De novo acute myeloid leukemia risk factors
A Texas case-control study
Acute myeloid leukemia (AML) is comprised of several bone marrow-based cancers and is the most common type of leukemia in the United States. The etiology of AML is not well understood. A case-control study was conducted at The University of Texas M. D. Anderson Cancer Center to investigate associations between lifestyle characteristics and the risk of AML in Texas.
This study included 638 adult patients with de novo AML (cases) and a group of 636 matched controls. Interviewer-administered questionnaires were used to collect demographic and occupational data. The distribution of cases by World Health Organization (WHO) subtype was 71 patients (11%) with recurrent cytogenetic abnormalities (AML-RCA), 134 patients (21%) with multilineage dysplasia (AML-MD), and 389 patients (61%) with AML not otherwise categorized (AML-NOC). Multivariate logistic regression analyses were performed among all AML cases and among both sexes and each WHO subgroup.
Among men, heavy smoking (≥30 pack-years; odds ratio [OR], 1.86) and occupational solvent exposure at low levels (OR, 2.87) or moderate/high levels (OR, 4.13) statistically significantly increased the risk of AML. Among women, obesity (OR, 1.62) and solvent exposure to low levels (OR, 2.73) or moderate/high levels (OR, 3.90) increased the risk of AML. Across WHO subtypes, obesity was associated with a statistically significantly increased risk of AML-RCA (OR, 3.15), whereas solvent exposure increased the risk in all subtypes at low levels (AML-RCA: OR, 4.11; AML-MD: OR, 2.54) and moderate/high levels (AML-RCA: OR, 5.13; AML-MD: OR, 3.02). A joint effect between smoking and solvent exposure was observed, and the highest risk was observed among smokers who had solvent exposure (OR, 4.51).
The current results suggested that several factors play a role in AML predisposition with possible joint effects. Risk profiles for AML differed by sex and WHO subtype. Cancer 2012. © 2012 American Cancer Society.
Acute myeloid leukemia (AML) is comprised of several bone marrow-based neoplasms that have clinical similarities but distinct morphologic, immunophenotypic, and cytogenetic subtypes. AML is the most common type of leukemia in the United States, accounting for 75% to 80% of all adult acute leukemias.1 It is estimated that there were 12,330 new cases of AML and 8950 deaths in the United States in 2010.2 The age-adjusted incidence rate for 2001 through 2005 was 3.6 per 100,000 population. AML cases are classified into de novo and therapy-related AML (t-AML), defined as AML preceded by chemotherapy and/or radiation treatment, which constitutes 5% to 15% of all AMLs.3 On the basis of morphologic and immunophenotypic characteristics, the World Health Organization (WHO) divides AML into 4 homogenous subgroups: AML with recurrent cytogenetic abnormalities (AML-RCA); AML with multilineage dysplasia (AML-MD), including AML after a myelodysplastic/myeloproliferative disorder; t-AML; and AML not otherwise categorized (AML-NOC).
Although several studies have examined the epidemiologic risk factors associated with AML, its etiology is still not well understood. Studies have been hampered by inconsistent diagnosis, changing classification standards, the inclusion of t-AML with de novo AML, and small sample sizes. Some of the previous studies have suggested several potential risk factors for AML, including exposure to benzene and other organic solvents,4-6 chemotherapeutic agents,7 agrichemicals,8, 9 tobacco smoke,10, 11 and obesity.12 However, exposure to these risk factors is relatively rare and does not explain the majority of AML cases.13
The current population-based case-control study was conducted at the University of Texas M. D. Anderson Cancer Center (MDACC), 1 of the largest AML treatment centers in the United States. The objectives were to identify demographic and epidemiologic factors associated with the risk of developing adult, de novo AML among Texas residents and explore differences in risk between sexes and WHO subtypes.
MATERIALS AND METHODS
This population-based case-control study included 638 adult patients with de novo AML (cases) and a control group of 636 individuals from Texas. Cases were adult patients (ages 18-80 years) who registered at MDACC between 2003 and 2007 with a confirmed diagnosis of AML, and there were no restrictions on sex or ethnicity for their inclusion. Cases were identified at their first visit and were enrolled prospectively into the study. The overall participation rate among cases was approximately 87%. Reasons for nonenrollment included refusals (9%) or too ill to participate/died before interview and no proxy was available (4%). Proxy interviews (with close family members) were required for 11% of the completed interviews, because the cases were either too ill or died before completing the full questionnaire after giving their informed consent. On the basis of WHO classification criteria, in the case group, there were 71 patients with AML-RCA, 134 patients with AML-MD, and 389 patients with AML-NOC in our study. WHO classification was not available for 44 patients. Clinical information, including karyotypes, was obtained from the MDACC clinical database. In accordance with previous conventions, cases were categorized according to their pretreatment cytogenetics as favorable (translocation 8,12 [t(8,12)], inversion 16 [inv(16)]), poor (−5, −7, −3, or complex [≥3 abnormalities]), or intermediate (diploid, other abnormalities not otherwise categorized).14 Distributions of WHO and cytogenetic categories are presented in Table 1.
Table 1. Demographic Characteristics of Cases and Controls
|Sex|| || || |
| Women||293 (45.92)||293 (46.07)||NS|
| Men||345 (54.08)||343 (53.93)|| |
|Race|| || || |
| White||504 (79)||492 (77.36)||NS|
| Hispanic||87 (13.64)||88 (13.84)|| |
| Black||34 (5.33)||44 (6.92)|| |
| Asian||13 (2.04)||12 (1.89)|| |
|Age at diagnosis/interview: Mean±SD, y||54.24±15.21||53.0±13.90||NSa|
|Education status|| || || |
| <Bachelors||430 (67.40)||298 (47.60)||< .0001|
| ≥Bachelors||208 (32.60)||328 (52.40)|| |
|Family history of hematopoietic cancerb|| || || |
| No||588 (92.16)||618 (97.17)||< .0001|
| Yes||50 (7.84)||18 (2.83)|| |
|WHO category|| || || |
| AML-RCA||71 (12)||—|| |
| AML-MD||134 (22.6)||—|| |
| AML-NOC||389 (65.5)||—|| |
|Cytogenetic category|| || || |
| Favorable||68 (11.1)||—|| |
| Intermediates||363 (59.2)||—|| |
| Poor||182 (29.7)||—|| |
By using our previously described methodology,15 controls were recruited using random digit dialing. Briefly, a standardized telephone script was used to introduce the study, establish eligibility, and determine willingness to participate. Controls had no previous history of invasive cancer and were frequency-matched to cases on age (±5 years), sex, race, and county of residence. Seventy-seven percent of the eligible controls participated in the study.
Informed consent was obtained before data collection in accordance with institutional review board requirements. Interviewers administered the structured questionnaire within 2 months of patient registration (cases) or enrollment (controls) to assess demographic information, medical history, occupational history, lifetime smoking, exposure to second-hand smoke, family history of cancer among first-degree relatives, and anthropometric information (height and weight).
Lifetime occupational history included job title, major duties, equipment used, work done by the company, and period of employment for each job. Occupational exposures (including benzene, gasoline, other organic solvents, pesticides, herbicides, and fertilizers) were estimated based on each occupation held full-time for at least 1 year using the Job-Exposure Matrix developed by the National Cancer Institute.16 Because the prevalence of exposure was low for individual chemicals, we combined the exposures of benzene, gasoline, and other organic solvents as a measure of organic solvent exposure; and we combined pesticides, herbicides, and fertilizers as a measure of agrichemical exposure. The Job-Exposure Matrix was used to assign an intensity level of exposure for each job title: none, low, moderate, and high. Because of the small number of individuals who were exposed to high levels, we combined moderate and high levels of exposure for analyses. In addition, an exposure index was calculated based on exposure level × number of years of exposure. Because exposure levels varied over a lifetime, a cumulative exposure index was calculated to reflect lifetime exposure history.
Participants who smoked >100 cigarettes in their lifetime were defined as “ever-smokers” and were further categorized as “current smokers” or “former smokers,” defined as those who had quit >1 year before diagnosis (cases) or interview (controls). Pack-years were calculated from the average number of packs smoked per day × number of years smoked. “Heavy smoking” was defined as smoking ≥30 pack-years over the lifetime of the participant. The body mass index (BMI) (in kg/m2) was calculated using self-reported weight and height at diagnosis/interview. Participants with a BMI ≥30 kg/m2 were categorized as obese. Participants who reported having any first-degree relatives with lymphoma, leukemia, multiple myeloma, or myelodysplastic syndromes were categorized as having a positive family history of hematopoietic cancer.
Descriptive analyses were conducted using chi-square tests and Student t tests with SPSS statistical software (version 17.0; SPSS, Inc., Chicago, Ill). For categorical variables, Cochran-Armitage trend tests were used to estimate P trend values. Odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were calculated using unconditional logistic regression. Variables with univariate ORs with P values ≤ .1 were evaluated for inclusion in a multivariable model that was constructed in a forward, stepwise manner and adjusted for relevant variables, including education and family history of hematopoietic cancer. Independent models were run for each sex and for each WHO subtype. Results were considered statistically significant at the α = .05 level.
Demographic characteristics of the study participants are presented in Table 1. Because of successful matching, there were no differences between cases and controls with respect to age, sex, or race/ethnicity. Controls were more likely to have completed a bachelor's degree (P < .0001) and were less likely to report having a positive family history of hematopoietic cancer (P < .0001).
Because the incidence of AML differs by sex,2 we determined crude ORs by sex (Table 2). In both men and women, being a current smoker (men: OR, 1.60; 95% CI, 1.08-2.38; women: OR, 1.93; 95% CI, 1.26-2.97) and a heavy smoking history (ie, ≥30 pack-years [men: OR, 2.45; 95% CI, 1.57-3.83; women: OR, 1.76; 95% CI, 0.98-3.16]) were associated with an increase in risk, which reached statistical significance only in men. Exposure to second-hand smoke was not associated significantly with AML risk in men or women (data not shown; N = 1059 [men: OR, 0.82; 95% CI, 0.55-1.22; women: OR, 1.06; 95% CI, 0.68-1.67]).
Table 2. Univariate Analysis by Sex
|Smoking status|| || || || || || |
| Never||170 (58)||196 (66.9)||1.00||145 (42)||190 (55.4)||1.00|
| Former||51 (17.4)||54 (18.4)||1.09 [0.71-1.68]||123 (35.7)||90 (26.2)||1.79 [1.27-2.53]a|
| Current||72 (24.6)||43 (14.7)||1.93 [1.26-2.97]a||77 (22.3)||63 (18.4)||1.60 [1.08-2.38]a|
|Pack years of smoking|| || || || || || |
| Nonsmoker||170 (58)||196 (66.9)||1.00||145 (42)||190 (55.4)||1.00|
| <10||46 (15.7)||46 (15.7)||1.15 [0.73-1.82]||63 (18.3)||55 (16)||1.50 [0.98-2.29]|
| ≥10 to <30||45 (15.4)||30 (10.2)||1.73 [1.04-2.87]a||64 (18.6)||59 (17.2)||1.42 [0.94-2.15]|
| ≥30||32 (10.9)||21 (7.2)||1.76 [0.98-3.16]||73 (21.6)||39 (11.4)||2.45 [1.57-3.83]a|
| Ptrend|| || ||.013|| || ||< .0001|
|Solvent exposure|| || || || || || |
| None||210 (71.7)||263 (89.8)||1.00||127 (36.8)||235 (69.3)||1.00|
| Low||33 (11.3)||15 (5.1)||2.76 [1.46-5.21]a||50 (14.5)||31 (9.1)||2.99 [1.82-4.91]a|
| Medium/high||50 (17.1)||15 (5.1)||4.18 [2.28-7.64]a||168 (48.7)||73 (21.5)||4.26 [3.00-6.04]a|
| Ptrend|| || ||< .0001|| || ||< .0001|
| Mean cumulative exposureb||5.97||2.05||1.03 [1.01-1.05]a||28.66||10.15||1.02 [1.01-1.03]a|
|Agrichemical exposure|| || || || || || |
| None||267 (91.1)||284 (96.9)||1.00||269 (78)||323 (95.3)||1.00|
| Low||11 (3.8)||6 (2)||1.95 [0.71-5.35]||25 (7.2)||3 (0.9)||—|
| Medium/high||15 (5.1)||3 (1)||—||51 (14.8)||13 (3.8)||4.71 [2.51-8.85]a|
|BMI, kg/m2|| || || || || || |
| <25||115 (39.3)||138 (47.3)||1.00||111 (32.2)||101 (29.5)||1.00|
| ≥25 to <30||77 (26.3)||89 (30.5)||1.04 [0.70-1.54]||134 (38.8)||173 (50.4)||0.71 [0.50-1.00]|
| ≥30||101 (34.5)||65 (22.3)||1.87 [1.25-2.78]a||100 (29)||69 (20.1)||1.32 [0.88-1.98]|
| Ptrend|| || ||.005|| || ||.320|
|Family history of hematopoietic cancerc|| || || || || || |
| No||272 (92.8)||285 (97.3)||1.00||316 (91.6)||333 (97.1)||1.00|
| Yes||21 (7.2)||8 (2.7)||2.75 [1.20-6.32]a||29 (8.4)||10 (2.9)||3.06 [1.47-6.37]a|
The magnitude of AML risk associated with intensity of exposure to solvents was similar between men and women. Low levels were associated with an almost 3-fold increase (men: OR, 2.99; 95% CI, 1.82-4.91; women: OR, 2.76; 95% CI, 1.46-5.21), and moderate/high levels were associated with an increase >4-fold (men: OR, 4.26; 95% CI, 3.00-6.04; women: OR, 4.18; 95% CI, 2.28-7.64). The OR associated with the cumulative exposure index indicated that both the duration and the intensity of exposure led to similar increases in risk between men and women. However, there were marked differences in the prevalence of exposures by sex. On the basis of the calculations of population-attributable risk, exposure to moderate/high levels of solvents accounted for 41% of the overall AML risk in men and 14% in women. Moderate/high levels of agrichemical exposure also increased AML risk in men (OR, 4.71; 95% CI, 2.51-8.85). Because of the small number of women exposed to agrichemicals, ORs were not calculated. Obesity was associated with an increased risk in both men (OR, 1.32; 95% CI, 0.88-1.98) and women (OR, 1.9; 95% CI, 1.3-2.8), reaching statistical significance only in women.
Including factors that were identified as significant in the univariate analyses, we constructed independent multivariable models for men and women (Table 3). A history of heavy smoking significantly increased the risk of developing AML only in men (OR, 1.86; 95% CI, 1.15-3.02). However, among women, being a current smoker was associated with AML risk (OR, 1.75; 95% CI, 1.12-2.72). Occupational exposure to solvents increased AML risk in both men (OR, 4.13; 95% CI, 2.79-6.12) and women (OR, 4.04; 95% CI, 2.14-7.64). The occupations most commonly associated with solvent exposure in men were auto mechanic (25%), oil field worker (13%), chemical plant operator/worker (13%), and gas station operator (10%); in women, the occupations were hairdresser (33%), chemical/rubber plant worker (23%), laborer (15%), and cosmetologist (8%). Obesity significantly increased the risk of AML among women (OR, 1.57; 95% CI, 1.02-2.41), and there was a similar but statistically nonsignificant association among men.
Table 3. Multivariate Logistic Regression Analysis by Sex
|Smoking, pack-years|| || |
| Never smoker||1.00||1.00|
| <10||1.06 (0.65-1.73)||0.99 (0.62-1.58)|
| ≥10 to <30||1.37 (0.80-2.35)||1.11 (0.7-1.74)|
| >30||1.34 (0.72-2.50)||1.86 (1.15-3.02)b|
|Solvent exposure|| || |
| Low||2.73 (1.41-5.28)b||2.87 (1.69-4.86)b|
| Moderate/high||3.90 (2.06-7.38)b||4.13 (2.79-6.12)b|
|BMI at diagnosis/ interview, kg/m2|| || |
| ≥25 to <30||0.91 (0.60-1.39)||0.70 (0.49-1.01)|
| ≥30||1.62 (1.06-2.47)b||1.24 (0.91-1.79)|
To investigate whether solvent exposure and smoking history had significant interactive effects, we evaluated the joint effects of these 2 exposures (Table 4). The greatest risk for AML was identified among smokers who also reported occupational solvent exposure (OR, 4.51; 95% CI, 2.83-7.18). There was no difference in the association when the analysis was stratified by sex (data not shown).
Table 4. Interaction Between Current Smoking and Solvent Exposure
|No||No||265 (41.5)||419 (66.3)||1.00|
|No||Yes||72 (11.3)||79 (12.5)||1.41 [1.01-2.05]b|
|Yes||No||224 (35.1)||107 (16.9)||3.31 [2.51-4.37]b|
|Yes||Yes||77 (12.1)||27 (4.3)||4.51 [2.83-7.18]b|
Because AML is a heterogeneous disease, data were analyzed according to major WHO subtypes. Demographic characteristics were similar across the WHO subtypes (data not shown). Independent multivariable models were run for each WHO subtype adjusting for age, sex, race, education, and family history of hematopoietic cancer. For the AML-RCA group, increased risks were associated with obesity (OR, 3.15; 95% CI, 1.51-6.57) and both low levels (OR, 4.11; 95% CI, 1.63-10.32) and moderate/high levels (OR, 5.13; 95% CI, 2.58-10.20) of occupational solvent exposure. In the AML-MD group, increased risks were associated with both low levels (OR, 2.54; 95% CI, 1.33-4.84) and moderate/high levels (OR, 3.02; 95% CI, 1.80-5.05) of occupational solvent exposure.
Associations between chromosomal aberrations and specific exposures were assessed to determine whether risk differed by cytogenetic status. Among the cases, 247 patients (57%) had diploid karyotypes (Table 5). The most common chromosomal aberrations observed were complex karyotypes (27%), −5/−7 (17%), +8 (9%), inv(16) (7%), and t(8;21) (7%). Associations between these abnormalities and risk factors were assessed to determine whether the risk differed according cytogenetic status. Compared with cases who had diploid karyotypes, those with −5/−7 abnormalities were more likely to have ever smoked (OR, 1.65; 95% CI, 1.01-2.68). Solvent exposure increased the risk of −5/−7 and +8 abnormalities, but the ORs did not reach statistical significance (OR, 1.35; 95% CI, 0.80-2.27; OR, 1.83; 95% CI, 0.92-3.67, respectively). No association was observed with respect to complex karyotypes, inv(16), or t(8;21) abnormalities and smoking or solvent exposure.
Table 5. Association Between Chromosomal Abnormalities With Smoking and Solvent Exposure
|Diploid|| || || || |
| No. (%)||122 (49.4)||125 (50.6)||132 (53.4)||115 (46.6)|
|Complexb|| || || || |
| No. (%)||60 (50)||60 (50)||61 (50.8)||59 (49.2)|
| OR [95% CI]||—||1.06 [0.68-1.67]||—||0.89 [0.56-1.40]|
|−5/−7|| || || || |
| No. (%)||26 (35.1)||48 (64.9)||34 (45.9)||40 (54.1)|
| OR [95% CI]||—||1.80 [1.05-3.09]c||—||1.35 [0.80-2.27]|
|+8|| || || || |
| No. (%)||17 (43.6)||22 (56.4)||15 (38.5)||24 (61.5)|
| OR [95% CI]||—||1.26 [0.64-2.49]||—||1.83 [0.92-3.67]|
|inv(16)|| || || || |
| No. (%)||16 (51.6)||15 (48.4)||18 (58.1)||13 (41.9)|
| OR [95% CI]||—||0.92 [0.43-1.93]||—||0.83 [0.39-1.77]|
|t(8;21)|| || || || |
| No. (%)||18 (60)||12 (40)||16 (53.3)||14 (46.7)|
| OR [95% CI]||—||0.65 [0.30-1.41]||—||1.00 [0.47-2.15]|
To our knowledge, this is the largest AML case-control study to date in the United States and the first to address risk factors by sex and WHO subtypes. Most published case-control studies have included fewer participants and analyzed risk across all subtypes and both sexes. Our results suggest that AML is a heterogeneous disease with sex-specific and subtype-specific risk factors that need to be considered when developing risk-prediction models.
Smoking generally has been recognized as a risk factor for AML, but results from previous studies have been inconsistent.17-21 Two case-control studies, 1 conducted in Canada17 and another in Sweden,18 indicated that increased risks were associated with heavy smoking, although each used different classification criteria for heavy smoking (≥20 pack-years and ≥40 pack-years, respectively). A large cohort study by Ma and colleagues indicated that both current smokers and former smokers who smoked more than a pack a day had a statistically increased risk of AML.19 However, a more recent Swedish case-control study indicated that there was no significant association between smoking and AML risk.20 Pogoda and colleagues suggested that previous nonsignificant results may stem from a lack of specificity in AML classification.21 Our results also indicate that smoking is a statistically significant risk factor for AML. Heavy smoking resulted in the greatest increase of risk among men, whereas current smoking resulted in a greater increase among women. To our knowledge, this is the first case-control study to investigate sex differences in the association between smoking and AML risk. Our results agree with data from previous prospective studies indicating that myeloid leukemia had a significant, positive association with smoking in men but not in women.22 The association between cigarette smoking and AML is biologically plausible, because smokers are exposed to a 10-fold increase in benzene inhalation compared with nonsmokers.23 Cigarettes also contain other known or suspected human carcinogens, including ethyl benzene, octane, xylele isomers, and radioactive lead-210.24
We observed a statistically significant increase in AML risk among both men and women who had occupational solvent exposure, and this association was observed consistently for all AML subtypes. AML risk increased not only with the intensity level of exposure but also with the duration of exposure according to the cumulative exposure index. Our results are in concordance with the findings from a recent meta-analysis of industry-based cohorts, which indicated that benzene at work increases the risk of AML in a dose-response pattern.25 Benzene is the most studied solvent in the context of leukemia risk and has long been recognized as hematotoxic.26, 27 Benzene exposure can contribute to the development of leukemia by multiple pathways, and the roles of different metabolites involved in benzene toxicity are currently being investigated.27, 28
Our results confirm the association between obesity and AML among women, in agreement with the Iowa Women's Health Study, which demonstrated a 2.4-fold increase of AML risk among obese women.12 We also observed an elevated risk associated with obesity in men, although the effect was not statistically significant, similar to data from a cohort of Swedish men who were construction workers (relative risk, 1.30; 95% CI, 0.77-2.17).29 To our knowledge, the only previous case-control study examining WHO subtypes (conducted in China) established no association between obesity and AML-RCA or AML-MD.30 However, differences among populations and the fact that the definition for obesity in China (BMI ≥28 kg/m2) differs from that in the United States may account for this disagreement. Research suggests that a decreased immune response or increases in levels of plasma leptin and insulin-like growth factor 1 may mediate the association between obesity and cancer risk.17, 31
Our findings on the joint effects of cigarette smoking and occupational exposure highlight the importance of tobacco as another major source of benzene in addition to occupational exposure. To our knowledge, no previous studies have examined the joint effects of these 2 factors on AML development. The elevated risks we observed suggest that the interaction between smoking and solvent exposure should be considered when determining overall risk.
Our cytogenetic data suggest that, in patients with AML, smoking is associated with −5/−7 monosomies, which are associated with a poor prognosis.32, 33 A few previous studies have examined the association between smoking and cytogenetic abnormalities in patients with AML and observed associations between smoking and −5/−7 abnormalities.20, 34 However, in those studies, statistical power was weak for the associations because of the limited number of AML cases and the small samples of each cytogenetic abnormality; thus, the ORs should be interpreted with caution. We also observed that solvent exposure was associated an elevated risk of −5/−7 and +8, although the ORs were not statistically significant. Two previous studies demonstrated that patients who had de novo AML with occupational solvent exposure had greater frequencies of −5/5q−, −7/7q−, and +8 chromosomal abnormalities compared with nonexposed patients.35, 36 Although our results are inconclusive, taken together with the previous literature, they indicate a correlation between carcinogenic agents and karyotypic patterns of tumor cells.
Although only a small number of participants among both cases and controls had a family history of hematopoietic cancer, our analyses indicate that such a history is associated with a 3-fold increase in the risk of AML for both sexes. A previous case-control study conducted in China established no association between a positive family history of hematopoietic cancer and the development of AML.30
Our current investigation, as a case-control study, has inherent limitations. Recall bias is inherent in all retrospective studies; however, to minimize this effect, we used trained interviewers to administer structured interviews to both cases and controls. Although we did not verify occupational histories, the utility and applicability of self-reported occupational history to determine exposure history has been demonstrated by other investigators.37 In addition, effects from residual confounding because of imperfect assessment or unknown factors cannot be ruled out.
This study also has several strengths. To our knowledge, it is the first case-control study in the United States to compare risk factors for de novo AML between sexes and WHO subtypes. In addition, this large, population-based, case-control study (638 cases and 636 controls) was conducted in Texas, a state with a strong petrochemical industry, which commonly is associated with exposures to benzene and gasoline. By collecting lifetime occupational history, we were able to more accurately estimate total occupational exposure rather than restricting the analyses to the most recent job.
In conclusion, in this case-control study, multiple risk factors were identified, including differences between sexes and subtypes, that play a significant role in the risk of AML. These findings highlight the need for multicenter collaborations to identify the epidemiologic and genetic risk factors associated with AML development to better understand the complexity of AML etiology and the underlying heterogeneity of the disease. Future studies should evaluate the interaction of epidemiologic risk factors with markers of genetic susceptibility and examine their effects in the context of specific types of AML.
This study was supported by National Cancer Institute grants CA100632 and CA115180 and by National Institute of Environmental Health Sciences grant ES007784.
CONFLICT OF INTEREST DISCLOSURES
The authors made no disclosures.