Agricultura l Health Study

1. An Excess Risk of Multiple Myeloma (MM) and a Premalignant MM Disorder in the Agricultural Health Study Cohort Provides Preliminary Evidence of a Link to Pesticides Michael Alavanja, Dr.P.H.Division of Cancer Epidemiology & Genetics, National Cancer Institute2010 Midwest Rural Agricultural safety & Health Forum: Cultivating Change November 17-18, 2010 Hotel Vero/Sheraton, Iowa City, IA Agricultural Health Study

2. Collaborating Federal Agencies • National Cancer Institute • National Institute of Environmental Health Science • U.S. Environmental Protection Agency • National Institute for Occupational Safety and Health

3. Agenda • Why study Multiple Myeloma and why talk about it here? • Background: Purpose & Scope of the AHS. • Background: Epidemiologic Design of the AHS. • Background: Exposure Assessment in the AHS. • Multiple Myeloma and Farming • Monoclonal Gammopathy of Undetermined Significance (MGUS) • MGUS in the AHS • Biomarkers of Exposure & Effects of Agriculture (BEEA) Study

4. Why study Multiple Myeloma and why talk about it here? • Multiple Myeloma is a malignancy of the plasma cell. • The cause (etiology) of multiple myeloma is uncertain but farmers and farming have been associated with this malignancy in many studies conducted in North America, Europe and New Zealand. • Five year survival is only 28% (i.e., 72% die within 5 years).

5. Background, Purpose & Scope of the AHS

6. Background • World-wide occupational exposure to pesticides exceeds 1 billion. • Previous health studies characterized as having inadequate exposure assessment. • Exposure misclassification can result from inadequate exposure assessment. • Exposure misclassification reduces our ability to identify agents responsible for disease (Zahm et al., 1997, Kromhout and Heedrick 2005). • Case-control studies. • Exposure information collected after disease onset-case-recall bias possible.

7. Background • The International Agency for Research on Cancer (IARC) classifies only 1 pesticide (arsenical insecticides) and one pesticide contaminant (dioxin) as human carcinogens. [1991, IARC monograph volume 53]. • Over 800 active ingredients in thousands of pesticide formulations are on the market world-wide. • Some of these widely used pesticides are suspected to be human carcinogens, but a final determination has not been made because of limited evidence.

8. Purpose of the Agricultural Health Study (AHS) (www.aghealth.org) • To study a wide range of health effects of agricultural exposures in farmers and their families and commercial pesticide applicators. • Generate research generalizable to a much wider population world-wide.

9. Scope of the Agricultural Health Study (AHS) (www.aghealth.org) • Major Exposures under study: • Pesticides • Animals • Engine exhaust • Solvents • Organic and inorganic dust • Health Effects under study: • Cancer • Respiratory Health • Reproductive Health • Neurologic Disease • Work Place Injuries

10. Family Exposure Opportunities

11. Epidemiology Research: potential contributions of the AHS • Characteristics: • No extrapolation of results from lab animal to human. • No extrapolation from high exposures in animal testing to lower exposure in the human experience. • Results generalizable for the chemical evaluated in adults (within the range of exposures of the study). • Comprehensive exposure assessment to minimize misclassification—accurately rank order exposures. • Prospective study to minimize information bias. • Control for confounders to obtain valid risk estimates. • Generate biological results to assess biological plausibility and modes of action and identify susceptible sub-populations.

12. Cancer Epidemiology Design

13. AHS Design • Prospective design (exposure assessed prior to cancer onset) • 52,000 private applicators (i.e., farmers and nursery operators) • 32,000 spouses of farmers • >4,000 commercial applicators (i.e., pesticide application for hire) • Two important agricultural states (Iowa & North Carolina) • Corn , soybean and hog production in both states • Distinctive agriculture in North Carolina: fruits, vegetables, tobacco, cotton

14. AHS Design • Target population--licensed pesticide applicators (private & commercial applicators) : • Knowledgeable about their chemical use, • Regularly exposed but not exposed every day, • Farmers tend not to move residence (easier to follow-up).

15. AHS Design • Comprehensive exposure assessment • Initial questionnaires –self administered (1993-1997) • Enrollment questionnaire administered at pesticide licensing examination/class—82% participation of target population. • Repeated questionnaires- Telephone interviews (1999-2003 & 2005-2010)-updated exposure & other information • Field measurements of pesticides (sample of study subjects) • EPA [n=69 2,4-D & n=17 chlorpyrifos on field crops] • NIOSH [n=74 captan users in orchards].

16. AHS Design • Questionnaire content • Lifetime pesticide use for >50 pesticides (days/year; years) • Exposure determinants(e.g., pesticide application methods, equipment repair, mixed and/or applied, PPE) • Other Farm activities and non-farm occupational exposures • Lifestyle factors (e.g., smoking, diet, alcohol consumption) • Medical history • Family cancer history

17. AHS Design • Little loss to cancer incidence follow-up (<2 per cent) • Population-based cancer registries in both states • Monitor date study subjects move from state • National Death Index (NDI) • Over one-million person-years of follow-up in the AHS • Buccal cells collected as a source of DNA • 35,000 study subjects, 1999 through present

19. Control of Confounding Factors in the AHS cohort (A variable will only confound if it is correlated with exposure and effect) • Concurrent pesticide exposures (e.g., information about >50 other pesticides and total pesticide use) • Pesticide exposures and other occupational exposures (e.g., Non-farming occupational history, farm exposures other than pesticide use). • Life-style (e.g., tobacco use, alcohol, diet, physical activity)

20. AHS Research Strategy for Cancer Studies Biological Initial Reevaluation Evidence in Findings later in time Humans Iowa North Carolina License Type Exposure- Response Exposure- Response Exposure- Response Exposure- Response Exposure- Response Exposure- Response YES YES YES

21. Exposure Assessment in the AHS

22. Exposure Assessment in the AHS • Duration of use (yrs of use of >50 pesticides). • Frequency of use (days per year of use >50 pesticides) . • Intensity weighting factors • Application method • Mix • Repair pesticide application equipment • Personal protective equipment • Reliability (repeatability) and Duration Validity. • Comparison of questionnaire data to field measurements of pesticides

23. Exposure Metric in AHS • Cumulative Exposure = Duration X Intensity

24. Questionnaire ExposureAssessment • Farmers in the AHS provide reproducible reports of pesticide use information (Blair et al, 2002) • Specific chemicals and application methods • Agreement 80-95% for ever vs. never use questionnaire data collected1 year apart • Accurate information on duration of use (Hoppin et al., 2002) • Compared to pesticide registration data • Most applicators provided complete information on lifetime use of pesticides

25. Questionnaire Exposure Assessment

26. Exposure Assessment AlgorithmDosemeci M, et al. (2002) Ann Occup Hyg. Vol 46 (2), 245-260. Intensity level=(Mix + Application Method + Repair)* PPE • Information from questionnaires administered to all applicators. • Information from monitoring studies from the published literature and the Pesticide Handlers Exposure Data Base (PHED –EPA data base). • Assessment of algorithm made with field measurements of pesticides. • Assessment of algorithm made by comparing results to Canadian exposure assessment study.

27. Comparison of AHS algorithm exposure score with urine concentration of 2,4-D in sample from AHS cohort. GM ug/L Algorithm Exposure Score (mean score: 5.5, 9.4, 15.2 ) (n=69, 2,4-D applicators in the Agricultural Health Study ) Thomas K. et al. (2009) J of Exposure Science and Environmental Epidemiology 1-11.

28. Comparison of AHS algorithm exposure score with urine concentration of 2,4-D in sample from Canadian study. GM (Ug/L in urine) Algorithm Exposure Score (1-<5, 5-10, >10-15) (n=39, 2,4-D applicators Pesticide Exposure Assessment Study) Coble J. et al. (2005) J of Occupational and Environmental Hygiene. 2: 194-201.

29. Comparison of AHS algorithm exposure score with urine concentration of MCPA in sample from Canadian study. GM ug/L in urine Algorithm Exposure Score (1-<5, 5-10, >10-15) (n=42, MCPA applicators Pesticide Exposure Assessment Study) Coble J. et al. (2005) J of Occupational and Environmental Hygiene.2: 194-201.

30. Exposure Assessment Summary • AHS exposure algorithm scores consistent with pesticide exposure measurements (Thomas et al & Coble et al) • Algorithm for the AHS were more closely related to measured urinary levels than any individual exposure determinant (e.g., acres, lbs active ingredient), consequently the algorithm should reduce misclassification (Blair A et al and Thomas et al.). • Continued refinement of algorithm weights will be necessary as more exposure data is collected (Hines et al. & Thomas et al.) • __________________________________ • Thomas K. et al. (2009) J of Exposure Science and Environmental Epidemiology e-pub . • Coble J. et al, (2005) J Occup and Environ Hyg 2: 194-201. • Blair A et al., submitted • Hines CJ. et al., (2008) Ann Occup Hyg 52(3):153-166.

31. Summary: Modified Bradford Hill Criteriain the Mode of Action Framework • Dose-response (exposure-response) • Total life-times days of exposure to specific pesticide • Intensity-weighted lifetime days. • Verify exposures (i.e., compare questionnaire to field measures of pesticides) • Temporal concordance • Prospective exposure assessment • Biological plausibility • Biomarker studies of exposure and effect (toxicity pathways) • Genetic susceptibility (GXE) • Coherence and consistency • Iowa vs. North Carolina • Private vs. Commercial

32. Multiple Myeloma, MGUS and Pesticides in the Agricultural Health Study Cohort Ola Landgren1,2, Robert Kyle3,, Jane A. Hoppin4, Laura E. Beane Freeman1, Gabriella Andreottti1, Jon Hofmann1, Neil Caporaso1, Sharon Savage1 , James R. Cerhan3, Jerry A. Katzmann3, S. Vincent Rajkumar3, Michael C. Alavanja1 1Division of Cancer Epidemiology and Genetics, NCI, Bethesda, MD; 2Medical Oncology Branch, NCI, Bethesda, MD; 3College of Medicine, Mayo Clinic , Rochester, MN; 4Epidemiology Branch, NIEHS, NIH, Research Triangle Park, NC.

33. Epidemiology 101 • Relative risk= incidence of disease in the exposed population/ incidence of disease in the unexposed population. [null RR=1] • Odds ratio is a good approximation for rare diseases

34. Multiple Myeloma: Background • Multiple Myeloma is a malignancy of plasma cells (differentiated B cells). • Characterized by the presents of elevated number of plasma cells in the bone marrow. • Very often, also characterized by monoclonal proteins in serum and urine (IgA, IgD, IgE, • IgG or light chains). • Five year survival is only 28%.

35. Multiple Myeloma: Background • The cause (etiology) of multiple myeloma is uncertain but farmers and farming have been associated with this malignancy in many studies conducted in North America, Europe and New Zealand. • Other associations: age, obesity, personal use of permanent hair dyes (>20 years), certain medications, autoimmune disorders, male gender, African-American greater than European-Americans greater than Asians-Americans, some other factors suspected.

36. Multiple Myeloma: Background • MGUS ----> Multiple Myeloma (MM) • MGUS=Monoclonal gammopathy of undetermined significance • MM always preceded by a premalignant disorder MGUS (Landgren O, et al., Blood 2009;113:5412-5417)

37. Multiple Myeloma: Background • Over 1 billion people are occupationally exposed to pesticides world-wide. • SIR of multiple myeloma in Agricultural Health Study (AHS) = 1.34 (0.97-1.98).

38. Biological Plausibility Human Carcinogen Epidemiology Exposure Assessment Causal Logic to Establish Human Carcinogenicity

39. Methods • The prevalence of MGUS in a sample (n=555) of men > 50 years from the AHS cohort was compared to the prevalence of MGUS in a population-based sample (n=9,469) of men from Olmsted County, MN > 50 years.

40. Table 1. MGUS prevalence (%) among 678 men in the Agricultural Health Study and 9,469 men in Olmsted, MN

42. Table 2. Specific pesticide use at enrollment and the risk of MGUS

44. Summary: Observation from AHS(follow-up evaluations scheduled) • Multiple Myeloma in excess in AHS SIR=1.34 (0.97-1.81) (Alavanja MCR, et al. Scand J Work Environ Health 2005:31 suppl 1:39-45). • Monoclonal gammopathy of undetermined significance (MGUS) – 2-fold significant excess in AHS (Landgren O. et al., Blood (2009), 113(25);6386-6391). • Several widely used pesticides are associated with elevated MGUS levels (Landgren O. et al., Blood (2009), 113(25);6386-6391). • Odds ration for multiple myeloma = 5.7 (2.1-7.2) for heaviest users of permethrin (Rusiecki J, et al., Environ. Health Perspectives (2009), 117;581-586).

45. SIGNIFICANCE • Several million Americans use pesticides for which we found an association with MGUS in the AHS. • Causes of MGUS risk might be an underlying explanation of the previously observed excess of multiple myeloma risk among persons exposed to pesticides, including those in the AHS.

46. STRENGTHS • Comprehensive pesticide exposure assessment for AHS cohort. • Minimal misclassification of MGUS since both the AHS cohort and Olmsted County samples analyzed by the Mayo Clinic Laboratory. • Any minimal misclassification of pesticide use would be non-differential

47. LIMITATIONS • Unable to evaluate certain pesticides due to limited number of exposed cases. • Results generalizable mostly to white males.

48. Future Research • To further explore the associations between pesticides and MGUS and multiple myeloma, a follow-up study of 1,600 AHS study subjects was initiated in June 2010 (approx. 3X size of the previous study). • Will examine additional pesticides with additional exposure assessment from 1998-2009. • Will examine other biomarkers of relevance to pesticide exposure and/or multiple myeloma

49. Bibliography • >110 peer reviewed manuscripts have been published from the Agricultural Health Study they are listed at: papers have been published from the Agricultural Health Study , the complete current bibliography can be found at • >http://aghealth.nci.nih.gov/publications.html. • Under the major topic headings: • 1. Methods • 2. Exposure Assessment • 3. Health Outcomes • 4. Diet • 5. Injury Under the major topic headings

50. Intramural Research Team • NIEHS • Jane Hoppin • Dale Sandler • Freya Kamel • Donna Baird • Olga Basso • Stephanie London • Beth Regan • David Umbach • Clarice Weinberg • Martha Montgomery • Sharon Myers • Tina Saldana • Martin Valcin • Jenna Waggoner • USEPA • Kent Thomas • Carol Christensen • National Cancer Institute: • Michael Alavanja • Laura Beane Freeman • Jay Lubin • Stella Koutros • Gabriella Andreotti • Jonathan Hofmann • Neil Caporaso • Ola Lundgren • Sharon Savage • Rashmi Sinha • Aaron Blair • Shelia Zahm • Kathryn Hughes • NIOSH • Cynthia Hines • Brian Curwin • Paul Henneberger • Greg Kullman