| ORIGINAL ARTICLE |
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| Year : 2018 | Volume
: 3
| Issue : 2 | Page : 45-51 |
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Association of secondhand smoke with increased sagittal abdominal diameter in the United States population: National health and nutrition examination survey 2011–2012
Naila Khalil1, Kyle D Wallace2, Omar T Tahtamooni3, Nikki L Rogers1, Ramzi W Nahhas4
1 Department of Population and Public Health Sciences, Boonshoft School of Medicine, Wright State University, Dayton, OH, USA
2 Epidemiologist Public Health-Dayton and Montgomery County, Dayton, OH, USA
3 Department of Environmental and Occupational Health, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, USA
4 Department of Population and Public Health Sciences; Department of Psychiatry, Boonshoft School of Medicine, Wright State University, Dayton, OH, USA
Correspondence Address:
Dr. Naila Khalil
Department of Population and Public Health Sciences, Boonshoft School of Medicine, Wright State University, 3123 Research Blvd, Suite #200, Dayton, OH 45420
USA
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/ed.ed_7_18
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Background: Tobacco smoke, an endocrine and metabolic disruptor, is associated with increased abdominal adiposity in active smokers (ASs). However, the role of secondhand smoke (SHS) exposure in central adiposity is unclear. Abdominal adiposity, measured as sagittal abdominal diameter (SAD), is associated with increased risk of cardiometabolic disease and mortality. We assessed the role of SHS exposure in explaining patterns of SAD and evaluated this relationship for differences by age.
Methods: Cross-sectional data from the National Health and Nutrition Examination Survey 2011–2012 were utilized for 6188 individuals aged 12–80 years. Using serum cotinine and self-reported smoking information, smoking status was categorized as nonsmoker (NS, <1 ng/ml), SHS (1–<10 ng/ml), and AS (≥10 ng/ml). SAD was compared across smoking categories including a Bonferroni correction. Age was grouped as 12–19, 20–49, and ≥50 years. Linear regression models assessing the association of SAD with smoking status were adjusted for sex, race/ethnicity (White, Black, Hispanic, and Asian/other), income, body mass index (BMI), and survey weights. The model (pooled over age) was adjusted for age, and the age-specific model included a smoking status by age group interaction.
Results: AS, NS, and SHS constituted an estimated 41%, 53%, and 6% of the population, respectively. The estimated mean population SAD was 21.6 cm (standard error: 0.1). Before adjusting for risk factors, SAD was marginally greater among SHS (21.0 cm) than NS (20.8 cm). Adjusting for covariates, AS had greater mean SAD (20.5 cm) than both SHS (20.2 cm, p-Bon [p-Bonferroni] = 0.009) and NS (20.0, p-Bon ≤0.001). However, SHS did not have significantly greater mean SAD than NS (p-Bon = 1.000). Association of SAD and smoking status differed by age (smoking status × age interaction, P = 0.013), with inconsistent patterning in the oldest age group. Among individuals aged 20–49 years, SHS exposed (20.7 cm) had greater mean SAD than NS (20.1 cm), although not significantly so (p-Bon = 0.347). Among those aged ≥50 years, SHS (20.5 cm) had significantly lower mean SAD than NS (21.2 cm) (p-Bon = 0.033).
Conclusion: Our results suggest a dose-response relationship between smoking and SAD. We discovered an unexpected U-shaped relationship between smoking and SAD among older adults that warrant further research.
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