Joint association of adiposity and smoking with mortality among U.S. adults

Determinants of smoking related weight-concern in smokers participating in a community-based cessation program

BACKGROUND

Concern about weight gain is a barrier to smoking-cessation, but determinants of postcessation weight-concern have not been comprehensively assessed in the context of community-based cessation programs.

METHODS

This cross-sectional analysis used baseline data from a cessation trial of 392 adults randomized to physical activity (PA) or general wellness counseling as adjunctive treatment for smoking. Outcomes were 1) smoking behaviors to control weight and 2) anticipating relapse due to weight gain. Independent variables were PA and perceptions, sociodemographics, psychosocial measures, smoking behavior and perceptions, diet, and BMI. From bivariable models examining main and sex interaction effects, significant variables were entered into a linear (control) or logistic (relapse) regression model to identify key determinants.

RESULTS

For both measures, weight-concern was greater (p < .05) for female smokers (standardized b = 0.52, SE = 0.10; OR = 0.29, 95 % CI = 0.17-0.49), White (b = 0.12, SE = 0.05; OR = 0.39, 95 % CI = 0.23-0.66), and less motivated to quit (b = -0.14, SE = 0.05; OR = 0.77, 95 % CI = 0.59-1.0). Higher scores for smoking to control weight were associated with less PA (b = -0.10, SE = 0.05) and higher BMI (b = 0.21, SE = 0.05). For men, higher BMI was associated with greater anticipation of relapse (OR = 2.54, 95 % CI = 1.42-4.56).

CONCLUSIONS

Among adults attempting cessation, women, White smokers, and those less motivated to quit were more likely to smoke for weight control and to relapse due to weight gain. Higher BMI was associated with greater anticipation of relapse for men, but not women. Weight-concerns, for both measures, were not related to smoking history, psychosocial functioning, PA engagement or attitudes, or dietary variables. Results suggest potential cessation intervention targets for weight-concerned smokers.

Understanding the risk of developing weight-related complications associated with different body mass index categories: a systematic review

BACKGROUND

Obesity and overweight are major risk factors for several chronic diseases. There is limited systematic evaluation of risk equations that predict the likelihood of developing an obesity or overweight associated complication. Predicting future risk is essential for health economic modelling. Availability of future treatments rests upon a model's ability to inform clinical and decision-making bodies.  This systematic literature review aimed to identify studies reporting (1) equations that calculate the risk for individuals with obesity, or overweight with a weight-related complication (OWRC), of developing additional complications, namely T2D, cardiovascular (CV) disease (CVD), acute coronary syndrome, stroke, musculoskeletal disorders, knee replacement/arthroplasty, or obstructive sleep apnea; (2) absolute or proportional risk for individuals with severe obesity, obesity or OWRC developing T2D, a CV event or mortality from knee surgery, stroke, or an acute CV event.

METHODS

Databases (MEDLINE and Embase) were searched for English language reports of population-based cohort analyses or large-scale studies in Australia, Canada, Europe, the UK, and the USA between January 1, 2011, and March 29, 2021. Included reports were quality assessed using an adapted version of the Newcastle Ottawa Scale.

RESULTS

Of the 60 included studies, the majority used European cohorts. Twenty-nine reported a risk prediction equation for developing an additional complication. The most common risk prediction equations were logistic regression models that did not differentiate between body mass index (BMI) groups (particularly above 40 kg/m²) and lacked external validation. The remaining included studies (31 studies) reported the absolute or proportional risk of mortality (29 studies), or the risk of developing T2D in a population with obesity and with prediabetes or normal glucose tolerance (NGT) (three studies), or a CV event in populations with severe obesity with NGT or T2D (three studies). Most reported proportional risk, predominantly a hazard ratio.

CONCLUSION

More work is needed to develop and validate these risk equations, specifically in non-European cohorts and that distinguish between BMI class II and III obesity. New data or adjustment of the current risk equations by calibration would allow for more accurate decision making at an individual and population level.

The effect of BMI on COVID-19 outcomes among older patients in South Korea: a nationwide retrospective cohort study

Background: The coronavirus disease 2019 (COVID-19) pandemic has caused deaths and shortages in medical resources worldwide, making the prediction of patient prognosis and the identification of risk factors very important. Increasing age is already known as one of the main risk factors for poor outcomes, but the effect of body mass index (BMI) on COVID-19 outcomes in older patients has not yet been investigated. Aim: We aimed to determine the effect of BMI on the severity and mortality of COVID-19 among older patients in South Korea. Methods: Data from 1272 COVID-19 patients (≥60 years old) were collected by the Korea Centers for Disease Control and Prevention. The odds ratios (ORs) of severe infection and death in the BMI groups were analyzed by logistic regression adjusted for covariates.Results: The underweight group (BMI<18.5 kg/m2) had a higher OR for death (adjusted OR = 2.23, 95% confidence interval [95% CI] = 1.06-4.52) than the normal weight group (BMI, 18.5-22.9 kg/m2). Overweight (BMI, 23.0-24.9 kg/m2) was associated with lower risks of both severe infection (adjusted OR = 0.55, 95% CI = 0.31-0.94) and death (adjusted OR = 0.50, 95% CI = 0.27-0.91). Conclusions: Underweight was associated with an increased risk of death, and overweight was related to lower risks of severe infection and death in older COVID-19 patients in Korea. However, this study was limited by the lack of availability of some information, including smoking status.KEY MESSAGESUnderweight is an independent risk factor of death in older COVID-19 patients.Overweight patients have a lower risk of death and severe infection than normal-weight patients.

Body mass index and all-cause mortality in older adults: A scoping review of observational studies

In older age, body composition changes as fat mass increases and redistributes. Therefore, the current body mass index (BMI) classification may not accurately reflect risk in older adults (65+). This study aimed to review the evidence on the association between BMI and all-cause mortality in older adults and specifically, the findings regarding overweight and obese BMI. A systematic search of the OVID MEDLINE and Embase databases was conducted between 2013 and September 2018. Observational studies examining the association between BMI and all-cause mortality within a community-dwelling population aged 65+ were included. Seventy-one articles were included. Studies operationalized BMI categorically (n = 60), continuously (n = 8) or as a numerical change/group transition (n = 7). Reduced risk of mortality was observed for the overweight BMI class compared with the normal BMI class (hazard ratios [HR] ranged 0.41-0.96) and for class 1 or 2 obesity in some studies. Among studies examining BMI change, increases in BMI demonstrated lower mortality risks compared with decreases in BMI (HR: 0.83-0.95). Overweight BMI classification or a higher BMI value may be protective with regard to all-cause mortality, relative to normal BMI, in older adults. These findings demonstrate the potential need for age-specific BMI cut-points in older adults.

Insights into the association between smoking and obesity: the 2014 Indonesian Family Life Survey

BACKGROUND Various findings on the relationship between smoking and obesity have been demonstrated. This study aimed to investigate the association between smoking behavior and obesity in the Indonesian adult population. METHODS A cross-sectional analysis was conducted using data from the 2014 Indonesian Family Life Survey. A body mass index of ≥25 kg/m² was employed to define obesity. Smoking behavior was assessed in terms of smoking status and its attributes. The potential confounders of gender, age, education, residential environment, economic status, physical activity, and education level were adjusted using logistic regression.  RESULTS Study subjects were 28,949 adults aged ≥20 years. Current smoking was a protective factor of obesity (adjusted odds ratio [aOR] = 0.53; 95% confidence interval [CI] = 0.48–0.58), whereas previous smoking habit showed no association with obesity (aOR = 0.96; 95% CI = 0.84–1.09). The risk of current smokers having obesity was lower than that of nonsmokers as smoking duration increased (aOR = 0.46–0.63). By contrast, the risk of obesity was relatively higher among former smokers than current smokers as the duration of quitting increased (aOR = 1.46–2.20). Heavy smokers had a higher risk of obesity than light smokers among former (aOR = 1.85; 95% CI = 1.27–  2.67) and current smokers (aOR = 1.38; 95% CI = 1.23–1.65). CONCLUSIONS Overall, smoking negatively affected obesity among the Indonesian adult population. By contrast, quitting smoking was associated with an increased risk of obesity. Thus, weight management along with smoking cessation intervention should be prescribed.

Obesity, smoking habits, and serum phosphate levels predicts mortality after life-style intervention

Background

Life-style interventions, including smoking cessation and weight control are of importance for managing future escalating prevalence of obesity. Smoking habits and obesity have jointly great impact on mortality, however mechanisms behind the effect and variables involved in the obesity paradox is still unknown.

Objectives

This study examines risk factors for all-cause, cardiovascular, and cancer mortality in males and females with high cardiovascular risk, mediated by smoking habits, body mass index (BMI, kg/m²), and serum phosphate (S-P) levels.

Methods

Patients were admitted to the Vindeln Patient Education Center in groups of 30 for a four-week residential comprehensive program (114 hours) focusing on smoking cessation, stress reduction, food preferences and selections, and physical exercise. The follow-up, in years from 1984 to 2014 corresponds to 30 years. This study included 2,504 patients (1,408 females and 1,096 males). Cox regression analysis was used to assess mortality risk associated with smoking habits, low and high BMI, and low and high S-P levels.

Results

High BMI (>34,2 kg/m²), current smoking, type 2 diabetes mellitus (T2DM), high serum calcium (S-Ca), mmol/L and high systolic blood pressure (SBP, mmHg) were associated with all-cause mortality irrespective of sex. Former and current smoking females had a high all-cause mortality (adjusted hazard ratio [HR] 1.581; 95% CI 1.108–2.256, adjusted hazard ratio [HR] 1.935; 95% CI 1.461–2.562, respectively) while current smoking and high BMI increased risk for cardiovascular mortality (adjusted hazard ratio [HR] 3.505; 95% CI 2.140–5.740 and [HR] 1.536; 95% CI 1.058–2.231, respectively). Neither low nor high levels of S-P predicted all-cause, cardiovascular disease (CVD) and cancer mortality in males or females while low levels of S-P predicted all-cause mortality in smokers (adjusted hazard ratio [HR] 1.713; 95% CI 1.211–2.424). In non-smokers, low BMI (<27.6 kg/m²) was protecting and high BMI a risk for all-cause mortality. In males, ischemic heart disease (IHD), and low serum albumin (S-Alb) were associated with all-cause mortality. In females, an interaction between high BMI and smoking (HbmiSM) decreased the cardiovascular mortality (adjusted hazard ratio [HR] 0.410; 95% CI 0.179–0.937, respectively).

Conclusions

High BMI and current smoking were associated with all-cause mortality in both males and females in the present high cardiovascular-risk cohort. In current smokers and non-smokers, T2DM and high S-Ca were associated with an increase in all-cause mortality, while low S-P was associated with all-cause mortality in smokers. Interaction between high BMI and smoking contribute to the obesity paradox by being protective for cardiovascular mortality in females.

Prediagnosis and postdiagnosis smoking and survival following diagnosis with ovarian cancer

Little is known about the influence of prediagnosis and postdiagnosis smoking and smoking cessation on ovarian cancer survival. We investigated this relationship in two prospective cohort studies, the Nurses' Health Study (NHS) and NHSII. Analyses included 1,279 women with confirmed invasive, Stage I-III epithelial ovarian cancer. We used Cox proportional hazards regression models to estimate hazard ratios (HR) and 95% confidence intervals (CI) for ovarian cancer-specific mortality by smoking status, adjusting for age and year of diagnosis, tumor stage, histologic subtype, body mass index and nonsteroidal anti-inflammatory use (postdiagnosis models only). When examining prediagnosis smoking status (assessed a median of 12 months before diagnosis), risk of death was significantly increased for former smokers (HR = 1.19, 95% CI: 1.02-1.39), and suggestively for current smokers (HR = 1.21, 95% CI: 0.96-1.51) vs. never smokers. Longer smoking duration (≥20 years vs. never, HR = 1.23, 95% CI: 1.05-1.45) and higher pack-years (≥20 pack-years vs. never, HR = 1.28, 95% CI: 1.07-1.52) were also associated with worse outcome. With respect to postdiagnosis exposure, women who smoked ≥15 cigarettes per day after diagnosis (assessed a median of 11 months after diagnosis) had increased mortality compared to never smokers (HR = 2.34, 95% CI: 1.63-3.37). Those who continued smoking after diagnosis had 40% higher mortality (HR = 1.40, 95% CI: 1.05-1.87) compared to never smokers. Overall, our results suggest both prediagnosis and postdiagnosis smoking are associated with worse ovarian cancer outcomes.

Tobacco Smoking, Eating Behaviors, and Body Weight: A Review

Purpose of review

This narrative review provides an overview of the relationships among tobacco smoking, eating behaviors, and body weight. The aims are to (1) examine the concurrent and longitudinal associations between tobacco smoking and body weight, (2) describe potential mechanisms underlying the relationships between smoking and body weight, with a focus on mechanisms related to eating behaviors and appetite, and (3) discuss management of concomitant tobacco smoking and obesity.

Recent findings

Adolescents who smoke tobacco tend to have body mass indexes (BMI) the same as or higher than nonsmokers. However, adult tobacco smokers tend to have lower BMIs and unhealthier diets relative to nonsmokers. Smoking cessation is associated with a mean body weight gain of 4.67 kg after 12 months of abstinence, though there is substantial variability. An emerging literature suggests that metabolic factors known to regulate food intake (e.g., ghrelin, leptin) may also play an important role in smoking-related behaviors. While the neural mechanisms underlying tobacco smoking-induced weight gain remain unclear, brain imaging studies indicate that smoking and eating cues overlap in several brain regions associated with learning, memory, motivation and reward. Behavioral and pharmacological treatments have shown short-term effects in limiting post-cessation weight gain; however, their longer-term efficacy is limited.

Summary

Further studies are needed to identify the exact mechanisms underlying smoking, eating behaviors, and body weight. Moreover, effective treatment options are needed to prevent long-term weight gain during smoking abstinence.

A meta-analysis but not a systematic review: an evaluation of the Global BMI Mortality Collaboration

Meta-analyses of individual participant data (MIPDs) offer many advantages and are considered the highest level of evidence. However, MIPDs can be seriously compromised when they are not solidly founded upon a systematic review. These data-intensive collaborative projects may be led by experts who already have deep knowledge of the literature in the field and of the results of published studies and how these results vary based on different analytical approaches. If investigators tailor the searches, eligibility criteria, and analysis plan of the MIPD, they run the risk of reaching foregone conclusions. We exemplify this potential bias in a MIPD on the association of body mass index with mortality conducted by a collaboration of outstanding and extremely knowledgeable investigators. Contrary to a previous meta-analysis of group data that used a systematic review approach, the MIPD did not seem to use a formal search: it considered 239 studies, of which the senior author was previously aware of at least 238, and it violated its own listed eligibility criteria to include those studies and exclude other studies. It also preferred an analysis plan that was also known to give a specific direction of effects in already published results of most of the included evidence. MIPDs where results of constituent studies are already largely known need safeguards to their validity. These may include careful systematic searches, adherence to the Preferred Reporting Items for Systematic Review and Meta-Analyses of individual participant data guidelines, and exploration of the robustness of results with different analyses. They should also avoid selective emphasis on foregone conclusions based on previously known results with specific analytical choices.

Associations of maternal obesity and smoking status with perinatal outcomes

Maternal obesity and smoking are associated with adverse perinatal outcomes. These prevalent conditions contribute to health disparities. In this study, we examine whether maternal BMI moderates the impact of smoking cessation on short-term perinatal outcomes. This is a secondary analysis of assessments conducted from several prospective clinical trials examining the efficacy of incentives to promote smoking cessation during pregnancy. Participants were randomly assigned to receive financial incentives contingent upon smoking abstinence or a control condition. Pregnancy outcomes were abstracted from the medical record. ANCOVA and multiple logistic regression were used for statistical analysis. Among 388 women, there was a significant interaction between maternal pre-pregnancy BMI and smoking status on gestational age at delivery (p = .03) and admission to the NICU (p = .04). Among underweight/normal weight gravidas, smoking resulted in earlier deliveries and a greater likelihood of NICU admission than in those who abstained. Among overweight/obese gravidas, there was no effect of smoking on gestational age at delivery and infants of smokers were less likely to be admitted to the NICU. Maternal obesity and smoking have significant individual effects on perinatal outcome. Maternal overweight/obesity appears to moderate the effect of smoking on gestational age at delivery and on NICU admissions.

Association Between Antioxidant Intake/Status and Obesity: a Systematic Review of Observational Studies

The global prevalence of obesity has doubled in recent decades. Compelling evidences indicated that obesity was associated with lower concentrations of specific antioxidants which may play a role in the development of obesity-related diseases such as cardiovascular disease. The present review aimed to synthesize the evidence from studies on the association between obesity and antioxidant micronutrients in a systematic manner. Data bases including MEDLINE, Science Direct, and Cochrane were searched from inception to October 2015. Thirty-one articles were reviewed using the MOOSE checklist. Lower concentrations of antioxidants have been reported in obese individuals among age groups worldwide. Circulatory levels of carotenoids, vitamins E and C, as well as zinc, magnesium, and selenium were inversely correlated with obesity and body fat mass. However, studies demonstrated inconsistencies in findings. Lower status of carotenoids, vitamins E and C, zinc, magnesium, and selenium appears to be associated with adiposity. Intervention studies may be needed to establish the causality of these associations.

BMI and all cause mortality: systematic review and non-linear dose-response meta-analysis of 230 cohort studies with 3.74 million deaths among 30.3 million participants

OBJECTIVE

To conduct a systematic review and meta-analysis of cohort studies of body mass index (BMI) and the risk of all cause mortality, and to clarify the shape and the nadir of the dose-response curve, and the influence on the results of confounding from smoking, weight loss associated with disease, and preclinical disease.

DATA SOURCES

PubMed and Embase databases searched up to 23 September 2015.

STUDY SELECTION

Cohort studies that reported adjusted risk estimates for at least three categories of BMI in relation to all cause mortality.

DATA SYNTHESIS

Summary relative risks were calculated with random effects models. Non-linear associations were explored with fractional polynomial models.

RESULTS

230 cohort studies (207 publications) were included. The analysis of never smokers included 53 cohort studies (44 risk estimates) with >738 144 deaths and >9 976 077 participants. The analysis of all participants included 228 cohort studies (198 risk estimates) with >3 744 722 deaths among 30 233 329 participants. The summary relative risk for a 5 unit increment in BMI was 1.18 (95% confidence interval 1.15 to 1.21; I(2)=95%, n=44) among never smokers, 1.21 (1.18 to 1.25; I(2)=93%, n=25) among healthy never smokers, 1.27 (1.21 to 1.33; I(2)=89%, n=11) among healthy never smokers with exclusion of early follow-up, and 1.05 (1.04 to 1.07; I(2)=97%, n=198) among all participants. There was a J shaped dose-response relation in never smokers (Pnon-linearity <0.001), and the lowest risk was observed at BMI 23-24 in never smokers, 22-23 in healthy never smokers, and 20-22 in studies of never smokers with ≥20 years' follow-up. In contrast there was a U shaped association between BMI and mortality in analyses with a greater potential for bias including all participants, current, former, or ever smokers, and in studies with a short duration of follow-up (<5 years or <10 years), or with moderate study quality scores.

CONCLUSION

Overweight and obesity is associated with increased risk of all cause mortality and the nadir of the curve was observed at BMI 23-24 among never smokers, 22-23 among healthy never smokers, and 20-22 with longer durations of follow-up. The increased risk of mortality observed in underweight people could at least partly be caused by residual confounding from prediagnostic disease. Lack of exclusion of ever smokers, people with prevalent and preclinical disease, and early follow-up could bias the results towards a more U shaped association.

How smoking affects the proportion of deaths attributable to obesity: assessing the role of relative risks and weight distributions

OBJECTIVE

Although ever-smokers make up the majority of the older adult population in the USA, they are often excluded from studies examining the impact of obesity on mortality. Understanding how smoking and obesity interact is critical to assessing the proportion of deaths attributable to obesity.

SETTING

Nationally representative sample of the non-institutionalised population of the USA. Baseline data were drawn from the National Health and Nutrition Examination Survey, 1988-1994 and 1999-2004.

PARTICIPANTS

US adults aged 50-74 (n=9835).

PRIMARY OUTCOME MEASURE

We used Cox models to estimate the mortality risks of obesity by smoking status. All-cause mortality was assessed prospectively through 31 December 2006 (n=1243 deaths). Maximum body mass index (BMI) was specified as the key exposure variable. We also calculated population attributable fractions (PAFs) by smoking status and investigated differences in PAFs in a decomposition analysis.

RESULTS

The HR associated with a one-unit increment in BMI beyond 25.0 kg/m(2) was 1.057 for never-smokers (95% CI 1.033 to 1.082; p<0.001), 1.036 for former smokers (95% CI 1.015 to 1.059; p<0.01) and 1.024 for current smokers (95% CI 0.997 to 1.052).We estimated that 19.8% of deaths were attributable to excess weight. The PAFs were 31.9, 20.4 and 11.3 for never-smokers, former and current smokers, respectively. The difference in PAFs between never-smokers and current smokers was almost entirely explained by the difference in HRs.

CONCLUSIONS

The proportion of deaths attributable to obesity is nearly 3 times as high among never-smokers compared with current smokers. This finding is consistent with the fact that smokers are subject to significant competing risks. Analyses that exclude smokers are likely to substantially overestimate the proportion of deaths attributable to obesity in the USA.

Heavy Smoking Is More Strongly Associated with General Unhealthy Lifestyle than Obesity and Underweight

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.

Are major behavioral and sociodemographic risk factors for mortality additive or multiplicative in their effects?

All individuals are subject to multiple risk factors for mortality. In this paper, we consider the nature of interactions between certain major sociodemographic and behavioral risk factors associated with all-cause mortality in the United States. We develop the formal logic pertaining to two forms of interaction between risk factors, additive and multiplicative relations. We then consider the general circumstances in which additive or multiplicative relations might be expected. We argue that expectations about interactions among socio-demographic variables, and their relation to behavioral variables, have been stated in terms of additivity. However, the statistical models typically used to estimate the relation between risk factors and mortality assume that risk factors act multiplicatively. We examine empirically the nature of interactions among five major risk factors associated with all-cause mortality: smoking, obesity, race, sex, and educational attainment. Data were drawn from the cross-sectional NHANES III (1988-1994) and NHANES 1999-2010 surveys, linked to death records through December 31, 2011. Our analytic sample comprised 35,604 respondents and 5369 deaths. We find that obesity is additive with each of the remaining four variables. We speculate that its additivity is a reflection of the fact that obese status is generally achieved later in life. For all pairings of socio-demographic variables, risks are multiplicative. For survival chances, it is much more dangerous to be poorly educated if you are black or if you are male. And it is much riskier to be a male if you are black. These traits, established at birth or during childhood, literally result in deadly combinations. We conclude that the identification of interactions among risk factors can cast valuable light on the nature of the process being studied. It also has public health implications by identifying especially vulnerable groups and by properly identifying the proportion of deaths attributable to a risk factor.

Estimating the Time-Varying Joint Effects of Obesity and Smoking on All-Cause Mortality Using Marginal Structural Models

Obesity and smoking are independently associated with a higher mortality risk, but previous studies have reported conflicting results about the relationship between these 2 time-varying exposures. Using prospective longitudinal data (1987-2007) from the Atherosclerosis Risk in Communities Study, our objective in the present study was to estimate the joint effects of obesity and smoking on all-cause mortality and investigate whether there were additive or multiplicative interactions. We fit a joint marginal structural Poisson model to account for time-varying confounding affected by prior exposure to obesity and smoking. The incidence rate ratios from the joint model were 2.00 (95% confidence interval (CI): 1.79, 2.24) for the effect of smoking on mortality among nonobese persons, 1.31 (95% CI: 1.13, 1.51) for the effect of obesity on mortality among nonsmokers, and 1.97 (95% CI: 1.73, 2.22) for the joint effect of smoking and obesity on mortality. The negative product term from the exponential model revealed a submultiplicative interaction between obesity and smoking (β = -0.28, 95% CI: -0.45, -0.11; P < 0.001). The relative excess risk of interaction was -0.34 (95% CI: -0.60, -0.07), indicating the presence of subadditive interaction. These results provide important information for epidemiologists, clinicians, and public health practitioners about the harmful impact of smoking and obesity.

Inverse relationship between body mass index and mortality in older nursing home residents: a meta-analysis of 19,538 elderly subjects

Body mass index (BMI) and mortality in old adults from the general population have been related in a U-shaped or J-shaped curve. However, limited information is available for elderly nursing home populations, particularly about specific cause of death. A systematic PubMed/EMBASE/CINAHL/SCOPUS search until 31 May 2014 without language restrictions was conducted. As no published study reported mortality in standard BMI groups (<18.5, 18.5-24.9, 25-29.9, ≥30 kg/m(2)), the most adjusted hazard ratios (HRs) according to a pre-defined list of covariates were obtained from authors and pooled by random-effect model across each BMI category. Out of 342 hits, 20 studies including 19,538 older nursing home residents with 5,223 deaths during a median of 2 years of follow-up were meta-analysed. Compared with normal weight, all-cause mortality HRs were 1.41 (95% CI = 1.26-1.58) for underweight, 0.85 (95% CI = 0.73-0.99) for overweight and 0.74 (95% CI = 0.57-0.96) for obesity. Underweight was a risk factor for higher mortality caused by infections (HR = 1.65 [95% CI = 1.13-2.40]). RR results corroborated primary HR results, with additionally lower infection-related mortality in overweight and obese than in normal-weight individuals. Like in the general population, underweight is a risk factor for mortality in old nursing home residents. However, uniquely, not only overweight but also obesity is protective, which has relevant nutritional goal implications in this population/setting.

Impact of Smoking and Excess Body Weight on Overall and Site-Specific Cancer Mortality Risk

BACKGROUND

Smoking and excess body weight are major preventable risk factors for premature death. This study aimed at analyzing their single and combined association with site-specific cancer mortality.

METHODS

Our study population comprised 35,784 men and women of ages 14 to 99 years, who participated in population-based health surveys conducted 1977-1993 in Switzerland and were followed up for mortality until 2008. Multivariable Cox proportional hazards models were calculated for different cancer sites, and population attributable fractions were derived.

RESULTS

The hazard ratio of dying from cancer (all sites) was 2.32 (95% confidence interval, 2.04-2.63) for heavy smokers (vs. never smokers) and 1.15 (1.01-1.32) for obese [body mass index (BMI) ≥ 30 kg/m(2)] vs. normal weight individuals. Heavy smoking (≥20 cigarettes/day) was associated with increased mortality due to cancer of the lung, upper aero-digestive tract, pancreas, bladder, liver, and the total of remaining sites. Obesity was associated with higher risk of dying from cancer of the liver and the female genital tract (essentially corpus or cervix uteri and ovary). More than 20% of all cancer deaths in our population were attributable to ever smoking and overweight (BMI ≥ 25 kg/m(2)).

CONCLUSIONS

Smoking was a much stronger risk factor for cancer than excess body weight. For lung, liver, and pancreatic cancer, the combination of excess body weight and smoking lead to cumulated higher risks.

IMPACT

Our findings support recommendations for obese persons to quit smoking despite potential postcessation weight gain.

Smoking Habits and Neuropeptides: Adiponectin, Brain-derived Neurotrophic Factor, and Leptin Levels

This study aimed to identify changes in the level of neuropeptides among current smokers, former smokers, and individuals who had never smoked, and how smoking habits affect obesity and metabolic syndrome (MetS). Neuropeptide levels, anthropometric parameters, and metabolic syndrome diagnostic indices were determined among male workers; 117 of these had never smoked, whereas 58 and 198 were former and current smokers, respectively. The total sample comprised 373 male workers. The results obtained from anthropometric measurements showed that current smokers attained significantly lower body weight, body mass index, waist circumference, and abdominal fat thickness values than former smokers and those who had never smoked. Current smokers’ eating habits proved worse than those of non-smokers and individuals who had never smoked. The level of brain-derived neurotrophic factor (BDNF) in the neuropeptides in the case of former smokers was 23.6 ± 9.2 pg/ml, higher than that of current smokers (20.4 ± 6.1) and individuals who had never smoked (22.4 ± 5.8) (F = 6.520, p = 0.002). The level of adiponectin among former smokers was somewhat lower than that of current smokers, whereas leptin levels were higher among former smokers than current smokers; these results were not statistically significant. A relationship was found between adiponectin and triglyceride among non-smokers (odds ratio = 0.660, β value = −0.416, p < 0.01) and smokers (odds ratio = 0.827, β value = −0.190, p < 0.05). Further, waist circumference among non-smokers (odds ratio = 1.622, β value = 0.483, p < 0.001) and smokers (odds ratio = 1.895, β value = 0.639, p < 0.001) was associated with leptin. It was concluded that cigarette smoking leads to an imbalance of energy expenditure and appetite by changing the concentration of neuropeptides such as adiponectin, BDNF, leptin, and hsCRP, and influences food intake, body weight, the body mass index, blood pressure, and abdominal fat, which are risk factors for MetS and cardiovascular disease.