Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: EC:6.3.5.5 (CPS)
 1,262 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Over the past 40 years, the American Cancer Society has led in large-scale, prospective studies of behavioral and environmental risk factors in association with cancer development. Through results of its 1952 study, cigarette smokers were found to have a 10-fold higher risk of lung cancer than nonsmokers. Cancer Prevention Study I (1959-1972) extended these results and also showed the relationship between age smoking began, depth of inhalation, smoking cessation, air pollution, body weight, etc., on all causes of death as well as specific cancer sites. Cancer Prevention Study II began in 1982 and after six years of follow-up has confirmed many earlier findings, and additionally has found: aspirin may be protective against colon cancer; persons reporting themselves to be heavy exercisers had higher standardized mortality ratios (SMR) for lung, colorectal, and pancreas cancer than moderate exercisers; more women who were long-term users of artificial sweeteners reported gaining weight during the past year than nonusers; diesel fume exposure elevated the risk of lung cancer among men ages 40-79; pesticide exposure was associated with an increased risk of multiple myeloma; and based on CPS II mortality rates, an estimated 250 million of the 1.25 billion persons living in developed countries will die because they smoke.
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PMID:Cancer Prevention Study II. The American Cancer Society Prospective Study. 147 48

Globally, oral cancer is one of the ten common cancers. In some parts of the world, including the Indian subcontinent, oral cancer is a major cancer problem. Tobacco use is the most important risk factor for oral cancer. The most common form of tobacco use, cigarette smoking, demonstrates a very high relative risk--in a recent cohort study (CPS II), even higher than lung cancer. In areas where tobacco is used in a smokeless form, oral cancer incidence is generally high. In the West, especially in the U.S. and Scandinavia, smokeless tobacco use consists of oral use of snuff. In Central, South, and Southeast Asia smokeless tobacco use encompasses nass, naswar, khaini, mawa, mishri, gudakhu, and betel quid. In India tobacco is smoked in many ways; the most common is bidi, others being chutta, including reverse smoking, hooka, and clay pipe. A voluminous body of research data implicating most of these forms of tobacco use emanates from the Indian subcontinent. These studies encompass case and case-series reports, and case-control, cohort, and intervention studies. Collectively, the evidence fulfills the epidemiological criteria of causality: strength, consistency, temporality, and coherence. The biological plausibility is provided by the identification of several carcinogens in tobacco, the most abundant and strongest being tobacco-specific N-nitrosamines such as N-nitrosonornicotine (NNN) and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK). These are formed by N-nitrosation of nicotine, the major alkaloid responsible for addiction to tobacco. The etiological relationship between tobacco use and oral cancer has provided us with a comprehensive model for understanding carcinogenesis.
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PMID:Epidemiology of cancer by tobacco products and the significance of TSNA. 868 60

The authors explored two methodological issues in the estimation of smoking-attributable mortality for the United States. First, age-specific and age-adjusted relative risk, attributable fraction, and smoking-attributable mortality estimates obtained using data from the American Cancer Society's second Cancer Prevention Study (CPS II), a cohort study of 1.2 million participants (1982-1988), were compared with those obtained using a combination of data from the National Mortality Follow-back Survey (NMFS), a representative sample of US decedents in which information was collected from informants (1986), and the National Health Interview Survey (NHIS), a nationally representative household survey (1987). Second, the potential for residual confounding of the disease-specific age-adjusted smoking-attributable mortality estimates was addressed with a model-based approach. The estimated smoking-attributable mortality based on the CPS II for the four most common smoking-related diseases-lung cancer, chronic obstructive pulmonary disease, coronary heart disease, and cerebrovascular disease-was 19% larger than the estimated smoking-attributable mortality based on the NMFS/NHIS, yet the two data sources yielded essentially the same smoking-attributable mortality estimate for lung cancer alone. Further adjustment of smoking-attributable mortality for disease-appropriate confounding factors (education, alcohol intake, hypertension status, and diabetes status) indicated little residual confounding once age was taken into account.
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PMID:Methodological issues in estimating smoking-attributable mortality in the United States. 1099 48

Although smoking is widely recognized as a major cause of cancer, there is little information on how it contributes to the global and regional burden of cancers in combination with other risk factors that affect background cancer mortality patterns. We used data from the American Cancer Society's Cancer Prevention Study II (CPS-II) and the WHO and IARC cancer mortality databases to estimate deaths from 8 clusters of site-specific cancers caused by smoking, for 14 epidemiologic subregions of the world, by age and sex. We used lung cancer mortality as an indirect marker for accumulated smoking hazard. CPS-II hazards were adjusted for important covariates. In the year 2000, an estimated 1.42 (95% CI 1.27-1.57) million cancer deaths in the world, 21% of total global cancer deaths, were caused by smoking. Of these, 1.18 million deaths were among men and 0.24 million among women; 625,000 (95% CI 485,000-749,000) smoking-caused cancer deaths occurred in the developing world and 794,000 (95% CI 749,000-840,000) in industrialized regions. Lung cancer accounted for 60% of smoking-attributable cancer mortality, followed by cancers of the upper aerodigestive tract (20%). Based on available data, more than one in every 5 cancer deaths in the world in the year 2000 were caused by smoking, making it possibly the single largest preventable cause of cancer mortality. There was significant variability across regions in the role of smoking as a cause of the different site-specific cancers. This variability illustrates the importance of coupling research and surveillance of smoking with that for other risk factors for more effective cancer prevention.
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PMID:Role of smoking in global and regional cancer epidemiology: current patterns and data needs. 1588 Apr 14

A stochastic two-stage cancer model is used to analyse the relation between lung cancer and cigarette smoking. The model contains the main rate-limiting stages of carcinogenesis, which include initiation, promotion (clonal expansion of initiated cells), malignant transformation and a lag time for tumour formation. Various data sets were used to test the model. These include the data of a large prospective collaborative project carried out in 10 different European countries, the European Prospective Investigation into Cancer and Nutrition (EPIC). This new data set has not been modelled before. The model is also tested on other published data from CPS-II (Cancer Prevention Study II) of the American Cancer Society and the British doctors' study. The analyses indicate that the EPIC data are best described with smoking dependence on the rates of malignant transformation and clonal expansion. With increasing smoking rates, saturation effects in the two exposure rate-dependent model parameters were observed. The results find confirmation in the biological literature, where both mutational effects and promotional effects of cigarette smoke are documented.
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PMID:Analysis of epidemiological cohort data on smoking effects and lung cancer with a multi-stage cancer model. 1641 Feb 61

We conducted an extended follow-up and spatial analysis of the American Cancer Society (ACS) Cancer Prevention Study II (CPS-II) cohort in order to further examine associations between long-term exposure to particulate air pollution and mortality in large U.S. cities. The current study sought to clarify outstanding scientific issues that arose from our earlier HEI-sponsored Reanalysis of the original ACS study data (the Particle Epidemiology Reanalysis Project). Specifically, we examined (1) how ecologic covariates at the community and neighborhood levels might confound and modify the air pollution-mortality association; (2) how spatial autocorrelation and multiple levels of data (e.g., individual and neighborhood) can be taken into account within the random effects Cox model; (3) how using land-use regression to refine measurements of air pollution exposure to the within-city (or intra-urban) scale might affect the size and significance of health effects in the Los Angeles and New York City regions; and (4) what exposure time windows may be most critical to the air pollution-mortality association. The 18 years of follow-up (extended from 7 years in the original study [Pope et al. 1995]) included vital status data for the CPS-II cohort (approximately 1.2 million participants) with multiple cause-of-death codes through December 31, 2000 and more recent exposure data from air pollution monitoring sites for the metropolitan areas. In the Nationwide Analysis, the influence of ecologic covariate data (such as education attainment, housing characteristics, and level of income; data obtained from the 1980 U.S. Census; see Ecologic Covariates sidebar on page 14) on the air pollution-mortality association were examined at the Zip Code area (ZCA) scale, the metropolitan statistical area (MSA) scale, and by the difference between each ZCA value and the MSA value (DIFF). In contrast to previous analyses that did not directly include ecologic covariates at the ZCA scale, risk estimates increased when ecologic covariates were included at all scales. The ecologic covariates exerted their greatest effect on mortality from ischemic heart disease (IHD), which was also the health outcome most strongly related with exposure to PM2.5 (particles 2.5 microm or smaller in aerodynamic diameter), sulfate (SO4(2-)), and sulfur dioxide (SO2), and the only outcome significantly associated with exposure to nitrogen dioxide (NO2). When ecologic covariates were simultaneously included at both the MSA and DIFF levels, the hazard ratio (HR) for mortality from IHD associated with PM2.5 exposure (average concentration for 1999-2000) increased by 7.5% and that associated with SO4(2-) exposure (average concentration for 1990) increased by 12.8%. The two covariates found to exert the greatest confounding influence on the PM2.5-mortality association were the percentage of the population with a grade 12 education and the median household income. Also in the Nationwide Analysis, complex spatial patterns in the CPS-II data were explored with an extended random effects Cox model (see Glossary of Statistical Terms at end of report) that is capable of clustering up to two geographic levels of data. Using this model tended to increase the HR estimate for exposure to air pollution and also to inflate the uncertainty in the estimates. Including ecologic covariates decreased the variance of the results at both the MSA and ZCA scales; the largest decrease was in residual variation based on models in which the MSA and DIFF levels of data were included together, which suggests that partitioning the ecologic covariates into between-MSA and within-MSA values more completely captures the sources of variation in the relationship between air pollution, ecologic covariates, and mortality. Intra-Urban Analyses were conducted for the New York City and Los Angeles regions. The results of the Los Angeles spatial analysis, where we found high exposure contrasts within the Los Angeles region, showed that air pollution-mortality risks were nearly 3 times greater than those reported from earlier analyses. This suggests that chronic health effects associated with intra-urban gradients in exposure to PM2.5 may be even larger between ZCAs within an MSA than the associations between MSAs that have been previously reported. However, in the New York City spatial analysis, where we found very little exposure contrast between ZCAs within the New York region, mortality from all causes, cardiopulmonary disease (CPD), and lung cancer was not elevated. A positive association was seen for PM2.5 exposure and IHD, which provides evidence of a specific association with a cause of death that has high biologic plausibility. These results were robust when analyses controlled (1) the 44 individual-level covariates (from the ACS enrollment questionnaire in 1982; see 44 Individual-Level Covariates sidebar on page 22) and (2) spatial clustering using the random effects Cox model. Effects were mildly lower when unemployment at the ZCA scale was included. To examine whether there is a critical exposure time window that is primarily responsible for the increased mortality associated with ambient air pollution, we constructed individual time-dependent exposure profiles for particulate and gaseous air pollutants (PM2.5 and SO2) for a subset of the ACS CPS-II participants for whom residence histories were available. The relevance of the three exposure time windows we considered was gauged using the magnitude of the relative risk (HR) of mortality as well as the Akaike information criterion (AIC), which measures the goodness of fit of the model to the data. For PM2.5, no one exposure time window stood out as demonstrating the greatest HR; nor was there any clear pattern of a trend in HR going from recent to more distant windows or vice versa. Differences in AIC values among the three exposure time windows were also small. The HRs for mortality associated with exposure to SO2 were highest in the most recent time window (1 to 5 years), although none of these HRs were significantly elevated. Identifying critical exposure time windows remains a challenge that warrants further work with other relevant data sets. This study provides additional support toward developing cost-effective air quality management policies and strategies. The epidemiologic results reported here are consistent with those from other population-based studies, which collectively have strongly supported the hypothesis that long-term exposure to PM2.5 increases mortality in the general population. Future research using the extended Cox-Poisson random effects methods, advanced geostatistical modeling techniques, and newer exposure assessment techniques will provide additional insight.
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PMID:Extended follow-up and spatial analysis of the American Cancer Society study linking particulate air pollution and mortality. 1962 30

Large, unexplained, but possibly related disparities exist between heart disease risks observed in differing genders, educational levels, times, and studies. Such heart disease disparities might be related to cumulative tobacco smoke damage (smoke load) disparities that are overlooked in standard assessments of point smoking status. So, I reviewed possible relationships between smoke load and heart disease levels across genders, educational strata, years, and leading studies. Smoker heart disease risk assessments in the Nurses Health Study (Nurses), Cancer Prevention Study-II (CPS-II), and British Doctors studies were compared and related to their likely selection and misclassification biases. Relationships between smoke loads and United States (US) education- and gender-related heart disease mortality disparities were qualitatively assessed using lung cancer rates as a smoke load proxy. The high heart disease mortality risks observed in smoking Nurses in 1980-2004 and in less educated US women in 2001 were qualitatively associated with their higher smoke loads and lower selection and exposure misclassification biases than in the CPS-II and Doctors studies. Smoking-attributable heart disease death tolls and disparities extrapolated from mortality ratios from the CPS-II and Doctors studies may be substantial underestimates. Such studies appear to have compared convenience samples of light smokers to lighter smokers instead of comparing representative smokers to the unexposed. Further efforts to minimize smoke exposures and better quantify cumulative smoking-attributable burdens are needed.
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PMID:Smoking and ischemic heart disease disparities between studies, genders, times, and socioeconomic strata. 1965 85

In a previous analysis (see Part I) we proposed a heuristic for assessing the efficacy of potential reduced-risk tobacco products (PRRPs) on lung cancer (LC) rates, using smoking cessation data published in a report from the Iowa Women's Health Study (IWHS) as a basis for sample size estimates. In this study, an additional analysis was performed using cessation data from the much larger Cancer Prevention Study II (CPS-II), which also provides data on different durations of cessation. Statistical methods were used to assess whether smokers switching to a PRRP would reduce their risk of LC. Furthermore, non-inferiority tests compared the LC risk in switchers to that in smokers who had quit smoking. The present work shows that similar sample size estimates were obtained whether the analysis was based on the IWHS or the CPS-II data sets, suggesting that the heuristic may be generally applicable to prospective real-life studies to evaluate PRRPs. Non-inferiority testing of switchers compared with quitters required approximately 10-fold more subjects than did superiority testing of switchers compared with smokers. Altogether, these estimates indicate that it is feasible, in terms of study duration and sample size, to clinically assess the LC risk-reducing potential of a PRRP.
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PMID:Further considerations on the evaluation of potential reduced-risk tobacco products. Part II: Re-assessment of a heuristic using the CPS-II database. 2001 23

Past studies have examined the relationship of lung cancer to smoking using longitudinal data for select samples. This study applies the two-stage clonal expansion (TSCE) model to U.S. +xsmoking data over a 25-year period. Smoking Base Case (SBC) data on actual smoking duration and intensity from the years 1975-2000 are applied by gender to separate TSCE models, which are then calibrated to historical trends in lung cancer death rates using regression analysis. The uncalibrated and calibrated TSCE models are also applied to SBC data for two scenarios: (1) no tobacco control and (2) complete tobacco control. The results are used to develop estimates of the number of lives saved as a result of tobacco control and how many lives would be saved if cigarette use had ceased in 1965. Predictions of lung cancer from the TSCE models with CPS-II and the CPS-I data for males and especially females are considerably below historical rates with the deviations from historical rates increasing over time. Residual trends unrelated to the smoking models were also found. Tobacco control activities saved approximately 625,000 lives between the years 1975 and 2000. An additional 2,110,000 lives would have been saved if all smoking was stopped in 1965. Tobacco control has successfully prevented lung cancer deaths, but many more lives could be saved with further reductions in smoking rates. Systematic biases were observed from TSCE models using CPS-I and CPS-II data to estimate smoking-related lung cancer deaths.
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PMID:Chapter 10: A macro-model of smoking and lung cancer: examining aggregate trends in lung cancer rates using the CPS-I and CPS-II and two-stage clonal expansion models. 2288 83

The relationship between smoking and lung cancer is well established and cohort studies provide estimates of risk for individual cohorts. While population trends are qualitatively consistent with smoking trends, the rates do not agree well with results from analytical studies. Four carcinogenesis models for the effect of smoking on lung cancer mortality were used to estimate lung cancer mortality rates for U.S. males: two-stage clonal expansion and multistage models using parameters estimated from two Cancer Prevention Studies (CPS I and CPS II). Calibration was essential to adjust for both shift and temporal trend. The age-period-cohort model was used for calibration. Overall, models using parameters derived from CPS I performed best, and the corresponding two-stage clonal expansion model was best overall. However, temporal calibration did significantly improve agreement with the population rates, especially the effect of age and cohort.
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PMID:Chapter 14: Comparing the adequacy of carcinogenesis models in estimating U.S. population rates for lung cancer mortality. 2288 88


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