Association of smoking with abdominal adipose deposition and muscle composition in Coronary Artery Risk Development in Young Adults (CARDIA) participants at mid-life: A population-based cohort study

James G Terry; Katherine G Hartley; Lyn M Steffen; Sangeeta Nair; Amy C Alman; Melissa F Wellons; David R Jacobs, Jr; Hilary A Tindle; John Jeffrey Carr

doi:10.1371/journal.pmed.1003223

. 2020 Jul 21;17(7):e1003223. doi: 10.1371/journal.pmed.1003223

Association of smoking with abdominal adipose deposition and muscle composition in Coronary Artery Risk Development in Young Adults (CARDIA) participants at mid-life: A population-based cohort study

James G Terry ^1,^*, Katherine G Hartley ¹, Lyn M Steffen ², Sangeeta Nair ¹, Amy C Alman ³, Melissa F Wellons ¹, David R Jacobs Jr ², Hilary A Tindle ^1,⁴, John Jeffrey Carr ¹

Editor: Sanjay Basu⁵

PMCID: PMC7373261 PMID: 32692748

Abstract

Background

Smokers have lower risk of obesity, which some consider a “beneficial” side effect of smoking. However, some studies suggest that smoking is simultaneously associated with higher central adiposity and, more specifically, ectopic adipose deposition. Little is known about the association of smoking with intermuscular adipose tissue (IMAT), an ectopic adipose depot associated with cardiovascular disease (CVD) risk and a key determinant of muscle quality and function. We tested the hypothesis that smokers have higher abdominal IMAT and lower lean muscle quality than never smokers.

Methods and findings

We measured abdominal muscle total, lean, and adipose volumes (in cubic centimeters) and attenuation (in Hounsfield units [HU]) along with subcutaneous (SAT) and visceral adipose tissue (VAT) volumes using computed tomography (CT) in 3,020 middle-aged Coronary Artery Risk Development in Young Adults (CARDIA) participants (age 42–58, 56.3% women, 52.6% white race) at the year 25 (Y25) visit. The longitudinal CARDIA study was initiated in 1985 with the recruitment of young adult participants (aged 18–30 years) equally balanced by female and male sex and black and white race at 4 field centers located in Birmingham, AL, Chicago, IL, Minneapolis, MN, and Oakland, CA. Multivariable linear models included potential confounders such as physical activity and dietary habits along with traditional CVD risk factors. Current smokers had lower BMI than never smokers. Nevertheless, in the fully adjusted multivariable model with potential confounders, including BMI and CVD risk factors, adjusted mean (95% CI) IMAT volume was 2.66 (2.55–2.76) cm³ in current smokers (n = 524), 2.36 (2.29–2.43) cm³ in former smokers (n = 944), and 2.23 (2.18–2.29) cm³ in never smokers (n = 1,552) (p = 0.007 for comparison of former versus never smoker, and p < 0.001 for comparison of current smoker versus never and former smoker). Moreover, compared to participants who never smoked throughout life (41.6 [41.3–41.9] HU), current smokers (40.4 [39.9–40.9] HU) and former smokers (40.8 [40.5–41.2] HU) had lower lean muscle attenuation suggesting lower muscle quality in the fully adjusted model (p < 0.001 for comparison of never smokers with either of the other two strata). Among participants who had ever smoked, pack-years of smoking exposure were directly associated with IMAT volume (β [95% CI]: 0.017 [0.010–0.025]) (p < 0.001). Despite having less SAT, current smokers also had higher VAT/SAT ratio than never smokers. These findings must be viewed with caution as residual confounding and/or reverse causation may contribute to these associations.

Conclusions

We found that, compared to those who never smoked, current and former smokers had abdominal muscle composition that was higher in adipose tissue volume, a finding consistent with higher CVD risk and age-related physical deconditioning. These findings challenge the belief that smoking-associated weight loss or maintenance confers a health benefit.

James Terry and colleagues reveal the higher levels of abdominal adipose tissue in smokers compared to nonsmokers.

Author summary

Why was this study done?

Smoking and obesity are, separately, well-known health risks for cancer and cardiovascular disease (CVD).
Smokers often have lower risk of obesity measured using BMI, leading to the misconception of a “beneficial side effect” to smoking.
Even with a lower BMI, smokers may have a higher risk of depositing fat (more properly called adipose tissue) in and around organs and tissues compared to those who never smoked. This type of fat carries higher risk and may interfere with organ and tissue functions.

What did the researchers do and find?

We used computed tomography (CT) to measure abdominal fat deposited just below the skin’s surface (subcutaneous fat), around organs including the intestines (visceral fat) and abdominal muscles (intermuscular fat), and inside the muscles (intramuscular fat) in 3,020 middle-aged participants in the Coronary Artery Risk Development in Young Adults (CARDIA) study.
We found that current smokers had higher proportions of fat within their abdominal muscles and visceral fat around their internal organs compared to never smokers, whereas those who had quit smoking had intermediate levels of visceral and intramuscular fat.

What do these findings mean?

Despite lower BMI and subcutaneous fat, smokers appear to be at risk of accumulating organ-associated fat and intramuscular fat that have been shown to increase circulating blood fats and sugar.
This may, in turn, explain some of the hidden, higher risk of CVD and disability in smokers.

Introduction

Historically, cigarette advertisements promoted the idea that a lower body weight was a possible beneficial side effect of smoking [1,2]. It is, therefore, not surprising that smokers cite concern about weight gain as a barrier to smoking cessation [3,4]. Indeed, BMI is lower in current smokers compared to nonsmokers, and those who quit smoking tend to gain weight [5–8]. However, smoking has also been associated with central fat patterning [9,10]. Central fat patterning suggests higher ectopic fat deposition within or around non-adipose tissues or organs (e.g., liver, muscle, or heart), which is, in turn, strongly associated with diabetes, cardiovascular disease (CVD) risk, and all-cause mortality [11–19].

Studies using computed tomography (CT) to delineate specific adipose depots suggest that smoking is associated with higher visceral adipose tissue (VAT) [20–23], but less is known about the role of smoking in ectopic adipose depots other than VAT. Ectopic adipose deposition within skeletal muscles (intermuscular adipose tissue [IMAT]) is an independent risk factor for coronary artery calcification (CAC), diabetes, and cardiovascular and all-cause death [16,18,19]. Accumulation of IMAT and associated changes in muscle composition and function are suspected contributors to disability in the elderly [24]. The prevalence of severe, clinically significant loss of lean muscle mass, known as sarcopenia, ranges from 5% to 10% in community-dwelling populations over age 65 to perhaps 30% in those over age 80 [24]. Further, the Women’s Health Initiative dual-energy X-ray absorptiometry (DXA) substudy found that 17% of participants had sarcopenic obesity [25]. Risk factors likely contributing to both ectopic adipose tissue deposition and muscle deconditioning include sedentary lifestyle [26,27], smoking [26,27], and diets rich in fast food (FF), sugar-sweetened beverages (SSBs), and alcohol [23,28–30], and smoking.

We evaluated the role of smoking history in abdominal adipose deposition and, specifically, its role in muscle composition and quality using CT in more than 3,000 Coronary Artery Risk Development in Young Adults (CARDIA) participants aged 43 to 55. Our overarching hypothesis is that, even at mid-life, current smokers have higher IMAT and poorer muscle quality than former or never smokers after adjusting for generalized obesity and potentially confounding factors such as diet quality, alcohol consumption, and physical activity.

Materials and methods

Study population

The CARDIA study began in 1985 with recruitment of 5,115 participants aged 18 to 30 years at field centers located in Birmingham, AL, Chicago, IL, Minneapolis, MN, and Oakland, CA [31]. Recruitment was balanced for equal inclusion of black and white and female and male participants, age (18–24, 25–30 years), and education (≤12 years, >12 years). The current study includes data from participants who agreed to undergo abdominal CT scan at the year 25 (Y25) examination. A total of 3,499 participants were examined in clinic at Y25, representing 72% of the original cohort. Of these 3,499, 3,172 underwent abdominal CT, and 327 were excluded due to weight, inability to fit in the CT scanner, or (rarely) pregnancy. Of 3,172 participants with abdominal CT scans, 3,020 participants (95.2% of those who underwent CT) had complete measures of abdominal adipose tissues and muscle composition along with smoking status and key covariables including BMI. The manuscript proposal and analysis plan for the present study was approved by the CARDIA Publications and Presentations Committee on March 14, 2018, and assigned #A-1811 (S1 CARDIA Proposal). The analysis protocol was followed as approved, and as with all CARDIA manuscripts, the manuscript underwent data confirmation and CARDIA peer review prior to study approval. This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S2). All participants provided written informed consent, and institutional review boards from each field center and the coordinating center approved the study annually.

Clinical evaluations

Clinical measures

Clinic visit procedures were standardized and consistent across examinations as previously published in detail [31]. Blood pressure was measured in triplicate after a 5-minute rest using an automated blood pressure monitor (Omron model HEM907XL; Omron Healthcare Inc., Lake Forest, IL) with the average of the second and third measurements used in analyses. Fasting plasma lipids and lipoproteins were measured using enzymatic methods at Northwest Lipids Research Laboratory (Seattle, WA). Serum glucose was measured using the Roche Modular P hexokinase method (Roche Diagnostics, Rotkreuz, Switzerland), and percent hemoglobin A1c (HbA1c) was measured using Tosoh G7 HPLC (Tosoh, San Francisco, CA). Plasma C-reactive protein (CRP) was measured using high-sensitivity nephelometry-based method (BNII nephelometer, Dade Behring, Eschborn, Germany). Diabetes was defined as fasting glucose ≥7.0 mmol/L (≥126 mg/dL), self-report of oral hypoglycemic medications or insulin, 2-hour postload glucose ≥11.1 mmol/L (≥200 mg/dL), or HbA1c ≥6.5% at the Y25 visit.

Use of antihypertensive and lipid-lowering treatments was collected through interviewer-administered questionnaires. At the baseline visit, when participants were 18 to 30 years of age, diabetes and use of antihypertensive and lipid-lowering medicines was extremely rare, so these measures were not included in any models adjusted for baseline exposures.

Lifestyle measures

Smoking history

Based on interviewer-administered questionnaires, cigarette smoking was classified as never, former, or current at each CARDIA visit (S1 Form, S2 Form). For the cross-sectional analyses at Y25, smoking data from each participant visit were reviewed, and participants were coded as follows: (1) never smokers only if they always reported never smoking and never reported past smoking at any visit; (2) former smokers if they denied current smoking but had admitted current smoking or past smoking at any other CARDIA visit; or (3) current smokers if the participant admitted current smoking at the Y25 visit. For those who reported past or current smoking at any CARDIA visit, the age at which the participant became a regular smoker was queried. The average number of cigarettes smoked per day across all CARDIA visits attended from baseline (Y0) through Y25 was calculated and divided by 20 cigarettes per package to determine mean packs per day. To reliably estimate the smoking exposure during the 25-year CARDIA follow-up, mean packs per day was multiplied by the number of years between consecutive visits in which the participant reported currently smoking. Thus, a participant who reported being a former smoker at baseline (age 18–30) and was never a current smoker during follow-up was coded as having 0 pack-years, while a participant who was a current smoker at all attended visits through Y25 and averaged smoking 20 cigarettes per day would be coded as having 25 pack-years’ exposure. Serum cotinine was measured only at the CARDIA baseline visit as an independent marker of active current smoking [32–35]. Participants with cotinine ≥14 ng/mL were coded positive for active smoking based on research establishing that cut-off as providing >98% accuracy in smoking status classification [32–35].

Other lifestyle and clinical measures

Alcohol consumption was computed as milliliters per day based upon self-reported drinks per week of beer (12-ounce glass), wine (5-ounce glass), and liquor (1.5-ounce shot). Self-reported usual weekly intake of FF and SSBs were used as surrogates for diet quality. FF intake was categorized as never consumes, 1–2 visits per week, or ≥3 meals per week. SSB was categorized as never consumes, 1–2 SSBs, or ≥3 SSBs per week. The CARDIA Physical Activity History questionnaire was used to estimate weekly leisure, occupational, and household physical exertion over the past 12 months [36]. Education was self-reported in years, and maximum years of education was included in models. Weight and height were measured with participants wearing light clothing and no shoes. Body weight was measured to the nearest 0.2 kg on a calibrated scale, whereas height was measured to the nearest 0.5 cm using a fixed vertical ruler. BMI was calculated as weight in kilograms divided by height in meters squared.

CT measures of arterial calcification and adipose deposition

Participants underwent a multidetector CT chest and abdomen scans using a standardized protocol [15,16,37–39]. The scans were performed at CARDIA field centers using 64-channel multidetector GE CT scanners (GE Healthcare Milwaukee, WI) at the Birmingham, AL, and Oakland, CA, centers and Siemens CT scanners (Siemens, Erlangen, Germany) at the Chicago, IL, and Minneapolis, MN, centers. ECG gating was used for the cardiac scan, and a quality control phantom was included (INTableTM Calibration Pad, Image Analysis, Columbia, KY). CT images were electronically transmitted to the central CT reading center located at Wake Forest University School of Medicine, Winston-Salem, NC. Experienced image analysts measured calcified plaque using FDA-approved workstations (Aquarius Workstation, TeraRecon, Foster City, CA) producing total calcium scores based on the Agatston method [38,40]. In the present study, CAC was defined as present for scores >0 Agatston units. Abdominal CT scans were analyzed using the 50-cm display field of view. Adipose tissue depots were measured volumetrically within a 10-mm block of 10 × 1 mm or 8 × 1.25 mm contiguous slices based on the nominal slice thickness produced by the specific scanner centered at the level of the disk between the 4th and 5th lumbar vertebrae as previously described [16–18]. Tissues with attenuation of −190 through −30 Hounsfield units (HU) were defined as adipose tissue. Medical Image Processing, Analysis, and Visualization (MIPAV) software was used to quantify subcutaneous (SAT) and VAT volume.

Abdominal muscle composition (fat, lean, and total) was measured volumetrically from a 10-mm block of contiguous slices centered between the 3rd and 4th lumbar disks [16,18]. The abdominal muscles were measured at the L3–L4 level to avoid changes in muscle orientation related to the pelvic bones in some individuals at the L4–L5 level. Muscle volumes at L3–L4 and L4–L5 were highly correlated ranging from 0.87 (rectus) to 0.98 (psoas and paraspinous). Pixels within muscle with attenuation of −190 to −30 HU were defined as adipose tissue and −29 to 160 HU as lean tissue. Fat, lean, and total muscle volumes were quantified for the psoas, paraspinous, lateral oblique, and rectus muscles using a custom MIPAV plug-in developed by study investigators [16,18]. Measures of left and right muscles in each group were highly correlated, so mean adipose, lean, and total volumes of the left and right sides were calculated and analyzed for all abdominal muscles. Mean muscle attenuation within the range of −29 to 160 HU was calculated across all muscles and considered a measure of intramuscular adipose content.

Analysis reliability of CT measures was assessed through blinded intra- and inter-reader re-reads of 158 scan pairs (approximately 5%). Overall (intra- and inter-reader) technical error in re-analysis of 158 pairs of scans was 6.6% for CAC, 6.0% for VAT, and 7.7% for psoas muscle total volume with correlations for re-reads >0.95 in each measure.

Statistics

Abdominal adipose tissue and muscle composition outcome variables were analyzed as continuous data. Smoking status (never, former, current) at Y25 and pack-years of smoking exposure were the primary independent variables. Model covariables were chosen a priori. Model 1 included age, sex, race, and field center, with model 2 adding education, alcohol consumption, FF consumption frequency, SSB consumption, physical activity, and BMI. Model 3 included model 2 covariables plus diabetes status, systolic blood pressure, CRP, triglycerides, use of anti-hypertensive and cholesterol medicines, and prevalent CAC. VAT/SAT ratio was added to model 3 for muscle outcomes to test associations after adjustment for central adipose deposition. Analyses were repeated with baseline smoking status and baseline cotinine as predictors in models including the above Y25 covariables (model 3) or baseline covariables age, education, height, physical activity, alcohol consumption, FF consumption, systolic blood pressure, fasting triglycerides and glucose, and BMI along with sex, race, and field center.

We tested two-way and three-way sex, race, and Y25 smoking status interactions in fully adjusted models predicting the primary variables of interest, IMAT volume, muscle attenuations, and IMAT/lean volume ratio. There were no significant interactions for sex, race, and smoking status (all interactions >0.15). All analyses were performed using STATA version 15.

Results

At the Y25 visit, just over half of the participants were never smokers (n = 1,552), 31% were former smokers (n = 944), and 17% were current smokers (n = 524) (Table 1). Compared to never smokers, current smokers were less likely to be female or white, had fewer years of education, were less physically active, had higher alcohol intake, and were more likely to consume SSBs and FF. Current smokers had lower BMI but higher blood pressure, triglycerides, and CRP compared to never smokers. Of note, baseline BMI did not differ by smoking status at CARDIA baseline (24.5 [4.8], 24.5 [4.8], and 24.4 [4.8] kg/m² among never, former, and current smokers, respectively, p = 0.95). Current smokers were much more likely to have prevalent CAC than either never smokers or former smokers.

Table 1. Participant characteristics at Y25 visit according to Y25 smoking status^*.

Characteristic	All (N = 3,020)	Smoking Status			p^†
Characteristic	All (N = 3,020)	Never (n = 1,552)	Former (n = 944)	Current (n = 524)	p^†
*Demographic*
Age, y	50.1 (3.6)	49.9 (3.6)	50.8 (3.5)	49.6 (3.7)	<0.001
Female, %	56.3%	56.4%	58.9%	51.2	0.02
White, %	52.6%	53.0%	60.1%	38.0	<0.001
Education, y	15.6 (2.6)	16.1 (2.4)	15.5 (2.6)	13.9 (2.2)	<0.001
*Lifestyle*
Age started smoking, y^‡	18.0 (4.6)	n/a	17.8 (3.6)	18.4 (5.7)	0.02
25-year smoking exposure, pack-years^‡	2.3 (0.1–10.0)	n/a	0.4 (0.0–2.9)	12.1 (5.7–19.6)	<0.001
Physical activity, units	277 (126–486)	271 (124–490)	312 (144–504)	227 (105–413)	<0.001
Alcohol intake, mL/d	2.4 (0–14.7)	0.0 (0.0–10.0)	4.8 (0.0–17.0)	8.9 (0.0–28.6)	<0.001
SSB, %
Never consumes	33.9%	35.2%	37.8%	23.3%
<1–2/wk	43.3%	44.5%	43.3%	39.7%
≥3/wk	22.8%	20.4%	18.9%	37.0%	<0.001
FF, %
Never consumes	33.3%	32.3%	39.2%	26.0%
<1–2 meals/wk	37.4%	36.5%	36.1%	42.2%
≥3 meals/wk	29.3%	31.2%	24.7%	31.9%	<0.001
BMI, kg/m²	30.2 (7.1)	30.6 (7.1)	30.2 (7.4)	29.2 (6.4)	<0.001
WC, cm	94.6 (15.8)	94.7 (15.7)	94.6 (16.3)	94.3 (14.7)	0.88
*Clinical*
Diastolic BP, mmHg	75.0 (11.2)	74.5 (11.0)	74.7 (11.0)	77.1 (11.5)	<0.001
Systolic BP, mmHg	119.8 (16.0)	119.1 (15.5)	119.3 (16.2)	122.9 (16.9)	<0.001
HTN treatment, %	27.4%	25.5%	28.0%	32.1%	0.01
Cholesterol treatment, %	15.8%	15.0%	17.0%	16.0%	0.43
Total cholesterol, mg/dL	192.5 (37.0)	192.0 (35.8)	192.8 (36.5)	193.3 (40.5)	0.76
LDL cholesterol, mg/dL	112.0 (32.8)	113.3 (31.8)	110.6 (32.5)	111.0 (35.4)	0.10
HDL cholesterol, mg/dL	58.0 (18.0)	57.4 (16.9)	59.5 (18.9)	57.0 (19.6)	0.006
Triglycerides, mg/dL	93 (68–134)	90 (65–129)	93 (68–134)	100 (75–157)	<0.001
CRP, mg/dL	1.4 (0.6–3.5)	1.4 (0.6–3.3)	1.3 (0.6–3.3)	1.8 (0.8–4.3)	<0.001
Diabetes, %	12.4%	11.3%	12.5%	15.3%	0.06
CAC prevalence, %	28.2%	23.7%	28.5%	41.0%	<0.001

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*Smoking history based on participant reported status at Y25 and confirmed across each CARDIA visit attended from baseline through Y25.

^†Based on ANOVA (continuous variables), Kruskal-Wallis (continuous, non-normally distributed variables), or chi-squared (categorical variables).

^‡Available for 943 former smokers and 523 current smokers (1,466 total).

Abbreviations: BP, blood pressure; CAC, coronary artery calcification; CARDIA, Coronary Artery Risk Development in Young Adults; CRP, C-reactive protein; FF, fast food; HDL, high-density lipoprotein; HTN, hypertension; LDL, low-density lipoprotein; n/a, not applicable; SSB, sugar-sweetened beverage; WC, waist circumference; Y25, year 25

Associations of smoking history at CARDIA Y25 visit with abdominal adipose tissues and muscle composition

CT measures of abdominal adipose tissue and muscle composition are shown in Table 2. Unadjusted SAT volume was >25 cm³ lower in current smokers than either former or never smokers. VAT volume was nominally higher in former smokers compared to never smokers. Compared to never smokers, VAT/SAT ratio was higher in former smokers and higher still in current smokers. Total and lean muscle volumes were slightly higher in current smokers compared to former smokers. IMAT volume and IMAT/lean ratio were each markedly higher in current and former smokers compared to never smokers. Lean muscle attenuation was lower in former or current smokers than never smokers (each comparison p < 0.0001), suggesting an association of smoking with higher intramyocellular fat.

Table 2. Unadjusted Y25 CT measures of abdominal adipose tissues and muscle composition [mean (SD]) by Y25 smoking status^*.

Abdominal Measure		All (N = 3,020)	Smoking Status
Abdominal Measure		All (N = 3,020)	Never (n = 1,552)	Former (n = 944)	Current (n = 524)	p_former _{vs. never}	p_{current vs. former}	p_{current vs. never}
Adipose Depots	SAT	334.9 (169.1)	344.4 (170.0)	333.9 (170.7)	308.8 (161.0)	0.13	0.006	<0.001
	VAT	132.0 (73.8)	130.1 (73.0)	136.0 (76.0)	130.8 (71.9)	0.05	0.20	0.85
	VAT/SAT ratio	0.456 (0.311)	0.431 (0.254)	0.469 (0.304)	0.505 (0.443)	0.003	0.034	<0.001
Muscle Composition	Total volume	20.4 (5.2)	20.4 (5.3)	20.3 (5.1)	20.9 (5.0)	0.42	0.029	0.09
	Lean volume	18.0 (4.6)	18.1 (4.8)	17.7 (4.5)	18.2 (4.4)	0.07	0.041	0.48
	IMAT volume	2.35 (1.62)	2.25 (1.53)	2.42 (1.64)	2.51 (1.82)	0.013	0.28	0.001
	IMAT/lean ratio	0.136 (0.098)	0.129 (0.092)	0.141 (0.102)	0.143 (0.105)	0.003	0.80	0.007
	Attenuation	41.1 (6.4)	41.7 (6.4)	40.4 (6.2)	40.6 (6.4)	<0.001	0.61	<0.001

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Abbreviations: CARDIA, Coronary Artery Risk Development in Young Adults; CT, computed tomography; HU, Hounsfield units; IMAT, intermuscular adipose tissue; SAT, subcutaneous adipose tissue; VAT, visceral adipose tissue; Y25, year 25

Multivariable models for the association of Y25 smoking status with adipose tissues and muscle composition are shown in Table 3. SAT was approximately 11% lower in current smokers than either former or never smokers in the minimally adjusted model 1, but these differences were attenuated in models 2 and 3. Former and current smokers had significantly higher VAT compared to never smokers in model 2 only. VAT/SAT ratio was higher in current smokers compared to never smokers in all models. Also shown in Table 3, total muscle volume was higher by 0.50 cm³ among current smokers compared to never smokers in the fully adjusted model (model 4). Current smokers had IMAT volume that was 0.30 cm³ (approximately 11%) higher than former smokers and 0.43 cm³ (approximately 16%) higher than never smokers in the fully adjusted model (model 4). Lean muscle attenuation was lower in current smokers or former smokers compared to those who never smoked in all models (p < 0.0001 for all comparisons).

Table 3. Multivariable models of Y25 CT abdominal adipose tissues and muscle composition (least squares mean [95% CI]) by Y25 smoking status^*.

Abdominal Depot	Measure	Model	Smoking Status			p_{former vs. never}	p_{current vs. former}	p_{current vs. never}
Abdominal Depot	Measure	Model	Never	Former	Current	p_{former vs. never}	p_{current vs. former}	p_{current vs. never}
Adipose Depots	SAT	1	344.3 (336.6–352.0)	338.5 (328.4–348.3)	301.6 (287.7–314.4)	0.36	<0.001	<0.001
		2	336.8 (332.9–340.6)	334.3 (329.4–339.1)	330.7 (323.9–337.6)	0.43	0.41	0.14
		3	335.8 (332.0–339.7)	334.30 (329.4–339.1)	333.5 (326.6–340.4)	0.61	0.86	0.57
	VAT	1	130.5 (127.1–134.0)	134.5 (130.0–139.0)	132.2 (126.0–138.2)	0.18	0.54	0.66
		2	128.8 (126.1–131.4)	133.7 (130.4–137.0)	138.8 (134.1–143.5)	0.022	0.08	<0.001
		3	130.3 (127.9–132.8)	133.5 (130.4–136.6)	134.4 (130.0–138.9)	0.12	0.74	0.13
	VAT/SAT ratio	1	0.432 (0.419–0.445)	0.461 (0.445–0.478)	0.514 (0.492–0.536)	0.007	<0.001	<0.001
		2	0.438 (0.425–0.451)	0.463 (0.447–0.480)	0.494 (0.470–0.517)	0.021	0.039	<0.001
		3	0.444 (0.431–0.457)	0.463 (0.447–0.479)	0.477 (0.453–0.500)	0.08	0.36	0.021
Muscle Composition	Total volume	1	20.4 (20.3–20.6)	20.6 (20.4–20.8)	20.2 (19.9–20.5)	0.25	0.04	0.20
		2	20.3 (20.2–20.4)	20.5 (20.3–20.7)	20.8 (20.6–21.1)	0.09	0.022	<0.001
		3	20.3 (20.2–20.5)	20.5 (20.3–20.6)	20.8 (20.5–21.0)	0.14	0.06	0.002
		4	20.3 (20.2–20.5)	20.5 (20.3–20.6)	20.8 (20.5–21.0)	0.16	0.06	0.003
	Lean volume	1	18.1 (17.9–18.2)	18.1 (17.9–18.3)	17.6 (17.4–17.8)	0.85	0.001	<0.001
		2	18.0 (17.9–18.1)	18.0 (17.9–18.2)	18.0 (17.8–18.2)	0.70	0.88	0.90
		3	18.0 (17.9–18.1)	18.0 (17.9–18.2)	17.9 (17.7–18.2)	0.85	0.72	0.81
		4	18.0 (17.9–18.1)	18.0 (17.9–18.2)	18.0 (17.7–18.2)	0.86	0.71	0.81
	IMAT volume	1	2.26 (2.18–2.34)	2.40 (2.30–2.50)	2.51 (2.37–2.65)	0.035	0.22	0.002
		2	2.22 (2.16–2.28)	2.36 (2.29–2.43)	2.69 (2.59–2.79)	0.003	<0.001	<0.001
		3	2.23 (2.17–2.29)	2.36 (2.29–2.43)	2.66 (2.56–2.77)	0.005	<0.001	<0.001
		4	2.23 (2.18–2.29)	2.36 (2.29–2.43)	2.66 (2.55–2.76)	0.007	<0.001	<0.001
	IMAT/lean ratio	1	0.130 (0.125–0.132)	0.138 (0.130–0.142)	0.148 (0.139–0.155)	0.042	0.068	<0.001
		2	0.129 (0.125–0.132)	0.136 (0.131–0.141)	0.155 (0.148–0.161)	0.014	<0.001	<0.001
		3	0.129 (0.125–0.133)	0.136 (0.132–0.141)	0.154 (0.147–0.160)	0.015	<0.001	<0.001
		4	0.129 (0.126–0.133)	0.136 (0.132–0.141)	0.153 (0.147–0.160)	0.021	<0.001	<0.001
	Attenuation	1	41.7 (41.4–42.0)	40.7 (40.4–41.1)	40.1 (39.7–40.7)	<0.001	0.07	<0.001
		2	41.7 (41.4–41.9)	40.8 (40.5–41.2)	40.2 (39.7–40.7)	<0.001	0.06	<0.001
		3	41.6 (41.3–41.9)	40.8 (40.4–41.2)	40.3 (39.8–40.8)	<0.001	0.14	<0.001
		4	41.6 (41.3–41.9)	40.8 (40.4–41.2)	40.4 (39.9,40.9)	<0.001	0.16	<0.001

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*Smoking status based on participant reported status at Y25 and confirmed across each CARDIA visit attended from baseline through Y25; comparisons based on ANOVA; tissue volumes are in cm³, and attenuation is in HU. Model 1: age, race, sex, and center; model 2: model 1 + education, physical activity, alcohol consumption, SSB consumption, FF consumption, and BMI; model 3: model 2 + diabetes, cholesterol treatment, hypertension treatment, systolic BP, triglycerides, CRP, and prevalent CAC; model 4: model 3 + VAT/SAT ratio (muscle composition measures only); tissue volumes are in cm³, and attenuation is in HU.

Abbreviations: BP, blood pressure; CAC, coronary artery calcification; CARDIA, Coronary Artery Risk Development in Young Adults; CRP, C-reactive protein; CT, computed tomography; FF, fast food; HU, Hounsfield units; SAT, subcutaneous adipose tissue; SSB, sugar-sweetened beverage; VAT, visceral adipose tissue; Y25, year 25

Among those with a history of smoking, we also tested the association of pack-years of smoking exposure during CARDIA follow-up with adipose volumes and muscle composition. As shown in Fig 1A, pack-years were positively associated with IMAT volume (p < 0.001) in fully adjusted models. For reference, 10 pack-years’ higher smoking exposure was associated with an additional 0.17 cm³ IMAT (approximately 7% higher volume per 10 pack-years based on mean 2.45 cm³ IMAT among ever smokers). Fig 1B and Fig 1C demonstrate the association of pack-years’ smoking exposure with higher IMAT/lean volume ratio (p < 0.001) and lower muscle attenuation (p = 0.015). There were no significant associations of pack-years of exposure with abdominal adipose or total or lean muscle volumes in fully adjusted models. In former smokers, we also tested the association between years since quitting and muscle composition and abdominal adipose measures and found no significant associations.

Fig 1 — (a) Predicted IMAT, (b) IMAT/lean ratio, and (c) lean muscle quality are plotted against continuous pack-years during CARDIA adjusted for age, race, sex, center, education, physical activity, alcohol consumption, SSB consumption, FF consumption, BMI, diabetes, cholesterol treatment, hypertension treatment, systolic BP, triglycerides, CRP, prevalent CAC, and VAT/SAT ratio. Pack-years was a significant predictor of IMAT volume (β [95% CI] 0.017 [0.010–0.025], *p <* 0.001), IMAT/lean volume ratio (β [95% CI] 0.001 [0.0006–0.0015], *p <* 0.001), and lean muscle quality (β [95% CI] −0.043 (−0.077 to −0.008], *p =* 0.016). BP, blood pressure; CAC, coronary artery calcification; CARDIA, Coronary Artery Risk Development in Young Adults; CRP, C-reactive protein; FF, fast food; HU, Hounsfield units; IMAT, intermuscular adipose tissue; SAT, subcutaneous adipose tissue; SSB, sugar-sweetened beverage; VAT, visceral adipose tissue; Y0, baseline; Y25, year 25.

Associations of smoking history at CARDIA baseline visit with Y25 abdominal adipose tissue and muscle composition

Associations of baseline smoking status and cotinine levels with Y25 adipose and muscle composition are shown in Supporting Information S1 and S2 Tables, respectively. Participants who were smokers at baseline (age 18–30 years) had higher Y25 VAT/SAT ratio than never smokers (p = 0.005) after adjusting for either concurrent baseline or Y25 factors (S1 Table). Compared to those who had never smoked at baseline, current smokers had 0.27 cm³ higher IMAT volume 25 years later along with lower muscle attenuation in all models. Participants with baseline cotinine ≥14 ng/mL, suggestive of current smoking, had higher VAT/SAT ratio 25 years later after adjustment for baseline or Y25 covariables (S2 Table). IMAT volume was approximately 0.2 cm³ higher and muscle attenuation was lower (each p < 0.001) in those with high cotinine.

Discussion

In this multicenter study of healthy middle-aged participants followed 25 years, we found that, compared to never smokers, current smokers had higher abdominal muscle adipose volume and lower lean muscle attenuation, suggesting lower muscle quality. The adverse association of smoking with muscle composition persisted after adjustment for other lifestyle factors and both BMI and visceral fat. The frequent longitudinal assessment of smoking status (mean 7.4 ± 1.1 assessments over 25 years) provided rigorous, comprehensive capture of cumulative smoking exposure throughout early to middle adult life. These data suggest that the lower BMI associated with smoking masks higher ectopic adipose tissue deposition and lower muscle quality.

Smoking is associated with lower risk of obesity, and smoking cessation is thought to contribute to weight gain [5–8]. Moreover, the inverse association between smoking and weight has been shown to strengthen with age [41], a finding replicated in CARDIA in that baseline BMI was not associated with smoking status but, 25 years later, current smokers had markedly lower BMI compared to either never or former smokers. However, a recent mendelian randomization study found that genes associated with higher BMI and waist circumference are associated with higher odds of being a current smoker and more intense smoking habits [42]. Thus, the direction of the association between smoking and measures of obesity could plausibly run in either direction. Regardless, the association of smoking with higher waist circumference is consistent with cross-sectional studies suggesting that smoking is associated with higher abdominal VAT [20,21,43]. Accumulation of VAT is strongly associated with poor CVD risk factor profiles and prevalent and incident CVD [11]. Though BMI was lower in current compared to never smokers, current smokers in CARDIA had higher VAT and VAT/SAT ratio, an index of central fat deposition, after adjustment for possible confounders and BMI. Although smoking has previously been linked to higher central fat deposition, our findings suggest that smoking is adversely associated with muscle composition with poorer muscle quality apparent as early as mid-life. Current smokers had higher IMAT volume and higher proportion of IMAT relative to lean muscle compared to either never smokers or former smokers, findings that were not explained by lower BMI in smokers. Perhaps as importantly, current and former smokers in CARDIA had significantly lower lean muscle attenuation compared to never smokers, suggesting higher intramyocellular adipose deposition and poorer quality lean muscle.

A recent meta-analysis estimates the excess risk for clinically significant sarcopenia associated with ever smoking at approximately 12%, although smoking-related risk was most apparent in elderly participants [44]. Extreme sarcopenic changes associated with functional decline and frailty would be expected to be rare at mid-life given that lean muscle mass peaks in early adulthood around age 30 and declines thereafter by about 1% per year in men [45]. However, a large study found that smoking and higher pack-years of exposure were associated with lower skeletal muscle mass among 845 healthy men aged 45 to 85 (mean age 64) [26]. In the present CARDIA analysis, lean muscle volume did not differ by smoking status. Total muscle volume was slightly higher in current smokers compared to never smokers, though quantitatively the difference was consistent with the amount of excess IMAT in smokers. Our data suggest that assessing only lean or total muscle volume not only may lead to potentially missing important associations between muscle composition and risk factors such as smoking but also could underestimate the pathological impact of IMAT accumulation [16,18,19,46,47].

IMAT accumulation is likely to contribute to decline in muscle function and mobility in older adults [48]. The Health, Aging, and Body Composition (Health ABC) study followed more than 1,600 healthy septuagenarians and found that thigh IMAT increased over 5 years even in those who lost weight [49]. A recent analysis from the Age, Gene/Environment Susceptibility (AGES)-Reykjavik Study reported that current smoking was associated with lower thigh muscle attenuation (suggesting fat infiltration) in septuagenarian men and women [50]. Further, higher pack-years of smoking was associated with lower muscle attenuation and peak muscle torque in women, but not men [50]. Taken together, the AGES-Reykjavik and CARDIA data suggest that smoking is associated with higher adipose tissue and poorer skeletal muscle quality from middle age through late in life. Increasing IMAT is associated with lower gait speed, grip strength, and other functional assessments that, in turn, portend falls and limited mobility with aging [48]. Indeed, in the Women’s Health Initiative DXA study, low lean body mass—especially when combined with obesity—was strongly associated with risk of falls [25].

IMAT accumulation is associated with diabetes risk as shown in CARDIA and other studies [18,51,52]. Despite its association with lower risk of BMI-assessed obesity, current smoking is associated with higher risk of diabetes [51,52]. Diabetes, in turn, contributes to risk of sarcopenia in the elderly [53]. As demonstrated in the present study, the association of current smoking and smoking exposure with higher IMAT suggests a plausible mechanism through which smoking might pose a risk for diabetes. The 0.4 cm³ difference in IMAT volume between current and never smokers is approximately 25% of the 1.6 cm³ SD for IMAT among all participants. In context, we have previously shown that a full 1-SD higher IMAT level is associated with approximately 90% higher diabetes prevalence [18]. Excess accumulation of IMAT impairs glucose disposal via muscle insulin resistance, interrupting glucose metabolism in skeletal muscles which account for approximately 80% of glucose utilization [54]. Though smoking is associated with diabetes risk, to date, neither the American Diabetes Association nor the International Diabetes Foundation include current smoking in risk calculators for type 2 diabetes.

Mechanisms potentially linking smoking to muscle compositional changes and deconditioning are numerous. Muscle biopsies taken from current, long-term smokers had muscle fiber cross-sectional area 25% smaller than nonsmokers, suggesting an association of smoking with muscle wasting [55–57]. These observational data in humans are consistent with laboratory experiments showing that direct exposure to cigarette smoke reduced muscle fiber cross-sectional area and increased succinate dehydrogenase (SDH) activity in rats [55,58]. Muscle fiber atrophy and higher SDH, a measure of oxidative activity, are likely adaptations to local hypoxia induced by cigarette smoke [55,58]. As a major source of reactive oxygen and nitrogen species, mainstream cigarette smoke likely foments oxidative stress and chronic inflammation, which could promote detrimental muscle morphologic and metabolic changes [59–60]. Though the present study showed higher IMAT and lower lean muscle quality rather than lean muscle loss per se, our findings stem from middle-aged participants at low risk of clinically significant sarcopenia. In light of the present CARDIA data, findings from an experimental mouse model fed a high-fat diet and given injections of nicotine or saline are of interest [61]. After 10 weeks on the high-fat diet, the nicotine-injected mice weighed less than controls but accumulated intramyocellular lipid and had higher oxidative stress [61]. IMAT accumulation is also associated with pro-inflammatory circulating cytokines, including interleukin-6 (IL-6), CRP, and tumor necrosis factor-α (TNF-α) [51]. In the presence of obesity, chronic oxidative and inflammatory stress are implicated in many disease processes, including sarcopenia, diabetes, and CVD [62].

Limitations

Though smoking history was verified by cotinine measurement at baseline, smoking was thereafter self-reported, potentially resulting in misclassifying of risk. The CT adipose tissue and muscle composition outcome variables were obtained 25 years after the CARDIA baseline visit. Therefore, we quantified the association of smoking exposure assessed at multiple time points with abdominal adipose deposition measured at a single time point and so the present findings should be confirmed with longitudinal measurement of tissue changes. As such, we cannot definitively rule out reverse causality as a contributor to these associations. Two plausible scenarios are that cigarette smoking increases organ-related fat or that cigarette smoking is started in part because of a tendency towards fatness but is effective only in reducing non–organ-related fat. Although we adjusted for dietary and other habitual factors that may explain variation in muscle composition, it is possible that residual confounding exists.

Conclusions

In the present study, IMAT volume was higher and muscle quality lower in current smokers compared to never smokers. Among ever smokers, having greater pack-years of exposure was directly associated with IMAT and inversely associated with muscle quality. Importantly, former smokers had muscle composition and quality intermediate between never smokers and current smokers, suggesting that cessation is worthwhile despite possible weight gain. These findings are important given the widespread misconception that smoking confers weight-related health benefits.

Supporting information

S1 STROBE checklist. STROBE, Strengthening the Reporting of Observational Studies in Epidemiology.

(DOCX)

Click here for additional data file.^{(32.8KB, docx)}

S1 Table. Multivariable models of Y25 muscle composition (least squares mean [95% CI]) by baseline smoking status.

Y25, year 25.

(DOCX)

Click here for additional data file.^{(16.4KB, docx)}

S2 Table. Multivariable models of Y25 muscle composition (least squares mean [95% CI]) by baseline cotinine level.

Y25, year 25.

(DOCX)

Click here for additional data file.^{(15.4KB, docx)}

S1 CARDIA Proposal. CARDIA study publications and presentations form detailing the study.

CARDIA, Coronary Artery Risk Development in Young Adults.

(DOCX)

Click here for additional data file.^{(68.5KB, docx)}

S1 Form. CARDIA study smoking questionnaire administered at each clinic visit.

CARDIA, Coronary Artery Risk Development in Young Adults.

(PDF)

Click here for additional data file.^{(154.9KB, pdf)}

S2 Form. CARDIA study smoking questionnaire administered at each clinic visit.

CARDIA, Coronary Artery Risk Development in Young Adults.

(PDF)

Click here for additional data file.^{(162.8KB, pdf)}

Acknowledgments

We thank the investigators, the staff, and the participants of the Coronary Artery Risk Development in Young Adults (CARDIA) study for their dedication and highly valued contributions. This article has been reviewed by CARDIA for scientific content.

Abbreviations

AGES-Reykjavik: Age, Gene/Environment Susceptibility-Reykjavik study
CAC: coronary artery calcification
CARDIA: Coronary Artery Risk Development in Young Adults
CRP: C-reactive protein
CT: computed tomography
CVD: cardiovascular disease
DXA: dual-energy X-ray absorptiometry
FF: fast food; HbA1c, hemoglobin A1c
Health ABC: Health, Aging, and Body Composition study
HU: Hounsfield units
IL-6: interleukin-6
IMAT: intermuscular adipose tissue
MIPAV: Medical Image Processing, Analysis, and Visualization
SAT: subcutaneous adipose tissue
SDH: succinate dehydrogenase
SSB: sugar-sweetened beverage
STROBE: Strengthening the Reporting of Observational Studies in Epidemiology
TNF-α: tumor necrosis factor-α
VAT: visceral adipose tissue
Y25: year 25

Data Availability

Data are available from the CARDIA Coordinating Center: http://www.cardia.dopm.uab.edu/contact-cardia. A description of the NHLBI policies governing the data and describing access to the data can be found at the following website: http://www.cardia.dopm.uab.edu/study-information/nhlbi-data-repository-data. Others have access to these data in the same manner as the authors, and the authors do not have any special access privileges that others would not have.

Funding Statement

Funding: JGT, LMS, DRJ, and JJC and the CARDIA Study as a whole are supported through NHLBI awards to University of Alabama at Birmingham (HHSN268201800005I & HHSN268201800007I), Northwestern University (HHSN268201800003I), University of Minnesota (HHSN268201800006I), and Kaiser Foundation Research Institute (HHSN268201800004I), and Vanderbilt School of Medicine (R01-HL098445) and NIA an Intramural Research Program of the National Institute on Aging (NIA) and an intra-agency agreement between NIA and NHLBI (AG0005).The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS Med. doi: 10.1371/journal.pmed.1003223.r001

Decision Letter 0

Emma Veitch

17 May 2020

Dear Dr. Terry,

Thank you very much for submitting your manuscript "Smoking is associated with abdominal adipose deposition and muscle composition in mid-life: The CARDIA Study" (PMEDICINE-D-19-04435) for consideration at PLOS Medicine.

Your paper was evaluated by a senior editor and discussed among all the editors here. It was also discussed with an academic editor with relevant expertise, and sent to independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, I am afraid that we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.

In revising the manuscript for further consideration, your revisions should address the specific points made by each reviewer and the editors. Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments, the changes you have made in the manuscript, and include either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please submit a clean version of the paper as the main article file; a version with changes marked should be uploaded as a marked up manuscript.

In addition, we request that you upload any figures associated with your paper as individual TIF or EPS files with 300dpi resolution at resubmission; please read our figure guidelines for more information on our requirements: http://journals.plos.org/plosmedicine/s/figures. While revising your submission, please upload your figure files to the PACE digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at PLOSMedicine@plos.org.

We expect to receive your revised manuscript by Jun 01 2020 11:59PM - we are suggesting a slightly shortened timeline for resubmission given this paper is for the special issue (Determinants, Consequences and Management of Obesity), and that the revisions suggested by reviewers are not substantial. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

We ask every co-author listed on the manuscript to fill in a contributing author statement, making sure to declare all competing interests. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. If new competing interests are declared later in the revision process, this may also hold up the submission. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT. You can see our competing interests policy here: http://journals.plos.org/plosmedicine/s/competing-interests.

Please use the following link to submit the revised manuscript:

https://www.editorialmanager.com/pmedicine/

Your article can be found in the "Submissions Needing Revision" folder.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see http://journals.plos.org/plosmedicine/s/submission-guidelines#loc-methods.

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

We look forward to receiving your revised manuscript.

Sincerely,

Emma Veitch, PhD

PLOS Medicine

On behalf of:

Adya Misra, PhD

Senior Editor

PLOS Medicine

plosmedicine.org

-----------------------------------------------------------

Requests from the editors:

*Please revise your title according to PLOS Medicine's style. Your title must be nondeclarative and not a question. It should begin with main concept if possible. "Effect of" should be used only if causality can be inferred, i.e., for an RCT. Please place the study design ("A randomized controlled trial," "A retrospective study," "A modelling study," etc.) in the subtitle (ie, after a colon) - in this case presumably "prospective cohort" would be appropriate.

*In the last sentence of the Abstract Methods and Findings section, please describe the main limitation(s) of the study's methodology - in the main discussion section the possibility of residual confounding is mentioned, but reverse causality (as noted by one reviewer) might also be a potential source of bias, and this could be discussed/mentioned as appropriate in either the main Discussion section or abstract, as appropriate.

*At this stage, we ask that you include a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract. Please see our author guidelines for more information: https://journals.plos.org/plosmedicine/s/revising-your-manuscript#loc-author-summary

*We'd ask you to modify the referencing format to match PLOS Medicine's style (this should be numbered callouts in square brackets (eg [1, 2]) rather than superscript numerals. If referencing software was used this should be fairly quick and straightforward.

*Did your study have a prospective protocol or analysis plan? Please state this (either way) early in the Methods section.

a) If a prospective analysis plan (from your funding proposal, IRB or other ethics committee submission, study protocol, or other planning document written before analyzing the data) was used in designing the study, please include the relevant prospectively written document with your revised manuscript as a Supporting Information file to be published alongside your study, and cite it in the Methods section. A legend for this file should be included at the end of your manuscript.

b) If no such document exists, please make sure that the Methods section transparently describes when analyses were planned, and when/why any data-driven changes to analyses took place.

c) In either case, changes in the analysis-- including those made in response to peer review comments-- should be identified as such in the Methods section of the paper, with rationale.

*In the Abstract, currently effect estimates for differences between compared groups are presented, along with p-values. The journal normally expects that 95% confidence intervals (CI's) will also be given so that readers can understand the imprecision associated with the effects seen in the study. We'd ask that these are given both in the abstract and in the main text along with the main effect estimates.

*As the paper reports findings from a prospective cohort, we'd suggest that the study is reported according to the STROBE guideline; please include the completed STROBE checklist as Supporting Information. Please add the following statement, or similar, to the Methods: "This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (SChecklist)." The STROBE guideline can be found here: http://www.equator-network.org/reporting-guidelines/strobe/. When completing the checklist, please use section and paragraph numbers, rather than page numbers.

*As noted by one reviewer, residual confounding (and/or reverse causation) are possible explanations for the effects seen in the analysis, which limit interpretation of the associations. We'd caution the authors to ensure that the writeup in the paper is not overly conclusive about a causal basis to the associations reported in the analysis - these should be presented as associations/links rather than definitive effects.

-----------------------------------------------------------

Comments from the reviewers:

Reviewer #1: I confine my remarks to statistical aspects of this paper. These were well done and I recommend publication

Peter Flom

-----------------------------------------------------------

Reviewer #2: Terry and co-authors examined the association between cigarette smoking and intermuscular adipose tissue and attenuation in the well-described CARDIA population. Although the authors used rather unique measures in a large sample, I have a number of concerns specifically focused on the clinical/medical translation of the findings.

1) The nature of the study is observational only, so no causality can be inferred on the basis of this data.

2) The analyses are done cross-sectional meaning that the direction of the effect cannot be explained. Previous studies (e.g., Mendelian Randomization studies) showed that individuals with obesity have a higher propensity to be a current smoker.

3) The clinical relevance of the findings is poorly examined in the present study. For example, there is an approximate 0.4 units difference in IMAT volume between never and current smokers. The impact of this difference on health (e.g., diabetes or CAD risk) is not discussed. A mediation analysis would be helpful to see whether the association between smoking and health outcomes is mediated by the differences observed in the present study. Especially because the authors also showed a lower BMI in the current smokers, this would be interesting. Combining all anthropometric difference in current smokers would provide a clearer image to the impact on diseases like T2D (or Hb1Ac or HOMA-IR, in case they measured this).

4) It would have been interesting to examine the association of years of smoking cessation and the study outcomes. It seems that past smokers have an IMAT/Attenuation which is a bit in the middle between past en never smokers, but this group is very heterogenous in terms of the duration of smoking cessation. This would give this study a somewhat longitudinal aspect.

5) It is unclear to this reviewer what the authors tried to do with the adjustment for the baseline variables (25 years ago!).

-----------------------------------------------------------

Reviewer #3: This is an excellent paper on an important topic. I am surprised the analysis has not been done before. The authors have been thorough in their statistical analysis and the discussion is well-written.

I only have very tiny amendments:

-"type 2 diabetes" rather than "type ii diabetes." (the latter term is older nomenclature)

-In the opening sentence of the introduction, change "was" to "is." Alternatively, add the word "historically" to the beginning of the sentence to make the tenses match.

-----------------------------------------------------------

Any attachments provided with reviews can be seen via the following link:

[LINK]

PLoS Med. 2020 Jul 21;17(7):e1003223. doi: 10.1371/journal.pmed.1003223.r002

Author response to Decision Letter 0

28 May 2020

Attachment

Submitted filename: PLOS Med Editor and Reviewer Response.docx

Click here for additional data file.^{(22.9KB, docx)}

PLoS Med. doi: 10.1371/journal.pmed.1003223.r003

Decision Letter 1

Adya Misra

8 Jun 2020

Dear Dr. Terry,

Thank you very much for re-submitting your manuscript "Smoking is associated with abdominal adipose deposition and muscle composition in CARDIA Study participants at mid-life: a prospective cohort study" (PMEDICINE-D-19-04435R1) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by xxx reviewers. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

Our publications team (plosmedicine@plos.org) will be in touch shortly about the production requirements for your paper, and the link and deadline for resubmission. DO NOT RESUBMIT BEFORE YOU'VE RECEIVED THE PRODUCTION REQUIREMENTS.

In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.

Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. If you haven't already, we ask that you provide a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract.

We expect to receive your revised manuscript within 1 week. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

We ask every co-author listed on the manuscript to fill in a contributing author statement. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT.

If you have any questions in the meantime, please contact me or the journal staff on plosmedicine@plos.org.

We look forward to receiving the revised manuscript by Jun 15 2020 11:59PM.

Sincerely,

Adya Misra, PhD

Senior Editor

PLOS Medicine

plosmedicine.org

------------------------------------------------------------

Requests from Editors:

Abstract

Could you please provide additional participant demographics? For instance, where the participants are from/where the registry is based

For p-values throughout, please use the format 0.xxx

Please clarify this sentence “compared to never smokers, current and former smokers had abdominal muscles higher in adipose tissue”

b) If no such document exists, please make sure that the Methods section transparently describes when analyses were planned, and when/why any data-driven changes to analyses took place.

c) In either case, changes in the analysis-- including those made in response to peer review comments-- should be identified as such in the Methods section of the paper, with rationale.

Throughout- please provide a space between text and reference square brackets

Introduction

I would suggest an alternative for “svelte figure” and “quitters”

I would suggest rephrasing “obese, ageing society” as it could be interpreted as stigmatising

Could you please provide citations to or copies of the questionnaires used in the study

Line 269- please provide the exact p-value here and also the data. PLOS do not permit instances of data not shown. The same goes for lines 317, 319 and 339

For all tables, please define CRP, CAC, HU in the footnotes

At line 371, please revisit the possibly contentious statement about the "tobacco industry's longstanding advertising campaign". The cited article appears to refer to adverts from the 1930s. We suggest deleting the relevant sentence.

Similarly, please ensure that the statement at lines 101-102 accurately reflects the content of the cited references.

At line 471, unless there are surveys documenting the beliefs of "millions of smokers", please amend the wording.

Please quote exact p values or "p<0.001".

Please add full access details to reference 1.

Please adapt the title so that it is non-declarative in style, e.g., "Smoking and abdominal adipose deposition ...".

Please remove the word "prospective" from the title (we view this as a retrospective analysis of a prospectively gathered dataset).

Please remove all iterations of "[Internet]" from the reference list.

Comments from Reviewers:

Reviewer #2: The authors addressed my concerns

Any attachments provided with reviews can be seen via the following link:

[LINK]

PLoS Med. 2020 Jul 21;17(7):e1003223. doi: 10.1371/journal.pmed.1003223.r004

Author response to Decision Letter 1

15 Jun 2020

Attachment

Submitted filename: PLOS Med Editor Response 20200615.docx

Click here for additional data file.^{(22.6KB, docx)}

PLoS Med. doi: 10.1371/journal.pmed.1003223.r005

Decision Letter 2

Adya Misra

18 Jun 2020

Dear Mr Terry,

On behalf of my colleagues and the academic editor, Dr. Sanjay Basu, I am delighted to inform you that your manuscript entitled "Association of smoking with abdominal adipose deposition and muscle composition in Coronary Artery Risk Development in Young Adults (CARDIA) participants at mid-life: a population-based cohort study" (PMEDICINE-D-19-04435R2) has been accepted for publication in PLOS Medicine.

PRODUCTION PROCESS

Before publication you will see the copyedited word document (in around 1-2 weeks from now) and a PDF galley proof shortly after that. The copyeditor will be in touch shortly before sending you the copyedited Word document. We will make some revisions at the copyediting stage to conform to our general style, and for clarification. When you receive this version you should check and revise it very carefully, including figures, tables, references, and supporting information, because corrections at the next stage (proofs) will be strictly limited to (1) errors in author names or affiliations, (2) errors of scientific fact that would cause misunderstandings to readers, and (3) printer's (introduced) errors.

If you are likely to be away when either this document or the proof is sent, please ensure we have contact information of a second person, as we will need you to respond quickly at each point.

PRESS

A selection of our articles each week are press released by the journal. You will be contacted nearer the time if we are press releasing your article in order to approve the content and check the contact information for journalists is correct. If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact.

PROFILE INFORMATION

Now that your manuscript has been accepted, please log into EM and update your profile. Go to https://www.editorialmanager.com/pmedicine, log in, and click on the "Update My Information" link at the top of the page. Please update your user information to ensure an efficient production and billing process.

Thank you again for submitting the manuscript to PLOS Medicine. We look forward to publishing it.

Best wishes,

Adya Misra, PhD

Senior Editor

PLOS Medicine

plosmedicine.org

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 STROBE checklist. STROBE, Strengthening the Reporting of Observational Studies in Epidemiology.

(DOCX)

Click here for additional data file.^{(32.8KB, docx)}

S1 Table. Multivariable models of Y25 muscle composition (least squares mean [95% CI]) by baseline smoking status.

Y25, year 25.

(DOCX)

Click here for additional data file.^{(16.4KB, docx)}

S2 Table. Multivariable models of Y25 muscle composition (least squares mean [95% CI]) by baseline cotinine level.

Y25, year 25.

(DOCX)

Click here for additional data file.^{(15.4KB, docx)}

S1 CARDIA Proposal. CARDIA study publications and presentations form detailing the study.

CARDIA, Coronary Artery Risk Development in Young Adults.

(DOCX)

Click here for additional data file.^{(68.5KB, docx)}

S1 Form. CARDIA study smoking questionnaire administered at each clinic visit.

CARDIA, Coronary Artery Risk Development in Young Adults.

(PDF)

Click here for additional data file.^{(154.9KB, pdf)}

S2 Form. CARDIA study smoking questionnaire administered at each clinic visit.

CARDIA, Coronary Artery Risk Development in Young Adults.

(PDF)

Click here for additional data file.^{(162.8KB, pdf)}

Attachment

Submitted filename: PLOS Med Editor and Reviewer Response.docx

Click here for additional data file.^{(22.9KB, docx)}

Attachment

Submitted filename: PLOS Med Editor Response 20200615.docx

Click here for additional data file.^{(22.6KB, docx)}

Data Availability Statement

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PERMALINK

Association of smoking with abdominal adipose deposition and muscle composition in Coronary Artery Risk Development in Young Adults (CARDIA) participants at mid-life: A population-based cohort study

James G Terry

Katherine G Hartley

Lyn M Steffen

Sangeeta Nair

Amy C Alman

Melissa F Wellons

David R Jacobs Jr

Hilary A Tindle

John Jeffrey Carr

Roles

Abstract

Background

Methods and findings

Conclusions

Author summary

Why was this study done?

What did the researchers do and find?

What do these findings mean?

Introduction

Materials and methods

Study population

Clinical evaluations

Clinical measures

Lifestyle measures

Smoking history

Other lifestyle and clinical measures

CT measures of arterial calcification and adipose deposition

Statistics

Results

Table 1. Participant characteristics at Y25 visit according to Y25 smoking status*.

Associations of smoking history at CARDIA Y25 visit with abdominal adipose tissues and muscle composition

Table 2. Unadjusted Y25 CT measures of abdominal adipose tissues and muscle composition [mean (SD]) by Y25 smoking status*.

Table 3. Multivariable models of Y25 CT abdominal adipose tissues and muscle composition (least squares mean [95% CI]) by Y25 smoking status*.

Fig 1. Predicted IMAT volume (cm3), IMAT/lean volume ratio, and lean muscle quality (HU) based on pack-years smoked from CARDIA Y0 through Y25.

Associations of smoking history at CARDIA baseline visit with Y25 abdominal adipose tissue and muscle composition

Discussion

Limitations

Conclusions

Supporting information

Acknowledgments

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Emma Veitch

Roles

Author response to Decision Letter 0

Decision Letter 1

Adya Misra

Roles

Author response to Decision Letter 1

Decision Letter 2

Adya Misra

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases

Table 1. Participant characteristics at Y25 visit according to Y25 smoking status^*.

Table 2. Unadjusted Y25 CT measures of abdominal adipose tissues and muscle composition [mean (SD]) by Y25 smoking status^*.

Table 3. Multivariable models of Y25 CT abdominal adipose tissues and muscle composition (least squares mean [95% CI]) by Y25 smoking status^*.

Fig 1. Predicted IMAT volume (cm³), IMAT/lean volume ratio, and lean muscle quality (HU) based on pack-years smoked from CARDIA Y0 through Y25.