COPD:
recognizing the susceptible smoker
S.J.M. Hoonhorst
The research described in this thesis was supported by a research grant from the Top
Institute Pharma project T1-108 ‘Acute and chronic inflammatory responses - COPD
and smoking’, with partners University Medical Center Groningen (UMCG), University
of Groningen (RUG), GRIAC Research Institute Groningen, University Medical Center
Utrecht (UMCU), Nycomed BV (now Takeda), GlaxoSmithKline and Foundation TI Pharma.
Printing of this thesis was financially supported by: University of Groningen, University Medical
Center Groningen, Boehringer Ingelheim BV, TEVA Pharmachemie, Takeda Nederland BV,
GlaxoSmith Kline and Diagnoptics BV.
Cover design and layout: Susan Hoonhorst
Printed by: Koninklijke Wöhrmann B.V., Zutphen.
ISBN: 978-94-6203-692-5
© S.J.M. Hoonhorst, 2014. All rights reserved. No part of this publication may be reproduced
or transmitted, in any form or by any means, without permission of the author, or when
appropriate, of the publishers of the publications.
COPD:
Recognizing the susceptible smoker
Proefschrift ter verkrijging van de graad van doctor aan de
Rijksuniversiteit Groningen
op gezag van de
rector magnificus prof. dr. E. Sterken
en volgens besluit van het College voor Promoties.
De openbare verdediging zal plaatsvinden op woensdag 10 December 2014 om 14.30 uur
door Susan Jolande Mariska Hoonhorst geboren op 19 Juni 1986
te Stad Delden
Promotor
Prof. Dr. D.S. Postma
Co-promotor
Dr. N.H.T. ten Hacken
Beoordelingscommissie
Prof. dr. P.S. Hiemstra
Prof. dr. A. Schols
Prof. dr. Smeenk
Paranimfen
Anke Kortier
Karin Snijders
Contents
Chapter 1
General introduction
Chapter 2
Acute and chronic inflammatory responses induced by
smoking in individuals susceptible and non-susceptible
to development of COPD: from specific disease
phenotyping towards novel therapy. Protocol of a crosssectional study
21
Chapter 3
Increased activation of blood neutrophils after cigarette
smoking in young individuals susceptible to COPD
39
Chapter 4
Advanced glycation end products in the skin are
enhanced in COPD
69
Chapter 5
Advanced glycation endproducts and their receptor in
different body compartments in COPD
93
Chapter 6
Lower corticosteroid skin blanching response is
associated with severe COPD
121
Chapter 7
Steroid resistance in COPD? Overlap and differential
anti-inflammatory effects in smokers and ex-smokers
141
Chapter 8
Summary and General Discussion
159
Chapter 9
Nederlands samenvatting
175
Dankwoord
183
9
1
Chapter
General Introduction
Chapter 1
10
General introduction
Chronic Obstructive Pulmonary Disease
Chronic obstructive pulmonary disease (COPD) is a lung disease characterized by chronic airflow
limitation which is generally progressive and associated with enhanced chronic inflammatory
responses in the airways and lungs to noxious particles and gases (1). COPD is a leading cause
of morbidity and the only chronic disease with ongoing increase in mortality. In 2002, COPD
was the fifth leading cause of death, but its prevalence is increasing and it is expected that it
will be the fourth leading cause of death in 2030 (2). The worldwide prevalence of COPD in
adults aged 40 years and older is 9-10% (3).
The main symptoms of COPD are chronic and progressive dyspnea, cough and
sputum production (4). Although COPD is a pulmonary disease, it is highly associated with
systemic manifestations and comorbidities like diabetes, cardiovascular disease and skeletal
muscle wasting (5,6). COPD is diagnosed by measuring of airflow limitation by spirometry,
expressed as the Forced Expiratory Flow in one second (FEV1) divided by the Forced Expiratory
Flow (FVC) (also known as the Tiffeneau index). A postbronchodilator FEV1/FVC < 0.70 confirms
the existence of irreversible airflow limitation and strongly suggest a diagnosis of COPD
(7). The disease severity of COPD is classified by the GOLD grading system based on FEV1 %
predicted: stage I (mild, FEV1 ≥ 80% predicted); stage II (moderate, FEV1 50-80% predicted);
stage III, (severe, FEV1 30-50% predicted); stage IV, (very severe, FEV1 ≤ 30% predicted) (7).
Although the GOLD classification is generally maintained, it has been demonstrated
that different kinds of phenotypes can be recognized. For this reason, assessment of disease
severity was reconsidered and a new classification was composed in which besides airflow
limitation, also patient’s symptom severity and risk of adverse future events (exacerbation risk)
are taken into account (1). In this classification severity stages are summarized in groups A-D
as follows: A – low risk (GOLD I-II, 0-1 exacerbations per year), less symptoms (mMRC grade
0-1 or CAT score <10); B – low risk, more symptoms (mMRC grade ≥2 or CAT score ≥10); C
– high risk (GOLD III-IV, ≥2 exacerbations per year and/or ≥1 hospitalized exacerbation per
year), less symptoms; D – high risk, more symptoms. It was considered that this classification
might reflect the complexity of COPD better than solely the use of the classification of airflow
limitation in stages I-IV. However, it turned out that the clinical presentation and course of
COPD is too heterogeneous to be classified in a two dimensional scale. Furthermore, it was
shown that mortality was even better predicted using the GOLD grading system rather than
using the new ABCD groups (8).
Currently, COPD is thus still is a complex heterogeneous disease with various clinical
expressions, which makes it therefore of importance to combine clinical, physiological,
immunological and radiographic parameters for optimal characterization of COPD patients.
This may already now, and even more so in the future, lead to better treatment targets to
ameliorate disease severity and prevent further progression of the various types of COPD.
Risk factors
Cigarette smoking is the main cause of COPD. It has been shown in multiple studies that
smokers show a higher prevalence of respiratory symptoms and abnormal lung function values,
accompanied by a faster decline in lung function with higher age as compared to non-smokers
11
1
Chapter 1
(9). Of all COPD cases, it is estimated that 80% of COPD patients are smoking related. Although
smoking is the most important cause of COPD development, other factors can increase the risk
and the prevalence of COPD in non-smokers as well. For example, some genetic factors have
been described to underlying COPD, from which polymorphisms in the alpha-1 antitrypsin
gene is the first and most well-known factor causing alpha-1 deficiency and thereby increased
tissue break down (10,11). Furthermore, bronchial hyperresponsiveness, childhood asthma,
impaired lung growth (from gestation until adolescence), passive exposure to cigarette smoke,
smoking during pregnancy, and occupational exposures like organic and inorganic dusts and
chemical agents and fumes are associated with airflow obstruction and chronic respiratory
symptoms (7,12,13).
Susceptibility to COPD
Not all of the individuals with the same smoking history will actually develop COPD, i.e. only
about 15-20 % of all smokers. It still remains unclear for which reasons COPD is manifested
only in this small proportion of smokers. Probably these so-called ‘susceptible’ smokers are
more sensitive to the detrimental effects of cigarette smoking than ‘non-susceptible’ smokers.
The exact mechanisms underlying this susceptibility are unknown. However, it is likely that
genetic background is an important underlying factor. Several family studies have investigated
the genetic predispostition in relation to smoking-related COPD. Silverman et al. showed that
first-degree relatives of early-onset COPD probands had significantly lower FEV1 and FEV1/FVC
values than control subjects, despite similar packyears of smoking (14). In a follow-up study
they showed that these relatives also had increased bronchodilator responsiveness compared
with controls (15). Another study also demonstrated significant familial risk of airflow
obstruction in smoking siblings of patients with severe COPD (16). Interestingly, airway wall
thickening and emphysema are independently aggregating within families of COPD patients
(17). Furthermore, parental history of COPD is a strong risk factor for COPD, independently of
the family history of smoking, personal lifetime smoking, and environmental smoke exposure
during childhood (18). Taken together, the combination of smoking and familial COPD
occurrence strongly associates with a higher risk to develop COPD. Although a familial risk for
COPD may help identifying susceptible smokers for COPD it is not 100% predictive, hence a
more discriminative biomarker is still needed.
Acute effects of cigarette smoking
To understand the underlying mechanisms of smoking-induced COPD it is valuable
to investigate the very first airway responses to cigarette smoke. Several studies have
investigated the effects of acute smoking on inflammation and oxidative stress in humans,
animals and in in vitro models. A few years ago, van der Vaart et al. extensively reviewed the
acute effects (<24 h) of cigarette smoking (19). In animal models, alveolar macrophages and
neutrophils were increased in lung tissue, whereas mast cells were increased in the airways
within 6 hours after acute smoking. In contrast, eosinophils were decreased 6 to 24 hours after
smoking. Alveolar macrophages and neutrophils were increased in BALF varying from 1 to 24
hours after acute smoking. Unfortunately, only a few studies are available investigating the
12
General introduction
effects of acute smoking in humans. In summary, these studies showed that neutrophils are
increased or unchanged in bronchoalveolar lavage fluid (BALF), that there are no effects on the
number of monocytes or leucocytes in BALF, and that the local concentration of radio-labelled
neutrophils in the lung increases after smoking. Furthermore, there are indications that the
epithelial barrier is impaired and fibroblast function decreased. With regard to the systemic
compartment, peripheral blood neutrophils were increased, whereas eosinophils decreased.
Acute smoking in susceptible individuals
To understand the mechanisms underlying the development of smoking-related COPD it might
be attractive to perform an acute smoke model in young individuals who are susceptible to
COPD and compare the effects with those in non-susceptible individuals. As mentioned before,
young susceptible individuals can be identified by a high familial risk for COPD. Probably, these
young susceptible individuals already have abnormal responses to cigarette smoke compared
to non-susceptible individuals. Available acute smoking studies have generally investigated
old smokers or COPD patients (age >40 years) with a high smoking history. However, in
these groups repetitive acute effects of cigarette smoke have already accumulated and thus
contributed to structural changes and irreversible lung damage. Therefore, it seems more
attractive to investigate the acute effects of cigarette smoking in ‘naive’ individuals in whom
the lungs and the systemic inflammatory component are not yet affected by chronic smoke
exposure.
Inflammation and COPD
The progressive airflow limitation that occurs in many patients with COPD is accompanied by
an increased inflammatory response of the lungs to noxious agents. This airway inflammation
persists with aging in COPD. Inflammation is treated with corticosteroids that suppress virtually
every step of the inflammatory pathway. However, in contrast with asthma, the majority of
COPD patients are less responsive to corticosteroid treatment, even at high doses of inhaled or
oral steroids. Inhaled corticosteroids (ICS) have no effects on disease progression or mortality
in COPD, but reductions in symptoms and exacerbations were observed in subgroups of
patients and health status has been shown to improve after ICS treatment as well (20-22).
Besides these clinical beneficial effects, some studies have demonstrated anti-inflammatory
effects in some COPD groups as well (23-26). The GLUCOLD study investigated mild-tomoderate COPD patients and found besides a reduction in FEV1 decline, an improvement in
airway hyperresponsiveness, positive effects on health status, and a reduction in inflammatory
cells in the airways (23). Bronchial T-lymphocyte and mast cell numbers were reduced after
treatment as well as sputum cell counts. Another study showed a reduction in the absolute
numbers of biopsy leukocytes (CD45+), CD8+ cells, and CD4+ cells accompanied by decreases
in cells expressing genes for the proinflammatory mediators IFN-γ and TNF-α, and a reduced
number of neutrophils and eosinophils in sputum (24). Also other studies have confirmed
anti-inflammatory effects of ICS treatment assessed in bronchial biopsies of COPD patients,
including reductions in CD8+ lymphocytes, mast cells and macrophages (25,26). It is not fully
understood why there is such a diverse responsiveness to corticosteroid treatment between
13
1
Chapter 1
different groups of patients and why, despite the depression of inflammation that appears to
occur, there is not a clear effect on FEV1 decline and mortality. Several molecular mechanisms
contributing to corticosteroid resistance have been described in the literature, including genetic
susceptibility, defective GR binding and nuclear translation, transcription factor activation, or
abnormal histone acetylation (27). Also cigarette smoking has found to be associated with
reduced corticosteroid responsiveness in asthma (28-30), and in COPD (31). Because of the
high variety of phenotypes within COPD it is hard to call corticosteroid unresponsiveness as
a general characteristic of COPD, especially given the sometimes reported long-term positive
responses in a subset of patients, or the beneficial effects in most patients when treating
exacerbations. Furthermore, we can speculate on whether corticosteroid unresponsiveness is
a characteristic of COPD by being a result of the disease, or that it earlier in life also contributes
to the development of COPD.
Advanced Glycation End products and their receptor
Advanced glycation end products (AGEs) are a heterogeneous group of compounds that are
formed from nonenzymatic glycation and oxidations of proteins and lipids which is irreversible
(32). Besides these endogenous pathways, tobacco smoking is an important exogenous source
of AGEs formation (33,34). Under normal circumstances, AGEs are slowly formed and they
accumulate in the body during aging, however, this process is accelerated in inflammatory
conditions and oxidative stress. The best known AGEs are Nε-(carboxymethyl)lysine (CML), Nε(carboxyethyl)lysine (CEL) and pentosidine. AGEs have damaging effects on tissues in which
they accumulate, by altering protein function, cross-linking proteins, and by binding the
receptor for AGEs (RAGE) (35,36).
RAGE is a multi-ligand receptor of the immunoglobulin super family and ligand
binding activates inflammatory and tissue remodeling processes. RAGE is implicated in the
pathogenesis of several chronic diseases including cardiovascular diseases and metabolic and
neurodegenerative disorders. Interestingly, RAGE is highly expressed in the lung compared
to other organ tissues (37). RAGE exists in two isoforms; membrane-bound (mRAGE) or in a
soluble form without the transmembrane domain (sRAGE). sRAGE can be generated through
alternative splicing or by cleavage of the cell-bound receptor at the cell surface (38,39). The
ligand binding domain between the different isoforms is similar. Therefore it is thought that
sRAGE acts as a decoy receptor, preventing the interaction of ligands with mRAGE.
The presence of chronic inflammation and oxidative stress in COPD, local as well as
systemic, may lead to increased formation and accumulation of AGEs in COPD. Unfortunately,
until now, AGEs are rarely studied in COPD. Only a few studies have shown that accumulation
is elevated in lung tissue and plasma of COPD patients (40,41). Importantly, sRAGE levels are
decreased in COPD patients (41-45) and there are indications that RAGE expression is elevated
in human lung tissue (40,46). These data may indicate an important role for the AGE-RAGE
interaction in the pathogenesis of COPD. As AGEs formation is highly associated with smoking,
we hypothesize that the AGE-RAGE interaction is increased in ‘susceptible smokers’ thereby
contributing to COPD development. Therefore, further research is needed.
14
General introduction
Aims of this thesis
In summary, it is of importance to gain more insight in the underlying mechanisms driving
the development of COPD in ‘susceptible’ individuals. We already know that familial COPD is
an important predictor of COPD development, however, a more discriminative biomarker to
establish susceptibility to COPD would be welcome in the field of preventive medicine. The
first aim of this thesis is to investigate if there exist differential local and systemic inflammatory
responses between young indidivuals being susceptible or non-susceptible to develop
COPD after exposure to a disease-specific challenge; smoking three cigarettes in one hour.
Additionally, we examined AGEs, RAGE and their interaction, and corticosteroid (in)sensitivity
in smoking and never-smoking young and older healthy controls and COPD patients, providing
more insight in the origins and pathology of COPD.
Outline of the thesis
Chapter 2 presents the research protocol of the clinical study investigating acute and
chronic effects of cigarette smoking in young and old individuals who are susceptible or nonsusceptible to develop COPD. In Chapter 3 results of the acute smoking study are presented in
which the local and systemic inflammatory responses to cigarette smoking were investigated
in young and old individuals who are susceptible and non-susceptible to the development
of COPD. The results of a study on accumulation of Advanced Glycation End-products (AGEs)
in the skin of young and old healthy smokers and never-smokers, and COPD patients are
presented in Chapter 4. Chapter 5 presents data on AGEs and RAGE in plasma, sputum,
bronchial biopsies and the skin of young and old healthy smokers and never-smokers, and
COPD patients. In Chapter 6 data of the skin blanching test are presented, investigating
corticosteroid sensitivity in the skin of healthy controls and COPD patients. Chapter 7 presents
a study on the differential effects of inhaled corticosteroids in smoking and ex-smoking COPD
patients. Chapter 8 contains the summary and a general discussion with future perspectives
of research.
15
1
Chapter 1
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General introduction
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18
General introduction
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19
1
2
Chapter
Acute and chronic inflammatory responses
induced by smoking in individuals
susceptible and non-susceptible to
development of COPD:
from specific disease phenotyping
towards novel therapy.
Protocol of a cross-sectional study
Adèle Lo Tam Loi*, Susan Hoonhorst*, Lorenza Franciosi, Rainer Bischoff, Roland
Hoffmann, Irene Heijink, Antoon van Oosterhout, Marike Boezen, Wim Timens,
Dirkje Postma, Jan-Willem Lammers, Leo Koenderman, Nick ten Hacken
* Authors contributed equally
BMJ Open 2013 Feb 1;3(2)
Chapter 2
ABSTRACT
Introduction: Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease
with pulmonary and extra-pulmonary manifestations. Although COPD is a complex disease,
the diagnosis and staging are still based on simple spirometry measurements. Different
COPD phenotypes exist based on clinical, physiological, immunological, and radiological
observations. Cigarette smoking is the most important risk factor for COPD, but only 15-20%
of smokers develop the disease, suggesting a genetic predisposition. Unfortunately, little is
known about the pathogenesis of COPD, and even less on the very first steps that are associated
with an aberrant response to smoke exposure.
This study aims to investigate the underlying local and systemic inflammation
of different clinical COPD phenotypes, and acute effects of cigarette smoke exposure in
individuals susceptible and non-susceptible for the development of COPD. Furthermore, we
will investigate mechanisms associated with corticosteroid insensitivity. Our study will provide
valuable information regarding the pathogenetic mechanisms underlying the natural course
of COPD.
Methods and analysis: This cross-sectional study will include young and old individuals
susceptible or non-susceptible to develop COPD. At young age (18-40 years) 60 “party smokers”
will be included that are called susceptible or non-susceptible based on COPD prevalence in
smoking family members. Additionally, 30 healthy smokers (age 40-75 years) and 110 COPD
patients will be included. Measurements will include questionnaires, pulmonary function, lowdose CT scanning of the lung, body composition, 6-min-walking distance, and biomarkers in
peripheral blood, sputum, urine, exhaled breath condensate, epithelial lining fluid, bronchial
brushes and biopsies. Non-biased approaches such as proteomics will be performed in blood
and epithelial lining fluid.
Ethics and dissemination: This multicenter study was approved by the medical ethical
committees of UMC Groningen and Utrecht, the Netherlands. The study findings will be
presented at conferences and will be reported in peer-reviewed journals.
Trial registration: ClinicalTrials.gov, NCT00807469 (study 1) and NCT00850863 (study 2).
22
Acute and chronic smoking effects and susceptibility to COPD
INTRODUCTION
Chronic obstructive pulmonary disease (COPD) is a major cause of morbidity and mortality
worldwide (1). The disease is characterized by persistent and progressive expiratory airflow
obstruction (post-bronchodilator FEV1/FVC<0.70) and its severity is based on FEV1 %predicted
(1). Cigarette smoking is the most important risk factor for COPD in the western world, but
only 15-20 % of young smokers will eventually develop the disease, suggesting a genetic
predisposition. So far, the genetic background of these susceptible smokers has not been
elucidated (2). Unravelling the underlying pathogenetic mechanisms of COPD is difficult
because it takes 20-30 years of smoking before susceptible smokers develop established
COPD. Also, there is the problem that COPD has many clinical expressions, and we just have
started to learn how to phenotype this heterogeneous disease. Finally, there are many other
risk factors that may modulate the complex interaction between the genetic background
and smoking, like in utero events, microbial infections, dietary factors, physical inactivity and
pharmacological treatment.
It is well accepted that the spirometry measurements (FEV1 and FVC) are largely insufficient to
diagnose and classify COPD (1). With the increased recognition of the various clinical expressions
of COPD, consensus is growing that COPD represents a spectrum of overlapping diseases with
important extra-pulmonary consequences. Phenotypes of COPD may be classified according
to four domains: clinical, physiological, immunological and radiographical (3).
• Clinical distinctions are generally based on dyspnea scores, frequency of exacerbations,
body mass, muscle wasting, corticosteroid responsiveness, depression / anxiety, comorbidity, and healthy status (4).
• Physiological distinctions may be based on the degree of airflow limitation, decline in
lung function, bronchodilator responsiveness, airway hyperresponsiveness, CO diffusion
capacity, hyperinflation, body-plethysmography, bio-impedance, and exercise tolerance.
• Immunological features comprise the type and severity of local and systemic
immunological processes in the lung and systemic compartment. In blood leukocytes
cytokines, and mediators may affect the functionality of extra-pulmonary tissues and
organs, leading to COPD-associated co-morbid conditions.
• Radiographic distinctions may be based on the presence of various forms and severity of
emphysema, thickened large airways, and small airways abnormality on high-resolution
computed tomography scans.
Although systemic inflammation and multi-organ pathology have been put forward as
important features of COPD, surprisingly little is known about the underlying pathogenesis.
Most COPD studies in this field included small numbers of individuals, focused on more severe
stages of COPD, characterized subjects clinically on the basis of few arbitrary pulmonary
measurements, did not take into account the genetic background and paid limited attention to
different aspects of systemic inflammation. In addition, most studies assumed that assessment
of cytokines by multiplex assays (e.g. Luminex) is sufficient to accurately describe the systemic
inflammatory response. Unfortunately, many caveats are present that preclude a complete
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Chapter 2
insight in this response, e.g.:
• not all cytokines implicated in COPD are known,
• little effort is taken to measure anti-inflammatory cytokines (the balance between pro- and
anti-inflammatory signals will probably determine the extent and type of inflammation),
different pro-inflammatory cytokines can act as heterologous antagonists (inhibit the
effects of other cytokines).
• the kinetics of cytokines is very dynamic and no consensus is present regarding an optimal
single time point for blood collection.
In the present study we set out to characterize systemic inflammation by an alternative
approach. Innate immune cells will be used as integrators of pro- and anti-inflammatory
signals. We hypothesize that subtle changes in the phenotype of granulocytes and monocytes
are caused by an “inflammatory imprinting” of these cells.
Cigarette smoking is the main risk factor for developing COPD. Repetitive acute effects of
cigarette smoke exposure may accumulate and after many years lead to irreversible lung
damage. To understand the changes in the lung due to chronic smoking we believe that it is
important to first investigate the exact immunological responses to an acute smoke exposure
event, particularly in “naive” lungs that are not yet affected by chronic smoke exposure.
The acute (<24 hours) effects of smoking in humans, animals and cell cultures have been
extensively reviewed some years ago by van der Vaart and colleagues (5). If we integrate all
available data on acute smoking we are able to construct a hypothetical time frame for the
acute effects of smoking (figure 1). One of the very first insults on the bronchial system is by
oxidants present in cigarette smoke. After local depletion of anti-oxidants, the first oxidative
stress products can be measured within 1 hour. These products will disappear within 6 hours.
There is a surprisingly fast influx of inflammatory cells; even faster than the synthesis of some
pro-inflammatory cytokines (TNF-alpha, IL-1beta, IL-8). The exact time period at which the
proteinase / antiproteinase balance is affected is unknown; however, protein degradation is
measurable within 6 hours after smoking. Unfortunately, until now, only a few studies have
investigated the acute effects of cigarette smoking in humans (5). These studies included
only small numbers of individuals, characterized subjects mainly on basis of pulmonary
measurements, paid no attention to the genetic background and paid limited attention to
different aspects of pulmonary inflammation.
Corticosteroids provide little therapeutic benefit in a relatively large group of COPD patients,
despite their broad anti-inflammatory effects. Our goal is to identify common markers
in peripheral blood monocytes, skin and lung epithelial cells that might contribute to
corticosteroid insensitivity. Recently, the GLUCOLD study demonstrated beneficial effects on
airway wall inflammation and decline in lung function yet with large inter-individual differences
(6). In vitro studies have shown that the ability of dexamethasone to suppress cytokine release
(e.g. IL-8) from alveolar macrophages is impaired in COPD patients as compared to healthy
smokers (7). Furthermore, alveolar macrophages from healthy smokers are more resistant
to corticosteroids than macrophages from non-smokers(8). This relative steroid insensitivity
24
Acute and chronic smoking effects and susceptibility to COPD
may, in part, be explained by a suppressive effect of cigarette smoke-induced oxidative
stress. This suppression may particularly play a role in the airway epithelium, where cells are
in first contact with cigarette smoke and form an important source of mediators involved in
the induction of neutrophilic airway inflammation (e.g. the chemoattractant IL-8). It may well
be that corticosteroid insensitivity is gradually acquired by smoking in COPD, and one might
hypothesize that smokers who develop COPD are more prone to have signs of corticosteroid
insensitivity.
Figure 1. Acute smoking effects in the lung
General hypotheses
There is a clear need to better understand all factors that contribute to the development
of COPD and its different phenotypes. This study focuses on the pathogenesis and clinical
expression of smoking-induced COPD, studied both in the pulmonary and the systemic
compartments. The following general hypothesis is put forward by our consortium (figure 2):
“COPD is a multi-organ disease situated in both the lung and extra-pulmonary organs and tissues.
Dysfunction of the latter tissues is exemplified by muscle atrophy, impaired muscle oxidative
capacity, osteoporosis, atherosclerosis and heart failure. A low-grade systemic inflammation plays
a pivotal role in the induction and perpetuation of this multi-organ disease. Smoking and persistent
production of inflammatory mediators from the lung are inducers of systemic inflammation.
Other risk factors such as diet deficiencies, sedentary life style, and frequent infections contribute
independently to further amplification of systemic inflammation. In more advanced COPD
the extra-pulmonary pathology starts to contribute to disease severity and a vicious circle of
persistent difficulty to treat inflammation. Consequently, local and systemic inflammation should
be reduced in all stages of disease by reversing negative life style factors and applying successful
anti-inflammatory treatment modalities. In more advanced stages multimodal interventions
additionally should improve impaired tissue functions. An important contributing problem is the
relative corticosteroid insensitivity of both lung and peripheral tissue responses in COPD.”
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Chapter 2
Figure 2. General hypothesis about the role of systemic inflammation
Aims of the study
• To assess systemic and local inflammation at baseline in:
a)
young healthy individuals with low number of pack years smoking who have a high and low familial risk to develop COPD;
b)
older individuals with higher number of pack years who either have normal lung function or COPD.
We hypothesize that young susceptible individuals and COPD patients demonstrate a
higher degree and different type of local and systemic inflammation at baseline.
• To study systemic and local inflammation after acute smoke exposure in the above groups.
We hypothesize that young susceptible individuals and COPD patients demonstrate a
higher and aberrant local and systemic inflammatory response to cigarette smoke.
• To compare in bronchial epithelial cells and PBMCs corticosteroid responsiveness in
vitro between susceptible and non-susceptible individuals. To study in these cells the
effects of cigarette smoking and to elucidate underlying mechanisms of corticosteroid
unresponsiveness.
• To determine whether the type and severity of the systemic inflammatory response
contributes to the clinical outcome of COPD. We hypothesize that the type and severity of
systemic inflammation have profound effects on the clinical picture of COPD.
• To investigate the relationship between downstream genetic effects (transcriptome,
proteome) and specific COPD phenotypes in peripheral blood and lung tissue (induced
sputum, bronchial biopsies, epithelial lining fluid).
26
Acute and chronic smoking effects and susceptibility to COPD
METHODS
Study population
In total 200 old and young individuals who are susceptible or not susceptible to develop COPD
will be recruited (table 1). At old age (>40 years), 30 healthy smokers (>20 pack years) and 110
COPD patients (>10 pack years) will be enrolled in the study. At young age, 60 “party smokers”
with a normal lung function will be included with a high or low prevalence of COPD in smoking
family members (see table 1). Party smoking was defined by irregularly smoking and/or able
to quit smoking for at least two days. Exclusion criteria are: α-1-anti-trypsin-deficiency, acute
pulmonary infections (like tuberculosis, pneumonia, flue, tracheo-bronchitis), prior history
of significant inflammatory lung disease other than COPD (sarcoidosis, pulmonary fibrosis,
silicosis, ect.), active infections (such as hepatitis A-C, cystitis, gastro-enteritis etc.), treatment
with antibiotics or corticosteroids within 8 weeks, taking part in another study, recent
diagnosis of cancer. Medication such as NSAIDs and immunosuppressive agents which could
affect the results of the study will be excluded, as well as substance abuse. Co-morbidities that
might lead to study-related (serious) adverse events will be excluded on basis of an arbitrary
selection of conditions listed in the ACE-27 co-morbidity scale (9).
Study design
This study is a bi-center cross-sectional study that takes place at the University Medical
Centers Utrecht (UMC Utrecht) and Groningen (UMC Groningen). Participating subjects will
undergo extensive clinical characterisation (table 2). Local and systemic inflammation will be
investigated in several ways. Special attention will be paid to acute smoking and corticosteroid
insensitivity in selected subgroups.
Table 1. Study population
Disease
Non-susceptible
Healthy [A]
Healthy [B]
Susceptible
Healthy [C]
COPD:
Stage I [D1]
Stage II [D2]
Stage III [D3]
Stage IV [D4]*
No
Age
(Yrs)
Smoking
status
Pack
years
FEV1
/VC,%
FEV1,
% pred
30
30
18-40
40-75
Party smoking
Ex or current
0-10
>20
> 70
> 70
> 85
> 85
30
18-40
Party smoking
0-10
> 70
> 85
30
30
30
20
40-75
Ex or current
>10
≤ 70
> 80
40-75
>10
≤ 70
50-80
40-75
>10
≤ 70
30-50
40-75
>10
≤ 70
< 30
< 53
< 30
Susceptibility in young individuals is based on family history. Not susceptible means that none of the smoking
family members who are at least 40 years of age have COPD. Susceptible means that the prevalence of COPD
in smoking family members older than 40 years is high: 2 out of 2, 2 out of 3 or 3 out of 3, 3 out of 4 or 4
out of 4. Party smoking was defined by irregularly smoking and/or able to quit smoking for at least two days.
Alpha-1-antitrypsin deficiency is excluded. *patients with a FEV1 30-50% predicted in combination with chronic
respiratory failure also have stage IV.
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Chapter 2
Table 2. Measurements
Clinical
Physiological
Immunological
Radiographical
Measurements
Demographics
Physical examination
Peripheral blood (routine measurements)
Presence of metabolic syndrome
ECG
Bode index
Fagerstrom Smoking Questionnaire
St Georges Respiratory Questionnaire (SGRQ)
Clinical COPD Questionnaire (CCQ)
SQUASH
Urine (microproteins)
AGE (Advanced Glycation Endproducts)-reader
Skin blanching test
Flow volume + reversibility
Body plethysmography
CO diffusion
Methacholine challenge test
Bioelectrical impedance
Six minute walking distance
Sputum induction (only baseline)
Peripheral blood (systemic inflammation)
Peripheral blood 4x (acute smoking)
Exhaled breath condensate 3x (acute smoking)
Exhaled CO 5x (acute smoking)
Bronchial biopsy 2x (acute smoking)
Epithelial lining fluid 2x (acute smoking)
Epithelial brushes 2x (acute smoking)
Low dose HRCT-scan lung
Group
All
All
All
All
B, D1-4
B, D1-4
All
D1-4
D1-4
All
All
All
All
All
All
All
A,B,C,D1-3
All
B,D1-4
A,B,C,D2
All
A,B,C,D2
A,B,C,D2
A,B,C,D2
A,B,C,D2
A,B,C,D2
A,B,C,D2
All
Clinical outcomes
Demographic variables include: age, sex, smoking habits, education, profession, other
exposures, height and weight. Risk factors of the metabolic syndrome will be determined
including blood pressure, waist hip circumference, lipid profile and fasting glucose (table 2).
Questionnaires will be the Clinical COPD Questionnaire (CCQ), the St Georges Respiratory
Questionnaire (SGRQ), the Dutch Fagerstrom test for nicotine dependence, and the Short
QUestionnaire to ASsess Health-enhancing physical activity (SQUASH)(10-12). Exacerbation
frequency will be recorded in COPD patients. The BODE-index will be calculated on basis of
FEV1, six-minute-walking distance, Body Mass Index (BMI) and MRC-dyspnea score (4). In urine
(micro) protein concentration will be assessed. Corticosteroid sensitivity will be measured by
the cutaneous vasoconstrictor response to topical budesonide using the skin blanching test
(13). Budesonide dissolved in 95% ethanol will be applied to the skin using eight different
concentrations (0-1000 μg/ml). Blanching will be scored with a 7-point scale: 0-3 (increasing
with steps of 0.5; 0 = no blanching and 3 = intense blanching). Cumulative oxidative stress will
be measured in the skin using the non-invasive AGE (Advanced Glycation Endproducts) reader
(DiagnOptics, Groningen, The Netherlands) (14).
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Acute and chronic smoking effects and susceptibility to COPD
Physiological outcomes
Spirometry will be performed according to international guidelines (ERS 2005) (15). We will
assess FEV1, FEV1/FVC, IVC, FEF50, FEF75, reversibility to salbutamol, TLC, FRC (body box), and
CO diffusion. Methacholine challenge tests are performed according to international guidelines
(ERS 2005), using serial doubling concentrations of methacholine-bromide (0.03 to 38.4 mg/
ml) with the 2-min tidal breathing method at 5-minute intervals. The six-minute-walkingdistance (6MWD) will be determined according the American Thoracic Society published
guidelines of 2002 (16). Individuals should walk at their own pace, can stop if necessary, and
are allowed to use oxygen. Body composition will be estimated using single frequency (50 kHz)
bioelectrical impedance (Biostat 500), and fat-free-mass will be calculated with the diseasespecific equation of Schols et al (17).
Immunological outcomes
Lung inflammation
• Sputum will be induced and processed according to a validated and standardized
technique(18), with some modifications. Differential cell counts of eosinophils, neutrophils,
lymphocytes, macrophages, epithelial and squamous cells will be performed on May
Grünwald Giemsa (MGG) stained cytospins by a qualified cytopathologist.
• Exhaled breath condensate (EBC) will be collected using The EcoScreen® (Jaeger,
Hoechberg, Germany). Hydrogen peroxide, pH, 8-isoprostane, nitrite, nitrate, 4-hydroxy2-nonenal and malondialdehyde will be measured.
• Bronchoscopy will be performed using established guidelines (19-21), and 6 bronchial
biopsies will be taken from subsegmental carinae in the right or left lower lobe. Epithelial
morphology, epithelial proliferation, and basement membrane thickness will be measured
(22). Submucosal density of inflammatory cells (AA1, EG2, CD68, CD3, CD4, CD4CD25, CD8,
mast cells, neutrophils) will be quantitated in a semi-automated way (22). Expression of
E-Cadherin, VEGF, ICAM, VCAM, E-selection, P-selectin, AGEs and RAGEs will be measured.
• Epithelial lining fluid will be sampled by advancing 3 microsample probes (BC -401C,
Olympus, Tokyo, Japan) in the lumen of the left main bronchus (23,24). Cytokines will be
measured by Luminex (Linco, Nuclilab BV, Ede, The Netherlands). 90% of the ELF will be
used for proteomic analysis. Briefly, each trypsin digested sample will be labeled (iTRAQ®
Reagent 8-plex, ABSciex, Foster City, CA, USA) according to the manufacturer’s protocol. The
individually labeled digests will be combined into a single sample mixture and subjected
to strong-cation exchange chromatography (AKTA Purifier, GE Healthcare Biosciences
AB, Uppsala, Sweden). The resulting peptide-containing fractions will be separated by
reversed-phase chromatography (Ultimate 3000 nanoflow liquid chromatography system,
Dionex, Amsterdam, The Netherlands). Fractions of 12 sec will be spotted on MALDI
targets (Probot, Dionex, Amsterdam, The Netherlands) and mass spectrometric analysis
will be carried out on a 4800 Proteomics Analyzer MALDI TOF/TOF instrument (Applied
Biosystems, Foster City, CA, USA) controlled by the 4000 Series Explorer v3.5 software.
Proteins will be identified using Protein Pilot® software v2.0 (Applied Biosystems).
• Bronchial epithelial cells will be harvested from the right or left main bronchus by
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Chapter 2
brushing as described elsewhere (25). Brushed epithelial cells will be cultured to enable
corticosteroid sensitivity experiments. In these experiments, cultured bronchial epithelial
cells will be incubated in vitro with steroids and the effects on chemokine production
(IL-8, GRO-a, RANTES) and MMP/TIMP expression (mRNA) will be established. In addition,
in peripheral blood mononuclear cells (PBMC) the following parameters will be studied:
1) plasma levels of chemokines/inflammatory cytokines 2) In vitro effects of steroids on
TNF-α, IL-1β, IL-10, TGF-β, signaling pathways (western/EMSA), TLRs and CD14 expression
as well as genes with a GRE in their promoter, e.g. 2-adrenergic receptor, MAPKP-1, FoxP3
(ELISA/RTPCR).
Systemic inflammation
Systemic inflammation will be measured in peripheral blood using several methods to study
systemic activation of innate immune cells at four different levels:
• Expression of established and newly markers on innate immune cells associated with preactivation (26,27). The established markers include proteins that are up-regulated on the
cell surface upon activation of neutrophils in vitro, and can be measured by flowcytometry:
CD11b (Mac-1), CD18 (integrin β2 chain), CD66b (CAECAM-8), CD63 (LAMP-3). New
markers directed against active integrins and Fc-receptors have been shown useful in
detecting more subtle activation such as induced by cytokines: active Mac-1 (CD11b/
clone CBRM1/5 (28)), active β1-integrin chain (CD29/ clone N29 (29)), and active FcγRII
(CD32/clones A17 (30)). These latter markers will be used to detect subtle priming signals
affecting the function of leukocytes in the peripheral blood.
• Determination of the sensitivity of innate immune cells for stimuli. One of the first
changes which can be observed in response to inflammatory stimuli in vivo is a change
in sensitivity for innate immune stimuli such as fMLF. Little activation is associated with
an enhanced responsiveness, whereas pronounced systemic activation is associated with
decreased responsiveness for fMLF (31)). Therefore, the responsiveness of leukocytes for
fMLF will be measured as read-out for systemic inflammatory signals in vivo.
• Genomic and proteomic analysis of innate immune cells in vivo (32). Total mRNA and
proteins are collected from leukocytes and will be analysed by unsupervised genomic
and proteomics techniques. Proteomics will be carried out by 2D-DIGE (33).
• Multiplex analysis of the presence of pro-and anti-inflammatory cytokines in plasma/
serum. Serum samples will be analysed for the presence of multiple cytokines and
chemokines by luminex technology (34).
Systemic inflammation will also be measured in peripheral blood using peripheral blood
mononuclear cells (PBMC’s):
• Expression of intracellular and cell-surface markers of adaptive immune cells (Th1-cells,
Th2-cells, Th17-cells, Treg-cells, B-cells, NK-cells) will be measured by flow cytometry.
Lung and systemic inflammation after acute smoking
Young and old subjects who are susceptible or not susceptible to develop COPD will smoke
3 cigarettes in 1 hour. Exhaled CO, blood samples, and urine, will be collected at baseline and
30
Acute and chronic smoking effects and susceptibility to COPD
after smoking according the scheme in table 3. Exhaled CO will be measured at baseline to
check if individuals did not smoke recently, and after smoking to check if individuals inhaled
cigarette smoke sufficiently. A first bronchoscopy will be performed after 24 hours. Bronchial
biopsies, epithelial brushes and microprobe sampling of epithelial lining fluid will be collected.
Six weeks after the acute smoking procedure a second bronchoscopy will be performed as a
baseline measurement, obtaining the same specimen.
Table 3. Acute smoke model
Baseline
Smoking
3
cigarettes
5
minutes
2
hours
24
hours
6
weeks
Exhaled CO
x
x
x
x
x
Blood
x
x
Exhaled breath condensate
x
x
x
Urine
x
x
x
Bronchial Biopsies
x
x
Epithelial brush
x
x
Microsampling probe (ELF)
x
x
Samples collected at baseline and during the acute smoking procedure including sputum supernatant, serum,
plasma, DNA and RNA of blood, urine, exhaled breath condensate, epithelial lining fluid, epithelial brushes, and
bronchial biopsies will be stored for further analyses.
Radiological outcomes
All subjects will undergo a low-dose CT-scan at full inspiration and expiration. Exposure
settings will be 30 mAs at 90 kVp for patients weighing less then 50 kg, 30 mAs at 120 kVp
for patients weighing between 50 and 80 kg and 30 mAs at 140 kVp for those weighing more
than 80 kg without dose modulation. During expiration the exposure settings will be 20 mAs
at 90 kVp (body mass < 80kg) or 20 mAs at 120 kVp (body mass > 80kg). Emphysematous
lung changes will be quantitated using automated software on low-dose CT scanning images
developed in the UMC Utrecht.
Sample size calculation
We concluded that the limited data in the literature do not allow to calculate a reliable sample
size according to a formal power-analysis. In general 20-30 subjects per group are needed in
studies to detect a significant pro- or anti-inflammatory effect in sputum, BAL or bronchial
biopsies. Looking to the available acute smoking studies in the literature this seems sufficient
to detect an effect at least in exhaled breath condensate.
Statistical analyses
Demographic variables as age, sex, smoking habits, education, work, other exposures, height
and weight will be expressed as means (SD) or medians (IQR) as appropriate for continuous
variables, and number (percentages) for dichotomous variables, according to group.
Exacerbation frequency will be described (with percentage) per groups. Spirometry data (FEV1,
FEV1/FVC, IVC, FEF50, FEF75, reversibility to salbutamol, TLCO TLC, FRC (body box), CO diffusion,
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Chapter 2
methacholine challenge tests), and data indicative of systemic inflammation will be described
likewise.
Comparisons between groups with regard to all of the above mentioned variables
will be tested using Chi-square tests in case of comparison of proportions, and parametric
(like the unpaired t-test) or non-parametric tests (like the M-W-U-test/ Wilcoxon rank sum) as
appropriate according to the distribution of the residuals. To test changes within groups over
time at various visits, additionally paired variants of the before mentioned tests will be used as
appropriate (for example, the paired-t-test and the Wilcoxon signed rank test).
Linear or logistic regression will be used to further analyze differences between
groups in the above mentioned outcome variables taking confounding factors into account.
Techniques like Linear Mixed Effects models will be used to estimate changes in variables over
time.
Ethics and dissemination
The two studies are registered at clinicaltrial.gov (identifier study 1: NCT00807469 and identifier
study 2: NCT 00850863). These two studies have been judged by the medical ethical committee
of UMC Groningen and additionally study 2 has been minimally judged by the medical ethical
committee of UMC Utrecht. The study findings will be presented at conferences and will be
reported in peer-reviewed journals.
DISCUSSION
There is a large backlog in the recognition of different phenotypes of COPD and their underlying
immunopathological processes. This importantly hinders the appropriate diagnosis, treatment
and prognosis of this disabilitating disease. Currently lung function (FEV1 and FEV1/FVC) is
still the standard for the diagnosis and classification of COPD (1). However, there is general
consensus that FEV1 poorly correlates with important patient-centred outcomes such as
quality of life, symptoms and exercise capacity (35). Celli et al showed an association between
FEV1 and mortality when FEV1 was combined with MRC-dyspnoea score, 6-minute walking
distance and BMI. The so-called BODE index was put forward as a composite measure to
characterise COPD in a more realistic way (4). In the last decades different approaches have
been put forward to characterize COPD leading to at least 16 different phenotypes (36).
Although clinically relevant in terms of presentation, triggers and treatment response these
phenotypes do not necessarily give insight into the underlying disease processes of COPD.
In this perspective the term intermediate phenotype or endotype has been put forward to
describe a subtype of a disease which is defined by a distinct functional or pathophysiological
mechanism (37). Together with genetic and environmental factors intermediate phenotypes
may explain the clinical presentation of a heterogeneous disease like COPD. Accordingly, the
present study will phenotype the induction and progression of COPD and associate this with
underlying pathophysiological mechanisms in a biased as well as non-biased way. As smoking
is the most important environmental risk factor for COPD we will use an acute smoking
model to evaluate differences in smoking-induced acute mechanisms differentially expressed
32
Acute and chronic smoking effects and susceptibility to COPD
between individuals with a high and low risk for development of COPD.
Recently, a large prospective cohort study (ECLIPSE) was initiated to study the natural
course of COPD in order to gain more insight in the underlying pathogenetic mechanisms (38).
The ECLIPSE study is a three year observational study including current and ex-smoking COPD
patients and healthy controls with and without a smoking history. Indeed the ECLIPSE study
confirmed that the clinical manifestations of COPD are highly variable and that the degree of
airflow limitation does not capture the heterogeneity of the disease(39). Particularly, the rate of
change in FEV1 among patients with COPD was highly variable, with increased rates of decline
among current smokers, patients with bronchodilator reversibility and with emphysema (40).
Several new susceptibility genes have been identified in the ECLIPSE study (41,42), as well
as potentially useful biomarkers (43-45). However, in contrast to our study ECLIPSE does not
include young subjects and is, therefore, not able to investigate the susceptibility for COPD
at young age. ECLIPSE investigates aspects of systemic inflammation (CRP, TNF-α, IL-6, IL-8,
SDP), but does not investigate the activation state of circulating neutrophils and lymphocytes,
nor does it perform unbiased proteomic analyses of epithelial lining fluid and peripheral
blood neutrophils. Therefore, our study will complement ECLIPSE data by focusing on the
pathogenesis of local and systemic inflammation by using unique approaches to link genomic
and inflammatory phenotypes in all stages of COPD from preclinical to advanced disease.
The present study has already been started and recruitment is still ongoing. The
study population has been described in ClinicalTrial.gov (NCT00807469, NCT 00850863) and
was divided into 9 groups. Initially, we planned to distinguish susceptible individuals into a
“susceptible” and “very susceptible” group. The group of “old” very susceptible individuals
should include early-onset COPD (FEV1/FVC<70%, FEV1< 40% predicted, age<53 years) and
COPD with low number of pack years (FEV1/FVC < 70%, FEV1 < 80%predicted, pack years<5).
The group of “young” very susceptible individuals should have included young individuals
with family members with early-onset COPD or COPD with low smoke exposure. Despite an
intensive search among lung transplantation (LTx) candidates/recipients and their family
members we were not able to recruit this group in sufficiently high numbers. Therefore, we
decided to combine the susceptible and very susceptible groups.
COPD is often accompanied by different co-morbidities, especially cardiovascular
conditions, which also affect the prognosis of the disease as well as quality of life and cost
of COPD (46,47). Consequently, we do not exclude subjects with cardiovascular co-morbidity
conditions unless the condition was acute or too severe. We use the selected grade 1-3 comorbidity list in the ACE-27 (9) to exclude patients with co-morbidities within grade 2 or 3
in all organ systems except the respiratory system. We also exclude subjects with systemic
inflammatory diseases such as rheumatoid arthritis, because we might investigate systemic
inflammation related to other systemic inflammatory diseases.
In conclusion this study will provide valuable information regarding the pathogenetic
mechanisms underlying the development of COPD, which in the future will help us to develop
new targets for the management of different phenotypes of COPD.
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Chapter 2
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Acute and chronic smoking effects and susceptibility to COPD
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29. Johansson MW, Kelly EA, Busse WW, Jarjour NN, Mosher DF. Up-regulation and activation
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expiratory volume in 1 second over time in COPD. N Engl J Med 2011 Sep 29;365(13):11841192.
41. Qiu W, Cho MH, Riley JH, Anderson WH, Singh D, Bakke P, et al. Genetics of sputum gene
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42. Castaldi PJ, Cho MH, Litonjua AA, Bakke P, Gulsvik A, Lomas DA, et al. The Association
of Genome-Wide Significant Spirometric Loci with Chronic Obstructive Pulmonary Disease
Susceptibility. Am J Respir Cell Mol Biol 2011 Dec;45(6):1147-1153.
36
Acute and chronic smoking effects and susceptibility to COPD
43. Lomas DA, Silverman EK, Edwards LD, Locantore NW, Miller BE, Horstman DH, et al. Serum
surfactant protein D is steroid sensitive and associated with exacerbations of COPD. Eur
Respir J 2009 Jul;34(1):95-102.
44. Sin DD, Miller BE, Duvoix A, Man SF, Zhang X, Silverman EK, et al. Serum PARC/CCL-18
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Intern Med 2005 Feb 15;142(4):233-239.
37
2
3
Chapter
Increased activation of blood neutrophils after
cigarette smoking
in young individuals susceptible to COPD
Susan Hoonhorst, Wim Timens, Leo Koenderman, Adèle Lo Tam Loi,
Jan-Willem Lammers, Marike Boezen, Antoon van Oosterhout,
Dirkje Postma, Nick ten Hacken
Respiratory Research 2014 15;121
Chapter 3
Abstract
Background: Cigarette smoking is the most important risk factor for Chronic Obstructive
Pulmonary Disease (COPD). Only a subgroup of smokers develops COPD and it is unclear why
these individuals are more susceptible to the detrimental effects of cigarette smoking. The risk
to develop COPD is known to be higher in individuals with familial aggregation of COPD. This
study aimed to investigate if acute systemic and local immune responses to cigarette smoke
differentiate between individuals susceptible or non-susceptible to develop COPD, both at
young (18-40 years) and old (40-75 years) age.
Methods: All participants smoked three cigarettes in one hour. Changes in inflammatory
markers in peripheral blood (at 0 and 3 hours) and in bronchial biopsies (at 0 and 24 hours)
were investigated. Acute effects of smoking were analyzed within and between susceptible
and non-susceptible individuals, and by multiple regression analysis.
Results: Young susceptible individuals showed significantly higher increases in the expression
of FcγRII (CD32) in its active forms (A17 and A27) on neutrophils after smoking (p = 0.016 and
0.028 respectively), independently of age, smoking status and expression of the respective
markers at baseline. Smoking had no significant effect on mediators in blood or inflammatory
cell counts in bronchial biopsies. In the old group, acute effects of smoking were comparable
between healthy controls and COPD patients.
Conclusions: We show for the first time that COPD susceptibility at young age associates with
an increased systemic innate immune response to cigarette smoking. This suggests a role of
systemic inflammation in the early induction phase of COPD.
Trial registration: Clinicaltrials.gov: NCT00807469 (http://clinicaltrials.gov/show/
NCT00807469)
40
Acute effects of smoking and COPD susceptibility
INTRODUCTION
Cigarette smoking is the most important risk factor for Chronic Obstructive Pulmonary Disease
(COPD) (1). However, only a proportion of all smokers, about 15-20%, will actually develop
COPD, the so-called ‘susceptible’ smokers. It is still unclear which factors determine why these
individuals are more sensitive to the detrimental effects of cigarette smoking compared with
‘non-susceptible’ smokers.
To better understand how cigarette smoking leads to irreversible lung damage and
chronic airflow obstruction, knowledge of the initial responses to cigarette smoking might be
very useful. Several studies investigated the acute inflammatory and oxidative stress responses
to cigarette smoking in animal and in vitro models, yet only a few studies investigated these
responses in humans (2). These studies focused generally on COPD patients and ‘healthy
smokers’ without airway obstruction. However, aging and the cumulative amount of packyears smoking may lead to changes in the airways and lung parenchyma in both groups, likely
affecting their response to cigarette smoking. Particularly in COPD, the structural changes in
the lung may lead to a different response to smoking. For this reason, it might be hypothesized
that the very first responses to cigarette smoking in healthy young individuals with a low
number of pack-years is an ideal model to investigate the induction and early progression
towards COPD.
Several family studies have provided evidence that a genetic predisposition is
involved in the smoking-related development of COPD. Silverman et al. showed that smoking
or ex-smoking in first degree relatives of early-onset COPD probands associates significantly
with lower forced expiratory volume in one second (FEV1) values compared to relatives of
control subjects (3). Several other studies have demonstrated that the combination of smoking
and familial clustering of COPD strongly associates with a higher risk for COPD (4-6). Although
a history of familial COPD may help to identify smokers who are susceptible to develop COPD
themselves, a more discriminative biomarker would be welcome in the field of preventive
medicine. Additionally, elucidating the smoking-induced pathogenesis of COPD in susceptible
individuals may ultimately lead to the identification of new drug targets.
The aim of this study was to identify early biomarkers of COPD susceptibility
by investigating acute responses to cigarette smoke in young (18-40 years) individuals
susceptible and non-susceptible to develop COPD, based on a high prevalence or absence
of COPD in smoking relatives. All subjects smoked three cigarettes in one hour. Before and
after smoking, inflammatory markers were determined in peripheral blood and bronchial
biopsies. We hypothesized that susceptible individuals exhibit a different systemic and local
inflammatory response compared to non-susceptible individuals. In addition, we investigated
the acute response to cigarette smoking in older (ex) smokers with and without COPD, to
assess if responses to cigarette smoking change after many years of smoking.
41
3
Chapter 3
Methods
Study population
Young individuals (age 18-40 years) who are susceptible or non-susceptible to develop COPD
were included (7). All young subjects were intermittent smokers, able to quit smoking for at
least 2 days and start smoking on request. Furthermore, we included mild-to-moderate COPD
patients (FEV1 30-80% predicted, FEV1/FVC <0.7, >10 pack-years), and smokers without airway
obstruction (FEV1/FVC >0.7, >20 pack-years). Exclusion criteria are mentioned in the online
supplement.
The study was performed at the University Medical Center Groningen (UMCG)
(NCT00807469, http://clinicaltrials.gov/show/NCT00807469). The medical ethics committee of
the UMCG approved the study protocol and all subjects gave written informed consent.
Study design
Baseline and follow-up measurements were performed after smoking three cigarettes within
one hour (Figure 1). Subjects quitted smoking for at least two days prior baseline visits, and
refrained from smoking between the acute smoking procedure and the 24-hrs bronchoscopy.
Refraining from smoking was verified by exhaled carbon monoxide (CO) measurements
being <5 parts per million (ppm) and sufficient inhalation of the three cigarettes by a rise in
CO (Micro+ Smokerlyzer®, Bedfont Scientific Ltd, Kent, England). Subjects were not allowed to
participate in the acute smoking procedure if their CO measurement was >5 ppm at baseline.
Figure 1. Time frame of the acute smoking procedure.
Definition of abbreviations: CO = carbon monoxide, min = minutes, h = hours. Exhaled CO was obtained at
baseline, directly after smoking, and 2 hours after smoking the last cigarette. Blood samples were collected at
baseline and 2 hours after smoking the last cigarette. Bronchial biopsies were obtained 24 hours after smoking.
Six weeks later bronchial biopsies were obtained as baseline measurement. Subjects refrained from smoking
during two days before the baseline measurements and the baseline bronchoscopy after 6 weeks. In addition,
subjects refrained from smoking after the acute smoking procedure until the 24 hrs bronchoscopy.
Measurements
Demographic characteristics were obtained and spirometry, body plethysmography and COdiffusion were performed according to standardized guidelines (8,9).
Before and after smoking, blood was collected in sodium heparin tubes or serum
tubes to perform flow cytometry analysis (FACs) on neutrophil activation markers and cytokine
quantification respectively. Detailed methods are described in the online supplement. Briefly,
42
Acute effects of smoking and COPD susceptibility
leucocytes were triple stained with antibodies against (FcγRII) CD32, Mac-1 (CD11b), ICAM1 (CD54), IL-8 receptors (CD181/CXCR1, CD182/CXCR2) combined with antibodies directed
against L-selectin (CD62L) and FcγRIII (CD16). Additionally, the expression of the active form
of FcγRII (CD32) was identified by monoclonal phages antibodies MoPhab A17 and A27 (10).
Cells were analyzed in a flow cytometer (FACScalibur; BD Biosciences). Within the granulocyte
population (identified based on forward (FCS) and side-scatter (SSC)), neutrophils were
identified by CD16high expression and eosinophils by CD16low expression. Flow cytometry data
was analysed by FCS Express Version 3 (De Novo software) and median fluorescence intensities
(MFI) were calculated. Cytokine quantification was performed by multiplex analyses (Milliplex,
Millipore Corporation, Billerica, MA, USA).
Bronchial biopsies were taken from subsegmental carinae of the right or left lower
lobe. Briefly, biopsies were fixed in 4% neutral buffered formalin, processed and embedded
in paraffin and cut in 3 μm sections. Immunohistochemical stainings were performed using
the DAKO autostainer (DAKO, Glostrup, Denmark) using antibodies against inflammatory cells.
Detailed immunohistochemistry and quantification procedures are presented in the online
supplement.
Data analyses
Group characteristics were analyzed using Mann-Whitney U tests or Chi-squared tests.
The Wilcoxon signed-rank test was performed to test acute smoking effects within groups.
Absolute changes with smoking were analyzed between groups using Mann-Whitney U tests.
Multiple linear regression analysis was performed with absolute change in the variables tested
as dependent variable and susceptibility to COPD (n/y) as predictor variable. Models were
adjusted for relevant co-variables. Data were normalized by log-transformation if necessary.
Linear regression models were considered valid if the residuals were normally distributed.
Statistical analyses were performed using the statistical program IBM SPSS Statistics version 20.
Results
Subjects
Table 1 presents the clinical characteristics of subjects that were included in the study: 50
young individuals, 29 non-susceptible and 21 susceptible, and 40 older subjects, 27 healthy
controls and 13 COPD patients. All subjects successfully performed the acute smoking
procedure. However, from the total group (n = 90) 6 subjects had missing data in the flow
cytometry analyses due to technical reasons and 19 subjects (young non-susceptible: n =
4, young susceptible: n = 7, healthy controls: n = 7, COPD patients: n = 1) had incomplete
bronchial biopsy data because subjects did not want to undergo a second bronchoscopy.
43
3
Chapter 3
Table 1. Group characteristics
Young (<40 years)
Old (>40 years)
Susceptible
Healthy controls
COPD
Non-susceptible
(n = 29)
(n = 21)
(n = 27)
(n = 13)
Age, years
21 (20-23)
31 (22-38)*
51 (46-62)
66 (64-70)†
Gender, male n (%)
17 (59)
11 (52)
23 (85)
13 (100)
Pack-years
1 (0-3)
5 (2-10)*
26 (23-36)
32 (23-46)
Current smokers, n (%)
29 (100)
13 (62)*
26 (96)
10 (77)
Ex-smokers, n (%)
0 (0)
0 (0)
1 (4)
3 (23)
Non-smoker, n (%)
0 (0)
8 (38)
0 (0)
0 (0)
Cig. / day for smoking subjects, n
3 (1-10)
8 (2-17)
14 (8-20)
6 (3-14)†
FEV1,%predicted
106 (101-112)
110 (104-114)
106 (102-116)
65 (60-75)†
FEV1/FVC,%
85 (83-91)
81 (78-87)*
78 (74-83)
50 (38-59)†
RV/TLC,%
22 (19-24)
25 (23-28)
32 (28-37)
39 (34-48)†
TLCO/VA,%predicted
100 (92-110)
95 (82-105)
100 (91-106)
75 (63-96)†
MEF50,%predicted
97 (85-119)
94 (85-108)
90 (80-151)
23 (12-29)†
hsCRP, mg/L
0.7 (1.6-1.9)
1.0 (0.6-2.2)
1.9 (0.6-3.8)
2.9 (1.0-5.0)
Blood neutrophils, x109/L
3.3 (2.7-3.9)
3.8 (2.9-4.4)
3.5 (2.7-4.7)
3.8 (3.3-5.0)
Blood eosinophils, x109/L
0.16 (0.13-0.26) 0.12 (0.10-0.19) 0.17 (0.10-0.20)
0.20 (0.1-0.4)
Definition of abbreviations: n = number, FEV1 = Forced Expiratory Volume in one second, FVC = Forced Vital
Capacity, RV = Residual Volume, TLC = Total Lung Capacity, TLCO/VA = transfer coefficient for carbon monoxide,
MEF50 = maximal expiratory flow at 50% of vital capacity, hsCRP = high-sensitivity C-Reactive Protein. Data are
expressed as medians with interquartile ranges (IQR), unless stated otherwise. * p-value <0.05, young susceptible
versus young non-susceptible. † p-value <0.05, COPD versus healthy controls.
Table 2. Neutrophil activation markers measured in blood by flow cytometry 2 hours
after smoking
Change with smoking
Young non-susceptible
Young susceptible
(n = 27)
(n = 20)
p-value †
CD16+ Neutrophils
3.4 (-1.3;6.7)*
1.8 (0.2;4.8)*
NS
CD16− Eosinophils
−3.6 (-5.6;-2.4)*
−2.3 (-3.9;-1.3)*
0.037
CD11b (Mac-1)
−4.6 (-20.3;18.6)
9.9 (-16.4;76.6)
NS
CD32 (FcγRII)
−12.6 (-23.9;2.67)
−12.3 (-31.7;-6.1)*
NS
CD54 (ICAM-1)
−0.8 (-1.6;0.48)
−1.6 (-23.0;-0.13)*
NS (0.072)
CD181/CXCR1 (IL-8 receptor)
−19.2 (-48.2;-2.6)*
−5.2 (-27.9;9.8)
NS (0.073)
CD182/CXCR2 (IL-8 receptor)
−23.5 (-53.0;-6.5)*
−27.7 (-46.5;0.7)*
NS
A17 (active FcγRII)
3.26 (-3.0;3.3)
14.8 (2.6;71.0)*
NS (0.067)
A27 (active FcγRII)
0.4 (-9.6;14.6)
19.0 (0.8;67.8)*
NS (0.078)
Values are expressed as median change (Tafter-Tbefore) in fluorescence intensity (MFI) with interquartile ranges (IQR),
two hours after smoking. * Significant response to cigarette smoke within the group (Wilcoxon signed-rank tests,
p < 0.05). † p-values for differences in responses to cigarette smoke between susceptible and non-susceptible
subjects (Mann-Whitney U tests, NS = not significant).
44
Acute effects of smoking and COPD susceptibility
Exhaled CO
The baseline median (IQR) exhaled CO value was 1 (1-2) in the whole group, and values were
significantly increased in all groups after smoking; 5 (3-10) in young non-susceptible, 7 (4-11)
in young susceptible, 8 (6-10) in old healthy controls and 5 (3-8) in COPD patients respectively.
There were no significant differences between the study groups in median exhaled CO levels
after smoking.
Flow cytometry on systemic inflammatory cells
Table 2 presents changes in cell-surface marker expression on neutrophils in the young
groups with smoking. Absolute values before and after smoking are presented in Table E1.
In the susceptible group, CD32 and CD54 expression decreased, and expression of active
FcγRII (clones A17 and A27) increased significantly as demonstrated in Figure 2. In the nonsusceptible group CD181/CXCR1 expression was significantly decreased. Figures 2 and 3 show
that CD182/CXCR2 expression, and percentages of eosinophils (CD16− granulocytes) and
neutrophils (CD16+ granulocytes) similarly decreased in the two groups.
Differential responses to smoking were borderline significant between groups as
follows: CD54 decreased more and A17 and A27 increased more in the young susceptible
group (Figure 2). CD181/CXCRI expression increased more in the young non-susceptible
group (Figure 2). Finally, eosinophil percentages decreased more in the non-susceptible group
(Figure 3).
Cytokine concentrations in serum
Changes in cytokine levels with smoking are presented in Table 3 (see Table E1 for absolute
values before and after smoking). IL-8 and GM-CSF were significantly decreased in the nonsusceptible group, whereas IL-7 significantly increased. Furthermore, TNFα was significantly
decreased in both groups. The changes with smoking were not significantly different between
the susceptibility groups.
45
3
Chapter 3
Figure 2. Effects of acute smoking on expression of neutrophil activation markers in
young subjects
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responses to cigarette smoke within groups were analyzed by Wilcoxon signed-rank tests. Differences in
responses to cigarette smoke between susceptible and non-susceptible groups were analyzed by comparing
delta’s (Tafter-Tbefore) using Whitney U tests. *p < 0.05, NS = not significant.
46
Acute effects of smoking and COPD susceptibility
Figure 3. Effects of acute smoking on total neutrophils and eosinophils in young subjects.
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Table 3. Cytokines measured in blood 2 hours after smoking
Change with smoking
Young non-susceptible
Young susceptible
(n = 29)
(n = 21)
p-value†
IL-1β
0.00 (0.00;0.00)
0.00 (0.00;0.00)
NS
IL-6
0.00 (-0.25;0.15)
−0.04 (-1.0;0.06)
NS
IL-8
−0.70 (-1.56;-0.01)*
−0.24 (-1.03;0.68)
NS
GM-CSF
0.00 (-0.20;0.00)*
0.00 (-0.54;0.00)
NS
TNFα
−0.27 (-0.89;0.00)*
−0.35 (-1.11;0.16)*
NS
IFNγ
0.00 (-0.84;1.10)
−0.49 (-1.59;1.13)
NS
IL-2
0.00 (-0.46;0.70)
0.00 (-0.70;0.48)
NS
IL-4
0.00 (-0.91;0.00)
0.00 (-0.11;0.00)
NS
IL-5
0.00 (-0.09;0.09)
0.00 (-0.05;0.00)
NS
IL-7
0.79 (0.00;3.97)*
0.00 (-1.07;3.39)
NS
IL-10
0.00 (-1.81;0.00)
0.00 (-1.35;1.46)
NS
IL-12p70
0.00 (-0.31;0.24)
0.00 (-0.83;0.31)
NS
IL-13
0.00 (0.00;1.65)
0.00 (-1.10;1.63)
NS
Values are expressed as median change (Tafter-Tbefore) in cytokine concentration (pg/ml) with interquartile ranges
(IQR), two hours after smoking. * Significant response to cigarette smoke within the group (Wilcoxon signedrank tests, p < 0.05). † p-values for differences in responses to cigarette smoke between susceptible and nonsusceptible subjects (Mann-Whitney U tests, NS = not significant).
47
3
Chapter 3
Inflammatory cells in bronchial biopsies after smoking
Table 4 presents the changes in inflammatory cell counts in bronchial biopsies 24 hours
after smoking (see Table E1 for absolute values before and after smoking). Eosinophils (EPX
immunopositivity) was significantly increased in the non-susceptible group. The changes of
bronchial cell counts were not significantly different with smoking between groups.
Table 4. Inflammatory cells in bronchial biopsies 24 hours after smoking
Change with smoking
Young non-susceptible
Young susceptible
(n = 25)
(n = 14)
p-value†
Submucosal
CD3+ T-cells
6.8 (-21.9;21.6)
3.2 (-9.1;15.3)
NS
CD4+ T-cells
0.0 (-2.8;11.8)
−0.4 (-7.4;1.5)
NS
CD8+ T-cells
0.2 (-0.8;16.5)
−8.9 (-29.9;11.3)
NS
FOXP3+ T-cells
0.9 (-1.9;2.2)
0.2 (-0.3;1.8)
NS
CD68+ macrophages
2.3 (-1.2;7.2)
−0.4 (-4.0;2.6)
NS
AA1+ mast cells
0.0 (-2.1;11.8)
1.1 (-3.5;2.7)
NS
EPX+ eosinophils
0.8 (0.0;1.0)*
0.0 (-0.7;1.0)
NS
NP57 + neutrophils
1.2 (-5.8;5.2)
1.7 (-6.8;7.4)
NS
% E-selectin pos. vessels
0.0 (-1.0;0.0)
0.0 (-5.2;0.0)
NS
Values are expressed as median change (Tafter-Tbefore) in cell counts with interquartile ranges (IQR), 24 hours after
smoking. Inflammatory cells are expressed as cell counts / 0.1 mm2. * Significant response to cigarette smoke
within the group (Wilcoxon signed-rank tests, p < 0.05). † p-values for differences in responses to cigarette
smoke between susceptible and non-susceptible subjects (Mann-Whitney U tests, NS = not significant).
Susceptibility as predictor of systemic responses to cigarette smoking
We assessed whether susceptibility predicted the changes in expression of CD54, CD181/
CXCR1, active FcγRII (clones A17 and A27) and percentage of eosinophils with smoking by
multiple linear regression models. Table 5 shows that susceptibility was a significant predictor
of the change in A17 and A27 expression after smoking, independently of expression at
baseline, age and smoking status. Susceptibility was not associated with CD54 and CD181/
CXCR1 expression, and percentage of eosinophils.
Table 5. Associations of susceptibility (no/yes) with the change in expression of
neutrophil markers after smoking
Dependent variable: change in expression with smoking
Predictor variable: susceptibility y/n†
n = 47
B
S.E.
p-value
(CD181/CXCR1 (IL-8 receptor)‡
0.130
0.106
0.227
CD54 (ICAM-1)
−1.043
0.878
0.241
A17 (active FcγRII)‡
0.127
0.051
0.016*
A27 (active FcγRII)‡
0.102
0.045
0.028*
CD16− Eosinophils
0.680
0.695
0.334
Different multiple regression models with susceptibility to COPD (y/n) as predictor value and change in
expression of neutrophil markers (CD181/CXCR1, CD54, A17 or A27) or % eosinophils after smoking (TafterTbefore) as dependent variable. B = regression coefficient. * Significant (p < 0.05). † all models were adjusted for
expression of marker at baseline, age and current smoking n/y. ‡ Data were log-transformed.
48
Acute effects of smoking and COPD susceptibility
Additionally, using multiple linear regression analysis, we investigated if the changes in
expression of neutrophil activation markers were predictors of the change in number
of neutrophils in bronchial biopsies. Both a higher increase in CD54 expression of blood
neutrophils and susceptibility were significant predictors of a higher increase of bronchial
neutrophils counts (Table 6, Figure 4).
Table 6. Association of change in expression of neutrophil activation markers and
susceptibility with change in number of bronchial NP57+ neutrophils in bronchial
biopsies
Outcome variable: change in number of bronchial NP57+ neutrophils with smoking
n = 39, R2 = 0.620
β
p-value
Change in CD54 expression (Tafter-Tbefore)
0.271
0.044 *
NP57 expression at baseline
−0.597
<0.001 *
Susceptibility, n/y
0.386
0.013 *
Current smoking, n/y
0.309
0.037 *
Multiple regression model with susceptibility to COPD (y/n) as predictor value and change in expression
of neutrophil count in bronchial biopsies after smoking (Tafter-Tbefore) as dependent variable. Β = standardized
regression coefficient.* Significant (p < 0.05).
! Figure 4. Association between change in expression of neutrophil activation markers and
change in number of bronchial NP57+ neutrophils in bronchial biopsies after smoking
! ! "! Values are expressed as change in median fluorescence intensity (MFI) (Tbefore – Tafter), two hours after smoking.
Effects of acute smoking in COPD patients and smokers without airway obstruction
Data of the acute smoking procedure in the old groups are presented in Table E2. Briefly,
we observed no significant differences in the change of neutrophil marker expression after
smoking between COPD patients and controls, whereas the decrease in the percentage of
eosinophils was larger in COPD patients. IL-6 and IL-8 levels in blood decreased after smoking
in healthy controls, a change that was close to significance when compared with the change
in COPD patients. Finally, the change in number of bronchial neutrophils significantly differed
between groups, showing an increase in healthy controls and a decrease in COPD patients.
49
3
Chapter 3
Discussion
This is the first human study using an acute smoking design in a population of young and
old individuals being susceptible or non-susceptible to develop COPD. The focus of this study
was on the comparison of the acute response to cigarette smoking in young individuals, older
subjects were investigated to assess if responses change after many years of smoking. We
demonstrated that susceptibility to develop COPD at young age associates with an enhanced
innate immune response to cigarette smoking in peripheral blood when compared with
non-susceptible individuals, suggesting that a systemic inflammatory component is involved
during the induction of COPD.
Our most important finding is that peripheral blood neutrophil activation markers
were differentially expressed after smoking between young susceptible and young nonsusceptible subjects. Previous human studies have shown that the number of peripheral blood
neutrophils increases after acute smoking (2), a finding that we confirmed, i.e. neutrophils
(CD16+ granulocytes) significantly increased in both the susceptible and non-susceptible
group (Figure 3). However, the activation of neutrophils is a well-described multi-step process,
generally starting with priming (pre-activation) caused by chemotaxins or cytokines, leading
to upregulation of integrins and adhesion molecules (e.g. CD11b, ICAM-1) (11,12). Additionally,
primed neutrophils can be recognized by MoPhab antibodies A17 and A27 since they bind
FcγRII (CD32) only in the context of primed cells and exquisitely capable to detect primed cells
in the circulation (10,13). We found that acute smoking significantly increased median A17 and
A27 expression only in the group of young susceptible subjects (Table 2, Figure 2). This effect
was further confirmed by regression analyses, showing that this increase was independently
of age, smoking status and marker expression at baseline. In contrast, receptors involved in
adhesion and migration tended to decrease after smoking, which was significant for ICAM-1
and CD182/CXCR2 markers in susceptible subjects and for CD181/CXCR1 and CD182/CXCR2
in non-susceptible subjects. Taken together these data suggest that circulating neutrophils
become more activated immediately after smoking, and particularly so in young susceptible
subjects.
The underlying mechanisms are complex. Some in vitro studies have shown that
circulating neutrophils of smokers are pre-activated or primed compared with never-smokers
and have a higher capacity to migrate towards chemotactic stimuli, or are more responsive to
activating agents (14,15). We did not investigate chemotactic characteristics of neutrophils,
however, our data are pointing at a mechanism by which neutrophils are more easily primed
in young susceptible individuals. This may contribute to a higher influx of neutrophils into
the airways, leading to more intense inflammation and tissue damage. The trend we found
in reduced expression of ICAM-1 in young susceptible individuals supports this hypothesis.
Neutrophils with upregulated expression of adhesion molecules may already have left the
circulation infiltrating the lung tissue. This concept has also been proposed for eosinophils
in allergic asthma by Johansson et al (16). Interestingly, we demonstrated that a higher
increase of ICAM-1 expression on circulating neutrophils upon smoking was associated with
a higher increase of bronchial biopsy neutrophils. In the same model, being susceptible to
develop COPD and current smoking were independent predictors of neutrophil influx after
50
Acute effects of smoking and COPD susceptibility
smoking, indicating that the influx of cells is higher in susceptible individuals who smoke.
However, no significant associations were found between bronchial cell counts and the other
neutrophil activation markers. This lack in association may be due to the fact that blood and
bronchial biopsies were collected at different time points. Nevertheless, it is encouraging that
our methods identified subtle alterations in the activation state of circulating neutrophils
associated with changes in neutrophil numbers in the airways.
Blood eosinophil numbers decreased after smoking both in young susceptible and
non-susceptible subjects, a finding in accordance with our previous study on acute smoking
effects in intermittent smokers (17). Another study in four young healthy women demonstrated
a decreased number of eosinophils two hours after smoking of 12 cigarettes (18). Interestingly,
eosinophil numbers also significantly decreased after smoking in COPD patients and healthy
controls. The underlying mechanism is yet to be defined, but a similar situation is found upon
systemic LPS challenge in man (19). Apparently, eosinophil homing signals are generated by
innate immune signals such as DAMPs (acute smoking) and PAMPs (LPS). Our study did not
show associations between smoking-induced changes in eosinophil numbers and cytokine
concentrations in blood, suggesting that remaining eosinophils were not responsive to the
cytokines with respect to homing of the cells. Another explanation might be that eosinophils
migrated from the circulation into the lung tissue. However, we did not find an associated rise
in eosinophil numbers in bronchial biopsies after smoking. A final explanation is that toxic
substances in cigarette smoke cause apoptosis (20), a phenomenon we did not investigate
specifically. Interestingly, susceptible subjects had a deeper fall in eosinophils than nonsusceptible subjects in both the young and old population, although this finding did not
remain significant in multiple regression analysis. Apparently, the eosinophilic response to
cigarette smoke is not contributing to susceptibility, in contrast to the neutrophilic response.
Next we investigated whether the differences in responses to cigarette smoke
between young susceptible and non-susceptible subjects were also present between COPD
patients and healthy controls. Here we found no differences in expression of neutrophil
activation markers in peripheral blood after smoking, which may be due to the fact that they
were older and had more pack-years. It is known that age and prolonged smoking increases
systemic inflammation (21). Further, several studies have demonstrated that neutrophils are
more activated in COPD patients (22), and this may have obscured a relatively subtle response
on recent smoking exposure. However, basal levels of expression markers in both our old
groups did not differ, thus a different explanation is required. There, we postulate that the
inflammatory response to cigarette smoking after long-term smoking has faded out or has
been switched into a more persistent inflammatory response, minimizing the ability to detect
subtle changes in neutrophil activation.
Smoking of three cigarettes did not affect inflammatory cell counts in bronchial
biopsies 24 hours later. This contrasts with findings in animal models, where acute smoking
results in an influx of inflammatory cells in lung tissue 6-24 hours later (23-25). The time point of
24 hours after smoking was chosen based on animal studies given the lack of data in men (2,7).
It may well be that the response to cigarette smoking in human occurs early after smoking,
or that animals were exposed to relatively much higher levels of cigarette smoke. Smoking in
51
3
Chapter 3
human has been shown to increase neutrophils in sputum (17), bronchoalveolar lavage fluid
(BALF) (26) and lung tissue using nuclear imaging techniques (27). Possibly, the main effects of
smoking do not take place in the large airways, but at other lung regions like peripheral airways
and lung parenchyma. Clearly, our negative biopsy findings can be explained in a number of
ways: collecting biopsies too late after smoking, smoking of too few cigarettes, or investigating
the wrong lung compartment. Future human studies must take these considerations into
account.
The strengths of our study are that we investigated young individuals with normal
lung function who are either susceptible or not susceptible to develop COPD and we used a
disease-specific challenge to find biomarkers of COPD susceptibility. There are some limitations
as well. First, we defined COPD susceptibility on familial history of COPD only; no lung function
measurements were performed to verify COPD in family members. However, family history
of COPD is a strong risk factor of COPD (5) and we maintained a strict inclusion algorithm (7).
Second, we used exhaled CO to verify smoking abstinence before the acute smoking procedure,
yet this is only reliable within 6 hours of smoking cessation. Third, the young susceptible group
smoked a higher number of pack-years compared with the susceptible group. Fourth, the
number of participants was relatively low, especially in the young susceptible and the COPD
group. Additionally, we lost some data because 21% of the subjects did not complete the two
bronchoscopies. However, our significant findings are relevant as they were found in spite of
the low sample size of this study.
In conclusion, we found that COPD susceptibility at young age associates with an
increased activation of peripheral neutrophils after cigarette smoking. This increased innate
immune response was not found at old age, likely because the inflammatory response to
cigarette smoking has faded out or has been switched into a more persistent inflammatory
response as a result of long-term smoking or aging. Our data emphasizes that systemic
inflammation contributes likely to the early induction phase of COPD.
52
Acute effects of smoking and COPD susceptibility
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Acute effects of smoking and COPD susceptibility
3
55
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Chapter
Supplement
Chapter 3
58
Acute effects of smoking and COPD susceptibility
METHODS
Exclusion criteria
Exclusion criteria were α-1-antitrypsin deficiency, acute pulmonary infections, prior history
of significant inflammatory lung disease other than COPD, active infections, treatment with
antibiotics or corticosteroids within 8 weeks, and recent diagnosis of cancer. In addition,
medication which could affect the results of the study was excluded, such as anti-inflammatory
drugs and immunosuppressive agents.
Flow cytometry analysis of neutrophilic surface receptors in blood
Before and after smoking, peripheral blood was collected in tubes containing sodium heparin.
Red blood cells were lysed and washed with 5 ml cold PBS + 10g/l albumin (Albuman) +
0.32% sodium citrate. 25 µl of leukocytes (5x106 cells/ml) were triple stained with antibodies
to identify the expression of activation markers on neutrophils; (FcγRII) CD32, Mac-1 (CD11b),
ICAM-1 (CD54), IL-8 receptors (CD181/CXCR1, CD182/CXCR2) combined with antibodies
directed against L-selectin (CD62L) and FcγRIII (CD16) (to identify neutrophil and eosinophil
populations, respectively). Cells were analyzed in a flow cytometer (FACScalibur; BD
Biosciences). Leucocyte populations were identified based on forward (FCS) and side-scatter
(SSC) characteristics. Neutrophils were identified by CD62Lhigh cells and eosinophils by CD16low
cells in the granulocytes population.
To identify the active form of FcγRII (CD32) on the surface of granulocytes directly
FITC-labelled monoclonal phages antibodies (MoPhab) A17 and A27 were used as described
previously (1). In short, 50 µl of whole blood was primed during 5 minutes by 37 °C. After 5
minutes, samples were incubated with antibodies A17, A27, and CD11b during 1 hour. Red
blood cells were lysed and analysed in a flow cytometer (FACSCalibur; BD Biosciences). The
neutrophil population was identified based on FCS and SSC characteristics.
Antibodies specifications: CD16 (clone 3G), CD62L (clone DREG56) and CD32 (clone
FLI8.26) were obtained from BD Pharmingen (San Diego, CA, USA); CD11b (clone 2LPM19c)
from DAKO (Copenhagen, Denmark); CD54 (clone MEM-111) from Caltag (San Francisco, CA,
USA); CD181/CXCR1 (clone 42705) and CD182/CXCR2 (clone 48311) from R&D systems (Europe,
UK).
All data of flow cytometry were analysed by FCS Express Version 3 (De Novo software)
and for each antibody the median fluorescence intensity (MFI) was calculated.
Multiplex analysis
Before and after smoking, blood was collected in serum tubes containing clot activator and
gel for serum separation. The tubes were stored at room temperature during 2-3 hours and
centrifugated afterwards (2000 rcfmax at room temperature, 10 minutes). Serum was obtained
and stored at -80°C until analyses were performed. Cytokine quantification was performed by
multiplex analyses (Milliplex, Millipore Corporation, Billerica, MA, USA).
59
3
Chapter 3
Inflammatory cells in bronchial biopsies
After administering of local anaesthesia (lidocain 2-4%), a flexible bronchoscope was
introduced and bronchial biopsies were taken from subsegmental carinae of the right lower
lobe. Biopsies were fixed in 4% neutral buffered formalin, processed and embedded in paraffin
and cut in 3 µm sections. Quality of biopsies was verified by hematoxylin and eosin (HE)
staining. Immunohistochemical stainings were performed using the DAKO autostainer (DAKO,
Glostrup, Denmark), by antibodies against T-lymphocytes (CD3 (DAKO), CD4 (Novocastra), CD8
(DAKO)), Treg-lymphocytes (FoxP3, Abcam), neutrophils (NP57, DAKO), macrophages (CD68,
DAKO), mast cells (AA1, DAKO), and eosinophils (EPX, Lee Labs). Sections were deparaffinized,
antigens were retrieved and then sections were incubated with primary antibodies. Primary
antibodies CD3, CD4, FoxP3, NP57, CD68 and AA1 were detected with NovaRed kit (Vector
Labs, Burlingame, USA). CD8 was detected using biotinylated anti-mouse IgGI and streptavidin
labelled peroxidase antibodies followed y the chromogen Nova red. EPX was detected using
biotiniylated anti-mouse and streptavidin alkalin phosphase antibodies followed by the
chrmogen Permant Red. All sections were counterstained with methylgreen (greenblue).
Quantification of all stainings was performed by one blinded observer, using
ImageScope (Aperio Technologies, version 11.2.0.780). Quantification was performed on
the largest of three biopsy sections. The number of positive stained inflammatory cells (i.e.
positively stained nuclei) was scored in the submucosal area, 100 µm under the basement
membrane and in a total area of 0.1mm2 per biopsy (2). Double positive blood vessels stained
for CD31 and E-selectin were also scored in the submucosal area, 100 µm under the basement
membrane and in the whole biopsy.
60
Young (<40 years)
Non-susceptible
Susceptible
(n=27)
(n=20)
Before
After
Before
After
96
88
104
96
(72-129)
(67-129)
(76-143)
(72-209)
169
154
175
154
(143-184)
(133-172)
(161-195)
(135-193)
10
8
12
9
(8-12)
(7-11)
(9-16)
(8-16)
107
78
118
101
(82-138)
(61-100)
(77-157)
(73-154)
100
67
93
63
(78-129)
(50-87)
(63-118)
(52-79)
21
20
38
58
(14-49)
(14-70)
(19-99)
(36-130)
21
22
63
71
(13-54)
(14-54)
(21-81)
(33-137)
6.1
2.6
4.8
1.3
(4.7-9.3)
(1.4-3.5)
(2.9-5.9)
(0.7-3.4)
Values are expressed as median fluorescence intensity (MFI) with interquartile ranges (IQR)
CD11b
CD32
CD54
CD181/CXCR1
CD182/CXCR2
A17
A27
Eosinophils
Old (>40 years)
Healthy controls
COPD patients
(n=24)
(n=13)
Before
After
Before
After
122
136
100
84
(87-166)
(89-198)
(62-133)
(70-157)
184
178
178
160
(165-205)
(165-191)
(165-184)
(143-169)
12
11
11
9
(8-17)
(8-17)
(7-13)
(6-12)
124
116
107
87
(90-151)
(87-169)
(72-124)
(66-120)
93
87
72
56
(63-115)
(58-114)
(56-98)
(51-85)
41
49
39
44
(27-62)
(32-104)
(17-188)
(27-127)
48
45
35
67
(34-107)
(34-107)
(22-114)
(36-124)
4.8
2.8
6.6
3.5
(2.4-7.7)
(1.5-4.5)
(3.3-11.1)
(1.6-5.2)
A. Neutrophil activation markers measured in blood by flow cytometry 2 hours after smoking
Table E1. Values before and after acute smoking in young and old groups
Acute effects of smoking and COPD susceptibility
3
61
62
Young (<40 years)
Susceptible
Non-susceptible
(n=29)
(n=21)
Before
After
Before
After
IL-1β
0.13
0.13
0.13
0.13
(0.13-0.13)
(0.13-0.13)
(0.13-0.13)
(0.13-0.13)
IL-6
0.94
0.87
1.62
1.51
(0.13-1.45)
(0.13-1.38)
(0.13-0.13)
(1.12-2.38)
IL-8
6.16
5.33
6.16
5.15
(4.91-7.47)
(4.03-6.69)
(4.62-7.70)
(3.86-7.64)
GM-CSF
0.13
0.13
0.13
0.13
(0.13-0.58)
(0.13-0.13)
(0.13-1.01)
(0.13-0.68)
TNFα
5.43
5.68
5.61
5.37
(4.20-7.12)
(4.35-6.71)
(3.88-6.97)
(3.68-5.97)
IFNγ
3.76
4.29
7.07
6.08
(2.80-6.77)
(2.33-6.59)
(1.02-9.53)
(3.46-9.43)
IL-2
1.10
1.84
1.84
1.84
(0.27-1.30)
(0.27-2.80)
(0.47-4.42)
(0.83-3.76)
IL-4
0.35
0.35
0.13
0.13
(0.13-4.54)
(0.13-1.72)
(0.13-1.72)
(0.13-1.72)
IL-5
0.18
0.18
0.24
0.26
(0.13-0.39)
(0.13-0.36)
(0.13-0.49)
(0.13-0.45)
IL-7
4.42
7.00
7.16
8.3
(2.84-7.17)
(4.27-9.60)
(4.55-12.20)
(4.27-13.22)
IL-10
5.24
4.54
4.25
4.77
(3.30-8.84)
(2.38-8.94)
(0.13-8.60)
(0.13-12.35)
IL-12p70
1.01
1.19
1.97
1.71
(0.19-1.87)
(0.25-1.84)
(1.23-2.98)
(0.81-2.92)
IL-13
2.21
2.57
2.57
4.13
(0.46-4.60)
(1.17-6.60)
(0.84-7.02)
(1.56-6.70)
Values are expressed as medians with interquartile ranges (IQR)
B. Cytokines measured in blood 2 hours after smoking
Old (>40 years)
Healthy controls
COPD patients
(n=27)
(n=13)
Before
After
Before
After
0.13
0.13
0.13
0.13
(0.13-0.13)
(0.13-0.13)
(0.13-0.13)
(0.13-0.13)
2.26
1.77
2.25
2.41
(1.78-4.60)
(1.40-3.65)
(1.19-4.46)
(1.31-4.70)
6.08
5.78
8.07
7.87
(4.46-9.10)
(3.88-8.02)
(7.2-10.12)
(6.27-10.35)
0.13
0.13
0.13
0.13
(0.13-0.68)
(0.13-0.13)
(0.13-0.13)
(0.13-0.13)
5.68
5.06
5.95
5.57
(4.22-8.00)
(3.92-6.77)
(2.63-9.10)
(3.16-8.75)
3.40
4.16
2.22
2.98
(1.59-9.68)
(1.59-6.48)
(1.44-3.95)
(1.74-6.76)
0.27
0.47
0.79
0.25
(0.13-1.95)
(0.13-1.42)
(0.13-1.44)
(0.15-0.90)
0.13
0.13
0.13
0.13
(0.13-1.72)
(0.13-0.35)
(0.13-4.48)
(0.13-4.54)
0.13
0.13
0.13
0.24
(0.13-0.34)
(0.03-0.24)
(0.13-0.32)
(0.13-0.33)
3.97
4.42
2.77
7.00
(1.22-8.84)
(1.22-10.02)
(1.24-8.41)
(3.98-8.84)
5.24
4.25
5.24
4.08
(0.13-9.85)
(0.13-8.96)
(3.85-9.66)
(0.13-5.92)
1.71
1.45
0.61
1.19
(0.80-3.33)
(0.94-2.77)
(0.19-1.22)
(0.25-1.33)
2.53
2.21
1.00
1.00
(0.13-7.60)
(0.13-7.59)
(0.55-2.39)
(0.67-2.21)
Table E1. Values before and after acute smoking in young and old groups
Chapter 3
Young (<40 years)
Susceptible
Non-susceptible
(n=25)
(n=14)
Before
After
Before
After
33.6
41.8
39
46.1
CD3+ T-cells
(19.6-59.9)
(25.7-56.9)
(26.6-51.8)
(33.1-67.0)
6.0
9.1
5.2
3.1
CD4+ T-cells
(0.4-11.5)
(3.2-19.2)
(0.5-16.8)
(0.6-11.6)
CD8+ T-cells
34.4
33.0
32.4
30.1
(9.9-64.8)
(22.4-51.4)
(22.4-55.8)
(9.0-47.5)
FOXP3+ T-cells
0.9
1.5
1.0
1.8
(0.0-2.8)
(0.8-3.6)
(0.0-3.6)
(0.8-4.2)
CD68+ macrophages
6.1
8
5.7
5.9
(1.4-10.3)
(5.5-13.4)
(2.2-12.1)
(0.9-11.6)
AA1+ mast cells
3.2
3.5
2.9
2.6
(1.9-5.8)
(1.1-5.9)
(0.8-6.9)
(0.0-3.8)
EPX+ eosinophils
0.0
0.8
0
0
(0.0-0.9)
(0.0-1.3)
(0-1.0)
(0.0-1.4)
NP57 + neutrophils
5.7
6.5
6.2
10.1
(0.6-9.9)
(2.9-9.9)
(1.9-13.6)
(4.0-12.8)
% E-selectin
0.0
0.0
0.0
0.0
positive vessels
(0.0-3.6)
(0.0-0.0)
(0.0-6.5)
(0.0-0.0)
Values are expressed as medians with interquartile ranges (IQR)
C. Inflammatory cells in bronchial biopsies 24 hours after smoking
Table E1. Values before and after acute smoking in young and old groups
Old (>40 years)
Healthy controls
COPD patients
(n=20)
(n=12)
Before
After
Before
After
23.4
22.8
14.8
18.6
(12.1-36.3)
(11.2-30.0)
(8.9-21.2)
(6.8-31.9)
2,0
2,0
1.2
2.8
(0.0-6.8)
(0.2-10.8)
(0.0-1.9)
(0-5.2)
14.4
22.9
11.7
14,0
(7.6-32.4)
(8.7-43.6)
(3.7-19.8)
(3.8-25.8)
0.8
1,0
0.0
0.8
(0.0-1.8)
(0.0-2.5)
(0.0-0.8)
(0.0-3.3)
3.7
4.5
4.2
1.9
(0.8-10.2)
(2.0-8.6)
(2.5-7.4)
(0.0-4.1)
2.2
3.4
1.8
2.9
(0.7-5.6)
(1.4-5.7)
(0.2-3.5)
(0.4-3.0)
0.0
0.4
0.4
0,0
(0.0-0.5)
(0.0-2.2)
(0.0-1.9)
(0.0-2.6)
4.0
6,0
5.7
2.9
(0.9-7.2)
(2.8-12.8)
(1.8-12.8)
(0.2-7.7)
0.0
0.0
0.0
2.9
(0.0-0.0)
(0.0-5.9)
(0.0-1.7)
(0.0-7.7)
Acute effects of smoking and COPD susceptibility
3
63
Chapter 3
Table E2. Acute smoking effects in old groups
A. Neutrophil activation markers measured in blood by flow cytometry 2 hours after
smoking
Change with smoking
Healthy controls
COPD
(n=24)
(n=13)
3.4 (0.4-10.2)*
3.4 (0.3-6.4)*
-1.8 (-3.0;-1.1)*
-2.9 (-5.1;-1.6)*
p-value†
CD16+ Neutrophils
NS
0.049
CD16- Eosinophils
CD11b (Mac-1)
-6.5 (-19.8;-1.3)
8.4 (-53.5;82.5)
NS
CD32 (FcγRII)
-6.5 (-19.8;-1.3)*
-12.4 (-20.8;-2.5)*
NS
CD54 (Icam-1)
-0.8 (-2.2;0.4)
-1.0 (-1.9;-0.1)*
NS
CD181/CXCR1 (IL-8 receptor)
-3.2 (-21.6;32.9)
-6.7 (-27.9;5.4)
NS
CD182/CXCR2 (IL-8 receptor)
-2.0 (-16.6;24.1)
-3.9 (-19.6;2.2)
NS
A17 (active FcγRII)
3.1 (-8.5;30.8)
7.0 (-13.1;32.9)
NS
A27 (active FcγRII)
0.6 (-15.2;16.6)
9.1 (-3.0;47.9)
NS
Values are expressed as median change (T3-T0) in fluorescence intensity (MFI) with interquartile ranges (IQR), two
hours after smoking. * Significant response to cigarette smoke within the group (Wilcoxon signed-rank tests,
p<0.05). † p-values for differences in responses to cigarette smoke between susceptible and non-susceptible
subjects (Mann-Whitney U tests, NS = not significant).
B. Cytokines measured in blood 2 hours after smoking
Change with smoking
Healthy controls
COPD
(n=27)
(n=13)
p-value†
IL-1β
0.00 (0.00;0.00)
0.00 (0.00;0.00)
NS
IL-6
-0.48 (-1.87;0.19)*
0.10 (-0.49;0.95)
0.081
IL-8
-0.62 (-1.34;0.21)*
0.37 (-1.09;2.24)
0.091
GM-CSF
0.00 (0.00;0.00)
0.00 (0.00;0.00)
NS
TNFα
-0.71 (-1.20;0.00)*
-0.45 (-0.82;0.14)
NS
IFNγ
-0.37 (-1.58;0.58)
0.50 (0.00;1.12)
NS
IL-2
0.00 (0.00;0.84)
0.00 (-0.70;0.07)
NS
IL-4
0.00 (0.00;0.00)
0.00 (-1.24;0.00)
NS
IL-5
0.00 (-0.10;0.09)
0.00 (0.00;0.06)
NS
IL-7
0.00 (-0.28;1.30)
1.21 (-0.85;4.58)
NS
IL-10
0.00 (-2.98;0.00)
-3.32 (-4.13;0.00)*
NS
IL-12p70
0.00 (-0.26;0.00)
0.00 (-0.08;0.20)
NS
IL-13
0.00 (-1.58;0.87)
0.00 (-1.15;1.65)
NS
Values are expressed as median change (T3-T0) in cytokine concentration (pg/ml) with interquartile ranges (IQR),
two hours after smoking. * Significant response to cigarette smoke within the group (Wilcoxon signed-rank tests,
p<0.05). † p-values for differences in responses to cigarette smoke between susceptible and non-susceptible
subjects (Mann-Whitney U tests, NS = not significant).
64
Acute effects of smoking and COPD susceptibility
Table E2. Acute smoking effects in old groups
C. Inflammatory cells in bronchial biopsies 24 hours after smoking
Change with smoking
Healthy controls
COPD
(n=20)
(n=12)
p-value†
Submucosal
-0.6 (-15.2;10.7)
-0.6 (-11.5;19.6)
NS
CD3+ T-cells
CD4+ T-cells
1.2 (-3.5;5.2)
1.3 (-0.7;3.2)
NS
CD8+ T-cells
7.9 (-3.1;19.8)*
1.7 (-4.3;13.9)
NS
FOXP3+ T-cells
0.0 (-0.7;0.9)
0.0 (0.0;2.6)
NS
CD68+ macrophages
0.1 (-8.3;5.6)
-1.3 (-5.6;0.8)
NS
AA1+ mast cells
0.7 (-2.7;3.1)
0.0 (-1.5;2.4)
NS
EPX+ eosinophils
0.41 (0.0;1.6)
0.0 (-1.5;2.3)
NS
NP57 + neutrophils
2.4 (-0.5;7.4)*
-2.4 (-7.3;1.7)
0.027
% E-selectin pos. vessels
0.0 (0.0-1.1)
1.2 (0.0;3.4)*
NS
Values are expressed as median change(T24-T0) in cell counts with interquartile ranges (IQR), 24 hours after
smoking. Inflammatory cells are expressed as cell counts / 0.1mm2. * Significant response to cigarette smoke
within the group (Wilcoxon signed-rank tests, p<0.05). † p-values for differences in responses to cigarette smoke
between susceptible and non-susceptible subjects (Mann-Whitney U tests, NS = not significant).
65
3
Chapter 3
REFERENCES
1.
Koenderman L, Kanters D, Maesen B, Raaijmakers J, Lammers JW, de Kruif J, Logtenberg T. Monitoring
of neutrophil priming in whole blood by antibodies isolated from a synthetic phage antibody library.
J Leukoc Biol 2000;68:58-64.
2.
ten Hacken NH, Aleva RM, Oosterhoff Y, Smith M, Kraan J, Postma DS, Timens W. Submucosa 1.0 x 0.1
mm in size is sufficient to count inflammatory cell numbers in human airway biopsy specimens. Mod
Pathol 1998;11:292-294.
3.
Broekema M, ten Hacken NH, Volbeda F, Lodewijk ME, Hylkema MN, Postma DS, Timens W. Airway
epithelial changes in smokers but not in ex-smokers with asthma. Am J Respir Crit Care Med
2009;180:1170-1178.
4.
Broekema M, Timens W, Vonk JM, Volbeda F, Lodewijk ME, Hylkema MN, Ten Hacken NH, Postma DS.
Persisting remodeling and less airway wall eosinophil activation in complete remission of asthma.
Am J Respir Crit Care Med 2011;183:310-316.
66
Acute effects of smoking and COPD susceptibility
3
67
4
Chapter
Advanced glycation end products
in the skin are enhanced in COPD
Susan Hoonhorst, Adèle Lo Tam Loi, Jorine Hartman, Eef Telenga,
Maarten van den Berge, Leo Koenderman, Jan-Willem Lammers,
Marike Boezen, Dirkje Postma, Nick ten Hacken
Metabolism 2014 Sep;63(9)
Chapter 4
ABSTRACT
Rationale: Cigarette smoking is the main cause of chronic obstructive pulmonary disease
(COPD) inducing oxidative stress and local tissue injury, resulting in pulmonary inflammation.
Advanced glycation end products (AGEs) are produced by glycation and oxidation of proteins
and lipids and their formation is accelerated in inflammatory conditions associated with
oxidative stress. AGEs are harmful by affecting protein function and triggering secondary
messenger pathways after binding to their receptor.
Objectives: To assess whether AGEs in the skin are associated with COPD.
Methods: Mild-to-very-severe COPD patients and old (40-75 years) and young (18-40
years) healthy smokers and never-smokers were included. AGEs were measured by skin
autofluorescence (SAF) using the AGE-ReaderTM. Demographic variables, smoking habits, comorbidities and lung function values, were obtained in all subjects.
Measurements and main results: 202 COPD patients (FEV1,%predicted=55) had significantly
higher SAF values than 83 old and 110 young healthy controls: 2.5 vs. 1.8 and 1.2 (arbitrary
units, p<0.05). No differences in SAF values were found between the four GOLD stages. Both
SAF and packyears contributed independently to lower lung function (FEV1/FVC(%), MEF50/
FVC, and RV/TLC(%)).
Conclusions: Skin autofluorescence of AGEs is equally increased in COPD patients of different
GOLD stages (I-IV), and is significantly enhanced compared to healthy controls, independent
of age, smoking and disease severity. We hypothesize that systemic AGEs may play a role in
the induction phase of COPD in susceptible smokers. Future studies should further investigate
mechanisms underlying AGEs formation and accumulation in COPD, by investigating other
tissues than the skin and performing genetic analyses.
70
AGE accumulation in the skin is increased in COPD
INTRODUCTION
Chronic obstructive pulmonary disease (COPD) is characterized by progressive airway
obstruction, chronic pulmonary inflammation and airway remodeling (1). The main cause of
COPD in the western world is cigarette smoking, which induces oxidative stress and tissue
injury in the lungs, finally resulting in chronic pulmonary inflammation (2). However, the
burden of COPD is not restricted to the lung as COPD patients may show an ongoing systemic
inflammation which induces systemic oxidative stress, and this may affect other organ systems
and tissues outside the lung (4, 5).
Advanced glycation end products (AGEs) are products of glycation and oxidation
of proteins and lipids. Under normal circumstances, AGEs are formed at a slow rate and
accumulate in the body with aging. However, their formation and accumulation is accelerated
in inflammatory conditions associated with oxidative stress. Importantly once formed, AGEs
may cause local tissue injury by cross-linking of proteins thereby affecting their structure
and function. In addition, AGEs may also have important consequences via binding the AGE
receptors (RAGEs), triggering secondary messenger pathways inducing an increase of oxidative
stress and inflammatory cytokine release (6, 7). AGEs are highly associated with glycemic and
oxidative stress conditions, leading to a higher accumulation of AGEs in patients with diabetes,
renal failure, and cardiovascular risk (6, 8, 9).
There are indications that AGEs might play a role in the pathogenesis of COPD.
Recently, a study demonstrated upregulated expression of AGEs and RAGEs in the airways of
COPD patients compared with healthy controls (10). In line with this, another study showed
over-expression of RAGEs in airway epithelial and smooth muscle cells of COPD patients (11).
In addition, soluble RAGEs in serum (sRAGE), which generally act as decoy receptors for RAGE
ligands, have been found to be lower in COPD patients than in healthy controls (12-14).
AGE accumulation in the skin can be measured non-invasively by an AGE ReaderTM
(15). This device measures intrinsic autofluorescence of some AGEs expressed as skin
autofluorescence (SAF). SAF has been extensively examined and proven to be a marker for both
cumulative glycaemic and oxidative stress since higher SAF values are strongly associated with
diabetes, cardiovascular risk, and renal failure (16-19). No studies are available investigating
SAF in COPD.
As older age, oxidative stress and systemic inflammation are important characteristics
and predictors of COPD, conditions that are also involved in accelerated AGE formation and
accumulation, we hypothesize that AGE accumulation in the skin is increased in COPD. In the
current study we aimed to assess if SAF is elevated in COPD patients and investigated SAF in
mild to very severe COPD patients and in old (40-75 years) and young (18-40 years) healthy
smokers and never-smokers using the AGE ReaderTM.
METHODS
We collected data from three observational studies performed in Groningen and Utrecht, The
Netherlands (ClinicalTrials.gov identifiers: NCT00807469, NCT00850863, NCT00848406, and
trialregister.nl identifier: NTR1497). These studies with overlapping baseline investigations
71
4
Chapter 4
performed SAF measurements following the same protocol. The studies were approved by
the medical ethic committee of the University Medical Centers Utrecht (UMCU) and Groningen
(UMCG), and all subjects provided signed informed consent.
Study population
COPD patients (age 40-75 years) were recruited from outpatient clinics of UMCG and UMCU.
Patients with a smoking history of >10 packyears and a post-bronchodilator FEV1/FVC<0.7 were
included and classified by Global initiative for chronic Obstructive Lung Disease (GOLD) stages I
to IV (1). Old (40-75 years) and young (18-40 years) healthy smokers and never-smokers without
airway obstruction (FEV1/FVC>0.7) were recruited by advertisements. Old current smokers had
to have a smoking history of >10 packyears, and young current smokers a smoking history of
>0.5 packyears. Non-smoking subjects with a smoking history <0.5 packyears were included
as never-smokers in both young and old groups. Exclusion criteria for all groups were alpha-1
antitrypsin deficiency and a doctors’ diagnosis of asthma; co-morbidities were not excluded.
Study design
Clinical characteristics
All subjects performed post-bronchodilator spirometry and body plethysmography according
to the European Respiratory Society guidelines (20). Furthermore, demographic variables,
smoking habits and co-morbidities (a.o. diabetes mellitus, cardiovascular diseases, renal
failure) were recorded.
Skin autofluorescence (SAF)
SAF was assessed non-invasively by the AGE-ReaderTM (DiagnOptics B.V., Groningen, The
Netherlands) (15). Technical details of this device have been extensively described elsewhere
(18). In short, the AGE reader illuminates approximately 1 cm2 of the skin, guarded against
surrounding light, with an excitation light source between 300 and 420 nm (peak excitation
flow ~350 nm). Only light from the skin is measured between 300 and 600 nm with a
spectrometer using a 200-µm glass fiber. SAF was calculated by dividing the average light
intensity emitted per nm over the 420- to 600-µm range by the average light intensity emitted
per nm over the 300- to 420-µm range, using the AGE Reader software version 2.2. The volar
surface of subject’s forearm was positioned on top of the device, taking care to perform the
measurement at normal skin site, i.e. without visible vessels, scars, or other skin abnormalities.
SAF was averaged from three consecutive measurements for each subject, measured within
a time period of approximately 2 minutes. In all analyses, SAF was expressed as the mean of
these three measurements in arbitrary units (AU).
Statistical methods
Mann-Whitney U tests or Chi-square tests were used to compare baseline characteristics and
SAF between groups. Linear regression analyses were performed to investigate associations
between SAF and relevant clinical characteristics, with SAF as dependent variable and vital
signs (blood pressure, BMI), lung function, smoking characteristics (packyears, current
72
AGE accumulation in the skin is increased in COPD
smoking) as independent variables. Each model was adjusted for age, gender and performing
center (UMCU/UMCG). Multiple linear regression analyses were performed to investigate the
independent effect of SAF on lung function (FEV1/FVC, MEF50/FVC, RV/TLC), models being
adjusted for age, gender, packyears, and performing center.
RESULTS
Subject characteristics
The characteristics of the study groups are presented in Table 1. We included 202 COPD patients,
distributed over GOLD stages I-V (n = 60, 54, 54, and 34 respectively) (Table E1 in online data
supplement). The old healthy group contained 83 participants, consisting of 28 never-smokers
and 55 current smokers. The young healthy group included 110 participants, consisting of 36
never-smokers and 74 current smokers. In the COPD group, GOLD IV patients were significantly
younger than GOLD I-III patients. GOLD I patients smoked a significantly higher number of
cigarettes per day than GOLD II-IV patients. In the old healthy group, never-smokers had a
significantly higher age than smokers. The characteristics of both young groups were similar.
Table 1. Subject characteristics of total groups
COPD
Old
Young
N
202
83
110
55 (50)
Males,n(%)
135 (67)
60 (72)
Age, years
64 (58-69)
54 (48-61)
22 (20-28)
BMI, kg/m2
24.9 (22.6-27.2)
24.7 (22.8-27.2)
22.7 (20.8-24.5)
BMI >30 kg/m2, n(%)
20 (10)
10 (12)
1 (1)
Current smokers, n(%)
79 (39)
55 (66)
74 (67)
Cigarettes per day
0 (0-10)
17 (11-20)
10 (3-15)
Packyears
35 (25-48)
22 (0-31)
1 (0-5)
FEV1, % predicted
55 (37-85)
109 (102-118)
108 (102-116)
FEV1/FVC, %
47 (34-58)
79 (76-83)
85 (81-90)
MEF50/FVC, /sec
0.19 (0.11-0.33)
0.88 (0.79-0.04)
1.02 (0.89-1.18)
RV/TLC, %
48 (39-56)
31 (28-34)
23 (20-25)
Diastolic blood pressure, mmHg
80 (75-87)
82 (76-90)
78 (70-82)
Diabetes, n (%)
10 (5)
0 (0)
1 (0.9)
Cardiovascular disease, n (%)
50 (25)
3 (4)
1 (0.9)
SAF, AU
2.5 (2.0-2.9)
1.8 (1.6-2.1)
1.2 (1.1-1.5)
Values are expressed as medians (inter quartile ranges). Values of all baseline characteristics were significantly
different between the three groups (p < 0.05). n = number, FEV1 = forced expiratory volume in one second, FVC =
forced vital capacity, MEF50= maximal expiratory flow at 50% of vital flow capacity, SAF = skin autofluorescence,
AU = arbitrary units.
Skin autofluorescence between groups
When comparing COPD patients, healthy old individuals, and healthy young individuals, SAF
was significantly different between all groups (2.5, 1.8, and 1.2 AU respectively, p<0.001) (Table
1). SAF was similar between COPD patients GOLD stage I-IV (2.4, 2.3, 2.5, and 2.5 respectively),
between healthy old smokers and never-smokers (1.8 and 1.8 respectively), and between
young smokers and young never-smokers (1.3 and 1.2 respectively) (Figure 1, Table E1 in online
73
4
Chapter 4
data supplement).
Figure 1. Skin auto fluorescence (SAF) in COPD patients, old and young healthy smokers
and never-smokers
SAF values are significantly different between the total COPD group, the total healthy old group, and total young
healthy group, with COPD patients having highest SAF and young groups having lowest SAF (p < 0.001). Values
are expressed as medians (ranges).
Associations with skin autofluorescence
In the total population, linear regression analyses showed that poorer lung function values
and a higher number of packyears were significantly associated with higher SAF values,
independently of gender, age, and performing center (p<0.010, Table 2 and Figure 2). In all
analyses, lung function and packyears were independently associated with SAF since both
predictor values remained significant when added together in the models.
In the stratified analyses no significant associations between lung function parameters
and SAF were present within any of the groups (Table 2). Only in the young healthy group
a lower FEV1 %predicted was associated with a higher SAF. A higher number of packyears
was associated with a higher SAF in both young and old healthy groups (p≤0.001), and this
association almost reached significance in COPD patients (p=0.053) (Table 2). Within the old
healthy group current smoking was associated with higher SAF (p=0.019). No associations
74
-0.007
0.002
BMI, kg/m2
Diastolic blood pressure, mmHg
-0.015
0.005
0.004
0.044
0.010
0.005
0.002
0.091
COPD patients
B
S.E.
-0.001
0.002
-0.002
0.003
-0.215
0.299
0.006
0.004
0.157
0.326
0.053
0.625
p
0.450
0.518
0.473
0.127
0.009
0.019
0.002
0.094
0.013
0.004
0.006
0.223
0.016
-0.003
0.215
0.559
p
0.420
0.783
0.724
0.211
Old healthy group
B
S.E.
-0.003
0.003
0.003
0.009
0.066
0.187
0.014
0.011
0.016
0.005
0.021
0.094
0.009
0.003
0.006
0.049
0.069
0.080
0.001
0.061
Young healthy group
B
S.E.
p
-0.005
0.002
0.045
-0.005
0.005
0.269
-0.100
0.116
0.391
0.001
0.005
0.790
β
-0.375
-0.107
-0.202
-0.278
FEV1/FVC
S.E.
0.059
1,616
0.045
1,391
p-value
0.000
0.003
0.000
0.000
β
-0.409
-0.073
-0.205
-0.263
MEF50/FVC
S.E.
0.001
0.031
0.001
0.020
p-value
0.000
0.038
0.000
0.000
β
0.430
0.213
0.140
0.250
RV/TLC
S.E.
0.042
1,149
0.032
0.995
p-value
0.000
0.000
0.004
0.000
Multiple regression analyses in the total study population. Dependent variables were FEV1/FVC, MEF50/FVC and RV/TLC. Values represented in bold are significant
associations. Β=standardized regression coefficient, S.E. = standard error, FEV1 = forced expiratory volume in one second, FVC = forced vital capacity, MEF50= maximal
expiratory flow at 50% of vital flow capacity, RV = residual volume, TLC = total lung capacity, SAF = skin autofluorescence, m/f = male / female.
Predictors of lung function:
Age, years
Gender, m/f
Packyears
SAF, AU
Table 3. Multiple linear regression analyses in the total study population
Linear regression analyses in the total study population and stratified for COPD patients, old healthy group and young healthy group. Predictor variables were separately
added to the regression model with SAF as dependent variable. All models were adjusted for gender, age and center.
Values represented in bold are significant associations (p<0.05).
B= unstandardized regression coefficient, S.E. = standard error, (n/y) = no / yes, n = number, FEV1 = forced expiratory volume in one second, FVC = forced vital capacity,
MEF50= maximal expiratory flow at 50% of vital flow capacity, RV = residual volume, TLC = total lung capacity
0.007
0.003
0.309
0.411
<0.001
0.153
0.008
0.079
Packyears
Current smoking, n/y
0.001
0.055
p
<0.001
<0.001
<0.001
<0.001
Total population
B
S.E.
-0.005
0.001
-0.008
0.002
-0.434
0.087
0.011
0.002
Predictors of SAF: FEV1, % predicted
FEV1/FVC, %
MEF50/FVC
RV/TLC, %
Table 2. Linear regression analyses in the total study population and stratified for COPD patients, the old healthy group and the
young healthy group
AGE accumulation in the skin is increased in COPD
4
75
Chapter 4
were found between SAF and BMI or blood pressure.
Multiple linear regression analyses in the total study population, including age,
gender, packyears and SAF showed that higher SAF was significantly associated with lower
lung function values (FEV1/FVC, MEF50/FVC, RV/TLC) (p<0.01, Table 3).
Figure 2. Associations of packyears and lung function variables with skin autofluorescence
in the total population
SAF = skin autofluorescence, AU = arbitrary units, R2 = correlation coefficient, FEV1 = forced expiratory volume in
one second, FVC = forced vital capacity, RV = residual volume, MEF50= maximal expiratory flow at 50% of vital
flow capacity, TLC = total lung capacity.
Comorbidities
We investigated the effects of relevant comorbidities, i.e. diabetes and cardiovascular disease
on SAF. Cardiovascular disease was most prevalent; in 25% of all COPD patients, 4% of old
healthy individuals and 1 % of young healthy individuals (Table 1). Furthermore, prevalence
of diabetes was 5%, 0% and 1 % respectively in each group. Median (IQR) SAF values of the 50
COPD patients with cardiovascular disease compared to those without were: 2.6 (2.3-2.9) vs.
2.4 (2.0-2.9)(p<0.05). Median (IQR) SAF values in COPD patients with diabetes mellitus (n=10)
and without (n=192) were 2.9 (2.5-4.2) vs. 2.4 (2.0-2.9) respectively (p<0.05).
76
AGE accumulation in the skin is increased in COPD
DISCUSSION
In this study we demonstrated for the first time that AGEs in the skin, measured as skin
autofluorescence, are elevated in COPD patients compared with healthy controls, independent
of age, current smoking and disease severity. A plausible explanation for this observation is
that AGEs reflect higher amounts of cumulative endogenous oxidative stress exposure as a
result from chronic systemic inflammation which is generally present in COPD patients (21).
The most important observation of this study is that SAF is elevated in COPD patients
compared with healthy controls. Regression models in the total population confirmed that
higher SAF associates with lower lung function, independently of age, gender and packyears.
These associations were also demonstrated for variables representing small airways dysfunction
(MEF50/FVC), which is important because smoking-induced COPD starts in the distal airways.
Interestingly, our data showed comparable SAF values between the different stages of disease
severity suggesting that disease progression of COPD is not associated with accumulation of
AGEs in the skin. Therefore we put forward the notion that AGEs formation may be accelerated
particularly during the induction phase of COPD. Apparently, such processes only take place
in so called susceptible smokers (22, 23). The old healthy smokers in our study clearly have
proven not to be susceptible to develop COPD because they still have normal lung function
values after many years of smoking. Even more importantly, their SAF values were completely
comparable to those of old healthy never-smokers. We therefore speculate that accelerated
AGEs formation in the induction phase of COPD is determined by the genetic make-up of
susceptible individuals and that studying the immunological pathways of AGEs formation
and accumulation might be very worth wile. Why accelerated AGE formation does not lead
to higher SAF values in the higher GOLD stages of COPD is an intriguing finding, however we
have to realize that our study is limited by its cross-sectional design.
Skin autofluorescence could be put forward as a useful biomarker of COPD, as it may
differentiate COPD patients from healthy controls in an easy and non-invasive way. However,
the overlap between groups was large, and it takes many years before SAF is elevated in
established COPD. In other words, at an individual level this appears not a useful biomarker
to recognize susceptible individuals in a preclinical stage of COPD. As AGEs could play an
important pathogenetic role in the pre-clinical stage of COPD it might be attractive to find
other surrogate markers for AGE-formation, better reflecting the actual process of AGEformation than SAF does, and investigate them in the context of susceptible versus nonsusceptible smokers with respect to COPD development.
Assuming that AGEs play a role in the development of COPD, a few mechanisms
supporting this hypothesis can be considered. Two genome-wide association (GWA) studies
have shown that the advanced glycosylation end product-specific receptor (AGER) gene is
associated with lower lung function (FEV1 and FEV1/FVC) (24, 25). In addition, the minor allele
of the AGER SNP was more frequently found in smokers with normal lung function than in
smokers with COPD (26). This allele is a missense mutation encoding the sRAGE protein. Thus,
one can speculate that in non-susceptible smokers this polymorphism may act as a protective
mechanism by increasing the extent of membrane bound RAGE (mRAGE) that is cleaved
into sRAGE, resulting in a higher amount of decoy receptors in the circulation protecting
77
4
Chapter 4
against AGEs driven inflammation and tissue injury in the lungs. As mentioned earlier, other
studies showed an upregulation of AGEs and RAGEs in the airways and lower levels of solube
RAGE (sRAGE) in the circulation of COPD patients, further indicating the possible important
contribution of AGEs in COPD (10-14). Taken together, we speculate that AGE formation is
increased in susceptible smokers at a certain time point due to their genetic background,
thereby causing harmful effects in cells and tissues, thereby affecting the structure and
function of airways and lung tissue.
Earlier studies demonstrated that cigarette smoke is an important exogenous
source of reactive glycation products and oxidative stress (27, 28). Therefore cigarette
smoking theoretically may induce AGEs directly or indirectly via oxidative stress. Indeed,
we demonstrate that a higher number of packyears contributed independently to SAF. This
finding is in accordance with a previous study in healthy subjects demonstrating a positive
association between the number of packyears and SAF (29). Packyears in our study were more
closely related to SAF than current smoking. This is not surprising, as deposition of AGEs in the
skin probably needs many years of cumulative oxidative stress, which is more closely related to
the number of packyears smoking than to current smoking habits.
Some other conditions affecting the level of SAF have been described in the
literature. First of all, older age was found to be strongly related with higher SAF (30). Our
study also demonstrated such an association and therefore we adjusted all regression analyses
for age. Secondly, it has been shown that individuals with diabetes, cardiovascular diseases,
and renal failure have higher SAF values. These comorbidities are very prevalent in COPD,
especially cardiovascular disease and diabetes (31, 32). Within our COPD population, SAF
was indeed significantly higher in patients with cardiovascular disease and diabetes than
those without. This finding is in line with the concept that COPD may be a component of a
chronic inflammatory syndrome involving many other organs (33), and that AGEs might act as
potential mediators of systemic inflammation. Finally, nutrition habits may have small effects
on SAF. One study demonstrated an 8.7 percent increase of SAF in healthy subjects, two hours
after a high fat meal containing a sufficient AGEs content (34). In our current study we had no
data on recent food intake, but we believe this has not affected our results.
The strength of the current study is that we included a large population of COPD
patients with different severity stages (GOLD I-IV). Furthermore, we used large control groups
strictly including young and old subjects who were never smokers or current smokers, in
this way avoiding contamination with ex-smokers. Of course there are some limitations. The
number of non-smokers was smaller than the number of smokers, both in young and old
healthy groups. Furthermore, there were no data available on systemic inflammatory markers
and on AGEs in other tissues than the skin. In future studies these parameters could help to
better interpret SAF data in COPD or COPD development.
In conclusion, we demonstrate that AGEs in the skin, as measured by skin
autofluorescence, are higher in COPD patients than healthy controls, independent of age,
current smoking and disease severity. We hypothesize that AGEs in the circulation and lung
may play a role in the induction phase of COPD in so-called susceptible smokers. Therefore, it is
important in future studies to assess mechanisms underlying AGE formation and accumulation
78
AGE accumulation in the skin is increased in COPD
in COPD, by investigating other tissues than the skin and performing genetic analyses and
gene expression.
4
79
Chapter 4
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28. Cerami C, Founds H, Nicholl I, Mitsuhashi T, Giordano D, Vanpatten S, Lee A, Al-Abed Y, Vlassara H,
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31. Agusti A, Calverley PM, Celli B, Coxson HO, Edwards LD, Lomas DA, MacNee W, Miller BE, Rennard S,
Silverman EK, Tal-Singer R, Wouters E, Yates JC, Vestbo J, Evaluation of COPD Longitudinally to Identify
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82
AGE accumulation in the skin is increased in COPD
4
83
4
Chapter
Supplement
Chapter 4
86
GOLD I
60
40 (67)
64 [59-72]
24.9 [22.8-26.7]
4 (7)
33 (55)
7 [0-16]§
35 [23-51]
92 [85-100]‡
62 [57-67]‡
0.39 [0.30-0.49]
38 [32-42]
80 [75-86]
0 (0)
19 (32)
1 (2)
2.4 [2.0-2.9
COPD
GOLD II
GOLD III
54
54
42 (78)
39 (72)
64 [60-69]
65 [60-69]
25.7 [23.8-28.3]‡
23.6 [22.4-25.8]
8 (15)
3 (6)
27 (50)
14 (26)
1 [0-12]
0 [0-4]
36 [28-47]
40 [26-51]
65 [55-71]‡
39 [35-45]‡
49 [45-55]‡
36 [30-40]‡
0.21 [0.17-0.28]
0.13 [0.10-0.15]
46 [39-49]
56 [48-61]
82 [76-88]
80 [76-88]
3 (6)
5 (9)
9 (17)
15 (28)
0 (0)
0 (0)
2.3 [2.0-2.8]
2.5 [2.0-3.0]
GOLD IV
34
14 (41)
58 [52-64]#
24.2 [20.2-27.6]
5 (15)
5 (15)
0 [0-0]
28 [23-42]
24 [20-28]‡
27 [23-33]‡
0.10 [0.08-0.11]
65 [57-71]
80 [70-87]
2 (6)
7 (21)
0 (0)
2.5 [2.1-2.8]
Smokers
55
40 (73)
51 [46-59]
24.7 (22.7-27.2)
6 (11)
55 (100)
17 [11-20]
26 [22-39]
107 [101-117]
78 [75-83]
0.88 [0.77-1.04]
31 [28-34]
80 [75-88]
0 (0)
3 (6)
0 (0)
1.8 [1.6-2.1]
Old
Never-smokers
28
20 (71)
58 [52-66]*
24.6 (22.8-27.1)
4 (14)
0 (100)
----111 [103-120]
79 [77-82]
0.85 [0.79-1.07]
32 [27-35]
83 [77-90]
0 (0)
0 (0)
0 (0)
1.8 [1.4-2.0]
Young Smokers
Never-smokers
74
36
42 (57)
13 (36)
22 [20-28]
22 [20-29]
23.1 [21-25]
21.8 [20.4-23.9]
1 (1)
0 (0)
74 (100)
0 (0)
10 [3-15]
--2.7 [1-6]
--108 [102-116]
108 [103-115]
85 [81-90]
86 [80-91]
1.03 [0.89-1.19]
1.00 [0.82-1.17]
22 [20-25]
24 [22-27]
75 [70-82]
80 [72-84]
1 (1)
0 (0)
0 (0)
1 (2,8)
0 (0)
0 (0)
1.3 [1.1-1.5]
1.2 [1.0-1.4]
Values are expressed as median (Inter Quartile Range) or as number (percentage).
N = number, m = male, FEV1 = Forced Expiratory Volume in one second, FVC = Forced Vital Capacity, MEF50= maximal expiratory flow at 50% of vital flow capacity, SAF
= skin autofluorescence, AU = arbitrary units.
*p < 0.05; †p < 0.05 compared with COPD GOLD I, II, III; ‡p < 0.05 compared with GOLD III; §p < 0.05 compared with GOLD III and IV; ‡p < 0.01 between all groups.
N
Males, n (%)
Age, years
BMI, kg/m2
BMI >30 kg/m2 , n (%)
Current smokers, n
Cigarettes per day
Packyears
FEV1 , % predicted
FEV1 / FVC, %
MEF50/FVC, /sec
RV/TLC, %
Diastolic bloodpressure, mmHg
Diabetes, n (%)
Cardiovascular disease, n (%)
Renal failure, n (%)
SAF, AU
Table E1.
AGE accumulation in the skin is increased in COPD
4
87
4
Chapter
Addendum
Chapter 4
90
AGE accumulation in the skin is increased in COPD
No differences were present between young susceptible and non-susceptible individuals. In
multiple regression analysis, no association was found between COPD susceptibility and SAF
after adjustment for gender, age and packyears.
Figure 1. Skin autofluorescence the susceptibility groups
4
"
!
#
%
#
"$
$
$
"$
"
"
#
#
Skin autofluorescence (SAF) values presented as median (range). * p>0.05 compared with all groups. AU=
arbitrary units.
Table 1. Group characteristics
Young
Young susceptible
Healthy controls
COPD
non-susceptible
N, n
33
21
28
94
Male, n (%)
20 (61)
11 (52)
24 (86)
68 (72)
Age, years
21 [20-23]
31 [25-38]
51 [47-62]
63 [58-67]
Packyears, n
1 [0-3]
6 [2-10]
27 [23-39]
35 [26-47]
4.6 [3.8-5.4]
4.0 [3.6-4.6]
4.0 [3.5-4.3]
1.8 [1.1-2.8]
FEV1, L
FEV1, % pred
104 [101-114]
110 [104-114]
108 [102-115]
57 [40-85]
86 [84-91]
81 [77-87]
78 [74-84]
48 [37-60]
FEV1/FVC (%)
Data are expressed as median [Inter Quartile Range]. n = number, FEV1 = Forced Expiratoy Volume in 1 second,
FVC = Forced Vital Capacity.
91
5
Chapter
Advanced glycation endproducts
and their receptor
in different body compartments in COPD
Susan Hoonhorst, Adèle Lo Tam Loi, Eef Telenga, Maarten van den Berge, Leo
Koenderman, Jan-Willem Lammers, H. Marike Boezen, Antoon van Oosterhout,
Monique Lodewijk, Wim Timens, Dirkje S. Postma, Nick H.T. ten Hacken
Submitted
Chapter 5
ABSTRACT
Background: Inflammation and oxidative stress caused by cigarette smoking contribute
to chronic obstructive pulmonary disease (COPD). Smoking and oxidative stress lead to
accelerated formation and accumulation of advanced glycation end products (AGEs), causing
local tissue damage either directly or by binding the receptor for AGEs (RAGE). This study
assessed the association of AGEs or RAGE in plasma, sputum, bronchial biopsies and skin with
COPD and lung function, and their variance between these body compartments.
Methods: Healthy smoking and never-smoking controls (n=191, age 18-40 years) and COPD
patients (n=97, GOLD stage I-IV) were included. Autofluorescence (SAF) was measured in
the skin, AGEs (pentosidine, CML and CEL) and sRAGE in blood and sputum by ELISA, and in
bronchial biopsies by immunohistochemistry.
Results: Higher SAF and lower sRAGE levels associated with COPD and lower lung function
(p <0.001; adjusting for relevant covariates). Lower plasma sRAGE levels significantly and
independently predicted higher SAF values (p<0.001).
Conclusion: In COPD, AGEs accumulate differentially in body compartments, i.e. they
accumulate in the skin, but not in plasma, sputum and bronchial biopsies. The association
between lower sRAGE and higher SAF levels supports the hypothesis that the protective
mechanism of sRAGE as a decoy-receptor is impaired in COPD.
94
AGEs and RAGE in COPD
INTRODUCTION
Chronic obstructive pulmonary disease (COPD) is characterized by chronic airflow limitation,
accompanied by persistent inflammation of the airways, mainly caused by cigarette smoking.
Both smoking and inflammation are associated with oxidative stress leading to accelerated
formation and accumulation of advanced glycation end products (AGEs) (1,2).
AGEs are a heterogeneous and complex group of compounds that are irreversibly
formed by non-enzymatic glycation and oxidation of proteins and lipids (3). They accumulate
in tissues with ageing, and under oxidative stress and inflammatory conditions their formation
and accumulation increases. Therefore, accumulation of AGEs can be used as a read-out system
for exposure to oxidative stress during life. This is particularly true in tissues with slow turnover,
more than in tissues or products from tissues with rapid turnover. The best known AGEs are Nε(carboxymethyl)lysine (CML), Nε-(carboxyethyl)lysine (CEL) and pentosidine. AGEs cause local
tissue damage by affecting protein structure, by formation of crosslinks between molecules,
or by binding the receptor for AGE (RAGE) (4,5). RAGE is a member of the immunoglobulin
superfamily and is a pattern-recognition receptor on cell surfaces. Ligation of RAGE triggers
inflammatory responses, induces oxidative stress, and in turn causes RAGE over-expression.
This finally leads to increased tissue remodeling (5). Interestingly, expression of RAGE in the
lung has shown to be relatively high when compared with other tissues (6).
A few studies have indicated that AGEs are involved in the pathology of COPD. One
study showed increased accumulation of AGEs in lung parenchyma and small airways of
COPD patients (7). We and others found increased AGEs accumulation in the skin of COPD
patients compared to healthy smoking and never-smoking controls (8,9). Furthermore,
plasma CML levels in COPD are elevated compared to non-COPD controls (9), suggesting a
systemic component that may contribute to AGEs accumulation outside the lung, and to extrapulmonary manifestations of COPD. Regarding RAGE, it has been shown that immunostaining
of the receptor is increased in bronchial biopsies and lung parenchyma of COPD patients
(7,10). Importantly, RAGE also exists as soluble form (sRAGE). It has been postulated that sRAGE
can act as a decoy receptor by clearance of circulating AGEs, in this way preventing ligation
of membrane bound RAGE. This possible ‘protective’ mechanism may be reduced in COPD, as
levels of sRAGE have found to be lower in COPD patients than in non-COPD controls (11-15).
Taken together, studies so far suggested that the AGE-RAGE axis is involved in the
pathology of COPD. In the current study we evaluated both AGEs and (s)RAGE levels in plasma,
sputum, bronchial biopsies and the skin in the same study subjects. Young (18-40) and old
(40-75) smokers and never-smokers, and mild-to-very severe COPD patients were included. We
studied whether the expression of AGEs or RAGE in the different tissues was associated with
COPD and lung function values, and whether the expression of AGEs and/or RAGE levels in
different tissues were associated.
METHODS
Subjects
Data were collected from two studies performed in Groningen and Utrecht, the Netherlands
95
5
Chapter 5
(Clinicaltrials.gov: NCT00807469 and NCT00848406 (A multi-center study (16)) and
NCT00848406). All participating subjects gave peripheral blood and performed an AGE-reader
measurement, while a subgroup of subjects underwent sputum induction and bronchoscopy
with collection of bronchial biopsies. All measurements were obtained by using standardized
protocols. The studies were approved by the medical ethics committees of University Medical
Centers Groningen (UMCG) and Utrecht (UMCU), the Netherlands.
Mild to very severe COPD patients (40-75 years, >10 packyears), as classified by
the Global Initiative for Chronic Obstructive Lung Disease (GOLD) (17) were recruited from
outpatient clinics of UMCG and UMCU. Old (40-75 years) and young (18-40 years) healthy
smokers and healthy never-smokers were recruited by advertisements. Old smokers had a
smoking history >10 packyears and young smokers >0.5 packyears. All never-smoking subjects
had smoked <0.5 packyears. Healthy participants had no history of pulmonary diseases and
showed normal spirometry. Exclusion criteria for all groups were alpha-1 antitrypsin deficiency
and a doctors’ diagnosis of asthma.
Determination of AGEs and RAGE in peripheral blood samples and sputum
Blood was collected in tubes containing EDTA and was immediately placed on ice. After
centrifugation (twice at 2000 rcfmax, 10 min, 4°C) samples were stored at -80°C until analysis.
Sputum induction was performed according standard protocols (detailed methods in online
supplement). Sputum samples were centrifuged (10 min, 450g, 4°C) and the supernatant was
stored at -80°C until analysis.
In plasma and sputum samples, ELISA was performed to determine levels of total
sRAGE (cleaved and secreted forms) (RAGE DuoSet; R&D Systems, Minneapolis, MN, USA), CEL
(Cell Biolabs Inc. San Diego, CA, USA), CML (Cell Biolabs Inc. San Diego, CA, USA) and Pentosidine
(Uscn Life Science Inc., Wuhan, China), all according to the manufacturer’s instructions.
Determination of AGEs and RAGE in bronchial biopsies
Bronchial biopsies were taken from subsegmental carinae of the right lower lobe. Biopsies
were fixed in 4% neutral buffered formalin, processed and embedded in paraffin and cut in
3 µm sections. After antigen retrieval, sections were incubated with the primary monoclonal
antibody against AGEs (anti-AGEs (clone 6D12), 1:750, Cosmo Bio Co, Ltd, Tokyo, Japan) or RAGE
(anti-RAGE (ab7764), 1:1500, Abcam, Cambridge, UK). Immunohistochemical stainings were
performed using the DAKO autostainer (DAKO, Glostrup, Denmark). Quantification of both
stainings was performed by calculating the percentage positive and strong positive pixels of
the total amount of pixels in whole biopsies, using ImageScope (Aperio Technologies, version
11.2.0.780). Detailed immunohistochemistry and quantification procedures are presented in
the online supplement.
Measurement of AGEs using Skin autofluorescence in the skin
Skin autofluorescence (SAF) was assessed non-invasively by the AGE-ReaderTM (DiagnOptics
B.V., Groningen, The Netherlands) (18). Technical details of this device have been extensively
96
AGEs and RAGE in COPD
described elsewhere and briefly in the online supplement. (19). In short, the volar surface of
subject’s forearm was positioned on top of the device and three consecutive measurements
were performed for each subject. In all analyses, SAF was expressed as the mean of these three
measurements in arbitrary units (AU).
Statistical analysis
Differences in expression of AGEs and RAGE between groups were analyzed by Kruskal-Wallis
test, followed by Mann-Whitney U tests if significant. Associations with COPD were examined
by multiple regression analyses with AGEs or RAGE expression as dependent variables, and
COPD or lung function values as predictor variables. Associations of AGEs and RAGE between
different compartments were additionally analyzed by multiple regression models. All models
were adjusted for co-variates that associate with AGEs formation, including age, gender,
packyears, BMI, LDL cholesterol, and triglycerides. Benjamini Hochberg corrections were
applied to correct for multiple testing (20). Regression models were considered valid if the
residuals were normally distributed. Statistical analyses were performed using the statistical
program IBM SPSS Statistics version 20.
RESULTS
Subject characteristics
In total, 108 young controls (including 36 never-smokers and 72 smokers), 83 old controls
(including 28 never-smokers and 55 smokers) and 97 COPD patients (32 GOLD I, 25 GOLD II, 24
GOLD III, 16 GOLD IV) were included. Group characteristics are presented in Table 1.
Table 1. Group characteristics
Young healthy
Old healthy
COPD
n=108
n=83
n=97
Age, years
25 (66)
54 (8.9)
62 (7.6)
Males, n (%)
54 (50)
60 (72)
68 (70)
Current smokers, n (%)
72 (67)
54 (65)
51 (53)
Cigarettes per day
9 (6.8)
16 (7.1)
11 (7.9)
Packyears
3.1 (4.8)
20 (18.0)
38 (16.8)
BMI, kg/m2
23.0 (2.8)
25.3 (3.6)
25.5 (4.7)
FEV1,%pred
108 (9.5)
110 (13.5)
62 (27)
FEV1/FVC (%)
85 (5.7)
79 (5.0)
48 (14)
RV/TLC (%)
23.3 (5.1)
31.1 (4.4)
45.8 (11.3)
FEF25-75, %pred
101 (18.7)
100 (30.4)
24 (17)
TLCOc/VA, %pred
97 (13.3)
98 (12.6)
67 (24)
LDL cholesterol, mmol/L
2.6 (0.8)
3.6 (1.0
3.4 (1.0)
Triglycerides, mmol/L
1.0 (0.8)
1.4 (1.0)
1.2 (0.7)
Fasting glucose, mmol/L
5.2 (1.3)
5.6 (0.7)
5.8 (0.7)
Creatinine, µmol/L
73.7 (10.9)
80.2 (14.5)
82.7 (14.8)
Data are presented as mean (sd) unless otherwise stated. n=number, BMI=body mass index, FEV1=forced
expiratory volume in one second, FVC=forced expiratory volume, RV=residual volume, TLC=total lung capacity,
FEF=forced expiratory flow, TLCOc/VA=transfer coefficient for carbon monoxide, LDL=low density lipoprotein.
97
5
Chapter 5
AGEs
Expression of AGEs in plasma, sputum, bronchial biopsies and the skin is presented in Figure
1 and Table 2.
In plasma (Figure 1A), CEL levels were significantly higher in young healthy subjects
than in old healthy subjects and COPD patients. Furthermore, plasma CML levels were
significantly higher in COPD patients than young and old subjects, and higher in the young
group than in the old healthy group. Plasma pentosidine levels did not differ between groups.
In sputum (Figure 1B), CEL and CML levels did not differ between groups, whereas pentosidine
levels were too low to be detected; only 11 sputum supernatant samples of the total 182
samples were above the detection limit of 1.45 ng/ml. AGEs immunopositivity In whole
bronchial biopsies, was not differently expressed between groups, (Figure 1C), neither were
quantitative analyses in the intact and basal epithelium, smooth muscle and connective tissue
(Figure 1, online supplement). However, accumulation of AGEs in the skin was significantly
different between all groups, with highest SAF values in COPD patients and lowest values in
the young group (Figure 1D)
In all measurements, levels of AGEs did not differ between the COPD severities (GOLD
I-IV, Table 1, online supplement). Additionally, AGEs levels are presented separately for healthy
never-smokers and smokers (Table 1, online supplement).
Table 2. AGE and RAGE expression in young and old subjects, and COPD patients
Young healthy
Old healthy
COPD GOLD I-IV
Kruskal-Wallis
<40 years
>40 years
>40 years
p-value
Plasma
n=105
n=82
n=95
6.8 (5.1-9.4) §
0.000*
CEL, µg/ml
10.2 (7.4-15.4)
7.0 (4.8-10.6) §
CML, µg/ml
10.5 (0.0-29.5)
9.2 (0.0-13.5) §
12.5 (0.0-21.6) §‡
0.042*
Pentosidine, ng/ml
36.8 (25.3-53.1)
39.9 (30.1-52.0)
46.1 (31.8-58.5)
0.181
0.000*
sRAGE, pg/ml
795 (614-1089)
805 (617-1032)
414 (292-592) §‡
Induced sputum
n=97
n=73
n=12
CEL, µg/ml
5.9 (1.9-8.9)
3.50 (0.0-6.9)
6.0 (2.9-11.0)
0.064
CML, µg/ml
10.6 (0.0-22.7)
10.4 (0.0-23.7)
18.3 (11.2-22.5)
0.409
Pentosidine, ng/ml
0.0 (0.0-0.0)
0.0 (0.0-0.0)
0.0 (0.0-0.0)
0.650
sRAGE, pg/ml
78.1 (0.0-160.2)
114.0 (0.0-240.5)
110.9 (49.7-161.8)
0.117
Bronchial Biopsies
n=85
n=68
n=12
AGEs, positivity (%)
31.1 (20.3-36.9)
25.9 (20.9-35.4)
25.5 (16.9-30.7)
0.418
RAGE, positivity (%)
9.8 (6.5-15.1)
8.2 (5.7-11.8)
8.0 (4.2-11.4)
0.204
Skin
n=107
n=83
n=96
AGE-reader, SAF
1.2 (1.1-1.5) *
1.8 (1.6-2.0) *
2.5 (2.2-2.9) *
0.000*
Data are expressed as medians (IQR). * p<0.05 between all groups, § p<0.05 compared with the young healthy
group, ‡ compared with the old healthy group. CEL= Nε-(carboxyethyl)lysine, CML= Nε-(carboxymethyl)lysine,
RAGE= receptor for advanced glycation endproducts, AGEs= advanced glycation endproducts, SAF= skin
autofluorescence.
98
AGEs and RAGE in COPD
Figure 1. AGEs expression in plasma, sputum, bronchial biopsies and the skin
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AGEs levels in A) plasma, B) sputum, C) bronchial biopsies and D) skin (SAF). Horizontal lines represent median
values with interquartile ranges, * p<0.05 between groups.
99
100
COPD, n/y
β
p-value
0.009
0.924
0.093
0.327
-0.035
0.711
0.093
0.311
0.029
0.757
-0.025
0.792
-0.065
0.550
0.426
<0.001
sRAGE
sRAGE
RAGE positivity
COPD, n/y
β
p-value
-0.422
<0.001
-0.110
0.194
-0.083
0.365
FEV1, % predicted
β
p-value
0.310
<0.001
0.084
0.289
-0.053
0.529
FEV1, % predicted
β
p-value
-0.465
0.642
-0.081
0.283
-0.020
0.788
-0.139
0.101
-0.030
0.728
-0.124
0.152
0.139
0.155
-0.302
<0.001
Predictor variables
FEV1/FVC (%)
β
p-value
0.405
<0.001
0.012
0.908
0.04
0.715
Predictor variables
FEV1/FVC (%)
β
p-value
0.581
0.562
-0.088
0.337
0.069
0.451
-0.044
0.691
0.043
0.706
0.022
0.844
0.075
0.551
-0.357
<0.001
RV/TLC (%)
β
p-value
-0.236
0.002
-0.015
0.895
0.241
0.060
RV/TLC (%)
β
p-value
0.117
0.204
0.052
0.572
0.093
0.314
0.11
0.353
0.021
0.862
-0.024
0.841
-0.126
0.420
0.265
<0.001
FEF25-75, %predicted
β
p-value
0.297
<0.001
-0.041
0.622
-0.038
0.678
FEF25-75, %predicted
β
p-value
0.0.41
0.635
-0.027
0.761
-0.011
0.901
-0.059
0.518
0.000
0.998
-0.053
0.568
0.094
0.379
-0.347
<0.001
Values in bold represent significant associations. β= standardized regression coefficient for predictor variables. Models in Table A were adjusted for age, gender,
packyears, BMI, LDL cholesterol and triglycerides; models in Table B were adjusted for age, gender and packyears. FEV1=forced expiratory volume in one second,
FVC=forced expiratory volume, RV=residual volume, TLC=total lung capacity, FEF=forced expiratory flow, CEL=Nε-(carboxyethyl)lysine, CML=Nε-(carboxymethyl)lysine,
AGEs=advanced glycation endproducts, SAF=skin autofluorescence, RAGE=receptor for advanced glycation endproducts.
Dependent variables
Plasma
Sputum
Bronchial biopsies
B. Associations of RAGE with COPD and lung function
Dependent variables Plasma
CEL
CML
Pentosidine
Sputum
CEL
CML
Pentosidine
Bronchial biopsies
AGE positivity
Skin
SAF
A. Associations of AGEs with COPD and lung function
Table 3. Associations of AGEs and RAGEs with COPD and lung function in the total population
Chapter 5
SAF
Skin
-0.10
0.13
0.06
0.00
0.10
0.01
0.06
0.00
-0.02
p
0.91
0.04
0.08
0.21
0.59
0.20
0.46
0.47
0.27
0.34
---
B
-1.50
-0.13
0.59
0.02
-0.25
-0.05
0.62
-0.01
0.13
---
0.42
p
0.72
0.78
0.03
0.28
0.77
0.34
0.22
0.13
0.07
---
0.34
CML
0.91
-0.29
0.33
0.01
0.58
-0.04
1.14
-0.01
---
0.11
-0.39
B
0.81
0.51
0.20
0.37
0.44
0.38
0.01
0.01
---
0.07
0.27
p
Pentosidine
Plasma
B
-154.97
-2.52
-4.99
0.18
6.77
-0.33
-6.92
---
-1.84
-1.01
3.21
p
0.64
0.09
0.30
0.46
0.57
0.21
---
0.01
0.13
0.47
0.00*
sRAGE
B
0.18
-0.06
0.14
0.00
0.47
0.03
---
0.00
0.04
0.02
0.07
p
0.91
0.53
0.01
0.23
0.00*
0.00*
---
0.21
0.01
0.22
0.46
CEL p
0.20
0.34
0.38
0.57
0.00*
--0.50
0.03
0.07
0.72
0.51
B
0.99
-0.13
-0.13
-0.01
2.93
--0.92
-0.06
0.92
0.32
9.76
CML
-0.62
-0.06
0.01
0.00
---
0.00
0.17
0.00
0.01
0.00
0.02
B
0.50
0.27
0.70
0.21
---
0.50
0.00*
0.46
0.44
0.77
0.59
p
Pentosidine
Sputum
23.76
-0.19
0.09
---
-5.94
-0.63
-3.30
0.04
0.46
0.51
-3.70
B
p
0.63
0.95
0.96
---
0.21
0.03
0.23
0.30
0.37
0.28
0.21
sRAGE
3.15
0.30
---
0.00
0.12
0.03
0.51
-0.01
0.04
0.07
0.43
0.39
0.07
---
0.96
0.70
0.07
0.01
0.09
0.20
0.03
0.08
p
0.37
---
0.10
0.00
-0.19
0.00
-0.07
0.00
-0.01
0.00
0.28
B
0.85
---
0.07
0.95
0.27
0.72
0.53
0.64
0.51
0.78
0.04
p
positivity
positivity
B
RAGE
Bronchial biopsies
AGE
B
p
---
0.00
0.00
0.00
-0.01
0.00
0.00
---
0.85
0.39
0.63
0.50
0.51
0.91
0.81
0.00*
0.00
0.72
0.91
0.00
0.00
0.00
SAF
Skin
B=regression coefficient for predictor variables. All models are adjusted for age, gender, packyears, BMI, LDL cholesterol, and triglycerides. * significant p-value after
Benjamini Hochberg correction for multiple testing. CEL=Nε-(carboxyethyl)lysine, CML=Nε-(carboxymethyl)lysine, sRAGE=soluble receptor for advanced glycation
endproducts, AGE=advanced glycation end products, RAGE=receptor for advanced glycation endproducts, SAF= skin autofluorescence.
AGE positivity
RAGE positivity
sRAGE
Bronchial
Pentosidine
biopsies
CEL
sRAGE
CML
Pentosidine
Sputum
CML
CEL
CEL
0.01
B
---
Predictor variables
Plasma
TABLE 4. Associations between AGEs and RAGE expression in different tissues
AGEs and RAGE in COPD
5
101
Chapter 5
RAGE
Levels of RAGE in plasma, sputum and bronchial biopsies are presented in Figure 2 and Table 2.
In plasma, sRAGE levels were significantly lower in COPD patients than in young and
old healthy subjects (Figure 2A). In addition, COPD GOLD stage III patients had lower sRAGE
levels than GOLD stage I patients, and GOLD stage IV patients had lower sRAGE levels than
GOLD stage I and II (Figure 2A and Table 1 in online supplement). No differences were found
between young and old healthy subjects. RAGE levels in sputum and RAGE immunopositivity
in whole sections from bronchial biopsies did not differ between groups (Figure 2B and 2C).
When studying different parts of the bronchial biopsies (intact and basal epithelium, smooth
muscle, connective tissue) no group differences were found (Figure 1, online supplement).
RAGE levels in healthy never-smokers and smokers are presented in Table 1 in the
online supplement.
Figure 2. RAGE expression in plasma, sputum and bronchial biopsies
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RAGE levels in A) plasma, B) sputum, and C) bronchial biopsies. Horizontal lines represent median values with
interquartile ranges, * p<0.05 between groups.
102
AGEs and RAGE in COPD
Associations between COPD, lung function and AGEs and RAGE
Table 3(A-B) shows the results of multiple regression analyses with COPD or lung function
values as predictors of AGEs and RAGE expression in the different compartments.
In established COPD, a lower FEV1 %predicted, FEV1/FVC, FEF22-75 % predicted, as
well as a higher RV/TLC were associated with a higher SAF, independently of age, gender,
number of packyears, BMI, LDL cholesterol and triglycerides (Table 3A). No associations were
observed between COPD or lung function values on one hand and AGEs in plasma, sputum
and bronchial biopsies on the other hand.
Regarding RAGE, both established COPD and impaired lung function values were
associated with higher levels of soluble RAGE in plasma (Table 3B). No associations were found
of COPD or lung function values with RAGE levels in sputum and bronchial biopsies.
Associations of AGEs and RAGE expression between different compartments
Results of multiple regression analyses are presented in Table 4, reflecting associations
between AGEs and RAGE expression in the different tissues after adjustment for age, gender,
packyears, BMI, LDL cholesterol and triglycerides. Lower plasma sRAGE levels were significantly
associated with higher SAF values. Furthermore no significant associations were found.
5
Figure 3. Associations between sRAGE and SAF
Rho=correlation coefficient, SAF=skin autofluorescence, sRAGE is soluble receptor for advanced glycation
endproducts. Association after adjustment for age, gender, packyears, BMI, LDL cholesterol and triglycerides was
in B=0.00, p=<0.01.
103
Chapter 5
DISCUSSION
In this study we investigated a large COPD and a non-COPD control population with respect to
the accumulation of AGEs and expression of its receptor RAGE in different body compartments
including plasma, induced sputum, bronchial biopsies and the skin. We performed this study
in COPD, a chronic disease which for a long time has been associated with chronic oxidative
stress, the most important accelerator of AGES formation. Our study shows that SAF values
in the skin were higher in COPD than in young and old non-COPD controls, whereas the
expression of AGEs in bronchial biopsies was not different between the groups. In addition,
sRAGE levels in plasma were lower in COPD patients. Of interest, lower sRAGE associated with
higher SAF, fitting the hypothesis of a ‘protective’ function of sRAGE by acting as a decoyreceptor preventing accumulation in the skin.
In COPD, oxidative stress is thought to be continuously increased as a consequence
of ongoing inflammation (endogeneous component) and chronic smoking (exogeneous
component). This continuous exposure to oxidative stress, both locally in lung tissue as well
as systemically in peripheral blood, might lead to increased accumulation of AGEs inside
and outside the lung. In the current study we demonstrated that AGEs accumulation was
elevated in the skin of COPD patients, a finding that we and others have observed before (8,9).
Interestingly, SAF values were comparable between the different severity stages of COPD.
This suggests that AGEs formation is not increased during disease progression, but may be
accelerated in the induction phase of COPD. Our data may suggest the following processes:
AGEs accumulate due to oxidative stress responses to some extent during aging in healthy
smokers, whereas this is accelerated with chronic smoking in healthy smokers. The highest
AGEs would be expected in ‘susceptible’ smokers, i.e. subjects who develop COPD. Here, AGEs
accumulate due to a combination of ageing and disease-related exaggerated response to
smoking and associated local and systemic oxidative stress.
In contrast with our findings in the skin, AGEs expression in bronchial biopsies was
not different between COPD patients and non-COPD controls and did not associate with lung
function values in the total population. This contradicts a previous study showing higher AGEs
expression in the lung parenchyma and small airways of COPD patients as compared to nonCOPD controls (7). In an effort to replicate these findings we analyzed immunopositivity of
our bronchial biopsies in numerous ways, e.g. by quantifying AGEs in different parts of the
bronchial biopsies and by using different antibodies (against total amount of AGEs, CML and
pentosidine). However, no differential expressions in COPD patients were observed. There are
several explanations for our negative finding in bronchial biopsies. First, we collected biopsies
from the central airways whereas oxidative stress might predominantly exist in the peripheral
airways, and thereby also AGEs formation. Unfortunately, studies comparing oxidative stress
in central and peripheral airways are scarce. One study showed that isoprostane levels in
ELF from the peripheral airways were higher than from the central airways, both in smokers
with and without airway obstruction (21). We also checked expression of AGEs in peripheral
lung tissue sections of smokers and non-smokers with and without COPD but did not find
differences between these (small) groups. Secondly, accumulation of AGEs in the lung might
be limited because of the relatively high turn-over rate of cells and extracellular matrix
104
AGEs and RAGE in COPD
(22). For example, the turn-over rate of epithelium from the tracheo-bronchial wall in adult
rodents is estimated to be more than 100 days (23), in contrast to about 20 years of the dermis
and an infinite turnover time of the ocular lens, both organs in which AGES are stored (24).
Finally, a quantification problem may contribute to a lower expression of AGEs in the lungs
of COPD patients, as extracellular matrix proteins are reduced in the central airways of COPD
patients (25). Obviously, more research at AGEs accumulation is needed in both the central
and peripheral airways before definitive conclusions can be drawn regarding our conflicting
results.
In plasma, we demonstrated that CML, CEL and pentosidine levels were comparable
between COPD patients and non-COPD controls, after correction for confounding factors.
Our results are in line with three previous studies in COPD investigating plasma CML levels
and showing no differences between COPD and non-COPD controls (12,14,26). In contrast,
one study showed comparable pentosidine levels as well, but lower CML levels and higher
CEL levels in COPD patients (9). The latter is surprising since CML and CEL both are formed by
the same pathway, namely via reactive carbonyl compounds. One explanation might be that
another technique was used, namely mass spectrometry. One also have to realize that AGEs
are very volatile, hence measure a ‘snap shot’ in time only and results can be affected by food
intake and smoking as well (1,27).
Besides their harmful local effects in tissue, AGEs can interact with RAGE thereby
triggering intracellular signaling in pro-inflammatory pathways. Two previous studies showed
that immunostaining of RAGE was increased in bronchial biopsies and in lung parenchyma
of COPD patients (7,10), but we observed no differences with non-COPD controls in the
current study. RAGE also exists as a soluble form, generated as a splice variant of the advanced
glycosylation end product-specific receptor (AGER) gene or by proteolysis of the receptor from
the cell surface. In line with previous studies (11-15), we demonstrated lower levels of sRAGE
in plasma of COPD patients and these reduced levels were associated with lower lung function
values in the total population. Of interest, we demonstrated for the first time that lower sRAGE
levels were associated with increased SAF values. This finding supports the idea that sRAGE
acts as a decoy receptor. AGEs binding to sRAGE may lead to clearance of AGEs preventing
to accumulate in body tissues, a protective mechanism that apparently is impaired in COPD
patients. There are indications that lower sRAGE levels are genetically determined, as a single
nucleotide polymorphism (SNP) in the AGER gene associates with lower sRAGE levels (11). In
this perspective, impaired sRAGE levels might contribute to higher levels of AGEs in tissues
specifically in COPD.
Finally, we assessed levels of AGEs and RAGE in sputum supernatant, which has not
been studied in COPD before. We hypothesized that AGEs and RAGE levels in sputum might
reflect expression in the lung. No differential levels of both AGEs and RAGE in induced sputum
from COPD patients and non-COPD controls were found, nor associations with expression in
bronchial biopsies. This observation fits with the comparable expression of AGEs and RAGE
between COPD and healthy individuals as observed in our bronchial biopsies. An explanation
may be that AGEs are released in more peripheral airways and not captured in sputum, as one
study in COPD demonstrated that CML was elevated in the epithelial lining fluid (ELF) collected
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Chapter 5
in the peripheral airways, but not in the central airways (28). Since this is the first study of AGEs
and RAGE in sputum, further research is needed.
This study is unique because of its large population of healthy smokers and neversmokers and a large group of COPD patients of all severities, as well as the availability of different
tissues from each participant. Unfortunately, only a subgroup of COPD patients performed
sputum induction and a bronchoscopy which may have affected the statistical power of this
study. Another limitation is that our study had a cross-sectional design. Longitudinal studies
are needed to investigate changes in AGEs and RAGE levels in the different tissues over time
and to further assess the potential contributing role of AGEs and RAGE in the development of
COPD.
To summarize, there is growing evidence in the literature for an AGEs - RAGE
interaction in the pathology of COPD. Our study contributes to this insight since we show an
increased AGEs accumulation in the skin of COPD patients compared to non-COPD smokers
and never-smokers. Moreover, we did not observe differences between COPD and non-COPD
controls in central bronchial biopsies, indicating that accumulation of AGEs is not similar in
different body compartments. No further associations were found between AGEs and RAGE in
the different compartments that were investigated. Interestingly, we demonstrated that lower
sRAGE levels associate with higher AGE accumulation in the skin. This fits the hypothesis of a
‘protective’ function of sRAGE by acting as a decoy-receptor preventing accumulation in the
skin.
106
AGEs and RAGE in COPD
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Gopal P, Reynaert NL, Scheijen JL, Engelen L, Schalkwijk CG, Franssen FM, et al. Plasma AGEs and skin
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12. Gopal P, Rutten EP, Dentener MA, Wouters EF, Reynaert NL. Decreased plasma sRAGE levels in COPD:
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13. Iwamoto H, Gao J, Koskela J, Kinnula V, Kobayashi H, Laitinen T, et al. Differences in plasma and
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15. Smith DJ, Yerkovich ST, Towers MA, Carroll ML, Thomas R, Upham JW. Reduced soluble receptor for
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16. Lo Tam Loi AT, Hoonhorst SJ, Franciosi L, Bischoff R, Hoffmann RF, Heijink I, et al. Acute and chronic
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inflammatory responses induced by smoking in individuals susceptible and non-susceptible to
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of skin autofluorescence. Ann N Y Acad Sci 2005 Jun;1043:290-298.
19. Meerwaldt R, Graaff R, Oomen PH, Links TP, Jager JJ, Alderson NL, et al. Simple non-invasive assessment
of advanced glycation endproduct accumulation. Diabetologia 2004 Jul;47(7):1324-1330.
20. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: a Practical and Powerful Approach to
Multiple Testing. J R Statist Soc 1995;57(1):289.
21. Kodama T, Kanazawa H, Tochino Y, Kyoh S, Asai K, Hirata K. A technological advance comparing
epithelial lining fluid from different regions of the lung in smokers. Respir Med 2009 Jan;103(1):35-40.
22. Rawlins EL, Hogan BL. Epithelial stem cells of the lung: privileged few or opportunities for many?
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23. Blenkinsopp WK. Proliferation of respiratory tract epithelium in the rat. Exp Cell Res 1967
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24. Verzijl N, DeGroot J, Thorpe SR, Bank RA, Shaw JN, Lyons TJ, et al. Effect of collagen turnover on the
accumulation of advanced glycation end products. J Biol Chem 2000 Dec 15;275(50):39027-39031.
25. Annoni R, Lancas T, Yukimatsu Tanigawa R, de Medeiros Matsushita M, de Morais Fernezlian S, Bruno
A, et al. Extracellular matrix composition in COPD. Eur Respir J 2012 Dec;40(6):1362-1373.
26. Boschetto P, Campo I, Stendardo M, Casimirri E, Tinelli C, Gorrini M, et al. Plasma sRAGE and
N-(carboxymethyl) lysine in patients with CHF and/or COPD. Eur J Clin Invest 2013 Jun;43(6):562-569.
27. Uribarri J, Cai W, Sandu O, Peppa M, Goldberg T, Vlassara H. Diet-derived advanced glycation end
products are major contributors to the body’s AGE pool and induce inflammation in healthy subjects.
Ann N Y Acad Sci 2005 Jun;1043:461-466.
28. Kanazawa H, Kodama T, Asai K, Matsumura S, Hirata K. Increased levels of N(epsilon)-(carboxymethyl)
lysine in epithelial lining fluid from peripheral airways in patients with chronic obstructive pulmonary
disease: a pilot study. Clin Sci (Lond) 2010 May 6;119(3):143-149.
29. Rutgers SR, Timens W, Kaufmann HF, van der Mark TW, Koeter GH, Postma DS. Comparison of induced
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2000 Jan;15(1):109-115.
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5
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Chapter
Supplement
Chapter 5
112
AGEs and RAGE in COPD
METHODS
Sputum induction
Sputum induction was performed according to the method described elsewhere with some
modifications (1). In short, 4,5% hypertonic saline was nebulized with an ultrasonic nebulizer
(Ultraneb, DeVillbiss, Somerset, PA, USA). Patients inhaled for three periods of five minutes
and were encouraged to cough and expectorate sputum after each period. The volume of the
whole sputum sample was determined and an equal volume of 0.1% dithiothreitol (Sputolysin;
Calbiochem, La Jolla, CA, USA) was added. The samples were agitated during 15 minutes in
a shaking water bath for 15 minutes at 37°C to complete homogenization and then filtered
through a 48 µm nylon gauze. The filtered sample was centrifuged (10 min, 450g, 4°C) and the
supernatant was stored at -80°C until analysis.
Bronchial biopsies
After administering of local anaesthesia (lidocain 2-4%), a flexible bronchoscope was introduced
and bronchial biopsies were taken from subsegmental carinae of the right lower lobe. Biopsies
were fixed in 4% neutral buffered formalin, processed and embedded in paraffin and cut in 3
µm sections. Quality of biopsies was verified by hematoxylin and eosin (HE) staining. Sections
were deparaffinized in xylene (2x10 min), rehydrated in alcohol dilations (2x100%, 2x96%, and
1x70%), and rinsed in demi-water. For AGEs staining, antigens were retrieved by incubating
the slides in 0,1M Tris-HCl pH 9.0 buffer at 80°C overnight. For RAGE staining, Citrate 10mM
pH 6.0 buffer was preheated, slides were placed in a plastic container and were heated in
microwave for 15 min at 400W. After antigen retrieval the slides were cooled down at room
temperature (RT) and were washed with PBS. All sections were incubated with 0.3% hydrogen
peroxide H2O2 (Merck, Germany) in PBS (500µl H2O2 30% in 50ml PBS) for 30 min at RT to block
endogenous peroxidase activity. After three washes with PBS, sections were incubated with
the primary monoclonal antibody against AGEs (anti-AGEs (clone 6D12), 1:750, Cosmo Bio
Co, Ltd, Tokyo, Japan) or RAGE (anti-RAGE (ab7764), 1:1500, Abcam, Cambridge, UK) diluted in
PBS/1%BSA for 1 hour at RT. For AGEs staining, sections were washed in PBS for three times and
incubated with the secondary antibody (EnvisionTM Detection Systems Peroxidase (DAKO)) for
30 min at RT. For RAGE staining, sections were washed in PBS for 3 times and incubated with
the secondary peroxidase labeled rabbit anti-goat antibody (DAKO, 1:100 diluted in PBS/1%
BSA + 1%AB serum) for 30 min at RT. After washing with PBS 3x, sections were incubated with
the tertiary peroxidase labeled goat anti-rabbit antibody (DAKO, 1:100 diluted in PBS/1% BSA +
1%AB serum) for 30 min at RT. After washing the sections for three times in PBS for three times,
peroxidase activity was visualised by incubating the slides in DAB (3-3’DiaminoBenzidine)
together with 50 µl of hydrogen peroxide for 10 min at RT. Sections were rinsed in demi water.
Finally, the sections were counterstained with haematoxilin for approximately 2 min, rinsed in
tap water, dehydrated in alcohol (70%, 96% and 100%), dried, and mounted with mounting
medium and covered with a coverslip. Both immunohistochemical stainings were performed
using the DAKO autostainer (DAKO, Glostrup, Denmark). Quantification of both stainings was
performed by calculating the percentage positive and strong positive pixels of the total amount
113
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Chapter 5
of pixels in whole biopsies, using ImageScope (Aperio Technologies, version 11.2.0.780).
Skin autofluorescence
SAF was assessed non-invasively by the AGE-ReaderTM (DiagnOptics B.V., Groningen, The
Netherlands) (2). Technical details of this device have been extensively described elsewhere
(3). In short, the AGE reader illuminates approximately 1 cm2 of the skin, guarded against
surrounding light, with an excitation light source between 300 and 420 nm (peak excitation
flow ~350 nm). Only light from the skin is measured between 300 and 600 nm with a
spectrometer using a 200-µm glass fiber. SAF was calculated by dividing the average light
intensity emitted per nm over the 420- to 600-µm range by the average light intensity emitted
per nm over the 300- to 420-µm range, using the AGE Reader software version 2.2. The volar
surface of subject’s forearm was positioned on top of the device, taking care to perform the
measurement at normal skin site, i.e. without visible vessels, scars, or other skin abnormalities.
SAF was averaged from three consecutive measurements for each subject, measured within a
time period of approximately 2 minutes. In all analyses, SAF is expressed in arbitrary units (AU).
114
AGEs and RAGE in COPD
Table 1. AGE and RAGE expression in young and old never-smokers and smokers,
and COPD GOLD stages
A. Young healthy never-smokers and smokers
Young healthy never-smokers
Young healthy smokers
Plasma
n=36
n=69
CEL
9.0 (6.7-15.0)
11.4 (8.2-16.2)
CML
10.9 (0.0-25.3)
10.5 (0.0-38.1)
Pentosidine
48.7 (32.6-70.9)
35.3 (18.7-47.8)*
RAGE
878.8 (573.3-1113.5)
777.0 (628.8-1077.1)
Sputum
n=34
n=63
CEL
4.0 (0.0-9.5)
6.4 (3.8-8.2)
CML
14.8 (0.0-44.8)
0.0 (0.0-17.6)*
Pentosidine
0.0 (0.0-0.0)
0.0 (0.0-0.0)
RAGE
0.0 (0.0-97.1)
101.3 (0.0-195.1)*
Bronchial biopsies
n=32
n=53
AGEs, positivity (%)
31.3 (18.5-37.4)
30.6 (20.7-36.9)
RAGE, positivity (%)
8.8 (6.1-14.8)
10.8 (6.8-15.2)
Skin
n=36
n=71
AGE-reader
1.20 (1.04-1.40)
1.3 (1.1-1.5)
CEL= Nε-(carboxyethyl)lysine, CML= Nε-(carboxymethyl)lysine, RAGE= receptor for advanced glycation endproducts,
AGEs= advanced glycation endproducts, SAF= skin autofluorescence. Values are expressed as median (IQR).
B. Old healthy never-smokers and smokers
Old healthy never-smokers
Old healthy smokers
Plasma
n=28
n=54
CEL
5.37 (3.29-8.55)
8.56 (5.06-11.26)*
CML
10.97 (0.00-13.64)
0.00 (0.00-12.00)
Pentosidine
39.53 (28.29-45.51)
41.1 (30.12-66.48)
RAGE
810.8 (672.9-1118.5)
795.6 (609.3-953.3)
Sputum
n=23
n=50
CEL
0.00 (0.00-3.23)
6.11 (2.47-8.97)*
CML
10.40 (0.00-23.49)
11.46 (0.00-24.41)
Pentosidine
0.00 (0.00-0.00)
0.00 (0.00-0.00)
RAGE
115.2 (0.0-206.9)
114.0 (0.0-262.2)
Bronchial biopsies
n=26
n=42
AGEs, positivity (%)
28.2 (19.1-37.4)
25.4 (21.3-33.7)
RAGE, positivity (%)
6.9 (5.0-10.6)
9.5 (6.3-12.5)
Skin
n=28
n=55
AGE-reader
1.774 (1.43-2.00)
1.80 (1.60-2.10)
CEL= Nε-(carboxyethyl)lysine, CML= Nε-(carboxymethyl)lysine, RAGE= receptor for advanced glycation endproducts,
AGEs= advanced glycation endproducts, SAF= skin autofluorescence. Values are expressed as median (IQR).
115
5
116
Kruskall-Wallis
COPD GOLD I
COPD GOLD II
COPD GOLD III
COPD GOLD IV
p-value
Plasma
n=32
n=23
n=24
n=16
CEL
6.61 (4.92-9.05)
5.37 (3.67-7.48)
8.23 (5.92-9.58)
7.34 (5.94-13.39)
0.084
CML
13.76 (0.00-23.31)
9.25 (0.00-18.70)
13.06 (0.00-45.76)
14.47 (0.00-21.66)
0.508
Pentosidine
45.61 (30.29-59.85)
49.17 (30.28-58.88)
39.63 (30.50-49.28)
53.67 (39.81-66.58)
0.111
295.8 (216.5-412.2)§‡
0.001*
RAGE
510.02 (397.75-672.20)
423.9 (372.3-635.8)
314.40 (249.45-500.79)§
Sputum
n=0
n=10
n=2
n=0
CEL
13.75 (nvt)
13.75 (nvt)
0.086
CML
18.61 (nvt)
18.6 (nvt)
0.83
Pentosidine
0.00 (0.00-0.00)
0.00 (0.00-0.00)
1,000
RAGE
110.9 (77.4-176.3)
74.3 (nvt)
0.667
Bronchial biopsies
n=0
n=10
n=2
n=0
AGEs, positivity (%)
23.6 (15.9-31.7)
26.7 (25.1-26.7)
RAGE, positivity (%)
7.3 (3.3-11.0)
12.4 (5.7-12.4)
Skin
n=32
n=25
n=24
n=15
AGE-reader
2.42 (2.20-2.86)
2.47 (2.03-2.95)
2.71 (2.20-3.14)
2.51 (2.17-2.89)
0.612
CEL= Nε-(carboxyethyl)lysine, CML= Nε-(carboxymethyl)lysine, RAGE= receptor for advanced glycation endproducts, AGEs= advanced glycation endproducts, SAF= skin
autofluorescence. Values are expressed as median (IQR).
C. COPD severities
Table 1. AGE and RAGE expression in young and old never-smokers and smokers, and COPD GOLD stages
Chapter 5
CEL
CML
Pentosidine
RAGE
CEL
CML
Pentosidine
RAGE
AGEs positivity
RAGE positivity
Plasma
Sputum
Bronchial
biopsies
p
0.06
0.41
-- -- -- -- -- -- -- ---
Pentosidine
Rho
p
Rho
0.12 0.05 -0.12
-- --0.05
-- ------ ------ ------ ------ ------ ------ ------ -----
CML Plasma
CEL Rho
p
Rho
p
0.10
0.12 0.28 <0.01
-0.22 <0.01 0.16 0.03
-0.16 0.01 0.05
0.54
-- ---0.15 0.06
-- ---- ---- ---- ---- ---- ---- ---- ---- ---- ---- ---- ---
RAGE p
0.38
0.92
0.99
0.97
<0.01
-- -- -- -- ---
CML Rho
0.07
0.01
0.00
0.00
0.26
-----------
Sputum
Rho
0.09
-0.01
-0.03
0.01
0.10
0.02
---------
p
0.25
0.90
0.67
0.94
0.18
0.83
-- -- -- ---
Pentosidine
Rho
p
Rho
-0.08 0.30
0.09
-0.01 0.94 0.21
-0.06 0.45
0.13
0.06
0.46 -0.17
0.04
0.56 0.20
-0.29 <0.01 -0.07
-0.19 0.01 -0.08
-- ---0.09
-- ------ -----
RAGE p
0.28
0.01
0.10
0.03
0.02
0.38
0.56
0.79
-- ---
AGEs
positivity
Bronchial biopsies
Rho
0.16
-0.00
-0.05
-0.09
0.12
0.07
-0.10
-0.03
0.24
---
SAF
Skin
p
Rho
p
0.05 -0.29 <0.01
0.99
0.04
0.55
0.52 0.12 0.05
0.28 -0.46 <0.01
0.15 -0.16 0.03
0.38
0.11
0.15
0.26 -0.10 0.18
0.72
0.10
0.18
<0.01 0.06
0.44
---0.12 0.14
RAGE
positivity
Spearman’s rank correlations, Rho=correlation coefficient. CEL= Nε-(carboxyethyl)lysine, CML= Nε-(carboxymethyl)lysine, RAGE= receptor for advanced glycation
endproducts, AGEs= advanced glycation endproducts, SAF= skin autofluorescence.
Table 2. Correlations of AGEs and RAGE between different compartments
AGEs and RAGE in COPD
5
117
Chapter 5
Figure 1. Quantitative analyses of AGEs and RAGE expression in bronchial biopsies
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Quantitative analyses of AGEs (left panel) and RAGE (right panel) expression in A) intact epithelium, B) basal
epithelium, C) smooth muscle, D) connective tissue of bronchial biopsies. Intensity of staining was scored
by a 4-points scale: 0=negative staining, 1=weak positive, 2=positive, and 3=strong positive. Horizontal bars
represent median values.
118
AGEs and RAGE in COPD
Figure 2. Quantitative analyses of AGEs and RAGE expression in peripheral airways
Quantitative analyses of AGEs (left panel) and RAGE (right panel) expression in A) epithelium and B) smooth
muscle of the peripheral airways. Intensity of staining was scored by a 4-points scale: 0=negative staining,
1=weak positive, 2=positive, and 3=strong positive. Horizontal bars represent median values.
REFERENCES
1.
Rutgers SR, Timens W, Kaufmann HF, van der Mark TW, Koeter GH, Postma DS. Comparison of induced
sputum with bronchial wash, bronchoalveolar lavage and bronchial biopsies in COPD. Eur Respir J
2000;15:109-115.
2.
Meerwaldt R, Links T, Graaff R, Thorpe SR, Baynes JW, Hartog J, Gans R, Smit A. Simple noninvasive
measurement of skin autofluorescence. Ann N Y Acad Sci 2005;1043:290-298.
3.
Meerwaldt R, Graaff R, Oomen PH, Links TP, Jager JJ, Alderson NL, Thorpe SR, Baynes JW, Gans RO, Smit
AJ. Simple non-invasive assessment of advanced glycation endproduct accumulation. Diabetologia
2004;47:1324-1330.
119
5
6
Chapter
Lower corticosteroid
skin blanching response
is associated with severe COPD
Susan Hoonhorst, Nick ten Hacken, Adèle Lo Tam Loi, Leo Koenderman,
Jan-Willem Lammers, Eef Telenga, Marike Boezen,
Maarten van den Berge, Dirkje Postma
PLoS One 2014 Feb 5;9(2)
Chapter 6
ABSTRACT
Background: Chronic obstructive pulmonary disease (COPD) is characterized by chronic airflow
limitation caused by ongoing inflammatory and remodeling processes of the airways and lung
tissue. Inflammation can be targeted by corticosteroids. However, airway inflammation is
generally less responsive to steroids in COPD than in asthma. The underlying mechanisms are
yet unclear. This study aimed to assess whether skin corticosteroid insensitivity is associated
with COPD and COPD severity using the corticosteroid skin blanching test.
Methods: COPD patients GOLD stage I-IV (n=27, 24, 22, and 16 respectively) and healthy neversmokers and smokers (n=28 and 56 respectively) were included. Corticosteroid sensitivity was
assessed by the corticosteroid skin blanching test. Budesonide was applied in 8 logarithmically
increasing concentrations (0-100 µg/ml) on subject’s forearm. Assessment of blanching was
performed after 7 hours using a 7-point scale (normal skin to intense blanching). All subjects
performed spirometry and body plethysmography.
Results: Both GOLD III and GOLD IV COPD patients showed significantly lower skin blanching
responses than healthy never-smokers and smokers, GOLD I, and GOLD II patients. Their area
under the dose-response curve values of the skin blanching response were 586 and 243 vs.
1560, 1154, 1380, and 1309 respectively, p<0.05. Lower FEV1 levels and higher RV/TLC ratios
were significantly associated with lower skin blanching responses (p=0.001 and p=0.004
respectively). GOLD stage I, II, III and IV patients had similar age and packyears.
Conclusions: In this study, severe and very severe COPD patients had lower skin corticosteroid
sensitivity than mild and moderate COPD patients and non-COPD controls with comparable
age and packyears. Our findings together suggest that the reduced skin blanching response
fits with a subgroup of COPD patients that has an early-onset COPD phenotype.
122
Corticosteroid skin blanching response in COPD
INTRODUCTION
Chronic obstructive pulmonary disease (COPD) is characterized by chronic airflow limitation
caused by ongoing inflammatory and remodeling processes of the airways and lung tissue
(1). Inflammation can be targeted with inhaled corticosteroids (ICS) and indeed ICS improve
symptoms, quality of life and exacerbation rates in COPD (2,3). However, most studies did not
find a reduction in lung function decline after long-term ICS treatment (4), indicating that the
underlying process of disease progression was generally not effectively modified. Two studies
showed beneficial effects of long-term ICS treatment on lung function decline (5,6). This
suggests that a subgroup of COPD patients may be more sensitive to ICS, a finding recently
corroborated by gene-expression profiling (7).
Notwithstanding above findings, it is clear that airway inflammation in COPD
generally responds less well to corticosteroids than in asthma patients. However, it has not
been fully established whether corticosteroid insensitivity is more pronounced in patients with
more severe COPD. The TORCH study has shown that treatment with combined salmeterol and
fluticasone reduced moderate-to-severe exacerbations and improved health status and forced
expiratory volume in one second (FEV1) across all GOLD stages. Depending on the outcome
parameters studied, the latter study showed additionally a better or equal ICS response in
severe COPD compared to patients with milder disease (8).
Corticosteroid sensitivity can indirectly be assessed by the McKenzie skin blanching
test (9). In this test, corticosteroids are topically applied to the skin in 8 logarithmically
increasing concentrations (0-100 µg/ml) and corticosteroid responsiveness can be determined
by the degree of blanching of the skin. Asthma patients with airway obstruction unresponsive
to corticosteroid treatment (i.e. a failure of FEV1 and peak expiratory flow to improve by at
least 15% after 2-week corticosteroid treatment) have lower skin blanching scores than steroid
responsive patients (10). Furthermore, skin blanching scores are lower in smokers with asthma
than in never-smokers with asthma and smokers without airway obstruction (11). Additionally,
we recently found that lower skin blanching scores were associated with a lower lung function
in asthma patients (12). So far, no data are available in COPD.
In this study we investigated the skin blanching test in patients with mild-to-very
severe COPD and healthy never-smokers and long-term smokers. Our main objective was to
ascertain whether COPD patients have lower corticosteroid sensitivity in the skin than neversmoking and heavy smoking healthy controls, as measured by the skin blanching response.
Secondly, we investigated if the skin blanching response was associated with disease severity.
METHODS Study population
COPD patients and healthy individuals were recruited by advertisements and from hospital
outpatient clinics. Mild-to-severe COPD patients were included with an FEV1/FVC<0.7 and
stages I to IV according to the Global initiative for chronic Obstructive Lung Disease (GOLD) (1).
Never-smokers and current smokers without airway obstruction (FEV1/FVC>0.7) were included
as healthy controls. Smokers had to have a smoking history of more than 20 packyears.
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6
Chapter 6
Patients with a doctor’s diagnosis of asthma or patients with alpha-1 antitrypsin deficiency
were excluded. The study was approved by the medical ethics committees of University
Medical Centers Groningen and Utrecht, The Netherlands. All subjects gave their written
informed consent. The study is a multicenter study performed by University Medical Centers
of Groningen and Utrecht, with trial register numbers NCT00807469 and NCT00850863 (www.
clinicaltrials.gov) (13). Healthy controls were obtained from the Norm study performed by
University Medical Center Groningen, trial register number NCT00848406.
Measurements
The corticosteroid skin blanching test was performed as described before (12). Briefly,
budesonide was dissolved in 95% ethanol to 8 logarithmically increasing concentrations:
0-0.33-1.0-3.3-10-33.3-100-333-1000 μg/ml. The different budesonide concentrations were
randomly applied at 2 cm diameter test sites on the subject’s forearm, 10 μl per test site.
Sites were covered with plastic film. Assessment of blanching was performed after 7 hours by
trained observers blinded for the concentration sequences using a 7-point scale, i.e. 0 (normal
skin), 0.5, 1, 1.5, 2, 2.5, 3 (intense blanching) (Figure 1).
Pulmonary function and lung volumes were measured by spirometry and body
plethysmography respectively, according to European Respiratory Society guidelines (14,15).
Figure 1. Blanching of the skin as a result of vasoconstriction, 7 hours after application of
budesonide in increasing concentrations
Statistical methods
Skin blanching response was expressed as the area under the dose-response curve (AUC)
for each participant using the trapezoidal method (16). Mann-Whitney U tests were used to
compare skin blanching responses between groups, using the Benjamini-Hochberg correction
for multiple testing (17). Spearman’s rank correlations were performed on skin blanching
response and clinical characteristics. Multiple regression analyses were performed on skin
blanching response with significant variables from univariate correlation analyses as predictor
variables. We included lung function variables representing airway obstruction (FEV1, L),
hyperinflation (RV/TLC, %), and small airway obstruction (MEF50, L/s). Since FEV1 and RV/TLC
are highly correlated to each other, they were not entered in the model simultaneously. All
models were adjusted for age, gender, and height. P-values <0.05 were considered significant.
Data were analyzed using IBM SPSS Statistics version 20.
124
Healthy
never-smokers
28
20 (71)
57 [51-66]
1560 [1018-2180]
0 (0)
0
0
3.6 [3.1-4.4]
79 [77-82]
111 [103-120]
32 [27-35]
4.0 [3.3-5.1]
0 (0)
0 (0)
Healthy smokers
COPD GOLD I
COPD GOLD II
COPD GOLD III
COPD GOLD IV
N
56
27
24
22
16
Male, n (%)
41 (73)
22 (82)
18 (75)
16 (73)
7 (44)
Age, years
51 [46-58]
64 [58-68]
63 [59-70]
63 [56-65]
60 [53-66]
Skin blanching, AUC
1154 [667-1771]
1380 [783-2481]
1309 [379-2189]
586 [17-1270]
243 [59-1385]
Current smokers, n (%)
55 (100)
18 (67)
16 (67)
11 (50)
3 (19)
Cigarettes per day, n
17 [11-20]
12 [6-23]
6 [4-15]
10 [4-20]
5 [5-6]
Packyears
26 [21-39]
40 [28-54]
31 [24-39]
42 [32-56]
31 [23-40]
FEV1, L
3.7 [3.3-4.2]
2.9 [2.7-3.4]
1.9 [1.7-2.1]
1.2 [1.1-1.4]
0.7 [0.6-0.8]
FEV1 / FVC, %
78 [75-83]
64 [58-68]
50 [42-57]
38 [34-41]
31 [25-37]
FEV1, % predicted
107 [101-117]
95 [87-100]
66 [55-73]
41 [36-47]
25 [21-29]
RV/TLC, %
31 [28-34]
38 [32-41]
44 [38-49]
52 [47-57]
64 [55-66]
MEF50, L/s
4.0 [3.6-4.9]
2.0 [1.7-2.4]
0.8 [0.7-1.2]
0.4 [0.4-0.5]
0.3 [0.2-0.3]
CS use:
0 (0)
10 (37)
18 (75)
20 (91)
16 (100)
ICS, n (%)
0 (0)
9 (33)
17 (71)
19 (86)
10 (63)
ICS, ug/ml per day§
0 [0-500]
1000 [125-1750]
1000 [200-1000]
1000 [250-1188]
OCS, n (%)
0 (0)
0 (0)
1 (4)
1 (4)
1 (5)
6 (38)
OCS, mg per day
0 [0-0]
0 [0-0]
0 [0-0]
0 [0-5]
Data are expressed as median [Inter Quartile Range]. n = number, AUC = area under the dose-response curve, FEV1 = forced expiratory volume in one second, FVC
= forced vital capacity, RV = residual volume, TLC = total lung capacity, MEF50 = maximal expiratory flow at 50% of vital capacity, CS = Corticosteroids, ICS = inhaled
corticosteroids, OCS = oral corticosteroids.§ calculated as µg/day beclomethasone.
Table 1. Characteristics of the study population
Corticosteroid skin blanching response in COPD
6
125
Chapter 6
RESULTS
Characteristics
We included 28 healthy never-smokers, 56 healthy smokers and 89 COPD patients; 27 GOLD
stage I, 24 GOLD stage II, 22 GOLD stage III and 16 GOLD stage IV. Group characteristics are
presented in Table 1. COPD patients had as higher age (p<0.01), were less frequently current
smokers (p<0.01), and had a higher number of packyears (p<0.04) than healthy controls. As
expected, lung function values were significantly lower (p<0.01) in COPD patients than in
controls. In total 9 patients used oral corticosteroids (OCS). The number of patients using OCS
was significantly higher (p=0.03) in the severe-to-very severe (GOLD III+IV, n=7) than in the
mild-to-moderate patient group (GOLD I+II, n=2).
Figure 2. Skin blanching responses in healthy controls and COPD patients GOLD stages
I–IV
#
!" "
%
$
!"
!
#
"
%
"
!
"
AUC = area under the dose-response curve. Values are expressed as median [range]. ** significantly different
from healthy never-smokers, healthy smokers, GOLD I and GOLD II patients (p-value <0.05) * significantly
different from healthy never-smokers, healthy smokers and GOLD I patients (p-value ≤0.01).
126
Corticosteroid skin blanching response in COPD
Figure 3. Dose-response curves of budesonide in healthy controls and COPD patients
GOLD stages I–IV
#!"
$' &"#!"#
$'#!"#
%#! Values are expressed as mean ± SEM.
Skin blanching response
COPD GOLD stage III patients had significantly lower skin blanching responses than healthy
never-smokers and smokers (both p≤0.01), GOLD stage I (p<0.01) and GOLD stage II patients
(p=0.02) (Figures 2 and 3). GOLD stage IV patients had also significantly lower skin blanching
responses than healthy never-smokers and smokers (both p<0.01) and GOLD stage I patients
(p=0.01), and had near significantly lower skin blanching responses than GOLD stage II patients
(p=0.06). The skin blanching response was comparable between healthy never-smokers and
smokers and between patients with GOLD stages I and II, as were the responses between GOLD
III and GOLD IV patients. Additionally, no significant differences in skin blanching responses
were found between smoking and ex-smoking COPD patients (p>0.05).
Predictors of skin blanching response
Table 2 presents results of monovariate correlations of the skin blanching response with
gender, age, corticosteroid use, smoking parameters, and lung function. A lower skin
blanching response was significantly correlated with female gender, corticosteroid use and
reduced pulmonary function (lower FEV1, FEV1/FVC ratio, FEV1,%predicted, MEF50, and higher
RV/TLC ratio)(p<0.05). The skin blanching response was not significantly correlated with age
and smoking (current smoking, cigarettes per day and packyears).
Table 3 presents the results of the two multivariate regression models. A lower skin
blanching response was significantly associated with a lower FEV1 (p<0.01), and with a higher
RV/TLC ratio (p<0.01). Effects of CS use in both models remained the same and R-square values
of both models were comparable, 0.144 and 0.118 respectively. No significant associations
were present between skin blanching response and MEF50. Importantly, corticosteroid use was
not a predictor of skin blanching response in both multiple regression models.
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6
Chapter 6
Table 2. Univariate correlations of skin blanching response with clincal characteristics
N=173
Rho
p-value
Gender, male/female
-0.177
0.020
Age, years
-0.035
0.644
Corticosteroid use, no/yes
-0.196
0.010
Current smoking, no/yes
-0.092
0.226
Cigarettes per day, n
0.073
0.469
Packyears
-0.107
0.162
FEV1, L
0.329
0.000
FEV1 /FVC, %
0.215
0.004
FEV1, %predicted
0.247
0.001
MEF50, L/s
0.257
0.001
RV/TLC, %
-0.299
0.000
Spearman’s rank correlations with skin blanching response (AUC) as dependent variable. FEV1 = forced expiratory
volume in one second, FVC = forced vital capacity, RV = residual volume, TLC = total lung capacity, MEF50 =
maximal expiratory flow at 50% of vital capacity, TLCO = carbon monoxide transfer factor, VA = accessible lung
volume. Values in bold represent significant correlations (p-value < 0.05).
Table 3. Linear regression analyses on skin blanching response (AUC)
Model 1
Model 2
R2=0.118
N=173
R2=0.144
B
S.E.
p-value
B
S.E.
p-value
FEV1, L
490.9
140.8
0.001
------RV/TLC, %
-------25.4
2.8
0.004
-130.1
78.5
0.099
-17.4
58.6
0.767
MEF50, L/s
Corticosteroid use, no/yes
218.7
209.5
0.298
115.4
210.0
0.583
Since FEV1 and RV/TLC are highly correlated to each other, they were not entered in the model simultaneously.
Dependent variable is skin blanching response (AUC). Both models were corrected for age, gender, and height.
B = regression coefficient, SE = standard error, FEV1 = forced expiratory volume in one second, FVC = forced
vital capacity, RV = residual volume, TLC = total lung capacity, MEF50 = maximal expiratory flow at 50% of
vital capacity, TLCO = carbon monoxide transfer factor, VA = accessible lung volume. Values in bold represent
significant p-values (< 0.05).
Effects of corticosteroid treatment on the skin blanching response
In the GOLD I+II patient group, 26 patients used ICS treatment, 2 patients OCS, and 23 patients
used no corticosteroids (Table 1). In the GOLD III+IV patient group, numbers were respectively
29, 7, and 2 (Table 1). COPD patients with and without ICS had similar median [IQR] skin
blanching responses as determined by AUC values, both in the GOLD I and II groups (1327
[718-2293] and 1380 [640-2480] respectively), and the GOLD III and IV groups (333 [95-1231]
and 645 [2-645] respectively) (Figure 4). The same was found for OCS, median [IQR] AUC values
being 795 [2.37-795] and 241 [23-1520] in the GOLD I+II and GOLD III+IV groups respectively.
128
Corticosteroid skin blanching response in COPD
!
&# &#'$&#
!
#$"! $
!#%
#$"! $
Figure 4. Corticosteroid treatment and skin blanching response in COPD patients
AUC = area under the dose-response curve, CS = corticosteroids, ICS = inhaled corticosteroids, OCS = oral
corticosteroids.
DISCUSSION
The current study shows that the skin blanching response to topical budesonide is lower in
patients with more severe COPD. Patients with COPD GOLD stage III and IV had significantly
lower skin blanching response than those with stage I and II and the latter groups had a
response similar to that in healthy never- and current-smokers. These observations were not
driven by corticosteroid use. In line, a lower skin blanching response was associated with a
lower FEV1 and a higher RV/TLC ratio in multivariate regression analyses, and not affected
by smoking. These data indicate that reduced corticosteroid sensitivity is present in a subpopulation of COPD patients.
The association we found between FEV1 and skin blanching response in COPD is
consistent with observations from our earlier study in asthma, in which patients with an FEV1
<80% predicted had a lower skin blanching response than those without airway obstruction
(12). Our findings are also in line with Brown et al, showing that corticosteroid resistant asthma
patients had lower skin blanching scores than steroid sensitive patients (10). This may be due
to their steroid resistance, but compatible with our observation steroid resistant patients had
also lower FEV1%predicted values than steroid sensitive patients. In our COPD population
we additionally focused on the relation between skin blanching response and lung function
parameters representing hyperinflation and small airway obstruction. Monovariate analyses
showed that besides a lower FEV1, lower skin blanching response correlated significantly with
a lower MEF50 and more hyperinflation (RV/TLC). Of these parameters, FEV1 and RV/TLC ratio
appeared to be the most important predictors of the skin blanching response, since both
remained significant in multiple linear regression analyses. These are novel observations which
have not been described before. Taken together, our data suggest an interesting link between
a lower skin blanching response and worse lung function.
The question arises whether the observed impaired corticosteroid sensitivity
originated during the progression of COPD, or whether it was already present earlier in life,
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Chapter 6
possibly contributing to the development of COPD. In this context, our findings that mild
and moderately severe COPD patients had skin blanching responses which were comparable
to those of healthy controls are of interest. This finding makes it less likely that a reduced
corticosteroid sensitivity originates as a result of COPD development. Importantly, all COPD
patient groups (GOLD I, II, III and IV) were of comparable age and had smoked the same high
number of packyears. However, despite similar age and packyears, main determinants of lung
function level, patients with GOLD stage III and IV had excessively lower lung function values.
This suggests that they were more susceptible to the detrimental effects of smoking and
possibly suffer from a different type of COPD e.g. by early accelerated lung function decline
resulting in early-onset of COPD (18,19). We therefore hypothesize that the skin blanching
response may have discriminative property to point at a subphenotype of COPD by recognizing
patients with an accelerated loss of lung function at an earlier stage in life.
Phenotyping of COPD patients aims to identify subgroups of patients, based on
clinically important characteristics, which could be useful to better understand the origin of
COPD and to optimize treatment. Traditionally, distinctions have been made between patients
with emphysema and chronic bronchitis, two clinical phenotypes which in most cases are
both present to a varying extent (20). We found no indications that corticosteroid insensitivity
is related with those phenotypes, since skin blanching response was not associated with
diffusion capacity and symptoms (phlegm production and shortness of breath, data not
shown). Several phenotypes of COPD have been proposed, based on outcomes which may
vary greatly between patients, e.g. clinical and physiological manifestations, radiologic
characterization, exacerbation frequency, systemic inflammation, and comorbidities (21).
Since COPD patients show individually different benefits to corticosteroids, this suggests that
a corticosteroid-responsive phenotype could also be present. If we assume that the response
in the skin can be translated to the airways, the skin blanching response might differentiate
between those responders and non-responders to corticosteroid treatment. This clearly needs
further prospective study.
There are indications that corticosteroid insensitivity is genetically determined.
The skin blanching response has been shown to be regulated by vascular smooth muscle
glucocorticoid receptors (GR) (22-24), leading to local vasoconstriction (25). Studies
investigating the association between GR single nucleotide polymorphisms (SNPs) and
corticosteroid sensitivity have shown that individuals with a BclI SNP express a lower skin
blanching response (24,26). This SNP may result in different GR isoform expression, receptor
affinity or GR expression. The change in GR function that affects corticosteroid response
is of putative importance, since endogenous corticosteroids are essential to normal lung
development during fetal growth (27). Thus, small changes in corticosteroid sensitivity may
have important consequences for lung development and lung growth in utero and early
childhood. It has been shown that lower lung function at birth or in early childhood increases
the risk of COPD development (18). Furthermore, corticosteroid insensitivity may associate
with a higher susceptibility to harmful external stimuli, like cigarette smoking and air pollution.
The subsequently enhanced inflammatory condition may contribute to further remodeling
and fibrosis of the airways and/or emphysematous changes in lung tissue together resulting
130
Corticosteroid skin blanching response in COPD
in airway obstruction (28). In this way our findings of reduced corticosteroid sensitivity in a
subgroup of COPD patients may reflect an underlying genetic contribution to an early onset
COPD phenotype (29).
Patients with severe COPD use more often corticosteroid treatment compared to
milder stages of COPD, in line with the GOLD guidelines that advocate ICS in this patient
category (1). Also in the current study corticosteroid use was higher prevalent in GOLD stage
III and IV patients. It is of importance to realize that corticosteroid treatment may interfere with
the GR receptor, thereby affecting the skin blanching response. We observed no differences
in skin blanching response between patients using ICS and/or OCS, and patients using no
corticosteroids at all. Monovariate correlation analyses showed that corticosteroid use was
correlated with a lower skin blanching response. However, corticosteroid use was no predictor
of skin blanching response in multiple regression models, demonstrating the more important
role of lung function as predictor of skin blanching. In addition, we have investigated the
effects of beta-2 agonists and anticholinergics on the skin blanching response, since these
drugs can affect vasoconstriction responses in humans (30). We did not find a significant
association between both drugs and the skin blanching response (data not shown).
In conclusion, the current study shows that severe and very severe COPD patients
(GOLD III and IV) have lower corticosteroid sensitivity in the skin compared with mild and
moderate COPD patients (GOLD I and II) and healthy never-smokers and smokers. It remains
speculative which mechanisms drive this observation. We put forward the hypothesis that
corticosteroid insensitivity is a genetically determined phenotype which may contribute to
the development of COPD. To further investigate, validate and extend our findings, a next step
would be to replicate this research in future studies, expanding the group size and associating
skin blanching response with differential genetic profiles in a population of COPD patients
and healthy controls. This would gain more insight in different COPD phenotypes and may be
helpful in understanding the corticosteroid resistant phenotype that can be present in COPD.
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Chapter 6
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24. Panarelli M, Holloway CD, Fraser R, Connell JM, Ingram MC, Anderson NH, et al. Glucocorticoid
receptor polymorphism, skin vasoconstriction, and other metabolic intermediate phenotypes in
normal human subjects. J Clin Endocrinol Metab 1998 Jun;83(6):1846-1852.
25. Haigh J, Meyer E, Smith E, Kanfer I. The human skin blanching assay for in vivo topical corticosteroid
assessment I. Reproducibility of the assay. Int J Pharm 1997;152:179-183.
26. Kumsta R, Entringer S, Koper JW, van Rossum EF, Hellhammer DH, Wust S. Glucocorticoid receptor
gene polymorphisms and glucocorticoid sensitivity of subdermal blood vessels and leukocytes. Biol
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27. Provost PR, Boucher E, Tremblay Y. Glucocorticoid metabolism in the developing lung: Adrenal-like
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28. Hogg JC, Chu F, Utokaparch S, Woods R, Elliott WM, Buzatu L, et al. The nature of small-airway
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29. Silverman EK, Chapman HA, Drazen JM, Weiss ST, Rosner B, Campbell EJ, et al. Genetic epidemiology
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133
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6
Chapter
Addendum
Chapter 6
136
Corticosteroid skin blanching response in COPD
The corticosteroid skin blanching response was also investigated in young and old susceptible
and non-susceptible subjects, to assess if susceptibility to COPD is associated with a lower skin
blanching response.
21 young susceptible subjects, 33 young non-susceptible subjects, 89 COPD
patients (GOLD stages I to IV) and 28 healthy controls (old non-susceptible) were included.
Group characteristics are presented in Table 1. In the young group, susceptible subjects had
significantly higher age (p < 0.01), higher number of packyears (p < 0.01), and lower FEV1/FVC
values (p = 0.025) than the non-susceptible subjects. In the ‘old’ group, COPD patients had
significantly higher age (p < 0.01) and were less frequently current smokers (p < 0.01) than
healthy controls. As expected, FEV1, FEV1/FVC, and FEV1 %predicted were significantly lower (p
< 0.01) in COPD patients than in controls.
Young susceptible subjects had significantly lower skin blanching responses than
young non-susceptible subjects (p = 0.04) (Figure 1A and 1B). No difference was found between
the total group of COPD patients and healthy controls (Figure 1A and 1B).
Table 2 presents results of monovariate correlation analyses of the skin blanching
response with gender, age, COPD susceptibility, corticosteroid use, smoking parameters, and
lung function. In the total group higher age, susceptibility to COPD, corticosteroid use, and
higher number of packyears were correlated with a lower skin blanching response. Higher
lung function values (FEV1, FEV1/FVC, and FEV1 %predicted) were correlated with higher
skin blanching responses. In the young group, a significant correlation was found between
susceptibility to COPD and lower skin blanching response. In the old group, corticosteroid use,
and lower lung function (FEV1, FEV1/FVC, FEV1 %predicted) were correlated with a lower skin
blanching response.
Results of linear regression analyses are presented in Table 3. In the young group,
no association with susceptibility to COPD or lung function and skin blanching response was
found. In the total group and in the old group, higher absolute FEV1, higher FEV1/FVC (%), and
higher FEV1 %predicted were associated with higher skin blanching responses.
Summarized, these data show that young individuals who are susceptible to develop
COPD already have lower skin blanching responses than young non-susceptible individuals.
This might reflect the presence of lower endogenous relative corticosteroid sensitivity in this
susceptible group, which could be an important contributor to COPD development in later life.
137
6
Chapter 6
Table 1. Group characteristics
Young nonYoung
Healthy controls
COPD
susceptible
susceptible
N, n
33
21
28
89
Male, n (%)
20 (61)
11 (52)
24 (86)
63 (71)
Current smokers, n (%)
33 (100)
13 (62)
27 (96)
48* (54)
Age, years
21 [19-39]
31* [18-42]
51 [39-71]
63* [34-74]
Cigarettes per day, n
4 [0-20]
8 [0-20]
15 [1-30]
9 [1-30]
Pack years, y
1 [0-9]
5* [0-20]
26 [17-62]
35* [10-88]
FEV1, L
4.6 [3.3-6.3]
4.1 [3.2-5.6]
4.0 [2.6-5.2]
1.7* [0.6-4.1]
FEV1 / FVC, %
85 [74-98]
81* [76-97]
79 [71-91]
47* [21-70]
FEV1, % predicted
106 [90-126]
110 [97-132]
108 [87-136]
56* [16-119]
Skin blanching, AUC
2252 [0-2857]
1862* [ 0-2497]
1572 [174-2448]
1132 [0-2797]
Group data are expressed as medians [range],*p < 0.05. n = number,FEV1 = Forced ExpiratoryVolume in one
second, FVC = Forced Vital Capacity, CS = Corticosteroids, AUC = Area Under the dose-response Curve.
Table 2. Correlations of clinical characteristics with skin blanching response
Total group
Young group
Old group
N = 171
N = 54
N = 117
ρ
p-value
ρ
p-value
ρ
p-value
Gender
-0.078
0.313
-0.154
0.267
-0.156
0.093
Age
-0.275
0.000
-0.072
0.607
-0.090
0.336
Susceptibility (n/y)
-0.217
0.004
-0.278
0.042
-0.130
0.161
Corticosteroid use (n/y)
-0.279
0.000
-----0.209
0.024
Current smoking (n/y)
0.021
0.787
0.007
0.962
-0.003
0.973
Number of cigarettes / day
-0.087
0.349
-0.005
0.973
0.081
0.500
Packyears
-0.234
0.003
-0.137
0.353
-0.054
0.560
FEV1 (L)
0.405
0.000
0.136
0.326
0.401
0.000
FEV1 /FVC (%)
0.340
0.000
0.023
0.870
0.310
0.001
0.295
0.000
-0.198
0.150
0.338
0.000
FEV1 %predicted
Spearman’s correlations with skin blanching response (AUC) as dependent variable.Values in bold represent
p-values < 0.05.ρ = spearman’s rho, FEV1= Forced Expiratory Volume in one second, FVC = forced vital capacity.
Table 3. Linear regression analyses on skin blanching response
Total group
Young group
Old group
N = 171
N = 54
N = 117
Predictor variabele
B
SE
p-value
B
SE
p-value
B
SE
p-value
FEV1 (L)
284.4
94.3
0.003 -353.3 248.8
0.163
398.2
104.4
0.000
12.2
6.0
0.044
-4.5
24.0
0.851
13.8
6.5
0.038
FEV1 /FVC (%)
FEV1 %predicted
9.0
3.4
0.008
-9.9
14.3
0.492
10.8
3.6
0.003
Susceptibility (n/y)
-57.3
186.4
0.759 -269.5 305.435 0.382
156.1 262.631 0.553
Dependent variable is skin blanching response (AUC). Values in bold represent p-values < 0.05. All models
were corrected for age, gender, corticosteroid use, and packyears. β = standardized regression coefficient, SE =
standard error, FEV1= forced expiratory volume in one second, FVC = forced vital capacity.
138
Corticosteroid skin blanching response in COPD
Figure 1. Skin blanching responses in the COPD susceptibility groups
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Skin blanching responses presented as (A) median (range) responses,and (B) dose-response curves of
budesonidein the young and old groups (mean ± SEM). AUC = area under the dose-response curve.
139
7
Chapter
Steroid resistance in COPD?
Overlap and differential anti-inflammatory
effects in smokers and ex-smokers
Susan Hoonhorst, Nick ten Hacken, Judith Vonk, Wim Timens, Pieter Hiemstra,
Thérèse Lapperre, Peter Sterk, Dirkje Postma
PLoS One 2014 Feb 5;9(2)
Chapter 7
ABSTRACT
Background: Inhaled corticosteroids (ICS) reduce exacerbation rates and improve health status
but can increase the risk of pneumonia in COPD. The GLUCOLD study, investigating patients
with mild-to-moderate COPD, has shown that long-term (2.5-year) ICS therapy induces antiinflammatory effects. The literature suggests that cigarette smoking causes ICS insensitivity.
The aim of this study is to compare anti-inflammatory effects of ICS in persistent smokers and
persistent ex-smokers in a post-hoc analysis of the GLUCOLD study.
Methods: Persistent smokers (n=41) and persistent ex-smokers (n=31) from the GLUCOLD
cohort were investigated. Effects of ICS treatment compared with placebo were estimated by
analysing changes in lung function, hyperresponsiveness, and inflammatory cells in sputum
and bronchial biopsies during short-term (0-6 months) and long-term (6-30 months) treatment
using multiple regression analyses.
Results: Bronchial mast cells were reduced by short-term and long-term ICS treatment in
both smokers and ex-smokers. In contrast, CD3+, CD4+ and CD8+ cells were reduced by shortterm ICS treatment in smokers only. In addition, sputum neutrophils and lymphocytes, and
bronchial CD8+ cells were reduced after long-term treatment in ex-smokers only. No significant
interactions existed between smoking and ICS treatment.
Conclusion: Even in the presence of smoking, long-term ICS treatment may lead to antiinflammatory effects in the lung. Some anti-inflammatory ICS effects are comparable in
smokers and ex-smokers with COPD, other effects are cell-specific. The clinical relevance of
these findings, however, are uncertain.
142
ICS effects in smokers and ex-smokers with COPD
INTRODUCTION
Chronic obstructive pulmonary disease (COPD) is characterized by chronic, partially reversible
airflow limitation (1). This airflow limitation is generally progressive and associated with an
inflammatory process in the airways. The main cause for the development of COPD is cigarette
smoking. Smoking cessation is the most effective way to reduce disease progression and to
prevent mortality (1). Interestingly, inflammation is reduced in asymptomatic smokers who
stopped smoking (2). In contrast, there is evidence that airway inflammation persists or even
increases in COPD patients after smoking cessation (2,3).
Inhaled corticosteroids (ICS) have anti-inflammatory effects in asthma. Clinical effects
of ICS in COPD have been investigated widely, providing positive effects on exacerbation
rates and health status especially in advanced stages (4-6), but also of harm (e.g. pneumonia)
(7). The GLUCOLD study has shown that besides positive effects on lung function decline,
hyperresponsiveness and health status, long-term (2.5-year) therapy with ICS additionally
induces anti-inflammatory effects in COPD patients with mild-to-moderate severe disease (8).
Bronchial T-lymphocyte and mast cell numbers were reduced after treatment as well as sputum
cell counts. Importantly, these anti-inflammatory effects were accompanied by a reduction
in FEV1 decline and improvement in airway hyperresponsiveness and health status (8). Other
studies either found no anti-inflammatory effects as assessed in sputum (9), or confirmed antiinflammatory effects of three-month ICS treatment assessed in bronchial biopsies of COPD
patients, including reductions in CD8+ lymphocytes, mast cells and macrophages (10-12).
There are several indications that cigarette smoking may induce resistance to the
anti-inflammatory effects of ICS. Smoking asthmatics are less sensitive to corticosteroids than
non-smoking asthmatics (13-15). One short-term study showed no anti-inflammatory effects
by ICS treatment in smokers with COPD, whereas limited beneficial effects were observed in
ex-smokers (16). In vitro studies have shown that oxidative and nitrative stress, as can be caused
by cigarette smoking, inactivates histone deacetylase-2, which is involved in the suppression
of activation of inflammatory genes (17,18).
The GLUCOLD study provided evidence for both clinical and anti-inflammatory
effects of long-term ICS treatment in COPD. So far, no studies have linked long-term clinical
and inflammatory effects of ICS treatment with active smoking in COPD. The aim of the current
study is to assess anti-inflammatory effects of ICS in persistent smokers and persistent exsmokers with COPD by a post-hoc analysis of the GLUCOLD study.
METHODS
Ethics Statement
The study was approved by the medical etics committee of Leiden University Medical Center
(LUMC) and by the medical etics committee of University Medical Center Groningen (UMCG).
All patients signed written informed consent. The study is registered at ClinicalTrials.gov with
identifier NCT00158847.
143
7
Chapter 7
Subjects
All persistent smokers and persistent ex-smokers from the GLUCOLD cohort were included (8).
To obtain the largest contrast possible, patients who switched in smoking status were excluded.
In- and exclusion criteria have been described in detail previously (8). Briefly, patients were
aged 45–75 years, had a smoking history of more than ten packyears, and irreversible airflow
limitation compatible with Global Initiative for Chronic Obstructive Lung Disease (GOLD)
stages II and III (1). Patients with asthma or other active lung disease were excluded.
Study design
Patients were randomly assigned into four groups as described before (8): 1) fluticasone
propionate (FP) 500 μg twice daily (b.i.d.) for the first six months, followed by placebo b.i.d. for
24 months; 2) FP 500 μg b.i.d. for 30 months; 3) FP 500 μg b.i.d., and salmeterol (S) 50 μg b.i.d.
in a single inhaler for 30 months; 4) placebo b.i.d. for 30 months.
Measurements
At baseline and after 6- and 30-month treatment, inflammatory cells in induced sputum and
bronchial biopsies, postbronchodilator forced expiratory volume in one second (FEV1), and the
provocative concentration of metacholine causing a 20% fall in FEV1 (PC20) were measured.
Sputum samples were obtained by the full sample method (19). Differential cell counts were
expressed as a percentage of nucleated cells excluding squamous cells as previously described
(19). Fiberoptic bronchoscopy, biopsy processing and quantification were performed as
described before (3). Immunohistochemistry was performed by specific antibodies against
T-lymphocytes (CD3, CD4, CD8), and mast cell tryptase (AA1) (3). The number of subepithelial
positively staining inflammatory cells was counted within the largest possible area of maximal
125 mm deep beneath the basement membrane, per biopsy section, and expressed as the
mean number of cells/0.1 mm2 of the two biopsies (3). FEV1 was assessed by standardized
procedures as well as the two-minute tidal breathing method to measure PC20 methacholine
(19).
Statistical analyses
The absolute changes between the measurements at baseline and 6 months, and between the
measurements at 6 months and 30 months, were calculated for FEV1, PC20, and inflammatory
cells in sputum and bronchial biopsies. These absolute changes were normalized by Lntransformation when necessary. Multiple regression analyses were performed on these (Lntransformed) absolute changes after short-term ICS treatment (baseline to 6 months) and after
long-term ICS treatment (6 to 30 months). For the analyses from baseline to 6 months, the
FP 6-month, FP 30-month, and FP/S 30-month groups were combined. For the analyses from
6 to 30 months, the placebo group was combined with the FP 6-month group, and the FP
30-month group was combined with the FP/S 30-month group. The effects of ICS treatment
in persistent smokers and persistent ex-smokers and the interaction of ICS with smoking were
investigated in the total population by multiple regression models. All models were adjusted
144
ICS effects in smokers and ex-smokers with COPD
for gender and age. The analyses were performed with SPSS version 18.0.3 and a p-value < 0.05
was considered statistically significant.
RESULTS
Of the 101 patients who adhered to therapy (>70% medication use) (8), 72 patients who were
persistent smokers (n=41) and persistent ex-smokers (n=31) were included in the current
analyses. Characteristics of these 72 COPD patients among the different treatment groups are
shown in Table 1 including the measurements at baseline, and after 6 or 30 months of ICS
treatment.
Table 1. Patient characteristics *
Smokers placebo
0
6
Patients, n
Ex-smokers ICS
0
6
11
30
6
25
10 / 1
23 / 7
6/0
25 / 0
63 [51-66]
59 [55-64]
62 [57-70]
67 [62-71]
Men/women
Age, years
0-6 months treatment
Smokers ICS
Ex-smokers placebo
0
6
0
6
FEV1, post, L
1.8
[1.7-2.6]
2.0
[1.5-2.5]
2.1
[1.7-2.3]
2.0
[1.6-2.2]
1.9
[1.4-2.2]
1.7
[1.5-2.1]
2.0
[1.7-2.4]
2.1
[1.8-2.3]
PC20, mg/mL
1.0
[0.3-1.5]
0.3
[0.3-2.2]
0.7
[0.2-2.8]
1.2
[0.2-7.6]
0.45
[0.1-2.7]
0.07
[0.04-20.3]
0.3
[0.1-2.4]
1.2
[0.6-14.4]
46.8
[.9-92.0]
113.5
[67.6-168.6]
25.0
[15.5-37.8]
134.8
[95.8-260.8]
57.3
[36.6-86.3]
169.5
[75.1-237.1]
28.0
[15.3-43.5]
Bronchial cell counts ‡
118.5
CD3+ cells
[45.0-171]
CD4+ cells
37.0
[14.0-58.5]
27.8
[17.1-66.1]
41.0
[27.3-71.6]
10.3
[6.4-17.1]
41.8
[18.9-177.8]
36.3
[7.4-54.6]
71.0
[43.1-95.8]
11.0
[7.3-20.3]
CD8+ cells
15.5
[9.0-52.0]
13.8
[8.9-32.6]
18.0
[9.5-38.6]
8.0
[4.9-10.4]
24.0
[12.4-75.8]
12.5
[7.3-18.3]
23.3
[8.0-40.9]
5.5
[3.0-8.5]
Mast cells
20.5
[16.5-33.5]
11.8
[8.6-15.6]
28.5
[21.6-33.9]
8.0
[5.9-10.25]
27.3
[23.1-35.0]
11.3
[7.1-12.5]
23.8
[16.6-39.4]
5.0
[2.0-8.0]
79.5
62.2
158.0
197.1
118.8
80.7
[31.2-146.6] [31.2-177.2] [89.2-215.1] [23.4-273.7] [68.5-340.6] [50.0-137.8]
Sputum cell counts §
Neutrophils
43.7
[20.2-89.4]
35.0
[15.0-70.8]
Eosinophils
0.7
[0.0-3.3]
0.4
[0.0-1.2]
1.2
[0.4-4.4]
0.9
[0.3-3.0]
2.0
[0.8-4.2]
0.9
[0.3-7.1]
2.7
[0.9-6.3]
0.9
[0.08-2.4]
1.6
[0.4-2.3]
21.8
[8.6-46.7]
0.7
[0.2-1.9]
10.9
[8.3-21.8]
1.9
[0.6-3.9]
26.5
[19.2-48.4]
2.2
[0.6-4.3]
31.4
[13.4-52.2]
8.3
[2.5-9.2]
47.3
[21.9-65.2]
6.6
[1.4-11.0]
56.7
[8.1-59.0]
4.4
[1.9-10.9]
48.5
[17.8-84.1]
3.0
[0.6-6.5]
24.4
[13.5-48.3]
Lymphocytes
Macrophages
* Patients were selected from the GLUCOLD cohort(8); only persistent smokers and ex-smokers were included
Values before and after treatment are presented of short-term ICS treatment (baseline and after 6 months), and
long-term ICS treatment (6 months to 30 months). Data are expressed as medians [Interquartile Ranges]; n=
number; FEV1, post, L = FEV1 after salbutamol expressein liters; PC20 = provocative concentration of metacholine
causing a fall in FEV1 of > 20%; ‡ Cell counts / 10-7 per m2 of subepithelium; § Cell counts x104 per mL.
145
7
Chapter 7
Table 1. Patient characteristics * (continuation)
Smokers placebo
6
30
Patients, n
6-30 months treatment
ICS
Ex-smokers placebo
30
6
30
Ex-smokers ICS
6
30
20
21
17
14
16 / 4
17 / 4
17 / 0
14 / 0
62 [51-66]
58 [56-64]
67
[61-70.0]
69
[60-71]
Men/women
Age, years
Smokers
6
FEV1, post, L
2.0
[1.5-2.4]
1.7
[1.3-2.2]
2.0
[1.5-2.3]
1.9
[1.5-2.4]
2.0
[1.6-2.4]
2.1
[1.5-2.4]
2.1
[1.9-2.3]
2.1
[1.9-2.3]
PC20, mg/mL
1.1
[0.3-4.8]
0.8
[0.4-1.7]
0.6
[0.2-6.1]
0.7
[0.4-3.6]
0.9
[0.07-9.9]
0.3
[0.1-2.6]
0.7
[0.5-13.8]
4.5
[0.5-27.1]
Bronchial cell counts ‡
CD3+ cells
35.3
[26.5-70.9]
26.8
[12.0-54.8]
25.0
[17.8-39.5]
16.5
[12.3-60.0]
38.0
[27.5-71.3]
69.5
[33.4-90.9]
22.8
[13.3-40.5]
18.8
[4.5-43.5]
CD4+ cells
17.0
[10.6-40.3]
11.0
[8.0-33.5]
10.8
[6.4-20.1]
14.0
[6.5-25.5]
18.0
[7.3-52.3]
29.5
[25.5-43.5]
10.8
[7.4-20.1]
15.0
[9.5-33.5]
CD8+ cells
9.5
[5.9-25.4]
11.0
[4.3-31.0]
8.5
[6.1-11.5]
9.3
[2.4-14.1]
7.0
[3.0-12.8]
15.5
[10.3-25.0]
6.3
[3.8-8.4]
3.5
[1.9-6.1]
Mast cells
11.0
[6.5-13.9]
13.3
[7.1-15.9]
8.0
[6.3-9.6]
5.3
[2.3-10.1]
6.0
[2.8-10.0]
12.8
[11.0-18.8]
5.8
[1.3-10.5]
1.3
[0.0-3.0]
Sputum cell counts §
Neutrophils
50.2
64.9
62.2
[19.4-142.8] [12.1-109.7] [22.4-177.2]
30.3
[12.1-81.2]
77.9
60.7
86.5
[32.9-234.2] [14.5-163.9] [52.3-144.9]
33.3
[8.2-74.6]
Eosinophils
0.7
[0.08-1.5]
0.6
[0.2-2.6]
0.8
[0.2-3.0]
0.5
[0.1-2.9]
0.9
[0.2-5.8]
0.5
[0.0-1.1]
0.9
[0.0-2.3]
0.3
[0.1-1.0]
Lymphocytes
0.9
[0.4-2.1]
1.2
[0.3-3.1]
2.4
[0.5-4.3]
0.7
[0.1-1.7]
4.7
[0.6-8.8]
2.0
[0.8-4.3]
3.0
[0.6-6.9]
1.2
[0.2-2.5]
Macrophages
21.8
[10.2-42.4]
17.6
[4.4-35.9]
26.4
[11.2-40.0]
7.3
[2.7-25.1]
23.9
[12.0-57.5]
18.3
[9.9-42.7]
25.9
[14.5-43.7]
12.9
[5.4-19.3]
* Patients were selected from the GLUCOLD cohort(8); only persistent smokers and ex-smokers were included
Values before and after treatment are presented of short-term ICS treatment (baseline and after 6 months), and
long-term ICS treatment (6 months to 30 months). Data are expressed as medians [Interquartile Ranges]; n=
number; FEV1, post, L = FEV1 after salbutamol expressein liters; PC20 = provocative concentration of metacholine
causing a fall in FEV1 of > 20%; ‡ Cell counts / 10-7 per m2 of subepithelium; § Cell counts x104 per mL.
Short-term effects
Multiple regression analyses showed that ICS treatment increased PC20 significantly in exsmokers (Table 2). ICS treatment significantly reduced bronchial mast cells in both smokers
and ex-smokers compared to placebo. Additionally, bronchial CD3+, CD4+, and CD8+ cells were
significantly reduced by ICS treatment in smokers only (Table 2).There were no significant
interactions between ICS treatment and smoking.
Long-term effects
Stratified analyses showed significant reductions in bronchial mast cells in both smokers and
ex-smokers with ICS treatment compared to placebo (Table 2, Figure 1). In contrast to smokers,
ex-smokers had significant reductions in bronchial CD8+ cells with ICS treatment as well as
sputum neutrophil counts (Table 2, Figure 1). There existed no significant interactions between
smoking and ICS treatment (Table 2).
146
ICS effects in smokers and ex-smokers with COPD
Table 2. Multiple regression analyses: effects of ICS treatment in smokers and ex-smokers,
and the interaction between ICS treatment and smoking (smoking x ICS) on changes in
lung function, hyperresponsiveness and inflammatory cells in biopsies and sputum*
0-6 months ICS treatment
Smoking
Ex-smokers
Smokers
x ICS
B
p
B
p
B
p
FEV1, post, L
0.09 0.27 -0.07 0.27 -0.17 0.12
2.65 0.02 0.49 0.59 -2.16 0.13
PC20, mg/mL
Bronchial cell counts ‡
-0.66 0.13 -0.92 0.01 -0.27 0.63
CD3+ cells
-0.86 0.07 -1.37 0.00 -0.51 0.40
CD4+ cells
CD8+ cells
-0.55 0.21 -0.70 0.05 -0.15 0.79
Mast cells
-0.83 0.00 -0.50 0.04 0.33 0.37
Sputum cell counts §
Neutrophils
-0.44 0.45 -0.04 0.93 0.40 0.59
Eosinophils
-1.55 0.12 0.25 0.74 1.80 0.15
Lymphocytes
-0.14 0.85 -0.08 0.89 0.06 0.95
Macrophages
-0.39 0.53 0.17 0.73 0.55 0.48
6-30 months ICS treatment
Smoking
Ex-smokers
Smokers
x ICS
B
p
B
p
B
p
0.09 0.26
0.14 0.04
0.05 0.62
2.40 0.02
0.41 0.68 -1.99 0.16
-0.71 0.11
-0.03 0.94
-1.08 0.02
-1.36 0.00
-1.24 0.04
0.39 0.67
-1.30 0.13
1.05 0.95
0.36 0.39
0.34 0.44
-0.03 0.96
-0.67 0.04
0.76
-0.60
-1.33
-13.99
1.07
0.37
1.06
0.69
0.08
0.55
0.11
0.17
0.21
0.61
0.44 -0.98
0.08 -0.03
0.34 -15.03
0.45
0.41
0.98
0.49
* Patients were selected from the GLUCOLD cohort(8); only persistent smokers and ex-smokers were included.
All analyses are adjusted for age and sex. Data are expressed as B, p (regression coefficient, p-value); significant
data (p<0.05) are presented in bold; FEV1, post, L = FEV1 after salbutamol expressed in liters; PC20 = provocative
concentration of metacholine causing a fall in FEV1 of > 20%; ‡ Cell counts / 10-7 per m2 of subepithelium; § Cell
counts x104 per mL. The absolute changes in bronchial cell counts and sputum cell counts were normalized by
Ln-transformation.
Figure 1. Effects of long-term ICS treatment on CD8+ cells and bronchial mast cells
! ! !
7
! ! !
Effects of ICS treatment from 6-30 months in persistent smokers and persistent ex-smokers on bronchial CD8+
cells (upper panel) and bronchial mast cells (lower panel). Data are presented as medians (Interquartile Ranges).
NS = not significant.
147
Chapter 7
DISCUSSION
This study investigated the confounding effect of concurrent smoking with respect to the shortand long-term effects of ICS treatment on clinical and inflammatory outcomes in persistent
smokers and persistent ex-smokers with COPD participating in the GLUCOLD study. We did
not find statistical interactions between ICS treatment and smoking, implying that the effect
of ICS treatment on clinical and inflammatory parameters was comparable in smokers and exsmokers. Multiple regression analyses showed that bronchial mast cell numbers significantly
were reduced both in persistent smokers and persistent ex-smokers. This occurred with
short- and long-term ICS treatment, showing the robustness of this observation. Furthermore,
a significant reduction in number of bronchial CD8+ lymphocytes was observed after longterm ICS treatment in ex-smokers only. This is compatible with results in the main paper
(8). Additionally, stratified analysis showed that sputum neutrophils were reduced during
long-term ICS treatment in ex-smokers only, a finding that fits published studies on steroid
responsiveness (12). Bronchial CD3+, CD4+, and CD8+ cells were reduced in smokers only after
short-term ICS treatment.
So far, studies investigating the effect of smoking on corticosteroid responsiveness
have been performed in asthmatics predominantly. Smoking asthmatics were shown to be less
sensitive to corticosteroids than non-smoking asthmatics (13-15). There have been less studies
published investigating the effect of smoking on corticosteroid responsiveness in COPD than
in asthma. One short-term study (10-week treatment) found no anti-inflammatory effects
of ICS treatment investigating inflammatory mediators like IL-8, IL-10, neutrophil elastase in
sputum in smokers with COPD, whereas limited beneficial effects were present in ex-smokers
(16). In a study focusing on inflammation in airway wall biopsies, effects of ICS + salmeterol
treatment were in the same direction in smokers and ex-smokers, although the reduction in
cell counts (CD8+, CD4+, CD45+, mast cells, CD68+) were generally greater in former smokers
(12). Our data link, to our knowledge for the first time, the effects of short-term and long-term
ICS treatment to the smoking status of patients with COPD. We show reductions particularly in
mast cells in both persistent smokers and persistent ex-smokers, whereas other cell types were
sometimes differentially affected in these two groups.
We believe that the fact that we analysed patient groups containing exclusively
persistent smokers or persistent ex-smokers, in addition to the long-term intervention and the
combined clinical and inflammatory outcomes represent the strengths of the current study.
There are some limitations as well. We did not find significant interactions of ICS treatment
response and smoking. This can be a real observation, yet it may also be due to low power,
caused by the reduction in number of participants since we excluded switchers in smoking
status. This resulted in small numbers in some subgroups, e.g. only 6 ex-smokers using ICS for
six months, and thus this asks for caution of providing firm conclusions. For instance, there
were differential effects with regard to short- and long-term treatment effects on lymphocytes,
which may particularly result from analysing small numbers of patients. However, the direction
of effects of other cell types was comparable with both short- and long-term follow-up
providing robust findings. Additionally, we have to take into account that combining treatment
groups in the 6-30 months period might have affected the results, for example by the effect of
148
ICS effects in smokers and ex-smokers with COPD
steroid withdrawal in the control group. However, when the individuals who stopped FP after
6 months were excluded, the analyses showed the same direction of effects and still reached
significance despite lower study power (Table 1 in the online supplement). We realize that the
current study is the only one available with long-term biopsy data. There may have occurred
some selection bias due to the strict definition of the patient groups. Notwithstanding this,
the effects observed were, with respect to clinical (PC20, FEV1) and inflammatory parameters, in
the same direction as the main results (8), and our stratified analyses confirmed the multiple
regression findings. One may argue that there are baseline differences in cell counts in the
various groups under study. Therefore, we analysed our data with adjustment of baseline cell
counts as well and found that results of biopsy and sputum analyses were comparable. The
(borderline) significant effects of long-term ICS treatment on neutrophils in the total group
and ex-smokers became insignificant.
How can we explain that mast cell numbers were reduced by short- and long-term ICS
treatment in smokers and ex-smokers in stratified analyses, whereas other cells were affected
either in smokers or ex-smokers, or not affected at all, as for instance macrophages? One
explanation may be that this reflects an epigenetic phenomenon (20) that may occur with DNA
methylation and histone modification. Histone deacetylase-2 can be inactivated by oxidative
and nitrative stress due to cigarette smoking (17) thereby inducing chromatin condensation
and repression of gene transcription. In this way it can interfere with the anti-inflammatory
actions of inhaled corticosteroids. Epigenetic regulation is known to be cell-specific and tissuespecific, selectively inactivating genes and signal transduction pathways (20). This may thus
explain differential effects of ICS in smokers and ex-smokers in specific cell types.
Gizycki et al. found that mast cells were reduced with 3-month ICS treatment in COPD
patients who had high packyears smoking and smoked on average 27 cigarettes per day (21),
corroborating our finding that mast cells are reduced by ICS treatment in persistent smokers
with COPD. This suggests that epigenetic effects of smoking, if present, do not take place in
mast cells with respect to the ultimate effects of ICS treatment. We found that bronchial CD8+
cells specifically were reduced in ex-smokers after long-term ICS treatment. An in vitro study
by Kaur et al. on lymphocytes obtained by bronchoalveolar lavage in COPD patients showed
that dexamethasone reduced IL-2 and INFgamma production after PHA/PMA stimulation
(22). Effects were comparable in smokers and ex-smokers, suggesting that activation of
lymphocytes is steroid sensitive in COPD regardless of smoking. We found that CD8 cells were
significantly reduced by long-term ICS treatment in ex-smokers and not in smokers with COPD.
Although, the interaction between smoking and ICS treatment was not statistically significant,
the estimated difference between the ex-smokers and current smokers with respect to the ICSeffect is striking. Whether these seemingly different observations are due to the relatively low
number of patients under study in our investigation, to differential effects of steroids on lung
influx and activation of lymphocytes, or to differences in steroid sensitivity when tested in all
lymphocyte subtypes (as studied by Kaur et al) versus the CD8 subset in our study has yet to
be determined. Our data together might reflect the idea that CD8+ cells, which are important
key players in COPD, are sensitive to e.g. histone modification induced by smoke exposure,
whereas mast cells are not.
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Chapter 7
We suggest that the clinical implication of our findings may be that, even in the
presence of smoking, long-term ICS treatment may lead to anti-inflammatory effects in the
lung. However, especially in COPD patients with frequent exacerbations, the balance between
positive clinical and anti-inflammatory effects and side effects (e.g. pneumonia (7)) still has to
be considered. We put forward the hypothesis that steroid unresponsiveness in COPD is not a
general characteristic. Some anti-inflammatory ICS effects are comparable in smokers and exsmokers with COPD, yet other effects are cell-specific, dependent on smoking status. Studies
in larger datasets are needed to prospectively study the underlying mechanisms of similar
or differential ICS effects in smokers and ex-smokers with COPD. This will open avenues for
further targeted therapy in persistent smokers and ex-smokers with COPD.
150
ICS effects in smokers and ex-smokers with COPD
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20. Yang Y, Haitchi HM, Cakebread J, Sammut D, Harvey A, Powell RM, et al. Epigenetic mechanisms
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21. Gizycki MJ, Hattotuwa KL, Barnes N, Jeffery PK. Effects of fluticasone propionate on inflammatory cells
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7
153
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Chapter
Supplement
Chapter 7
156
ICS effects in smokers and ex-smokers with COPD
Table 1.
FP 6-month treatment included
6-30 months ICS treatment
Smoking
Ex-smokers
Smokers
x ICS
B
p
B
p
B
p
FEV1, post, L
0.09 0.26
0.14 0.04
0.05 0.62
2.40 0.02
0.41 0.68 -1.99 0.16
PC20, mg/mL
Bronchial cell counts ‡
-0.71 0.11
0.36 0.39
1.07 0.08
CD3+ cells
-0.03 0.94
0.34 0.44
0.37 0.55
CD4+ cells
CD8+ cells
-1.08 0.02
-0.03 0.96
1.06 0.11
Mast cells
-1.36 0.00
-0.67 0.04
0.69 0.17
Sputum cell counts §
Neutrophils
-1.24 0.04
0.76 0.21
0.61 0.45
Eosinophils
0.39 0.67
-0.60 0.44 -0.98 0.41
Lymphocytes
-1.30 0.13
-1.33 0.08 -0.03 0.98
Macrophages
1.05 0.95 -13.99 0.34 -15.03 0.49
FP 6-month treatment excluded
6-30 months ICS treatment
Smoking
Ex-smokers
Smokers
x ICS
B
p
B
p
B
p
0.09 0.43
0.18 0.03
0.09 0.48 -0.05 0.98
0.09 0.94
0.14 0.94 -0.57 0.35
0.15 0.78
-1.33 0.02
-1.13 0.03
0.89 0.11
1.02 0.04
1.20 0.06
-0.87 0.04
-0.75 0.40
-1.25
1.51 0.25
-1.38
-0.90 0.48
-2.00
6.12 0.79 -24.16
1.45
0.87
1.70
0.26
0.08 0.23 0.03 0.69 0.06
-0.50
0.15
-2.89
0.04
-1.10
0.15 -30.28
0.64 0.08 0.49 0.28 Patients were selected from the GLUCOLD cohort[7]; only persistent smokers and ex-smokers were included.
All analyses are adjusted for age and sex. Data are expressed as B, p (regression coefficient, p-value); significant
data (p<0.05) are presented in bold; FEV1, post, L = FEV1 after salbutamol expressed in liters; PC20 = provocative
concentration of metacholine causing a fall in FEV1 of > 20%; ‡ Cell counts / 10-7 per m2 of subepithelium; § Cell
counts x104 per mL. The absolute changes in bronchial cell counts and sputum cell counts were normalized by
Ln-transformation.
7
157
8
Chapter
Summary and General Discussion
Chapter 8
160
Summary and general discussion
SUMMARY
The scope of this thesis was on recognizing the ‘susceptible smoker’. Smoking is the main
cause of COPD, a disease which is characterized by chronic airflow limitation which is generally
progressive and associated with enhanced inflammatory responses in the airways and lungs.
COPD is a leading cause of morbidity and mortality worldwide, with increasing rates over time.
Importantly, not all individuals with the same smoking history will develop COPD, i.e. only 1520% of all smokers. It is still unclear why COPD is manifested only in this small proportion of
smokers. Therefore we aimed to investigate several biomarkers which might help to identify
the susceptible smoker, and to provide more insight in the origins and pathogenesis of COPD.
To this aim, an acute smoking study was performed in old and young individuals susceptible
and non-susceptible to COPD. Furthermore, advanced glycation end products (AGEs) and their
receptor (RAGE) were investigated in COPD patients and healthy smoking and never-smoking
controls. Formation of AGEs is accelerated in conditions of inflammation and oxidative stress,
and accumulated deposition of AGEs in tissue may has harmful effects. Finally, corticosteroid
(in)sensitivity was investigated in the skin as a potential characteristic of COPD. Additionally,
the role of smoking on the anti-inflammatory responses to ICS-treatment was investigated in
smokers and ex-smokers with COPD.
Study design
Chapter 2 describes the outlines of the cross-sectional study that was performed at the
University Medical Centers Groningen and Utrecht. The overall aim of this clinical study was to
investigate the underlying local and systemic inflammation that is present in different clinical
COPD phenotypes, and the acute effects of cigarette smoke exposure in individuals who are
susceptible and non-susceptible for the development of COPD. To this aim, COPD patients
GOLD stage I-IV and healthy controls (both groups aged >40 years) were recruited, being
susceptible and non-susceptible (i.e. they smoked but did not develop COPD) to develop COPD
respectively. In addition, young healthy subjects, susceptible and non-susceptible to develop
COPD (both groups aged <40 years) were recruited. In these young individuals susceptibility
was based on their family history, i.e. children in families where many family members smoked
and either developed COPD (hence susceptible offspring) or not developed COPD (nonsusceptible). All subjects were characterized in terms of clinical, physiological, immunological
and radiographical measurements. In addition, young and old susceptible and non-susceptible
groups performed the acute smoking procedure, which is extensively described in Chapter 3.
Acute smoking model in young and old susceptible or non-susceptible individuals
In Chapter 3 we investigated the effects of acute smoking in young and old individuals being
susceptible or non-susceptible to the development of COPD. After smoking of 3 cigarettes in
one hour inflammatory responses in peripheral blood (after 3 hours) and bronchial biopsies
(after 24 hour) were analyzed within and between susceptible and non-susceptible groups.
This study showed that activation markers A17 and A27 on circulating neutrophils in young
susceptible individuals were significantly more increased with smoking than in non-susceptible
161
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Chapter 8
subjects. No effects of acute smoking were present in mediators in blood or inflammatory cell
counts in bronchial biopsies. In the old group, the effects of acute smoking were comparable
between healthy controls and COPD patients. In conclusion, we showed for the first time that
susceptibility to COPD at young age associates with an increased innate immune response to
a disease-specific challenge. This suggests that systemic inflammation may play a role in the
early induction phase of COPD.
Advanced glycation endproducts (AGEs) and their receptor
In Chapter 4, we questioned whether AGEs accumulation in the skin is associated with COPD,
measured by skin autofluorescence (SAF). A total of 202 mild-to-very severe COPD patients,
83 smoking and never-smoking healthy controls (aged >40 years), and 110 younger smoking
and never-smoking controls (aged <40 years) were included. We demonstrated that SAF values
were significantly elevated in COPD patients compared with old and young healthy controls.
Interestingly, SAF did not significantly differ between the four disease severity stages in COPD
patients. Lower lung function values (FEV1/FVC, MEF50/FVC, RV/TLC) and a higher number
of packyears were associated with higher SAF values in the total population. Since SAF was
higher in COPD patients than in healthy controls, but was not associated with disease severity
within the COPD group, we concluded that AGEs may play a role in the induction phase of
COPD in susceptible smokers.
We further investigated the accumulation of AGEs in other body compartments in Chapter 5.
In this study we examined the levels of AGEs (CEL, CML, and pentosidine) in plasma, induced
sputum, bronchial biopsies and the skin. In addition, we studied the expression of the receptor
for AGEs (RAGE) in these compartments. We included 97 COPD patients (GOLD I-IV), 83 smoking
and never-smoking controls (>40 years), and 108 younger smoking and never-smoking
controls (<40 years). In this population we confirmed that SAF was increased in COPD patients
compared with healthy controls. Furthermore, sRAGE levels in plasma were significantly
decreased in COPD patients, and this decrease was significantly associated with an increase
in SAF. No further differences of AGEs and RAGE levels were detected in bronchial biopsies,
sputum supernatant and plasma levels. We conclude that AGEs accumulation is not necessarily
associated between different body tissues in COPD. This might be due to differences in tissue
turnover rates of AGEs. Lower sRAGE levels in plasma in COPD might indicate an impaired
protective mechanism, reducing the clearance ability of circulating AGEs.
Corticosteroid (in)sensitivity
In Chapter 6 we assessed whether corticosteroid sensitivity was lower in COPD patients
than in smoking and never-smoking healthy controls, by performing the corticosteroid skin
blanching test. We included 89 COPD patients (GOLD I-IV), 28 never-smokers and 56 smokers.
We demonstrated that GOLD III and IV COPD patients showed lower skin blanching responses
than healthy never-smokers and smokers, as well as GOLD I and GOLD II patients despite the
fact that they had comparable age. Additionally we showed that lower FEV1 values and higher
RV/TLC ratios were significantly associated with lower skin blanching responses. These findings
162
Summary and general discussion
suggest that the reduced skin blanching response fits with a subgroup of COPD patients that
has an early-onset COPD phenotype.
In Chapter 7 we questioned whether anti-inflammatory effects of ICS are different in 41
persistent smokers and 31 ex-smokers in a post-hoc analysis of the GLUCOLD study. We
investigated if there were differences in treatment effects compared with placebo, by analyzing
changes in lung function, hyperresponsiveness, and inflammatory cell counts in sputum and
bronchial biopsies during short-term (0-6 months) and long-term (6-30 months) treatment
with ICS. We showed that mast cells were reduced by short-term and long-term ICS treatment
in both smokers and ex-smokers. In addition, sputum neutrophils and lymphocytes, and
bronchial CD8+ cells were reduced after long-term treatment in ex-smokers only. The clinical
implication of these findings is that, even in the presence of smoking, long-term ICS treatment
may lead to anti-inflammatory effects in the lung.
8
163
Chapter 8
GENERAL DISCUSSION
We investigated different biomarkers to identify susceptible smokers in an early phase of COPD
and to gain more insight in the underlying mechanisms driving this development towards
COPD. Familial presence of COPD is an important risk factor of COPD susceptibility, but more
discriminative biomarkers are needed to recognize individuals susceptible to develop COPD
later in life, and to better understand the pathogenesis of the disease. In this section we reflect
our findings presented in this thesis and discuss their implications, limitations and future
perspectives.
Biomarkers of COPD susceptibility in young individuals
Below we will discuss the potential biomarkers that were investigated in this thesis, in relation
to their ability to be a ‘diagnostic’ parameter of COPD susceptibility at young age.
Inflammatory biomarkers with acute smoking
An appropriate method to differentiate susceptible and non-susceptible individuals might be
to investigate their initial response to cigarette smoking, assuming that there is a differential
response. The number of acute smoking studies that has been performed in humans is limited,
although the acute smoke model has been postulated as a usefull method to better understand
the inflammatory and oxidative stress responses to smoke (1). In Chapter 3 we investigated
responses of local and systemic inflammatory biomarkers to this disease-specific challenge in
susceptible and non-susceptible subjects. We showed that young susceptible individuals could
be recognized by a higher activation state of circulating neutrophils as a result from smoking.
The expression of the markers A17 and A27, which both specifically recognize the active form
of FCRγII (2,3), was more upregulated in young susceptible individuals when compared with
young non-susceptible subjects. The associations with COPD susceptibility were present after
adjustment for age, smoking status and expression of the markers at baseline. This suggests
they might be potential biomarkers of COPD susceptibility. On the other hand, the changes
were subtle and not uniform, which poses questions about the sensitivity and specificity of
such a potential biomarker. Since this biomarker can only be recognized as a response to
an activating stimulus like smoking, and smoking of 3 cigarettes within one hour is not very
attractive in the frame of a diagnostic test, a more easy diagnostic test is needed to recognize
susceptible individuals.
One way to develop such a test is by isolating neutrophils from individuals, treating
these cells with cigarette smoke extract or an LPS challenge in vitro, and measure their change
in activation status. In this way, some in vitro studies have shown that circulating neutrophils
of smokers are pre-activated compared with never-smokers, as they have a higher capacity
to migrate towards chemotactic stimuli or are more responsive to activating agents (4,5).
Perhaps it is possible to develop a reliable in vitro test that discriminates between high and low
responders on in vitro challenge of leucocytes with cigarette smoke or a surrogate of cigarette
smoke. Another approach might be to challenge participants with a surrogate of cigarette
smoke in vivo, for example challenging participants with lipopolysaccharide (LPS) (6,7).
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Summary and general discussion
However, this is also an invasive procedure with potential side effects. The next step would
then be to validate this in the perspective of COPD development, which is not very attractive
in the light of a disease with a 30-year latency time. We conclude that a reliable biomarker test
on susceptibility to develop COPD is still far away.
We did not find differential responses with the other neutrophils markers between groups,
but significant ‘within’ group differences were observed for several markers. For example, the
adhesion molecule ICAM-1 significantly decreased in susceptible subjects, while no significant
effects was present in the non-susceptible group. These markers need further investigation,
since differential effects may be detected in a larger population or a population with objectively
defined COPD as a marker of familial susceptibility.
AGE-reader
In Chapter 4 we have demonstrated that AGEs accumulation in the skin, as measured by
SAF, is elevated in COPD patients. A logical explanation is that the ongoing inflammation
and oxidative stress that is present in COPD has contributed to this elevated AGEs levels over
time. Interestingly, SAF values were comparable between the different disease severity stages
who had the same age. This suggests that AGEs accumulation is not associated with disease
progression, but might take place during the developmental phase of COPD. We therefore
investigated SAF in our young population containing susceptible and non-susceptible
individuals, as presented in the Addendum of Chapter 4. We could not demonstrate that SAF
values were higher in susceptible than non-susceptible subjects, also not after adjustment for
gender, age and smoking. An explanation for this negative finding might be that accumulation
of AGEs takes some years, in other words, maybe our young subjects were too young to show
differences between the two groups. A comparison between susceptible and non-susceptible
individuals aged 35-45 years was also negative (data not shown); however the two groups
had a small sample size. Although a larger study might be able to detect differences in SAF
in such individuals, we believe the overlap between the two groups is too large to become
a discriminative tool at an individual level. On the other hand, we cannot exclude that SAF
in combination with determination of AGE-receptor expression or with acute rises of AGEs in
plasma after smoking would be worthwhile. At the moment we have to conclude that SAF is
not suitable as a biomarker of COPD susceptibility, and potentially suitable as a biomarker of
COPD.
Corticosteroid (in)sensitivity
The corticosteroid skin blanching test is an easy non-invasive test to assess responses to
corticosteroids, and might reflect corticosteroid sensitivity in the airways. Interestingly, we
observed that the young susceptible group had a lower corticosteroid response to topically
applied budesonide in the corticosteroid skin blanching test than non-susceptible individuals,
as presented in the Addendum of Chapter 6. This finding suggests that reduced corticosteroid
sensitivity might be a characteristic, particularly present in subjects with established COPD or
with susceptibility of COPD. However, susceptibility remained no predictor of skin blanching
response after adjustment for age, packyears, gender and corticosteroid use. Also here we
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have to realize that there is a large variability in the skin blanching outcomes, which makes it
hard to compare between individuals. Moreover, the read-out is subjective. In the past, several
more objective techniques like Doppler velocimitry and reflectance spectrophotometry were
compared with this visual scoring system, but visual assessment of the skin blanching response
appeared to be the most sensitive tool (8). Although, determination of individual steroid
responsiveness might be improved in the future, it is difficult to imagine that a corticosteroid
test would be discriminative to identify subjects with such a multifactorial and heterogeneous
disease like COPD.
Mechanisms underlying COPD susceptibility
Inflammatory responses to acute smoking
How can we explain increased activity of the innate immune response as a result from
smoking in young susceptible individuals and in which way could this response contribute
to development of COPD at higher age? We speculated that neutrophils of susceptible
individuals are more easily to prime, and therefore more sensitivity for inflammatory triggers.
The activation of neutrophils is a multi-step process, which generally starts with priming (preactivation) caused by chemotaxins or cytokines, leading to upregulation of integrins and
adhesion molecules like CD11b or ICAM-1 (9,10). Acute smoking might have triggered local
inflammation, thereby increasing the expression of activation molecules on neutrophils. Our
data indicate that circulating neutrophils in susceptible individuals are more sensitive to this
response, resulting in a faster influx of neutrophils to the site of inflammation. The trend we
showed in decreased expression of the adhesion molecule ICAM-1 supports this idea, since
neutrophils with increased adhesion molecules might have left the circulation by migration
into the lung tissue. This may lead to a more intense inflammatory response in the airways
of susceptible individuals, which may contribute to lung tissue damage at higher age. It is
contradictive that we were not able to detect a higher release of mediators in peripheral blood,
which was measured at the same time point. Additionally, no increased influx of neutrophils was
found in bronchial biopsies, which can be explained e.g. by the fact that biopsies were taken 24
hours after smoking. In general, the inflammation in COPD is predominantly characterized by a
neutrophilic inflammation type, which might further strengthen our hypothesis.
Role of AGEs
We concluded before that AGEs might be involved in the induction phase of COPD as AGE
accumulation is elevated in the skin of COPD patients, but did not associate with COPD
severity. We hypothesize that AGEs in non-smokers are slowly formed with aging, and that
accumulation of AGEs in smokers is slightly increased due to smoking as an additional source
of AGEs formation (11,12). Probably, in ‘susceptible’ smokers AGEs formation and accumulation
is increased during COPD development, as a result of chronically increased oxidative stress due
to smoking (exogenous factor) and systemic inflammation (endogenous factor). A contributing
factor might be a reduction in levels of the soluble form of the receptor for AGEs (sRAGE),
which is lower in COPD patients as we show in Chapter 5 and as presented by previous
research (13-16). sRAGE can act as a decoy receptor, by scavenging AGEs from the circulation.
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Summary and general discussion
In this way, sRAGE facilitates clearance of AGEs and prevents AGEs to bind cellular bound RAGE
which otherwise would activate pro-inflammatory intracellular pathways. This protective
mechanism probably is impaired in COPD patients and might be genetically determined, as
polymorphisms in the advanced glycosylation end product-specific receptor (AGER) gene are
associated with lower lung function (FEV1 and FEV1/FVC) (17,18). These polymorphisms can
affect the proportion of sRAGE expression and thereby influence their protective function.
Role of corticosteroid (in)sensitivity
While it might be hard to recognize susceptible individuals using the skin blanching test, this
test can give more insight in the potential role of corticosteroid sensitivity in the pathogenesis
of COPD. It is generally accepted that the majority of COPD patients are poor-responders
to corticosteroid treatment, as inhaled corticosteroids have generally no or little effects on
disease progression or mortality. It is unclear if this reduced corticosteroid sensitivity it is a
general characteristic of the disease or if it contributes to disease development.
In Chapter 6 we showed that patients with a more severe COPD (GOLD stage III
and IV) have lower corticosteroid responses compared with healthy controls, while milder
disease stages (GOLD stage I and II) showed comparable levels of skin blanching. Because all
patients were age-matched and smoked the same high number of packyears, it suggests that
the skin blanching response identifies a subgroup or subphenotype of COPD patients with
a reduced corticosteroid sensitivity. In Chapter 7 we investigated the effect of smoking on
anti-inflammatory effects of inhaled corticosteroid treatment (ICS) in the GLUCOLD cohort,
as there are indications that smoking may induce resistance to the anti-inflammatory effects
of corticosteroids (19-21). Here we showed that some anti-inflammatory effects were present
in the airways after short-term (0-6 months) and long-term treatment (6-30 months) with
inhaled corticosteroid (ICS) treatment, in both persistent smokers and ex-smokers of the
GLUCOLD cohort. So apparently, even in the presence of smoking ICS treatment may lead to
some anti-inflammatory effects in the lung. From both studies we have learned that reduced
corticosteroid responsiveness is not a general characteristic of the disease, but is variable
between patients and might be associated with different COPD phenotypes.
We hypothesize that this variability in corticosteroid responses might have its
origins earlier in life. If the corticosteroid response in susceptible individuals at young age
is already reduced, as suggested earlier in this section, one can imagine this may have large
consequences at higher age after many years of smoking. One mechanism might be via reduced
glucocorticosteroid receptor function (GR). The skin blanching has been shown to be regulated
by vascular smooth muscle GRs (22-24). SNPs in the GR gene affect receptor structure and have
been shown to be associated with a lower skin blanching response (24,25). Since endogenous
corticosteroids are of importance during lung development in fetal growth (26), even small
changes in the GR structure might affect lung growth. And lower lung function values at birth
or in early childhood have been shown to increase the risk of COPD development (27). Another
consequence of lower steroid responsiveness might be that endogenous anti-inflammatory
pathways are suppressed, leading to increased local inflammation resulting in tissue damage,
particularly after many years of chronic smoking.
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Other mechanisms underlying COPD susceptibility
In this thesis, our main focus was on differential mechanisms that are present in susceptible
individuals which may contribute to COPD development. This focus on disease processes is in
line with the normal strategy to investigate diseases in general. Another strategy might be to
focus on the disease-protective processes, in other words to pose the question why subjects
stay healthy. Regarding COPD it might be worthwhile to extend our work by a more critical view
on the non-susceptible individuals, and how they are protected against the damaging effects
of cigarette smoke. For example, it may be that non-susceptible smokers have less depletion
of the anti-oxidant capacity as a response to smoking is present in the airways, because of a
different oxidant/anti-oxidant balance. This might be an important focus in future studies.
Future studies
Acute smoking study design
In the acute smoking study we observed that, besides neutrophil activation markers, the
majority of the measured inflammatory markers were not affected by acute smoking at all.
This lack of response can be explained by several causes.
First of all, the number of cigarettes smoked may have been insufficient. Previous
acute smoking studies in human used very diverse study designs regarding smoke exposure
(1), varying from 1-2 cigarette in 10 minutes, to 4 cigarettes in 1 hour, to 24 in 8 hours, or
even 24 cigarettes in 8 hours (28-31). Smoking 3 cigarettes within one hour was the lowest
dose that had an a priori good chance to elicit an inflammatory response in bronchial biopsies
without inducing cigarette smoke addiction. However, not one inflammatory marker in our
bronchial biopsies responded, perhaps because we projected the cigarette dose from animal
studies. Indeed future studies might increase the number of cigarettes and number of days of
smoking, but this seems not very ethical.
We asked participants to refrain from smoking during two days prior to the acute
smoking procedure. This might be a relatively short wash-out period, since the variation in
smoking behavior between participating subjects was high. In future studies this period might
be extended to for example ten days. On the other hand, this might be difficult to verify. In
our study we used exhaled CO measurements to control if subjects adequately refrained from
smoking, however this method can only detect smoking during the last 6 hours. Another
method is measuring urinary cotinine levels, which gives information of the last 10 days. This
might be more precise, but is time consuming.
As the number of human studies is small and diverse in their design, it was hard to
decide at what time point after smoking samples should be obtained. This was particularly
true for bronchial biopsies, because no previous studies investigated this tissue in the context
of acute smoking effects. Cell influx in the lungs occurs very quickly after smoking in BALF.
Therefore 24 hours may have been too long after smoking. In addition, there was a minimum of
6 weeks between the bronchoscopy after smoking and the bronchoscopy at baseline. We chose
for this time span for comfort and safety reasons of the patient. However, this is a relatively long
period of time and immunological events like airway infections may have occurred, in this way
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Summary and general discussion
affecting our results. Future studies should consider to take biopsies somewhat earlier after
the acute smoking event and to shorten the time period between the two bronchoscopies, for
example doing a bronchoscopy 6 hours after smoking with a minimum interval of two weeks
between the two bronchoscopies.
Furthermore, we can question if we used the reliable tools to investigate acute smoke
responses. From our study, assuming that we chose the right time points, it turned out that
bronchial biopsies are not very sensitive and specific to investigate the acute inflammatory
response to cigarette smoking. It could be that these responses mainly take place in the
more peripheral airways. Taking transbronchial biopsies might be an alternative, however
quantification of inflammatory responses in this material is very complicated and inaccurate,
leading to the need of high sample sizes, and many more people at risk for this invasive
procedure.
To end, we selected our young susceptible individuals based on prevalence of COPD
in their families, as we know that COPD has a genetic component and family studies have
shown that the combination of smoking and familial clustering of COPD associates with the
risk to develop COPD (32-35). We gained this information based on an extensive questionnaire.
We did not invite their family members to perform flow volume measurements to confirm
the prevalence of COPD. Maybe even more importantly, as COPD still is an underdiagnosed
disease, we did not confirm that smoking family members did not develop COPD. This may
have affected the correct categorization of susceptible and non-susceptible family members
and thus the correct categorization of subjects participating in this study. Of course, performing
flow volume measurements in all family members is time consuming and logistically a
challenge. Another way to improve selection of the young susceptible group is to define more
strict inclusion criteria and to select ‘very’ susceptible subjects, e.g. by selecting children of
very severe early-onset COPD patients. One disadvantage of this approach is that the study
population might not represent “normal” COPD but a special subphenotype of COPD, e.g. early
onset only.
Follow-up
One limiting factor in studying the ‘susceptible’ smoker is that COPD is manifested relatively
late in life, around the age of 50-60 years. Ideally, young susceptible individuals should be
followed-up in longitudinal studies to see if COPD is actually developed at higher age. It would
be interesting to link responses to cigarette smoke in early life with clinical and pathological
manifestations in later life. Ideally, this should be combined with genetic profiling and gene
expression in different tissues. In this way, future research may lead to hopefully better
understanding the mechanisms underlying smoking-induced COPD, leading to an earlier
diagnosis of COPD, and to new treatment targets of this disabling disease.
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and chronic bronchitis. Am J Respir Crit Care Med 1998 Jun;157(6 Pt 1):1770-1778.
33. McCloskey SC, Patel BD, Hinchliffe SJ, Reid ED, Wareham NJ, Lomas DA. Siblings of patients with
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34. Hersh CP, Hokanson JE, Lynch DA, Washko GR, Make BJ, Crapo JD, et al. Family history is a risk factor
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35. Patel BD, Coxson HO, Pillai SG, Agusti AG, Calverley PM, Donner CF, et al. Airway wall thickening and
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Chapter
Nederlandse samenvatting
Chapter 9
176
Nederlandse samenvatting
COPD: het herkennen van de ‘gevoelige’ roker.
Chronisch Obstructieve Longziekte (COPD)
De chronisch obstructieve longziekte COPD is een longaandoening die wordt gekenmerkt
door een chronische luchtwegobstructie die in het algemeen progressief is en geassocieerd
is met een toename van chronische ontstekingsreacties van de longen en luchtwegen op
schadelijke deeltjes of gassen. COPD is een één van de voornaamste oorzaken van morbiditeit
en is de enige chronische aandoening in de wereld waarbij het sterftecijfer blijft stijgen. In
2002 was COPD de vijfde belangrijkste doodsoorzaak wereldwijd, maar het aantal gevallen
met COPD blijft stijgen waardoor naar schatting COPD op de vierde plek zal staan in 2030.
Wereldwijd gezien komt COPD voor in 9-10% van de volwassenen boven de 40 jaar.
De belangrijkste symptomen van COPD zijn chronische en progressieve
kortademigheid, hoesten en slijmproductie. Hoewel COPD een longziekte is, zijn er vaak
ook ziekteverschijnselen buiten de longen aanwezig, zoals diabetes, hart- en vaatziekten
en spierdysfunctie. De diagnose COPD wordt gesteld door het meten van de mate van
luchtwegobstructie. Deze wordt uitgedrukt in de maximale hoeveelheid lucht die in één
seconde kan worden uitgeblazen (FEV1) ten opzichte van de maximale hoeveelheid lucht die kan
worden uitgeblazen (FVC). Als deze ratio lager is dan 70% nadat iemand een luchtwegverwijder
heeft ingeademd om de longfunctie zo goed mogelijk te laten zijn, dan spreken we van
luchtwegobstructie. De ernst van COPD wordt daarna geclassificeerd door de vastgestelde
GOLD criteria, gebaseerd op het percentage FEV1 ten opzichte van de normaalwaarde: stadium
1: (mild, FEV1 ≥ 80% van voorspeld); stadium II (matig, FEV1 50-80% van voorspeld); stadium III,
(ernstig, FEV1 30-50% van voorspeld); stadium IV, (zeer ernstig, FEV1 ≤ 30% van voorspeld). Er
zijn ook andere manieren om de ernst uit te drukken, maar deze wordt het meest gebruikt in
de literatuur. COPD is een complexe ziekte met verschillende klinische uitingen. Het is daarom
van belang om klinische-, fysiologische-, immunologische- en radiologische parameters met
elkaar te combineren om de karakterisatie van COPD patiënten te optimaliseren.
Roken is de belangrijkste oorzaak voor het ontwikkelen van COPD. Onderzoek heeft
aangetoond dat rokers vaker respiratoire symptomen en abnormale longfunctiewaardes
hebben vergeleken met niet-rokers. COPD wordt in ongeveer 80% van de gevallen veroorzaakt
door roken. Hoewel roken de meest belangrijke oorzaak is van COPD, zijn er ook andere factoren
die kunnen bijdragen aan het ontstaan van COPD bij mensen die niet roken, zoals genetische
factoren (bijvoorbeeld het alfa-1 antitrypsine gen), verminderde groei van de longen (van voor
de geboorte tot volwassenheid), roken tijdens de zwangerschap en beroepsmatig gerelateerde
blootstelling aan schadelijke stoffen.
Niet alle rokers ontwikkelen COPD, ook al hebben ze evenveel gerookt gedurende
hun leven. Ongeveer 15-20% van alle rokers ontwikkelt COPD. Het is nog steeds onduidelijk
waarom COPD optreedt in een kleine groep rokers. Waarschijnlijk zijn de rokers die COPD
krijgen meer ‘gevoelig’ voor de schadelijke effecten van sigarettenrook dan de rokers die
geen COPD krijgen (‘niet-gevoelig’). De mechanismen die deze gevoeligheid veroorzaken
zijn niet bekend. Het is aannemelijk dat de genetische achtergrond hierbij een belangrijke rol
speelt. Uit onderzoek in families is onder andere gebleken dat eerstegraads familieleden van
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COPD patiënten een slechtere longfunctie hebben dan familieleden van gezonde controles
(ondanks dat ze hetzelfde aantal jaren gerookt hebben), dat broers en zussen van patiënten
met een ernstig COPD een hoger risico hebben op luchtwegobstructie en dat verdikking van
de luchtwegwand en emfyseem voorkomen in families van COPD patiënten. Als laatst blijkt
dat het hebben van ouders met COPD een sterke risicofactor is voor COPD, onafhankelijk van
roken of roken in de omgeving. Samengevat: de combinatie van roken en het voorkomen van
COPD in de familie is sterk geassocieerd met het risico om COPD te ontwikkelen op latere
leeftijd. Hoewel een hoger familie risico de gevoeligheid van COPD kan helpen voorspellen, zijn
we nog steeds op zoek naar biologische markers die de ‘gevoelige roker’ kunnen herkennen.
Acute effecten van sigarettenrook in mensen die gevoelig of niet-gevoelig zijn voor
het ontwikkelen van COPD
Om de onderliggende mechanismen van het ontstaan van COPD door roken te kunnen
begrijpen is het erg waardevol om de eerste reactie op sigarettenrook te onderzoeken. Tot nu
toe is er weinig onderzoek gedaan naar de effecten van sigarettenrook in mensen. Een klein
aantal onderzoeken heeft gekeken naar de mate van ontsteking en oxidatieve stress in onder
andere de longen, in het spoelvocht van de longen en in het bloed na roken. Deze onderzoeken
zijn uitgevoerd in rokers of COPD patiënten die ouder zijn dan 40 jaar en veel hebben gerookt.
Door het vele roken hebben deze mensen waarschijnlijk al veranderingen ondergaan in hun
longen, waardoor het moeilijker wordt om het effect van roken te meten. Omdat een groot
deel van de rokers geen COPD ontwikkelt, is het ook belangrijk om de genetische aanleg mee
te nemen. Daarom hebben wij in dit proefschrift de reactie op sigarettenrook onderzocht bij
‘jonge’ en ‘oude’ mensen, die gevoelig of niet-gevoelig zijn voor het ontwikkelen van COPD. De
gevoeligheid voor COPD werd dan bepaald door uit te vragen of familieleden die rookten ook
COPD hadden.
Hoofstuk 2 beschrijft het protocol van het klinische onderzoek dat we hebben
uitgevoerd in de Universitaire Medische Centra van Utrecht en Groningen. Het doel van dit
onderzoek was de onderliggende ontstekingsreacties na roken te onderzoeken in zowel
de longen als in het bloed, in verschillende stadia van COPD, en om de acute effecten van
sigarettenrook te onderzoeken bij mensen die gevoelig of niet-gevoelig zijn voor het
ontwikkelen van COPD. Hiervoor zijn patiënten met een mild tot een zeer ernstig COPD en
gezonde controles (40-75 jaar) geïncludeerd, ook wel gedefinieerd als gevoelig voor het
ontwikkelen van COPD (gerookt en COPD ontwikkeld) en niet-gevoelig (gerookt en geen
COPD ontwikkeld). Daarnaast zijn er ‘jonge’ mensen (18-40 jaar) geïncludeerd die gevoelig (zij
hebben rokende familieleden met COPD) en niet-gevoelig (zij hebben rokende familieleden die
geen COPD hebben ontwikkeld) zijn voor het ontwikkelen van COPD. Alle deelnemers aan het
onderzoek zijn uitgebreid onderzocht op basis van klinische, fysiologische, immunologische
en radiologische factoren. In een subgroep van de COPD patiënten, de gezonde controles, en
de jonge groepen (gevoelig en niet-gevoelig voor het ontwikkelen van COPD) hebben we het
acute effect van roken onderzocht.
In hoofdstuk 3 van dit proefschrift worden de effecten van het acuut roken
onderzoek beschreven, dat werd uitgevoerd in ‘jonge’ (18-40 jaar) en ‘oude’ (40-75)
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Nederlandse samenvatting
deelnemers die gevoelig en niet-gevoelig zijn voor het ontwikkelen van COPD. Het doel van
dit onderzoek was om te kijken of mensen die gevoelig zijn voor het ontwikkelen van COPD
anders reageren op sigarettenrook dan mensen die niet gevoelig zijn. De focus lag hierbij
op de jonge groepen. Alle deelnemers werden gevraagd om 2 dagen helemaal niet te roken
en daarna tijdens het onderzoek 3 sigaretten binnen een uur te roken. Voor en na roken
werd er bloed afgenomen (3 uur na roken) en werden er luchtwegbiopten afgenomen (24
uur na roken). De ontstekingsreacties die optraden werden onderzocht in zowel het bloed
als in de luchtwegbiopten. We hebben hierbij het verschil in expressie van activatie markers
op ontstekingscellen in het bloed onderzocht, het verschil in expressie van signaaleiwitten
(cytokines) in het bloed en het verschil in het aantal van de soorten ontstekingscellen in de
luchtwegbiopten.
We vonden dat bepaalde activatie markers van neutrofiele granulocyten toenemen
na roken in de jonge groep die gevoelig is voor het ontwikkelen voor COPD vergeleken met de
niet-gevoelige groep. We vonden geen verschillen na roken op de expressie van signaaleiwitten
in het bloed en op de aantallen van de soorten ontstekingscellen in de luchtwegbiopten. In
de oude groepen vonden we geen verschillen in de ontstekingsreacties na roken tussen COPD
patiënten en gezonde controles. Deze bevindingen suggereren dat de ontsteking in het bloed
mogelijk een rol speelt bij de ontwikkeling van COPD.
Advanced Glycation endproducts (AGEs) en de receptor voor AGEs (RAGE)
AGEs zijn eiwitten die onomkeerbaar gevormd worden als gevolg van ontsteking en
oxidatieve stress. Naast deze endogene factoren, is roken één van de belangrijkste exogene
factoren die bijdraagt aan het vormen van AGEs. Naarmate we ouder worden, worden AGEs
langzaam opgeslagen in de weefsels in ons lichaam. Onder omstandigheden van ontsteking
en oxidatieve stress wordt dit proces versneld. AGEs kunnen lokale schade aanrichten in de
weefsels waarin ze worden opgeslagen. Daarnaast kunnen ze binden aan de receptor voor
AGEs (RAGE) waardoor schadelijke processen in een cel ontstaan. RAGE komt ook voor in het
bloed (sRAGE). Welke functie sRAGE heeft is nog niet exact bekend, maar er zijn indicaties dat
sRAGE beschermend werkt door de AGEs uit de circulatie weg te vangen zodat ze niet in het
weefsel terecht komen. Aangezien COPD gepaard gaat met een (lichte) chronische ontsteking
in het lichaam, zou dit kunnen leiden tot een verhoogde vorming en opslag van AGEs in het
lichaam. Omdat de vorming van AGEs ook geassocieerd is met roken, zou de vorming van
AGEs kunnen bijdragen aan het ontwikkelen van COPD in de gevoelige roker.
In hoofdstuk 4 hebben we de opstapeling van AGES in de huid gemeten met een
AGE reader. Dit is een apparaat dat de hoeveelheid AGEs via autofluorescentie (belichting met
UVA stralen) op de huid kan meten door weerkaatst UVA licht van een bepaalde frequentie te
meten (SAF). Dit werd gedaan op de onderarm van de deelnemers. We wilden uitzoeken of de
opstapeling van AGEs in de huid is toegenomen bij COPD patiënten vergeleken met gezonde
controles. We hebben laten zien dat de opstapeling van AGEs in de huid groter is bij COPD
patiënten vergeleken met gezonde controles. We vonden geen associaties tussen SAF en de
ernst van COPD. Deze bevinding suggereert dat AGEs mogelijk een rol spelen in de inductie
fase van COPD.
179
9
Chapter 9
Vervolgens hebben wij onderzocht of AGEs ook in andere lichaamscomponenten
(bloed, sputum en luchtwegbiopten) verhoogd waren en hebben we de expressie van de
receptor voor AGEs (RAGE) onderzocht. Dit onderzoek wordt beschreven in Hoofdstuk 5 van dit
proefschrift. We vonden dat sRAGE levels in het bloed van COPD patiënten lager zijn vergeleken
met gezonde controles. Deze verlaagde RAGE levels waren geassocieerd met een hogere
accumulatie van AGEs in de huid. Mogelijk hebben RAGE levels in het bloed een beschermende
functie door AGEs weg te vangen uit de circulatie, een beschermingsmechanisme dat bij COPD
verminderd is. We hebben geen verschillen gevonden in AGE en RAGE expressie in de andere
lichaamscomponenten tussen COPD patiënten en gezonde controles. Dit suggereert dat de
accumulatie niet perse tegelijkertijd in alle weefsels optreedt. De vernieuwingssnelheid van de
verschillende weefsels kan hierbij een belangrijke factor zijn.
Corticosteroid (on)gevoeligheid
Inhalatiecorticosteroïden (ICS) hebben een ontstekingsremmende werking en worden
veel gebruikt bij de behandeling van chronische obstructieve longaandoeningen. Veel
astmapatiënten reageren hier goed op, maar de werking bij COPD patiënten is minder
duidelijk. De behandeling met ICS heeft geen remmend effect op de progressie van de ziekte
bij de meeste mensen met COPD en er is geen verlaging op de sterftecijfers t.g.v. COPD bij
gebruik van ICS. Toch zijn de meningen over de effecten verdeeld. Zo zijn er veel onderzoeken
die hebben laten zien dat symptomen en het aantal exacerbaties afnemen als COPD patiënten
ICS gebruiken, en dat de gezondheidstoestand verbetert. Naast deze klinische effecten zijn er
aanwijzingen een vermindering van ontsteking bij sommige patiënten. Het is niet duidelijk
waarom er door COPD patiënten zo verschillend op de behandeling met ICS wordt gereageerd.
Het effect van een steroid kun je ook in de huid meten. In Hoofdstuk 6 hebben we
met behulp van de corticosteroïdhuidtest onderzocht of COPD patiënten een lagere reactie
hebben op corticosteroïden in de huid dan gezonde controles. Tijdens deze test werd de
corticosteroïd budesonide in verschillende concentraties aangebracht op de huid van de
onderarmen. De gevoeligheid voor corticosteroïden kan bepaald worden door de mate van
bleekheid te analyseren. We hebben laten zien dat COPD patiënten met een ernstiger COPD
minder gevoelig zijn voor corticosteroïden dan patiënten met een mildere vorm van COPD en
gezonde controles. Dit suggereert dat de corticosteroïdhuidtest een sub-fenotype van COPD
patiënten identificeert.
Er zijn ook aanwijzingen dat roken de effecten van corticosteroïden kunnen
verminderen. In Hoofdstuk 7 hebben we onderzocht of er verschillen waren in de
ontstekingsremmende effecten van ICS bij rokers en ex-rokers met COPD in een vervolg
analyses op de Glucold studie. Tijdens deze studie zijn patiënten met een matig tot ernstig
COPD kortdurend (6 maand) of langdurig (3 jaar) behandeld met ICS. Op verschillende
tijdstippen zijn de longfunctie, bronchiale hyperreactiviteit en ontstekingscellen in opgehoest
slijm (sputum) en luchtwegbiopten gemeten. We vonden dat het aantal mestcellen
afnam na zowel een kortdurende of langdurige behandeling met ICS, bij zowel rokers als
ex-rokers. Na een langdurige behandeling was het aantal neutrofiele granulocyten en
lymfocyten afgenomen in sputum en was het aantal CD8-positieve lymfocyten afgenomen in
180
Nederlandse samenvatting
luchtwegbiopten. Dit suggereert dat zowel bij rokers als ex-rokers met COPD een behandeling
met inhalatiecorticosteroïden, zowel op korte (6 maand) als lange (3 jaar) termijn, bepaalde
ontstekingscellen in de luchtwegen doet afnemen.
Dit onderzoek laat zien dat jonge mensen die een risico hebben om COPD te ontwikkelen anders
reageren op sigaretten roken dan jongeren zonder een dergelijk risico. Een beperkende factor
in onderzoek naar de ‘gevoelige’ roker is dat COPD pas laat tot uiting komt, namelijk rond de
leeftijd van 50-60 jaar. Het zou daarom zinvol zijn om jonge mensen die gevoelig zijn voor
het ontwikkelen voor COPD langdurig te volgen om te kijken of ze ook daadwerkelijk COPD
ontwikkelen op hogere leeftijd. In dat opzicht zou het van belang zijn om de reacties op
sigarettenrook op jonge leeftijd te koppelen aan klinische en pathologische uitingen op latere
leeftijd. Op deze manier zou toekomstig onderzoek kunnen leiden tot een eerdere diagnose
van COPD en tot nieuwe aangrijpingspunten voor medicatie.
9
181
Dankwoord
184
Dankwoord
Het is af! Ik kan terugkijken op mooie promotietijd die ik met veel verschillende mensen
heb mogen beleven. Graag wil ik dan ook iedereen bedanken die heeft bijgedragen aan het
voltooien van dit proefschrift, zowel werk gerelateerd als daar buiten. Een aantal mensen wil ik
graag in het bijzonder noemen.
Allereerst wil ik alle deelnemers bedanken die hebben meegedaan aan dit onderzoek. Zij
hebben pittige onderzoeken moeten doorstaan in het kader van de wetenschap. Bedankt voor
jullie inzet en enthousiasme! Zonder jullie was dit proefschrift er niet geweest.
Dan natuurlijk mijn promotor Prof. dr. Dirkje Postma en co-promotor dr. Nick ten Hacken.
Beste Dirkje, onvoorstelbaar hoe ver jouw kennis rijkt. In combinatie met jouw gedrevenheid,
enthousiasme en kritische maar vooral inspirerende blik heeft dit geleid tot een geslaagd
promotietraject. Beste Nick, bedankt voor je grote betrokkenheid en enthousiasme de
afgelopen jaren. Of het nou ging om praktische zaken voor het project of om het brainstormen
over de verschillende artikelen, ik kon altijd bij jou terecht. Met veel geduld wist je alles altijd
op een rijtje te zetten zodat we samen weer tot nieuwe inzichten konden komen. Allebei heel
erg bedankt voor de fijne samenwerking en voor alles wat ik van jullie heb geleerd!
Ruth, we zijn tegelijkertijd begonnen aan dit project. Een grote studie die we ons binnen korte
tijd eigen moesten maken. Dat is ons volgens mij goed gelukt! Ik wil je heel erg bedanken voor
de grote bijdrage die je aan het onderzoek hebt geleverd en voor de fijne samenwerking. Het
was elke keer weer een hele uitdaging om alle visites te organiseren en om de deelnemers te
begeleiden. Bedankt voor je inzet en je enthousiasme!
De leescommissie, bestaande uit Prof. dr.Frank Smeenk, Prof. dr. Pieter Hiemstra en Prof. dr.
Annemie Schols wil ik bedanken voor de bereidheid tot het lezen en beoordelen van dit
proefschrift.
Mijn collega’s uit Utrecht, Prof. dr. Jan-Willem Lammers, Prof. dr. Leo Koenderman en dr. Adèle
Lo Tam Loi wil ik bedanken voor de fijne samenwerking. We hebben elkaar aardig wat keren
opgezocht om de onderzoeken op elkaar af te stemmen en om te sparren over de data die
we samen verzamelden. Leo, bedankt voor je enthousiasme en waardevolle input bij het
interpreteren van de data. Adèle, bedankt voor het wegwijs maken in het project en de
goede samenwerking binnen onze overlappende onderzoeken. Jij kunt al terugkijken op een
geslaagde promotie, ik wens je veel succes met je opleiding.
Het onderzoek beschreven in dit proefschrift is uitgevoerd binnen het TIP consortium met
partners Universitair Medisch Centrum Utrecht, Universiteit Maastricht, GlaxoSmithKline,
Nycomed (nu Takeda) en TI Pharma. Ik wil dan ook alle collega’s binnen dit consortium
bedanken voor de prettige samenwerking en de inspirerende bijeenkomsten. Met z’n allen
werkten we aan een groot project waarin ieder zijn of haar eigen aandeel had. Dit heeft veel
mooie resultaten en interessante discussies opgeleverd.
185
Prof. dr. Wim Timens (lab Pathologie) en Prof. dr. Antoon van Oosterhout (lab Longziekten/
Allergologie) wil ik bedanken voor het mede mogelijk maken van het verwerken en analyseren
van alle biopten en samples die we verzameld hebben. Beste Wim en Antoon, bedankt voor al
jullie input en de prettige bijeenkomsten die we hadden.
Dan de analisten van het lab Longziekten/Allergologie en Pathologie: Judith, Monique, Theo,
en later ook Sharon en Mark. Wat hebben jullie veel werk gehad aan de immense hoeveelheid
samples die we gedurende het onderzoek verzameld en geanalyseerd hebben. Het was elke
dag weer afwachten of de verschillende samples volgens onze strakke planning binnen zouden
komen, want dit bleek vaak niet zo vanzelfsprekend te zijn als we van te voren dachten. Dankzij
jullie motivatie en flexibiliteit kon de deur van de vriezer aan het einde van de dag altijd met
een voldaan gevoel dicht! Ook het uitvoeren van de FACs analyses, de analyses op de overige
samples en het scoren van de biopten was een enorme klus. Zonder jullie was dit boekje er
zeker niet gekomen. Onwijs bedankt!
Alle medewerkers van de longfunctie afdeling wil ik bedanken voor het uitvoeren van de
uitgebreide longfunctieonderzoeken en sputuminducties en natuurlijk voor het klaarzetten
van de skin blanching potjes! Daarnaast voor de flexibiliteit bij het inplannen van de
onderzoeken en het beschikbaar stellen van de onderzoekskamers. Het was erg prettig om
met jullie samen te werken.
Het endoscopiecentrum, met in het bijzonder Alie, voor de mogelijkheid tot het verrichten van
alle bronchoscopieën en de goede zorgen voor de deelnemers.
Iedereen van het GRIAC: bedankt voor de leerzame wekelijkse bijeenkomsten, het meedenken
en de discussies tijdens presentaties en posters en de gezelligheid op congressen.
De secretaresses van de longafdeling, Trudy, Heleen, Renée, Sietske en Stephanie. Bedankt
voor jullie hulp, ondersteuning en gezelligheid de afgelopen jaren.
Mijn directe collega onderzoekers van de afdeling longziekten: Jorine, Maartje, Erica, Grietje,
Akkelies, Ilse, Fransien, Wytske, Anda, Ruth, Eef en Karin. Wat was het leuk om met jullie samen
te werken. Ik denk met plezier terug aan alle gekkigheid (zoals A4-tjes), juichmomentjes
(koekjes en taart), frustraties (waar is die boksbal), sportiviteit (bowlen, curling, skieën, Evert)
en feestmomentjes (wijn en bitterballen). Bedankt voor de gezellige jaren!
Anke en Karin, met z’n drieën zijn we begonnen aan dezelfde studie en ondertussen hebben
we alle drie ons eigen plekje gevonden. Het was een prachtige tijd (en nog steeds)! Ik vind het
dan ook super dat jullie mijn paranimfen willen zijn. Met jullie aan mijn zijde moet het zeker
gaan lukken!
Dan natuurlijk vrienden en familie die naast mijn promotie van onschatbare waarde zijn
geweest. Ook al begrepen jullie niet altijd waar ik nou precies mee bezig was, jullie waren altijd
geïnteresseerd en betrokken. AMSH’ers, bedankt voor alles wat samen hebben beleefd na al
186
Dankwoord
die jaren vriendschap. Nu breken er nieuwe tijden aan en ik kijk erg uit naar alle momenten die
nog gaan komen! Arnold, Trijnie, Saskia, Stefan, Daniëlle en Jarno ik heb mij vanaf het begin
altijd thuis gevoeld bij jullie, bedankt voor alle gezelligheid! Lieve pap en mam, Marleen en
René, jullie hebben altijd achter mij gestaan en ik weet dat jullie er altijd voor mij zullen zijn.
Lieve Bas, bedankt voor al je steun en je oneindige vrolijkheid. Als ik even in het dal zat, wist jij
me altijd weer die berg op te krijgen. Ik hou van je!
187

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