smoking declines with time quit, but the shape of the decline has never been precisely modelled, or meta-analyzed. From a database of studies of at least 100 cases, we extracted 106 blocks of RRs (from 85 studies) comparing current smokers, former smokers (by time quit) and never smokers. Corresponding pseudo-numbers of cases and controls (or at-risk) formed the data for fitting the negative exponential model. We estimated the half-life (H, time in years when the excess risk becomes half that for a continuing smoker) for each block, investigated model fit, and studied heterogeneity in H. We also conducted sensitivity analyses allowing for reverse causation, either ignoring short-term quitters (S1) or considering them smokers (S2). Model fit was poor ignoring reverse causation, but much improved for both sensitivity analyses. Estimates of H were similar for all three analyses. For the best-fitting analysis (S1), H was 9.93 (95% CI 9.31-10.60), but varied by sex (females 7.92, males 10.71), and age (<50years 6.98, 70+years 12.99). Given that reverse causation is taken account of, the model adequately describes the decline in excess risk. However, estimates of H may be biased by factors including misclassification of smoking status.

Systematic review with meta-analysis of the epidemiological evidence relating FEV1 decline to lung cancer risk.

BACKGROUND: Reduced FEV1 is known to predict increased lung cancer risk, but previous reviews are limited. To quantify this relationship more precisely, and study heterogeneity, we derived estimates of ß for the relationship RR(diff) = exp(ßdiff), where diff is the reduction in FEV1 expressed as a percentage of predicted (FEV1%P) and RR(diff) the associated relative risk. We used results reported directly as ß, and as grouped levels of RR in terms of FEV1%P and of associated measures (e.g. FEV1/FVC). METHODS: Papers describing cohort studies involving at least three years follow-up which recorded FEV1 at baseline and presented results relating lung cancer to FEV1 or associated measures were sought from Medline and other sources. Data were recorded on study design and quality and, for each data block identified, on details of the results, including population characteristics, adjustment factors, lung function measure, and analysis type. Regression estimates were converted to ß estimates where appropriate. For results reported by grouped levels, we used the NHANES III dataset to estimate mean FEV1%P values for each level, regardless of the measure used, then derived ß using regression analysis which accounted for non-independence of the RR estimates. Goodness-of-fit was tested by comparing observed and predicted lung cancer cases for each level. Inverse-variance weighted meta-analysis allowed derivation of overall ß estimates and testing for heterogeneity by factors including sex, age, location, timing, duration, study quality, smoking adjustment, measure of FEV1 reported, and inverse-variance weight of ß. RESULTS: Thirty-three publications satisfying the inclusion/exclusion criteria were identified, seven being rejected as not allowing estimation of ß. The remaining 26 described 22 distinct studies, from which 32 independent ß estimates were derived. Goodness-of-fit was satisfactory, and exp(ß), the RR increase per one unit FEV1%P decrease, was estimated as 1.019 (95%CI 1.016-1.021). The estimates were quite consistent (I2 =29.6%). Mean age was the only independent source of heterogeneity, exp(ß) being higher for age <50 years (1.024, 1.020-1.028). CONCLUSIONS: Although the source papers present results in various ways, complicating meta-analysis, they are very consistent. A decrease in FEV1%P of 10% is associated with a 20% (95%CI 17%-23%) increase in lung cancer risk.

Using the negative exponential distribution to quantitatively review the evidence on how rapidly the excess risk of ischaemic heart disease declines following quitting smoking.

No previous review has formally modelled the decline in IHD risk following quitting smoking. From PubMed searches and other sources we identified 15 prospective and eight case-control studies that compared IHD risk in current smokers, never smokers, and quitters by time period of quit, some studies providing separate blocks of results by sex, age or amount smoked. For each of 41 independent blocks, we estimated, using the negative exponential model, the time, H, when the excess risk reduced to half that caused by smoking. Goodness-of-fit to the model was adequate for 35 blocks, others showing a non-monotonic pattern of decline following quitting, with a variable pattern of misfit. After omitting one block with a current smoker RR 1.0, the combined H estimate was 4.40 (95% CI 3.26-5.95) years. There was considerable heterogeneity, H being <2years for 10 blocks and >10years for 12. H increased (p<0.001) with mean age at study start, but not clearly with other factors. Sensitivity analyses allowing for reverse causation, or varying assumed midpoint times for the final open-ended quitting period little affected goodness-of-fit of the combined estimate. The US Surgeon-General's view that excess risk approximately halves after a year's abstinence seems over-optimistic.

Reassessing the evidence relating smoking bans to heart disease.

Recent studies have compared rates of acute myocardial infarction before and after introducing smoking bans. Some meta-analyses report post-ban reductions up to 19%, implausibly large considering likely changes in smoking habits and passive smoke exposure. Our literature reassessment demonstrates major weaknesses in many studies and meta-analyses, including failure to consider data from control areas or existing trends in acute myocardial infarction rates, incorrect estimation of variability, and use in some meta-analyses of results for population subsets or estimates apparently unrelated to the data reported. We report meta-analyses using a consistent approach to derive estimates of the ban effect, taking account of time trends and control data, which indicate a much smaller reduction. Preferring national to regional estimates where available, we estimate a 5% reduction (95% CI 3-8%). Omitting estimates where trend adjustment was impossible, this becomes 2.7% (2.1-3.4%), consistent with reported declines of 2-3% in large national populations (England, France, Italy, USA). We discuss some limitations of these estimates. Further evidence is needed, possibly by analyzing national mortality data. Our findings highlight the need for a valid approach when estimating the effect of bans, and demonstrate major weaknesses in many previous publications.

Systematic review of the evidence relating FEV1 decline to giving up smoking.

BACKGROUND: The rate of forced expiratory volume in 1 second (FEV1) decline ("beta") is a marker of chronic obstructive pulmonary disease risk. The reduction in beta after quitting smoking is an upper limit for the reduction achievable from switching to novel nicotine delivery products. We review available evidence to estimate this reduction and quantify the relationship of smoking to beta. METHODS: Studies were identified, in healthy individuals or patients with respiratory disease, that provided data on beta over at least 2 years of follow-up, separately for those who gave up smoking and other smoking groups. Publications to June 2010 were considered. Independent beta estimates were derived for four main smoking groups: never smokers, ex-smokers (before baseline), quitters (during follow-up) and continuing smokers. Unweighted and inverse variance-weighted regression analyses compared betas in the smoking groups, and in continuing smokers by amount smoked, and estimated whether beta or beta differences between smoking groups varied by age, sex and other factors. RESULTS: Forty-seven studies had relevant data, 28 for both sexes and 19 for males. Sixteen studies started before 1970. Mean follow-up was 11 years. On the basis of weighted analysis of 303 betas for the four smoking groups, never smokers had a beta 10.8 mL/yr (95% confidence interval (CI), 8.9 to 12.8) less than continuing smokers. Betas for ex-smokers were 12.4 mL/yr (95% CI, 10.1 to 14.7) less than for continuing smokers, and for quitters, 8.5 mL/yr (95% CI, 5.6 to 11.4) less. These betas were similar to that for never smokers. In continuing smokers, beta increased 0.33 mL/yr per cigarette/day. Beta differences between continuing smokers and those who gave up were greater in patients with respiratory disease or with reduced baseline lung function, but were not clearly related to age or sex. CONCLUSION: The available data have numerous limitations, but clearly show that continuing smokers have a beta that is dose-related and over 10 mL/yr greater than in never smokers, ex-smokers or quitters. The greater decline in those with respiratory disease or reduced lung function is consistent with some smokers having a more rapid rate of FEV1 decline. These results help in designing studies comparing continuing smokers of conventional cigarettes and switchers to novel products.

Further investigation of gateway effects using the PATH study.

Background: Interest exists in whether youth e-cigarette use ("vaping") increases risk of initiating cigarette smoking. Using Waves 1 and 2 of the US PATH study we reported that adjustment for vaping propensity using Wave 1 variables explained about 80% of the unadjusted relationship. Here we use data from Waves 1 to 3 to avoid over-adjustment if Wave 1 vaping affected variables recorded then. Methods: Our main analysis M1 concerned Wave 2 never smokers who never vaped by Wave 1, linking Wave 2 vaping to Wave 3 smoking initiation, adjusting for Wave 1 predictors. We conducted sensitivity analyses that: excluded Wave 1 other tobacco product users; included other product use as an extra predictor; or considered propensity for smoking or any tobacco use, rather than vaping. We also conducted analyses that: adjusted for propensity as derived originally; ignored Wave 1 data; used exact age (not previously available) as a confounder rather than grouped age; attempted residual confounding adjustment by modifying predictor values using data recorded later; or considered interactions with age. Results: In M1, adjustment removed about half the excess OR (i.e. OR-1), the unadjusted OR, 5.60 (95% CI 4.52-6.93), becoming 3.37 (2.65-4.28), 3.11 (2.47-3.92) or 3.27 (2.57-4.16), depending whether adjustment was for propensity as a continuous variable, as quintiles, or for the variables making up the propensity score. Many factors had little effect: using grouped or exact age; considering other products; including interactions; or using predictors of smoking or tobacco use rather than vaping. The clearest conclusion was that analyses avoiding over-adjustment explained about half the excess OR, whereas analyses subject to over-adjustment explained about 80%. Conclusions: Although much of the unadjusted gateway effect results from confounding, we provide stronger evidence than previously of some causal effect of vaping, though some doubts still remain about the completeness of adjustment.

Investigating the effect of e-cigarette use on quitting smoking in adults aged 25 years or more using the PATH study.

Background: The evidence on harms and benefits of e-cigarettes partly concerns whether their use encourages smokers to quit.  We addressed this using data from the nationally representative PATH study, with detailed accounting for potential confounding variables. Methods: We considered adults aged 25+.  Our original analyses, reported in version 1 of this paper, used data for Waves 1 to 3, separate analyses considering Waves 1 to 2, 2 to 3 and 1 to 3.  These related baseline ever e-cigarette use (or e-product use at Wave 2) to quitting at follow-up, adjusting for confounders derived from 55 candidates.  Sensitivity analyses omitted ever other product users, linked quitting to current e-cigarette use, and used values of some predictors modified using follow-up data.  Additional analyses used data for Waves 1 to 4, separately considering sustained, delayed and temporary quitting during Waves 1 to 3, 2 to 4 and 1 to 4.  Sensitivity analyses considered 30-day quitting, restricted attention to smokers attempting to quit, and considered ever smokeless tobacco or snus use. Results: In the original analyses, unadjusted odds ratios (ORs) of quitting smoking for ever e-cigarette use were 1.29 (95% CI 1.01-1.66), 1.52 (1.26-1.83) and 1.47 (1.19-1.82) for the Wave 1 to 2, 2 to 3, and 1 to 3 analyses.  These reduced after adjustment, to 1.23 (0.94-1.61), 1.51 (1.24-1.85) and 1.39 (1.11-1.74).  Quitting rates remained elevated in users in all sensitivity analyses.  The additional analyses found associations of e-cigarette use with sustained, delayed and temporary quitting, associations little affected by considering 30-day quitting, and only slightly reduced restricting attention to quit attempters.  Ever use of smokeless tobacco or snus also predicted increased quitting.   Conclusions: As does most evidence from clinical trials, other analyses of PATH, and other epidemiological studies, our results suggest using e-cigarettes helps adult smokers to quit.

Does use of flue-cured rather than blended cigarettes affect international variation in mortality from lung cancer and COPD?

We compared risk of lung cancer and chronic obstructive pulmonary disease (COPD) associated with flue-cured and blended cigarettes. Mortality and smoking data were collected for 1971-2000 by sex, age, and period for three countries with a mainly flue-cured market and four with a blended market. Epidemiological relative risk estimates for current and ex smoking were summarized. Smoking statistics and mortality were compared between flue-cured cigarette and blended cigarette countries. Unadjusted mortality rates were generally lower in blended cigarette countries early on, with the difference diminishing or reversing by the 1990s. Differences by cigarette type were rarely significant, due to variations, particularly for COPD, between countries within cigarette type. Current smoking prevalence was generally lower in blended cigarette countries in 1971-1975, with the difference reducing over time. Differences by type were never significant, with blended cigarette countries varying markedly. Ex-smoking increased over time and was lower for blended cigarette countries, generally not significantly. Consumption per smoker was somewhat lower for blended cigarette countries. Relative risk estimates for smoking, derived mainly from U.S. and UK studies, varied little by cigarette type. Conclusions based on estimated smoking-related excess mortality were similar to those based on unadjusted mortality rates. There was little indication of any difference between flue-cured and blended cigarettes on risk of lung cancer or COPD. Our approach could have detected differences of about 40% for male lung cancer, or twofold differences for females or for COPD, had they existed. Between-country differences in rates of two major diseases predominantly caused by smoking cannot materially be explained by whether the countries use flue-cured or blended cigarettes.

2-Methoxy antimycin reveals a unique mechanism for Bcl-x(L) inhibition.

Overexpression of Bcl-x(L) in multiple cancers correlates with resistance to chemotherapy and radiation therapy, and provides a rationale for development of small-molecule Bcl-x(L) inhibitors. Based on knockout studies, nonneoplastic cells also require Bcl-x(L) survival functions, particularly when challenged with cytotoxic agents. We analyze the selective cytotoxicity of one Bcl-x(L) inhibitor, 2-methoxy antimycin A, toward cells with excess exogenous Bcl-x(L) in isogenic cell line pairs. This selectivity, characteristic of a gain-of-function mechanism, is not shared by other known Bcl-x(L) inhibitors, including BH3I-2, HA14-1, ABT-737, gossypol, or the stapled BH3 helical peptide SAHB-BID. We show that Bcl-x(L) overexpression induces a shift in energy metabolism from oxidative phosphorylation to glycolysis. Treatment with 2-methoxy antimycin A acutely reverses the metabolic effects of Bcl-x(L), causing mitochondrial hyperpolarization and a progressive increase in mitochondrial NAD(P)H. We identify an additional small-molecule Bcl-x(L) inhibitor, NSC 310343, establishing a class of Bcl-x(L) inhibitors with gain-of-function activity. In contrast to other Bcl-x(L) inhibitors, combining gain-of-function Bcl-x(L) inhibitors with a standard inducer of apoptosis, staurosporine, enhances selective cytotoxicity toward Bcl-x(L)-overexpressing cells. These results provide an example of the intersection of bioenergetic metabolism and Bcl-x(L) functions and suggest a metabolic basis for the gain-of-function mechanism of Bcl-x(L) inhibitors.

The effect of quitting smoking on HDL-cholesterol - a review based on within-subject changes.

A higher concentration of high density lipoprotein cholesterol (HDL-C) in ex-smokers than smokers has consistently been observed. Better evidence of quitting effects comes from within-subject changes. We extend an earlier meta-analysis to quantify the reduction, and investigate variation by time quit and other factors. We conducted Medline and Cochrane searches for studies measuring HDL-C in subjects while still smoking and later having quit. Using unweighted and inverse-variance weighted regression analysis, we related changes (in mmol/l) to intra-measurement period, and estimated time quit, and to study type, location and start year, age, sex, product smoked, validation of quitting, baseline HDL-C, baseline and change in weight/BMI, and any study constraints on diet or exercise. Forty-five studies were identified (17 Europe, 16 North America, 11 Asia, 1 Australia). Thirteen were observational, giving changes over at least 12 months, with most involving >1000 subjects. Others were smoking cessation trials, 12 randomized and 20 non-randomized. These were often small (18 of <100 subjects) and short (14 of <10 weeks, the longest a year). Thirty studies provided results for only one time interval. From 94 estimates of HDL-C change, the unweighted mean was 0.107 (95% CI 0.085-0.128). The weighted mean 0.060 (0.044 to 0.075) was lower, due to smaller estimates in longer term studies. Weighted means varied by time quit (0.083, 0.112, 0.111, 0.072, 0.058 and 0.040 for <3, 3 to <6, 6 to <13, 13 to <27, 27 to <52 and 52+ weeks, p=0.006). After adjustment for time quit, estimates varied by study constraint on diet/exercise (p=0.003), being higher in studies requiring subjects to maintain their pre-quitting habits, but no other clear differences were seen, with significant (p<0.05) increases following quitting being evident in all subgroups studied, except where data were very limited. For both continuing and never smokers, the data are (except for two large studies atypically showing significant HDL-C declines in both groups, and a smaller decline in quitters) consistent with no change, and contrast markedly with the data for quitters. We conclude that quitting smoking increases HDL-C, and that this increase occurs rapidly after quitting, with no clear pattern of change thereafter.