Original Paper
Diseases
Neurodegener Dis 2014;14:31–38 DOI: 10.1159/000355344
Received: January 22, 2013 Accepted after revision: August 27, 2013 Published online: November 12, 2013
Environmental and Occupational Risk Factors for Amyotrophic Lateral Sclerosis: A Case-Control Study Angela M. Malek a Aaron Barchowsky b Robert Bowser f Terry Heiman-Patterson g David Lacomis e Sandeep Rana g Ada Youk c David Stickler a Daniel T. Lackland a Evelyn O. Talbott d a
Department of Neurosciences, College of Medicine, Medical University of South Carolina, Charleston, S.C., Departments of b Environmental and Occupational Health, c Biostatistics and Epidemiology and d Epidemiology, Graduate School of Public Health, University of Pittsburgh, and e Departments of Neurology and Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, Pa., f Departments of Neurology and Neurobiology, Barrow Neurological Institute, St. Joseph’s Hospital and Medical Center, Phoenix, Ariz., and g Department of Neurology, College of Medicine, Drexel University, Philadelphia, Pa., USA
Abstract Background/Aims: Environmental and occupational exposures are implicated as risk factors for amyotrophic lateral sclerosis (ALS), the etiology of which is largely unknown, although no causal relationships have been established. Objective: The aim of the study was to evaluate the associations of personal risk factors and self-reported environmental and occupational exposures with risk of ALS. Methods: The cases involved ALS patients (n = 66) identified from major neurological centers in Pittsburgh and Philadelphia, Pa., USA, from 2008 to 2010. The age-, race- and sex-matched controls included outpatient hospital and population-based controls (n = 66). A detailed questionnaire obtaining data on occupation, vocational and avocational exposure as well as personal lifestyle factors was administered. Results: Occupational
© 2013 S. Karger AG, Basel 1660–2854/13/0141–0031$38.00/0 E-Mail [email protected] www.karger.com/ndd
exposure to metals (odds ratio, OR = 3.65; 95% CI: 1.15, 11.60) and pesticides (OR = 6.50; 95% CI: 1.78, 23.77) was related to increased risk of ALS after controlling for smoking and education. No associations were found for occupational exposure to organic or aromatic solvents. Conclusion: Workers exposed to metals and pesticides may be at greater risk of ALS. Future research should involve more accurate exposure assessment through the use of job exposure matrices, confirmation of occupation and biomarkers. © 2013 S. Karger AG, Basel
Introduction
Background The annual global incidence of amyotrophic lateral sclerosis (ALS) is approximately 1–2.6 per 100,000 persons [1]. Male sex, older age and genetic alterations, such
The study was conducted at the University of Pittsburgh.
Angela M. Malek, PhD, MPH Department of Neurosciences, College of Medicine Medical University of South Carolina Suite 501, 19 Hagood Avenue, Charleston, SC 29425 (USA) E-Mail malek @ musc.edu
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Key Words Amyotrophic lateral sclerosis · Epidemiology · Environmental exposure · Occupational exposure · Risk factors
Objective This study aims to further elucidate the epidemiological evidence for ALS etiology by investigating potential risk factors in two geographic regions of Pennsylvania (W. Pa. and the Greater Philadelphia area), both of which have a long-term presence of industry as well as many life-long residents.
Subjects and Methods Study Population This study received approval from the appropriate institutional review boards. All participants provided written informed consent. The specific aims were to evaluate the associations of personal risk factors and self-reported environmental and occupational exposure with risk of ALS.
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Neurodegener Dis 2014;14:31–38 DOI: 10.1159/000355344
Cases contacted about the study (n = 106)
Cases participating in the study (n = 78 or 73.4%)
Cases excluded due to inability to identify controls (n = 12)
Cases included in analysis (n = 66)
W. Pa. cases (n = 55)
Greater Philadelphia area cases (n = 9)
Fig. 1. ALS case enrollment flow diagram.
Sporadic ALS patients were recruited from 3 major medicalcenter neurology clinics with ALS centers, 2 in Pittsburgh, Pa., and 1 in Philadelphia, Pa., between December 2008 and July 2010. The 2 ALS centers in Pittsburgh saw an average of 75 new patients (total) per year, while the Philadelphia ALS center saw about 45 new patients per year; however, these estimates also include familial ALS patients who were excluded from our study. ALS was diagnosed according to El Escorial World Federation of Neurology Criteria by board-certified neurologists and included possible, probable or definite ALS [31]. The diagnosis also included (as available) data from the neurologic examination, clinical history, electrophysiological tests, and laboratory and imaging studies. Cases and controls were required to live in one of the W. Pa. study counties: Allegheny, Armstrong, Beaver, Butler, Washington or Westmoreland, or in one of the following Greater Philadelphia area study counties: New Castle County, Del.; Union, Somerset or Sussex County, N.J.; or Montgomery or Philadelphia County, Pa., for at least 1 year, due to their close proximity to ALS centers. Exclusion criteria for both cases and controls included having a 1st-, 2nd- or 3rd-degree blood relative with ALS, a history of travel to Guam or other islands of the Mariana group, or a history of neurological conditions such as poliomyelitis/postpolio syndrome, Parkinson’s disease, parkinsonism, Alzheimer’s disease or dementia. Cases and controls were required to speak English. Controls were selected from the corresponding geographic region (W. Pa. or Greater Philadelphia area) and 1:1 matched to cases by age of onset/first ALS symptoms (±5 years), sex and race. Patients were informed about the study at ALS clinics or received a letter describing the study from their neurologist. Two trained research coordinators were available to conduct the interview after patients were screened and consented by the neurologist or research coordinator. Of the 106 patients contacted about the study, 78 participated, with a response rate of 73.6% (fig. 1). However, only 66 (57 from W. Pa. and 9 from Philadelphia) of the 78
Malek et al.
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as in C9orf72, SOD1, TDP-43 and FUS, are the only established risk factors for sporadic ALS [2, 3]. ALS increases in incidence in those over 50 years of age until the age of 75 years [4, 5]. Men have been found to develop ALS at 1.6 times the rate of women [6]. An environmental etiology has been supported by epidemiologic studies and is suggested by the gender discrepancy [2, 7]. Also implicated in the development of ALS is a genetic predisposition to certain environmental toxicants [8, 9]. Many occupational and personal risk factors have been investigated over the years, the majority of which have produced conflicting results. Since the 1970s, most studies have reported an association between exposure to metals, such as lead, mercury or others, and risk of ALS [10–17], although others have failed to find a statistically significantly increased risk of disease [18, 19]. The relationship between exposure to pesticides, agricultural occupations and residing in a rural area and risk of ALS has been inconsistent as only about half of the epidemiologic studies, including two meta-analyses, have found an association [14, 20–25]. However, this finding has not been replicated by some studies [26–29]. The association of occupational electromagnetic field exposure with ALS has also been investigated, with most published reports indicating a positive relationship [30]. Western Pennsylvania (W. Pa.), USA, is known for its long history of steel making and is home to several National Priorities List sites, landfills, chemical plants, coal mines and coal-fired power plants, with Pittsburgh being the largest coke producer. In addition, many industrial and Superfund sites, oil refineries, metal fabricators, chemical plants and power-generating stations are located in the Greater Philadelphia area.
Exposure Assessment A modified version of the ALS Consortium of Epidemiologic Studies ALS risk factor questionnaire was administered by personal interview to obtain self-reported lifetime residential and occupational history, vocational and avocational exposure, and personal lifestyle factors [33]. Occupations held for at least 2 years since the age of 19 years were coded according to the 1980 US Census industrial and occupational classification system as follows: (1) managerial and professional specialty; (2) technical, sales and administrative support; (3) service; (4) precision production, craft and repair; (5) operators, fabricators and laborers; and (6) farming, forestry and fishing [34]. A 7th category was added for full-time homemakers. The 1980 coding was used due to the small number of participants in each category. Occupational exposure to 21 agents exposed to 10 times or more while working on any job since the age of 19 years was ob-
Risk Factors for ALS
tained and grouped under the following 7 main categories: metals; pesticides; organic/chlorinated solvents; aromatic solvents, petroleum and rubber; diesel and gasoline fuel; antifreeze and coolants; and electrical and electronic equipment and machinery. Lifetime days of exposure were calculated for each agent by multiplying the number of years exposed by the number of days (per year) exposed, and categorized into 3 levels: 1–399, 400–1,999 or ≥2,000 days, as based upon previously defined levels [35]. Statistical Analysis Univariate analyses were carried out to explore the demographic characteristics of cases and controls. The Wilcoxon signedrank test was used for nonnormally distributed data. Risk of ALS was evaluated by odds ratios (ORs) and corresponding 95% confidence interval (CI) derived from conditional logistic regression models. Exact logistic regression was performed to explore the association of exposure to individual metals and pesticides with risk of ALS. Earlier studies have suggested a relationship between education and ALS; therefore, final conditional logistic regression models were fit adjusting for smoking (ever vs. never) and education (high school or less vs. more than high school) [26, 27]. SPSS (Statistical Package for the Social Sciences) version 19 (IBM, Chicago, Ill., USA) and SAS (Statistical Analysis System) version 9.3 (SAS Institute Inc., Cary, N.C., USA) were used to conduct all statistical analyses [36, 37]. Ever smoking was characterized as smoking ≥100 cigarettes in a lifetime. Pack years were calculated by the number of packs (or proportion of a pack) smoked per day multiplied by the number of years smoked. Drinking was defined as consuming at least 1 alcoholic drink per month for 6 months. Individuals who had worked in more than one occupation were counted separately in each occupational category. Analyses were stratified by occupational exposure to account for the possibility of interactions. Participant remuneration was made possible by funding provided by the University of Pittsburgh Center for ALS Research and the ALS Hope Foundation.
Results
The demographic characteristics of cases and controls are displayed in table 1. The majority of cases and controls were male (68.2%), Caucasian/White (98.5%) and from W. Pa. (86.4%). The cases (mean ± SD: 57.1 ± 13.2 years) were slightly older than the controls (56.4 ± 13.5 years), with a trend for older cases (p = 0.07). Most cases and controls had obtained further education following high school. Both cases and controls reported living on farms ≤20 years; however, only cases (n = 5) reported living on a farm for >20 years. Cases were twice as likely as controls to use residential well water for >20 years (p = 0.03). Somewhat fewer cases than controls had ever smoked. Among those who smoked, the majority smoked for >20 years. Controls were more likely than cases to smoke >2 packs per day, though smoking 20 drinks/month
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Neurodegener Dis 2014;14:31–38 DOI: 10.1159/000355344
Cases (n = 66)
Controls χ2 (n = 66)
p
Frequency4 (n = 50; n = 44) Once a month 2–4 times per month 5–8 times per month 9–16 times per month ≥17 times per month
3 (6) 15 (30) 9 (18) 8 (16) 14 (28)
– 10 (23) 13 (30) 9 (20) 12 (27)
Use of residential well water, n None 1–3 years 4–9 years 10–14 years 15–20 years >20 years Do not know
32 (48) 8 (12) 2 (3) 5 (8) 4 (6) 12 (18) 3 (5)
49 (74) 14.19 0.03* 2 (3) 1 (2) 2 (3) 6 (9) 6 (9) –
Years lived on a farm, n None 1–3 years 4–9 years 10–14 years 15–20 years >20 years Don’t know
52 (79) – 4 (6) 1 (2) 3 (5) 5 (8) 1 (2)
58 (88) 2 (3) 2 (3) 1 (2) 3 (5) – –
4.68 0.32
8.99 0.17
Values in parentheses denote percentages. * p < 0.05. 1 For cases, age at first symptoms was used, and age at interview was used for controls. 2 A paired t test was carried out. 3 Four cases and 1 control were excluded from analysis due to refusal to disclose the frequency or number of drinks consumed per month. 4 Two cases and 1 control were excluded from analysis due to refusal to disclose the frequency of drinking per month.
consumed 1–19 alcoholic drinks per month; however, controls were more likely to consume ≥20 drinks per month. Frequency of drinking was similar for cases and controls. Table 2 presents the self-reported unadjusted results of occupational exposure characterized by agent of exposure. Those with occupational exposure to pesticides potentially had 3.17 times the risk of developing ALS compared with those without exposure (OR = 3.17; 95% CI: 1.27, 7.93). Occupational exposure to electrical and electromagnetic equipment and machinery and electromagnetic fields appeared protective for cases (OR = 0.41; 95% CI: 0.20, 0.82). No differences were found among cases and controls for any of the other kinds of occupational exposure. Malek et al.
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Cases (n = 66)
Table 2. Risk of ALS according to self-reported occupational exposure among matched cases and controls1
Exposure2
Cases (n = 66), n
Conditional,3 OR
Metals4 Pesticides5 Organic/chlorinated solvents6 Aromatic solvents, petroleum and rubber7 Diesel and gasoline fuel Antifreeze and coolants Electrical/electromagnetic equipment or machinery8
19 21 28 28 23 14 33
0.89 (0.84, 4.24) 3.17 (1.27, 7.93)* 0.75 (0.38, 1.47) 1.13 (0.57, 2.27) 0.77 (0.34, 1.75) 1.25 (0.49, 3.17) 0.41 (0.20, 0.82)
Values in parentheses denote 95% CI. * p < 0.05. The analysis excluded 2 controls who did not hold jobs as of the reference date. 2 The analysis included occupational exposure occurring ≥10 times throughout the cases’ occupational history. 3 The reference group included those without the occupational exposure. 4 Metals included lead and mercury. 5 Pesticides included: insecticides, herbicides, fungicides and fumigants. 1
6
Organic/chlorinated solvents included: paint strippers; adhesives; degreasers and other cleaning agents; dry cleaning agents; and dyes or printing inks. 7 Aromatic solvents, petroleum and rubber included: solvents (such as toluene and xylene); mineral spirits or white spirits; varnishes; oil-based paint; paint thinners; and cutting, cooling and lubrication oils. 8 Electrical and electronic equipment and machinery included electromagnetic fields such as power lines or transformer stations.
Table 3. Multivariate conditional logistic regression of the association of self-reported occupational exposure with ALS among matched
cases and controls All participants1 (n = 130)
Metals Pesticides Organic solvents Aromatic solvents Electrical/electronic equipment or machinery or electromagnetic fields Education2 Smoking3
95% CI
B
SE
p
OR
lower
upper
1.30 1.87 0.004 –0.52
0.59 0.66 0.53 0.60
0.03 0.01 0.99 0.38
3.65* 6.50* 1.00 0.59
1.15 1.78 0.35 0.18
11.60 23.77 2.86 1.91
–1.29 –0.11 –0.85
0.47 0.23 0.49
0.01 0.65 0.08
0.28 0.90 0.43
0.11 0.57 0.16
0.69 1.42 1.11
* p < 0.05. analysis excluded 2 controls who did not hold jobs as of the reference date. 2 Education was categorized as ‘high school or less’ or ‘more than high school’ (referent). 3 Smoking was categorized as ‘ever smoked’ (≥100 cigarettes) or ‘never smoked’ (referent).
A final multivariate conditional logistic model was carried out to evaluate the association of occupational exposure with risk of ALS (table 3). After controlling for smoking (ever vs. never) and education (high school or less vs. more than high school), cases reported significantly greater occupational exposure to metals (OR = 3.65; 95% CI: 1.15, 11.60) and pesticides (OR = 6.50; 95% CI: 1.78, 23.77) than controls. Occupational exposure to electrical or electronic equipment, machinery or electro-
magnetic fields again appeared protective for cases (OR = 0.28; 95% CI: 0.11, 0.69). Additional analyses were conducted to compare occupational exposure to individual metals (lead and mercury) and pesticides (insecticides, herbicides, fungicides and fumigants) between cases and controls, although the sample sizes were very small and thus exact logistic regression was used (data not shown). No type of occupational exposure was associated with an increased risk of
Risk Factors for ALS
Neurodegener Dis 2014;14:31–38 DOI: 10.1159/000355344
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1 The
Discussion
ALS cases reported greater occupational exposure to metals and pesticides compared with controls, after adjustment for smoking and education in the multivariate model. Our findings confirm results of previous research suggesting a potential relationship between exposure to metals [11–17] and pesticides [23, 35, 38, 39] and risk of ALS. Although not causally related, lead has been the most consistent environmental risk factor 36
Neurodegener Dis 2014;14:31–38 DOI: 10.1159/000355344
over time [11, 13, 16, 17]. Several risk factors and kinds of exposure including electric shocks, living near farms, physical activity, trauma and family history of neurological diseases, which have been inconsistently associated with ALS, could not be confirmed to be associated through our study. We failed to find an effect of smoking in our sample after examining it dichotomously as ever/never smoking as well as by mean pack years smoked. However, a relationship was found between well water use and ALS that would be interesting to explore further, as metals may be present in unregulated residential well water. In addition, cases consumed caffeinated coffee significantly longer (4.6 years) than controls. Possible reasons for inconsistent findings include different classifications of occupations by US Census coding or other methods and varying definitions of exposure or occupations, such as pesticides and farming. Most exposure assessment is obtained through self-report without verification or biological sampling. In addition, exposure to specific agents or chemicals is not always asked about by researchers and may not be known by participants. Another explanation may be multifactorial, involving a combination of kinds of exposure and genetic factors [5, 40]. Several limitations of our study should be considered. Recall bias may be present with a retrospective design and selection bias may be a concern as two different control populations were used, of which one was outpatient based and the other included population-based individuals identified from a readily available consumer list. As occupational exposure to electrical or electronic equipment, machinery or electromagnetic fields appeared to be protective for cases in our cohort, there may be a possible neuroprotective effect on neurons. However, our study was underpowered to detect an association. In addition, misclassification may have occurred if the exposure was misinterpreted to include computers or other electronic devices and thus was overreported. Philadelphia joined the study later, and as a result did not have the opportunity to contact and enroll all potentially eligible patients. A number of interested cases who completed the questionnaire were excluded because they did not meet the regional study criteria in W. Pa. or due to a lack of controls for the Greater Philadelphia area. Our study was underpowered to assess any potential relationships in women and racial/ethnic groups other than Caucasians. The matched case-control design was an important asset to help account for the possibility of confounding Malek et al.
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ALS among cases compared with controls, following adjustment for education and smoking. Several supplementary tables have also been provided. Demographic characteristics and personal risk factors for paired data were compared by conditional logistic regression models (OR and 95% CI; online suppl. table 1; for all online suppl. material, see www.karger.com/ doi/10.1159/000355344). Cases (mean ± SD: 40.4 ± 18.2 years) drank caffeinated coffee significantly longer (4.6 years) than controls (35.8 ± 14.7 years; p = 0.045). No other significant differences in demographic characteristics or personal risk factors were found between cases and controls. Occupation was categorized according to the 1980 US Census occupational classification system (data not shown). There were no significant differences among occupational categories for cases and controls; however, cases were more likely to be employed in a managerial and professional specialty, in service, and in operator, fabricator and laborer positions. More controls worked in technical, sales and administrative support, and in precision production, craft and repair positions. Occupational exposure would most likely be directly related to the occupation process itself among blue-collar positions such as operators, fabricators and laborers, or in precision production, craft and repair. People in other professions (managerial and professional specialty, service, and technical, sales and administrative support) may not work directly with certain kinds of occupational exposure but rather may work in close proximity. Self-reported occupational exposure was also examined by lifetime days of exposure (1–399, 400– 1,999 and ≥2,000 days), and a potential protective effect was found for ≥2,000 lifetime days of exposure to electrical/electronic equipment or machinery against the risk of developing ALS, compared with those without the exposure (OR = 0.40; 95% CI: 0.19, 0.84; online suppl. table 2).
among factors matched upon. Additionally, it sought to focus on occupational exposure and personal risk factors while controlling for smoking and education. Enrolling patients from 2 ALS centers in Pittsburgh captured the majority of ALS patients in the area. Although 6 W. Pa. counties were included in our study, 5 of which surround the city of Pittsburgh, over half of the patients resided in Allegheny County (31 of 57). This is similar to the 71 MND deaths occurring in Allegheny County over a 2-year period (1999–2000; or 443 for the 12-year period of 1999–2010) as reported by the CDC Wonder database [32]. As metals and pesticides were detected to be potential risk factors for ALS and the long-term presence of industry in Philadelphia and W. Pa. is well known, mortality from MND was compared with other regions of the state and observed 6 additional counties with mortality rates of ≥3.0 deaths per 100,000 persons in rural areas. Findings of previous studies investigating rural versus urban residency have been heterogeneous, with some reporting an increased risk in rural areas [24, 25] and others refuting this relationship [26, 27, 29]. Inclusion of the Philadelphia ALS center facilitated patient enrollment and helped improve the sample size. As other studies have
lacked additional sources of individual-level exposure such as smoking, occupation, avocation, etc., this is a major strength of our study.
Conclusion
Our study adds significantly to the existing literature with regard to the potential relationship of occupational exposure to metals and pesticides and risk of ALS, and it also highlights potential risk factors, such as well water use and caffeinated coffee, to be investigated by future studies. These potential relationships are in need of further examination and confirmation with larger and more widespread samples. Moreover, future research should involve population-based controls, job exposure matrices, confirmation of occupation and biomarkers to obtain more accurate and precise exposure assessments.
Acknowledgments This study was supported by the University of Pittsburgh Center for ALS Research and the ALS Hope Foundation.
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Diseases
Neurodegener Dis 2014;14:31–38 DOI: 10.1159/000355344
Received: January 22, 2013 Accepted after revision: August 27, 2013 Published online: November 12, 2013
Environmental and Occupational Risk Factors for Amyotrophic Lateral Sclerosis: A Case-Control Study Angela M. Malek a Aaron Barchowsky b Robert Bowser f Terry Heiman-Patterson g David Lacomis e Sandeep Rana g Ada Youk c David Stickler a Daniel T. Lackland a Evelyn O. Talbott d a
Department of Neurosciences, College of Medicine, Medical University of South Carolina, Charleston, S.C., Departments of b Environmental and Occupational Health, c Biostatistics and Epidemiology and d Epidemiology, Graduate School of Public Health, University of Pittsburgh, and e Departments of Neurology and Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, Pa., f Departments of Neurology and Neurobiology, Barrow Neurological Institute, St. Joseph’s Hospital and Medical Center, Phoenix, Ariz., and g Department of Neurology, College of Medicine, Drexel University, Philadelphia, Pa., USA
Abstract Background/Aims: Environmental and occupational exposures are implicated as risk factors for amyotrophic lateral sclerosis (ALS), the etiology of which is largely unknown, although no causal relationships have been established. Objective: The aim of the study was to evaluate the associations of personal risk factors and self-reported environmental and occupational exposures with risk of ALS. Methods: The cases involved ALS patients (n = 66) identified from major neurological centers in Pittsburgh and Philadelphia, Pa., USA, from 2008 to 2010. The age-, race- and sex-matched controls included outpatient hospital and population-based controls (n = 66). A detailed questionnaire obtaining data on occupation, vocational and avocational exposure as well as personal lifestyle factors was administered. Results: Occupational
© 2013 S. Karger AG, Basel 1660–2854/13/0141–0031$38.00/0 E-Mail [email protected] www.karger.com/ndd
exposure to metals (odds ratio, OR = 3.65; 95% CI: 1.15, 11.60) and pesticides (OR = 6.50; 95% CI: 1.78, 23.77) was related to increased risk of ALS after controlling for smoking and education. No associations were found for occupational exposure to organic or aromatic solvents. Conclusion: Workers exposed to metals and pesticides may be at greater risk of ALS. Future research should involve more accurate exposure assessment through the use of job exposure matrices, confirmation of occupation and biomarkers. © 2013 S. Karger AG, Basel
Introduction
Background The annual global incidence of amyotrophic lateral sclerosis (ALS) is approximately 1–2.6 per 100,000 persons [1]. Male sex, older age and genetic alterations, such
The study was conducted at the University of Pittsburgh.
Angela M. Malek, PhD, MPH Department of Neurosciences, College of Medicine Medical University of South Carolina Suite 501, 19 Hagood Avenue, Charleston, SC 29425 (USA) E-Mail malek @ musc.edu
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Key Words Amyotrophic lateral sclerosis · Epidemiology · Environmental exposure · Occupational exposure · Risk factors
Objective This study aims to further elucidate the epidemiological evidence for ALS etiology by investigating potential risk factors in two geographic regions of Pennsylvania (W. Pa. and the Greater Philadelphia area), both of which have a long-term presence of industry as well as many life-long residents.
Subjects and Methods Study Population This study received approval from the appropriate institutional review boards. All participants provided written informed consent. The specific aims were to evaluate the associations of personal risk factors and self-reported environmental and occupational exposure with risk of ALS.
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Cases contacted about the study (n = 106)
Cases participating in the study (n = 78 or 73.4%)
Cases excluded due to inability to identify controls (n = 12)
Cases included in analysis (n = 66)
W. Pa. cases (n = 55)
Greater Philadelphia area cases (n = 9)
Fig. 1. ALS case enrollment flow diagram.
Sporadic ALS patients were recruited from 3 major medicalcenter neurology clinics with ALS centers, 2 in Pittsburgh, Pa., and 1 in Philadelphia, Pa., between December 2008 and July 2010. The 2 ALS centers in Pittsburgh saw an average of 75 new patients (total) per year, while the Philadelphia ALS center saw about 45 new patients per year; however, these estimates also include familial ALS patients who were excluded from our study. ALS was diagnosed according to El Escorial World Federation of Neurology Criteria by board-certified neurologists and included possible, probable or definite ALS [31]. The diagnosis also included (as available) data from the neurologic examination, clinical history, electrophysiological tests, and laboratory and imaging studies. Cases and controls were required to live in one of the W. Pa. study counties: Allegheny, Armstrong, Beaver, Butler, Washington or Westmoreland, or in one of the following Greater Philadelphia area study counties: New Castle County, Del.; Union, Somerset or Sussex County, N.J.; or Montgomery or Philadelphia County, Pa., for at least 1 year, due to their close proximity to ALS centers. Exclusion criteria for both cases and controls included having a 1st-, 2nd- or 3rd-degree blood relative with ALS, a history of travel to Guam or other islands of the Mariana group, or a history of neurological conditions such as poliomyelitis/postpolio syndrome, Parkinson’s disease, parkinsonism, Alzheimer’s disease or dementia. Cases and controls were required to speak English. Controls were selected from the corresponding geographic region (W. Pa. or Greater Philadelphia area) and 1:1 matched to cases by age of onset/first ALS symptoms (±5 years), sex and race. Patients were informed about the study at ALS clinics or received a letter describing the study from their neurologist. Two trained research coordinators were available to conduct the interview after patients were screened and consented by the neurologist or research coordinator. Of the 106 patients contacted about the study, 78 participated, with a response rate of 73.6% (fig. 1). However, only 66 (57 from W. Pa. and 9 from Philadelphia) of the 78
Malek et al.
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as in C9orf72, SOD1, TDP-43 and FUS, are the only established risk factors for sporadic ALS [2, 3]. ALS increases in incidence in those over 50 years of age until the age of 75 years [4, 5]. Men have been found to develop ALS at 1.6 times the rate of women [6]. An environmental etiology has been supported by epidemiologic studies and is suggested by the gender discrepancy [2, 7]. Also implicated in the development of ALS is a genetic predisposition to certain environmental toxicants [8, 9]. Many occupational and personal risk factors have been investigated over the years, the majority of which have produced conflicting results. Since the 1970s, most studies have reported an association between exposure to metals, such as lead, mercury or others, and risk of ALS [10–17], although others have failed to find a statistically significantly increased risk of disease [18, 19]. The relationship between exposure to pesticides, agricultural occupations and residing in a rural area and risk of ALS has been inconsistent as only about half of the epidemiologic studies, including two meta-analyses, have found an association [14, 20–25]. However, this finding has not been replicated by some studies [26–29]. The association of occupational electromagnetic field exposure with ALS has also been investigated, with most published reports indicating a positive relationship [30]. Western Pennsylvania (W. Pa.), USA, is known for its long history of steel making and is home to several National Priorities List sites, landfills, chemical plants, coal mines and coal-fired power plants, with Pittsburgh being the largest coke producer. In addition, many industrial and Superfund sites, oil refineries, metal fabricators, chemical plants and power-generating stations are located in the Greater Philadelphia area.
Exposure Assessment A modified version of the ALS Consortium of Epidemiologic Studies ALS risk factor questionnaire was administered by personal interview to obtain self-reported lifetime residential and occupational history, vocational and avocational exposure, and personal lifestyle factors [33]. Occupations held for at least 2 years since the age of 19 years were coded according to the 1980 US Census industrial and occupational classification system as follows: (1) managerial and professional specialty; (2) technical, sales and administrative support; (3) service; (4) precision production, craft and repair; (5) operators, fabricators and laborers; and (6) farming, forestry and fishing [34]. A 7th category was added for full-time homemakers. The 1980 coding was used due to the small number of participants in each category. Occupational exposure to 21 agents exposed to 10 times or more while working on any job since the age of 19 years was ob-
Risk Factors for ALS
tained and grouped under the following 7 main categories: metals; pesticides; organic/chlorinated solvents; aromatic solvents, petroleum and rubber; diesel and gasoline fuel; antifreeze and coolants; and electrical and electronic equipment and machinery. Lifetime days of exposure were calculated for each agent by multiplying the number of years exposed by the number of days (per year) exposed, and categorized into 3 levels: 1–399, 400–1,999 or ≥2,000 days, as based upon previously defined levels [35]. Statistical Analysis Univariate analyses were carried out to explore the demographic characteristics of cases and controls. The Wilcoxon signedrank test was used for nonnormally distributed data. Risk of ALS was evaluated by odds ratios (ORs) and corresponding 95% confidence interval (CI) derived from conditional logistic regression models. Exact logistic regression was performed to explore the association of exposure to individual metals and pesticides with risk of ALS. Earlier studies have suggested a relationship between education and ALS; therefore, final conditional logistic regression models were fit adjusting for smoking (ever vs. never) and education (high school or less vs. more than high school) [26, 27]. SPSS (Statistical Package for the Social Sciences) version 19 (IBM, Chicago, Ill., USA) and SAS (Statistical Analysis System) version 9.3 (SAS Institute Inc., Cary, N.C., USA) were used to conduct all statistical analyses [36, 37]. Ever smoking was characterized as smoking ≥100 cigarettes in a lifetime. Pack years were calculated by the number of packs (or proportion of a pack) smoked per day multiplied by the number of years smoked. Drinking was defined as consuming at least 1 alcoholic drink per month for 6 months. Individuals who had worked in more than one occupation were counted separately in each occupational category. Analyses were stratified by occupational exposure to account for the possibility of interactions. Participant remuneration was made possible by funding provided by the University of Pittsburgh Center for ALS Research and the ALS Hope Foundation.
Results
The demographic characteristics of cases and controls are displayed in table 1. The majority of cases and controls were male (68.2%), Caucasian/White (98.5%) and from W. Pa. (86.4%). The cases (mean ± SD: 57.1 ± 13.2 years) were slightly older than the controls (56.4 ± 13.5 years), with a trend for older cases (p = 0.07). Most cases and controls had obtained further education following high school. Both cases and controls reported living on farms ≤20 years; however, only cases (n = 5) reported living on a farm for >20 years. Cases were twice as likely as controls to use residential well water for >20 years (p = 0.03). Somewhat fewer cases than controls had ever smoked. Among those who smoked, the majority smoked for >20 years. Controls were more likely than cases to smoke >2 packs per day, though smoking 20 drinks/month
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Cases (n = 66)
Controls χ2 (n = 66)
p
Frequency4 (n = 50; n = 44) Once a month 2–4 times per month 5–8 times per month 9–16 times per month ≥17 times per month
3 (6) 15 (30) 9 (18) 8 (16) 14 (28)
– 10 (23) 13 (30) 9 (20) 12 (27)
Use of residential well water, n None 1–3 years 4–9 years 10–14 years 15–20 years >20 years Do not know
32 (48) 8 (12) 2 (3) 5 (8) 4 (6) 12 (18) 3 (5)
49 (74) 14.19 0.03* 2 (3) 1 (2) 2 (3) 6 (9) 6 (9) –
Years lived on a farm, n None 1–3 years 4–9 years 10–14 years 15–20 years >20 years Don’t know
52 (79) – 4 (6) 1 (2) 3 (5) 5 (8) 1 (2)
58 (88) 2 (3) 2 (3) 1 (2) 3 (5) – –
4.68 0.32
8.99 0.17
Values in parentheses denote percentages. * p < 0.05. 1 For cases, age at first symptoms was used, and age at interview was used for controls. 2 A paired t test was carried out. 3 Four cases and 1 control were excluded from analysis due to refusal to disclose the frequency or number of drinks consumed per month. 4 Two cases and 1 control were excluded from analysis due to refusal to disclose the frequency of drinking per month.
consumed 1–19 alcoholic drinks per month; however, controls were more likely to consume ≥20 drinks per month. Frequency of drinking was similar for cases and controls. Table 2 presents the self-reported unadjusted results of occupational exposure characterized by agent of exposure. Those with occupational exposure to pesticides potentially had 3.17 times the risk of developing ALS compared with those without exposure (OR = 3.17; 95% CI: 1.27, 7.93). Occupational exposure to electrical and electromagnetic equipment and machinery and electromagnetic fields appeared protective for cases (OR = 0.41; 95% CI: 0.20, 0.82). No differences were found among cases and controls for any of the other kinds of occupational exposure. Malek et al.
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Cases (n = 66)
Table 2. Risk of ALS according to self-reported occupational exposure among matched cases and controls1
Exposure2
Cases (n = 66), n
Conditional,3 OR
Metals4 Pesticides5 Organic/chlorinated solvents6 Aromatic solvents, petroleum and rubber7 Diesel and gasoline fuel Antifreeze and coolants Electrical/electromagnetic equipment or machinery8
19 21 28 28 23 14 33
0.89 (0.84, 4.24) 3.17 (1.27, 7.93)* 0.75 (0.38, 1.47) 1.13 (0.57, 2.27) 0.77 (0.34, 1.75) 1.25 (0.49, 3.17) 0.41 (0.20, 0.82)
Values in parentheses denote 95% CI. * p < 0.05. The analysis excluded 2 controls who did not hold jobs as of the reference date. 2 The analysis included occupational exposure occurring ≥10 times throughout the cases’ occupational history. 3 The reference group included those without the occupational exposure. 4 Metals included lead and mercury. 5 Pesticides included: insecticides, herbicides, fungicides and fumigants. 1
6
Organic/chlorinated solvents included: paint strippers; adhesives; degreasers and other cleaning agents; dry cleaning agents; and dyes or printing inks. 7 Aromatic solvents, petroleum and rubber included: solvents (such as toluene and xylene); mineral spirits or white spirits; varnishes; oil-based paint; paint thinners; and cutting, cooling and lubrication oils. 8 Electrical and electronic equipment and machinery included electromagnetic fields such as power lines or transformer stations.
Table 3. Multivariate conditional logistic regression of the association of self-reported occupational exposure with ALS among matched
cases and controls All participants1 (n = 130)
Metals Pesticides Organic solvents Aromatic solvents Electrical/electronic equipment or machinery or electromagnetic fields Education2 Smoking3
95% CI
B
SE
p
OR
lower
upper
1.30 1.87 0.004 –0.52
0.59 0.66 0.53 0.60
0.03 0.01 0.99 0.38
3.65* 6.50* 1.00 0.59
1.15 1.78 0.35 0.18
11.60 23.77 2.86 1.91
–1.29 –0.11 –0.85
0.47 0.23 0.49
0.01 0.65 0.08
0.28 0.90 0.43
0.11 0.57 0.16
0.69 1.42 1.11
* p < 0.05. analysis excluded 2 controls who did not hold jobs as of the reference date. 2 Education was categorized as ‘high school or less’ or ‘more than high school’ (referent). 3 Smoking was categorized as ‘ever smoked’ (≥100 cigarettes) or ‘never smoked’ (referent).
A final multivariate conditional logistic model was carried out to evaluate the association of occupational exposure with risk of ALS (table 3). After controlling for smoking (ever vs. never) and education (high school or less vs. more than high school), cases reported significantly greater occupational exposure to metals (OR = 3.65; 95% CI: 1.15, 11.60) and pesticides (OR = 6.50; 95% CI: 1.78, 23.77) than controls. Occupational exposure to electrical or electronic equipment, machinery or electro-
magnetic fields again appeared protective for cases (OR = 0.28; 95% CI: 0.11, 0.69). Additional analyses were conducted to compare occupational exposure to individual metals (lead and mercury) and pesticides (insecticides, herbicides, fungicides and fumigants) between cases and controls, although the sample sizes were very small and thus exact logistic regression was used (data not shown). No type of occupational exposure was associated with an increased risk of
Risk Factors for ALS
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1 The
Discussion
ALS cases reported greater occupational exposure to metals and pesticides compared with controls, after adjustment for smoking and education in the multivariate model. Our findings confirm results of previous research suggesting a potential relationship between exposure to metals [11–17] and pesticides [23, 35, 38, 39] and risk of ALS. Although not causally related, lead has been the most consistent environmental risk factor 36
Neurodegener Dis 2014;14:31–38 DOI: 10.1159/000355344
over time [11, 13, 16, 17]. Several risk factors and kinds of exposure including electric shocks, living near farms, physical activity, trauma and family history of neurological diseases, which have been inconsistently associated with ALS, could not be confirmed to be associated through our study. We failed to find an effect of smoking in our sample after examining it dichotomously as ever/never smoking as well as by mean pack years smoked. However, a relationship was found between well water use and ALS that would be interesting to explore further, as metals may be present in unregulated residential well water. In addition, cases consumed caffeinated coffee significantly longer (4.6 years) than controls. Possible reasons for inconsistent findings include different classifications of occupations by US Census coding or other methods and varying definitions of exposure or occupations, such as pesticides and farming. Most exposure assessment is obtained through self-report without verification or biological sampling. In addition, exposure to specific agents or chemicals is not always asked about by researchers and may not be known by participants. Another explanation may be multifactorial, involving a combination of kinds of exposure and genetic factors [5, 40]. Several limitations of our study should be considered. Recall bias may be present with a retrospective design and selection bias may be a concern as two different control populations were used, of which one was outpatient based and the other included population-based individuals identified from a readily available consumer list. As occupational exposure to electrical or electronic equipment, machinery or electromagnetic fields appeared to be protective for cases in our cohort, there may be a possible neuroprotective effect on neurons. However, our study was underpowered to detect an association. In addition, misclassification may have occurred if the exposure was misinterpreted to include computers or other electronic devices and thus was overreported. Philadelphia joined the study later, and as a result did not have the opportunity to contact and enroll all potentially eligible patients. A number of interested cases who completed the questionnaire were excluded because they did not meet the regional study criteria in W. Pa. or due to a lack of controls for the Greater Philadelphia area. Our study was underpowered to assess any potential relationships in women and racial/ethnic groups other than Caucasians. The matched case-control design was an important asset to help account for the possibility of confounding Malek et al.
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ALS among cases compared with controls, following adjustment for education and smoking. Several supplementary tables have also been provided. Demographic characteristics and personal risk factors for paired data were compared by conditional logistic regression models (OR and 95% CI; online suppl. table 1; for all online suppl. material, see www.karger.com/ doi/10.1159/000355344). Cases (mean ± SD: 40.4 ± 18.2 years) drank caffeinated coffee significantly longer (4.6 years) than controls (35.8 ± 14.7 years; p = 0.045). No other significant differences in demographic characteristics or personal risk factors were found between cases and controls. Occupation was categorized according to the 1980 US Census occupational classification system (data not shown). There were no significant differences among occupational categories for cases and controls; however, cases were more likely to be employed in a managerial and professional specialty, in service, and in operator, fabricator and laborer positions. More controls worked in technical, sales and administrative support, and in precision production, craft and repair positions. Occupational exposure would most likely be directly related to the occupation process itself among blue-collar positions such as operators, fabricators and laborers, or in precision production, craft and repair. People in other professions (managerial and professional specialty, service, and technical, sales and administrative support) may not work directly with certain kinds of occupational exposure but rather may work in close proximity. Self-reported occupational exposure was also examined by lifetime days of exposure (1–399, 400– 1,999 and ≥2,000 days), and a potential protective effect was found for ≥2,000 lifetime days of exposure to electrical/electronic equipment or machinery against the risk of developing ALS, compared with those without the exposure (OR = 0.40; 95% CI: 0.19, 0.84; online suppl. table 2).
among factors matched upon. Additionally, it sought to focus on occupational exposure and personal risk factors while controlling for smoking and education. Enrolling patients from 2 ALS centers in Pittsburgh captured the majority of ALS patients in the area. Although 6 W. Pa. counties were included in our study, 5 of which surround the city of Pittsburgh, over half of the patients resided in Allegheny County (31 of 57). This is similar to the 71 MND deaths occurring in Allegheny County over a 2-year period (1999–2000; or 443 for the 12-year period of 1999–2010) as reported by the CDC Wonder database [32]. As metals and pesticides were detected to be potential risk factors for ALS and the long-term presence of industry in Philadelphia and W. Pa. is well known, mortality from MND was compared with other regions of the state and observed 6 additional counties with mortality rates of ≥3.0 deaths per 100,000 persons in rural areas. Findings of previous studies investigating rural versus urban residency have been heterogeneous, with some reporting an increased risk in rural areas [24, 25] and others refuting this relationship [26, 27, 29]. Inclusion of the Philadelphia ALS center facilitated patient enrollment and helped improve the sample size. As other studies have
lacked additional sources of individual-level exposure such as smoking, occupation, avocation, etc., this is a major strength of our study.
Conclusion
Our study adds significantly to the existing literature with regard to the potential relationship of occupational exposure to metals and pesticides and risk of ALS, and it also highlights potential risk factors, such as well water use and caffeinated coffee, to be investigated by future studies. These potential relationships are in need of further examination and confirmation with larger and more widespread samples. Moreover, future research should involve population-based controls, job exposure matrices, confirmation of occupation and biomarkers to obtain more accurate and precise exposure assessments.
Acknowledgments This study was supported by the University of Pittsburgh Center for ALS Research and the ALS Hope Foundation.
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