Epidemiology of Multiple Myeloma
10th INTERNATIONAL MYELOMA WORKSHOP
10-14 April 2005
Sydney, Australia
Dalsu Baris, MD, PhD
Occupational and Environmental Epidemiology Branch
Division of Cancer Epidemiology and Genetics
National Cancer Institute
NIH/DHHS
USA

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Outline
1.
Descriptive Data
2.
Environmental and Genetic Risk Factors
3.
Future Directions

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International variation in multiple myeloma incidence by continent and sex
Males Females
AFRICA
Zimbawi
AFRICA
CENTRAL AND SOUTH AMERICA
USA, Puerto Rico
Uruguay
Argentiana
France, Martinique
CENTRAL AND SOUTH AMERICA
Brazil
Ecuador
Costa Rica
Colombia
Cuba
NORTH AMERICA
USA, SEER Blacks
USA, California, LA, Angeles, Hispanic White
Canada
NORTH AMERICA
USA, SEER Whites
USA, California, LA, Non-Hispanic White
ASIA
Israel, All Jews
Israel, Non-Jews
China, Hong Kong
India
ASIA
Japan, Osaka
Korea, Seoul
Philipppines, Manila
Thailand, Bangkok
EUROPE
Italy, Florence
Switzerland, Zurich
Ireland
Italy, NE Cancer Surveillance Network
France, Isere
The Netherlands
Sweden
Norway
EUROPE
Belgium
UK, England
Denmark
Spain, Asturias
Germany, Saarland
Finland
Austria, Tyrol
Slovakia
Poland, Lower Silesia
Croatia
Chzech Republic
Luthuania
Estonia
Latvia
Belarus
Russia, St Petersburg
(Graphed using data from
OCEANIA
New Zealand
Parkin et al., IARC , Cancer
Australia, New South Wales
OCEANIA
Australia, Victoria
Australia, Queensland
Incidence in Five Continents,
Vol. VIII, 2002)
10
505
10
Rate per 100,000 person-years

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U.S. Mortality and Incidence Trends by Race
U.S. Mortality Trends
SEER Incidence Trends
(1950-2001)
(1975-2001)
20
20
10
10
Black Male
Black Male
Black Female
Black Female
White Male
White Male
White Female
White Female
person-years
0person-years
100,00 1
1
100,000
eper
per
Rat
Rate
0.2
0.2
1970 1980 1990 2000 2010
1950 1960 1970 1980 1990 2000 2010
Year of death
Year of diagnosis

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Age-Specific Incidence Rates (SEER, 1975-1999)
100
Black Male
Black Female
White Male
White Female
ears
10
person-y
100,000
1
per
ateR
0.1
0
20406080
100
Age at diagnosis
Pgm185/Plot06

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Suspected Risk Factors
Ionizing Radiation
Farming and Agricultural Exposures
Other occupational exposures (metal workers, rubber
manufacturing, wood workers, paint industry workers,
textile workers, firefighters, meat workers)
Specific Exposures (benzene, trichloroethylene, dioxins)
Other factors (HHV-8, chronic immune stimulation,
diet, obesity, low socioeconomic status)

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U.S. Multiple Myeloma Mortality Maps (1970-1994)
White Males
White Females
http://www3.cancer.gov/atlasplus/links.html

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U.S. Melanoma of Skin Mortality Maps (1970-1994)
White Males
White Females

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Multiple Myeloma in Connecticut Women
(Yale/NCI Myeloma Study)
OBJECTIVE:
To examine environmental and genetic
factors and risk of myeloma
DESIGN:
A population-based case-control study in
Connecticut women (1996-2002)
CASES:
179 cases
CONTROLS:
691 controls shared with the Yale non-
Hodgkin's lymphoma study
PARTICIPATION
RATES:
57% for cases
69% for RRD controls <65 years old
47% for Medicare controls >=65 years old

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Questionnaire Data
Demographic data (educational attainment, income)
Smoking and alcohol use
Obesity, diet
Farm and agricultural pesticide use
Home and family garden pesticide use
Occupational history
Past medical history
Medication, x-ray history
Residential history
EMF, UV exposures
Hair coloring product use
Family history
Biological Sample Collection
Blood or buccal cell collection (SNP analyses)

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Educational Attainment, Income and Multiple Myeloma
Education
Cases
Controls
ORs
95% CI
Graduate degree/Professional
16
101
1.0
Ref.
College degree
21
124
1.2
0.6-2.4
Vocational/Some college
48
211
1.4
0.8-2.7
High school graduate
64
181
2.0
1.1-3.8
<12 years schooling
30
74
2.1
1.1-4.3
P for trend =0.0044
Income
+$50,000
39
240
1.0
Ref.
>$50,000 - <=$30,000
29
130
1.2
0.7-2.0
>$30,000 - <=$10,000
17
167
1.3
0.7-2.2
<$10,000
15
31
2.1
1.0-4.4
P for trend =0.007
Don't know
41
63
3.1
1.6-5.7
Refused
15
58
1.4
0.7-2.8
Odds ratios (ORs) are adjusted of age and race

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Socioeconomic Status (SES) and Multiple Myeloma
(Baris et al., AJPH 2000; 90:1277-81)
% Population Attributable Risk
Blacks
Whites
Indicator of low SES
%%
Occupation-based
37
17
Income
19
3
Education
14
10
% of black excess in incidence explained by the low SES indicators
Low education
Occupation-based low SES
Low income
28%
17%
49%

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SES as a Surrogate for Exposure
Jobs with hazardous exposures
Unemployment
Less access to medical care
Stressful work and home environment
Poor diet
Poor housing
Exposure to infectious agents

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Smoking, Alcohol Use, Obesity and Multiple Myeloma
Smoking (pack-years)
Cases
Controls
ORs
95% CI
Never smoked
100
309
1.0
Ref.
<=6.3
19
96
0.7
0.4-1.3
>6.3 - <=16.5
15
98
0.6
0.3-1.0
>16.5 -<=33.0
21
94
0.7
0.4-1.3
>33.0
20
93
0.6
0.4-1.0
Alcohol use
Never used
79
224
1.0
Ref.
Ever used
100
467
0.7
0.5-1.0
Body Mass Index (BMI)
<25
77
342
1
Ref.
25-29.99
57
205
1.1
0.7-1.6
30-34.99
26
88
1.2
0.7-2.0
35+
18
51
1.5
0.7-2.5
Odds ratios (ORs) are adjusted of age and race

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Obesity and Multiple Myeloma
4.0
Brown et al., Cancer Causes and Control, 2001
Calle et al., NEJM, 2003
3.5
ORs
3.0
Men
Women
BMI
Blacks
Whites
2.5
Normal
1.0
1.0
Risk 2.0
Overweight
1.5
1.3
1.5
Obese
1.9
1.9
Relative 1.0
p trend
<0.001
NS
0.5
p trend=0.002
p trend=0.004
ORs adjusted for age, race and study area
0.0
BMI
18.5-24.925.0-29.930.0-34.935.0-39.9
--
--
18.5-24.925.0-29.930.0-34.935.0-39.9

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Medical and family history and multiple myeloma
Cases
Controls
ORs
95% CI
Asthma
No
163
628
1.0
Ref.
Yes
15
63
1.0
0.5-1.8
Rheumatoid Arthritis
No
170
681
1.0
Ref.
Yes
8
10
2.9
1.1-7.5
History of myeloma in first degree relative
No
157
659
1.0
Ref.
Yes
5
13
1.8
0.6-5.4
ORs are adjusted for age and race

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Myeloma Risk and Environmental Exposures
Appears to have an environmental component
Not completely understood
Low levels of exposure/small effects
Effects may be missed or underestimated
Exposures to follow-up: SES, obesity/diet,
infectious agents

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Single Nucleotide Polymorphysms (SNP) Analyses
152 cases and 597 controls
64 SNPs in selected 37 inflammatory mediated genes
Inflammatory cytokines
Th1/Th2 Cytokines
Chemokines
IL1A
IL10
IL4
IL8RB
IL1B
CTLA4
IL5
CCR2
IL1RN
IL12A
IL12B
CCR5
TNF
IL2
IL15RA
CX3CR1
LTA
IL15
IL10RA
IL8
IL6
IL7R
IL4R
CXCL12
IL16
IL13
JAK3
Adhesion molecules
Innate Immunity
Interferons
VCAM
FCGR2A
STAT1
ICAM1
TLR4
IFNGR1
SELE
MPO
IFNG
IFNGR2

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Inflammatory mediated genes and susceptibility to myeloma:
Selected inflammatory cytokines
Gene
SNP
Genotype
Controls Cases
ORs
95%CI
IL6
-174G/C
GG
206
40
1.0
Ref.
CG
252
58
1.2
0.8-1.8
CC
80
25
1.6
0.9-2.8
P for trend=0.12
TNF
-308G/A
GG
388
99
1.0
Ref.
AG+AA
155
26
0.7
0.4-1.1
TNF
-238G/A
GG
448
103
1.0
Ref.
AG+AA
12
60
0.9
0.5-1.8
LTA
+252A/G
AA
253
56
1.0
Ref.
AG
229
58
1.2
0.5-1.8
GG
60
13
0.9
1.1-0.7
ORs are for non-Hispanic Caucasians, and adjusted for age

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Inflammatory mediated genes and susceptibility to myeloma:
Innate immunity
Gene
SNP
Genotype
Controls
Cases
ORs
95%CI
FCGR2A
-120A/G
AA
230
46
1.0
Ref.
(Low-affinity Fc receptors)
AG
226
52
1.3
0.7-1.8
GG
52
20
1.9
1.1-3.6
P for trend=0.06
MPO
-463G/A
GG
327
64
1.0
Ref.
(Myeloperoxsidase)
AG+AA
177
50
1.5
1.0-2.2
TLR4
+636A/G
AA
424
96
1.0
Ref.
(Toll-like receptor 4)
AG+GG
43
10
1.1
0.5-2.3
FCGR2A, MPO, TLR4 combined
All common genotype
18
119
1.0
Ref.
One rare genotype
64
260
1.6
0.9-2.9
Two rare genotype
28
107
1.8
0.9-3.4
Three rare genotype
4
8
3.4
0.9-12.7
P for trend=0.04
ORs are for non-Hispanic Caucasians, and adjusted for age

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Innate Immunity Genes
Low-affinity Fc receptors (FCGRs)
- Bind to Fc fragments of IgG and transmit effector signals in immune cells
- Play a role in pathogenesis of immune complex related autoimmune diseases
Toll like receptors (TLRs)
- Involve in the initial phase of host defense against pathogens
- Activate signaling pathways leading to induction of immune and inflammatory genes
Myeloperoxidase (MPO)
- Hemoprotein secreted during activation of neutrophils, which plays a role in defense
by catalyzing the production of hypochloric acid
- Involves oxidative stress

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Sometimes research is easy

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Future Studies
Caretaker Genes
Macro Environment
Drug/carcinogen metabolizing
Chemicals
DNA repair
Viruses
Radiation
Other specific exposures
Immune
Individual Variations
Regulation
GENES
ENVIRONMENT
Micro Environment
CANCER
Oxyradicals
Hormones
Gatekeeper Genes
Growth factors
Cell cycle control
Programmed cell death
(Modified from Shields et al.)

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How can we adequately study joint effects
of environmental and genetic risk factors?
Large studies with biospecimens and exposure data
Power to study
Main effects
Gene-environment interactions
Gene-gene interactions
False positive reporting probability
Development of strategic partnerships
Effective utilization of large data resources
Case-control consortium

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Collaborators
Yale University
Tongzhang Zheng, MD, PhD
New York State University at Albany
Erin Bell, PhD
NCI Collaborators
Office of the Director
Shelia Zahm, ScD
Occupational and Environmental Epidemiology Branch
Nathaniel Rothman, MD; Qing Lan, MD, PhD
Hormonal Epidemiology Branch
Sophia Wang, PhD
Viral Epidemiology Branch
Elizabeth E. Brown, MPH, PhD
Genetic Epidemiology Branch
Lynn Goldin, PhD
Core Genotyping Facility
Stephen Chanock, MD, PhD; Meredith Yeager, PhD

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