Exposure to polycyclic aromatic hydrocarbons, volatile organic compounds, and tobacco-specific nitrosamines and incidence of esophageal cancer

Arash Etemadi; Hossein Poustchi; Cindy M Chang; Antonia M Calafat; Benjamin C Blount; Deepak Bhandari; Lanqing Wang; Gholamreza Roshandel; Apostolos Alexandridis; Julianne Cook Botelho; Baoyun Xia; Yuesong Wang; Connie S Sosnoff; Jun Feng; Mahdi Nalini; Masoud Khoshnia; Akram Pourshams; Masoud Sotoudeh; Mitchell H Gail; Sanford M Dawsey; Farin Kamangar; Paolo Boffetta; Paul Brennan; Christian C Abnet; Reza Malekzadeh; Neal D Freedman

doi:10.1093/jnci/djad218

. 2023 Oct 19;116(3):379–388. doi: 10.1093/jnci/djad218

Exposure to polycyclic aromatic hydrocarbons, volatile organic compounds, and tobacco-specific nitrosamines and incidence of esophageal cancer

Arash Etemadi ^1,^2,^✉, Hossein Poustchi ³, Cindy M Chang ⁴, Antonia M Calafat ⁵, Benjamin C Blount ⁶, Deepak Bhandari ⁷, Lanqing Wang ⁸, Gholamreza Roshandel ⁹, Apostolos Alexandridis ¹⁰, Julianne Cook Botelho ¹¹, Baoyun Xia ¹², Yuesong Wang ¹³, Connie S Sosnoff ¹⁴, Jun Feng ¹⁵, Mahdi Nalini ¹⁶, Masoud Khoshnia ¹⁷, Akram Pourshams ¹⁸, Masoud Sotoudeh ¹⁹, Mitchell H Gail ²⁰, Sanford M Dawsey ²¹, Farin Kamangar ²², Paolo Boffetta ^23,²⁴, Paul Brennan ²⁵, Christian C Abnet ²⁶, Reza Malekzadeh ²⁷, Neal D Freedman ²⁸

¹ Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

² Digestive Oncology Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran

³ Liver and Pancreaticobilliary Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran

⁴ Center for Tobacco Products, Food and Drug Administration, Silver Spring, MD, USA

⁵ Division of Laboratory Sciences, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA, USA

⁶ Division of Laboratory Sciences, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA, USA

⁷ Division of Laboratory Sciences, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA, USA

⁸ Division of Laboratory Sciences, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA, USA

⁹ Golestan Research Center of Gastroenterology and Hepatology, Golestan University of Medical Sciences, Gorgan, Iran

¹⁰ Center for Tobacco Products, Food and Drug Administration, Silver Spring, MD, USA

¹¹ Division of Laboratory Sciences, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA, USA

¹² Division of Laboratory Sciences, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA, USA

¹³ Division of Laboratory Sciences, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA, USA

¹⁴ Division of Laboratory Sciences, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA, USA

¹⁵ Division of Laboratory Sciences, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA, USA

¹⁶ Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

¹⁷ Golestan Research Center of Gastroenterology and Hepatology, Golestan University of Medical Sciences, Gorgan, Iran

¹⁸ Digestive Oncology Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran

¹⁹ Digestive Oncology Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran

²⁰ Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

²¹ Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

²² Department of Biology, School of Computer, Mathematical and Natural Sciences, Morgan State University, Baltimore, MD, USA

²³ Stony Brook Cancer Center, Stony Brook University, Stony Brook, NY, USA

²⁴ Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy

²⁵ International Agency for Research on Cancer, Lyon, France

²⁶ Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

²⁷ Digestive Oncology Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran

²⁸ Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

^✉

Correspondence to: Arash Etemadi, MD, PhD, Division of Cancer Epidemiology and Genetics, National Cancer Institute, 9609 Medical Center Dr, Rockville, MD 20850, USA (e-mail: arash.etemadi@nih.gov).

Roles

Arash Etemadi: MD, PhD, Conceptualization, Data curation, Formal analysis, Methodology, Project administration, Supervision, Validation, Writing - original draft, Writing - review & editing

Hossein Poustchi: MD, PhD, Data curation, Project administration, Resources, Writing - review & editing

Cindy M Chang: PhD, Funding acquisition, Methodology, Resources, Writing - review & editing

Antonia M Calafat: PhD, Data curation, Investigation, Resources, Writing - review & editing

Benjamin C Blount: PhD, Data curation, Methodology, Project administration, Supervision, Writing - review & editing

Deepak Bhandari: PhD, Data curation, Writing - review & editing

Lanqing Wang: PhD, Data curation, Writing - review & editing

Gholamreza Roshandel: MD, PhD, Data curation, Investigation, Project administration, Writing - review & editing

Apostolos Alexandridis: PhD, Writing - review & editing

Julianne Cook Botelho: PhD, Data curation, Methodology, Writing - review & editing

Baoyun Xia: PhD, Data curation, Writing - review & editing

Yuesong Wang: PhD, Data curation, Writing - review & editing

Connie S Sosnoff: PhD, Data curation, Writing - review & editing

Jun Feng: PhD, Data curation, Writing - review & editing

Mahdi Nalini: MD, PhD, Data curation, Methodology, Writing - review & editing

Masoud Khoshnia: MD, Data curation, Writing - review & editing

Akram Pourshams: MD, Data curation, Writing - review & editing

Masoud Sotoudeh: MD, Data curation, Writing - review & editing

Mitchell H Gail: PhD, Formal analysis, Methodology, Validation, Writing - review & editing

Sanford M Dawsey: MD, Project administration, Writing - review & editing

Farin Kamangar: MD, PhD, Conceptualization, Supervision, Writing - review & editing

Paolo Boffetta: MD, Project administration, Supervision, Writing - review & editing

Paul Brennan: PhD, Funding acquisition, Resources, Writing - review & editing

Christian C Abnet: MPH, PhD, Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Supervision, Writing - review & editing

Reza Malekzadeh: MD, Data curation, Funding acquisition, Project administration, Resources, Supervision, Writing - review & editing

Neal D Freedman: MPH, PhD, Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Validation, Writing - original draft, Writing - review & editing

PMCID: PMC10919344 PMID: 37856326

Abstract

Background

Studying carcinogens in tobacco and nontobacco sources may be key to understanding the pathogenesis and geographic distribution of esophageal cancer.

Methods

The Golestan Cohort Study has been conducted since 2004 in a region with high rates of esophageal squamous cell carcinoma. For this nested study, the cases comprised of all incident cases by January 1, 2018; controls were matched to the case by age, sex, residence, time in cohort, and tobacco use. We measured urinary concentrations of 33 exposure biomarkers of nicotine, polycyclic aromatic hydrocarbons, volatile organic compounds, and tobacco-specific nitrosamines. We used conditional logistic regression to calculate odds ratios (ORs) and 95% confidence intervals for associations between the 90th vs the 10th percentiles of the biomarker concentrations and incident esophageal squamous cell carcinoma.

Results

Among individuals who did not currently use tobacco (148 cases and 163 controls), 2 acrolein metabolites, 2 acrylonitrile metabolites, 1 propylene oxide metabolite, and one 1,3-butadiene metabolite were significantly associated with incident esophageal squamous cell carcinoma (adjusted odds ratios between 1.8 and 4.3). Among tobacco users (57 cases and 63 controls), metabolites of 2 other volatile organic compounds (styrene and xylene) were associated with esophageal squamous cell carcinoma (OR = 6.2 and 9.0, respectively). In tobacco users, 2 tobacco-specific nitrosamines (NNN and N’-Nitrosoanatabine) were also associated with esophageal squamous cell carcinoma. Suggestive associations were seen with some polycyclic aromatic hydrocarbons (especially 2-hydroxynaphthalene) in nonusers of tobacco products and other tobacco-specific nitrosamines in tobacco users.

Conclusion

These novel associations based on individual-level data and samples collected many years before cancer diagnosis, from a population without occupational exposure, have important public health implications.

Esophageal cancer is the eighth most common cancer worldwide and one of the major causes of cancer death across the globe because of its poor prognosis (1). In 2020, the number of new cases was estimated to be 604 100 worldwide, with 544 076 deaths (2). Esophageal squamous cell carcinoma is the most common histological subtype of esophageal cancer and has a distinct geographic pattern, with high rates found in the Asian esophageal cancer belt, East Africa, and parts of South America (3). In low-incidence areas, tobacco and alcohol use are the main risk factors for esophageal squamous cell carcinoma, but in high-incidence areas other risk factors (eg, environmental pollution, opiate use, nutritional deficiencies, and hot drinks) play important roles (4). Studying toxicants and carcinogens from tobacco and nontobacco sources may be key to understanding the pathogenesis and geographic distribution of esophageal squamous cell carcinoma.

Tobacco can cause cancer by exposing users to high concentrations of carcinogens and toxicants, among other mechanisms (5). Some of these compounds are unique to tobacco products (eg, tobacco-specific nitrosamines), whereas other compounds such as polycyclic aromatic hydrocarbons and volatile organic compounds, produced as a consequence of incomplete combustion, are found in tobacco smoke and other sources. Understanding the role of individual smoke-related carcinogens in disease etiology is important both for mechanistic understanding and informing regulation (6). This is best achieved by studying people who actively use tobacco and those who do not, as carcinogens can exert effects regardless of exposure source. However, only a few prospective studies have investigated the associations of particular smoke-related biomarkers and esophageal squamous cell carcinoma incidence among tobacco users and nonusers (7). This is partly because most smoke-related carcinogens are optimally assessed via urinary biomarker studies. Yet, only a few prospective cohort studies have collected urine, and even fewer such studies have been conducted in populations at high risk for esophageal squamous cell carcinoma.

The Golestan Cohort Study (GCS) is a prospective cohort study that collected urine samples from a large sample of the general population (8). The site of this study, Golestan, Iran, is a region with one of the highest rates of esophageal squamous cell carcinoma in the world. Prior works in the cohort and in the larger region indicate that the population is highly exposed to combustion byproducts including polycyclic aromatic hydrocarbons and volatile organic compounds from tobacco and nontobacco sources, such as opiate use (9-11). As such, the cohort serves as a unique setting for a nested study to rigorously evaluate associations of smoke-related carcinogens and toxicants with esophageal squamous cell carcinoma.

Methods

GCS recruited 50 045 people aged 40-75 years from January 2004 to June 2008, who are being followed actively for cancer incidence and cause-specific mortality since recruitment. The protocol for outcome detection and confirmation has been described before (12). At baseline, all participants completed a comprehensive questionnaire and provided spot urine samples. Trained interviewers administered the GCS questionnaire that included demographic information and years of formal education, in addition to detailed questions about tobacco use (cigarette, waterpipe [hookah], chewed tobacco [nass]), opiate use, and household fuel use for heating and cooking (natural gas, kerosene, and biomass). A wealth score was calculated from appliance ownership and home size, using multiple correspondence analyses (13). Physical examinations included counting the number of teeth missing in oral examination and measurement of body weight and height using standard protocols. Body mass index (BMI) was calculated as weight (in kilograms) divided by height (in meters) squared. Tea intake and favored tea temperature was measured during the interview as described previously (14). All participants provided written informed consent. GCS has been approved by appropriate ethics committees at the Tehran University of Medical Sciences, National Cancer Institute, and the International Agency for Research on Cancer (IARC).

Nested cases and controls

For the present study, cases comprised of all individuals who were diagnosed as having an incident esophageal cancer, reported through annual follow-up calls, by January 1, 2018, and confirmed as having esophageal squamous cell carcinoma through linkage to the provincial cancer registry, with urine samples available in the National Cancer Institute biorepository. For each case, we randomly selected a control from cohort participants who were alive and upper gastrointestinal (esophageal and gastric) cancer-free at the time of case diagnosis (risk-set sampling), individually matched to the case by age group (44 years and younger, 45-49, 50-54, 55-59, 60-64, 65-69, and 70 years and older), sex, place of residence (urban or rural), time of enrollment in the cohort, and tobacco use at baseline. Because our objective was to examine biomarker-cancer associations in current tobacco users and nonusers separately, we matched cases and controls on the basis of 3 common types of tobacco products (cigarettes, waterpipe, and nass) and period of use (never, former, current). Current use of tobacco was defined as the use of 1 of the 3 types of tobacco at least once a week including the year before enrollment in the study, and former use meant that the individual had quit at least 1 year before the study. The analytical dataset included 205 esophageal squamous cell carcinoma cases and 226 controls after the exclusions detailed in Supplementary Figure 1 (available online).

Laboratory analyses

We measured urinary concentrations of 4 classes of exposure biomarkers: tobacco alkaloids, polycyclic aromatic hydrocarbons, volatile organic compounds, and tobacco-specific nitrosamines. Table 1 shows the full list of urinary biomarkers: 2 nicotine metabolites, 7 metabolites of polycyclic aromatic hydrocarbons, 20 volatile organic compounds (15), and 4 tobacco-specific nitrosamines. Because the concentrations of tobacco-specific nitrosamines are commonly below the limit of detection in individuals with low or undetectable concentrations of urinary cotinine, we first tested all urine samples with cotinine immunoassay test strips (Acro Biotech, Montclair, CA, USA) (16) and measured tobacco-specific nitrosamines only in samples with a cotinine-positive result above a 50 ng/mL cutoff. Details of laboratory methods are explained in the Supplementary Material (available online). Two sets of quality control samples (smoker and nonsmoker) were used to calculate the coefficients of variation of the methods, with all coefficients of variation in both pools below 20%, showing excellent method performance (see Table 1).

Table 1.

Metabolites used in the biomarker panel developed by the Centers for Disease Control and Prevention National Center for Environmental Health

Biomarker class	Full biomarker name	Parent compound	Abbreviation	CV (%)^c
Biomarker class	Full biomarker name	Parent compound	Abbreviation	Pool 1	Pool 2
Nicotine metabolites	Cotinine	Nicotine	COTT	5.3	3.2
Nicotine metabolites	trans-3’-hydroxycotinine	Nicotine	HCTT	5.3	5.3
Metabolites of polycyclic aromatic hydrocarbons	1-hydroxynaphthalene	Naphthalene and carbaryl^a	1-nap	NA^b	3.5
	2-hydroxynaphthalene	Naphthalene	2-nap	7.4	3.5
	1-hydroxyphenanthrene	Phenanthrene	1-phe	4.0	4.0
	Sum of 2- and 3-hydroxyphenanthrene	Phenanthrene	∑_2,3phe	4.5	3.1
	2-hydroxyfluorene	Fluorene	2-flu	3.8	3.5
	3-hydroxyfluorene	Fluorene	3-flu	4.2	3.9
	1-hydroxypyrene	Pyrene	1-pyr	13.0	5.0
Metabolites of volatile organic compounds	2-methylhippuric acid	Xylene	2MHA	17.3	14.4
	3-methylhippuric acid + 4 methylhippuric acid	Xylene	3MHA and 4MHA	6.6	3.9
	N-acetyl-S-(2-carbamoylethyl)-L-cysteine	Acrylamide	2CaEMA	7.8	6.4
	N-acetyl-S-(1-carbamoyl-2-hydroxyethyl)-L-cysteine	Acrylamide	1CaHEMA	NA^b	3.9
	N-acetyl-S-(1-cyano-2-hydroxyethyl)-L-cysteine	Acrylonitrile	1CyHEMA	NA^b	4.5
	N-acetyl-S-(2-cyanoethyl)-L-cysteine	Acrylonitrile	2CyEMA	13.3	6.3
	N-acetyl-S-(2-hydroxyethyl)-L-cysteine	Acrylonitrile, ethylene oxide	2HEMA	16.3	11.0
	N-acetyl-S- (2-carboxyethyl)-L-cysteine	Acrolein	2CoEMA	5.9	6.0
	N-acetyl-S- (3-hydroxypropyl)-L-cysteine	Acrolein	3HPMA	13.0	10.7
	N-acetyl-S-(benzyl)-L-cysteine	Toluene^a	BzMA	6.0	4.8
	Mandelic acid	Styrene	MADA	12.0	10.9
	Phenylglyoxylic acid	Ethylbenzene and styrene	PhGA	7.6	8.0
	N-acetyl-S-(phenyl)-L-cysteine	Benzene	PhMA	15.6	11.7
	N-acetyl-S- (2-hydroxypropyl)-L-cysteine	Propylene oxide	2HPMA	13.0	10.7
	N-acetyl-S-(N-methylcarbamoyl)-L-cysteine	Dimethylformamide^a	MCaMA	15.1	6.5
	N-acetyl-S- (3,4-dihydroxybutyl)-L-cysteine	1,3-Butadiene	34HBMA	5.4	4.3
	N-acetyl-S-(4-hydroxy-2-buten-1-yl)-L-cysteine	1,3-Butadiene	4HBeMA	12.4	4.5
	N-acetyl-S-(3-hydroxy-1-methylpropyl)-L-cysteine	Crotonaldehyde	3HMPMA	4.2	3.6
	N-acetyl-S-(4-hydroxy-2-methyl-2-buten-1-yl)-L-cysteine	Isoprene	4HMBeMA	19.2	17.3
	2-thioxothiazolidine-4-carboxylic acid	Carbon disulfide	TTCA	10.9	NA^b
Tobacco specific nitrosamines	N′-nitrosoanabasine	NAB	NABT^d	NA	3.7
	N′-nitrosoanatabine	NAT	NATT^d	NA	3.0
	4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol	NNK	NNAL^d	NA	4.9
	N′-nitrosonornicotine	NNN	NNNT^d	NA	19.0

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Multiple other parent chemicals can also be metabolized to these compounds. CV = coefficient of variation.

CV could not be calculated because measurements were below limit of detection.

Pool 1: nonusers of tobacco; pool 2: tobacco users.

Measured only among those with a positive cotinine strip test.

Statistical analysis

For each biomarker, concentrations below the limit of detection were replaced by the limit of detection value divided by the square root of 2 (17). All biomarker concentrations were then divided by urinary creatinine to adjust for urinary concentration and log-transformed to conform to a normal distribution. Geometric means and 95% confidence intervals (CIs) of creatinine-corrected concentrations were calculated for tobacco users and nonusers. We calculated odds ratios (ORs) and 95% confidence intervals for associations between exposure biomarkers and incident esophageal squamous cell carcinoma in current tobacco users and “individuals who did not currently use tobacco at baseline” (current nonusers) using conditional logistic regression, conditioned on case-control matching. Odds ratios were compared at the 90th vs the 10th percentiles of the log-relative biomarker concentrations [ie, OR = e ^β(x90-x10)], where β is the conditional logistic regression coefficient and (X10, X90) are the 10th and 90th percentiles of the log-biomarker concentrations, respectively. In this method, each log-metabolite is divided by a constant to transform the biomarker values into a new unit, which is equal to a change from the 10th to the 90th percentile, without changing other model properties (statistical significance and goodness of fit). Although the whole data are used in the analysis, the model estimates should be interpreted as the outcome odds ratio for the highest vs the lowest tenth of the log-metabolite values (18). We built 2 sets of models in each stratum of current tobacco use; base models were adjusted for known risk factors in this population: ethnicity, education, wealth score, BMI, tea temperature, and tooth loss. The second set of models were further adjusted for urinary cotinine as an indicator of recent tobacco exposure intensity and opium use, which had been identified in our previous work as important sources of carcinogen exposure in this cohort (11). Inclusion of heating and/or cooking fuel type and dietary factors did not change estimates more than 10%, so these variables were not included in the final models.

Results

Cases and controls were similar in most baseline characteristics including matching factors (age, sex, place of residence, and tobacco use) and opium use (Table 2). Among cases, there were slightly more people with Turkmen ethnicity (86.8% vs 78.3%) and without formal education (85.4% vs 78.3%). Cases were also more likely to be in the 2 low quartiles of wealth score and had more tooth loss than controls, although these differences did not reach statistical significance. There were significantly more individuals with a BMI below 25 kg/m² (62.0% vs 50.9%) among cases, and more cases drank hot tea at temperatures above 60°C (68.3% vs 57.1%) compared with controls.

Table 2.

Baseline characteristics of esophageal squamous cell carcinoma cases and matched controls in the Golestan Cohort Study^a

Characteristics		Controls Raw No. (%) (n = 226)	Esophageal cancer cases Raw No. (%) (n = 205)
Mean age (SD)		59.2 (9.0)	59.7 (9.1)
Sex	Female	101 (44.7)	90 (43.9)
Sex	Male	125 (55.3)	115 (56.1)
Ethnicity	Turkmen	177 (78.3)	178 (86.8)
Ethnicity	Non-Turkmen	49 (21.7)	27 (13.2)
Residence	Urban	18 (8.0)	16 (7.8)
Residence	Rural	208 (92.0)	189 (92.2)
Education	No formal education	177 (78.3)	175 (85.4)
Education	Some education	49 (21.7)	30 (14.6)
BMI^b	Normal or underweight (BMI < 25 kg/m²)	115 (50.9)	127 (62.0)
	Overweight (BMI 25-29.9 kg/m²)	78 (34.5)	50 (24.4)
	Obese (BMI ≥ 30 kg/m²)	33 (14.6)	28 (13.7)
Wealth score, quartiles	1st, lowest	81 (35.8)	78 (38.1)
	2nd	53 (23.4)	59 (28.8)
	3rd	60 (26.6)	40 (19.5)
	4th, highest	32 (14.2)	28 (13.7)
Cigarette use	Never	169 (74.8)	160 (78.0)
	Past	19 (8.4)	12 (5.9)
	Current	38 (16.8)	33 (16.1)
Hookah use, waterpipe	Never	223 (98.7)	204 (99.5)
	Past	3 (1.3)	1 (0.5)
	Current	0	0
Nass use, chewed tobacco	Never	196 (86.7)	178 (86.8)
	Past	2 (0.9)	1 (0.5)
	Current	28 (12.4)	26 (12.7)
Any current tobacco use	Yes	63 (27.9)	57 (27.8)
Any current tobacco use	No	163 (72.1)	148 (72.2)
Opium use	Never	172 (76.1)	157 (76.6)
Opium use	Ever	54 (23.9)	48 (23.4)
Tea temperature^b	<60ºC	97 (42.9)	65 (31.7)
Tea temperature^b	≥60ºC	129 (57.1)	140 (68.3)
Tooth loss, quintiles	Expected or fewer	52 (23.0)	33 (16.1)
	Q1	29 (12.8)	32 (15.6)
	Q2	47 (20.8)	49 (23.9)
	Q3	54 (23.9)	43 (21.0)
	Q4	44 (19.5)	48 (23.4)
Fuel type used for heating and cooking	Kerosene	174 (77.0)	151 (73.7)
	Natural gas	13 (5.8)	10 (4.9)
	Other	36 (16.0)	41 (19.9)
	Unknown	3 (1.2)	3 (1.5)

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BMI = body mass index; Q = quintiles.

P < .05.

After excluding individuals with cotinine levels discordant with self-reported tobacco use, the tobacco current nonuser group had 148 confirmed esophageal squamous cell carcinoma cases and 163 controls, and the tobacco-user group had 57 confirmed cases and 63 controls. Table 3 shows the geometric means (95% CI) of the exposure biomarkers in cases and controls stratified by current tobacco use. All polycyclic aromatic hydrocarbons were significantly higher in individuals using tobacco, regardless of being a case or a control. Among volatile organic compounds, all were significantly higher among current tobacco users, except for PhGA, PhMA, and TTCA. BzMA concentrations were substantially higher among controls who used tobacco than among cases who used tobacco or cases and controls who did not use tobacco. Polycyclic aromatic hydrocarbon biomarkers (except 1-hydroxynapththalene) were also significantly higher among nontobacco users who lived in rural areas compared with residents of urban areas (Supplementary Table 1, available online). Such difference between rural and urban residents was not observed among individuals who used tobacco.

Table 3.

Geometric means and 95% confidence intervals of study biomarkers among esophageal squamous cell carcinoma cases and controls stratified by tobacco use in Golestan Cohort Study

Biomarkers	Do not currently use tobacco		Currently use tobacco
	Controls Geometric means (95% CI)	Esophageal cancer cases Geometric means (95% CI)	Controls Geometric means (95% CI)	Esophageal cancer cases Geometric means (95% CI)
	(n = 163)	(n = 148)	(n = 63)	(n = 57)
Nicotine metabolites, ng/mg creatinine
COTT	1.3 (1.0 to 1.5)	1.1 (0.9 to 1.4)	3733.4 (3001.7 4643.3)^b	3381.8 (2628.4 to 4351.1)^b
HCTT	2.6 (2.1 to 3.2)	2.3 (1.8 to 2.8)	6065.6 (4778.8 to 7699.0)^b	5800.3 (4459.9 to 7543.5)^b
Polycyclic aromatic hydrocarbons, ng/g creatinine
1-nap	11 032.5 (9417.5 to 12 924.5)	11 966.9 (9974.3 to 14 357.6)	17 300.7 (13 867.9 to 21 583.2)^a	17 346.1 (14 133.3 to 21 289.2)^a
2-nap	2198.5 (1909.5 to 2531.3)	2563.8 (2206.4 to 2979.0)	8846.1 (7003.7 to 11 173.1)^b	8621.3 (6616.5 to 11 233.6)^b
1-phe	258.8 (234.5 to 285.8)	243.2 (220.5 to 268.1)	341.2 (300.1 to 387.9)^b	372.5 (326.6 to 424.9)^b
∑_2,3phe	417.9 (351.5 to 496.8)	422.9 (355.0 to 503.7)	1359.3 (955.5 to 1933.7)^b	1241.7 (860.6 to 1791.6)^b
2-flu	453.8 (403.0 to 511.1)	472.5 (417.1 to 535.3)	1021.9 (845.5 to 1235.0)^b	1134.4 (931.6 to 1381.2)^b
3-flu	217.5 (182.8 to 258.7)	246.3 (206.1 to 294.4)	1049.2 (784.0 to 1404.1)^b	1083.1 (810.1 to 1448.1)^b
1-pyr	406.4 (359.5 to 459.4)	430.9 (379.3 to 489.4)	757.4 (640.3 to 895.9)^b	759.6 (634.8 to 908.9)^b
Volatile organic compounds, µg/g creatinine
2MHA	81.0 (67.1 to 97.9)	79.6 (66.1 to 95.9)	119.3 (92.1 to 154.5)^a	163.9 (121.4 to 221.2)^b
3MHA and 4MHA	336.3 (283.7 to 398.6)	318.3 (267.2 to 379.3)	600.4 (463.7 to 777.4)^b	749.9 (552.3 to 1018.1)^b
2CaEMA	45.0 (40.0 to 50.6)	47.9 (42.2 to 54.4)	145.5 (115.0 to 184.3)^b	128.2 (97.3 to 168.9)^b
1CaHEMA	10.3 (9.2 to 11.5)	10.6 (9.3 to 12.0)	21.7 (17.8 to 26.3)^b	19.6 (16.1 to 23.8)^b
1CyHEMA	2.4 (2.1 to 2.7)	2.7 (2.3 to 3.1)	12.3 (8.8 to 17.0)^b	12.8 (8.8 to 18.4)^b
2CyEMA	1.2 (1.0 to 1.5)	1.5 (1.2 to 1.9)	35.7 (22.5 to 56.7)^b	33.7 (19.1 to 59.3)^b
2HEMA	1.4 (1.2 to 1.6)	1.6 (1.4 to 1.8)	3.2 (2.5 to 4.0)^a	3.1 (2.3 to 4.1)^a
2CoEMA	74.5 (68.3 to 81.4)	82.2 (74.5 to 90.6)	152.6 (125.6 to 185.5)^b	157.9 (120.8 to 206.5)^b
3HPMA	204.7 (185.0 to 226.6)	231.3 (209.0 to 256.1)	533.2 (415.9 to 683.6)^b	603.3 (437.1 to 832.9)^b
BzMA	5.2 (4.6 to 6.0)	5 (4.4 to 5.7)	7.7 (6.0 to 9.8)	4.6 (3.7 to 5.8)
MADA	213.2 (196.6 to 231.1)	221.7 (204.5 to 240.4)	361.5 (316.3 to 413.3)^b	424.4 (367.8 to 489.7)^b
PhGA	96.9 (82.6 to 113.6)	99.6 (86.4 to 114.7)	115.1 (88.7 to 149.3)	112.6 (85.9 to 147.6)
PhMA	1.3 (1.1 to 1.4)	1.3 (1.2 to 1.5)	1.5 (1.2 to 1.7)	1.5 (1.2 to 1.7)
2HPMA	22.8 (20.8 to 25.0)	26.3 (23.6 to 29.4)	42.6 (34.1 to 53.2)^a	41.0 (31.5 to 53.3)^a
MCaMA	104.1 (92.6 to 117.0)	110.5 (98.7 to 123.8)	395.0 (324.5 to 480.7)^b	377.8 (292.5 to 487.9)^b
34HBMA	293.8 (271.5 to 317.8)	312.7 (289.5 to 337.8)	374.7 (332.1 to 422.7)^a	370.2 (312.5 to 438.5)^a
4HBeMA	4.9 (4.5 to 5.4)	5.1 (4.6 to 5.7)	17.0 (12.8 to 22.5)^b	15.5 (11.0 to 22.0)^b
3HMPMA	178.5 (156.2 to 204.0)	168.5 (143.1 to 198.4)	460.7 (327.8 to 647.5)^b	384.6 (263.0 to 562.4)^b
4HMBeMA	3.3 (3.1 to 3.5)	3.6 (3.3 to 3.9)	15.6 (10.9 to 22.3)^b	16.0 (10.8 to 23.7)^b
TTCA	12.5 (10.7 to 14.6)	14.2 (12.0 to 17.0)	9.7 (8.2 to 11.4)	12.0 (9.8 to 14.8)
Tobacco-specific nitrosamines, pg/mg creatinine
NABT	NA	NA	12.1 (9.1 to 16.2)	12.7 (9.1 to 17.7)
NATT	NA	NA	55.4 (41.9 to 73.2)	61.2 (43.4 to 86.4)
NNAL	NA	NA	153.0 (121.4 to 192.9)	192.8 (152.0 to 244.5)
NNNT	NA	NA	7.4 (5.6 to 9.8)	8.0 (6.3 to 10.2)

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P < .05. CI = confidence interval.

P < .01 in tobacco users compared with nonusers.

When comparing incident cases and controls who did not currently use tobacco at baseline, most polycyclic aromatic hydrocarbons, especially the 2 hydroxynaphthalene metabolites, were higher in cases compared with controls, but none of these differences were statistically significant (Table 4). Four volatile organic compound biomarkers (2HEMA: an acrylonitrile metabolite; 2CoEMA: an acrolein metabolite; 2HPMA: a propylene oxide metabolite; and 34HBMA: a 1,3-butadiene metabolite) were significantly associated with incident esophageal squamous cell carcinoma in the base models. These 4 volatile organic compound biomarkers were still significantly associated with esophageal squamous cell carcinoma after adjusting for opium use and urinary cotinine (2HEMA: OR_{90th vs10th} = 2.3, 95% CI = 1.1 to 5.1; 2CoEMA: OR_{90th vs 10th} = 2.3, 95% CI = 1.1 to 4.6; 2HPMA: OR_{90th vs 10th} = 2.8, 95% CI = 1.3 to 5.8; 34HBMA: OR_{90th vs 10th} = 2.1, 95% CI = 1.1 to 4.1; all P < .05). Additionally, another metabolite of acrylonitrile (2CyEMA: OR_{90th vs 10th} = 4.3, 95% CI = 1.4 to 13.5; P < .05) and one more acrolein metabolite (3HPMA: OR_{90th vs 10th} = 1.8, 95% CI = 1.0 to 3.5; P < .05) were significantly associated with incident esophageal squamous cell carcinoma in fully adjusted models. Supplementary Table 2 (available online) shows the distribution of cases and controls who did not use tobacco by quartiles of biomarker concentrations among controls and adjusted odds ratios for each of the 3 highest quartiles compared with the first quartile. The models confirmed the above findings, with an additional significant association between the highest quartile of 2-hydroxynaphthalene and esophageal squamous cell carcinoma (2-nap: OR_{q4 vs q1} = 2.9, 95% CI = 1.1 to 7.6; P < .05).

Table 4.

Odds ratios (95% CI) for the associations between study biomarkers (90th vs 10th percentile) and esophageal squamous cell carcinoma stratified by tobacco use in Golestan Cohort Study

Biomarkers	Do not currently use tobacco		Currently use tobacco
	OR (95% CI)		OR (95% CI)
	Base model^a	Full model^b	Base model^a	Full model^b
Nicotine metabolites
COTT	0.8 (0.4 to 1.6)	NA	0.1 (0.0 to 1.0)	NA
HCTT	0.8 (0.4 to 1.6)	NA	0.2 (0.0 to 1.1)	NA
Polycyclic aromatic hydrocarbons
1-nap	1.5 (0.8 to 3.0)	1.6 (0.8 to 3.2)	1.3 (0.3 to 5.1)	3.5 (0.6 to 20.0)
2-nap	1.6 (0.8 to 3.4)	2.0 (0.9 to 4.5)	0.3 (0.1 to 1.6)	0.8 (0.1 to 5.3)
1-phe	0.8 (0.4 to 1.6)	0.9 (0.4 to 1.8)	0.6 (0.1 to 4.1)	1.2 (0.2 to 9.2)
∑_2,3phe	0.9 (0.5 to 1.7)	1.2 (0.5 to 2.6)	0.6 (0.2 to 2.1)	0.7 (0.2 to 2.5)
2-flu	1.0 (0.5 to 2.0)	1.1 (0.5 to 2.3)	0.6 (0.1 to 3.6)	2.3 (0.3 to 21.6)
3-flu	1.1 (0.5 to 2.3)	1.5 (0.6 to 3.7)	0.4 (0.1 to 1.9)	0.7 (0.1 to 3.6)
1-pyr	1.3 (0.7 to 2.6)	1.6 (0.8 to 3.4)	0.5 (0.1 to 2.1)	0.6 (0.1 to 3.0)
Volatile organic compounds
2MHA	0.9 (0.4 to 1.8)	1.0 (0.5 to 2.1)	2.7 (0.6 to 12.4)	6.2 (1.0 to 39.5)^c
3MHA and 4MHA	0.8 (0.4 to 1.6)	0.8 (0.4 to 1.8)	1.8 (0.4 to 7.9)	4.3 (0.9 to 24.4)
2CaEMA	1.2 (0.6 to 2.3)	1.6 (0.7 to 3.5)	0.3 (0.1 to 1.8)	0.4 (0.1 to 3.2)
1CaHEMA	1.1 (0.5 to 2.2)	1.2 (0.6 to 2.7)	0.3 (0.1 to 1.8)	0.4 (0.1 to 3.0)
1CyHEMA	1.7 (0.7 to 3.8)	2.1 (0.9 to 4.9)	0.4 (0.1 to 3.1)	1.2 (0.1 to 12.9)
2CyEMA	1.6 (0.8 to 3.5)	4.3 (1.4 to 13.5)^c	0.3 (0.0 to 2.2)	0.6 (0.1 to 5.8)
2HEMA	2.1 (1.0 to 4.3)^c	2.3 (1.1 to 5.1)^c	0.5 (0.1 to 2.2)	0.4 (0.1 to 2.4)
2CoEMA	2.2 (1.1 to 4.4)^c	2.3 (1.1 to 4.6)^c	0.9 (0.3 to 3.3)	1.6 (0.4 to 6.1)
3HPMA	1.7 (0.9 to 3.0)	1.8 (1.0 to 3.5)^c	0.8 (0.2 to 3.7)	2.3 (0.4 to 13.4)
BzMA	1.1 (0.6 to 2.0)	1.1 (0.6 to 2.0)	0.1 (0.0 to 0.8)^c	0.2 (0.0 to 1.0)
MADA	1.2 (0.6 to 2.3)	1.3 (0.7 to 2.7)	2.3 (0.5 to 11.1)	9.0 (1.0 to 85.3)^c
PhGA	1.1 (0.5 to 2.2)	1.1 (0.5 to 2.3)	1 (0.4 to 2.4)	1.3 (0.4 to 3.6)
PhMA	1.2 (0.7 to 2.2)	1.2 (0.6 to 2.3)	1.1 (0.3 to 3.9)	1 (0.3 to 3.9)
2HPMA	2.7 (1.3 to 5.7)^d	2.8 (1.3 to 5.8)^d	0.9 (0.3 to 3.1)	1.1 (0.3 to 4.2)
MCaMA	1.3 (0.7 to 2.4)	1.9 (0.9 to 4.1)	0.4 (0.1 to 1.9)	0.8 (0.2 to 4.0)
34HBMA	2.1 (1.1 to 3.9)^c	2.1 (1.1 to 4.1)^c	1 (0.4 to 2.5)	1.2 (0.4 to 3.3)
4HBeMA	1.1 (0.6 to 2.1)	1.2 (0.7 to 2.3)	0.6 (0.1 to 2.4)	1 (0.2 to 4.6)
3HMPMA	1.2 (0.7 to 2.0)	1.2 (0.7 to 2.1)	0.9 (0.3 to 2.8)	1.4 (0.4 to 5.0)
4HMBeMA	1.4 (0.8 to 2.6)	1.6 (0.9 to 3.0)	0.6 (0.1 to 2.6)	2 (0.3 to 12.2)
TTCA	1.4 (0.8 to 2.6)	1.4 (0.8 to 2.6)	1.5 (0.3 to 6.6)	2.4 (0.4 to 13.2)
Tobacco-specific nitrosamines
NABT	NA	NA	0.8 (0.2 to 2.9)	10.6 (0.9 to 124.2)
NATT	NA	NA	0.7 (0.2 to 2.9)	51.3 (1.6 to 166.5)^c
NNAL	NA	NA	1.2 (0.4 to 3.9)	4.7 (0.9 to 25.4)
NNNT	NA	NA	1.1 (0.3 to 4.6)	2.9 (0.8 to 19.0)

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90th vs 10th percentile in conditional logistic regression models (conditioned on matching) and further adjusted for ethnicity, education, wealth score, body mass index, tea temperature, and tooth loss. CI = confidence interval; OR = odds ratio.

90th vs 10th percentile in base models further adjusted for opium use and urinary cotinine.

P < .05.

P < .01.

Among current tobacco users, there was an inverse association between BzMA (a nonspecific biomarker of several volatile organic compounds, especially toluene) and esophageal squamous cell carcinoma in the base models. In the fully adjusted models, after adjusting for opium use and urinary cotinine, 2 volatile organic compounds were associated with incident esophageal squamous cell carcinoma. These included a xylene metabolite (2MHA: OR_{90th vs 10th} = 6.2, 95% CI = 1.0 to 39.5; P < .05) and a styrene metabolite (MADA: OR_{90th vs 10th} = 9.0, 95% CI = 1.0 to 85.3; P < .05). Tobacco-specific nitrosamines were only measured in individuals who currently used tobacco. All tobacco-specific nitrosamines were higher in cases compared with controls, though these differences were not statistically significant in base models. In the fully adjusted models, the odds ratios for each tobacco-specific nitrosamines increased, and one of them (N’-Nitrosoanatabine or NATT) reached statistical significance (OR_{90th vs 10th} = 51.3, 95% CI = 1.6 to 166.5; P < .05). Supplementary Table 3 (available online) shows the distribution of cases and controls who used tobacco by quartiles of biomarker concentrations among controls and adjusted odds ratios for each of the 3 highest quartiles compared with the first quartile. The models confirmed the above findings, with an additional significant association between the highest 2 quartiles of another tobacco-specific nitrosamines (NNN) and esophageal squamous cell carcinoma (OR_{q3 vs q1} = 25.2, 95% CI = 1.3 to 501.9; and OR_{q4 vs q1} = 17.7, 95% CI = 1.0 to 317.9; P < .05).

Discussion

In this high-risk region for esophageal cancer, we saw statistically significant associations between several metabolites of volatile organic compounds in urine samples collected at baseline and incident esophageal squamous cell carcinoma during more than 10 years of follow-up. Acrolein, acrylonitrile, propylene oxide, and 1,3-butadiene metabolites were associated with esophageal squamous cell carcinoma in individuals who did not use tobacco. In tobacco users, metabolites of 2 other volatile organic compounds (styrene and xylene) in addition to 2 tobacco-specific nitrosamines (NNN and N’-Nitrosoanatabine) were associated with increased risk, but the number of cases was much smaller than nonusers. Additional associations were seen with polycyclic aromatic hydrocarbons (especially 2-hydroxynaphthalene) in nonusers of tobacco products, and other tobacco-specific nitrosamines in tobacco users, but most of these associations were not statistically significant.

We previously showed that this population is highly exposed to polycyclic aromatic hydrocarbons, even among people who do not use tobacco; in our prior work, measured concentrations of several polycyclic aromatic hydrocarbon biomarkers among participants who didn’t use tobacco were higher than among US tobacco users (19). We confirmed these high concentrations in the current study. High concentrations of polycyclic aromatic hydrocarbon biomarkers among people who do not use tobacco are a ubiquitous finding in high-incidence areas for esophageal squamous cell carcinoma, including Northeast Iran (20), China (21), Southern Brazil (22) and East Africa (23). Further supporting the importance of polycyclic aromatic hydrocarbon exposure and esophageal squamous cell carcinoma, a previous study showed strong dose-response relationship between the polycyclic aromatic hydrocarbon content of esophageal tissue and the risk of esophageal squamous cell carcinoma (24). Although we did find suggestive associations between polycyclic aromatic hydrocarbon biomarkers and esophageal squamous cell carcinoma in nonusers of tobacco products in the current study, only the association between the highest quartile of 2- hydroxynaphthalene and esophageal squamous cell carcinoma reached statistical significance (ie, the same biomarker associated with esophageal squamous dysplasia in a study from East Africa) (23). As shown previously in a study of polycyclic aromatic hydrocarbon exposure and lung cancer risk in the Shanghai cohort, high concentrations of polycyclic aromatic hydrocarbon biomarkers in the general population may blunt the power of the study to detect associations with these biomarkers, as background exposure is relatively high in cases and controls (25). Besides, in the current study, polycyclic aromatic hydrocarbon metabolite concentrations were almost twice as high in rural areas compared with urban areas. Esophageal squamous cell carcinoma risk is also higher in rural areas (26), but because our nested study was matched on the place of residence, we were unable to analyze potential differences by rural vs urban areas. Unpublished analyses of polycyclic aromatic hydrocarbon metabolites in rural vs urban areas in GCS suggest that the type of household fuel used for cooking and heating (especially kerosene and solid fuel vs natural gas) may be one of the sources of these differences, but it cannot explain them completely.

Among individuals who did not use tobacco products, the strongest associations with incidence of esophageal squamous cell carcinoma were found for 2 acrylonitrile metabolites. Humans can be exposed to environmental acrylonitrile from tobacco smoke, fires, and residues in some commercial polymeric materials such as synthetic rubbers (27). Acrylonitrile has been found to be a multisite carcinogen in animal models (28), and the US National Toxicology Program showed elevated incidences of forestomach, ovary, lung, and Harderian gland cancers in mice (29). Despite being a potent carcinogen in rodents, acrylonitrile was downgraded by IARC from group 2A (probably carcinogenic) to 2B (possibly carcinogenic), because more recent cohort studies failed to confirm earlier results suggesting an association between occupational exposure to acrylonitrile and human cancer (30). Yet, these prior cohort studies were conducted among occupationally exposed workers, and they assessed exposure using questionnaire data, industrial hygiene monitoring, and expert judgment (27). Our study is the first to use biomonitoring, and we found associations between specific biomarkers of acrylonitrile exposure in urine with incidence of esophageal squamous cell carcinoma. Biomonitoring provides a more direct method of assessing the internal dose of xenobiotics as opposed to estimating the dose through indirect methods (31). Additionally, studies of occupationally exposed populations can be prone to potential biases, such as better health and younger age, the so-called healthy worker effect (32), which can make it difficult to extrapolate findings to the general population.

We also observed statistically significant associations between 2 acrolein metabolites and esophageal squamous cell carcinoma in individuals who did not use tobacco. Acrolein is probably carcinogenic to humans (IARC group 2A) based on sufficient evidence in experimental animals and strong mechanistic evidence (33). Acrolein is formed endogenously, and tobacco smoke, combustion of fuel, wood and plastic, and ambient air pollution are among its external sources of exposure (34). Kitchen fumes formed during high-temperature cooking are among household sources of acrolein (34). Acrolein has been shown to induce malignant lymphoma and nasal cavity squamous cell carcinoma in animal models (33). Acrolein–DNA adducts are elevated in tobacco smokers and are preferentially formed at TP53 mutational hotspots for lung cancer in human lung cells (35). In a previous report from this population (36), we showed a higher concentration of acrolein metabolites (particularly 3HPMA) among tobacco smokers with lung cancer compared with controls, which was statistically significant after adjustment for demographics (OR = 2.34, 95% CI = 1.03 to 4.03). These associations were attenuated after adjustment for smoking intensity and opiate use, the main sources of acrolein in the cohort. The same acrolein biomarker was also found to be significantly associated with lung cancer in Chinese cigarette smokers, although this association was attenuated after adjustment for smoking variables, especially cotinine level (25). The 2 mercapturic acids we tested in the current study reflect the 2 primary acrolein excretion products in urine (33), and our study is the first to show associations between both of these metabolites and esophageal squamous cell carcinoma and the first study to document an association between acrolein exposure and any cancer in nonsmokers.

A biomarker for propylene oxide (2HPMA) was associated with esophageal squamous cell carcinoma in individuals who did not use tobacco. Propylene oxide can be present in a variety of household and industrial products and food additives (37). It is also found in tobacco smoke and is considered to be an environmental pollutant (37). Propylene oxide is classified as possibly carcinogenic to humans (IARC group 2B) based on sufficient evidence in experimental animals in spite of inadequate evidence in humans (38). Propylene oxide has been shown to cause several different types of tumors in 2 rodent species and by different routes of exposure, including forestomach cancer (primarily squamous cell carcinoma). Human studies have included a few cohort studies of mixed exposures (including propylene oxide) among chemical workers (39). These cohort studies could not distinguish the specific effects of propylene oxide vs other exposures. A case-control study of lymphohematopoietic cancer that evaluated specific exposure to propylene oxide reported some associations (40), but IARC concluded that the study suffered limitations in exposure assessment and potential confounding by other risk factors (38). Again, there have been no previous reports of any association between propylene oxide exposure and esophageal cancer risk or any study of cancer risk among the general population.

Of the exposures associated with esophageal squamous cell carcinoma among nonusers of tobacco in our study, 1,3-butadiene is the only one that is a group 1 human carcinogen (41). This designation is based on sufficient evidence in experimental animals and humans. The main mechanistic pathway is thought to involve genotoxicity leading to the formation of reactive epoxides, which interact with DNA. Although occupational exposure is the main source of butadiene, it has also been widely detected in ambient air but at much lower levels. Wood and bush fires, vehicle emissions, and gasoline volatilization, in addition to cigarette smoke, are some common sources (41). The majority of human epidemiologic studies have again been conducted in occupational settings, and the main risk was seen for hematolymphoid malignancies (42). Among studies in nonoccupational settings, an ecological study in southeastern Texas assessed the association between estimates of airborne concentrations of 1,3-butadiene based on census tracts, showing that census tracts with the highest 1,3-butadiene concentrations had the highest rates of different types of leukemia in children (43). Our findings suggest that nonoccupational 1,3-butadiene exposure may also contribute to the risk of other types of cancer.

Tobacco is a complex mixture of many different carcinogens, and because tobacco users are simultaneously exposed to all these compounds, specific biomarkers may not stand out to show individual-level risk (44). In contrast to individuals who never used tobacco, in tobacco users, we saw fewer associations for volatile organic compound biomarkers, with associations limited to biomarkers of styrene and xylene. Styrene is probably carcinogenic to humans (group 2A) and is present in tobacco smoke and air pollution (45). Increased incidence and mortality of leukemia subtypes and lymphomas have been seen in large occupational cohorts in the reinforced plastics industry from several countries (45). Additionally, evidence has been judged to be sufficient in experimental animals for the carcinogenicity of styrene. For xylene, however, there are insufficient data available, and IARC has not been able to classify its carcinogenicity to humans (group 3) (45).

Nicotine and other tobacco alkaloids are nitrosated into tobacco-specific nitrosamines during curing and processing of tobacco, and 2 tobacco-specific nitrosamines, NNK and NNN, are known carcinogens (46). NNAL, the main metabolite of NNK excreted in urine, has been shown to be associated with lung cancer risk (47), and urinary NNN has been associated with esophageal cancer risk in the Shanghai cohort (25). We also observed statistically significant associations between the highest 2 quartiles of NNN and esophageal cancer. In addition, we saw a strong association between another tobacco-specific nitrosamine, which is a metabolite of anatabine (N’-Nitrosoanatabine), another tobacco alkaloid, and esophageal squamous cell carcinoma. Although this tobacco-specific nitrosamine is not known to be a carcinogenic biomarker, we have previously shown high correlations among tobacco-specific nitrosamines in tobacco users (19), and the totality of the associations between the tobacco-specific nitrosamines and esophageal squamous cell carcinoma in our study may support a causal relationship despite our relatively small number of tobacco smokers with esophageal squamous cell carcinoma. Of note, tobacco use is not a major contributor to esophageal squamous cell carcinoma risk in many high-incidence areas, including Golestan (48).

In the current study, we showed important associations between several exposure biomarkers and esophageal squamous cell carcinoma using samples and data collected in a well-defined longitudinal general population cohort. The fact that sample collection was done many years before cancer incidence is an important advantage because it precludes reverse causation (ie, changes in exposure after cancer diagnosis). Urinary biomarkers have relatively short half-lives, but we previously used data from 2 measurements 5 years apart to show that most of our study biomarkers are appropriate for exposure assessment in a longitudinal study (19). Urinary biomarkers reflect individual-level internal dose, which is the result of the combined effect of external exposure and metabolism, but evidence shows that external dose plays a far more prominent role (25). A major limitation of previous epidemiological studies was that records of exposure from environmental or occupational assessments were often incomplete or missing. This study relies on biomonitoring using urinary biomarkers, which is a direct assessment of exposures from all different sources (7). Previous work was mainly conducted in occupational settings, where the exposure is higher than the general population and the individuals are generally healthier and younger (the healthy worker effect), making public health conclusions challenging. The large number of cases who never used tobacco in our study provided the opportunity to investigate less-studied nontobacco and nonoccupational sources of exposure. The relatively small number of cases who used tobacco, combined with the complex and correlated nature of exposures among tobacco users, limited our statistical power to detect associations in this group. We think that our findings among individuals who used tobacco should be interpreted with caution, because the models were relatively unstable because of the small number of cases. Most nested studies of biomarker-cancer associations (including ours) have been designed as matched case-control studies. This design provides an excellent platform to investigate the internal dose of carcinogens independent of important confounders. However, matching may lead to lowering the variation of biomarker concentrations between cases and controls. For example, matching on the place of residence (rural or urban) in our study may have precluded our ability to detect some associations, especially between polycyclic aromatic hydrocarbon metabolites and esophageal squamous cell carcinoma as differences in exposures have been matched between cases and controls. Finally, we did not adjust for multiple comparisons for 2 reasons:

The panel of biomarkers used in this study included smoke-related biomarkers for which a priori data suggested their involvement in the association between exposure to tobacco and chronic diseases, especially cancer.
For acrylonitrile and acrolein, more than 1 biomarker showed statistically significant associations with esophageal squamous cell carcinoma.

Still, some of our statistically significant associations may have arisen by chance and should be confirmed in future studies.

Our study provides evidence for the association between exposure to polycyclic aromatic hydrocarbons, acrolein, acrylonitrile, propylene oxide, 1,3-butadiene, styrene, xylene, tobacco-specific nitrosamines, and esophageal squamous cell carcinoma in a high-incidence area where tobacco use is not a major contributor to esophageal cancer risk. Many of these chemicals are classified by IARC as probable or possible carcinogens but have lacked epidemiologic data. Identifying these novel associations using individual-level data and samples collected many years before cancer incidence, from a population without occupational exposure, has important public health implications. That the most prominent exposure-cancer associations were seen among nonusers of tobacco products underlines the potential value of more strict monitoring and control of these exposures from nontobacco sources.

Supplementary Material

djad218_Supplementary_Data

djad218_supplementary_data.pdf^{(210.3KB, pdf)}

Acknowledgements

We thank the study participants and the Behvarz (community health workers) in the study areas for their help. We also thank the general physicians, nurses, and nutritionists in the enrollment teams for their collaboration and assistance and Golestan University of Medical Sciences (Gorgan, Iran), the Golestan health deputies, and the chiefs of the Gonbad and Kalaleh health districts for their close collaboration and support. The study sponsor(s) had no role in the design of the study; the collection, analysis, and interpretation of the data; the writing of the manuscript; and the decision to submit the manuscript for publication.

The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Department of Health and Human Services or any of its affiliated institutions or agencies. Use of trade names is for identification only and does not imply endorsement by the US Department of Health and Human Services or its agencies.

Contributor Information

Arash Etemadi, Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA; Digestive Oncology Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.

Hossein Poustchi, Liver and Pancreaticobilliary Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.

Cindy M Chang, Center for Tobacco Products, Food and Drug Administration, Silver Spring, MD, USA.

Antonia M Calafat, Division of Laboratory Sciences, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA, USA.

Benjamin C Blount, Division of Laboratory Sciences, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA, USA.

Deepak Bhandari, Division of Laboratory Sciences, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA, USA.

Lanqing Wang, Division of Laboratory Sciences, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA, USA.

Gholamreza Roshandel, Golestan Research Center of Gastroenterology and Hepatology, Golestan University of Medical Sciences, Gorgan, Iran.

Apostolos Alexandridis, Center for Tobacco Products, Food and Drug Administration, Silver Spring, MD, USA.

Julianne Cook Botelho, Division of Laboratory Sciences, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA, USA.

Baoyun Xia, Division of Laboratory Sciences, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA, USA.

Yuesong Wang, Division of Laboratory Sciences, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA, USA.

Connie S Sosnoff, Division of Laboratory Sciences, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA, USA.

Jun Feng, Division of Laboratory Sciences, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA, USA.

Mahdi Nalini, Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.

Masoud Khoshnia, Golestan Research Center of Gastroenterology and Hepatology, Golestan University of Medical Sciences, Gorgan, Iran.

Akram Pourshams, Digestive Oncology Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.

Masoud Sotoudeh, Digestive Oncology Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.

Mitchell H Gail, Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.

Sanford M Dawsey, Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.

Farin Kamangar, Department of Biology, School of Computer, Mathematical and Natural Sciences, Morgan State University, Baltimore, MD, USA.

Paolo Boffetta, Stony Brook Cancer Center, Stony Brook University, Stony Brook, NY, USA; Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy.

Paul Brennan, International Agency for Research on Cancer, Lyon, France.

Christian C Abnet, Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.

Reza Malekzadeh, Digestive Oncology Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.

Neal D Freedman, Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.

Data availability

Data available upon reasonable request through study portal: https://dceg2.cancer.gov/gemshare/.

Author contributions

Arash Etemadi, MD, PhD (Conceptualization; Data curation; Formal analysis; Methodology; Project administration; Supervision; Validation; Writing—original draft; Writing—review & editing), Christian C. Abnet, PhD (Conceptualization; Formal analysis; Funding acquisition; Investigation; Methodology; Supervision; Writing—review & editing), Paul Brennan, PhD (Funding acquisition; Resources; Writing—review & editing), Paolo Boffetta, MD (Project administration; Supervision; Writing—review & editing), Farin Kamangar, MD, PhD (Conceptualization; Supervision; Writing—review & editing), Sanford M Dawsey, MD (Project administration; Writing—review & editing), Mitchell H. Gail, PhD (Formal analysis; Methodology; Validation; Writing—review & editing), Masoud Sotoudeh, MD (Data curation; Writing—review & editing), Akram Pourshams, MD (Data curation; Writing—review & editing), Masoud Khoshnia, MD (Data curation; Writing—review & editing), Mahdi Nalini, MD, PhD (Data curation; Methodology; Writing—review & editing), Jun Feng, PhD (Data curation; Writing—review & editing), Connie S. Sosnoff, PhD (Data curation; Writing—review & editing), Yuesong Wang, PhD (Data curation; Writing—review & editing), Baoyun Xia, PhD (Data curation; Writing—review & editing), Julianne Cook Botelho, PhD (Data curation; Methodology; Writing—review & editing), Apostolos Alexandridis, PhD (Writing—review & editing), Gholamreza Roshandel, MD, PhD (Data curation; Investigation; Project administration; Writing—review & editing), Lanqing Wang, PhD (Data curation; Writing—review & editing) Deepak Bhandari, PhD (Data curation; Writing—review & editing), Benjamin C. Blount, PhD (Data curation; Methodology; Project administration; Supervision; Writing—review & editing), Antonia M. Calafat, PhD (Data curation; Investigation; Resources; Writing—review & editing), Cindy M. Chang, PhD (Funding acquisition; Methodology; Resources; Writing—review & editing), Hossein Poustchi, MD, PhD (Data curation; Project administration; Resources; Writing—review & editing), Reza Malekzadeh, MD (Data curation; Funding acquisition; Project administration; Resources; Supervision; Writing—review & editing), and Neal D. Freedman, MPH, PhD (Conceptualization; Formal analysis; Funding acquisition; Investigation; Methodology; Project administration; Resources; Validation; Writing—original draft; Writing—review & editing)

Funding

The current project was supported by Federal funds from the Center for Tobacco Products, FDA, Department of Health and Human Services, through interagency agreements between the Center for Tobacco Products, FDA, the Centers for Disease Control and Prevention, and the NCI, NIH.

The Golestan Cohort Study was supported by Tehran University of Medical Sciences (grant no: 81/15); Cancer Research UK (grant no: C20/A5860); the Intramural Research Program of the NCI, NIH; and various collaborative research agreements with the IARC.

Conflicts of interest

MG, who is a JNCI Associate Editor and co-author on this paper, was not involved in the editorial review or decision to publish the manuscript. The authors declare they have no actual or potential conflicts of interest.

References

1. Kocarnik JM, Compton K, Dean FE, et al. ; Global Burden of Disease 2019 Cancer Collaboration. Cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life years for 29 cancer groups from 2010 to 2019: a systematic analysis for the Global Burden of Disease Study 2019. JAMA Oncol 2022;8(3):420-444. [DOI] [PMC free article] [PubMed] [Google Scholar]
2. Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2021;71(3):209-249. [DOI] [PubMed] [Google Scholar]
3. Murphy G, McCormack V, Abedi-Ardekani B, et al. International cancer seminars: A focus on esophageal squamous cell carcinoma. Ann Oncol 2017;28(9):2086-2093. [DOI] [PMC free article] [PubMed] [Google Scholar]
4. Abnet CC, Arnold M, Wei WQ.. Epidemiology of esophageal squamous cell carcinoma. Gastroenterology. 2018;154(2):360-373. [DOI] [PMC free article] [PubMed] [Google Scholar]
5. IARC Working Group on the Evaluation of Carcinogenic Risks to Humans. Tobacco smoke and involuntary smoking. IARC monographs on the evaluation of carcinogenic risks to humans/World Health Organization, International Agency for Research on Cancer. 2004;83:1-1438. [PMC free article] [PubMed] [Google Scholar]
6. Hecht SS. Tobacco carcinogens, their biomarkers and tobacco-induced cancer. Nat Rev Cancer. 2003;3(10):733-744. [DOI] [PubMed] [Google Scholar]
7. Yuan JM, Butler LM, Gao YT, et al. Urinary metabolites of a polycyclic aromatic hydrocarbon and volatile organic compounds in relation to lung cancer development in lifelong never smokers in the Shanghai Cohort Study. Carcinogenesis. 2014;35(2):339-345. [DOI] [PMC free article] [PubMed] [Google Scholar]
8. Pourshams A, Khademi H, Malekshah AF, et al. Cohort profile: The Golestan Cohort Study—a prospective study of oesophageal cancer in northern Iran. Int J Epidemiol 2010;39(1):52-59. [DOI] [PMC free article] [PubMed] [Google Scholar]
9. Etemadi A, Islami F, Phillips DH, et al. Variation in PAH-related DNA adduct levels among non-smokers: the role of multiple genetic polymorphisms and nucleotide excision repair phenotype. Int J Cancer 2013;132(12):2738-2747. [DOI] [PMC free article] [PubMed] [Google Scholar]
10. Islami F, Boffetta P, van Schooten FJ, et al. Exposure to polycyclic aromatic hydrocarbons among never smokers in Golestan province, Iran, an area of high incidence of esophageal cancer—a cross-sectional study with repeated measurement of urinary 1-OHPG in two seasons. Front Oncol 2012;2:14. [DOI] [PMC free article] [PubMed] [Google Scholar]
11. Etemadi A, Poustchi H, Calafat AM, et al. Opiate and tobacco use and exposure to carcinogens and toxicants in the Golestan Cohort Study. Cancer Epidemiol Biomarkers Prev 2020;29(3):650-658. [DOI] [PMC free article] [PubMed] [Google Scholar]
12. Khademi H, Etemadi A, Kamangar F, et al. Verbal autopsy: reliability and validity estimates for causes of death in the Golestan Cohort Study in Iran. PLoS One 2010;5(6):e11183. [DOI] [PMC free article] [PubMed] [Google Scholar]
13. Islami F, Kamangar F, Nasrollahzadeh D, et al. Socio-economic status and oesophageal cancer: results from a population-based case-control study in a high-risk area. Int J Epidemiol 2009;38(4):978-988. [DOI] [PMC free article] [PubMed] [Google Scholar]
14. Islami F, Poustchi H, Pourshams A, et al. A prospective study of tea drinking temperature and risk of esophageal squamous cell carcinoma. Int J Cancer. 2020;146(1):18-25. [DOI] [PMC free article] [PubMed] [Google Scholar]
15. Tevis DS, Flores SR, Kenwood BM, et al. Harmonization of acronyms for volatile organic compound metabolites using a standardized naming system. Int J Hyg Environ Health 2021;235:113749. [DOI] [PMC free article] [PubMed] [Google Scholar]
16. Achilihu H, Feng J, Wang L, Bernert JT.. Tobacco use classification by inexpensive urinary cotinine immunoassay test strips. J Anal Toxicol. 2019;43(2):149-153. [DOI] [PMC free article] [PubMed] [Google Scholar]
17. Hornung RW, Reed LD.. Estimation of average concentration in the presence of nondetectable values. Appl Occup Environ Hyg. 1990;5(1):46–51. [Google Scholar]
18. Moore SC, Playdon MC, Sampson JN, et al. A metabolomics analysis of body mass index and postmenopausal breast cancer risk. J Natl Cancer Inst. 2018;110(6):588-597. [DOI] [PMC free article] [PubMed] [Google Scholar]
19. Etemadi A, Poustchi H, Chang CM, et al. Urinary biomarkers of carcinogenic exposure among cigarette, waterpipe, and smokeless tobacco users and never users of tobacco in the Golestan Cohort Study. Cancer Epidemiol Biomarkers Prev 2019;28(2):337-347. [DOI] [PMC free article] [PubMed] [Google Scholar]
20. Kamangar F, Schantz MM, Abnet CC, Fagundes RB, Dawsey SM.. High levels of carcinogenic polycyclic aromatic hydrocarbons in mate drinks. Cancer Epidemiol Biomarkers Prev. 2008;17(5):1262-1268. [DOI] [PubMed] [Google Scholar]
21. Roth MJ, Qiao YL, Rothman N, et al. High urine 1-hydroxypyrene glucuronide concentrations in Linxian, China, an area of high risk for squamous oesophageal cancer. Biomarkers. 2001;6(5):381-386. [DOI] [PubMed] [Google Scholar]
22. Lopes AB, Metzdorf M, Metzdorf L, et al. Urinary concentrations of polycyclic aromatic hydrocarbon metabolites in mate drinkers in Rio Grande do Sul, Brazil. Cancer Epidemiol Biomarkers Prev. 2018;27(3):331-337. [DOI] [PMC free article] [PubMed] [Google Scholar]
23. Mwachiro MM, Pritchett N, Calafat AM, et al. Indoor wood combustion, carcinogenic exposure and esophageal cancer in southwest Kenya. Environ Int. 2021;152:106485. [DOI] [PMC free article] [PubMed] [Google Scholar]
24. Abedi-Ardekani B, Kamangar F, Hewitt SM, et al. Polycyclic aromatic hydrocarbon exposure in oesophageal tissue and risk of oesophageal squamous cell carcinoma in north-eastern Iran. Gut. 2010;59(9):1178-1183. [DOI] [PMC free article] [PubMed] [Google Scholar]
25. Hecht SS, Murphy SE, Stepanov I, Nelson HH, Yuan JM.. Tobacco smoke biomarkers and cancer risk among male smokers in the Shanghai cohort study. Cancer Lett. 2013;334(1):34-38. [DOI] [PMC free article] [PubMed] [Google Scholar]
26. Etemadi A, Buller ID, Hashemian M, et al. Urinary nitrate and sodium in a high-risk area for upper gastrointestinal cancers: Golestan Cohort Study. Environ Res 2022;214(Pt 2):113906. [DOI] [PMC free article] [PubMed] [Google Scholar]
27. Cole P, Mandel JS, Collins JJ.. Acrylonitrile and cancer: a review of the epidemiology. Regul Toxicol Pharmacol. 2008;52(3):342-351. [DOI] [PubMed] [Google Scholar]
28. Walker VE, Walker DM, Ghanayem BI, Douglas GR.. Analysis of biomarkers of DNA damage and mutagenicity in mice exposed to acrylonitrile. Chem Res Toxicol. 2020;33(7):1623-1632. [DOI] [PMC free article] [PubMed] [Google Scholar]
29. Toxicology and carcinogenesis studies of acrylonitrile (CAS No. 107-13-1) in B6C3F1 mice (gavage studies). Natl Toxicol Program Tech Rep Ser. 2001;(506):1-201. [PubMed] [Google Scholar]
30. IARC Working Group on the Evaluation of Carcinogenic Risks to Humans . Re-evaluation of Some Organic Chemicals, Hydrazine and Hydrogen Peroxide: Other Compounds Reviewed in Plenary Sessions: IARC. 1999. [PMC free article] [PubMed]
31. Hecht SS, Yuan JM, Hatsukami D.. Applying tobacco carcinogen and toxicant biomarkers in product regulation and cancer prevention. Chem Res Toxicol. 2010;23(6):1001-1008. [DOI] [PMC free article] [PubMed] [Google Scholar]
32. Kirkeleit J, Riise T, Bjorge T, Christiani DC.. The healthy worker effect in cancer incidence studies. Am J Epidemiol. 2013;177(11):1218-1224. [DOI] [PubMed] [Google Scholar]
33. IARC Monographs Vol 128 group. Carcinogenicity of acrolein, crotonaldehyde, and arecoline. Lancet Oncol. 2021;22(1):19-20. [DOI] [PubMed] [Google Scholar]
34. Gregg EO, Minet E, McEwan M.. Urinary biomarkers of smokers’ exposure to tobacco smoke constituents in tobacco products assessment: a fit for purpose approach. Biomarkers. 2013;18(6):467-486. [DOI] [PMC free article] [PubMed] [Google Scholar]
35. Feng Z, Hu W, Hu Y, Tang MS.. Acrolein is a major cigarette-related lung cancer agent: Preferential binding at p53 mutational hotspots and inhibition of DNA repair. Proc Natl Acad Sci USA. 2006;103(42):15404-15409. [DOI] [PMC free article] [PubMed] [Google Scholar]
36. Rostron BL, Wang J, Etemadi A, et al. Associations between biomarkers of exposure and lung cancer risk among exclusive cigarette smokers in the Golestan Cohort Study. Int J Environ Res Public Health. 2021;18(14):7349. [DOI] [PMC free article] [PubMed] [Google Scholar]
37. Lunn RM, Mehta SS, Jahnke GD, Wang A, Wolfe MS, Berridge BR.. Cancer hazard evaluations for contemporary needs: highlights from new national toxicology program evaluations and methodological advancements. J Natl Cancer Inst. 2022;114(11):1441-1448. [DOI] [PMC free article] [PubMed] [Google Scholar]
38. McDonald AJO, Medicine E.. Some industrial chemicals: IARC monographs on the evaluation of carcinogenic risks to humans. Vol 60. 1995;52(5):360. [Google Scholar]
39. Hogstedt LC. Methods for detecting DNA damaging agents in humans. IARC Sci Publ. 1988;(89):21-22. [PubMed] [Google Scholar]
40. Ott MG, Teta MJ, Greenberg HL.. Lymphatic and hematopoietic tissue cancer in a chemical manufacturing environment. Am J Ind Med. 1989;16(6):631-643. [DOI] [PubMed] [Google Scholar]
41. IARC Working Group on the Evaluation of Carcinogenic Risks to Humans. IARC monographs on the evaluation of carcinogenic risks to humans. Volume 97. 1,3-butadiene, ethylene oxide and vinyl halides (vinyl fluoride, vinyl chloride and vinyl bromide). IARC monographs on the evaluation of carcinogenic risks to humans/World Health Organization, International Agency for Research on Cancer. 2008;97:3-471. [PMC free article] [PubMed] [Google Scholar]
42. Sathiakumar N, Tipre M, Leader M, Brill I, Delzell E.. Mortality among men and women in the North American Synthetic Rubber Industry, 1943 to 2009. J Occup Environ Med. 2019;61(11):887-897. [DOI] [PubMed] [Google Scholar]
43. Whitworth KW, Symanski E, Coker AL.. Childhood lymphohematopoietic cancer incidence and hazardous air pollutants in southeast Texas, 1995-2004. Environ Health Perspect. 2008;116(11):1576-1580. [DOI] [PMC free article] [PubMed] [Google Scholar]
44. Etemadi A, Abnet CC, Dawsey SM, Freedman ND.. Biomarkers of tobacco carcinogenesis in diverse populations: challenges and opportunities. Cancer Epidemiol Biomarkers Prev. 2023;32(3):289-291. [DOI] [PubMed] [Google Scholar]
45. IARC Monographs Vol 121 Group. Carcinogenicity of quinoline, styrene, and styrene-7,8-oxide. Lancet Oncol. 2018. [DOI] [PubMed] [Google Scholar]
46. IARC. (International Agency for Research on Cancer). Working Group on the Evaluation of Carcinogenic Risks to Humans. Personal habits and indoor combustions. Volume 100 E. A review of human carcinogens. IARC monographs on the evaluation of carcinogenic risks to humans/World Health Organization, International Agency for Research on Cancer. 2012:1-538. [PMC free article] [PubMed]
47. Hecht SS, Hatsukami DK.. Smokeless tobacco and cigarette smoking: chemical mechanisms and cancer prevention. Nat Rev Cancer. 2022;22(3):143-155. [DOI] [PMC free article] [PubMed] [Google Scholar]
48. Sheikh M, Poustchi H, Pourshams A, et al. Individual and combined effects of environmental risk factors for esophageal cancer based on results from the Golestan Cohort Study. Gastroenterology. 2019;156(5):1416-1427. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

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

Supplementary Materials

djad218_Supplementary_Data

djad218_supplementary_data.pdf^{(210.3KB, pdf)}

Data Availability Statement

Data available upon reasonable request through study portal: https://dceg2.cancer.gov/gemshare/.

[djad218-B1] 1. Kocarnik JM, Compton K, Dean FE, et al. ; Global Burden of Disease 2019 Cancer Collaboration. Cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life years for 29 cancer groups from 2010 to 2019: a systematic analysis for the Global Burden of Disease Study 2019. JAMA Oncol 2022;8(3):420-444. [DOI] [PMC free article] [PubMed] [Google Scholar]

[djad218-B2] 2. Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2021;71(3):209-249. [DOI] [PubMed] [Google Scholar]

[djad218-B3] 3. Murphy G, McCormack V, Abedi-Ardekani B, et al. International cancer seminars: A focus on esophageal squamous cell carcinoma. Ann Oncol 2017;28(9):2086-2093. [DOI] [PMC free article] [PubMed] [Google Scholar]

[djad218-B4] 4. Abnet CC, Arnold M, Wei WQ.. Epidemiology of esophageal squamous cell carcinoma. Gastroenterology. 2018;154(2):360-373. [DOI] [PMC free article] [PubMed] [Google Scholar]

[djad218-B5] 5. IARC Working Group on the Evaluation of Carcinogenic Risks to Humans. Tobacco smoke and involuntary smoking. IARC monographs on the evaluation of carcinogenic risks to humans/World Health Organization, International Agency for Research on Cancer. 2004;83:1-1438. [PMC free article] [PubMed] [Google Scholar]

[djad218-B6] 6. Hecht SS. Tobacco carcinogens, their biomarkers and tobacco-induced cancer. Nat Rev Cancer. 2003;3(10):733-744. [DOI] [PubMed] [Google Scholar]

[djad218-B7] 7. Yuan JM, Butler LM, Gao YT, et al. Urinary metabolites of a polycyclic aromatic hydrocarbon and volatile organic compounds in relation to lung cancer development in lifelong never smokers in the Shanghai Cohort Study. Carcinogenesis. 2014;35(2):339-345. [DOI] [PMC free article] [PubMed] [Google Scholar]

[djad218-B8] 8. Pourshams A, Khademi H, Malekshah AF, et al. Cohort profile: The Golestan Cohort Study—a prospective study of oesophageal cancer in northern Iran. Int J Epidemiol 2010;39(1):52-59. [DOI] [PMC free article] [PubMed] [Google Scholar]

[djad218-B9] 9. Etemadi A, Islami F, Phillips DH, et al. Variation in PAH-related DNA adduct levels among non-smokers: the role of multiple genetic polymorphisms and nucleotide excision repair phenotype. Int J Cancer 2013;132(12):2738-2747. [DOI] [PMC free article] [PubMed] [Google Scholar]

[djad218-B10] 10. Islami F, Boffetta P, van Schooten FJ, et al. Exposure to polycyclic aromatic hydrocarbons among never smokers in Golestan province, Iran, an area of high incidence of esophageal cancer—a cross-sectional study with repeated measurement of urinary 1-OHPG in two seasons. Front Oncol 2012;2:14. [DOI] [PMC free article] [PubMed] [Google Scholar]

[djad218-B11] 11. Etemadi A, Poustchi H, Calafat AM, et al. Opiate and tobacco use and exposure to carcinogens and toxicants in the Golestan Cohort Study. Cancer Epidemiol Biomarkers Prev 2020;29(3):650-658. [DOI] [PMC free article] [PubMed] [Google Scholar]

[djad218-B12] 12. Khademi H, Etemadi A, Kamangar F, et al. Verbal autopsy: reliability and validity estimates for causes of death in the Golestan Cohort Study in Iran. PLoS One 2010;5(6):e11183. [DOI] [PMC free article] [PubMed] [Google Scholar]

[djad218-B13] 13. Islami F, Kamangar F, Nasrollahzadeh D, et al. Socio-economic status and oesophageal cancer: results from a population-based case-control study in a high-risk area. Int J Epidemiol 2009;38(4):978-988. [DOI] [PMC free article] [PubMed] [Google Scholar]

[djad218-B14] 14. Islami F, Poustchi H, Pourshams A, et al. A prospective study of tea drinking temperature and risk of esophageal squamous cell carcinoma. Int J Cancer. 2020;146(1):18-25. [DOI] [PMC free article] [PubMed] [Google Scholar]

[djad218-B15] 15. Tevis DS, Flores SR, Kenwood BM, et al. Harmonization of acronyms for volatile organic compound metabolites using a standardized naming system. Int J Hyg Environ Health 2021;235:113749. [DOI] [PMC free article] [PubMed] [Google Scholar]

[djad218-B16] 16. Achilihu H, Feng J, Wang L, Bernert JT.. Tobacco use classification by inexpensive urinary cotinine immunoassay test strips. J Anal Toxicol. 2019;43(2):149-153. [DOI] [PMC free article] [PubMed] [Google Scholar]

[djad218-B17] 17. Hornung RW, Reed LD.. Estimation of average concentration in the presence of nondetectable values. Appl Occup Environ Hyg. 1990;5(1):46–51. [Google Scholar]

[djad218-B18] 18. Moore SC, Playdon MC, Sampson JN, et al. A metabolomics analysis of body mass index and postmenopausal breast cancer risk. J Natl Cancer Inst. 2018;110(6):588-597. [DOI] [PMC free article] [PubMed] [Google Scholar]

[djad218-B19] 19. Etemadi A, Poustchi H, Chang CM, et al. Urinary biomarkers of carcinogenic exposure among cigarette, waterpipe, and smokeless tobacco users and never users of tobacco in the Golestan Cohort Study. Cancer Epidemiol Biomarkers Prev 2019;28(2):337-347. [DOI] [PMC free article] [PubMed] [Google Scholar]

[djad218-B20] 20. Kamangar F, Schantz MM, Abnet CC, Fagundes RB, Dawsey SM.. High levels of carcinogenic polycyclic aromatic hydrocarbons in mate drinks. Cancer Epidemiol Biomarkers Prev. 2008;17(5):1262-1268. [DOI] [PubMed] [Google Scholar]

[djad218-B21] 21. Roth MJ, Qiao YL, Rothman N, et al. High urine 1-hydroxypyrene glucuronide concentrations in Linxian, China, an area of high risk for squamous oesophageal cancer. Biomarkers. 2001;6(5):381-386. [DOI] [PubMed] [Google Scholar]

[djad218-B22] 22. Lopes AB, Metzdorf M, Metzdorf L, et al. Urinary concentrations of polycyclic aromatic hydrocarbon metabolites in mate drinkers in Rio Grande do Sul, Brazil. Cancer Epidemiol Biomarkers Prev. 2018;27(3):331-337. [DOI] [PMC free article] [PubMed] [Google Scholar]

[djad218-B23] 23. Mwachiro MM, Pritchett N, Calafat AM, et al. Indoor wood combustion, carcinogenic exposure and esophageal cancer in southwest Kenya. Environ Int. 2021;152:106485. [DOI] [PMC free article] [PubMed] [Google Scholar]

[djad218-B24] 24. Abedi-Ardekani B, Kamangar F, Hewitt SM, et al. Polycyclic aromatic hydrocarbon exposure in oesophageal tissue and risk of oesophageal squamous cell carcinoma in north-eastern Iran. Gut. 2010;59(9):1178-1183. [DOI] [PMC free article] [PubMed] [Google Scholar]

[djad218-B25] 25. Hecht SS, Murphy SE, Stepanov I, Nelson HH, Yuan JM.. Tobacco smoke biomarkers and cancer risk among male smokers in the Shanghai cohort study. Cancer Lett. 2013;334(1):34-38. [DOI] [PMC free article] [PubMed] [Google Scholar]

[djad218-B26] 26. Etemadi A, Buller ID, Hashemian M, et al. Urinary nitrate and sodium in a high-risk area for upper gastrointestinal cancers: Golestan Cohort Study. Environ Res 2022;214(Pt 2):113906. [DOI] [PMC free article] [PubMed] [Google Scholar]

[djad218-B27] 27. Cole P, Mandel JS, Collins JJ.. Acrylonitrile and cancer: a review of the epidemiology. Regul Toxicol Pharmacol. 2008;52(3):342-351. [DOI] [PubMed] [Google Scholar]

[djad218-B28] 28. Walker VE, Walker DM, Ghanayem BI, Douglas GR.. Analysis of biomarkers of DNA damage and mutagenicity in mice exposed to acrylonitrile. Chem Res Toxicol. 2020;33(7):1623-1632. [DOI] [PMC free article] [PubMed] [Google Scholar]

[djad218-B29] 29. Toxicology and carcinogenesis studies of acrylonitrile (CAS No. 107-13-1) in B6C3F1 mice (gavage studies). Natl Toxicol Program Tech Rep Ser. 2001;(506):1-201. [PubMed] [Google Scholar]

[djad218-B30] 30. IARC Working Group on the Evaluation of Carcinogenic Risks to Humans . Re-evaluation of Some Organic Chemicals, Hydrazine and Hydrogen Peroxide: Other Compounds Reviewed in Plenary Sessions: IARC. 1999. [PMC free article] [PubMed]

[djad218-B31] 31. Hecht SS, Yuan JM, Hatsukami D.. Applying tobacco carcinogen and toxicant biomarkers in product regulation and cancer prevention. Chem Res Toxicol. 2010;23(6):1001-1008. [DOI] [PMC free article] [PubMed] [Google Scholar]

[djad218-B32] 32. Kirkeleit J, Riise T, Bjorge T, Christiani DC.. The healthy worker effect in cancer incidence studies. Am J Epidemiol. 2013;177(11):1218-1224. [DOI] [PubMed] [Google Scholar]

[djad218-B33] 33. IARC Monographs Vol 128 group. Carcinogenicity of acrolein, crotonaldehyde, and arecoline. Lancet Oncol. 2021;22(1):19-20. [DOI] [PubMed] [Google Scholar]

[djad218-B34] 34. Gregg EO, Minet E, McEwan M.. Urinary biomarkers of smokers’ exposure to tobacco smoke constituents in tobacco products assessment: a fit for purpose approach. Biomarkers. 2013;18(6):467-486. [DOI] [PMC free article] [PubMed] [Google Scholar]

[djad218-B35] 35. Feng Z, Hu W, Hu Y, Tang MS.. Acrolein is a major cigarette-related lung cancer agent: Preferential binding at p53 mutational hotspots and inhibition of DNA repair. Proc Natl Acad Sci USA. 2006;103(42):15404-15409. [DOI] [PMC free article] [PubMed] [Google Scholar]

[djad218-B36] 36. Rostron BL, Wang J, Etemadi A, et al. Associations between biomarkers of exposure and lung cancer risk among exclusive cigarette smokers in the Golestan Cohort Study. Int J Environ Res Public Health. 2021;18(14):7349. [DOI] [PMC free article] [PubMed] [Google Scholar]

[djad218-B37] 37. Lunn RM, Mehta SS, Jahnke GD, Wang A, Wolfe MS, Berridge BR.. Cancer hazard evaluations for contemporary needs: highlights from new national toxicology program evaluations and methodological advancements. J Natl Cancer Inst. 2022;114(11):1441-1448. [DOI] [PMC free article] [PubMed] [Google Scholar]

[djad218-B38] 38. McDonald AJO, Medicine E.. Some industrial chemicals: IARC monographs on the evaluation of carcinogenic risks to humans. Vol 60. 1995;52(5):360. [Google Scholar]

[djad218-B39] 39. Hogstedt LC. Methods for detecting DNA damaging agents in humans. IARC Sci Publ. 1988;(89):21-22. [PubMed] [Google Scholar]

[djad218-B40] 40. Ott MG, Teta MJ, Greenberg HL.. Lymphatic and hematopoietic tissue cancer in a chemical manufacturing environment. Am J Ind Med. 1989;16(6):631-643. [DOI] [PubMed] [Google Scholar]

[djad218-B41] 41. IARC Working Group on the Evaluation of Carcinogenic Risks to Humans. IARC monographs on the evaluation of carcinogenic risks to humans. Volume 97. 1,3-butadiene, ethylene oxide and vinyl halides (vinyl fluoride, vinyl chloride and vinyl bromide). IARC monographs on the evaluation of carcinogenic risks to humans/World Health Organization, International Agency for Research on Cancer. 2008;97:3-471. [PMC free article] [PubMed] [Google Scholar]

[djad218-B42] 42. Sathiakumar N, Tipre M, Leader M, Brill I, Delzell E.. Mortality among men and women in the North American Synthetic Rubber Industry, 1943 to 2009. J Occup Environ Med. 2019;61(11):887-897. [DOI] [PubMed] [Google Scholar]

[djad218-B43] 43. Whitworth KW, Symanski E, Coker AL.. Childhood lymphohematopoietic cancer incidence and hazardous air pollutants in southeast Texas, 1995-2004. Environ Health Perspect. 2008;116(11):1576-1580. [DOI] [PMC free article] [PubMed] [Google Scholar]

[djad218-B44] 44. Etemadi A, Abnet CC, Dawsey SM, Freedman ND.. Biomarkers of tobacco carcinogenesis in diverse populations: challenges and opportunities. Cancer Epidemiol Biomarkers Prev. 2023;32(3):289-291. [DOI] [PubMed] [Google Scholar]

[djad218-B45] 45. IARC Monographs Vol 121 Group. Carcinogenicity of quinoline, styrene, and styrene-7,8-oxide. Lancet Oncol. 2018. [DOI] [PubMed] [Google Scholar]

[djad218-B46] 46. IARC. (International Agency for Research on Cancer). Working Group on the Evaluation of Carcinogenic Risks to Humans. Personal habits and indoor combustions. Volume 100 E. A review of human carcinogens. IARC monographs on the evaluation of carcinogenic risks to humans/World Health Organization, International Agency for Research on Cancer. 2012:1-538. [PMC free article] [PubMed]

[djad218-B47] 47. Hecht SS, Hatsukami DK.. Smokeless tobacco and cigarette smoking: chemical mechanisms and cancer prevention. Nat Rev Cancer. 2022;22(3):143-155. [DOI] [PMC free article] [PubMed] [Google Scholar]

[djad218-B48] 48. Sheikh M, Poustchi H, Pourshams A, et al. Individual and combined effects of environmental risk factors for esophageal cancer based on results from the Golestan Cohort Study. Gastroenterology. 2019;156(5):1416-1427. [DOI] [PMC free article] [PubMed] [Google Scholar]

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Exposure to polycyclic aromatic hydrocarbons, volatile organic compounds, and tobacco-specific nitrosamines and incidence of esophageal cancer

Arash Etemadi, MD, PhD

Hossein Poustchi, MD, PhD

Cindy M Chang, PhD

Antonia M Calafat, PhD

Benjamin C Blount, PhD

Deepak Bhandari, PhD

Lanqing Wang, PhD

Gholamreza Roshandel, MD, PhD

Apostolos Alexandridis, PhD

Julianne Cook Botelho, PhD

Baoyun Xia, PhD

Yuesong Wang, PhD

Connie S Sosnoff, PhD

Jun Feng, PhD

Mahdi Nalini, MD, PhD

Masoud Khoshnia, MD

Akram Pourshams, MD

Masoud Sotoudeh, MD

Mitchell H Gail, PhD

Sanford M Dawsey, MD

Farin Kamangar, MD, PhD

Paolo Boffetta, MD

Paul Brennan, PhD

Christian C Abnet, MPH, PhD

Reza Malekzadeh, MD

Neal D Freedman, MPH, PhD

Roles

Abstract

Background

Methods

Results

Conclusion

Methods

Nested cases and controls

Laboratory analyses

Table 1.

Statistical analysis

Results

Table 2.

Table 3.

Table 4.

Discussion

Supplementary Material

Acknowledgements

Contributor Information

Data availability

Author contributions

Funding

Conflicts of interest

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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