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  • Research article
  • Open Access
  • Open Peer Review

Total Exposure Study Analysis consortium: a cross-sectional study of tobacco exposures

  • 1Email author,
  • 1,
  • 1,
  • 2,
  • 3,
  • 4,
  • 5,
  • 6 and
  • 6
BMC Public Health201515:866

https://doi.org/10.1186/s12889-015-2212-5

  • Received: 6 September 2014
  • Accepted: 2 September 2015
  • Published:
Open Peer Review reports

Abstract

Background

The Total Exposure Study was a stratified, multi-center, cross-sectional study designed to estimate levels of biomarkers of tobacco-specific and non-specific exposure and of potential harm in U.S. adult current cigarette smokers (≥one manufactured cigarette per day over the last year) and tobacco product non-users (no smoking or use of any nicotine containing products over the last 5 years). The study was designed and sponsored by a tobacco company and implemented by contract research organizations in 2002–2003. Multiple analyses of smoking behavior, demographics, and biomarkers were performed. Study data and banked biospecimens were transferred from the sponsor to the Virginia Tobacco and Health Research Repository in 2010, and then to SRI International in 2012, for independent analysis and dissemination.

Methods

We analyzed biomarker distributions overall, and by biospecimen availability, for comparison with existing studies, and to evaluate generalizability to the entire sample. We calculated genome-wide statistical power for a priori hypotheses. We performed clinical chemistries, nucleic acid extractions and genotyping, and report correlation and quality control metrics.

Results

Vital signs, clinical chemistries, and laboratory measures of tobacco specific and non-specific toxicants are available from 3585 current cigarette smokers, and 1077 non-users. Peripheral blood mononuclear cells, red blood cells, plasma and 24-h urine biospecimens are available from 3073 participants (2355 smokers and 719 non-users). In multivariate analysis, participants with banked biospecimens were significantly more likely to self-identify as White, to be older, to have increased total nicotine equivalents per cigarette, decreased serum cotinine, and increased forced vital capacity, compared to participants without. Effect sizes were small (Cohen’s d-values ≤ 0.11). Power for a priori hypotheses was 57 % in non-Hispanic Black (N = 340), and 96 % in non-Hispanic White (N = 1840), smokers. All DNA samples had genotype completion rates ≥97.5 %; 68 % of RNA samples yielded RIN scores ≥6.0.

Conclusions

Total Exposure Study clinical and laboratory assessments and biospecimens comprise a unique resource for cigarette smoke health effects research. The Total Exposure Study Analysis Consortium seeks to perform molecular studies in multiple domains and will share data and analytic results in public repositories and the peer-reviewed literature. Data and banked biospecimens are available for independent or collaborative research.

Keywords

  • Cotinine
  • Cigarette Smoke Exposure
  • Current Cigarette Smoker
  • Serum Cotinine
  • Smoking Duration

Background

The Total Exposure Study (TES) was designed by a tobacco company sponsor in the 1990s with the primary objectives of estimating exposure of current U.S. adult cigarette smokers to cigarette smoke constituents and of investigating relationships between FTC tar categories and cigarette smoke exposure. Other objectives included investigating associations of smoking behavior and biomarkers of exposure (BOE), comparing BOE in adult smokers and non-users, and investigating relationships between BOE and biomarkers of potential harm (BOPH) [1]. From 2002 to 2003, internationally-recognized contract research organizations (CROs), under contract to the tobacco company sponsor, collected questionnaire data, clinical data, and biological samples from 3,585 smokers and 1077 non-users at 39 clinical sites in 31 U.S. states and performed clinical chemistry, laboratory and statistical analyses [13]. TES participants were recruited using Institutional Review Board-approved advertisements [1, 2], with defined inclusion and exclusion criteria (Additional file 1). The study was approved by an Institutional Review Board at each clinical site and conducted in accordance with Good Clinical and Laboratory Practices and principles of the Declaration of Helsinki. Using blood and urine biospecimens and mass spectrometry-based and clinical chemistry-based analyses, the CROs determined levels of BOE and BOPH in smokers and non-users. Additional blood and urine biosamples were collected from consenting subjects for possible future analyses.

The Virginia Tobacco Health Research Repository (VTHRR) was formed in 2010 as a Virginia non-profit, non-stock corporation by authorization of the Virginia BioTechnology Research Partnership Authority Board, a political subdivision of the Commonwealth of Virginia. The VTHRR received TES data and biospecimens as a contribution from the tobacco company sponsor. The mission of the VTHRR is to make the TES data and banked biospecimens available to scientists, research institutions, regulatory agencies and industry for research to increase the scientific knowledge base of the health effects of cigarette smoking [4].

Under a 2012 Asset Transfer Agreement between the VTHRR and SRI International (SRI), an independent, non-profit research institute incorporated in 1946 in the state of California, TES data and biospecimens were transferred to SRI in 2012. The agreement between SRI and VTHHR provides SRI with complete independence to pursue valid scientific objectives. The principal intended result of any analysis of TES data or biospecimens is the generation of knowledge related to smoking and health that is shared in the scientific peer-reviewed literature and in appropriate databases. SRI will independently maintain, curate, and make both data and biospecimens available to the research community for this purpose.

In order to optimize the validity and utility of the TES data and banked biospecimens to support its full use by the global public health research community, there is a need for thoughtful, objective scientific analysis of the resource. The purpose of this analysis was to review TES data and biospecimens, investigate distributions of self-reported, clinical and laboratory measures of exposure and potential harm (biomarker), and pote`ntial differences in biomarker levels between those participants with banked biospecimens and those without, calculate statistical power for genomic analyses, and perform analyses of plasma and peripheral blood monocyte analytes.

Methods

We obtained ethical approval from the SRI International Human Subjects Committee to conduct these analyses of TES data and biospecimens.

Each study site selected to use either their individual site-specific IRB or a central IRB contracted by the primary clinical and laboratory CRO responsible for the conduct of the study. TES participants were recruited, provided informed consent and were screened in a two-visit, multicenter process as current cigarette smokers, stratified by their regular cigarette’s Federal Trade Commission (FTC) tar level (≤2.9, 3.0–6.9, 7.0–12.9, and ≥ 13 mg), and as non-users [1, 2]. Inclusion and exclusion criteria are described in Additional file 1. Participants were paid up to 300 U.S. dollars for completion of all study components. Recruitment sites were distributed in 31 States over four regions [Midwest (19.7 %), Northeast (13.0 %), South (37.8 %) and West (29.5 %)] and among urban (68.5 %) and non-urban (31.5 %) areas.

All participants provided vital signs (at both visits), medical history and concomitant medication data (at the first visit), and completed a questionnaire survey regarding smoking history and attitudes and preferences regarding smoking (in current smokers), demographics, lifestyle and environmental exposures (at the second visit). Between the first and second visit, smokers collected cigarette butts over a 24-h period and smoking topography information using a portable instrument which measured the number of puffs, the length of puffs and the length of the inter-puff interval. Both smokers and non-users collected their urine over 24-h. At the second visit, lung function tests were performed and blood was collected for processing, biomarker assays and, under a separate consent for future research, for banking.

Four tubes of whole blood [two 10 ml potassium ethylenediaminetetraacetic acid (KEDTA) and two 8.5 ml acid citrate dextrose solution A (ACDA) tubes] were obtained from each participant at the second visit after a minimum 6 h fast and processed for plasma, red blood cells and monocytes [1]. The TES biospecimen aliquots in SRI’s possession include approximately: a) 6000 peripheral blood mononuclear cell (PBMC) samples; b) 7000 red blood cell samples; c) 5000 24-h urine samples; and d) 3000 plasma samples. TES biospecimens have been stored at −80 °C by the VTHRR and SRI.

We examined TES publications, accessed the University of California San Francisco Legacy Tobacco Documents Library (UCSF LTDL) website [5] TES-related documents to 1) compare with documents we had received from the VTHRR and 2) to learn more about the study design and analysis goals of the TES, and engaged with colleagues regarding the potential value of the TES for tobacco research. We reviewed data collection, sample preservation, and laboratory assay protocols followed by the CROs that conducted the TES. We inspected TES clinical and biospecimen data and labeled biospecimens to confirm that the dataset was deidentified.

We queried the TES clinical data to assess the distribution of participant data among the analysis strata (age, sex, and BMI) among the four smoking categories defined by the smoker’s usual cigarette FTC tar level, and among non-users. We evaluated the distributions of analysis strata among all participants, by banked biospecimen availability, and by biospecimen type. We analyzed additional behavioral, demographic, biomarker and tobacco product variable distributions among participants, and compared distributions between participants with and without banked biospecimens.

We constructed logistic regression models predicting the availability of biospecimens in self-identified non-Hispanic Black and White smokers using individuals with complete data in three increasingly complex models. Model 1 comprised BMI and demographic covariates, Model 2 added BOE to the covariates in model 1, and Model 3 added BOPH to Model 2. We imputed missing data for each model and repeated analyses with the larger sample sizes. To determine the extent to which random variability was responsible for the ability of the demographic variables and biomarkers to predict biospecimen availability, we randomly permuted the variable indicating the availability of biospecimens and determined a 95 % confidence interval for the percent reduction in the variance of this randomly permuted variable attributable to the covariates.

Plasma biospecimens were randomly selected (women and men, aged 35–49 years, with BMI < 25 kg/m2, both current smokers and non-users) and sent to the SRI Clinical Analysis Laboratory (CAL). Six clinical laboratory assays were performed on 47 plasma samples to measure levels of glucose, aspartate and alanine aminotransferases, total bilirubin, albumin, and total cholesterol. We estimated the correlation between SRI CAL plasma and original CRO serum analyte values.

PBMCs randomly selected from TES participants (N = 30, ~1 % of participants with available biospecimens) from defined strata [ages 35–49 and with BMI < 25 kg/m2] resulted in a sample that was 37 % female, 70 and 20 % self-identified White and Black, with 67 % current smokers. Initially, we performed DNA extraction from a limited number of pellets using Gentra Puregene reagents (Qiagen). To conserve biospecimen resources, we reviewed several multiple analyte protocols, and selected a protocol for simultaneous DNA and RNA extraction (NORGEN 48700 kit with Proteinase K). We modified lysis buffer amounts by available white blood cell count data and extracted ~1X106 cells from each lysed pellet. DNA was sent to the Rutgers University Cell and DNA resource for genotyping with the Smokescreen® Array [6]. RNA quality (RNA integrity score, RIN) was analyzed using the Agilent 2100 BioAnalyzer using the Eukaryote Total RNA Nano assay.

Statistical analyses were performed using SAS version 9.2 (Cary, North Carolina) and STATA SE version 12.0 (Stata Corp, College Station, Texas). Except where specified, the alpha used for statistical significance was 0.05. We evaluated power to detect genetic variants for serum cotinine at genome-wide significance using Quanto [7].

Results

Review of the published TES literature

Scientists employed by the tobacco company sponsor have published analyses in peer-reviewed scientific journals using data from the TES pilot study [8] and the TES main study [2, 3, 916]. Analyses included population estimates of BOE levels for smokers and non-users [2], estimates of levels of BOPH in smokers and non-users [3], the relationships between machine-derived tar yields of cigarette products and BOE in smokers [9], models of BOPH [11], the impact of menthol-containing cigarettes on selected BOE in White and Black smokers [10], and the relationships between selected BOE and BOPH in smokers [14]. These scientists have also reported on the relationships between BOE and nicotine dependence [13] and between nicotine and carbon monoxide BOE and other factors, including smoking topographical variables [12]. These authors utilized TES data to examine the relationships between smoking mentholated cigarettes or non-mentholated cigarettes and glucuronide metabolite ratios [15], and with measures of nicotine dependence [16]. We review Roethig et al. [2] and Frost-Pineda et al. [3] here to introduce TES BOE (Additional file 2: Table S1) and BOPH (Additional file 3: Table S2).

Roethig et al. published estimates of BOE (Additional file 2: Table S1) in smokers and non-users and, within smokers, within different age, sex, BMI, and self-identified racial strata [2]. Mean levels of BOE were weighted by age, sex and BMI variance estimates from the U.S. Behavioral Risk Factor Surveillance System (BRFSS), an annual telephone-based behavioral survey established in 1984 [17], to produce weighted estimates of BOE reported and described by Roethig et al. as population estimates [2]. The BRFSS used post-stratification weighting based on United States Census data from the 1980s until 2011 [17]. Lee and Messiah criticized the application of weights extracted from a nationally representative sample to a sample for which inclusion rates at recruitment sites were not known or not reported [18]. In response, Sarkar and Liang noted that the weighted means were similar to or unchanged from unadjusted means [19]. Weighted estimates of tobacco-specific biomarkers [nicotine, cotinine and trans-3′-hydroxycotinine and their glucoronides (nicotine equivalents, NE), serum cotinine, and total 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol and glucuronide (total NNAL)] suggested that the younger participants (21–34 years) and female participants had the lowest tobacco-specific exposures, and that individuals with BMIs < 25 kg/m2 compared with individuals with BMIs ≥ 25 kg/m2 had higher serum cotinine levels and lower total NNAL levels, suggesting reduced cotinine clearance and NNK metabolism in heavier individuals [2]. Significant differences in serum cotinine by BMI similar to those reported in the TES have been previously observed in the National Health and Nutrition Examination Survey [20]. In the TES, self-identified White smokers smoked significantly more cigarettes per day and had greater NE and total NNAL exposure over 24 h, but lower NE and total NNAL exposure per cigarette, and lower serum cotinine exposure, than self-identified Black smokers [2]. It has previously been observed that White smokers smoke more cigarettes per day than Black smokers and that nicotine intake per cigarette measured by serum cotinine is higher in Black smokers than in White smokers [21, 22], which is related to significantly reduced nicotine clearance in Blacks compared to Whites [23, 24].

Frost-Pineda et al. [3] reported mean values for 29 BOPH (Additional file 3: Table S2) in both smokers and non-users. The BOPH represented various physiological functions: cardiovascular, endothelial, hematologic, inflammation, lipid, hepatic, renal, respiratory, metabolic, and oxidative stress [3]. The effects of multiple BOE [cigarettes per day (CPD), NE, and smoking duration] on the BOPH in current smokers versus non-users were evaluated in two stepwise regression models (model A with CPD and smoking duration, and model B with NE and smoking duration) with age, sex, BMI and self-identified race as additional independent variables [3]. The three most elevated mean BOPH in current smokers versus non-users were those reflecting oxidative stress, platelet activation and inflammation. The oxidative stress biomarker 8-epi-prostaglandin F exhibited the largest difference between smokers and non-users (+42 %), while BMI and age, and BMI and NE, were the most important correlates in models A and B, respectively. The platelet activation biomarker 11-dehydrothromboxane B2 exhibited the second largest difference between smokers and non-users (+29 %), and sex and BMI, and sex and NE were the most important correlates in models A and B, respectively. The inflammation biomarker white blood cell count exhibited the third largest difference between smokers and non-users (+19 %) and BMI and self-identified race were the most important correlates in both models. Overall, BMI and sex were the first and second most common significant correlates reported by Frost-Pineda et al. [3].

Our search of the UCSF LTDL identified multiple documents we had received from the VTHRR, including: the Amended Final Research Protocol, dated 19 August 2002, that describes the clinical protocol, laboratory testing and biospecimen banking procedures to be conducted by the primary clinical and laboratory CRO [1]; the TES Adult Smoker Survey [25]; and the TES Adult Non-Smoker Survey [26]. We found no differences between the documents we had received from VTHRR and those available on the UCSF LTDL. We also identified summary documents that provided information on the design and analysis goals of the TES, including: a Statement of Work for data management and analysis to be conducted by the primary data analysis CRO dated 30 April 2004 [27]; a draft version of the Statistical Analysis Plan dated 1 September 2004 [28]; and a PowerPoint presentation dated 7 February 2005 that presented TES pilot results, and design and initial analyses of the TES [29]. Review of these documents enriched our understanding of the design and conduct of the study and confirmed study parameters, e.g., numbers of individuals recruited within design strata. With the assistance of a UCSF Industry Documents Digital Librarian, we identified SAS datasets available in the Philip Morris collection but these refer to an unrelated study [30].

TES recruitment and analysis strata

The original enrollment goal of the TES [1] was 1000 smokers among four strata defined by FTC tar levels of the smoker’s usual cigarette (≤2.9, 3–6.9, 7–12.9, and ≥ 13 mg), and 1000 non-users [1]. The distribution of evaluable subjects in the five categories (504, 953, 1066, and 1062 smokers, and 1077 non-users) was significantly different from the design (Pearson χ24d.f. = 159.9, P < 0.0001). The distribution of participants with clinical data by enrollment strata and by demographic strata is shown in Table 1.
Table 1

TES participants with clinical data, by recruitment variables and by previously utilized analysis strata

Analysis Variable

≤2.9 mg

3.0–6.9 mg

7.0–12.9 mg

≥13 mg

Smokersa

Non-usersb

Sex

Female

293 (58.1)

648 (68.0)

604 (56.7)

514 (48.4)

2059 (57.4)

639 (59.3)

N (%)

Male

211 (41.9)

305 (32.0)

462 (43.3)

548 (51.6)

1526 (42.6)

438 (40.7)

Age, years

21–34

82 (16.3)

273 (28.6)

397 (37.2)

387 (36.4)

1139 (31.8)

358 (33.2)

N (%)

35–49

224 (44.4)

394 (41.3)

377 (35.4)

428 (40.3)

1423 (39.7)

358 (33.2)

≥50

198 (39.3)

286 (30.0)

292 (27.4)

247 (23.3)

1023 (28.5)

361 (33.5)

BMI (kg/m2)

<25

177 (35.1)

392 (41.1)

425 (39.9)

411 (38.7)

1405 (39.2)

398 (37.0)

N (%)

≥25

327 (64.9)

561 (58.9)

641 (60.1)

651 (61.3)

2180 (60.8)

679 (63.0)

Total

 

504 (14.1)

953 (26.6)

1066 (29.7)

1062 (29.6)

3585 (76.9)

1077 (23.1)

aParticipants smoking ≥ one manufactured cigarette per day during the last year. bNon-users of tobacco or nicotine products for the last five years, and throughout the study

TES demographics and smoking status

The demographic composition of TES participants with clinical data (N = 4662) was 57.9 % female, with mean (standard deviation, SD) age 42.1 (13.2) years and mean (SD) BMI 27.9 (6.7) kg/m2 (Table 2). Self-identified race distributions were 77.1 % “Caucasian or White”, 16.5 % “African American or Black”, and four other self-identified race categories comprising 6.5 % of participants. Only a small fraction of TES participants self-identified as Hispanic ethnicity (3.8 % of total participants). Most (76.9 %) TES participants were current smokers with mean (SD) CPD of 16.0 (8.9). Age, self-identified race, and education distributions differed significantly by smoking status (current smokers were significantly more likely to be older, self-identify as Black, and significantly less likely to have a college degree), while sex, BMI, and self-identified ethnicity (Hispanic versus Not Hispanic) did not differ by smoking status.
Table 2

TES demographics and smoking status, overall and among those with and without banked biospecimens

Characteristic

All

With

Without

χ2 or t, P

Sexa

   

2.33, .13

 Female

2698 (57.9)

1754 (57.1)

944 (59.4)

 

 Male

1964 (42.1)

1319 (42.9)

645 (40.6)

 

Age (Years)b

42.1 (13.2)

42.5 (13.0)

41.2 (13.5)

3.0, .002

 21–34

1497 (32.1)

927 (30.2)

570 (35.9)

16.83, .0002

 35–49

1781 (38.2)

1222 (39.8)

559 (35.2)

 

  ≥ 50

1384 (29.7)

924 (30.1)

460 (28.9)

 

BMI (kg/m2)c

27.9 (6.7)

28.1 (6.8)

27.5 (6.5)

2.6, .009

  < 25

1803 (38.7)

1161 (37.8)

642 (40.4)

3.05, .08

  ≥ 25

2859 (29.7)

1912 (30.1)

947 (59.6)

 

Self-identified Racee

   

26.5, <.0001

 White

3578 (77.1)

2420 (79.1)

1158 (73.2)

23.8, <.0001d

 Black

765 (16.5)

447 (14.6)

318 (20.1)

 

 Other

121 (2.6)

80 (2.6)

41 (2.6)

 

 Multi-racial

73 (1.6)

48 (1.6)

25 (1.6)

 

 Native American

67 (1.4)

44 (1.4)

23 (1.5)

 

 Asian

24 (0.5)

12 (0.4)

12 (0.8)

 

Ethnicityf

   

0.07, .79

 Hispanic

178 (3.8)

119 (3.9)

59 (3.7)

 

 Not Hispanic

4458 (96.2)

2937 (96.1)

1521 (96.3)

 

Educationg

   

4.0, .26

  < High School

356 (7.7)

230 (7.6)

126 (8.0)

 

 HS/some College

3292 (71.3)

2199 (72.2)

1093 (69.5)

 

  ≥ Bachelors

969 (21.0)

615 (20.2)

354 (22.5)

 

Smoking status

   

0.35, .55

 Non-smoker

1077 (23.1)

718 (23.4)

359 (22.6)

 

 Smoker

3585 (76.9)

2355 (76.6)

1230 (77.4)

 

aχ2smoking status = 1.22, P = .27.btsmoking status = 3.2, P = .001. ctsmoking status = 1.33, P = .18. dWhite and Black. eAll races, χ2smoking status = 5.81, P = .016. fχ2smoking status = 2.04, P = .15. gχ2smoking status = 223, P = <.0001

TES banked biospecimen availability and smoking status

Two-thirds (66 %) of TES participants have banked biospecimens. PBMCs are the most common biospecimen type while urine is the least common (Table 3). Among participants with banked biospecimens, and among the four biospecimen types, there are no significant differences in sex, age and BMI proportions, but there are significant differences in smoking status (Table 4). Compared to participants with banked PBMC biospecimens, participants with banked urine biospecimens are significantly more likely to be smokers (OR = 1.26, 95 % CI 1.11–1.44, P = 0.0004).
Table 3

TES banked biospecimen aliquots, by biospecimen type

Aliquots per participant

PBMCa

RBCb

Plasmac

Urined

Total

1 Aliquot

40

94

2914

321

 

2 Aliquots

2923

905

 

2299

 

3 Aliquots

 

1771

   

Total N Aliquots

5886

7217

2914

4919

20936

Total N participants with Aliquots

2963

2770

2914

2620

3073

aFrom one 8.5 mL ACDA yellow-top tube processed in two vials. bFrom one 10 mL K2EDTA tube processed into three vials. cFrom one 10 mL K2EDTA tube of whole blood processed into two vials. dAliquots of 100 mL from a 24 h urine sample

Table 4

TES banked biospecimen availability, by biospecimen type, and by strata previously used for analysis

 

Any

PBMC

RBC

Plasma

Urine

 

3073

2963

2770

2914

2620

Smoking status

     

 Smoker

2355 (76.6)

2272 (76.7)

2120 (76.5)

2223 (76.3)

2112 (80.6)

 Non-user

718 (23.4)

691 (23.3)

650 (23.5)

691 (23.7)

508 (19.4)

Sex

     

 Female

1714 (55.8)

1686 (56.9)

1582 (57.1)

1648 (56.6)

1495 (57.1)

 Male

1359 (44.2)

1279 (43.1)

1188 (42.9)

1266 (43.4)

1125 (42.9)

Age (years)

     

 21–34

927 (30.2)

880 (29.7)

822 (29.7)

876 (30.1)

783 (29.9)

 35–49

1222 (39.8)

1184 (40.0)

1109 (40.0)

1171 (40.2)

1057 (40.3)

  ≥ 50

924 (30.1)

899 (30.3)

839 (30.3)

867 (29.8)

780 (29.8)

BMI, kg/m2

     

  < 25

1161 (37.8)

1117 (37.7)

1046 (37.8)

1092 (37.5)

1000 (38.2)

  ≥ 25

1912 (62.2)

1846 (62.3)

1724 (62.2)

1822 (62.5)

1620 (61.8)

Pearson χ2, by biospecimen type: Smoking Status, χ2 = 19.59 (P = .00021); Sex, χ2 = 0.22 (P = .97); Age, χ2 = 0.44 (P = .99); BMI, χ2 = 0.29 (P = .96)

TES demographics

Participants with banked biospecimens are significantly older (age, continuous or categorical), have significantly increased BMI (continuous), and are more likely to self-identify as White compared to those without (Table 2). When stratified by ethnicity and race, self-identified non-Hispanic Black participants with banked biospecimens are significantly older and have significantly increased BMI than those without biospecimens [mean (SD) age 40.8 (10.7) vs 38.8 (11.1) years, t = 2.45, P = 0.0144, N = 755; mean (SD) BMI 30.5 (8.0) vs 28.5 (6.9) kg/m2, t = 3.58, P = 0.0004, N = 755, data not shown]. Significant differences in age and BMI among all participants and stratified by self-identified ethnicity and race are small (Cohen’s d-values = 0.10, 0.09, 0.18 and 0.27, respectively). Smoking duration, CPD (continuous and categorical), and usual cigarette FTC tar level (categorical) are significantly increased in those with banked biospecimens compared to those without, overall, and when stratified by self-identified race (Table 5). Significant differences are small; d-values for smoking duration overall, and among self-identified non-Hispanic Blacks and Whites are 0.13, 0.22 and 0.08, respectively, and d-values for CPD among self-identified non-Hispanic smokers, and among self-identified non-Hispanic White smokers, are 0.14 and 0.09, respectively.
Table 5

TES self-reported BOE, non-Hispanic current smokers by self-identified race, and by banked biospecimens

Characteristic

All

With

Without

χ2 or t, P

Years smoked [N (%)]a

22.0 (12.9)

22.6 (12.8)

20.9 (13.0)

3.62, .0003

 Black

18.6 (11.8)

19.7 (11.8)

17.1 (11.5)

2.69, .0074

 White

22.8 (13.0)

23.1 (13.0)

22.0 (13.2)

2.05, .0404

CPD [N (%)]b

16.3 (8.9)

16.7 (8.9)

15.5 (8.9)

3.60, .0003

 Black

11.3 (6.3)

11.4 (6.4)

11.1 (6.1)

0.61, .5393

 White

17.4 (9.1)

17.6 (9.0)

16.8 (9.2)

2.13, .0329

CPDc 1–10

952(28.8)

579 (26.6)

373 (33.1)

17.7, .0005

 11–20

1487 (45.0)

996 (45.8)

491 (43.6)

 

 21–30

643 (19.5)

453 (20.8)

190 (16.9)

 

  ≥ 31

221 (6.7)

148 (6.8)

73 (6.5)

 

Black 1–10

327 (54.0)

179 (52.7)

148 (55.6)

0.705, .8720

 11–20

227 (37.5)

130 (38.2)

97 (36.5)

 

 21–30

43 (7.1)

26 (7.7)

17 (6.4)

 

  ≥ 31

9 (1.5)

5 (1.5)

4 (1.5)

 

White 1–10

625 (23.2)

400 (21.8)

225 (26.1)

7.701, .0526

 11–20

1260 (46.7)

866 (47.2)

394 (45.8)

 

 21–30

600 (22.3)

427 (23.3)

173 (20.1)

 

  ≥ 31

212 (7.9)

143 (7.8)

69 (8.0)

 

FTC tar, mgd

9.16 (5.4)

9.18 (5.3)

9.13 (5.6)

0.23, .8187

 Black

10.9 (6.9)

10.7 (6.9)

11.2 (6.9)

–0.74, .4622

 White

8.8 (5.0)

8.9 (4.9)

8.5 (5.0)

1.86, .0624

  ≤ 2.9

472 (14.3)

314 (14.4)

158 (14.0)

13.28, .0041

 3–6.9

887 (26.8)

551 (25.3)

336 (29.8)

 

 7–12.9

985 (29.8)

689 (31.6)

296 (26.2)

 

  ≥ 13

965 (29.2)

627 (28.8)

338 (30.0)

 

Black  ≤ 2.9

135 (22.3)

81 (23.8)

54 (20.3)

1.35, .7183

 3–6.9

76 (12.5)

40 (11.8)

36 (13.5)

 

 7–12.9

74 (12.2)

40 (11.8)

34 (12.8)

 

  ≥ 13

321 (53.0)

179 (52.7)

142 (53.4)

 

White  ≤ 2.9

337 (12.5)

233 (12.7)

104 (12.1)

14.62, .0022

 3–6.9

811 (30.0)

511 (27.8)

300 (34.8)

 

 7–12.9

911 (33.7)

649 (35.3)

262 (30.4)

 

  ≥ 13

644 (23.8)

448 (24.3)

196 (22.7)

 

aN = 3274. bCigarette butts returned, 24 h, N = 3303. cFTND coding (1–10 = 0, 11–20 = 1, 21–30 = 2, >30 = 3). dN = 3309

TES BOE

Most tobacco-specific (NE, serum cotinine and total NNAL) and non-specific BOE are significantly higher in smokers with banked biospecimens than in smokers without, except for serum cotinine, 4-ABP and MHBMA (Table 6). Metabolites of acreolein and 1,3 butadiene are significantly greater in non-users with banked biospecimens than in non-users without. All statistically significant differences in BOE by banked biospecimen availability have small effect sizes, ranging from 0.10 to 0.24. When stratified by self-identified ethnicity and race, more BOE differ significantly by biospecimen availability among non-Hispanic Whites than among non-Hispanic Blacks (Tables 7 and 8). The effect sizes of the two BOE differences in self-identified non-Hispanic Black smokers are small, and the effect size of the one BOE difference in self-identified non-Hispanic Black non-users is a medium effect size (d = 0.47). Among self-identified non-Hispanic White smokers, NE, total NNAL, carboxyhemoglobin and an acreolin metabolite, and among self-identified non-Hispanic White non-users, a 1,3 butadiene metabolite, exhibit significant differences. All these significant differences are of small effect size.
Table 6

TES laboratory-based BOEa among smokers and non-users, and among those with and without banked biospecimens

 

All

With

Without

t

P

Among smokers

     

NE per cigb

0.89 (0.7)

0.91 (0.7)

0.86 (0.7)

2.33

.020

NE mg/24 hrc

12.78 (7.9)

13.3 (7.9)

11.76 (7.8)

5.48

<.0001

Serum cotinine ng/mld

188.9 (103.4)

190.0 (103.1)

186.7 (147.7)

0.9

.37

Total NNAL ng/cige

29.4 (23.7)

30.3 (23.9)

27.8 (23.1)

3.03

.0025

Total NNAL ng/24 hrf

425.2 (303.9)

445.5 (308.2)

385.7 (291.5)

5.67

<.0001

COHb % saturationg

5.26 (2.3)

5.35 (2.3)

5.09 (2.3)

3.25

.0012

1-OHP ng/24 hrh

259.8 (345)

268.3 (343)

243.3 (349)

2.32

.02

3-HPMA ug/24 hri

1941.9 (1326)

2017.6 (1302)

1796.6 (1361)

4.73

<.0001

4-ABP pg/g Hbj

43.5 (53.6)

44.2 (56.8)

42.3 (47.1)

0.96

.34

MHBMA ug/24 hrk

3.52 (3.3)

3.60 (3.3)

3.38 (3.4)

1.87

.062

DHBMA ug/24 hrl

530.1 (276.3)

541.1 (255.7)

508.9 (311.2)

3.11

.0019

Among non-smokers

     

COHb % saturationm

1.46 (0.5)

1.46 (0.6)

1.45 (0.5)

0.08

.94

3-HPMA ug/24 hrn

461.7 (532)

484.3 (571)

416.3 (441.0)

2.14

.033

4-ABP pg/g Hbo

14.5 (70.8)

13.2 (71.5)

17.0 (69.6)

−0.68

.5

MHBMA ug/24 hrp

0.49 (1.0)

0.48 (0.8)

0.50 (1.3)

−0.16

.88

DHBMA ug/24 hrq

385.6 (172.5)

398.8 (182.3)

359.1 (147.7)

3.82

<.0001

aDefinitions (and parent compounds) of BOE from Additional file 2: Table S1: NE Nicotine Equivalents (Nicotine); Total NNAL Total 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol, and its glucuronide (4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone); COHb carboxyhemoglobin (carbon monoxide); 1-OHP Total 1-hydroxypyrene (polycyclic aromatic hydrocarbons); 3-HPMA 3-hydroxy-propylmercapturic acid (acreolein); 4-ABP 4-aminobiphenyl hemoglobin (Hb) adducts (4-aminobiphenyl); MHBMA monohydroxyl-butenylmercapturic acid (1,3 butadiene); DHBMA = dihydroxy-butyl-mercapturic acid (1,3 butadiene)

bN = 3529, d = 0.10. cN = 3535, d = 0.18. dN = 3469. eN = 3529, d = 0.11. fN = 3535, d = 0.20. gN = 3558, d = 0.11. hN = 3554, d = 0.07. iN = 3556, d = 0.17. jN = 2801. kN = 3415. lN = 3558, d = 0.11. mN = 1069. nN = 1058, d = 0.13. oN = 723. pN = 629. qN = 1074, d = 0.24

Table 7

TES Non-Hispanic Black laboratory-based BOE by smoking status, and by banked biospecimen availability

 

All

With

Without

t

P

Among smokers

     

NE per ciga

1.04 (0.8)

1.05 (0.8)

1.02 (0.9)

0.49

.6254

NE mg/24 hra

10.3 (6.7)

10.6 (6.6)

9.82 (6.7)

1.46

.1449

Serum cotinine ng/mlb

205.3 (113)

206.8 (114.5)

203.2 (111.2)

0.37

.7081

Total NNAL ng/cigc

33.4 (27.5)

34.8 (28.0)

31.5 (26.8)

1.47

.1414

Total NNAL ng/24 hr3

331.8 (233.9)

349.7 (236.9)

308.6 (228.3)

2.13

.0336

COHb % saturationd

4.74 (2.1)

4.75 (2.1)

4.74 (2.1)

0.08

.9355

1-OHP ng/24 hre

326.4 (421)

333.8 (379.8)

316.9 (469)

0.47

.6352

3-HPMA ug/24 hrf

1605 (1083)

1670 (1005)

1521 (1172)

1.64

.1012

4-ABP pg/g Hbg

39.6 (28.2)

40.1 (29.8)

39.0 (26.6)

0.41

.6831

MHBMA ug/24 hrh

2.65 (2.8)

2.9 (3.2)

2.3 (2.1)

2.75

.0062

DHBMA ug/24 hri

495.5 (270.2)

511.7 (276.9)

474.4 (260.4)

1.67

.0946

Among non-smokers

     

COHb % saturationj

1.5 (0.7)

1.49 (0.7)

1.52 (0.6)

−0.24

.8116

3-HPMA ug/24 hrk

433.5 (379.8)

458.8 (430.1)

380.3 (237.3)

1.43

.1546

4-ABP pg/g Hbl

12.9 (16.6)

14.7 (19.7)

9.24 (5.7)

1.96

.054

MHBMA ug/24 hrm

0.57 (0.9)

0.64 (1.1)

0.42 (0.6)

1.41

.1619

DHBMA ug/24 hrn

401.3 (177)

426.3 (188.3)

349.2 (138.8)

2.83

.0054

aN = 574. bN = 576. cN = 593. dN = 603. eN = 603. fN = 601. gN = 478. hN = 569. iN = 598. jN = 148. kN = 149. lN = 87. mN = 103. nN = 151

Table 8

TES non-Hispanic White laboratory-based BOE, by smoking status, and by banked biospecimen availability

 

All

With

Without

t

P

Among smokers

     

NE per ciga

0.86 (0.6)

0.88 (0.6)

0.82 (0.6)

2.4

.0166

NE mg/24 hrb

13.5 (8.1)

14.0 (8.1)

12.6 (8.1)

4.01

<.0001

Serum cotinine ng/mlc

187.1 (101.2)

188.1 (100.6)

184.9 (102.6)

0.75

.4545

Total NNAL ng/cigd

28.4 (22.4)

29.2 (22.4)

26.7 (22.1)

2.7

.0069

Total NNAL ng/24 hre

450.9 (314.2)

467.1 (315.6)

416.2 (308.5)

3.91

<.0001

COHb % saturationf

5.43 (2.3)

5.49 (2.3)

5.29 (2.4)

2.12

.0343

1-OHP ng/24 hrg

300.4 (362.3)

307.1 (361.0)

286.3 (364.7)

1.38

.1669

3-HPMA ug/24 hrh

2048 (1368)

2100 (1337)

1936 (1427)

2.89

.0039

4-ABP pg/g Hbi

45.2 (59.6)

45.7 (62.3)

44.1 (53.6)

0.6

.5475

MHBMA ug/24 hrj

3.78 (3.4)

3.76 (3.2)

3.82 (3.7)

−0.39

.6985

DHBMA ug/24 hrk

541.6 (281.2)

548.3 (252.9)

527.4 (333.4)

1.63

.1029

Among non-smokers

     

COHb % saturationl

1.44 (0.5)

1.44 (0.5)

1.44 (0.4)

0.07

.9449

3-HPMA ug/24 hrm

463 (557.3)

480.2 (595)

429.3 (474.6)

1.35

.1791

4-ABP pg/g Hbn

15.1 (78.1)

13.1 (79.3)

18.7 (76.0)

−0.83

.4085

MHBMA ug/24 hro

0.47 (1.0)

0.44 (0.7)

0.53 (1.5)

−0.71

.4782

DHBMA ug/24 hrp

386.1 (170.6)

397.1 (180.1)

364.7 (148.5)

2.8

.0053

aN = 2680. bN = 2686. cN = 2635. dN = 2667. eN = 2673. fN = 2680. gN = 2683. hN = 2681. iN = 2119. jN = 2590. kN = 2684. lN = 846. mN = 837. nN = 589. oN = 484. pN = 848

TES BOPH

The distribution of BOPH by banked biospecimen availability is shown in Table 9. Six of 29 BOPH measures have nominally significantly higher levels in TES participants with available banked biospecimens versus those without, while the respiratory function measure FVC and hemoglobin remain significantly different after false discovery rate correction (q-values = 0.0128 and 0.0496, respectively) [31]. After excluding individuals with implausible FEV1 values < 35 % or > 125 % of predicted, as suggested by Frost-Pineda et al. [3], and then stratifying by self-identified ethnicity and race, and then by smoking status, we observed that self-identified non-Hispanic White smokers with banked biospecimens have significantly increased FVC compared to those without [93.9 (23.2) vs 90.8 (17.9), t = 3.81, P = 0.0001, N = 2584]. The statistically significant increase in % predicted FVC among self-identified non-Hispanic White smokers with available biospecimens is unexpected because multiple BOE are significantly increased in self-identified non-Hispanic White smokers with banked biospecimens and lung function is expected to be reduced in individuals with increased measures of exposure. Evidence for the influence of current smoking on longitudinal decline in FEV1 and FVC suggests that current smoking influences longitudinal FEV1 decline more than FVC [32], though this would not explain an increase in FVC. We constructed another regression model including education and household income, but these potential confounders [33] had no effect on the observed differences in FVC within non-Hispanic White smokers (data not shown). Further analyses of lung function measures and other variables in the TES may identify possible explanatory factors or confounders. After stratifying by self-identified race and ethnicity, and then by smoking status, we observed that self-identified non-Hispanic White non-users exhibit a significant difference in hemoglobin by banked biospecimen availability [14.50 (1.42) vs 14.31 (1.25), t = 1.84, P = 0.033, N = 828]. Statistically significant differences in FVC and hemoglobin in these strata are small (d-values are 0.15 and 0.14, respectively).
Table 9

TES participant BOPH, by banked biospecimen availability

Analyte

With

Without

N

t

P

8-epi-prostaglandin-Fa

1745 (1000)

1657 (1056)

4557

2.74

.006

Total bilirubinb

0.48 (0.3)

0.49 (0.3)

4420

−0.92

.36

Hematocrit (%)

43.2 (4.1)

42.9 (4.1)

4560

2.61

.01

Hemoglobinc

14.7 (1.52)

14.6 (1.50)

4572

2.74

.003

Platelets (103/uL)

275.2 (70.2)

277.9 (71.0)

4527

−1.25

.21

WBC (103/uL)

7.68 (2.3)

7.61 (2.2)

4572

0.99

.32

Microalbumin (mg/24 h)

43.7 (338.3)

31.6 (188.4)

3235

1.31

.19

11-dehydrothromboxane-B21

1343.0 (931)

1277.2 (1027)

4286

2.05

.04

hs-CRP (mg/L)

4.45 (7.0)

4.25 (6.4)

4433

0.92

.36

Fibrinogenb

323.8 (75.8)

320.1 (77.7)

4498

1.56

.12

von Willebrand Factor (%)

102.8 (46.1)

100.9 (48.6)

4601

1.26

.21

Serum creatinineb

0.82 (0.2)

0.81 (0.2)

4640

0.32

.75

Blood urea nitrogenb

13.5 (4.4)

13.1 (4.5)

4640

2.39

.02

Total cholesterolb

196.8 (40.9)

194.6 (42.3)

4639

1.68

.09

HDLb

52.2 (16.3)

52.6 (16.2)

4590

−0.95

.34

LDLb

115.3 (35.0)

113.5 (37.4)

4428

1.60

.11

Triglyceridesb

153.9 (125)

148.9 (125)

4639

1.29

.19

Alkaline phosphatased

74.9 (25.3)

74.9 (26.1)

4637

−0.02

.99

Alanine aminotransferased

26.1 (24.7)

26.5 (24.1)

4617

−0.45

.65

Aspartate aminotransferased

24.8 (19.0)

25.4 (21.1)

4561

−0.96

.34

Lactate dehydrogenased

154.8 (32.8)

153.1 (32.2)

4482

1.63

.10

FEV1 (% of predicted)

85.1 (21.7)

84.7 (20.9)

4539

0.48

.63

FVC (% of predicted)

94.4 (25.1)

91.8 (22.0)

4541

3.52

.0004

Serum albuminc

4.31 (0.4)

4.30 (0.3)

4629

0.27

.79

Serum glucoseb

96.8 (30.7)

97.1 (29.8)

4626

−0.29

.77

Uric acidb

5.28 (1.5)

5.26 (1.4)

4640

0.40

.69

Diastolic BP (mmHg), Visit 1

77.0 (10.7)

76.5 (10.3)

4659

1.26

.21

Diastolic BP (mmHg), Visit 2

76.5 (10.3)

76.5 (10.2)

4660

−0.05

.96

Systolic BP (mmHg), Visit 1

123.5 (16.6)

122.6 (16.0)

4659

1.78

.07

Systolic BP (mmHg), Visit 2

121.5 (15.9)

120.8 (15.5)

4660

1.32

.19

Heart rate (bpm), Visit 1

72.7 (10.2)

72.8 (10.2)

4656

−0.12

.90

Heart rate (bpm), Visit 2

73.4 (10.3)

72.9 (10.4)

4659

1.44

.15

ang/24 hrs. bmg/dL. cg/dL. dU/L

TES participant usual cigarette brand

Information on participant’s usual cigarette brand is available from 606 and 1336 self-identified non-Hispanic Black and non-Hispanic White smokers, respectively. The top 20 brands account for 66.0 and 49.4 % of the brand information available from self-identified non-Hispanic Black and non-Hispanic White smokers, respectively (Table 10). Usual cigarette brand distributions do not differ significantly among self-identified non-Hispanic Black or among non-Hispanic White smokers by the presence or absence of banked biospecimens (Table 10).
Table 10

TES participant usual cigarette brand, by self-identified race/ethnicity and by banked biospecimen availability

Usual cigarette brand identification

All

With [N, %]

Without [N, %]

 

Self-identified non-Hispanic Black smokersa

Newport KFMHP 263101

101

51

50.5

50

49.5

Newport 100’s 100FMHP

64

35

54.7

29

43.5

Carlton menthol KFMSP

26

17

65.4

9

34.6

Newport KFMSP 278012

23

12

52.2

11

47.8

Carlton 100’s 100FHP

20

10

50.0

10

50.0

Carlton 100’s 100FSP

18

9

50.0

9

50.0

Newport 100’s 100FMSP

17

7

41.2

10

58.8

Carlton 100’s menthol 100FMSP

16

12

75.0

4

25.0

Carlton 100’s menthol 100 FMSP

15

9

60.0

6

40.0

Carlton KFSP

12

8

66.7

4

33.3

Benson & Hedges 100’s menthol 100FMSP

11

9

81.8

2

18.2

Kool filter kings KFMHP

11

7

63.6

4

36.4

Merit ultra lights KFHP

11

4

36.4

7

63.6

Kool filter king KFMSP

9

6

66.7

3

33.3

Kool super longs 100’s 100 FMHP

9

4

44.4

5

55.6

Kool super longs 100’s 100FMSP

9

5

55.6

4

44.4

Merit ultima 100’s 100FHP

8

5

62.5

3

37.5

Now 100 s menthol 100 FMSP

8

5

62.5

3

37.5

Carlton KFHP

6

4

66.7

2

33.3

Doral full flavor 100’s menthol 100FMSP

6

3

50.0

3

50.0

Total

400

222

55.5

178

44.5

 

Self-identified non-Hispanic White smokersb

Marlboro lights KFHP

227

160

70.5

67

29.5

Marlboro ultra lights KFHP

186

124

66.7

62

33.3

Marlboro KFHP

147

103

70.1

44

29.9

Marlboro ultra lights 100’s 100FHP

106

70

66.0

36

34.0

Marlboro lights 100’s 100FHP

90

68

75.6

22

24.4

Camel Turkish lights KFHP

81

55

67.9

26

32.1

Carlton 100’s 100FHP

67

46

68.7

21

31.3

Marlboro 100’s 100FHP

61

42

68.9

19

31.1

Carlton 100’s 100FSP

47

28

59.6

19

40.3

Camel filters KFHP

41

26

63.4

15

36.6

Carlton KFSP

35

26

74.3

9

25.7

Newport KFMHP 263101

33

25

75.8

8

24.2

Now 100 s 100FSP

31

24

77.4

7

22.6

Doral ultra lights 100’s 100FHP

30

19

63.3

11

36.7

Virginia slims ultra lights menthol 100FMHP

29

19

65.5

10

34.5

Benson & Hedges deluxe ultra lights 100’s 100FHP

26

12

46.2

14

53.9

Now 100 s menthol 100FMSP

26

21

80.8

5

19.2

Carlton 100’s menthol 100FMSP

25

20

80.0

5

20.0

Marlboro KFSP

24

17

70.8

7

29.2

Virginia slims ultra lights 100FHP

24

18

75.0

6

25.0

Total

1336

923

69.1

413

30.9

aχ219d.f. = 13.85, P = .79. bχ219d.f. = 18.34, P = .50

Modeling banked biospecimen availability by demographics, BOE and BOPH

Sample sizes among self-identified non-Hispanic Black and White smokers with complete data and with imputed data for the progressively more complex models were 3236 and 3318 (2.5 % of participants had missing data in Model 1), 2317 and 3318 (30.2 % of participants had missing data in Model 2), and 1090 and 3053 (64.3 % had missing data in Model 3), respectively. However, while a large fraction of the population was missing one or more variable values, on average they were only missing a single value out of a large number of independent variables. The number of missing values that were imputed was relatively small; 0.3 % of all values required imputation in Model 1, 1.9 % in Model 2, and 2.5 % in Model 3. Significant demographic variables, BOE and BOPH in progressively more complex multivariate models of banked biospecimen availability with imputed data were: Model 1) BMI, self-identified race, age and age squared; Model 2) self-identified race, age, age squared, and NE/24 h; and Model 3) self-identified race, age, age squared, NE/24 h, serum cotinine, MHBMA and FVC (Table 11). The mean (SD) predicted probabilities of banked biospecimen availability, in progressively more complex multivariate models without and with imputed data are: Model 1) 0.647 (0.069) and 0.665 (0.060); Model 2) 0.643 (0.077) and 0.668 (0.070); and Model 3) 0.625 (0.102) and 0.669 (0.095). Explanatory power estimates (r2) of the anthropometric, demographic, BOE and BOPH variables in progressively more complex multivariate models with imputed data among self-identified non-Hispanic Black and White smokers to predict banked biospecimen availability are 0.018, 0.024, and 0.037, respectively. In permutation analyses of self-identified non-Hispanic Black and White smokers with imputed data in Model 3, the mean (95 % confidence interval) r2 was 0.020 (0.016 - 0.023) suggesting that about half of the explanatory power of variables is due to random variability (0.020/0.037 = 0.54).
Table 11

Multivariate model of banked biospecimen availability, self-identified non-Hispanic black and non-Hispanic white smokers

 

β

SE

t

P

FMIa

Sex

0.191

0.123

1.550

0.121

0.009

BMIb

0.015

0.008

1.860

0.063

0.010

Self-identified race

−0.598

0.125

−4.780

0.000

0.006

Education (HS/some College)

0.001

0.146

0.010

0.993

0.011

Education (≥Bachelors)

−0.129

0.173

−0.740

0.457

0.010

Age

0.057

0.020

2.800

0.005

0.003

Age squared

−0.001

0.000

−2.930

0.003

0.003

Smoking duration

0.007

0.006

1.280

0.201

0.031

11–20 CPD

0.101

0.114

0.890

0.374

0.009

21–30 CPD

0.143

0.162

0.880

0.379

0.010

>30 CPD

0.215

0.243

0.880

0.377

0.020

CPDb

0.001

0.009

0.070

0.944

0.011

FTC tar

−0.002

0.008

−0.300

0.767

0.004

Nicotine equivalents/cigarette

−0.085

0.162

−0.530

0.599

0.042

Nicotine equivalents/24 h

0.034

0.015

2.350

0.019

0.028

Serum cotinine

−0.001

0.001

−2.070

0.038

0.027

Total NNAL/cigarette

0.006

0.005

1.220

0.224

0.034

Total NNAL/24 h

0.000

0.000

−0.190

0.849

0.025

Carboxyhemoglobin

−0.019

0.027

−0.700

0.486

0.007

1-OHP

0.000

0.000

0.000

0.999

0.005

3-HPMA

0.000

0.000

0.010

0.990

0.010

4-ABP

0.001

0.001

0.840

0.400

0.317

MHBMA

−0.030

0.015

−1.980

0.048

0.069

DHBMA

0.000

0.000

−0.820

0.410

0.006

8-epi-prostaglandin-Fb

0.000

0.000

−0.630

0.527

0.010

Total bilirubin

−0.147

0.177

−0.830

0.406

0.051

Hematocrit

0.018

0.026

0.680

0.495

0.027

Hemoglobin

−0.071

0.077

−0.930

0.351

0.024

Platelets

−0.001

0.001

−1.280

0.200

0.051

WBC

−0.007

0.020

−0.370

0.715

0.026

Microalbumin

0.000

0.000

0.960

0.341

0.409

11-dehydrothromboxane-B2

0.000

0.000

0.720

0.472

0.108

hs-CRP

0.007

0.007

1.020

0.306

0.072

Fibrinogen

0.000

0.001

0.560

0.575

0.052

von Willebrand factor

0.000

0.001

0.260

0.798

0.019

Serum creatinine

0.220

0.259

0.850

0.396

0.021

Blood urea nitrogen

−0.004

0.011

−0.360

0.720

0.005

Total cholesterol

0.021

0.138

0.160

0.877

0.030

HDL

−0.019

0.138

−0.140

0.890

0.030

LDL

−0.022

0.138

−0.160

0.872

0.030

Triglycerides

−0.004

0.028

−0.150

0.882

0.031

Alkaline phosphatase

−0.001

0.002

−0.840

0.401

0.009

Alanine aminotransferase

0.001

0.003

0.380

0.700

0.012

Aspartate aminotransferase

−0.006

0.004

−1.580

0.113

0.022

Lactate dehydrogenase

0.003

0.001

1.880

0.060

0.041

FEV1 excluding extreme valuesb

−0.003

0.003

−0.930

0.352

0.002

FVC excluding extreme valuesb

0.010

0.003

4.030

0.000

0.003

Serum albumin

0.178

0.141

1.260

0.206

0.022

Serum glucose

−0.003

0.001

−1.850

0.065

0.019

Uric acid

−0.027

0.037

−0.730

0.464

0.004

Diastolic BP, Visit 1

0.005

0.006

0.830

0.405

0.003

Diastolic BP, Visit 2

−0.005

0.006

−0.760

0.446

0.002

Systolic BP, Visit 1

0.000

0.004

0.100

0.919

0.003

Systolic BP, Visit 2

0.003

0.004

0.680

0.498

0.002

Heart rate, Visit 1

−0.007

0.005

−1.500

0.134

0.003

Heart rate, Visit 2

0.003

0.005

0.580

0.563

0.003

Constant

−2.243

0.988

−2.270

0.023

0.006

aProportion of variability in the SE due to multiple imputation. blmputation truncated at observed values

Correlations of clinical chemistry results in 47 plasma samples from the SRI CAL (2013) and those from serum reported by the CRO (2002–2003) were high and statistically significant [glucose (0.922), aspartate aminotransferase (0.993), alanine aminotransferase (0.997), total bilirubin (0.960), albumin (0.702), and total cholesterol (0.913), all p-values < 0.001] (Table 12 and Fig. 1). The lower correlation for blood albumin may be due to the two different matrices, the increased variance of some albumin clinical chemistry analysis methods [34], or the use of different methods in the clinical analyzers in the two different clinical chemistry laboratories.
Table 12

Comparison of six circulating analytes in TES participant plasma and serum

SRI CAL analyte values, plasma

CRO analyte values, serum

GLUa

ASTb

ALTc

TBId

ALBe

CHOf

GLU

AST

ALT

TBI

ALB

CHO

89

455

495

1.8

4.4

223

82

381

403

1.19

4.2

192

117

20

18

1.4

4.0

213

114

17

18

1.05

4.0

202

173

22

42

0.4

4.7

238

161

21

37

0.28

4.5

234

84

45

50

0.5

4.2

184

83

37

37

0.23

3.9

167

86

30

29

0.4

4.3

202

83

31

27

0.28

4.4

185

119

27

22

0.6

4.6

142

115

20

19

0.41

4.6

142

65

22

22

0.5

4.5

207

68

22

21

0.38

4.4

203

85

31

36

0.6

4.5

237

85

27

38

0.36

4.6

234

70

32

45

0.2

4.4

163

69

26

39

0.18

4.4

148

113

16

10

0.2

4.1

176

115

13

11

0.16

4.0

165

85

22

22

0.8

4.7

304

74

22

20

0.60

4.8

288

80

38

57

0.3

4.7

160

79

34

52

0.24

4.6

164

94

17

12

0.4

4.6

148

93

14

11

0.27

4.3

136

94

24

21

0.3

5.0

231

91

28

22

0.22

4.9

213

93

18

25

0.3

4.0

238

92

17

27

0.27

4.2

230

103

21

27

0.3

4.1

173

98

18

23

0.29

4.2

167

93

27

28

0.8

4.3

145

93

20

24

0.55

4.0

138

79

23

22

0.6

4.2

217

79

19

19

0.48

4.7

218

83

19

16

0.5

4.6

174

82

15

13

0.33

4.3

166

93

22

15

0.4

4.8

149

86

16

14

0.24

4.3

130

85

26

24

0.6

5.0

262

79

19

18

0.29

4.6

239

93

25

30

0.4

4.4

176

75

21

31

0.23

4.2

167

81

15

14

0.7

4.8

214

50

28

21

0.47

4.9

229

91

18

10

0.3

4.4

211

81

18

10

0.18

4.3

200

90

26

25

0.5

4.6

177

84

22

21

0.33

4.4

174

92

36

56

0.4

3.9

228

91

30

50

0.21

4.0

221

84

13

11

0.4

4.1

232

79

42

25

0.26

4.2

230

90

16

16

 

4.8

123

86

17

13

0.15

4.6

116

82

21

10

0.3

3.8

196

57

39

7

0.25

4.2

190

82

25

20

0.5

3.9

159

85

20

17

0.29

3.7

142

74

48

29

0.9

4.8

181

68

44

24

0.40

4.4

161

107

42

70

0.7

4.9

200

102

37

60

0.51

4.8

187

88

22

25

0.5

3.8

167

85

21

20

0.34

3.9

164

90

25

27

0.5

4.4

183

88

25

27

0.34

4.6

183

98

27

18

0.4

4.5

207

98

25

18

0.27

4.3

192

94

44

47

0.2

4.1

163

89

39

44

0.16

4.1

162

89

20

17

0.3

4.3

139

92

15

10

0.30

4.3

165

93

32

51

0.4

4.8

225

82

26

42

0.24

4.5

203

81

15

16

0.5

4.3

181

75

12

12

0.26

4.2

153

81

53

47

0.6

4.5

153

73

48

41

0.39

4.3

153

45

15

17

0.3

4.3

120

17

18

16

0.16

4.4

121

89

23

23

0.5

4.9

168

77

18

18

0.32

4.4

144

86

33

30

0.3

4.4

267

82

30

23

0.16

4.4

257

101

16

16

0.2

4.4

239

85

16

15

0.13

4.3

218

90

33

40

0.4

4.3

177

83

27

34

0.27

4.3

166

84

26

24

0.4

4.3

175

80

27

23

0.25

4.3

169

88

14

12

0.2

4.1

153

92

28

27

0.18

4.7

230

aGlucose (mg/dL). bAspartate aminotransferase (U/L). cAlanine aminotransferase (U/L). dTotal bilirubin (mg/dL). eAlbumin (g/dL). fTotal cholesterol (mg/dL)

Fig. 1
Fig. 1

Comparison of six circulating analytes in TES plasma (CAL) and serum (CRO)

Mean (SD) DNA and RNA from ~1 M cells was 4.63 (1.63) ug, and 2.10 (0.62) ug, respectively. We sent four DNA samples from Gentra Puregene extraction and 27 DNA samples from NORGEN extraction for Smokescreen Array genotyping at the Rutgers University Infinite Biologics facility. All DNA samples had genotype completion rates ≥ 97.5 % and passed the 97 % rate threshold; the mean genotype completion rate was 99.4 %. Mean (SD) RNA Integrity (RIN) scores from 28 RNA samples analyzed were 6.4 (2.2); 68 % of RIN scores were ≥ 6.0, a standard used in RNA sequence analysis [35]. There were no significant differences in sex, race, smoking status, or total nicotine equivalents between RNA samples with RIN ≥ 6.0 and < 6.0 (all p-values > 0.12). Thus, from PBMC pellets frozen at ultralow temperatures for over a decade, DNA quality and genotyping results were excellent, while RNA quality was good, but requires evaluation using transcriptome-wide methods.

Finally, we assessed statistical power to detect a priori genetic loci of interest from an example of a large-scale (1000 s) candidate gene association scan, and an example from a locus nominated by genome-wide association scans (GWAS), with genome-wide significance (GWS) as the statistical threshold. For self-identified non-Hispanic Black current smokers, we selected rs11187065 as an example, identified in the insulin-degrading enzyme gene as the gene-centric SNP most significantly associated with serum cotinine in the Coronary Artery Risk Development in Young Adults (CARDIA) study by Hamidovic et al. [36]. The influence of rs11187065 on serum cotinine was substantial with a β of −85.1 ng/ml, with mean (SE) of 236.5 (8.1) ng/ml from 365 African American smokers [36]. Mean (SD) CPD in the CARDIA sample was 10.5 (7.4), similar to that of the TES (Table 4). Using the sample size of self-identified non-Hispanic Black smokers with banked PBMCs in the TES (N = 340), there is 57 % power to detect the locus at genome-wide significance (and 83 % power to detect this locus at the original study’s Bonferroni adjustment level of 2.3 × 10−6) using an additive model, a one-sided test, the mean (SD) of serum cotinine among self-identified non-Hispanic Black smokers (Table 6), the rs11187065 minor allele frequency of 0.083 in the HapMap [37] African Americans in the Southwest sample, and the effect size from Hamidovic et al. For assessing power to detect a priori loci of interest among self-identified non-Hispanic White current smokers, we selected rs1051730 in the nicotinic acetylcholine receptor (nAChR) subunit gene cluster on chromosome 15q25.1, associated with smoking intensity and related phenotypes [38], including cotinine level [39], as an example. In an analysis of 2932 smokers with serum or plasma cotinine estimates, Munafo et al. estimated that each minor allele contributed to a mean increase in the unadjusted level of cotinine in European ancestry samples of 138.72 nmol/L [(95 % CI) 97.91 - 179.53 nmol/L, P = 2.7 × 10-11] [39], or 24.42 ng/mL, although this was reduced 18 % upon adjustment for self-reported CPD. Using the sample size of self-identified non-Hispanic White smokers with banked PBMCs in the TES (N = 1840), there is 70–96 % power to detect this locus at a genome wide significance level (5 × 10−8) using an additive model, a one-sided test, the mean (SD) of serum cotinine among self-identified non-Hispanic White smokers (Table 7), the rs1051730 minor allele frequency of 0.385 in HapMap Utah Residents with Northern and Western European Ancestry sample, and estimated allele effect sizes of Munafo et al. (adjusted and unadjusted for CPD).

Discussion

TES research opportunities

TES data and banked biospecimens, together with current biotechnologies, offer opportunities for the tobacco research community to identify behavioral, clinical, environmental and molecular factors that may influence cigarette smoke exposures (susceptibility model) and identify molecular factors that may be modulated by cigarette smoke exposures (response model). In particular, the TES can provide existing BOE and BOPH data from a large sample of generally healthy individuals, as well as banked biospecimens for the generation of novel BOE and BOPH. We will conduct biomarker research in the context of an Analysis Consortium that will enhance the TES by adding novel biomarkers and biomarker analyses to elucidate relationships between cigarette smoke exposures and health effects. We will share data with other collaborations engaged in the analyses and meta-analyses of susceptibility and response models. We will deposit data with the repositories designed for genome-wide data per Federal guidance or journal practice and conditional on Human Subjects Committee approval.

Specifically, GWAS using TES PBMC DNA may contribute to the elucidation of relationships between germline variation and self-report and laboratory measures of exposures [38, 39], including genetic loci influencing the non-nicotine tobacco-specific BOE NNAL. Analyses of TES PBMC mitochondrial DNA (mtDNA) via copy number and deletion analysis [40] may enhance knowledge of the factors that influence mtDNA damage [41, 42]. Analysis of PBMC DNA and RNA will provide additional data to examine the effects of cigarette smoking on the PBMC epigenome [43, 44] and transcriptome [45]. Analyses of the plasma and urine proteome [46, 47] and metabolome [4850], may make a contribution to the developing literature of the impact of tobacco and other exposures defining the exposome, an integrated approach to biomarker discovery for exposure and disease paradigms [51]. Validation, integration and extension of these susceptibility and response models can be conducted in independent datasets and in meta-analyses, and may contribute to the development of biomarker panels for diagnostic, prognostic and therapeutic research in tobacco-attributable disease.

There are a number of differences in the landscape of smoking behaviors, tobacco/nicotine products and tobacco control between the time in which the TES was conducted and the present day [52]. These differences include: 1) the prevalence of cigarette use in U.S. adults has declined from ~21 to ~18 %; 2) the regular use of electronic cigarettes has increased in prevalence from 0 to almost 3 %; 3) the annual spending on advertising of tobacco products in the U.S. has declined from an all-time high of $15.4 billion in 2003 to $9.6 billion in 2012; 4) there has been a substantial increase in restrictions on smoking in public places due to increased recognition of harm associated with exposure to second and third-hand smoke; and 5) the passage in 2009 of the Family Smoking Prevention and Tobacco Control Act which prohibited the use of terms in advertising related to “light” cigarettes and created a regulatory framework by which the FDA can evaluate new tobacco products prior to their marketing to the public. Even with these temporal differences, there are several similarities concerning the cigarettes themselves that are of most relevance to the present investigation of cigarette smoking and its impact on BOE and BOPH. These include: 1) despite various changes in cigarette design over the past 12 years, there is no evidence that any of these have resulted in a “safer” cigarette; 2) the amount of tar and nicotine in cigarettes has remained relatively stable since 1993; 3) the most popular brands of cigarettes smoked (see Table 10) remain the same (Marlboro, Camel, and Newport); 4) the effects of exposure to combustible tobacco products both with respect to BOE and BOPH remain the same; and 5) the health consequences of exposure to cigarette smoke (either mainstream or sidestream) including cancer, cardiovascular disease, and respiratory disease remain the same. Since the primary focus of the present investigation is on BOE and BOPH that reside within pathways resulting in negative health outcomes, the TES remains as relevant today as in 2003.

TES biospecimens provide a sample of current smokers powered at GWS to identify the chr15q25.1 nAChR loci associated with BOE (cotinine levels [39, 53, 54], and NNAL [55]). These biospecimens may provide data for future meta-analyses of BOE in both European ancestry and African ancestry samples. TES participants who are current smokers, have smoking topography data, BOE and banked biospecimens are suitable subjects for pharmacogenetic or pharmacometabolic research, e.g., to identify drug metabolizing enzyme and transporter gene associations with existing tobacco-specific BOE, or with as yet undetermined metabolic profiles in 24-h urine. TES biospecimens and data can be used to identify or replicate novel susceptibility or response models, especially in collaborative meta-analyses. Such results may be validated in larger datasets focused on the analysis of tobacco exposures, such as the Population Assessment of Tobacco and Health (PATH) study [56].

Limitations to the resource

The TES was a multi-site, cross-sectional study with collection sites distributed across the U.S. The sample has limited numbers of individuals with self-identified race other than Black or White, and has limited numbers of individuals with self-identified ethnicity of Hispanic. The diversity in geographical collection is an opportunity to evaluate region as a covariate in both cigarette smoke exposure susceptibility and response-to-tobacco models, e.g., comparing BOE by region or state. However, regional diversity also represents a challenge for future analyses due to potential confounding. Some potential confounders can be measured at a molecular level and used as a covariate in analyses, e.g., principal components of population genetic variation [57] can be evaluated by region or by state.

TES participants with banked biospecimens exhibit small statistically significant differences in demographics and biomarkers compared to TES participants without banked biospecimens. With respect to differences in demographics, participants with banked biospecimens were significantly older and more likely to self-identify as White. The smaller proportion of Black TES participants with banked biospecimens compared to White TES participants with banked biospecimens is consistent with contemporaneous observations in epidemiologic cohorts of reduced willingness to provide consent for future genetic testing in the National Health and Nutrition Examination Survey of 1999–2000 [58], and reduced willingness to provide consent for storage of DNA for future genetic testing in the Baltimore Epidemiological Catchment Area study of 2004–2005 [59], even though the TES was not a representative population-based survey based on national or local sampling. With respect to differences in exposure, smokers with banked biospecimens had increased NE per 24 h and reduced serum cotinine, consistent with the differences observed in demographic characteristics. Despite these small statistically significant differences in demographics and exposure between TES participants with and without biospecimens, TES participants with banked biospecimens can be selected by specific clinical and laboratory criteria to create defined datasets for molecular analyses.

Use and availability of TES data and biospecimens

The principal intended result of any analysis of the TES is the generation of knowledge related to smoking and health that is shared with the public health community and in the scientific peer-reviewed literature. SRI and the VTHRR agreed on the following principles regarding use of TES data and biospecimens. First, maintain the integrity of the data and samples, i.e., establish infrastructure to track and make the data and biospecimens secure. Second, ensure that potential users of the TES data and/or biospecimens are scientific researchers or organizations focused on the intended analysis goals of the TES, as assessed by education, experience, or by publication track record. Third, include terms in Material Transfer Agreements requiring recipients of data and/or samples to make reasonable efforts to publish the results of studies approved after scientific advisory committee review in the peer-reviewed scientific literature. Under data-sharing guidance for researchers using Federal (e.g., NIH) funds [6062] and an Office of Science and Technology Policy memorandum [63], scientists who generate molecular data, using array-based or high-throughput genomic technologies are obligated to submit both phenotype and molecular data to qualifying databases.

This is the first time that TES data and biospecimens will be made available to independent scientists in any life sciences area. There is a need for careful, objective scientific analysis of the resource. Consistent with the 2012 recommendation of the U.S. Institute of Medicine to incorporate an independent Tobacco Research Governance Entity [64], SRI has engaged leading experts to form a TES Scientific Advisory Board. This board will provide oversight, review and adjudication of research applications to use the TES data and biorepository resources.

Due to the large size of the TES research resource and the possibilities for integrative analyses, we emphasize our interest in collaborating with individual or groups of investigators, institutions and/or sponsors. Analysis of multiple domains of molecular signatures from TES biospecimens will elucidate the contribution of the genome to exposure susceptibility and the subsequent response of multiple –omic domains to cigarette smoke exposure. Investigators interested in collaborative or independent investigations using the TES data and biospecimens are encouraged to contact the SRI authors.

Conclusions

The TES research resource represents a sample of 4662 current cigarette smokers and tobacco product and nicotine non-users and includes: behavioral and demographic data; cigarette product characteristics; self-reported clinical data and laboratory-based BOE and BOPH; and banked biospecimens suitable for molecular analyses from >3000 participants. We identified small but statistically significantly greater self-reported measures of cigarette consumption and NE in participants who had consented to contribute biospecimens for banking and future analysis, primarily in self-identified non-Hispanic White smokers, compared to those not contributing biospecimens. The sample of TES participants with biospecimens is statistically powered to provide information on existing susceptibility biomarkers in self-identified Blacks and in self-identified Whites, and represents a well-powered resource to identify novel biomarkers of susceptibility and response to cigarette smoke exposures. The TESAC will seek support to enable research efforts to generate and contribute –omic data to research consortia and to public databases, and findings to the peer-reviewed literature. Such findings will contribute to the understanding of the relationship between cigarette smoke exposures and attributable disease.

Abbreviations

TES: 

Total Exposure Study

BOE: 

Biomarkers of exposure

BOPH: 

Biomarkers of potential harm

U.S.: 

United States

VTHRR: 

Virginia tobacco and health research repository

BMI: 

Body mass index

FTC tar: 

Federal trade commission machine-rated tar

CROs: 

Clinical research organizations

TESAC: 

TES Analysis Consortium

KEDTA: 

Potassium ethylenediaminetetraacetic acid

ACDA: 

Acid citrate dextrose solution A

PBMC: 

Peripheral blood mononuclear cell

UCSF: 

University of California San Francisco

HIPAA: 

Health insurance portability and accountability act

CAL: 

Clinical analysis laboratory

NE: 

Total nicotine equivalents

LC-MS/MS: 

Liquid chromatography-mass spectrometry-mass spectrometry

NNAL: 

4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol

NNK: 

4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone

COHb: 

Carboxyhemoglobin

1-OHP: 

1-hydroxypyrene

3-HPMA: 

3-hydroxy-propylmercapturic acid

4-ABP: 

4-aminobiphenyl

Hb: 

Hemoglobin

GC-MS: 

Gas chromatography–mass spectrometry

MHBMA: 

Monohydroxyl-butenylmercapturic acid

DHMBA: 

Dihydroxy-butyl-mercapturic acid

BRFSS: 

Behavioral Risk Factor Surveillance System

EIA: 

Enzyme immunoassay

WBC: 

White blood cell

hs-CRP: 

High-sensitivity C-reactive protein

HDL: 

High-density lipoprotein

LDL: 

Low-density lipoprotein

BP: 

Blood pressure

FEV1

Forced expiratory volume in 1 second

FVC: 

Forced expiratory vital capacity

CPD: 

Cigarette per day

N: 

Total number

SD: 

Standard deviation

CARDIA: 

Coronary artery risk development in young adults

GWAS: 

Genome-wide association study

NHANES: 

National health and nutrition examination survey

nAChR: 

Nicotinic acetylcholine receptor

PATH: 

Population assessment of tobacco and health

IOM: 

Institute of medicine

Declarations

Acknowledgements

We thank the following individuals for helping us get to this stage: Robert T Skunda, Krishna Kodukula, Jocelyn To, Walter Moos, Ian Colrain, Joe Rogers, Laleh Shayesteh, Naseem Chini, Denise Nishita, Vinu Rathee, Jennifer Miller, Gabrielle Leblanc, Joe Perrone, Greg Stauber, Hua Lin, Tom Shaler, Lauren Haberland and Rachel Taketa. The Center for Advanced Drug Research, now SRI Shenandoah Valley, houses TES biospecimens and was established with support from the Commonwealth of Virginia to SRI. We acknowledge funding from SRI International. Andrew W Bergen acknowledges funding from the National Institute of Drug Abuse (DA033813, PI: Bergen). SRI International and the National Institute of Drug Abuse played no role in the design, collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to submit the manuscript for publication. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Center for Health Sciences, SRI International, 333 Ravenswood Avenue, Menlo Park, CA 94025, USA
(2)
Stanford Prevention Research Center, Stanford University School of Medicine, Palo Alto, CA 94305, USA
(3)
Department of Psychiatry and Neurobehavioral Sciences, University of Virginia, Charlottesville, VA 22911, USA
(4)
BioRealm, LLC, Culver City, CA 90230, USA
(5)
University of Nevada, Las Vegas, NV 89154, USA
(6)
Venebio Group, Richmond, VA 23225, USA

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© Bergen et al. 2015

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