There is substantial evidence that low consumption of fruit and vegetables (FV) is a major risk factor for many chronic diseases. The aim of this study was to assess FV consumption and the variables that influence it among elderly individuals in Iran aged 60 and over.This was a cross-sectional study to investigate FV intake by a randomly-selected sample of members of elderly centers in Tehran, Iran. A multidimensional questionnaire was used to collect data on demographic characteristics, daily consumption of FV, knowledge, self-efficacy, social support, perceived benefits, and barriers against FV. Data were analyzed using t-tests, one way analysis of variance, Pearson correlation, and logistic regression.In total, 400 elderly individuals took part in the study. The mean age of the participants was 64.07 (SD = 4.49) years, and most were female (74.5%). The mean number of FV servings per day was 1.76 (SD = 1.15). Ninety-seven percent of participants (n = 388) did not know the recommended intake was at least five servings of FV per day. Similarly, 88.3% (n = 353) did not know the size of a single serving. The most frequent perceived benefits of and barriers against FV consumption were availability and expense, respectively. Knowledge , perceived benefits  and barriers , self-efficacy  and family support  were significantly associated with fruit and vegetable consumption.The findings of this study indicate that FV intake among elderly individuals in Iran was lower than the recommended minimum of five daily servings and varied greatly with age, marital status, educational attainment, and income level. The results also indicated that low perceived benefits, low self-efficacy, and perceived barriers could lead to lower consumption of FV. It seems that in order to improve FV consumption among elderly individuals in Iran, raising awareness, improving perception of benefits and enhancing self-efficacy regarding FV consumption should receive more attention. Indeed, it is essential to plan health education programs and nutritional interventions for this group of the population. Coronary heart disease (CHD), cancer and stroke are leading causes of death  that areIn general, many factors contribute to FV intake. For instance, one study showed that knowledge, attitudes, skills and self-efficacy were among the factors that might influence an individual's likelihood of taking five servings of FV per day . It has Many studies have revealed variables that influence FV consumption among elderly populations worldwide ,19, but The present study was conducted to investigate factors that contribute to FV consumption by elderly individuals in Iran. We were particularly interested in studying the effects of the following determinants: age, gender, education, marital status, employment and economic status, chronic disease, body mass index (BMI), stages of change, self efficacy, support from family and friends, knowledge regarding FV, and perceived benefits and barriers.This was a cross-sectional study among a randomly selected sample of members of elderly centers in Tehran, Iran. The study was conducted between September 2007 and April 2008. Tehran has 23 elderly centers in which free educational and social services are offered to members. Membership is free of charge and all individuals aged 60 and above are eligible to join. A random sample was selected from all listed members. The sample size was estimated on the basis of a single proportion design. We assumed that at best 50% of the elderly would have adequate FV intake. Thus, a study with a sample of 385 elderly would have 80% power to detect a difference of 5% (45-55%) at the 0.05 significance level. The sample size actually obtained for this study was 454. Those who did not agree to take part in the study and those who were suffering from serious illness or had undergone a surgical operation up to 3 months before the date of data collection were excluded (n = 422).The Ethics Committee of Tehran University of Medical Sciences approved the study. Before the study was conducted, the aim, method and confidentiality were explained fully to the potential participants and if they were satisfied and willing to take part they were asked to read and sign a consent form. The headmasters of all 23 elderly centers were also approached about their willingness to participate. To collect data, trained interviewers conducted face-to-face interviews. The interviewers had high school diplomas. Each interview lasted for approximately 45 minutes.We used several instruments to collect data. Their reliability was assessed among a sub-sample of 20 participants using Cronbach's alpha coefficient. The instruments and findings are explained as follows:2, and economic status was measured using the asset-based approach developed by Ferguson and colleagues [This comprised three sections covering demographic and anthropometrics data including information on age, sex, education, income, marital status, health status (having a chronic disease or not) and BMI. Chronic disease was indicated by asking each individual to respond to the question: 'Do you have any long-standing diseases'? Anyone who responded positively was asked to indicate the name of the disease. Weight was measured using the same digital scales  while the subjects were minimally clothed and not wearing shoes. Height was measured by a tape measure while the subjects were standing and not wearing shoes and the shoulders were in a normal position. BMI was calculated and expressed in kg/mlleagues  and usedlleagues . AccordiThis part of the questionnaire comprised five statements by which the participants were categorized into different stages of change: pre-contemplation, contemplation, preparation, action and maintenance. This part included multiple-choice questions adapted from the literature . The parSelf-efficacy was assessed using a five-item questionnaire developed by Ma et al. . Each itIn order to assess the influences of family and friends on healthy eating, the 6-item family support for healthy eating habits scale and the 6-item friend support for healthy eating habits scale were used . Each itThe knowledge of participants was measured using a six-item questionnaire. The first question was: Would you say what is the recommended number of servings for FV consumption per day?" The response categories were '1', '2-3' and '5 or more'. The second item was: Would you say that a single serving of beans is 'more,' 'less,' or 'about as much' as can fit in the palm of your hand? The next four items used an agree/disagree response format. The items were: FV are a good source of fiber; if you take vitamin pills you do not have to eat a lot of FV; boiling and evaporation is the best method to cook vegetables; as long as you eat FV, it does not matter what color they are. Correct responses were summed to create a total knowledge score of 0 to 6. This scale was found to be valid  and a reliable measure .This part was generated from previous studies and focus group discussions with convenient samples of elderly individuals. Participants were asked about their perceptions regarding amounts of FV intake. The final perceived benefit questionnaire consists of 15 items. Each item is rated on a 5-point scale ranging from very important to not at all important. The perceived barrier consists of 11 items. Each item is also rated on a 5-point scale ranging from very important to not at all important. The total score for the perceived benefits ranges from 15 to 75 and for perceived barriers from 11 to 55. The Cronbach's alpha coefficient for the benefit scale was 0.73 and for the barrier scale it was 0.69.This section comprised two parts as follows:7.1. Food frequency questionnaire:  This consisted of two main questions related to fruits and vegetables  available in Tehran's markets. Response categories were: never, 1-2 times per week, 3-4 times per week, 5-6 times per week, and every day. Accordingly the respondents were asked to indicate the amount of intake. We estimated the daily FV intake for each individual on the basis of this information.7.2. A 24-hour recall:  Participants were asked to estimate their daily servings of FV at breakfast, lunch, dinner, and between meals as snacks or deserts in accordance with a nutrition guideline card. The nutrition guideline card categorized one serving of vegetables into one of three following groups: (1) one cup of raw green leafy vegetables such as spinach or salad; (2) one-half cup of other vegetables cooked or chopped raw, such as tomatoes, carrots, pumpkin, corn, Chinese cabbage, beans, or onions; and (3) one-half cup of vegetable juice. The nutrition guideline categorized one serving of fruit into one of three groups: (1) one medium size fruit such as an apple, banana, or orange; (2) one-half cup of cooked, chopped, or canned fruit; and (3) one-half cup of fruit juice, not artificially flavored. We then calculated daily serving FV consumption for each individual.Data were analyzed using both descriptive and analytic statistics. Mean values of daily FV consumption were provided by the characteristics of the study sample; and for knowledge, benefits, barriers, and self-efficacy, mean values were assessed by stages of change.To compare FV consumption per day among different subgroups of the study sample, an independent samples t-test and one-way analysis of variance (ANOVA) were used to compare two or more independent mean values, respectively.Pearson correlation was used to assess the correlation between independent variables and FV consumption.Logistic regression was used to identify the magnitude of association between FV servings eaten per day and independent variables including age, gender, education, marital status, economic status, self-reported chronic disease, perceived benefits and barriers, self efficacy, knowledge regarding FV consumption, and social support (family and friends). To avoid infinite odds ratios, some categories were merged. For example, marital status was categorized into 'married' and 'widowed and divorced'. For the purpose of logistic regression analysis the sample was divided into quartiles of FV consumption and the 1st (inadequate) and 4th (adequate) quartiles were compared in order to increase statistical power.Of 454 eligible individuals, 32 did not agree to be interviewed. Thus, 422 individuals who signed the consent form entered the study. A total of 22 questionnaires were excluded from analysis owing to incomplete answers. In total, 400 elderly individuals (102 men and 298 women) from 23 elderly centers took part in the study. The mean age of participants was 64.07 (SD = 4.49) years (range 60-87). Most participants were married (55%) and unemployed (80%) with BMI between 25 and 29 (48%). The results showed that FV consumption among the participants was low. Overall, the mean serving of FV intake eaten per day for the whole sample was 1.76 (SD = 1.15) and varied significantly with age (P = 0.003), educational attainment (P < 0.001), marital status (P < 0.001), economic status (P < 0.001), and BMI (P < 0.001). However, there were no significant differences between males and females or with different employment status. Table The data analysis indicated that 97% of participants (n = 388) did not know that the recommended intake is at least five servings of fruit and vegetables per day. Similarly, 93% (n = 372) did not know about the importance of FV color, and 88.3% (n = 353) did not know the recommended size of one serving. However, 74.3% of participants (n = 297) acknowledged that FV are an important source of fiber, 70.3% (n = 281) correctly reported that vitamin pills were not as valuable as FV, and 76% (n = 304) were aware that boiling and evaporating are healthy methods of cooking vegetables. The mean scores of participants' knowledge were 2.07 (SD = 0.47), 2.91 (SD = 0.84), 3.15 (SD = 0.82), 4.33 (SD = 0.52) and 5.75 (SD = 0.46) for the pre-contemplation, contemplation, preparation, action and maintenance stages, respectively.The perceived benefits and barriers regarding FV intake are shown in table TTM variables, knowledge, social support and FV intake were all found to be significantly correlated Table .Logistic regression was used to identify variables that contribute to inadequate FV consumption. The results showed that knowledge , perceived benefits  and barriers , self-efficacy , and support from family  were significant predictors of FV consumption, while age, gender, education, marital status, economic status, employment, chronic diseases, and support from friends were not. The results are presented in Table This study revealed that FV consumption among elderly Iranians is much lower than the daily consumption recommended by WHO . FurtherAlthough previous studies ,39 have As expected, this study showed that participants who were more educated  and wealthier consumed more FV. These results are similar to those reported by other investigators ,41. It sAlthough this study showed that most participants had good knowledge regarding the different health benefits of FV and also believed that vitamin pills are not real substitutes for fresh FV, very few participants knew about the daily amount of FV consumption or the correct size of an FV serving as recommended by WHO. Van Duyn and co-workers indicated that awareness of how many FV portions a person should eat per day is associated with higher consumption .The current results provide further support to studies indicating that FV prices are a barrier to consumption by low-income consumers, so developing public policies to make FV more affordable for low-income families should be encouraged . A previThe findings of this study indicated that married participants consumed more FV than the widowed and divorced. A study showed that married men consumed more FV than single men but there were no differences among women in this regard . This miParticipants in more advanced stages of change of FV consumption behavior were more likely to consume these foods. This is consistent with findings by other researchers . These rConsistent with other previous findings , our stuGiven that all our respondents were members of elderly centers, the findings of this study might not be generalized to all elderly Tehran residents. These elderly might differ from others in terms of socioeconomic status, family cohesiveness, social support, and availability and access to FV. Further studies are needed to examine the mediating factors affecting FV consumption in a larger and more diverse group of elderly in Iran. In addition, it should be noted that our findings on FV intake were based on self-reported information and thus might be associated with measurement errors. Seasonal aspects were not investigated in this study; since season might influence the availability of FV, it is recommended that this be considered in future studies.The findings demonstrated that FV consumption among elderly Iranian individuals was low and varied greatly with age, education and income level. In addition, the results indicated that low perceived benefits, low self-efficacy, and perceived barriers could lead to lower consumption of FV. Therefore, it seems that in order to improve FV consumption among elderly Iranians, raising awareness, improving perception benefits and enhancing self-efficacy regarding FV consumption should receive more attention. Indeed, it is essential to plan health education programs and nutritional interventions for this group of the population.FV: Fruit and vegetable; CHD: Coronary heart disease; CVD: Cardiovascular disease; BMI: Body Mass Index; TTM: Transtheoretical Model; OR: odds ration.The authors declare that they have no competing interests.LS was the main investigator, analyzed the data and involved in drafting the manuscript. HE supervised the study, and contributed to the study design. KM contributed to the study design, performed the statistical analysis, and supervised the study. SST involved in drafting, and revising it critically for important intellectual content. AJ helped in writing process. AM contributed to the analysis, edited the paper and provided the final version. All authors read and approved the final manuscript.


The aim of this study was to conduct a comprehensive examination of the independent associations of a wide range of serum markers of nutritional status with lung function, measured as the one-second forced expiratory volume , selenium, normalized calcium, chloride, and iron were independently associated with higher levels of FEVMaintaining higher serum concentrations of dietary antioxidant vitamins and selenium is potentially beneficial to lung health. In addition other novel associations found in this study merit further investigation. Although smoking is the main identified risk factor for COPD it is clear that other aetiological factors are also involved. There is now substantial observational evidence, based predominantly on food frequency questionnaire measures of intake, that a diet high in antioxidants is associated with better lung function , yielded effect sizes that were reduced slightly, but did not affect the overall conclusions of the analysis. Similar conclusions were made when lung function was modelled as FVC.Finally, we conducted sensitivity analyses to examine whether the results were similar after the exclusions of selected participants. When the results were examined excluding individuals who used vitamin or mineral supplements , the majority of the results were similar; increases in the effect size were seen for vitamin E and iron, whereas the effect sizes for lycopene and selenium were slightly reduced. Excluding people with asthma  from the study population did not alter the effect estimates. Excluding participants with COPD  [self-reported physician-diagnosed emphysema and/or chronic bronchitis, and/or by GOLD spirometry criteria  was found between iron and transferrin saturation: these two nutrients were never included in the same model. Within Model 3, 95% of the correlations between nutrients were less than 0.3 and the strongest correlation found was between lutein/zeaxanthin and beta-cryptoxanthin (r = 0.50), however modelling them separately did not alter findings; it only increased the size of the effects in the final model. The final model was examined for collinearity and there was no strong evidence of collinearity within the model as all of the variance inflation factors were less than 5 and the mean variation inflation factor was 1.86. Lastly, if we apply the Bonferoni correction to the p-values, then only p-values of less than 0.002 would be considered as statistically significant. This multiple comparison approach would have excluded the following nutrients from the final model lutein/zeaxanthin (p = 0.092), lycopene (p = 0.01) iron (p = 0.005), red blood cell folate (p = 0.021), and sodium (p = 0.022).1. The a priori objective of this study was to test all available serum nutrients in the NHANES III dataset in a single model, to identify the relative importance of individual nutrients, and the statistical power available in the dataset has allowed us to distinguish independent effects of several exposures. In addition, the majority of the nutrients had very weak correlations with other serum nutrients. A systematic approach to modelling was taken and one of the explicit goals of the analyses was to discover new associations. Most of the p-values were very small (p < 0.002), however if we applied the conservative approach of the Bonferroni correction, 4 of the nutrients in the final model would no longer be considered statistically significant. However, the results from this approach must also be interpreted with caution due to the potential of type II error[There is already an extensive literature on the relation between measures of dietary intake, lung function and various other respiratory disease outcomes, which has been reviewed elsewhere,4,18,19, II error. SimilarAlthough all of the nutrient levels we analysed are dependent at least to some degree on dietary intake, some  are closely regulated by homeostatic systems in the body, so in these and in some other cases levels are likely to be low only when intake is extremely low. However we have included these nutrients in the analysis since all have potential links with lung defences, airway calibre or other factors relevant to COPD. In addition, for the majority of study participants, the diet will tend to track through their lifetime, and therefore these cross-sectional relations are important to investigate.1 was independently and directly related to levels of vitamin A, beta-cryptoxanthin, lutein/zeaxanthin, lycopene, vitamin C, vitamin E, selenium, normalised calcium, chloride and iron, and was inversely related to potassium and sodium. Of nutrients with linear associations with FEV1 the strongest effects per standard deviation change were evident for chloride and vitamin A. Of variables with non-linear associations, the strongest category effects were seen with beta-cryptoxanthin and selenium.We found that in our mutually adjusted models, FEV1, but after adjustment for these nutrients the effect serum beta-carotene was not independently associated with lung function in both analyses of the data. If lung function is modelled in a similar fashion to the prior paper, the effect sizes for vitamin C, vitamin E and selenium are similar to the previously published mutually-adjusted model even after adjustment for the other serum antioxidants that were associated with lung function. This finding is consistent with a relatively predominant role of vitamin C in serum and interstitial fluid in maintaining membrane-bound vitamin E in a reduced state [1[1 [Our findings for the nutrients in the antioxidant group were predictably similar to previous findings from a less extensive analysis of data from NHANES III , with vied state . Three o state [1,13,15, t state [1. There iate [1[1 , with thate [1[1 ,10,15,22ate [1[1 ,14,22.A growing body of evidence suggests that higher levels of selenium are associated with a reduced risk of asthma -30, but 1, and we are not aware of any previous reports of these associations. There is substantial literature suggesting an association between sodium intake and self-reported asthma and/or other respiratory symptoms [1 [1, and an intervention study in exercise-induced asthma reported findings that contradict ours, in that dietary chloride was associated with a reduction in lung function after an exercise challenge test [Our analysis of mineral effects found strong effects of serum chloride and sodium on FEVsymptoms -33, exersymptoms -37, and symptoms ,39, althsymptoms ,40-44. Tptoms [1 ,45. Thernge test . Our fin1 and serum potassium level is also, to our knowledge, new. It is also consistent with reported associations between increased urinary potassium and increased airway hyperresponsiveness[Our finding of a negative association between FEVnsiveness,46 and ansiveness, but twonsiveness,48 and onsiveness.1.To our knowledge the associations reported herein between lung function and serum levels of iron, calcium and folate have not previously been reported, though all have biologically plausible effects on the lung. In the case of iron, effects may be mediated through peripheral involvement in antioxidant processes -55, whil1 and hence may mediate reduced susceptibility to COPD. In addition to the antioxidant vitamins, selenium is also potentially important. Although the effects of the antioxidant vitamins have been recognised for some time, the potential beneficial role of selenium deserves further investigation. Likewise, our finding that sodium, chloride and potassium levels are also related to lung function needs to be tested in other datasets, and if confirmed, the relative importance of intake and homeostatic control mechanisms investigated.To summarise, our study confirms that antioxidant levels in blood are associated with higher levels of FEVFEV1: Forced expiratory volume in 1 second; BMI: Body mass index; COPD: Chronic obstructive pulmonary disease; SD: Standard deviation; WHR: Waist to Hip Ratio; TIBC: Total iron binding capacity.The authors declare that they have no competing interests.TM was responsible for the statistical analyses and draft of the manuscript. SL, HS, PB, PC & JB all contributed to the design of the study and draft of the manuscript. All authors read and approved of the final manuscript.


The relative contribution of lean and fat to the determination of bone mineral density (BMD) in postmenopausal women is a contentious issue. The present study was undertaken to test the hypothesis that lean mass is a better determinant of BMD than fat mass.2).This cross-sectional study involved 210 postmenopausal women of Vietnamese background, aged between 50 and 85 years, who were randomly sampled from various districts in Ho Chi Minh City (Vietnam). Whole body scans, femoral neck, and lumbar spine BMD were measured by DXA . Lean mass (LM) and fat mass (FM) were derived from the whole body scan. Furthermore, lean mass index (LMi) and fat mass index (FMi) were calculated as ratio of LM or FM to body height in metre squared (mIn multiple linear regression analysis, both LM and FM were independent and significant predictors of BMD at the spine and femoral neck. Age, lean mass and fat mass collectively explained 33% variance of lumbar spine and 38% variance of femoral neck BMD. Replacing LM and FM by LMi and LMi did not alter the result. In both analyses, the influence of LM or LMi was greater than FM and FMi. Simulation analysis suggested that a study with 1000 individuals has a 78% chance of finding the significant effects of both LM and FM, and a 22% chance of finding LM alone significant, and zero chance of finding the effect of fat mass alone.These data suggest that both lean mass and fat mass are important determinants of BMD. For a given body size -- measured either by lean mass or height --women with greater fat mass have greater BMD. Several prospective studies in the last three decades have consistently indicated that bone mineral density (BMD) is the best indicator of fracture risk ,2. Each Body weight is largely made up of two components: fat mass (FM) and lean mass . The relative contribution of these two components to the variation in BMD has been highly contentious. While some studies have suggested that LM, not FM, is associated with BMD -18; otheMost previous studies were based on Caucasian populations, and the results can not necessarily be extrapolated to Asian populations, whose body fat is believed to be higher than in Caucasian populations . We askeThe study setting was Ho Chi Minh city (formerly Saigon), a major city and an economic hub in Vietnam. The study was designed as a cross-sectional investigation, in which 210 women aged between 50 and 85 were randomly sampled by the cluster sampling scheme. None of the participants had any diseases deemed to affect osteoporosis  or previous use of therapies that interfere with bone metabolism . The study protocol and procedures were approved by the ethics committee of Hospital 115 and Pham Ngoc Thach University of Medicine.BMD was measured at the lumbar spine (LS), femoral neck (FN) and whole body (WB) in all participants. The measurement was done with a dual energy X-ray absorptiometry (DXA) densitometer . The precision error (%CV) in our laboratory was 2% for lumbar spine and 1.8% for femoral neck BMD, and 1.5% for whole body BMD. Fat tissue mass (FM) and lean tissue mass (LM) were derived from the whole body scan. In addition, we calculated the percent body fat (PBF) by dividing FM by body weight. The densitometer was standardized by a standard phantom every time before measurement.Anthropometrical parameters including age, weight, and standing height were obtained. Body weight was measured by using an electronic balance with indoor clothing without shoes. Height was determined without shoes on a portable stadiometer with mandible plane parallel to the floor.a and LMi = LM/(height)b, where height is expressed in meters [a and b were derived by fitting the equation of log FM or log LM against height: log(FM) = k + a×log(height), and log(LM) = c + b×log(height). Using the observed data from our study, we found a = 1.96 and b = 1.70, which is close to 2. Thus, FMi = FM/(height)2 and LMi = LM/(height)2 was calculated, which is similar to the calculation of body mass index. Multiple linear regression analysis was used to analyze the relative contributions of FMi and LMi to BMD.We were specifically interested in the following question: for a given body size, what is the association between fat mass and BMD? Therefore, the primary purpose of analysis was to assess the association between lean mass, fat mass, and BMD. However, since body size is associated with all of these measures, any association between lean mass or fat mass and BMD should ideally be adjusted for body size. In this study, we chose height, rather than weight, as a proxy for body size, because the correlation between fat mass and height (r = 0.25) is lower than the correlation between fat mass and weight (r = 0.80). We derived the fat mass index (FMi) and lean mass index (LMi) by the following formulae: FMi = FM/(height)n meters . The powP < 0.05) of each regression parameters was noted. The distribution of regression parameters and their statistical significance were then obtained. All analyses and simulation were done with the R program [In the next analysis, we addressed the following question: if the present study were repeated many times, what would be the distribution or variability of the effects of lean mass and fat mass on BMD, and how would sample size affect the distribution? In order to address the question, we conducted a simulation study, in which 10,000 pseudo studies were generated, with each study having a sample size of 50, 100, 200, 300, 400, 500, and 1000 women. In each study, weight, height, lean mass, fat mass, and lumbar spine BMD were generated using the means, variance-covariance matrix of the real study. In each study, lumbar spine BMD was modeled as a linear function of lean mass and fat mass in the multiple linear regression, from which the statistical significance . Advancing age was associated with decreased lean mass , but with a non-significantly increased fat mass . As a result, there was a significant positive correlation between age and percent body fat .The study included 210 participants, whose anthropometric and demographic characteristics are shown in Table 2 increase in BMD at the lumbar spine, femoral neck, and whole body, respectively. However, each 5 kg increase in fat mass was associated with a 0.022, 0.017, and 0.001 g/cm2 increase in BMD at the lumbar spine, femoral neck, and whole body, respectively. In fact, after accounting for lean mass, the association of fat mass and whole body BMD was not statistically significant (P = 0.90). Age, lean mass, and fat mass collectively "explained" 33% variance in lumbar spine BMD and 38% variance in femoral neck BMD. When lean mass and fat mass were replaced by lean mass index and fat mass index (Model 4), similar associations were also observed. However, a simpler model with only weight or BMI could also explain the same amount of variance in BMD.In univariate analysis, greater lean mass or fat mass was associated with greater BMD at the lumbar spine, femoral neck, and whole body Figure . In the 2 higher lumbar spine BMD than those in the first tertile of lean mass (23 - 30 kg); but the difference in BMD between the first tertile of fat mass (5 - 17 kg) and the third tertile of fat mass (21 - 34 kg) was 0.05 g/cm2. Moreover, for a given fat mass, individuals with more lean mass had greater BMD at all sites.In a further analysis, we estimated mean and standard deviation of BMD stratified by tertile of lean mass and fat mass. Results of this analysis Figure  confirm 2 increase in lumbar spine BMD, which was about three times the increase associated with a 1 kg of fat mass. The distribution of regression coefficients associated with fat mass suggested that there is a 10% chance that the coefficient of fat mass could be negative. However, virtually 100% regression coefficients associated with lean mass were positive.The distribution of 10,000 simulated regression coefficients associated with LM and FM is shown in Figure Results of further simulation analyses are shown in Table The relative contribution of lean mass and fat mass to bone mineral density remains a contentious issue. While a majority of studies have found a positive association between lean mass and BMD -18, few Distinguishing the role of lean mass versus fat mass as a determinant of BMD has clinical relevance, because an association between BMD and LM suggests that increase in physical activity may directly translate into protection against osteoporosis; while an association between BMD and FM implies that obesity may have protective effect against bone loss. However, delineating the independent effects of LM and FM on BMD is not straightforward, because the two measurements are correlated, and depending on the magnitude of correlation, when they are considered in a multiple linear regression model, it is sometimes impossible to separate one effect from another.r = 0.55) was not much different from the correlation between fat mass and BMD (r = 0.60), and when the two variables were statistically significant in the multiple linear regression. Our results suggest that both lean mass and fat mass are positively associated with BMD, even though the correlation between the two body composition measures was moderate (r = 0.37), which is consistent with previous studies [It was initially reported that fat mass was a major determinant of BMD , but sub studies . HoweverStatistically, whether lean mass or fat mass is associated with BMD is dependent on the inter-correlations among lean mass, fat mass, and BMD. However, the correlation could be different among studies due to sample size and sampling variability. In such a heuristic situation, simulation is a reasonable approach to gain insight into the variability. Using the observed correlations among the three variables, we have conducted a simulation study, and found that studies with 200 individuals or less have between 1 and 6% chance of finding a significant effect of fat mass without a significant effect of lean mass, and 55% to 68% chance of finding the effect of lean mass without a significant effect of fat mass. However, studies with more than 200 individuals have a much better chance (32% to 78%) of detecting the effects of both lean mass and fat mass. These results are in broad agreement with the literature so far, in which the effects of both lean mass and fat mass were reported in studies with at least 300 individuals, and only small studies reported the effect of fat mass alone on BMD. Our finding has important implications for the design of future studies, in which sample size is a crucial factor for the identification and delineation of the effects of lean mass and fat mass on BMD.It has recently been suggested that for a given body weight, there was a negative correlation between fat mass and BMD ,29. This2 with regression coefficient of 0.034 (for lumbar spine BMD). Such an inverse relation of height to BMD is hardly biologically interpretable. In other words, a model with BMI does not capture adequately the relationship between BMD and body size.Body mass index is often used as a predictor or a factor of adjusting BMD in the literature. However, we found that this usage is sub-optimal, because of lack of empirical evidence and difficulty in interpretation. Empirically, we found that weight and height are better determinants of BMD than BMI is. For example, in our study, body weight and height collectively explained ~15% of the variance of lumbar spine and femoral neck BMD, whereas BMI explained only 3 to 7% of the BMD variance. Since BMI is derived as the ratio of weight over height squared, a positive association between BMI and BMD in a regression model without height implies that within levels of weight, height is inversely associated with BMD in a quadratic fashion. For example, model 2 , women with greater fat mass have greater BMD. Because lean mass is related to physical activity, this finding re-inforces the concept that physical activity is an important component in the prevention of bone loss and osteoporosis in postmenopausal women.All abbreviations are defined in the text.All authors declare that they have no competing interests with regard to this work. Professor T. Nguyen received honorarium for speaking and providing consultant services to MSD Vietnam Ltd, Sanofi-Aventis, Norvatis, and Roche.Study concept and design: Lan Ho-Pham and Tuan Nguyen; Acquisition of data: Lan Ho-Pham and Thai Q. Lai. Analysis and interpretation of data: Lan Ho-Pham, Nguyen Nguyen, Thai Q. Lai, and Tuan Nguyen; Drafting the manuscript: Lan Ho-Pham, Tuan Nguyen and Nguyen Nguyen; Statistical analysis: Nguyen Nguyen, Thai Q. Lai and Tuan Nguyen; Critical revision of the manuscript: Lan Ho-Pham, Tuan Nguyen and Nguyen Nguyen, Thai Q. Lai. All authors read and approved the final manuscript.Contributions of the authors to the manuscript included The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2474/11/59/prepub


In recent years, there has been growing interest in theoretical studies integrating cognitions and environmental variables in the prediction of behaviour related to the obesity epidemic. This is the approach adopted in the present study in reference to the theory of planned behaviour. More precisely, the aim of this study was to determine the contribution of cognitive and environmental variables in the prediction of active commuting to get to and from work or school.A prospective study was carried out with 130 undergraduate and graduate students . Environmental, cognitive and socio-demographic variables were evaluated at baseline by questionnaire. Two weeks later, active commuting  to get to and from work or school was self-reported by questionnaire. Hierarchical multiple regression analyses were performed to predict intention and behaviour.p < 0.001). Combining cognitive and environmental variables with socio-demographic variables to predict behaviour yielded a final model explaining 41.1% (p < 0.001) of the variance. The significant determinants were intention, habit and age. Concerning intention, the same procedure yielded a final model explaining 78.2% (p < 0.001) of the variance, with perceived behavioural control, attitude and habit being the significant determinants.The model predicting behaviour based on cognitive variables explained more variance than the model based on environmental variables (37.4% versus 26.8%; Z = 3.86, The results showed that cognitive variables play a more important role than environmental variables in predicting and explaining active commuting. When environmental variables were significant, they were mediated by cognitive variables. Therefore, individual cognitions should remain one of the main focuses of interventions promoting active commuting among undergraduate and graduate students. Obesity has reached endemic proportions, affecting developed and developing countries . UnfortuThe TPB suggests that behaviour is predicted by intention and perceived behavioural control (PBC) ,9. IntenTo date, only a small number of studies have investigated the cognitive determinants of active commuting, and variations in findings have been reported. Some authors have reported significant associations between walking/bicycling for transport/leisure and intention  and PBC Likewise, growing interest has been paid to environmental variables and it has been suggested that these variables should be considered in the promotion of physical activity . One of Recently, Sallis, Cervero, Ascher, Henderson, Kraft and Kerr  suggesteThus, the aims of this study were: 1) to identify cognitive and environmental variables predicting active commuting; 2) to identify cognitive and environmental variables explaining intentions to adopt active commuting; 3) to test if environmental variables are mediated by cognitive variables in the prediction of behaviour and intentions; and 4) to test if environmental variables moderate the intention-behaviour relation.Participants were students at one of the major universities in the Province of Quebec during the fall term of 2007. We first contacted a few professors known to the authors who were teaching during the term. For those who agreed, all of their students attending the lecture at the time of the baseline survey were asked to participate. Students who agreed to do so were included in the study. A total of 256 undergraduate and graduate students from different academic programs  were contacted, and 161 agreed to participate (response rate of 62.9%). Of these 161 respondents, 134 successfully completed the follow-up measure two weeks later (response rate of 83.2%). Four respondents were excluded because it was not possible to match pre- and post-questionnaires. Thus, data from 130 participants were retained for the analysis.A prospective design was adopted for this study. Students were contacted in class and invited to participate in the study. The study was first explained, and those who agreed to participate had to complete a baseline questionnaire assessing environmental, cognitive and socio-demographic variables. At two-week follow-up, a self-report of use of active commuting frequency  was obtained. The project received the approval of the ethics committee of the local university.The baseline questionnaire contained five sections: 1) neighbourhood environment; 2) physical activity habits; 3) variables from the theory of planned behaviour (TPB) and related cognitions; 4) habit of using active commuting; and 5) socio-demographic variables [see Additional files As suggested by the International Physical Activity & the Environment Network (IPEN), the Neighbourhood Environment Walkability Survey (NEWS) was used to assess environmental variables related to using walking and bicycling for commuting. It is also considered a reliable and valid instrument -34. HoweThe guidelines provided by Ajzen  were folActive commuting was defined as an activity performed for at least 10 minutes, as recommended by the Public Health Agency of Canada (PHAC) and the American College of Sports Medicine (ACSM) ,36. ThusSocio-demographic variables assessed included gender, age, working/study status, height and body weight. Height and body weight were used to compute body mass index (BMI).In the past 2 weeks, how many times (for a period of a least 10 minutes) did you walk/bicycle to get to your workplace or your school?"At two-week follow-up, participants were asked to report the number of times they had used active commuting to get to and from work or school. The same question used to assess past behaviour at baseline was also used to assess behaviour at follow-up, but in reference to the last two weeks: "To ensure the psychometric qualities of the study questionnaire, 32 participants not involved in the present study completed a two-week test-retest reliability study. The evaluation of the internal consistency and temporal stability of the variables showed appropriate values [see Additional files Z test procedure described by Tabachnick & Fidell [Pearson correlation coefficients were computed to investigate relations between active commuting , intention and other variables. Items with a correlation of p ≤ .20 were retained and tested in subsequent regression analyses. Two separate regression analyses were first performed to evaluate the efficacy of environmental and cognitive variables, respectively, to predict active commuting. Then, hierarchical multiple regression analyses were performed to predict active commuting. The variables were entered according to the following steps: 1) past behaviour; 2) cognitive variables; 3) environmental variables; 4) socio-demographic variables and 5) habit. After each step, non-significant variables (p > .05) were withdrawn one by one, starting with the less significant, until all variables left in the model were significant. For all regression analyses, past behaviour was first entered in order to control for its effect. Habit was entered in the last step to ascertain its additional contribution to the model. Since the habit index used in this study is not a measure of behavioural frequency, past behaviour and habit can be used independently . Finally& Fidell  was emplThe role of intention as mediator in the relationship between environmental variables and active commuting was tested using the method suggested by Baron and Kenny . As inteA three-step hierarchical regression was applied, as suggested by Aiken and West , to testA total of 130 respondents completed both baseline and follow-up questionnaires. The mean age of the sample was 24.0 ± 4.9 years (range: 19 to 48 years), with a predominance of female participants (71.5%). The majority of the participants studied full time (86.2%) and were also working (60.8%). More than half of the participants (52.3%) always had access to a car. In general, participants self-reported having a healthy weight with an average BMI of 22.0 (SD = 3.4). The majority of participants could get to school (74.6%) or work (73.4%) within a 10-minute walk. Also, the majority had been living in their neighbourhood for more than a year (63.8%). No significant differences were observed between participants who completed and those who did not complete the follow-up questionnaire.Concerning active commuting at follow-up, participants reported using an active commuting mode an average 6.3 times per week (SD = 5.81). Walking was a more popular activity than bicycling . However, 36.2% of the respondents used active commuting ten or more times per week to get to and from work or school.r = 0.60, p < .001). For environmental variables, active commuting and intention were positively correlated with « apartments » and negatively associated with « detached single-family residence », « time to access services », « time to get to school/work » and « car accessibility ». Concerning socio-demographic variables, only age was negatively associated with active commuting, whereas work status was negatively related to intention.Students had a slightly positive intention to use active commuting in the next two weeks . Intention was significantly associated with active commuting (p < 0.001) was explained by past behaviour  and intention . When habit was added to the model, only intention  and habit  remained significant predictors, explaining 38.7% of variance (p < 0.001). None of the other cognitive variables reached significance (p > 0.05).Regression of active commuting at follow-up on past behaviour and cognitions at baseline showed that 37.4% of the variance in active commuting (p < 0.001). Three variables were significant predictors: past behaviour , time to get to work or school  and car accessibility . The latter two environmental variables varied as expected; that is, longer time to get to work or school and having access to a car played a negative role.Regression of follow-up behaviour on past behaviour and environmental variables at baseline yielded to a significant model explaining 26.8% of variance  was significantly higher than the model of environmental variables  in the prediction of active commuting .The model predicting behaviour based on cognitions was compared with the model predicting behaviour based on environmental variables. The p > 0.05) (see Table p < 0.001); the significant determinants were intention (p < 0.01), age (p < 0.05) and habit (p < 0.01).When both cognitive and environmental variables were combined in the same hierarchical linear regression analysis, none of the environmental variables reached significance  = 114.85, p < 0.001) in intention was explained by PBC , attitude  and subjective norm . When past behaviour was included in the model, intention was predicted by past behaviour , PBC  and attitude ; the explained variance was 0.74 (p < 0.001). The final model for the cognitive variables explained 78.2% of the variance of intention (p < 0.001), with significant determinants being PBC , attitude  and habit .Given that intention is the most important predictor of behaviour according to the TPB, analyzing its determinants was justified. When only TPB variables were considered, 72.6% of variance (p < 0.001), two factors contributed to the prediction of intention: time to get to work or school ; and car accessibility . The contribution of these two environmental variables varied as expected, i.e. negatively. This model of the environmental variables explained 40.4% of the variance (p < 0.001).In the context of environmental variables and controlling for past behaviour  showed that the proportion of variance in intention explained by cognitive variables  was significantly greater than the model based on environmental variables .The models predicting intention from cognitions and environmental variables were compared. The p < 0.001), attitude (p < 0.05) and habit (p < 0.001); it explained 78.2% (p < 0.001) of the variance in the intention to use active commuting.Combining environmental and cognitive variables indicated that no environmental variable contributed significantly to the prediction of intention ; apartments ; time to access services ; time to get to work or school ; and car accessibility . In fact, mediation analyses indicated that environmental variables were mediated by the determinants of intention. Time to get to work or school was mediated by PBC  and habit , whereas detached single-family residence , apartments , time to access services  and car accessibility  were mediated by PBC. No other significant relation was found. Figure Mediation tests were performed to identify how environmental factors contribute to the formation of intention and prediction of behaviour. Five environmental variables met Baron and Kenny's 1986) criteria and were tested for mediation by intention in the prediction of active commuting, controlling for age and habit. None of these factors reached significance: detached single-family residence (CI986 critep > 0.05).Detached single-family residences, apartments, time to access services, time to get to work or school and car accessibility were environmental variables tested as potential moderators of the intention-behaviour relationship, since they were significant correlates of active commuting. However, none of these variables interacted significantly with intention in the prediction of active commuting  than males participated. Therefore, the present observations cannot be extended to the general population. In addition, the sample size was modest (N = 130). Notwithstanding this point, we obtained a power of 100% for our final model predicting behaviour. Participants were volunteers, consequently more likely to be interested in the studied behaviour. Also, an objective measure of active commuting would have been preferable to a self-report measure, although our measure presented good test-retest value. The inclusion of objective measures of environmental variables would also have been interesting, since it was recently documented that objective and subjective environmental measurements are not necessarily equivalent . NotwithCompared to previously published work, one of the major strengths of this study is its use of a prospective design, allowing behaviour prediction. Moreover, as it has been suggested in the past , our stuIn summary, intention, habit and age predicted behaviour significantly. These results indicate, along with the findings of Giles-Corti and Donavan , that coThe authors declare that they have no competing interests.ML carried out the study, performed the statistical analysis and drafted the manuscript. ML and GG participated in the study design. GG revised critically the manuscript. All authors read and approved the final manuscript.Environmental variables: Questions, scales and psychometric qualities of the survey. Table presenting all the questions and answer scales used in the baseline survey and the psychometric values obtained at the test-retest study. Items used to assess the environmental variables and their psychometric qualities.Click here for fileBehaviour, habit and cognitive variables: Questions, scales and psychometric qualities of the survey. Table presenting all the questions and answer scales used in the baseline survey and the psychometric values obtained at the test-retest study. Items used to assess behaviour, habit and cognitive variables and their psychometric qualities.Click here for fileBehaviour, habit and cognitive variables: Questions, scales and psychometric qualities of the survey (continued). Table presenting all the questions and answer scales used in the baseline survey and the psychometric values obtained at the test-retest study. Items used to assess behaviour, habit and cognitive variables and their psychometric qualities.Click here for file


Interest is rising in smokeless tobacco as a safer alternative to smoking, but published reviews on smokeless tobacco and cancer are limited. We review North American and European studies and compare effects of smokeless tobacco and smoking.We obtained papers from MEDLINE searches, published reviews and secondary references describing epidemiological cohort and case-control studies relating any form of cancer to smokeless tobacco use. For each study, details were abstracted on design, smokeless tobacco exposure, cancers studied, analysis methods and adjustment for smoking and other factors. For each cancer, relative risks or odds ratios with 95% confidence intervals were tabulated. Overall, and also for USA and Scandinavia separately, meta-analyses were conducted using all available estimates, smoking-adjusted estimates, or estimates for never smokers. For seven cancers, smoking-attributable deaths in US men in 2005 were compared with deaths attributable to introducing smokeless tobacco into a population of never-smoking men.n = 19) and prostate cancer . The oropharyngeal association disappeared for estimates published since 1990 , for Scandinavia , and for alcohol-adjusted estimates . Any effect of current US products or Scandinavian snuff seems very limited. The prostate cancer data are inadequate for a clear conclusion.Eighty-nine studies were identified; 62 US and 18 Scandinavian. Forty-six (52%) controlled for smoking. Random-effects meta-analysis estimates for most sites showed little association. Smoking-adjusted estimates were only significant for oropharyngeal cancer  if as many used smokeless tobacco as had smoked, and 2,081 (2.0%) if everyone used smokeless tobacco.An increased risk of oropharyngeal cancer is evident most clearly for past smokeless tobacco use in the USA, but not for Scandinavian snuff. Effects of smokeless tobacco use on other cancers are not clearly demonstrated. Risk from modern products is much less than for smoking. Over the last 10 years, interest in smokeless tobacco (ST) as a possible safer alternative to smoking has risen. Although a number of recent reviews have considered the evidence relating ST to cancer, some have not included meta-analyses , and othThe review described in this paper is restricted to studies in Western populations. In practice this predominantly means studies in the USA and Sweden, the only North American and European countries where the two major types of ST – chewing tobacco and snuff – are commonly used . AlthougOur first objective is to carry out a comprehensive review of the available epidemiological evidence in Western countries relating ST to cancer, including meta-analyses for as many cancer types as the data justify. In meeting this objective, we take proper account of the potential confounding role of smoking by distinguishing effect estimates which are unadjusted for smoking and those which take smoking into account . Our second objective is to provide a quantitative indication of the relative effects of ST and cigarette smoking.All reports had to satisfy the following inclusion criteria: published in a peer reviewed journal or the results publicly available, epidemiological study in humans, of cohort or case-control design, study location specified, any form of cancer as the outcome, and chewing tobacco, oral snuff or unspecified ST as the exposure. They also had to fall outside the exclusion criteria: conducted in an Asian or African population, no control group, or inappropriate design . Relevant papers were sought from a MEDLINE search conducted in May 2008 of "cancer" AND ("smokeless tobacco" OR "chewing tobacco" OR "snuff" OR "snus"), supplemented by citations in recent reviews -6,10 andno information – effect estimates are provided but no details are given of any adjustments made; B. no adjustment – effect estimates are available for the whole population, but smoking is not taken into account; C. never smokers – the only effect estimates available are for never smokers; D. some adjustment – effect estimates adjusted for smoking are available, but the adjustment is relatively simple, using two or three level broad groupings , and takes no account of daily amount smoked or duration of smoking; and E. more adjustment – effect estimates are available that take into account daily amount smoked, duration of smoking and/or their product (pack-years). Studies were categorised under D or E if smoking-adjusted effect estimates are available, regardless of whether some results for never smokers are also presented. The method used to adjust for smoking is not always clear. Studies where the authors merely report that they 'adjusted for cigarette smoking' are included in category D.Reports were grouped by study, and for each study details were abstracted , were selected, with results for each grouping tabulated in a standard way, with details given of the source, exposure to ST, smoking group, sex, number of cases and adjustment factors for each effect estimate or indication of association . For each study the intent is to extract the relative risk (RR) or odds ratio (OR) adjusted for the most factors, relevant to current, former or ever exposure to chewing tobacco, snuff or overall/undefined ST. Where relevant results for a study are reported in more than one paper, those based on the greatest number of cases are used.Results are included, where available, for the whole population and for never smokers, and for sexes separately. RR or OR estimates based on zero exposed cases (or controls) are not included as providing too little information and because a valid confidence interval (CI) cannot be calculated. Suitable estimates of effect (RR or OR) and precision (CI) provided by the authors are used if possible, estimates otherwise being calculated from available data presented in the source publication, based on methods -118 summStudy-specific results for the different types of cancer are presented in an essentially identical format, with a standard set of information included for each effect estimate included. Points to note about the entries in the various columns are discussed below.For the case-control studies, the source reference is shown. For the cohort studies, the source reference is also shown, but the study is also identified by name.The exposure is identified as chewing tobacco ('chew'), 'snuff' or smokeless tobacco ('ST'). ST implies the results relate to smokeless tobacco unspecified by the author, or to use of either chewing tobacco or snuff or both.Results are presented for current, former or ever use, or simply for 'use' where the timing of exposure was unspecified by the author. For current, former or ever use, the comparison is with never use; for use, it is with non-use.Results are presented only for any smoking  and for never smokers.Results are shown, where available, for the sexes separately, though in some studies results are given only for the sexes combined.Within each table, each effect estimate (RR or OR) is given a unique identification number, so that those which are included in specific meta-analyses can readily be seen.The number of ST-exposed cases is shown. Total numbers of cases are given elsewhere. Estimates are not presented unless there is at least one exposed case.et al. 2007 [This is the RR for cohort studies or the OR for case-control studies, together with its 95% CI. For many studies, the estimates are not given directly in the source paper, but were calculated from data provided. This involved one or more of the following: estimating numbers of exposed and unexposed cases and controls from proportions exposed given numerically or graphically and, where appropriate, combining numbers over level of exposure or cancer subtype; calculating estimates from a 2 × 2 table, or multiple independent 2 × 2 tables using standard methods , and calal. 2007 . Fuller verbatim the wide variety of variable descriptions given by the original authors. Thus 'res' (area of residence) includes any variable based on the location of the subject and, for example, includes centre in multicentre case-control studies. 'Diet' includes any aspect of diet, and 'alc'  any aspect of alcohol use. Estimates relevant to never smokers are not listed as being adjusted for smoking ('smok').The adjustment factors used for each estimate are shown. For matched case-control studies, the matching factors are not included unless the estimate specifically took this into account . The factors used have been simplified into a relatively short consistent list, rather than repeating For the five columns, ST use – type, ST use – exposure, smoking, sex and adjustment factors, any blank entry for a particular effect estimate is assumed to be the same as in the first previous non-blank entry in that column. This avoids needless repetition and makes the tables easier to read.Estimates with no CI are not included in the meta-analyses. The standard error of the logarithm of estimates of effect size was calculated from its reported or estimated CI, assuming that the effect size was log-normally distributed. The logarithms of the effect sizes and their corresponding standard errors form the data points for fixed-effect and random-effects meta-analysis .any, there is no restriction of estimates on type of exposure or region. In the second set, any ST use (USA), estimates are restricted to those from studies conducted in the USA (or on occasion in Puerto Rico), while in the third set, snuff (Scandinavia), estimates are restricted to those for snuff and for studies conducted in Scandinavia. Each of the three sets of meta-analyses is divided into overall data, smoking-adjusted and never smokers. In the overall data analyses, estimates are not restricted on smoking status or on adjustment for smoking. The smoking-adjusted analyses only include estimates that are for the whole population and adjusted for smoking or are for never smokers. The never smokers analyses are restricted to estimates for never smokers. For oropharyngeal cancer, where more estimates are available, some additional meta-analysis results are shown, based on estimates that are smoking and alcohol adjusted, and on estimates published since 1990.For most cancer groupings, results of nine random-effect meta-analyses are presented, subject to availability of data . In the first set of three, To avoid double-counting multiple non-independent estimates from the same study, estimates from each study are selected for inclusion in the meta-analyses using order of preference lists for ST exposure (ever use/unspecified use/current use/former use), then smoking status (any – based on the combined population of smokers and non-smokers/never smokers), and then ST type (ST/snuff/chew), with each list being in order of most to least preferred. At each step we retain those estimates highest up the list, discarding any estimate lower in the preference order. If the procedure ends up with separate estimates for males and for females, both are included in the analysis. In one study , the resP value of homogeneity [2 statistic [P < 0.1 are indicated.The presentation of the meta-analyses shows the number of estimates combined; the identification numbers of these estimates ; the combined random-effects estimate, with its 95% CI , the chiogeneity  and the tatistic . The mettatistic  where fiForest plots are also included for most of the cancers. These are generally based on the smoking-adjusted analyses, with the estimates split by region and shown with cohort data first, then case-control, presented in order of publication year.2 is calculated by w  heterogeneity of estimates, sensitivity to potentially outlying estimates is tested by removing that with the largest Q2 value and rerunning the analyses. This process is continued until there is no longer significant heterogeneity.For each estimate included, the value of Qtatistic . Where tSensitivity to the criterion for including estimates based on ST exposure is also tested by rerunning the meta-analyses with the preference list for ST exposure changed from ever use/unspecified use/current use/former use to current use/ever use/unspecified use/former use.P < 0.05) heterogeneity, the meta-regression analyses use a more limited variable list: region, sex, and smoking as above, and also study type .For oropharyngeal cancer, fixed-effects regression analysis is used to investigate how the estimates selected for the first set of meta-analyses vary by region , period × study type , sex , ST exposure , smoking  and alcohol adjustment . For those other cancers where more than five estimates are available and where there was evidence of significant (P < 0.05 can be added. Significance is estimated by treating the ratio of the deviance per degree of freedom (d.f.) explained by the factor to the residual deviance per d.f. as an F statistic. For oropharyngeal cancer some additional analyses investigate the drop in deviance resulting from introducing each factor individually, and others are conducted having excluded 'outlying' observations with a very high Q2 value.Regression analyses are only conducted based on the overall data and smoking-adjusted data. The analyses successively introduce the most significant factor into the model, stopping when no further factor significant at RRs for current and former cigarette smokers (compared with never cigarette smokers) for men aged 35+ for seven major cancers caused by smoking  were obtained from the American Cancer Society Cancer Prevention Study II (CPS-II) . Numbersi as the number of deaths for cancer i , Rci and Rfi as the RRs for current and former cigarette smokers for cancer i, and pc and pf as the proportions of current and former cigarette smokers in the population, the estimated number of deaths, Defining DThe number of deaths avoided from these seven cancers, had the whole population the risk of never smokers  is then estimated by:si as the estimated relative risk from ST for cancer i based on the meta-analyses using smoking-adjusted effect estimates. Where Rsi is estimated to be less than 1, it is taken to be 1 for the purposes of calculating deaths attributable to ST.Let us further define RFor a population of never smokers, the number of deaths from cancer i that would have occurred had the same proportion of men used ST as had ever smoked is then estimated by:The increase in overall deaths from these seven cancers is then given by:1 can then be compared with E as an indicator of the relative effects of ST and smoking.IAlso for a population of never smokers, the number of deaths from cancer i that would have occurred had all the men used ST, is estimated by:The increase, compared with E, is then calculated by:The MEDLINE search identified 690 publications. Two hundred and thirty-eight were rejected as describing studies conducted in Asia or Africa or relating to products typically used there, 96 as not describing epidemiological studies, 112 as not relating to cancer and 163 as being reviews, letters or comments not providing primary data. Seventeen were rejected as having an inappropriate study design and three as not providing relevant results. This left 61 apparently relevant publications. Taking into account also citations in recent reviews , and eliet al. [Results relating ST use to mortality or incidence have been reported for nine cohort studies, with results provided by multiple publications for some studies. Six studies have been conducted in the USA and are based on the Lutheran Brotherhood cohort -14, the et al.  restrictet al.  where soet al. .et al. [et al. Of the 80 studies, 56 were conducted in the USA, 11 in Sweden, three in each of Canada, Denmark and the UK, and one in each of Brazil, Norway and Puerto Rico, with one study conducted in five countries. Most of the studies involve only one or a small number of cancer types, but one study [Results relating ST use to cancer have been reported for 80 case-control studies, with Table et al.  is to a ne study  involvesne study ,99,106. ST use is not a major subject for many of the publications from which results have been extracted. While reference is made to ST in the title of one or more papers relating to six of the nine cohort studies , the same is true for only 15 of the 80 case-control studies. For many of the other studies ,113,114,To summarise the extent to which the available effect estimates were adjusted for smoking, the studies were divided into five groups  as described more fully in the methods. Of the nine cohort studies, the numbers in the five categories were, respectively, 0, 1, 3, 3 and 2. The Iowa study  failed tOf the 80 case-control studies considered, details of the adjustment factors used are not provided in either of the studies reported by Bjelke  or in twP < 0.001). Limiting consideration to smoking-adjusted data, the estimate reduces substantially, to 1.36 , though it is still significant, and marked heterogeneity remains (P < 0.001). Further limiting attention to estimates adjusted for both smoking and alcohol, the two major risk factors for oropharyngeal cancer [n = 10). A significant relationship is seen in never smokers , though the estimates are heterogeneous (P = 0.044), and generally based on a very small number of oropharyngeal cancer cases that used ST.Table 6 1.04–1.7, n = 19l cancer , eliminaWhen the analyses are restricted to US studies, the pattern is similar to that for the overall data, with the effect estimates reduced when attention is limited to those that are smoking-adjusted, and close to 1.0 when estimates that are adjusted both for smoking and alcohol are considered. The effect estimate for never smokers is significantly increased , based on five small studies, in total involving 19 ST-exposed oropharyngeal cancer cases.No real evidence of a relationship with snuff use is seen in studies conducted in Scandinavia, where seven estimates, all adjusted for smoking, and four additionally adjusted for alcohol, give a combined estimate of 0.97 (0.68–1.37). However some heterogeneity should be noted, a high RR of 3.1 (1.5–6.6) in the Uppsala County study  conflictMany of the higher estimates seen in Table a priori, the change in estimates about that time is very clear. As shown in Figure P = 0.34 for pre-1990 and P = 0.93 for post-1990) and a highly significant (P < 0.001) difference between estimates in the two periods. Smoking-adjusted estimates for the cohort studies which, though published between 2005 and 2008, generally cover a long follow-up period extending from before 1990, give an intermediate result .While the choice of 1990 as the cut-point was not defined The findings are very similar to those in an earlier review . That reThe evidence presented suggests that snuff as used in Scandinavia has no effect on oropharyngeal cancer risk. Products used in the past in the USA may have increased the risk but any effect that exists now seems likely to be quite small.Table n = 7), somewhat lower than when there is no restriction to smoking-adjusted data . The corresponding analyses show no real indication of an effect for snuff in Scandinavia, but are more suggestive for the USA. Even here, the smoking-adjusted estimate is not significant , though this is based on only three small studies, involving a total of 11 cases using ST. The estimates based on all the available smoking-adjusted data include an any smoking RR of 1.00 (0.79–1.27) from the study with the largest weight, the Swedish Construction Workers study [n = 4) for any ST use, mainly because they use a higher (combined adeno/squamous) estimate of 1.92 (1.00–3.68) for the Swedish Construction Workers study [The meta-analyses . No significant associations are seen in the separate meta-analyses for the USA and Scandinavia.Table At most, the overall data weakly suggest a possible effect of ST on pancreatic cancer risk. A fuller discussion of these data is available elsewhere .et al. [Table et al.  of 18.0 et al. [n = 5), with significant evidence of heterogeneity (P = 0.002). The analysis for never smokers removes the case-control study and eliminates the heterogeneity. However the combined estimate of 1.14  remains non-significant.There are rather more data for the combined category of all cancers of the digestive system. Of the four studies providing data, all conducted in the USA, NHANES I  and CPS-et al.  a signifMore data are needed before any conclusion can be drawn for these cancers.n = 5).The data shown in Table Given the independent role of smoking and alcohol in larynx cancer ,8, and tP < 0.001), the major contributors to this being the high RR of 6.80 (1.60–28.5) in never smokers in NHANES I [n = 5), it is not statistically significant.Table NHANES I , the sigNHANES I , and theNHANES I . While tWhile the data have unexplained heterogeneity, they do not provide any clear indication of a relationship of lung cancer to ST use.et al. in 2007 [Not included in Table  in 2007  based onet al. [Table et al. . Based oProstate cancer is not considered smoking related ,8, and mTable Considered together, the data provide no real evidence of an association between ST and bladder cancer.Table While there is a suggestion of a possible relationship, more data are needed before any firm conclusions can be reached.P = 0.01), due mainly to the Bracci and Holly estimate. The evidence for other endpoints – multiple myeloma, Hodgkin's disease, leukaemia, and overall haematopoietic cancer – is more limited, and does not suggest any relationship with ST use.Table et al. [et al. [et al. [et al. [Table et al.  report aet al. . The Wilet al.  provides [et al. , in a st [et al.  report a [et al. ,33 repor [et al. ,126 whicn = 7). However, the combined estimate for never smokers, which excludes the low estimate from the case-control study, is a significant 1.10 . The estimate for never smokers is similar for the US data  and the Scandinavian snuff data . The data are consistent with any excess risk of cancer in ST users being small.As shown in Table P < 0.01, and two significant at P < 0.05, similar to the numbers one would expect by chance. Both the significant cases  heterogeneity was evident, and removal continues until no significant heterogeneity is seen. For pancreatic, lung and bladder cancer and for non-Hodgkin's lymphoma, only relatively high estimates are removed, and the random-effects estimate decreased, though only for lung cancer was the estimate now significantly below 1.0. For digestive cancer, the effect is to increase the estimate, but the significance is unchanged. For overall cancer, the effect is also to increase the estimate, here to marginal significance, 1.07 (1.00–1.15). For oropharyngeal cancer, the original substantial heterogeneity (P < 0.001) is seen to be due mainly to four estimates, three high and one low. The excess decreases from a significant 1.36 (1.04–1.77) to a non-significant 1.17 (0.95–1.45) after the removal of these estimates.Table Similar analyses for the overall data (not shown) were also carried out. They also did not help to demonstrate any clear effect of ST on risk. For oropharyngeal cancer, where heterogeneity is very marked indeed, this is mainly due to estimates with atypically high values  also did not materially affect the results obtained (data not shown).2) is 69.5 (P < 0.001), significant reductions in deviance in 'one factor at a time' analysis are seen for period by study type , sex  and region . However, the tendency for estimates to be high in females and the USA was no longer significant after adjustment for period by study type, this relationship reflecting the tendency for estimates to be high in case-control studies published before 1990, low in case-control studies published after 1990, and intermediate in prospective studies , the most significant factor is sex . Though drops in deviance of 20 or more are also seen for region, period by study type and smoking status, with estimates high for females, USA, old case-control studies and data unadjusted for smoking, no other factor is significant at P < 0.05 after adjustment for sex. The high deviance of 335.6 is clearly due to very high Q2 values for some estimates, and further analyses were run excluding these estimates . However, again sex was the most significant factor (P = 0.02), with no further factor significant at P < 0.05 after adjusting for sex.Based on the 41 overall estimates (whether smoking-adjusted or not) for oropharyngeal cancer, where the deviance is 335.6 (P < 0.05) explained the heterogeneity. For overall cancer, study type was significant (P = 0.001), but this merely reflected the low estimate for the single case-control study, evident also in the sensitivity analysis shown in Table P = 0.025) and the overall data (P = 0.029). This difference reflected the two high estimates already noted in the sensitivity analysis.Meta-regression analyses were not attempted for larynx, nasal or prostate cancer or for overall digestive cancer or non-Hodgkin's lymphoma because of insufficient numbers of estimates, or for oesophageal, stomach and kidney cancer because of lack of heterogeneity. For pancreatic and bladder cancer, none of the factors investigated significantly (at P < 0.05) are seen for oropharyngeal cancer, though not based on studies published since 1990, and for prostate cancer, but not for any other cancer considered. For never smokers, significant increases are seen for oropharyngeal cancer (again not when based on studies published since 1990), for oesophageal cancer and also for overall cancer. Compared with the smoking-adjusted estimates, the estimates for never smokers tend to be more variable, due to smaller numbers of ST-exposed cases studied, though they consistently exceed 1.0.Table Table As shown in Table Results relating the various cancers to dose of exposure to ST are only reported in a few studies and are not presented in detail here.P < 0.001) trend in risk with increasing duration of exposure is seen, though only for cancers of the gum and buccal mucosa, and not for other mouth and pharynx cancers.For oropharyngeal cancer, eight studies were identified that related risk to extent and/or duration of exposure. In seven of these studies, which all show no overall relationship of ST with risk in Table P < 0.01) is reported with daily amount of snuff consumed for pancreatic cancer [P < 0.05) trend for risk to increase with frequency of use of chewing tobacco, and a study of pancreatic cancer [P = 0.04) trend for risk to increase with ounces per week (oz/wk) ST used, though with the odds ratios forming an erratic pattern . Generally the rather sparse dose-response data add little to the overall evidence.For other cancer sites relatively few studies report dose-response data. In the CPS-II study  no trendc cancer  in neverc cancer ,111,114,c cancer  which reTable The smoking-adjusted relative risks for any ST use taken from Table We have analysed data relating cancer risk to the consumption of chewing tobacco and snuff as used in Western countries. We have identified 12 cancers (or combined categories) where, as shown in Table P < 0.05. Indeed, based on smoking-adjusted data, which might be argued to provide a good compromise between avoidance of bias and loss of power, only the estimates for oropharyngeal and prostate cancer are significant, with that for oropharyngeal cancer not evident in more recently published studies. However, it should be noted that while many of the estimates in Table It is notable that no strong association at all is evident and that few of the associations are significant at There are a number of difficulties in interpreting the results of these meta-analyses. The studies are of varying design, size and quality. Many of the individual study reports have limitations and present less information than is ideal for a meta-analysis. Shortcomings include small numbers of cases, and in particular of cases exposed to ST, lack of histological confirmation, lack of division by cancer site, as well as an unclear description of inclusion and exclusion criteria, details of case and control selection, and methods of exposure assessment. Furthermore, details such as the type of ST used, and duration and frequency of use, are often not considered. The products used vary by country and over time, and increased risks seen in older studies for some cancers may not reflect the risks of more modern products, with reduced nitrosamine levels . For mosA major problem is that many of the studies fail to adjust for smoking and other important potential confounding variables. Although recent major reviews ,8 considn = 19) can be compared with the estimate adjusted for smoking and alcohol of 1.07 . Restricting attention to estimates adjusted for both factors also eliminated the highly significant (P < 0.001) heterogeneity seen in the smoking-adjusted data. Alcohol is also an important factor in the aetiology of oesophageal, larynx and liver cancer [Helicobacter pylori infection for stomach cancer and diet for digestive cancer.Adjustment for other risk factors is also important, as shown by the case of oropharyngeal cancer where the smoking-adjusted estimate of 1.36 , pancreas, larynx, lung and bladder, as well as for overall cancer and overall digestive cancer. As noted above, the evidence is too limited for most of the cancers to allow a proper investigation of the sources of this heterogeneity.Another difficulty in interpreting the overall results is the variability of the findings. Heterogeneity significant at least at Based on the data analysed, there is little or no evidence of publication bias. However, it should be noted that the number of studies reporting results in a form that cannot be included in the meta-analyses is fairly high, representing up to about 30% for some cancers ), oesophageal cancer , pancreatic cancer  and lung cancer  show much less evidence of a relationship with ST than do corresponding estimates recently reported in a review by Boffetta In 2005 in US men aged 35 or over, there were a total of 142,205 deaths from seven cancers considered to be caused by smoking. Based on relative risks from CPS-II for current and former smoking  and estiThere are a number of objections that can be made in respect of this comparison. These include the following:inter alia changes in cigarettes that have occurred since then. However, CPS-II is widely used as a source of data for calculating deaths attributed to smoking .2. The RR estimates used for ST use are not specifically for the USA, or for males. However, 62 of the 89 studies considered in this review were conducted in the USA, and 41 of the 58 estimates used in the smoking-adjusted meta-analyses for the seven cancers are for males .3. The RR estimates used for ST are for any ST use, and do not separate current and former use, due to most studies not providing such data.n = 8), while the more limited data for leukaemia also showed no clear evidence of a relationship.4. The calculations are limited to those seven cancers which the US Surgeon General, in his 1989 report  consider5. It is theoretically possible that ST use might increase the risk of some cancers not increased by smoking. Here one should note the significant association for prostate cancer .6. The calculations do not take into account the fact that a proportion of US males aged 35+ already use ST. Given the relatively weak association between cancer and ST use, any attempt to do this would have had relatively little effect.7. The calculations also do not take pipe and cigar smoking into account.8. The approach used is somewhat simplistic, and a more realistic (but more complex) calculation might be to compare predicted cancer deaths over a long-term period in a population continuing to smoke as at present, with the predicted number in a population switching from cigarettes to ST.Despite all these points, it is clear that any effect of ST on risk of cancer, if it exists at all, is quantitatively very much smaller than the known effects of smoking. This is in any case apparent from a simple comparison of the RRs for cigarette smoking and for ST use.The available data relating to ST use have a number of weaknesses, including inadequate control for smoking in many, and limited data for never smokers. Nevertheless, it is possible to conduct meta-analyses based on smoking-adjusted estimates for a relatively wide range of cancers. These show no indication of an increased risk of cancer for snuff, as used in Scandinavia. The overall data for oropharyngeal cancer shows a significant increase in risk associated with ST use, but this is not evident for estimates adjusted for smoking and alcohol, or for studies published since 1990. Any effect of ST may relate mainly to products used in the past in the USA. A weak but significant association with prostate cancer, based on limited data from US studies, requires more confirmatory evidence. Reports of significant associations with pancreatic and oesophageal cancer in an earlier review  are not CI: 95% confidence interval; CPS-I: American Cancer Society Cancer Prevention Study I; CPS-II: American Cancer Society Cancer Prevention Study II; d.f.: degrees of freedom; NHANES I: First National Health and Nutrition Examination Survey; OR: odds ratio; RR: relative risk; ST: smokeless tobacco.PNL, founder of PN Lee Statistics and Computing Ltd., is an independent consultant in statistics and an advisor in the fields of epidemiology and toxicology to a number of tobacco, pharmaceutical and chemical companies. JH works for PN Lee Statistics and Computing Ltd.PNL previously contributed to reviews of some of the data considered here ,5,10. HeThe pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1741-7015/7/36/prepub


Pityriasis Rosea (PR) is an acute inflammatory and self-limiting skin disorder, sometimes with troublesome symptoms. To date, there are few treatments available for this disorder.Compare the traditional treatment with erythromycin to a newly introduced antiviral treatment acyclovir for PR.Patients with clinically confirmed diagnosis of PR, matching our exclusion criteria, were enrolled. They were randomized in two groups that received high-dose oral acyclovir or erythromycin. The participants were evaluated two, four, and eight weeks after commencement of the study and followed for one year.P < 0.05). Also, patients in the acyclovir group experienced faster resolution of pruritus in comparison with the erythromycin group (not significant). No adverse drug reaction was detected in both groups.A total of 30 patients including 15 males and 15 females completed the study. After eight weeks, 13 patients in the acyclovir group experienced complete response, while in the erythromycin group only six patients had complete response (It seemed that a high-dose of oral acyclovir was a safe and effective therapy for PR, although this remained to be confirmed in larger studies. Pityriasis rosea (PR) is an acute inflammatory and self- limiting disease of the skin. It typically begins as a single thin oval scaly plaque called a herald patch, and is typically asymptomatic. PR is co6We were unable to find a study comparing these two drugs. The aim of this study is to compare the efficacy of the traditional treatment with erythromycin and the recently introduced treatment with acyclovir.Venereal Disease Research Laboratory (VDRL) test was performed on all patients and positive results were confirmed by the fluorescent treponemal antibody absorbed (FTA-ABS) test, to exclude second-stage syphilis. The patients were randomized in two groups, A and B. Group A received high-dose acyclovir  while group B received oral erythromycin (400 mg QID for 10 days). Acyclovir and erythromycin used in the study were in similar white pill forms, with the same packaging. The patients were visited two, four, and eight weeks after commencement of the study, to evaluate response to treatment and then followed for one year to detect any recurrences. They were evaluated for improvement in symptoms (mainly pruritus), appearance of new lesions or resolution of previous ones, and increase or decrease in erythema and scaling. Count of lesions on the body was documented on every visit, for further evaluation of response to treatment. The responses were categorized as:This randomized clinical trial was conducted for a period of one year from May 2007 to April 2008. Patients with diagnosis of PR, confirmed clinically by two academic dermatologists, were selected for the study, if they visited our clinic during the first week of their disease and if they had extensive generalized lesions. An informed consent was taken from each patient or parent for inclusion in the study. Patients were excluded if they were pregnant or breast feeding women, or had any history of sensitivity to acyclovir or erythromycin, and renal or hepatic impairment, or were suspected to have fungal infection, psoriasis, or eczema. Complete response: No new lesions followed by disappearing of all previous lesions, with or without residual post-lesional pigmentationPartial response: Few new lesions plus regression or disappearing of some previous lesionsNo response: No regression of lesions, along with appearance of new lesions.P < 0.05 was considered significant.Student t-test was used for comparing means, while the chi-square test was used for comparing differences in proportions wherever necessary. The participants in the study were 30 patients with PR, who passed our inclusion and exclusion criteria. The average age of the patients who attended this study was 32.9 ± 16 years; 15 (50%) female and 15 (50%) male.Six patients in the erythromycin group and eight patients in acyclovir group had pruritus before the commencement of the study.At the end of the study, in group A, which received high- dose acyclovir, 13 patients had complete response, while in group B, treated with erythromycin, only six patients had complete response (P < 0.05). After eight weeks, in the patients treated with acyclovir, two had partial response, while in the erythromycin group, nine had partial response (P > 0.05). Pruritis is one of the most distressing symptoms of PR. As we can see, in group A, pruritus in all patients resolved after two weeks, while in group B two patients after four weeks and one patient after eight weeks had pruritus, although this was not significant.In our study, the male to female ratio is about one in both treatment groups. None of our patients in both groups achieved complete response during the first two weeks of treatment. This is contrary to some other trials1516 that151223