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Airway inflammation and respiratory health in the Southern California children's health study

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AIRWAY INFLAMMATION AND RESPIRATORY HEALTH IN THE
SOUTHERN CALIFORNIA CHILDREN’S HEALTH STUDY

by

Theresa Meredith Bastain

_____________________________________________________________

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

May 2012

Copyright 2012 Theresa Meredith Bastain
ii

ACKNOWLEDGEMENTS
I am forever indebted to Frank Gilliland for his guidance and support during the
period of my dissertation research and as a role model, mentor, colleague and friend for
the past 10 years.
In addition I am sincerely grateful to the following people:
John Peters, for being an inspiration in life and in death and for teaching me the
importance of the little things: meetings should always end with a good pun and a bottle
of good red wine should be shared with good friends.
Rob McConnell, Jim Gauderman, Kiros Berhane and Louis Dubeau, for serving
as my committee and for their valuable advice and direction.
Talat Islam, Towhid Salam and Carrie Breton, for helping with data analysis and
for thoughtful and critical reviews of papers and presentations.
Ed Avol, Andrea Hricko, Heather Volk, Scott Fruin, JC Chen and Jim Zhang, for
their support and encouragement.
The Children’s Health Study (CHS) data management team and CHS field teams,
for their tireless efforts in collecting, processing and managing the rich resources of the
CHS cohorts.
The school principals, teachers, students and parents in each of the 13 CHS study
communities, for their cooperation and participation.
Kristin Dessie, Cassandra Sutton, Katie Cettie, Helen Tol Dosta, Ashley Nielsen,
Shanelle Ueyama, Jessica Higginbotham, Krissy Nielsen, Joan Howland, Christine
Tidwell, Maria Rodriguez, Leticia Gracia, Celia Cedillo, Keyisha Dantzler and Erin
West, for being patient, hard-working, supportive and the best staff anyone could ever
ask for.
And last but not least, my family—for encouraging me to always persevere no
matter what curveballs life may throw at me.
iii

TABLE OF CONTENTS

Acknowledgements ................................................................................. ii
List of Tables ....................................................................................... viii
List of Figures ......................................................................................... x
Abstract .................................................................................................. xi
Chapter 1: Introduction .............................................................. 1
Chapter 2: Pathophysiology, Mechanisms and Descriptive
Epidemiology of Childhood Asthma and
Pulmonary Function Development ........................... 6
Asthma .................................................................................. 6
Definition, Diagnostic Criteria and
Pathophysiology of Childhood Asthma .......................... 6
Clinical Diagnostic Issues ......................................... 7
Diagnostic Issues in Asthma ............................... 7
Allergic vs. Non-allergic Asthma ....................... 8
Age-Related Phenotypes: Early-Onset
Transient, Late Onset, and Early Onset
Persistent ............................................................. 8
Use of Questionnaire-based Report of
Physician-Diagnosed Asthma in
Epidemiological Studies ................................... 10
Mechanisms and Pathophysiology of Asthma ........ 10
Airway Inflammation ........................................ 11
Airway Remodeling .......................................... 12
Bronchoconstriction .......................................... 13
Airway Hyperresponsiveness............................ 13
Descriptive Epidemiology of Childhood Asthma ... 14
Twin Studies and Genetic Determinants........... 14
Global Variation in Prevalence ......................... 15
Migrant Studies ................................................. 18
The Hygiene Hypothesis and Infections ........... 19
Social and Demographic Factors ...................... 20
Environmental Influences on Childhood
Asthma Incidence and Prevalence .................... 20
Tobacco Smoke Exposure ........................... 21
Maternal Dietary Factors and Childhood
Obesity ........................................................ 21
Early Life Environmental Exposures in
the Home ..................................................... 22
iv

Ambient Air Pollution Exposure ................ 23
Pulmonary Function Development in Childhood ............... 24
Normal Pulmonary Function Development .................. 24
Pulmonary Function Testing and Definitions of
Measures ....................................................................... 25
Descriptive Epidemiology of Pulmonary Function
Development ................................................................. 27
Relationship with Asthma ....................................... 27
Genetic Studies ....................................................... 27
Social Factors and Racial Differences in
Pulmonary Function ................................................ 29
Environmental Influences on Pulmonary Growth
and Development .................................................... 30
Tobacco Smoke Exposure ................................. 31
Ambient Air Pollution Exposure ...................... 32
Summary ............................................................................. 33
Chapter 2 References .......................................................... 34

Chapter 3: FeNO and HRCT as Non-invasive Techniques
to Measure Airway Inflammation and Airway
Remodeling ............................................................. 50
Exhaled Nitric Oxide (FeNO) ............................................. 51
Biological Determinants of Nitric Oxide ...................... 51
Nitric Oxide and Inflammation ..................................... 52
Measurement of Nitric Oxide in Exhaled Breath ......... 53
Online FeNO Measurement .................................... 54
Offline FeNO Measurement ................................... 54
Online Versus Offline FeNO Measurement ........... 55
Reproducibility of FeNO Measurement ................. 55
Relationship of FeNO to Inflammatory Markers
Collected From Bronchoalveolar Lavage, Induced
Sputum or Bronchial Biopsy ......................................... 56
Determinants of FeNO .................................................. 58
Genetic Determinants ............................................. 59
Age, Race, Sex, and BMI........................................ 59
Diet and Infections .................................................. 60
Personal Smoking and Secondhand Smoke
Exposure ................................................................. 60
Air Pollution Exposure ........................................... 61
Relationship of FeNO with Childhood Respiratory
Diseases......................................................................... 63
FeNO and Inflammatory Subtypes in Asthma ........ 63
Interrelationships of FeNO with Asthma and
Atopy....................................................................... 64
FeNO in Clinical Settings ....................................... 65
v

FeNO and Pulmonary Function .............................. 66
The Future of FeNO ...................................................... 67
HRCT as a Measure of Airway Structural Changes for
use in Epidemiologic Studies of Air Pollution ................... 68
Structural Components of the Lower Respiratory Tract
Visualized through Volumetric Reconstructions of
HRCT Images ............................................................... 69
Airway Dimensions Obtained from HRCT Images ...... 70
Research Studies Using HRCT ..................................... 71
HRCT Studies of Asthma and Pulmonary Function
in Children and Adults ............................................ 71
HRCT Studies in Healthy Individuals .................... 73
HRCT Studies of Environmental Exposures on
Health Outcomes ..................................................... 74
Is There a Future for Imaging the Impacts of
Environmental Exposures in Healthy Populations? ...... 75
Summary ............................................................................. 76
Chapter 3 References .......................................................... 77

Chapter 4: Rationale, Specific Aims, Methods, and Study
Procedures ............................................................... 91
Specific Aims and Theoretical Model ................................ 91
Methods and Procedures ..................................................... 94
Study Populations and Design: Children’s Health
Study Overview ............................................................ 94
Study Population for Manuscript 1 ................... 96
Study Population for Manuscript 2 ................... 96
Study Population for Manuscript 3 ................... 96
Study Procedures: Indices of Health Status .................. 97
Ascertaining Incident Asthma Cases ................ 97
Assessing Pulmonary Function ......................... 97
Assessing Airway Structure with HRCT .......... 98
Study Procedures: Assessing Exposures ...................... 99
Measurement of Exhaled Nitric Oxide ............ 99
Measurement of Air Pollution Exposure ........ 100
Assessment of Covariate Information,
Exposure History and Other Health
Information ..................................................... 102
Study Procedures: Ascertaining Informed Consent .... 102
Statistical Methods ...................................................... 103
Manuscript 1 Methods .................................... 103
Manuscript 2 Methods .................................... 104
Manuscript 3 Methods .................................... 105
Chapter 4 References ........................................................ 107

vi

Chapter 5: Exhaled Nitric Oxide, Susceptibility and New-
Onset Asthma in the Children’s Health Study
(Manuscript 1) ....................................................... 109
Chapter 5 Abstract ............................................................ 109
Introduction ....................................................................... 110
Methods............................................................................. 111
Study Subjects ............................................................. 111
New Onset Asthma Definition .................................... 111
Socio-demographic and Medical History .................. 111
FeNO Collection and Analysis ................................... 112
Statistical Methods ...................................................... 113
Results ............................................................................... 115
Study Population and Cohort Follow-up .................... 115
Selected Health and Exposure Characteristics and
Risk of New Onset Asthma......................................... 117
Distribution of FeNO .................................................. 118
FeNO and Risk of New-Onset Asthma ....................... 119
FeNO and New-Onset Asthma by Allergy Status
and Parental History of Asthma .................................. 121
Discussion ......................................................................... 126
Conclusions ....................................................................... 130
Chapter 5 Acknowledgements .......................................... 131
Chapter 5 References ........................................................ 132

Chapter 6: Exhaled Nitric Oxide, Susceptibility and New-
Onset Asthma in The Children’s Health Study
(Manuscript 1 Online Methods and Data
Supplement) .......................................................... 135
Statistical Methods ............................................................ 135
Statistical Methods for Fitting Spline-Based Cox
Regression Models ............................................................ 137
Description of Figure 5.2 ............................................ 137
Description of Figure 6.1 ............................................ 137
Chapter 6 References ........................................................ 143

Chapter 7: Exhaled Nitric Oxide and Pulmonary Function in
the Southern California Children’s Health Study
(Manuscript 2) ...................................................... 144
Chapter 7 Abstract ............................................................ 144
Introduction ....................................................................... 145
Methods............................................................................. 146
Study Subjects and Design.......................................... 146
Pulmonary Function Collection and Analysis ............ 147
FeNO Collection and Analysis ................................... 147
vii

Socio-demographic and Medical History
Information ................................................................. 148
Statistical Methods ...................................................... 148
Results ............................................................................... 149
Demographics and Descriptive Characteristics .......... 149
FeNO and Pulmonary Function .................................. 152
Relationship of FeNO with MMEF and FEV1/FVC
by Physician-Diagnosed Asthma ................................ 152
Discussion ......................................................................... 155
Conclusions ....................................................................... 158
Chapter 7 Acknowledgements .......................................... 159
Chapter 7 References ........................................................ 160

Chapter 8: The Association Between Lung Function
Deficits, Air Pollution Exposure, and Small
Airways Structure Assessed by HRCT
(Manuscript 3) ....................................................... 163
Chapter 8 Abstract ............................................................ 163
Introduction ....................................................................... 164
Methods............................................................................. 165
Study Subjects and Design.......................................... 165
Pulmonary Function Methods ..................................... 165
CT Methods and Analysis ........................................... 166
Air Pollution Exposure Assessment Methods............. 167
Statistical Methods ...................................................... 167
Results ............................................................................... 168
Descriptive Results ..................................................... 168
Differences in Lung Function and Airway
Dimensions by Physician-Diagnosed Asthma ............ 170
Associations of Lung Function and Air Pollution
with Airway Dimensions ........................................... 171
Discussion ......................................................................... 173
Conclusions ....................................................................... 176
Chapter 8 Acknowledgements .......................................... 177
Chapter 8 References ........................................................ 178

Chapter 9: Summary and Future Research Directions ........... 180
Summary ........................................................................... 180
Future Directions: Manuscripts 1 and 2 ............................ 184
Future Directions: Manuscript 3 ....................................... 185
Conclusions ....................................................................... 189
Chapter 9 References ........................................................ 190

Bibliography ....................................................................................... 192
viii

LIST OF TABLES

5.1. Subject Characteristics and Associations with New-Onset Asthma

5.2. Association of Exhaled Nitric Oxide (FeNO) with New-Onset Asthma in the
Children’s Health Study

5.3. Exhaled Nitric Oxide (FeNO) and Risk of New-Onset Asthma, Restricted to
Children without Lifetime Wheezing and without Wheezing in the 12 Months
Prior to Study Entry

5.4. Exhaled Nitric Oxide (FeNO) and Risk of New-Onset Asthma: Restricted Case
Definitions

5.5. Association of Exhaled Nitric Oxide (FeNO) with New-Onset Asthma by
Respiratory Allergy Status

5.6. Association of Exhaled Nitric Oxide (FeNO) with New-Onset Asthma by Parental
History of Asthma

6.1. FeNO Quartile Cutpoints by Age at Study Entry

6.2. Incidence Rates of Asthma Among Study Participants

6.3. Exhaled Nitric Oxide (FeNO) and Risk of New-Onset Asthma: Restricted Case
Definitions to those Reporting Recent Medication Use in the Diagnosis Year

7.1. Demographic, Household and Health Characteristics for Children’s Health Study
Participants, 2007-2008

7.2. Association of Pulmonary Function with Quartiles of Exhaled Nitric Oxide (FeNO),
Children’s Health Study, 2007-2008

7.3. Association of Pulmonary Function with Quartiles of Exhaled Nitric Oxide (FeNO)
by History of Physician-Diagnosed Asthma, Children’s Health Study, 2007-2008

8.1. Selected Demographic, Health and Exposure Characteristics in Former Southern
California Children’s Health Study Participants in the HRCT Study

8.2. Sex Differences in Airway Dimensions Measured by CT and Mean Pulmonary
Function Measures Assessed by Spirometry in Former Participants in the
Southern California Children’s Health Study
ix

8.3. Differences by Asthma in Airway Dimensions Measured by CT and Mean
Pulmonary Function Measures Assessed by Spirometry in Former Participants in
the Southern California Children’s Health Study

8.4. Correlations of Airway Dimensions Measured by CT with Pulmonary Function Tests
in Former Participants in the Southern California Children’s Health Study

8.5. Associations of Airway Dimensions Measured by CT with Community-Specific
Average Annual Air Pollution in Former Children’s Health Study Participants
x

LIST OF FIGURES
3.1. Cross-section of Airway with Metrics of Airway Structure

4.1. Theoretical Model of the Effects of Airway Inflammation on Childhood Respiratory
Diseases, Using Exhaled Nitric Oxide (FeNO) as a Marker of Airway
Inflammation and HRCT as a Non-invasive Technique to Assess of Airway
Remodeling

5.1. Distribution of Exhaled Nitric Oxide at Baseline

5.2. Hazard Ratio Function of the Effect of Exhaled NO on New Onset Asthma

6.1. The Estimated Family History of Asthma Specific Hazard Ratio for Asthma
Incidence as a Function of Exhaled NO in the Study Cohort

xi

ABSTRACT
The burden of childhood respiratory diseases is an important public health
problem. Asthma is the most common childhood chronic disease and numerous studies
have documented its rise in worldwide prevalence over the past several decades.
Moreover, normal pulmonary function development during childhood is important for
reaching maximum attainable adult lung function. While the etiology of childhood
asthma and development of normal pulmonary function development are complex, a
growing body of evidence shows that a variety of environmental exposures are important
determinants of childhood airway diseases and normal development. A greater
understanding of the biological mechanisms influencing childhood asthma and the natural
course of pulmonary function development is critical to minimizing the adverse effects of
environmental exposures. We investigated pathophysiologic mechanisms using advances
in non-invasive technologies including exhaled nitric oxide (FeNO) and high resolution
computed tomography (HRCT) scanning to assess airway inflammation and airway
structural changes in the Southern California Children’s Health Study. We demonstrated
that higher measured inflammation could predict the onset of asthma and was associated
with lower pulmonary function levels. Moreover, we found that adults who had been
exposed to higher levels of air pollution during childhood had anatomically smaller
airways than adults exposed to lower levels during childhood. These findings have the
potential to identify early markers of disease and aid in preventive strategies to lessen the
substantial burden that exists with childhood asthma and airway diseases. Further
research is greatly needed to examine the effects of environmental exposures on key
xii

pathophysiologic mechanisms that lead to or mitigate the development of childhood and
adult respiratory diseases.
1

CHAPTER 1: INTRODUCTION
The burden of childhood respiratory diseases is an important public health
problem. Asthma is the most common childhood chronic disease and numerous studies
have documented its rise in worldwide prevalence over the past several decades.
1
It has
been estimated that 300 million people worldwide suffer from asthma and that 35 million
of these are in the United States alone.
2-3
According to the Centers for Disease Control
and Prevention (CDC), asthma prevalence increased from 3.6% in 1980 to 9.1% in 2007
in children 17 years and younger.
4
Similar increasing rates are seen around the developed
world, and there is a great need for further study in developing countries where it has
been suggested that the burden is likely to be even more substantial.
1

Current annual asthma-related health care costs and expenditures in the United
States are estimated to be more than $30 billion.
5
Moreover, it has been estimated that
asthma accounts for 1.1 million annual hospital outpatient visits, 1.6 million annual
emergency department visits, and 10.6 million annual physician office visits.
6-7

In addition to the burden of childhood asthma and other childhood airway
diseases, normal pulmonary function development during childhood is important for
reaching maximum attainable adult lung function. Deficits in pulmonary function
development have been associated with increased risk for asthma, chronic obstructive
pulmonary diseases (COPD), cardiovascular diseases, mental health problems and even
mortality.
8-11

While the etiology of childhood asthma and pulmonary function development is
complex, a growing body of evidence shows that a variety of environmental exposures
including tobacco smoke exposure and ambient air pollution exposures are important
2

determinants of childhood airway diseases.
12-13
Moreover, early and prenatal exposures
have been implicated as important for childhood pulmonary function development and
support the hypothesis that early exposures set the stage for later deficits in pulmonary
function.
14-16
Exposure to certain environmental pollutants during critical periods of
development alter the normal course of lung development and result in permanent
changes that affect the structure and function of the respiratory system.
17

A greater understanding of the biological mechanisms influencing childhood
asthma and the natural course of pulmonary function development is critical to
minimizing the adverse effects of environmental exposures. These mechanisms have
been difficult to investigate directly in epidemiologic studies of the effects of
environmental exposures on children’s respiratory health as direct measurement of
airway pathophysiology previously required the use of invasive techniques.
In this dissertation, we examine these pathophysiologic mechanisms using
advances in non-invasive technologies to assess airway inflammation and airway
structural changes in children and adults who are collectively part of the southern
California Children’s Health Study (CHS)—the more than 17-year population-based
prospective study of the effects of air pollution and other environmental exposures on the
respiratory health of more than 11,000 current and former schoolchildren in 13 southern
California communities.
The context for the three original manuscripts presented is provided through a
review of the pathophyisology, known mechanisms and descriptive epidemiology of
childhood asthma and normal pulmonary function development (Chapter 2); an
investigation of the development of non-invasive techniques for assessing airway
3

inflammation and airway structure (Chapter 3); and a detailed presentation of the
specific aims and methods used in the three original manuscripts (Chapter 4). Where
possible, results and previous findings from the CHS have been emphasized to place the
manuscripts’ results in the context of the CHS to date.
In Chapters 5 and 6 (Manuscript 1 and Manuscript 1 online supplement), we
prospectively examine whether children participating in the CHS with elevated airway
inflammation, as measured by fractional concentration of nitric oxide in exhaled air
(FeNO), are at increased risk for new-onset asthma. In Chapter 7 (Manuscript 2), we
examine whether airway inflammation in children, as measured by FeNO, is associated
with deficits in pulmonary function, and whether this relationship varies by history of
physician-diagnosed asthma. In Chapter 8 (Manuscript 3), we examine whether air
pollution exposure during childhood and adolescence is associated with adult airway
anatomy and whether pulmonary function in early adulthood is associated with airway
anatomy using high resolution computed tomography (HRCT). Finally, we provide
overall conclusions and a brief discussion of future research directions (Chapter 9).

4

Chapter 1 References

1. Eder W, Ege MJ, von Mutius E. The asthma epidemic. N Engl J Med
2006;355:2226-35.

2. American Lung Association Epidemiology & Statistics Unit. Trends in Asthma
Morbidity and Mortality; 2007.

3. World Health Organization. Global surveillance, prevention and control of
chronic respiratory diseases: a comprehensive approach; 2007.

4. Centers for Disease Control and Prevention. 2007 National Health Interview
Survey Data. Table 4-1 Current Asthma Prevalence Percents by Age, United States:
National Health Interview Survey, 2007. Atlanta, GA: U.S. Department of Health and
Human Services, CDC; 2010 Accessed November 21, 2010.

5. Kamble S, Bharmal M. Incremental direct expenditure of treating asthma in the
United States. J Asthma 2009;46:73-80.

6. DeFrances CJ, Lucas CA, Buie VC, Golosinskiy A. 2006 National Hospital
Discharge Survey. Natl Health Stat Report 2008:1-20.

7. Cherry DK, Hing E, Woodwell DA, Rechtsteiner EA. National Ambulatory
Medical Care Survey: 2006 summary. Natl Health Stat Report 2008:1-39.

8. Sobol BJ, Herbert WH, Emirgil C. The high incidence of pulmonary functional
abnormalities in patients with coronary artery disease. Chest 1974;65:148-51.

9. Keys A, Aravanis C, Blackburn H, et al. Lung function as a risk factor for
coronary heart disease. Am J Public Health 1972;62:1506-11.

10. Knuiman MW, James AL, Divitini ML, Ryan G, Bartholomew HC, Musk AW.
Lung function, respiratory symptoms, and mortality: results from the Busselton Health
Study. Ann Epidemiol 1999;9:297-306.

11. Goodwin RD, Chuang S, Simuro N, Davies M, Pine DS. Association between
lung function and mental health problems among adults in the United States: findings
from the First National Health and Nutrition Examination Survey. Am J Epidemiol
2007;165:383-8.

12. Arruda LK, Sole D, Baena-Cagnani CE, Naspitz CK. Risk factors for asthma and
atopy. Curr Opin Allergy Clin Immunol 2005;5:153-9.

5

13. Kasznia-Kocot J, Kowalska M, Gorny RL, Niesler A, Wypych-Slusarska A.
Environmental risk factors for respiratory symptoms and childhood asthma. Ann Agric
Environ Med 2010;17:221-9.

14. Bush A. COPD: a pediatric disease. COPD 2008;5:53-67.

15. Canoy D, Pekkanen J, Elliott P, et al. Early growth and adult respiratory function
in men and women followed from the fetal period to adulthood. Thorax 2007;62:396-402.

16. Shi W, Bellusci S, Warburton D. Lung development and adult lung diseases.
Chest 2007;132:651-6.

17. Kajekar R. Environmental factors and developmental outcomes in the lung.
Pharmacol Ther 2007;114:129-45.

6

CHAPTER 2: PATHOPHYSIOLOGY, MECHANISMS AND DESCRIPTIVE
EPIDEMIOLOGY OF CHILDHOOD ASTHMA AND PULMONARY FUNCTION
DEVELOPMENT
In this chapter, we introduce the key features of childhood asthma; discuss the
complexity of the pathophysiology of asthma; and provide a summary of the
epidemiologic evidence of social, demographic, and environmental contributions to the
current asthma epidemic. We also discuss the development of pulmonary function during
childhood and summarize environmental, social and demographic factors that contribute
to normal pulmonary function development. We further discuss the relationship between
asthma and pulmonary function development.

Asthma
Definition, Diagnostic Criteria and Pathophysiology of Childhood Asthma
While no definitive diagnostic instrument exists, asthma is considered to be a
chronic inflammatory disease that is diagnosed based on a pattern of symptoms and/or
responsiveness to treatment.
1
Asthma is characterized by chronic airway inflammation
and airway remodeling resulting in excess mucous production and airway thickening, as
well as airway hyperresponsiveness (AHR) and bronchoconstriction resulting in a
narrowing of the airways.
2
In contrast to conditions such as chronic obstructive
pulmonary disease (COPD), asthma is traditionally considered to have reversible airflow
obstruction. However, if untreated, the chronic inflammation associated with asthma can
cause the airflow obstruction to become irreversible due to airway remodeling.
3

7

As described in the first and most recent guidelines for asthma diagnosis by the
National Heart, Lung and Blood Institute (NHLBI) [Expert Panel Report 3 (EPR3):
Guidelines for the Diagnosis and Management of Asthma
2
commissioned by the National
Asthma Education and Prevention Program Coordinating Committee and coordinated by
the NHLBI]:
Asthma is a chronic inflammatory disorder of the airways in which many cells
and cellular elements play a role: in particular, mast cells, eosinophils, T
lymphocytes, macrophages, neutrophils, and epithelial cells. In susceptible
individuals, this inflammation causes recurrent episodes of wheezing,
breathlessness, chest tightness, and coughing, particularly at night or in the early
morning. These episodes are usually associated with widespread but variable
airflow obstruction that is often reversible either spontaneously or with treatment.
The inflammation also causes an associated increase in the existing bronchial
hyperresponsiveness to a variety of stimuli. Reversibility of airflow limitation
may be incomplete in some patients with asthma (EPR 1991; EPR ⎯2 1997; EPR-3
2007).
2, 4-5

Clinical Diagnostic Issues
Diagnostic Issues in Asthma
As defined by the NHLBI committee in EPR-3, asthma is a clinical syndrome that
is diagnosed primarily by a profile of symptoms. The current recommended guidelines
for asthma diagnosis by EPR-3 include the presence of episodic symptoms of wheeze,
cough, shortness of breath, and chest tightness; determination of reversible airflow
8

obstruction (forced expiratory volume in one second (FEV
1
) <80% predicted; ratio of
FEV
1
to forced vital capacity (FVC) <65% or below the lower limit of normal; and
exclusion of alternative diagnoses. The EPR-3 also recommends further testing for
suspected asthma cases including assessing diurnal variation in peak flow, determining
airway hyperresponsiveness (AHR), obtaining chest X-ray, or determining allergic
sensitization.
Allergic Versus Non-Allergic Asthma
Asthma can be either allergic or non-allergic. Allergic asthma is characterized by
an IgE-mediated response to common allergens and exposures

and is the most common
form of childhood asthma.
6
In contrast, non-allergic asthma is not IgE-mediated;
however, it has been shown that the profile of inflammatory cells and cytokine gene
expression is similar in both subtypes of asthma.
7-8

Age-Related Phenotypes: Early Onset Transient, Late Onset and Early Onset Persistent
In 1995, Martinez et al.
9
proposed three asthma phenotypes: transient early, late
onset, and persistent wheeze based on observations that children with wheeze who were
diagnosed before 3 years often did not have asthma symptoms in later childhood.
Therefore, children were classified as transient early wheezers if wheeze was present at
anytime during the first 3 years but not between 3 and 6 years. Children with an onset of
wheezing symptoms after age 3 were classified as late onset and those with wheezing
before age 3 who continued to have wheezing episodes at age 6 were classified as
persistent. The classification system that Martinez et al. proposed has important
implications for sorting out differences in asthma etiology. They proposed that the group
of children with transient wheeze experienced symptoms due to respiratory infections or
9

maternal smoking, which implies that age at onset of symptoms is important to
understanding the role of environmental factors in asthma etiology.
In recent years, it has been widely recognized that asthma is a heterogeneous
syndrome with potentially many more phenotypes than earlier classification schemes
documented.
10-11
The NHLBI Severe Asthma Research Program (SARP), a multi-center
study of adults and children with severe asthma, has used cluster analysis to determine
how symptoms in children with severe asthma are distributed and how these symptoms
are related to standard definitions of severity. Cluster analysis determined that among 161
children participating in SARP, four clusters of asthma were identified.
10
Children who
were in cluster 1 had approximately normal lung function and low presence of atopy;
children in cluster 2 had lower lung function, higher presence of atopy, and increased
asthma symptoms and medication use; children in cluster 3 had greater presence of
comorbid conditions, increased airway hyperresponsiveness and still lower lung function;
and children in cluster 4 had the lowest lung function and reported the most asthma
symptoms and medication use. They also found that important predictors of cluster
designation were duration, number of controller medications used and baseline lung
function. Moreover, children with severe asthma were assigned to all clusters and no one
cluster conformed to standard definitions of asthma severity.
Similar cluster analyses have been conducted within large cohort studies to better
understand phenotypes among children with asthma and wheeze. One study compared
wheezing phenotypes in 5760 children in the Avon Longitudinal Study of Parents and
Children (ALSPAC) with 2810 children in the Prevention and Incidence of Asthma and
Mite Allergy (PIAMA) study using longitudinal latent class analysis. They found
10

evidence for a 5-class model with evidence for the existence of an intermediate-onset
phenotype onset of wheeze after 2 years of age.
11
These cluster-analysis based
approaches appear to be promising and have the potential to lead to further understanding
of the complicated and heterogeneous nature of childhood asthma.
Use of Questionnaire-based Report of Physician-Diagnosed Asthma in Epidemiologic
Studies
The use of questionnaire-based report of physician-diagnosed asthma has been
widely accepted as a valid method of classifying asthma status in large epidemiologic
studies.
12-13
These validated questionnaires ask the parent (if research participant is a
child) about symptoms of asthma including the presence of wheeze, cough, bronchitis,
and chest tightness as well as diagnostic history by a physician.
12, 14-15
In the Southern
California Children’s Health Study (CHS) a prospective population-based study of
11,000 children in southern California, we independently verified self-reported
physician-diagnosed asthma through a review of medical records and found that more
than 95% of the children with a self-reported diagnosis had either a definite or probable
asthma diagnosis noted on the medical record.
16

Mechanisms and Pathophysiology of Asthma
In this section, we will outline the major pathophysiological elements currently
accepted as central to asthma, including chronic airway inflammation, airway
remodeling, bronchoconstriction and airway hyperresponsiveness.

11

Airway Inflammation
Airway inflammation has a fundamental role in the pathophysiology of asthma.
17

It involves the interaction of many cell types and inflammatory mediators that eventually
leads to the physiologic symptoms of the disease. The cellular processes involved in
airway inflammation have shown consistent patterns in asthma—irrespective of
phenotype—and they have not been shown to depend on disease severity, persistence or
duration of disease.
2
While advances in understanding the pathophysiology of asthma
have been made, there are still unanswered questions about how these interrelated
processes unfold.
The processes of acute and chronic airway inflammation have both been well
documented. Acute airway inflammation can arise as a response to many external forces
including exposure to allergens, viruses, indoor and outdoor air pollutants.
6, 18-20
The
progression of the acute allergic (Immunoglobulin E, IgE) inflammatory response has
been well studied.
21
There are two reactions that characterize the acute inflammatory
response: the early-phase response and the late-phase response. The early-phase response
is initiated when allergen binds to IgE on mast cells in the airways causing degranulation
of mast cells. Proinflammatory mediators (e.g. histamine, eicosanoids, and reactive
oxygen species) are produced which lead to contraction of airways smooth muscle and
increase mucous secretion. Plasma leakage, airway edema and narrowing of the airway
lumen are all part of the inflammatory cascade.
The late-phase acute response involves the recruitment and activation of
inflammatory cells including eosinophils, CD4+T-cells, basophils, neutrophils and
macrophages. Mast cells also produce cytokines [e.g. Interleukin-3 (IL-3), IL-4, IL-5, IL-
12

6, IL-8, IL-13, tumor necrosis factor (TNF)] that promote a type 2 T-helper cell (Th2)
immune response. A vicious cycle ensues with further recruitment of leukocytes,
prolonged survival of eosinophils through generation of IL-4 and granulocyte-
macrophage colony stimulating factor (GM-CSF) leading to persistent and sometimes
chronic airway inflammation.
In contrast to acute airway inflammation, chronic airway inflammation is
characterized by the persistence of inflammatory cells including eosinophils,
lymphocytes, and activated macrophages; epithelial cell shedding and fragility which can
lead to airway hyperresponsiveness; and activation of epithelial cells and release of
cytokines, eotaxin as well as growth factors (e.g. IGF and TGF- ) which can lead to
airway remodeling.
17

Airway Remodeling
While the acute inflammatory response is a normal response of cells to insult or
injury, chronic inflammation in some individuals with asthma is followed by airway
remodeling, which is an attempt to repair the damage that can permanently alter the
structure of the airways.
22
Airway remodeling includes both regeneration of injured tissue
of the same type and replacement by connective tissue which later results in the
formation of scar tissue.
Characteristics of airway remodeling include increased smooth muscle mass in
large and small airways, increased mucous glands, thickening of reticular basement
membrane, increased blood vessels, and increased collagen in subepithelial tissues
leading to “scaring.” Remodeling can lead to altered airway structure, increased airway
13

wall thickness and changes in pulmonary function and, in some cases, irreversible airflow
obstruction.
3

Bronchoconstriction
One of the key events leading to symptoms in asthma is bronchocontriction or
airway smooth muscle contraction. Airway narrowing and subsequent restriction of
airflow occur in response to respiratory tract irritants or other stimuli. When exposed to
sensitive allergens (e.g. pollens, pet dander, dust mite), bronchoconstriction results due to
a release of mediators from mast cells (e.g. histamine, tryptase, leukotrienes, and
prostaglandins) that directly contract the airway smooth muscle and are dependent on
IgE.
23
Other factors (including exercise, cold air and other irritants) can also cause
airflow restriction but the mechanisms regulating the response to these factors are less
understood.
2

Airway Hyperresponsiveness
Airway hyperresponsiveness (AHR) is another common feature of asthma and is
defined as the exaggerated narrowing of the airways after inhalation of a trigger or
specific stimulus.
24-26
Many,
27-33
but not all,
34
studies have shown associations of AHR
with airway inflammation and airway remodeling. The degree of airway
hyperresponsiveness can be quantified using inhalation challenges with histamine or
methacholine, which both serve to constrict the airways. AHR is measured by a decline in
pulmonary function following an inhalation challenge using standard methodology.
35
The
ability to quantify AHR has important clinical utility as AHR has been identified as one
of the defining characteristics of asthma.
36

14

It is not known precisely what drives AHR in patients with asthma but it has been
suggested that a number of different mechanisms might be involved, including an
increase in the number of eosinophils which can result in epithelial damage, basement
membrane thickening, and the release of inflammatory mediators that cause bronchial
smooth muscle contraction and release of plasma proteins into the airway wall, resulting
in airway wall thickening.
37

Descriptive Epidemiology of Childhood Asthma
Twin Studies and Genetic Determinants
It is widely accepted that genetic factors account for a significant proportion of
allergy and asthma occurrence.
38-39
Twin and family-based studies have shown that there
are higher rates of asthma among monozygotic (MZ) than dizygotic (DZ) twins, with
estimates of heritability between 50 and 90%.
40
One recent study from more than 20,000
twin pairs in the Danish Twin Registry showed that MZ twins had a six-fold increased
risk of asthma while DZ twins had a three-fold increased risk of asthma, compared to the
general population if his/her co-twin had asthma.
41

The field of asthma genetics has made substantial progress over the past
decades.
42
By 2008, nearly 1000 studies had been published that examined genetic
associations with asthma and allergic diseases.
43
Recently, the focus of genetic studies of
asthma has been on genomewide approaches (genomewide association studies, GWAS)
involving large consortia.
44
In the past few years, more than 10 GWAS involving large
consortia have been published regarding genetic associations with asthma, with the
largest being the GABRIEL consortium. GABRIEL identified that the IL18R1, IL33,
15

SMAD3, ORMDL3, HLA-DQ and IL2RB loci were all associated with asthma.
45
Results
from the GWAS collectively have identified associations with genes expressed in the
respiratory epithelium, but have not supported strong associations with genes controlling
IgE levels, suggesting that asthma and atopy may be regulated by different pathways.
44

While the GWAS studies have identified novel genes to pursue, the risk estimates
associated with these variants and asthma are low, leaving much of the variability in
asthma risk unexplained.
46-47

A more detailed discussion of the genetic determinants of asthma is beyond the
scope of this review; however, a critical point worth noting is that even with rapid
changes in technology (e.g. high throughput capabilities for GWAS), these studies have
demonstrated that asthma is a complex disease and individual genes are likely to play
small roles unless combined with modifying environmental factors.
43

Global Variation in Prevalence
Although genetic factors are important risk factors for individuals, it is unlikely
that they are responsible for the large global variations in asthma and allergic airway
diseases which exist between populations.
48-49
Moreover, genetic factors are not likely to
explain the rise in worldwide prevalence documented over the past several decades.
50

The International Study of Asthma and Allergies in Childhood (ISAAC) was
initiated in the early 1990s in order to establish standardized methodology for
epidemiological studies and facilitate international collaboration to investigate the
variations in asthma prevalence.
51
The original ISAAC aims were to describe the
prevalence and severity of asthma and allergic diseases in children living in different
countries; to obtain baseline measures for assessment of future trends in the prevalence
16

and severity of these diseases; and to provide a framework for further research into
genetic, lifestyle, environmental and medical care factors affecting these diseases.
There were three phases of ISAAC: Phase One was a prevalence study that used a
written questionnaire in two age groups and involved 156 centers in 56 developed and
developing countries with a total of 721,601 children. The two age groups were children
13-14 years of age and children 6-7 years of age. Phase Two of ISAAC investigated the
determinants of the observed differences in prevalence rates found in Phase One, and
ISAAC Phase Three repeated the Phase One questionnaires approximately 5-10 years
later to reassess trends in prevalence of asthma and allergic diseases.
ISAAC Phase One demonstrated a very large variation in the prevalence of
asthma and asthma symptoms in children throughout the developed and developing
world. Each country surveyed had some children who reported having asthma or asthma
symptoms during their lifetimes but there was up to a 15-fold difference in prevalence
between countries. In Albania, Estonia, Ethiopia, Indonesia, Iran, Poland, Russia, South
Korea and Uzebekistan, prevalence estimates were between 1.6 and 3.0%; in Australia,
New Zealand, Oman, Peru, Singapore and the UK, prevalence estimates were between
20.7 and 28.2%.
52

Phase Two of ISAAC was a more in-depth study of some of the determinants of
the differences in prevalence rates observed in Phase One. Phase Two examined these
determinants using objective markers of asthma and allergic diseases in approximately
30,000 children ages 9-11 such as pulmonary function testing, airway
hyperresponsiveness (AHR), and skin prick testing, as well as blood collection to
investigate genetic determinants and gene-gene and gene-environment interactions
17

between different study centers. Dust samples were also collected for analysis of
allergens and endotoxin exposure.
53

One ISAAC Phase Two study showed that prevalence of wheeze ranged from
4.4% in Albania to 21.9% in New Zealand and prevalence of AHR ranged from 2.1% in
Albania to 48% in India. Wheeze and AHR were positively associated in children in both
developing and developed countries, but this association was primarily seen in atopic
children.
54

Phase Three of ISAAC repeated the Phase One survey at least five years after
Phase One ended to assess changes in asthma prevalence in the decade following the
Phase One data collection. The survey was conducted in approximately 500,000 children
in two age groups. Children aged 13-14 years of age were recruited from 106 centers in
56 countries (n = 304,679) and children aged 6-7 years were recruited from 66 centers in
37 countries (n = 193,404).
55
Patterns related to change in prevalence were observed in
most countries. Among 6–7 year olds, the prevalence of asthma symptoms changed by 1
standard error or more in many centers (59%) from Phase One to Phase Three. Of the 39
centers with changes, prevalence increased in 25 centers and decreased in 14 centers.
Among 13–14 year olds, the prevalence of asthma symptoms changed by 1 standard error
or more in 77% of centers, but about equal numbers of centers showed an increase in
prevalence and a decrease in prevalence of symptoms of asthma. The authors concluded
that there is likely to be a variety of environmental factors associated with these
worldwide changes.
56

Collectively, the three phases of the ISAAC have demonstrated that large
variations exist in the worldwide prevalence of asthma and allergic diseases, even in
18

those of similar ethnic background (e.g., Williams et al.
57
), suggesting that environmental
factors are largely responsible for the variations observed. Many environmental factors
have been examined in subsequent analyses from ISAAC Phase One and have provided
evidence that environmental factors are important.
53, 58-63
Among these environmental
factors, Brunekreef et al.
59
reported that higher self-reported exposure to truck traffic on
the child’s street of residence was associated with increased symptoms of asthma,
rhinitis, and eczema in many of the developed and developing countries studied in
ISAAC.
Migrant Studies
In addition to studies of the global variation in asthma, migrant studies have
provided support for environmental factors in asthma etiology.
64
Migration involves
exposure to a new set of pollutants and allergens, as well as new socioeconomic and
cultural issues such as housing, diet, and medical services. Studies of children whose
families have migrated from developing to developed countries have suggested an
increased risk of asthma associated with the environment of the developed world.
65-66
A
recent paper from the ISAAC comparing centers from Canada with centers in China
showed considerable differences in prevalence rates of asthma between children of
similar genetic makeup living in different environments. Rates among children in the 13-
14 year old age group were lowest for Chinese children born and living in China, higher
for Chinese children who had migrated to Canada during their lifetimes and higher still
for Chinese children who had been born and raised in Canada.
67
In addition, in the US,
studies have documented lower asthma rates in foreign-born Chinese, Dominican, and
Mexican populations than among those born in the US.
68-71
Differences in asthma rates
19

have also been shown within countries (e.g. in Papua New Guinea and in some countries
in southern Africa), with more urban areas having higher rates of asthma than rural
areas.
72-74

The Hygiene Hypothesis and Infections
The Hygiene Hypothesis has received widespread attention and provides one
explanation for the higher rates of asthma and allergic diseases observed in industrialized
nations compared to developing countries and in urban areas compared to rural areas. The
Hygiene Hypothesis was first described in 1989 by David Strachan who observed that
allergic conditions, such as eczema and hayfever, were less common in children who had
a higher number of siblings. He proposed that the exposure to more infections from
siblings protected those children from developing subsequent allergic conditions.
75
The
Hygiene Hypothesis has been further used to explain the rise in asthma and allergic
diseases seen in industrialized nations as well as the difference in rates in urban and rural
areas, due to increased hygiene and healthcare access which has changed exposures to
infection in early life resulting in an alteration of the immune system.
76-77

The Hygiene Hypothesis is controversial for a number of reasons. Some recent
studies have suggested that a larger number of children in a family is associated with a
decreased risk of asthma, but birth order does not appear to be involved.
78-79
Moreover, it
has been argued that the Hygiene Hypothesis cannot explain some patterns seen, such as
why some South American countries have high prevalence rates for asthma and allergy
and also have higher rates of infections than in some countries with lower rates of
asthma.
12

20

Social and Demographic Factors
Numerous social and psychological factors, such as socioeconomic status (SES)
as well as and exposure to violence, and psychological illnesses such as depression, stress
and anxiety, have been associated with asthma.
80-81
However, the findings with respect to
socioeconomic status and asthma prevalence are mixed,
82-84
suggesting that the
relationship between social and economic factors and asthma is complex.
In prospective studies conducted in the U.S., it has been shown that the
prevalence of asthma varies greatly among racial and ethnic groups, which may reflect
differences in genetic makeup, but likely also reflects differences in environmental, social
and cultural factors between different groups.
85
In the U.S., Puerto Ricans, African
Americans, Filipinos, and Native Hawaiians have the highest rates of asthma.
68, 86-87

However, among Hispanics and Asians in the U.S., there is considerable heterogeneity in
asthma prevalence. Among the lowest rates of asthma are seen in Mexican Americans
and Korean Americans.
86

Sex is another important determinant in asthma occurrence that follows a time-
dependent pattern. Asthma is more common among boys than girls through
approximately age 13-14. After puberty, studies have shown a greater incidence of
asthma among girls than boys.
88-90

Environmental Influences on Childhood Asthma Incidence and Prevalence
Studies have shown a wide variety of environmental exposures such as smoking,
diet, obesity, allergens, respiratory infections, and ambient air pollution exposures to be
important determinants of childhood airway diseases.
91-92

21

Tobacco Smoke Exposure
Exposure to tobacco smoke has been consistently implicated as a significant risk
factor for childhood asthma.
93-98
In the CHS, in utero exposure to maternal smoking was
associated with increased asthma prevalence and wheezing symptoms, and secondhand
smoke exposure was consistently associated with wheezing symptoms.
99
Further
investigation revealed that the effects of in utero exposure to maternal smoking on
asthma and wheezing were largely restricted to children with GSTM1 null genotype, with
little effect among those with GSTM1 (+) genotype, indicating the importance of
identifying susceptible subgroups.
100
In addition to the effects of maternal smoking on
asthma development in the child, later CHS studies have indicated that grandmaternal
smoking during the mother's fetal period may increase the risk of asthma in her
grandchildren.
101

Maternal Dietary Factors and Childhood Obesity
There is an extensive literature on the role of maternal diet on children’s asthma
risk. Prompted partly by observations that the increase in asthma prevalence in
industrialized countries was paralleled by lower dietary intake of vegetables and fish (or
from a so-called Mediterranean diet to one that included more trans fats and animal-based
foods), numerous studies began to carefully examine of the role of dietary factors such as
antioxidants and lipids/omega 3 fatty acids in asthma occurrence. Maternal intake during
pregnancy of vitamin E and zinc, but not vitamin A, C, copper, magnesium, selenium, or
manganese, were found to be associated with wheeze and asthma in their children.
102-104

An emerging body of evidence suggests that maternal supplementation with
omega-3 fatty acids may reduce the risk of allergic airway diseases in the child.
105-106
In
22

several studies, maternal intake of oily fish (a source of omega-3 fatty acids) has been
associated with lower risk of asthma and atopy in infancy (e.g. Romieu et al.,
107
Willers
et al.,
108
Salam et al.
109
). In the CHS, higher maternal intake of oily fish was associated
with a reduced risk of asthma in their children while higher maternal intake of fish sticks
(a source of trans fats) was associated with increased risk of asthma in the child.
109

In addition to maternal dietary factors during pregnancy, a child’s dietary habits
in early childhood have also been shown to be important in asthma occurrence. An
increasing body of evidence suggests that childhood obesity may play a role in the
development of asthma in later childhood.
110-112
Results from the CHS have
demonstrated an elevated risk of new-onset asthma among children who were overweight
or obese and this risk was particularly evident among boys and children without
respiratory allergies.
113

Early-Life Environmental Exposures in the Home
Early life exposures to indoor air pollutants as well as other exposures in the
home have often been implicated as key factors in studies of childhood asthma. In a
recent review of studies conducted at the Johns Hopkins Center for Childhood Asthma in
the Urban Environment, important indoor airborne

determinants of asthma morbidity in
urban environments included

particulate matter and nitrogen

dioxide from cooking,
smoking, and sweeping as well as airborne mouse allergen exposure.
114
In the CHS,
early onset asthma was significantly associated with a number of home environmental
exposures in the first year of life, including wood or oil smoke, soot or exhaust,
cockroaches, herbicides, pesticides, farm crops, dust, and animals.
16

23

Studies on the effects of exposure to domestic cats and dogs have been
inconsistent and it remains unclear whether an observed protective effect of pets on
sensitization to allergens may prevent the occurrence of asthma and other allergic
diseases.
115-119
In the CHS, an increased risk of developing asthma was associated
owning a pet, and, in particular, a dog.
120
In contrast, a prospective study conducted in the
Prevention and Incidence of Asthma and Mite Allergy (PIAMA) study birth cohort found
that pet exposure in early life may prevent allergic sensitization but does not affect the
development of asthma up to 8 years of age.
121

Ambient Air Pollution Exposure
In recent years, studies have supported an increasingly important role for the
effects of ambient air pollution on asthma, especially when considering the role of traffic-
related pollution and genetic susceptibility.
122
The effects of air pollution on asthma
symptoms and morbidity are reasonably well-established.
123
In the CHS, living within 75
meters of a major road was associated with a 1.5-fold increased risk of lifetime asthma
and wheeze.
124
Results from the CHS have also shown that children with asthma had
more bronchitis and persistent phlegm production if they lived in communities with
higher levels of regional nitrogen dioxide or particulate pollution.
125

The role of air pollution on the risk of new-onset asthma has been controversial;
however, recent studies have more strongly supported a role for air pollution on the
development of new-onset asthma (e.g. Brauer et al.
126
). In the CHS, children who played
at least three team sports had more than three-fold increased risk of new onset asthma if
they lived in communities with high ozone levels, but there was no increased risk if they
lived in communities with low ozone levels and played three or more team sports.
127

24

Pulmonary Function Development in Childhood
Deficits in pulmonary function have been associated with a variety of health
outcomes including asthma, chronic obstructive pulmonary diseases (COPD),
cardiovascular diseases, mental health problems and even mortality.
128-131
Pulmonary
function has frequently been used as a quantitative index of respiratory function or as an
intermediate outcome in epidemiologic and other studies investigating etiologic factors of
respiratory diseases. A greater understanding of the natural course of pulmonary function
development is critical to understanding and mitigating respiratory diseases.

Normal Pulmonary Function Development
In 1979, Polgar and Weng
132
described the normal development of the pulmonary
system from gestation to adulthood and presented standard values for mean lung
function. They argued, however, that there was a general lack of studies using consistent
methodology, and that there was a need for normal growth charts for children’s
pulmonary function development. The majority of early studies were cross-sectional
which did not take into account changes during puberty.
133-137
There were a few early
longitudinal studies conducted, but these studies were limited to specific age ranges, had
relatively small cohorts, and had a limited number of pulmonary function
measurements.
138-141

The Harvard Six-Cities Study was one of the first studies to examine pulmonary
function growth in a large cohort of children (n=13,737), ages 6-18 years in six cities
across the United States. Over the course of the 15 years of the study, children performed
25

annual pulmonary function tests. The cohort was predominantly white (approximately
90%).
Wang et al. developed growth charts for normal pulmonary function development
based on the annual measurements from the Harvard Six Cities study.
142
They found that
median pulmonary function growth followed a similar pattern to height growth curves in
that there was a linear increase with age until the adolescent growth spurt (age 10 for
girls, age 12 for boys). They demonstrated that pulmonary function growth for girls
plateaus at approximately age 16, and at approximately age 18 for boys. Girls’ median
pulmonary function levels are similar, but slightly lower, than boys during the
prepubescent stage, but because girls’ growth spurts start early, they briefly surpass boys
in median pulmonary function levels in early puberty. They further showed the growth
spurt for boys for pulmonary function growth is larger than for girls, and therefore, the
difference between boys and girls continues to widen until at least age 18, when the
plateau for boys is achieved.

Pulmonary Function Testing and Definitions of Measures
Documentation of pulmonary function is commonly acquired using forced
maximal effort maneuvers using a spirometer. Standard recommendations from the
American Thoracic Society dictate that participants perform pulmonary function tests
(PFTs) in a seated position, pinch their nose shut with their fingers, and then perform at
least 3 satisfactory maximal expiratory maneuvers with a maximum of 7 efforts
attempted.
143

26

Common measures of pulmonary function include: (1) Forced Vital Capacity
(FVC): the total volume of air expired from the lung (in milliliters, ml); (2) Forced
Expiratory Volume in the First Second (FEV
1
): The volume of air expired during the first
second of the PFT (in ml); (3) Maximal Mid-Expiratory Flow (MMEF) rate: The flow at
which air was being expired during the middle 50% of the PFT, after 25% of FVC had
been expired but before 75% of FVC had been expired (in ml/sec); (4) Forced Expiratory
Flow at 75% of FVC (FEF
75
): The flow at which air was being expired at the point when
75% of the FVC had already been expired (in ml/sec); and (5) Peak Expiratory Flow Rate
(PEFR): The maximum flow at which air was being expired during the PFT (in ml/sec).
FEV
1
is often considered the best single measure of lung function, as it captures
information about both lung volume and flow rate. FVC is considered the best measure of
lung capacity, and the ratio of these two measures (FEV
1
/FVC) is a measure of airflow
obstruction. Clinically, an FEV
1
/FVC ratio less than 70% of predicted combined with an
FEV
1
less than 80% of predicted after administration of a short-acting bronchodilator
(beta-2 agonist) is considered to be Chronic Obstructive Pulmonary Disease (COPD)
according to the NHLBI/WHO Global Initiative for Chronic Obstructive Lung Disease
(GOLD) criteria.
144
MMEF and FEF
75
are commonly considered measures of the lungs’
small airways function, while PEFR is considered a measure of the health status of the
larger airways.

27

Descriptive Epidemiology of Pulmonary Function Development
Relationship with Asthma
Although it is well established that pulmonary function is reduced during an acute
asthma exacerbation,
145-146
the chronic

effects of asthma on pulmonary function
development have not been examined extensively. The few prospective studies that have
examined the effects of asthma on pulmonary function development have suggested that
persistent deficits

occur.
147
In the CHS, asthma was associated with large deficits in
MMEF and FEF
75
especially among males.
148
These deficits were smaller in females;
however, larger deficits in FEV
1
, MMEF, FEF
75
and PEFR were seen in both males and
females with longer time since diagnosis, indicating duration of asthma is important for
pulmonary function deficits. Moreover, deficits in flow rates in both large and small
airways were observed among both males and females with early onset asthma (before
age 3 years). The CHS study supported earlier studies that showed pulmonary function
deficits in children with asthma continue into adulthood.
148
A later CHS study showed
that there was a protective effect of higher level of airway flows on the risk of new-onset
asthma. Specifically, relative those with levels of MMEF in the 10
th
percentile, those with
levels in the 90
th
percentile had a 50% reduced risk of new onset asthma.
149

Genetic Studies
It has been suggested that pulmonary function has a strong genetic component.
150-
151
Twin studies in Europe and the US have provided evidence that heritability is as high
as 0.77 for FEV
1
.
152-153
Family-based studies have also indicated that genetic effects
appear to be consistent over time.
154

28

Several important candidate genes have been implicated as important factors in
childhood pulmonary function growth and development. In the CHS, early efforts to
understand genetic variation in pulmonary function used a candidate gene approach to
investigate regions with common functional variants in pathways known to be important
for lung growth and development.
20
In one study, three genes from the glutathione-S-
transferase (GST) family of genes, a group of genes involved in antioxidant defense
pathways, were investigated—GSTT1, GSTMI, and GSTP1. GSTM1 null genotype was
associated with growth deficits in FVC and FEV
1
. In addition, children who had two
copies of the GSTP1 val105 allele had larger lung function growth deficits in FVC and
FEV
1
compared to children with one or more ile105 alleles.
155
Additional CHS studies
have found associations of pulmonary function growth across the GST family of genes.
156

The CHS has also investigated the NOS2A pathway on pulmonary function
growth, as it is involved determining nitrosative stress by producing large amounts of
nitric oxide in response to environmental stressors. A pair of “yin-yang” haplotypes in
NOS2A were identified in which the “yin” haplotype was associated with increased risk
of asthma and reduced FEV
1
growth and the “yang” haplotype was associated with better
FEV
1
growth and reduced risk of asthma.
157

While pathway-driven and candidate-gene approaches have provided important
information on the genetic determinants of pulmonary function development, recently, a
consortium of genome-wide association studies on pulmonary function, SpiroMeta,
comprised of more than 20,000 European participants with normal lung function,
identified five novel genes that reached genome-wide significance: GSTCD, TNS1 and
HTR4 for FEV
1
, and AGER and THSD4 for FEV
1
/FVC.
158
In addition, SpiroMeta
29

identified a region suggestive of association with FEV
1
/FVC in DAAM2. Around the
same time, the CHARGE Consortium, a GWAS of lung function in 20,890 participants,
also identified genome-wide significant associations at GSTCD, HTR4 and AGER.
159

Both consortia also confirmed previously reported associations between FEV
1
and
FEV
1
/FVC and a region of HHIP.
160

While the genes identified in these GWASs are all biologically relevant to the
lung and involved in pathways important in immune function, muscle function or
inflammation,
161
the SpiroMeta investigators estimated that the five loci identified in their
study only account for approximately 0.14% of the variation in FEV
1
/FVC ratio.
158
It is
likely that results from large-scale genomewide-environment interaction studies will
provide greater clues to understanding the natural course of pulmonary function
development.
Social Factors and Racial Differences in Pulmonary Function
An emerging body of evidence has implicated social factors, including
socioeconomic status, in normal development and achievement of maximum attained
lung function in early adulthood.
162-165
Historically, clinical practice has employed
different standards for interpretation of pulmonary function tests between racial groups,
based largely on assumed differences in anthropomorphic differences between racial
groups in the US.
166-168
Recently, however, this long-standing practice has received
criticism as studies have shown similarities in the relationship of pulmonary function and
height among different racial groups.
169-171

Nevertheless, the effects of social factors and race/ethnicity differences in
pulmonary function development and decline are difficult to disentangle. A number of
30

studies published in the 1980s suggested that social factors associated with race, rather
than differences between races, may have more important effects on pulmonary function,
including differential economic stressors as well as exposures to environmental agents
including tobacco smoke.
172-174
A literature review covering more than 20 years
published in 2007 concluded a significant inverse association between pulmonary
function and socio-economic factors persists even after adjusting for smoking,
occupational factors and race and the authors noted that this relationship is an important
under-recognized contributor to pulmonary disease.
175

A study recently published using data from the National Health and Nutrition
Survey III (NHANES III) employed quantile regression to attempt to disentangle the
contribution of socioeconomic factors to racial differences across the distribution of
FEV
1
among a population of approximately 10,000 white and African-American adults
aged 20-80.
176
They found a clear racial difference for both males and females in FEV
1

with African-American participants consistently having lower FEV
1
than white
participants. They also found a consistent dose-response relationship between educational
attainment and FEV
1
for both races; however, white participants with the same level of
educational attainment had higher FEV
1
than African-American participants. The results
indicate that better methodologies are needed for disentangling these relationships and
that further research is warranted in understanding the effects of these social
determinants.
Environmental Influences on Pulmonary Function Growth and Development
There is a growing body of evidence documenting adverse effects of
environmental exposures on pulmonary function growth and development in childhood.
31

In recent years, early and prenatal exposures have emerged as important for childhood
pulmonary function development and support the hypothesis that early exposures set the
stage for later deficits in pulmonary function.
177-179

Poor fetal nutrition can lead to low birth weight which in turn has been associated
with alterations in the development of the respiratory system and later deficits in
pulmonary function.
180
Endogenous estrogens and androgens have also been shown to be
important in modulating lung development.
181

Tobacco Smoke Exposure
The effects of tobacco smoke exposure on pulmonary function development have
been extensively studied and are perhaps the most hazardous of all children’s
environmental exposures.
182
It is thought that exposure to secondhand tobacco smoke
(SHS) during critical periods alter the normal course of lung development and result in
permanent changes that affect the structure and function of the respiratory system.
183

As previously described, normal lung growth begins in the in utero period and
continues through the late teens (for girls) and early 20s (for boys).
184
Among healthy
non-smokers, pulmonary function then reaches a plateau that remains steady for up to 10
years when it begins a slow decline. For those exposed to SHS or exposure to maternal
smoking in utero, lung function may plateau at a lower level than is normal and lungs
may not reach their full development potential.
185

In the CHS, exposure to maternal smoking in utero was associated with reduced
PEFR, MMEF, and FEF
75
in the child. Exposure to SHS was also associated with
reduced MMEF and FEF
75.
186
Further examination in the CHS of whether deficits in
lung function were associated with tobacco smoke exposure varied by sex or asthma
32

status, indicated that in utero exposure was independently associated with deficits in
MMEF, FVC, or FEV
1
/FVC ratio that were larger in children with asthma. The effect of
SHS exposure varied by sex and asthma status; in particular, current SHS exposure was
significantly associated with deficits in flows in children with asthma.
187

Ambient Air Pollution Exposure
There is considerable evidence that ambient air pollution at current levels has
substantial negative effects on pulmonary function growth and development in children
and adolescents.
123, 188-194
In the CHS, early cross-sectional results showed associations
of regional particulate matter (PM
10
and PM
2.5
), acid vapor and nitrogen dioxide
exposures with lower FVC, FEV
1
and MMEF; and ozone with lower PEFR and
MMEF.
195
The first longitudinal analyses in the CHS showed significant deficits in
FEV
1
, FVC, MMEF, and FEF
75
growth over a four-year period with higher exposures to
particulate matter (PM
10
, PM
2.5
, and PM
10-2.5
) as well as NO
2
and acid vapor.
196-197

Further follow-up over an eight year period of growth (from age 10 to age 18) indicated
that clinically-significant deficits in FEV
1
growth were observed with exposure to
nitrogen dioxide, acid vapor, particulate matter and elemental carbon. It was determined
that that proportion of 18-year-olds with clinically significant deficits in FEV
1
was nearly
5 times greater at the highest level of exposure to particulate matter (PM
2.5
) compared to
the lowest level of exposure.
198

Recent studies have acknowledged the importance of local pollution sources,
especially from traffic sources, in pulmonary function development and other respiratory
outcomes.
199
Evidence from the CHS indicated that living within 500 meters of a
freeway was associated with significant deficits in 8-year growth of FEV
1
and MMEF
33

compared to children living farther away from a freeway (>1500 meters).
200
Moreover,
joint models showed that traffic-related pollution was independent from regional air
pollution; indicating that both local and regional air pollution can have important and
devastating effects on pulmonary function development.

Summary
In this Chapter, we discussed the key features of childhood asthma and pulmonary
function development and provided a summary of the determinants of childhood asthma
and normal pulmonary function development. We provided evidence that environmental
exposures are increasingly important in the course of both of these conditions and that
further research is needed to understand the complexity of childhood respiratory disease
and health.

34

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50

CHAPTER 3: FENO AND HRCT AS NON-INVASIVE TECHNIQUES TO
MEASURE AIRWAY INFLAMMATION AND AIRWAY REMODELING
The current understanding of the pathogenesis of asthma suggests that oxidative
and nitrosative stress and dysregulated inflammatory responses are important
mechanisms that lead to chronic airway inflammation and airway remodeling.
1-4
These
mechanisms have been difficult to investigate in large population-based prospective
studies as direct measurement of airway pathophysiology previously required the use of
invasive methods such as bronchial biopsies, bronchoalveolar lavage, and induced
sputum or were limited to indirect measurement by pulmonary function tests that measure
the amount and speed of exhaled air.
5

In the past two decades, substantial progress has been made in developing and
validating non-invasive biomarkers of airway inflammation, in particular exhaled nitric
oxide (FeNO),
6
as well as in developing imaging techniques, such as high resolution
computed tomography (HRCT), that have made it feasible to non-invasively study
structural changes of the airways.
7

In this chapter, we briefly examine the biology and biochemical synthesis of nitric
oxide (NO); evaluate the relationship of FeNO with other biomarkers of airway
inflammation; discuss reproducibility and standard measurement practices for FeNO for
use in clinical settings as well as for use in large population-based prospective cohort
studies investigating children’s respiratory health; and provide an overview of the
epidemiologic determinants of FeNO. We further provide an overview of volumetric
reconstruction from HRCT imaging; describe automated quantitative dimensions that can
be derived from HRCT images; examine HRCT imaging studies of children and adults
51

with and without asthma; and discuss the potential for the use of HRCT in studies of
environmental exposures on human health.

Exhaled Nitric Oxide (FeNO)
Biological Determinants of Nitric Oxide
Nitric oxide (NO) is an important cellular signaling molecule involved in
numerous biological processes in the human body. At appropriate levels of production,
NO has critical functions in the nervous, immune and cardiovascular systems, including
smooth muscle relaxation resulting in widening of blood vessels and increasing blood
flow, neurotransmission, and host defense against pathogens.
8
At constant or chronic
levels of production, however, NO can have detrimental effects contributing to a number
of serious health conditions including cancer, septic shock, diabetes, multiple sclerosis,
arthritis, ulcerative colitis, hypertension, stroke and neurodegenerative diseases.
9-10

NO is produced from L-arginine, oxygen, and NADPH by a family of enzymes
called the nitric oxide synthases (NOSs). Arginase also utilizes L-arginine, in
competition with NOS, in the urea cycle. Three forms of NOS exist, each with a unique
function: neuronal NOS (nNOS) encoded by NOS1, endothelial NOS (eNOS) encoded by
NOS3, and inducible NOS (iNOS) encoded by NOS2A. Each of the three NOS isoforms
is expressed in airway epithelial cells; however, iNOS is thought to be the main
contributor to FeNO.
11-14
Both nNOS and eNOS are constitutively active in neurons and
endothelial cells, respectively, are calcium dependent, and produce low levels of NO. In
contrast, inducible NOS (iNOS) as the name implies, can be induced in the process of
52

inflammation or by other stimuli, is not dependent on calcium, and can produce high
concentrations of NO which can be toxic to cells.
15-16

Nitric Oxide and Inflammation
Several cells involved in host immunity use iNOS to produce NO as a central
mechanism of the inflammatory response.
17
Phagocytes, including monocytes,
macrophages, and neutrophils, use the cytotoxic properties of NO, to kill invading
bacteria or pathogens. The immune system can regulate activity of phagocytes that play a
role in inflammation and immune responses: iNOS is activated by interferon-gamma
(IFN-γ) and by tumor necrosis factor (TNF),

and is strongly inhibited by transforming
growth factor-beta (TGF-β) and weakly inhibited by interleukin(IL)-4 and IL-10.
17

In addition to NO, the inflammatory response results in the release of other
molecules including hydrogen peroxide and superoxide. In extreme conditions of
inflammation or under oxidative/nitrosative stress (such as during exposure to high levels
of ambient air pollution), high levels of NO produced can react with these molecules to
produce a variety of reactive nitrogen species (RNS) and reactive oxygen species (ROS),
including the formation of the powerful oxidant peroxynitrite.
8
Among its most damaging
effects, peroxynitrite promotes nitrotyrosine formation in proteins and irreversibly
inhibits tyrosine phosphorylation, a process that is important in cell cycle control.
Peroxynitrite is associated with airway hyperresponsiveness and airway epithelial
damage, promotes the recruitment of inflammatory cells, and inhibits pulmonary
surfactant.
18-19

53

It has been discussed that proinflammatory

cytokines and oxidants both increase
the expression of iNOS resulting in high concentrations of NO. Studies have
demonstrated that expression of iNOS is increased in airway epithelial cells among
patients with asthma,
20
which is likely to explain the increased FeNO associated with
asthma.
21
We will discuss the relationship of FeNO with asthma and respiratory diseases
in greater detail later in this chapter.

Measurement of Nitric Oxide in Exhaled Breath
Despite publications numbering in the thousands, the field of FeNO measurement
has been characterized by considerable differences in FeNO levels in health and disease,
largely attributed to differences in measurement practices. In 1997, a task force
commissioned by the European Respiratory Society (ERS) released the first guidelines
for the measurement of FeNO
22
which was followed by a statement from the American
Thoracic Society (ATS) in 1999.
23
The 1999 ATS statement was updated based on the
recommendations that came out of an international conference sponsored by the ATS in
2002 which resulted in joint ERS/ATS recommendations for online and offline
measurement.
24
A statement was approved by both the ATS and ERS in 2002 on
measurement of FeNO in children.
25

The current understanding is that the NO in exhaled breath is formed in both the
upper and lower respiratory tracts
26-32
and diffuses into the airway lumen down a
concentration gradient.
33-34
As a result of diffusion, the concentration of FeNO increases
with decreasing flow rates below 150 ml/sec.
35
Breath holding as well as the nasal
54

cavities and paranasal sinuses can produce high levels of NO and can contaminate the
FeNO sample if ATS/ERS guidelines are not closely followed.
36

Online FeNO Measurement
The gold standard for FeNO collection is single breath online measurement of
FeNO.
23, 25
During online collection the participant deeply inhales NO free air to
approximately total lung capacity and then immediately exhales into a NO analyzer
attached to a computer. The participant is coached by a trained technician to watch a
visual cue on screen to maintain a fixed flow rate (50 ml/sec is standard) against a 5-
20cm H
2
0 pressure, which serves to exclude nasal sources of NO, until a plateau is
reached during an exhalation of at least 4 seconds. The procedure described above is
repeated until levels are in agreement within 5% and then the mean of these maneuvers is
typically reported.
Offline FeNO Measurement
While online collection has been the gold standard, instrumentation in the early
part of the decade was too cumbersome to transport for field testing for large
epidemiologic studies. Therefore the collection method of choice for early field studies
(until online analyzers became more easily transported) was offline measurement using
single breath collection with controlled flow rate. Offline measurement has been shown
to produce levels of FeNO similar to online measurement.
37-38
During offline collection,
the participant inhales NO-free air through a scrubber to total lung capacity and blows
through a mouthpiece at a constant flow rate (100 ml/sec) into an NO-free Mylar balloon,
while maintaining a pressure of 5-20cmH20 by flow restriction. Participants are coached
to not hold their breaths and the first 4 seconds of dead space volume is discarded. The
55

collected samples are then analyzed by a chemiluminescent NO analyzer (typically in a
central laboratory).
Online versus Offline FeNO Measurement
Studies have indicated that there was excellent agreement between offline and
online measurement techniques in laboratory-based studies, using a variety of
techniques.
39-41
Moreover, it has been shown that standardization of flow rates during
offline collection decreases variability and results in levels similar to online collection,
even among children.
37-38
Using these standardized techniques, in one study, 93% of
children aged 4-8 years were able to successfully perform the offline maneuver.
42

In the Children’s Health Study (CHS), both online and offline collection of FeNO
have been utilized. When portable NO analyzers became available, the CHS changed
from using offline collection to online collection in its field studies. A small CHS
substudy involving 362 children who completed online and offline FeNO measurements
in the same session determined a nearly linear relationship between the two procedures
(R
2
=0.90). Important artifacts were found in the offline collection including the lagtime
for the bag samples to transport to the laboratory as well as ambient NO. Including these
two factors in a prediction model of online FeNO, using offline values as the predictor,
gave a model R
2
=0.94.
43

Reproducibility of FeNO Measurement
Both online and offline FeNO measurement have shown good reproducibility
within a subject, in particular for short time frames. For example, among approximately
300 young adults who performed offline collection, the intra-class correlation was 0.98 in
repeated samples.
44
Another offline study conducted in healthy children and adolescents
56

reported that the intraclass correlation ranged from 0.92-0.99.
38
Using the online method,
a study in healthy adults showed coefficients of variation were very low within a 10
minute period (5.1%, ri=0.95) and increased to 10.8% at 6 hours and 11.7% at 24 hours.
45

Another study in 33 healthy adults over a five-day period of repeated online
measurements, found that day-to-day coefficient of variation was 13.3 +/- 5.3% (range
4.6 - 23.9%), with an intraclass correlation of 0.84.
46

Collectively, the results from these studies indicate that both online and offline
measurements of FeNO are acceptable for large-scale epidemiologic studies in the field;
however, in the case of offline measurement, ambient NO and transport time to the
analytical facility should be considered.

Relationship of FeNO to Inflammatory Markers Collected from Bronchoalveolar
Lavage, Induced Sputum or Bronchial Biopsy
Measurement of airway inflammation previously required the use of invasive
methods such as bronchial biopsies, bronchoalveolar lavage (BAL), and induced
sputum.
29-31
Bronchial biopsy is the most invasive method which involves insertion of a
bronchoscope through the nose or mouth down into the airways where inflammatory cells
or tissue samples are collected. BAL also involves the insertion of a bronchoscope
thorough the mouth or nose into the lungs, but rather than collecting tissue at the site,
fluid is inserted and then recollected to sample the epithelial lining fluid (ELF), protein
composition of the airways, or inflammatory cells of the respiratory system primarily
from the lower respiratory tract (e.g. alveoli). For this reason, the fluid collected may not
adequately represent the inflammatory process that occurs in the airway walls. Induced
57

sputum is the third method commonly used to measure inflammation and it involves
inhaling an aerosolized saline over different time periods and then coughing up the
resulting sputum into a collection container. Common inflammatory markers assessed in
induced sputum include total cell counts, % eosinophils, %neurophils, %lymphocytes,
eosinophilic cationic protein (ECP), elastase, fibrinogen, albumin, interleukin-5 (IL-5)
and IL-8.
It has been shown that FeNO is modestly correlated with sputum eosinophils.
Barry et al. found a significant association between FeNO and sputum eosinophils in 566
adult asthma patients and also found that FeNO greater than 8.3 ppb at 250 mL/s flow
gave 71% sensitivity and 72% specificity for identifying clinically-significant esosinophil
counts greater than 3%.
47
Piacentini et al. found a significant correlation between FeNO
and sputum eosinophils but failed to find a significant correlation with sputum eosinophil
cationic protein (ECP).
48
Mattes et al. found a positive significant correlation in children
with asthma between FeNO and sputum ECP, as well as a positive correlation between
FeNO and sputum eosinophils, although the latter was not statistically significant.
49

Recently Schleich et al. found that FeNO greater than 41 ppb gave 65% sensitivity and
79% specificity for identifying sputum eosinophil count ≥3% and an FeNO threshold of
42 ppb was found to discriminate between eosinophilic and non-eosinophilic asthma.
50

FeNO has also been shown to correlate with inflammatory cells obtained from
bronchial biopsies and from BAL. In one study among children with asthma, there was a
statistically significant correlation between FeNO and eosinophils obtained by bronchial
biopsy.
51
In another study of 20 stable lung transplant patients and 20 healthy controls,
% neutrophils from BAL were significantly associated with FeNO.
52

58

In addition to samples collected from the respiratory tract, several studies have
investigated the association of FeNO with serum or peripheral blood markers to address
whether FeNO is a satisfactory method for assessing inflammation. Silvestri et al. found a
significant correlation between peripheral blood eosinophils and FeNO levels in allergic
asthmatic children.
53
Another study showed a higher correlation of FeNO levels with
peripheral blood eosinophils, and also showed significant association with IgE (r =.48, p
<0.0001) and serum ECP.
54
In contrast, Piacentini et al. failed to find a significant
correlation of FeNO with serum ECP.
48

Compared with BAL, induced sputum and bronchial biopsy, FeNO has multiple
advantages. It has been shown to be highly reproducible if the collection guidelines are
adhered to.
55
It is non-invasive and fairly easy to perform in the clinic as well as in large
population-based field studies, allowing real-time evaluation of airway inflammation.
Moreover, FeNO may be a more direct measure of proximal airway inflammation
because it provides an assessment of the state of the airway wall contraction and
relaxation of the smooth muscle.
56
Other methods such as BAL are likely to collect cells
from the lower respiratory tract or distal airways (e.g. alveoli). New methods of extended
FeNO have an even greater potential for distinguishing proximal airway inflammation
from distal airway inflammation. However, a discussion of obtaining FeNO at multiple
flow rates is beyond the scope of this review.

Determinants of FeNO
Many factors have been studied that influence FeNO levels. Here we briefly
review genetic, demographic, dietary and environmental determinants of FeNO.
59

Genetic Determinants
Candidate gene studies have primarily focused on the nitric oxide synthase (NOS)
pathway and have found inconsistent results. Wechsler et al.
57
found an association
between NOS1 genotype and FeNO in approximately 100 patients with asthma. In a
subset of this same cohort of participants with asthma, Storm van’s Gravesande et al.
58

found an association of a known missense mutation in NOS3 with level of FeNO. In
contrast, Leung et al. found that neither NOS1 or NOS3
59
nor NOS2
60
were associated
with level of FeNO in a cohort of Chinese children. Ali et al. also failed to confirm earlier
associations of NOS1 with FeNO in 87 healthy children.
61

In more than 2000 children participating in the Children’s Healthy Study (CHS),
Salam et al.
62
investigated the relationship of the NOS pathway genes (NOS1, NOS2, and
NOS3) as well as variants in arginases (encoded by ARG1 and ARG2) with FeNO. It was
found that variants in NOS2A and ARG2 were associated with FeNO level, with the
association of a NOS2A promoter haplotype varying by asthma status.
62

Further collaborative efforts, such as the EAGLE consortium, an ongoing
consortium of genome-wide association studies (GWAS) primarily in Europe, is likely to
lead to additional insight on the impact of genetic determinants on FeNO using a GWAS
meta-analysis approach.
Age, Sex, Race, and BMI
The effects of age, sex, race and body mass index (BMI) on FeNO have been
inconsistent.
25
Some studies have reported that FeNO levels in children are not
associated with age, gender, race/ethnicity, or BMI
63-66
while others have found
significant relationships with these predictors and FeNO.
38, 67
In the CHS, there were no
60

significant differences found between boys and girls, but compared to younger
participants, children over 9 years of age had significantly higher FeNO levels.
68
In
addition, Asian-American boys had significantly higher FeNO levels than children of all
other race/sex combination groups and Hispanics and African-Americans had slightly
higher levels of FeNO than non-Hispanic whites. Increasing weight-for-height was
associated with decreasing FeNO suggesting BMI might be an important determinant of
FeNO level.
68
A recently published study in China confirmed these preliminary findings
in a large-population based study of children: FeNO levels were estimated to decrease
1.5% per each kg/m
2
increase in BMI.
69

Diet and Infections
Dietary intake has been shown to affect FeNO levels. Increased FeNO levels have
been observed after eating nitrate-containing foods with a maximum effect after 2
hours.
70-71
Intake of water and/or of caffeine may lead to temporarily altered FeNO
levels.
72-73
Acute alcohol ingestion has been associated with reduced FeNO in individuals
with asthma and in healthy individuals.
74-75

Studies have shown upper and lower respiratory tract viral infections may lead to
increased FeNO.
76-77
Pneumonia has not been shown to be associated with increased
FeNO
78
but active tuberculosis has been associated with increased FeNO
79-80
and HIV
infection has been associated with decreased FeNO levels.
81

Personal Smoking and Secondhand Smoke Exposure
It is widely accepted that smoking acutely lowers FeNO levels in active smokers
with a strong relationship seen between FeNO level and number of cigarettes smoked.
82-
84
In addition, smoking cessation has been associated with increases in FeNO level.
85-86

61

The studies investigating the effects of smoking and asthma on FeNO have been
inconsistent. Some studies have shown patients with asthma who smoke also have
reduced FeNO levels
87
while others have shown these patients to have increased FeNO
levels.
88

Experimental studies suggest that secondhand smoke exposure (SHS) also
reduces FeNO levels
89
but observational studies have shown mixed results, especially in
children.
90-91
Perzanowksi et al.
92
found lower levels of FeNO among inner-city children
exposed to household SHS at age 7 years (β = -0.74, p < 0.001) but found higher levels of
FeNO among these 7-year-old children when considering their SHS exposure at age 4 (β
= 0.36, p = 0.015). Another recent study showed that both active smoking and SHS
exposure reduced FeNO levels.
84
In this study, both active smoking and SHS exposure of
at least two hours a day were associated with lower FeNO levels in both healthy
individuals and patients with asthma, independent of age, sex and height.
84

Air Pollution Exposure
In the past decade, several studies have examined the acute effects of ambient air
pollutants on FeNO. Nickmilder et al. found higher FeNO levels with higher ambient
ozone concentrations in a sample of 72 healthy children participating in summer camps in
Belgium.
93
Fischer et al. found that ambient levels of particulate matter less than 10
microns in diameter (PM
10
), blacksmoke, and NO from the previous day were
significantly related to FeNO levels, and nitrogen dioxide (NO
2
), carbon monoxide (CO)
and NO two days before FeNO measurement were significantly related to levels of FeNO
in a 7-week panel study of 68 children ages 10-11 living in an urban environment.
94

Delfino et al. found the strongest associations of FeNO with 2-day average personal
62

exposures to particulate matter less than 2.5 microns in diameter (PM
2.5
), elemental
carbon (EC), and NO
2
in a 10-day panel study of 45 children involving both personal and
ambient exposures.
95
Flamant-Hulin et al.
96
found that FeNO levels were significantly
elevated in a study of 104 children with and without asthma who were exposed to high
levels of formaldehyde, acetaldehyde, and PM
2.5
sampled at schools. In a 22-week panel
study of 158 children with asthma and 50 children without asthma, Barraza-Villarreal et
al. found that higher 8- hour average PM
2.5
was associated with increased FeNO.
97

Berhane et al. found that 8 day-lagged exposure to ambient PM
2.5
, 7 day-lagged exposure
to ambient PM
10
, and 23 day-lagged exposure to ambient ozone were significantly
associated with higher levels of FeNO in 2240 children participating in the CHS. The
effects of the ambient pollutants were higher in the warm season.
98

While the previous studies have reasonably established an important acute effects
role for ambient air pollution on FeNO, it is less clear whether there are chronic effects of
ambient pollution on FeNO. In a recently published paper, Eckel et al.
99
investigated
whether residential traffic proximity, a surrogate for chronic air pollution exposure, was
associated with level of FeNO, among 2143 children participating in the Children’s
Health Study. It was found that among children with asthma, those living in homes in
proximity to a greater density of roadways (operationalized as total length of roads within
a defined buffer size) had higher FeNO levels. The study confirmed earlier results by
Dales et al.
100
who found that roadway length within a 200 meter buffer was significantly
associated with increased FeNO in a cohort of more than 1600 children, indicating the
effects of chronic air pollution exposure may lead to chronic airway inflammation.

63

Relationship of FeNO with Childhood Respiratory Diseases
In 1993, Alving et al. first described elevated levels of FeNO among eight adults
with asthma.
101
Since then, numerous studies have confirmed these findings in both
children and adults
102-103
and further studies have shown that FeNO levels were decreased
when patients with asthma were treated with inhaled corticosteroids.
104
In this section, we
discuss several controversial issues related to FeNO and childhood respiratory diseases,
including relationships with inflammatory subtypes in asthma, the interrelationships of
asthma and atopy with FeNO, FeNO in clinical settings, and the future of FeNO in
epidemiologic research.
FeNO and Inflammatory Subtypes in Asthma
Results from these early studies lead to the hypothesis that FeNO is a non-
invasive biomarker of airway inflammation, in particular eosinophilic airway
inflammation, as studies (previously discussed) comparing FeNO to other methods of
assessing airway inflammation showed significant relationships FeNO and eosinophils,
but often not other inflammatory cell types, collected from BAL, bronchial biopsies and
induced sputum
48, 105

Given the rising interest in the role of neutrophils in asthma, several studies have
suggested that at least two phenotypes of asthma may exist in which each is characterized
by the dominance of eosinophils or neutrophils.
106-108
A recent study has suggested that
FeNO has the potential to distinguish four inflammatory subtypes of asthma—
esosinophilic, mixed granulocytic, neutrophilic, and paucigranulocytic—and that patterns
of inflammatory response in non-eosinophilic asthma may not be homogeneous.
109

Porsbjerg et al. found that while the highest levels of FeNO were observed in study
64

participants with the eosinophilic subtype, only subjects with the neutrophilic subtype
(non-eosinophilic) had significantly lower levels of FeNO, whereas the paucigranuloctyic
subtype (non-eosinophilic) had levels similar to those of the mixed granulocytic subtype
(eosinophilic), suggesting that the presence of eosinophils and neutrophils may not be the
driving force behind the inflammatory response.
109

Interrelationships of FeNO with Asthma and Atopy
A number of studies previously showed that FeNO is associated with atopy—the
tendency to mount an IgE-mediated immune response in the presence of an antigen—
even in the absence of asthma.
67, 110
A recent study conducted in the UK confirmed these
earlier results: Fourteen hundred participants from a birth cohort were reassessed at age
18 to determine interrelationships between asthma, FeNO and atopy. It was found that
atopy was associated with higher levels of FeNO; however, the level of FeNO was
similar among asthmatic participants with and without atopy. The highest levels were
observed among participants who were both atopic and had asthma. The authors
concluded that FeNO was a marker of atopy and allergic asthma rather than asthma
alone.
111

Another study conducted in the Netherlands reassessed 300 participants from a
birth cohort at age 21 and found that FeNO levels were higher among those with atopic
versus non-atopic asthma as well as those with eczema or allergic rhinitis (versus
without).
112
Similarly, in a study of 112 children with asthma, Silvestri et al. found that
FeNO was increased among the children with confirmed atopy, but was not elevated
among the children without atopy.
53
Welsh et al. found that asthma combined with atopic
dermatitis yielded higher levels of FeNO than asthma alone in a study of 644 children.
113

65

However, other studies in adults and children have concluded that FeNO is not
just a marker of atopic status.
114
Among a group of adult atopic patients with allergic
rhinitis but without a diagnosis of asthma, Prieto et al. found that FeNO was higher in
those patients with airway hyperresponsiveness or other signs suggestive of asthma such
as wheeze.
115
In a prospective study of 90 children in an outpatient hospital setting,
Hervas et al. found that that FeNO levels were higher among children with asthma than
among non-atopic children, atopic children without symptoms as well as children with
rhinitis without symptoms. Only atopic children with active rhinitis had higher levels of
FeNO than children with asthma.
116

In the CHS, it was found that children with asthma and no history of allergy (as a
surrogate for atopic status) as well as children with allergy and no asthma had higher
levels of FeNO than healthy children. Children with both asthma and allergy had
substantially higher levels of FeNO than healthy children.
68
Similarly, a study of 368
school children aged 8-10 years in the UK found that children categorized as atopic
asthmatic, non-atopic asthmatic, and atopic only by ISAAC questionnaire responses had
increased levels of FeNO compared to normal controls, with the highest levels seen in
children with both atopy and asthma.
117

FeNO in Clinical Settings
Despite associations of FeNO with asthma and allergic airway diseases, the role of
FeNO in clinical practice remains unclear.
118
A number of studies have supported the use
of FeNO in monitoring adherence to medication,
119
maintaining asthma control and
predicting relapse and asthma exacerbation.
120-123
Especially in the presence of
symptoms, elevated FeNO (e.g., above 35 ppb in steroid-naïve patients) has been shown
66

to be helpful in confirming an of asthma diagnosis.
124-125
FeNO has been shown to be
superior to spirometry in identifying children with asthma, particularly allergic mild
asthma,
126
but it has also been shown that FeNO in combination with spirometry
predicted asthma exacerbation better than FeNO or spirometry alone.
127

However, others have argued that the literature is inconsistent and that FeNO cannot
discriminate between asthma and atopy.
128
In addition, it has been argued that the costs
outweigh the benefits for use in clinical practice, especially because a single
measurement of FeNO can be influenced by many factors other than allergic symptoms,
medication adherence or exacerbation.
129
It remains to be seen whether advances in
multiple-flow FeNO measurement may help to resolve these uncertainties.
FeNO and Pulmonary Function
While the role of FeNO in asthma has been the subject of intense investigation,
118-119,
125, 130
its relationship with pulmonary function in children has been less extensively
studied. The studies that have investigated the relationship of FeNO with pulmonary
function in children with and without asthma have shown mixed results.
54, 92, 131-134
A
recently published study of 437 children with mild to moderate persistent asthma with
normal FEV
1
%predicted participating in two asthma clinical trials showed that FEF
25-75
% predicted and FEV
1
/FVC % predicted were negatively correlated FeNO.
134
Another
study showed a statistically significant association of FeNO with FEV
1
/FVC among 144
children with mild to moderate asthma, but did not find a significant association with
FEV
1
% predicted or FVC % predicted.
54

In contrast, Covar et al. showed no significant association of FEV
1
, FVC, or
FEV
1
/FVC with FeNO among 118 children with asthma in the Childhood Asthma
67

Management Program (CAMP) study.
131
Perzanowski et al. also found that FeNO was
not statistically significantly correlated with FVC %predicted, FEV
1
%predicted or
FEF
25-75
%predicted among a cohort of 89 inner city healthy children and children with
asthma and asthma-like symptoms recruited from Head Start centers.
92

Debley et al. investigated the relationship of pulmonary function with single breath
exhaled nitric oxide (SB-eNO) among a small cohort of 44 infants and very young
children with wheeze.
135
They found that SB-eNO was not associated with baseline
FVC, FEV
0.5
, FEF
25-75
, or FEF
75
, after adjustment for sex, eczema, family history of
asthma, and tobacco smoke exposure. However, higher baseline SB-eNO was associated
with decreases in FEV
0.5
, FEF
25-75,
and FEF
75
at 6 month follow-up after adjustment for
age at enrollment, sex, family history of asthma, history of eczema, sustained use of
inhaled corticosteroids during follow-up, and secondhand tobacco smoke exposure,
indicating that elevated FeNO might be more strongly associated with deficits in
pulmonary function growth rather than level alone.

The Future of FeNO
It remains largely unexplored as to whether FeNO could be useful as a non-
invasive marker of the inflammatory process during asthma pathogenesis or formation of
other airway diseases. The potential usefulness of FeNO in studies of asthma etiology is
illustrated by a recent Swedish prospective population-based study of healthy adults
without respiratory symptoms that found that baseline FeNO over the 90
th
percentile
predicted new-onset wheeze at four-year follow-up.
136
Further research is needed to
investigate whether FeNO could be used as a marker of early respiratory disease.
68

The jury is still out on widespread recommendation for the use of FeNO in asthma
clinical practice. Some have argued that the literature remains inconsistent with regard to
the clinical usefulness of FeNO
128
and that the costs of FeNO in clinical practice far
outweigh the benefits
129
while others have argued for the routine use of FeNO in clinical
practice.
125, 137
Unfortunately, the continued debate over the value of FeNO in clinical
practice has affected other fields, including population-based epidemiologic studies that
stand to greatly benefit from this non-invasive marker of airway inflammation that can be
taken into the field and provide additional mechanistic information beyond questionnaire
indices of health status. The utility of measurements of FeNO will likely depend on
context and a greater understanding of the potential of this biomarker outside of the
clinician’s office.

HRCT as a Measure of Airway Structural Changes for Use in Environmental
Epidemiologic Studies
In the first part of this chapter, we discussed advances in non-invasive methods of
assessing airway inflammation, namely exhaled nitric oxide (FeNO), that have been used
in the monitoring and management of childhood asthma. Developments in lung imaging
techniques have also made it feasible to non-invasively study lung diseases. In particular,
high resolution computed tomography (HRCT) scans have the spatial resolution required
to quantify airway remodeling and other aspects of small airways diseases. Unlike
conventional pulmonary function tests, HRCT improves phenotypic capabilities because
it can look at individual airways and determine specific sites of airway insult that may
lead to changes or deficits observed in standard pulmonary function testing.
5

69

As discussed in Chapter 2, airway remodeling is a key component of asthma
pathogenesis.
7
Thickened airway walls result from airway remodeling due to increases in
smooth muscle, increased presence of inflammatory cells, replacement of injured tissue
with connective tissue, as well as vascular changes.
138-140
HRCT studies have become
increasingly used to evaluate airway wall thickening in studies of patients with asthma
since the method is non-invasive and has been shown to be highly reproducible for
assessing individual airways.
141

In the following sections, we briefly describe volumetric reconstruction of the
lower respiratory system in HRCT studies; discuss metrics of airway size and structure
used in HRCT studies; evaluate the use of HRCT in studies of adults and children with
asthma; highlight the use of HRCT in studies investigating the effects of environmental
exposures on disease status; and discuss the plausibility for using imaging techniques,
such as HRCT, for investigating the impacts of environmental exposures in healthy
populations.

Structural Components of the Lower Respiratory Tract Visualized Through
Volumetric Reconstruction of HRCT Images
The lower respiratory tract includes the trachea, the main bronchi (large airways),
the secondary bronchioles (small airways), the tertiary bronchioles (smaller airways), the
terminal bronchioles (smallest airways), and finally, the alveolar ducts which are lined
with alveoli. The large and small airways are structurally comprised of the airway wall
and the airway lumen through which air passes. The alveoli—the structures responsible
70

for gas exchange—are encased in collagen and elastin fibers that provide a structural
framework of connective structures for the respiratory system.
142

While early attempts to understand lung structure used wood or plastic models or
foam sheets,
143-145
considerable progress has been made to visualize lung structure using
computer models based on three-dimensional volumetric reconstructions of HRCT image
slices.
146
Lengths of airways, spatial relationships, areas, and lung volumes are able to be
obtained based on volumetric reconstructions
146-147
and they allow for determining the
number and type of branching patterns of airway generations and of number of alveoli.
145,
148-149
HRCT has been shown to provide images of airways to less than 5 mm in
diameter
150
although at 2 mm and below, visualization of the small airways becomes
more difficult.
151
Three-dimensional reconstructions of HRCT scans provide a valuable
means to visualize the airways.

Airway Dimensions Obtained from HRCT Studies
While airway measurement methods are constantly evolving, there are several
measured and calculated dimensions of the airways that are commonly produced in
studies involving HRCT. Depending on the protocol used (e.g. hand tracing or automated
computer algorithm), a specified number (and size range) of airways are visualized in
cross-section and analyzed. In Figure 3.1, adapted from Kosciuch et al.,
152
the external
airway diameter (D) and airway luminal diameter (L) are measured and then the
remaining dimensions are calculated.
Wall thickness (WT) is calculated as the difference between external and luminal
diameter, divided by two, assuming that the wall thickness is constant across the airway
71

cross-section (WT=D-L/2). Airway wall thickness (AWT) is calculated as the ratio of
wall thickness to the external diameter (AWT=WT/D). Total airway area (A
O
) is defined
as the area inside the outer perimeter. The lumen area (A
L
) is defined as the area inside
the internal perimeter. The airway wall area (Aaw) is defined as the area between these
two perimeters (A
O
-A
L
). Wall area percentage (WA%) is calculated as A
O
-A
L
/A
O
x
100%.

Figure 3.1. Cross-section of airway with metrics of airway structure.
D=external airway diameter; L=airway lumen diameter; WT=wall thickness; A
o
=airway outer area;
A
L
=airway lumen area; Aaw=airway wall area. Figure adapted from Kosciuch et al.
152

Research Studies Using HRCT
HRCT Studies of Asthma and Pulmonary Function in Adults and Children
Early HRCT studies measured several airway dimensions indicative of airway
remodeling including airway wall thickness, airway wall area, and total airway area,
using a qualitative scoring or tracing approach by radiologists or trained radiologic
technicians. The majority of the early studies indicated that individuals with asthma have
72

thicker airways than healthy controls and the degree of airway thickness is further related
to disease severity, airflow obstruction and airway hyperreactivity.
153

Gono et al. showed that airway wall thickness and wall area were significantly
larger for 14 adult patients with asthma and airflow obstruction (FEV
1
<80% predicted)
compared with 10 adult patients with asthma and no airflow obstruction and 7 normal
controls.
154
Kasahara et al. found that wall area percentage and wall thickness were
higher among 49 patients with asthma than among 18 healthy controls and that the
metrics of airway wall thickness assessed were inversely associated with FEV
1
%
predicted in the patients with asthma.
155
Little et al. failed to find a significant
relationship between FEV
1
% predicted and airway wall thickness among patients with
asthma but they did find statistically significantly positive associations between wall area
percentage and asthma severity.
156
Similarly, Niimi et al. found that wall area was
significantly increased in 81 patients with mild, moderate, and severe asthma compared
with 28 healthy controls, with thicker airways in the more severe disease.
157

Most studies of asthma and airway wall thickening using HRCT have been
conducted in adults as HRCT involves the use of radiation; however, the few studies that
have been conducted in children have generally been consistent with the findings in
adults. Marchac et al.
158
showed that airway wall thickening (AWT) score—computed
based on the number of visible sections of bronchi—was larger among 27 children with
severe asthma than among 21 control children. However, there was no difference in
AWT score found between children with and without airway obstruction or between
children with severe and mild asthma. Similarly, Ketai et al.
159

found that wall thickness
was significantly larger in 21 children, aged 7 to 17 years, with moderate to severe
73

chronic asthma compared with similar sized airways in 19 control children, aged 7 to 16
years. However, they did not find a significant association of thickness score with
measures of airway obstruction.
Later developments in automated analytic approaches for assessing airway
dimensions have improved objectivity, reproducibility, and efficiency.
141
These advances
have eliminated possible biases of observer tracings by measuring all proximal airways
rather than only those traced by hand or were able to be analyzed by semi-automated
methods. Using fully automated methods, Aysola et al.
141
found that the 63 patients with
severe asthma on average had thickened airway walls compared to 35 with mild to
moderate asthma and 25 healthy controls. In addition, similar to the earlier findings of
Kasahara et al.,
155
Aysola et al. found that found that thicker airway walls were
associated with lower levels of FEV
1
at baseline among the patients with severe
asthma.
141

HRCT Studies in Healthy Individuals
While few HRCT studies have been conducted in healthy individuals alone (e.g.
not as a control group in a case-control study), Brown et al. have conducted studies in
healthy individuals primarily as a model of airway hyperresponsiveness in asthma. In one
study, Brown et al.
160
challenged five healthy participants with methacholine and asked
them to refine from taking deep breaths (to mimic effects in patients with asthma) while
concurrently conducting partial spirometry and measuring changes in luminal area via
HRCT. The group had previously shown that deep inspiration is protective against airway
narrowing in healthy subjects
161
and hypothesized that the effect of deep inspiration is a
key pathophysiologic mechanism behind airway hyperresponsiveness. They found that
74

the luminal area measurements made via HRCT were highly correlated with FEV
1
, FVC,
FEV
1
/FVC and MMEF and suggested that this method could be used further to
understand the mechanism of airway hyperresponsiveness.
HRCT Studies of Environmental Exposures on Health Outcomes
A number of studies have used HRCT methods to quantify effects of
environmental exposures on the disease status of affected individuals. Studies in
developing countries have used HRCT to quantify the effects of indoor biomass fuels on
respiratory symptoms. For example, Kara et al. found that women in Turkey with
exposure to biomass fuels had significantly greater parenchymal structural changes than
did women without exposure to indoor biomass fuels, and these structural changes
correlated with degree of symptoms of obstructive pulmonary disease.
162

Investigators have also used HRCT to quantify and diagnose asbestos-related
pulmonary disease as well as investigate smoking-related effects on pulmonary
outcomes.
163-168
HRCT parameters have also been used to predict the rate of FEV
1

decline in smokers leading to chronic obstructive pulmonary disease.
169

A growing body of evidence has confirmed that HRCT can distinguish structural
abnormalities among asymptomatic smokers and non-smokers. Remy-Jardin et al. found
that HRCT could detect structural differences in subpleural and parenchymal
micronodules and emphysema between 175 healthy current smokers, ex-smokers and
non-smokers with normal pulmonary function tests.
170
Soejima et al. showed that HRCT
could detect lung density abnormalities between non-smokers and smokers, with little
structural improvement after smoking cessation.
171
Among 36 asymptomatic current
smokers, former smokers and non-smokers, Spaggiari et al.
172
found that air trapping was
75

observed in 30.7% of current and former smokers and emphysema was found in 34.6% of
current and former smokers, while no areas of emphysema or air trapping were found
among the non-smokers, leading the authors to conclude that HRCT may be a useful tool
to aid in early diagnosis of smoking-related lung disease.
172

Given these findings, HRCT may have the potential to identify airway structural
differences between those exposed and unexposed to other environmental exposures,
such as ambient air pollution, as well as serve as a biomarker for early disease to assist in
primary prevention efforts.

Is There a Future for Imaging the Impacts of Environmental Exposures in Healthy
Populations?
A recent editorial by Tschumperlin
173
published in the New England Journal of
Medicine, suggests that eosinophilic airway inflammation may not be required for airway
remodeling to occur in asthma, but rather physical insults leading to bronchoconstriction
may be sufficient to cause airway remodeling. His invited comments were based on a
paper in the same issue by Grainge et al.
174
that found that bronchoconstriction alone led
to airway remodeling.
In the study by Grainge et al., mild asthmatic patients exposed to allergen had
eosinophilic airway inflammation and bronchoconstriction as well as manifestations of
airway remodeling including increased expression of transforming growth factor β (TGF-
β), greater subepithelial collagen thickness, epithelial mucus staining, and epithelial
proliferation.
174
Mild asthmatic patients exposed to methacholine also experienced
bronchoconstriction similar to the group challenged with allergen and had similar
76

manifestations of airway remodeling; however, they did not show evidence of
eosinophilic airway inflammation. Tschumperlin
173
felt the evidence from this study was
compelling enough to suggest that physical insults could potentially lead to airway
remodeling even among healthy individuals. These results imply that an alternative
mechanism may exist for the effects of the physical environment on respiratory health
independent of the inflammatory pathway. Tools such as HRCT scanning may have the
ability to test these hypotheses directly.

Summary
In this Chapter, we examined two non-invasive methods currently available to
assess aspects of respiratory disease in children and adults. We explored the biological
and epidemiologic determinants of FeNO, a biomarker of airway inflammation, as well as
examined its use in clinical and research settings. We provided an overview of studies
that have utilized HRCT as a method for visualizing airway remodeling and structural
airway abnormalities.
We determined that further research is needed to investigate whether FeNO and
HRCT could be used to identify early disease and/or to address whether these biomarkers
could be used to differentiate the effects of environmental impacts among exposed
individuals.

77

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172. Spaggiari E, Zompatori M, Verduri A, et al. Early smoking-induced lung lesions
in asymptomatic subjects. Correlations between high resolution dynamic CT and
pulmonary function testing. Radiol Med 2005;109:27-39.

173. Tschumperlin DJ. Physical forces and airway remodeling in asthma. N Engl J
Med 2011;364:2058-9.

174. Grainge CL, Lau LC, Ward JA, et al. Effect of bronchoconstriction on airway
remodeling in asthma. N Engl J Med 2011;364:2006-15.

91

CHAPTER 4: RATIONALE, SPECIFIC AIMS, METHODS, AND STUDY
PROCEDURES
Specific Aims and Theoretical Model

As demonstrated in the previous chapters, childhood respiratory diseases are an
important public health problem. While advances have been made in identifying multiple
exposures and host factors involved in asthma etiology and childhood respiratory
diseases, more research is needed to identify the mechanisms and relevant susceptibility
factors influencing these diseases in order to improve treatment and ultimately to lead to
preventive strategies.
Among the many exposures and factors investigated in asthma and airway
diseases include genetic factors, allergens and environmental exposures. Ambient air
pollution is an exposure that has been extensively investigated with regard to asthma
exacerbation and pulmonary function.
1
Evidence from the Southern California Children’s
Health Study (CHS) has documented associations between exposure to ambient air
pollution and traffic and risk of new onset asthma;
2-4
however, decreasing trends air
pollution levels have raised uncertainty about these conclusions.
Understanding the biological processes that mediate the effects of environmental
exposures and host factors on childhood airway diseases may help resolve these
uncertainties. A number of oxidant pollutants are present in ambient air pollution
including ozone, nitric oxide, nitrogen dioxide and reactive oxygen and nitrogen species.
While the respiratory system has a variety of host defenses to combat these oxidants,
these defenses are sometimes inadequate to control their effects, possibly leading to
92

oxidative and nitrosative stress and eventually to chronic airway inflammation, airway
structural changes including airway remodeling, and impaired respiratory function.
5-6

This dissertation examines these mechanistic processes that may mediate the
effects of environmental exposures and host factors on childhood respiratory diseases,
using advances in non-invasive technologies to assess airway inflammation and airway
structural changes.
The Specific Aims (SA) of this research are:
Specific Aim 1:
SA1a: To investigate whether children with elevated airway inflammation, as
indicated by elevated exhaled nitric oxide levels (FeNO), are at increased risk of new-
onset asthma. (Manuscript 1)
SA1b: To investigate whether the risk of new-onset asthma associated with
elevated airway inflammation varies by child’s allergic status or by parental history of
asthma. (Manuscript 1)
Specific Aim 2:
SA2a: To investigate whether children with elevated airway inflammation, as
indicated by elevated exhaled nitric oxide levels (FeNO), have deficits in small airways
flows. (Manuscript 2)
SA2b: To investigate whether the relationship between airway inflammation, as
indicated by elevated exhaled nitric oxide levels (FeNO), and pulmonary function flows
varies by child’s asthma status. (Manuscript 2)

93

Specific Aim 3:
SA3a: To investigate whether deficits in pulmonary function assessed by
spirometry are associated with anatomic changes in airway structure, size or thickness
assessed by high resolution computed tomography (HRCT) scan in adults. (Manuscript 3)
SA3b: To investigate whether associations of pulmonary function assessed by
spirometry with anatomic changes in airway structure, size or thickness assessed by
HRCT vary by history of physician-diagnosed asthma. (Manuscript 3)
SA3c: To investigate whether chronic air pollution exposure during childhood is
associated with anatomic changes in airway structure, size or thickness assessed by
HRCT in adults with maximum attained lung growth. (Manuscript 3)

Figure 4.1. Theoretical model of the effects of airway inflammation on childhood respiratory
diseases, using exhaled nitric oxide (FeNO) as a marker of airway inflammation and HRCT as a
non-invasive technique to assess airway remodeling.

94

Methods and Procedures
Study Populations and Design: Children’s Health Study Overview
The Southern California Children’s Health Study (CHS) is a 17-year prospective
school-based cohort study designed to assess the long-term effects of air pollution on the
respiratory health of children.
7-8
Approximately 3000 4
th
, 7
th
, and 10
th
grade public school
children (cohorts “C”, “B”, and “A”, respectively) were enrolled 1993 from 12 Southern
California communities, selected to maximize the diversity of air pollution exposures. An
additional 3000 4
th
grade children were enrolled in 1996 (cohort “D”). Annual
questionnaires were collected from participants to assess subjects’ residential and medical
status. This included demographic and household information, asthma status, wheezing
and allergy histories, and other health information. Buccal cells were collected and stored
for genotyping. Annual pulmonary function testing was conducted in school, from study
entry through high school graduation, using low-resistance rolling-seal

spirometers
(Spiroflow Model 132; P.K. Morgan Ltd., Gillingham, UK) connected

to personal
computers. Test performance was based on American Thoracic Society guidelines,
9

modified for children. Subjects’ height and weight were measured at each annual testing.
Additional details of the pulmonary testing protocol have been previously published.
10

A few years after high school graduation, approximately 900 former CHS subjects
from cohorts A-D living within 500 miles of Los Angeles participated in a follow-up
study investigating whether previously identified air pollution-related deficits in
pulmonary function growth persisted beyond age 18. These study participants had
pulmonary function re-measured using portable pressure-transducer-based spirometers
95

(Screenstar Spirometers, Morgan Scientific, Haverhill, MA) and completed a follow-up
questionnaire containing residential, occupational, and other exposures for the time
period since age 18. The follow-up study included adults who were still living in their
childhood CHS community of residence as well as those who had moved to different
communities.
In 2002, approximately 5500 kindergarten and 1
st
grade children (cohort “E”)
were enrolled in a new cohort in 13 Southern California communities, with a key aim of
assessing whether air pollution contributes to the incidence of asthma and respiratory
diseases. The study design and annual procedures were similar to those of cohorts A-D.
Parents completed an annual self-administered questionnaire (that included socio-
demographic and child’s health characteristics and a brief exposure history, including
exposure to secondhand tobacco smoke and in utero exposure to maternal smoking) until
children were old enough to reliably compete the annual questionnaires themselves in
school. Buccal cells were collected and stored for genotyping. Eligible children without a
physician diagnosis of asthma at study entry (and approximately 200 children with a
physician diagnosis of asthma for comparison with other studies) performed annual
exhaled nitric oxide (FeNO) testing beginning in 2004. Children performed annual
pulmonary function testing when they reached 4
th
or 5
th
grade.
The number of actively-followed members of cohort E was reduced substantially
in 2006 due to a decrease in funding. The study concentrated on 8 instead of the
originally-enrolled 13 communities, with all but one of the communities inside of the Los
Angeles Basin. In order to augment the cohort with a larger within-community gradient
96

in traffic exposure, approximately 300 additional participants were added in the reduced
8 community schools in 2007 (unofficially called cohort E+).
Study Population for Manuscript 1. During the 2004–2005 school year, FeNO
testing was performed by 2585 subjects out of 3146 eligible cohort E members without a
physician diagnosis of asthma (82%). We further excluded from analysis children whose
parent reported a physician diagnosis of asthma during the school year of FeNO testing
(n=62); children who were lost to follow-up during the year after FeNO testing (n=241);
and children whose breath samples were invalidated due to storage or technical problems
with the analyzer (n=76); resulting in 2206 final analytic subjects for Manuscript 1.
Study Population for Manuscript 2. During the 2007-2008 school year, concurrent
FeNO and pulmonary function testing were performed by 76% of 2064 active cohort E
members, resulting in 1560 final analytic subjects for Manuscript 2. The majority of
study participants for Manuscript 2 (1288) were enrolled in cohort E during 2002–2003
when they were in kindergarten or first grade (average 5–6 years old). An additional 272
participants for Manuscript 2 were from an augmentation of the cohort enrolled in 2007-
2008.
Study Population for Manuscript 3. During 2003-2004 home-based study visits
for the adult follow-up study of 900 former CHS participants from cohorts A-D,
participants were given a brochure describing the high resolution computed tomography
(HRCT) study of air pollution and lung structure. Forty former CHS participants had CT
scans performed at USC University Hospital on a GE LightSpeed Pro16 (GE Medical
Systems, Milwaukee, WI) multi-detector row CT scanner within one year of the follow-
up pulmonary function testing (mean lag time between PFT and CT scan=121 days;
97

range 7-308 days). HRCT study entry criteria required that the participants were over 18
years of age and live within 200 miles of Los Angeles. Eleven of the participants’ HRCT
scan data were not able to be analyzed because of incorrect specifications entered in the
scanner by the radiology technician performing the scan. The remaining scans were
analyzed using EmphylxJ software, a graphics-based lung analysis program for
quantitative analysis of lung CT scans, resulting in 29 final analytic subjects for
Manuscript 3.

Study Procedures: Assessing Indices of Health Status
Ascertaining Incident Asthma Cases.
For Manuscript 1, the primary outcome is new onset (or incident) asthma. At
study entry, selected aspects of the child’s and parents’ medical histories were collected,
and in each successive school year, an update questionnaire inquiring about the child’s
intervening year of health was completed by parents and returned to study staff. An
incident asthma case was defined as a child with no prior parental report of a physician
diagnosis of asthma at FeNO testing whose parent reported a physician diagnosis of
asthma in an annual follow-up questionnaire during the three-year follow-up period for
Manuscript 1.
Assessing Pulmonary Function.
The primary outcome assessed in Manuscript 2 is pulmonary function. During the
school year, each cohort E community was visited at least twice in different seasons, to
minimize confounding of location and season effects. Health status at testing was
evaluated by questionnaire and subjects with symptoms of acute respiratory infection
98

within the previous 3 days were excluded or rescheduled. Pulmonary function was
measured using pressure-transducer-based spirometers (Screenstar Spirometers, Morgan
Scientific, Haverhill, MA) by trained field technicians who traveled to study schools to
perform maximal effort spirometric testing of the children. We assessed forced expiratory
volume during the first second (FEV
1
), forced vital capacity (FVC), and average
expiratory flow over the middle half of FVC (FEF
25-75%
, also known as maximal mid-
expiratory flow, or MMEF) from a series of lung function testing maneuvers performed
on each child as stipulated by American Thoracic Society recommendations.
In Manuscript 3, pulmonary function was measured using portable commercially-
available ScreenStar/ComPAS spirometers (Morgan Scientific, Haverhill, MA) by trained
field technicians who traveled to the homes of follow-up study participants to perform
maximal effort spirometric testing. These spirometers were chosen because they were
small and easily transported to the homes of participants for testing. The testing protocol
remained largely unchanged from the original CHS study
7-8
and we assessed FEV
1
, FVC,
and MMEF from a series of lung function testing maneuvers performed according to
American Thoracic Society recommendations.
Assessing Airway Structure with HRCT.
CT scans were performed at USC University Hospital on a GE LightSpeed Pro16
(GE Medical Systems, Milwaukee, WI) multi-detector row CT scanner. Inspiratory CT
scans were acquired at suspended full inspiration with the following parameters: 120
peak kilovoltage (kVp), 80 tube current (mA), 1.25-mm collimation, slice thickness
1.25 mm, 0.5 seconds/rotation, pitch 1 and reconstructed using both a low spatial
frequency reconstruction (Standard) and an edge enhancing (Lung) kernel. In addition, 4
99

expiratory CT images were obtained at Residual Volume (RV-after maximal expiration).
Female participants were required to provide a urine sample immediately prior to the CT
scan to verify that they were not pregnant.
De-identified images were archived in Digital Imaging and Communications in
Medicine (DICOM) 3.0 format and transferred to the University of British Columbia
(UBC) for analysis. CT scans were analyzed using EmphylxJ software, a graphics-based
lung analysis program for quantitative analysis of lung CT scans. Briefly, all airways that
were cut in cross section on CT were analyzed using the full-width at half maximum
method. This technique produces measurements of airway lumen area, wall area, and the
percentage of the total airway cross sectional area that is wall (WA%).

Study Procedures: Assessing Exposures
Measurement of Exhaled Nitric Oxide (FeNO)
For Manuscript 1, offline breath collection was performed in the morning at
schools to avoid traffic-related peaks of ambient NO and possible effects of recent eating
on FeNO, according to American Thoracic Society recommendations
11-12
and subjects
with symptoms of acute respiratory infection within the past 3 days were excluded or
rescheduled. Exhaled breath samples for offline testing were obtained using Bag
Collection and Sampling Kits and 1.5-l aluminized Mylar bags (Sievers Division, GE
Analytical Instruments, Boulder, CO) by the deadspace-discard method at 100 ml/sec
expiratory flow, and were stored in temperature-controlled coolers and transported to and
analyzed at a central laboratory (Sievers Model 280i chemiluminescent NO analyzer).
Indoor air samples were also collected to estimate subjects’ ambient NO exposure at
100

testing. Lag times between collection and analysis ranged from 2 to 26 hours. Offline
FeNO values were then converted to online FeNO values for all children, as would be
measured at 50 ml/sec expiratory flow,
12
using a prediction model (model adjusted
R
2
=0.94) determined in a later substudy of 362 children with concurrent online and
offline FeNO measurements.
13
The substudy included 1 or 2 testing days at each of 15
schools in 8 communities to cover most of the geographic and seasonal range. Online
measurements were performed at 50 ml/sec expiratory flow using EcoMedics CLD-88-
SP analyzers, with DeNOx accessories to provide NO-free inhaled air (EcoPhysics Inc.,
Ann Arbor, MI/Duernten, Switzerland), according to the manufacturer’s instructions
based on professional societies' recommendations.
11-12, 14
The prediction model included
adjustment for ambient NO concentration and lag time between collection and analysis.
We used predicted online FeNO in subsequent analyses for Manuscript 1.
The protocol for exhaled breath collection was similar for Manuscript 2; however,
beginning in the 2006-2007 school year, online FeNO was collected from all children in
the field. Online FeNO measurements were performed at standard 50 ml/sec expiratory
flow using EcoMedics CLD-88-SP analyzers, with DeNOx accessories to provide NO-
free inhaled air (EcoPhysics Inc., Ann Arbor, MI/Duernten, Switzerland), according to
established guidelines.
11-12, 14
Additional details of breath collection and FeNO analysis
used in this study were reported previously.
13, 15-16

Measurement of Air Pollution Exposure
One of the major strengths of the CHS has been its characterization of ambient air
pollution in Southern California over the past two decades. Details of the CHS air
pollution exposure assessment methods for cohorts A-D have been reported previously.
7,
101

17
In brief, ambient air pollution data were obtained from regional air monitoring stations
located in each of the twelve study communities. Several regional pollutants were
measured, including particulate matter less than 10 microns in diameter (PM
10
) and less
than 2.5 microns in diameter (PM
2.5
), nitrogen dioxide (NO
2
), ozone (O
3
), elemental
carbon (EC), and acid vapor. Annual averages of the 24-hour PM
10
and NO
2
averages
were computed, as were annual averages of 2-week samples of PM
2.5
, EC, and acid vapor
averages. For O
3
, the computed annual average was restricted to include only data
collected between 10:00 A.M. and 6:00 P.M. The summary measure of each regional
pollutant level used in analyses for Manuscript 3 is the mean of each pollutant’s annual
averages from 1994 to 2005, in each community.
While regional pollution derived from community-specific air monitoring stations
has been a consistent metric for the CHS methods for air pollution exposure assessment,
exposure to traffic and within-community variability in air pollution exposures has been
the focus of the air pollution exposure assessment methods for cohort E. Two separate
intra-community exposure campaigns were undertaken at the homes of many cohort E
study participants to assess the within-community variability to O
3
, NO
2
, and NO as well
as to assess three size cuts of PM (PM
0.20,
PM
2.5
, PM
10
) and PM components. Although
measurements were not made at all cohort members’ homes, models based on these
measurements were developed for the whole cohort. These models have the potential to
provide considerably greater estimates of air pollution exposure for cohort E children
than were possible with the relatively limited community-based estimates available for
cohorts A-D. While these models are beyond the scope of this dissertation, this rich
102

resource is likely to greatly enhance the future directions of the work initiated in the three
studies in this dissertation.
Assessment of Covariate Information, Exposure History and Other Health Information
For Manuscripts 1 and 2, parents completed an annual self-administered
questionnaire that included socio-demographic and child’s health characteristics and a
brief exposure history, including exposure to secondhand tobacco smoke and in utero
exposure to maternal smoking.
For Manuscript 3, in addition to the annual questionnaires that were collected
from participants (and their parents) during their participation in the CHS before age 18
(cohorts A-D) that assessed demographic and household information, asthma status,
wheezing and allergy status, and other health status, participants in the adult follow-up
study completed a follow-up questionnaire containing residential, occupational, and other
exposures for the time period since age 18.

Study Procedures: Ascertaining Informed Consent
For Manuscripts 1 and 2, informed consent from a parent/guardian and assent
from each child were obtained by trained field staff before FeNO testing and lung
function testing, using procedures approved by the USC’s Institutional Review Board
(IRB). For Manuscript 3, informed consent from participants was obtained by trained
field staff before HRCT scan and lung function testing, using procedures approved by the
USC’s IRB and the USC Radiation Safety Committee.

103

Statistical Methods
Manuscript 1 Methods
In order to investigate the relationship of FeNO with new-onset asthma, we
calculated incidence rates and conducted descriptive analyses, and we explored a series
of multivariate modeling approaches to account for potential confounders and
heterogeneity of effects within subgroups of children. Crude incidence rates for new-
onset asthma were calculated by dividing the number of cases by the total person-years at
risk. For children who developed new-onset asthma, follow-up was considered complete
at the time of reported diagnosis. Incidence rates were calculated for age-specific
quartiles of FeNO. We have previously shown that FeNO varies with age.
15
Therefore,
age-specific FeNO quartiles were computed for each of three age strata (defined as less
than 8years, 8 to 9 years, greater than 9 years) for analyses.
To further investigate the association between FeNO and new-onset asthma, we
fitted Cox proportional hazards models with sex- and age-specific baseline hazards (with
age defined as integer age at FeNO testing). All models were adjusted for community of
residence and race/ethnicity to account for the study design, and we assessed potential
confounders identified a priori including parental education, annual family income,
health insurance, parental history of asthma, BMI, household pets or pests, humidifier
use, average outdoor air pollution levels on the day of the FeNO measurement (NO
2
, O
3
,
PM
2.5
, PM
10
), lifetime secondhand smoke (SHS) exposure, and in utero exposure to
maternal smoking. Covariates were considered confounders if the hazard ratio changed
by 10% after addition to the base model. The final model was additionally adjusted for
lifetime history of wheezing. Heterogeneity of associations among subgroups was
104

assessed by fitting models with appropriate interaction terms, and statistical significance
was tested by partial likelihood ratio tests.
18
Stratified analyses were performed in the
presence of significant interaction (p-value< 0.05). The nature of the nonlinearity in
FeNO effects was explored using splines, piecewise cubic polynomials that are joined
smoothly at a number of breakpoints known as knots.
19

Sensitivity analyses were conducted by limiting the asthma case definition to
those (1) reporting a diagnosis greater than one year after FeNO testing; (2) without a
history of respiratory allergy; and (3) reporting use of inhaled medication in the diagnosis
year follow-up questionnaire. To explore the role of wheezing prior to the onset of
asthma, we restricted the analysis to children without a history of ever wheezing and 12
months prior to FeNO testing.
All analyses for Manuscript 1 were conducted using SAS software (SAS Institute,
Cary, NC) version 9.1. All hypothesis testing was conducted assuming a 0.05
significance level and a two-sided alternative hypothesis.
Manuscript 2 Methods
Multiple linear regression models were fitted to examine the association between
FeNO and pulmonary function. Outcome measures analyzed in this investigation include
FEV
1
, FVC, MMEF, and the ratio of FEV
1
to FVC. We used a log-transformation of
each pulmonary function measure in order to satisfy the assumptions of linear regression.
We examined FeNO as a continuous variable and determined that the dose-response
relationship of FeNO with pulmonary function was not best explained as a linear
function. Therefore, we examined the relationship of quartiles of FeNO with log-
transformed pulmonary function.
105

Included in all of our models were design variables or known determinants of
pulmonary function including height, age, sex, body-mass index (BMI, the weight in
kilograms divided by the square of the height in meters), race/ethnicity, recent respiratory
infections and indicators for community of residence and field technician. We examined
several possible confounding variables, including parental education, income, health
insurance, asthma status, asthma medication usage, family history of asthma, lifetime
secondhand smoke (SHS) exposure, and in utero exposure to maternal smoking. A
change of 10% or greater in the FeNO effect estimates was used as the criterion for
confounding. Interaction terms were added to the model to test for effect modification by
sex and asthma status, and subsequent stratified analyses were used to further explore
relationships when an interaction was significant. Trend tests were conducted by using an
ordinal variable for FeNO quartiles.
All analyses for Manuscript 2 were conducted using SAS software (SAS Institute,
Cary, NC) version 9.2. All hypothesis testing was conducted assuming a 0.05
significance level and a two-sided alternative hypothesis.
Manuscript 3 Methods
We conducted descriptive analyses of sex differences in pulmonary function and
of anatomic airway measures and tested these differences by t-test of equality of means.
We investigated three measures of pulmonary function: FEV
1
, FVC, and MMEF. We
investigated three measures of airway dimensions from the volumetric reconstructions of
the HRCT scans: wall area percent (WA%), average total airway area and average lumen
area. We used Pearson correlations to assess the association of the three pulmonary
function metrics and three airway dimensions.
106

In order to first investigate the relationship between regional long-term average
air pollution (PM
10,
PM
2.5
, NO
2
, O
3
, EC and acid vapor) and the three airway
dimensions, we used Pearson correlations. We used multiple linear regression to further
investigate the association between air pollution and the three measures of airway
structure. We assessed the impact of smoking history and race on these associations, in
addition to height and age at HRCT scan, sex, and history of physician-diagnosed asthma.
To aid in interpretation of the associations, each pollutant was scaled to its interquartile
range.
All analyses for Manuscript 3 were conducted using SAS software (SAS Institute,
Cary, NC) version 9.2. All hypothesis testing was conducted assuming a 0.05
significance level and a two-sided alternative hypothesis.

107

Chapter 4 References
1. Brunekreef B. Health effects of air pollution observed in cohort studies in Europe.
J Expo Sci Environ Epidemiol 2007;17 Suppl 2:S61-5.

2. McConnell R, Berhane K, Gilliland F, et al. Asthma in exercising children
exposed to ozone: a cohort study. Lancet 2002;359:386-91.

3. McConnell R, Islam T, Shankardass K, et al. Childhood incident asthma and
traffic-related air pollution at home and school. Environ Health Perspect 2010;118:1021-
6.

4. Jerrett M, Shankardass K, Berhane K, et al. Traffic-related air pollution and
asthma onset in children: a prospective cohort study with individual exposure
measurement. Environ Health Perspect 2008;116:1433-8.

5. van der Vliet A, Cross CE. Oxidants, nitrosants, and the lung. Am J Med
2000;109:398-421.

6. Gilliland FD, McConnell R, Peters J, Gong H, Jr. A theoretical basis for
investigating ambient air pollution and children's respiratory health. Environ Health
Perspect 1999;107 Suppl 3:403-7.

7. Peters JM, Avol E, Gauderman WJ, et al. A study of twelve Southern California
communities with differing levels and types of air pollution. II. Effects on pulmonary
function. Am J Respir Crit Care Med 1999;159:768-75.

8. Peters JM, Avol E, Navidi W, et al. A study of twelve Southern California
communities with differing levels and types of air pollution. I. Prevalence of respiratory
morbidity. Am J Respir Crit Care Med 1999;159:760-7.

9. Standardization of spirometry--1987 update. Statement of the American Thoracic
Society. Am Rev Respir Dis 1987;136:1285-98.

10. Peters J, Avol E, NAVIDI W, et al. A Study of Twelve Southern California
Communities with Differing Levels and Types of Air Pollution I. Prevalence of
Respiratory Morbidity. American Journal of Respiratory and Critical Care Medicine
1999;159:760-7.

11. Recommendations for standardized procedures for the on-line and off-line
measurement of exhaled lower respiratory nitric oxide and nasal nitric oxide in adults and
children-1999. This official statement of the American Thoracic Society was adopted by
the ATS Board of Directors, July 1999. Am J Respir Crit Care Med 1999;160:2104-17.
108

12. Berry MA, Shaw DE, Green RH, Brightling CE, Wardlaw AJ, Pavord ID. The use
of exhaled nitric oxide concentration to identify eosinophilic airway inflammation: an
observational study in adults with asthma. Clin Exp Allergy 2005;35:1175-9.

13. Linn WS, Berhane KT, Rappaport EB, Bastain TM, Avol EL, Gilliland FD.
Relationships of online exhaled, offline exhaled, and ambient nitric oxide in an
epidemiologic survey of schoolchildren. J Expo Sci Environ Epidemiol 2008;19:674-81.

14. Baraldi E, de Jongste JC. Measurement of exhaled nitric oxide in children, 2001.
Eur Respir J 2002;20:223-37.

15. Linn WS, Rappaport EB, Berhane KT, Bastain TM, Avol EL, Gilliland FD.
Exhaled nitric oxide in a population-based study of southern California schoolchildren.
Respir Res 2009;10.

16. Linn WS, Rappaport EB, Berhane KT, Bastain TM, Salam MT, Gilliland FD.
Extended exhaled nitric oxide analysis in field surveys of schoolchildren: a pilot test.
Pediatr Pulmonol 2009;44:1033-42.

17. Peters JM, Avol E, Navidi W, et al. A study of twelve Southern California
communities with differing levels and types of air pollution. I. Prevalence of respiratory
morbidity. Am J Respir Crit Care Med 1999;159:760-7.

18. Cox. Regression models and life tables. J Royal Statistical Soc 1972;B:187-202.

19. Hastie TJ, Tibshirani RJ. Generalized Additive Models. New York: Chapman and
Hall; 1990.

109
CHAPTER 5: EXHALED NITRIC OXIDE, SUSCEPTIBILITY AND NEW-
ONSET ASTHMA IN THE CHILDREN’S HEALTH STUDY
1
(MANUSCRIPT 1)

Chapter 5 Abstract
A substantial body of evidence suggests an etiologic role of inflammation and
oxidative/nitrosative stress in asthma pathogenesis. Fractional concentration of nitric
oxide in exhaled air (FeNO) may provide a non-invasive marker of oxidative/nitrosative
stress and aspects of airway inflammation. We examined whether children with elevated
FeNO are at increased risk for new-onset asthma. We prospectively followed 2206
asthma-free children (age 7–10 years) who participated in the Children’s Health Study.
We measured FeNO and followed these children for three years to ascertain incident
asthma cases. Cox proportional hazard models were fitted to examine the association
between FeNO and new-onset asthma. We found that FeNO was associated with
increased risk of new-onset asthma. Children with the highest quartile of FeNO had more
than a two-fold increased risk of new-onset asthma compared to those with the lowest
quartile (hazard ratio: 2.1; 95% confidence interval: 1.3-3.5). This effect did not vary by
child’s history of respiratory allergic symptoms. However, the effect of elevated FeNO
on new-onset asthma was most apparent among those without a parental history of
asthma. Our results indicate that children with elevated FeNO are at increased risk for
new-onset asthma, especially if they have no parental history of asthma.

110
Introduction
Asthma is the most common childhood chronic disease and studies have
documented its rise in prevalence over the past several decades.
2
Although the etiology of
asthma has been extensively studied, the pathogenesis and the factors causing the rapid
rise in prevalence have yet to be firmly established. To reduce the burden from asthma,
more research that focuses on asthma pathogenesis is needed. The current understanding
of the pathogenesis of asthma suggests that oxidative and nitrosative stress and
dysregulated inflammatory responses play a role in asthma etiology.
3-4
Fractional exhaled
nitric oxide concentration (FeNO) provides a non-invasive marker of oxidative and
nitrosative stress and aspects of airway inflammation that may have a role in childhood
asthma and allergic airway disease pathogenesis.
5-6
The potential usefulness of FeNO in
studies of asthma etiology is illustrated by a recent Swedish population-based study of
healthy adults without respiratory symptoms that found that elevated FeNO predicted the
development of wheeze.
7

The Children’s Health Study (CHS), a longitudinal population-based study of
respiratory health among school-age children in 13 Southern California communities,
provided an opportunity to investigate whether children with elevated FeNO are at
increased risk of new-onset asthma. We also hypothesized that the elevated risk of new-
onset asthma associated with FeNO differs by child’s allergic status and by parental
history of asthma. We examined the association of new-onset asthma with FeNO using
data collected annually from 2004–2007 from a cohort of 2206 children whose parents
did not report a physician diagnosis of asthma at study entry.

111
Methods
Study Subjects
Participants were from a CHS cohort enrolled during 2002–2003 when they were
in kindergarten or first grade (average 5–6 years old). Informed consent from a
parent/guardian and assent from each child were obtained before FeNO testing. The
University of Southern California’s Institutional Review Board approved the protocol.
Parents completed an annual self-administered questionnaire that included socio-
demographic and child’s health characteristics and a brief exposure history, including
exposure to secondhand tobacco smoke and in utero exposure to maternal smoking.
During 2004–2005, parental consent was obtained and FeNO testing was performed on
2585 subjects out of 3146 eligible cohort members (82%). We further excluded children
whose parent reported a physician diagnosis of asthma during the school year of FeNO
testing (n=62); children who were lost to follow-up during the year after FeNO testing
(n=241); and children whose breath samples were invalidated due to storage or technical
problems with the analyzer (n=76); resulting in 2206 subjects.
New-Onset Asthma Definition
An incident asthma case was defined as a child with no prior parental report of a
physician diagnosis of asthma at FeNO testing whose parent reported a physician
diagnosis of asthma in an annual follow-up questionnaire during the three-year follow-up
period.
Socio-demographic and Medical History Information
Race/ethnicity was defined as non-Hispanic white, Hispanic, African American,
Asian/Hawaiian/Pacific Islander, and mixed/other ethnicities, based on parental report.
Education was defined as the highest level of education attainment of the parent or

112
guardian who completed the questionnaire. Annual household income was used to assess
the role of socioeconomic status. We dichotomized self-reported health insurance
coverage to assess the role of access to health care.
At study entry, selected aspects of the child’s and parents’ medical histories were
collected, and in each successive school year, an update questionnaire inquiring about the
child’s intervening year of health was completed by parents and returned to study staff.
Child’s history of respiratory allergy included any hay fever or allergic rhinitis. Child’s
history of ever wheezing and wheezing in the past 12 months were defined as yes/no.
Parental history of asthma was defined as an asthma diagnosis in either biological parent.
During annual school visits, subjects’ height and weight were measured using
standardized protocols.
FeNO Collection and Analysis
Details of breath collection and FeNO analysis used in this study were reported
previously.
8-9
In brief, offline breath collection was performed in the morning at schools
to avoid traffic-related peaks of ambient NO and possible effects of recent eating on
FeNO, according to American Thoracic Society recommendations.
10-11
Each CHS
community was visited at least twice in different seasons, to minimize confounding of
location and season effects. Health status at testing was evaluated by questionnaire;
subjects with symptoms of acute respiratory infection within the past 3 days were
excluded or rescheduled. Exhaled breath samples for offline testing were obtained using
Bag Collection and Sampling Kits and 1.5-l aluminized Mylar bags (Sievers Division,
GE Analytical Instruments, Boulder, CO) by the deadspace-discard method at 100
ml/sec expiratory flow, and were stored in temperature-controlled coolers and transported

113
to and analyzed at a central laboratory (Sievers Model 280i chemiluminescent NO
analyzer). Indoor air samples were also collected to estimate subjects’ ambient NO
exposure at testing. Lag times between collection and analysis ranged from 2 to 26 hours.
When this study began, offline collection was the most feasible method of collection for
large field studies. Excellent agreement has been demonstrated between offline and
online measurements in laboratory-based studies, using a variety of techniques.
12-14
In
this study, offline FeNO values were converted to online FeNO values for all children, as
would be measured at 50 ml/sec expiratory flow,
11
using a prediction model (model
adjusted R
2
=0.94) determined in a later substudy of 362 children with concurrent online
and offline FeNO measurements.
8
The substudy included 1 or 2 testing days at each of
15 schools in 8 communities to cover most of the geographic and seasonal range. Online
measurements were performed at 50 ml/sec expiratory flow using EcoMedics CLD-88-
SP analyzers, with DeNOx accessories to provide NO-free inhaled air (EcoPhysics Inc.,
Ann Arbor, MI/Duernten, Switzerland), according to the manufacturer’s instructions
based on professional societies' recommendations.
5, 10-11
The prediction model included
adjustment for ambient NO concentration and lag time between collection and analysis.
We used predicted online FeNO (hereafter, FeNO) in subsequent analyses.
Statistical Methods
In order to investigate the relationship of FeNO with new-onset asthma, we
calculated incidence rates and conducted descriptive analyses, and we explored a series
of multivariate modeling approaches to account for potential confounders and
heterogeneity of effects within subgroups of children. Crude incidence rates for new-
onset asthma were calculated by dividing the number of cases by the total person-years at
risk. For children who developed new-onset asthma, follow-up was considered complete

114
at the time of reported diagnosis. Incidence rates were calculated for age-specific
quartiles of FeNO. We have previously shown that FeNO varies with age.
9
Therefore,
age-specific FeNO quartiles were computed for each of three age strata (defined as less
than 8 years, 8 to 9 years, greater than 9 years) for analyses (see Table 6.1 in the online
supplement, Chapter 6).
To further investigate the association between FeNO and new-onset asthma, we
fitted Cox proportional hazards models with sex- and age-specific baseline hazards (with
age defined as integer age at FeNO testing). All models were adjusted for community of
residence and race/ethnicity to account for the study design, and we assessed potential
confounders identified a priori including parental education, annual family income,
health insurance, parental history of asthma, BMI, household pets or pests, humidifier
use, average outdoor air pollution levels on the day of the FeNO measurement (NO
2
, O
3
,
PM
2.5
, PM
10
), lifetime secondhand smoke (SHS) exposure, and in utero exposure to
maternal smoking. Covariates were considered confounders if the hazard ratio changed
by 10% after addition to the base model. The final model was additionally adjusted for
lifetime history of wheezing. Heterogeneity of associations among subgroups was
assessed by fitting models with appropriate interaction terms, and statistical significance
was tested by partial likelihood ratio tests.
15
Stratified analyses were performed in the
presence of significant interaction (p-value< 0.05). The nature of the nonlinearity in
FeNO effects was explored using splines, piecewise cubic polynomials that are joined
smoothly at a number of breakpoints known as knots.
16

Sensitivity analyses were conducted by limiting the asthma case definition to
those (1) reporting a diagnosis greater than one year after FeNO testing; (2) without a

115
history of respiratory allergy; and (3) reporting use of inhaled medication in the diagnosis
year follow-up questionnaire. To explore the role of wheezing prior to the onset of
asthma, we restricted the analysis to children without a history of ever wheezing and 12
months prior to FeNO testing.
All analyses were conducted using SAS software (SAS Institute, Cary, NC)
version 9.1. All hypothesis testing was conducted assuming a 0.05 significance level and
a two-sided alternative hypothesis.
Results
Study Population and Cohort Follow-up
Descriptive analyses are presented in Table 5.1. There were approximately equal
numbers of boys and girls, and sex was not associated with new-onset asthma. Nearly
50% of children were 8–9 years old at initial FeNO measurement. The population was
ethnically diverse: 55% of the participants were Hispanic white. This was largely a
middle-class population: the majority of children lived in households earning more than
$50,000 per year, had health insurance, and had parents with at least some college
education. None of these characteristics was associated with new-onset asthma.

116

Table 5.1. Subject Characteristics and Associations with New-Onset Asthma
Subject Characteristic* N=2206 %
Hazard
Ratio† 95% CI
Female 1155 52% 1.01 0.71-1.44
Age at FeNO testing

<8 years 699 32% 0.77 0.48-1.25
8-9 years 1064 48% 0.74 0.48-1.16
>9 years 443 20% 1

Race/Ethnicity

Non-Hispanic White 788 36% 1

Hispanic 1212 55% 0.92 0.60-1.41
African American 36 2% 1.82 0.62-5.31
Asian/Hawaiian/ Pacific Islander 67 3% 0.64 0.20-2.09
Other 101 5% 1.30 0.61-2.78
Allergic Status at Study Entry

Never allergy 1104 50% 1

Former hayfever or allergic rhinitis 543 25% 1.65 1.05-2.60
Current hayfever or allergic rhinitis 557 25% 2.38 1.57-3.60
History of Wheeze

No lifetime history of wheeze 1598 73% 1

Lifetime wheeze 604 27% 4.85 3.38-6.96
No wheeze 12 months prior to study entry 1983 95% 1

Wheeze 12 months prior to study entry 110 5% 4.95 3.12-7.86
Any Family History of Asthma 317 16% 1.99 1.32-3.01
Exposure to Secondhand Smoke (SHS) 206 11% 0.99 0.55-1.80
In Utero Exposure to Maternal Smoking 131 6% 0.87 0.40-1.90
Annual Household Income

<$15,000 268 14% 1.29 0.69-2.40
$15,000-$49,999 594 32% 1.04 0.64-1.69
>=$50,000 1003 54% 1

No Health Insurance 260 13% 1.14 0.64-2.03

Parent Education Level

< 12
th
grade
425 21%
0.92 0.43-1.99
12
th
grade
368 18%
0.73 0.35-1.53
Some college
754 36%
1.04 0.58-1.87
College
284 14%
0.86 0.43-1.74
Some graduate
242 12%
1

BMI Percentile Category

Underweight (<5
th
percentile) 54 2% 2.05 0.82-5.14
Normal weight (5
th
to <85
th
percentile) 1371 62% 1

Overweight/Obese ( ≥85
th
percentile) 781 35% 1.44 0.99-2.08
*Numbers may not equal 2206

†Adjusted for race/ethnicity and community with baseline strata for age and gender

117
We ascertained 129 cases of new-onset asthma over a three-year period of follow-
up (69 females, 60 males). The overall crude incidence rate was 22.2 per 1000 person-
years (pyrs) (see Table 6.2 in the online supplement, Chapter 6, for rates by sex,
ethnicity, and other select characteristics). The overall mean and median follow-up times
were 2.59 and 2.93 years, respectively, and about 25% of the participants were lost prior
to the end of follow-up. The proportion of possible follow-up time did not vary
substantially by sex, ethnicity, or quartile of FeNO; however, there were small but
significant differences in loss to follow-up rates with respect to parental education, family
income, and child’s health insurance coverage (data not shown). Based on telephone
interviews conducted in the CHS with the families of subjects who left the study schools,
loss to follow-up was primarily due to family moves out of the school catchment area
related to a change in employment.
17

Selected Health and Exposure Characteristics and Risk of New-Onset Asthma
Fifty percent of the participants reported having no history of respiratory allergy
at study entry; the remaining 50% were split evenly between past history of respiratory
allergy (history of hay fever or allergic rhinitis but no current symptoms) and current
respiratory allergy (symptoms within the previous 12 months) (Table 5.1). Children with
any history of respiratory allergy showed an increased risk of new-onset asthma relative
to the never-allergy group. Any history of wheezing and wheezing in the 12 months prior
to FeNO testing were present in 27% and 5% in the participants, respectively, and were
associated with a nearly five-fold increased risk of new-onset asthma. Sixteen percent
reported a parental history of asthma which was associated with a two-fold increased risk
of new-onset asthma in the child.

118
Sixty-two percent of study participants had normal body mass index (BMI) at
baseline. Neither underweight (<5
th
percentile) nor overweight/obese (≥85
th
percentile)
was significantly associated with increased risk of new-onset asthma. Neither lifetime
secondhand smoke exposure nor in utero exposure to maternal smoking was associated
with risk of new-onset asthma. Prevalence of SHS exposure and in utero exposure to
maternal smoking in this population was lower than in previous CHS cohorts
18
(11% and
6%, respectively).
Distribution of FeNO
As we previously reported,
9
FeNO followed an approximately log normal
distribution (mean: 13.6 ppb; median: 10.1 ppb; standard deviation: 12.0 ppb; range: 2.3-
132.4 ppb) (Figure 5.1). The median concentrations of FeNO and ranges (at baseline)
among subjects who developed new-onset asthma was 10.9 ppb (range: 3.2-132.4) versus
10.1 ppb (range: 2.3-107.2) among subjects who did not develop asthma.

Figure 5.1. Distribution of Exhaled Nitric Oxide at Baseline.

119
FeNO and Risk of New-Onset Asthma
Elevated FeNO was associated with an increased risk of new-onset asthma (Table
5.2 and Figure 5.2). Children with FeNO in the highest quartile at the start of follow-up
had more than a two-fold increased risk of incident asthma compared to those with FeNO
in the lowest quartile (HR: 2.11; 95% CI: 1.26-3.51), after adjusting for race/ethnicity,
community of residence, and lifetime history of wheeze. We observed an increasing trend
of asthma risk with increasing quartiles of FeNO (p trend <0.01). The association of new-
onset asthma with FeNO was not substantially affected by adjustment for parental
education, family income, health insurance, family history of asthma, household pets or
pests, humidifier use, BMI, daily average air pollution levels (on the day of FeNO
measurement) secondhand smoke exposure, or in utero exposure to maternal smoking
(data not shown).

Table 5.2. Association of Exhaled Nitric Oxide (FeNO) with New-Onset Asthma in the Children's
Health Study
Age-Specific
Quartiles of FeNO
at Baseline
New-onset
Asthma No Asthma
(N= 129) % (N= 2077) % HR
1
95% CI
Quartile 1 24 19% 528 25% 1
Quartile 2 30 23% 521 25% 1.53 0.89-2.63
Quartile 3 30 23% 523 25% 1.68 0.97-2.90
Quartile 4 45 35% 505 24% 2.11 1.26-3.51
p
trend
<0.01
1
HR=Hazard ratio, adjusted for race/ethnicity, lifetime wheeze and community with baseline strata for
age and gender

120

Figure 5.2. Hazard Ratio Function of the Effect of Exhaled NO on New Onset Asthma.
The model was fit for subjects with FeNO <= 40 ppb (approximately 95% of subjects) due
to sparseness of data above 40 ppb.

To assess the role of past wheezing, we adjusted the risk estimates for wheezing
history and conducted analyses among children without any history of wheezing.
Adjustment for any history of wheezing changed the risk estimates by slightly more than
10%. In sensitivity analyses restricting the cohort to children without a history of
wheezing (n=1602) or without wheezing in the 12 months prior to FeNO measurement
(n=2096), we found a similar increased risk of new-onset asthma for those with the
highest quartile of FeNO (Table 5.3).
To assess the effect of asthma case definition, we conducted analyses restricting
cases to those with recent inhaled medication use. We found that the estimates for the
effects of FeNO on new-onset asthma were larger (over four-fold risk comparing the
highest to the lowest quartile of FeNO, Tables 5.4 and 6.3). To investigate the
contribution of delayed asthma diagnosis, we restricted the analysis to follow-up starting

121
in the second year and found that eliminating the first year of follow-up did not
substantially alter the relative risk estimates for FeNO (Table 5.4).
FeNO and New-Onset Asthma by Allergy Status and Parental History of Asthma
In contrast to our hypothesis, we did not find evidence that the effect of FeNO on
new-onset asthma depended on respiratory allergy status (Table 5.5). The pattern of
increasing risk of new-onset asthma with increasing FeNO was observed in children with
and without a reported history of respiratory allergy.
The effect of FeNO on new-onset asthma differed among children with and
without a parental history of asthma (Table 5.6 and Figure 6.1), p interaction<0.05. The
observed increasing risk of asthma development was most apparent among children
without a parental history of asthma. Compared to children with the lowest quartile of
FeNO and no parental history of asthma, children with the highest quartile of FeNO and
no parental history of asthma had more than a three-fold increased risk of new-onset
asthma (HR: 3.18, 95% CI: 1.66, 6.08). This pattern of increasing risk with higher levels
of FeNO was observed among children with either maternal or paternal asthma (data not
shown). Although parental history of asthma was directly associated with elevated risk of
new-onset asthma, we observed little evidence for association of increasing FeNO with
increasing risk of new-onset asthma in children with a parental history of asthma
(although power was limited by sample size and number of cases in this group).
In further analyses, the association of FeNO with new-onset asthma did not differ
in boys and girls, children of different ethnicity, those exposed to secondhand smoke, or
those exposed in utero to maternal smoking (data not shown).

122

123

124

125

126
Discussion
We found that children with higher FeNO had a substantial risk of incident
asthma compared to children with low FeNO. The use of FeNO in asthma clinical
practice has been extensively investigated.
19
While the role of FeNO in clinical practice
remains unclear,
20
a number of studies have supported the use of FeNO in monitoring
adherence to medication,
21
maintaining asthma control and predicting relapse.
22-23

Especially in the presence of symptoms, elevated FeNO (e.g., above 35 ppb in steroid-
naïve patients) has been supportive of asthma diagnosis.
19, 24
However, to our
knowledge, this is the first investigation to demonstrate the predictive value of FeNO for
identifying children at risk for developing asthma, thereby extending the utility of this
marker beyond monitoring medication adherence, predicting asthma exacerbations or
verifying a diagnosis.
A number of studies have identified a subgroup of individuals with elevated
FeNO without asthma or asthma-related symptoms.
25-28
In a cross-sectional study of 13-
14-year-old schoolchildren, Nordvall et al.
28
suggested that a small subset of participants
with elevated levels of FeNO who reported no symptoms of asthma in the ISAAC
questionnaire
29
may represent “early asthma.” Sivan et al.
30
compared the use of FeNO,
spirometry, and induced sputum for eosinophil count in consecutive school-age children
referred for evaluation of possible asthma. The sensitivity, specificity, and positive and
negative predictive values for best cutoff points of FeNO (19 parts per billion) were all
above 80% and were very similar to those for sputum eosinophil count, suggesting that
FeNO testing is about as effective as sputum induction in aiding the diagnosis of
childhood asthma.

127
In a population-based prospective study of adults who were free of asthma or
wheeze at study entry, Olin et al.
7
found that baseline FeNO over the 90
th
percentile
predicted new-onset wheeze at four-year follow-up among adults. Because their study
was underpowered to investigate new-onset asthma, the authors used new-onset wheeze
as a surrogate and early marker of asthma. Regardless of wheeze history, elevated FeNO
was predictive of new-onset asthma in our population of schoolchildren.
It is widely accepted that genetic factors account for a significant proportion of
allergy and asthma occurrence.
31-32
In this study, we found nearly a two-fold increased
risk of new-onset asthma in children with a parental history of asthma; however, the size
of that risk did not vary significantly by quartiles of FeNO. The effect of elevated FeNO
on new-onset asthma was more marked among children without a parental history of
asthma. We previously reported that 23% of the parent cohort indicated a parental history
of asthma at cohort establishment
33
(two years before FeNO measurement). The
prevalence of parental asthma in the current study was 16%, likely due to the exclusion of
prevalent asthma cases and cases diagnosed in the two years before FeNO measurement.
Moreover, the proportion of cases with a parental history of asthma was quite similar by
child’s history of allergy; 29% of allergic and 24% of non-allergic cases had a parental
history of asthma (data not shown).
While our analysis is based on small numbers, the absence of an increased risk in
children with higher FeNO and a parental history of asthma [relative to children with
lower FeNO and a parental history of asthma] may indicate that the new-onset asthma
associated with FeNO is not mediated by the same pathways that account for the asthma
in children with a parental history of asthma. Alternatively, our study may demonstrate

128
that beyond the age of 5 to 8 years, the impact of parental history on the development of
asthma may be reduced.
Elevated inflammation and oxidative/nitrosative stress could also arise from
exposure and/or susceptibility to environmental stressors, such as secondhand smoke or
ambient air pollution. We have previously reported that the effects of air pollution on
asthma risk may differ in children with and without a parental history of asthma. In a
CHS cohort recruited in the 1990s, we showed that children who exercised heavily in
high-ozone environments were at increased risk of new-onset asthma especially in the
absence of a parental history of asthma.
34
We also demonstrated that traffic-related
pollution was associated with a two-fold increased risk of lifetime asthma in children
without a parental history of asthma.
33
Taken together, our results and previous findings
support an etiologic role for inflammation in asthma pathogenesis. Further research is
needed to determine whether pro-inflammatory environmental stressors may help to
explain why we see the largest effects of FeNO in children without a parental history of
asthma.
A body of evidence indicates that FeNO is elevated in allergic airway disease
5-6

and studies have shown FeNO to be elevated in healthy atopic children.
35
We did not find
evidence that the effect of FeNO depended on the child’s allergy status. Children with
and without a history of respiratory allergy showed similar patterns of increasing risk of
new-onset asthma by increasing quartiles of FeNO.
While our results suggest that the relationship of FeNO with new-onset asthma
may be independent of the allergic pathway, it is important to note that we used parent-
report of hayfever or allergic rhinitis as a measure of children’s respiratory allergy status,

129
which may result in measurement error of true atopic status. However, measurement error
is not likely to explain our entire findings as significant residual confounding by atopic
status would likely occur only if the true associations between atopy and FeNO and
between atopy and new onset asthma are both very strong (e.g. RRs=10.0).
36
The strong
relationships of self-reported measures of allergy with FeNO and asthma provide
evidence that measurement error of atopic status is not likely to explain our findings.
The incidence rate of physician-diagnosed asthma in the present study (22.2 per
1000 pyrs) was higher than rates reported in earlier periods.
37
However, in recent
decades, rates are more comparable, likely reflecting the increasing occurrence of
childhood asthma.
38-39
The incidence rate in the present study is in line with earlier CHS
cohorts of approximately the same age (17.8 per 1000 pyrs).
18
By restricting our case
definition to children without any history of wheezing, the incidence rate remained
substantial (11.1 per 1000 pyrs).
A recognized limitation of our study is our reliance on self-report of physician-
diagnosed asthma for defining eligibility and for case ascertainment. However, physician
diagnosis of asthma has been widely accepted as a valid method of classifying asthma
status in large epidemiologic studies.
40-41
In a subset of a previous CHS cohort, we
independently verified self-reported physician-diagnosed asthma through a review of
medical records and found that more than 95% of the children with a self-reported
diagnosis had either a definite or probable asthma diagnosis noted on the medical
record.
42

Another potential influence on asthma diagnosis is differential access to care and
differences in medical practice. We found that adjustment for various indicators of

130
socioeconomic status did not change our results, and we found that by restricting our
analysis to cases using recent inhaled medication or by restricting to cases categorized as
moderate or severe resulted in stronger risk estimates at each FeNO quartile; therefore,
any bias that might arise from differences in medical practice is likely to attenuate the
risk estimate toward the null. Children who reported use of inhaled medication after
diagnosis could represent children with more bronchial inflammation before asthma onset
and more severe asthma at diagnosis.
While this cohort was initially established when the participants were young (ages
5–6 years on average), we cannot definitively state that a new asthma diagnosis
represents a true incident case. Misclassification of asthma status at cohort entry is not
likely to explain our findings, as excluding cases reporting a diagnosis in the first year of
follow-up did not substantially alter the relative risk estimates. To further limit the
inclusion of possible undiagnosed asthma, we restricted our analysis to children without a
history of wheeze or without wheeze in the twelve months prior to FeNO measurement.
The results remained consistent with the highest risk of new-onset asthma occurring in
children with the highest quartile of FeNO.

Conclusions
Our results suggest that FeNO is a marker of risk for the development of asthma
especially among children without a parental history of asthma. FeNO may be valuable in
developing primary prevention strategies.

131
Chapter 5 Acknowledgements
We are indebted to the school principals, teachers, students and parents in each of
the 13 study communities for their cooperation and especially to the members of the
health testing field team for their efforts. This work was supported by the Southern
California Environmental Health Sciences Center (grant # 5P30ES007048) funded by the
National Institute of Environmental Health Sciences, the Children’s Environmental
Health Center (grant #s 5P01ES009581, R826708-01 and RD831861-01) funded by the
National Institute of Environmental Health Sciences and the Environmental Protection
Agency, the National Institute of Environmental Health Sciences (grant #
5P01ES011627) the National Heart, Lung and Blood Institute (grant #s 5R01HL061768
and 5R01HL076647) and the Hastings Foundation.

132
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25. Ludviksdottir D, Janson C, Hogman M, Hedenstrom H, Bjornsson E, Boman G.
Exhaled nitric oxide and its relationship to airway responsiveness and atopy in asthma.
BHR-Study Group. Respir Med 1999;93:552-6.

26. Henriksen AH, Lingaas-Holmen T, Sue-Chu M, Bjermer L. Combined use of
exhaled nitric oxide and airway hyperresponsiveness in characterizing asthma in a large
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134

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Exhaled nitric oxide in a population-based study of asthma and allergy in schoolchildren.
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30. Sivan Y, Gadish T, Fireman E, Soferman R. The use of exhaled nitric oxide in the
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31. Moffatt MF, Cookson WO. Gene identification in asthma and allergy. Int Arch
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32. Liu T, Valdez R, Yoon PW, Crocker D, Moonesinghe R, Khoury MJ. The
association between family history of asthma and the prevalence of asthma among US
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33. McConnell R, Berhane K, Yao L, et al. Traffic, susceptibility, and childhood
asthma. Environ Health Perspect 2006;114:766-72.

34. McConnell R, Berhane K, Gilliland F, et al. Asthma in exercising children
exposed to ozone: a cohort study. Lancet 2002;359:386-91.

35. Franklin PJ, Taplin R, Stick SM. A community study of exhaled nitric oxide in
healthy children. Am J Respir Crit Care Med 1999;159:69-73.

36. Kelsey JL, Thompson WD, Evans AS. Methods in Observational Epidemiology.
New York: Oxford University Press; 1986.

37. Dodge RR, Burrows B. The prevalence and incidence of asthma and asthma-like
symptoms in a general population sample. Am Rev Respir Dis 1980;122:567-75.

38. Basagana X, Sunyer J, Zock JP, et al. Incidence of asthma and its determinants
among adults in Spain. Am J Respir Crit Care Med 2001;164:1133-7.

39. Strachan DP, Butland BK, Anderson HR. Incidence and prognosis of asthma and
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135
40. Worldwide variation in prevalence of symptoms of asthma, allergic
rhinoconjunctivitis, and atopic eczema: ISAAC. The International Study of Asthma and
Allergies in Childhood (ISAAC) Steering Committee. Lancet 1998;351:1225-32.

41. Burr ML. Diagnosing asthma by questionnaire in epidemiological surveys. Clin
Exp Allergy 1992;22:509-10.

42. Salam MT, Li YF, Langholz B, Gilliland FD. Early-life environmental risk
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2004;112:760-5.

135

CHAPTER 6: EXHALED NITRIC OXIDE, SUSCEPTIBILITY AND NEW-
ONSET ASTHMA IN THE CHILDREN’S HEALTH STUDY (MANUSCRIPT 1)
ONLINE METHODS AND DATA SUPPLEMENT
Statistical Methods
In order to investigate the relationship of FeNO with new-onset asthma, we
calculated incidence rates of new-onset asthma, conducted descriptive analyses of
exposure variables with new-onset asthma, and explored a series of multivariate
modeling approaches to account for potential confounders and heterogeneity of effects
within subgroups of children. Crude incidence rates for new-onset asthma were
calculated by dividing the number of cases by the total person-years at risk. For children
who developed new-onset asthma, follow-up was considered complete at the time of
reported diagnosis.
Incidence rates were calculated for age-specific quartiles of FeNO. We have
previously shown that FeNO varies with age.
1
Therefore, three age strata were defined
with cutpoints at 8 and 9 years and these age strata were used to define FeNO quartiles
for analyses.
To further investigate the association between FeNO and new-onset asthma with
adjustment for potential confounders, we fitted Cox proportional hazards models with
sex- and age-specific baseline hazards (with age defined as integer age at FeNO testing).
All models were adjusted for community of residence and race/ethnicity to account for
the study design and we assessed potential confounders identified a priori including
parental education, family income, health insurance, family history of asthma, BMI,
household pets or pests, humidifier use, lifetime secondhand tobacco smoke exposure, or
136

in utero exposure to maternal smoking. Covariates were considered to be confounders if
the hazard ratio changed by 10% after addition to a base model (that included adjustment
for community and race/ethnicity). The final model was additionally adjusted for
lifetime history of wheezing with stratified baseline hazards for sex and age.
Heterogeneity of associations among subgroups was assessed by fitting models with
appropriate interaction terms, and statistical significance was tested by partial likelihood
ratio tests.
2
Stratified analyses were performed in the presence of significant interaction
(p-value< 0.05). The nature of the nonlinearity in FeNO effects was explored using
splines, piecewise cubic polynomials that are joined smoothly at a number of breakpoints
known as knots.
3

Sensitivity analyses were conducted by limiting the asthma case definition to
those (1) reporting a physician diagnosis more than one year after FeNO testing; (2)
without a history of allergy; and (3) reporting use of inhaled medication within the
previous twelve months. To explore the role of wheezing prior to the onset of asthma, we
restricted to children without a history of wheezing and without wheezing in the twelve
months prior to FeNO testing.
All analyses were conducted using SAS software (SAS Institute, Cary, NC)
version 9.1. All hypothesis testing was conducted assuming a 0.05 significance level and
a two-sided alternative hypothesis.
137

Statistical Methods for Fitting Spline-based Cox Regression Models

Description of Figure 5.2
The Cox regression model with one-knot quadratic regression spline term for the
effect of FeNO as depicted in Figure 5.2 has the following form:
( ) ( ) ) ( exp ) (
3
2
1 2
2
1 0 , 0 ,
T
gender age gender age
X Knot eNO eNO eNO t t ⋅ + − ⋅ + ⋅ + ⋅ ⋅ =
+
β β β β λ λ
, where X denoted a set of the confounders and age and gender-specific baseline
hazard was used. Note that the knot was selected based on a grid search that
considered multiple knots and varying locations. The depicted smooth curve for the
hazard ratio as function of FeNO is based on
( ) ) ( exp
2
1 2
2
1 0
+
− ⋅ + ⋅ + ⋅ Knot eNO eNO eNO β β β
, which describes the effect of baseline FeNO on the hazard of new-onset asthma,
given other factors are the same. The entire cohort was used in fitting the above
regression model. Figure 5.2 depicts the estimated overall hazard ratio for asthma
incidence as a function of exhaled NO in the study cohort.

Description of Figure 6.1
The quadratic regression spline-based Cox regression model for Figure 6.1 has the
following form:
( ) ( )
( ) )
( exp ) (
7 6
2
0 0 5
2
0 4 0 3
2
1 1 2
2
1 1 1 0 , 0 ,
T
famhx famhx famhx famhx
famhx famhx famhx famhx gender age gender age
X famhx Knot eNO eNO eNO
Knot eNO eNO eNO t t
⋅ + ⋅ + − ⋅ + ⋅ + ⋅ +
− ⋅ + ⋅ + ⋅ ⋅ =
+
= = = =
+
= = = =
β β β β β
β β β λ λ

, where X denotes a set of confounders, “famhx” represents an indicator of family
history of asthma, incorporating age and gender-specific baseline hazard. The primary
statistics of interest are family history of asthma-specific hazard ratios
( ) ) ( exp
6
2
1 1 2
2
1 1 1 0
β β β β + − ⋅ + ⋅ + ⋅
+
= = = = famhx famhx famhx famhx
Knot eNO eNO eNO for subjects
with family history of asthma
and
( ) ) exp(
2
0 0 5
2
0 4 0 3
+
= = = =
− ⋅ + ⋅ + ⋅
famhx famhx famhx famhx
Knot eNO eNO eNO β β β for subjects
without family history of asthma
138

, which describe the effect of baseline FeNO on the hazard new-onset asthma, given
other factors are the same, in both subjects with family history of asthma and those
without family history of asthma. Figure 6.1, therefore, describes the estimated family
history of asthma specific hazard ratio for asthma incidence as a function of exhaled
NO in the study cohort. The two curves in this plot are comparable because the main
effect of family history of asthma was considered in the modeling framework.

Figure 6.1. The estimated family history of asthma specific hazard ratio for asthma
incidence as a function of exhaled NO in the study cohort.
139

140

Table 6.2. Incidence Rates of Asthma Among Study Participants

Asthma Cases
Person-Years
(pyrs)
Incidence
Rate (per
1000 pyrs)
95% CI
All Children 129 5802.6 22.2
Sex

Female 69 3051.5 22.6 17.9-28.6
Male 60 2751.1 21.8 16.9-28.1
Race/Ethnicity

White 47 2141.9 21.9 16.5-29.2
Hispanic 67 3150.1 21.3 16.7-27.0
African American 4 80.4 49.7 18.7-132.5
Asian 3 161.3 18.6 6.0-57.7
Other 8 262.6 30.5 15.2-60.9
Family History of Asthma

No 86 4388.3 19.6 15.9-24.2
Yes 34 869.0 39.1 28.0-54.8
Age at Study Entry

<8 years 40 1863.9 21.5 15.7-29.3
8 to 9 years 58 2818.5 20.6 15.9-26.6
>9 years 31 1120.2 27.7 19.5-39.4
Age-specific Quartile of FeNO

FeNO quartile 1 24 1501.3 16.0 10.7-23.8
FeNO quartile 2 30 1438.4 20.9 14.6-29.8
FeNO quartile 3 30 1454.5 20.6 14.4-29.5
FeNO quartile 4 45 1408.3 32.0 23.9-42.8

141

Table 6.2 continued

Asthma Cases
Person-
Years
(pyrs)
Incidence
Rate
(per 1000
pyrs)
95% CI

Children Without History of
Wheezing 48 4310.2 11.1
Sex

Female 29 2289.6 12.7 8.8-18.2
Male 19 2020.6 9.4 6.0-14.7
Race/Ethnicity

White 18 1517.3 11.9 7.5-18.8
Hispanic 23 2412.9 9.5 6.3-14.3
African American 4 57.6 69.5 26.1-185.1
Asian 2 135.3 14.8 3.7-59.1
Other 1 180.9 5.5 0.8-39.3
Family History of Asthma

No 33 3356.1 9.8 7.0-13.8
Yes 12 532.8 22.5 12.8-39.7
Age at Study Entry

<8 years 13 1365.8 9.5 5.5-16.4
8 to 9 years 25 2117.4 11.8 8.0-17.5
>9 years 10 827.1 12.1 6.5-22.5
Age-specific Quartile of FeNO

FeNO quartile 1 8 1065.4 7.5 3.8-15.0
FeNO quartile 2 16 1119.5 14.3 8.8-23.3
FeNO quartile 3 8 1144.0 7.0 3.5-14.0
FeNO quartile 4 16 981.3 16.3 10.0-26.6

142

143

Chapter 6 References

1. Linn WS, Rappaport EB, Berhane KT, Bastain TM, Avol EL, Gilliland FD.
Exhaled nitric oxide in a population-based study of southern California schoolchildren.
Respir Res 2009;10:28.

2. Cox. Regression models and life tables. J Royal Statistical Soc 1972;B:187-202.

3. Hastie TJ, Tibshirani RJ. Generalized Additive Models. New York: Chapman and
Hall; 1990.

144

CHAPTER 7: EXHALED NITRIC OXIDE AND PULMONARY FUNCTION IN
THE CHILDREN’S HEALTH STUDY (MANUSCRIPT 2)

Chapter 7 Abstract
Understanding the role of airway inflammation in pulmonary function
development may be important for childhood airway diseases. We examined the
relationship between exhaled nitric oxide (FeNO), a non-invasive biomarker for airway
inflammation, and pulmonary function among 1560 schoolchildren (246 with asthma)
from the Southern California Children’s Health Study. Pulmonary function was measured
by trained field technicians using ScreenStar (pressure-transducer-based) spirometers.
We measured FeNO using EcoMedics nitric oxide (NO) analyzers with ambient NO
scrubbers at standard expiratory flows of 50 ml/sec. Linear regression was used to
examine the relationship between pulmonary function and FeNO. Elevated FeNO was
associated with deficits in small-airway flows. Children with FeNO in the highest quartile
had approximately 3% lower maximum mid-expiratory flow (MMEF) than did children
with the lowest quartile of FeNO (p<0.05) and had approximately 1% lower ratio of
FEV
1
to FVC (p<0.01), after adjustment for potential confounders. Higher FeNO was
associated with lower ratio of FEV
1
to FVC and lower MMEF in children with and
without a history of physician-diagnosed asthma. Airway inflammation, as assessed by
FeNO, was associated with deficits in small airway flows.
145

Introduction
Normal pulmonary function development during childhood is important for
reaching maximum attainable adult lung function. Multiple determinants for childhood
pulmonary function development have been identified including asthma, early childhood
respiratory infections, airway hyper-responsiveness and exposures to oxidant pollutants
such as those in air pollution and tobacco smoke.
1-5

Airway inflammation during childhood has the potential to adversely affect
pulmonary function development. Asthma is an airway inflammatory disease that has
been distinguished from other chronic pulmonary diseases by reversible airflow
obstruction. However, in response to chronic airway inflammation, airway remodeling in
asthma can cause permanent structural changes.
6
It has yet to be firmly established
whether airway inflammation affects pulmonary function in children. Understanding the
role of airway inflammation in pulmonary function development could have important
implications for clinical interventions and improve prevention efforts for childhood
airway diseases.
Fractional concentration of nitric oxide in exhaled breath (FeNO) has provided a
non-invasive method to investigate important aspects of airway inflammation that may
have a role in childhood asthma and airway diseases.
7-8
The role of FeNO in asthma has
been the subject of intense investigation,
9-12
but its relationship with pulmonary function
in children has been less extensively studied. The studies that have investigated the
relationship of FeNO with pulmonary function in children with and without asthma have
shown mixed results.
13-18
The Southern California Children’s Health Study (CHS) is a
prospective population-based cohort study of the effects of air pollution on the respiratory
146

health of school-age children. Using this resource, we investigated whether airway
inflammation was associated with decreased pulmonary function, and whether this
relationship varied by history of physician-diagnosed asthma.

Methods
Study Subjects and Design
Concurrent FeNO and pulmonary function testing were performed during the
2007-2008 school year by 1560 of 2064 (76%) active cohort members (251 participants
did not perform FeNO testing and 253 did not perform pulmonary function testing). The
majority of study participants (1288) were from a cohort enrolled during 2002–2003
when they were in kindergarten or first grade (average 5–6 years old) as part of the
southern California Children’s Health Study (CHS), a prospective cohort study designed
to assess the long-term effects of air pollution on the respiratory health of children. An
additional 272 participants were from an augmentation of the cohort enrolled in 2007-
2008. Informed consent from a parent/guardian and assent from each child were obtained
before FeNO and lung function testing, using procedures approved by the University of
Southern California’s Institutional Review Board. Parents completed an annual self-
administered questionnaire that included information on socio-demographic
characteristics and health of the child and an updated exposure history, including
secondhand tobacco smoke exposure. There were no differences in annual household
income, parental education, health insurance coverage, or child’s age among the 504
children (of 2064 active cohort members) who did not perform both lung function and
FeNO testing.
147

Pulmonary Function Collection and Analysis
During the 2007-2008 school year, each CHS community was visited at least
twice in different seasons, to minimize confounding of location and season effects.
Health status at testing was evaluated by questionnaire and subjects with symptoms of
acute respiratory infection within the previous 3 days were excluded or rescheduled.
Pulmonary function was measured using pressure-transducer-based spirometers
(Screenstar Spirometers, Morgan Scientific, Haverhill, MA) by trained field technicians
who traveled to study schools to perform maximal effort spirometric testing of the
children. We assessed forced expiratory volume during the first second (FEV
1
), forced
vital capacity (FVC), and maximal mid-expiratory flow (MMEF) from a series of lung
function testing maneuvers performed on each child as stipulated by American Thoracic
Society recommendations. Details of the testing protocol have been published
previously.
19

FeNO Collection and Analysis
Online FeNO measurements were performed at 50 ml/sec expiratory flow using
EcoMedics CLD-88-SP analyzers, with DeNOx accessories to provide NO-free inhaled
air (EcoPhysics Inc., Ann Arbor, MI/Duernten, Switzerland), according to established
guidelines.
7, 20-21
Breath collection was performed at schools in the late morning to avoid
traffic-related peaks of ambient NO and possible effects of recent food consumption on
FeNO, according to American Thoracic Society recommendations.
20-21
Additional details
of breath collection and FeNO analysis used in this study were reported previously.
22-24

148

Socio-demographic and Medical History Information
Race/ethnicity was defined as non-Hispanic white, Hispanic, African American,
Asian/Hawaiian/Pacific Islander, and mixed/other ethnicities, based on parental report.
Parental education was defined as the highest level of education attainment of the parent
or guardian who completed the questionnaire. Annual household income was used to
assess the role of socioeconomic status. We used parent-reported health insurance
coverage to assess the role of access to health care.
At study entry, selected aspects of the child’s and parents’ medical histories were
collected, and in each successive school year, an update questionnaire inquiring about the
child’s intervening year of health was completed by parents and returned to study staff.
The child’s asthma status was defined by parental report of a physician diagnosis of
asthma at study entry or in an annual follow-up questionnaire. Child’s history of ever
wheezing and wheezing in the past 12 months were defined as yes/no based on
questionnaire report. Child’s history of respiratory allergy included any hay fever or
allergic rhinitis. During annual school visits, subjects’ height and weight were measured
using standardized protocols.
Statistical Methods
Multiple linear regression models were fitted to examine the association between
FeNO and pulmonary function. Outcome measures analyzed in this investigation include
FEV
1
, FVC, MMEF
,
and the ratio of FEV
1
to FVC. We used a log-transformation of
each pulmonary function measure in order to satisfy the assumptions of linear regression.
We examined FeNO as a continuous variable and determined that the dose-response
relationship of FeNO with pulmonary function was not best explained as a linear
149

function. Therefore, we examined the relationship of quartiles of FeNO with log-
transformed pulmonary function.
Included in all of our models were design variables or known determinants of
pulmonary function including height, age, sex, body-mass index (BMI, the weight in
kilograms divided by the square of the height in meters), race/ethnicity, recent respiratory
infections and indicators for community of residence and field technician. We examined
several possible confounding variables, including parental education, income, health
insurance, asthma status, asthma medication usage, family history of asthma, lifetime
secondhand smoke (SHS) exposure, and in utero exposure to maternal smoking. A
change of 10% or greater in the FeNO effect estimates was used as the criterion for
confounding. Interaction terms were added to the model to test for effect modification by
sex and asthma status, and subsequent stratified analyses were used to further explore
relationships when an interaction was significant. Trend tests were conducted by using an
ordinal variable for FeNO quartiles.
All analyses were conducted using SAS software (SAS Institute, Cary, NC)
version 9.2. All hypothesis testing was conducted assuming a 0.05 significance level and
a two-sided alternative hypothesis.

Results
Demographic and Descriptive Characteristics
There were approximately equal numbers of boys and girls and the average age at
testing was 11 years (Table 7.1). Nearly 60% of the study population was Hispanic white,
30% were non-Hispanic white and the remaining participants were African American,
150

Asian/Hawaiian/Pacific Islander or other race/ethnicity. The majority of the study
population came from households earning greater than $50,000 per year with college-
educated parents. A small percentage of participants (3%) reported secondhand smoke
exposure in the home. Sixteen percent of the participants reported a physician-diagnosis
of asthma and 19% reported a history of physician-diagnosed asthma in either biological
parent. Nearly 40% of the participants had no history of respiratory allergy at the time of
testing; the remaining 60% were distributed between having past history of respiratory
allergy (history of hay fever or allergic rhinitis but no current symptoms) and having
current respiratory allergy (symptoms within the previous 12 months). Twenty-four
percent of participants overall had a lifetime history of wheeze and 10% had wheeze in
the previous 12 months. Children with a history of asthma had higher prevalence of
wheeze than children without a diagnosis of asthma (74% versus 13% for lifetime
wheeze and 35% versus 4% for wheeze within the previous 12 months, respectively).
FeNO followed an approximately log normal distribution (geometric mean: 12.4
ppb; standard deviation (SD): 2.1 ppb). The geometric mean concentration of FeNO
among children with physician-diagnosed asthma was 16.3 ppb (SD: 2.4 ppb) versus 11.9
ppb (SD: 2.0 ppb) among children without physician-diagnosed asthma. We computed
adjusted mean pulmonary function volumes and flows, adjusted for age, sex, race,
community, and height by history of physician-diagnosed asthma. Among children
without asthma, adjusted mean FEV
1
was 2471 ml versus 2398 ml among those with
asthma. Adjusted mean FVC was 2843 ml versus 2818 ml; and adjusted mean MMEF
was 2916 versus 2710 ml/sec for children without asthma and children with asthma,
respectively.
151

Characteristic* N
% or
Mean (SD)
Sex
Males 737 47%
Females 823 53%
Race/Ethnicity
White/Non-Hispanic 470 30%
Hispanic 914 59%
African-American 26 2%
Asian/Hawaiian/Pacific Islander 64 4%
Other 78 5%
Household Second Hand Smoke Exposure 46 3%
Responding Parent > HS Education 922 63%
Annual Household Income > $50,000 737 57%
Age at Testing (yrs) 1560 11.2 (0.6)
Height at Testing (cm) 1560 146.9 (7.9)
Body Mass Index (kg/m
2
) 1560 20.3 (4.4)
Exhaled Nitric Oxide (FeNO), (ppb) [geo. mean (SD)] 1560 12.4 (2.1)
FeNO Quartile 1 389 5.8 (1.2)
FeNO Quartile 2 391 8.8 (1.1)
FeNO Quartile 3 390 13.4 (1.2)
FeNO Quartile 4 390 34.6 (1.6)
Physician-Diagnosed Asthma 246 16%
Any History of Wheeze 355 24%
Wheeze within Past 12 Months 136 10%
Parental History of Asthma 264 19%
*Not all characteristics sum to 1560 due to missing data.
Table 7.1. Demographic, Household and Health Characteristics for
Children's Health Study Participants, 2007-2008

152

FeNO and Pulmonary Function
Elevated FeNO was associated with deficits in small airway flows in the overall
study population (Table 7.2). Children with FeNO in the highest quartile had
approximately 3% lower maximum mid-expiratory flow (MMEF) than did children with
the lowest quartile of FeNO (p<0.05) and had approximately 1% lower ratio of FEV
1
to
FVC (p<0.01), after adjustment for potential confounders. We observed an increasing
trend of deficits in small airway flows with increasing quartiles of FeNO (p trend for
MMEF <0.05; p trend for FEV
1
/FVC<0.01). The association of elevated FeNO with
these measures of pulmonary function was not substantially affected by adjustment for
history of physician-diagnosed asthma, parental education, annual household income, or
secondhand smoke exposure (data not shown). There was not a significant relationship
between elevated FeNO and FVC or FEV
1
level.
Relationship of FeNO with MMEF and FEV
1
/FVC by Physician-Diagnosed Asthma
There were no statistically significant differences in the relationship of higher
FeNO and MMEF or FEV
1
/FVC between those with and without physician-diagnosed
asthma (e.g., for MMEF, p
interaction
=0.62) (Table 7.3). Higher FeNO was associated with
lower ratio of FEV
1
to FVC and lower MMEF in the subgroups of children with and
without a history of physician-diagnosed asthma.
We limited our analysis to children who reported no wheezing within 12 months
of the questionnaire in order to determine whether our results were sensitive to symptoms
of asthma. We found similar magnitudes of deficits in MMEF and FEV
1
/FVC ratio for
children with the highest quartile of FeNO compared to children with the lowest quartile
of FeNO among children without current wheeze (data not shown).
153

154

155

In order to determine whether our results were sensitive to the use of inhaled
corticosteroids (ICS) specifically, we limited our asthma cases to those who did not
report using ICS during the previous year. We found similar magnitudes of deficits in
MMEF and FEV
1
/FVC in children with asthma not using ICS in the highest quartile of
FeNO compared to the lowest quartile of FeNO (data not shown).

Discussion
We found that airway inflammation, as measured by elevated FeNO, was associated
with small airway flow rates in healthy school-aged children and school-aged children
with asthma. Elevated FeNO levels were associated with deficits in MMEF after
adjustment for potential confounders including medication use for asthma. A similar
pattern of effects was observed for FEV
1
/FVC ratio.
The long-term effects of airway inflammation on the growing lungs of children are of
concern and may have clinical and public health significance. The magnitude of deficits
observed in small airway flows is similar to those we have previously seen among
children exposed to in utero tobacco smoke and secondhand smoke in an earlier CHS
cohort.
4
The deficits in small airways flows shown may reflect larger underlying
pathology and alternations in small airways function than the relatively small magnitude
suggests. If the deficits persist, these children may be at increased risk for chronic
obstructive pulmonary disease and other chronic health conditions associated with
pulmonary function deficits later in life. Reducing the sources that lead to increased
airway inflammation in healthy children and children with physician-diagnosed asthma is
likely to reduce the burden of chronic respiratory diseases in adulthood.
156

The majority of studies in children have not investigated the relationship of FeNO
with pulmonary function in healthy children. Perzanowski et al. found that FeNO was not
statistically significantly correlated with FVC %predicted, FEV
1
%predicted or FEF
25-75
%predicted among a cohort of 89 inner city healthy children and children with asthma
and asthma-like symptoms recruited from Head Start centers.
14
However, this study was
potentially limited by small sample size and possible selection bias from convenience
sampling.
Among studies of pulmonary function in children with asthma, some have shown
statistically significant associations with FeNO while others have found no association. A
recent study of 437 children with mild to moderate persistent asthma with normal FEV
1
%predicted participating in two asthma clinical trials showed that FEF
25-75
% predicted
and FEV
1
/FVC % predicted were negatively correlated with log-transformed FeNO (r=-
0.22, p<0.0001 and r=-0.25, p<0.0001, respectively).
18
Another study showed a
statistically significant association of FeNO with FEV
1
/FVC (r=-0.19, p=0.03) among
144 children with mild to moderate asthma, but did not find a significant association with
FEV
1
% predicted or FVC % predicted.
17
Covar et al. showed no significant correlation
of FEV
1
, FVC, or FEV
1
/FVC with FeNO among 118 children with asthma in the
Childhood Asthma Management Program (CAMP) study.
13

Debley et al. investigated the relationship of pulmonary function with single breath
exhaled nitric oxide (SB-eNO) among a small cohort of 44 infants and very young
children with wheeze.
25
They found that SB-eNO was not associated with baseline FVC,
FEV
0.5
, FEF
25-75
, or FEF
75
, after adjustment for sex, eczema, family history of asthma,
and tobacco smoke exposure. However, higher baseline SB-eNO was associated with
157

decreases in FEV
0.5
, FEF
25-75,
and FEF
75
at 6 month follow-up after adjustment for age at
enrollment, sex, family history of asthma, history of eczema, sustained use of inhaled
corticosteroids during follow-up, and secondhand tobacco smoke exposure, indicating
that elevated FeNO might be more strongly associated with deficits in pulmonary
function growth rather than level of function alone.
This was a cross-sectional study of the relationship between FeNO and pulmonary
function. The Children’s Health Study is an ongoing prospective investigation, and in the
future, multiple yearly concurrent FeNO and pulmonary function measurements will
provide an opportunity to assess the relationship of FeNO with subsequent lung function
growth during the period of rapid lung development in adolescence. Given the findings
of the recent investigation in infants by Debley et al.,
25
it will be useful to assess the
utility of FeNO in predicting deficits in pulmonary function growth during follow-up in
our cohort.
We recognize that our reliance on self-report of physician-diagnosed asthma is a
potential limitation; nevertheless, physician diagnosis of asthma has been widely
accepted as a valid method of classifying asthma status in large epidemiologic studies.
26-
27
Moreover, in a subset of a previous CHS cohort, we verified self-reported physician-
diagnosed asthma through a review of medical records and found that more than 95% of
children with a self-reported diagnosis had either a definite or probable asthma diagnosis
noted on the medical record.
28

158

Conclusions
Airway inflammation, as assessed by FeNO, is associated with deficits in small
airway flows in healthy children and in children with physician-diagnosed asthma.
Further research is needed to determine whether elevated FeNO is predictive of future
deficits in pulmonary function growth.
159

Chapter 7 Acknowledgements
We are indebted to the school principals, teachers, students and parents in each of
the 13 study communities for their cooperation and especially to the members of the
health testing field team for their efforts. This work was supported by the Southern
California Environmental Health Sciences Center (grant # 5P30ES007048) funded by the
National Institute of Environmental Health Sciences, the Children’s Environmental
Health Center (grant #s 5P01ES009581, R826708-01 and RD831861-01) funded by the
National Institute of Environmental Health Sciences and the Environmental Protection
Agency, the National Institute of Environmental Health Sciences (grant # 5P01ES011627
and 5R01 ES016535) the National Heart, Lung and Blood Institute (grant #s
5R01HL061768 and 5R01HL076647) and the Hastings Foundation. The funding
agencies had no role in the design, collection, analysis or interpretation of data; nor did
they have any role in the writing of the manuscript or the decision to submit the
manuscript for publication.
160

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163

CHAPTER 8: THE ASSOCIATION BETWEEN LUNG FUNCTION DEFICITS,
SMALL AIRWAYS STRUCTURE AND CHRONIC AIR POLLUTION
EXPOSURE (MANUSCRIPT 3)
Chapter 8 Abstract
Chronic exposure to air pollution in childhood is associated with reduced lung
function growth and with lower maximum attained measures of airway flows in young
adulthood. We hypothesized that these observed deficits in lung function occur as a result
of anatomic changes in airway structure. The study population was recruited, post-high
school, from a larger pool of subjects originally enrolled in the Children’s Health Study
(CHS), a prospective cohort study designed to assess the long-term effects of air pollution
on the respiratory health of children. Twenty-nine of these participants underwent
computed tomography (CT) scanning within one year of follow-up maximal effort
spirometry testing. Airway dimensions were calculated using custom software. Long-
term ambient air pollution exposures were calculated using community air monitoring
data averaged over the period of lung development. We found that pulmonary function
measures were significantly associated with quantitative airway dimensions (e.g., r=0.54,
p<0.01 for the relationship of MMEF with average luminal area). We found that higher
exposure to ozone was significantly associated with lower total average airway area
(p<0.05) and lower average luminal area (p=0.058) after adjusting for sex, log-
transformed height, and history of physician-diagnosed asthma. Similar associations were
found for other regional pollutants; however, these relationships were not statistically
significant. Differences in airway structure may underlie the reported adverse effects of
air pollution on childhood lung function development.
164

Introduction
Findings from the Southern California Children's Health Study (CHS), a
prospective study of the chronic respiratory effects of air pollution among public
schoolchildren from 12 Southern California communities, have shown that chronic
exposure to air pollution is associated with reduced lung function growth during
adolescence and with lower maximum attained measures of airway flow in young
adulthood.
1-2
The basis for these long-term air pollution effects on spirometric indices of
airflow is unknown but one possible mechanism is irreversible anatomic changes in the
airway structure. Changes in airway lumen size or airway wall thickness may indicate
elevated long-term risk for adverse health outcomes such as airway remodeling in asthma
or chronic obstructive pulmonary disease.
Until recently, it has been difficult to measure airway dimensions in young people
because this previously required tissue to be collected with invasive procedures. Recent
developments in lung imaging have made it feasible to study the structure of the lung
3

without the need for tissue removal. Computed tomography (CT) has proven very useful
in assessing lung structure in chronic lung diseases such as asthma and chronic
obstructive pulmonary disease (COPD).
4-8
Studies have shown that CT measurements of
airway wall thickness correlate with pulmonary function.
5, 8
Therefore, CT is a non-
invasive and useful technique to study the relationship of airway size with pulmonary
function.
Using the resources of the CHS and of developments in lung imaging, we
investigated the hypothesis that that long-term exposure to air pollution results in
165

structural changes in airway anatomy as measured by CT in young adults. We also
investigated whether pulmonary function deficits in young adults are associated with CT
measurement of airway dimensions and whether these relationships vary by lifetime
history of asthma.

Methods
Study Subjects and Design
Participants in the CT lung structure study were a subset (N=29) of former
participants of the Southern California Children’s Health Study (CHS), a prospective
cohort study established in 1993 to assess the long-term effects of air pollution on the
respiratory health of children.
9
Participants were recruited from a CHS follow-up study of
pulmonary function beyond age 18. CT scans were completed within one year of follow-
up pulmonary function testing (mean time between PFT and CT scan=121 days; range
7—308 days). In addition, a follow-up questionnaire characterizing residential,
occupational, and other exposures for the time period since high school graduation
(nominal age 18) was completed by all participants. Informed consent from participants
was obtained before CT scan and lung function testing, using procedures approved by the
University of Southern California’s Institutional Review Board and the USC Radiation
Safety Committee.
Pulmonary Function Testing Methods
Documentation of current lung function performance was measured at subjects’
homes, using portable pressure-transducer-based spirometers (Screenstar Spirometers,
Morgan Scientific, Haverhill, MA) by trained field technicians. Forced expiratory volume
166

during the first second (FEV
1
), forced vital capacity (FVC), and maximal mid-expiratory
flow (MMEF) were measured from a series of lung function testing maneuvers performed
as stipulated by American Thoracic Society recommendations. Details of the testing
protocol have been published previously.
9

CT Methods and Analysis
Twenty-nine former CHS participants received CT scans at the Keck Hospital of
USC using a GE LightSpeed Pro16 (GE Medical Systems, Milwaukee, WI) multi-
detector row CT scanner within one year of follow-up study home visit for pulmonary
function testing. Inspiratory CT scans were acquired at suspended full inspiration with
the following parameters: 120 peak kilovoltage (kVp), 80 tube current (mA), 1.25-mm
collimation, slice thickness 1.25 mm, 0.5 seconds/rotation, pitch 1 and reconstructed
using both a low spatial frequency reconstruction (Standard) and an edge enhancing
(Lung) kernel. In addition, 4 expiratory CT images were obtained at Residual Volume
(RV-after maximal expiration). Female participants were required to provide a urine
sample immediately prior to the CT scan to verify that they were not pregnant.
De-identified images were archived in Digital Imaging and Communications in
Medicine (DICOM) 3.0 format and transferred to the University of British Columbia for
analysis. CT scans were analyzed using EmphylxJ software, a graphics-based lung
analysis program for quantitative analysis of lung CT scans. Briefly, all airways that
were cut in cross section on CT were analyzed using the full-width at half maximum
method. This technique produces measurements of airway lumen area, wall area, and the
percentage of the total airway cross sectional area that is wall (WA%).

167

Air Pollution Exposure Assessment Methods
Details of the CHS air pollution exposure assessment methods have been reported
previously.
9-10
In brief, ambient air pollution data were obtained from air monitoring
stations located in each of the twelve study communities. Several regional pollutants
were measured, including particulate matter less than 10 microns in diameter (PM
10
) and
less than 2.5 microns in diameter (PM
2.5
), nitrogen dioxide (NO
2
), ozone (O
3
), elemental
carbon (EC), and acid vapor. Annual averages of the 24-hour PM
10
and NO
2
averages
were computed, as were annual averages of 2-week samples of PM
2.5
, EC, and acid vapor
averages. For O
3
, the computed annual average was restricted to include only data
collected between 10:00 A.M. and 6:00 P.M. The summary measure of each pollutant
level used in the CT analysis is the mean of each pollutant’s annual averages from 1994
to 2005, in each childhood community of residence.
Statistical Methods
We conducted descriptive analyses of pulmonary function (FEV
1
, FVC, and
MMEF) and of airway dimensions (airway lumen area, wall area, and WA%), stratified
by sex and asthma status. We used Pearson correlations to assess the association of
pulmonary function with airway dimensions. Because of concerns about small sample
size, we also conducted non-parametric correlations using Spearman rank correlation
analysis and found qualitatively similar results; therefore, results from the Pearson
correlation analyses are presented.
We used multiple linear regression to investigate the association between regional
long-term average air pollution (PM
10,
PM
2.5
, NO
2
, O
3
, EC and acid vapor) and the three
measures of airway structure. We assessed the impact of lifetime smoking status, race,
168

log-transformed height, sex, age at CT scan, and history of physician-diagnosed asthma
on these associations. Covariates were included in the final model if they were known to
be associated with the outcome and the exposure or if a change of greater than 10% of the
beta estimate for air pollution was observed when added to a model with only the air
pollution parameter and the outcome. To aid in interpretation of the associations, each
pollutant was scaled to its interquartile range.
All analyses were conducted using SAS software (SAS Institute, Cary, NC)
version 9.2. All hypothesis testing was conducted assuming a 0.05 significance level and
a two-sided alternative hypothesis.

Results
Descriptive Results
Descriptive results are presented in Table 8.1: 55% were female, approximately
70% self-identified as non-Hispanic white, and more than 85% were 24 years or older at
CT scan. Approximately 20% reported a lifetime history of physician diagnosed asthma.
Less than 20% of participants were classified as “ever smokers” based on whether they
reported having smoked at least five packs of cigarettes in their lifetimes. Sixty-five
percent of the participants had originally been CHS subjects in communities classified as
having higher regional PM exposures and the remaining 35% were from CHS
communities classified as having lower regional PM exposures.
169

Table 8.1. Selected Demographic, Health and Exposure
Characteristics in Former Southern California Children's
Health Study Participants in the HRCT Study (N=29)
Characteristic N Frequency
Sex

Male 13 44.8%

Female 16 55.2%

Age at HRCT Scan

<=23 years 4 13.8%

24-26 years 13 44.8%

>=27 years 12 41.4%

Race/Ethnicity

White 20 69.0%

Asian American 1 3.4%

African American 1 3.4%

Mixed 2 6.9%

Other 5 17.2%

Physician-Diagnosed Asthma

No 23 79.3%

Yes 6 20.7%

Smoked at least 5 packs in Lifetime

No 24 82.8%

Yes 5 17.2%

CHS Community of Residence age 10-18, by Regional
Air Pollution Category
Atascadero 1 3.4%

Lancaster 2 6.9%

Lompoc 1 3.4%

Santa Maria 1 3.4%

Long Beach 5 17.2%

Upland 2 6.9%

Lake Gregory 5 17.2%

Lake Elsinore 2 6.9%

Mira Loma 5 17.2%

Riverside 1 3.4%

San Dimas 4 13.8%
PM=particulate matter; HRCT=high resolution computed
tomography

170

Males had significantly larger FEV
1
, FVC, and MMEF levels than females (all p-
values<0.01) (Table 8.2). Males also had significantly larger average total airway area
and average luminal area (p<0.01 and p<0.05) than females.

Airway Dimensions, Measured by CT Mean SD Mean SD p-value
#
Average lumen area (of all airways measured), cm
2
0.13 0.03 0.10 0.02 <0.05
Average total airway area (of all airways measured), cm
2
0.38 0.06 0.33 0.05 <0.01
Wall area percent (WA%) 70.08 2.71 72.12 2.79 0.06
Pulmonary Function Measures
FEV
1
(ml) 5039 560 3624 561 <0.0001
FVC (ml) 6094 855 4246 739 <0.0001
MMEF (ml/sec) 5545 1416 4170 812 <0.01
#
p-value is from a t-test for equality of means for men versus women
Table 8.2. Sex Differences in Airway Dimensions Measured by CT and Mean Pulmonary Function
Measures Assessed by Spirometry in Former Participants in the Southern California Children's
Health Study (N=29)
FEV
1
=Forced expiratory volume in one second; FVC=forced vital capacity; MMEF=maximal mid-expiratory flow; WA%=percentage of
all assessed airways that is wall (wall/wall+lumen * 100%)
Men (n=13) Women (n=16)

Differences in Lung Function and Airway Dimensions by Physician-Diagnosed Asthma
Lower levels of FEV
1
, FVC, and MMEF were observed among the small number
of participants with a lifetime history of physician-diagnosed asthma (n=6) compared to
those without a physician-diagnosis of asthma (n=23); however, these differences were
not statistically significant (Table 8.3). Among those with lifetime history of asthma,
average total airway area and average lumen area were 15% and 23% smaller,
respectively, than among those without a history of asthma (both p values=0.05). Those
with lifetime history of physician-diagnosed asthma also had a larger percentage of
airway wall area (73% versus 71% for no history of asthma), but this difference did not
reach statistical significance (p=0.09).
171

Airway Dimensions, Measured by CT Mean SD Mean SD p-value
#
Average lumen area (of all airways measured), cm
2
0.12 0.03 0.09 0.03 0.05
Average total airway area (of all airways measured), cm
2
0.36 0.06 0.31 0.05 0.05
Wall area percent (WA%) 70.74 2.84 73.00 2.53 0.09
Pulmonary Function Measures
FEV
1
(ml) 4402 873 3708 871 0.09
FVC (ml) 5219 1210 4522 1175 0.21
MMEF (ml/sec) 4948 1281 4165 1302 0.19
#
p-value is from a t-test for equality of means for those with and with asthma
Table 8.3. Differences by Asthma in Airway Dimensions Measured by CT and Mean Pulmonary
Function Measures Assessed by Spirometry in Former Participants in the Southern California
Children's Health Study (N=29)
FEV
1
=Forced expiratory volume in one second; FVC=forced vital capacity; MMEF=maximal mid-expiratory flow; WA%=percentage of
all assessed airways that is wall (wall/wall+lumen * 100%)
History of Physician-Diagnosed Asthma
No (n=23) Yes (n=6)

Associations of Lung Function and Air Pollution with Airway Dimensions
Higher FEV
1
, FVC, and MMEF levels were significantly associated with lower
WA%, higher average luminal area and higher total airway area (Table 8.4). The
strongest correlations were seen for small airway flows (MMEF) (for example, with
average luminal area, r=0.54, p<0.01).

FEV
1
FVC MMEF
r r r
Average lumen area 0.54** 0.41* 0.54**
Average total airway area 0.53** 0.42* 0.55**
Wall area percent (WA%) -0.39* -0.29 -0.42*
**p<0.01, *p<0.05
Pulmonary Function
Table 8.4. Correlations of Airway Dimensions
Measured by CT with Pulmonary Function Tests
in Former Southern California Children's Health
Study Participants (N=29)
FEV
1
=Forced expiratory volume in one second; FVC=forced vital
capacity; MMEF=maximal mid-expiratory flow; WA%=percentage
of all assessed airways that is wall.
Airway Dimensions
Measured by CT

172

We investigated the association of community-specific average annual air
pollution between 1994 and 2005 with WA%, average total airway area and average
luminal area and (Table 8.5). We found that higher exposure to ozone was significantly
associated with lower total average airway area (p<0.05) and lower average luminal area
(p=0.058) after adjusting for sex, log-transformed height, and history of physician-
diagnosed asthma. Across an interquartile range of ozone exposure, participants in the
75
th
percentile of long-term average ozone exposure (50.3 ppb) had 0.016 cm
2
lower
average total airway area, and 0.008 cm
2
lower average luminal area compared to
participants in the 25
th
percentile of long-term average ozone exposure (41.2 ppb).
Similar patterns were observed for long-term average exposure to NO
2
, PM
10
, PM
2.5
,
elemental carbon, and acid vapor, but these relationships were not statistically significant.
There were no significant associations between WA% and long-term average exposure to
air pollution.
173

β
Adjusted
2
β β
Adjusted
2
β β
Adjusted
2
β
24-hour NO
2
-0.006 0.002 -0.011 0.003 0.218 -0.403
O
3 ,
10am-6pm
-0.005 -0.008^ -0.010 -0.016* 0.167 0.410
24-hour PM
10
-0.004 -0.003 -0.005 -0.002 0.289 0.189
PM
2.5
-0.009 -0.004 -0.012 0.000 0.619 0.241
EC
-0.009 0.001 -0.012 0.008 0.532 -0.204
Acid Vapor
-0.014† -0.011 -0.026 -0.019 0.623 0.372
*p<0.05, ^p<0.10, †p<0.15
1
Beta estimates (both unadjusted and adjusted) are change in cm
2
for average lumen area and aveage total airway area and change in % for WA%,
scaled to the interquartile range of each pollutant: NO 2 IQR=18.04 ppb; O 3 IQR=9.08 ppb; PM 10 IQR=11.57 μg/m
3
; PM 2.5 IQR=12.67 μg/m
3
; EC
IQR=0.92 μg/m
3
; acid vapor IQR=4.40 ppb.
2
Adjusted for sex, log(height), and history of physician-diagnosed asthma
Table 8.5. Associations of Airway Dimensions Measured by CT with Community-
Specific Average Annual Air Pollution in Former Children's Health Study
Community-
Specific
Average Annual
Air Pollution,
1994-2005
Airway Dimensions Measured by CT
1
Average lumen
area (cm
2
)
Average total
airway area (cm
2
)
Wall area percent
(WA%)

Discussion
The purpose of this study was to investigate whether pulmonary function deficits
are associated with quantitative CT measurements of airway dimensions and whether
lifetime air pollution is associated with these airway dimensions. This study is the first
step to investigate whether deficits in pulmonary function associated with chronic
exposure to regional air pollution previously observed in the Children’s Health Study
occur as a result of structural changes in airway anatomy.
We assessed the direct relationship of long-term community-specific annual
average air pollution exposure with airway dimensions in adults. We found that higher
174

ozone exposure during childhood was significantly associated with smaller total airway
area and smaller luminal area. We believe these initial results merit further study to
determine whether air pollution is causally linked with airway structural abnormalities,
especially given our relatively small sample size. If the suggestive associations of long-
term average air pollution with smaller airways are documented with a larger study, these
results would be the first step to demonstrating that air pollution could cause permanent
structural changes of the airway. Such findings would have important public health and
policy implications and could lead to primary prevention efforts for childhood and adult
lung diseases.
We also found that pulmonary function level, and in particular small airways
flow, was significantly associated with wall area percent (WA%), average luminal area
and average total airway area. Higher FEV
1
, FVC, and MMEF levels were significantly
associated with lower WA%, higher average luminal area and higher total airway area.
Additionally, we found that average luminal area and average total airway area were
significantly smaller among our small sample of participants with a history of a
physician-diagnosis of asthma. This observation has the potential to lead to better
phenotyping of children with asthma and may help to identify pre-clinical markers of
airway wall thickening.
Our ability to show a relationship between airway structure and physiologic
measures of pulmonary function provides a biological basis for the changes in lung
function that we have observed over the course of the Children’s Health Study. Our
findings are consistent with some but not all studies that have found significant
associations between airway structure and pulmonary function. Among 22 patients with
175

chronic obstructive pulmonary disease and 20 healthy smokers and nonsmokers, Deveci
et al.
8
found a significant negative correlation between wall area percentage and FEV
1
%
predicted. Kasahara et al.
5
found that wall area percentage and wall thickness were
inversely associated with FEV
1
% predicted among 49 patients with asthma. However,
Kosciuch et al.
11
failed to find a significant relationship between airway wall thickness
with FEV
1
% predicted or with FVC %predicted among 10 patients with asthma or 12
patients with stable, mild to moderate COPD. Similarly, Little et al.
6
did not find a
significant relationship between FEV
1
% predicted and airway wall thickness among
patients with asthma.
This study has some limitations. First, misclassification of lifetime exposure may
have limited our ability to detect statistically significant effects between airway
dimensions and long-term average air pollution levels. These assigned estimates were
based on annual averages of central site air pollution stations and it is known that there
are considerable within-community differences in some pollutants, such as NO
2
, due to
differences in exposure to traffic. This may be a more important consideration than
average regional pollution between communities. Perhaps more importantly, our
exposure assignments did not account for differences in exposures to ambient air
pollution due to moving, occupational exposures, or many other personal exposures that
could have effects on airway anatomy after our CHS participants reached age 18.
However, given the period of lung growth is often complete or nearly complete by age 18
for many individuals, exposures after age 18 may have a smaller effect on airway
anatomy.
176

Finally, airway measurements were obtained using “low radiation dose” CT
scans. Due to the relatively young age of the study population, we minimized radiation
exposures to the lowest levels possible. While the noise in CT scans increases with
decreasing x-ray dose, these low dose CT scans are becoming standard for studies of
humans
12
and it is thought that the measurements of airway wall dimensions are
comparable between subjects.

Conclusions
We found that small airways flows as assessed by maximal-effort spirometry
were significantly associated with airway dimensions as assessed by CT. We also found
that long-term average regional ozone exposure was associated with smaller airways.
Further research is needed to determine whether airway structure may mediate air
pollution-associated pulmonary function deficits in large-scale epidemiologic studies
which would lead to better understanding of the harmful effects of exposure to ambient
air pollution and would have important policy implications for primary prevention
efforts.

177

Chapter 8 Acknowledgements
We are indebted to the school principals, teachers, students and parents in each of
the 12 study communities for their cooperation and especially to the members of the
health testing field teams for their efforts. This work was supported by the Southern
California Environmental Health Sciences Center (grant # 5P30ES007048) funded by the
National Institute of Environmental Health Sciences, the Children’s Environmental
Health Center (grant #s 5P01ES009581, R826708-01 and RD831861-01) funded by the
National Institute of Environmental Health Sciences and the Environmental Protection
Agency, the National Institute of Environmental Health Sciences (grant # 5P01ES011627
and 5R01 ES016535) the National Heart, Lung and Blood Institute (grant #s
5R01HL061768 and 5R01HL076647) and the Hastings Foundation. The funding
agencies had no role in the design, collection, analysis or interpretation of data; nor did
they have any role in the writing of the manuscript or the decision to submit the
manuscript for publication.
178

Chapter 8 References
1. Gauderman WJ, Avol E, Gilliland F, et al. The effect of air pollution on lung
development from 10 to 18 years of age. N Engl J Med 2004;351:1057-67.

2. Pickering T. Persistence of pollution-induced lung function deficits in early
adulthood: Evidence from the Children's Health Study [M.S. dissertation]. Los Angeles:
Available from: Dissertations & Theses @ University of Southern California. Accessed
August 3, 2011, Publication Number: AAT 1479938; 2010.

3. de Blic J, Scheinmann P. The use of imaging techniques for assessing severe
childhood asthma. J Allergy Clin Immunol 2007;119:808-10.

4. Gono H, Fujimoto K, Kawakami S, Kubo K. Evaluation of airway wall thickness
and air trapping by HRCT in asymptomatic asthma. Eur Respir J 2003;22:965-71.

5. Kasahara K, Shiba K, Ozawa T, Okuda K, Adachi M. Correlation between the
bronchial subepithelial layer and whole airway wall thickness in patients with asthma.
Thorax 2002;57:242-6.

6. Little SA, Sproule MW, Cowan MD, et al. High resolution computed
tomographic assessment of airway wall thickness in chronic asthma: reproducibility and
relationship with lung function and severity. Thorax 2002;57:247-53.

7. Niimi A, Matsumoto H, Amitani R, et al. Airway wall thickness in asthma
assessed by computed tomography. Relation to clinical indices. Am J Respir Crit Care
Med 2000;162:1518-23.

8. Deveci F, Murat A, Turgut T, Altuntas E, Muz MH. Airway wall thickness in
patients with COPD and healthy current smokers and healthy non-smokers: assessment
with high resolution computed tomographic scanning. Respiration 2004;71:602-10.

9. Peters JM, Avol E, Gauderman WJ, et al. A study of twelve Southern California
communities with differing levels and types of air pollution. II. Effects on pulmonary
function. Am J Respir Crit Care Med 1999;159:768-75.

10. Peters JM, Avol E, Navidi W, et al. A study of twelve Southern California
communities with differing levels and types of air pollution. I. Prevalence of respiratory
morbidity. Am J Respir Crit Care Med 1999;159:760-7.

11. Kosciuch J, Krenke R, Gorska K, Zukowska M, Maskey-Warzechowska M,
Chazan R. Relationship between airway wall thickness assessed by high-resolution
computed tomography and lung function in patients with asthma and chronic obstructive
pulmonary disease. J Physiol Pharmacol 2009;60 Suppl 5:71-6.

179

12. Pauwels EK, Bourguignon M. Cancer induction caused by radiation due to
computed tomography: a critical note. Acta Radiol 2011;52:767-73.

180

CHAPTER 9: SUMMARY AND FUTURE RESEARCH DIRECTIONS
Summary
Children’s respiratory health remains an important and ongoing public health
concern. While the Centers for Disease Control and Prevention (CDC) has documented a
more than two-fold increase in asthma prevalence over the past 25 years
1
(with similar
increased rates seen around the developed world
2
), the reason behind these observed
increases remains unknown. Among the many factors proposed as contributing to this
increase include environmental factors such as exposure to ambient air pollution,
although decreasing trends in pollution levels in the U.S. have raised uncertainty about
the validity of this hypothesis. The current investigation aimed to contribute to resolving
these uncertainties by trying to better understand the biological mechanisms that mediate
the effects of environmental exposures on children’s respiratory diseases by employing
the use of non-invasive technologies to assess airway inflammation and airway structural
changes.
In the first manuscript (Exhaled Nitric Oxide, Susceptibility and New-Onset
Asthma in the Children’s Health Study), published in the European Respiratory Journal,
we examined whether children with elevated airway inflammation, as indicated by
elevated level of fractional concentration of nitric oxide in exhaled breath (FeNO), were
at increased risk for new-onset asthma. In order to investigate this hypothesis, we
prospectively followed 2206 asthma-free children (age 7–10 years) participating in the
Southern California Children’s Health Study (CHS) for three years after measuring FeNO
to ascertain incident asthma cases. We found that level of FeNO at baseline was
associated with increased risk of new-onset asthma over three years of follow-up.
181

Children with the highest quartile of FeNO had more than a two-fold increased risk of
new-onset asthma compared to children with the lowest quartile of FeNO (hazard ratio:
2.1; 95% confidence interval: 1.3-3.5). The increased risk did not vary by child’s history
of respiratory allergies. However, the effect of elevated FeNO on new-onset asthma was
stronger among those without a parental history of asthma.
To our knowledge, this is the first study to demonstrate the predictive value of
FeNO for identifying children at risk for developing asthma. As discussed in Chapter 3,
while the role of FeNO in clinical practice remains unclear,
3
studies have supported the
use of FeNO in monitoring adherence to medication,
4
maintaining asthma control and
predicting relapse.
5-6
This study thereby has extended the utility of this marker beyond
monitoring medication adherence, predicting asthma exacerbations or verifying a
diagnosis to potentially identifying a subset of children at higher risk of later asthma
development. As our results were strongest in children without a family history of
asthma, FeNO may be valuable in developing critically needed primary prevention
strategies, as relatively few alternatives are currently available.
In the second manuscript (Exhaled Nitric Oxide and Pulmonary Function in the
Southern California Children’s Health Study), currently in preparation for resubmission,
we examined whether airway inflammation, as assessed by FeNO, was associated with
pulmonary function in childhood. We examined the relationship between FeNO and
pulmonary function among 1560 schoolchildren (246 with asthma) from the CHS. We
found that elevated FeNO was associated with deficits in small-airway flows. Children
with FeNO in the highest quartile had approximately 3% lower maximum mid-expiratory
flow (MMEF) than did children with the lowest quartile of FeNO (p<0.05) and had
182

approximately 1% lower ratio of FEV
1
to FVC (p<0.01). Higher FeNO was associated
with lower ratio of FEV
1
to FVC and lower MMEF in children with and without a history
of physician-diagnosed asthma
While the magnitude of the deficits in pulmonary function observed appears
relatively small, they may reflect larger underlying pathology and alternations in small
airways function. The magnitude of deficits observed in small airway flows is similar to
those we have previously seen among children exposed to in utero tobacco smoke and
secondhand smoke in an earlier CHS cohort.
7
If the deficits persist, these children may be
at increased risk for chronic obstructive pulmonary disease and other chronic health
conditions associated with pulmonary function deficits later in life. In addition, while this
study was cross-sectional, the CHS is an ongoing prospective investigation, and in the
future, multiple yearly concurrent FeNO and pulmonary function measurements will
provide an opportunity to assess the relationship of FeNO with subsequent lung function
growth during the period of rapid lung development in adolescence.
As discussed in Chapter 2, results from the CHS indicated that long-term
exposure to nitrogen dioxide, acid vapor, particulate matter and elemental carbon over the
period of rapid lung development resulted in clinically-significant deficits in FEV
1

growth. It was determined that that proportion of 18-year-olds with clinically significant
deficits in FEV
1
was nearly 5 times greater at the highest level of exposure to particulate
matter (PM
2.5
) compared to the lowest level of exposure.
8
In the third manuscript (The
Association Between Lung Function Deficits, Air Pollution Exposure, and Small Airways
Structure Assessed by HRCT), currently in preparation, we hypothesized that air
pollution-associated deficits in lung function observed in the CHS over the period of
183

rapid lung development occur as a result of permanent anatomic changes in airway
structure.
We examined the relationship between lung function level and various
quantitative measures of airway structure as assessed by high resolution computed
tomography (HRCT) among 29 former CHS participants who were participating in a
follow-up study. Calculated measures of airway structure included wall area percent
(WA%), average total airway area and average lumen area. We computed an estimate of
long-term ambient air pollution exposure using air monitoring data from each of the CHS
study communities averaged over the period of lung development. We found that small
airways flows were significantly associated with all three measures of airway structure
visualized by HRCT (e.g., r=0.54, p<0.01 for the relationship of MMEF with average
luminal area). We further found that higher childhood exposure to ozone was
significantly associated with lower total average airway area and lower average luminal
area after adjusting for sex, log-transformed height, and history of physician-diagnosed
asthma.
Our ability to show a relationship between airway structure and physiologic
measures of pulmonary function provides a biological basis for the changes in lung
function that we have observed over the course of the Children’s Health Study. The
associations between air pollution and airway structure merit further study to determine
whether air pollution is causally linked to anatomical airway structural abnormalities.
Further research is needed to determine whether airway structure may mediate air
pollution-associated pulmonary function deficits in large-scale epidemiologic studies.
184

This body of work has demonstrated that airway inflammation and airway
remodeling play important roles in understanding the effects of environmental exposures
on children’s respiratory health. We showed in particular that FeNO is a useful non-
invasive measure of airway inflammation that is feasible to use in large-scale
epidemiologic studies. Our studies also demonstrated that FeNO has the potential to aid
in primary prevention efforts for asthma and later obstructive lung diseases. Moreover,
our studies showed that HRCT is a useful non-invasive measure of airway remodeling,
that could potentially be used to determine direct effects of environmental exposures on
respiratory health.
In the following sections, we outline possible directions to pursue related to each
of the three manuscripts, with a focus on future directions for further investigating the
effects of ambient air pollution on airway inflammation and airway remodeling as
biological mechanisms leading to long-term effects on the respiratory health of children
and young adults.

Future Directions: Manuscripts 1 and 2

There are several potential lines of investigation to explore related to
understanding environmental impacts on airway inflammation and airway remodeling in
childhood respiratory diseases. To further follow-up the first manuscript (Exhaled Nitric
Oxide, Susceptibility and New-Onset Asthma in the Children’s Health Study), we could
add of additional years of follow-up time for incident asthma case ascertainment
(Manuscript 1 ascertains cases for 3 years while available follow-up time now is 7 years)
to see if our results are robust over a longer study period. We could also change our
185

modeling approach by incorporating multiple years of FeNO measurement prior to
asthma diagnosis to determine whether additional markers of “exposure” enhance our
risk estimates on incident asthma. Finally, we could investigate whether the effects of
airway inflammation (as measured by exhaled FeNO) on incident asthma risk differ in
regions of higher or lower particulate air pollution by taking advantage of the large intra-
community variability PM study that was conducted during the period of follow-up.
Because the second manuscript was cross-sectional, (Exhaled Nitric Oxide and
Pulmonary Function in the Southern California Children’s Health Study), taking
advantage of now-available yearly concurrent FeNO and pulmonary function
measurements would provide an opportunity to assess the relationship of FeNO with lung
function growth during the period of rapid lung development in adolescence and
determine whether FeNO can predict later deficits in pulmonary function. There are two
additional years of concurrent FeNO and pulmonary function measurements for a total of
five years of follow-up. Debley et al. recently found that single-breath FeNO was
predicted deficits in pulmonary growth in infants at 6 months of follow-up.
9

Future Directions: Manuscript 3
There are many exciting next steps to pursue for future directions for the third
manuscript (The Association Between Lung Function Deficits, Air Pollution Exposure,
and Small Airways Structure Assessed by HRCT). We found significant associations
between quantitative airway dimensions and pulmonary function and significant
associations with average childhood ozone exposure.
186

There are many aspects of Manuscript #3 that warrant further investigation. There
are additional metrics of lung structure that have not yet been analyzed including metrics
that estimate air trapping, lung density, as well as additional metrics of airway wall and
lumen parameters. Each of these could be assessed for relationships with pulmonary
function and lifetime air pollution.
It is important to note that Manuscript #3 was a pilot study based on a volunteer
pool of 40 of adult participants from the original Children’s Health Study (Cohorts A-D);
however, only 29 of the participants’ HRCT scans were formatted correctly by the
radiology technicians for the software to analyze the airway parameters. Given the small
sample size and the subject selection (volunteer convenience sample from the follow-up
study), we believe these initial results merit further study to determine whether air
pollution is causally linked with airway structural abnormalities. Moreover, our estimates
of lifetime exposure likely suffered from misclassification due to a lack of information on
air pollution exposure after age 18, no information on occupational exposures that could
contribute to airway structural deficits, as well as reliance on central site community
monitors for air pollution assignment.
We propose to follow-up the preliminary results summarized in the third
manuscript with a larger study sample. We would selectively recruit participants based on
exposure to air pollution in childhood and adolescence (roughly half exposed to higher
levels of air pollution and the other half to lower levels of air pollution) from the cohort
currently under active follow-up (Cohort E) who will be age 18 in 2-3 years. The benefits
of recruiting from the current active cohort are numerous: (1) we have maintained
personal contact with these participants for nearly ten years which would likely facilitate
187

recruitment; (2) we have follow-up health status and covariate information annually for
these participants; and (3) we have enhanced information on their exposure to air
pollution including home- and school-based measurements of gases (NO, NO
2
, O
3
) and
particulate matter (PM
0.20
, PM
2.5
, PM
10
and constituents) for a sample of the cohort, with
sophisticated models for estimates of exposure for the whole cohort.
In order to assess the impacts of airway inflammation and childhood air pollution
exposure on the respiratory health of young adults, we would assess pulmonary function
(through pulmonary function tests), airway inflammation (through measurements of
FeNO), and CT measures of airway dimensions to assess airway remodeling. We would
collect questionnaires on health outcomes and detailed information on possible
confounding exposures. This study design would also allow us to test the hypothesis that
environmental insults could have direct effects on airway structure (e.g. airway
remodeling) independent of the inflammatory pathway, as suggested by the recent work
by Grainge et al. who showed that bronchoconstriction may be sufficient to cause airway
remodeling.
10

The challenges of conducting a large-scale HRCT study are not to be
underestimated, however. While we had a waiting list of volunteers to participate in the
pilot study (beyond the 40 for whom we had funding), this study would expose healthy
individuals to ionizing radiation. In the pilot study, we minimized radiation exposures to
the lowest dose levels possible to maintain an adequate image quality. While it could be a
challenge to convince the Institutional Review Board that a large-scale study in healthy
volunteers is worthy of merit, there are currently several cohort studies currently using
HRCT in healthy adults. For example, the Multi-Ethnic Study of Atherosclerosis
188

(MESA) cohort study, a study of subclinical cardiovascular disease, has more than 3000
older healthy and asthmatic participants with HRCT scans (personal communication).
While the MESA study also has considerable air pollution data (MESA Air), the study
sample is limited in its ability to assess the effects of childhood air pollution exposure on
lung structure directly since the cohort is 45 to 84 years of age. Follow-up of the
currently active Children’s Health Study participants would provide a unique opportunity
to test the hypothesis that childhood air pollution exposure permanently alters the
structure of the airways.
In addition to HRCT, there are other lung imaging techniques that have become
available that do not use radiation exposure; however, each has its own unique
disadvantages. Hyperpolarized
3
Helium (
3
He) MRI measurements have been shown to be
correlated with HRCT airway measurements (e.g. at the 5
th
generation
11
). While
3
He MRI
has the advantage of assessing regional lung function (perfusion, ventilation etc.) and
recently has been used to demonstrate new alveolarization during adolescent
development,
12
HRCT is still recommended over
3
He MRI for structural imaging because
of its greater resolution for visualizing the airways and other lung structures.
13

Another new technique, optical coherence tomography (OCT), has been shown to
be more sensitive than HRCT at detecting small airway wall changes, as it can measure at
even higher resolution than HRCT
14
(2 mm in diameter or less for OCT versus 5 mm in
diameter for HRCT
15
). However, while OCT does not expose participants to ionizing
radiation, it does require the use of broncoscopy which is an invasive procedure not likely
to be acceptable to healthy adults. In summary, despite its limitations, HRCT is still the
method of choice to currently to visualize the airways in a large-scale study.
189

Conclusions
To conclude, further research is needed to more acutely examine the effects of
environmental exposures on key pathophysiologic mechanisms that lead to or mitigate
childhood respiratory diseases. Such avenues of research have the potential to identify
early markers of disease and aid in preventive strategies to lessen the substantial burden
that exists with childhood asthma and adult airway diseases.

190

Chapter 9 References
1. Centers for Disease Control and Prevention. 2007 National Health Interview
Survey Data. Table 4-1 Current Asthma Prevalence Percents by Age, United States:
National Health Interview Survey, 2007. Atlanta, GA: U.S. Department of Health and
Human Services, CDC; 2010 Accessed November 21, 2010.

2. Eder W, Ege MJ, von Mutius E. The asthma epidemic. N Engl J Med
2006;355:2226-35.

3. Petsky HL, Cates CJ, Li AM, Kynaston JA, Turner C, Chang AB. Tailored
interventions based on exhaled nitric oxide versus clinical symptoms for asthma in
children and adults. Cochrane Database Syst Rev 2008:CD006340.

4. Jones SL, Kittelson J, Cowan JO, et al. The predictive value of exhaled nitric
oxide measurements in assessing changes in asthma control. Am J Respir Crit Care Med
2001;164:738-43.

5. Fritsch M, Uxa S, Horak F, Jr., et al. Exhaled nitric oxide in the management of
childhood asthma: a prospective 6-months study. Pediatr Pulmonol 2006;41:855-62.

6. Pijnenburg MW, Hofhuis W, Hop WC, De Jongste JC. Exhaled nitric oxide
predicts asthma relapse in children with clinical asthma remission. Thorax 2005;60:215-
8.

7. Gilliland FD, Berhane K, McConnell R, et al. Maternal smoking during
pregnancy, environmental tobacco smoke exposure and childhood lung function. Thorax
2000;55:271-6.

8. Gauderman WJ, Avol E, Gilliland F, et al. The effect of air pollution on lung
development from 10 to 18 years of age. N Engl J Med 2004;351:1057-67.

9. Debley JS, Stamey DC, Cochrane ES, Gama KL, Redding GJ. Exhaled nitric
oxide, lung function, and exacerbations in wheezy infants and toddlers. J Allergy Clin
Immunol 2010;125:1228-34 e13.

10. Grainge CL, Lau LC, Ward JA, et al. Effect of bronchoconstriction on airway
remodeling in asthma. N Engl J Med 2011;364:2006-15.

11. Kirby M, Krowchuk NM, Wheatley A, McCormack DG, Coxson HO, Parraga G.
Evaluation Of COPD Airway Function And Structure Using Hyperpolarized 3he
Magnetic Resonance Imaging And X-Ray Computed Tomography. In: American
Thoracic Society. Denver, CO; 2011.
191

12. Narayanan M, Owers-Bradley J, Beardsmore CS, et al. Alveolarization Continues
During Childhood and Adolescence: New Evidence from 3He Magnetic Resonance. Am
J Respir Crit Care Med 2011.

13. Ley-Zaporozhan J, Ley S, Kauczor HU. Morphological and functional imaging in
COPD with CT and MRI: present and future. Eur Radiol 2008;18:510-21.

14. Coxson HO, Quiney B, Sin DD, et al. Airway wall thickness assessed using
computed tomography and optical coherence tomography. Am J Respir Crit Care Med
2008;177:1201-6.

15. Stern RL, Cline HE, Johnson GA, Ravin CE. Three-dimensional imaging of the
thoracic cavity. Invest Radiol 1989;24:282-8.

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Abstract (if available)

Abstract The burden of childhood respiratory diseases is an important public health problem. Asthma is the most common childhood chronic disease and numerous studies have documented its rise in worldwide prevalence over the past several decades. Moreover, normal pulmonary function development during childhood is important for reaching maximum attainable adult lung function. While the etiology of childhood asthma and development of normal pulmonary function development are complex, a growing body of evidence shows that a variety of environmental exposures are important determinants of childhood airway diseases and normal development. A greater understanding of the biological mechanisms influencing childhood asthma and the natural course of pulmonary function development is critical to minimizing the adverse effects of environmental exposures. We investigated pathophysiologic mechanisms using advances in non-invasive technologies including exhaled nitric oxide (FeNO) and high resolution computed tomography (HRCT) scanning to assess airway inflammation and airway structural changes in the Southern California Children’s Health Study. We demonstrated that higher measured inflammation could predict the onset of asthma and was associated with lower pulmonary function levels. Moreover, we found that adults who had been exposed to higher levels of air pollution during childhood had anatomically smaller airways than adults exposed to lower levels during childhood. These findings have the potential to identify early markers of disease and aid in preventive strategies to lessen the substantial burden that exists with childhood asthma and airway diseases. Further research is greatly needed to examine the effects of environmental exposures on key pathophysiologic mechanisms that lead to or mitigate the development of childhood and adult respiratory diseases.

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Asset Metadata

Creator Bastain, Theresa Meredith (author)

Core Title Airway inflammation and respiratory health in the Southern California children's health study

School Keck School of Medicine

Degree Doctor of Philosophy

Degree Program Epidemiology

Publication Date 03/26/2012

Defense Date 01/09/2012

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

Tag children's health,exhaled nitric oxide,OAI-PMH Harvest,respiratory disease

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Gilliland, Frank D. (committee chair), Berhane, Kiros (committee member), Dubeau, Louis (committee member), Gauderman, W. James (committee member), McConnell, Robert (committee member)

Creator Email bastain@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c127-678888

Unique identifier UC1369117

Identifier usctheses-c127-678888 (legacy record id)

Legacy Identifier etd-BastainThe-547.pdf

Dmrecord 678888

Document Type Dissertation

Rights Bastain, Theresa Meredith

Type texts

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

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

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