Consequences of smoking for body weight, body fat distribution, and insulin resistance

Chioléro, Arnaud; Faeh, David; Paccaud, Fred; Cornuz, Jacques

doi:10.1093/ajcn/87.4.801

Cited by 968 publications

(840 citation statements)

References 117 publications

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“…Smoking is an important risk factor for many diseases and premature death, but smokers generally have a lower BMI than non-smokers 41. Thus, smoking may significantly confound and attenuate the association between body weight and mortality 42.…”

Section: Discussionmentioning

confidence: 99%

Trajectory of body shape in early and middle life and all cause and cause specific mortality: results from two prospective US cohort studies

Song

et al. 2016

BMJ

101

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Objective To assess body shape trajectories in early and middle life in relation to risk of mortality.Design Prospective cohort study.Setting Nurses’ Health Study and Health Professionals Follow-up Study.Population 80 266 women and 36 622 men who recalled their body shape at ages 5, 10, 20, 30, and 40 years and provided body mass index at age 50, followed from age 60 over a median of 15-16 years for death.Main outcome measures All cause and cause specific mortality.Results Using a group based modeling approach, five distinct trajectories of body shape from age 5 to 50 were identified: lean-stable, lean-moderate increase, lean-marked increase, medium-stable/increase, and heavy-stable/increase. The lean-stable group was used as the reference. Among never smokers, the multivariable adjusted hazard ratio for death from any cause was 1.08 (95% confidence interval 1.02 to 1.14) for women and 0.95 (0.88 to 1.03) for men in the lean-moderate increase group, 1.43 (1.33 to 1.54) for women and 1.11 (1.02 to 1.20) for men in the lean-marked increase group, 1.04 (0.97 to 1.12) for women and 1.01 (0.94 to 1.09) for men in the medium-stable/increase group, and 1.64 (1.49 to 1.81) for women and 1.19 (1.08 to 1.32) for men in the heavy-stable/increase group. For cause specific mortality, participants in the heavy-stable/increase group had the highest risk, with a hazard ratio among never smokers of 2.30 (1.88 to 2.81) in women and 1.45 (1.23 to 1.72) in men for cardiovascular disease, 1.37 (1.14 to 1.65) in women and 1.07 (0.89 to 1.30) in men for cancer, and 1.59 (1.38 to 1.82) in women and 1.10 (0.95 to 1.29) in men for other causes. The trajectory-mortality association was generally weaker among ever smokers than among never smokers (for all cause mortality: P for interaction <0.001 in women and 0.06 in men). When participants were classified jointly according to trajectories and history of type 2 diabetes, the increased risk of death associated with heavier body shape trajectories was more pronounced among participants with type 2 diabetes than those without diabetes, and those in the heavy-stable/increase trajectory and with a history of diabetes had the highest risk of death.Conclusions Using the trajectory approach, we found that heavy body shape from age 5 up to 50, especially the increase in middle life, was associated with higher mortality. In contrast, people who maintained a stably lean body shape had the lowest mortality. These results indicate the importance of weight management across the lifespan.

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Section: Discussionmentioning

confidence: 99%

Trajectory of body shape in early and middle life and all cause and cause specific mortality: results from two prospective US cohort studies

Song

et al. 2016

BMJ

101

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“…Smoking increases 24-h energy expenditure by ~ 10% and increases energy expenditure more during exercise and after eating than while at rest [38]. A 10% increase in metabolic rate, corresponding to an expenditure of 200 kcal per 24 h, seems small; however, assuming that there is no change in caloric intake, this increase in energy expenditure caused by nicotine can result in the loss of 10 kg in body weight over 1 year [39]. …”

Section: Discussionmentioning

confidence: 99%

Gender differences in sociodemographic and behavioural factors associated with BMI in an adult population in rural Burkina Faso – an AWI-Gen sub-study

Boua

Sorgho

Rouamba

et al. 2018

Global Health Action

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Background: The global health transition is linked with an increased burden of non-communicable diseases with cardiovascular diseases leading the epidemic. In sub-Saharan Africa (SSA), the prevalence of obesity has increased during the past decades and there is a need to investigate the associated driving factors. In Burkina Faso obesity remains low, especially in rural areas. In this study we recruited middle-aged adults, as part of a larger study on genetic and environmental contributions to cardiometabolic disease among Africans. Objectives: To investigate the distribution of BMI and prevalence of obesity in a cross-sectional population-based study and to determine the sociodemographic and behavioural correlates with BMI. Methods: Participants (N = 2,076) were recruited from the Nanoro Health and Demographic Surveillance System area and were aged 40–60 years. We applied hierarchical modelling to identify factors associated with BMI and structural equation modelling to identify mediated effects of sociodemographic and behavioural variables on BMI. Results: Data are presented on 2,076 participants (49.9% female). Men had significantly higher BMI than women with medians of 21.1 (19.2 – 23.4) vs 19.8 (18.1 – 21.6) (p < 0.001), and there were significantly more underweight women compared to men (31.0% vs 17.4%) (p < 0.001). More men were overweight and obese than women (11.9% vs 5.2% and 2.2% vs 1.4%). Socioeconomic status was the major contributor to increased BMI for men, and education was the main contributor in women. Tobacco smoking and chewing, and problematic alcohol consumption were associated with a decrease in BMI in men and women. Conclusion: Overweight and obesity are relatively low among adults in rural Burkina Faso, and men had a higher median BMI than women. Behavioural factors, including tobacco use and alcohol consumption, contributed to a decrease in BMI, whereas socioeconomic status and education (which were both generally low in this community) contributed to an increase in BMI.

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“…From a non‐exhaustive review of the literature, we identified potential confounders: insurance coverage, 12 , 13 number of primary care physicians per 100 000 population, 14 , 15 environmental humidity, 11 , 16 chronic disease and pregnancy rates, 17 , 18 , 19 , 20 percentage of the county population living below the poverty level, 12 , 21 , 22 vaccination uptake, 23 , 24 , 25 racial composition, 26 , 27 , 28 , 29 population density, 30 , 31 and prevalence of smoking 32 , 33 , 34 . A concise description of data sources and variable definitions for all covariates is provided in Table 1.…”

Section: Methodsmentioning

confidence: 99%

Relationship between community prevalence of obesity and associated behavioral factors and community rates of influenza‐related hospitalizations in the United States

Charland

Buckeridge

Hoen

et al. 2012

Influenza Resp Viruses

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Please cite this paper as: Charland et al.(2012) Relationship between community prevalence of obesity and associated behavioral factors and community rates of influenza‐related hospitalizations in the United States. Influenza and Other Respiratory Viruses DOI: 10.1111/irv.12019. Background Findings from studies examining the association between obesity and acute respiratory infection are inconsistent. Few studies have assessed the relationship between obesity‐related behavioral factors, such as diet and exercise, and risk of acute respiratory infection. Objective To determine whether community prevalence of obesity, low fruit/vegetable consumption, and physical inactivity are associated with influenza‐related hospitalization rates. Methods Using data from 274 US counties, from 2002 to 2008, we regressed county influenza‐related hospitalization rates on county prevalence of obesity (BMI ≥ 30), low fruit/vegetable consumption (<5 servings/day), and physical inactivity (<30 minutes/month recreational exercise), while adjusting for community‐level confounders such as insurance coverage and the number of primary care physicians per 100 000 population. Results A 5% increase in obesity prevalence was associated with a 12% increase in influenza‐related hospitalization rates [adjusted rate ratio (ARR) 1·12, 95% confidence interval (CI) 1·07, 1·17]. Similarly, a 5% increase in the prevalence of low fruit/vegetable consumption and physical inactivity was associated with an increase of 12% (ARR 1·12, 95% CI 1·08, 1·17) and 11% (ARR 1·11, 95% CI 1·07, 1·16), respectively. When all three variables were included in the same model, a 5% increase in prevalence of obesity, low fruit/vegetable consumption, and physical inactivity was associated with 6%, 8%, and 7% increases in influenza‐related hospitalization rates, respectively. Conclusions Communities with a greater prevalence of obesity were more likely to have high influenza‐related hospitalization rates. Similarly, less physically active populations, with lower fruit/vegetable consumption, tended to have higher influenza‐related hospitalization rates, even after accounting for obesity.

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Consequences of smoking for body weight, body fat distribution, and insulin resistance

Cited by 968 publications

References 117 publications

Trajectory of body shape in early and middle life and all cause and cause specific mortality: results from two prospective US cohort studies

Trajectory of body shape in early and middle life and all cause and cause specific mortality: results from two prospective US cohort studies

Gender differences in sociodemographic and behavioural factors associated with BMI in an adult population in rural Burkina Faso – an AWI-Gen sub-study

Relationship between community prevalence of obesity and associated behavioral factors and community rates of influenza‐related hospitalizations in the United States

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