Heavy Smoking Is More Strongly Associated with General Unhealthy Lifestyle than Obesity and Underweight.
ABSTRACT: BACKGROUND:Smoking and obesity are major causes of non-communicable diseases. We investigated the associations of heavy smoking, obesity, and underweight with general lifestyle to infer which of these risk groups has the most unfavourable lifestyle. METHODS:We used data from the population-based cross-sectional Swiss Health Survey (5 rounds 1992-2012), comprising 85,575 individuals aged?18 years. Height, weight, smoking, diet, alcohol intake and physical activity were self-reported. Multinomial logistic regression was performed to analyse differences in lifestyle between the combinations of body mass index (BMI) category and smoking status. RESULTS:Compared to normal-weight never smokers (reference), individuals who were normal-weight, obese, or underweight and smoked heavily at the same time had a poorer general lifestyle. The lifestyle of obese and underweight never smokers differed less from reference. Regardless of BMI category, in heavy smoking men and women the fruit and vegetable consumption was lower (e.g. obese heavy smoking men: relative risk ratio (RRR) 1.69 [95% confidence interval 1.30;2.21]) and high alcohol intake was more common (e.g. normal-weight heavy smoking women 5.51 [3.71;8.20]). In both sexes, physical inactivity was observed more often in heavy smokers and obese or underweight (e.g. underweight never smoking 1.29 [1.08;1.54] and heavy smoking women 2.02 [1.33;3.08]). A decrease of smoking prevalence was observed over time in normal-weight, but not in obese individuals. CONCLUSIONS:Unhealthy general lifestyle was associated with both heavy smoking and BMI extremes, but we observed a stronger association for heavy smoking. Future smoking prevention measures should pay attention to improvement of general lifestyle and co-occurrence with obesity and underweight.
Project description:Several studies have reported direct associations of smoking with body mass index (BMI) and abdominal obesity. However, the interplay between them is poorly understood. Our first aim was to investigate the interaction between smoking status and BMI on abdominal obesity (waist circumference, WC). Our second aim was to examine how the association of smoking status with WC varies among normal and overweight/obese men and women. We examined 5833 participants from the National FINRISK 2007 Study. The interactions between smoking and BMI on WC were analyzed. Participants were categorized into eight groups according to BMI (normal weight vs. overweight/obese) and smoking status (never smoker, ex-smoker, occasional/light/moderate daily smoker, heavy daily smoker). The associations between each BMI/smoking status -group and WC were analyzed by multiple regressions, the normal-weight never smokers as the reference group. The smoking status by BMI-interaction on WC was significant for women, but not for men. Among the overweight/obese women, ex-smokers (? = 2.73; 1.99, 3.46) and heavy daily smokers (? = 4.90; 3.35, 6.44) had the highest estimates for WC when adjusted for age, BMI, alcohol consumption and physical activity. In comparison to never smoking overweight/obese women, the ?-coefficients of ex-smokers and heavy daily smokers were significantly higher. Among men and normal weight women the ? -coefficients did not significantly differ by smoking status. An interaction between smoking status and BMI on abdominal obesity was observed in women: overweight/obese heavy daily smokers were particularly vulnerable for abdominal obesity. This risk group should be targeted for cardiovascular disease prevention.
Project description:Obesity has been proposed as a potential protective factor against lung cancer. We examined the association between BMI and lung cancer risk in a pooled analysis based on nested case-control studies from four cohort studies.A case-control study was nested within four cohorts in USA, Europe, China and Singapore that included 4172 cases and 8471 control subjects. BMI at baseline was calculated as weight in kilograms divided by height in meters squared (kg/m2), and classified into 4 categories: underweight (BMI <?18.5), normal weight (18.5???BMI <?25), overweight (25???BMI <?30) and obese (?30). Odds ratios (ORs) and 95% confidence intervals (CIs) for BMI-lung cancer associations were estimated using unconditional logistic regression, adjusting for potential confounders.Considering all participants, and using normal weight as the reference group, a decreased risk of lung cancer was observed for those who were overweight (OR 0.77, 95% CI: 0.68-0.86) and obese (OR 0.69, 95% CI: 0.59-0.82). In the stratified analysis by smoking status, the decreased risk for lung cancer was observed among current, former and never smokers (P for interaction 0.002). The adjusted ORs for overweight and obese groups were 0.79 (95% CI: 0.68-0.92) and 0.75 (95% CI: 0.60-0.93) for current smokers, 0.70 (95% CI: 0.53-0.93) and 0.55 (95% CI: 0.37-0.80) for former smokers, 0.77 (95% CI: 0.59-0.99), and 0.71 (95% CI: 0.44-1.14) for never smokers, respectively. While no statistically significant association was observed for underweight subjects who were current smokers (OR 1.24, 95% CI: 0.98-1.58), former smokers (OR 0.27, 95% CI: 0.12-0.61) and never smokers (OR 0.83, 95% CI: 0.5.-1.28).The results of this study provide additional evidence that obesity is associated with a decreased risk of lung cancer. Further biological studies are needed to address this association.
Project description:Weight gain after quitting smoking is a common concern for smokers and can discourage quit attempts. The purpose of this analysis was to describe the long-term weight gain, smoking cessation attributable (SCA) weight gain and describe their relationship to cigarette consumption and body mass index (BMI) 10 years ago in a contemporary, nationally representative sample of smokers who continued to smoke and those who quit.In all, 12,204 adults ?36 years old were selected from the 2003-2012 National Health and Nutrition Examination Survey (NHANES). Ten-year weight gain for never, continuing and former smokers (who quit 1-10 years ago) was calculated by body mass index (BMI) 10 years ago and cigarettes per day (CPD). SCA weight gain was calculated by taking the difference between the adjusted mean 10-year weight gain of former smokers and that of continuing smokers.Mean 10-year weight gain among continuing smokers was 3.5? versus 8.4?kg among former smokers; the SCA weight gain was 4.9?kg. After Bonferroni correction, there was no significant difference in overall weight gain between continuing and former smokers of 1-14 CPD, and SCA weight gain was lowest in this group (2.0?kg, confidence interval (CI): 0.3, 3.7). SCA weight gain was highest for former smokers of ?25 CPD (10.3?kg, CI: 7.4, 13.2) and for those who were obese (7.1?kg, CI: 2.9, 11.3) mostly because of lower than average weight gain or weight loss among continuing smokers in these groups.In a current, nationally representative sample, baseline BMI and CPD were important factors that contributed to the magnitude of long-term weight gain following smoking cessation. Light to moderate smokers (<15 CPD) experienced little SCA weight gain, whereas heavy smokers (?25 CPD) and those who were obese before quitting experienced the most.
Project description:BACKGROUND: Cigarette smoking, adiposity, unhealthy diet, heavy alcohol drinking and physical inactivity together are associated with about half of premature deaths in Western populations. The aim of this study was to estimate their individual and combined impacts on residual life expectancy (RLE). METHODS: Lifestyle and mortality data from the EPIC-Heidelberg cohort, comprising 22,469 German adults ?40 years and free of diabetes, cardiovascular disease and cancer at recruitment (1994-1998), were analyzed with multivariable Gompertz proportional hazards models to predict lifetime survival probabilities given specific baseline status of lifestyle risk factors. The life table method was then used to estimate the RLEs. RESULTS: For 40-year-old adults, the most significant loss of RLE was associated with smoking (9.4 [95% confidence interval: 8.3, 10.6] years for male and 7.3 [6.0, 8.9] years for female heavy smokers [>10 cigarettes/day]; 5.3 [3.6, 7.1] years for men and 5.0 [3.2, 6.6] years for women smoking ?10 cigarettes/day). Other lifestyle risk factors associated with major losses of RLE were low body mass index (BMI <22.5 kg/m(2), 3.5 [1.8, 5.1] years for men; 2.1 [0.5, 3.6] years for women), obesity (BMI ?30, 3.1 [1.9, 4.4] years for men; 3.2 [1.8, 5.1] years for women), heavy alcohol drinking (>4 drinks/day, 3.1 [1.9, 4.0] years for men), and high processed/red meat consumption (?120 g/day, 2.4 [1.0, 3.9] years for women). The obesity-associated loss of RLE was stronger in male never smokers, while the loss of RLE associated with low BMI was stronger in current smokers. The loss of RLE associated with low leisure time physical activity was moderate for women (1.1 [0.05, 2.1] years) and negligible for men (0.4 [-0.3, 1.2] years). The combined loss of RLE for heavy smoking, obesity, heavy alcohol drinking and high processed/red meat consumption, versus never smoking, optimal BMI (22.5 to 24.9), no/light alcohol drinking and low processed/red meat consumption, was 17.0 years for men and 13.9 years for women. CONCLUSIONS: Promoting healthy lifestyles, particularly no cigarette smoking and maintaining healthy body weight, should be the core component of public health approaches to reducing premature deaths in Germany and similar affluent societies.
Project description:BACKGROUND:Lifestyle behaviors have been widely reported to influence the survival of patients with head and neck cancer. However, the relationship between pretreatment lifestyle behaviors and survival among patients with nasopharyngeal carcinoma (NPC) is unclear. METHODS:A prospective cohort study was designed to determine the relationship between lifestyle behaviors and survival in 1,533 NPC patients recruited from October 2005 to October 2007. Pretreatment lifestyle behaviors (such as body-mass index [BMI], smoking, alcohol, diet) of the patients were investigated. Univariate and multivariate proportional-hazards models were used to assess the impact of lifestyle behaviors on patient survival. RESULTS:Smoking was a predictor of survival; both current smokers (hazard ratio [HR] = 1.88; 95% CI, 1.33 to 2.65) and heavy smokers (? 25 Pack-years; HR = 1.84; 95% CI, 1.30 to 2.60) showed associations with poor survival. Higher BMI was significantly associated with a lower risk of death (P(trend) = 0.002). Compared with under/normal-weight patients (BMI less than 22.99 kg/m(2)), the multivariate HR for survival was 0.66 (95% CI, 0.48 to 0.90) and 0.47 (95% CI, 0.23 to 0.97) for overweight and obese patients, respectively. No alcohol intake and high fruit intake were associated with favorable survival in the univariate analysis but lost significance in the multivariate model. CONCLUSION:Our findings indicate that pretreatment lifestyle behaviors, especially smoking status and BMI, as easily available data, provide prognostic value for survival in NPC patients.
Project description:Few studies have examined the association between body mass index (BMI: kg/m(2)) and pancreatic cancer risk in Asian populations. We examined this relationship in 51,251 Chinese men and women aged 45-74 who enrolled between 1993 and 1998 in the population based, prospective Singapore Chinese Health Study. Data were collected through in-person interviews. By December 31, 2011, 194 cohort participants had developed pancreatic cancer. A Cox proportional hazards model was used to estimate hazard ratios (HR) and their 95% confidence intervals (95% CI). We hypothesized the association between BMI and pancreatic cancer risk may vary by smoking status (ever v. never) and there was evidence for this as the interaction between BMI and smoking status was significant (p = 0.018). Among ever smokers, being classified as underweight (BMI <18.5 kg/m(2)), was associated with a significantly elevated risk of pancreatic cancer relative to smokers with a BMI of 21.5-24.4 kg/m(2) (HR = 1.99, 95% CI = 1.03-3.84). This association was strengthened after exclusion of the first three years of follow-up time. Among never smokers, there was no association between BMI and pancreatic cancer risk. However, after excluding pancreatic cancer cases and person-years in the first three years of follow-up, never smokers with a BMI ≥ 27.5 kg/m(2) showed a suggestive increased risk of pancreatic cancer relative to never smokers with a BMI of 21.5-24.4 kg/m(2) (HR = 1.75, 95% CI = 0.93-3.3). In conclusion, Singaporean Chinese who were underweight with a history of smoking had an increased risk of developing pancreatic cancer, whereas there was no significant association between BMI and pancreatic cancer in never smokers.
Project description:OBJECTIVE:To investigate the shape of the causal relation between body mass index (BMI) and mortality. DESIGN:Linear and non-linear mendelian randomisation analyses. SETTING:Nord-Trøndelag Health (HUNT) Study (Norway) and UK Biobank (United Kingdom). PARTICIPANTS:Middle to early late aged participants of European descent: 56 150 from the HUNT Study and 366 385 from UK Biobank. MAIN OUTCOME MEASURES:All cause and cause specific (cardiovascular, cancer, and non-cardiovascular non-cancer) mortality. RESULTS:12 015 and 10 344 participants died during a median of 18.5 and 7.0 years of follow-up in the HUNT Study and UK Biobank, respectively. Linear mendelian randomisation analyses indicated an overall positive association between genetically predicted BMI and the risk of all cause mortality. An increase of 1 unit in genetically predicted BMI led to a 5% (95% confidence interval 1% to 8%) higher risk of mortality in overweight participants (BMI 25.0-29.9) and a 9% (4% to 14%) higher risk of mortality in obese participants (BMI ≥30.0) but a 34% (16% to 48%) lower risk in underweight (BMI <18.5) and a 14% (-1% to 27%) lower risk in low normal weight participants (BMI 18.5-19.9). Non-linear mendelian randomisation indicated a J shaped relation between genetically predicted BMI and the risk of all cause mortality, with the lowest risk at a BMI of around 22-25 for the overall sample. Subgroup analyses by smoking status, however, suggested an always-increasing relation of BMI with mortality in never smokers and a J shaped relation in ever smokers. CONCLUSIONS:The previously observed J shaped relation between BMI and risk of all cause mortality appears to have a causal basis, but subgroup analyses by smoking status revealed that the BMI-mortality relation is likely comprised of at least two distinct curves, rather than one J shaped relation. An increased risk of mortality for being underweight was only evident in ever smokers.
Project description:OBJECTIVE:To study the magnitude and predictors of underweight, incident underweight and recovery from underweight among rural Indian adults. DESIGN:Prospective cohort study. Each participant's BMI was measured in 2008 and 2012 and categorized as underweight (BMI<18·5 kg/m2), normal (BMI=18·5-22·9 kg/m2) or overweight/obese (BMI ?23·0 kg/m2). Incident underweight was defined as a transition from normal weight or overweight/obese in 2008 to underweight in 2012, and recovery from underweight as a transition from underweight in 2008 to normal weight in 2012. Bivariate and multivariable logistic regression analyses were employed. SETTING:The Birbhum Health and Demographic Surveillance System, West Bengal, India. SUBJECTS:Predominantly rural individuals (n 6732) aged ?18 years enrolled in 2008 were followed up in 2012. RESULTS:In 2008, the prevalence of underweight was 46·5 %. From 2008 to 2012, 25·8 % of underweight persons transitioned to normal BMI, 12·9 % of normal-weight persons became underweight and 0·1 % of overweight/obese persons became underweight. Multivariable models reveal that people aged 25-49 years, educated and wealthier people, and non-smokers had lower odds of underweight in 2008 and lower odds of incident underweight. Odds of recovery from underweight were lower among people aged ?36 years and higher among educated (Grade 6 or higher) individuals. CONCLUSIONS:The current study highlights a high incidence of underweight and important risk factors and modifiable predictors of underweight in rural India, which may inform the design of local nutrition interventions.
Project description:<h4>Background</h4>Little is known as to how health-related quality of life (HRQoL) when measured by generic instruments such as EQ-5D differ across smokers, ex-smokers and never-smokers in the general population; whether the overall pattern of this difference remain consistent in each domain of HRQoL; and what implications this variation, if any, would have for economic evaluations of tobacco control interventions.<h4>Methods</h4>Using the 2006 round of Health Survey for England data (n = 13,241), this paper aims to examine the impact of smoking status on health-related quality of life in English population. Depending upon the nature of the EQ-5D data (i.e. tariff or domains), linear or logistic regression models were fitted to control for biology, clinical conditions, socio-economic background and lifestyle factors that an individual may have regardless of their smoking status. Age- and gender-specific predicted values according to smoking status are offered as the potential 'utility' values to be used in future economic evaluation models.<h4>Results</h4>The observed difference of 0.1100 in EQ-5D scores between never-smokers (0.8839) and heavy-smokers (0.7739) reduced to 0.0516 after adjusting for biological, clinical, lifestyle and socioeconomic conditions. Heavy-smokers, when compared with never-smokers, were significantly more likely to report some/severe problems in all five domains--mobility (67%), self-care (70%), usual activity (42%), pain/discomfort (46%) and anxiety/depression (86%). 'Utility' values by age and gender for each category of smoking are provided to be used in the future economic evaluations.<h4>Conclusion</h4>Smoking is significantly and negatively associated with health-related quality of life in English general population and the magnitude of this association is determined by the number of cigarettes smoked. The varying degree of this association, captured through instruments such as EQ-5D, may need to be fed into the design of future economic evaluations where the intervention being evaluated affects (e.g. tobacco control) or is affected (e.g. treatment for lung cancer) by individual's (or patients') smoking status.