|Year : 2018 | Volume
| Issue : 2 | Page : 45-51
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
|Date of Submission||13-Apr-2018|
|Date of Acceptance||30-May-2018|
|Date of Web Publication||12-Jul-2018|
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
Source of Support: None, Conflict of Interest: None
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.
Keywords: Abdominal adiposity, endocrine disruptor, environmental tobacco smoke, metabolic disruptor, sagittal abdominal diameter, secondhand smoke
|How to cite this article:|
Khalil N, Wallace KD, Tahtamooni OT, Rogers NL, Nahhas RW. Association of secondhand smoke with increased sagittal abdominal diameter in the United States population: National health and nutrition examination survey 2011–2012. Environ Dis 2018;3:45-51
|How to cite this URL:|
Khalil N, Wallace KD, Tahtamooni OT, Rogers NL, Nahhas RW. Association of secondhand smoke with increased sagittal abdominal diameter in the United States population: National health and nutrition examination survey 2011–2012. Environ Dis [serial online] 2018 [cited 2022 Nov 28];3:45-51. Available from: http://www.environmentmed.org/text.asp?2018/3/2/45/236534
| Introduction|| |
Secondhand smoke (SHS) exposure occurs when people breathe in smoke from burning tobacco products or inhale smoke that has been exhaled by other smokers. SHS may occur in homes, workplaces, and public places such as restaurants and bars and in private vehicles. More than 7000 chemicals can be found in tobacco smoke; hundreds are known to be toxic and at least 70 are known carcinogens. Some commonly found carcinogens in tobacco smoke include hydrogen cyanide, carbon monoxide, ammonia, cadmium, arsenic, benzene, and beryllium. Globally, more than 600,000 premature deaths were associated with SHS in 2011.
Evidence regarding the association between SHS and chronic diseases such as cardiovascular disease is well known. Exposure to SHS can have deleterious effects on the heart and blood vessels. Between 2005 and 2009, SHS exposure caused more than 7300 lung cancer deaths among adult nonsmokers (NSs) in the United States (US).
Exposure to SHS may also increase the risk of metabolic syndrome (MetS). MetS is a precursor to cardiometabolic conditions of great public health concern. MetS is a combination of common risk factors including high blood pressure, raised blood glucose, high cholesterol levels, and increased abdominal adiposity. Although the cause is likely complex and multifactorial, abdominal adiposity is a contributing factor to MetS. Increased abdominal adiposity contributes to other MetS factors such as dyslipidemia, hyperglycemia, beta cell dysfunction, and insulin resistance. According to Han and Lean, MetS affects about 30%–40% of people by age 65, mainly through weight gain, and by a genetic or epigenetic predisposition to intra-abdominal/ectopic abdominal fat accumulation.
Abdominal adiposity is comprised of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT). Compared to SAT, VAT has a closer association to obesity-related metabolic outcomes., Accurate assessment of VAT can help in estimating the risk of metabolic diseases. Although computed tomography and magnetic resonance imaging are more precise methods for measuring VAT, they are impractical to use in clinical or research settings due to cost and training requirements. In contrast, sagittal abdominal diameter (SAD) is an inexpensive, simple clinical measurement of VAT. SAD is measured through the anteroposterior diameter of the abdomen when patient is supine (lying down): it, therefore, reflects VAT, as the subcutaneous fat is displaced inferiorly by gravity. Increased SAD has been associated with elevated risk of cardiovascular  and metabolic diseases.
Evidence suggests that the odds of having abdominal obesity increases as the number of daily cigarettes increases; the relationship between central obesity and smoking is dose dependent. However, the relationship between SHS and abdominal adiposity has not been well studied. The aim of our study was to investigate the metabolic effects of tobacco smoke, in terms of SAD, across varying levels of smoking status: active smoker (AS), SHS, and NS and to determine how smoking impacts SAD among different age groups.
| Methods|| |
Study methods and participants
Publicly available data from the National Health and Nutrition Examination Survey (NHANES) 2011–2012 cycle were utilized for this study; lack of individually identifiable data makes it exempt from human subject research review under 45 CFR part 46. A detailed description of the survey design and methodology are available on the NHANES website. Briefly, NHANES is an ongoing survey of the noninstitutionalized US population collected using a stratified, multistage probability sampling design. After providing informed consent, participants visited a mobile examination center for physical assessment, examination, and laboratory measures. Analysis of serum cotinine (SCOT) was conducted in a random, one-third subsample of participants aged 12 years and above. The present study sample consisted of 6188 NHANES participants aged 12–80 years with available SAD and SCOT measures.
Smoking status and serum cotinine
In the present study, SCOT (a metabolite of nicotine) concentration and related questionnaire data regarding tobacco use and exposure to SHS were used to categorize smoking status. SCOT was analyzed at the National Center for Environmental Health and Centers for Disease Control and Prevention with isotope dilution-high-performance liquid chromatography/atmospheric pressure chemical ionization tandem mass spectrometry.
Smoking status was categorized based on (a) SCOT levels ,, and (b) questionnaire responses from the following datasets “(a) SMQ_G: smoking-cigarette use,” “(b) SMQFAM_G: Smoking-household smokers” (including occupational smoking exposure), and “(c) SMQRTU_G: smoking-recent tobacco use.”
AS was defined as those with (a) SCOT ≥10.0 ng/mL, OR responding (b) “Yes” to survey questions “SMQ020: smoked at least 100 cigarettes in life” OR (c) “everyday” or “some days” to question “SMQ040: Do you now smoke cigarettes?” or (d) responded “Yes” to the question asking recent use of tobacco “used tobacco/nicotine last 5 days” (SMQ680 = 1), or “last 5 days use of cigarettes, pipes, cigars, and smokeless tobacco” (SMQ690A = 1, OR SMQ690B = 2, OR SMQ690C = 3, OR SMQ690D = 4, OR SMQ690E = 5).
SHS exposed was defined as those with (a) SCOT 1.0–<10 ng/mL, OR (b) those who responded “Yes” to the questions “SMD410: Does anyone smoke inside home?” OR (c) “OCQ275-Has anyone smoked where you work?”
NS was defined as those who had (a) SCOT <1.0 ng/mL OR (b) answered “No” to “smoking at least 100 cigarettes in lifetime” OR (c) “No” to “current cigarette smoking.”
SAD (cm) was measured using calipers [Figure 1]. NHANES staff measured participants' supine anteroposterior (back to front of abdomen) diameter twice (reported as mean of two measures) at the level of anterior iliac crest with knees at a 90° angle, feet resting flat on the table, and arms crossed over the chest (19 p3-24). This supine SAD variable was introduced in NHANES 2011–2012, providing the initial national reference data (19 p3-21; p3-27).
Covariates selected a priori based on their relationship to SAD or SHS cited in the literature included age, race/ethnicity, gender, BMI, and income. Sociodemographic information such as age, gender, race/ethnicity, and income were available from interviewer-administered questionnaires. We categorized age in three levels as 12–19 years, 20–49 years, and ≥50 years. Self-reported race/ethnicity was categorized as non-Hispanic White, non-Hispanic Black, Mexican American, and Asian/Other (including multiracial). Annual household income was grouped as <$25,000, $25,000–$54,999, and ≥$55,000. Body weight was measured by NHANES staff to the nearest 0.01 kg using an electronic load cell scale and standing height was measured with a fixed stadiometer. BMI (kg/m 2) was calculated as body weight (kilograms) divided by the square of height (meters squared) and categorized as underweight <18.5, normal weight 18.5–24.9, overweight 25–29.9, and obese ≥30.
Population characteristics and SAD were summarized as mean ± standard error (SE) or number (and %) of observations. Differences in SAD between smoking status groups were tested using ANOVA (Proc GLM) or Rao Scott Chi-square test as recommended by the National Center for Health Statistics.
As decided a priori, analyses were conducted to examine the relationship between smoking status and SAD, both overall and by age groups. We constructed multivariable linear regression models with SAD as the dependent variable and smoking status as the primary exposure variable, adjusting for the aforementioned covariates. To assess if age modified the relationship between SHS and SAD, a smoking status by age interaction was tested. Sampling weights, strata, and primary sampling units were adjusted for in all analyses to account for the complex NHANES survey design. All tests were two sided and conducted at the 5% significance level. To adjust for multiple comparisons between smoking status groups, Bonferroni corrections were applied (a) over the three pairwise comparisons of mean SAD between smoking status groups in the overall model and (b) over the nine age group-specific pairwise comparisons in the model with the interaction. Corrected P values were reported as p-Bonferroni [p-Bon] = 3p or 9p, respectively, with values greater than one truncated to one. SAS survey procedures were used (SAS Institute, Inc., version 9.3) by applying Taylor series linearization method for the calculation of SEs.
| Results|| |
AS, NS, and SHS comprised an estimated 41%, 53%, and 6% of the population, respectively [Table 1]. The mean age of the study population was 41.1 (SE: 0.7) years; AS had higher mean age compared to NS and SHS [P< 0.001, [Table 1]. The percentage of men was higher in AS and SHS than in NS (P< 0.001). The study population was predominantly composed of non-Hispanic White participants (65%). Between age groups, the proportion of non-Hispanic Black participants in SHS was twice as large as in either NS or AS; AS had the largest proportion of non-Hispanic Whites (P< 0.001). Mean BMI was incrementally greater with greater smoking exposure (P< 0.001). Almost half of the population had annual household income ≥$55,000; however, between smoking status groups, SHS had lower income than AS and NS (P< 0.001). Mean SAD overall was 21.6 cm (SE: 0.1), and greater mean SAD was observed with greater smoking exposure (P< 0.001). Although both SAD and BMI exhibited a dose-response relationship with smoking exposure, these measures were only slightly greater in SHS compared to NS, with larger means observed for AS.
|Table 1: Estimated population characteristics based on 2011-2012 National Health and Nutrition Examination Survey participants, overall, and by smoking status|
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After adjusting for covariates and pooled over age categories, smoking status was a significant predictor of SAD (P< 0.001). After adjusting for multiple comparisons, mean SAD was incrementally greater with greater smoke exposure [NS, 20.0 cm; SHS, 20.2 cm; AS, 20.5 cm; see [[Figure 2], Panel A]. As shown in [Table 2], AS had significantly greater mean SAD than both SHS (D = 0.35, p-Bon = 0.009) and NS (D = 0.47, p-Bon <0.001), although SHS did not have significantly greater mean SAD than NS (D = 0.12, p-Bon = 1.000).
|Figure 2: Panel (a) Multivariate adjusted association of SAD (cm) by smoking status. Panel (b) Multivariate adjusted association of SAD (cm) by smoking status. AS: Active smoking, SHS: Secondhand smoking, NS: Non-smoking and age groups|
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|Table 2: Adjusted mean sagittal abdominal diameter (cm) difference and 95% confidence interval across smoking status, overall and by age categories (adjusted for gender, ethnicity, body mass index, and income; overall model also adjusted for age)|
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The overall smoking status by age interaction was significant (P = 0.013); the difference between age groups in the pattern of association between SAD and smoking is shown in [Figure 2], Panel B. Among individuals aged 12–19 years and those 50 years and older, AS had significantly greater mean SAD than either SHS or NS [Table 2]. However, SHS (20.7 cm) had greater mean SAD than NS (20.1 cm) only in a single age group (20–49 years) and not significantly so (p-Bon = 0.347). Surprisingly, among those 50 years and older, SHS (20.5 cm) had significantly lower mean SAD than NS (21.2 cm) (p-Bon = 0.033).
| Discussion|| |
The results of the current study suggest that tobacco smoke may have metabolic disrupting effects evidenced by increasing BMI and SAD with increasing smoke exposure. These findings coincide with existing literature that indicates a positive correlation between the number of cigarettes smoked and central obesity.,,,, Results from our multivariate analysis indicate a dose-response relationship between smoking and SAD; SAD values were incrementally higher for each incremental increase in smoke exposure (NS, 20.0 cm; SHS, 20.2 cm; AS, 20.5 cm). SHS also had higher mean SAD than NS, though this difference was not statistically significant.
The smoking status by age interaction was significant; in particular, the pattern of association between smoking and SAD was not consistent across age groups. There was a counterintuitive pattern for the 20–49-year age group, where SHS exposed had higher mean SAD than AS. This unexpected difference was not statistically significant but requires examination. Among individuals ages 12–19 years and those 50 years and older, the results indicate a statistically significant higher mean SAD among AS than SHS and NS, consistent with our overall finding. It appears that SHS may be associated with lower mean SAD (protective) compared to NS, significantly so in the oldest age group. This notion that SHS is protective against SAD is an interesting finding that deserves further investigation.
In recent literature, certain endocrine disruptors have been noted to follow atypical dose-response relationships or nonmonotonic dose-response (NMDR) association. An NMDR relationship is distinguished by a direction change in the curve's slope that occurs within the range of doses tested. Since nicotine is known to have endocrine and metabolic disruptor effects, an NDMR relationship is possible, as noted with other endocrine or metabolic disruptors. One study identified a U-shaped relationship (a common NMDR relationship) between BMI and number of cigarettes smoked. It explains that heavy smokers have weight gain, but compared to NSs, light smokers tend to have a reduced body weight due to low levels of nicotine exposure suppressing appetite and increasing energy expenditure. This U-shaped relationship may explain the protective effect of SHS against higher mean SAD discovered in our study. A confounded relationship with another, yet unidentified variable is also a possibility.
Although not shown in our results, when the age group 50 years and older was stratified by gender, the U-shaped SAD pattern was more pronounced among females. The greater mean differences in SAD observed among AS (compared to SHS and NS) in women 50 years and older is accordant with a stronger association detected between smoking and abdominal fat accumulation among older women.,, However, the U-shaped relationship between smoke exposure and SAD is not well defined and deserves further research.
There are several strengths of our study. First, the NHANES sample, after adjusting for survey weights, is representative of the US population and provides the first-ever population data for SAD. We also adjusted for many covariates to account for possible confounding effects. To our knowledge, no other studies have investigated the effects of smoking status on SAD and differences in the impact of smoking status on SAD between age groups. Our interesting findings beg for further investigation of SHS smoking and SAD. The cross-sectional design of our study is a limitation: it does not allow for the determination of causality between different levels of smoking status and SAD. A second limitation arises with the small sample size for the SHS 50 years and older group (n = 70). Although the protective effect for this group was statistically significant, this result is perhaps the most variable because of the small sample size.
| Conclusion|| |
Our study sought to assess the potential metabolic impact of smoking on SAD in a nationally representative sample. The results provide valuable insight into SAD patterning with smoke exposure and suggest a potentially complex relationship between SHS and SAD with age that may reflect unexpected metabolic pathways or confounding variables that are not yet identified. In general, our study suggests a dose-response relationship between smoking and SAD, underscoring tobacco smoke as a metabolic disruptor. Our analysis of the smoking impact on SAD across age groups reveals a possible U-shaped relationship, especially among older adults. Further research is needed to substantiate and elucidate these findings and build upon our description of SHSs metabolic disrupting effects.
The contribution of study participants is gratefully acknowledged. This research was nonfunded.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Pagani LS, Nguyen AK, Fitzpatrick C. Prospective associations between early long-term household tobacco smoke exposure and subsequent indicators of metabolic risk at age 10. Nicotine Tob Res 2016;18:1250-7.
Gaillard T. Consequences of abdominal adiposity within the metabolic syndrome paradigm in black people of African ancestry. J Clin Med 2014;3:897-912.
Han TS, Lean ME. A clinical perspective of obesity, metabolic syndrome and cardiovascular disease. JRSM Cardiovasc Dis 2016;5:2048004016633371.
Fox CS, Massaro JM, Hoffmann U, Pou KM, Maurovich-Horvat P, Liu CY, et al.
Abdominal visceral and subcutaneous adipose tissue compartments: Association with metabolic risk factors in the Framingham Heart Study. Circulation 2007;116:39-48.
Hayashi T, Boyko EJ, McNeely MJ, Leonetti DL, Kahn SE, Fujimoto WY, et al.
Visceral adiposity, not abdominal subcutaneous fat area, is associated with an increase in future insulin resistance in Japanese Americans. Diabetes 2008;57:1269-75.
van der Kooy K, Seidell JC. Techniques for the measurement of visceral fat: A practical guide. Int J Obes Relat Metab Disord 1993;17:187-96.
Kvist H, Chowdhury B, Grangård U, Tylén U, Sjöström L. Total and visceral adipose-tissue volumes derived from measurements with computed tomography in adult men and women: Predictive equations. Am J Clin Nutr 1988;48:1351-61.
Empana JP, Ducimetiere P, Charles MA, Jouven X. Sagittal abdominal diameter and risk of sudden death in asymptomatic middle-aged men: The Paris Prospective Study I. Circulation 2004;110:2781-5.
Risérus U, Arnlöv J, Brismar K, Zethelius B, Berglund L, Vessby B, et al.
Sagittal abdominal diameter is a strong anthropometric marker of insulin resistance and hyperproinsulinemia in obese men. Diabetes Care 2004;27:2041-6.
Clair C, Chiolero A, Faeh D, Cornuz J, Marques-Vidal P, Paccaud F, et al.
Dose-dependent positive association between cigarette smoking, abdominal obesity and body fat: Cross-sectional data from a population-based survey. BMC Public Health 2011;11:23.
Hukkanen J, Jacob P
, Benowitz NL. Metabolism and disposition kinetics of nicotine. Pharmacol Rev 2005;57:79-115.
Centers for Disease Control and Prevention (CDC). Fourth National Report on Human Exposure to Environmental Chemicals. Atlanta (GA): CDC; 2009. Available from: https://www.cdc.gov/exposurereport/pdf/fourthreport.pdf
. [Last updated on 2017 Apr 14; Last accessed on 2018 Jun 07].
Khalil N, Chen A, Lee M, Czerwinski SA, Ebert JR, DeWitt JC, et al.
Association of perfluoroalkyl substances, bone mineral density, and osteoporosis in the U.S. Population in NHANES 2009-2010. Environ Health Perspect 2016;124:81-7.
Jain RB. Trends in serum cotinine concentrations among daily cigarette smokers: Data from NHANES 1999-2010. Sci Total Environ 2014;472:72-7.
Akbartabartoori M, Lean ME, Hankey CR. Relationships between cigarette smoking, body size and body shape. Int J Obes (Lond) 2005;29:236-43.
Bamia C, Trichopoulou A, Lenas D, Trichopoulos D. Tobacco smoking in relation to body fat mass and distribution in a general population sample. Int J Obes Relat Metab Disord 2004;28:1091-6.
Barrett-Connor E, Khaw KT. Cigarette smoking and increased central adiposity. Ann Intern Med 1989;111:783-7.
Canoy D, Wareham N, Luben R, Welch A, Bingham S, Day N, et al.
Cigarette smoking and fat distribution in 21,828 British men and women: A population-based study. Obes Res 2005;13:1466-75.
Lagarde F, Beausoleil C, Belcher SM, Belzunces LP, Emond C, Guerbet M, et al.
Non-monotonic dose-response relationships and endocrine disruptors: A qualitative method of assessment. Environ Health 2015;14:13.
Vandenberg LN, Colborn T, Hayes TB, Heindel JJ, Jacobs DR Jr., Lee DH, et al.
Hormones and endocrine-disrupting chemicals: Low-dose effects and nonmonotonic dose responses. Endocr Rev 2012;33:378-455.
Tweed JO, Hsia SH, Lutfy K, Friedman TC. The endocrine effects of nicotine and cigarette smoke. Trends Endocrinol Metab 2012;23:334-42.
[Figure 1], [Figure 2]
[Table 1], [Table 2]