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Study of risk factors of B-cell non-Hodgkin lymphoma (NHL) in the California Teachers Study

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Study of risk factors of B-cell non-Hodgkin lymphoma (NHL) in the California Teachers Study

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STUDY OF RISK FACTORS OF B-CELL NON-HODGKIN
LYMPHOMA (NHL) IN THE CALIFORNIA TEACHERS STUDY

by
Yani Lu

A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(EPIDEMIOLOGY)

August 2009

Copyright 2009 Yani Lu

ii

Acknowledgements

My sincerest thanks, tremendous appreciation, and deepest respect go to Dr. Leslie
Bernstein, for being not only a brilliant academic mentor, but also a supporter, a listener,
a motivator, a challenger, and an inspiration. Working with Dr. Bernstein has been an
invaluable privilege.

I would also like to extend my special thanks and gratitude to Dr. Wendy Mack for her
academic advice, exceptional guidance, her insight, and her knowledge throughout my
Ph.D. study. I would like to thank Dr. Dennis Deapen and Dr. Wendy Cozen for their
countless advice and suggestions throughout the course of my research and in the
preparation of this manuscript. I would also like to express my thanks to Dr. Clive
Taylor who as the outside member on my committee provided me with the necessary
insight into the matter of my research and taught me the joy of critical scientific thinking.

The moral support I received from friends and family over the past three years was
crucial to the success of my studies. Most of all I owe my sincerest thanks to my husband
and my son for being so supportive and my parents for being so close despite of the long
distance that separated us.

iii

Finally, I want to express my thanks to all my colleagues and friends at USC and
elsewhere who inspired, supported me in different ways, namely Jane, Xinhui, Nitzon,
Carmen, Ellen, Tina, and many others. I also want to extend my sincere thanks to all the
participants in the California Teachers Study who made my project possible.

iv

Table of Contents

Acknowledgements

ii
List of Tables

vi
Abstract

viii
Chapter 1 Non-Hodgkin Lymphoma Study Background

1
1.1 Introduction

1
1.2 NHL Classification

2
1.3 NHL Identified Risk Factors

3
1.4 NHL Tentative Risk Factors

4
1.5 Study Aims

6
Chapter 2 Family history of hematopoietic malignancies and Non-
Hodgkin lymphoma risk in the California Teachers Study

8
2.1 Summary

8
2.2 Introduction

9
2.3 Materials and Methods

10
2.4 Results

13
2.5 Discussion

17
2.6 Conclusions

23
Chapter 3 Body size, recreational physical activity and B-cell Non-
Hodgkin lymphoma risk among women in the California Teachers
Study

24
3.1 Summary

24
3.2 Introduction

25
3.3 Materials and Methods

27

v

3.4 Results

34
3.5 Discussion

45
3.6 Conclusions

51
Chapter 4 Oral contraceptive use, hormone therapy and risk of Non-
Hodgkin lymphoma in the California Teachers Study

53
4.1 Summary

53
4.2 Introduction

54
4.3 Materials and Methods

57
4.4 Results

64
4.5 Discussion

73
4.6 Conclusions

84
Bibliography

85

vi

List of Tables

Table 2.1 Selected baseline characteristics of 121,216 eligible women in the
California Teachers Study in relation to family history of hematopoietic
malignancy at baseline

14
Table 2.2 Relative risk estimates and 95% confidence intervals for the
association between family history of hematopoietic malignancy and B-cell
NHL risk among women in the California Teachers Study

15
Table 2.3 Relative risk estimates and 95% confidence intervals for the
association between family history of hematopoietic malignancy and B-cell
NHL risk among women with sibling in the California Teachers Study

18
Table 3.1 Selected baseline characteristics of 121216 eligible women in the
California Teachers Study in relation to long-term physical activity, height
and weight at baseline

36
Table 3.2 Relative risk estimates (and 95% confidence intervals) for the
association between body size and B-cell NHL among women in the
California Teachers Study

39
Table 3.3 Relative risk estimates (and 95% confidence intervals) for the
association between physical activity and B-cell NHL among women in the
California Teachers Study

42
Table 3.4 Relative risk estimates (and 95% confidence intervals) for the
association between waist, hip, waist/hip ratio and B-cell NHL among women
in the California Teachers Study

43
Table 3.5 Relative risk estimates (and 95% confidence intervals) for the
association between height and B-cell NHL among women in the California
Teachers Study, by body mass index, alcohol consumption, smoking status,
long-term physical activity and family history of lymphoma

44
Table 4.1 Selected baseline characteristics among eligible women in relation
to oral contraceptive (OC) and hormone therapy (HT) use status at cohort
entry

65
Table 4.2 Oral contraceptive (OC) use, menopausal status and hormone
therapy (HT) use in two analytic cohorts
67

vii

Table 4.3 Relative risk (RR) estimates (and 95% confidence intervals (CI))
for the association between oral contraceptive (OC) use and B-cell NHL risk

69
Table 4.4 Relative risk (RR) estimates (and 95% confidence intervals (CI))
for the association between menopause and B-cell NHL risk

70
Table 4.5 Relative risk (RR) estimates (and 95% confidence intervals (CI))
for the association between hormone therapy (HT) use and B-cell NHL risk

74

viii

ABSTRACT

The incidence of Non-Hodgkin lymphoma (NHL) has risen for several decades.
However, few risk factors have been identified and most of the etiology of NHL has not
been explained.

This dissertation presents the results for family history of hematopoietic malignancy,
body size, physical activity, and exogenous hormone use on B-cell NHL risk among
women aged 22 to 84 years old at cohort entry in the California Teachers Study.
Multivariable adjusted relative risks (RR) and 95% confidence intervals (CI) were
estimated by fitting Cox proportional hazards models. A history of hematopoietic
malignancy in any first-degree relative was associated with increased B-cell NHL risk
(RR=1.52, 95% CI=1.11-2.07). Risk patterns vary by type of hematopoietic malignancy
and the gender of the relative. Height was positively associated with all B-cell NHL
combined (P for trend=0.0002) and B-cell chronic lymphocytic leukemias/small
lymphocytic lymphomas (CLL/SLL) subtype (P for trend=0.001). Weight and body mass
index (BMI, kg/m
2
) at age 18 were predictors for B-cell NHL overall. Women who used
oral contraceptives (OC) had lower risk of B-cell NHL than women who had never used
OC (RR=0.82, 95%CI=0.66-1.03). Although the overall association between B-cell NHL
and HT use was null (RR=1.05, 95%CI=0.82-1.34), current users of unopposed estrogen
therapy (ET) had greater risk (RR=1.36, 95%CI=1.00-1.87) and current users of estrogen
plus progestin therapy (EPT) had lower risk (RR=0.80, 95%CI=0.58-1.10) than women

ix

with no HT use. Furthermore, risk of B-cell NHL declined as duration of EPT use
increased, whereas no duration of use effect was observed for ET use.

These findings indicate: 1) Family history of hematopoietic malignancy is positively
associated with B-cell NHL risk and risk patterns vary by type of hematopoietic
malignancy and the gender of the relative; 2) Greater height, which may reflect early life
immune function, infectious exposures, nutrition, or growth hormone levels, may play a
role in NHL etiology; body size at age 18 may be more relevant to NHL etiology than
that in later life; 3) Unopposed estrogen use may increase the risk of B-cell NHL, but
combinations of estrogen and progestin, as EPT or OC, may decrease risk.

1
Chapter 1
Non-Hodgkin Lymphoma Study Background

1.1 Introduction
Non-Hodgkin lymphoma (NHL), as an etiologically and clinically heterogeneous group
of lymphoid malignancies, is expected to account for 4.6% of cancer diagnoses and 3.4%
of cancer deaths in 2008 (Jemal et al., 2008). Its occurrence has risen steadily and
dramatically in most parts of the world for several decades. For example, the age-
adjusted incidence almost doubled in the second half of the 20
th
century, and NHL has
become the fifth most common cancer among men and women in the United States. The
known risk factors, HIV epidemic, and changes in detection and diagnosis of lymphoma
can only explain a small part of the rise, leaving the much of the increase in incidence
largely unexplained (Hartge & Devesa, 1992). Since 1991, the increase in NHL incidence
reached a plateau among males, partly due to improved treatment for HIV. However, the
age-adjusted incidence rate for females continues to increase at approximately 0.7% per
year since 1995, as estimated by Surveillance, Epidemiology and End Results (SEER)
Program ("SEER Incidence & US Mortality Trends,1996-2005," 2008).

Lymphomas originate in lymphatic organs during progressive stages of lymphoid
development. B and T lymphocytes develop and mature in the primary, or central,
lymphoid organs, the B-cells in the bone marrow and the T-cells in the thymus. The

2
mature lymphocytes then migrate to the secondary, or peripheral, lymphoid organs
(lymph nodes, spleen, blood, tonsils, and other lymphoid tissues located in the
gastrointestinal and respiratory tract) (Harris et al., 2001). SEER data show that 65% of
lymphomas present in lymph nodes in the United States in recent years (Hartge, Wang,
Bracci, Devesa, & Holly, 2006).

1.2 NHL Classification
The great heterogeneity of NHL creates enormous challenges for the study of NHL
etiology. The World Health Organization (WHO) classification of lymphoid neoplasm
has become the most consistent and reliable disease classification system; and has
become the international standard (Jaffe, Harris, Stein, & Vardiman, 2001). The WHO
classification combines morphologic, immunophenotypic, genetic, and clinical features to
group lymphomas into three major categories: B-cell neoplasm, T- and NK- cell
neoplasms, and Hodgkin lymphoma. Within the B- and T-cell neoplasms, the WHO
scheme distinguishes between precursor and mature neoplasms. Worldwide, it is
estimated that mature B-cell lymphomas account for over 90% of all lymphomas, with
the largest two subsets, diffuse large B-cell lymphoma (DLBCL) and follicular
lymphoma, accounting for approximately 31% and 22% of all lymphomas, respectively
(Jaffe et al., 2001).

3
1.3 NHL Identified Risk Factors
Advancing age is a major risk factor for NHL. Men are more likely than women to
develop NHL, generally by a 3:2 margin. There are also racial differences in NHL
incidence rates with Caucasians having higher risk than other racial groups for both men
and women; this racial disparity is particularly evident at older ages (Hartge, Wang et al.,
2006).

Most of the etiology of NHL has not been explained although previous studies have
established several associations and have provided numerous leads to pursue. The most
clearly established risk factors for NHL are immuno-suppressed states due either to
primary immunodeficiency conditions or to acquired immunodeficiency conditions which
occur in iatrogenic immuno-suppression among organ transplants patients, Sjogren’s
syndrome, and infection with HIV and other viruses (Bernstein & Cozen, 2002).
Widespread use of Highly Active Antiretroviral Therapy (HAART) has reduced the
incidence of CNS lymphoma and other NHL subtypes associated with HIV, which has
been demonstrated in SEER data (Clarke & Glaser, 2001; Eltom, Jemal, Mbulaiteye,
Devesa, & Biggar, 2002). However, considerable risk still remains (Biggar, 2001;
Jacobson et al., 1999). Many NHL cases with an infectious agent etiology are of B-cell
origin or of rare histology, and considerable geographic heterogeneity exists for these
lymphomas (Alexander et al., 2007). Although positive associations for several viral and

4
bacterial agents have been reported across numerous studies, the specific etiologic
mechanisms for NHL are not known.

1.4 NHL Tentative Risk Factors
The risk of NHL is slightly elevated among male farmers in the United States, which has
raised concerns that pesticide exposure may increase the risk of NHL (Alexander et al.,
2007). However, epidemiologic studies of pesticide exposure have not identified
consistent associations with pesticides as a general category, or with classes of pesticides
or specific chemicals. Case-control studies are limited by the low prevalence of exposure
to uncommon pesticides or of related occupations, whereas cohort studies of exposed
populations generally accrue few NHL cases, reducing precision. The lack of consistency
across different exposure groups and outcomes detracts from the hypothesis that pesticide
exposure is a cause of NHL. It is possible that pesticide exposure serves as a surrogate for
another farm-related exposure, one that is more ubiquitous and could affect other
segments of the population, such as oncogenic viruses carried by farm animals or
ultraviolet (UV) radiation (Bernstein & Cozen, 2002).

UV radiation, principally from sunlight, has been suggested as a risk factor for NHL
partly based on co-occurrence of skin cancers and lymphomas. In SEER registry data,
melanoma survivors showed a relative risk of 1.42 for NHL, and survivors of NHL
showed a relative risk of 1.75 for skin melanoma (Goggins, Finkelstein, & Tsao, 2001).

5
This pattern of second primary co-occurrence of skin cancer and NHL is now established,
but the relative contributions of therapy, genetic susceptibility, and environmental
exposures remain to be discerned. Recent case-control studies from Australia (Hughes et
al., 2004), from Sweden and Denmark (Smedby et al., 2005) and the United States
(Hartge, Lim et al., 2006) show a modest inverse association between level of UV
exposure and NHL risk. This apparent protective association agrees with the gradient in
risk in the United States (Hartge, Devesa, Grauman, Fears, & Fraumeni, 1996) and could
indicate a beneficial effect of vitamin D (Egan, Sosman, & Blot, 2005). More
epidemiologic data are needed before a firm conclusion can be drawn on the role of
ultraviolet radiation in NHL development.

The effects of ionizing radiation on NHL risk have been evaluated among different
groups of patients exposed to radiation treatments and among occupational groups
exposed to radiation, such as atomic energy workers, uranium workers, and radiologists.
Based on these studies, no convincing evidence exists for an increased risk of NHL
associated with radiation exposure (Bernstein & Cozen, 2002).

As to lifestyle factors, diet, exercise, and energy balance might influence the risk of
development lymphoma through a variety of pathways (Skibola, Forrest et al., 2004) and
dietary patterns. These patterns have changed markedly during the years of the long-term
increases in NHL. The current data do not support firm conclusions, but several studies

6
have reported excess lymphoma risk in association with diets high in fat and protein or
low in vegetables and fruits (Chiu et al., 1996; Tavani, Gallus, La Vecchia, & Franceschi,
2001; S. Zhang et al., 1999). Nutritional status and physical activity are known to alter
immune function, a suspected risk factor for NHL (Swerdlow, 2003). Adult height partly
reflects early life nutrition, health experience and growth related hormones. Obesity and
physical activity may also play an important role in the etiology of NHL because they are
involved in hormone metabolism, immune response, inflammatory response and
angiogenesis (Calle & Kaaks, 2004). Exogenous estrogens have been evaluated in case-
control and cohort studies with inconsistent results that vary by type of exposure and
NHL subtype (Cerhan, Vachon et al., 2002; Lee, Bracci, & Holly, 2008; Skibola et al.,
2005). Reproductive hormones affect immune function, and estrogen and estrogen-like
compounds influence B-cell development (Medina, Strasser, & Kincade, 2000); further
research is needed to clarify the role of exogenous hormones on risk of NHL.

1.5 Study Aims
In summary, few risk factors have been identified for NHL. Although familial
aggregation of NHL is well established, the mode of inheritance is not. Furthermore,
there is increasing evidence of etiologic heterogeneity among subtypes of NHL, with
different incidence patterns according to age, sex, race, and geography. Thus, we propose
here to use the California Teachers Study, a cohort study among female teachers and
administrators, to investigate the pattern of B-cell NHL heritability; the association

7
between body size, and recreational physical activity and risk of B-cell NHL and its main
subtypes; the association between oral contraceptive use, and hormone replacement
therapy and risk of B-cell NHL and its main subtypes.

8
Chapter 2
Family history of hematopoietic malignancies and
Non-Hodgkin lymphoma risk in the California Teachers Study

2.1 Summary
Risk of non-Hodgkin lymphoma (NHL) appears to be increased among persons with a
family history of hematopoietic malignancies, but whether risk varies by gender or
affected family member’s relationship is unclear. We explored these issues among
121,216 members of the California Teachers Study, a cohort of female teachers and
administrators in California followed from 1995 to 2005 for diagnosis of incident B-cell
NHL (N=478). Relative hazards (RR) and 95% confidence intervals (CI) were estimated
using Cox proportional hazards methods. A history of hematopoietic malignancy in any
first-degree relative (parent, sibling or child) was associated with increased B-cell NHL
risk (RR=1.52, 95% CI=1.11-2.07). Risk was elevated for women reporting a first-degree
relative with lymphoma (RR=1.74, 95% CI=1.16-2.60), but not leukemia (RR=1.35, 95%
CI=0.87-2.09). Having a lymphoma-affected parent (RR=2.01, 95% CI=1.20-3.37) or
sibling (RR=1.95, 95% CI=1.04-3.67) increased risk; this association was restricted to
female relatives (RR=2.48, 95% CI=1.53-4.04). For women with a leukemia-affected
relative, risk was elevated if the relative was male (RR=1.76, 95% CI=1.05-2.94). Family
history of hematopoietic malignancy is positively associated with B-cell NHL risk and
risk patterns vary by type of hematopoietic malignancy and the gender of the relative.

9
2.2 Introduction
Evidence from epidemiologic studies shows that the risk of non-Hodgkin lymphoma
(NHL) is increased by approximately 2-fold for individuals who have a first-degree
family history (parent, sibling or child) of hematopoietic malignancy (Altieri, Bermejo, &
Hemminki, 2005; Chang, Smedby et al., 2005; Chatterjee et al., 2004; Chiu et al., 2002;
Chiu et al., 2004; Goldin et al., 2005; Mensah, Willett, Ansell, Adamson, & Roman,
2007; Negri et al., 2006; Pottern et al., 1991; S. S. Wang et al., 2007; Y. Zhang et al.,
2007; Zhu et al., 1998). However, the genetic factors underlying NHL are poorly
understood.

A recent analysis from the International Lymphoma Epidemiology (InterLymph)
Consortium of NHL case-control studies found an increased risk of NHL for individuals
who reported having a first-degree relative with a hematopoietic malignancy (NHL,
Hodgkin lymphoma, leukemia, or multiple myeloma) (S. S. Wang et al., 2007). They also
found that NHL risk differed by the gender and familial relationship of the affected
relative. These findings were based on case-control studies using retrospectively self-
reported data on family history, and were therefore potentially subject to survival bias
and differential recall bias (Chang, Smedby, Hjalgrim, Glimelius, & Adami, 2006). To
understand the role of genetic factors and the underlying disease mechanisms in NHL
better, we used data from the prospective California Teachers Study (CTS) to investigate
NHL risks associated with first-degree family history.

10
2.3 Materials and Methods
2.3.1 Study Cohort
The CTS is a cohort of 133,479 female California public school professionals, identified
through the California State Teachers Retirement System, who responded to an initial
mailed questionnaire in 1995. A detailed description of the CTS has been published
elsewhere (Bernstein et al., 2002). In this analysis, we included 121,216 CTS participants
who were California residents, under age 85 years at baseline, who had no prior history
of a hematopoietic malignancy at the time of recruitment into the cohort. Use of human
subjects in this study was approved by the Institutional Review Boards at the City of
Hope, University of Southern California, Northern California Cancer Center, and
University of California Irvine; and by the Committee for the Protection of Human
Subjects, California Health and Human Services Agency.

Because of etiologic heterogeneity among NHL subtypes (Morton et al., 2007), we
classified B-cell NHL and T-cell NHL separately. Incident diagnoses of B-cell NHL
(International Classification of Diseases for Oncology, ICDO, third edition morphology
codes: 9590, 9591, 9670-9675, 9678-9699, 9727, 9823, 9832, 9835 and 9836), including
chronic lymphocytic and prolymphocytic leukemias, were identified through annual
linkages with the California Cancer Registry, a population-based cancer registry. Thirty-
six women diagnosed with a T-cell NHL (IDCO codes: 9700-9702, 9705, 9709, 9714,
9718, 9719, 9834) were followed until their diagnoses but not included as cases (events).

11
Person-time was accrued from the date a participant completed her baseline questionnaire
until the first occurrence of either a diagnosis of a B-cell NHL or a censoring event,
including a move outside of California for at least 4 months, a first diagnosis of another
hematopoietic malignancy (T-cell NHL, Hodgkin lymphoma, multiple myeloma or
leukemia other than those included in our NHL case definition), death, or December 31,
2005. Maintenance of a California residence was monitored through annual newsletter
mailings, questionnaire mailings, annual linkage with the U.S. Postal Service national
change of address database, and change-of-address postcards submitted by participants.
Information on dates and causes of death was obtained from the California state mortality
files, the Social Security Administration death master files, and the National Death Index.

2.3.2 Exposure Assessment
The self-administered, baseline questionnaire collected detailed information on personal
and family history of cancer. For this study, having “no family history” of a
hematopoietic malignancy was defined as reporting no first-degree relative (mother,
father, sister, brother, daughter, or son) with 1) NHL or Hodgkin lymphoma or 2)
leukemia. When reporting family history, participants were not asked to distinguish
among lymphoma or leukemia subtypes, as valid recall of subtype has been shown to be
inaccurate (Glaser, Chang, Horning, & Clarke, 2007; Morton et al., 2007). We
categorized the affected relatives according to their relationship to the participant,

12
including male or female relative; parent, child, or sibling; and father, mother, brother, or
sister.

We assigned all CTS participants a socioeconomic status (SES) score. This was based on
area-level SES summary score assigned to each of the 1990 census block groups in
California. The score equally weighted education, occupation, and income quartile
rankings of the California population in the 1990 census (Reynolds et al., 2004).
Participants were assigned an SES summary score according to their residential addresses
at baseline. For this analysis, we categorized each participant’s SES score into one of four
quartiles, based on the SES distribution across all California residents in 1990.

2.3.3 Statistical Analysis
We used Cox proportional hazards regression models, stratified by age in years at
baseline, to estimate hazard rate ratios (RR) and 95% confidence intervals (CI) for B-cell
NHL, using age in days from baseline until the end of follow-up for the time under
observation. Women who were adopted were assigned unknown family history in
analyses evaluating the association of having an affected parent or sibling on NHL risk,
but they were included in analyses of the effect of having an affected child. Where
relevant, analyses included a category for those who were missing family history
information. Results for this category are not presented in the tables. We assessed
potential confounders including race, residential SES, history of a full-term pregnancy

13
(yes, no, or unknown), smoking status at baseline (current, former, never, or unknown),
alcohol consumption one year before baseline (0, <15, or ≥15 grams/day, or unknown),
height (<62, 62-63, 64-65, 66-67, ≥68 inches, or unknown) and body mass index at
baseline (BMI; <20, 20-24.9, 25-29.9, or ≥30 kg/m
2
, or unknown). We did not include
any of these variables in our final statistical models as none changed the risk estimate by
as much as 5%. We adjusted for number of siblings in models that assessed B-cell NHL
risks among individuals who had an affected brother or sister. We conducted two
sensitivity analyses. In one of these, we restricted the analysis of a family history in a
sibling to woman with at least one sibling. In the second we assessed the association with
NHL of having a leukemia-affected relative by excluding women with CLL who were
censored on the dates of their diagnoses. All statistical analyses were performed using
SAS version 9.1 (SAS Institute, Inc.).

2.4 Results
During an average of 9.3 years of follow-up, 478 CTS participants were diagnosed with
B-cell NHL; their mean age ± standard deviation at diagnosis was 69.3 ± 11.7 years
(range, 33-92 years). Table 2.1 summarizes the distribution of several characteristics
according to family history of lymphoma or leukemia. Overall, 5.1% of the participants
had a first-degree family history of lymphoma or leukemia. Non-Hispanic white women,
older women, and women with higher BMI were more likely to have an affected first-

14
Table 2.1 Selected baseline characteristics of 121,216 eligible women in the California
Teachers Study in relation to family history of hematopoietic malignancy at baseline
N Family history of lymphoma or leukemia
No Yes Not known
Total 121,216 113,225 6,165 1,826
Age at cohort entry (year)
<35 12,741 10.7 4.0 18.2
35 – 44 21,448 17.9 13.5 19.2
45 – 54 36,468 30.1 29.3 30.8
55 – 64 23,475 19.2 23.3 15.3
65 – 74 17,797 14.5 19.1 11.5
75 – 84 9,287 7.5 10.8 5.1
Age-adjusted percentages:
Race
Non-Hispanic white 104,814 86.6 89.0 81.4
All other races/ethnicities 15,405 12.7 10.3 13.9
Unknown race/ethnicity 997 0.7 0.7 4.7
Socioeconomic status quartile rank
1
st
(low) 5,287 4.1 4.0 4.5
2nd 20,799 16.3 16.0 17.6
3
rd
39,228 32.0 31.5 33.7
4
th
(high) 54,356 46.4 47.3 43.0
Unknown 1,546 1.3 1.2 1.2
Smoking status
Never 79,627 64.8 63.2 62.8
Former 34,684 29.4 31.2 29.2
Current 6,181 5.2 5.2 7.6
Unknown 724 0.6 0.4 0.5
Alcohol consumption (grams/day)
None 38,619 31.3 31.8 30.4
< 15 56,970 48.3 46.3 47.4
≥ 15 19,301 16.1 17.3 17.4
Unknown 6,326 4.3 4.6 4.8
Body mass index (kg/m
2
)
<20 12,651 48.4 47.0 44.1
20 - 24.9 58,204 10.1 9.3 9.8
25 - 29.9 29,100 24.1 25.0 24.2
≥30 16,414 14.1 15.4 17.6
Unknown 4,847 3.3 3.3 4.3
Number of siblings
0 11,878 10.1 8.3 0.2
1 35,560 30.4 28.0 0.1
2 30,147 25.6 24.7 -
3 18,147 15.3 16.1 -
4+ 21,804 17.3 21.7 0.1
Adopted/unknown 3,680 1.4 1.3 99.6

15

Table 2.2 Relative risk estimates and 95% confidence intervals for the association between
family history of hematopoietic malignancy and B-cell NHL risk among women in the
California Teachers Study
Family history Total person-years Number of cases RR (95% CI)
None 1,053,996 432 1.00

Lymphoma 29,620 25 1.74 (1.16-2.60)
Male relative 16,572 9 1.14 (0.59-2.21)
Female relative 13,601 17 2.48 (1.53-4.04)
Parent 18,412 15 2.01 (1.20-3.37)
Father 9,824 4 1.06 (0.40-2.85)
Mother 8,756 11 2.92 (1.60-5.31)
Sibling* 9,178 10 1.95 (1.04-3.67)
Brother* 5,478 5 1.66 (0.69-4.02)
Sister* 3,810 5 2.28 (0.94-5.54)
Child 2,617 1 0.52 (0.07-3.68)
Parent/Child 20,904 16 1.72 (1.04-2.83)

Leukemia 29,211 21 1.35 (0.87-2.09)
Male relative 16,653 15 1.76 (1.05-2.94)
Female relative 12,960 7 0.95 (0.45-2.01)
Parent 18,974 15 1.62 (0.97-2.71)
Father 10,966 11 2.17 (1.19-3.95)
Mother 8,097 4 0.95 (0.35-2.54)
Sibling* 8,143 7 1.42 (0.67-3.00)
Brother* 4,643 5 1.80 (0.74-4.35)
Sister* 3,585 2 0.89 (0.22-3.60)
Child 2,516 1 0.71 (0.10-5.01)
Parent/child 21,451 16 1.50 (0.91-2.48)

Any lymphoma or
Leukemia 57,153 44 1.52 (1.11-2.07)
Male relative 32,635 24 1.49 (0.99-2.25)
Female relative 26,037 22 1.58 (1.03-2.43)
Parent 36,674 29 1.76 (1.21-2.57)
Father 20,563 15 1.71 (1.02-2.86)
Mother 16,582 14 1.77 (1.04-3.02)
Sibling* 16,924 16 1.64 (0.99-2.72)
Brother* 9,953 10 1.77 (0.94-3.32)
Sister* 7,268 6 1.39 (0.62-3.12)
Child 5,064 2 0.60 (0.15-2.42)
Parent/child 41,477 31 1.58 (1.10-2.28)
RR = relative risk; CI = confidence interval.
* Model adjusted for number of siblings (0, 1, 2, 3, 4+, adopted/unknown)

16
degree relative. Family history did not differ according to SES, smoking status, or level
of alcohol consumption.

A history of hematopoietic malignancy in any first-degree relative was associated with a
statistically significant increased risk of B-cell NHL (RR=1.52, 95% CI=1.11-2.07)
(Table 2.2). Risk of B-cell NHL was statistically significantly elevated for women who
reported having a first-degree relative with NHL or Hodgkin lymphoma (RR=1.74, 95%
CI=1.16-2.60), but was not associated with having a first degree relative diagnosed with a
leukemia (RR=1.35, 95% CI=0.87-2.09), when compared to women with no family
history of any hematopoietic malignancy. The association with a parental-based family
history of lymphoma or leukemia did not differ from that of a sibling-based family
history (Table 2.2). However, risks varied significantly by the gender of the affected
family member. Women reporting a female relative with lymphoma had a 2.5-fold
greater risk of B-cell NHL than those with no family history of any hematopoietic
malignancy (RR=2.48, 95% CI=1.53-4.04), but those reporting a male relative with
lymphoma were not at increased risk (RR=1.14, 95% CI=0.59-2.21). In contrast, an
increased risk of B-cell NHL was found among women who had a first-degree male
relative with leukemia (RR=1.76, 95% CI=1.05-2.94), but not a female-affected relative
(RR=0.95, 95% CI=0.45-2.01), compared to women with no family history of any
hematopoietic malignancy.

17
When family history of both lymphoma and leukemia were combined, risk estimates
were elevated for most family history categories (Table 2.2). In a secondary analysis
limited to women who had at least one sibling, risk estimates for having an affected
sibling were essentially unchanged (Table 2.3).

2.5 Discussion
Our cohort study results confirm the evidence from population-based case-control studies
and registry-based linkage studies of an increased NHL risk among individuals with a
family history of hematopoietic malignancy (Altieri et al., 2005; Chang, Smedby et al.,
2005; Chatterjee et al., 2004; Chiu et al., 2002; Chiu et al., 2004; Goldin et al., 2005;
Mensah et al., 2007; Negri et al., 2006; Pottern et al., 1991; Y. Zhang et al., 2007; Zhu et
al., 1998). The magnitudes of the RR estimates in our cohort study are closer to those
from registry-based studies, in which family history data are verified through linkage to
population-based cancer registries, than to those reported for case-control studies, in
which family history was generally self-reported and, therefore, subject to
misclassification and recall bias.

In our study, we found that the association between a first-degree family history of
hematopoietic malignancy and risk of B-cell NHL varied by gender, with stronger
associations when female relatives had a history of lymphoma and when male relatives
had a history of leukemia; and that it did not matter whether the relatives were parents or

18
Table 2.3 Relative risk estimates and 95% confidence intervals for the association between family history
of hematopoietic malignancy and B-cell NHL risk among women with sibling in the California Teachers
Study
Total Number
Family history person-years of cases RR (95% CI) RR (95% CI)*
No 932,391 379 1.00

Hodgkin's or other lymphoma 26,941 22 1.64 (1.07-2.53)
Male relative 15,120 8 1.09 (0.54-2.20)
Female relative 12,291 15 2.37 (1.41-3.97)
Parent 16,203 13 1.98 (1.14-3.44)
Father 8,730 3 0.89 (0.29-2.77)
Mother 7,600 10 3.07 (1.64-5.76)
Sibling 9,079 10 1.84 (0.98-3.45) 1.95 (1.03-3.66)
Brother 5,409 5 1.58 (0.65-3.83) 1.67 (0.69-4.03)
Sister 3,779 5 2.12 (0.87-5.12) 2.26 (0.93-5.49)
Offspring 2,187 0
Parent/offspring 18,314 13 1.57 (0.90-2.73)

Leukemia 26,322 18 1.25 (0.78-2.00)
Male relative 15,165 14 1.76 (1.03-3.00)
Female relative 11,512 5 0.74 (0.31-1.79)
Parent 16,598 13 1.58 (0.91-2.75)
Father 9,671 10 2.21 (1.18-4.13)
Mother 6,987 3 0.81 (0.26-2.52)
Sibling 7,995 7 1.33 (0.63-2.82) 1.42 (0.67-3.00)
Brother 4,593 5 1.69 (0.70-4.09) 1.80 (0.74-4.35)
Sister 3,487 2 0.84 (0.21-3.36) 0.90 (0.22-3.64)
Offspring 2,121 0
Parent/offspring 18,710 13 1.36 (0.79-2.37)

Any lymphoma or leukemia 51,717 38 1.41 (1.01-1.97)
Male relative 29,733 22 1.47 (0.96-2.26)
Female relative 23,323 18 1.41 (0.88-2.26)
Parent 32,173 25 1.72 (1.15-2.58)
Father 18,204 13 1.66 (0.95-2.88)
Mother 14,341 12 1.74 (0.98-3.10)
Sibling 16,678 16 1.55 (0.94-2.56) 1.65 (0.99-2.73)
Brother 9,835 10 1.68 (0.89-3.14) 1.77 (0.94-3.33)
Sister 7,140 6 1.29 (0.58-2.90) 1.39 (0.62-3.12)
Offspring 4,259 0
Parent/offspring 36,249 25 1.43 (0.95-2.14)
RR = relative risk; CI = confidence interval.
* Model adjusted for number of siblings (0, 1, 2, 3, 4+, adopted/unknown)

19
siblings. Our cohort study results differ from the InterLymph consortium results for
women, a pooled analysis of 17 NHL case-control studies (11 population-based, 6
hospital-based) (S. S. Wang et al., 2007). In InterLymph, RRs were higher when the
family member with the history of hematopoietic malignancy was a sibling than when the
family member was a parent and when the family history was one of lymphoma in a male
relative or leukemia in a female relative. Response rates for the participating case-control
studies varied, with some case response rates below 60% and some control response rates
below 50%. Moreover, the authors noted that RR estimates varied widely across
individual studies, a finding that likely reflected different study designs (hospital-based
versus population-based), sampling variability, response rates, and wording of the
questionnaires. Selection bias in case-control studies, especially from a survival bias, is
of particular concern if the pattern of NHL heritability is also associated with
aggressiveness of NHL subtype or with NHL survival more generally. Although the
InterLymph report provided risk estimates for major B-cell NHL subtypes (S. S. Wang et
al., 2007), we were unable to evaluate risk by NHL subtype due to the small number of
cases with a family history.

Family history may represent shared environment as well as familial genetic
predisposition to develop NHL and whether one interpretation prevails over the other is
still in question. A prospective (Mack et al., 1995) twin study conducted among
volunteers identified a slightly higher risk of NHL overall in monozygotic twins than in

20
dizygotic twins. The difference was about 1.7-fold, which is compatible with either a
modest genetic effect or a familial effect (since monozygotic twins share more early
environmental factors than dizygotic twins).

The results of previous studies are inconsistent with respect to the magnitude of the RR
for NHL associated with a history of hematopoietic malignancy among siblings
compared to the RR associated with having affected parents. We found similar results
whether the family history of hematopoietic malignancy was that of an affected parent or
of an affected sibling; this is consistent with the results from three studies that were not
included in the InterLymph analysis (Altieri et al., 2005; Czene, Adami, & Chang, 2007;
Zhu et al., 1998). Other studies have found that having an affected sibling was associated
with a greater increase in NHL risk than having an affected parent (Chang, Smedby et al.,
2005; Chatterjee et al., 2004; Pottern et al., 1991; S. S. Wang et al., 2007). Our findings
for gender are consistent with those of Czene and colleagues who used population-based
registries, to assess NHL risk. Among women in their study, those who had an affected
female relative with hematopoietic malignancy had a higher RR of NHL than those with
an affected male relative (Czene et al., 2007).

To collect family history of cancer in a large cohort study, self reports are most practical
and validation can only be done for a limited subgroup. A limitation of our study is that
our family history reports were self-reported and not confirmed. Concerns regarding the

21
accuracy of self-reported family history of lymphoma were raised previously in a
population-based case-control study of malignant lymphoma in Sweden (Chang et al.,
2006), in which self-reported family history of cancer was validated against family
history of cancer recorded in population-based registries. The study investigators found
the specificity of reporting hematopoietic malignancy was extremely high for both
lymphoma cases and controls (98 and 99 percent, respectively); but the sensitivity was 60
percent for lymphoma cases and only 38 percent for controls. This resulted in a higher
estimated RR of NHL associated with family history of hematopoietic malignancy when
using self-reported data rather than registry-based data. It is probable that our measure of
family history reflects some degree of misclassification. However, we consider it unlikely
that this misclassification is differential, since our family history reports were obtained at
study entry, prior to any NHL diagnosis. Further, it is unlikely that the accuracy of self-
reported data differed by gender of the affected relative, as we observed higher risk for
NHL if the affected relative who had lymphoma was a female, and a higher risk for NHL
if the affected relative who had leukemia was a male. Taken together, it is likely that any
misclassification of family history in our study is non-differential, and would be expected
to bias our RR estimates toward the null.

Since we asked about lymphoma family history separately from leukemia family history,
it is possible that participants with a family history of CLL reported this as a leukemia
family history, not as a lymphoma family history. Yet CLL is part of the NHL case

22
definition (Jaffe et al., 2001). When we excluded women with CLL from our case group,
censoring them on their dates of diagnosis, we obtained essentially the same results as
shown in Table 2.2 where risk for NHL was associated with having a male relative with
leukemia.

Another limitation is that we did not ascertain cancer diagnoses among participants’
relatives during the follow-up period. Given that cohort members who developed NHL,
compared with those who did not, would be more likely to have had additional familial
diagnoses of hematopoietic cancer, our RR estimates may underestimate the true
association with first-degree family history.

A final limitation of our study is our inability to examine risk by subtype of
hematopoietic malignancy, with respect to either the proband or the familial diagnoses. It
is known that Hodgkin lymphoma subtypes, NHL subtypes, and leukemia subtypes differ
epidemiologically, etiologically, pathologically and clinically (Cozen, Katz, & Mack,
1992; Jaffe et al., 2001; Morton et al., 2007; Morton et al., 2006; Mueller & Grufferman,
2006). However, we were not able to evaluate specificity of risk due to the lack of
detailed information on the type of hematologic neoplasm occurring in the family
members, and the limited numbers of incident cases diagnosed with each subtype of B-
cell NHL. Given that we found an association despite these limitations, we conclude that
either a strong association within a specific subtype is muted by the misclassification that

23
occurs when subtypes are grouped together, or that some early hematopoietic or immune
response abnormality is responsible for the general increase in risk across all B-cell NHL
subtypes.

2.6 Conclusions
We confirmed a positive association between family history of hematopoietic malignancy
and NHL risk. Our results from a prospective cohort of women differ from those from
the case-control study-based InterLymph Consortium, in that we found higher RRs in
association with an affected female relative with lymphoma, and an affected male relative
with leukemia and observed no differences in risk by level of familial relationship. These
findings underscore the importance of future studies, ideally with validated family history
data, to reveal specific associations among hematologic neoplasm subtypes, and to
determine which genetic or environmental factors account for the increased familial risks.

24
Chapter 3
Body size, recreational physical activity and B-cell
Non-Hodgkin lymphoma risk among women in the California Teachers Study

3.1 Summary
Few risk factors have been identified for non-Hodgkin lymphoma (NHL), an etiologically
and clinically heterogeneous group of lymphoid malignancies. Nutritional status and
physical activity are known to alter immune function, suspected as relevant to
lymphomagenesis. We explored body size measures and recreational physical activity in
relation to subsequent development of B-cell NHL in the prospective California Teachers
Study. Between 1995 and 2006, 533 women were diagnosed with incident B-cell NHL
among 121,216 eligible women aged 22 to 84 years old at cohort entry. Multivariable
adjusted relative risks and 95% confidence intervals were estimated by fitting Cox
proportional hazards models for all B-cell NHL combined as well as for the 3 most
common subtypes: diffuse, large B-cell lymphomas; follicular lymphomas; and B-cell
chronic lymphocytic leukemias/small lymphocytic lymphomas (CLL/SLL). Height was
positively associated with all B-cell NHL combined (P for trend=0.0002) and CLL/SLL
subtype (P for trend=0.001). Weight and body mass index (BMI, kg/m
2
) at age 18 were
predictors for B-cell NHL overall. No associations were observed for weight and BMI at
cohort entry, waist circumference, hip circumference, waist/hip ratio, or long-term or
recent recreational physical activity, except for an inverse association between BMI at

25
cohort entry and CLL/SLL risk. These findings indicate that greater height, which may
reflect early life immune function, infectious exposures, nutrition, or growth hormone
levels, may play a role in NHL etiology. Body size at age 18 may be more relevant to
NHL etiology than that in later life.

3.2 Introduction
Non-Hodgkin lymphoma (NHL), as an etiologically and clinically heterogeneous group
of lymphoid malignancies, is expected to account for 4.6% of cancer diagnoses and 3.4%
of cancer deaths in 2008 (Jemal et al., 2008). Its incidence has increased steadily since
the 1940s and NHL is now the fifth most common cancer among men and women in the
United States. HIV-associated NHL and changes in detection and diagnosis of lymphoma
explain only a small part of the increase in incidence (Hartge & Devesa, 1992). Since
1991, NHL incidence rates have reached a plateau among men, partly due to improved
treatment for HIV; but incidence rates among women have continued to increase at
approximately 0.7% per year since 1995 ("SEER Incidence & US Mortality Trends,1996-
2005," 2008).

Nutritional status and physical activity are known to alter immune function, a suspected
risk factor for NHL (Hance, Rogers, Hursting, & Greiner, 2007). Adult height partly
reflects early life nutrition, health experience and growth related hormones. Numerous
studies have shown its positive associations with several types of cancers, especially

26
breast cancer (Gunnell et al., 2001). Obesity and physical activity may also play an
important role in the etiology of NHL because they are involved in hormone metabolism,
immune response, inflammatory response and angiogenesis (Calle & Kaaks, 2004).
Moreover, height has increased historically and the prevalence of overweight and obesity
has been increasing markedly over the past several decades (Crimmins & Finch, 2006;
Pischon, Nothlings, & Boeing, 2008). If greater height and body size are associated with
NHL risk, then these increases may explain the observed increase of NHL incidence.
Nevertheless, results from studies of body size and physical activity in relation to NHL
risk have been mixed (Bosetti et al., 2005; Brownson, Chang, Davis, & Smith, 1991;
Cerhan et al., 2005; Cerhan, Janney et al., 2002; Chang, Hjalgrim et al., 2005; Chiu et al.,
2007; Fernberg et al., 2006; Larsson & Wolk, 2007; Lim et al., 2007; Maskarinec, Erber,
Gill, Cozen, & Kolonel, 2008; Morton et al., 2003; Oh, Yoon, & Shin, 2005; Pan, Mao,
Ugnat, & Canadian Cancer Registries Epidemiology Research, 2005; Samanic et al.,
2004; Skibola, Holly et al., 2004; Y. Wang, Rimm, Stampfer, Willett, & Hu, 2005;
Willett et al., 2008; Willett et al., 2005; Wolk et al., 2001; Zahm, Hoffman-Goetz,
Dosemeci, Cantor, & Blair, 1999).

In this study, we used the data from the California Teachers Study (CTS), a large
prospective cohort of women, to investigate whether body size and physical activity are
associated with risk of B-cell NHL, which accounts for more than 85% of all NHL in the
US. We further explored associations between the three main subtypes (diffuse, large B-

27
cell lymphomas (DLBCL); follicular lymphomas (FL); and B-cell chronic lymphocytic
leukemias/small lymphocytic lymphomas (CLL/SLL)) as defined by the WHO
modification to the REAL (Revised European-American Lymphoma) classification for
hematopoietic malignancies (Jaffe et al., 2001).

3.3 Materials and Methods
3.3.1 Study Population
A detailed description of CTS has been published elsewhere (Bernstein et al., 2002).
Briefly, this prospective study is comprised of 133,479 female public school
professionals recruited through the California State Teachers Retirement System in 1995.
All participants completed a self-administered baseline questionnaire, which collected
information on demographic factors, height, weight, menstrual and reproductive factors,
personal and family cancer and health history, oral contraceptive and menopausal
hormone therapy use and lifestyle factors (recreational physical activity, diet, alcohol
consumption, and smoking). Participants completed a second self-administered
questionnaire in 1997-1998 providing information on passive smoking exposure,
radiation history, and waist and hip measurements.

Use of human subjects in this study has been approved by the Institutional Review
Boards at the City of Hope, University of Southern California, Northern California
Cancer Center, and University of California at Irvine, in accord with assurances filed

28
with and approved by the Committee for the Protection of Human Subjects, California
Health and Human Services Agency.

For analyses of height, weight and body mass index (BMI) at cohort entry, weight and
BMI at age 18, and physical activity, we excluded sequentially women who were not
California residents at the time the baseline questionnaire was completed (n = 8,867),
who had limited their participation to breast cancer research (n = 18), who had a prior
history of a hematopoietic malignancy (n = 536) or whose history of cancer was unknown
(n = 663), and who were 85 years of age or older at cohort entry (n = 2179). The resulting
analytic cohort based on the baseline questionnaire consisted of 121,216 women. Among
these women, 90,640 participants returned the second questionnaire. During the time
interval between submission of the baseline questionnaire and the second questionnaire,
92 women were diagnosed with hematopoietic malignancy and 1224 women moved out
of California. As a result, 89,324 women were considered eligible for the analyses of
waist circumference, hip circumference, and waist/hip ratio.

3.3.2 Case Ascertainment and Follow-up
Incident diagnoses of B-cell NHL (International Classification of Diseases for Oncology,
third edition morphology codes: 9590, 9591, 9670-9675, 9680-9699, 9727, 9823, 9832,
9835, 9836) were identified through annual linkages with the California Cancer Registry,
the population-based cancer registry for California. The California Cancer Registry

29
receives over 99% of all cancer diagnoses occurring in California residents from its
regional registries as part of a state mandate.

The status of California residence was monitored through the annual mailing of
newsletters or questionnaires, annual linkage with the U.S. Postal Service national change
of address database, and change-of-address postcards submitted by participants.
Information on the date and cause of death were obtained from the California state
mortality file, the National Death Index, and the Social Security Administration death
master files (Bernstein et al., 2002).

3.3.3 Measures of Body Size and Recreational Physical Activity
Participants provided information regarding their height (feet and inches) and weight
(pounds) at age 18 and at cohort entry, and their participation in recreational physical
activities during different life periods (throughout high school; between the ages of 18
and 24, 25 and 34, 35 and 44, and 45 and 54 years; and the 3 years before submitting the
baseline questionnaire). For each time period, participants were asked the average hours
per week (none, 0.5, 1, 1.5, 2, 3, 4-6, 7-10, and ≥11 hours) and the average months per
year (1-3, 4-6, 7-9, and 10-12 months) they spent in strenuous activities (e.g., swimming
laps, aerobics/calisthenics, running, jogging, basketball, cycling on hills, racquetball) and
in moderate activities (e.g., brisk walking, golf, volleyball, cycling on level streets,
recreational tennis, or softball). For each time period, we created average annual hours

30
per week activity variables by multiplying the hours per week by the portion of the year
in which the woman engaged in the activity. We assigned the midpoint value of the
categories in making these calculations, assigning a value of 12 for the category 11 h/wk
or more. To create a measurement of long-term strenuous or moderate recreational
physical activity, we then multiplied the average hours per week for a particular time
period by the number of years in that time period. After summing up across all time
periods, we divided by the total number of years from high school to age 54 years or age
at cohort entry, whichever was younger. We then summed long-term strenuous and long-
term moderate activity to get overall long-term recreational physical activity. Strenuous
plus moderate recreational physical activity in the past 3 years was examined separately
from long-term activity. Both measures of physical activity were categorized into three
groups ( ≤0.50, 0.51-3.99, and ≥4.00 h/wk/y).

Height at cohort entry was categorized by the quintile distribution within the analytic
cohort. Height and weight at age 18 and at cohort entry were used to calculate BMI
(kg/m
2
) at different life points. BMI at cohort entry cut points were based on values used
in the literature to categorize women as lean (<20 kg/m
2
), normal (20-24.9 kg/m
2
),
overweight (25-29.9 kg/m
2
), or obese (30+ kg/m
2
) (Holly, Lele, Bracci, & McGrath,
1999). Due to the limited number of women whose BMI was above 25 kg/m
2
at age 18,
the quartile distribution of BMI at age 18 among eligible participants defined categories;
similarly, weight at 18 and at cohort entry, waist circumference, hip circumference, and

31
waist/hip ratio were categorized according to the quartile distribution among all cohort
members eligible for analysis of the variable. To limit measurement error, we excluded
subjects from analyses of waste and hip measures if the individual’s waist circumference
was below the 10
th
percentile but her hip circumference was above the 90
th
percentile or
vice versa.

3.3.4 Assessment of Other Potential Risk Factors
We collected detailed information on a number of potential NHL risk factors including
self-reported race (non-Hispanic white, non-white, or unknown), first-degree family
history of lymphoma (no, yes or adopted/unknown), alcohol consumption in the year
before joining the cohort (none, <15 grams/day, >15 grams/day, unknown), smoking
status at cohort entry (never, former, current, or unknown), age at menarche (never had
period, ≤11, 12, ≥13 years old or unknown), and socioeconomic status (SES). Area-level
SES was generated for all census block groups in California in 1990 using education,
occupation, and income quartile ranks of the California population and creating a
summary score that weighted each variable equally (Reynolds et al., 2004). Participants
were assigned to an SES summary score according to residential address at cohort entry.
For this analysis, we used the 50
th
percentile distribution of SES among all California
residents to categorize our participants into two SES groups.

32
3.3.5 Statistical Analyses
We fit multivariable Cox proportional hazards regression models stratified by age in
years at cohort entry to investigate whether body size and recreational physical activity
are associated with B-cell NHL risk. We conducted analyses for all B-cell NHL
combined (n=533) as well as separate analyses for DLBCL (ICDO-morphology codes
9678-9680, 9684, n=147), FL (ICDO codes 9690-9698, n=115), and CLL/SLL (ICDO
codes 9670, 9823, n=116).

For analyses of height, weight at age 18 and at cohort entry, BMI at age 18 and at cohort
entry, and physical activity, person-time was accrued from the date that a woman
completed her baseline questionnaire until the first occurrence of one of the following: a
diagnosis of B-cell NHL (n = 533), a move outside of California (n = 9,645), a first
diagnosis of a T-cell NHL, Hodgkin lymphoma, multiple myeloma, or leukemia other
than CLL (n = 312), death (n = 8,824), or December 31, 2006 (n = 101,902). Among
eligible women who returned the second questionnaire, for analyses of waist and hip
circumferences, person-time was accrued from the date that a woman completed her
second questionnaire until the first occurrence of a B-cell NHL (n = 348), a move outside
of California (n = 5,807), a first diagnosis of a T-cell NHL, Hodgkin lymphoma, multiple
myeloma, or leukemia other than CLL (n = 203), death (n = 5,707), or December 31,
2006 (n = 77,259). Relative risks (RR) and 95% confidence intervals (CI) were estimated
using age in days from cohort entry (or submission of the second questionnaire) until the

33
end of follow-up (exit) for the time under observation in the Cox regression models. We
included weight, height, age at menarche, and long-term strenuous plus moderate
recreational physical activity as appropriate in our models. We assessed other potential
risk factors (race, SES, family history of lymphoma, current smoking status, and alcohol
consumption one year before cohort entry), but none of these factors altered risk
estimates by as much as 5% and therefore none were included in the final models. We
performed tests for trend for ordinal variables by fitting the median value for each
category of a variable as a continuous variable. We examined effect modification of the
height association by BMI at cohort entry (<25 kg/m
2
versus ≥25 kg/m
2
), current
smoking status (never versus ever), alcohol consumption one year before cohort entry
(drinker versus non-drinker), and long-term recreational physical activity (below median,
< 3.3 h/w/y, versus above median, > 3.3 h/w/y, of combined strenuous and moderate
activities). The base model, which included one variable for height, was compared with a
model with two variables for height, one for each of the two categories of the potential
effect modifier (likelihood ratio test for homogeneity of trends with 1 df) (Kleinbaum,
Kupper, & Morganstern, 1982). Due to the small number of cases with a first degree
relative of lymphoma, we did not have the statistical power to test whether the effect of
height on NHL is modified by family history of lymphoma (no versus yes). Therefore, we
conducted an analysis confined to women without a family history of lymphoma.

34
To exclude the possibility that a previous diagnosis of cancer influenced anthropometric
measures at cohort entry, we performed analyses that excluded participants who had any
non-hematopoietic cancer diagnosis (except basal or squamous cell skin cancers) before
returning the baseline questionnaire (for height, weight and BMI at cohort entry, and
physical activity); for waist and hip circumference analyses, we then excluded any
participant diagnosed with cancer prior to completion of the second questionnaire. To
avoid the possibility that recent recreational physical activity (i.e., in the past 3 years)
could be affected by underlying but as yet undiagnosed NHL, we conducted additional
analyses which excluded women diagnosed with NHL during the first 2 years of follow-
up.

Two-sided P-values are reported for tests for trend and tests for effect modification. We
did not adjust P-values for multiple comparisons. All statistical analyses were performed
using SAS version 9.1 (SAS Institute Inc, Cary, NC).

3.4 Results
The mean age at cohort entry was 52.7 years for women in the analytic cohort. The
average length of follow-up was 10.1 years. The mean ages at diagnosis ± standard
deviation were 69.6 ± 11.7 years (range, 33-92) for women diagnosed with any B-cell
NHL, 69.6 ± 11.7 years (range, 33-92) for DLBCL, 66.2 ± 12.7 years (range 35-90) for
FL, and 72.1 ± 9.7 (range, 48-92) for CLL/SLL.

35
In the analytic cohort at baseline, 38% of the participants were at least 66 inches tall and
42.2% participants reported averaging at least 4 h/wk of some moderate or strenuous
recreational physical activity from high school through age 54 years (Table 3.1). Women
with greater height or higher levels of recreational physical activity were more likely to
be white, be younger in age, have a higher level of alcohol consumption in the year prior
to joining the cohort and have lower BMI at cohort entry.

Increasing height was statistically significantly associated with B-cell NHL (P for trend =
0.0002) and CLL/SLL (P for trend = 0.001) risk (Table 3.2). Risk for B-cell NHL was
47% greater among tall women (> 68 inches) than among women in the middle height
category (64-65 inches, RR = 1.47, 95% CI = 1.12-1.94); risk for CLL/SLL among tall
women was nearly double that of women in the middle height category (RR = 1.99, 95%
CI = 1.10-3.59). A weak positive association was observed between height and DLBCL
(P for trend = 0.12), but no association was observed for FL (P for trend = 0.24). The
associations of height with B-cell NHL and with CLL/SLL persisted after excluding
women with histories of other cancers at cohort entry (P for trend < 0.0001 and P for
trend < 0.001, respectively, data not shown).

36
Table 3.1 Selected baseline characteristics of 121216 eligible women in the California Teachers Study in relation to
long-term physical activity, height and weight at baseline
Characteristic N
Long-term physical activity
(h/wk/y; %)† Height (inches; %) Weight (pounds; %)
<=0.5 0.51-3.99 ≥4 Unk < 66 ≥ 66 Unk < 140 ≥ 140 Unk
Total 121,216 9.9 47.4 42.2 0.6 61.6 38.1 0.4 44.0 52.2 3.8
Age at cohort entry (years)
<35 12,741 2.9 36.2 60.7 0.2 54.0 45.9 0.1 54.9 43.7 1.4
35 - 44 21,448 3.9 43.3 52.5 0.3 57.3 42.6 0.2 48.8 49.4 1.8
45 - 54 36,468 7.8 51.6 40.1 0.5 60.3 39.5 0.2 43.3 54.6 2.2
55 - 64 23,475 12.1 50.6 36.8 0.6 61.5 38.2 0.3 38.1 58.4 3.6
65 - 74 17,797 16.4 47.8 34.8 1.0 67.7 31.7 0.7 39.4 54.0 6.7
75 - 84 9,287 23.3 46.4 28.6 1.7 75.5 23.0 1.6 44.5 41.9 13.6
Age-adjusted percentages:
Race
Non-Hispanic white 104,814 8.5 48.8 42.2 0.5 58.5 41.3 0.3 42.7 54.3 3.0
All other races 15,405 12.9 48.3 38.2 0.6 77.5 21.9 0.6 48.1 48.0 3.9
Unknown race 997 12.7 43.6 42.2 1.5 67.0 32.3 0.8 41.0 49.8 9.2
First-degree family history of lymphoma
No 114,276 8.9 48.7 41.8 0.5 60.9 38.8 0.3 43.5 53.4 3.0
Yes 3,177 9.6 51.1 38.8 0.5 59.8 39.8 0.3 40.1 56.9 3.1
Unknown 3,763 12.4 46.2 40.4 1.0 62.9 36.4 0.7 42.2 51.9 6.0
Socioeconomic Status
Below Median 26,086 9.9 48.5 41.0 0.6 62.1 37.6 0.4 37.2 59.5 3.3
Above Median 93,584 8.8 48.8 41.8 0.6 60.7 39.1 0.3 45.0 51.9 3.1
Unknown 1,546 7.8 44.7 47.1 0.4 58.8 40.9 0.4 41.1 55.7 3.2
Alcohol consumption (grams/day)
None 38,619 11.7 49.9 38.0 0.5 63.3 36.4 0.3 40.7 56.0 3.3
< 15 56,970 7.6 48.9 43.1 0.4 60.6 39.2 0.3 44.3 53.0 2.7
≥ 15 19,301 7.4 46.7 45.4 0.5 56.8 43.0 0.3 45.5 51.4 3.1
Unknown 6,326 11.9 46.3 38.9 2.8 63.2 36.1 0.7 44.5 48.5 7.1

37
Table 3.1 continued.
Characteristic N
Long-term physical activity
(h/wk/y; %)† Height (inches; %) Weight (pounds; %)
<=0.5 0.51-3.99 ≥4 Unk < 66 ≥ 66 Unk < 140 ≥ 140 Unk
Smoking status
Never 79,627 9.2 48.6 41.7 0.5 61.6 38.1 0.3 44.7 52.3 3.0
Former 34,684 8.5 49.3 41.6 0.5 59.5 40.2 0.3 40.7 56.1 3.2
Current 6,181 9.8 46.8 42.9 0.5 60.3 39.3 0.5 42.2 54.0 3.8
Unknown 724 9.4 47.5 32.5 10.7 63.2 36.1 0.7 43.5 49.2 7.3
Body mass index at cohort entry (kg/m
2
)
<20 12,651 8.1 45.2 46.1 0.6 55.1 44.9 99.2 0.8
20 - 24.9 58,204 7.9 47.7 44.0 0.5 61.0 39.0 65.9 34.1
25 - 29.9 29,100 9.7 50.4 39.4 0.5 60.8 39.2 6.1 93.9
≥30 16,414 11.0 52.7 35.7 0.5 65.8 34.2 100.0
Unknown 4,847 15.9 45.8 36.9 1.4 56.8 34.4 8.8 2.8 2.2 95.1
Abbreviation: Unk: Unknown
†: Long-term physical activity combines strenuous and moderate physical activity

38
Weight at cohort entry was not associated with NHL risk overall, but a marginal
statistically significant positive trend was observed for FL risk (P for trend = 0.06). The
risk of developing FL among women in the highest quartile (>161 pounds) was 80%
greater than that for women in the lowest quartile of weight (<125 pounds, 95% CI =
0.94-3.46) (Table 3.2). This association was attenuated after exclusion of women with a
cancer diagnosis prior to cohort entry (P for trend = 0.26). Although B-cell NHL risk
overall was not significantly related to BMI at cohort entry, obese women had an
increased risk of NHL that was of borderline significance compared to women with
normal BMI (RR = 1.23, 95%=0.95-1.58). However, an inverse marginal statistically
significant association was detected between BMI and risk of CLL/SLL with the highest
risk for lean women and the lowest risk for obese women (P for trend = 0.08) (Table 3.2).

The exclusion of women with cancer diagnoses before cohort entry made this association
stronger (P for trend = 0.04), but no meaningful changes were observed for associations
of BMI with B-cell NHL overall or subtypes other than CLL/SLL (P for trend=0.04)
(data not shown).

Weight and BMI at age 18 were associated with NHL risk. Compared to women in the
lowest quartiles of weight and BMI at age 18, women in the highest quartiles had 31% (P
for trend=0.03) and 25% increased risk (P for trend=0.04), respectively (Table 3.2). A
positive but non-statistically significant association was observed between weight at age

39

Table 3.2 Relative risk estimates (and 95% confidence intervals) for the association between body size and B-cell NHL among women in the
California Teachers Study
All cases Diffuse, large B-cell Follicular CLL/SLL
Person- Cases RR Cases RR Cases RR Cases RR Baseline
variable years (533) (95% CI) (147) 95% CI (115) 95% CI (116) 95% CI
Height (inches)†
<62 123,491 50 0.80 (0.57-1.11) 19 0.98 (0.56-1.71) 11 0.90 (0.45-1.82) 7 0.50 (0.21-1.15)
62 – 63 273,620 115 0.93 (0.72-1.18) 27 0.73 (0.45-1.18) 17 0.64 (0.36-1.14) 29 1.10 (0.66-1.84)
64 – 65 358,890 149 1.00 43 1.00 35 1.00 30 1.00
66 – 67 295,256 133 1.18 (0.93-1.49) 35 1.10 (0.70-1.73) 34 1.19 (0.74-1.92) 31 1.45 (0.88-2.41)
≥ 68 174,474 83 1.47 (1.12-1.94) 22 1.41 (0.83-2.40) 17 1.09 (0.60-1.98) 19 1.99 (1.10-3.59)
Unknown 4,200 3 1 1 0
P trend 0.0002 0.12 0.24 0.001
Weight (pounds)‡
<125 249,608 93 1.00 31 1.00 14 1.00 21 1.00
125 –139 292,094 100 0.85 (0.64-1.13) 30 0.78 (0.47-1.31) 23 1.28 (0.65-2.51) 25 0.87 (0.48-1.57)
140 – 61 344,489 155 0.97 (0.74-1.27) 38 0.73 (0.44-1.20) 33 1.38 (0.72-2.65) 33 0.79 (0.45-1.40)
>161 298,935 152 1.09 (0.82-1.43) 41 0.90 (0.54-1.49) 38 1.80 (0.94-3.46) 25 0.67 (0.36-1.25)
Unknown 44,804 33 7 7 12
P trend 0.16 0.99 0.06 0.31
Body Mass Index (kg/m
2
)‡
<20 127,799 48 1.14 (0.83-1.55) 16 1.43 (0.82-2.48) 8 0.89 (0.42-1.88) 15 1.70 (0.96-3.04)
20 - 24.9 593,401 226 1.00 60 1.00 47 1.00 49 1.00
25 - 29.9 295,373 144 1.07 (0.87-1.32) 38 1.05 (0.70-1.59) 34 1.28 (0.82-1.99) 30 1.00 (0.63-1.58)
≥30 166,606 81 1.23 (0.95-1.58) 26 1.46 (0.92-2.32) 18 1.31 (0.76-2.27) 10 0.70 (0.35-1.38)
Unknown 46,751 34 7 8 12
P trend 0.25 0.38 0.21 0.08

40

Table 3.2 continued.
All cases Diffuse, large B-cell Follicular CLL/SLL
Person- Cases RR Cases RR Cases RR Cases RR Baseline
variable years (533) (95% CI) (147) 95% CI (115) 95% CI (116) 95% CI
Weight at age 18(pounds)*
<116 323,852 107 1.00 31 1.00 22 1.00 21 1.00
116 - 125 364,409 142 1.02 (0.78-1.33) 41 1.09 (0.67-1.79) 30 0.96 (0.54-1.7) 36 1.26 (0.72-2.21)
126 - 135 212,575 113 1.40 (1.06-1.86) 28 1.30 (0.75-2.25) 29 1.55 (0.85-2.81) 23 1.36 (0.72-2.56)
>135 280,018 134 1.31 (0.99-1.74) 40 1.48 (0.87-2.51) 25 1.02 (0.54-1.91) 25 1.15 (0.60-2.18)
Unknown 49,076 37 7 9 11
P trend 0.03 0.11 0.79 0.92
Body Mass Index at age 18 (kg/m
2
)*
<19.5 301,353 124 1.00 34 1.00 24 1.00 27 1.00
19.5 - 20.7 284,165 114 0.97 (0.75-1.25) 35 1.09 (0.68-1.74) 24 1.05 (0.60-1.86) 28 1.11 (0.65-1.90)
20.8 - 22.4 286,889 116 1.00 (0.78-1.30) 30 0.94 (0.57-1.55) 31 1.40 (0.82-2.40) 22 0.88 (0.50-1.55)
>22.4 302,250 141 1.25 (0.98-1.60) 41 1.31 (0.83-2.08) 27 1.22 (0.70-2.13) 28 1.19 (0.70-2.03)
Unknown 55,274 38 7 9 11
P trend 0.04 0.25 0.39 0.65
Abbreviations: RR = relative risk; CI = confidence interval; CLL = chronic lymphocytic leukemia; SLL = small lymphocytic lymphoma.
†: Model was adjusted for weight, age at menarche (<=11, 12, 13+, never had period, missing) and long-term strenuous plus moderate physical
activity.
‡: Model was adjusted for height, age at menarche (<=11, 12, 13+, never had period, missing) and long-term strenuous plus moderate physical
activity.
*: Model was adjusted for height at age 18, age at menarche (<=11, 12, 13+, never had period, missing) and high school strenuous plus moderate
physical activity level

41
18 and DLBCL (P for trend=0.11), the risk of DLBCL increased with the increasing level
of weight at age 18. No association was observed for BMI at age 18 and risk of any
specific NHL subtype (Table 3.2).

We observed no association between NHL risk and long-term recreational physical
activity or activity in the past 3 years (Table 3.3). The exclusion of NHL diagnoses
within the first 2 years of follow-up or the exclusion of women with previous cancer
diagnoses did not affect these findings (data not shown).

Waist circumference, hip circumference, and waist/hip ratio were not associated with the
risk of B-cell NHL overall (348 NHL cases) or with any of the NHL subtypes evaluated
separately (Table 3.4). The results did not change after exclusion of women who had a
cancer diagnosis prior to submitting the second questionnaire (data not shown).

Table 3.5 provides data on the association between height and all B-cell NHL stratified
by possible effect modifiers. We did not detect any statistically significant interactions,
and the association between height and B-cell NHL remained strong among women
without a family history of lymphoma (p=0.0002).

42

Table 3.3 Relative risk estimates (and 95% confidence intervals) for the association between physical activity and B-cell NHL among women in the
California Teachers Study
All cases Diffuse, large B-cell Follicular CLL/SLL
Person- Cases RR Cases RR Cases RR Cases RR Baseline
variable years (533) (95% CI) (147) 95% CI (115) 95% CI (116) 95% CI
Long-term strenuous plus moderate physical activity*
0-0.50 117,645 79 1.00 25 1.00 12 1.00 19 1.00
0.51-3.99 583,664 236 0.87 (0.67-1.13) 61 0.72 (0.45-1.15) 53 1.18 (0.63-2.23) 55 0.90 (0.53-1.53)
≥4.00 521,574 211 1.04 (0.80-1.35) 60 0.94 (0.59-1.52) 48 1.39 (0.73-2.65) 41 0.93 (0.53-1.61)
Unknown 7,047 7 1 2 1
P trend 0.20 0.44 0.29 0.90
Recent strenuous plus moderate physical activity†*
0-0.50 279,954 123 1.00 36 1.00 24 1.00 24 1.00
0.51-3.99 500,531 210 1.13 (0.89-1.44) 54 1.02 (0.65-1.60) 52 1.28 (0.77-2.14) 48 1.41 (0.84-2.37)
≥4.00 442,399 193 1.09 (0.84-1.41) 56 1.07 (0.66-1.75) 37 0.96 (0.53-1.73) 43 1.34 (0.76-2.37)
Unknown 7,047 7 1 2 1
P trend 0.79 0.74 0.48 0.55
Abbreviations: RR = relative risk; CI = confidence interval; CLL = chronic lymphocytic leukemia; SLL = small lymphocytic lymphoma.
*Model was adjusted for age at menarche (<=11, 12, ≥13, never had period, missing), height, and weight.
†: Recent activity=activity in the 3 years before completing the baseline questionnaire; model was also adjusted for long-term strenuous plus
moderate physical activity.

43

Table 3.4 Relative risk estimates (and 95% confidence intervals) for the association between waist, hip, waist/hip ratio and B-cell NHL among
women in the California Teachers Study
All cases Diffuse, large B-cell Follicular CLL/SLL
Person- Cases RR Cases RR Cases RR Cases RR
Measure years (348) (95% CI) (95) 95% CI (80) 95% CI (74) 95% CI
Waist circumference(inches)*
<29 180,413 56 1.00 15 1.00 11 1.00 13 1.00
29 – 31 153,996 72 1.16 (0.81-1.66) 23 1.49 (0.76-2.94) 17 1.24 (0.57-2.73) 18 1.27 (0.61-2.66)
>31 – 35 170,092 82 0.90 (0.62-1.32) 20 0.97 (0.46-2.03) 19 0.95 (0.42-2.14) 19 0.90 (0.41-1.98)
>35– 160,070 97 0.98 (0.65-1.48) 26 1.22 (0.56-2.69) 25 1.11 (0.46-2.65) 19 0.85 (0.35-2.02)
Unknown 89,514 41 11 8 5
P trend 0.66 0.84 0.77 0.41
Hip circumference(inches)*
>37 186,327 79 1.00 23 1.00 17 1.00 22 1.00
>37 – 39 153,471 64 0.89 (0.62-1.27) 19 1.03 (0.53-1.99) 12 0.61 (0.28-1.33) 14 0.74 (0.36-1.52)
>39 – 42 171,558 85 0.90 (0.63-1.30) 22 0.99 (0.49-1.99) 20 0.74 (0.35-1.57) 15 0.60 (0.28-1.31)
>42– 153,215 79 0.93 (0.61-1.40) 20 0.97 (0.44-2.14) 23 0.91 (0.40-2.06) 18 0.95 (0.41-2.19)
Unknown 89,514 41 11 8 5
P trend 0.64 0.87 0.99 0.81
Waist/hip ratio*
<0.750 162,966 46 1.00 14 1.00 11 1.00 7 1.00
0.750-0.794 173,500 72 1.20 (0.83-1.75) 16 0.90 (0.44-1.86) 23 1.59 (0.77-3.28) 18 1.95 (0.81-4.69)
0.795-0.854 165,976 88 1.20 (0.84-1.74) 23 1.10 (0.56-2.18) 14 0.82 (0.37-1.84) 23 1.97 (0.83-4.67)
≥0.855 162,127 101 1.03 (0.71-1.50) 31 1.18 (0.60-2.33) 24 1.06 (0.49-2.29) 21 1.31 (0.54-3.23)
Unknown 89,514 41 11 8 5
P trend 0.86 0.44 0.91 0.87
Abbreviations: RR = relative risk; CI = confidence interval; CLL = chronic lymphocytic leukemia; SLL = small lymphocytic lymphoma.
*Model was adjusted for age at menarche (<=11, 12, 13+, never had period, missing), long-term strenuous plus moderate physical activity, height and
weight.

44
Table 3.5 Relative risk estimates (and 95% confidence intervals) for the association between height and B-cell NHL among women in the California
Teachers Study, by body mass index, alcohol consumption, smoking status, long-term physical activity and family history of lymphoma
Exposure Height (inches)†
Category‡ Cases <62 62 – 63 64 - 65 66 - 67 ≥ 68
P for
trend
P for homogeneity
of trend
Body mass index
<25.0 kg/m
2
274 24 67 67 70 46
1.00 1.32 (0.90-1.93) 1.08 (0.74-1.58) 1.47 (1.00-2.15) 1.79 (1.16-2.75) 0.0009 0.55
≥25.0 kg/m
2
225 24 41 74 56 30
1.00 0.90 (0.57-1.43) 1.33 (0.87-2.03) 1.33 (0.84-2.12) 1.4 9(0.86-2.58) 0.07
Alcohol consumption
Non-drinker 184 13 47 53 44 27
1.00 1.37 (0.91-2.05) 1.38 (0.93-2.06) 1.59 (1.04-2.42) 1.98 (1.21-3.23) <0.0001 0.16
Drinker 326 35 63 90 85 53
1.00 1.02 (0.70-1.49) 1.17 (0.82-1.67) 1.43 (0.99-2.07) 1.76 (1.16-2.66) 0.002
Smoking status
Never smoker 317 35 71 87 73 51
1.00 1.15 (0.80-1.65) 1.20 (0.84-1.71) 1.36 (0.94-1.98) 1.95 (1.29-2.94) 0.0009 0.69
Ever smoker 212 15 43 62 60 32
1.00 1.15 (0.76-1.73) 1.33 (0.91-1.95) 1.63 (1.11-2.41) 1.68 (1.05-2.67) 0.0004
Long-term physical activity (hrs/wk per year)
≤3.3 256 23 53 77 64 39
1.00 1.16 (0.76-1.77) 1.45 (0.97-2.16) 1.64 (1.09-2.50) 2.07 (1.30-3.30) 0.0004 0.53
>3.3 243 20 55 64 63 41
1.00 1.32 (0.85-2.04) 1.20 (0.78-1.85) 1.49 (0.96-2.31) 1.84 (1.14-2.99) 0.006
First-degree family history of lymphoma
No 443 49 105 139 121 80
1.00 1.09 (0.78-1.54) 1.21 (0.87-1.70) 1.40 (0.99-1.99) 1.86 (1.27-2.72) 0.0002
†: For each exposure category the number of subjects is presented in the first line and the relative risk estimates with 95% confidence intervals are
presented in the second line. Models were adjusted for weight, age at menarche (<=11, 12, 13+, never had period, missing) and long-term strenuous
plus moderate physical activity.
‡: Unknown categories were excluded from the relevant analyses.

45
3.5 Discussion
In this large cohort of female public school professionals, we detected statistically
significant associations between height and B-cell NHL risk overall and the CLL/SLL
subtype, a weak association with DLBCL risk, and no association with FL. Weight and
BMI at age 18 were associated with B-cell NHL risk overall, but weight and BMI at
cohort entry were not except within the CLL/SLL subgroup where risk decreased with
increasing BMI. Neither long-term nor recent recreational physical activity was
associated with NHL risk. Furthermore, waist circumference, hip circumference, and
waist/hip ratio, were not associated with NHL risk. The association between height and
NHL risk was not modified by BMI at cohort entry, smoking status, alcohol
consumption, or physical activity.

The association between height and NHL risk has been examined in several studies with
inconsistent results. Among the studies that reported the associations separately for
women, the Nurse’s Health Study (S. Zhang et al., 1999) and the Multiethnic Cohort
study (MEC) (Maskarinec et al., 2008) reported positive associations between height and
overall NHL risk, while the Iowa Women’s Health Study cohort (Cerhan, Janney et al.,
2002) reported no impact of height on risk of NHL overall or any subtype. The pooled
analysis from InterLymph consortium (Willett et al., 2008; Willett et al., 2005) reported
that the tallest men were at increased risk of NHL but no association was observed for
women. Results from a British case-control study, which is included in the InterLymph

46
consortium report, showed that the shortest women had increased risk for NHL overall
and for the DLBCL subtype (Willett et al., 2005). One of two studies reporting results
for men and women combined, found increased risk for DLBCL and CLL/SLL with
increasing height (Lim et al., 2007); the second study reported a significant increased risk
for FL among tall individuals (Cerhan et al., 2005). A positive association between
height and CLL risk, but not NHL overall, was also observed in the case-control study by
Chang et al. (Chang, Hjalgrim et al., 2005). Studies among children and adolescents (<
18 years at diagnosis) have found that girls with NHL or Hodgkin lymphoma are taller
than population norms (Pui, Dodge, George, & Green, 1987). Furthermore, studies of
mortality have shown that height and mortality from haematopoietic cancers (lymphomas
and Hodgkin’s lymphomas) are positively associated (Leon, Smith, Shipley, & Strachan,
1995). Height has also been positively associated with several other types of cancer
(Gunnell et al., 2001; Schouten et al., 2008).

Adult height is influenced by both genetic factors and environmental factors exposures
that may occur in utero, or during infancy, childhood, or puberty. Frequent infections in
early life may influence height and with persistent effects later in life affecting risk of
NHL and other cancers (Blackwell, Hayward, & Crimmins, 2001; McDade, 2003). One
possibility is that individuals exposed to high-pathogen environments may invest energy
in developing antipathogen defenses, which could result in impaired growth (shorter
stature) (Butte, Wong, & Garza, 1989; Lunn, 2000). Early exposures to pathogens may

47
also prime the immune system to optimize protection against the specific pathogens that
are most likely to be encountered later in life. Data showing significant differences in
IgM, IgG, and IgA levels between urban and rural residents in a relatively genetically
homogenous population in Nigeria support this hypothesis (Mohammed, Tomkins, &
Greenwood, 1973). On this basis, we might hypothesize that the immune function of
taller individuals is less developed and therefore, not primed to handle unfamiliar antigen
exposures in adulthood; and further, that the response of T and B cells in adulthood to
unfamiliar antigens may result in malignancy.

Nutritional status during childhood is another important factor that may influence adult
height and subsequent B-cell NHL risk. Both dietary energy and height are positively
associated with circulating levels of insulin-like growth factor-I (IGF-I) in childhood
(Hoppe et al., 2004; I. Rogers, Emmett et al., 2006; I. Rogers, Metcalfe et al., 2006; I. S.
Rogers et al., 2005) but not in adulthood (Bray et al., 2006). IGF-1 is a major regulator of
childhood growth and mediates most of the anabolic actions of growth hormone (Butler
& Le Roith, 2001). IGF-I protects cells from apoptosis (Buckbinder et al., 1995). In a
rodent model, IGF-I administration increased the number of lymphocytes and the size of
lymphoid organs (e.g., spleen and thymus) (Clark, 1997; Clark, Strasser, McCabe,
Robbins, & Jardieu, 1993). IGF-I also acts through increased differentiation to potentiate
pro-B to pre-B cell maturation (Landreth, Narayanan, & Dorshkind, 1992), and increase
B-cell proliferation (Gibson, Piktel, & Landreth, 1993), which may increase the potential

48
to initiate and promote NHL development. Thus, IGF-I levels in childhood, as reflected
by growth patterns and ultimately, height, may have long-lasting impact on the
development of NHL.

Evidence from many recent studies suggests that obesity, especially obesity in early life,
is associated with a modest increase in risk of NHL; and that the association varies by
NHL subtype. A meta-analysis (Larsson & Wolk, 2007) of 10 cohort studies and 6 case-
control studies reported that, among women, the risk of NHL increased 12% with every 5
kg/m
2
increase in BMI (RR=1.12, 95%CI=1.02-1.23). Obese individuals (men and
women) also had an increased risk of DLBCL, but no increased risk of FL or CLL/SLL.
The pooled analysis from the InterLymph consortium of 18 case-control studies from 13
countries (Willett et al., 2008), however, reported no association between BMI and
overall NHL risk for men or women, but an increased risk of DLBCL for extremely
obese (BMI ≥ 40 kg/m
2
) men and women.

Consistent with most previous studies, we found a non-statistically significant modest
increased risk of NHL overall among women who were obese at cohort entry ( ≥ 30
kg/m
2
). Although a similar increase in risk for DLBCL was observed in obese women
relative to normal women (20-24.9 kg/ m
2
), thin women (<20 kg/m
2
) also had non-
significantly elevated risk. For CLL/SLL, risk was elevated among thin women; this
observation has not been previously reported. Our findings that weight and BMI at age 18

49
are more strongly associated with NHL risk than weight and BMI in later life are
supported by the evidence from the MEC (Maskarinec et al., 2008) and a British case-
control study (Willett et al., 2005). In the MEC (Jemal et al., 2008), weight and BMI at
age 21 were more strongly associated with NHL risk than were these measures at cohort
entry, among both men and women. The British case-control study reported that the
positive associations between BMI and NHL risk were more pronounced among
individuals diagnosed at younger ages (Willett et al., 2005).

Several explanations for the inconsistencies across studies can be proposed including
differences in study design with the potential for case-control studies to suffer from
survival bias, differences in study populations, or variations by geographic locale which
represent different exposure experiences during childhood affecting growth patterns and
different lifestyle patterns during the adult years. Some cohort studies included in the
meta-analysis (Larsson & Wolk, 2007) recruited participants at relatively young ages,
with a mean age of approximately 40 years in several studies (Chiu, Gapstur, Greenland,
Wang, & Dyer, 2006; Oh et al., 2005), whereas the mean age at cohort entry was 52.7
years in our study and 60 years in the MEC (Maskarinec et al., 2008). Thus, weight and
BMI at early life time may better represent lifetime body size status since a large
proportion of women may have undergone age-related body size changes due to disease,
transition to postmenopausal status, or lifestyle by the time of cohort entry in our study
and the MEC. Besides the potential for higher levels of IGF-1 due to greater caloric

50
intake during childhood and adolescence noted above (Schernhammer et al., 2007), other
possible mechanisms that mediate an association between obesity during childhood,
adolescence and as a young adult and NHL risk include insulin resistance, chronic
hyperinsulinaemia due to insulin and IGFs, or increased bioavailability of steroid
hormones (Calle & Kaaks, 2004; Hojgaard, Gyrd-Hansen, Olsen, Sogaard, & Sorensen,
2008; Pischon et al., 2008). Recently, studies of polymorphisms in the leptin (LEP) and
leptin receptor (LEPR) genes have shown that leptin, as an adipocyte-derived hormone
regulating food intake and immune function, may be mediator in NHL pathogenesis
(Skibola, Holly et al., 2004; Willett et al., 2005).

We found no association between long-term or recent recreational physical activity and
NHL risk overall or by subtype, consistent with the results from the Iowa Women’s
Health Study (Cerhan, Janney et al., 2002). Two case-control studies have reported
inverse associations between total recreational physical activity and NHL risk (Cerhan et
al., 2005; Pan et al., 2005). Case-control studies of recent activity may provide spurious
associations. During the early stages of NHL, patients often experience fatigue, loss of
appetite weight loss, and feelings of weakness, which could result in reduced activity and
therefore more reported activity among controls than among cases.

The major strengths of this study include its prospective design, an extensive evaluation
of anthropometric variables, comprehensive follow-up procedures, the virtually complete

51
ascertainment of cancer outcomes and comprehensively adjustment for confounders.
Several limitations must be considered. First, the number of patients available by subtype
was limited resulting in lack of precision in our risk estimates. Second, self-reported
measures of body size may be inaccurate with heavier women being more likely to
underreport weight and shorter women were more likely to over-report height (Gorber,
Tremblay, Moher, & Gorber, 2007). With our cohort design, these measurement errors
would be expected to be non-differential between affected women (cases) and unaffected
women, and thus would be expected to attenuate the true underlying associations with
NHL risk. Another limitation of our study is that we did not collect information on
occupational physical activity. Existing data from three studies that included
occupational physical activity measures showed no association between occupational
physical activity and NHL risk (Brownson et al., 1991; Cerhan et al., 2005; Zahm et al.,
1999). Finally, this cohort of mostly non-Hispanic white (86.4%) college-educated
women is not representative of all women in the United States. Nevertheless, the obesity
distribution within the cohort is comparable with that of the US non-Hispanic white
population (Ogden et al., 2006).

3.6 Conclusions
We found that height was positively associated with NHL risk and this association was
consistent across subgroups defined by other potential NHL risk factors. Height reflects
in large part early life nutrition and possibly exposure to pathogens, suggesting growth

52
hormone levels or maturation of the immune system may play a role in the NHL etiology.
In addition, body size at age 18 which reflects early life nutrition status and obesity at
cohort entry which may reflect an inflammatory condition, may be modestly associated
with B-cell NHL risk. Further studies are needed to provide insight into the mechanisms
involved in these associations.

53
Chapter 4
Oral contraceptive use, hormone therapy and risk of
Non-Hodgkin lymphoma in the California Teachers Study

4.1 Summary
To evaluate whether use of oral contraceptives (OC) or menopausal hormonal therapy
(HT) is associated with B-cell non-Hodgkin lymphoma (NHL). Within the prospective
California Teachers Study, women under age 85 with no history of hematopoietic cancer
were followed from 1995 through 2006 for diagnosis of B-cell NHL. Overall, 508
women of 116,779 women eligible for analysis of OC use and 308 of 54,758
postmenopausal women eligible for analysis of HT use developed B-cell NHL.
Multivariable adjusted relative risks (RR) and 95% confidence intervals (CI) were
estimated by fitting Cox proportional hazards models for all B-cell NHL combined and
for the three most common subtypes: diffuse, large B-cell lymphomas (DLBCL);
follicular lymphomas (FL); and B-cell chronic lymphocytic leukemias/small lymphocytic
lymphomas (CLL/SLL). Women who used OCs had lower risk of B-cell NHL than
women who had never used OCs (RR=0.82, 95%CI=0.66-1.03). Although the overall
association between B-cell NHL and HT use was null (RR=1.05, 95%CI=0.82-1.34),
current users of unopposed estrogen therapy (ET) had greater risk (RR=1.36,
95%CI=1.00-1.87) and current users of estrogen plus progestin therapy (EPT) had lower
risk (RR=0.80, 95%CI=0.58-1.10) than women with no HT use. Furthermore, risk of B-

54
cell NHL declined as duration of EPT use increased, whereas no duration of use effect
was observed for ET use. These patterns of risk were observed for FL and DLBCL, but
not for CLL/SLL. These data suggest that unopposed estrogen use may increase the risk
of B-cell NHL, but combinations of estrogen and progestin, as EPT or OCs, may
decrease risk.

4.2 Introduction
Non-Hodgkin lymphoma (NHL) is the fifth most commonly diagnosed cancer among US
men and women (Jemal et al., 2008). While the causes of NHL remain elusive, the
greater proportion of NHL cases that occurs in males as opposed to females (Morton et
al., 2006) suggests that sex steroid hormones may influence the etiology of the disease.
Biological evidence from animals and humans shows that sex steroid hormones,
including pharmacologic formulations, modulate the immune system (Forsberg, 1984;
Giltay et al., 2000; Jungers et al., 1982; Kincade et al., 2000). This suggests the potential
for exogenous hormone use to influence lymphomagenesis. Previous studies
investigating the effects of exogenous hormone use on NHL risk have yielded
inconsistent results that vary by type of exposure and NHL subtype (Altieri et al., 2004;
Beiderbeck et al., 2003; Bernstein & Ross, 1992; Cerhan, Vachon et al., 2002; Cerhan et
al., 1997; Fernandez et al., 2003; Lee et al., 2008; Morton et al., 2009; Nelson, Levine, &
Bernstein, 2001; Norgaard et al., 2006; Skibola et al., 2005; Y. Zhang, Holford, Leaderer,
Boyle et al., 2004; Y. Zhang, Holford, Leaderer, Zahm et al., 2004).

55
Four of six case-control studies assessing oral contraceptive (OC) use reported a reduced
risk of NHL (Bernstein & Ross, 1992; Lee et al., 2008; Nelson et al., 2001; Skibola et al.,
2005; Smedby et al., 2007), but other case-control (Beiderbeck et al., 2003; Y. Zhang,
Holford, Leaderer, Boyle et al., 2004) and two cohort studies (Cerhan et al., 1997;
Morton et al., 2009) reported no association. Among these studies, only one case-control
study (Lee et al., 2008) and one cohort study (Morton et al., 2009) examined whether OC
use was associated with the risk of common NHL subtypes; the two studies reported
different associations. The explanation for inconsistent results with hormonal therapy
(HT) is complicated by the secular changes in the types of available HT and patterns of
use over the last several decades (Stefanick, 2005). Before 1980, almost all HT use
consisted of unopposed estrogen therapy (ET) and the use of HT decreased sharply from
1975 till 1980 due to reports of that endometrial cancer risk was associated with ET
(Smith, Prentice, Thompson, & Herrmann, 1975; Ziel & Finkle, 1975). The use of ET
(among women with hysterectomy) and combined estrogen-plus-progestin therapy (EPT)
formulations (among women with an intact uterus) steadily increased from 1982, with
evidence of protective effects of progestins on estrogen-induced endometrial changes,
until 2002, when the Women’s Health Initiative clinical trial data documented
unfavorable risk-benefit profiles for HT use among post-menopausal women (Rossouw et
al., 2002). Studies have addressed the different formulations in several ways. One cohort
study assessed the separate effect of ET and EPT (Morton et al., 2009); another cohort
study estimated the proportion of ET use in their cohort (Cerhan, Vachon et al., 2002); a

56
case-control study examined HT use according to hysterectomy status which is a strong
predictor of ET use (Skibola et al., 2005); all other studies only evaluated the non-
specific effect of HT use on NHL risk (Altieri et al., 2004; Beiderbeck et al., 2003;
Bernstein & Ross, 1992; Cerhan et al., 1997; Fernandez et al., 2003; Lee et al., 2008;
Nelson et al., 2001; Norgaard et al., 2006; Y. Zhang, Holford, Leaderer, Zahm et al.,
2004). It is clearly important to determine whether ET and EPT have similar or different
effects on women’s risk of NHL.

In this study, we used the data from the California Teachers Study (CTS), a large
prospective cohort of women, to investigate if OC use and HT use are associated with
risk of B-cell NHL, which accounts for more than 85% of all NHL in the US. In our
study, we collected detailed information on HT use, such as formulation and type. We
further explored associations between the three main subtypes (diffuse, large B-cell
lymphomas (DLBCL); follicular lymphomas (FL); and B-cell chronic lymphocytic
leukemias/small lymphocytic lymphomas (CLL/SLL)) as defined by the WHO
modification to the REAL (Revised European-American Lymphoma) classification for
hematopoietic malignancies (Jaffe et al., 2001).

57
4.3 Materials and Methods
4.3.1 Study Population
A detailed description of CTS has been published elsewhere (Bernstein et al., 2002).
Briefly, this prospective study is comprised of 133,479 female public school
professionals recruited through the California State Teachers Retirement System in 1995.
All participants completed a self-administered baseline questionnaire, which collected
information on demographic factors, menstrual and reproductive events, family and
personal history of cancer and other diseases, oral contraceptive and menopausal
hormone therapy use and lifestyle factors (recreational physical activity, diet, smoking
and alcohol use).

We excluded women sequentially from the cohort of 133,479 women who were not
California residents at the time the baseline questionnaire was completed (n = 8,867),
who had limited their participation to breast cancer research (n = 18), who had a prior
history of a hematopoietic malignancy (n = 536) or whose history of cancer was unknown
(n = 663), and who were 85 years of age or older at cohort entry (n = 2179). For the
analysis of oral contraceptive use, we further excluded women with unknown oral
contraceptive use status at cohort entry (n= 4437). For the analysis of hormonal therapy,
we further excluded women who were premenopausal (n= 47,928) or perimenopausal
(n=6410) at cohort entry, women who only used progestin (n= 990), and women with
unknown hormonal therapy status at cohort entry (n= 11,130). Thus, a final cohort of

58
116,779 women was available for the analysis of oral contraceptive use and a final cohort
of 54,758 women was available for the analysis of hormonal therapy.

Use of human subjects in this study has been approved by the Institutional Review
Boards at the City of Hope, University of Southern California, Northern California
Cancer Center, and University of California at Irvine, in accordance with assurances filed
with and approved by the Committee for the Protection of Human Subjects, California
Health and Human Services Agency.

4.3.2 Case Ascertainment and Follow-up
Incident diagnoses of B-cell NHL (International Classification of Diseases for Oncology
(ICDO), third edition morphology codes: 9590, 9591, 9670-9675, 9680-9699, 9727,
9823, 9832, 9835, 9836) were identified through annual linkages with the California
Cancer Registry, the population-based cancer registry for California. The California
Cancer Registry receives over 99% of all cancer diagnoses occurring in California
residents from its regional registries as part of a state mandate. Follow-up of each woman
started on the date that a woman completed her baseline questionnaire and ended at the
first occurrence of one of the following: a diagnosis of B-cell NHL, a move outside of
California, a first diagnosis of a T-cell NHL, Hodgkin lymphoma, multiple myeloma, or
leukemia other than CLL and prolymphocytic leukemia, death, or December 31, 2006.
During follow-up period, 508 B-cell NHL patients were identified from the 116,779

59
women eligible for oral contraceptive analysis, including 139 DLBCL (ICDO-
morphology codes 9678-9680, 9684), 107 FL (ICDO codes 9690-9698), and 113
CLL/SLL (ICDO codes 9670, 9823). Among 54,758 women eligible for hormonal
therapy, 378 B-cell NHL patients were identified, including 107 DLBCL, 74 FL, and 88
CLL/SLL.

The status of California residence was monitored through the annual mailing of
newsletters or questionnaires, annual linkage with the U.S. Postal Service national change
of address database, and change-of-address postcards submitted by participants.
Information on the date and cause of death were obtained from the California state
mortality file, the National Death Index, and the Social Security Administration death
master files (Bernstein et al., 2002).

4.3.3 Exposure Assessment
OC use was determined by the following questions: “Did you ever take birth control pills
(oral contraceptives) for one month or longer?” Response categories were “No,” “Yes,
and I am currently taking them,” and “Yes, but I am no longer taking them.” Subjects
responding yes were then asked “How old were you when you first used birth control
pills, and (if no longer taking) how old were you when you last used them?” Participants
were further asked “How many years in total have you used birth control pills (exclude
those periods when you temporarily stopped)?” Response categories for duration of use

60
were “less than 1 year,” “1-2,” “3-4,” “5-9,” “10-14,” “15-19,” “20-24,” and “25 years or
more.” Based on the information above, we defined participants’ OC use status at cohort
entry, age at first OC use, OC use duration and whether OC use was before or after 1970.
Information on menopausal status at cohort entry was collected by asking a participant if
her menstrual periods had stopped permanently, when she had her last period and why
her periods stopped. We further asked if and when a woman had a hysterectomy or ovary
removal surgery. Premenopausal women were defined as women who reported
continuing menstrual periods and who had never used hormones for menopausal
symptoms; perimenopausal women were defined as women whose menstrual periods had
stopped within the last 6 months and they were not currently pregnant; or women who
started to use HT before their periods had stopped and they were younger than age 56 at
cohort entry; postmenopausal women were defined as women whose menstrual periods
had stopped (due to natural menopause or bilateral oophorectomy) more than 6 months
before cohort entry or who were greater than 55 years old and not considered
premenopausal or perimenopausal. For the analysis of menopausal variables, we defined
type of menopause among postmenopausal women according to the reasons why their
periods stopped as natural menopause, bilateral oophorectomy, hysterectomy without
bilateral oophorectomy (defined as hysterectomy, with ≥ 1 ovary), other reason and
unknown type. We further created another variable that combined type of menopause and
age at menopause with the categories of natural menopause before age 50 or after,

61
bilateral oophorectomy before age 50 or after, hysterectomy without bilateral
oophorectomy, other reason, and unknown type of or age at menopause.

To collect information on HT use, we first asked "Have you ever taken estrogen (“female
hormones”) for symptoms of menopause (the change of life) or for other reasons?"
Response categories were "No," "Yes, and I am currently taking estrogens" and "Yes, but
I am no longer taking estrogens." If a subject answered yes, following questions
collected information on type of estrogen used, age first and age last used, duration and
the way estrogen was administered (by mouth, by patch, or other methods of
administration). For the purposes of this study we limited exposure to administration by
mouth or patch. Similar questions were asked for progestin use. Based on the
information of estrogen and progestin use, we determined HT use status among
postmenopausal women at cohort entry (never, past or current), the pattern of HT use
over time, with respect to formulation (ET or EPT), the total durations of HT use and
years since last use of any HT.

4.3.4 Assessment of other potential risk factors
We collected detailed information on a number of potential NHL risk factors including
self-reported race (non-Hispanic white or all other races), first-degree family history of
lymphoma (no, yes or adopted/unknown), any prior diagnosis of diabetes (no or yes),
alcohol consumption in the year before joining the cohort (none, <15 grams/day, >15

62
grams/day, or unknown), smoking status at cohort entry (never, former, current, or
unknown), age at menarche (never had period, ≤11, 12, ≥13 years old or unknown),
height ( ≤61, 62-63, 64-65, 66-67, >67 inches or unknown), body mass index (BMI) at
cohort entry ( ≤24.9 kg/m
2
, 25-29.9 kg/m
2
, ≥30 kg/m
2
, or unknown), number of full-term
pregnancy (FTP) (never pregnant, ever pregnant but no FTP, 1 FTP, 2 FTP, ≥3 FTP, or
unknown), and socioeconomic status (SES). Area-level SES was generated for all census
block groups in California in 1990 using education, occupation, and income quartile
ranks of the California population and creating a summary score that weighted each
variable equally (Reynolds et al., 2004). Participants were assigned to an SES summary
score according to residential address at cohort entry. Quartile distributions of California
population were used to categorize SES into four groups.

4.3.5 Data Analyses
We used multivariate Cox proportional hazards regression models to compute the hazard
rate ratio as a measure of relative risk (RR) and 95% confidence intervals (CI), using age
in days from cohort entry until the end of follow-up as the time scale. All the models
were stratified by age in years at cohort entry and adjusted for race. We assessed potential
risk factors such as family history of lymphoma, SES, current smoking status, alcohol
consumption one year before cohort entry, age at menarche, height, BMI, and number of
full-term pregnancy for all the exposures of interest, but none of these factors altered risk
estimates by as much as 5% and therefore none were included in the final models. For the

63
analysis of OC use exposure, we examined menopausal status and HT use status
[Premenopausal, Peri- and Post-menopausal (all past HT use, current ET use, current
EPT use, current or past progestin use only, unknown HT use status), HT use started
before menopause, and unknown menopausal status]; in the analysis of HT use, we
examined OC use status and age at first OC use (Never user, first use OC less than 25
years old, first use OC 25 years or older, and unknown OC use history). However,
neither of these factors was included as a confounder in the respective final models
because the additional adjustment for the other hormonal exposure had no impact on the
risk estimates.

Age at menopause and type of menopause were included in a single model, we then
examined the effect of the exposure that combined these two variables into a single
variable classifying natural menopause, bilateral oophorectomy according to whether
they occurred before or after age 50 years. All models were further adjusted for type of
HT use (ET only, EPT only, and other use pattern).

In the analysis of HT use, all exposures were adjusted for the combined single variable
that defined type of menopause and age at menopause. ET use was included in models
assessing duration of EPT use; similarly, EPT use was included in models assessing
duration of ET use.

64
For each exposure, we evaluated potential associations with all B-cell NHL combined
and separately for the 3 most common NHL subtypes: DLBCL, FL, and CLL/SLL. Two-
sided P-values are reported. All statistical analyses were performed using SAS version
9.1 (SAS Institute Inc, Cary, NC).

4.4 Results
The mean age at cohort entry was 52.6 years for women in the analytic cohort of OC use
and 63.1 years for women in the analytic cohort of HT use. The average length of follow-
up was 10 years for both groups. The mean ages at diagnosis ± standard deviation were
69.3 ± 11.8 years (OC cohort; range, 33-92) and 72.9 ± 9.0 years (HT cohort; range, 47-
92) for women diagnosed with any B-cell NHL, 69.2 ± 11.7 years (OC cohort; range,
33-92) and 73.5 ± 8.8 years (HT cohort; range, 50-92) for DLBCL, 65.7 ± 12.8 years (OC
cohort; range 35-90) and 71.0 ± 8.9 years (HT cohort; range, 50-90) for FL, and 72.0 ±
9.7 (range, 48-92) and 73.5 ± 8.9 years (HT cohort; range, 51-92) for CLL/SLL.

In the analytic cohort of OC use, 61% of the participants were past OC users and 6%
were current OC users at the time of cohort entry (Table 4.1). In the analytic cohort of
HT use among post-menopausal women, 57% of the participants were current HT users
and 17% were past HT users (Table 4.1). Women who reported past OC use or current

65
Table 4.1 Selected baseline characteristics among eligible women in relation to oral contraceptive (OC) and hormone therapy (HT) use
status at cohort entry
Characteristic N (total) OC use status (%) N (total) HT use status (%)
Never Past Current Never Past Current
Total 116,779 38,892 71,239 6,648 54,758 14,077 9,381 31,300
Age at cohort entry (mean ± SD) 52.6±9.9 62.4±14.3 49.0±10.0 34.1±7.1 63.1±9.6 65.3±9.7 67.0±9.5 60.9±8.9
Age-adjusted percentages:
Race
Non-Hispanic white 101,115 25.7 70.4 3.9 48,939 23.8 16.1 60.1
All other races/ethnicities 15,664 29.3 66.2 4.5 5,819 33.3 16.7 49.9
First-degree family history of lymphoma
No 110,110 26.0 70.0 4.0 51,493 24.7 16.2 59.0
Yes 3,074 27.1 69.8 3.1 1,696 22.4 16.7 60.9
Unknown/Adopted 3,595 29.3 66.2 4.5 1,569 28.9 14.0 57.1
Alcohol consumption (grams/day)
None 37,165 31.0 65.7 3.3 17,361 29.4 17.3 53.2
< 15 55,039 23.0 72.6 4.5 24,343 22.3 15.3 62.5
> 15 18,575 24.6 71.9 3.5 10,277 22.7 15.9 61.4
Unknown 6,000 31.9 62.4 5.7 2,777 27.1 18.9 54.0
Smoking status
Never 76,759 26.8 68.3 4.9 31,855 25.8 16.0 58.3
Former 33,411 24.8 72.8 2.4 19,105 22.0 16.5 61.6
Current 5,925 25.5 72.5 2.0 3,474 31.4 16.6 52.0
Unknown 684 29.7 66.8 3.5 324 27.9 15.5 56.6
Height (inches)
54-61 11,736 33.5 63.2 3.3 6,334 27.6 17.7 54.8
62-63 25,991 27.9 68.6 3.5 13,047 25.8 17.0 57.2
64-65 34,011 25.6 70.5 3.9 16,150 24.0 16.0 60.0
66-67 28,025 24.5 71.2 4.3 12,491 23.8 15.8 60.4
>67 16,594 21.9 73.0 5.1 6,453 23.4 14.4 62.2
Unknown 422 49.2 48.0 2.9 283 43.9 16.5 39.7

66
Table 4.1 continued.
N (total) OC use status (%) N (total) HT use status (%)
Never Past Current Never Past Current
Body Mass Index (kg/m
2
)
16 - 24.9 68,426 23.3 71.8 4.9 29,019 22.2 15.1 62.7
25 - 29.9 28,007 28.8 68.2 3.0 14,861 24.7 17.2 58.1
30 - 54.9 15,784 29.2 68.3 2.5 7,863 31.2 17.4 51.3
Unknown 4,562 44.0 53.8 2.2 3,015 34.0 18.2 47.8
Number of full-term pregnancy (FTP)
Never Pregnant 23,725 38.4 54.2 7.5 9,479 29.4 16.1 54.4
Pregnant, but no FTP 7,076 16.4 77.2 6.4 2,001 21.8 15.1 63.2
1 FTP 18,182 19.7 75.4 4.8 7,141 25.6 16.0 58.4
2 FTP 38,086 18.9 78.0 3.1 16,539 22.0 15.8 62.2
3+ FTP 28,658 33.3 65.2 1.5 18,442 24.7 16.6 58.6
Unknown 1,052 54.8 43.3 1.9 1,156 29.3 17.3 53.5

67
HT use at cohort entry were more likely to be white, be younger in age, drink alcohol in
the year prior to joining the cohort, be taller in height or lower in BMI at cohort entry,
and have been pregnant but never have had a full-term pregnancy. Women who used OCs
in the past were more likely to use HT and be a current HT user compared to women who
never used OCs. Furthermore, women who were current HT users were more likely to
have had an early age at first OC use (Table 4.2).

Table 4.2 Oral contraceptive (OC) use, menopausal status and hormone therapy (HT) use in
two analytic cohorts.
Characteristic OC use status (%)
OC use cohort N (total) Never Past Current
Total 116,779 38,892 71,239 6,648
Combination of menopausal status and HT
Premenopausal 46,712 14.8 75.9 9.4
Peri- and post-menopausal
No HT 14,486 50.3 49.6 0.1
All past HT 9,187 48.2 51.8 -
Current E 14,803 37.2 62.9 -
Current E+P 16,186 27.9 72.1 -
Current or past progestin only therapy 585 27.1 72.2 0.7
Unknown HT use status 5,578 46.3 53.5 0.2
HT started before menopause 4,619 10.1 88.9 1.1
Unknown menopausal status 4,623 12.0 86.4 1.6
Characteristic HT use status (%)
Post-menopausal women: HT use cohort N (total) Never Past Current
Total 54,758 14,077 9,381 31,300
Age at first OC use
Never 26,670 31.7 18.6 49.7
<25 years 9,642 16.7 10.7 72.7
≥25 years 15,302 19.2 15.3 65.5
Unknown 3,144 22.8 16.3 60.9
Abbreviations: ET = Unopposed estrogen therapy; EPT = estrogen plus progestin therapy

68
Using women who never used OC as the reference group, women who used OC had a
decreased risk of B-cell NHL that was of borderline statistical significance (RR=0.82,
95%CI=0.66-1.03) (Table 4.3). Moreover, women who started OC use at an early age
(<25 years) appeared to have a greater decrease in risk (RR=0.73, 95%CI=0.51-1.03)
than with women who started OC use at age 25 or older (RR=0.87, 95%CI=0.69-1.10).
Although no dose-response effect was found for the duration of OC use, women who
used OC for 5 to 9 years had the most greatest decrease in risk (RR=0.75, 95%CI=0.54-
1.03). Risk of B-cell NHL risk differed little when women who started OC use before
1970 were compared to those whose first use was in 1970 or later. No statistically
significant association was observed for OC use and risk of DLBCL, FL or CLL/SLL
subtype.

Age at menopause has no statistically significant association with B-cell NHL (P for
trend=0.57) (Table 4.4). Further adjustment for type of HT use did not alter the risk
estimates. The risk of B-cell NHL was statistically significantly associated with having
had a bilateral oophorectomy relative to having had natural menopause (RR=1.39,
95%CI= 1.05-1.85); however, this increased risk did not remain statistically significant
after adjustment for type of HT used. Women who had bilateral oophorectomy before age
50 had more than 70% increased risk (RR=1.71, 95%CI=1.19-2.45) and women who had
hysterectomy but without bilateral oophorectomy had almost a 50% increased risk
(RR=1.48, 95%CI=1.04-2.10) when compared to women who had natural menopause

69
Table 4.3 Relative risk (RR) estimates (and 95% confidence intervals (CI)) for the association between oral contraceptive (OC) use and B-cell NHL
risk
All cases DLBCL FL CLL/SLL
Person- Cases RR Cases RR Cases RR Cases RR
OC use Years 508 (95%CI) 139 (95%CI) 107 (95%CI) 113 (95%CI)
Never use 381,047 292 1.00 78 1.00 53 1.00 66 1.00
Ever use* 805,109 216 0.82 (0.66-1.03) 61 0.92 (0.60-1.40) 54 0.83 (0.52-1.34) 47 1.07 (0.67-1.71)
Age at first use
<25 539,501 95 0.73 (0.51-1.03) 26 0.82 (0.41-1.61) 28 0.85 (0.43-1.69) 15 0.86 (0.37-2.00)
25+ 247,031 117 0.87 (0.69-1.10) 34 0.97 (0.62-1.51) 24 0.81 (0.48-1.37) 32
1.16 (0.72-
1.86)
Unknown 18,577 4 1 2
OC use duration
<5 years 365,012 105 0.88 (0.68-1.15) 25 0.83 (0.50-1.40) 27 0.91 (0.53-1.57) 26 1.32 (0.77-2.24)
5-9 years 249,021 55 0.75 (0.54-1.03) 22 1.20 (0.69-2.09) 12 0.64 (0.32-1.29) 11 0.93 (0.46-1.90)
10+ years 171,551 52 0.87 (0.63-1.20) 12 0.80 (0.41-1.54) 14 0.95 (0.50-1.82) 10 0.98 (0.47-2.00)
Unknown 19,525 4 2 1
P for trend 0.17 0.74 0.58 0.86
Year began OC use
Before 1970 419,900 159 0.82 (0.65-1.04) 48 0.99 (0.64-1.54) 33 0.73 (0.43-1.23) 39 1.07 (0.66-1.73)
1970 and after 366,631 53 0.90 (0.61-1.32) 12 0.68 (0.31-1.48) 19 1.21 (0.60-2.48) 8 1.53 (0.66-3.58)
Unknown 18,577 4 1 2
Abbreviations: DLBCL = diffuse large B-cell lymphoma; FL = follicular lymphoma; CLL = chronic lymphocytic leukemia; SLL = small lymphocytic
lymphoma.
All models adjusted for race.
*: Among "ever use", only 7 cases were current users.

70
before age 50 years. Again, the adjustment for type of HT use attenuated strength of the
association and both risk estimates no longer were statistically significant (Table 4.4).
No statistically significant association was observed for the common subtypes except that
the risk of CLL/SLL increased significantly among women who had age at menopause
between 47 and 49 (RR=2.59, 95%CI=1.03-6.55). However, no dose-response effect was
observed between age at menopause and CLL/SLL risk (P for trend=0.28).
Table 4.4 Relative risk (RR) estimates (and 95% confidence intervals (CI)) for the association
between menopause and B-cell NHL risk
All cases
Person- Cases RR RR†
Years 378 (95%CI) (95%CI)
Age at menopause*
<44 64,770 35 1.00 1.00
44-46 57,450 28 0.84 (0.51-1.39) 0.84 (0.51-1.39)
47-49 86,544 68 1.45 (0.94-2.21) 1.46 (0.95-2.24)
50-52 119,203 73 1.08 (0.70-1.66) 1.10 (0.71-1.69)
53+ 107,718 82 1.16 (0.76-1.78) 1.20 (0.78-1.84)
Unknown 106,176 92
P for trend 0.57 0.49
Type of menopause*
Natural 315,641 203 1.00 1.00
Bilateral oophorectomy 108,265 77 1.39 (1.05-1.85) 1.19 (0.86-1.64)
Hyster ec to my, with ≥1 ovary 66,041 65 1.42 (0.79-2.56) 1.24 (0.68-2.56)
Others 45,503 30 1.25 (0.77-2.02) 1.25 (0.77-2.02)
Unknown 6,412 3
Type and age at menopause
Natural, age < 50 121,601 64 1.00 1.00
Natural, age ≥ 50 191,949 137 1.22 (0.90-1.64) 1.23 (0.91-1.67)
Bilateral oophorectomy, age <50 75,432 56 1.71 (1.19-2.45) 1.45 (0.98-2.15)
Bilateral oophorectomy, age ≥50 23,980 13 0.94 (0.52-1.71) 0.82 (0.45-1.51)
Hysterectomy, with ≥1 ovary 66,041 65 1.48 (1.04-2.10) 1.30 (0.90-1.88)
Others 45,503 30 1.34 (0.87-2.08) 1.35 (0.87-2.08)
Unknown 17,356 13
Abbreviations: ET = Unopposed estrogen therapy; EPT = estrogen plus progestin therapy.
All models were adjusted for race.
*: Age at menopause and type of menopause were mutually adjusted for each other.
†: Models were further adjusted for type of HT use.

71
Table 4.4 continued.
DLBCL FL CLL/SLL
Cases RR† Cases RR† Cases RR†
107 (95%CI) 74 (95%CI) 88 (95%CI)
Age at menopause*
<44 12 1.00 7 1.00 6 1.00
44-46 8 0.70 (0.28-1.73) 7 0.98 (0.34-2.84) 3 0.53 (0.13-2.14)
47-49 13 0.89 (0.39-2.02) 8 0.66 (0.23-1.87) 22 2.59 (1.03-6.55)
50-52 27 1.27 (0.60-2.67) 13 0.73 (0.28-1.94) 13 1.10 (0.40-3.02)
53+ 25 1.11 (0.52-2.35) 17 0.97 (0.38-2.51) 20 1.69 (0.64-4.42)
Unknown 22 22 24
P for trend 0.46 0.90 0.28
Type of menopause*
Natural 60 1.00 40 1.00 46 1.00
Bilateral oophorectomy 23 1.45 (0.85-2.48) 13 0.83 (0.38-1.81) 19 1.56 (0.81-2.99)
Hysterectomy, with ≥1 ovary 13 0.87 (0.46-1.62) 16 0.71 (0.20-2.58) 17 1.05 (0.33-3.30)
Others 8 1.20 (0.64-2.27) 5 0.57 (0.17-1.92) 6 0.84 (0.29-2.45)
Unknown 3
Type and age at menopause
Natural, age < 50 17 1.00 11 1.00 15 1.00
Natural, age ≥ 50 41 1.39 (0.78-2.46) 29 1.44 (0.71-2.91) 31 1.19 (0.64-2.22)
Bilateral oophorectomy, age <50 13 1.20 (0.55-2.61) 10 1.39 (0.54-3.54) 14 1.84 (0.83-4.08)
Bilateral oophorectomy, age ≥50 9 2.05 (0.88-4.78) 1 0.32 (0.04-2.57) 2 0.60 (0.13-2.72)
Hysterectomy, with ≥1 ovary 13 0.91 (0.43-1.96) 16 1.77 (0.76-4.15) 17 1.69 (0.80-3.57)
Others 8 1.35 (0.58-3.14) 5 1.15 (0.40-3.33) 6 1.16 (0.45-3.02)
Unknown 6 2 3
Abbreviations: ET = Unopposed estrogen therapy; EPT = estrogen plus progestin therapy; DLBCL = diffuse large B-cell
lymphoma; FL = follicular lymphoma; CLL = chronic lymphocytic leukemia; SLL = small lymphocytic lymphoma
All models were adjusted for race.
*: Age at menopause and type of menopause were mutually adjusted for each other.
†: Models were further adjusted for type of HT use.

72
Compared to women who had never used HT at cohort entry, HT users had a slight, but
not statistically significant, increased risk of B-cell NHL (Table 4.5). Considering
formations separately, current ET users had a statistically significant increased risk
(RR=1.36, 95%CI=1.00-1.87) compared to never users of HT, whereas current EPT users
had a decreased risk that was of marginal statistical significance (RR=0.80, 95%CI=0.58-
1.10). Similarly, women who only used ET before cohort entry (i.e., no EPT use) had an
increased risk but women who only used EPT before cohort entry (i.e., no ET use) had a
decreased risk although neither risk estimate was statistically significant. Combining ET
use and EPT use, no statistically significant associations were observed for HT use
duration overall or for HT use duration defined separately for past and current users (data
not shown). However, when examining duration of use for ET and EPT separately, an
inverse association was observed for duration of EPT use; the association became more
pronounced after adjustment for use of ET (P for trend=0.07) (Table 4.5). Women who
used EPT for more than 15 years had 48% decreased risk of B-cell NHL (RR=0.52,
95%CI=0.29-0.93). The restriction to women who used EPT only before cohort entry
showed similar results (data not shown). In contrast to the results for EPT use, the risk of
B-cell NHL associated with ET use increased after a short period of use (RR=1.23,
95%CI=0.87-1.73), remained fairly constant between 5-15 years (RR=1.29,
95%CI=0.92-1.82), and then declined somewhat for more than 15 yrs of use (RR=1.12,
95%CI=0.79-1.58). Adjustment for EPT use did not affect these risk estimates.
Restriction to women who used ET only also showed similar results (data not shown).

73
With respect to the common subtypes, we observed consistent increased risk of FL with
all HT use exposures except for duration of EPT use which showed a decreased risk with
longer duration (Table 4.5). For DLBCL, the increased risk was most pronounced among
ET users, especially current ET users (RR=1.78, 95%CI=1.00-3.18) or women who used
ET for 5-15 years (RR=1.42, 95%CI=0.94-3.53). There was no consistent association
between HT use and the risk of CLL/SLL.

4.5 Discussion
In this large cohort of female public school professionals, OC use decreased the risk of B-
cell NHL, especially among women started OC use at a young age. However, the risk did
not decline with increasing duration of OC use. We found a positive association between
current ET use, but an inverse association between current EPT use and the risk of B-cell
NHL overall. Furthermore, the risk associated with EPT use decreased with longer
duration of use, but longer duration of ET use was not associated with a greater risk than
shorter durations of use. We observed consistent increased risk of FL with all HT use
exposures except for duration of EPT use. ET use was also associated with increased risk
of DLBCL. No consistent associations between OC use was observed for common B-cell
NHL subtypes; HT use was no associated with CLL/SLL risk.

74

Table 4.5 Relative risk (RR) estimates (and 95% confidence intervals (CI)) for the
association between hormone therapy (HT) use and B-cell NHL risk
All cases
Person- Cases RR RR†
Years 378 (95%CI) (95%CI)
Never HT user 136,545 97 1.00 1.00

Ever HT user 405,317 281 1.05 (0.82-1.34)
HT use status and formulation
Past ET or EPT 89,880 78 1.08 (0.80-1.46)
Current HT use 315,437 203 1.03 (0.80-1.34)
Current ET 154,179 134 1.36 (1.00-1.87)
Cureent EPT 161,258 69 0.80 (0.58-1.10)
Type of HT use
ET only 171,621 156 1.21 (0.91-1.61)
EPT only 160,076 67 0.83 (0.60-1.15)
ET and EPT or others 73,619 58 1.14 (0.82-1.59)
Duration of ET use
< 5 years 66,231 56 1.23 (0.87-1.73) 1.23 (0.87-1.73)
5-15 years 76,443 64 1.29 (0.92-1.82) 1.28 (0.88-1.86)
> 15 years 78,601 78 1.12 (0.79-1.58) 1.11 (0.77-1.61)
Unknown 18,575 15
P for trend 0.47 0.55
Duration of EPT use
< 5 years 93,159 40 1.08 (0.72-1.61) 1.03 (0.69-1.54)
5-15 years 87,880 47 0.96 (0.66-1.39) 0.72 (0.47-1.12)
> 15 years 32,781 25 0.83 (0.53-1.32) 0.52 (0.29-0.93)
Unknown 16,824 11
P for trend 0.57 0.07
Years Since Last HT Use for Past Users
≤ 5 years since last use 36,344 22 1.04 (0.64-1.67)
> 5 years since last use 53,206 56 1.17 (0.82-1.65)
Unknown 330
Abbreviations: ET = Unopposed estrogen therapy; EPT = estrogen plus progestin
therapy.
All models were adjusted for race, type and age at menopause.
†: For duration of ET use, models adjusted for if ever used EPT; for duration of EPT
use, models adjusted for if ever used ET.

75

Table 4.5 continued.
DLBCL FL CLL/SLL
Cases RR† Cases RR† Cases RR†
107 (95%CI) 74 (95%CI) 88 (95%CI)
Never HT user 27 1.00 14 1.00 23 1.00

Ever HT user 80 1.14 (0.72-1.81) 60 1.48 (0.81-2.71) 65 0.98 (0.60-1.63)
HT use status and formulation
Past ET or EPT 17 0.87 (0.47-1.61) 18 1.81 (0.89-3.67) 19 1.05 (0.57-1.95)
Current HT use 63 1.29 (0.79- 2.10) 42 1.34 (0.71-2.54) 46 0.95 (0.55-1.63)
Current ET 42 1.78 (1.00- 3.18) 24 1.51 (0.70-3.29) 32 1.31 (0.69-2.50)
Cureent EPT 21 0.96 (0.53-1.74) 18 1.23 (0.60-2.53) 14 0.68 (0.34-1.35)
Type of HT use
ET only 46 1.38 (0.81-2.35) 31 1.72 (0.84-3.52) 33 0.94 (0.52-1.71)
EPT only 18 0.87 (0.46-1.62) 19 1.34 (0.65-2.77) 16 0.87 (0.44-1.72)
ET and EPT or others 16 1.17 (0.62-2.21) 10 1.34 (0.59-3.09) 16 1.29 (0.67-2.50)
Duration of ET use
< 5 years 14 1.13 (0.58-2.21) 13 2.27 (1.03-5.00) 10 0.85 (0.40-1.83)
5-15 years 22 1.83 (0.94-3.53) 10 1.42 (0.56-3.64) 17 1.12 (0.53-2.37)
> 15 years 21 1.20 (0.60-2.40) 14 1.81 (0.72-4.56) 20 0.88 (0.41-1.86)
Unknown 4 4 2
P for trend 0.40 0.21 0.64
Duration of EPT use
< 5 years 11 1.11 (0.52-2.35) 12 1.88 (0.82-4.34) 9 1.09 (0.47-2.53)
5-15 years 15 0.81 (0.36-1.83) 9 0.95 (0.36-2.48) 12 0.91 (0.37-2.21)
> 15 years 6 0.42 (0.13-1.32) 3 0.57 (0.13-2.55) 10 0.92 (0.32-2.68)
Unknown 2 4 1
P for trend 0.28 0.92 0.76

76
Table 4.5 continued.
DLBCL FL CLL/SLL
Cases RR† Cases RR† Cases RR†
107 (95%CI) 74 (95%CI) 88 (95%CI)
Never HT user 27 1.00 14 1.00 23 1.00
Years Since Last HT Use for Past Users
≤ 5 years since last use 5 0.91 (0.34-2.43) 6 1.91 (0.71-5.17) 5 0.99 (0.36-2.73)
> 5 years since last use 12 0.92 (0.45-1.88) 12 1.81 (0.80-4.09) 14 1.20 (0.59-2.45)
Unknown
Abbreviations: ET = Unopposed estrogen therapy; EPT = estrogen plus progestin therapy; DLBCL = diffuse large B-
cell lymphoma; FL = follicular lymphoma; CLL = chronic lymphocytic leukemia; SLL = small lymphocytic
lymphoma
All models were adjusted for race, type and age at menopause except for the duration of EPT use and CLL/SLL risk
where race was not included in the model because all cases were non-Hispanic White women.
†: For duration of ET use, models adjusted for if ever used EPT; for duration of EPT use, models adjusted for if ever
used ET.

77
Consistent with previous studies, we did not find statistically significant associations
between age at menopause or type of menopause and risk of NHL (Cerhan, Vachon et al.,
2002; Lee et al., 2008; Morton et al., 2009; Nelson et al., 2001; Y. Zhang, Holford,
Leaderer, Boyle et al., 2004). Four previous case-control studies assessing OC use
reported a reduced risk of NHL (Bernstein & Ross, 1992; Lee et al., 2008; Nelson et al.,
2001; Skibola et al., 2005), but other case-control (Beiderbeck et al., 2003; Denny et al.,
1992; Y. Zhang, Holford, Leaderer, Boyle et al., 2004) and cohort studies (Calle &
Kaaks, 2004; Cerhan et al., 1997; Morton et al., 2009) reported no association. Our study
results on OC use are most consistent with those from the case-control study by Lee et al.
(Lee et al., 2008), where the decrease in NHL risk was statistically significant for women
who used OCs and for women who started OCs at an early age. In this study, similar to
our observation, the authors did not observe decreasing risk with increasing duration of
OC use (Lee et al., 2008). However, Lee et al. reported a lower risk among women who
started OCs before 1970, which was not observed in our study. Lee et al. also examined
specific NHL histologic subtypes which were not examined in other case control studies;
they reported non-significant decreases in risk for DLBCL and FL. In our study, we
observed somewhat decreased risk of DLBCL and FL, but the relatively small number of
women diagnosed with each subtype and the low magnitude of effect on risk limited our
ability to draw firm conclusions about these potential associations. Only one cohort
study (Morton et al., 2009) has examined the risk of common NHL subtypes, reporting
no association between OC use and risk of any specific NHL subtype.

78
The association between HT use and NHL risk has been less consistent. The substantial
changes in the types of available HT and patterns of use over the last several decades
(Stefanick, 2005), together with the different NHL histologic subtype distributions in
each study may account for part of the observed inconsistencies in risk estimates. For
example, an early case-control study conducted in Los Angeles between 1979 and 1982,
which included patients with all grades and subtypes, reported an odds ratio of 1.58
(95%CI=1.09-2.29) for women with a history of at least 1 year of use of estrogen
replacement therapy (Bernstein & Ross, 1992). However, a second study by these
investigators which was conducted between 1989 and 1992, and which was restricted to
women diagnosed with Working Formulation classifications (Rosenberg, Berard, Brown,
& al:, 1982) of high or intermediate grade NHL, showed an odds ratio of 0.64
(95%CI=0.32-1.29) for women who ever used HT compared to women who never used
HT (Nelson et al., 2001). Between these two study periods, patterns of HT use also
experienced a substantial change (Stefanick, 2005; Wysowski, Golden, & Burke, 1995).
The number of women using estrogen alone decreased sharply from 1975 onward due to
reports of increased endometrial cancer risk (Smith et al., 1975; Ziel & Finkle, 1975), and
then increased gradually after 1980 (Hemminki, Kennedy, Baum, & McKinlay, 1988).
Progestin use increased from 1982 onward; concomitant use of estrogens and progestins
increased over time and was common by 1986 (Hemminki et al., 1988). One study
(Wysowski et al., 1995) showed that dispensed prescriptions for oral
medroxyprogesterone increased from 2.3 million prescriptions in 1982 to11.3 million in

79
1992, a 4.9 fold increase. The use of ET (among women with hysterectomy) and EPT
(among women with an intact uterus) steadily increased from 1982 until the early 2000s,
when the Women’s Health Initiative clinical trial data documented unfavorable risk-
benefit profiles for HT use among post-menopausal women (Stefanick, 2005). Thus, at
the time the first study was conducted (Bernstein & Ross, 1992), almost all HT use was
ET only. But when the second study was conducted (Nelson et al., 2001), a greater
proportion of HT use was EPT.

Among previous studies, only the National Institutes of Health (NIH) –American
Association of Retired People (AARP) Diet and Health Study Cohort (Morton et al.,
2009) provided information on type of HT used (ET and EPT) and hysterectomy status,
and few other studies accounted for hysterectomy status or oophorectomy status (Skibola
et al., 2005), which are strong predictors of ET use. The NIH -AARP study, which was
initiated at about the same time as the California Teachers Study, reported null
associations between NHL risk and EPT use among women with intact uteri, but an
inverse association for ET use and all NHL risk (RR=0.63, 95%CI=0.29-1.37) and for
DLBCL risk (RR=0.49, 95%CI=0.25-0.96) among women with hysterectomy (Morton et
al., 2009), these results differ from our study results which had longer follow-up (1996 to
2002 for the NIH/AARP study versus 1995-2005 for the California Teachers Study). An
early report of HT use and NHL risk from the Iowa Women’s Health Study Cohort which
had seven years of follow-up, initially reported no association between HT use and NHL

80
risk (Cerhan et al., 1997); five years later (13 years follow-up), they reported that HT use
increased the risk of NHL, particularly for nodal, follicular lymphoma (Cerhan, Vachon
et al., 2002). Data on HT use in Iowa Women’s Health Study Cohort was collected in
1986, when most HT use was ET alone. The authors pointed out that the increased risk
was driven by ET use instead of EPT use because they estimated that no more than 20%
of the current users, and a smaller proportion of former users had taken EPT at cohort
entry (Cerhan, Vachon et al., 2002). We have confirmed these results, showing ET
increased NHL risk, and particularly, FL risk.

Although Skibola et al. (Skibola et al., 2005) did not observe an overall association
between HT use and NHL risk in their case-control study, they detected a decreasing risk
with longer duration of HT use among women without hysterectomy or oophorectomy,
and an increased risk for short duration HT use ( ≤5 years) among women with
hysterectomy or oophorectomy. In our study, 68% women who used ET only reported
having hysterectomy and/or oophorectomy, 81% of women who use EPT only reported
having a natural menopause. Since the study reported by Skirbola et al. was a population-
based case-control study conducted in San Francisco, California, it is possible that
women in this study had similar HT use patterns as women in our cohort study. Thus, the
measure of HT duration in this case-control study roughly measured ET use duration
among women with hysterectomy or oophorectomy, and roughly measured EPT use
among women without hysterectomy or oophorectomy. If this were true, then their results

81
are comparable to our results. We found a null association between HT use and overall
B-cell NHL risk, but an increased risk among current ET users and a decreased risk
among current EPT users. Furthermore, the risk of all B-cell NHL combined increased
shortly after ET use was initiated and remained fairly constant with longer duration of ET
use; but risk decreased continuously with increasing duration of EPT use.

Other studies without detailed information of HT use that were conducted in the United
States between the late 1980’s and the late 1990’s either showed decreased risks (Y.
Zhang, Holford, Leaderer, Zahm et al., 2004) or null associations (Lee et al., 2008;
Skibola et al., 2005). Studies conducted in other countries during 1980s and 1990s also
reported null associations between HT use and NHL risk (Altieri et al., 2004; Beiderbeck
et al., 2003; Fernandez et al., 2003; Norgaard et al., 2006). Among these studies, two
evaluated HT use in relation to specific NHL subtypes (Lee et al., 2008; Y. Zhang,
Holford, Leaderer, Zahm et al., 2004); both reported a decreased risk of diffuse large-cell
lymphoma.

HT use information in our study was collected in 1995 and 1996. Among postmenopausal
women, 40% of past HT users used EPT and 61% of current HT users used EPT. If the
previous studies in the United States were to have had a similar distribution of ET and
EPT use as we did in our study, then we might hypothesize that the null associations
observed were due to the lack of information on types of HT use (ET and EPT) in each

82
study (Altieri et al., 2004; Beiderbeck et al., 2003; Fernandez et al., 2003; Lee et al.,
2008; Nelson et al., 2001; Skibola et al., 2005). Thus, one cannot conclude that HT use
has no association with NHL risk without further exploring the associations separately for
ET and EPT, because their combined impact of opposing effects on NHL risk yields a
null association.

Studies show that reproductive hormones affect immune function and play a central role
in tumor development (Beagley & Gockel, 2003; Forsberg, 1984; Gurka & Rocklin,
1987; Wilder, 1998), but the exact roles and mechanisms of estrogen and progestin in the
immune system, especially in lymphomagenesis, are poorly understood (Giltay et al.,
2000; Kincade et al., 2000). Accumulating evidence has shown that estrogen and
estrogen-like compounds influence B-cell development (Medina et al., 2000).
Polymorphisms in genes in estrogen-related pathways also provide support of a role for
prolaction and estrogen in NHL pathogenesis (Skibola et al., 2005). Progestin, on the
other hand, may play a different role. In a previous report from the California Teachers
Study, we showed that women who had at least one full-term pregnancy had a decreased
risk of B-cell NHL, but this decreased risk was not observed in women who only had
incomplete (not full-term) pregnancies. The steady increase in circulating progesterone
levels that occurs throughout pregnancy, alone or in conjunction with high estrogen
exposure, is essential for a successful Th1/Th2 shift that leads to a full-term pregnancy
(Druckmann & Druckmann, 2005; Moor, 1968; Raghupathy & Kalinka, 2008). Th2 cells

83
promote B-cell antibody production (e.g., IL-4, IL-5, IL-6, and IL-10), some of which
may be anti-inflammatory. Studies also show that progestins may affect tumor
development by modifying circulating levels of sex hormone binding globulin (SHBG)
and decrease insulin-like growth factor-1 (IGF-1) (Schindler et al., 1998). IGF-I protects
cells from apoptosis (Buckbinder et al., 1995) and increases B-cell proliferation (Gibson
et al., 1993), thus low levels of IGF-1 may decrease the potential to initiate and promote
NHL development. Although our study showed a possible protective effect of EPT on
NHL, future studies examining cytokine profiles and subsequent B-cell NHL risk among
women according to type and dose of exogenous hormone used may help to clarify the
specific effects of estrogen and progestin.

The major strengths of this study include its prospective design, an extensive evaluation
of OC and HT use exposures including OC and HT use duration, HT formulation and
type, comprehensive follow-up procedures, the virtually complete ascertainment of
cancer outcomes, and the use of the most current WHO Classification system for NHL
subtypes. The main limitation of our study is the small number of patients available for
subtype analysis. Even with small numbers of cases in each subtype, we observed
different associations, supporting the notion that the different NHL histologic subtypes
have different etiologies, and therefore should be considered as separate disease entities.

84
4.6 Conclusions
We found a weak inverse association of OC use with B-cell NHL overall. Although HT
use regardless of formulation and type was not associated with B-cell NHL risk, a
positive association was detected for ET use, particularly for the FL subtype and
potentially for the DLBCL subtype. Further, an inverse association was detected for EPT
use. No duration of use effect was noted for ET use, but NHL risk decreased with
increasing duration of EPT use. No consistent association was detected between OC use
and specific B-cell NHL subtypes or HT use and CLL/SLL risk. Future research with
detailed information of HT formulation and duration will help clarify the role of ET and
EPT in the development of NHL, and may lead to new insights into the etiology of this
disease.

85
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Abstract (if available)

Abstract The incidence of Non-Hodgkin lymphoma (NHL) has risen for several decades. However, few risk factors have been identified and most of the etiology of NHL has not been explained.

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Asset Metadata

Creator Lu, Yani (author)

Core Title Study of risk factors of B-cell non-Hodgkin lymphoma (NHL) in the California Teachers Study

School Keck School of Medicine

Degree Doctor of Philosophy

Degree Program Preventive Medicine (Health Behavior)

Publication Date 08/04/2009

Defense Date 06/10/2009

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag body size,family history,menopausal hormonal therapy,non-Hodgkin lymphoma,OAI-PMH Harvest,oral contraceptive,physical activity

Place Name California (states)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Bernstein, Leslie (committee chair), Deapen, Dennis (committee member), Mack, Wendy J. (committee member), Taylor, Clive R. (committee member)

Creator Email yanilu@usc.edu,yanilucoh@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-m2467

Unique identifier UC1480273

Identifier etd-Lu-3156 (filename),usctheses-m40 (legacy collection record id),usctheses-c127-172717 (legacy record id),usctheses-m2467 (legacy record id)

Legacy Identifier etd-Lu-3156.pdf

Dmrecord 172717

Document Type Dissertation

Rights Lu, Yani

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Repository Name Libraries, University of Southern California

Repository Location Los Angeles, California

Repository Email cisadmin@lib.usc.edu