Immunologic profile of excessive body weight

Lymphoma; pre-diagnostic blood markers

and occupational and environmental

exposures

Fatemeh Saberi Hosnijeh

2012

Lymphoma; Pre-diagnostic blood markers and occupational and

environmental exposures

© F. Saberi Hosnijeh, 2012

All rights reserved. No part of this thesis may be reproduced or transmitted in

any form or by means, electronic or mechanical, including photocopy,

recording, any information storage and retrieval system or otherwise,

without prior written permission of the author. The copyrights on the articles

that have been published or accepted for publication have been transferred

to the respective journals.

Thesis Utrecht University, the Netherlands

ISBN: 978-90-393-5749-1

Cover illustration: © Eraxion | Dreamstime.com

Cover lay-out: Anneloes M. Berns, Multi Media Division of Veterinary

Medicine, Utrecht University

Printing: drukkerij Ridderprint, Ridderkerk

Printing of this thesis was financially supported by:

Institute for Risk Assessment Sciences, Utrecht University, The Netherlands

Roche Nederland B. V., Woerden, The Netherlands

Lymphoma; pre-diagnostic blood markers and occupational

and environmental exposures

Lymfomen; pre-diagnostische merkers in bloed en beroepsmatige en

milieugerelateerde blootstellingen

(met een samenvatting in het Nederlands)

م؛ �� در �ن و ��س �� ا�� ش�� و زی)' �&�% ه�# "�! ��

)ا# �+ ز��ن �0رس �+ ه��ا. �-ص+(

Proefschrift

ter verkrijging van de graad van doctor aan de Universiteit Utrecht op gezag van de

rector magnificus, prof. dr. G.J. van der Zwaan, ingevolge het besluit van het college

voor promoties in het openbaar te verdedigen op dinsdag 10 april 2012 des

middags te 12.45 uur

door


geboren op 31 december 1970

te Shahreray, Iran

Promotoren: Prof. dr.ir. D.J.J. Heederik

Prof. dr. P. Vineis

Co-promotor: Dr. ir. R.C.H. Vermeulen

If we knew what it was we were doing, it would not be called research, would it?

Albert Einstein

To Reza,

Hadi and Hatef

Table of contents

Chapter 1 General introduction 9

Section one: Biomarker validation

Chapter 2 Stability and reproducibility of simultaneously detected

plasma and serum cytokine levels in asymptomatic

subjects

21

Chapter 3 Plasma cytokines and future risk of non-Hodgkin

lymphoma (NHL): a case-control study nested in the

Italian European Prospective Investigation into Cancer and

Nutrition

39

Section two: How can exposures modulate the immune system?

Chapter 4 Long-term effects on humoral immunity among workers

exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD)

55

Chapter 5 Changes in lymphocyte subsets in workers exposed to

TCDD

71

Chapter 6 Plasma cytokine concentrations in workers exposed to

TCDD

87

Chapter 7 Immunologic profile of excessive body weight

103

Chapter 8 General discussion

123

Summary

147

Samenvatting (Summary in Dutch)

151

Affiliation of contributors

157

About the author

161

List of publications

163

Acknowledgments

167

�� (Summary in Persian) 174

CHAPTER 1

General introduction

Chapter 1

10

Incidence of non-Hodgkin Lymphomas

Lymphomas are the most common form of hematological malignancies in

developed countries (SEER Cancer Statistics Review, 2010)(1). Most of

lymphomas arise from lymphocytes, critical cells in the immune system that

originate in the lymph nodes, bone marrow, and thymus (2). Among the two

main types of lymphomas, non-Hodgkin lymphomas (NHL) are more common

than Hodgkin lymphoma (HL). Based on cases diagnosed between 2004 and

2008 from 17 SEER geographic areas in the United States, the age-adjusted

incidence rate was 22.7 for total lymphoma, 2.8 for HL and 19.8 for NHL per

100,000 men and women per year (1). Incidence rates for NHL are

comparable in the Netherlands (Figure 1).

Occurrence of NHL has risen in most parts of the world for several

decades (3). Since the early 1970s, the number of new cases of NHL has

doubled in the United States and accounted for 4.2% of cancers diagnosed

and 3.3% of cancer deaths in 2006 (4, 5). Although incidence rates for NHL

stabilized in the 1990s, it is still increasing by approximately 1–2% annually (4).

In Europe, increase of NHL was reported 4.2% per annum, representing an

increase of 4.8% in males and 3.4% in females per annum in the period 1985-

1992 (6). Figure 1 shows the incidence rates of NHL for men and women in

the period of 1989 to 2009 in The Netherlands. As has been observed in other

developed countries an increase in NHL incidence is apparent between 1989

and 2004 with a leveling of in more recent years. Although determinants of

these increasing trends in incidence rates of NHL are not clear, it is likely

attributable to changes in the NHL morphology classification, modern

diagnostic tools, and infectious, environmental, and occupational risk factors

of NHL (4).

Figure 1 NHL incidence for male and female in Netherlands between 1989 and 2009

(Source: www.dutchcancerfigures.nl).

Non-Hodgkin Lymphoma incidence in Netherlands

0

5

10

15

20

25

30

1989 1992 1995 1998 2001 2004 2007 2009

Year

Nu

mb

er

per

100,0

00

Men

Women


11

Risk factors of non-Hodgkin Lymphomas

A variety of risk factors have been identified for lymphomas that are mostly

associated with a severely reduced immune function. These include immune

suppression due to primary (genetically) immunodeficiency diseases (7, 8),

infection with human immunodeficiency virus (HIV) (9) and

immunosuppressive drugs taken after organ transplantation (10). Although

severe immune deficiency is the best characterized and strongest known risk

factor of NHL, the prevalence of these risk factors is relatively low in the

general population and can not explain the majority of NHL cases. Given the

central role of the immune system in lymphomagenesis, it is postulated that

moderate perturbations of the immune system may be a risk factor as well

(8).

Epidemiological studies have shown that individuals with

autoimmune conditions such as rheumatoid arthritis, systemic lupus

erythematosis, Sjogren’s syndrome and Psoriasis are at a higher risk of

developing NHL (8, 11-13). Moreover, it has been shown that infection with

specific agents including Epstein-Barr virus (EBV) (14, 15), the human T cell

leukemia/lymphotropic virus (HTLV-1) (16), Helicobacter pylori (17, 18), and

Hepatitis C viruses (HCV) (19-21) carry an increased NHL risk. Several

mechanisms by which infections could develop NHL have been described

including transformation of lymphocytes (EBV, human herpes virus 8, HTLV-1),

depletion of CD4+ T lymphocytes (HIV) and through chronic immune

stimulation (HCV, H pylori) (21).

Additional evidence of the central role of the immune system in

lymphomagenesis can be discerned from recent genetic studies that have

shown that genetic variation in genes encoding for cytokines that modulate

the inflammatory process or are linked to B cell activation, including tumor

necrosis factor (TNF), lymphotoxin alpha (LTA), interleukin 10 (IL10) and IL4

are associated with an increased risk of NHL (22-25).

Do other risk factors operate through modulation of the immune system?

Excess of NHL risk has been observed among individuals exposed to solvents

(26- 29) radiation (30) and pesticides such as chlorophenoxy herbicides,

chlorophenols and their contaminants such as dioxins (31-33). Moreover,

recent studies have suggested that modifiable lifestyle factors including

obesity (34-36), diet (37, 38), smoking (39) and alcohol intake (40) may

contribute to the rising rates of NHL.

There is evidence that environmental and occupational NHL risk

factors such as exposure to benzene, trichloroethylene (TCE) and pesticides

influence functioning of the immune system. The Agency for Toxic Substances

Chapter 1

12

and Disease Registry (ATSDR) reported that an association between benzene

exposure and adverse effects on functional immunity is supported by the

literature where benzene solicits an immunosuppressive response (41).

Another chemical that has been considered as a probable lymphomagen is

TCE (42). A recent study showed that occupational exposure to TCE is

associated with a decline in lymphocyte subsets and soluble CD27 and CD30

markers which play an important role in the regulation of cellular activity in

subsets of T, B and NK cells and are associated with lymphocyte activation

(43).

Exposure to pesticides such as organochlorines (e.g., DDT),

organophosphates (e.g., malathion, parathion), chlorophenoxy herbicides,

and carbamates can cause a number of effects on the immune system,

varying from a slight modulation of immune functions to the development of

clinical immune diseases (44, 45). Phenoxy herbicides (i.e. 2,4,5-

trichlorophenoxyacetic acid) are often contaminated with dioxins such as

2,3,7,8-tetrachlorodibenzo-p dioxin (TCDD), a highly persistent environmental

toxic contaminant. There is suggestive evidence that TCDD and other dioxin-

like compounds with similar structures impair both humoral and cell-

mediated immunity (46, 47).

Nutritional status and obesity have an important influence on the

immune system as immune functions are sensitive to both under- and

overnutrition via for example leptin, the leptin receptor and adiponectin.

These protein hormones are expressed by adipocytes to regulate food intake

and energy expenditure (48, 49). Obesity promotes chronic inflammation and

increased production of pro-inflammatory cytokines, such as IL6, tumor

necrosis factor alpha (TNF-α), IL1ß, and leptin (35). Moreover, the leptin

receptor is expressed in several immune cell types and appears to activate a

number of cytokine-like signaling pathways (49). Although epidemiological

data support the idea that obesity is associated with alterations in immune

function, the mechanisms implicated in this process remain controversia (48).

Use of biomarkers in cancer risk assessment

In recent years, new advances in laboratory methods allow the incorporation

of biological measurements at the cellular and molecular level into large-scale

epidemiological studies. Molecular biology offers insights into mechanistic

pathways underlying the causation of disease as well as interaction of genetic

and environmental factors, which may determine individual susceptibility to

toxic exposures (50, 51). Applications of biomarkers in epidemiological

studies allow researchers to improve measurement of study variables and

better assess health effects of small doses and past exposures to certain

hazards. However, before using a biomarker in large-scale studies its validity


13

and reliability need to be assessed in preliminary field studies. A major aim of

biomarker validation is to characterize biomarker variability including

biological variability related to the subject (i.e., variability between and within

subjects), variability due to measurement error including intra- and inter-

laboratory variability, and random error. Reliability measures the extent to

which a marker provides consistent results across repeated measurements

(52). Moreover, when using biomarkers in etiologic studies, it is important to

understand whether an intermediate biomarker belongs to the causal

pathway between exposure and disease or it is simply a side effect of

exposure or disease, or whether their measurement is confounded by some

other exposures (52).

The pleiotropic and redundant nature of immunological markers

raises the need to measure more biomarkers at the same time at a system

level. Recent advances in laboratory methods (i.e. multiplex methods) enable

the quantification of a range of biomarkers of the immune system in small

amounts of biological samples. The latter is of importance because often the

collected samples are valuable (either limited in volume, especially in

prospective cohort studies or difficult to obtain, or both).

Hypothesis

Recent findings of genetic variations associated with NHL risk offer important

evidence linking immune function to lymphomagenesis among non-

immunocompromised individuals (22, 53-55). In addition, the relation

between lymphoma development and medical conditions in which the

immune system has been altered such as Sjogren syndrome, systemic lupus

erythematosus, and Celiac disease (11) suggest that a failure to modulate/or

regulate the immune response might be associated with an increased risk of

NHL. We hypothesize that other risk factors of lymphoma such as

environmental and occupational exposures and lifestyle factors operate

through perturbations of the immune system and propose that such immune

deregulation will be seen and can be quantified directly by measuring blood

immune components including among others cytokines.

Study population

This thesis work was conducted to study changes in immunological factors

due to occupational and environmental exposures and their association with

NHL risk using two data sets; The European Prospective Investigation into

Cancer and Nutrition (EPIC)-Italy cohort and the Dutch herbicide cohort,

which are described in more detail below.

Chapter 1

14

EPIC-Italy cohort

The European Prospective Investigation into Cancer and Nutrition (EPIC) is an

ongoing multi-center prospective cohort study designed to investigate the

relationship between nutrition and cancer. The study currently includes

519,978 participants (366,521 women and 153,457 men, mostly aged 35–70

years) in 23 centers located in 10 European countries (France, Germany,

Greece, Italy, the Netherlands, Spain, the UK, Sweden, Denmark and Norway),

to be followed for cancer incidence and cause-specific mortality for several

decades (56).

In this thesis, we used the Italian part of the EPIC cohort. In the

period 1993 to 1998, EPIC Italy completed the recruitment of 47,749

volunteers (15,171 men and 32,578 women, ages 35-65 y) in four different

areas covered by cancer registries: Varese (12,083 volunteers) and Turin

(10,604) in the northern part of the country and Florence (13,597) and

Ragusa (6,403) in central and southern Italy, respectively (57). An associated

center in Naples enrolled 5,062 women. At enrollment, all participants

provided detailed information on their dietary and lifestyle habits and to have

their health status followed prospectively. They also donated a blood sample

for long-term storage and bio-molecular assays to be carried out for research

purposes.

Dutch herbicide cohort

The Dutch herbicide cohort consists of workers from two chemical factories in

the Netherlands involved in the production and formulation of chlorophenoxy

herbicides (58). In factory A (workers employed between 1955 and 1985), one

of the main products was 2,4,5-trichlorophenoxyacetic acid (2,4,5-T). Other

pesticides manufactured in factory A were 2,4,5-trichlorophenol (2,4,5-TCP),

lindane, dichlobenil and tetradifon. Contamination with TCDD and other

dioxins is possible during production of 2,4,5-T and 2,4,5-TCP. In March 1963,

an uncontrolled reaction occurred in an autoclave in factory A where 2,4,5-

TCP was synthesized at the time. After the explosion, the contents of the

autoclave were released into the factory hall, including dioxins such as TCDD.

In factory B (workers employed between 1965 and 1986), the main products

were 4-chloro-2-methylphenoxyacetic acid (MCPA), 4-chloro-2-

methylphenoxy propanoic acid (MCPP) and 2,4-dichlorophenoxyacetic acid

(2,4-D), which are unlikely to be contaminated with TCDD (59). Increased risks

of all cancer mortality and NHL were reported previously by Hooiveld et al.

for factory A (58). A possible association between TCDD exposure and NHL

risk was recently confirmed in a follow-up of this cohort (59). These findings

are supported by other studies among occupationally and environmentally

exposed individuals (60-62).


15

TCDD is a highly immunosuppressive chemical in laboratory animals

and induces potent suppression of both the humoral and cell-mediated

immunity (47). However, there is still limited and inconclusive human

evidence on immunological effects of TCDD.

Aims and outlines of this thesis

Overall aim of this thesis is to study the possible perturbations of the immune

system by occupational and environmental risk factors of NHL and to study

these changes in relation to NHL risk in prospective cohorts. The

methodology is based on measurements of several intermediate markers

potentially related to NHL, and in particular changes in cytokine levels.

Section1: Biomarker validation

In the first part of this thesis we validate the application of single blood

cytokines measurement as a biomarker of the immune status in prospective

epidemiological studies. Chapter 2 describes the application of a bead-based

array system to measure cytokines in plasma/serum of healthy individuals.

We investigated the best blood matrix and whether freeze-thaw cycles

influence the levels of different cytokines and their interrelations and

investigated the intra- and inter-individual variability in cytokines. In Chapter

3 we studied the possible association between these intermediate markers

(blood cytokines) and lymphoma incidence.

Section 2: How can exposures modulate the immune system?

In the second part of this thesis we focus on changes of immune system

components possibly related to NHL risk due to exposure to environmental

and occupational risk factors of lymphoma including TCDD exposure and

obesity. In Chapter 4 we describe the possible long-term effects of TCDD

exposure on humoral immunity approximately 35 years since last exposure

among workers historically exposed to high levels of TCDD. Subsequently, we

studied changes in blood immune cells in particular lymphocyte subsets

(Chapter 5), and blood cytokine, chemokine and growth factors (Chapter 6).

In Chapter 7 we describe the interaction between excessive body weight, a

potential risk factor for NHL, and blood cytokine levels, among Italian EPIC

participants.

Finally, in Chapter 8 the main findings are reviewed in light of

previous studies and future steps and challenges in lymphoma research are

discussed.

Chapter 1

16

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Sacredote C, Purdue M, Lan Q, Rothman N & Vineis P. (2011). Circulating Soluble

Chapter 1

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CD30 and Future Risk of Lymphoma; Evidence from Two Prospective Studies in

the General Population. Cancer Epidemiol Biomarkers Prev 20: 1925-1927.

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B, Casagrande C & Vignat J. (2002). European Prospective Investigation into

Cancer and Nutrition (EPIC): study populations and data collection. Public Health

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Ceroti M & Pala V. (2003). A molecular epidemiology project on diet and cancer:

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DG & Bueno-de-Mesquita HB. (1998). Second Follow-up of a Dutch Cohort

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(2010). Cause-specific mortality of Dutch chlorophenoxy herbicide manufacturing

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tetrachlorodibenzo-para-dioxin. Epidemiology 4: 398-406.

CHAPTER 2

Stability and reproducibility of simultaneously detected plasma and serum cytokine levels in

asymptomatic subjects


Esmeralda J.M. Krop

Lützen Portengen

Charles S. Rabkin

Jakob Linseisen

Paolo Vineis

Roel Vermeulen

Biomarkers (2010); 15(2): 140–148

Chapter 2

22

Abstract

Blood levels of cyto- and chemokines might reflect immune deregulations

which might be related to lymphomagenesis. Potential utility of stored blood

samples of a prospective cohort was evaluated by the effect of different

blood sample types and freeze-thaw cycles on analyte levels. Bead-based

immunoassays were performed on two fresh samples (serum, citrate and

heparin plasma) of 10 asymptomatic adults collected fourteen days apart and

on aliquots of the first samples which were put through one to three freeze-

thaw cycles to measure 11 cytokines, 4 chemokines and 2 adhesion

molecules. Median CVs of the measured analytes were 20%, 24% and 32% in

serum, citrate and heparin plasma, respectively. Strong correlations (Rsp,

0.74-0.98) were observed between sample types, although small differences

in analyte levels were observed for most analytes. Freeze-thaw cycles did not

markedly change analyte levels. Our study supports the use of this assay

among asymptomatic subjects in epidemiological studies.

Array-based cytokine analyses in asymptomatic persons

23

Introduction

Cytokines are humoral signal molecules that are involved in cellular

communications especially in immune response. T lymphocytes are the major

source of cytokines with T helper cells being the most prolific cytokine

producers. Interleukin (IL)2, interferon gamma (IFN-γ) and tumor necrosis

factor beta (TNF-β) produced by T helper 1 lymphocyte cells (Th1) are

involved in pro-inflammatory immune responses (cell-mediated immune

response), whereas T helper 2 cells (Th2) produce IL4, IL5, IL9 and IL13 which

promote anti-inflammatory antibody dependent immune responses (1).

There are other types of T cells that can influence the expression and

activation of T helper cells, such as natural regulatory T cells, along with less

common cytokine profiles such as the Th3 subset. Both regulatory T cells and

Th3 cells produce the cytokine transforming growth factor beta (TGF-β) and

IL10. Recently, the characterization of another novel T helper sub-type, T

helper 17 cells (Th17), which has a potential function in the pathogenesis of

autoimmune diseases and host defense has been described (2, 3). Cytokine

signaling is characterized by considerable redundancy (several cytokines

respectively elicit the same cellular response) and pleiotropy (each cytokine

acts on multiple molecular targets) (4). Thus blood levels of multiple

cytokines collectively might reflect subtle status of a deregulated immune

response or a failure to modulate the immune response appropriately.

Recently, genetic studies have shown an association of the TNF G-

308A polymorphism with an increased risk of non-Hodgkin’s lymphoma

(NHL), particularly diffuse large B cell lymphoma (DLBCL), but not follicular

lymphoma. The IL10 T-3575A polymorphism was also associated with

increased risk of NHL, particularly DLBCL. For individuals homozygous for the

TNF -308A allele and carrying one or both IL10-3575A alleles, risk of DLBCL

was doubled. These reports offer important evidence linking immune

function to lymphomagenesis among non-immunocompromised individuals

(5). Given the pivotal role of cytokines in immune function it is of interest to

study prospectively if subtle deregulation of cytokine levels and/or their

interrelations is related to the occurrence of NHL.

Plasma and serum cytokine concentrations can be quantified by

standard enzyme linked immunosorbent assay (ELISA). However for each

cytokine a volume of 50 – 100 μl would be required resulting in a large

volume demand if multiple analytes are to be quantified. In contrast, the

Luminex xMAP platform (Luminex Corp., Austin, TX, USA) enables the

quantification of up to a hundred different analytes in volumes of 25 to 50 μl

(6, 7). The latter is of importance especially in prospective cohort studies as

only small volumes are generally available.

Chapter 2

24

To test the applicability of the Luminex xMAP system to quantify a

suite of cytokines and chemokines in prospectively collected plasma or

serum, we studied the reliability and levels of the assay in serum, sodium

citrate plasma and heparinized plasma specimens from healthy donors. The

choice for these specimen types was based on the available biological

materials within the European Prospective Investigation into Cancer and

Nutrition (EPIC). Type of media for blood fraction is considered an important

issue in biomarker measurements. Wong et al. (4)

showed that plasma

(heparin and acid citrate dextrose) measurements generally are not reflective

of serum levels. On the other hand, Tworoger et al. (8) concluded that EDTA

plasma is the best sample type for cytokines, although serum and heparin

plasma were deemed acceptable as well. Effects of multiple freeze-thaw

cycles on immune markers levels have been evaluated in several studies.

According to Aziz et al. (9) no significant differences in mean value of

examined cytokines and soluble activation markers in plasma and serum after

10 freeze-thaw cycles have been shown; while another study reported that

the levels of TNF-α increased significantly after three cycles (10). We studied

the influence of freeze-thaw cycles on cytokines levels and their

interrelations. The latter study was performed as often biological specimens

are used for multiple molecular assays necessitating freeze-thaw cycles

before the specimen can be tested.

Material and Methods

Subjects and samples

Venous blood samples (serum, citrate and heparinized plasma) were

collected from 10 healthy individuals (age 20-40 years). After collection, the

fresh samples were analyzed within two hours of collection for 11 cytokines

(including pro-inflammatory, anti-inflammatory, Th1 and Th2 cytokines), 4

chemokines and 2 adhesion molecules. Samples were subsequently aliquoted

and stored at -80°C. Subsets of the stored aliquots were put through one, two

or three freeze-thaw cycles (Figure 1). Samples were thawed over night at 4°C

and re-frozen the next day at -80°C. A second fresh sample was collected

approximately two weeks after the initial blood draw. Again these samples

were analyzed within two hours after blood draw at which time the stored

freeze-thaw samples were re-analyzed (Figure 1).


25

Figure 1 Protocol for evaluating the effects of freeze-thaw cycles, type of sample

preparation and test day; Whole blood samples collected from asymptomatic adults

(n=10) for two week time interval.

Cytokine measurements

All specimens were analyzed for IL1β, IL1α, IL2, IL4, IL5, IL6, IL8, IL10, IL12,

IL13, IFN-γ, TNF-α, regulated upon activation, normal T cell expressed and

secreted (RANTES), inter-cellular adhesion molecule (ICAM), vascular cell

adhesion molecule (VCAM), eotaxin, and IFN-induced protein (IP10) in

duplicate, using the Luminex multi-analyte profiling technology (Lab-MAP™),

according to the protocol described by Jager et al. (11) except that instead of

an 1-hr incubation, an overnight incubation at 4°C was used. Capture and

detection antibodies and recombinant proteins were purchased from

different commercial sources as described previously (12). As a reporter;

streptavidin-PE (BD Biosciences pharmingen, San Diego CA, USA) was used.

Mean fluorescence intensities were calculated from duplicates for each

sample. Standard curves were derived from recombinant protein standards

with twelve-fold dilutions. Detection limits were 1.22 (pg/ml) for IL1β, IL1α,

IL2, IL5, IL6, IL8, IL10, IL13; 2.44 (pg/ml) for IL12 and eotaxin and 24.4(pg/ml)

for ICAM and VCAM on both the first and second test days. However, limits of

detection varied between the first and second test days for IL4 (1.22 vs. 0.24

pg/ml, respectively), IFN-γ (2.44 vs. 1.22), TNF-α (4.88 vs. 1.22), RANTES (1.22

vs. 2.44) and IP10 (2.44 vs. 4.88). Cytokines measured in concentrations

below their respective detection limit or above the maximum detectable

range (RANTES; 5000 pg/ml) were imputed based on a maximum likelihood

estimation (MLE) procedure (13-15). This procedure generates unbiased

reproducibility statistics when at least 50% of measurements are above the

limit of detection (LOD) and below the maximum detectable level (14).

Therefore, no statistics were calculated for analytes with more than 50% of

measures below the LOD or above the maximum detectable level.

Blood Draw 1

(Serum, Heparin and Citrate plasma) Test Day 1

(Serum, Heparin and Citrate plasma)

Fresh 1

Freeze- Thaw cycles (1-3) (Serum, Heparin and Citrate plasma)

Blood Draw 2


Test Day 2


Fresh 2

+

Freeze- Thaw cycles (1-3)


Chapter 2

26

Statistical analysis

Cytokine concentrations were log-transformed as measured levels appeared

to follow an approximate log-normal distribution. For each analyte

reproducibility between duplicate samples by blood type (serum, citrate and

heparin plasma) was evaluated by the coefficient of variation (CV, standard

deviation /mean x100). To estimate the effect of measurement error (based

on duplicates) and intra-individual variance (based on two separate blood

draws) on the ability to compare subjects, the Intra-class Correlation

Coefficient (ICC, inter-individual variance / (inter- + intra-individual variance))

was calculated based on the two fresh samples.

Spearman’s rank correlation coefficients (Rsp) were calculated to

compare results from the different sample types (e.g. serum, citrate and

heparin plasma) and between samples that had gone through different

freeze-thaw cycles (e.g. fresh or one, two and three freeze-thaw cycles).

We also evaluated the effects of blood sample type, freeze-thaw

cycles and test day (could also be interpreted as batch effect) on cytokines

concentrations using a linear mixed effects model:

Ln (γij) = µ+ β1* sample type+ β2 * freeze-thaw cycle+ β3 * test day+ αi+ εij

Where yij denotes the cytokine levels for subjects (i=1… 10) on the jth blood

draw (j=1, 2). The overall mean concentration of the first fresh sample in

serum type is denoted by μ, αi= normally distributed subject effect, εij=

normally distributed error term and the βs are regression coefficients. Sample

type is citrate (1), heparin plasma (2) and serum (reference category (ref.)),

freeze-thaw cycle is fresh (ref.), one, two or three freeze-thaw cycles and test

day represents the first (ref.) or second test day.

Finally to construct the smallest, most informative set of markers and to

study if these sets differed by blood sample type or freeze-thaw cycles we

performed principal component analyses (PCA) using Varimax rotation. These

PCA analyses were restricted to cytokines whose presumed targets are

principally leukocytes and have function as immunomodulating agents (IL6,

IL1β, IL13, IL5, IL12, IL1α, IL10, IFN-γ, TNF-α, IL2). The number of principal

components (PCs) was determined with scree plots using a cut off of 1 for the

sorted Eigenvalues of the covariance matrix. The correlation between inter-

individual cytokines and each PC was assessed by factor loadings.

Statistical analyses were performed using SPSS software (ver. 11.5,

SPSS Inc.) and SAS (ver. 9.1, SAS institute). All p values are two-sided, with

P<0.05 considered as statistically significant.


27

Results

In total 5100 numbers of cytokine measurements were successfully

performed. We detected unexpected reproducible high levels (higher than

mean+2SD based on log-transformed values) of most cytokines in one of the

10 asymptomatic volunteers without any medical explanation. As these levels

were not deemed representative for asymptomatic subjects in the general

population we removed this person from the analyses. However, analyses

including this subject would not have materially changed the results of the

study. Serum and plasma ICAM levels were all above the maximum

detectable levels in the assay and therefore results are not included. For the

following analytes more or equal than 50% of the measurements were either

below the LOD or above the maximum detectable level: IL4 and RANTES

(citrate plasma); IL1β, IL4, IL13, TNF-α and RANTES (heparin plasma); IL4,

IL13, TNF-α and RANTES (serum). Therefore, no reproducibility statistics for

these analytes are given. As IL4 and RANTES were not detected in the

majority of samples for all blood sample types this analyte was not included

in any of the statistical analyses. IL1β, IL13, and TNF-α were retained in some

of the statistical analyses as these analytes were detected in more than 50%

for at least one blood sample type. Median coefficient of variation for

duplicate measurements for all analytes were 20% (8-105), 24% (8-90) and

32% (8-98) in serum, citrate, and heparin plasma, respectively (data not

shown).

Descriptive statistics (geometric mean and standard deviation) of

cytokines by type of blood samples are presented in table 1. Geometric mean

for the different cytokines, chemokines and adhesion molecules varied four

orders of a magnitude from 0.4 to 3435, 0.3 to 5005 and 0.1 to 6786 pg/ml

for citrate, heparin and serum levels, respectively. Although absolute levels of

analytes varied by sample type; overall levels were quite similar between the

different blood sample types. Interestingly, the number of samples below the

LOD seemed to be fewer for citrate than heparin plasma and serum.

Table 1 Descriptive statistics of cytokine concentrations by type

Citrate Heparin Serum

Subject Sample GM GSD <LOD >Range ICC GM GSD <LOD >Range ICC GM GSD <LOD >Range ICC

IL1β 9 18 3.58 2.2 1 0.39 NC NC 14 NC 1.86 2.19 8 0.78

IL1α 9 18 16.73 3.46 0.89 19.72 4.13 1 0.85 17.37 3.09 0.85

IL2 9 18 32.83 3.27 0.79 34.71 3.43 0.80 41.29 3.72 0.89

IL4 9 18 NC NC 12 NC NC NC 13 NC NC NC 14 NC

IL5 9 18 25.89 5.4 1 0.93 12.38 9.1 4 0.89 27.29 5.39 0.96

IL6 9 18 8.12 2.42 0.74 2.26 3.44 8 0.55 3.82 3.56 5 0.73

IL8 9 18 14.58 3.62 0.99 13.68 4.17 0.91 22.03 2.98 0.98

IL10 9 18 40.1 2.98 0.90 48.94 3.33 0.83 43.8 2.98 0.90

IL12 9 18 52.34 2.96 0.90 44.67 3.45 0.93 68.9 2.73 0.92

IL13 9 18 1.45 2.03 8 0.79 NC NC 13 NC NC NC 14 NC

IFN-γ 9 18 5.1 5.78 6 0.86 4.19 6.78 6 0.75 5.52 5.25 6 0.79

TNF-α 9 18 22.43 5.34 5 0.55 NC NC 9 NC NC NC 10 NC

RANTES 9 18 NC NC 11 NC NC NC 9 NC NC NC 17 NC

VCAM 9 18 271. 5 3.22 0.86 245.15 3.88 0.90 317.04 2.83 0.86

Eotaxin 9 18 124.99 1.54 0.83 292.5 1.29 0.77 170.01 1.43 0.76

IP10 9 18 48.91 1.57 0.92 39.8 1.64 0.84 43.16 1.45 0.86

Geometric means (GM) and standard deviations (GSD) calculated by means of two fresh samples; Number of samples with cytokine value

lower than limit of detection (<LOD) and maximum value (>Range); Intra-class correlation (ICC) between two fresh samples based on log-

transformed values; Interleukin(IL); Interferon gamma(IFN-γ); Tumour necrosis factor alpha (TNF-α); Regulated upon activation, normal T

cell expressed and secreted (RANTES); Vascular cell adhesion molecule (VCAM); Interferon-induced protein 10 (IP10); Not calculated (NC)

due to more or equal than 50% missing.


29

Correlation analyses of selected protein markers between blood sample

types

Correlation analyses between the different blood sample types are presented

in table 2. In general, strong correlations in analyte levels between serum and

plasma blood types (Rsp= 0.74-0.98) were observed except for IL1β (0.14) for

citrate vs. serum samples. Results were similar when only values above the

LOD were taken into account (data not shown).

Table 2 Correlation of fresh samples by blood sample type

Subject Sample Rsp Citrate vs.

Serum

Rsp Heparin vs.

Serum

Rsp Citrate vs.

Heparin

IL1β 9 18 0.14 NC NC

IL1α 9 18 0.97 0.94 0.95

IL2 9 18 0.97 0.97 0.98

IL5 9 18 0.96 0.84 0.87

IL6 9 18 0.79 0.81 0.82

IL8 9 18 0.95 0.92 0.88

IL10 9 18 0.96 0.93 0.93

IL12 9 18 0.90 0.89 0.94

IL13 9 18 NC NC NC

IFN-γ 9 18 0.95 0.98 0.96

TNF-α 9 18 NC NC NC

VCAM 9 18 0.98 0.96 0.98

Eotaxin 9 18 0.84 0.79 0.80

IP10 9 18 0.95 0.91 0.85

Spearman’s correlation coefficients (rho) between serum and plasma levels of log-

transformed cytokines including two fresh samples; Not calculated (NC).

Influence of storage time, type of blood samples and freeze-thaw cycles on

cytokine levels

Results from the freeze-thaw experiments for citrate plasma are presented in

table 3. Results for serum and plasma heparin were essentially similar and

therefore only the results for citrate are presented in detail. Overall levels

were comparable between the samples that had gone through various freeze-

thaw cycles (i.e. 1, 2, and 3), but differences were observed between cytokine

levels in the fresh samples and in samples that had gone through at least one

freeze-thaw cycle (Median percentage of changes for all cytokines 22 %,

Range -128-83).

Table 3 Correlations of up to three freeze-thaw cycles vs. first fresh sample in citrate

Fresh 1 Freeze-thaw 1 Freeze-thaw 2 Freeze-thaw 3 GM GSD GM GSD Rsp vs. fresh GM GSD Rsp vs. fresh GM GSD Rsp vs. fresh

IL1β 3.89 2.21 2.02 1.92 0.05 2.11 1.83 0.14 2.76 1.79 0.58

IL1α 20.96 3.62 13.49 3.33 0.73 11.18 3.98 0.90 10.17 2.63 0.82

IL2 32.23 4.1 33.63 2.96 0.90 33.15 2.94 0.90 31.42 2.51 0.93

IL5 25.47 5.2 24.31 4.96 0.95 16.4 6.42 0.93 16.53 5.13 0.93

IL6 8.76 2.6 5.22 2.69 0.57 4.65 2.47 0.42 4.85 2.71 0.50

IL8 14.64 3.8 13.01 3.84 0.73 11.54 3.99 0.82 11.57 2.97 0.95

IL10 44.33 3.27 33.67 3.21 0.93 29.69 3.42 0.97 26.41 3.04 0.83

IL12 51.93 3.27 50.76 3.1 0.87 44.98 3.03 0.93 40.28 2.38 0.88

IL13 1.19 2 1.34 1.89 0.33 1.17 2.08 0.43 1.5 1.89 0.57

IFN-γ 3.32 5.88 6.13 6.7 0.98 5.03 8.62 0.98 5.52 5.05 0.98

TNF-α 25.71 4.32 7.05 9.31 0.27 6.04 10.06 0.57 6.02 24.87 0.33

VCAM 357.96 3.16 215.54 3.3 0.97 187.45 3.55 1.00 173.83 3.03 1.00

Eotaxin 115.89 1.46 130.47 1.57 1.00 123 1.57 0.98 118.85 1.55 0.93

IP10 49.49 1.53 53.4 1.46 0.90 53.94 1.61 0.90 51.61 1.65 0.90

Spearman’s correlation coefficient (rho); Geometric mean (GM); Geometric standard deviation (GSD); Not calculated (NC); (n=9).


31

However, it is important to note that the first fresh sample was not analyzed

concurrently with the frozen samples which may account for part of the

observed differences (i.e. batch or test-day effect). Nevertheless, high rank

correlations between the test results of the fresh sample and of the samples

that had gone through at least one freeze-thaw cycle were still observed for

most cytokines, except for IL1β, IL6, IL13 and TNF-α that had rank correlations

less than 0.5 and IL4 and RANTES for which we could not calculate any

correlation statistics as more than 50% of the measurements were below

50%.

The effect of freeze-thaw cycles, blood sample type and test day

were further explored in a linear mixed effects model (table 4). In this

multiple regression model differences in cytokine levels between blood

sample types were observed for all cytokines except for IFN-γ. Median ratio

for citrate and heparin plasma vs. serum respectively were 0.85 (range 0.23-

4.26) and 0.83 (range 0.53-1.77). Freeze-thaw cycles did not significantly

influence cytokine levels (except IL1β, IL13 and TNF-α), although there

appeared to be a general tendency for lower levels with increasing number of

freeze-thaw cycles. For some analytes (IL1α, IL10, IL13, IFN-γ, VCAM and

eotaxin) a significant effect of test day was found. No interaction between

sample type and freeze-thaw cycles was observed (data not shown).

Within subject variability in cytokine levels

The ICC was calculated for analytes with less than 50% missing values based

on the results of the two fresh samples collected over a two week time period

(table 1). Overall ICCs were high indicating that the intra-individual variance

was minimal as compared to the inter-individual variance, except for IL1β and

TNF-α in citrate, and TNF-α, IL6 and RANTES in heparin plasma.

Inter-relationship between immune markers

For citrate plasma, heparin plasma and serum three PCs were found which in

the case of citrate plasma and serum were comparable (table 5). The first

component consisted of IL6, IL1β, IL13 and IL5. The second component

included IL2, IFN-γ and TNF-α while the third component consisted of IL12,

IL1α and IL10. The PCs for heparin plasma differed in that IL6, IL5 and IL12

were grouped in the first component, IL1β, IL13 and TNF-α in the second PC

and IL1α, IL10, IFN-γ and IL2 in the third component. PCA analyses on the

samples that had gone through multiple freeze-thaw cycles rendered similar

results (data not shown).

Table 4 Fixed effect estimations of the effect of freeze-thaw cycles, sample types and test day based on a linear mixed-effects

model

Freeze-thaw cycle

Sample type

Test day

Cytokines (Ln) Intercept coefficienta P- value

b coefficient

c P- value

b coefficient

d P-value

b

IL1β 0.55 1 -0.420f 0.01 1 0.677

e <0.0001 0.057 0.73

2 -0.529e 2 -0.519

e

3 -0.223

IL1α 2.687 -0.021 0.06 -0.141 0.02 -0.442 <0.0001

-0.105 0.038

-0.216f

IL2 3.74 -0.007 0.990 -0.25e 0.001 0.027 0.76

-0.015 -0.184e

-0.004

IL5 3.148 0.044 0.069 -0.029 <0.0001 -0.1002 0.584

-0.29 -0.63e

-0.35

IL6 1.467 -0.067 0.197 0.493 e

<0.0001 0.289 0.09

-0.3 -0.274f

-0.27

IL8 3. 058 -0.015 0.379 -1.476e <0.0001 -0.065 0.41

-0.105 -0.429e

-0.105

IL10 3.678 -0.0001 0.198 -0.185e 0.0005 -0.285 0.0008

-0.067 0.068

-0.156

(Continued on the following page)

Table 4 Continued Freeze-thaw cycle

Sample type

Test day

Cytokines (Ln) Intercept coefficienta P value

b coefficient

c P value

b coefficient

d P value

b

IL12 4.23 -0.009 0.23 -0.318e <0.0001 0.02 0.82

-0.072 -0.372e

-0.167

IL13 0.113 -0.32f 0.008 0.45

e <0.0001 0.453 0.0006

-0.43e 0.157

-0.223

IFN-γ 2.08 -0.134 0.198 -0.21f 0.123 0.79 <0.0001

-0.28 -0.113

-0.175

TNF-α 1.06 -0.153f 0.009 1.45

e <0.0001 -0.49 0.31

-0.578e -0.18

-0.654

VCAM 5.50 0.078 0.071 -0.226 e

<0.0001 -0.54 <0.0001

0.002 -0.238e

-0.09

Eotaxin 5.2 -0.044 0.286 -0.31e <0.0001 0.145 0.001

-0.072 0.57e

-0.079

IP10 3.78 0.08 0.099 0.106e <0.0001 0.035 0.374

0.085 -0.083e

0.08

Fixed effect estimations of different blood sample types, freeze-thaw cycles and test day for individual log-transformed cytokines, results obtained by Multiplex analyses (n=9);

a freeze-thaw cycle 1, 2 and 3, reference category is fresh sample;

b type III p- value;

c citrate (1) and

heparin (2), reference category is serum; d

second test day; e p-value<0.01;

f p- value<0.05; IL, interleukin; IFN, interferon; TNF, tumour

necrosis factor; VCAM, vascular cell adhesion molecule; IP, interferon-induced protein.

Chapter 2

34

Table 5 Principal component analysis using Varimax rotation with Kaiser

normalization using log-transformed cytokine concentrations

Citrate Heparin Serum

C.1 C.2 C.3 C.1 C.2 C.3 C.1 C.2 C.3

IL6 0.96 0.91 0.75 -0.45 0.42

IL1β 0.93 0.88* 0.87

IL13 0.91 0.59* 0.71* 0.89*

IL5 0.63 0.46 0.92 0.70 0.53

IL12 0.92 0.71 0.59 0.94

IL1α 0.90 0.63 0.66 0.91

IL10 0.86 0.44 0.48 0.51 0.84

IFN-γ 0.89 0.91 0.85

TNF-α 0.80 0.80 0.95*

IL2 0.76 0.92 0.89

Factors with an Eigenvalue > 1 are displayed. The correlation between each cytokine

and factors presented as factor loadings if above 0.4. Total variances explained by

three components (Cs) are 84.8, 86.9 and 86.7 for citrate, heparin and serum

respectively. * More than 50% below the LOD.

Discussion

Simultaneous detection of multiple cytokines could provide a comprehensive

assessment of an individual immune status. As the multiple cytokine assay

only requires a minimum amount of biological material, its application in

nested case-control studies in prospective studies, such as EPIC seems

feasible. However, many factors may influence the measurements of these

immune cell factors including sample collection, processing and storage (8, 9,

16-19). Therefore, before such assays can be used successfully in prospective

studies these factors need to be known (20). We therefore studied the

reproducibility of the assay, explored differences between plasma and serum

cytokine levels and studied the influence of freeze-thaw cycles on cytokines

levels.

In this study we used a single MLE imputation method to impute

cytokine levels below the LOD and above the maximum range. Statistical

analyses based on multiple imputations did not change the results and

therefore the statistical inferences based on the partly imputed data

presented in this manuscript are robust. We did not re-analyze samples for

which results were above the maximum range, by diluting the biological


35

sample, as the purpose of the study was to explore the suitability of the

multiplex bead assay to analyze a range of analytes in a single assay. Re-

analyzing the biological samples by diluting the sample would lead to an

increase in the necessary sample volume which is often is not feasible in

prospective cohort studies.

Median coefficient of variation (CV %) for the analytes in serum,

citrate and heparin plasma was between 20 and 32%. This laboratory

variability was similar to another study reporting on cytokines multiplex

assays in serum and/or plasma (4). Although these CV%s were higher than

one would normally accept for a single analyte assay they are not likely to

affect the ability to distinguish between individuals. This was clearly

demonstrated in our study where the ICCs for most cytokines, based on two

repeated blood samples collected 1 to 2 weeks apart and tested on two

different days, were generally above 0.8. This indicated that besides

laboratory error, temporal differences in individual cytokine levels were

relatively minor as compared to differences between asymptomatic persons,

at least within a 2-week period.

In several studies, comparisons have been made between serum and

plasma immune markers; however the results have been conflicting (8). In

our study small but statistically significant differences in cytokine levels,

except IFN-γ between the different blood sample types were detected.

Furthermore, it was clear that for heparin plasma more cytokine

measurements were below LOD or out of range making heparin plasma less

suitable for multiplex cytokine analyses than citrate plasma or serum.

Rank correlations between measured analyte levels in different

sample types were relatively high for most of the analytes, except for IL1β. A

reason for this low correlation is unknown but might be related to the

relative high number of values below the LOD for serum samples. These

results are different from those of a recent study that employed multiplex

cytokine assays in samples from asymptomatic persons where large

differences in cytokines levels between serum, heparin and citrate plasma

were reported (4). It is unclear why our results differ from this study but

differences in sampling processes and antibodies used might play a role. The

latter might indicate that the choice of antibodies or commercial kits is crucial

and that results obtained with different kits/antibodies might be difficult to

compare, at least in absolute sense.

Assessment of the freeze-thaw stability is important in studies of

biomarkers because of the use of previously thawed samples and/or

repeated analysis for a failed run. Ray et al. (21) reported that IL1β for 2

cycles, TNF-α for 3 and IL8 for 4 freeze-thaw cycles were stable. We found

similar results in that no statistically significant differences were observed in

Chapter 2

36

analyte levels for up to three freeze-thaw cycles except for IL1β, IL13 and

TNF-α. However, closer inspection of the results indicate that for most

analytes the concentration levels were decreasing slightly by each freeze-

thaw cycle as indicated by the increasing negative parameter estimates for

each subsequent freeze-thaw cycle in the mixed-effects model. As such it

seemed that freeze-thaw cycles did lead to some degradation of cytokines,

chemokines and adhesion molecules but that this is a relative minor effect.

Cytokines have been classified to sub-groups by their structural

homology and by the kind of T helper cells (Th1, Th2) they originate from. We

examined the inter-relationships among ten cytokines. Three out of the four

cytokines in the first component are classically considered to be Th2

cytokines (IL5, IL13) or involved in Th2 regulation (IL6). From the second

component, IL2 and IFN-γ are in agreement with the Th1 cytokines. IL10 as a

member of the newly introduced Th3 secreted cytokine group can be found

in the third component (table 5). These findings were relatively consistent

with an earlier study (4) on different panel of cytokines that grouped IL7,

IL10, IL12 and IFN-γ and IL4 and IL6 together. Results of these two studies

indeed indicate that based on a multiple cytokine measures a pattern can be

distinguished that reflects the redundancy, synergism and antagonism

present in the cytokine network that can be related to the more classical

categorization of Th’s responses and could provide important insights in

Th1/Th2/Th3 shifts in immune response. This observed pattern seemed to be

similar for citrate plasma and serum and not to be affected by freeze-thaw

cycles. Patterns observed in heparin plasma tended to be different.

Our study is one of the first to comprehensively study the influence of

different blood sample types and freeze-thaw cycles on the reproducibility,

levels and interrelations of a suite of cytokines, chemokines and adhesion

molecules. Furthermore, it has been shown that cytokine levels do not vary

much in time within a subject (at least in a two week period). This latter is

important if indeed single plasma or serum cytokine measurements are used

to classify prospectively an individual’s immune response. However, it would

be of interest to repeat this latter experiment on a larger population and with

a larger time interval between the two sample collections to test the

temporal variance over a large period of time. In addition, due to the long

storage times of prospective cohort samples, what we found can not be

necessarily extrapolated to those cohorts. The influence of long-term freezing

(years) should be further analyzed. In conclusion, the results of our study

tend to support the use of the Luminex-based cytokine assay among

asymptomatic subjects in (prospective) epidemiological studies.


37

Acknowledgements

We thank all the anonymous blood donors who participated in this study. This

work was supported by the ECNIS Network of Excellence (Environmental

Cancer Risk, Nutrition and Individual Susceptibility), operating within the

European Union 6th Framework Program, Priority 5: “Food Quality and

Safety” (FOOD-CT-2005-513943). The first author also acknowledges the

Iranian Ministry of Health, Treatment and Medical Education for support of a

PhD program at Utrecht University.

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16. Aziz N, Nishanian P, Taylor JMG, Mitsuyasu RT, Jacobson JM, Dezube BJ,

Lederman MM, Detels R, Fahey JL. (1999/b). Stability of plasma Levels of

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CHAPTER 3

Plasma cytokines and future risk of non-Hodgkin lymphoma: A case-control study nested in the

Italian European Prospective Investigation into Cancer and Nutrition


Esmeralda J.M. Krop

Chiara Scoccianti

Vittorio Krogh

Domenico Palli

Salvatore Panico

Rosario Tumino

Carlotta Sacredote

Niga Nawroly

Lützen Portengen

Jakob Linseisen

Paolo Vineis

Roel Vermeulen

Cancer Epidemiology, Biomarkers & Prevention (2010); 19(6): 1577–84

Chapter 3

40

Abstract

Background

Recently, biological markers related to the immune system such as cytokines

have been studied to further understand the etiology of non-Hodgkin

lymphoma (NHL). However, to date, there are no studies that have studied

cytokine levels prospectively in relation to NHL risk in the general population.

Methods

Using bead-based immunoassays, plasma levels of 11 cytokines, 4

chemokines, and 1 adhesion molecules were measured in pre-diagnostic

blood samples of 86 NHL cases and 86 matched controls (average time

between blood collection and diagnosis, 4.5 y). Conditional logistic regression

adjusted for body mass index and alcohol consumption was used to analyze

the association between individual plasma cytokine levels and the risk of

developing NHL.

Results

In multivariate models, excluding cases diagnosed within 2 years after

inclusion, we observed a significant association for interleukin 2 (IL2; P trend

= 0.004), interferon gamma (IFN-γ) (P trend = 0.05), and inter-cellular

adhesion molecule (ICAM) (P trend = 0.04). Sub-analyses of B cell NHL

patients showed a significant association with IL2 (P trend = 0.003), tumor

necrosis factor alpha (TNF-α; P trend = 0.03), and ICAM (P trend = 0.04) and a

borderline association with IL5 (P trend = 0.07) and IFN-γ (P trend = 0.08).

Conclusions

The results of this study suggest, in a prospective setting, a possible

association between plasma levels of IL2, ICAM, IFN-γ, and TNF-α with NHL

risk and provide some evidence that risk of NHL might be related to a down-

regulation of T helper 1 cytokines.

Impact

Identification of subtle changes in immune response regulation quantified by

plasma cytokine levels possibly provides new insights in the etiology of NHL.

Plasma cytokines and non–Hodgkin lymphoma risk

41

Introduction

Non–Hodgkin lymphomas (NHL) are a heterogeneous disease group

belonging to malignancies of the lymphoid tissue, which vary in histologic

characteristics, clinical manifestations, and etiologic factors (1-5). Incidence

rates of NHL have shown a significant rise especially in highly aggressive sub-

types during the past several decades worldwide (6). In European countries,

an overall increase in NHL incidence of 4.2% per annum was observed from

1985 to 1992 (7) and there is still a small (3%) but continuing increase in

incidence (8). Despite extensive research in recent years, reasons for this

increase are still largely unknown (9).

The most established risk factors of NHL are those related to severe

immunocompromised individuals such as genetically determined or acquired

immune deficiencies, including HIV infection or iatrogenically induced

immune suppression after transplantation (3-5). As an extension of this

observation, it has been hypothesized that many of the known and unknown

NHL risk factors might influence NHL risk through (subtle) modulations of the

immune system (10). A possible role of cytokines in the development of NHL

is supported by recent reports on NHL risk related to genetic variation in

genes encoding pro-inflammatory and anti-inflammatory cytokines (tumor

necrosis factor (TNF), LTA, interleukin (IL) 10, and IL4; refs. 11-13). These

reports offer among non-immunocompromised individuals. However,

functional relevance of the found single-nucleotide polymorphisms (SNP) is

still unclear. Therefore, studies incorporating direct measurements of the

immune environment, in addition to investigation of genetic polymorphisms,

are needed to further elucidate the role of immunomodulatory factors in

lymphomagenesis.

Biological markers related to the immune system such as cytokines

have been studied to explain the etiology of NHL (14-16). However, there are

only few studies that have studied cytokine levels prospectively in relation to

NHL risk and none have been in the general population. In a study nested

within the Multicenter AIDS Cohort Study (17) in which serum samples were

obtained preceding a diagnosis of AIDS lymphoma, Breen et al. reported an

increased serum level of IL6 in Burkitt's lymphoma patients compared with

CD4-matched AIDS controls who did not have lymphoma. In another study

(18), a significant increase in detectable serum levels of IL10 in AIDS

lymphoma patients was found compared with HIV+/HIV− subjects. However,

in this study, >81% of the cases developed NHL within 1 year of blood

collection, and as such, it can be questioned if the observed increases in IL10

were a result of the disease itself or the cause of the disease. Furthermore, as

this study was conducted among HIV+ subjects, it is questionable if these

results can be extrapolated to the general population.

Chapter 3

42

We hypothesize that given the pivotal role of cytokines in immune

function; blood levels of multiple cytokines collectively might reflect the

subtle status of a deregulated immune response or a failure to modulate the

immune response appropriately. To illustrate innate and acquired variations

in immune responses that affect the risk of developing NHL, a prospective

study is the only appropriate epidemiologic design. Only in this way factors

related to disease can be studied years before the onset of the disease,

excluding the possibility that the measured response is actually caused by the

disease and not the cause of the disease itself. We therefore did a case-

control study nested within the Italian contribution to the European

Prospective Investigation into Cancer and Nutrition (EPIC) on plasma cytokine

and chemokine levels potentially related to NHL risk.

Materials and Methods

Study population

The EPIC project is a European network of prospective cohorts that was set

up to examine relationships of cancer risk with nutrition and metabolic risk

factors (19). In the period 1993 to 1998, EPIC Italy completed the recruitment

of 47,749 volunteers (15,171 men and 32,578 women, ages 35-65 y) in four

different areas covered by cancer registries: Varese (12,083 volunteers) and

Turin (10,604) in the northern part of the country and Florence (13,597) and

Ragusa (6,403) in central and southern Italy, respectively. An associated

center in Naples enrolled 5,062 women. The present study included incident

cases of NHL until the end of 2004 according to the International

Classification of Diseases for Oncology, third edition.

For each case subject, one random control was selected among all

cohort members alive and free of cancer at the time of diagnosis of the index

case matched by center, gender, date of recruitment, age at diagnosis, and

age at recruitment (±3 y). A total of 91 patients with NHL were eligible to be

included in the study. Excluded were cases without suitable control samples

(n = 2) and cases for whom plasma specimen was missing (n = 3), so a total of

86 pairs entered the study. NHL diagnosis was the second malignancy for five

cases. Analyses excluding these cases did not change the results of the study.

Laboratory assay

Blood samples collected from EPIC participants were stored at −196°C in

liquid nitrogen until they were pulled for laboratory analysis. None of the

plasma specimens were thawed before the analyses. We measured 11

cytokines (IL1α, IL1β, IL2, IL4, IL5, IL6, IL10, IL12, IL13, interferone gamma

(IFN-γ), and tumor necrosis factor alpha (TNF-α)), 4 chemokines (IL8, RANTES,

eotaxin, and interferon-induced protein 10 (IP10)), and 1 adhesion molecules


43

(inter-cellular adhesion molecule (ICAM)) in stored citrate plasma samples (50

μL) of all cases and controls using the Luminex multianalyte profiling

technology (Lab-MAP) according to the protocol described by de Jager et al.

(20), except that, instead of 1-hour incubation, an overnight incubation at 4°C

was used (21). The Luminex multianalyte assay has been validated previously

against standard ELISA tests (20). In a recent study, we showed that this assay

provides reproducible results in plasma and serum samples and that the rank

correlations between measured and analyte levels in serum and plasma were

relatively high (Spearman rank correlation, 0.74-0.98) for most of the analytes

(21). All laboratory personnel were blinded with regard to case-control status.

Median time interval between sample collection and freezing was 4 hours for

both cases and controls (Table 1). All samples were run in duplicate with

matched case-control sets assayed in the same batch. Quality control sets

(low- and high-concentration cytokine quality control samples) were run in

duplicate with the case-control sets in each batch. The median intra-batch

coefficient of variation for all of cytokines based on these quality control

duplicate sets was 6.7% (4.3-30), and the median inter-batch coefficient of

variation was 30.7% (9.6-110). The lower limits of detection (LOD) based on

the standard curve were 0.24 pg/mL for IL4; 0.61 pg/mL for IL12; 1.22 pg/mL

for IL1β, IL2, IL5, IL6, IL8, IL10, IL13, IFN-γ, and TNF-α; 2.44 pg/mL for IL1α,

RANTES, and eotaxin; 4.88 pg/mL for IP10; and 73.24 pg/mL for ICAM.


Values of cytokine levels below the LOD or above the maximum range of

detection were imputed based on a maximum likelihood estimation method

(22). In all analyses, levels of cytokines were log-transformed to normalize

their distributions. For individuals without self-reported and measured

anthropometric data, center/age/gender-specific average values of

anthropometric variables have been imputed (n = 7). Differences between

cases and controls in mean plasma concentrations of cytokines and baseline

covariates were assessed using paired t-test. For categorical variables, the

statistical significances of case-control differences were tested by the χ2 test.

Odds ratios (OR) and 95% confidence intervals (95% CI) for NHL in

relation to plasma cytokine concentrations (as continuous variables) were

calculated by conditional logistic regression (CLR) using the PHREG procedure

(SAS statistical software, version 9.1; SAS Institute). Risk estimates were

computed both as crude and with additional adjustments for potential

confounders, including body mass index (in kg/m2; continuous) and alcohol

intake (g/d; continuous). The effect of physical activity (sex-specific quartiles

of combined recreational, household, and occupational physical activity) and

educational level (indicator of socioeconomic status, categorical) as potential

confounding variable was examined, but they did not appreciably change the

Chapter 3

44

risk estimates and therefore were not included in the models. Tertiles of

plasma cytokine concentrations were calculated based on the distribution in

control subjects, and CLR models were used to estimate the association

between tertiles of cytokine levels and risk of NHL. We also investigated the

possible associations by histologic sub-type of B cell NHL (B-NHL) and by

excluding samples that were not stored within 12 hours of collection and

cases diagnosed with <2 years of follow-up. The latter exclusion was made to

remove the possibility that cytokine levels may have been changed in cases

compared with control subjects due to pre-clinical disease status (n = 18

cases). In addition, potential modification of the effect of cytokines by gender

and centers was tested.

Statistical analyses were done using SPSS software (version 11.5;

SPSS, Inc.) and SAS (version 9.1; SAS institute). All P values are two-sided, with

P < 0.05 considered as statistically significant.

Results

Description of the study population

This study included 86 cases and an equal number of controls (68 men and

104 women). The median age at cancer diagnosis was 60 (range, 41-69) and

59.5 (range, 41-77) years for men and women, respectively (Table 1). Median

time between recruitment (i.e., blood collection) in the study and diagnosis of

NHL was 4.5 years (range, 0.12-10.4). Of all NHL cases, 92% were diagnosed

with B-NHL, with the most common diagnosed sub-types being follicular

lymphoma (31.6%), miscellaneous nodal lymphomas (21.5%), and diffuse

large B cell lymphoma (17.7%). Cases and control subjects did not differ

considerably with regard to most risk factors and covariates (Table 1).

Plasma cytokine concentration

Geometric mean and SD for plasma levels of individual cytokines are shown in

Table 2. Cytokine levels for cases seemed to be lower than for controls. For

chemokines, this seemed to be reversed. Cases and controls showed small

differences in mean level of IFN-γ (P = 0.05), IP10 (P = 0.07), TNF-α (P = 0.07),

and ICAM (P = 0.07) in the paired t test. By excluding case patients (n = 18)

diagnosed within less than 2 years of follow-up, significant differences

between levels of TNF-α (P = 0.04) and ICAM (P = 0.02) and borderline

significant differences for IFN-γ (P = 0.06) and IL5 (P = 0.06) between cases

and controls were found (data not shown).


45

Table 1 Baseline characteristics of non-Hodgkin lymphoma (NHL) cases and

control subjects

Case (n=86) Control (n=86) Pdiff

Matching variables

Age at recruitment, y* 55 (36-74) 54 (35-73) 0.85

Age at diagnosis, y*

Male (%), y* 39.5%; 60 (41-69)

Female (%), y* 60.5%; 59.5 (41-77)

Center (%)

Florence 20.9% 20.9%

Naples 7% 7%

Ragusa 5.8% 5.8%

Turin 20.9% 20.9%

Varese 45.3% 45.3%

Other variables

Body Mass Index * 24.8 (17.8-36.6) 25.3(18.8-35.3) 0.90

Physical activity (sex-specific quartiles) (%) 0.67

Qrt1 14% 15.1%

Qrt2 27.9% 20.9%

Qrt3 45.3% 52.3%

Qrt4 9.3% 10.5%

missing 3.5% 1.2%

Education 0.24

None 4.8% 0

Primary school completed 53% 55.3%

Technical/professional school 10.8% 16.5%

Secondary school 22.9% 18.8%

Longer education (incl. University deg.) 8.4% 9.4%

Alcohol intake at recruitment, grams/day 12.3 (0-104.4) 11.7 (0-93.9) 0.69

Storage time of samples (hours) * 4 (1:30-30:07) 4 (1:30-74:23) 0.22

Histological sub-type of NHL (%)

B-NHL 92%

Follicle center cell 29.1% (31.6%) ª

Diffuse large B cell 16.3% (17.7%) ª

Marginal zone B cell lymphoma 11.6% (12.7%) ª

Chronic lymphocytic leukemia

(CLL)

10.5% (11.4%) ª

Mantle-cell lymphoma 4.7% (5.1%) ª

Miscellaneous nodal lymphomas 19.8% (21.5%) ª

T cell NHL 8%

Extra nodal T cell 4.7%

Nodal Peripheral T cell lymphoma 3.5%

* Median (range); for individuals without self-report and measured anthropometric data, center, age, and gender-specific average values of anthropometric variables have been imputed; ª Percentage of B cell NHL (B-NHL) subjects.

Table 2 Descriptive statistics of plasma cytokines levels for cases and control subjects

Control (n=86) Case (n=86)

Geometric Mean g. SD Min-Max <LOD >Rang Geometric Mean g. SD Min-Max <LOD >Rang *p

IL1β 1.65 3.39 0.14-71.95 28 1.51 3.422 0.07-51.2 23 0.75

IL1α 67.56 8.97 0.46-32,976.9 3 48.62 9.89 0.7-29,806.4 4 2 0.24

IL2 70.61 8.76 0.16-3,854.4 5 2 41.13 12.63 0.0004-18,044.2 4 0.11

IL4 0.71 4.17 0.02-25.8 7 0.59 4.44 0.02-45.45 5 0.39

IL5 27.84 6.78 0.2-3,640.1 9 18.23 6.38 0.19-5,509.4 13 0.09

IL6 58.38 6.04 0.61-7,122.8 5 61.73 6.28 0.25-2,785.1 3 0.56

IL8 6.97 6.16 0.37-951.8 14 5.31 7.29 0.16-1,474.9 16 0.35

IL10 46.44 6.8 0.77-7,117.2 1 42.39 7.58 0.38-8,754.2 1 0.76

IL12 14.91 7.05 0.17-1,931.2 3 1 11.77 8.04 0.23-3,395.9 1 1 0.40

IL13 1.88 4.69 0.06-199.3 24 1.45 5.23 0.09-103.01 24 0.21

IFN-γ 6.81 7.97 0.16-702.7 21 3.47 13.9 0.0002-2,539.6 29 0.05

TNF-α 1.03 4.42 0.04-208.1 32 0.65 6.47 0.001-76.21 30 0.07

RANTES 9,459.68 2.51 1,237-16,581,337 1 30 9,981.54 2.09 1320-74,929 31 0.38

ICAM 117,761.8 1.66 28,986-179,640 128,143.4 1.4 38,274.8-210,085 0.07

Eotaxin 74.34 2.16 8.9-837.5 1 83.82 2.15 22.24-985.7 0.28

IP10 40.54 1.68 10.9-180.1 1 46.95 1.97 13.01-397.9 0.07

Geometric standard deviations (g. SD); Minimum (Min) and Maximum (Max) values of cytokines concentration; Number of samples with cytokine value

lower than limit of detection (<LOD) and higher than maximum value (>Range) were imputed; Interleukin (IL); Interferon gamma (IFN-γ); Tumour necrosis

factor alpha (TNF-α); Regulated upon activation, normal T cell expressed and secreted (RANTES); inter-cellular adhesion molecule (ICAM); Interferon-

induced protein 10 (IP10); * P Value of paired t-test (two-sided) based on log-transformed values of cytokines concentrations.


47

Risk estimation

CLR analyses based on tertiles of cytokine levels showed a significant inverse

association for IL2 and a borderline significant association for IFN-γ and ICAM

with the occurrence of NHL in both the crude and fully adjusted regression

model (Table 3). When we restricted the analyses to the case patients

diagnosed after the first 2 years of follow-up (n = 136), these associations

became slightly stronger: IL2 (P trend = 0.004), IFN-γ (P trend = 0.05), and

ICAM (P trend = 0.04). A further restriction to B-NHL patients (n = 130)

showed a significant association for IL2 (P trend = 0.003), TNF-α (P trend =

0.03), and ICAM (P trend = 0.04) and a borderline association for IL5 (P trend

= 0.07) and IFN-γ (P trend = 0.08; data not shown).

Discussion

To date, little is known about blood immune marker changes that may be

related to the development of NHL, except for a few small studies among HIV

patients (17, 18). In several studies, it has been documented that an

immunosuppressed state plays a key role in development of lymphomas (3-

6). Of the many mechanisms contributing to immune suppression, much

attention was recently given to inflammatory cells and to inflammatory

mediators in general. Adhesion molecules and their ligand(s) such as ICAM-1

play an important regulatory role in the inflammatory process. It is shown

that ICAM can be up-regulated on many cell types during an inflammatory or

immune response, particularly under the influence of various cytokines (23,

24). Interaction of all activated leukocytes with ICAM-1 may be a crucial step

in the induction and protraction of an inflammatory response (23). In the

multivariate CLR model, we found that increased levels of ICAM were

associated with higher NHL risk. Several clinical studies have shown that

blood level of ICAM is increased in NHL patients; however, to our knowledge,

there are no reports until now relating this adhesion molecule prospectively

to the risk of NHL.

As immune dysfunction is thought to be the underlying basis of

lymphomagenesis, an imbalance in the regulation and expression of T helper

1 (Th1) and Th2 cytokines could play an important role in the etiology of NHL

and its major sub-types (9, 25). Th1 cytokines generate and activate cytotoxic

T lymphocytes and natural killer cells, which play crucial roles in anti-tumor

immune responses. Mori et al. (25) studied CD4+ cells of diffuse large B cell

lymphoma patients and concluded that the Th1/Th2 balance was polarized to

Th2 in untreated patients and to Th1 in patients in complete remission. We

found similar results in that a lower risk of NHL with increasing IL2, IFN-γ, and

TNF-α (Th1) plasma levels was observed. It should be noted, however, that

re-evaluation of the Th1 and Th2 paradigm and discovery of other Th cells,

Table 3 Crude and multivariable-adjusted odds ratios (ORs) and 95% confidence intervals (CIs) of NHL cases by tertiles of

plasma cytokine concentrations in EPIC Italy.

Univariate model Multivariate model

N=172 N=172 More than 2 years follow-up (n=136)

Tertiles limits No. cases / No. controls OR (95% CI) †P P trend OR (95% CI) †P P trend OR (95% CI) †P P trend

IL1β 0.91 0.7 0.98 0.98 0.8 0.5 < -0.242 31/29 1.00 (reference) 1.00 (reference) 1.00 (reference) -0.242-0.848 29/29 0.94 (0.46-1.92) 0.93 (0.44-1.96) 0.85 (0.39-1.86) >0.848 26/28 0.83 (0.36-1.93) 0.97 (0.39-2.45) 0.73 (0.25-2.07) IL1α 0.98 0.8 0.86 0.9 0.5 0.4 <3.078 29/28 1.00 (reference) 1.00 (reference) 1.00 (reference) 3.078-4.896 29/29 0.95 (0.44-2.04) 0.80 (0.36-1.82) 0.50 (0.21-1.42) >4.896 28/29 0.92 (0.41-2.07) 0.80 (0.33-1.98) 0.60 (0.23-1.66) IL2 0.05 0.01 0.05 0.04 0.02 0.004 <3.524 43/28 1.00 (reference) 1.00 (reference) 1.00 (reference) 3.524-5.166 21/29 0.39 (0.16-0.91) 0.34 (0.14-0.86) 0.17 (0.04-0.6) >5.166 22/29 0.41 (0.18-0.96) 0.38 (0.14-1.00) 0.23 (0.07-0.78) IL4 0.59 0.5 0.9 0.9 0.9 0.7 <-1.129 32/29 1.00 (reference) 1.00 (reference) 1.00 (reference) -1.129- -0.115 22/28 0.68 (0.29-1.59) 0.83 (0.34-2.06) 0.80 (0.28-2.32) >-0.115 32/29 0.95 (0.19-2.24) 1.04 (0.41-2.54) 0.80 (0.29-2.23) IL5 0.25 0.2 0.15 0.1 0.1 0.08 <2.639 40/29 1.00 (reference) 1.00 (reference) 1.00 (reference) 2.639-3.742 21/29 0.57 (0.28-1.15) 0.46 (0.2-1.07) 0.38 (0.15-0.98) >3.742 25/28 0.67 (0.30-1.45) 0.61 (0.27-1.38) 0.52 (0.2-1.3) IL6 0.85 0.8 0.77 0.5 0.9 0.8 <3.906 28/29 1.00 (reference) 1.00 (reference) 1.00 (reference) 3.906-4.620 27/29 0.99 (0.44-2.23) 1.2 (0.51-2.79) 1.17 (0.45-3.05) >4.620 31/28 1.24 (0.50- 3.1) 1.42 (0.54-3.72) 1.00 (0.33-3.04)


Table 3 Continued


N=172 N=172 More than 2 years follow-up ( n=136)

Tertiles limits No. cases / No. controls OR (95% CI) †P P trend OR (95% CI) †P P trend OR (95% CI) †P P trend

IL8 0.32 0.2 0.15 0.3 0.1 .06 <1.021 37/29 1.00 (reference) 1.00 (reference) 1.00 (reference) 1.021-2.546 20/28 0.55 (0.25-1.19) 0.43 (0.18-1.0) 0.35 (0.13-0.95) >2.546 29/29 0.79 (0.36-1.73) 0.73 (0.3-1.77) 0.58 (0.21-1.58) IL10 0.74 0.7 0.5 0.4 0.3 0.3 <2.912 33/29 1.00 (reference) 1.00 (reference) 1.00 (reference) 2.912-4.512 25/29 0.73 (0.34-1.59) 0.61 (0.26-1.42) 0.48 (0.18-1.27) >4.512 28/28 0.87 (0.38-1.98) 0.71 (0.28-1.8) 0.62 (0.22-1.7) IL12 0.54 0.3 0.42 0.3 0.3 0.2 <1.845 35/29 1.00 (reference) 1.00 (reference) 1.00 (reference) 1.845-3.146 23/28 0.63 (0.28-1.43) 0.58 (0.25-1.35) 0.47 (0.17-1.26) >3.146 28/29 0.76 (0.35-1.67) 0.64 (0.27-1.55) 0.61 (0.23-1.61) IL13 0.52 0.3 0.6 0.4 0.4 0.2 <-0.049 36/29 1.00 (reference) 1.00 (reference) 1.00 (reference) -0.49-1.125 25/29 0.67 (0.31-1.43) 0.7 (0.32-1.56) 0.82 (0.33-2.03) >1.125 25/28 0.70 (0.33-1.48) 0.7 (0.32-1.55) 0.53 (0.22-1.3) IFN-γ 0.17 0.06 0.13 0.04 0.1 0.05 <1.019 39/29 1.00 (reference) 1.00 (reference) 1.00 (reference) 1.019-2.910 28/29 0.69 (0.33-1.44) 0.64 (0.28-1.44) 0.61 (0.24-1.56) >2.910 19/28 0.44 (0.19-1.03) 0.38 (0.15-0.97) 0.34 (0.12-0.96) TNF-α 0.38 0.2 0.41 0.18 0.3 0.1 <-0.928 36/28 1.00 (reference) 1.00 (reference) 1.00 (reference) -0.928-0.3 27/29 0.71 (0.34-1.47) 0.73 (0.33-1.62) 0.66 (0.27-1.59) >0.3 23/29 0.58 (0.26-1.28) 0.56 (0.24-1.31) 0.46 (0.18-1.17)


Table 3 Continued


N=172 N=172 More than 2 years follow-up ( n=136)

Tertiles limits No. cases / No. controls

OR (95% CI) †P P trend OR (95% CI) †P P trend OR (95% CI) †P P trend

RANTES 0.34 0.2 0.28 0.16 0.09 0.1

<8.828 24/29 1.00 (reference) 1.00 (reference) 1.00 (reference)

8.828-9.15 25/29 1.06 (0.46-2.46) 0.98 (0.39-2.47) 0.57 (0.2-1.71)

>9.15 37/28 1.69 (0.76-3.74) 1.81 (0.78-4.2) 2.1 (0.8-5.46)

ICAM 0.15 0.06 0.14 0.05 0.1 0.04


11.68-11.89 27/29 1.40 (0.64-3.09) 1.36 (0.6-3.15) 1.68 (0.64-4.41)

>11.89 40/29 1.25 (0.99-5.12 ) 2.44 (0.98-6.08) 3.1 (1.08-8.92)

Eotaxin 0.61 0.6 0.5 0.8 0.7 0.9

<4 28/28 1.00 (reference) 1.00 (reference) 1.00 (reference)

4-4.435 24/29 0.78 (0.35-1.73) 0.64 (0.28-1.48) 0.67 (0.26-1.73)

>4.435 34/29 1.16 (0.58-2.31) 0.91 (0.43-1.94) 0.91 (0.42-2)

IP10 0.23 0.4 0.18 0.3 0.4 0.8


3.537-3.91 20/29 0.61 (0.24-1.55) 0.58 (0.19-1.66) 0.52 (0.16-1.65)

>3.91 37/29 1.27 (0.53-3.05) 1.32 (0.51-3.44) 0.97 (0.35-2.73)

Odds ratios (ORs) estimated by conditional logistic regression models for tertiles of plasma cytokine concentrations (Tertiles cut points

based on the distribution of cytokine levels for control subjects); † Type ΙΙΙ p-value; P trend = p-value of Wald Chi-Square test, p-value

(two-sided) were calculated by including the median of each tertile of cytokine concentrations as continues variables in addition to all

covariates to the multivariate models.


51

such as Th3 and Th17, have led to the realization that a number of

inflammatory conditions in which Th1 was previously considered central may

actually be related to other types of Th cells. So, although the Th1/Th2 model

still provides a valuable framework to describe the immune changes found in

this study, the actual regulatory mechanisms involved are likely more

complicated.

Recent genetic studies showed that SNPs from candidate genes,

including TNF (10, 12-14); LTA (10); IL4, IL5, and IL6 (11); and IL10 (10-12, 14),

may be risk factors for NHL overall or for certain NHL sub-types. Our study

showed evidence of a possible role of TNF-α in lymphomagenesis, particularly

B-NHL. Based on laboratory evidence, both TNF G308A and IL10 T357A

polymorphisms result in overall elevated expression of TNF-α and thus

contribute to a shift in the Th1/pro-inflamatory immune response (13),

whereas our results showed low expression of Th1 cytokines (IL2, IFN-γ, and

TNF-α) in prospective NHL cases compared with controls. Lan et al. (12), in a

study of cytokine polymorphisms in the Th1/Th2 pathway, documented that

SNPs in Th2 cytokine genes may be associated with risk of NHL. These findings

raise the possibility that a shift in the balance of the Th1/Th2 response could

have crucial consequences for lymphomagenesis. However, it should be

considered that malignant diseases develop and progress as a result of multi-

factorial processes, in which genomic alterations and modifications in gene

expression in pre-malignant cells are joined by a tumor-supporting

microenvironmental setting (23). Further, functional significance of cytokine

SNPs is not clear, as genotype-phenotype correlation studies have shown

conflicting results possibly due to the extensive networks of gene products

regulating the production, modulation, and decay of cytokines. It is therefore

likely that any influence of individual cytokine genes on respective

phenotypes will be relatively minor.

Our study is one of the first studies that used prospectively collected

samples, thus avoiding inverse causation bias that may occur when variation

in blood level of cytokines is induced by the disease or by cancer treatments

or lifestyle changes after cancer diagnosis. There were, however, several

limitations in our study. First, we only had one blood sample for each

individual to characterize their immune profile. There are little data on the

inter-individual and intra-individual variation of cytokines. However, in a

previous study (21), we looked at the inter-individual and intra-individual

variability of cytokines measured in this study. Intra-class correlation

coefficients for most cytokines, based on two repeated blood samples

collected 1 to 2 weeks apart and tested on two different days, were generally

above 0.8, indicating that cytokine levels did not vary much in time within a

subject compared with the variance between subjects at least in a 2-week

period. It was concluded that, therefore, a single plasma or serum cytokine

Chapter 3

52

measurement possibly could be used to characterize an individual's immune

profile.

We had a relatively high inter-batch coefficient of variation (~30%) in

the current study. However, as the case-control pair samples were analyzed

next to each other (randomized order) in the same batch, the inter-batch

variability will not have affected the case-control comparisons. In contrast,

the intra-batch coefficient of variation was on average small (~7%). Another

potential limitation is that after blood collection cytokines are being excreted

from cells as an emergency response. Therefore, the time between blood

collection and storage can be important. In our study, the time between

blood collection and storage was relatively short (median, 4 h) and did not

differ between cases and controls. We explored this issue further by

excluding samples that were not stored within 12 hours of collection. These

analyses resulted in observations similar to the main analyses. Although we

could not control for some of the known risk factors for NHL (i.e., occupation

exposures, viral infections, or some previous immune diseases), they are

unlikely to have confounded our results, as the prevalence of these risk

factors is low in the general population. Previous studies on polymorphisms in

cytokine genes have shown that the effects of these genes were related to

specific NHL sub-types. Unfortunately, our study was too small to investigate

the association of cytokines with specific sub-types of NHL.

Blood cytokines are produced not only by those cell types considered

to play pivotal roles in the immune system as well as in inflammatory

responses, including lymphocytes, monocytes, and mast cells, but also by

macrophages and, for some cytokines, also fibroblasts, neutrophils, and

endothelial cells (26). So, it should be noted that plasma level of cytokines

may not necessarily reflect activity in the target tissue (lymph nodes). Lastly,

although we found some evidence of cytokine levels being associated with

NHL risk, it is important to notice that analyses of a large number of cytokines

may produce statistically significant associations simply by chance. Moreover,

due to small sample size of the study, it is possible that some associations

between these immune system molecules and NHL were missed. It is

therefore important that our findings are replicated in larger studies.

In conclusion, our findings suggest a possible association between

plasma levels of IL2, ICAM, IFN-γ, and TNF-α with NHL risk and provided some

evidence that risk of NHL might be related to a chronic inflammatory

environment as well as a shift in the balance of Th1/Th2 cytokines.

Acknowledgements

The EPIC study funded by “Europe Against Cancer” Programme of the

European Commission (SANCO), Italian Association for Research on Cancer,


53

Italian National Research Council, Compagnia di San Paolo. This work was

supported by the ECNIS Network of Excellence (Environmental Cancer Risk,

Nutrition and Individual Susceptibility), operating within the European Union

6th

Framework Program, Priority 5: “Food Quality and Safety” (FOOD-CT-

2005-513943).

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CHAPTER 4

Long-term effects on humoral immunity among workers exposed to 2,3,7,8-tetrachlorodibenzo-p-

dioxin (TCDD)


Daisy Boers

Lützen Portengen

H. Bas Bueno-de-Mesquita

Dick Heederik

Roel Vermeulen

Occupational and Environmental Medicine (2011);68:419-424

Chapter 4

56

Abstract

Objectives

Epidemiological studies have shown inconsistent effects on immunological

parameters in subjects exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin

(TCDD). In this study we investigated changes in humoral immunity and

prevalence of atopic diseases among workers from a Dutch historical cohort

occupationally exposed to chlorophenoxy herbicides and contaminants

including TCDD.

Methods

Forty-five workers who had been exposed to high levels of TCDD in the past

and 108 non-exposed workers (39 from the same factory as the exposed

subjects (internal control group) and 69 from a comparable factory but

without TCDD exposure (external control group)) were included in the current

investigation. Blood immunoglobulin (Ig) and complement factor (C)

concentrations were measured using quantitative nephelometry (IgA, IgG,

IgM, IgD, C3 and C4) and enzyme-linked immunosorbent assay (ELISA) (total

IgE). Specific IgE antibodies to a panel of common allergens were measured

by ELISA. Current plasma levels of TCDD were measured and back-

extrapolated to the time of last exposure (TCDDmax) using a one-compartment

first order kinetic model.

Results

Univariate analyses showed a significant association between both current

TCDD and TCDDmax and IgA levels (p-levels 0.03 and 0.01 respectively).

However, in a fully adjusted regression model this association was no longer

statistically significant. A borderline significant negative association between

both current and predicted TCDD levels and C4 was found in multivariate

analyses (p-levels 0.07 and 0.04, respectively). History of eczema was

significantly associated with current TCDD levels in both crude (OR = 1.5; 95%

CI = 1.03-2.2) and adjusted models (OR = 1.7; 95% CI = 1.08-2.7).

Conclusions

Overall our results do not support an association between TCDD exposure

and markers of humoral immunity except possibly for C4. Interestingly,

decreased levels of C4 have been linked to lymphoma risk which provides

some support to the putative link between TCDD and non-Hodgkin

lymphoma.

Effects of TCDD exposure on humoral immunity

57

Introduction

Previous epidemiological studies have shown a possible relationship between

occupational exposure to chlorophenoxy herbicides, chlorophenols and their

contaminants (e.g. dioxins and furans) and risk of several cancers including

soft tissues sarcomas, non-Hodgkin lymphoma (NHL) and lung cancer (1-6).

Manufacturing of some chlorophenoxy herbicides (e.g. 2,4,5-

trichlorophenoxyacetic acid (2,4,5-T) has been prohibited in most countries

because of possible contamination with polychlorinated dibenzo-p-dioxins

(PCDDs), including the highly toxic 2,3,7,8-tetrachlorodibenzo-p-dioxin

(TCDD)(7). TCDD is an unwanted by-product of numerous chemical reactions

involving chlorine compounds and is highly persistent in the environment and

biologic organisms (8).

Immunotoxicity related to TCDD has been described in several animal

studies (9, 10). Results of human epidemiological studies on this topic,

however, have been largely inconsistent. For example, several

epidemiological studies, but not all, have shown perturbations in

immunoglobulin levels in TCDD exposed subjects (11-15). Immune

suppression increases susceptibility to various infectious diseases and

lymphoproliferative diseases such as lymphoma. It is well known that severe

immune deficiency in humans increases the risk for NHL (16, 17). Moreover,

NHL has been associated with exposure to chlorophenoxy herbicides or

chlorophenols in several case-control (18, 19) and some recent cohort studies

(7, 20, 21) particularly with TCDD exposure. As such one could hypothesis

that the possible link between NHL and TCDD might be governed by TCDD

related perturbations (i.e. suppression) of the immune system.

Besides possible effects on immunological parameters several animal

and in vitro studies have suggested that TCDD may exacerbate atopic

conditions in particular atopic dermatitis (22, 23). However, to date there are

only a few investigations that have studied TCDD exposure in relation to

prevalence of atopic diseases in humans.

Given the limited evidence for an association between TCDD and

immunologic parameters and given the putative link between immunological

factors and NHL we set out to explore the possible long-term immunological

effects among subjects historically exposed to TCDD. Moreover, occurrence

of different atopic diseases in exposed workers compared to non-exposed

workers was investigated, with the inference that the excess prevalence may

indicate immunological related health impacts of TCDD. Workers were

selected from a retrospective cohort of workers exposed to chlorophenoxy

herbicides, chlorophenols, and dioxins. The cohort study was part of the IARC

multinational study of workers exposed to chlorophenoxy herbicides,

chlorophenols, and dioxins (3, 6, 7, 24).

Chapter 4

58

Material and methods

Cohort population

The Dutch herbicide cohort has been described in detail elsewhere (3, 6, 24).

Briefly, the cohort consists of workers from two chemical factories; factory A

(workers employed between 1955 and 1985; n = 1167) and factory B (workers

employed between 1965 and 1986; n = 1143) involved in the production and

formulation of chlorophenoxy herbicides. In factory A one of the main

products was 2,4,5,-T. Other pesticides manufactured in factory A were 2,4,5-

trichlorophenol (2,4,5-TCP), lindane, dichlobenil and tetradifon.

Contamination with TCDD and other dioxins is possible during production of

2,4,5-T and 2,4,5-TCP. In March 1963, an uncontrolled reaction occurred in an

autoclave in factory A where at the time 2,4,5-TCP was synthesized. After the

explosion, the contents of the autoclave were released in the factory hall,

including dioxins like TCDD (3, 24). In factory B the main products were 4-

chloro-2-methylphenoxyacetic acid (MCPA), 4-chloro-2-methylphenoxy

propanoic acid (MCPP) and 2, 4-dichlorophenoxyacetic acid (2,4-D) which are

unlikely to be contaminated with TCDD (6, 25).

Study population

Subjects were selected for blood collection based on stratified sampling of

(assumed) exposed and unexposed workers. For each worker, exposure

status was based on a detailed occupational history including periods of

employment in different departments and positions held. Exposed workers

were selected for blood collection if; 1] they had been exposed due to the

accident in factory A (both factory workers and hired contract workers that

had also been involved in clean-up after the accident) (n = 28) or if; 2] they

worked in departments of main production (n = 20). Exposed workers were

individually matched to three presumably non-exposed workers (based on an

“a-priori” exposure classification (26) in which several departments were

assumed not to be exposed to TCDD) employed in other departments by

factory, sex, age (within 5 years) and current residence (at first two digits of

postal code) as an internal control group. If an exposed worker agreed to

participate, the first non-exposed worker was invited. If the first non-exposed

worker was unable to participate the second non-exposed worker was

invited, etcetera. Some non-exposed subjects (n=4) were matched as control

subject to two exposed workers. A random sample of workers from factory B

(n = 78) was used as an external control group in the analyses, since they

were unlikely to be exposed to TCDD. These subjects were not individually

matched on age. All study subjects were male. Written informed consent

from each study subject was obtained after the study was explained.

Participants were asked to fill in a self-administered questionnaire, which


59

included information on their occupational history, personal medical history,

medication used in the weeks before the study, anthropometric

characteristics, smoking status and alcohol intake. Peripheral non-fasting

blood samples were collected during home visits between May 2007 and

September 2008. Heparinized blood samples were centrifuged within the

hour and plasma was kept cold at 4°C till stored within 6hrs at -80°C.

Serum immunoglobulin concentrations

Blood immunoglobulin (Ig) and complement factor (C) concentrations were

measured by quantitative nephelometry (IgA, IgG, IgM, IgD, C3 and C4) and

Enzyme-linked immunosorbent assay (ELISA) (total IgE). Specific

immunoglobulin E (IgE) antibodies to common allergens (house dust mite, cat

skin scrape, dog skin scrape, birch pollen and two grass pollens (Timotee and

English Rye)) (Allergon AB, Angelholm, Sweden) were measured by ELISA (27).

A sample was considered positive if corrected optical density (OD) was higher

than 0.05. Atopy was defined as a positive reaction to one or more common

allergens.

Exposure measurements

Heparin plasma samples of all subjects were analyzed for presence of

polychlorinated dibenzo-p-dioxins (PCDDs, including TCDD), polychlorinated

dibenzofurans (PCDFs) and polychlorinated biphenyls (PCBs) at the Centers

for Diseases Control and Prevention (CDC) Atlanta, USA using high-resolution

gas chromatography/isotope-dilution high resolution mass spectrometry with

results reported as parts per trillion (ppt), lipid adjusted (26).

Atopic diseases

Lifetime history of atopic diseases including asthma, hay fever, eczema and

allergy during the past years was ascertained by self-administered

questionnaire (26).

Exposure metrics

As TCDD is highly persistent with a long half-life in blood and human tissue,

exposures to TCDD can be measured in blood or fatty tissues years after the

initial exposure has ended. In this study we measured current levels of TCDD

(TCDDcurrent), approximately 35 years since last exposure. To predict TCDD

blood levels at the time of last exposure (TCDDmax) we extrapolated current

TCDD levels to time of assumed last exposure, which differs between

subjects, using the following one-compartment first order kinetic model with

7.1 years as half-life (t1/2)(26):

TCDDmax = background + (measured TCDD – background) * exp (ln (2) * lag / t1/2)

Chapter 4

60

Current TCDD levels and predicted maximum TCDD levels were subsequently

used to investigate exposure-response relations between TCDD levels and

blood immune markers.


Immune markers measured in concentrations below their respective

detection limit were imputed based on maximum likelihood estimation (MLE)

procedure (28). TCDD and immune markers concentrations were log-

transformed as measured levels appeared to follow a log-normal distribution.

Differences in continuous and categorical parameters between exposed and

non-exposed subjects were tested using t-test (paired t-test in matched and

two sample t-test in unmatched analyses) and chi-square test, respectively.

We calculated exposure-response relations between immune

markers as dependent variable and exposure to TCDDcurrent and TCDDmax using

linear regression analyses for continuous outcome variables or logistic

regression for binary outcome variables (i.e. specific IgE antibodies and

presence of atopic diseases). Additional adjustments for potential

confounders including body mass index (in kg/m²; continuous variable);

alcohol intake (unit/week; continuous variable), smoking (categorical

variable), medication used (categorical variable) and chronic and acute

medical conditions (categorical variable) were considered.

Statistical analyses were performed using SPSS software (ver. 16,

SPSS Inc.) and SAS (ver. 9.1, SAS institute). All p-values were two-sided, with

p<0.05 considered as statistically significant.

Results

Characteristics of participants

Blood immunoglobulin and complement factor concentrations were

measured successfully in 169 (out of 170) workers. TCDD was measured

successfully in 164 workers. We excluded 16 subjects (7 workers of factory A

and 9 of factory B) with a previous cancer diagnosis (except skin cancer) from

the analyses to remove the possibility that immune markers levels may have

been changed due to malignant disease or medications used. This resulted in

a total of 153 subjects available for analysis, 84 workers from factory A (45

exposed workers and 39 non-exposed workers) and 69 external non-exposed

workers from factory B.

Subject characteristics (n=153) are shown in Table 1. The mean age

differed between exposed workers (69.7 ± 7.03) from factory A and non-

exposed subjects from factory B (59.2 ± 9.1). Around 50% of exposed and

non-exposed workers of both factories suffered from chronic diseases such as

diabetes, cardiovascular diseases and hypertension. The proportion of


61

current smokers among exposed workers and both groups (internal and

external) of non-exposed workers was similar, whereas exposed workers had

lower amount of alcohol intake compared to both groups of non-exposed

workers which was significant when comparing with the internal non-exposed

group (p = 0.02). Geometric mean (GM) and geometric standard deviation

(GSD) of TCDDcurrent were 3.3 ppt ± 7.7 (10th - 90th percentiles (P10 - P90) 0.1-

30.9) in exposed workers and 1.2 ppt ± 5.4 (P10 - P90 = 0.08-7.2) and 0.4 ppt ±

5.1 (P10 - P90 = 0.07-3.8) in internal and external non-exposed groups

respectively. Historical maximum exposure (TCDDmax) was significantly higher

in exposed workers (GM ± GSD 81.9 ± 35.6; P10 - P90 = 0.1-2269.7) compared to

both internal (8.9 ± 26.6; 0.08-433.22) and external non-exposed workers (0.4

± 5.1; 0.07-3.8).

Table 1 General characteristics of exposed and non-exposed workers

Factory A Factory B Exposed

(n=45) Non-exposed (n=39)

P value

Non-exposed (n=69)

P value

Age* 69.7 (7.03) 68.8 (7.9) 0.6 59.2 (9.1) <0.001

Body Mass Index* 27.2 (3.0) 26.4 (3.1) 0.2 27.2 (3.6) 0.9

Alcohol intake (units/week) * 10.8 (13.5) 17.6 (13.1) 0.02 15.1 (16.5) 0.2

Smoking status, n (%) 0.9 0.9

Non-smoker 8 (17.8%) 7 (17.9%) 14 (20.3%)

Former smoker 27 (60.0%) 23 (59.0%) 38 (55.1%)

Smoker 10 (22.2%) 9 (23.1%) 17 (24.6%)

Medication, n (%) 0.3 0.1

Immunosuppressants 4 (8.9%) 4 (10.3%) 2 (2.9%)

NSAIDs 15 (33.3%) 7 (17.9%) 14 (20.3%)

Antibiotics 0 0 1 (1.4%)

Skin Cancer, n (%) 4 (8.9%) 3 (7.7%) 0.8 3 (4.3%) 0.3

Infectious diseases in the past

4 weeks, n (%)

3 (6.8%) 4 (10.3%) 0.6 6 (8.7%) 0.7

Chronic diseases, n (%) † 24 (53.3%) 21 (53.8%) 0.9 32 (46.4%) 0.5

Chronic inflammatory

diseases, n (%) ‡

12 (26.3%) 9 (23.1%) 0.7 18 (26.1%) 0.9

TCDDcurrent ppt § 3.3 (7.7) 1.2 (5.4) 0.001 0.4 (5.1) <0.001

TCDDmax ppt ¶ 81.9 (35.6) 8.9 (26.6) 0.001 0.4 (5.1) <0.001

*Mean (SD); † Chronic diseases included Diabetes, coronary heart disease and hypertension; ‡ Chronic inflammatory diseases: Chronic obstructive pulmonary disease, psoriasis, sarcoidosis, asthmatic bronchitis, rheumatoid arthritis, liver failure, Crohn’s disease, fibromyalgia and allergy; § Current levels of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) (TCDDcurrent) parts per trillion, geometric mean (geometric SD); ¶ Back-extrapolated levels of TCDD (TCDDmax) parts per trillion, geometric mean (geometric SD); NSAIDs, non-steroidal anti inflammatory drugs.

Chapter 4

62

Serum immunoglobulin and complement factor concentrations

Table 2 shows serum levels of all immune markers and percentages of

subjects with positive specific antibodies against common allergens for

exposed and non-exposed subjects. By excluding cancer subjects, a total of 41

matched pairs (four non-exposed workers were matched to 8 exposed

workers) were included in the paired t-test while 45 exposed workers from

factory A and 69 non-exposed workers of factory B were included in the

independent two-sample t-test. No consistent differences were observed in

immunological markers between the exposed workers and either of the

control groups.

Table 2 Serum immunoglobulins and complement factors (geometric mean

(geometric SD)) of exposed and non-exposed subjects

Exposed (n=41)

Non-exposed Factory A

(n=41)

P * Exposed (n=45)

Non-exposed Factory B

(n=69)

P

IgG (g / l) 11.0 (1.2) 11.1 (1.2) 0.8 10.9 (1.2) 10.4 (1.3) 0.3

IgM (g / l) 0.8 (1.7) 0.9 (1.7) 0.7 0.8 (1.7) 0.8 (1.7) 0.7

IgA (g / l) 2.5 (1.6) 2.6 (1.4) 0.8 2.5 (1.5) 2.4 (1.6) 0.6

IgD mg / l† 22.2 (4.5) 23.0 (4.0) 0.7 21.2 (4.6) 13.3 (6.8) 0.2

IgE (g / l) 27.2 (7.3) 37.5 (9.6) 0.5 22.9 (8.6) 18.8 (8.6) 0.6

C3 (g / l) 1.1 (1.2) 1.1 (1.1) 0.6 1.1 (1.2) 1.2 (1.2) 0.4

C4 (g / l) 0.2 (1.3) 0.2 (1.3) 0.2 0.2 (1.3) 0.3 (1.4) 0.2

Birch pollen, n (%) 2 (4.9%) 1 (2.4%) 0.6 2 (4.4%) 1 (1.4%) 0.3

Grass-mix pollen, n (%) ‡ 4 (9.8%) 3 (7.3%) 0.7 4 (8.9%) 4 (5.8%) 0.5

Dog skin scrape, n (%) 0 0 - 0 1 (1.4%) 0.4

Cat skin scrape, n (%) 0 1 (2.4%) 0.3 0 1 (1.4%) 0.4

House dust mite, n (%) 2 (4.9%) 3 (7.3%) 0.6 3 (6.7%) 5 (7.2%) 0.9

Atopy, n (%) § 6 (14.6%) 5 (12.2%) 0.7 7 (15.6%) 8 (11.6%) 0.5

* Paired t-test were used for continues variables; † More than 50% of IgD samples were imputed; ‡ Timotee and English rye grass pollen; § Atopy was defined as a positive reaction to one or more common allergens.

Dose-response analyses

Current measured levels of TCDD (TCDDcurrent) in workers of factory A were

back extrapolated to time of assumed last exposure and regression analyses

were carried out for both current and maximum TCDD exposure levels

(TCDDmax). We found a significant linear association for IgA and IgM with

current TCDD levels (see table 3). As around 50% of subjects suffered of

medical chronic conditions which might affect immune markers; we restricted

the analyses to subjects without chronic diseases (n = 75). In these analyses

the association of IgA with current TCDD exposure remained significant.

However when including other covariates in the model; the association with

Table 3 Dose-response relationships between immunological parameters and TCDDcurrent and TCDDmax

General linear model ‡ Univariate All subjects ( n=148) Subjects without chronic disease ( n=75) Multivariate § ( n=148)

Estimate 95% CI Estimate 95% CI Estimate 95% CI LnTCDDcurrent*

IgG 0.009 -0.008 to 0.026 0.013 -0.011 to 0.036 0.004 -0.020 to 0.020 IgA 0.039 0.004 to 0.074 0.049 0.004 to 0.094 0.030 -0.010 to 0.070 IgM -0.044 -0.087 to 0.00 -0.037 -0.096 to 0.022 -0.050 -0.100 to 0.004 IgD¶ -0.073 -0.212 to 0.065 -0.109 -0.290 to 0.072 -0.050 -0.200 to 0.080 IgE -0.016 -0.197 to 0.166 -0.154 -0.404 to 0.096 -0.040 -0.480 to 0.096 C3 -0.006 -0.021 to 0.010 -0.013 -0.034 to 0.008 -0.010 -0.020 to 0.010 C4 -0.016 -0.040 to 0.008 -0.028 -0.060 to 0.003 -0.020 -0.040 to 0.010

LnTCDDmax† IgG 0.006 -0.003 to 0.016 0.010 -0.005 to 0.022 0.010 -0.010 to 0.020 IgA 0.022 0.003 to 0.041 0.034 0.009 to 0.060 0.020 -0.002 to 0.040 IgM -0.016 -0.040 to 0.008 -0.010 -0.043 to 0.024 -0.020 -0.050 to 0.010 IgD¶ -0.010 -0.087 to 0.067 -0.002 -0.106 to 0.103 -0.004 -0.090 to 0.080 IgE 0.016 -0.084 to 0.116 -0.045 -0.189 to 0.099 -0.010 -0.120 to 0.110 C3 -0.006 -0.014 to 0.003 -0.010 -0.021 to 0.003 -0.010 -0.020 to 0.001 C4 -0.012 -0.026 to 0.001 -0.015 -0.033 to 0.002 -0.020 -0.030 to 0.00

The parameter estimate reflects a change per unit of exposure (parts per trillion) on the log scale; * Current levels of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) (TCDDcurrent); † back-extrapolated levels of TCDD (TCDDmax); ‡ all analyses are based on Log-transformed values of immunoglobulins and dioxin; § covariate included in the multivariate models were chronic inflammatory disease, infectious disease within 4 weeks before blood sampling, medication, alcohol intake, smoking, age and body mass index. ¶ More than 50% of IgD samples were imputed.

Chapter 4

64

IgA became statistically non-significant. A consistent borderline statistically

significant inverse association for C4 was found with current TCDD.

The regression analyses with TCDDmax rendered essentially similar

results in that a significant increase in IgA level and a significant decrease in

C4 with increasing TCDDmax level were observed in univariate and fully

adjusted regression models respectively.

Specific IgE for at least one of the five common allergens (atopy) was

detected in 19 (12.8%) workers and was not associated with current TCDD

levels in univariate (OR = 1.13; 95% CI = 0.88-1.46) and adjusted models (OR =

1.11; 95% CI = 0.84-1.48) (see table 4). Most sensitized workers had IgE to

house dust mite (6.8%) or grass pollen (7.4%); however none of specific IgE

antibodies were associated with current TCDD levels (data not shown). Life

time history of eczema was significantly associated with current TCDD levels

in both crude (OR = 1.51; 95% CI = 1.03-2.20) and adjusted models (OR = 1.71;

95% CI = 1.08-2.71). We found no significant associations between back-

extrapolated TCDD levels and specific IgE or any other reported atopic

diseases.

Table 4 Dose-response relationships between atopy, history of eczema,

asthma, hay fever and allergic disease and TCDD current and TCDD max (n=148)

Univariate Multivariate‡

OR (95% CI) OR (95% CI)

LnTCDDcurrent*

History of allergic diseases 1.13 (0.80-1.58) 1.14 (0.79-1.64)

History of eczema 1.51 (1.03-2.20) 1.71 (1.08-2.71)

History of hay fever 1.08 (0.80-1.47) 1.08 (0.77-1.52)

History of asthma 1.28 (0.84-1.96) 1.35 (0.75-2.43)

Atopy 1.13 (0.88-1.46) 1.11 (0.84-1.48)

LnTCDDmax†

History of allergic diseases 1.07 (0.89-1.27) 1.09 (0.89-1.34)

History of eczema 1.13 (0.95-1.35) 1.20 (0.97-1.48)

History of hay fever 1.08 (0.92-1.28) 1.10 (0.91-1.36)

History of asthma 1.17 (0.94-1.45) 1.36 (0.94-1.96)

Atopy 1.08 (0.95-1.24) 1.10 (0.93-1.30)

The OR reflects a change per unit of exposure (parts per trillion) on the log scale; * Current levels of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) (TCDDcurrent); † Back-extrapolated levels of TCDD (TCDDmax), all analyses are based on log-transformed values of both TCDD; ‡ Covariates included in the models: age, body mass index, smoking and alcohol intake.


65

Discussion

Immunotoxicity related to TCDD in humans has been investigated in several

studies showing largely inconsistent results. In the current investigation we

explored the effect of exposure to TCDD on humoral immune markers among

workers occupationally exposed to TCDD approximately 35 years since last

exposure. Overall levels of all immunoglobulins were slightly lower in exposed

workers compared with internal non-exposed workers while we found that

exposed workers had higher levels of immunoglobulin compared with

external non-exposed workers. However these differences were not

statistically significant. In addition, regression models adjusted for chronic

inflammatory and infectious diseases within 4 weeks before blood collection,

medication used, alcohol consumption, smoking, age and BMI did not show

significant changes in immunoglobulins levels in relation to TCDD exposure. In

a study on waste incineration workers exposed to TCDD in South Korea, slight

decreases in several immunoglobulin (IgG, IgA, IgM and IgE) levels were

detected compared to a control group, but these differences were

statistically not significant (11). However in this study workers were also

exposed to other toxicants like heavy metals, polycyclic aromatic

hydrocarbons (PAHs) and other organic materials. Other recent studies have

shown significant changes in one or more immunoglobulins including

decreased IgG levels (12-14, 29), increased IgG levels (30) or increased IgE

levels (14). It is important to note that TCDD levels in the present study were

measured approximately 35 years since last exposure which is relatively long

compared to other studies (10 to 20 years after exposure)(12, 13).

There is limited evidence in the literature on blood levels of

complement factors in relation to TCDD exposure. A study of TCDD exposed

subjects conducted 20 years after the Seveso accident showed a non-

significant positive association between blood TCDD and C4 (13). Similarly, in

another study a significant positive association for C4 with current TCDD

levels was reported (30). These results are in contrast to our results which

seemed to indicate decreasing C4 levels with increasing measured TCDD and

TCDDmax levels. In the study conducted by Ott et al. regression models were

only corrected for age, BMI and smoking. However, other potential

confounders like acute and chronic disease and use of medication were not

taken into account. Compared with the Seveso study which included both

males and females within a wide age range, our study subjects were middle-

aged or elderly men. Moreover, the magnitude of TCDD exposure and long

time interval between past maximum exposure and current measurement

which are different between studies might explain some of the differences

observed between studies.

Chapter 4

66

We did not find any significant relation between the presence of IgE

specific antibodies and TCDD levels. To our knowledge there are no previous

investigations that relate IgE specific antibodies to TCDD levels except for a

study on Flemish adolescents (Belgium) that showed an effect of exposure to

dioxin-like compounds and IgE specific antibodies. In this study a negative

association between the odds of having a positive response to house dust

mites, cat dander, or grass pollen was related to the serum concentrations of

dioxin-like compounds. In addition, a negative correlation between serum IgE

and dioxin-like compounds was observed (15). However, given the small

sample size of our study, the elder study population and the low prevalence

of sensitization to common allergens it is possible that we missed a possible

weak association between positive IgE specific antibodies and TCDD levels.

Alterations of allergic immune responses after TCDD exposure have

been investigated in several studies. Recent animal studies documented that

dioxin exposure exacerbates atopic dermatitis with no increase of IgE

antibody production (22, 31). A study on Korean Vietnam War veterans

exposed to Agent Orange (mixture of 2,4,5-T and 2,4-D used as a defoliant in

the Vietnam War, which could be contaminated with TCDD) indicated that

not only IgE levels increased, but also the immune system response skewed

toward type 2 immune responses, which could indicate an enhanced

susceptibility to various allergic diseases (14). In our study, a non significant

inverse association between total IgE and TCDDcurrent and TCDDmax levels was

found. Moreover, we found a significant association between history of

eczema and measured TCDD levels. Possible confounding of history of

eczema by other skin manifestations of TCDD exposure such as chloracne was

explored. None of the subjects with self-reported eczema (n=11) reported a

history of chloracne while under the subjects without eczema (n=137) three

subjects reported a history of chloracne. As such there is no indication that

the reported association between TCDD and eczema was confounded by a

history of chloracne. Consistent with our findings, Kim et al. reported that

Korean Vietnam War veterans exposed to Agent Orange had an increased

frequency of eczema compared to non-Vietnam veterans (adjusted for

potential confounders) (32). These findings raise the possibility that TCDD

affects the pathogenesis of eczema independent of IgE signaling (22).

However, our study did not show a significant association between eczema

and TCDDmax which might indicate that the association found with TCDDcurrent

could be a chance-finding. Given the strong correlation between TCDDcurrent

and TCDDmax (Rsp=0.97) it would be expected that both TCDD measures

provide similar results.

There were several limitations in our study. First, we selected an

internal non-exposed group of workers assumed to be non-exposed to TCDD

based on detailed job information (exposure classified as exposure to


67

chlorophenoxy herbicides) from factory A. However, recent analyses have

shown that exposure to TCDD was more widespread than previously thought

within this factory (26). Moreover, our results showed that the average levels

of TCDD in non-exposed workers of factory A are higher than the levels of

non-exposed workers of factory B. Therefore, it seems that using an internal

group of non-exposed workers might have biased the results towards the null

through misclassification of non-exposed workers. The external non-exposed

workers, however, were not closely matched to the exposed workers with

regard to age. Although outcomes were adjusted for age, some residual

confounding might have remained due to regional differences and differences

in work setting. Furthermore, examination of long-term immunological

effects of TCDD exposure was complex as it was difficult to differentiate

between effects that might be related to high levels of exposure in the past

(TCDDmax) or current measured levels of TCDD. Differences in associations

between TCDDmax and TCDDcurrent with eczema may indicate this complexity.

Finally, bias due to selective survival may have influenced our results.

Although the study did not provide strong support for possible long-

term effects on humoral immunity of TCDD, it is noteworthy that decreased

levels of C4 have been linked to lymphoma risk (33, 34). Low C4 levels might

potentially have a role in the survival of auto-reactive B cells. Prolonged

survival of B cells could increase the risk that unfavorable mutations might

occur, resulting in malignancy (35). Therefore, our findings for C4 might

provide some support to the putative link between TCDD and non-Hodgkin

lymphoma.

Conclusions

Our study showed that plasma TCDD levels were not associated with markers

of humoral immunity with the possible exception of a borderline significant

decrease in C4 levels. Given the observed heterogeneity in results from

different studies, it can be hypothesized that perturbations of the humoral

immune response due to TCDD exposure, if it occurs at all, may be subtle.

However, the immune system is complex with both humoral and cellular

components playing an important role. Therefore, more in-depth

characterization of both the humoral (for example cytokine expression

profiles) and cellular components might provide additional insights in the

possible immunological effects of TCDD in humans.

Acknowledgements

We would like to thank Wayman E. Turner for blood plasma analyses of

PCDDs, PCDFs and PCBs (Division of Environmental Health Laboratory

Sciences, Centers for Disease Control and Prevention, Atlanta GA, USA).

Chapter 4

68

Furthermore, we gratefully acknowledge Jack Spithoven for IgE analyses

(Institute for Risk Assessment Sciences, Utrecht University, the Netherlands).

We wish to thank all workers for participation in the study. The first author

also acknowledges the Iranian Ministry of Health, Treatment and Medical

Education for support of a PhD program at Utrecht University in the

Netherlands.

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28. Helsel DR. (2005). More Than Obvious: Better methods for interpreting

nondetect data. Environ Sci Technol 39: 419A-423A.

29. Halperin W, Vogt R, Sweeney MH, et al. (1998). Immunological markers among

workers exposed to 2,3,7,8- tetrachlorodibenzo-p-dioxin. Occup Environ Med 55:

742-749.

30. Ott MG, Zober A & Germann C. (1994). Laboratory results for selected target

organs in 138 individuals occupationally exposed to TCDD. Chemosphere 29:

2423-2437.

31. Fujimaki H, Nohara K, Kobayashi T, et al. (2002). Effect of a Single Oral Dose of

2,3,7,8-Tetrachlorodibenzo-p-dioxin on Immune Function in Male NC/Nga Mice.

Toxicol Sci 66: 117-124.

32. Kim J-S, Lim H-S, Cho S-II, et al. (2003). Impact of Agent Orange Exposure among

Korean Vietnam Veterans. Ind Health 41: 149-157.

33. Ioannidis JPA, Vassiliou VA & Moutsopoulos HM. (2002). Long-term risk of

mortality and lymphoproliferative disease and predictive classification of primary

Sjögren's syndrome. Arthritis Rheum 46: 741-747.

34. Theander E, Henriksson G, Ljungberg O, et al. (2006). Lymphoma and other

malignancies in primary Sjögren's syndrome: a cohort study on cancer incidence

and lymphoma predictors. Ann Rheum Dis 65: 796-803.

35. Pillemer S R. (2006). Lymphoma and other malignancies in primary Sjögren’s

syndrome. Ann Rheum Dis 65: 704–706.

CHAPTER 5

Changes in lymphocyte subsets in workers exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD)


Virissa Lenters

Daisy Boers

Lützen Portengen

Ellen Baeten


Dick Heederik

Andries C. bloem

Roel Vermeulen

Submitted for publication

Chapter 5

72

Abstract

Objectives

2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) is known to have toxic effects on

the hematopoietic system in animals but epidemiological studies in humans

have shown inconsistent results. In this study we investigated changes in

peripheral blood cell counts and lymphocyte subsets among workers from a

Dutch historical cohort occupationally exposed to chlorophenoxy herbicides

and contaminants including TCDD.

Methods

Forty-seven workers who had been exposed to high levels of TCDD in the past

and 38 low-exposed workers were included in the current investigation.

Complete blood count and differential and major lymphocyte subsets were

analyzed. Current plasma levels of TCDD (TCDDCurrent) were determined by

high-resolution gas chromatography/isotope-dilution high resolution mass

spectrometry. TCDD blood levels at the time of last exposure (TCDDmax) were

estimated using a one-compartment first order kinetic model.

Results

Cell counts and lymphocyte subsets were similar between high and low

exposed workers, except for a non-dose dependent increase in CD4+/CD8+

ratio among high exposed workers. Interestingly most lymphocyte subsets, in

particular the B cell compartment, showed a decrease with increasing levels

of both current and TCDDmax.

Conclusions

Overall, our study showed that plasma TCDD levels had no effect on white

blood cell counts and major subsets. However, a decrease in most

lymphocyte subsets was noted with the strongest effect for B cells. The latter

finding may suggest that dioxin exposure can have an adverse impact on the

hematopoietic system and lend support to B cell lymphoma induction by

dioxin.

Changes in lymphocyte subsets in workers exposed to TCDD

73

Introduction

2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) is a persistent environmental

contaminant generated as an unwanted by-product of numerous chemical

reactions involving chlorine compounds. It produces a broad spectrum of

effects on human organs including skin, liver, reproductive, nervous,

hematopoietic and immune systems (1). This spectrum of toxicities is known

to be mediated via its binding to the aryl hydrocarbon receptor (AhR), a

specific intracellular protein expressed by major cell types of the immune

system (2).

There is suggestive evidence from human observational studies that

TCDD and other dioxin-like compounds with similar structures impair cell

mediated immunity. Several cellular targets within the immune-

hematopoietic system have been shown to be altered by TCDD such as

antigen specific populations of lymphocytes, including CD4+ and, CD8+ T cells,

and B cells (3-6). However, other studies among TCDD exposed individuals did

not observe such changes (7-10).

Lymphocyte subsets and their microenvironments, including

cytokines and chemokines, likely play a role in the genesis of lymphomas, in

particular non-Hodgkin lymphomas (NHL) (11-15). NHL has been associated

with exposure to chlorophenoxy herbicides (e.g. 2,4,5-trichlorophenoxyacetic

acid) or chlorophenols and their contaminants including TCDD and higher

chlorinated dioxins in some previous studies (16-19). Therefore, the possible

link between NHL and TCDD might be governed by TCDD-related

perturbations (i.e., suppression) of immune cells.

Given the inconsistent evidence for the effect of TCDD on the

immune-hematopoietic system, we set out to investigate the association

between exposure to TCDD and hematological measures, including peripheral

blood cell counts and lymphocyte subsets, in subjects historically exposed to

high TCDD levels. Study subjects comprised a subset of a retrospective cohort

of Dutch workers, part of the IARC multinational study of workers exposed to

chlorophenoxy herbicides, chlorophenols and dioxins (20-22).


Study population

The cohort study design and exposure assessment have been previously

described in detail (20, 21). The cohort consists of workers from two

chlorophenoxy herbicide producing factories. Current analyses utilized a

subset of workers from one factory (labeled “A” in previous publications) who

were exposed to TCDD as a by-product of production of 2,4,5-

trichlorophenoxyacetic acid and 2,4,5-trichlorophenol during 1953 to 1969,

Chapter 5

74

and/or during an occupational accident in 1963. High-exposed workers were

selected for blood collection if they were still alive at the end of follow-up

(December 31st, 2006) and (1) they had been exposed due to the industrial

accident (both factory workers and contract workers hired to clean-up after

the accident) (n = 29), or (2) they worked in departments of main production

(n = 21). High-exposed workers were matched to three presumably low-

exposed workers (based on an a priori exposure classification in which several

departments were assumed not to be exposed to TCDD) employed in other

departments by factory, sex, age (within 5 years) and current residence (first

two digits of postal code). If an exposed worker agreed to participate, the

first low-exposed worker was invited. If the first low-exposed worker was

unable to participate, the second low-exposed worker was invited, and so on.

In total 43 low-exposed workers were selected. All study subjects were male.

Written informed consent was obtained from each study subject after the

study was explained. Participants were asked to complete a self-administered

questionnaire, which included questions on occupational history, personal

medical history, medication used in the weeks prior to the blood collection,

anthropometric characteristics, smoking status and alcohol intake. Blood

plasma samples were collected according to a standard protocol during home

visits between May 2007 and September 2008 (21, 22).

Hematological measurements

Blood samples were collected in 4 mL EDTA tubes via venipuncture from the

participants in sitting position. Blood samples were generally collected in the

afternoon, but occasionally also early morning or in the evening.

Samples were stored on ice at 0°C, and were transported to the Department

of Medical Immunology, UMCU. Hematological parameters including number

and proportion of white blood cells (WBC) and red blood cells (RBC),

monocytes, granulocytes and platelets as well as hemoglobin concentration

and hematocrit were determined by an automated Beckman Coulter AC

Tdiff2 counter (Beckman-Coulter Corporation, Miami, USA). Flow cytometric

analysis of the B- and T cell compartments were performed as described

previously (23). The lymphocyte cell subset populations were analyzed by

flow cytometry on a FACSCalibur (Becton Dickinson) using Cell Quest Pro data

analysis software (Becton Dickson). Absolute counts of lymphocyte subsets,

including T cell and B cell compartments, were calculated by multiplying the

percentages of the indicated subset as determined by flow cytometry and

absolute lymphocyte counts determined by the Beckman Coulter AC Tdiff2

counter.


75


Heparin plasma samples of all subjects were analyzed for TCDD, at the

Centers for Diseases Control and Prevention (CDC; Atlanta, USA) using high-

resolution gas chromatography/isotope-dilution high-resolution mass

spectrometry. Results were lipid adjusted and reported as parts per trillion

(ppt) (24).

Exposure metrics

TCDD is highly persistent with a long half-life in blood and human tissues. As

we measured current levels of TCDD (TCDDcurrent) approximately 35 years

since last exposure (lag), a one-compartment first order kinetic model with a

TCDD half-life (t1/2) of 7.1 years was used to estimate TCDD blood levels at the

time of last exposure (TCDDmax) (21, 22, 25):

TCDDmax = background + (measured TCDD – background) * exp (ln (2) * lag / t1/2)

The lag in this equation is defined as the time since last assumed exposure

and was calculated as follows:

• For workers exposed as a result of the accident in 1963, lag was

defined as years since the last date they worked in the clean-up.

• For workers working in a “production” department, lag was defined

as years since the last date they worked in this department (the

department “production” closed in January 1971).

• For other workers (not working in any of the above mentioned

departments) lag was defined as years since the last date they

worked in the factory but no later than December 31st 1976.

Exposure to TCDD strongly declined after 1976 when formulation of

2,4,5-T ceased.

Due to the biological persistence, TCDD can be found in plasma from virtually

everybody living in an industrialised country. Therefore only measured TCDD

levels above the estimated background (0.4 ppt) were back-extrapolated,

after which the background was added back in. Current TCDD levels and

estimated maximum TCDD levels were subsequently used to investigate

exposure-response relations between TCDD levels and hematologic

measures.


Individual TCDD levels (high exposed, n=15; low exposed, n=20), which were

below the limit of detection were imputed using a maximum likelihood

estimation method (26). TCDD and hematologic measures were log-

transformed as measured levels appeared to follow a log-normal distribution.

Differences in continuous and categorical parameters between high-exposed

Chapter 5

76

and low-exposed subjects were tested using a two sample t-test and chi-

square test, respectively.

We explored exposure-response relations between log-transformed

hematologic measures as the dependent variable and log-transformed

exposure to TCDDcurrent or TCDDmax as the independent variable using linear

regression analyses. Models were adjusted for potential confounders

including body mass index (in kg/m²; continuous variable); alcohol intake

(unit/week; continuous variable), smoking status (never, former, current),

medication that could affect cell populations (yes/no), and chronic and acute

medical conditions (yes/no). As more than 50% of high- and low-exposed

workers had a chronic disease, exposure-response relations were also

investigated using subjects free of chronic disease at the time of blood draw.

Statistical analyses were performed using SAS (ver. 9.2; SAS Institute

Inc., Cary, NC, USA). All p-values were two-sided, with p<0.05 considered

statistically significant.

Results


Peripheral blood cell counts were successfully measured in all workers. We

excluded two subjects as their TCDD results were missing and 6 subjects (2

high-exposed and 4 low-exposed workers) with a previous (non-skin) cancer

diagnosis from the analyses to remove the possibility that blood cell counts or

plasma TCDD levels may have been changed due to malignant disease or

treatment. This resulted in a total of 85 subjects available for analysis; 47

high-exposed workers and 38 low-exposed workers.

Subject characteristics (n=85) are shown in Table 1. More than 50% of

workers suffered from chronic diseases such as diabetes, cardiovascular

diseases and hypertension. High-exposed workers had lower alcohol intake

compared to low-exposed workers (p = 0.05). Smoking status among high-

and low-exposed workers was similar.

Geometric mean (GM) and geometric standard deviation (GSD) levels

of TCDDcurrent and historical maximum exposure (TCDDmax) were significantly

higher in high-exposed workers (TCDDcurrent: 3.25 ± 7.43 ppt; TCDDmax: 79.82 ±

33.28 ppt) compared to low-exposed workers (TCDDcurrent: 1.07 ± 6.42 ppt;

TCDDmax: 7.53 ± 32.14 ppt).


77

Table 1 General characteristics of high and low TCDD exposed workers

High-exposed

(n=47)

Low-exposed

(n=38) P value

£

Age (years) * 69.07 (7.45) 68.55 (7.93) 0.75

Body mass index (kg/m2) * 27.33 (3.10) 26.65 (3.04) 0.31

Alcohol intake (units/week) * 11.48 (13.56) 17.26 (13.16) 0.05

Smoking status, N (%) 0.84

Current smoker 12 (25.5%) 8 (21.1%)

Former smoker 28 (59.6) 23 (60.5%)

Never smoker 7 (14.9%) 7 (18.4%)

Skin cancer, N (%) 4 (8.5%) 3 (7.9%) 0.92

Infectious disease in the past 4 weeks, N (%) 4 (8.7%) 4 (10.5%) 0.78

Chronic disease, N (%) † 24 (51.1%) 20 (52.6%) 0.89

Chronic inflammatory disease, N (%) ‡ 13 (27.7%) 11 (28.9%) 0.90

Medication, N (%) 0.33

Immunosuppressant 5 (10.6%) 4 (10.5%)

NSAIDs 14 (29.8%) 6 (15.8%)

Antibiotics 0 1 (2.6%)

TCDDcurrent (ppt) ª 3.25 (7.43) 1.07 (6.42) 0.002

TCDDmax (ppt) b 79.82 (33.28) 7.53 (32.14) 0.001

* Mean (standard deviation); NSAIDs: non-steroidal anti-inflammatory drugs;

† Chronic diseases included: diabetes, coronary heart disease, and hypertension;

‡ Chronic inflammatory diseases: chronic obstructive pulmonary disease, psoriasis,

sarcoidosis, asthmatic bronchitis, rheumatoid arthritis, liver failure, Crohn’s disease,

fibromyalgia and allergy; ª Current levels of 2,3,7,8-tetrachlorodibenzo-p-dioxin,

geometric mean (geometric standard deviation); b Estimated maximum levels of

TCDD, geometric mean (geometric standard deviation); £ P values from t-tests for

continuous variables and Χ2 tests for categorical variables.

Hematologic measures

Table 2 shows measured blood cell counts, hemoglobin concentration and

hematocrit separately for high and low-exposed subjects. T helper cell

numbers increased non-significantly in high-exposed workers compared to

low-exposed workers while cytotoxic T cells were higher among low-exposed

workers resulting in a significant difference in the CD4+/CD8+ ratio between

high-exposed and low-exposed workers. No consistent differences were

observed in other hematologic measures between the high- and low-exposed

workers.

Chapter 5

78

Table 2 Hematologic measurements of high-exposed and low-exposed

workers (n=85)

High-exposed (n=47) Low-exposed (n=38)

n GM (GSD) n GM (GSD) P-value*

Red blood cells (109/L) 4740.00 (1.08) 4710.87 (1.07) 0.71

Hemoglobin (g/dL) 9.01 (1.08) 9.09 (1.08) 0.63

Hematocrit 0.44 (1.08) 0.44 (1.08) 0.89

Platelet count (109/L) 243.08 (1.35) 242.70 (1.27) 0.98

White blood cells (106/L) 7360.3 (1.26) 7295.7 (1.29) 0.87

Monocytes (106/L) 333.15 (1.86) 371.77 (1.42) 0.34

Granulocytes (106/L) 4765.00 (1.26) 4785.92 (1.38) 0.94

Lymphocytes (106/L) 2074.31 (1.52) 2007.08 (1.37) 0.69

B cells(CD19+) (106/L) 192.34 (2.46) 202.24 (2.19) 0.79

Naive B cells (106/L) 44 108.25 (2.14) 37 109.34 (1.91) 0.94

IgM+ memory B cells (106/L) 45 11.18 (5.24) 37 13.52 (2.91) 0.65

IgG/IgA+ memory B cells (106/L) 45 16.19 (2.72) 37 19.00 (2.28) 0.48

T cells (106/L) 1400.20 (1.48) 1359.60 (1.40) 0.72

T helper cells (CD4+) (106/L) 46 867.99 (1.51) 38 787.17 (1.54) 0.29

CD38+CD4+ (106/L) 46 6.43 (2.55) 38 6.59 (2.45) 0.90

Naive CD4+ (106/L) 42 289.95 (2.64) 37 231.45 (2.57) 0.30

Memory CD4+ (106/L) 42 495.68 (1.65) 37 492.16 (1.53) 0.95

Cytotoxic T cells (CD8+) (106/L) 46 375.26 (1.88) 38 441.46 (1.61) 0.19

CD38+CD8+ (106/L) 46 5.14 (2.92) 38 4.67 (2.62) 0.67

Naive CD8+ (106/L) 42 87.56 (2.35) 38 85.24 (2.70) 0.90

Memory CD8+ (106/L) 42 258.75 (2.25) 38 310.40 (1.78) 0.25

LGL cells (106/L) 45 149.95 (2.44) 38 117.65 (2.32) 0.21

Natural killer cells (106/L) 338.13 (1.75) 312.45 (1.64) 0.50

CD4+/CD8+ ratio 46 2.75 (1.71) 38 2.09 (1.16) 0.05

GM: geometric mean; GSD: geometric standard deviation; IgM: immunoglobulin M;

IgG: immunoglobulin G; IgA: immunoglobulin A; LGL: large granular lymphocytes; * P

values are from t-tests of log-transformed values.

Linear regression analysis showed that most of hematological

parameters had an inverse, albeit non significant, association with TCDDmax

levels (Table 3 and Figure 1). We found a significant linear association for B

cell and IgG/IgA+ memory B cell counts with TCDDmax levels in univariate

analyses (Table 3). The association between TCDDmax and B cells remained

significant after adjustment for covariates. Subsequently, we restricted the

regression analyses to high- and low-exposed workers separately. These

analyses showed that the association, though weak, was present among both

high- and low-exposed workers (Figure 2).

Table 3 Dose-effect relationships between hematologic measurements and TCDDmax

Univariate, N=85 Multivariate*, N=85

Estimate 95% CI Estimate 95% CI Red blood cells (10

9/L) -0.001 -0.005-0.004 -0.001 -0.006-0.004

Hemoglobin (g/dL) -0.0001 -0.005-0.006 0.0001 -0.005-0.005 Hematocrit -0.001 -0.006-0.003 -0.001 -0.006-0.004 Platelet count (10

9/L) -0.011 -0.027-0.005 -0.006 -0.024-0.013

White blood cells (106/L) -0.009 -0.024-0.005 -0.009 -0.024-0.006

Monocytes (106/L) -0.026 -0.057-0.005 -0.029 -0.063-0.005

Granulocytes (106/L) -0.007 -0.023-0.009 -0.007 -0.023-0.010

Lymphocytes (106/L) -0.013 -0.034-0.010 -0.010 -0.035-0.014

B cells (106/L) -0.058 -0.106- -0.009 -0.056 -0.108- -0.004

Naive B cells (106/L) -0.032 -0.077-0.012 -0.024 -0.066-0.018

IgM+ memory B cells (106/L) -0.033 -0.123-0.056 -0.017 -0.115-0.080

IgG/IgA+ memory B cells (106/L) -0.068 -0.125- -0.012 -0.024 -0.082-0.033

T cells (106/L) -0.014 -0.036-0.008 -0.003 -0.026-0.019

T helper cells (CD4+) (106/L) -0.015 -0.040-0.010 -0.005 -0.030-0.020

CD38+CD4+ (106/L) 0.006 -0.049-0.060 0.001 -0.062-0.065

Naive CD4+ (106/L) -0.036 -0.094-0.023 -0.021 -0.087-0.044

Memory CD4+ (106/L) -0.017 -0.045-0.011 -0.010 -0.041-0.022

Cytotoxic T cells (CD8+) (106/L) -0.021 -0.055-0.013 -0.007 -0.046-0.031

CD38+CD8+ (106/L) 0.011 -0.050-0.072 0.006 -0.066-0.078

Naive CD8+ (106/L) -0.044 -0.100-0.011 -0.037 -0.099-0.025

Memory CD8+ (106/L) -0.017 -0.060-0.027 -0.010 -0.060-0.040

LGL cells (106/L)† 0.017 -0.035-0.069 0.029 -0.032-0.091

Natural killer cells (106/L) 0.021 -0.010-0.052 0.011 -0.025-0.046

CD4+/CD8+ ratio 0.021 -0.070-0.112 0.014 -0.092-0.120

The parameter estimate reflects a change per unit of TCDDmax exposure (parts per trillion) on the log scale; all analyses are based on log-transformed values of hematologic measures and TCDDmax: estimated maximum levels of 2,3,7,8-tetrachlorodibenzo-p-dioxin; IgM: immunoglobulin M; IgG: immunoglobulin G; IgA: immunoglobulin A; LGL: large granular lymphocytes; * Covariates included in the multivariate models were age, body mass index, alcohol intake, smoking, chronic disease, chronic inflammatory disease, and infectious disease within 4 weeks before blood sampling, and medication.

Chapter 5

80

The regression analyses with current TCDD yielded similar results;

significant inverse associations between TCDDcurrent and B cell and IgG/IgA+

memory B cell counts in univariate, and B cell counts in multivariate analyses

(data not shown).

The sensitivity analysis restricted to chronic disease-free subjects

(high-exposed, n=23; low-exposed, n=18) and among non-smoking subjects

(n= 65) showed similar trends although results became statistically non-

significant because of limited power.

Figure 1 Linear regression between TCDDmax levels and B and T cell subsets adjusted

for age, body mass index, alcohol intake, smoking, chronic disease, chronic

inflammatory disease, infectious disease within 4 weeks before blood sampling, and

medication; Gray indicates low- exposed and black, high-exposed individuals on the

rug plot; Black lines are B cells/ subtypes and the gray lines T cells/ subtypes.

-4 -2 0 2 4 6 8

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B cells

Naive B cells

IgM+ memory B cells

IgG/IgA+ memory B cells

T cells

T helper cells

CD38+CD4+

Naive CD4+

Memory CD4+

Cytotoxic T cells

CD38+CD8+

Naive CD8+

Memory CD8+


81

Figure 2 Correlation between B cell counts and TCDDmax levels among high- and low-exposed workers; both values are log-transformed; ß= Linear regression estimate and p-value of the slope estimate.

Discussion

In this study we explored the potential hematotoxic effects of exposure to

TCDD among workers occupationally exposed to TCDD approximately 35

years prior to blood analysis. Overall numbers of blood cell and lymphocyte

subsets were the same among high-exposed and low-exposed workers,

except for a significant difference in CD4+/CD8+ ratio between high- and low-

exposed workers. This difference did not seem to be dose-dependent. In

most lymphocyte subsets a dose-dependent decrease with TCDD exposure

was observed, although this was statistically significant only in B cells.

In our study, a higher CD4+/CD8+ ratio among high-exposed workers

compared to low-exposed workers was found. Oh et al. reported a non-

significant higher ratio of T helper cells (CD4+) to T cytotoxic cells (CD8+)

among South Korean waste incineration workers, exposed to, among other

compounds, dioxins (6). In contrast, Webb et al. reported a significant

increase in CD8+ percents and a significant decrease in CD4+/CD8+ ratio in

exposed subjects among residents of an environmentally contaminated site in

Missouri, including dioxin contamination as compared to non-exposed

subjects (3). Another study among industrial workers showed a significant

increase in CD8+ counts among TCDD exposed workers compared to the

control group of non-exposed workers although the CD4+/CD8+ ratio did not

Chapter 5

82

change significantly (10). However other studies have found no difference in

CD4+ and CD8+ counts and CD4+/CD8+ ratio among dioxin exposed subjects

as compared to non-exposed subjects (4, 5, 8, 27-34). As such, the evidence

for a possible association between TCDD and the CD4+/CD8+ ratio is highly

inconsistent.

Our results showed a non-significant inverse association between

TCDD exposure levels and most lymphocyte subsets, in particular the B cell

compartment. A reduction in lymphocyte counts among TCDD exposed

subjects as compared to non-exposed subjects has been shown in some

studies (4-6, 32). However, other studies have shown no changes (7, 9, 29,

35) or an increase in some lymphocyte subsets (3, 31) in TCDD exposed

subjects.

We found a significant reduction in B cell counts with increasing TCDD

levels. A study among Operation Ranch Hand veterans exposed to Agent

Orange, which was usually contaminated with TCDD, showed that B cell

counts significantly increased in the lowest exposed category of TCDD

exposure, but not in the higher exposed categories, compared to a non-

exposed control group (5, 36); other studies did not observe significant

changes in B cell counts (3, 4, 6, 9, 31, 32, 35). However some of these studies

suffer from certain inadequacies such as small numbers (3, 9, 31, 35), no

report of quantitative blood TCDD exposure level (31, 35), TCDD measured in

other tissues than blood (3) or estimated from work history/or environment

(4, 6, 32), and potential residual confounding (4, 32). Despite the inconsistent

findings in epidemiologic studies, there is a significant body of animal and in

vitro research demonstrating a direct effect of TCDD on B cell maturation,

differentiation, and Ig gene regulation (37).

Although linear regression analysis showed weak but statistically

significant declines in B cell counts, the results of t-tests between subjects

classified as high- and low-exposed based on job titles did not reveal a

difference. Analyses restricted to the high- and low-exposed workers

separately showed that log-transformed continuous TCDD levels significantly

correlated with B cell counts among both groups (Figure 2). Exposure levels

between high- and low-exposed workers largely overlap, and although

average levels of TCDD between high- and low-exposed workers are different,

the contrast was not great enough to provide a significant difference in TCDD

levels between the two groups. The fact that the association is similar among

the high- and low-exposed groups strengthens the observation that TCDD

plasma levels were related to a decrease in many lymphocyte subsets, in

particular B cells. Whether these effects are related to past or peak

exposures, or due to current or cumulative TCDD levels is difficult to

disentangle as in our study TCDDmax and TCDDcurrent are highly correlated

(Pearson correlation coefficient 0.97, p<0.01)). However, TCDDcurrent could be


83

more relevant for effects on current cell counts, whereas estimated

maximum TCDD could reflect long-term effects on the blood forming system.

There are some limitations to our study. Small sample size prohibited

more extensive sensitivity analyses. Moreover, analyses of a large number of

blood outcomes may produce statistically significant associations simply by

chance. Finally, workers with relatively high exposures may have died or been

unable to participate, which might have led to selective survival bias in our

results.

Although the study did not provide strong support for possible

hematotoxicity of TCDD, our finding of B cell declines may be noteworthy in

light of previous studies which have implicated B cell activation and the B-

and T cell interactions as relevant for the development of lymphomas (14,

38). The fact that we see a specific effect on B lymphocyte is interesting as

most lymphomas originate from B cells. In our previous study of the long-

term effects of TCDD on humoral immunity, lower complement component 4

(C4) levels were found with increasing TCDD exposure (22). Previous studies

showed that decreased C4 levels might have a role in the survival of auto-

reactive B cells (12). Given that altered immunity including

immunosuppression is an established risk factor for NHL (39), these results

support the biologic plausibility that TCDD could be involved in the

development of lymphomas and provide some support to the observed

suggestion of an increased risk of NHL in this cohort of workers exposed to

chlorophenoxy herbicides, chlorophenols and contaminants (40).

Conclusion

In conclusion, our study showed that plasma TCDD levels were associated

with a decrease in most lymphocyte subsets with the strongest effect for B

cells. The later finding may suggest that dioxin exposure can have an adverse

impact on the hematopoietic system and that B cell lymphoma induction by

dioxin is biologically plausible.

Acknowledgments

We would like to thank Wayman E Turner for blood plasma analyses of TCDD

(Division of Environmental Health Laboratory Sciences, Centers for Disease

Control and Prevention, Atlanta, Georgia, USA). Furthermore, we gratefully

acknowledge Jack Spithoven and Siegfried de Wind for their help in fieldwork

and lab-analyses (Institute for Risk Assessment Sciences (IRAS), Utrecht

University, the Netherlands) as well as Jeanette Kimmel, Ingrid Wiegers and

Patrick Aerts (Department of Medical Immunology, UMCU). We wish to thank

all workers for participation in the study. The first author also acknowledges

the Iranian Ministry of Health, Treatment and Medical Education and IRAS

Chapter 5

84

foundation for supporting a PhD program at Utrecht University in the

Netherlands.

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40. Boers D, Portengen L, Turner WE, Bueno-de-Mesquita HB, Heederik D &

Vermeulen R. (2011). Plasma dioxin levels and cause-specific mortality in an

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doi:10.1136/oem.2010.060426 [published online].

CHAPTER 6

Plasma cytokine concentrations in workers exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD)


Daisy Boers

Lützen Portengen


Dick Heederik

Roel Vermeulen

Submitted for publication

Chapter 6

88

Abstract

Objectives

Few epidemiological studies have studied the effect of 2,3,7,8-

tetrachlorodibenzo-p-dioxin (TCDD) on blood cytokine levels. In this study we

investigated changes in plasma levels of a large panel of cytokines,

chemokines and growth factors among workers from a Dutch historical

cohort occupationally exposed to chlorophenoxy herbicides and

contaminants including TCDD.

Methods

Eighty-five workers who had been exposed to either high (n=47) or low

(n=38) TCDD levels more than 30 years before serum collection were included

in the current investigation. Plasma level of 16 cytokines, 10 chemokines and

6 growth factors were measured. Current plasma levels of TCDD (TCDDCurrent)

were determined by high-resolution gas chromatography/isotope dilution

high resolution mass spectrometry. TCDD blood levels at the time of last

exposure (TCDDmax) were estimated using a one-compartment first order

kinetic model.

Results

Blood levels of most analytes had a negative association with current and

estimated past maximum TCDD levels. These decreases reached formal

statistical significance for fractalkine, transforming growth factor alpha (TGF-

α) and fibroblast growth factor 2 (FGF2) with increasing TCDD levels.

Conclusions

Our study showed a general reduction in most analyte levels with the

strongest effects for fractalkine, FGF2 and TGF-α. These findings suggest that

TCDD exposure could suppress the immune system and that chemokine and

growth factor-dependent cellular pathway changes by TCDD may play role in

TCDD toxicity and associated health effects.

Plasma cytokine concentrations in workers exposed to TCDD

89

Introduction

Immune suppression by 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) has been

established in animal and experimental studies (1, 2). These studies showed

that TCDD produces a wide range of toxic effects on all components of the

immune system including cytokines and chemokines and might cause

imbalance between T helper1 and T helper2 cytokines (3-6). However, few

epidemiological studies have evaluated TCDD effects on cytokine levels in

humans. These studies showed that TCDD exposure could reduce serum

Interleukin (IL)4 levels (7) and interferon gamma (INF-γ) (7, 8) and/or increase

IL4 and IL10 levels (8). Another study found no changes in measured cytokine

levels (9).

Exposure to chlorophenoxy herbicides or chlorophenols has been

shown to be associated with non-Hodgkin lymphoma (NHL), in particular with

TCDD exposure (10-14). As the immune system has a possible role in the

development of lymphomas in particular NHL (15-20), the possible link

between NHL and TCDD might be governed by TCDD-related perturbations

(i.e., suppression) of the immune system.

Cytokines play a pivotal role in immune function. Therefore, blood

levels of multiple cytokines collectively might appropriately reflect subtle

status of a deregulated immune response or a failure to modulate the

immune response due to TCDD exposure. Previous studies investigating the

effects of TCDD exposure on blood cytokine levels only studied a few selected

cytokines and results showed considerable discrepancies. Here, we set out to

investigate the association between exposure to TCDD and plasma

concentration of a large panel of cytokines, chemokines and growth factors in

subjects historically exposed to TCDD. Study subjects comprised a subset of a

retrospective cohort of Dutch workers, part of the IARC multinational study of

workers exposed to chlorophenoxy herbicides, chlorophenols and dioxins

during their working life (10, 21-24).


Study population

The cohort study design and exposure assessment have been previously

described in detail (21-23). Briefly, the cohort consists of workers from two

chlorophenoxy herbicide producing factories. Current analyses utilized a

subset of workers from one factory (labeled “A” in previous publications) who

were exposed to TCDD as a by-product of the production of 2,4,5-

trichlorophenoxyacetic acid and 2,4,5-trichlorophenol during 1953 to 1969,

and/or during an uncontrolled reaction in 1963. High-exposed workers were

selected for blood collection if (1) they were still alive at the end of follow-up

Chapter 6

90

(December 31st, 2006); (2) they had been exposed due to the industrial

accident (both factory workers and contract workers hired to clean-up after

the accident) (n = 29) or if (3) they worked in departments of main production

(n = 21). High-exposed workers were matched to 43 low-exposed workers

employed in non-production departments by factory, sex, age (within 5 years)

and current residence (at first two digits of postal code). All study subjects

were male. Written informed consent was obtained from each study subject

after the study was explained. Participants were asked to complete a self-

administered questionnaire, which included questions on occupational

history, personal medical history, medication used in the weeks before the

study, anthropometric characteristics, smoking status and alcohol intake.

Blood plasma samples were collected according to a standard protocol during

home visits between May 2007 and September 2008 (23, 24).

Cytokines measurements

Blood samples were generally collected in the afternoon, but occasionally

also early morning or in the evening. Samples were processed within 1 hour

of being collected and stored on ice at 0°C, and were transported to the lab

where they subsequently were stored at -80°C. Heparinized plasma samples

were analyzed for a large panel of cytokines, chemokines and growth factors

including interleukin (IL)1β, IL2, IL4, IL5, IL6, IL7, IL8, IL10, IL12, IL13,

interferon alpha (INF-α), interferon gamma (INF-γ), tumor necrosis factor

alpha (TNF-α), eotaxin, IL1 receptor antagonist (IL1ra), interferon-induced

protein 10 (IP10), granulocyte-macrophage colony stimulating factor

(GMCSF), epidermal growth factor (EGF), fibroblast growth factor 2 (FGF2),

fms-like tyrosine kinase receptor 3 (Flt3) ligand protein (Flt3ligand),

granulocyte colony-stimulating factor (GCSF), melanoma growth stimulatory

activity/growth-related oncogene (GRO), monocyte chemotactic protein 1

(MCP-1), monocyte chemotactic protein 3 (MCP-3), macrophage derived

chemokine (MDC), macrophage inflammatory protein 1 alpha (MIP-1α),

macrophage inflammatory protein 1 beta (MIP-1ß), soluble CD40 ligand

(sCD40L), soluble IL2 receptor alpha (sIL2Rα), transforming growth factor

alpha (TGF-α) and vascular endothelial growth factor (VEGF) using the

milliplex HCYTOMAG-60SK and HSCYTMAG-60SK kits, according to the

protocol described by the manufacturer. Blood samples of high- and low-

exposed workers were randomly analyzed on two plates. A subset of samples

(15 high-exposed and 14 low-exposed) were analyzed in duplicate. The

median coefficient of variation for all analytes measured in duplicate was

11.9% (0.69-79.8). Intra-class correlation coefficients (ICCs) varied from 0.31

to 0.98 with a median of 0.72. The lower limits of detection (LOD) were 0.04

pg/mL for IL5, 0.05 pg/mL for IL8, 0.06 pg/mL for IL1β, 0.07 pg/mL for TNF-α,

0.15 pg/mL for GMCSF, 0.18 pg/mL for IL13 and INF-γ, 0.20 pg/mL for IL6 and


91

IL7, 0.26 pg/mL for IL2, 0.34 pg/mL for IL12, 0.42 pg/mL for IL4, 0.48 pg/mL

for IL10, 0.8 pg/mL for TGF-α, 1.8 pg/mL for GCSF, 1.9 pg/mL for MCP-1, 2.8

pg/mL for EGF, 2.9 pg/mL for MIP-1α and INF-α, 3.0 pg/mL for MIP-1ß, 3.6

pg/mL for MDC, 3.8 pg/mL for MCP-3, 4.0 pg/mL for eotaxin, 5.1 pg/mL for

sCD40L, 5.4 pg/mL for Flt3ligand, 7.6 pg/mL for FGF2, 8.3 pg/mL for IL1ra, 8.6

pg/mL for IP10, 9.9 pg/mL for GRO, 11.2 pg/mL for sIL2Ra, 22.7 pg/mL for

fractalkine, 26.3 pg/mL for VEGF.


Heparin plasma samples of all subjects were analyzed for TCDD, at the

Centers for Diseases Control and Prevention (CDC; Atlanta, USA) using high-

resolution gas chromatography/isotope-dilution high resolution mass

spectrometry. Results were lipid adjusted and reported as parts per trillion

(ppt) (25).

Exposure metrics

TCDD is highly persistent with a long half-life in blood and human tissues. As

we measured current levels of TCDD (TCDDcurrent) approximately 35 years

since last occupational exposure, a one-compartment first order kinetic

model with a TCDD half-life (t1/2) of 7.1 years was used to estimate TCDD

blood levels at the time of last occupational exposure (TCDDmax) (24, 26):

TCDDmax = background + (measured TCDD – background) * exp (ln (2) * tag / t1/2)

Current TCDD levels and estimated maximum TCDD levels were subsequently

used to investigate exposure-response relations between TCDD levels and

analytes measured.


Individual TCDD levels and analyte concentrations which were below the LOD

were imputed via a maximum likelihood estimation method (27). This

procedure generates unbiased statistics when at least 50% of measurements

are above the limit of detection (27). Therefore, no summary statistics were

calculated for cytokines with more than 50% of measures below the LOD

(IL1ß, IL2, IL12, IL13, INF-γ, Flt3ligand, INF-α, IL1ra, MCP-3, sIL2Ra and VEGF).

TCDD levels and analytes concentrations were natural log (ln)-transformed as

measured levels appeared to follow a log-normal distribution. Differences in

continuous and categorical parameters between high-exposed and low-

exposed subjects were tested using a two sample t-test and chi-square test,

respectively.

We modeled exposure-response relations between log-transformed

analyte concentrations as the dependent variable and log-transformed

Chapter 6

92

TCDDcurrent or TCDDmax as the independent variable using linear regression

analyses. Models were adjusted for potential confounders including body

mass index (in kg/m²; continuous variable); alcohol intake (unit/week;

continuous variable), smoking (categorical variable; current, former, never),

medication (yes/no; immunosuppressant, antibiotics, anti-inflammatory

drugs) and chronic and acute medical conditions (present/absent) and

laboratory testing day/batch (day 1, 2). As more than 50% of high- and low-

exposed workers had a chronic disease, exposure-response relations were

also investigated in subjects free of chronic disease as a sensitivity analysis. A

second sensitivity analyses was performed among workers who did not use

any immunosuppressant drugs. Finally, we corrected the associations for

blood cell counts (major cell types including white blood cells, lymphocytes, B

cells and monocytes (28)) in order to remove the possibility that effects in

analyte concentrations were driven by a reduction in absolute cell counts.

Statistical analyses were performed using SAS (ver. 9.2; SAS Institute

Inc., Cary, NC, USA). All p-values were two-sided, with p<0.05 considered

statistically significant.

Results


Two subjects with missing TCDD results as well as 6 subjects with a previous

cancer (non-skin) diagnosis (2 high-exposed and 4 low-exposed workers)

were removed from the analyses. The latter exclusion was made to remove

the possibility that analytes levels may have been changed in subjects due to

malignant disease or treatment. Therefore, a total of 85 subjects (47 high-

exposed workers and 38 low-exposed workers) were available for analysis.

Plasma cytokines, chemokines and growth factors concentrations were

successfully measured in all workers.

Subject characteristics (n=85) are shown in Table 1. Low-exposed

workers had higher alcohol intake compared to high-exposed workers (p =

0.05). Smoking status among high- and low-exposed workers was similar.

Although the distribution of chronic diseases, chronic inflammatory diseases

and infection were similar among high- and low-exposed workers, more than

50% of high and low-exposed workers suffered from chronic diseases such as

diabetes, cardiovascular diseases and hypertension. Geometric mean (GM)

and geometric standard deviation (GSD) levels of TCDDcurrent and historical

maximum exposure (TCDDmax) were significantly higher in high-exposed

workers (TCDDcurrent: 3.25 ± 7.43 ppt; TCDDmax: 79.82 ± 33.28 ppt) compared

to low-exposed workers (TCDDcurrent: 1.07 ± 6.42 ppt; TCDDmax: 7.53 ± 32.14

ppt).


93

Table 1 General characteristics of high and low TCDD exposed workers

High-exposed (n=47)

Low-exposed (n=38 )

P value

£

Age (years) * 69.07 (7.45) 68.55 (7.93) 0.75

Body mass index (kg/m2) * 27.33 (3.10) 26.65 (3.04) 0.31

Alcohol intake (units/week) * 11.48 (13.56) 17.26 (13.16) 0.05

Smoking status, N (%) 0.84

Current smoker 12 (25.5%) 8 (21.1%)

Former smoker 28 (59.6) 23 (60.5%)

Never smoker 7 (14.9%) 7 (18.4%)

Skin cancer, N (%) 4 (8.5%) 3 (7.9%) 0.92

Infectious disease in the past

4 weeks, N (%) 4 (8.7%) 4 (10.5%) 0.78

Chronic disease, N (%) † 24 (51.1%) 20 (52.6%) 0.89

Chronic inflammatory disease, N (%) ‡ 13 (27.7%) 11 (28.9%) 0.90

Medication, N (%) 0.33

Immunosuppressant 5 (10.6%) 4 (10.5%)

NSAIDs 14 (29.8%) 6 (15.8%)

Antibiotics 0 1 (2.6%)

TCDDcurrent (ppt) ª 3.25 (7.43) 1.07 (6.42) 0.002

TCDDmax (ppt) b 79.82 (33.28) 7.53 (32.14) 0.001

* Mean (standard deviation); NSAIDs: non-steroidal anti-inflammatory drugs;

† Chronic diseases included: diabetes, coronary heart disease, and hypertension;

‡ Chronic inflammatory diseases: chronic obstructive pulmonary disease, psoriasis,

sarcoidosis, asthmatic bronchitis, rheumatoid arthritis, liver failure, Crohn’s disease,

fibromyalgia and allergy; ª Current levels of 2,3,7,8-tetrachlorodibenzo-p-dioxin

(TCDDcurrent) parts per trillion, geometric mean (geometric standard deviation); b

Estimated maximum levels of TCDD (TCDDmax) parts per trillion, geometric mean

(geometric standard deviation); £ P values from t-tests for continuous variables and Χ

2

tests for categorical variables.

Cytokine concentrations

Table 2 shows cytokine, chemokine and growth factor levels for high- and

low-exposed workers. Blood levels of most analytes were lower in high-

exposed workers as compared to low-exposed workers. This reduction varied

from 1% for MDC to 67% for GMCSF (mean=28.5%). A significant decrease in

IL5 and GMCSF levels was found among high- and low-exposed workers.

Chapter 6

94

Table 2 Plasma cytokines concentrations among high and low TCDD exposed

workers (n=85)

High-exposed

(n=47)

Low-exposed

(n=38)

Out of 114ª measurements,

n<LOD

GM GSD GM GSD P

value*

IL4 40 0.84 6.68 1.63 7.06 0.12

IL5 21 0.19 5.21 0.37 3.78 0.05

IL6 6 2.38 3.19 3.13 4.66 0.35

IL7 17 0.41 2.61 0.58 3.94 0.16

IL8 0 3.71 1.75 3.39 2.18 0.58

IL10 9 4.39 3.86 4.35 3.78 0.97

GMCSF 24 0.32 8.08 0.97 11.47 0.03

TNF-α 2 5.37 2.61 5.70 2.44 0.77

EGF 19 16.12 5.37 25.53 6.05 0.24

Eotaxin 1 340.36 2.53 395.44 2.16 0.41

FGF2 47 8.1 4.69 8.67 6.23 0.88

Fractalkine 56 14.01 4.10 19.89 3.29 0.23

GCSF 9 12.68 3.97 17.64 2.5 0.22

GRO 4 333.62 3.35 391.51 2.59 0.51

IP10 4 445.86 3.29 411.58 2.92 0.75

MCP-1 3 262.43 3.78 239.85 4.39 0.77

MDC 0 772.78 1.68 780.55 1.80 0.95

MIP-1α 43 2.72 4.39 0.38 4.95 0.91

MIP-1ß 19 14.73 4.53 20.91 4.44 0.28

sCD40L 2 854.06 3.82 1312.91 2.64 0.10

TGF-α 29 0.65 4.57 1.04 5.75 0.19

GM: geometric mean; GSD: geometric standard deviation; IL: Interleukin; GMCSF: Granulocyte-macrophage colony stimulating factor; TNF-α: Tumor necrosis factor alpha; EGF: Epidermal growth factor; FGF2: Fibroblast growth factor 2; GCSF: Granulocyte colony-stimulating factor; GRO: melanoma growth stimulatory activity/growth-related oncogene; IP10: Interferon-induced protein 10; MCP-1: Monocyte chemotactic protein-1; MDC: Macrophage derived chemokine; MIP-1α: Macrophage inflammatory protein 1 alpha; MIP-1ß: Macrophage inflammatory protein 1 beta; sCD40L: Soluble CD40 ligand; TGF-α: Transforming growth factor alpha; * P values are from t-tests of natural log-transformed values; ª all measurements including duplicates.

Dose-response analyses

Linear regression analysis showed that most of measured analytes had a

negative, although non-significant, association with TCDDcurrent levels (table 3).

We found a significant linear association for FGF2, fractalkine and TGF-α with


95

TCDDcurrent levels in both univariate and multivariate analyses. Sensitivity

analysis restricted to chronic disease-free subjects and to subjects using no

immunosuppressant drugs showed similar results except that TGF-α and FGF2

became non-significant because of limited power (data not shown). When we

corrected observed multivariate associations for major blood cell counts

(Saberi Hosnijeh et al. submitted, Chapter 5), fractalkine and TGF-α remained

significantly associated.

Table 3 Dose-effect relationships between log-transformed cytokines

measurements and log- transformed TCDDcurrent concentrations

Univariate, N=85 Multivariate*, N=85

Estimate 95% CI Estimate 95% CI

IL4 -0.150 -0.358-0.059 -0.222 -0.455-0.111

IL5 -0.044 -0.211-0.123 -0.061 -0.232-0.110

IL6 -0.094 -0.238-0.051 -0.148 -0.312-0.017

IL7 -0.022 -0.149-0.105 -0.046 -0.197-0.105

IL8 0.010 -0.062-0.082 -0.006 -0.088-0.076

IL10 -0.051 -0.196-0.093 -0.056 -0.218-0.107

GMCSF -0.102 -0.351-0.147 -0.258 -0.539-0.023

TNF-α 0.020 -0.080-0.121 0.030 -0.136-0.077

EGF 0.010 -0.179-0.200 0.044 -0.166-0.254

Eotaxin 0.002 -0.092-0.096 0.017 -0.090-0.124

FGF2 -0.184 -0.361- -0.008 -0.180 -0.364-0.003

Fractalkine -0.197 -0.333- -0.060 -0.216 -0.368- -0.064

GCSF 0.012 -0.122-0.146 -0.067 -0.199-0.066

GRO -0.011 -0.130-0.108 -0.0001 -0.120-0.120

IP10 0.091 -0.030-0.212 0.063 -0.067-0.193

MCP-1 0.107 -0.042-0.256 0.127 -0.037-0.290

MDC 0.004 -0.055-0.064 0.024 -0.044-0.093

MIP-1α -0.102 -0.266-0.062 -0.051 -0.216-0.115

MIP-1ß -0.154 -0.315-0.005 -0.146 -0.304-0.012

sCD40L -0.012 -0.142-0.119 -0.008 -0.144-0.129

TGF-α -0.222 -0.392- -0.051 -0.249 -0.431- -0.067

* Covariates included in the multivariate models were age, body mass index, alcohol intake, smoking, chronic disease, chronic inflammatory disease, infectious disease within 4 weeks before blood sampling, medication and test day/batch.

Chapter 6

96

Subsequently, regression analyses were done for high- and low-exposed

subjects separately. Although these analyses showed negative trends for

fractalkine, FGF2 and TGF-α with current TCDD levels among both high- and

low-exposed workers (Figure 1), the associations were statistically significant

only among high-exposed workers. Regression analyses with estimated

maximum TCDD levels at time of last exposure (TCDDmax) rendered essentially

similar results albeit associations were slightly weaker (data not shown).

Figure 1 Linear regression between log-transformed TCDD levels and log-transformed

analytes (i.e. FGF2, Fractalkine and TGF-α) adjusted for age, body mass index, alcohol

intake, smoking, chronic disease, chronic inflammatory disease, infectious disease

within 4 weeks before blood sampling, medication and test day/batch; Solid line

indicates high-exposed subjects and dashed line, low-exposed subjects on the rug

plot. A group mean centering parameterization (group’s mean is subtracted from the

corresponding individual values) for the models was used to have equivalent models

with identical deviance and residual errors; TCDD exposure levels between high- and

low-exposed workers largely overlap.

-4 -2 0 2 4

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

Log TCDD (ppt)

Log A

naly

tes (

mean c

ente

red)

FGF2

Fractalkine

TGF-a

Solid line: High-exposed

Dashed line: Low-exposed


97

Discussions

The present paper reports on associations between past occupational

exposure to high levels of TCDD and plasma levels of several cytokines,

chemokines and growth factors in a peripheral blood sample of workers

exposed to the high levels of TCDD approximately 35 years prior to blood

collection. We found a non-significant reduction in plasma levels of most

measured analytes with increasing levels of TCDD. However only fractalkine,

TGF-α, and FGF2 showed a statistically significant association with TCDD

levels. Our study is the first study to evaluate plasma levels of a large panel of

cytokines in relation to TCDD exposure in humans.

Our results showed an overall decrease in plasma levels of most

cytokines, chemokines and growth factors with increasing TCDD levels. There

is limited evidence in the literature on plasma levels of cytokines in relation

to TCDD exposure, probably because populations exposed exclusively or

primarily to TCDD are rare. Oh et al. measured serum IL4 and INF-γ among

waste incineration workers and showed a reduction in both cytokines in the

exposed workers (n=31) compared to the matched, non-exposed healthy

control group (n=84) which was statistically significant for IL4 (p=0.004)(7). In

this study the level of dioxin exposure was evaluated by measuring the aerial

dioxin concentrations in the working area of the company that was 100 times

higher than that of the control population. In a study among company

workers involved in decontamination work, Neubert et al. reported no

changes in plasma levels of IL1α, IL1ß, IL6 and TNF-α among TCDD exposed

subjects (n=12, TCDD level=23-140 ppt) compared to low-exposed (n=33,

TCDD level=1-3 ppt) and medium-exposed subjects (n=28, TCDD level=4-9

ppt) despite a general reduction in the median plasma levels of most

measured analytes among the high-exposed group (9). Similar to these

studies on blood cytokines we found a decrease in most analytes among

which IL4, IL6 and TNF-α, albeit non-significant. Immune suppression

characterized by suppressed humoral and cell-mediated immunity is a well-

known toxic endpoint of TCDD exposure in animal and experimental studies

(1). It is accepted that the toxic effects of TCDD generally are mediated

through the aryl hydrocarbon receptor (AhR) signalling pathway (1). However,

the mechanistic pathways are unclear in humans. In a study on the relation

between immune blood cells and TCDD levels among the same study

population as presented here (28), we found an overall reduction in most

blood cell counts in particular B lymphocyte cells. When we corrected the

observed associations of the present study for white blood cells, monocytes,

lymphocytes and B lymphocyte cell counts results did not change for

fractalkine, TGF-α and FGF2 and the other analytes with the exception of IL4,

IL6 and GMCSF for which the association with TCDD became borderline

Chapter 6

98

statistically significant. Therefore, it seems that the general suppression

observed for the measured analytes are independent of the observed

reduction in cell counts among exposed subjects and that TCDD exposure in

humans might therefore influence on both function and the number of

immune cells.

We found a significant negative association between plasma levels of

fractalkine and TCDD levels. Fractalkine is an atypical chemokine existing as

membrane-bound and in soluble form. The soluble form of fractalkine is

chemotactic to cytotoxic T cells and monocytes and natural killer cells (29,

30). It is likely that fractalkine might induce an anti-tumor immune response

by recruiting these cells into tumor tissues and stimulating their adhesion to

tumor cells. Moreover, the results of a study by Shiraishi et al. showed that

the fractalkine gene is a direct target of the tumor suppressor p53. They have

suggested that p53 may eliminate aberrant/damaged cells by the host

immune response system through transcriptional regulation of fractalkine

(31). However, to date, there is no published literature on fractalkine levels in

relation to TCDD exposure. Our finding for fractalkine is interesting as it has

been shown that a decrease in fractalkine receptor (CX3CR1) expression

might play a role in immune responses against adult T cell lymphoma (32).

Cell growth and differentiation are regulated by numerous growth

factors and cytokines. There is growing animal and in vitro evidence that

TCDD influences growth factor-dependent cellular pathways (33). It has been

shown that inhibited expression of some growth factors including EGF, TGF-α

and TGF-ß levels are main influencing factors during TCDD-induced

developmental effects in animals (34, 35). A recent animal study on prostatic

budding impairment after TCDD exposure suggested that TCDD likely acts

through downstream of FGFR2 and extracellular signal–regulated kinase (36).

In vitro human studies showed that TCDD exposure induced post-

transcriptional TGF-α expression in culture of human keratinocyte cell line

(37, 38). Although TGF-α is expressed by epithelial cells, keratinocytes, and

macrophages (39, 40), it is not clear whether other cell types can also

produce TGF-α in response to TCDD exposure. Although we observed a

statistical significant decrease in plasma levels of TGF-α and FGF2, the clinical

significance of the results is not clear.

There are some limitations to our study including limited sample size,

possible bias due to selective survival and multiple testing. Moreover,

residual confounding by smoking (no information on intensity and duration of

smoking) is unlikely as smoking in general did not change the results. We had

only a cross-sectional blood sampling that limited us in obtaining more insight

into acute vs. long-term changes in cytokines, chemokines and growth factors

levels. Finally, as in our study TCDDmax and TCDDcurrent are highly correlated

(Pearson correlation coefficient 0.97, p<0.01)), it was difficult to differentiate


99

between effects that might be related to high levels of exposure in the past

(TCDDmax) and effects related to current TCDD levels.

In conclusions, our results showed that plasma levels of TCDD were

associated with an overall decrease in blood levels of most cytokines,

chemokines and growth factors which were statistically significant for

fractalkine, FGF2 and TGF-α. These results provide evidence of immunological

effects of TCDD in humans and provide additional support for

lymphomagenesis of TCDD.

Acknowledgements

We would like to thank Wayman E Turner for blood plasma analyses of

PCDDs, PCDFs and PCBs (Division of Environmental Health Laboratory

Sciences, Centers for Disease Control and Prevention, Atlanta, Georgia, USA).

Furthermore, we gratefully acknowledge Nena Burger for cytokine analyses

(Institute for Risk Assessment Sciences, Utrecht University, the Netherlands).

We wish to thank all workers for participation in the study. The first author

also acknowledges the Iranian Ministry of Health, Treatment and Medical

Education and IRAS foundation for supporting a PhD program at Utrecht

University in the Netherlands.

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CHAPTER 7

Immunologic profile of excessive body weight

Mansour Taghavi Azar Sharabiani

Roel Vermeulen

Chiara Scoccianti


Liliana Minelli

Carlotta Sacredote

Domenico Palli

Vittorio Krogh

Rosario Tumino

Paolo Chiodini

Salvatore Panico

Paolo Vineis

Biomarkers (2011); 16(3): 243–251

Chapter 7

104

Abstract

The purpose of this paper is to identify immunologic hallmarks of excessive

bodyweight. The analysis is based on 176 adults (106 women, 70 men) who

participated in a nested case-control study in Italy. All participants were

healthy at the time of blood collection and aged between 36 and 75 years.

We employed multivariate analysis of variance and a non-parametric

Bayesian additive regression tree approach along with a receiver operating

characteristic (ROC) curve analysis to determine the immunologic signature of

excessive body weight (i.e., obesity and overweight). Interleukin 8 (IL8), IL10,

interferon gamma, and interferon-induced protein 10 were shown to be

predictive of excessive body weight with an area under the ROC curve of 71%

(p < 0.0002). We propose that by using this profile-based approach to define

immunologic signatures, it might be possible to identify unique immunologic

hallmarks of specific types of obesity.


105

Introduction

Obesity is a complex and incompletely understood disorder (1). According to

the World Health Organization (WHO), overweight and obesity are defined as

abnormal or excessive fat accumulation that may impair health, leading to

reduced life expectancy (1). Obesity and overweight are associated with

many health problems, including breathing difficulties during sleep and

osteoarthritis, and are considered major risk factors for a number of chronic

diseases, including diabetes, cardiovascular diseases and cancer. Obesity is a

leading preventable cause of death worldwide with increasing prevalence in

adults and children. Obesity is now considered one of the most serious public

health problems of the 21st century (2). Since 1980, obesity rates have been

rising with alarming trends in several parts of the world. It is estimated that

overweight and obesity are responsible for more than 1 million deaths and 12

million life-years of ill health every year in the WHO European Region (1, 3, 4).

Obesity and overweight are usually measured by anthropometry

indices. Body mass index (BMI), weight in kilograms divided by height squared

in meters, is commonly used to classify adults into underweight, overweight

and obese categories (5). Other measures are waist circumference (WC), hip

circumference (HC) and waist-to-hip ratio (WHR). The WHO defines a BMI less

than 18.5 kg/m2 as underweight, a BMI between 18.5 and 24.9 kg/m2 as

normal, a BMI 25.0–29.9 kg/m2 as overweight and a BMI more than 30 kg/m2

as obese, which in turn is divided into further classes of obesity (1, 6, 7). The

association between BMI and risk of death has often been described as J-

shaped or U-shaped (8-22). The literature suggests an increasing risk of

mortality and other adverse health effects with BMI ≥ 25 kg/m2 as well as

WHR ≥ 0.95 for men and WHR ≥ 0.80 for women (22, 23-37).

Over the past decade, we have been witnessing substantial progress

into the understanding of physiologic processes regulating the balance of

energy (38). A burgeoning of research on cytokines has been made possible

since the pure recombinant cytokines and molecular probes for their genes

became available (39). Also, it is not a long time ago that adipose tissue began

to be viewed as an active organ in hormonal regulation (40). Obesity is

associated with substantial macrophage infiltration into adipose tissue (41-

44). It seems that there is a considerable overlap between the biology of

adipocytes and of innate immune cells such as macrophages (45). A number

of molecules involved in glucose homeostasis, vascular biology, tumor

development, lipoprotein metabolism and inflammation that are derived

from adipose tissue have already been identified (46). This growing body of

information indicates a broad range of overlapping cell regulatory activities

both in vitro and in vivo and may require systems biology approaches (47) to

make better sense of the observations (39).

Chapter 7

106

Here we examine the association of plasma levels of 11 cytokines, 4

chemokines and 1 adhesion molecule with bodyweight indicators (i.e., BMI,

WHR) and we propose hallmarks of excessive body weight resulting from

perturbations in immunologic factors.

Materials and Methods

Study population

The study is based on 176 adults (106 women, 70 men) who participated in a

case-control study nested in the Italian European Prospective Investigation

into Cancer and Nutrition (EPIC) whose original aim was to explore the

association of plasma cytokine and chemokine levels with increased risk of

non-Hodgkin lymphomas (NHL)(48). All participants were healthy at the time

of blood collection and aged between 36 and 75 years. EPIC, the European

network of prospective cohorts, was designed to investigate the relationships

between diet, nutritional status, lifestyle and environmental factors and the

incidence of cancer and other chronic diseases (49). EPIC-Italy recruited

47,749 volunteers (15,171 men, 32,578 women, aged 35–65 years) in 1993–

1998 from five different administrative centers covered by cancer registries,

including Varese (12,083 volunteers) and Turin (10,604) in the Northern part

of the country; Florence (13,597) in Central Italy and Ragusa (6403) and

Naples (5062 women) in Southern Italy. The nested case-control study

included 88 cases (53 women, 35 men) diagnosed with NHL before the end of

2004 according to the ICD-O-3 classification of diseases. For each case, one

control subject was selected out of all cohort members on a random basis,

using the following criteria: alive and free of cancer at the time of diagnosis of

the index case, matched by center, gender, date of recruitment, age at

diagnosis and age at recruitment (±3 years). Here we present results for cases

and controls (n = 176) together and also separately for the controls (n = 88).

Measures of anthropometry, physical activity and smoking

Weight, height, WC and HC were measured by trained personnel at the time

of recruitment. WC was measured at the torso circumference (at the point

where the front profile was narrowest) and HC was measured at the widest

circumference (below the iliac crest and above the great trochanter where

the front profile is wider). BMI was calculated as a person’s body weight (in

kilograms) divided by squared height (in meters). WHR is the ratio of WC to

HC. Information related to physical activity was collected (the type of physical

activity at work, physical exercise to keep fit and vigorous physical activity,

time spent on specific activities including walking, cycling, gardening,

housework and number of stairs climbed per day) (50). Energy expenditure

values were assigned using a standardized coding system developed by the


107

Compendium of physical activities (51). Depending on the duration and the

type of recreational and household activity reported on the baseline

questionnaire, the average of metabolic equivalent-hours (MET-hr) was

assigned separately in winter and summer.

Occupational activity has been coded as sedentary occupation,

standing occupation, manual work, heavy manual work, unemployed or

missing, as reported in the questionnaire. Subjects were cross-classified

based on sex-specific quartiles of recreational and household activity and on

categories of occupational work to generate a total physical activity variable

coded as inactive, moderately inactive, moderately active and active (52).

Smoking status was coded as never smoker, former smoker and current

smoker.

Laboratory assay

Citrate plasma samples (50 μl) were used to measure eleven cytokines, that

is, interleukin (IL) 1α, IL1β, IL2, IL4, IL5, IL6, IL10, IL12, IL13, interferon gamma

(IFN-γ) and tumor necrosis factor alpha (TNF-α); four chemokines, that is, IL8,

RANTES (regulated upon activation, normal T cell expressed, and secreted),

eotaxin and interferon-induced protein 10 (IP10), and one adhesion molecule

(inter-cellular adhesion molecule (ICAM)). We used the Luminex multianalyte

profiling technology (Lab-MAP™) according to the protocol described by de

Jager et al. (53), except that, instead of 1-hour incubation, an overnight

incubation at 4°C was used (54). Median time interval between sample

collection and freezing was 4 hr.

Due to the case-control study design, all samples were run in

duplicate with matched case-control sets assayed in the same batch. Quality

control sets (low- and high-concentration cytokines quality control samples)

were run in duplicate with the case-control sets in each batch. The median

intra-batch coefficient of variation for all cytokines based on these quality

control duplicate sets was 6.7% (4.3–30) and the median inter-batch

coefficient of variation was 30.7% (9.6–110). The lower limits of detection

were.24 (pg/ml) for IL4;.61(pg/ml) for IL12; 1.22 (pg/ml) for IL1β, IL2, IL5, IL6,

IL8, IL10, IL13, IFN-γ and TNF-α; 2.44 (pg/ml) for IL1α, RANTES and eotaxin;

4.88 (pg/ml) for IP10; and 73.24 (pg/ml) for ICAM (48).


Outliers were removed (using Box-plot) before further statistical analysis.

Numbers of missing values, including deleted outliers, varied from 19 to 26

depending on the model and the type of cytokines included in the model.

Chapter 7

108

Multivariate analysis of variance

We classified individuals into two groups of normal (optimal) weight and

excessive weight using the anthropometry indices. Cut-off point for BMI was

set at 25 kg/m2, for WC at 94 cm in men and 80 cm in women, and for WHR

at 0.95 in men and 0.79 in women according to guidelines and the literature

(7, 27). For HC, the cut-off point was set at 98.4 in men and 98.8 in women

based on the median of the HC in each gender. The grouping of individuals by

bodyweight indices was used to calculate the (adjusted) odds ratios (logistic

regression) for cytokines, smoking status, physical activity and case/control

status in relation to bodyweight.

The grouping was used to carry out multivariate analysis of variance

(MANOVA) with one dichotomous independent variable (e.g., BMI) and

multiple dependent variables. The statistics that are normally used for

MANOVA, that is, Wilks’ lambda, Lawley–Hotelling trace, Pillai’s trace, Roy’s

largest root, yielded similar results. The final selection of variables in the

model was based on the impact of each variable on the separation of classes

(loadings) and the overall statistical significance of the model. To perform

MANOVA, we normalized the data to address the differences in variability in

each marker, by dividing each variable by its standard deviation (univariate

scaling).

BART, logistic regression, and 10-fold cross validation

We employed a non-parametric Bayesian additive regression tree (BART)

analyses (55) that uses dimensionally adaptive random basis elements and

enables full posterior inference including point and interval estimates of the

unknown regression function as well as the marginal effects of potential

predictors. We used BART to predict two classes of BMI (i.e., BMI ≥ 25 kg/m2

and BMI < 25 kg/m2), WC, WHR and HC (the same cut-off points as MANOVA)

based on different sets of predictors. We used 10-fold cross validation (in

each iteration, 90% of the dataset was used to build the BART model and 10%

was used to predict the classes). Subsequently, a receiver operating

characteristic curve (ROC)(56, 57) was used to measure the Area-under-ROC

curve (AUC). Initially, we included all the variables such as age, physical

activity and smoking status. However, these variables were removed after

showing no added prognostic value. BART takes into account non-linear

associations as well as all potential interactions. We employed BART based on

the assumption that immunologic complex systems consist of networks of

interconnected and interactive elements with linear and non-linear

associations. We also employed logistic regression with 10-fold cross

validation.


109

MANOVA analyses were carried out using Stata (SE 10.1 for

Windows). BART and ROC analyses were based on freely available R

packages: BART (55, 58, 59).

Results

Table 1 shows the characteristics of the study population stratified by cases

and controls. Only immunologic elements with a statistically significant effect

in our analysis are shown. There were altogether 70 men and 106 women in

our study. IL8, IL10 and IP10 together with gender, included in a MANOVA

model (p < 0.0003), best separated individuals with BMI above and below 25

kg/m2. Among the predictors, IP10 (p < 0.001), IL8 (p < 0.017) and IL10 (p <

0.026) had significant impact on separating the classes, with IP10 having the

most significant impact. Table 2 shows the results of MANOVA analysis for

two classes of normal versus excessive weight (i.e., BMI > 25 kg/m2)

individuals. BMI is a crude measure of fatness in both genders, while WHR is

more representative of the common type of obesity, central adiposity. A

MANOVA model (p = 0.0003) including IL4, IL8, IL12, IL13, IP10 and gender

best separated two classes of WHR groups (i.e., above and below 0.95 in men

and 0.79 in women), in which IL13 (p = 0.003), IL8 (p = 0.014), IL12 (p = 0.018),

IP10 (0.038) and IL4 (p = 0.043) had significant impact.

An ROC curve based on 10-fold cross validated BART analyses

provided prognostic values for the candidate cytokines, that is, IL8, IL10, IFN-γ

and IP10 for BMI, as shown in Figure 1, with AUC of 0.69 (95% CI: 0.67, 0.71),

and p < 0.00004. When the same analysis was repeated by limiting the data

to BMI ≥ 23.5, the prediction improved marginally adding 2% to AUC. Similar

to MANOVA model, the set of IL4, IL8, IL12, IL13, IP10 significantly (p = 0.005)

separated two classes of WHR groups using the same cut-off points (Figure 2).

The 10-fold cross validated logistic regression analysis indicated an AUC of

0.65 (0.63, 0.67), p = 0.0006. A higher AUC derived from the BART model is

consistent with our assumption about the complexity and non-linearity of the

immunologic network, particularly with regard to obesity. The ROC curves

derived after a BART model based on either control subjects (shown in Figure

2) or based on cases only (not shown) are comparable with each other and

with the model including all subjects. In addition, examining the binary

outcome of case/control status for NHL by using age-adjusted logistic

regression, we found that there was no statistically significant difference

between NHL cases and controls with regard to BMI and other

anthropometric measures as well as the set of the predictors (IL8, IL10, IFN-γ,

IP10), nor was the logistic model itself statistically significant (p = 0.1997).

Table 1 Characteristics of the study population stratified by cases and controls

Controls Cases

Men Women Both Men Women Both

Age 53.6 (SD: 7.4) 54.0 (SD: 8.3) 53.8 (SD: 7.9) 53.4 (SD: 7.7) 54.5 (SD: 8.6) 54.1 (SD: 8.2)

Physical activity 2.6 (SD: 1.0) 2.6 (SD: 0.9) 2.6 (SD: 0.9) 2.4 (SD: 0.9) 2.7 (SD: 0.9) 2.6 (SD: 1.0)

BMI 26.2 (SD: 3.0) 25.5 (SD: 3.6) 25.7 (SD: 3.4) 25.7 (SD: 3.4) 25.8 (SD: 4.3) 25.8 (SD: 3.9)

WHR 0.9 (SD: 0.1) 0.8 (SD: 0.1) 0.8 (SD: 0.1) 0.9 (SD: 0.1) 0.8 (SD: 0.1) 0.8 (SD: 0.1)

IL4 -0.7 (SD: 1.1) -0.7 (SD: 1.3) -0.7 (SD: 1.2) -0.8 (SD: 1.1) -1.1 (SD: 1.2) -1.0 (SD: 1.2)

IL6 0.4 (SD: 1.8) 0.4 (SD: 2.1) 0.4 (SD: 2.0) 0.8 (SD: 1.8) 0.7 (SD: 2.0) 0.7 (SD: 1.9)

IL8 4.5 (SD: 1.9) 3.7 (SD: 1.9) 4.0 (SD: 2.0) 3.8 (SD: 2.3) 3.1 (SD: 2.4) 3.4 (SD: 2.4)

IL10 2.5 (SD: 1.7) 3.5 (SD: 2.1) 3.1 (SD: 2.0) 2.8 (SD: 2.2) 3.0 (SD: 2.4) 2.9 (SD: 2.3)

IL12 5.6 (SD: 1.9) 6.6 (SD: 2.6) 6.2 (SD: 2.4) 5.2 (SD: 2.7) 6.1 (SD: 2.9) 5.7 (SD: 2.8)

IL13 1.8 (SD: 1.3) 1.7 (SD: 1.3) 1.8 (SD: 1.3) 1.8 (SD: 1.2) 1.6 (SD: 1.4) 1.7 (SD: 1.3) IP10 3.6 (SD: 0.6) 3.9 (SD: 0.7) 3.8 (SD: 0.7) 3.7 (SD: 0.7) 4.0 (SD: 0.9) 3.9 (SD: 0.9)

BMI, body mass index; SD, standard deviation; WHR, waist-to-hip ratio


111

Table 2 MANOVA for two classes of normal versus excessive weight (i.e. BMI

> 25 kg/m2) individuals

Statistic P Value

MANOVA W 0.851 0.0003 P 0.149 0.0003 L 0.175 0.0003 R 0.175 0.0003 Ln-IL8 W 0.9373 0.0165 P 0.0627 0.0165 L 0.0669 0.0165 R 0.0669 0.0165 Ln-IL12 W 0.9532 0.0255 P 0.0468 0.0255 L 0.0491 0.0255 R 0.0491 0.0255 Ln-IP10 W 0.9326 0.0008 P 0.0674 0.0008 L 0.0723 0.0008 R 0.0723 0.0008 Sex W 0.9674 0.0296 P 0.0326 0.0296 L 0.0337 0.0296 R 0.0337 0.0296

BMI, body mass index; Ln, log-transformed; IL, Interleukin; IP10, interferon-induced

protein 10; L, Lawley–Hotelling trace; MANOVA, multivariate analysis of variance; R,

Roy’s largest root; P, Pillai’s trace; W, Wilks’ lambda.

Chapter 7

112

Figure 1 Receiver operating characteristic (ROC) curve of 10-times cross validated

Bayesian additive regression tree prediction of body mass index classes (i.e., above or

below 25 kg/m2); AUC > 69%, and p value < 0.00004; total observations: 154; events:

79; non-events: 75; predictors: IL8, IL10, IP10 and IFN-γ.

Figure 2 As in Figure 1, but controls only, AUC curve 63%, p = 0.019.


113

Discussion

Ideally, during the analysis of the associations between a complex condition

such as obesity and a set of variables such as immunological factors, which

constitute a complex network of interconnected elements, we would like to

infer a full model of all possible immunological variables and all possible

interactions between them. In practice, this is computationally impossible,

and conceptually it would be difficult to interpret such a model. Here we

suggest that it may be useful to look at the combination of immunologic

changes that take place in obesity, and identify a set of predictor variables

covering a network of linear and/or non-linear associations. Within this

context, we propose the concept of “immunological signature” of a

pathological condition such as obesity. To our knowledge, this approach has

not been applied in this field yet.

The MANOVA analyses indicated that IP10, IL8 and IL10 have a

significant impact on separation of the two classes of bodyweight based on

BMI. IL13, IL8, IL12, IP10 and IL4 had the maximum impact on the separation

of WHR-based classes of bodyweight.

The BART enabled us to cross validate the prediction and measure

the AUC. Tenfold cross validation of the BART model using various

combinations of the predictors (cytokines) led to a highly significant

prediction (AUC > 0.69%, p < 0.00004) using a set of IL8, IL10, IP10 and IFN-γ,

as shown in Figure 1. This is the same set of predictors (except IFN-γ in

MANOVA) that we observed through MANOVA. This level of consistency

reassures about the solidity of our findings. Among the predictors, IP10 had

the maximum prognostic value (IP10 nearly 62%, IL8 nearly 2%, IL10 nearly

3% and IFN-γ nearly 1%).

We repeated all calculations in controls and cases separately and

found similar predictive results (although with a slightly lower statistical

power).

Concepts such as the “immunological signature” and “metabonomic

fingerprint” have already been developed in genomic and metabonomic

investigations (60) of various diseases and there are similar examples in

proteomics and lipidomics (61-64). Here we have taken initial steps in a

similar direction by introducing an immunologic profile or immunologic

signature of excessive bodyweight based on our data.

Overall, our proposed approach, including MANOVA and BART

analyses, showed significant impacts of IL10, IP10 and IL8 and IL4, IL8, IL12,

IL13 and IP10 in separating BMI and WHR classes, respectively. Both

predictive models of, i.e., BMI and WHR classes share IL8 and IP10 between

the set of their predictors.

Chapter 7

114

IP10 belongs to the CXC superfamily (65). Monocytes, endothelial

cells and fibroblasts express IP10 (CXCL10)(65, 66). The expression and

secretion of IP10 by human monocytic cells are selectively increased by

leptin. IP10 levels are positively associated with leptin levels (67). Therefore,

the observed predictive role of IP10 in our study might indicate increased

levels of plasma leptin. In this regard, the observed association between

female gender and higher levels of IP10 is consistent with the fact that in

general women have higher levels of leptin (68-70). An enhanced expression

of IP10 has been reported among coronary heart disease (CHD) patients (71,

72), which is consistent with the well-established association of CHD and

excessive body (22, 73-98).

While obesity is associated with increased risk of cancer, IP10 inhibits

bone marrow colony formation and has shown to have anti-tumor activity in

vivo

(99) and may participate in the regulation of angiogenesis during

inflammation and tumorigenesis (99). IP10 is chemoattractant for human

monocytes, activates T cells and a number of other cells. Moreover, it

promotes T cell adhesion to endothelial cells (99, 100). Thus, despite adverse

effects of IP10 on coronary events, its rising levels might hypothetically be

beneficial in the cancer risk of obese people and could be viewed as a

compensatory defense mechanism (99).

The association of IL8 with BMI (101) WHR (101) and WC (102) has

been indicated in the literature. Scientific evidence based on in vitro

experiments has shown that adipocytes produce IL8 in human (103, 104). IL8

has already been introduced as a potential candidate linking obesity with

obesity-related metabolic complications (102). Moreover, circulating levels of

IL8 were associated with BMI and WHR (102).

IL10 is an anti-inflammatory cytokine that can inhibit production of

pro-inflammatory cytokines like IFN-γ, IL2, IL3 and TNF-α by macrophages and

the type 1 T helper cells. There is evidence that leptin may promote an

optimal pro-inflammatory response because, on one hand, it plays a pivotal

role in the systemic inflammatory response and, on the other hand, restrains

the inflammatory response via IL10 production (105). Therefore, we

hypothesize the key role of leptin in pathogenesis of obesity, and its

association with various cytokines indirectly has been reflected in the results

of our analyses. Also the role of adiponectin and its association with cytokines

need to be considered. Adiponectin is an adipocyte-derived protein and its

levels are inversely related to the degree of adiposity (106). Decreased

plasma adiponectin levels were reported in insulin-resistant states such as

obesity and type 2 diabetes and in patients with coronary artery diseases

(107, 108). Adiponectin and leptin perform complementary actions and can

have additive effects (107). Scientific evidence suggests a critical relevance of

adiponectin for regulation of cytokine in obesity. Adiponectin has been


115

shown to induce the production of IL10 in primary human monocytes,

monocyte-derived macrophages and dendritic cells and to significantly impair

the production of IFN-γ in human macrophages (109). Thus, the significant

effect of adiponectin in changing the production of IL10 and IFN-γ in human

and its critical role in energy balance might be a potential explanation of the

predictive value of IL10 and IFN-γ in our analysis.

In summary, our analyses suggest that IL8, IL10, IFN-γ and IP10

together are an overall predictor (immunologic profile) of obesity (BMI) with

up to 71% area-under-ROC curve. Changes of these immunological factors are

likely to be mediated by leptin and adiponectin. We also propose that further

studies are needed to determine the immunologic profiles of specific

subgroups of overweight/obese individuals with regard to their underlying

causes [e.g., those with known polymorphisms that are associated with both

obesity and cytokine production (110-113), those on high fat diet, those on

diets used for weight loss, those who recently gained/lost weight]. Most

likely, these specific sub-groups of obese/overweight individuals will have

their unique pattern of immunologic profiles with the same applications as

lipidomics, genomics and metabonomics. Ultimately, this approach (based on

human and/or animal research) could pave the way for integrating

immunologic research into wider systems biology investigations (47).

Acknowledgements

The study was funded by the “Determinants of obesity and its prevention”

project, Regione Umbria, Direzione Regionale Sanita e Servizi Sociali, and by

ECNIS.

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314:84–89.

CHAPTER 8

General discussion

Chapter 8

124

Lymphomagenesis; the role of the immune system

Non-Hodgkin lymphomas (NHL), solid tumors of lymphocyte origin, are the

most common hematopoietic cancers in both men and women in the

developed world (1, 2). The etiology of most NHL cases remains unclear, but

the strongest and most consistent risk factors are related to altered immunity

conditions. Severe immunodeficiency, including both hereditary

immunodeficiency disorders and acquired conditions such as those observed

in patients infected with the human immunodeficiency virus (HIV) and in

transplant patients receiving immunosuppressive drugs, is a well-described

and strong risk factor for NHL. Moreover, individuals with auto-immune

conditions such as rheumatoid arthritis, systemic lupus erythematosis,

Sjogren’s syndrome and psoriasis are at a higher risk of developing NHL (3-6).

Additional evidence of the integral role of the immune system in

lymphomagenesis can be seen from epidemiologic studies which examined

polymorphisms in genes coding for cytokines that modulate the inflammatory

process or are linked to B cell activation (7-9). In addition, recent genome-

wide association studies (GWAS) of the follicular subtype of NHL identified

associations with two variants within the human leukocyte antigen (HLA)

region (10, 11).

Given the central role of the immune system in lymphomagenesis, it

is hypothesized that moderate perturbations of the immune system may be a

risk factor as well (3). The main objective of this thesis is to study the possible

perturbations of the immune system by occupational and environmental risk

factors of NHL and to study these changes in relation to NHL risk in

prospective cohorts.

Methodological issues

Complementary Study Designs: The ‘‘Meet in the Middle’’ Concept

The U.S. National Institute for Environmental Health Sciences (NIEHS) has

suggested to integrate environmental exposures within the study of the

natural history of disease. Indeed the study of how an environmental agent

affects molecular targets, cellular function, tissue function, and survival

ultimately informs us about the etiology, pathogenesis, and distribution of

the disease (12). The recent introduced ‘‘meet in the middle approach’’ has

the potential to open new avenues for prevention by identifying the specific

environmental factors involved in the disease process (12). In this approach,

the relationship between putative intermediate markers and disease

outcome is explored. In parallel, associations between exposure estimates

and intermediate markers are assessed as well. Subsequently, the overlap

between markers of exposure and predictive markers of disease outcome

General discussion

125

would classify relevant intermediate markers of environmental driven

diseases (Figure 1). An example of this approach was recently published by

Chadeau and co-workers who identified a dietary colon cancer biomarker (a

derivative of benzoic acid produced by fiber-digesting gut bacteria) by

correlating a prospectively measured metabolic profile with both dietary fiber

intake and reduced colon cancer risk (13).

Figure 1 The ‘‘meet in the middle approach’’ concept

Prospective studies which study the disease process from exposure to

preclinical response and subsequently to a clinically diagnosed disease are

conceptually suitable for the identification of new biomarkers of both

exposure and early biological effect, since they are based on pre-clinical

biological samples that are not influenced, if the disease occurs years later, by

the inherent metabolic changes due to the disease itself. However, one

prospective sample might not reflect both the exposure and outcome of

interest in an optimal way. Moreover, exposure measurements cannot be

optimized in prospective studies due to some inherent limitations including 1)

if the exposure does not occur in isolation from other hazardous exposures; 2)

if a sufficient induction period since exposure has not yet passed; 3) or if the

observed biomarker does not reflect the exposure of interest due to the short

half-life of the marker. Therefore, use of other study designs with optimally

designed exposure measurements (i.e. cross-sectional or semi-longitudinal

molecular epidemiological studies) combined with prospective studies has

been recommended (14).

Use of biomarkers in cross-sectional molecular epidemiological studies

Cross-sectional studies can be used to evaluate intermediate biologic effects

from a wide range of exposures in the diet, environment and lifestyle factors.

Results of these studies are interpreted based on the assumption that the

intermediate endpoints reflect biologic changes considered relevant to a

particular disease development (15). However, these studies are not capable

of directly establishing or refuting a causal relation between an exposure or a

Exposure Biomarkers of

exposure

Intermediate –omics biomarkers of early effects

Disease

Chapter 8

126

level of exposure and risk for developing a disease. Cross-sectional studies

could however provide mechanistic insight into well established exposure–

disease relations and supplement suggestive but inconclusive evidence of the

carcinogenicity of an exposure. For intermediate endpoints with etiologic

fractions (Proportion of cases that the marker or the pathway that is reflected

by the marker had played a causal role in its development) that are close to

1.0, either positive or negative results are particularly informative, while for

intermediate endpoints linked to the risk of developing cancer but with a

substantially lower etiologic fraction, the interpretation is more circumspect.

Specifically, a positive association between an exposure and an intermediate

biomarker is informative, but a null association does not rule out that the

exposure is carcinogenic as the exposure may act through a mechanism not

reflected by the particular endpoint under study (16).

Given the limitations in both the cross-sectional and prospective

“meet in the middle” study designs, it seems that more than one study design

is warranted for biomarker discovery in environmentally driven diseases. In

this thesis, we implemented two approaches to study the possible

perturbations of the immune system by occupational and environmental risk

factors of NHL. First we used a prospective study to investigate pre-diagnostic

immune markers of NHL. Subsequently, we studied the effects of obesity on

these validated immune markers in the same prospective study (“meet in the

middle concept”). Secondly, using the cross-sectional study design, we

investigated changes in markers of the immune system due to exposure to

another NHL risk factor (i.e. Dioxin).

Predictive blood markers of lymphomas Current advances in the understanding of the pathogenesis of hematopoietic

malignancies and the introduction of high-throughput technologies facilitate

translational research toward the discovery, development, and clinical

validation of novel biomarkers for the early detection as well as for disease

progression and recurrence (17). As early detection of hematopoietic

malignancies has been shown to improve the survival rates, there is an

obvious benefit from early identification of at-risk individuals for disease

development (18). Early studies that examined pre-diagnosis materials for

lymphoma biomarkers are those among HIV patients that due to severe

immunosuppression were found at high risk of lymphoma development.

Lymphoma blood biomarkers among HIV patients

The CD4+ lymphocyte count was among the first biomarkers that have been

studied in relation to AIDS-NHL. Early studies suggested that nadir (lowest

ever) CD4+ lymphocyte count was an important predictor of acquired

General discussion

127

immunodeficiency syndrome-related to non-Hodgkin lymphoma (AIDS-

NHL)(8). Two recent studies reported that the most recent CD4+ lymphocyte

count rather than the nadir lymphocyte count is predictive of AIDS-NHL (19-

20). Interestingly, it has been shown that HIV viremia was related to NHL risk

independently of CD4+ lymphocyte count (20, 21). HIV virions that carry the

CD40 ligand, an activator of induced cytidine deaminase (AID), might

stimulate polyclonal B cell activation and generate the molecular lesions that

contribute to the development of NHL (8).

Early studies among HIV patients have shown that serum levels of

some cytokines and chemokines including interleukin (IL) 6 (22, 23), IL10 (24),

C-X-C motif chemokine 13 (CXCL13)(25) are related to the occurrence of

lymphoma among HIV patients. Recent studies did not only confirm these

previous findings of IL6 (26-28), IL10 (27) and CXCL13 (29), but also reported

associations for other cytokines. Rabkin and co-workers reported significant

associations between elevated levels of several cytokines including IL1α, IL4,

IL5, IL6, IL8, IL12p70, IL13, granulocyte-macrophage colony-stimulating factor

(GMCSF), Transforming growth factor alpha (TGF-α), vascular endothelial

growth factor (VEGF) and Interferon-induced protein 10 (IP10) measured in

pre-diagnostic blood samples of HIV patients and the risk of developing NHL

(28). They concluded that cytokine-mediated hyper-stimulation of B cell

proliferation (T helper 2 cytokines) may play a role in AIDS-NHL.

Moreover, in several studies elevated levels of soluble CD23 (a B cell

activation molecule) (26, 27, 30-32), sCD27 (26, 27, 33), sCD30 (26, 27, 34)

and sCD44 (26, 35) were reported in prospective blood samples of AIDS-NHL

patients as compared with HIV+ control and/or with AIDS control patients.

Soluble CD30 level is indicative of a type 2 T cell and/or cytokine response,

which would be expected to contribute to chronic B cell stimulation. Both

sCD27 and sCD30, as a biomarker of immune activation, are members of the

TNF receptor family, while sCD44 is a cell-surface adhesion molecule involved

in inflammatory cell function as well as tumor cell growth and metastasis (35).

Abnormal blood levels of some immunoglobulins (Ig) due to B cell

dysfunction also have been reported in some studies. These studies showed

an association with either high levels of serum globulin (36), IgE (30) and free

light chains of kappa (κ) and lambda (λ) or a decreased level of IgG (34) with

increased risk of AIDS-NHL, while other studies did not show changes in IgE

(27), IgG, IgM and IgA levels prior to the development of AIDS-NHL (37).

Overall, the findings of studies on HIV patients support that chronic B

cell activation is preceding the AIDS-NHL occurrence. It has been shown that

B cell activation is associated with DNA-modifying processes including Ig class

switch recombination (CSR) in Ig heavy chain genes (IgH) and somatic

hypermutation (SHM) (38). Moreover, higher levels of cytidine deaminase, a

DNA-mutating enzyme that plays a role in both IgH CSR and SHM, have been

Chapter 8

128

found to be related to AIDS-NHL development (26). Therefore, it can be

concluded that chronic sustained B cell stimulation leads to the accumulation

of genetic changes implicated in B cell lymphomagenesis (34).

Lymphoma blood biomarkers among the general population

Recently, the observations on the importance of chronic sustained B cell

activation in AIDS-NHL led to subsequent studies to investigate the possible

role of B cell activation pathways in lymphoma risk in the general population.

In our study within the Italian European Prospective Investigation into

Cancer and Nutrition cohort (EPIC-Italy) (Chapter 3) we observed a significant

association between NHL risk and lower plasma levels (collected on average

4.5 years prior to NHL diagnosis) of IL2 and tumor necrosis factor alpha (TNF-

α) and higher levels of inter-cellular adhesion molecule (ICAM) which

remained significant after excluding cases diagnosed within 2 years of follow-

up. In a nested case-control study within the New York University–Women’s

Health Study cohort, fifteen cytokines measured in blood samples collected a

median 8.2 years prior to the NHL diagnosis were studied. The results showed

that increased serum levels of IL13 were associated with decreased NHL risk

while elevated levels of soluble IL2 receptor (sIL2R) were associated with

increased risk of NHL (39). They also reported a marginally positive

association between TNF-α and sTNF-R2 and B-NHL as well as a negative

association for IL5.

A recent case-control study, nested within the Prostate, Lung,

Colorectal, and Ovarian (PLCO) Cancer Screening Trial with a median length of

5 years follow-up from blood collection to case diagnosis, conducted by

Purdue and colleagues (40) showed that elevated sTNF-R1 and sCD27 levels

were associated with higher risk of developing NHL. These associations

remained significant in analyses of cases diagnosed longer than 6 years

following blood collection. They also reported that elevated levels of IL10,

TNF-α and sTNF-R2 were significantly associated with increased risk of NHL

overall, but these associations weakened with increasing time from blood

collection to case diagnosis and were null for cases diagnosed longer than 6

years post collection. Both soluble TNF-R1 and sTNF-R2 bind with TNF-α and

may play a regulatory role by limiting its circulating levels in the blood.

Moreover, elevated levels of sCD27, a member of the TNF receptor

superfamily, involved in activation of both T cells and B cells, has been

observed in relation with many infectious and autoimmune diseases and has

been proposed as a marker of immune activation. These findings support that

sub-clinical inflammation and chronic B cell stimulation might play role in

lymphomagenesis in the general population.

Purdue et al. (41) also reported on the association between elevated

circulating soluble CD30 levels in pre-diagnostic serum of healthy subjects

General discussion

129

and NHL risk. A recent replication study within the EPIC-Italy cohort (42)

provided support for an elevated risk of lymphoma with increasing plasma

sCD30 levels. Soluble CD30 is cleaved from the cell surface of activated B and

T cells during the process of immune activation and can be detected at low

levels in normal serum. Most B cell NHL tumors do not express CD30 and

therefore, sCD30 levels may serve as a surrogate for the activation status of

the immune system, rather than reflecting tumor burden. As sCD30 is

expressed by activated B cells and the type 2 subset of T cells that secrete B

cell stimulatory cytokines, increases in serum sCD30 might indicate increased

B cell activation as well as an immune environment conducive to chronic B

cell stimulation.

Although these studies have shown that dysregulation of cytokine

production may precede the development of NHL in immunocompetent

people; a clear pattern has not yet been established. Additional evidence

from large prospective studies therefore is needed to clarify the relationship

with NHL risk for these markers and related cytokines. However, taken

together, findings to date provide considerable evidence that B cell activation,

chronic inflammation, and/or a shift in the balance of Th1/Th2 cytokines are

important phenomena in NHL development.

Effect of possible environmental/occupational lymphomagens on blood immune markers of lymphomas We have postulated that possible risk factors of lymphoma such as

environmental and occupational exposures may operate through

perturbations of the immune system. In this thesis we studied the possible

effects of two potential risk factors of lymphoma including occupational

exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) and a lifestyle risk

factor; obesity on (validated) markers of the immune system. A possible

association between TCDD exposure and NHL risk is supported by several

studies among occupationally and environmentally exposed individuals (43-

47). In addition, recent studies have suggested that modifiable lifestyle

factors such as obesity (48-50) may have contributed to the rising rates of

NHL.

TCDD exposure

We assessed a broad range of immunologic parameters directed toward

detecting changes in both the humoral and cellular arms of the immune

system among workers occupationally exposed to high levels of TCDD

approximately 35 years after last exposure. As demonstrated in Chapter 5

most lymphocyte subsets, in particular the B cell compartment, showed a

decrease in absolute counts with increasing TCDD exposure levels. Moreover,

Chapter 8

130

we observed (in Chapter 6) that blood levels of most cytokines, chemokines

and growth factors had a negative association with TCDD levels with a formal

statistical significance for fractalkine, transforming growth factor alpha (TGF-

α) and fibroblast growth factor 2 (FGF2). These changes were independent

from the previous described changes in blood cell counts. As such TCDD

exposure in humans might influence both function and the number of

immune cells.

In addition, we studied possible changes in immunoglobulins and

complement factors in relation to plasma TCDD levels (Chapter 4). Plasma

TCDD levels were not associated with markers of humoral immunity with the

possible exception of a borderline significant decrease in complement factor

4 (C4) levels. Overall, our findings support that dioxin exposure could have an

adverse impact on the immune system, in particular indicative of a

suppression of the immune system. Moreover, our results provide support to

the hypothesis that changes in cell-mediated immunity by TCDD may play

role in TCDD toxicity and associated health effects.

The participants in our study are among the highest exposed

occupational individuals, based on blood TCDD concentrations, and certainly

higher than levels that have been found in environmental settings. Here we

summarize the main findings of the relevant published studies that

investigated immunological changes in relation to dioxin exposure (for an

overview see table 1).

Studies on industrial workers and/or exposed subjects of industrial accidents

A study on persons exposed to TCDD due to an industrial accident in England

(17 years after exposure) reported no differences between exposed workers

and controls in T and B cell lymphocyte counts and subsets but the number of

natural killer cells was significantly higher in exposed workers (51). Another

study among workers exposed to TCDD due to an industrial accident (more

than 35 years before blood collection) showed a non-significant decrease of

lymphocyte counts, B-, T-, and T helper cells and a significant increase of IgA,

IgG and C4 for exposed workers compared to a referent group (52). Results of

a study on TCDD exposed subjects of Seveso, Italy, 20 years after the

industrial accident, showed that plasma IgG levels of a random sample of the

population in the most highly exposed zones decreased with increasing TCDD

plasma concentration, but IgM, IgA, C3 and C4 plasma concentrations did not

exhibit any consistent association with TCDD levels (53). Nagayama et al.

reported no significant changes on serum levels of IgG, IgA and IgM and

lymphocyte subsets in exposed subjects of the Yusho industrial accident (54).

Neubert et al. reported no significant change in immunological cell

subsets and multiple monoclonal antibody markers among workers exposed

to polychlorinated dibenzo-p-dioxins (PCDD)/ polychlorinated dibenzofurans

General discussion

131

(PCDFs) in a German study on workers involved in the decontamination of a

chemical plant, who had moderately increased dioxin body burdens (55).

However, in a follow-up study, they reported a significant decrease in plasma

concentrations of IgG1 in exposed workers (56). No differences were

reported for other immunoglobulins (IgM, IgA, IgD, IgG2, IgG3 and IgG4) and

cytokines of IL1α, IL1ß, IL6 and TNF-α. No significant differences could be

detected between 11 exposed workers and 10 age-matched, healthy controls

for lymphocyte subsets in another study among German industrial workers

exposed to high levels of TCDD 20 years before blood collection (57). Halperin

et al. reported a decrease in CD26+ cells (activated T cells) among subjects

occupationally exposed to TCDD compared to a non-exposed reference group

(58). Phenotype analysis of peripheral blood mononuclear cells revealed no

significant differences in the proportion of CD3+, CD4+, CD8+ T lymphocytes,

CD16+ natural killer cells, or CD19+ B lymphocytes of industrial workers

exposed to high concentration of TCDD in an independent other study (59). A

study among former workers of a pesticide-producing plant exposed to

PCDDs and PCDFs, in Germany showed that all blood cell counts, IgA, IgG, and

IgM, were not significantly correlated with exposure to PCDD/PCDF (60).

While another study among workers exposed to TCDD during the

manufacture of trichlorophenol (TCP) showed that level of gamma globulins

decreased with increasing TCDD concentration (61).

Studies on Vietnam War veterans exposed to Agent Orange

In a study on US Air force veterans of the Vietnam War who were exposed to

herbicides contaminated with TCDD, Michalek et al. reported no significant

differences in lymphocyte/subsets counts and immunoglobulin levels (IgA,

IgG and IgM) among a high-exposed group as compared to a non-exposed

control group. However a non-significant decrease in most lymphocyte

subsets was reported among the high-exposed group (62). Another study

among Vietnam War Korean veterans conducted by Kim et al. showed lower

plasma IgG levels as compared to controls, with a significant decrease in the

IgG1 levels. In contrast, increased plasma levels of IgE were observed among

exposed veterans (63).

Studies on waste incineration workers

A study in South Korea investigated the effects of dioxin exposure on three

different immune parameters; leukocyte sub-populations (CD3+, CD4+, CD8+,

CD19+, and CD69+), plasma immunoglobulin levels (IgA, IgG, IgM, and IgE),

and cytokines (IL4 and Interferon gamma (INF-γ)) among waste incineration

workers. There was no significant difference in T- and B cell profiles between

waste incineration workers and control subjects. However, T cell activation

was found to be significantly higher in the waste incineration workers than in

Chapter 8

132

the control subjects, but B cell activation did not exhibit this trend.

Immunoglobulins and cytokines were found in lower amounts in the waste

incineration workers, but this difference was not statistically significant

except for IL4 (64).

Studies on a population at risk from environmental contamination of TCDD

A Study among Missouri residents (Times beach area) exposed to TCDD

contaminated soil reported no significant changes in T cell numbers, T helper,

T suppressor cells and T4+/T8+ ratio. However, there was a greater

percentage of individuals with a T4+/T8+ ratio≤1 in the high exposed group

(65). Studies on community residents of Quail Run Mobile Home Park

(Missouri residents, USA) environmentally exposed to contaminated

industrial sludge showed a significantly decreased percentage of lymphocytes

expressing CD3+, CD4+, and CD2+, but the mean numbers of each of the T cell

subsets was comparable between groups (66, 67). Subsequent studies (68, 69)

showed significant increases associated with higher concentrations of TCDD

for IgG, %CD3+, %CD8+, number of T cells, percentages of CD2+, and

CD4+/Leu-8 position.

Svensson and co-workers indicated that high consumption of fatty

fish from the Baltic Sea, contaminated with persistent organochlorine

compounds, is associated with lower numbers of natural killer cells (70).

Another study among employees who worked in day-care centers where the

floor had been treated with wood preservatives containing

pentachlorophenol (PCP) and γ-hexachloro-cyclohexane (contaminated with

PCDD and furans) showed no association between inhalation dioxin exposure

and the number of peripheral CD4, CD8 cells, or their ratio (71).

All together, the existing epidemiologic studies in humans including

ours have not revealed consistent patterns of perturbations of the immune

system by TCDD (Table 1). This can be due to several factors that affected the

results of the above mentioned studies including: 1] effect assessment of

acute exposure in some studies compare to effects of long-term (chronic)

exposure to TCDD, 2] heterogeneity of the applied exposure assessment

methods (lack of individual quantification of the body burden), 3] different

statistical analysis (between-group differences instead of dose-effect or dose-

response relationships), 4] differences in time between blood measurements

and exposure, 5] differences in the magnitude of the exposure, 6] correlated

(confounding) exposures, 7] residual confounding, and 8] survival bias.

Nevertheless taken together the observed findings might suggest that

dioxin exposure can have an adverse impact on both the humoral and cell

mediated immune system and as such may provide support for

lymphomagenesis of TCDD.

General discussion

133

Table 1 Qualitative assessment of blood immune marker changes related to

TCDD exposure

Re

f. 6

5,

19

84

Re

f. 6

6,

19

86

Re

f. 6

7,

19

87

Re

f. 6

9,

19

88

Re

f. 5

1,

19

88

Re

f. 6

8,

19

89

Re

f. 5

5,

19

93

Re

f. 6

1,

19

94

Re

f. 5

2,

19

94

Re

f. 7

0,

19

94

Re

f. 7

1,

19

95

Re

f. 5

7,

19

96

Exposed, n 100 154 135 27 18 40 89 8 133 23 215 11

Non-exposed, n - 155 142 15 15 - - - 196 20 186 10

TCDD level, ppt NM NM NM NM NM 0-

750

1-

140

163-

1935

1-

553

NM NM ND-

874

Years from last

exposure

NR 1-

14

4-

16

1-

14

17 NR >10 15-28 20-

35

NR NR 20

Blood markers

WBC ↑ ↔ ↔ ↔ ↔ ↓ ↔

Lymphocyte ↓ ↔ ↑ ↔ ↔ ↓ ↔ ↔

B cell ↔ ↔ ↔ ↔ ↔ ↓ ↔ ↔

T cell ↔ ↔ ↔ ↔ ↔ ↔ ↔ ↓ ↔ ↔

CD4+ ↔ ↔ ↔ ↔ ↔ ↔ ↔ ↓ ↔ ↔ ↔

CD8+ ↔ ↔ ↔ ↑ ↔ ↔ ↔ ↔ ↔ ↔

CD8+ memory

cell

↔

CD4+/CD8+ ratio ↔ ↔ ↓ ↓ ↔ ↔ ↔ ↔ ↔

CD45+

Activated T cells ↔

Activated B cells

NK ↑ ↔ ↓ ↔

γ-Globulines ↓

IgG ↔ ↔ ↔ ↑ ↑ ↔

IgA ↔ ↔ ↑ ↔

IgM ↔ ↔ ↔ ↔

IgD ↔

IgE ↔ ↔

C3 ↔

C4 ↑

IL1α

IL1β

IL4

IL6

IL10

TNF-α

IFN-γ

TGF-α

Fractalkine

FGF2

(Continued in the following page)

Not reported (NR), Not detectable (ND), Not measured (NM), Increase ↑, Decrease ↓, No change ↔, 2,3,7,8-tetrachlorodibenzo-p dioxin (TCDD), White blood cell (WBC), Natural killer cell (NK), Immunoglobulin (Ig), Complement (C), Interleukin (IL), Tumor necrosis factor alpha (TNF-α), Interferon gamma (IFN-γ), Transforming growth factor alpha (TGF-α), Fibroblast growth factor 2 (FGF2).

Chapter 8

134

Table 1 (Continued) Qualitative assessment of blood immune marker changes

related to TCDD exposure

Re

f. 5

9,

19

98

Re

f. 5

8,

19

98

Re

f. 6

0,

19

98

Re

f. 6

2,

19

99

Re

f. 5

6,

20

00

Re

f. 5

4,

20

01

Re

f. 5

3,

20

02

Re

f. 6

3,

20

03

Re

f. 6

4,

20

05

Ch

ap

ter

4

Ch

ap

ters

5,

6

Exposed, n 21 259 187 894 12 16 62 51 31 79 85

Non-exposed, n 28 243 - 1167 33 - 58 36 84 69 -

TCDD level, ppt 2.9-

2252

0-

3389

1.2-

893

0-

3290

1-

140

NR 1-

89.9

NM NM 0-

61

0.01-

61

Years from last

exposure

10-

23

15-37 >10 16-

30

>10 27 20 >30 NR 35 35

Blood markers

WBC ↔ ↔ ↓

Lymphocyte ↓ ↔ ↔ ↔ ↓

B cell ↔ ↔ ↔ ↓ ↔ ↔ ↓

T cell ↔ ↔ ↔ ↓ ↔ ↔ ↓

CD4+ ↔ ↔ ↔ ↔ ↔ ↔ ↓

CD8+ ↔ ↔ ↔ ↓ ↔ ↓ ↓

CD8+ memory

cell

↑ ↓

CD4+/CD8+ ratio ↔ ↔ ↔ ↑

CD45+ ↓

Activated T cells ↓ ↓

Activated B cells ↔

NK ↔ ↔ ↔ ↓ ↑

γ-Globulines

IgG ↓ ↔ ↔ ↓ ↔ ↓ ↓ ↓ ↑

IgA ↔ ↔ ↔ ↔ ↔ ↔ ↓ ↑

IgM ↔ ↔ ↔ ↔ ↔ ↔ ↓ ↓

IgD ↔ ↓

IgE ↑ ↓ ↓

C3 ↑ ↔ ↓

C4 ↔ ↓

IL1α ↔

IL1β ↔

IL4 ↓ ↓

IL6 ↓

IL10 ↓

TNF-α ↔ ↑

IFN-γ ↓

TGF-α ↓

Fractalkine ↓

FGF2 ↓

Not reported (NR), Not detectable (ND), Not measured (NM), Increase ↑, Decrease

↓, No change ↔, 2,3,7,8-tetrachlorodibenzo-p dioxin (TCDD), White blood cell

(WBC), Natural killer cell (NK), Immunoglobulin (Ig), Complement (C), Interleukin (IL),

Tumor necrosis factor alpha (TNF-α), Interferon gamma (IFN-γ), Transforming growth

factor alpha (TGF-α), Fibroblast growth factor 2 (FGF2).

General discussion

135

According to the ‘‘meet in the middle’’ concept, the overlap between

observed markers of TCDD exposure and the putative markers of NHL could

identify relevant intermediate markers. However, to date, most of the

markers that were studied in this thesis have not been validated in relation to

NHL risk in prospective studies (72; Chapter 3, 39-41). Therefore, the value of

these observations is not clear. Nevertheless, our findings support the

possible effects of TCDD on suppression of the immune system, a process

which has been linked to NHL in prospective studies (3).

Obesity

Results described in chapter 7 suggested that IL8, IL10, IFN-γ and IP10 are

related to obesity (Body mass index; BMI). Among these predictors, IP10 had

the highest prognostic value for obesity. Changes in these immunological

factors are likely to be mediated by leptin and adiponectin. There is evidence

that leptin may promote an optimal pro-inflammatory response because, on

one hand, it plays a pivotal role in the systemic inflammatory response and,

on the other hand, restrains the inflammatory response via IL10 production

(73).

Results of previous studies also have shown that obesity causes

alterations to the immune system (Table 2). A community-based cross-

sectional study showed that obesity is linked to elevated leukocyte and

lymphocyte subsets, IL6 and IL1α levels (74). Two other cross sectional

studies showed that levels of C-reactive protein (CRP), and concentrations of

the pro-inflammatory cytokines IL6 and TNF-α, were related to all measures

of obesity (75, 76). In a study on 189 untreated asymptomatic men in Canada,

IL6 appeared to be clearly associated with visceral adiposity while TNF-α

showed an association with indices of total body fatness (77). Elevated CRP

levels were associated to obesity among participants of the Third National

Health and Nutrition Examination Survey (78). In addition, an association

between IL8, (79, 80), sTNF-R2 (79) and MCP-1 (80) and BMI has been

reported. In another cross sectional study among 148 non-diabetic subjects,

serum concentrations of sCD40L, an inflammatory marker, were significantly

higher in obese subjects compared with normal weight subjects (81). In

contrast, Ciftci et al. showed that serum IL6 and TNF-α level were not

correlated with BMI in both patients and controls of a case-control study (82).

Results of the Acute Respiratory Distress Syndrome Network (ARDSNet) trials

showed that plasma IL6, IL8 levels were inversely related to BMI, and that

white blood cell count increased proportionally with BMI (83).

Recently, adipose tissue is recognized as an endocrine organ that

secretes adipokines, including cytokines and chemokines (84, 85). Both IL6

and TNF-α which are of the most important pro-inflammatory cytokines, are

Chapter 8

136

expressed in adipose tissues (75). Overall, obesity seems to promote chronic

inflammation and increased production of pro-inflammatory cytokines (49).

An important issue in analyzing immune function/parameters in

obese individuals is that the effect of obesity itself on the immune system can

be confounded by nutritional status, physical activity and the coexistence of

metabolic conditions. Our studies described in Chapters 3 and 7 are one of

the first studies that used prospectively collected samples, thus avoiding

inverse causation bias in that some biomarkers might be affected by the

disease process or treatment of the disease. We only had one prospectively

collected blood sample for each individual to characterize their immune

profile. To increase the accuracy of the biomarker assessment, repeated

measures of the biomarkers might be necessary, because biomarker levels

may vary substantially from day to day. However, when the inter- and intra-

individual variability in cytokine levels was considered (Chapter 2), it was

found that cytokine levels did not vary much over time within an individual as

compared to the variance between individuals over at least a 2-week period.

Therefore, in this particular context, a single cytokine measurement is

expected to represent an individual's immune profile adequately.

In the two studies described in Chapter 3 and 7, no overlap was

observed between marker levels of obesity and NHL (Based on the original

concept of ‘‘meet in the middle’’ approach). However, a potential role of

obesity in lymphomagenesis cannot be excluded because, on one hand,

several studies showed that obesity is a low-grade chronic inflammatory state

and, on the other hand, a chronic inflammatory condition has been linked to

NHL risk in previous epidemiological studies (11). It seems therefore that a

broader investigation of markers of the immune system, such as for instance

markers of chronic B cell activation (sCD30), might be warranted to further

investigate the possible immune mediated association between obesity and

NHL.

General discussion

137

Table 2 Qualitative assessment of blood immune marker changes related to

obesity

Re

f. 7

4,

19

99

Re

f. 7

8,

19

99

Re

f. 7

5,

19

99

Re

f. 7

9,

20

02

Re

f. 8

2,

20

04

Re

f. 7

6,

20

05

Re

f. 8

0,

20

06

Re

f. 7

7,

20

08

Re

f. 8

1,

20

10

Re

f. 8

3,

20

10

Ch

ap

ter

7,

20

10

Study design C-S C C-S C-S C-C C-S C-S C-S C-S RCT C-C

Number of

subjects

157 16616 107 75 65 100 100 189 148 1409 154

Blood markers

White blood

cells

↑ ↑ ↑ ↑

Neutrophils ↑

Monocytes ↑

Lymphocyte ↑

B cell ↑

T cell ↑

CD4+ ↑

CD8+ ↔

NK ↔

IL1α ↑ ↔

IL6 ↑ ↑ ↔ ↑ ↑ ↓ ↔

IL8 ↑ ↑ ↓ ↓

IL10 ↑

TNF-α ↑ ↔ ↑ ↑ ↓

IFN-γ ↓

Ip10 ↑

MCP-1 ↑

MIP-1α ↔

CRP ↑ ↑ ↑ ↑ ↑ ↑

LKN-1 ↔ ↔

sICAM ↔ ↔

sCD40L ↑

TNFR1 ↔

sTNF-R2 ↑

Study design: Cohort (C), case-control(C-C), cross-sectional (C-S) and randomized

clinical trial (RCT); Increase ↑; Decrease ↓; No change ↔ ; Interleukin (IL); Tumor

necrosis factor alpha (TNF-α); Interferon gamma (IFN-γ); C reactive protein (CRP);

Interferon-induced protein 10 (IP10); Monocyte chemotactic protein-1 (MCP-1);

Macrophage inflammatory protein-1α; leukotactin-1 (LKN-1); Soluble tumor necrosis

factor alpha receptor 2 (TNF-R2); Soluble inter-cellular adhesion molecule (sICAM).

Chapter 8

138

Next steps in environmental lymphoma research Biomarkers have contributed greatly to our understanding of mechanistic

pathways from early exposures to cancer development as well as early

detection of cancers. Although our findings are informative on the

immunological changes that precede the development of lymphomas and

their possible relations with putative lymphomagens, the precise role of each

individual biomarker in the etiology of these cancers is still not completely

clear. More studies with larger sample sizes are needed to confirm the clinical

relevance of the observed changes in biomarkers.

As many large prospective cohort and case-control studies with

biologic samples have now been established, more-and-more biomarkers are

becoming available for epidemiologic studies. The first challenge, that

molecular epidemiology faces, is standardization of biomarker measurement

including standardization of sample collection, processing, banking and

analysis. Proper handling of biological samples from the time of collection to

the analysis protects the quality of the specimens and the validity of the

results (86). Standard procedures and quality check schemes are necessary

because there is a lack of definition to guarantee reproducibility of new

procedures (87). Secondly, standardization of exposure measurements across

studies is of critical importance. Misclassification of exposure can severely

affect estimates of disease risks, and even in some extreme situations, cause

misleading interpretations about exposure–disease associations. Accurate

assessment of exposure to occupational and environmental risk factors is

needed to ensure that epidemiological studies meet their objectives in

investigating the exposure–disease relationship.

In this thesis, we identified several putative intermediate blood

markers of lymphoma and tried to link the markers of exposures to

lymphoma status. Although we were successful in part of the work, the

approaches presented in this thesis need further development. These

developments need to be sought in better exposure assessment, better study

designs, more in-depth measurements of the immune system, and improved

statistical methods that are better suited to deal with the dynamic process

from exposure to intermediate endpoints and to clinical disease. However,

the improvement in laboratory techniques together with the increasing

number of well collected and documented biological samples from

prospective studies should facilitate such an improvement.

General discussion

139

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Epidemiology-Molecular Epidemiology (STROBE-ME): An extension of the

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Summary

Summary

148

The incidence of non-Hodgkin lymphoma (NHL) has risen in most parts of the

developed world steadily across all age groups and in both sexes, by about 3–

5% per year in the past 20 years. Reasons for this increase are unclear but are

unlikely explained by changes in NHL classification of borderline types of

lesions; less histopathological misdiagnosis of NHL as Hodgkin’s disease;

greater use of immunohistological techniques to examine cancers of

uncertain cell type; and modern diagnostic tools.

Severe immunosuppression (i.e. Primary immunodeficiency diseases,

infection with human immunodeficiency virus (HIV) and immunosuppressive

drugs taken after organ transplantation) is the most clearly defined risk factor

of NHL, leading to 50–100-fold excess risk of NHL. However, given the low

prevalence of these factors in the general population, it cannot explain the

majority of NHL cases. In addition to severe immunosuppression, medical

conditions in which the immune system has been altered such as Sjogren

syndrome, systemic lupus erythematosus, and Celiac disease have been

linked to lymphoma development as well. Therefore, it has been postulated

that moderate perturbations of the immune system may be a risk factor of

NHL development as well. Evidence has shown that possible environmental

and occupational NHL risk factors such as obesity, diet, exposure to solvents

(i.e. benzene, trichloroethylene (TCE)), pesticides and radiation influence

functioning of the immune system. We hypothesized that environmental and

occupational risk factors of lymphoma operate through perturbations of the

immune system.

The overall aim of this thesis is to study the possible perturbations of

the immune system by occupational and environmental risk factors of NHL

and to study these changes in relation to NHL risk in prospective cohorts. The

use of reliable intermediate biomarkers may improve our understanding of

pathways involved in lymphomagensis. Therefore, in the first part of this

thesis we validated the application of single blood cytokine measurements as

a biomarker of the immune status in prospective epidemiological studies. In

Chapter 2 potential utility of stored blood samples of a prospective cohort

was evaluated by the effect of different blood sample types and freeze-thaw

cycles on cytokine levels. We measured eleven cytokines, four chemokines

and two adhesion molecules in fresh samples (serum, citrate and heparin

plasma) of 10 asymptomatic adults collected 14 days apart and on aliquots of

the first samples which were put through one to three freeze-thaw cycles. We

used a multiplex fluorescent bead based immunoassay to measure cytokines

because it is fast and convenient and can measure a large number of analytes

in a small volume of biological sample, an important consideration in

prospective epidemiologic studies with a limited amount of biological

material from each participant. This study showed strong correlations

between sample types, although small differences in analyte levels were

Summary

149

observed for most analytes. Moreover, freeze-thaw cycles did not markedly

change cytokine levels. Furthermore, the observation that the intra-individual

variance in cytokine levels was much smaller than the inter-individual

variance supported the notion that a single plasma or serum cytokine

measurement possibly could be used to characterize an individual's immune

profile prospectively.

Subsequently, we assessed the levels of these cytokines in pre-

diagnostic blood samples of 86 NHL cases and 86 matched controls (average

time between blood collection and diagnosis, 4.5 y) among the participants of

the Italian European Prospective Investigation into Cancer and Nutrition (EPIC)

cohort and determined their association with the risk of developing NHL

(Chapter 3). The results of this study suggest, in a prospective setting, a

possible association between increased/decreased plasma levels of

interleukin (IL)2, inter-cellular adhesion molecule (ICAM), interferon gamma

(IFN-γ), and tumor necrosis factor alpha (TNF-α) with NHL risk and provide

some evidence that risk of NHL might be related to a down-regulation of T

helper 1 cytokines. The initial polarization of Th0 cell responses towards Th1

or Th2 is self-perpetuating. As such, down-regulation of Th1 cytokines

enhances responses toward Th2 cells that promote antibody-mediated

immunity and B cell activation.

In the second part of this thesis we studied the possible

immunological effects of two possibly lymphomagens: an occupational

exposure (i.e. 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD)) and an

environmental risk factor (i.e. obesity). First, we assessed a broad range of

immunologic parameters directed toward detecting abnormalities in both the

humoral and cellular arms of the immune system among workers from a

retrospective cohort exposed to chlorophenoxy herbicides, chlorophenols

and dioxins in particular TCDD. As demonstrated in Chapter 4, blood

immunoglobulins (Ig) and complement factor (C) concentrations were

measured for selected workers who had been exposed to high levels of TCDD

in the past and for two matched groups of low- or non-exposed workers (one

from the same factory as the exposed subjects and one from a comparable

factory but without TCDD exposure, respectively). TCDD plasma levels of all

subjects were measured and back-extrapolated to the time of last exposure

(more than 35 years ago) using a one-compartment first order kinetic model.

This study showed that plasma TCDD levels were not associated with markers

of humoral immunity with the possible exception of a borderline significant

decrease in C4 levels. Low C4 levels might potentially have a role in the

survival of auto-reactive B cells. Prolonged survival of B cells could increase

the risk that unfavorable mutations might occur; resulting in malignancy.

Therefore, our findings for C4 might provide some support for the putative

link between TCDD and NHL.

Summary

150

To assess changes in cell-mediated immunity the complete blood

count and differential and major lymphocyte subsets were analyzed for 47

workers who had been exposed to high levels of TCDD in the past and 38 low-

exposed workers from the same factory (Chapter 5). We found that most

lymphocyte subsets, in particular the B cell compartment, showed a decrease

in cell counts with increasing levels of TCDD. The observation of TCDD leading

to hematological changes in B cells provides support to B cell lymphoma

induction by dioxin. Subsequently, we measured plasma levels of 16

cytokines, 10 chemokines and 6 growth factors for the previous study

subjects described in chapter 5 (Chapter 6). We observed that blood levels of

most cytokines, chemokines and growth factors had a negative association

with TCDD levels with a formal statistical significance for fractalkine,

transforming growth factor alpha (TGF-α) and fibroblast growth factor 2

(FGF2). These changes were independent from the previous described

changes in blood cell counts. Overall, our findings support that dioxin

exposure could have an adverse impact on the immune system and likely

suppresses the immune system and that change in cell-mediated immunity by

TCDD may play role in TCDD toxicity and associated health effects.

To identify immunologic hallmarks of excessive bodyweight as a

possible risk factor (Chapter 7) of NHL we measured plasma levels of 11

cytokines, 4 chemokines and one adhesion molecule in pre-diagnostic blood

samples of 176 adults (106 women, 70 men) who participated in a nested

case-control study in EPIC Italy (Described in chapter 3). All participants were

healthy at the time of blood collection and aged between 36 and 75 years. IL8,

IL10, IFN-γ, and interferon-induced protein 10 (IP10) were shown to be

related to excessive body weight. We found that there was no statistically

significant difference between NHL cases and controls with regard to body

mass index and other anthropometric measures. In addition, the observed set

of predictors (IL8, IL10, IFN-γ, IP10) of excess body weight was not associated

to NHL risk.

Although our findings in this thesis are informative on the

immunological changes that may precede the development of lymphomas or

related to a particular exposure, the precise role of each individual biomarker

in the etiology of NHL is still not completely clear. Our data support that

subtle perturbations in the immune system may precede lymphoma

development and suggest that B cell activation, chronic inflammation and/or

an unbalance in Th1/Th2-responses are likely important phenomena in

lymphomagenesis. More studies with larger sample sizes are needed to

confirm our observations and to determine the clinical relevance of some of

the observed perturbation in the immune system.

Samenvatting

Samenvatting

152

Het aantal gevallen van het non-Hodgkin lymfoom (NHL) is in de laatste 20

jaar in de meeste delen van de wereld gestaag toegenomen in alle

leeftijdsgroepen en voor beide geslachten. De achterliggende oorzaken voor

deze toename zijn niet duidelijk en zijn niet alleen te verklaren door een

verandering in de classificatie van vermeende subtypes van NHL; een afname

van verkeerde histopathologische diagnose van NHL als de ziekte van

Hodgkin’s; een toename van het gebruik van immunohistologische

technieken bij de diagnose kanker van onbekende celtypes en andere

moderne diagnostische technieken.

Sterke immuno suppressie (zoals bijvoorbeeld voorkomt bij primaire immuno

deficiëntie ziekten, infecties met het human immuno deficiëntie virus (HIV)

en gebruik van immunosuppressiva na orgaantransplantatie) is de best

gedefinieerde risicofactor voor NHL. Dit leidt namelijk tot een 50-100-voudige

toename in het risico op NHL. Omdat deze factoren, over het algemeen,

weinig voorkomen in de algemene populatie vormen zij niet de verklaring

voor de meeste gevallen van NHL en is het onwaarschijnlijk dat deze factoren

de toename in NHL incidentie kunnen verklaren. Behalve sterke immuno

suppressie worden ook aandoeningen die gepaard gaan met aantasting van

het immuunsysteem in verband gebracht met de ontwikkeling van lymfomen,

zoals het syndroom van Sjögren, systemische lupus erythematodes en

Coeliakie. Er wordt daarom verondersteld dat milde verstoringen van het

immuunsysteem eveneens een risicofactor kunnen zijn voor het ontwikkelen

van NHL. Er is aangetoond dat mogelijke milieu- en arbeidsgerelateerde

risicofactoren voor NHL zoals overgewicht (obesitas), voeding en blootstelling

aan oplosmiddelen (bijv. benzeen, trichloorethyleen), bestrijdingsmiddelen

en straling van invloed zijn op het immuunsysteem. Onze hypothese is dan

ook dat milieu- en arbeidsgerelateerde risicofactoren voor NHL (voor een

deel) gemedieerd worden door verstoringen in het immuunsysteem.

Het hoofddoel van dit proefschrift is het onderzoeken van mogelijke

veranderingen van het immuunsysteem door milieu- en arbeidsgerelateerde

risicofactoren voor NHL en te kijken naar deze veranderingen in relatie tot het

risico op NHL in prospectieve cohort studies. Het gebruik van betrouwbare,

gevalideerde effect biomerkers kan een belangrijke bijdrage leveren aan de

kennis omtrent het ontstaan van lymfomen. In het eerste deel van dit

proefschrift is gekeken naar de validiteit van het gebruik van eenmalige

cytokine metingen in het bloed als een biomerker van de persoonlijke

immuunstatus. In hoofdstuk 2 wordt beschreven of opgeslagen

bloedmonsters van een prospectieve cohortonderzoek gebruikt kunnen

worden voor dergelijke bepalingen door te kijken naar het effect van

verschillende anticoagulantia en vries-dooi cycli op het cytokine niveau. Er

Samenvatting

153

zijn elf cytokines, vier chemokines en twee adhesie moleculen gemeten in

verse monsters (serum, citraat en heparine plasma) van tien gezonde

volwassenen op twee tijdstippen, met veertien dagen tussen de

monstername. Tevens zijn er monsters die onderworpen zijn aan één tot drie

vries-dooi cycli bemeten. Cytokines werden gemeten met een op

flowcytometrie gebaseerd multiplex meetsysteem. Deze techniek is snel en

gebruikt bovendien geringe volumina van biologische monsters. Dat laatste is

niet onbelangrijk in een prospectief cohortonderzoek waarin een beperkte

hoeveelheid biologisch materiaal van elke deelnemer beschikbaar is. Het

onderzoek toonde sterke correlaties aan in cytokine niveaus tussen

verschillende anticoagulantia, hoewel er wel kleine verschillen werden

gevonden in absolute niveaus voor de meeste merkers. Vries-dooi cycli waren

niet van invloed op de cytokine niveaus. Verder werd waargenomen dat de

intra-individuele variantie in cytokine niveaus veel kleiner was dan de inter-

individuele variantie. Dit ondersteunt het idee dat enkelvoudige plasma of

serum cytokine metingen mogelijk een redelijk prospectief beeld geven van

iemands individueel immuunprofiel.

Vervolgens zijn de niveaus van deze cytokines in pre-diagnostische

bloedmonsters van 86 NHL patiënten en 86 overeenkomstige controles (de

gemiddelde tijd tussen bloedafname en diagnose was 4,5 jaar), afkomstig van

deelnemers van de Italiaanse EPIC (European Prospective Investigation into

Cancer and Nutrition) cohortstudie gemeten en de mogelijke associatie met

het ontwikkelen op NHL bepaald (hoofdstuk 3). De resultaten van deze studie,

in een prospectief cohort, suggereren een mogelijk verband tussen

veranderingen in plasma niveaus van interleukine 2 (IL2), inter-cellular

adhesion molecule (ICAM), interferon γ (IFN-γ), en tumor necrosis factor

alpha (TNF-α) met risico op het ontwikkelen van NHL later in het leven en

geven een mogelijk bewijs dat het risico op NHL gerelateerd is aan een

verlaging van het aantal T helper 1 cytokines.

In het tweede deel van dit proefschrift worden mogelijke immunologische

effecten beschreven van twee mogelijke risicofactoren van NHL; een

arbeidsgerelateerde blootstelling aan dioxines (in het bijzonder 2,3,7,8-

tetracloordibenzo-p dioxine (TCDD)) en een milieugerelateerde risicofactor

(overgewicht). Eerst is een groot aantal immunologische parameters

onderzocht om veranderingen in zowel het humorale als het cellulaire

gedeelte van het immuunsysteem van arbeiders in een retrospectief cohort

blootgesteld aan chloorphenoxy herbiciden, chloorfenolen en dioxines (in het

bijzonder TCDD) te onderzoeken. Hoofdstuk 4 beschrijft hoe bloed

immunoglobulines (Ig) en complement factor (C) concentraties werden

gemeten bij een aantal werknemers die blootgesteld zijn geweest aan hoge

Samenvatting

154

concentraties TCDD in het verleden en bij twee overeenkomstige groepen van

weinig en niet blootgestelden (een groep van dezelfde fabriek als de groep

hoog blootgestelden en respectievelijk een groep van een vergelijkbare

fabriek maar zonder TCCD blootstelling). De TCDD plasma niveaus werden bij

iedereen gemeten en terug geëxtrapoleerd naar het laatste moment van

blootstelling (meer dan 35 jaar geleden) gebruik makend van een één-

compartiment eerste-orde kinetiek model. Hierbij bleek dat TCDD plasma

niveaus niet te relateren zijn aan merkers voor humorale immuniteit,

mogelijk uitgezonderd van een minimale significante afname van plasma C4

niveaus. Lage C4 niveaus kunnen een mogelijke rol spelen in de levensduur

van zelf reactieve B cellen. Een verlengde levensduur van B cellen kan leiden

tot een toename van het risico op ongewenste mutaties, die kunnen leiden

tot de vorming van kwaadaardig weefsel (kanker). Deze resultaten voor C4

ondersteunen de mogelijke relatie tussen TCDD en NHL.

Om de veranderingen in cellulaire immuniteit te bestuderen zijn

bloedceltellingen en subtyperingen van lymfocyten geanalyseerd bij 47

werknemers die in het verleden blootgesteld werden aan hoge concentraties

TCDD en 38 laag blootgestelden van dezelfde fabriek (hoofdstuk 5). Deze

analyses gaven aan dat voor de meeste subtyperingen van lymfocyten, in het

bijzonder de B cellen, er een afname was in aantal cellen bij een toegenomen

TCDD niveau. De waarneming dat TCDD leidt tot hematologische

veranderingen in B cellen geeft mogelijk ondersteuning aan de mogelijke

relatie tussen B cel geïnduceerde lymfomen en dioxine. Vervolgens is er

gekeken naar de plasma niveaus van 16 cytokines, 10 chemokines en 6

groeifactoren bij de hiervoor in hoofdstuk 5 beschreven studie, de resultaten

hiervan zijn beschreven in hoofdstuk 6. In deze analyses werd er voor de

meeste cytokines, chemokines en groeifactoren een negatief verband

gevonden met de TCDD niveaus met een statische significantie relatie voor

fractalkine, TGF- α (transforming growth factor α) en FGF2 (fibroblast growth

factor 2). Deze veranderingen waren onafhankelijk van de hiervoor

beschreven veranderingen in de bloedcel tellingen. Algemeen kan gezegd

worden dat deze resultaten enige onderbouwing geven aan het idee dat

blootstelling aan TCDD kan leiden tot een nadelig effect op het

immuunsysteem en mogelijke het immuunsysteem kan onderdrukken. Deze

veranderingen in de immuniteit van cellen door TCDD kan mogelijk een rol

spelen in de toxiciteit en gerelateerde gezondheidseffecten van TCDD.

Om immunologische kenmerken van extreem overgewicht, een mogelijke

risicofactor voor NHL, te bepalen is in hoofdstuk 7 gekeken naar de

plasmaniveaus van 11 cytokines, 4 chemokines en een adhesie molecuul in

pre-diagnostische bloedmonsters van 176 volwassenen (106 vrouwen en 70

Samenvatting

155

mannen) die meededen in de nested case-control studie in EPIC Italië (zoals

beschreven in hoofdstuk 3). Alle deelnemers waren gezond op het moment

van bloedafname en waren tussen de 36 en 75 jaar. IL8, IL10, IFN-γ en

interferon-induced protein 10 vertoonden een relatie met extreem

overgewicht. Er is echter geen statisch significant verschil gevonden tussen

NHL patiënten en controles in vergelijking tot BMI (body mass index een maat

voor overgewicht) en andere antropometrische waarden. Bovendien was de

bestudeerde groep van voorspellers van extreem overgewicht (IL8, IL10, IFN-γ,

IP10) niet gerelateerd aan een verhoogd risico op NHL.

Hoewel de bevindingen in dit proefschrift veel informatie geven over

mogelijke immunologische veranderingen die vooraf kunnen gaan aan de

ontwikkeling van lymfomen of gerelateerd zijn aan een bepaalde blootstelling,

is de precieze rol van elke individuele biomerker in de etiologie van NHL nog

niet geheel duidelijk. Onze resultaten wijzen in de richting dat subtiele

veranderingen van het immuunsysteem kunnen leiden tot de ontwikkeling

van lymfomen en deze suggereren dat activatie van B cellen, chronische

ontstekingen en/of een disbalans in de Th1/Th2 regulatie belangrijke

parameters zijn in het ontstaan van lymfomen. Meer onderzoek in grotere

groepen zijn nodig om onze bevindingen te bevestigen en om de klinische

relevatie van sommige waargenomen veranderingen in het immuunsysteem

te bevestigen.



158

Ellen Baeten

Department of Medical Immunology, University Medical Center Utrecht,

Utrecht, the Netherlands

Andries C. Bloem

Department of Medical Immunology, University Medical Center Utrecht,

Utrecht, the Netherlands

Daisy Boers (formerly)

Institute of Risk Assessment Sciences (IRAS), Division Environmental

Epidemiology, Utrecht University, the Netherlands


1- The National Institute for Public Health and Environmental Protection

(RIVM), Bilthoven, the Netherlands

2- Department of Gastroenterology and Hepatology, University Medical

Centre Utrecht (UMCU), Utrecht, the Netherlands

Paolo Chiodini

Universita’ di Napoli, Italy

Dick Heederik



Vittorio Krogh

Nutritional Epidemiology Unit, National Cancer Institute, Milan, Italy

Esmeralda J.M. Krop



Virissa Lenters



Jakob Linseisen

Institute of Epidemiology, Helmholtz Zentrum München, Germany

Liliana Minelli

University of Perugia, Perugia, Italy


159

Niga Nawroly

Imperial College, London, UK

Domenico Palli

1- Molecular and Nutritional Epidemiology Unit, Cancer Research and

Prevention Centre, Scientific Institute of Tuscany, Florence, Italy

2- ISPO, Firenze, Italy

Salvatore Panico

Department of Clinical and Experimental Medicine, Federico II University of

Naples, Napoli, Italy

Lützen Portengen



Charles S. Rabkin

National Cancer Institute, Rockville, MD, USA

Carlotta Sacredote

1- Institute for Scientific Interchange Foundation, Torino, Italy

2- CPO-Piemonte, Torino, Italy

Chiara Scoccianti

1- IARC, Lyon, France

2- Imperial College, London, UK

Mansour Taghavi Azar Sharabiani

School of Public Health, Imperial College London, London, UK

Rosario Tumino

Ragusa Cancer Registry, Ragusa, Italy

Roel Vermeulen

1- Institute of Risk Assessment Sciences (IRAS), Division Environmental


2- Julius Center for Health Sciences and Primary Care, University Medical

Center Utrecht, the Netherlands

Paolo Vineis

1- MRC/HPA Centre for Environment and Health, School of Public Health,

Imperial College, London, UK


160

2- Imperial College, London, UK

3- HuGeF Foundation, Torino, Italy

About the author

About the author

162

Fatemeh Saberi Hosnijeh was born in Tehran (Iran), on December 31, 1970.

After she completed her secondary school (Shahid Habibi Adl, Tehran, Iran) in

1990, she started studying medicine in Medical School at Tehran university of

Medical science and graduated in 1998. During her studies, she joined to the

Breast Cancer Research group (1995-1997) that was the first screening

program for breast cancer in Iran. After obtaining her degree of Doctor of

Medicine (MD) in 1998, she started working in the public health system as

manager and general practitioner in health care centers and hospitals

affiliated with Zanjan University of Medical Science (ZUMS) in Zanjan, Iran.

She also worked in “Addiction Treatment Clinic” in Zanjan as consultant and

physician which was valuable experience to learn more about medical, social

and personal problems of addicted persons. In 2004 she joined to the

“Healthy Heart Project” which is an ongoing prospective cohort on Lifestyle

factors of metabolic syndromes in general population in ZUMS. In 2006, she

was granted a scholarship to study epidemiology by the Ministry of Health,

Treatment and Medical Education of Iran. She started her study at the Julius

Center for Health Sciences and Primary Care, Utrecht University in 2007 and

obtained a Master of Science degree in Epidemiology. In 2008, she joined the

Institute for Risk Assessment Sciences (IRAS), Utrecht University to continue

her research studies described in this thesis.



164

Vermeulen R, Saberi Hosnijeh F, Portengen L Purdue M, Lan Q, Rothman N,

Krogh V, Palli D, Panico S, Tumino R, Sacredote C & Vineis P. (2011).

Circulating soluble CD30 and future risk of lymphoma; evidence from two

prospective studies in the general population. Cancer Epidemiol Biomarkers

Prev 20: 1925-1927.

Chuang S-C, Vermeulen R, Taghavi Azar Sharabiani M, Sacerdote C, Saberi

Hosnijeh F, Berrino F, Krogh V, Palli D, Panico S, Tumino R, Athersuch T &

Vineis P. (2011). The intake of grain fibres modulates cytokine and chemokine

levels in blood. Biomarkers 16 (6): 504-10.

Taghavi Azar Sharabiani M, Vermeulen R, Scoccianti C, Saberi Hosnijeh F,

Minelli L, Sacerdote C, Palli D, Krogh V, Tumino R, Chiodini P, Panico S &

Vineis P. (2011). Immunologic profile of excessive body weight. Biomarkers

16(3): 243-51.

Saberi Hosnijeh F, Boers D, Portengen L, Bueno-de-Mesquita HB, Heederik D

& Vermeulen R. (2011). Long-term effects on humoral immunity among

workers exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). Occupational

and environmental medicine 68(6): 419-24.

Saberi Hosnijeh F, Krop EJM, Scoccianti C, Krogh V , Palli D, Panico S, Tumino

R, Sacerdote C, Nawroly N, Portengen L, Linseisen J, Vineis P & Vermeulen R.

(2010). Plasma cytokines and future risk of non-Hodgkin lymphoma (NHL): A

case-control study nested in the Italian European Prospective Investigation

into Cancer and Nutrition. Cancer Epidemiol Biomarkers Prev 19(6): 1577-84.

Saberi Hosnijeh F, Krop EJM, Portengen L, Rabkin CS, Linseisen J, Vineis P &

Vermeulen R. (2010). Stability and reproducibility of simultaneously detected

plasma and serum cytokine levels in asymptomatic persons. Biomarkers

15:140–8.

Saberi Hosnijeh F, Lenters V, Boers D, Portengen L, Baeten E, Bueno-de-

Mesquita HB, Heederik D, Bloem A & Vermeulen R. Changes in lymphocyte

subsets in workers exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD),

Submitted.


165

Saberi Hosnijeh F, Boers D, Portengen L, Bueno-de-Mesquita HD, Heederik D

& Vermeulen R. Plasma cytokine concentrations in workers exposed to

2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), Submitted.

Saberi Hosnijeh F, Vermeulen R, Christopher Y, Peeters P, Vieneis P, et al.

Occupation and risk of leukemia within the European Prospective

Investigation into Cancer and Nutrition (EPIC), submitted.

Saberi Hosnijeh F, Vermeulen R, Vieneis P, et al. Anthropometric

characteristics and risk of leukemia within the European Prospective

Investigation into Cancer and Nutrition (EPIC), submitted.

Saberi Hosnijeh F, Vermeulen R, Peeters P, Vieneis P, et al. Dietary factors

and risk of leukemia within the European Prospective Investigation into

Cancer and Nutrition (EPIC), submitted.

Acknowledgements

Acknowledgements

168

I would like to acknowledge all those people who directly or indirectly

contributed to the completion of this study and helped me to grow as a

researcher.

First of all, I would like to express my deepest gratitude to my

supervisor and co-promoter Dr. Roel Vermeulen. Dear Roel, you have always

supported me throughout my research with your insightful knowledge while

at the same time allowing me to work independently. Although you

frequently were on trip between France, USA, China, Italy and the

Netherlands (even more?), at the same time you were responsive to my

questions. Thanks for all enjoyable discussions that we had, I learned so much

from you and I am looking forward to having further cooperation. I am

extremely thankful to my promoters Prof. Dr. Dick Heederik and Prof. Dr.

Paolo Vineis who I had the chance to learn a lot from these excellent persons,

thanks for this opportunity.

I wish to thank my friends, colleagues and other staff in IRAS who

have contributed immensely to my personal and professional development

especially my old and new office-mates: Virrisa (thank for all your English

language help and nice discussions), Marlos, Maciek, Rob (thank you for

helping me when I came to IRAS), Tim, Sussane, marten and Moniek. I am

extremely grateful to Lutzen for his valuable help in statistics. Special thank to

Ingrid (also for the summary in Dutch), Evelyn and Machteld for their great

helps in administrative tasks. I also would like to express my appreciation to

Prof. Dr. Bert Brunekreef for his support through IRAS foundation. I am

thankful to Parvin and Virissa for being my paranymphs.

My special gratitude goes to Ministry of Health, Treatment and

Medical Education of Iran for their financial support. In particular I would like

to thank Dr. Nafissi, Ms. Akbarpour, Ms. Aminpour and Dr. Abdollahi.

In addition, I would like to thank my Iranian friends in the

Netherlands: Dear Hadi and Parvin, I never forget your warm welcome and

generous support during these years, thanks for all you have done for me.

Hossein and Shahnaz, thank you for all your help and enjoyable time I spent

with you in all parties with delicious traditional Iranian foods. Very warm

thanks go to Homeira, for her friendship and supports. Also, I am thankful to

Prof. Dr. Majid Hassanizadeh and Forouz for constant support of my family. I

never forget wonderful parties in your beautiful garden. I would like to thank

Sahar and Hamid, Fahime and Esmaeil, Melika and Hadi, Zohre and

Mohamad, Maryam and Javad, Esmat and Mahdi, Maryam and Payam, Raha

and Abbas, Tala and Jahangir, Najibeh, Samad and Marjan, Gholam, Hamid

(Ghaem Maghami), Sheyda and Vahid, Amir, Pasha, Marzieh, Maryam and

Mojtaba for making an enjoyable stay for me in the Netherlands during these

years.

Acknowledgements

169

I wish to express my special appreciation to my parents, sisters and

brothers who have been always invaluable sources of inspiration, love,

encouragement and moral support.

Finally, my heartfelt thanks to my beloved husband Reza who has

supported me and is always next to me while being busy with his own PhD

research and to my sons, Hadi and Hatef who are joy of my life.

Persian summary

Persian summary ��

172

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� م�5 9م�ر% از ��8ظFGF2 و fractalkine، TGF-α در م�رد و ای ارت��ط م�45 دا�� م�45�ات ای. دار ب�د�ات ت;�' م=�>! از ت;�م�� در ��(� ه�% در ت��اد .��ل ��ح داد'

�ع، ی��. ب�د ��Aدر م��ض در م �ار &�� را �C�� ه�% م� ایTCDD � م��ا(� ت�ث� ح �یG ب� .=�F ای �5 ب�ن م�45� ��د' و و ب� اح� �ل زی�د .��ب .=�F ای �5 دا�� ب�9�4��J) �Kن �� 5 .�� (�� م� ده در م M ا.TCDD�J<) G از ت �س ب� ه�% .=�F ای

� ب�دن .TCDD�� . و اث�ات م�ت�N ب� 9ن دا�� ب�

�K)�J) �5.�ی�� در ، ب� 5T�ان ی�T Qم! اح� ��)��S(ه�% ای �(��ژیQ ا�PایO وزن ب�ا% ��ه-، در یQ م�� م�رد NHLب�وز '��% P& �)Yی)nested) ( !"� ٣در��ح داد' (

�ی� .�` ١١.�0 ،��. a�5�=S ل�M��م Qو ی �� ' ��! از ��&� در ( �(� ��ن &�� bcJر&=�ل �١٧تPم�د٧٠ زن و�١٠( ��د ب ( �� %�55�&�ن در زم�ن .ا(�از' &� G�� ه

�)� 5��٧ ت��٣ه�% ��ن .��F ب�د' و ب ج e 9ور% (�� T م�ر��ه�%. .�ل . داAم IFN-γ ، IL10 ، IL8 و IP10� دار% در ��b هh ت�4وت 9م�ر% م��5. ب� ا�PایO وزن در ارت��ط ب�د(

�Tو' . ه� وج�د (�ا�G و ��ه�NHLب�ن ب م�ارد ه�% دی�K و ا(�از' &�% )BMI(ت�د' % ب�ن �5O ب` �T� A، مب� ای ��S '�55� )IL8 ،IL10 ،IFN-γ ،IP10 ( ب� ��ب� �� ابNHL در

.ارت��ط (��د

�ات ای �(�� ا&� �S ی��م M ا.G ��! از ی�M ��ژه�% م� در ای `�ی�ن (�م� در م�رد ت;� ی�)�� ده5�' 9&�ه� وج�د 9ی5�� ب ب� G�T ��ار &�� در م��ض T�ام! �� ت�.�� 45�م ای�Aد

=nGه5�ز ب� NHL ه� درات��ژ% (�K)�Jب�د، (>O د�m ه� یQ از ای ) �� ی .�ر ��م! روو م8=�س در .=�F ای �5 ب�ن ��! از ب�وز �45�م را ح �یG ��د'ه�% (� م� وج�د 9�4��K ه�%

�ن .��ل��ه� �� اح� �Yً ��ل �م ت��دل در وا�O5 ی� ، ا��pب مPم وBه�% (�Jن مT %ه�Th1/Th2 در� � ای�Aد �45��ای5pم O<) م � . دار(


173

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European Prospective Investigation into Cancer and)یQ ��ه�رت 9ی5�' (�Kای��ی�

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G ه�% .�45�Bدد م��&.

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174

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Immunologic profile of excessive body weight

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