Early childhood health experience & adult phenotype in twins

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EARLY CHILHDOOD HEALTH EXPERIENCE & ADULT PHENOTYPE IN TWINS

by

Amie Eunah Hwang

_____________________________________________

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 Amie Eunah Hwang

ii

Dedication

I dedicate this dissertation to my parents whose selfless and unconditional support
made this achievement possible. They gave me life, love and most of all, faith in God.
They live their life honestly, diligently and faithfully. They taught me the true value of
hard work, a lesson that I will live by for the rest of my life and strive to teach to my son.
They believe in me wholeheartedly and inspire me to do my best. It is with their daily
encouragement and physical and emotional support, not to mention countless hours of
babysitting, that I’m able to finish this work. I am thankful to be able to dedicate this
work to my parents, as I embrace the next chapter of my life with them.
Last and foremost, I dedicate this work to God. For only through God’s grace and
blessing has this pursuit been possible and purposeful.

iii

Acknowledgements

First of all, I want to thank Dr. Wendy Cozen, whose unwavering support and
guidance led me through this long and challenging journey. She taught me passion for
science, truth, and knowledge. I thank her for her mentorship, friendship, persistence and
unwillingness to give up on me. I thank Dr. Thomas Mack who taught me how to think
outside the box and how to tell a story. I thank Dr. Leslie Bernstein and Dr. James
Gauderman for their mentorship and teachings in statistics. I thank Dr. Eileen Crimmins
for supporting my research with NIH funding.
I would like to thank friends and colleagues, Kristin Rand, Laura Buchanan,
David Hyde, Manny Garcia, Eunjung Lee, Kathy Lee, and Yujung Chung for their daily
support and encouragement.
I would also like to acknowledge my son Jonathan, my little Yeinie, who
unwittingly yet unquestionably sparked in me a stubborn determination, commitment,
and just a little bit of desperation to continue. He has made my life infinitely more worth
living and that much more fun. More than anything I look forward to from now is to see
you grow up to be someone that I know I will be proud of. I love you.

iv

Table of Contents

Dedication ii
Acknowledgements iii
List of tables vi
Lit of figures viii
Abbreviation ix
Abstract xi
Chapter 1. Background
1.1. Introduction 1
1.2. Specific infections with specific outcome 4
1.2.1. Timing of exposure 4
1.2.2. Delayed exposure to Epstein-Barr virus and Infectious
Mononucleosis 5
1.2.3. The effect of timing of exposure of childhood infections on
adult morbidity 7
1.2.4. Reactivation of varicella zoster virus infection, herpes zoster 9
1.3. Non-specific infections with adult diseases 10
1.3.1. The hygiene hypothesis and Hodgkin lymphoma 11
1.3.2. Barker hypothesis 13
1.3.3. The relationship of childhood infection to adult height 14
1.3.4. Energy Redirection 15
1.3.5. Inflammation from infections lowers growth hormone 18
1.4. Twin Studies 19
1.4.1. History of twin research 20
1.4.2. Advantages of Twin Studies 21
1.4.3. Epidemiologic study designs using twins as participants 23
1.5. Conclusion 26
Chapter 1 Reference 28

Chapter 2. Evidence of Genetic Susceptibility to Infectious Mononucleosis:
A Twin Study
2.1. Abstract 35
2.2. Introduction 35
2.3. Materials and Methods 37
2.3.1. Source of subjects and data instruments 37
2.3.2. Statistical analysis 38
2.4. Results 41

v

2.5. Discussion 43
Chapter 2 Reference 48

Chapter 3. Reports of Childhood Experience from Young Adult Twins and
Their Mothers
3.1. Abstract 51
3.2. Introduction 52
3.3. Materials and Methods 54
3.3.1. Source of Participants 54
3.3.2. Collection of Exposure Information 55
3.3.3. Statistical Analysis 57
3.4. Results 58
3.5. Discussion 69
Chapter 3 Reference 74

Chapter 4. The Effect of Childhood Infections on Adult Height in
Monozygotic Twins
4.1. Abstract 77
4.2. Introduction 77
4.3. Materials and Methods 79
4.3.1. Source of Participants 79
4.3.2. Questionnaire & exposure assessment 81
4.3.3. Statistical analysis 82
4.4. Results 85
4.5. Discussion 95
Chapter 4 Reference 99

Chapter 5. Conclusion 103

Comprehensive References 107

Appendices
Appendix A: Questionnaire for twins 120
Appendix B: Questionnaire for mothers 159

vi

List of Tables

Table 1.1. Leading Cause of Mortality in Children Under Age 5 By
Economic Development 1

Table 1.2. Infectious Agents Implicated in Causal Association With
Adult Diseases 2

Table 1.3. Comparison of 5 Prospective Cohort Studies of EBV
Seroconversion and IM Development Among University
Students 6

Table 2.1. Demographic Information for California Twin Pairs Born
in 1957-1982 40

Table 2.2. Pairwise Concordance for Infectious Mononucleosis 42

Table 3.1. Questions Inquiring Frequency of Occurrence and
Subjective Rankings. 56

Table 3.2. Demographic Distribution of Participating Twin Pairs. 58

Table 3.3. From Each Twin, the Relative Ranking of the Pair vs. the
Frequency-based Ranking of the Pair, Selected
Events/Characteristics 59

Table 3.4. Pattern of Paired Twin Summary Rankings Provided by Twins
and Their Mothers, Selected Events and Characteristics 65

Table 4.1. Demographic Distribution of Participating Twin Pairs 85

Table 4.2. Correlation Coefficients for Illness Measures in Toddler
Years and All Ages 86

Table 4.3. The Effect of Childhood Illness on Shorter Stature Within
Monozygotic Twin Pairs Discordant for Adult Height 87

Table 4.4. The Effect of Childhood Illness on Shorter Stature Within
Monozygotic Twin Pairs Discordant for Adult Height but with
Similar Birth Length 87

Table 4.5. The Effect of Infectious Illnesses during Toddler Years on
horter Stature Within Monozygotic Twin Pairs Stratified,
by Adult Height Differences (1 inch vs. > 1 inch) 90

vii

Table 4.5a. The Effect of Smaller Birth Size on Illness During Toddler
Years (1-5 Years) Among Height-Discordant Monozygotic
Twin Pairs. 92

Table 4.5b. Effect of Smaller Birth Size on Illness in Childhood
(0-18 Years) Among Height-Discordant Monozygotic Twin
Pairs. 92

Table 4.7. The Effect of Smaller Body Size at Different Ages on Shorter
Adult Stature Within Height-Discordant Monozygotic Twin
Pairs. 93

Table 5.1. Previously Reported Associations Between Various Cancers,
Height and Childhood Infections 103

viii

List of Figures

Figure 1.1. Important Energy Consuming Functions of Immune Cells. 16

Figure 1.2. Energy Distribution of Children Between Maintenance
and Growth Function. Cost of Trade-off is Dependent on
Available Energy Source. 17

Figure 2.1. Kaplan-Meier Curve of Freedom From Infectious
Mononucleosis in Co-Twins of Index Cases by Zygosity 43

Figure 3.1. Percentage of Informative Reports Provided From Subjective
Ranking and Frequency-based Ranking. 61

Figure 3.2. Correlation of the Percent of Question-Specific Informative
Responses Within Identical Twin Pairs. 62

Figure 3.3. Ratio of Rankings Reported by Identical Twins and Their
Matched Co-twins. 63

Figure 3.4. Percentage of Informative Reports Provided From Subjective
Ranking and Comparative Frequency Ranking 67

Figure 3.5. Correlation of the Percent of Question-Specific Informative
Responses From Mothers with that from their Individual Twins 68

Figure 3.6. Ratio of Ranking Measured by Rankings Reported From
Mothers and Twin Pairs in Agreement 68

Figure 4.1. Forest Plot Showing Effect of Illnesses in Childhood on Shorter
Stature Within Height-Discordant Monozygotic Twin Pairs,
Listing All Childhood Illness Factors by Age Group. 84

Figure 5.1. Overview of Rationale for Proposed Hypothesis 104

ix

Abbreviations

VZV Varicella zoster virus
EBV Epstein Barr virus
H. pylori Helicobacter pylori
HZ Herpes zoster
IM Infectious mononucleosis
SES Socioeconomic status
CTP California Twin Program
MZ Monozygotic
DZ Dizygotic
OR Odds ratio
CI Confidence interval
CRP C-Reactive Protein
HL Hodgkin’s lymphoma
GH Growth hormone
IGF Insulin-like growth factor-1
IL Interleukin
TNF Tumor necrosis factor
IGFBP Insulin-like growth factor binding protein
EEA Equal environment assumption
SIR Standardized incidence ratio
HR Hazard ratio

x

HLA Human leukocyte antigen
CVD Cardiovascular disease
CHD Coronary heart disease
Th T-helper lymphocyte
Th-1 Th type 1
Th-2 Th type 2
BMI Body mass index
SE Standard error

xi

Abstract

Childhood health experience may have a long term effects lasting into adulthood.
Childhood infections have an etiologic role in the development of cancers and chronic
diseases. For example, Epstein Barr virus (EBV) and Helicobactor pylori are well
established causes of EBV-positive Hodgkin’s lymphoma and stomach cancer,
respectively. Height has also been commonly associated with many cancers and other
chronic conditions. It is usually interpreted as a proxy for childhood socioeconomic status
or nutrition. However the effect of height on diseases has been consistently reported
independent of the effects of childhood socioeconomic status and diet, suggesting height
may represent other underlying biological mechanisms. Studies examining childhood
experiences are challenging due to unreliable recall by study participants and the
unavailability and incomplete of medical records.
Twins offer unique advantages for studying childhood health experience because
they can provide relative differences in exposure, they can validate each other’s answers,
and they are either partially or entirely matched on genome. This dissertation consists of
several projects examining childhood health in which twins identified from the California
Twin Program were the participants. For two of the studies, both young adult twins and
their mothers were interviewed, providing consistent and reliable information pertaining
to the twins’ childhood illness history. In a descriptive study, comparing responses from
mothers and twins about childhood exposures, I found all subjects were able to provide
the most information on differences between the twins when questions were framed in a
comparative fashion with ordinal answers. Although the number of pairs reporting

xii

differences in exposures was small, their answers were generally consistent with their
mothers.
Infectious mononucleosis, a disease caused by a delayed infection of EBV, is
associated with EBV positive Hodgkin’s lymphoma and multiple sclerosis. I conducted a
heritability study in monozygotic and dizygotic twins and found evidence suggesting a
genetic component in the development of infectious mononucleosis.
The effect of childhood illnesses on adult height was assessed in healthy identical
twins differing in height as adults, to control for genetic factors, childhood
socioeconomic status and parental exposures. The twin who reported more frequent
episodes of febrile illnesses was twice as likely to become the shorter twin as an adult.
This effect was consistent after adjusting for birth weight and birth height and was
strongest and most significant during toddler years (1-5 years of age).
In conclusion, these studies suggest that childhood illnesses are a determinant of
adult height. In the future, these findings can be applied to elucidate the relationship
between childhood infections and adult diseases.

Chapter 1. Background

1.1 INTRODUCTION
Childhood infectious diseases are a substantial cause of morbidity in children
throughout the world. In developing countries, infectious diseases are also a significant
cause of mortality in children. According to the Global Disease Burden report in 2001,
out of 56 million total deaths in the world, 10.6 million occurred in children, 99% of
whom were of lower income (Table 1.1) (Lopez et al. 2006). The major infections
associated with childhood mortality are diarrheal diseases, acute respiratory infections,
measles, malaria and HIV/AIDS (Lopez et al. 2006).

Table 1.1. Leading Cause of Mortality in Children Under Age 5 By Economic
Development. Adopted from (Lopez et al. 2006)

Cause of mortality in children
Percentage of all death in children
< 5 years of age
Low- and Middle-
income countries (%)
High-income
countries (%)
Infection related diseases
Acute respiratory infections 18.4 2.3
Diarrheal disease 15.2 0.6
Malaria 10.3 0.1
Measles 5.3 0.1
HIV/AIDS 3.2 0.1
Non-infection related diseases
Perinatal conditions 23.7 44.5
Other causes 17.0 17.9
Congenital anomalies 4.3 3.6
Injuries 2.9 9.8

1

Children in developing countries experience about 5 to 19 episodes of severe
diarrheal diseases per year, with the highest attack rate within the first 2 years of life,
whereas children in developed countries experience an average of 2 episodes per year of
a much milder course (Guerrant et al. 1990). In developed countries, the incidence and
severity of infectious diseases are considerably lower due to better hygiene, nutrition,
vaccination, and access to health care. Infections contribute less than 4% of all causes of
mortality in U.S. children under 5 years of age (Gardner et al. 1996); in contrast, in
developing countries, infections are responsible for over 50% of childhood deaths (Lopez
et al. 2006). Nonetheless, infections cause considerable morbidity in young children in
developed countries, with some long term consequences to be discussed later in this
chapter. Infections account for the majority of pediatric office visits in the U.S.,
specifically otitis media, upper respiratory infection, acute pharyngitis, bronchitis and
tonsillitis (Cypress 1983).

Table 1.2. Infectious Agents Implicated in Causal Association With Adult Diseases

Pathogen Initial Infectious disease Adult sequelae
Epstein-Barr virus Asymptomatic or Hodgkin lymphoma
Infectious mononucleosis Nasopharyngeal cancer
Burkitt’s lymphoma
Multiple sclerosis
Helicobacter pylori Asymptomatic Peptic ulcer
Gastric cancer
Varicella zoster virus Chicken pox Herpes zoster (shingles)
Hepatitis B virus Asymptomatic or Liver cancer
Acute hepatitis Non-Hodgkin lymphoma
2

Even if children survive their bouts of infectious illnesses, there may be long-term
effects. Childhood infections have been linked to adult morbidity and longevity
(Gutensohn et al. 1981; Albonico et al. 1998; Blackwell et al. 2001; Kleef et al. 2001;
Hart et al. 2003; Finch et al. 2004; Glaser et al. 2005; Crimmins et al. 2006; Paltiel et al.
2006). Several viruses that commonly cause childhood infections, such as Epstein Barr
virus (EBV), Helicobactor pylori (H. pylori), varicella zoster virus (VZV), and hepatitis
B virus, are known to be specifically linked to adult sequelae (Table 1.2) (Smith et al.
1995; Nielsen et al. 2007; de Martel et al. 2009; Rook et al. 2011). Additionally, timing
of exposure to infectious pathogens can impact susceptibility to adult disease years after
the initial infection (Blaser et al. 1995; Hjalgrim et al. 2007). For example, delayed
exposure to EBV during childhood results in infectious mononucleosis (IM) and has been
linked to Hodgkin lymphoma and multiple sclerosis (Hjalgrim et al. 2007). Early
exposure to H. pylori is associated with gastric carcinoma (Blaser et al. 1995). Surrogate
measures of non-specific exposure to infectious illnesses in childhood, such as doctor
visits, hospitalization, sibship size, and daycare attendance, have been implicated in
etiology of many cancers (Albonico et al. 1998; Kleef et al. 2001; Glaser et al. 2005;
Paltiel et al. 2006), chronic heart disease (Hart et al. 2003), and overall mortality
(Blackwell et al. 2001; Gurven et al. 2008). Several mechanisms for the association
between childhood infections and adult chronic diseases have been proposed and studied
extensively, and will be discussed later in this chapter.

3

1.2. SPECIFIC INFECTIONS WITH SPECIFIC OUTCOME
A few common childhood infectious agents are considered to be direct causes of
cancer and chronic diseases later in life (Table 1.2) (de Maat et al. 2004; Parkin 2006).
Childhood infectious pathogens such as H. pylori, hepatitis B and VZV, virus have been
implicated with gastric carcinoma, hepatocellular carcinoma (de Maat et al. 2004), and
herpes zoster (Smith et al. 1995) respectively. EBV acquired in childhood has been
linked to Burkitt’s lymphoma and nasopharyngeal cancer (de Martel et al. 2009).
Infectious mononucleosis (IM), a consequence of delayed exposure to EBV in
adolescence, has been associated with increased risk of Hodgkin lymphoma and multiple
sclerosis (Hjalgrim et al. 2003; Ambinder 2007; Nielsen et al. 2007; Levin et al. 2010).

1.2.1 Timing of exposure
The timing of exposure to infectious agents during childhood influences severity
of the subsequent infectious disease, as demonstrated by the polio model (Nielsen et al.
2002). Poliomyelitis, caused by the highly contagious poliovirus, was historically
acquired in childhood and caused asymptomatic or mild nonspecific symptoms. But 4-8 %
of infected individuals experienced symptomatic non-paralytic polio with pain in the
neck, back and leg. Only a few infected patients (<2%) developed paralytic polio with
muscle pain and flaccid paralysis (Nielsen et al. 2002). In the late 19
th
century,
poliomyelitis became increasingly severe and a shift from an endemic and largely
asymptomatic infection in infancy to an epidemic and symptomatic infection during
childhood and adulthood with a sharp increase in adult paralytic cases was noted
4

(Horstmann 1963). It was postulated that due to improvements in sanitation and hygienic
living conditions, children were no longer being exposed to polio virus during infancy
when the infection is likely to be asymptomatic, and that delayed exposure with infection
acquired in adolescence or adulthood increased the risk of developing a more severe
infection with paralysis (Nielsen et al. 2002). Factors that facilitated earlier exposure with
a lower risk of developing paralysis included large sibship size and late birth order and/or
low socioeconomic status (Weinstein 1957). This risk pattern was referred to as the polio
model, and suggests that delayed exposure to a specific infectious agent results in more
severe sequelae. The model explains the risk pattern for symptomatic or severe disease
due to Hepatitis A, measles, varicella, cytomegalovirus and other infections. Hepatitis A
is also an asymptomatic disease when acquired in infancy but is symptomatic when
acquired at an older age (Tapia-Conyer et al. 1999). Clinical hepatitis A was previously
rare in Latin America because children acquired it early, but in recent years, with
improvement in sanitation and a trend toward smaller family size, clinical hepatitis A in
adults is now common indicating a shift to later infection as seen with pre-vaccine
poliomyelitis (Tapia-Conyer et al. 1999).

1.2.2. Delayed exposure to Epstein-Barr virus and Infectious Mononucleosis
A EBV belongs to one of 8 distinct human herpesvirus, known for its ubiquitous global
prevalence of over 90% and persistent lifelong latency (Bannister 1996). EBV has been
extensively explored as a cause of Hodgkin lymphoma, Burkitt’s lymphoma, nasopharyngeal
cancer (de Martel et al. 2009), and multiple sclerosis (Nielsen et al. 2007; Levin et al. 2010). EBV
5

is easily transmitted thus it is very common in young children and is typically asymptomatic. In
developing countries, nearly 100% of the population is EBV positive by age 5 and IM is rare
(Shulman et al. 1992). In developed countries, approximately 50% of population enter
adolescence as EBV seronegative, and encounter EBV throughout the course of young adulthood
(Shulman et al. 1992). IM is a systemic febrile lymphoproliferative disease, is a clinically
symptomatic manifestation of primary EBV infection, typically due to delayed exposure to the
virus. IM, in turn, may produce a lifelong scar in the immune response that results in changes in
the lymphocyte population for years and possibly confers a significant vulnerability to Hodgkin
lymphoma and multiple sclerosis later in life (Ambinder 2007).

Table 1.3. Comparison of 5 Prospective Cohort Studies of EBV Seroconversion and IM
Development Among University Students. Adopted from (Sawyer et al. 1971; University
of Health Physicians and P.H.L.S Laboratories 1971; Hallee et al. 1974)

Proportion of
seronegative at entry
(%)
Proportion of
seroconversion*
(%)
Proportion of
developing IM*
(%)
US Military Academy 511/1401 (37%) 54/437 (12%) 15/54 (28%)
5 British Universities 622/1457 (43%) 60/496 (12%) 22/60 (37%)
Yale University 175/355 (49%) 23/175 (13%) 17/23 (74%)
Edinburg University 510/2006 (25%) 110/241 (46%) ** 27/110 (25%)
* During follow up of 1 year, except Edinburg university with follow up of 3 years
** Follow up for 3 years

Prospective serological studies among college and military academy students in
the U.S and England showed a consistent 12-13% annual seroconversion, however, the
incidence of IM among the seroconverted was highly variable between institutions (range
25-74%), implying the existence of risk factors other than age at exposure (Table 1.3)
(Sawyer et al. 1971; University of Health Physicians and P.H.L.S Laboratories 1971;
Hallee et al. 1974; Crawford et al. 2006). The host age, host immune system, viral strain,
6

size of primary inoculum and possibly infection with multiple strains are some of the
possible determinants of IM (Ambinder 2007). Recently, it was found that human
leukocyte antigen (HLA) polymorphism associated with EBV-positive Hodgkin
lymphoma was also associated with IM (McAulay et al. 2007). Thus it is possible that
individual variations in IM susceptibility have heritable component. As a part of this
dissertation, I have examined heritability of IM susceptibility in twins which will be
discussed in chapter 2.

1.2.3. The effect of timing of exposure of childhood infections on adult morbidity
Young adult Hodgkin lymphoma has a risk pattern that is similar to that of pre-vaccine
paralytic polio. Risk factors include early birth order, small sibship size, high parental
socioeconomic status and less crowded conditions (Glaser et al. 2005; Ambinder 2007; Hjalgrim
et al. 2007). Thus it is thought that, like paralytic polio, delayed exposure to a childhood virus
results in the more serious sequelae of young adult Hodgkin lymphoma. EBV is a good
candidate because it has a predilection for lymphocytes, it is present in tumors in about
30% of cases (Ambinder 2007), and elevated antibody levels precede disease (Mueller et
al. 2011). IM has been associated with an increased risk of Hodgkin lymphoma, up to 20-fold
risk within 5 years (Hjalgrim et al. 2003). However this increased risk is exclusively associated
with Hodgkin lymphoma that is EBV tumor positive, and not with the majority of EBV tumor
negative cases occurring in young adults (Hjalgrim et al. 2003). It is suspected that a different
virus may be the result of EBV tumor negative Hodgkin lymphoma, although the specific agent
is not yet known.
7

Early exposure to infectious pathogens during childhood can also affect risk of
adult diseases decades later in life. H. pylori infection is commonly acquired in early
childhood and establishes a chronic infection (Pounder et al. 1995). Prevalence is high
worldwide but varies by country and socioeconomic status (SES). In developing
countries, the infection is prevalent in 80% of the population, majority of who acquire the
infection before age 10 (Pounder et al. 1995). In developed countries, the incidence of
infection is rare in childhood but increases gradually to a prevalence of 20-50% in adults
(Pounder et al. 1995; Suerbaum et al. 2002). Surrogate measures of early life exposure to
infection such as large sibship size and late birth order in a large family are well-
established predictors for H. pylori infection (Goodman et al. 1996).
During chronic infection, H. pylori induces intense and continuous inflammation
in infected individuals, causing increases in proinflammatory cytokines such as IL-1 β,
IL-2, IL-6, IL-8 and TNF- α, and epithelial cell turnover and apoptosis leading to
development of atrophic gastritis and peptic ulcer (Suerbaum et al. 2002). Lifetime risk
of developing peptic ulcers from H. pylori infection range from 3% in United States to 25%
in Japan, which in turn may lead to gastric cancer (Suerbaum et al. 2002). About 3% of
1246 Japanese gastritis patients infected with H. pylori developed gastric cancer during 8
years of follow-up, whereas none of 280 uninfected gastritis patients or 253 H. pylori
treated patients (eradication therapy) from the same cohort developed gastric cancer
(Uemura et al. 2001). A pooled analysis 12 nested case control studies found that
individuals with H. pylori infection had 3-fold risk of developing gastric cancer and that
among those who has had H. pylori infection for more than 10 years before diagnosis, the
8

risk was 6-fold (Helicobacter and Cancer Collaborative Group 2001). Blaser and
colleagues studied reported that late birth order in a large family was associated with
earlier H. pylori infection and higher gastric cancer risk (Blaser et al. 1995; Blaser et al.
2007). These studies suggest that H. pylori-associated gastric cancer is more likely to
occur if the infection was acquired early in childhood, another example of childhood
infections and timing of infection conferring susceptibility to a disease later in life.

1.2.4. Reactivation of varicella zoster virus infection, herpes zoster.

A childhood infectious agent can establish a lifelong latency and reactivate later
to induce a disease that is different from the initial infection. An example of this is
varicella zoster virus (VZV), another member of human herpesvirus family and herpes
zoster. Primary infection with VZV causes chicken pox or varicella which occurs
relatively early in life. Varicella incidence peaks around age 6 and because of it highly
contagious nature, more than 90% of cases occur before age 9 (Marcy et al. 1997).
Latent VZV can become reactivated causing substantial morbidity. The reactivated
infection is herpes zoster (HZ), a disease of older and immune-compromised people.
During primary infections, the virus establishes a chronic latent infection in the
dorsal root ganglion of the brainstem and spinal cord where the virus genome will reside
for the life of the host. Virus may sporadically overwhelm the host cell but are normally
neutralized by circulating antibodies or cellular immune system before replication occurs.
However ,when the host resistance wans, reactivated virus multiplies and produces
inflammation in the affected sensory ganglion and surrounding meninges, following the
9

sensory nerve to skin producing blistering lesions and vesicular eruption of HZ of the
dermatome enervated by the nerve (Hope-Simpson 1965; Bannister 1996). During a
herpes zoster infection, the host’s humoral and cell-mediated immune response are
stimulated, producing more memory T and B lymphocytes, thus a second herpes zoster
attack is rare (Marcy et al. 1997). A preliminary analysis assessing heritability of herpes
zoster showed no difference in concordance between identical, or monozygotic (MZ) and
fraternal, dizygotic (DZ) twins, however there was insufficient power to definitively
demonstrate a null result (unpublished data, see chapter 2).

1.3. NON-SPECIFIC INFECTIONS WITH ADULT DISEASES
Individuals with history of frequent childhood infectious diseases, hospitalization
due to infections, poor childhood health and large number of siblings have an increased
risk of coronary heart disease, lung disease, stomach cancer, and Non-Hodgkin
lymphoma as adults (Barker et al. 1991; Blackwell et al. 2001; Hart et al. 2003; Barker
2004; Crimmins et al. 2006; Paltiel et al. 2006; Campbell et al. 2007; McEniry 2011). In
contrast, risks of Hodgkin lymphoma, atopy, multiple sclerosis and type I diabetes are
inversely associated with large sibship, late birth order, and daycare attendance (Strachan
2000; Glaser et al. 2005; Cozen et al. 2009).
Several mechanisms have been proposed to explain the association between
general (non-specific) childhood infection and adult diseases. The ‘hygiene hypothesis’
states immune stimulation from microbial molecules early in life is necessary for normal
maturation of the immune response, and that a deficit of early childhood exposures leads
to an immature immune response and a higher risk of atopy and other immune-related
10

diseases (Strachan 1989). The ‘Barker hypothesis’ suggests that developmental events at
very early life stages affect susceptibility to coronary heart disease (CHD) and
cardiovascular diseases (CVD) later in life (Barker 2004). Although this theory does not
specifically address childhood infections, close association between birth weight and
childhood infection warrants a closer look at how infection may play a role. One novel
theory proposed in this dissertation, is that height may act as a proxy for the underlying
mechanism of the association between childhood infection and adult disease.

1.3.1. The hygiene hypothesis and Hodgkin lymphoma

Humans have naturally coexisted with a range of microorganisms such as
intestinal helminthes, fecal organisms and environmental fermenting species. Through
co-evolutionary process, we have learned to tolerate them by a regulatory pathway which
ultimately would establish a normal level of immunoregulation (Rook 2012). Diminished
exposures to these organisms weakens the ability to terminate inflammatory responses,
and the consequent defective immune regulation results in an increase in chronic
inflammatory disorders, autoimmune diseases, and allergic disorders (Rook 2012).
The underlying observation that socially advantaged may be at higher risk of
atopy dates back to the 1870s when a physician named Charles Blackley reported that
aristocrats were more likely to get hay fever than farmers (Blackley 1873). As above, the
term ‘hygiene hypothesis’ was coined by Strachan in 1989 after he observed that hay
fever was less common among individuals with older siblings (Strachan 1989). After the
Industrial revolution, autoimmune disease and allergy became more common in affluent
11

Western countries (Weiss 2002). This increase in prevalence was accompanied by a
decrease in childhood infections in these same countries due to improved hygiene and
changes in lifestyle, and the two trends were presumed to be causally related (Okada et al.
2010).
When the immune system is activated by detection of a foreign substance or
infection, T helper (Th) lymphocyte cells proliferate and produce a polarized specific
immune response to fight infections- an imbalance in these responses can lead to other
diseases. The Th-type-1 (Th1) response produces cytokines that promote the
differentiation and activation of cytotoxic lymphocytes that kill infected cells. If the T
lymphocytes do not properly mature, an overproduction of Th1 cytokines can lead to the
attack of the body’s own proteins by cytotoxic lymphocytes, resulting in autoimmune
diseases (type 1 diabetes, Crohn’s disease, and multiple sclerosis) (Romagnani 2000). Th-
type-2 (Th2) responses produce cytokines that promote the differentiation of B
lymphocytes to plasma cells that then produce antibodies to neutralize extracellular
antigens (bacteria, parasites and viruses prior to cell entry). A lack of regulation and
overproduction of Th2 cytokines leads to elevated levels of the antibody IgE which
promotes the development of allergic conditions (asthma, atopic dermatitis, allergic
rhinitis) (Romagnani 2000). The Th1 and Th2 cytokine responses are mutually inhibitory.
Initially, it was postulated that hygienic living conditions prevented the necessary stimuli
to induce a mature Th1 response, resulting in an increase of Th2 diseases (Sheikh et al.
2003; Vercelli 2006). However in recent years, the hygiene hypothesis has evolved to
suggest that the hygienic conditions alter immune regulatory functions orchestrated by T
12

regulatory cell subsets (Th17 and Tregs) that carefully balance Th1 and Th2 responses,
resulting in an increase of both Th1 (autoimmune) and Th2 (atopic) diseases (Wills-Karp
et al. 2001).
Although the focus for this hypothesis has been on autoimmune and atopic
disease, a few investigators have explored a possible connection between the hygiene
hypothesis and cancer (Cozen et al. 2009). Young adult Hodgkin lymphoma occurs more
commonly in high SES populations. EBV-Hodgkin lymphoma comprises majority of the
cancer young adults and appears to be independent of EBV infection (Hjalgrim et al.
2003). Risk factors fit the poliomyelitis model (Gutensohn et al. 1981). Our USC twin
group found that factors associated with a Th2-skewed immune response (eczema,
smoking, decreased Th1 cytokines) and a deficit of oral exposures were associated with
an increased risk of young adult Hodgkin lymphoma in twins and proposed that the risk
pattern was compatible with the hygiene hypothesis mechanism (Cozen et al. 2004;
Cozen et al. 2009).

1.3.2. Barker hypothesis
Numerous studies have shown that birth weight is inversely associated with
hypertension, coronary heart disease and mortality (Bergvall et al. 2007; Eriksson et al.
2007). In 1992, David Barker, a cardiovascular specialist, examined cause of mortality in
a cohort of,5,000 men born in 1911-1930 and reported that low birth weight and
childhood respiratory infection was associated with death from chronic obstructive
airways diseases (Barker 1992). It has since become known as the ‘Barker hypothesis’ or
13

‘Fetal origin of disease hypothesis’ (Paneth et al. 1995). The Barker hypothesis
emphasizes that a deficient nutritional environment in utero results in low birth weight,
and an accelerated growth thereafter encodes a predisposition for developing obesity,
hypertension and cardiovascular diseases and ultimately mortality (Barker 2004).
Barker’s earlier work reported that lower birth weight, whopping cough,
bronchitis and pneumonia in infancy reduced adult lung function (Barker et al. 1991). A
prospective birth cohort study demonstrated that after >20 years of follow up, infection in
early life may be an important effect modifier for an association between birth weight,
postnatal growth and blood clotting factors known to be associated with elevated risk of
cardiovascular disease (Fraser et al. 2008). Another study in Puerto Ricans aged 60-74
showed that those born during the lean season (July – December), assumed to be exposed
to poorer nutrition and increased infections relative to those born in the rainy season, had
a 70% increase in risk of adult heart disease (McEniry 2011). However, birth cohort
study in Sweden that followed 1,319 men for 85 years did not observe an association
between gestational adjusted birth weight and CHD and cardiovascular mortality
(Eriksson et al. 2004).
It is not clear whether childhood infection is directly involved as a modifier of the
association between birth weight and cardiovascular disease, but is worth examining.

1.3.3. The relationship of childhood infection to adult height
Height is a well-established predictor of overall mortality and many chronic
diseases (Barker et al. 2001; Gunnell et al. 2001; Smith et al. 2001; Batty et al. 2006;
14

Green et al. 2011). Taller stature is linked to increased risks of Hodgkin lymphoma,
breast cancer, and leukemia (Batty et al. 2006; Green et al. 2011) and is inversely
associated with cardiovascular disease, stroke and stomach cancer (Gunnell et al. 2001;
Smith et al. 2001; Eriksson et al. 2004). Aside from genes, adult height is largely
determined by diet, SES and the timing of puberty, therefore in disease association
studies it is assumed that height is a marker for these specific environmental factors.
However, some studies have reported a consistent association between adult height and
disease independent of childhood SES, BMI, timing of puberty, and nutritional intake
(Gunnell et al. 2001; Smith et al. 2001; Batty et al. 2006; Green et al. 2011) suggesting
that height may represent other factors related to disease etiology. In this dissertation, I
propose that height may reflect the cumulative experience of childhood infections. I will
discuss the results of a study I conducted to test that hypothesis in chapter 4.

1.3.4. Energy Redirection
One possible mechanism that could explain a possible association between
childhood infections and height is energy redirection, based on the ‘life history theory’
which states that each life stage (gestation, infancy, childhood, adolescence and
adulthood) has a unique set of biological needs and adaptive challenges (McDade 2003).
Energy resources to address these needs are limited and are allocated to 3 life functions;
growth, reproduction and maintenance. They are mutually exclusive and limited, and a
tradeoff is inevitable (McDade 2003).

15

F
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16

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17

Children with infection will have less energy available for growth, unless the
depleted energy is replenished (Figure 1.3) (McDade 2003). An elevated level of C-
reactive protein (CRP), a marker for immune activation, was associated with smaller
weight gain in children, especially for those 2-4 years of age due to limited energy
reserves (body fat) at this age (McEniry 2011). Sick children also experience loss of
appetite, diminished food intake and nutrition malabsorption (Mata, Kromal et al. 1977;
Martorell, Yarbrough et al. 1980). The resulting negative energy balance is detrimental in
a nutritionally deprived environment, and can result in a loss of up to 30g protein per day
(Rowland et al. 1988), thus weight loss and growth faltering in sick children are common
in developed countries (Mata et al. 1977). Even when additional energy sources become
available after an illness, energy required for catch up growth is much higher than that for
regular growth (Wiskin et al. 2011). The weight loss experienced during acute illness
may be overcome, but repeated or chronic infections may have a long-term impact in
final adult height (Wiskin et al. 2011).

1.3.5. Inflammation from infections lowers growth hormone
Systemic hormones such as growth hormones (GH), insulin-like growth factor 1
(IGF-1), insulin, thyroid hormone, glucocorticoid, sex steroid regulate bone growth
(Ahmed et al. 2009). GH and IGF-1 promote linear growth via cell division and
differentiation at the level of long bone growth plate (Ahmed et al. 2009; Clayton et al.
2011). Inflammation due to infections results in increase in pro-inflammatory cytokines
IL-6, TNF- α, and IL-1 β production, which are thought to mediate decreases in GH and
18

IGF-1 and inhibit growth plate chondrocyte differentiation (De Benedetti et al. 1997;
Ahmed et al. 2009). In animal models, inflammation-induced high levels of interleukin-6
(IL-6) and low levels of IGF did not return to normal with nutritional supplementation
(Ballinger et al. 2000).
Cancer risk has been linked to elevated levels of circulating IGF-1 and decreased
levels of the main binding protein for IGF-1 (IGFBP-1) (Clayton et al. 2011). The main
function of GH and IGF-1 is to promote cell division, differentiation and metabolism,
thus if they are present within oncogenic environment, they may facilitate in promoting
tumor growth and differentiation.
A meta-analysis showed that IGF-1 levels, but not IGFBP-1, was modestly but
significantly associated with prostate, breast, colorectal and lung cancer (Clayton et al.
2011). Multiple myeloma risk has been associated with polymorphisms in IGF-1 and
IGFBP-1 (Birmann et al. 2009). Acrommegaly, an endocrine condition characterized by
hypersecretion of GH, is strongly associated with colorectal, colon, rectal and thyroid
cancer with statistically significant odd rations ranging from 2.5 to 13.4 (Clayton et al.
2011). GH and IGF-1’s promoting role of cell division and differentiation may escalate
tumor cell growth.

1.4. TWIN STUDIES
Researchers studying early childhood life history face the challenging task of
collecting reliable data. Due to impracticality of obtaining decades-old pediatric medical
records, epidemiologic studies focusing on childhood health typically rely in
questionnaire information obtained by adult participants’ recall of their own experience.
19

We believe that twins can reliably provide relative exposure differences between them in
the absence of clinical data, thus serve as an advantageous source of data to assess early
childhood life history. Work evaluating this premise will be presented in Chapter 3. Due
to their similar genetic and childhood environmental exposures, twins as study subjects
can help disentangle genetic and environmental causes of diseases and facilitate the
biological interpretation of research findings with less chance of confounding. These
distinctive characteristics allow twins to be valuable research participants in
epidemiology and other areas of research.

1.4.1. History of twin research
The first mention of twins in medical literature dates back to Hippocrates, who
attributed resemblance of twins to their shared material environment (Hippocrates 1931).
The first descriptions of modern methods of twin research is typically credited to Francis
Galton who examined similarities in physical characteristics and personalities of twins
and in association with their shared life history (Rende et al. 1990). In 1869, Galton
proposed that twins who are initially similar grow to be different due to non-shared
environment in their adulthood once they leave their parents’ home, and that twins who
are initially different grow to be similar due to their shared early environment (Rende et
al. 1990). Merriman was the first to recognize the value of comparing a trait of identical,
or monozygotic (MZ) twins to that of fraternal, or dizygotic (DZ) twins to assess
heritability. In 1924, he published a paper describing the fundamental difference between
DZ and MZ twins: that DZ twins are genetically no different than ordinary siblings,
20

because they develop from a different ovum and sperm, while MZ twins are genetically
identical because they develop from the same ovum and sperm (Merriman 1924; Rende
et al. 1990). That same year, Hermann Seimens published a description of the classic
twin method, comparing the concordance of physical traits between MZ and DZ twins,
and interpreting the difference between the two concordance estimates as a reflection of
the heritability of the trait (Siemens 1924; Rende et al. 1990). For example, if there is
more than a two-fold concordance rate ratio for a dichotomous trait among the MZ
compared to the DZ twin pairs, the interpretation would be that the trait was partially
heritable (because MZ twins share twice the genome compared to DZ twins). Since these
early heritability studies, many other study designs were developed to utilize the unique
advantages of twins, discussed in the sections below.

1.4.2. Advantages of twin studies
Two major advantages of using twins as participants are their ability to provide a
relative measure of exposure and proxy information about their each other. Twins grow
up being compared to each other and are aware of their developmental and behavioral
differences. Having been compared all their lives, twins can report relative qualitative
differences that might be difficult to quantify on an absolute basis (ie. number of ear
infections vs. which twin had more ear infections) (Reynolds et al. 2005). A study of
breast cancer in female twins by our group found that twins are dependable proxies for
each other and that the information obtained from single respondents about both members
of the pair is reliable (Hamilton et al. 2000). Twin studies also offer the advantage of
21

using the genetic similarity to assess heritability or to control for genetic influence on the
disease etiology. Comparing disease occurrence between MZ and DZ twins serves a as a
crude indicator of possible genetic susceptibility (Martin et al. 1997). Additionally,
restricting studies to MZ twins can remove any confounding that might result from
genetic differences, so that environmental determinants can be more essentially studied.
Finally, an unaffected MZ co-twin of a case can serve as a genetic proxy for the case if
the case is deceased or unavailable. Susceptibility phenotype, to the extent that it is
heritable, can also be approximated using the unaffected co-twin of a case as a surrogate
case, when disease or treatment affects the trait being measured. This approach was used
by our group to evaluate whether elevated or decreased cytokine levels (mostly heritable)
were associated with susceptibility to young adult Hodgkin lymphoma by measuring the
cytokines in the unaffected MZ twin of a case and comparing those levels to a non-
related control (Cozen et al. 2004; Cozen et al. 2008).
Twins also share early environmental factors to a greater degree than any other
matched research subject pairs. Thus, they are generally matched on early SES, lifestyle
factors and parental behaviors (such as smoking). That said, a proportion of even MZ
twins are discordant for environmental exposures and disease (Cockburn et al. 2002).
Practical advantages include high compliance and excellent study adherence.
Family members of twins are also more likely to participate in research studies. Having
been raised from by same mother, twins understand and interpret questions similarly.
Twins are also easy to locate and follow up, as establishing a communication with one
twin can facilitate locating the other twin.
22

1.4.3. Epidemiologic study designs using twins as participants
Twin studies can be used to study both environmental and genetic determinants of
a disease. Comparing concordance among MZ twins to that of DZ twins crudely assesses
whether genetics contribute to the expression of a given trait (Martin et al. 1997).
However, the interpretation of the data is often based on the ‘Equal Environment
Assumption’ (EEA), that MZ and DZ twins equally share environmental influences and
exposures (Kendler et al. 1993; Evans et al. 2000). However, MZ twins are treated more
similarly than DZ twins by parents, and tend to remain closer and influence each other as
peers more than DZ twins do; thus MZ twins tend to more closely share environmental
factors (Guo 2001; Tishler et al. 2007). If EEA is not met, then the excess in effect
estimate observed in MZ twins may be attributed to both greater environmental and
genetic similarity in MZ compared to DZ twin pairs. Thus when this method is used,
similarity of environmental exposures should also be considered.
A few classic study designs utilizing twins are presented below. There are many
other ways of employing twins in research studies especially to determine gene-
environment interaction with more complex genetic heritability modeling (MacGregor et
al. 2000). However, the following discussion will focus on study designs that are relevant
to this dissertation.

Heritability studies using concordance rates (see chapter 2)
If population-based age-specific incidence rates for the disease of interest are
readily available, a standard cohort analysis can be used assess heritability (Mack et al.
23

1995). A sample of disease discordant twins pairs are selected as a cohort, and the
unaffected cotwins of case twins are followed for a given period. Based on the population
age-specific incidence rate of the disease, the expected number of disease occurrences in
the unaffected cotwins is compared to observed numbers to calculate standardized
incidence ratio (SIR), separately for MZ and DZ twins. Increased observed/expected
incidence in the unaffected cotwins suggests familiality, which could have environmental
and/or genetic causes. If the SIR is substantially higher in MZ compared to DZ
unaffected cotwins, a role for genetics in the etiology of the disease is likely.
If population-based incidence rates are not available but the age of diagnosis is
available, risk to the initially unaffected MZ and DZ cotwins over time can be calculated,
conditioned on the time of diagnosis of the index case. The time interval is defined as the
time from the disease onset in the index case to the disease onset in the initially
unaffected cotwin or censor. A hazard ratio (HR) using a proportional hazard regression
model and survival curve comparison can be assessed for MZ and DZ initially unaffected
cotwins. A higher risk over time to initially unaffected MZ compared to DZ cotwins
suggests heritability, if the EEA is met. As previously mentioned, interpretation should
be conservative because, for this analysis, there is no standard population with which to
make a comparison. If the excess of risk in MZ unaffected cotwins is large in magnitude,
a role for heritability is suggested.
In the absence of population-based incidence rates, pairwise or probandwise
concordance can also be computed in MZ and DZ twin pairs as a crude measure of
24

heritability. The two types of concordance rates are calculated as follows (Witte et al.
1999);
Pairwise concordance rate =
C
C+D

Probandwise concordance rate =
2C
2C+D

C = number of twin pairs concordant for a given trait
D = number of twin pairs discordant for a given trait

The difference between pairwise and probandwise concordance lies in the method
of participant ascertainment. There are two ways to assemble twin study populations. In
one method, a cohort of twins is created based on an individual’s status of being a twin
(ie. birth records), and the twins are then followed prospectively to capture disease
occurrences. Because data is collected from a population-based set of participants, there
is no bias and thus the conventional method of pairwise concordance rate is sufficient. In
the other method, subjects are selected based on their disease status, (ie. cancer registry)
and linked to another data source to identify their twin status (ie. birth records). In this
case, disease status of each twin contributes to a probability of being recruited into the
study (Mcgue). Concordant twin pairs (in whom both members of the pair have the
disease) would essentially appear twice in the disease registry, once for each twin; hence
as a pair, they are twice as likely to be ascertained as discordant twin pairs. Therefore, to
correct for the ascertainment bias, probandwise concordance rate is the more appropriate
formula to use (McGue et al. 1992; Witte et al. 1999). An two-fold or higher excess
25

concordance rate in MZ compared to DZ twin pairs is suggestive of genetic component in
the disease etiology, given EEA is met (Tishler et al. 2007). As previously mentioned,
results should be interpreted conservatively as there is no standard population.

Matched case-control study in disease or trait discordant pairs (see chapter 4)
This is a standard case-control study in which the twin with the disease or trait is
designated as the ‘case’, and their unaffected cotwin is designated as the ‘control’.
Exposures are measured as in any other case-control study. This study design is
equivalent to other matched case-control studies, except that the twins are matched on
age, race/ethnicity, possibly sex, childhood SES, and parental factors. A conditional
logistic regression is used to assess odds ratio (OR) as an effect estimate.

1.5. CONCLUSION

Exposure to infectious agents during childhood has a significant impact on adult
health. Some exposures to infections are necessary to establish a balanced immune
system, while others produce acute or late effects. Some pathogens establish latent and
lifelong infections that can cause morbidity decades later. Some effects of early
childhood infections may not be organism specific, but may affect adult disease and traits
in more general ways. The associations between early childhood illnesses and adult
disease are difficult to study because of confounding and recall bias. Twins as subjects
can offer a valuable advantages to studies examining early childhood factors. In this
dissertation, I have examined the heritability of a sequelae of delayed infection with a
26

ubiquitous childhood virus (infectious mononucleosis) (chapter 2), tested the hypothesis
that early childhood illnesses affect an adult trait (height) (chapter 3), and evaluated the
value and credibility of information on early childhood illnesses provided by young adult
twins and their mothers (chapter 4).

27

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34

Chapter 2. Evidence of Genetic Susceptibility to
Infectious Mononucleosis: A Twin Study

This paper has been published in Epidemiology and Infection, 2011, Dec 8, page 1-7
(PMID:22152594)

2.1. ABSTRACT
Infectious mononucleosis is a clinical manifestation of primary Epstein-Barr virus
infection. It is unknown whether genetic factors contribute to risk. To assess heritability,
we compared disease concordance in monozygotic to dizygotic twin pairs from the
population-based California Twin Program and assessed the risk to initially unaffected
co-twins. One member of 611 and both members of 58 twin pairs reported a history of
infectious mononucleosis. Pairwise concordance in monozygotic and dizygotic pairs was
respectively 12.1% (SE = 1.9%) and 6.1% (SE= 1.2%). The relative risk (hazard ratio) of
monozygotic compared to dizygotic unaffected co-twins of cases was 1.9 (95% CI = 1.1-
3.4, p = 0.03), over the follow-up period. When the analysis was restricted to like-sex
twin pairs, that estimate was 2.5 (95% CI = 1.2-5.3, p = 0.02). The results are compatible
with a heritable contribution to the risk of infectious mononucleosis.

2.2. INTRODUCTION
Epstein-Barr virus (EBV) is an ubiquitous herpesvirus infection that persists
indefinitely in the host. It is transmitted via close oral contact in the setting of childhood
crowding, in which infection is common in young children and is typically asymptomatic
35

(Bannister 1996). Infectious mononucleosis (IM) is a clinical manifestation of primary
EBV infection that occurs chiefly in adolescents and young adults who escaped infection
as children (Bannister 1996).
Even when primary EBV infection occurs relatively late, symptomatic IM occurs
in only a fraction of primary infections (Evans et al. 1989). Prospective serological
studies among college and military academy students in the U.S, Scotland and Hong
Kong show a consistent 12-13% seroconversion rate to EBV positivity in the first year of
college, but the incidence of IM among the seroconverted varies greatly between
institutions (range 28-74%), implying the existence of important risk factors other than
age (Sawyer et al. 1971; University of Health Physicians and P.H.L.S Laboratories 1971;
Hallee et al. 1974; Dan et al. 1990; Crawford et al. 2006).
The extent to which genetic factors play a role in host response to initial infection,
seroconversion, and manifestation of clinical symptoms of IM has been examined in a
limited number of studies (Hurme et al. 1998; Helminen et al. 2001; Hurme et al. 2003;
McAulay et al. 2007). Some have reported that polymorphisms in genes coding for HLA
(McAulay et al. 2007) and cytokines (Hurme et al. 1998; Helminen et al. 2001; Hurme et
al. 2003) are associated with an increased risk of IM. However none of these studies had
enough power to adjust for other potential confounding factors.
Because identical (monozygotic, MZ) twins and fraternal (dizygotic, DZ) twins
share 100% or, on average, 50% of the genotype, respectively, a comparison of twin
concordance by zygosity can be used as a crude indicator of heritable susceptibility
(Martin et al. 1997). In addition, twins are matched on early childhood environment,
36

important because early childhood EBV infection may mitigate risk of later IM. For this
reason we sought to investigate the heritability of IM in twin pairs identified from a
population-based registry of California-born twins.

2.3. MATERIALS AND METHODS
This study was conducted according to the guidelines set by the Declaration of
Helsinki, and was completed without reference to any data that might personally identify
the selected subjects. Approval for the study was obtained from the University of
Southern California Institutional Review Board.

2.3.1. Source of subjects and data instruments
The California Twin Program (CTP) is a population-based registry of twins born
in California between 1908 and 1982, and described in detail elsewhere (Cockburn et al.
2001). Briefly, twins were identified from California birth records and contact addresses
were obtained by linkage with records of the California Department of Motor Vehicles.
From 1991 to 2001, a 16-page screening questionnaire was sent to the 115,733 individual
twins with verifiable addresses; 51,609 individual twins returned the completed
questionnaire yielding a response rate similar to or better than those reported among
similarly aged persons in other cohort studies (Kolonel et al. 2000; Bernstein et al. 2002).
This study is based on the subset of 6,926 double-respondent twin pairs born from 1957
to 1982 who received an updated questionnaire requesting more detailed information on
medical conditions.
37

Each twin was asked whether either the respondent or their co-twin ever had one
of several infectious diseases including IM, and if so, to indicate the age at onset.
Information on zygosity, gender, race/ethnicity and education was also obtained. The
twin pair constituted the unit of analysis, and the study was restricted to those twin pairs
whose members agreed on their zygosity. Study variables included zygosity (MZ, DZ),
sex of the pair (male-male, female-female, unlike-sex), race/ethnicity (White, Latino,
African-American, Native American, Asian, Other), education level (less than college
graduate, equal to or more than college graduate) and age at onset (continuous).

2.3.2. Statistical analysis
To determine the reliability of the proxy report from a twin about their co-twin,
we assessed the percent agreement and the kappa statistic of the self-reported and proxy-
reported occurrence of IM from the members of double-respondent pairs. The kappa
statistic showed that the agreement between self and proxy reports was higher than
predicted by chance (p<0.0001) but the magnitude was low (kappa =0.25-0.28). Although
inclusion of proxy respondents produced identical results, we judged proxy reports to be
insufficiently reliable and conservatively limited the formal analysis to self-reported IM
from double-respondent pairs. Pairs from which only one member reported IM were
designated “discordant”, and those from which both members reported the same
condition were designated “concordant”.
38

Pairwise concordance estimating the probability of disease occurring in both
members of a pair given that one twin was affected with the disease was calculated as
follows:
Pairwise concordance: = Number of concordant pairs
Number of concordant pairs + Number of discordant pairs
Asymptotic variance and standard errors (SE) were calculated using methods
suggested by Witte et al (Witte et al. 1999).
DZ twinning is associated with maternal age and parity (Bonnelykke 1990;
Hoekstra et al. 2008), and because large California families tend to be of lower
socioeconomic status, it is a potential confounder. Pairwise concordance was reported
separately by education level and sex. Because over 90% of the study population was
white, we did not stratify by race.
The Cox proportional hazard function and survival curve modeling (chi-square
test using PHREG and LIFETEST procedures) were used to determine the rate of
developing infectious mononucleosis in the initially unaffected co-twin, conditioned on
the age of diagnosis in the first diagnosed (index) case-twin, in MZ relative to DZ co-
twins [hazard ratio]. The interval of risk was defined as the time from the age of disease
onset in the index case-twin to the onset of disease in the co-twin or the time of
questionnaire completion (total 8257.3 person-years). We also evaluated whether the age
of the index twin’s onset of the disease modified the risk to the co-twin. Age at disease
onset was available for 87% of twins (n= 585). Because males and females may have
39

different social behavior and therefore interpersonal contact and exposure, we repeated
the analysis using only the like-sex twin pairs.
Table 2.1. Demographic Information for California Twin Pairs Born in 1957-1982

All Twins* Infectious Mononucleosis
Concordant Pairs† Discordant Pairs‡
n (%) n (%) n (%) p value§
Race/Ethnicity
White 5265 (76) 56 (96) 554 (91)
Latino 857 (12) 1 (2) 22 (4)
Black 186 (3) 0 (0) 8 (1)
Native American 91 (1) 0 (0) 2 (0)
Asian 235 (4) 0 (0) 6 (1)
Other 88 (1) 0 (0) 7 (1)

Unknown¶

204 (3) 1 (2) 12 (2) 0.82
Gender
Female-Female 3686 (53) 36 (62) 358 (59)
Male-Male 1733 (25) 11 (19) 117 (19)
Male-Female 1507 (22) 11 (19) 136 (22) 0.84
Zygosity
Monozygotic 2957 (43) 35 (60) 255 (42)
Dizygotic 3969 (57) 23 (40) 356 (58) 0.006
Zygosity-Gender
Monozygotic
Female-Female 1994 (29) 27 (46) 190 (31)
Male-Male 963 (14) 8 (14) 65 (11)
Dizygotic
Female-Female 1692 (24) 9 (16) 168 (27)
Male-Male 770 (11) 3 (5) 52 (9)
Male-Female 1507 (22) 11 (19) 136 (22) 0.08
Age# 30.8 ± 6.9 32.3 ± 5.7 31.9 ± 6.4 0.65
Total 6926 58 611

*All twin pairs in whom both members completed the screening questionnaire.
†Both members of the pair were diagnosed.
‡One member of the pair was diagnosed.
§p values are based on Chi-Square test for the categorical variables and is based on 2-
sample t test for the continuous variables.
¶Twin pairs in which members’ responses did not agree with each other.
#Mean age at completion of the questionnaire ± standard deviation.
40

Statistical analyses were performed using SAS 9.1 (SAS Institute, Cary, NC) and
95% confidence intervals (CI) and/or p values have been reported when applicable.

2.4. RESULTS
Of the 6,926 double-respondent twin pairs who completed the updated version of
the CTP questionnaire, at least one member of 669 pairs reported IM (Table 1). Twins
were slightly more likely to be white but were otherwise demographically similar to the
total twin population. Age at completion of the questionnaire ranged from 18 to 44 and
was similar among twins reporting IM and the total twin population. With the exception
of zygosity, IM-concordant and -discordant twin pairs were similar demographically.
Pairwise concordance for IM was twice as high among MZ compared to DZ twins
(Table 2, concordance ratio = 2.0, 95% CI = 1.2-3.3), and the difference was statistically
significant (difference in concordance = 6%, SE of difference = 2%, p
chi sq
= 0.008). The
higher concordance among MZ twins persisted when stratified by gender and education.
Differences were stronger when unlike-sex DZ twins were excluded (concordance ratio =
2.3, 95% CI = 1.2-4.4, difference in concordance = 7%, SE of difference = 3%, p
chi sq
=
0.008).

41

Table 2.2. Pairwise Concordance for Infectious Mononucleosis.

Concordant
*
Discordant †
Percent Pairwise
Concordance (SE)

Monozygotic Twins 35 255 12.1 (2)
Dizygotic Twins 23 356 6.1 (1)
Like-sex Dizygotic Twins‡ 12 220 5.1 (2)

By Gender
Monozygotic Twins
Female-Female 27 190 12.4 (2)
Male-Male 8 65 11.0 (4)
Dizygotic Twins
Female-Female 9 168 5.1 (2)
Male-Male 3 52 5.5 (3)
Male-Female 11 136 7.5 (2)

By Education
Monozygotic Twins
< 16 years of school 20 134 13.0 (3)
16+ years of school 15 121 11.0 (3)
Dizygotic Twins
< 16 years of school 13 204 6.0 (2)
16+ years of school 10 152 6.2 (2)
Like-sex Dizygotic Twins‡
< 16 years of school 9 129 6.5 (2)
16+ years of school 3 91 3.2 (2)

*
Both members of the pair were diagnosed.
†One member of the pair was diagnosed.
‡Subset of dizygotic twins who are male-male or female-female pairs.

Age at data collection was normally distributed (data not shown). The overall
mean age at onset for IM was 17 ± 5. Generally, the age at onset for cases in discordant
pairs was similar to the average of cases in concordant pairs (data not shown). MZ co-
twins of cases remained at consistently higher risk of IM over time relative to DZ twins
42

(hazard ratio = 1.9, 95% CI = 1.1-3.4, p = 0.03) (Figure 1). MZ co-twins had an even
higher risk when compared to like-sex DZ co-twins (hazard ratio = 2.5, 95% CI = 1.2-5.3,
p = 0.02). Age at IM onset in the index case did not modify risk to the co-twin (p
interaction

= 0.7, data not shown).

Figure 2.1. Kaplan-Meier Curve of Freedom From Infectious Mononucleosis in Co-
Twins of Index Cases By Zygosity.

2.5. DISCUSSION
Concordance for IM among MZ twins was twice that of DZ twins when considered
together and separately by sex and education level. Because age at exposure to EBV is a
determinant of IM, it is possible that the higher MZ concordance could be due to more
shared exposures in early childhood and adolescence between MZ and DZ twins.
DZ
MZ
43

Exposure to infections transmitted by the oral or respiratory route requires person-to-
person contact, and therefore the behavior of twins, parents and others plays a role.
Critiques of twin studies have argued that MZ twins are treated more similarly by parents
and peers, and thus have more similar early childhood exposures compared to DZ twins,
which could contribute to higher trait concordance among MZ compared to DZ twins
(Kendler 1983; Kendler et al. 1993; Evans et al. 2000; Eaves et al. 2003). However,
because IM concordance among MZ pairs was double that of DZ pairs, and because MZ
twins share on average twice as many genetic characteristics as DZ twins, a genetic
contribution to IM aetiology is also a plausible explanation (Evans et al. 2000; Eaves et al.
2003). If concordance was based mainly on environmental factors (e.g. time of exposure,
number of potential contacts), we might expect concordance to differ by sex, because
male and female twin pairs have different levels of shared behavior, including
interpersonal contact, and therefore exposure (Hamilton et al. 2006). Instead, we
observed that the two-fold higher concordance for MZ twin pairs was similar for both
male and female pairs, consistent with an underlying sex-independent genetic
susceptibility. The higher concordance among unlike-sex vs. like-sex DZ pairs is likely a
reflection of chance variation.
The seroconversion rate during college age is consistent across surveyed groups,
but the IM attack rate among seroconverters is variable (Sawyer et al. 1971; University of
Health Physicians and P.H.L.S Laboratories 1971; Hallee et al. 1974; Crawford et al.
2006), indicating that the rate of exposure to EBV remains consistent, but the rate of IM
among the exposed shows individual variation. Crawford and colleagues have postulated
44

that multiple exposures to sources of infection could be a risk factor for developing IM
(Crawford et al. 2002). Another possible, but not mutually exclusive, explanation is that
heritable factors play a role in host response and susceptibility.
Results from several studies of genetic susceptibility to primary EBV infection
suggest that a heritable factor might produce differences in antigen recognition, immune
response or the magnitude of cytokine production in response to infection (Hurme et al.
1998; Helminen et al. 2001; Hurme et al. 2003; McAulay et al. 2007). The target cell of
primary EBV infection is B-cells and the symptoms of IM are a result of immune
response to the EBV infection, including excessive production of inflammatory
cytokines by T cells (Farrell 2007). A genetic contribution to the variability in cytokine
secretion in response to antigens has been demonstrated, thus could also partially explain
a heritable IM susceptibility (de Craen et al. 2005). An association between
polymorphisms in genes encoding IL1- β and IL1 receptor antagonist (IL-1R α) has been
associated with EBV seropositivity and IM severity in children (Hurme et al. 1998). A
haplotype in the IL10 promoter region has been linked to increased IL-10 levels in blood
and protection against primary EBV infection and IM (Helminen et al. 2001). IL-10 is an
anti-inflammatory cytokine that is also involved in cytotoxic T cell differentiation and
induction (Hislop et al. 2007). Variation in HLA epitopes could result in differential
binding and activation of cytotoxic T lymphocytes against the lytic viral epitopes
contributing to clinical disease (Hislop et al. 2007). HLA class I polymorphisms are
associated with EBV-positive Hodgkin’s lymphoma (Diepstra et al. 2005; Hjalgrim et al.
2010), and McAulay et al. found that these same polymorphisms are present more
45

frequently in IM patients than in EBV-seronegative or asymptomatic EBV-seropositive
individuals (McAulay et al. 2007). These findings suggest that genetic variation in
antigen recognition and cytokine production may be important in IM susceptibility and
support a biologically plausible mechanism for heritability.
One limitation of this study is reliance on self-reported disease status and zygosity.
The validity of self-reported IM has been assessed several times with good results.
Crawford and colleagues reported over 90% accuracy of self-reported IM compared to
medical records among college students (Crawford et al. 2002). Self-reported zygosity is
more than 95% predictive of molecular zygosity, based on studies including subjects
from the present source population (Cockburn et al. 2001; Jackson et al. 2001). Even if
present, a minor non-differential misclassification of zygosity would slightly dampen,
and not increase, the estimated concordance difference.
The unaffected co-twin of any recognized case might be scrutinized more
carefully at time of presentation of the index case and thus produce a diagnostic bias, i.e.
over-reporting of the IM in the second twin, which could lead to overestimation of
concordance. However, such a bias is unlikely to vary much by zygosity.
We restricted our analysis to double respondent twins with disease status
ascertained by self-report from each member of the pair. As expected, a higher proportion
of MZ than DZ pairs responded in tandem. Although concordance is usually the most
important motivating factor for twin participation, in this situation, response is not likely
to be related to IM concordance because IM was one of many exposures queried and not
the main focus of the questionnaire. Therefore, although a lower response rate for DZ
46

(compared to MZ) twins in the CTP is likely, we would not expect it to affect the
proportion of zygosity-specific IM concordance.
Twins in the CTP have been shown to be representative of the general California
population with respect to age, sex, race and residential distribution (Cockburn et al.
2001). The average age at IM diagnosis among both MZ and DZ twin cases is similar to
that among IM cases in the general population (Heath et al. 1972).
Our results provide suggestive evidence for a genetic contribution to IM. Because
IM has been linked to chronic conditions, such as EBV-positive Hodgkin lymphoma
(Hjalgrim et al. 2003) and multiple sclerosis (Thacker et al. 2006), understanding the
nature of this genetic susceptibility may provide valuable clues to the aetiology of these
and other important chronic diseases.

47

CHAPTER 2 REFERENCES
Bannister, B. A. (1996). Upper Respiratory Track Infections. Infectious Disease. B. A.
Bannister, N. T. Begg and S. H. Gillespie. Oxford, Blackwell Science: 103-125.
Bernstein, L., Allen, M., et al. (2002). High breast cancer incidence rates among
California teachers: results from the California Teachers Study (United States).
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Bonnelykke, B. (1990). Maternal age and parity as predictors of human twinning. Acta
Geneticae Medicae et Gemellologiae 39(3): 329-334.
Cockburn, M. G., Hamilton, A. S., et al. (2001). Development and representativeness of a
large population-based cohort of native Californian twins. Twin Research 4(4):
242-250.
Crawford, D. H., Macsween, K. F., et al. (2006). A cohort study among university
students: identification of risk factors for Epstein-Barr virus seroconversion and
infectious mononucleosis. Clinical Infectious Diseases 43(3): 276-282.
Crawford, D. H., Swerdlow, A. J., et al. (2002). Sexual history and Epstein-Barr virus
infection. Journal of Infectious Diseases 186(6): 731-736.
Dan, R. and Chang, R. S. (1990). A prospective study of primary Epstein-Barr virus
infections among university students in Hong Kong. American Journal Tropical
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de Craen, A. J., Posthuma, D., et al. (2005). Heritability estimates of innate immunity: an
extended twin study. Genes & Immununity 6(2): 167-170.
Diepstra, A., Niens, M., et al. (2005). Association with HLA class I in Epstein-Barr-
virus-positive and with HLA class III in Epstein-Barr-virus-negative Hodgkin's
lymphoma. Lancet 365(9478): 2216-2224.
Eaves, L., Foley, D., et al. (2003). Has the "Equal Environments" assumption been tested
in twin studies? Twin Research 6(6): 486-489.
Evans, A. S. and Niederman, J. C. (1989). Epstein-Barr Virus. Viral Infections of
Humans: Epidemiology and Control. A. S. Evans. New York, Plenum Medical
Book Company.
Evans, D. M. and Martin, N. G. (2000). The validity of twin studies. GeneScreen 1: 77-
79.
Farrell, P. J. (2007). Role for HLA in susceptibility to infectious mononucleosis. Journal
of Clinical Investigation 117(10): 2756-2758.
48

Hallee, T. J., Evans, A. S., et al. (1974). Infectious mononucleosis at the United States
Military Academy. A prospective study of a single class over four years. Yale
Journal Biology and Medicine 47(3): 182-195.
Hamilton, A. S., Lessov-Schlaggar, C. N., et al. (2006). Gender differences in
determinants of smoking initiation and persistence in California twins. Cancer
Epidemiology, Biomarkers and Prevention 15(6): 1189-1197.
Heath, C. W., Jr., Brodsky, A. L., et al. (1972). Infectious mononucleosis in a general
population. American Journal of Epidemiology 95(1): 46-52.
Helminen, M. E., Kilpinen, S., et al. (2001). Susceptibility to primary Epstein-Barr virus
infection is associated with interleukin-10 gene promoter polymorphism. Journal
of Infectious Diseases 184(6): 777-780.
Hislop, A. D., Taylor, G. S., et al. (2007). Cellular responses to viral infection in humans:
lessons from Epstein-Barr virus. Annual Review of Immunology 25: 587-617.
Hjalgrim, H., Askling, J., et al. (2003). Characteristics of Hodgkin's lymphoma after
infectious mononucleosis. New England Journal of Medicine 349(14): 1324-1332.
Hjalgrim, H., Rostgaard, K., et al. (2010). HLA-A alleles and infectious mononucleosis
suggest a critical role for cytotoxic T-cell response in EBV-related Hodgkin
lymphoma. Proceedings of the National Academy of Sciences of the United States
of America 107(14): 6400-6405.
Hoekstra, C., Zhao, Z. Z., et al. (2008). Dizygotic twinning. Human Reproduction Update
14(1): 37-47.
Hurme, M., Haanpaa, M., et al. (2003). IL-10 gene polymorphism and herpesvirus
infections. Journal of Medical Virology 70 Suppl 1: S48-50.
Hurme, M. and Helminen, M. (1998). Polymorphism of the IL-1 gene complex in
Epstein-Barr virus seronegative and seropositive adult blood donors.
Scandinavian Journal of Immunology 48(3): 219-222.
Jackson, R. W., Snieder, H., et al. (2001). Determination of twin zygosity: a comparison
of DNA with various questionnaire indices. Twin Research 4(1): 12-18.
Kendler, K. S. (1983). Overview: a current perspective on twin studies of schizophrenia.
American Journal of Psychiatry 140(11): 1413-1425.
Kendler, K. S., Neale, M. C., et al. (1993). A test of the equal-environment assumption in
twin studies of psychiatric illness. Behavior Genetics 23(1): 21-27.
49

Kolonel, L. N., Henderson, B. E., et al. (2000). A multiethnic cohort in Hawaii and Los
Angeles: baseline characteristics. American Journalof Epidemiology 151(4): 346-
357.
Martin, N., Boomsma, D., et al. (1997). A twin-pronged attack on complex traits. Nature
Genetics 17(4): 387-392.
McAulay, K. A., Higgins, C. D., et al. (2007). HLA class I polymorphisms are associated
with development of infectious mononucleosis upon primary EBV infection.
Journal of Clinical Investigation 117(10): 3042-3048.
Sawyer, R. N., Evans, A. S., et al. (1971). Prospective studies of a group of Yale
University freshmen. I. Occurrence of infectious mononucleosis. Journal of
Infectious Disease 123(3): 263-270.
Thacker, E. L., Mirzaei, F., et al. (2006). Infectious mononucleosis and risk for multiple
sclerosis: a meta-analysis. Annals of Neurology 59(3): 499-503.
University of Health Physicians and P.H.L.S Laboratories (1971). Infectious
mononucleosis and its relationship to EB virus antibody. A joint investigation by
university health physicians and P.H.L.S. laboratories. British Medical Journal
4(5788): 643-646.
Witte, J. S., Carlin, J. B., et al. (1999). Likelihood-based approach to estimating twin
concordance for dichotomous traits. Genet Epidemiol 16(3): 290-304.

50
Chapter 3. Reports of Childhood Experience from
Young Adult Twins and Their Mothers

3.1. ABSTRACT
Increasingly, adult diseases are being linked to childhood exposures. In case-
control studies, usually required for the study of rare conditions, the study participants’
early childhood health history is typically assessed by recall, but results tend to be at best
highly misclassified and at worst seriously biased. Identical twin subjects may offer an
advantage. A proportion of twins can accurately rank their childhood experiences and
these rankings can be validated by their mothers. We examined the reproducibility of the
intra-pair rankings of the frequency, sequence, and magnitude of childhood febrile
illnesses, developmental milestones and body size respectively among the members of
126 adult identical twin pairs, validating the results by interviewing 126 mothers of the
twins. Rankings were obtained both by subjective comparisons and by comparing
estimated frequencies of childhood health-related exposures using self and proxy
information. The two methods were compared in the responses from each individual
twin, and, using summary rankings, the proportion of informative responses and the intra-
pair ratio of analysis were compared between co-twins and between twins and their
mothers. Mothers could provide informative rankings of their twins with respect to
childhood exposures more often than the twins themselves. We found consistency
between the results of the intra-pair rankings in all three comparisons, and the patterns
suggest that certain individual twins are more consistently willing or able to report subtle
distinctions than others. Rankings were especially informative for puberty milestones and
51
for body size at birth and in young adulthood. When enough identical twins can be
identified and especially when the subjects have a living mother, links between these
early observations and adult conditions can be validated.

3.2. INTRODUCTION
It is becoming clear that events in early childhood determine, at least in part, adult
diseases. Neonatal body size and the subsequent growth pattern have also been closely
linked with numerous chronic adult morbidities such as diabetes, coronary artery disease
(Barker 2004; Bergvall et al. 2007; Eriksson et al. 2007) and cancers of the colon (Nilsen
et al. 2005) and breast (Kaijser et al. 2003). Childhood infections have been suggestively
linked to a few neurological diseases such as Parkinson’s disease, motor neuron disease
and multiple sclerosis (Nielsen et al. 2007; Levin et al. 2010). Exposure to common
childhood infections has been associated with decreased risk of young adult Hodgkin
lymphoma (Glaser et al. 2005). Helicobacter pylori, an infection commonly acquired
during early childhood, is a known risk factor for gastric carcinoma and lymphoma
(Blaser et al. 1995; Helicobacter and Cancer Collaborative Group 2001). Infectious
mononucleosis in childhood has been linked to an increased risk of both Hodgkin
lymphoma and multiple sclerosis (Hjalgrim et al. 2007; Levin et al. 2010). However, long
term follow-up of childhood cohorts is infeasible, and case-control studies of early
history and adult disease usually must rely on the necessarily inaccurate recall of events
occurring decades in the past.
52
Even today, there is no mandate for pediatricians to keep medical records into
adulthood even when adult patients can accurately recall the location of their childhood
providers Although many records are now electronically stored for longer periods, future
cases still would have to remember multiple’ provider names and addresses. Finally, even
if the records were available, charting by pediatricians is often incomplete and the
completeness of information would be considered questionable (Hedin et al. 2006).
Unlike other case-control subjects, twins offer a unique opportunity to obtain
validated information. Twins are accustomed to comparisons, and have “tracked” each
other throughout childhood. Sharing early experience in tandem, they can recall even
small relative differences (Hamilton et al. 2000). Paired twins have acquired the same
vocabulary together and understand questions similarly. Moreover, the mothers of twins,
who have monitored the growth, health, and development of their children throughout
childhood, can also rank their twin progeny in respect to pertinent milestones and
experiences.
In order to assess the validity and reproducibility of such self- and proxy- reported
information on childhood health experiences, we have compared responses to questions
about childhood health, growth, and development from the members of adult identical
twin pairs and their mothers.

53
3.3. MATERIALS AND METHODS
The study was conducted with approval from the University of Southern
California Institutional Review Board in accordance with the Declaration of Helsinki. All
subjects provided written or verbal informed consent.

3.3.1. Source of Participants
Subjects were twins identified from the California Twin Program (CTP) and their
mothers were identified through the twins. The CTP is a population-based registry of
twins born in California between 1908 and 1982 and has been described in detail
elsewhere (Cockburn et al. 2002; Cockburn et al. 2006). Briefly, twin births were
selected from California birth records and contact addresses were obtained by linkage to
the records of California Department of Motor Vehicles. Recruitment was carried out in 4
waves from 1991 to 2001. A 16-page screening questionnaire was sent to each twins
requesting information about demographic characteristics, zygosity, growth and
development, reproductive history, life style factors, dietary preferences and medical
history. Of 115,733 questionnaires mailed, 51,609 (representing 36,965 pairs) were
completed and returned, yielding of a response rate of 44.6%, a rate at least comparable
to that from other cohorts (Kolonel et al. 2000; Bernstein et al. 2002). Comparison with
census samples of comparable native California birth cohorts indicated the twins to be
representative in terms of age, sex, race, social class and California County of birth
(Cockburn et al. 2001).
54
A subset of 305 identical twin pairs born between 1968 and 1982 who reported
themselves to be discordant in height by at least one inch and who were free of cancer,
immunodeficiency, neurological disease and autoimmune disease including diabetes,
rheumatoid arthritis, Crohn’s disease, and inflammatory bowel disease were selected for
recruitment into a study of the determinants of height focusing on childhood exposures.
Female twins who were not concordant on age at menarche (within 6 months) were
excluded. Once twins agreed to participate, their mothers were also invited to participate.
Paired twins and their mothers were asked to complete a questionnaire with emphasis on
childhood health history, puberty markers and physical development. Of the 610
individual twins initially identified, we located and contacted 378 twins by telephone,
349 of whom, representing 168 twin pairs, agreed to participate. Mothers of 161 pairs
also consented to participate. Of those who agreed to participate, 295 twins representing
162 pairs and 157 of their mothers successfully completed the questionnaires. The current
study utilizes the responses of the 126 complete trios consisting of both twins and their
mothers.

3.3.2. Collection of Exposure Information (see Appendix 1 & 2).
Information was collected from mothers via telephone interview. Twins were
given a choice of a self-administered hard copy or online questionnaire. The
questionnaire was divided into 3 sections: general health events & lifestyle factors before
age 13 (e.g., history of tonsillitis, appendectomy, stomach flu, diarrhea, participation in
sports, appetite, vegetarianism); illness (febrile illness, doctor visit due to sickness,
55
antibiotics, ear infections, missing school due to sickness), each with reference to age-
specific categories of infancy (0-1 years old), toddler age (2-5 years old), elementary
school years (6-10 years old), adolescence (11-13 years old), and teenage years (14-18
years old); age-specific physical development (weight and height at birth, age 6, 10, 13,
18 and current) and pubertal milestones (shaving, voice change, menarche, and breast
development). Additional questions were asked about flu, infectious mononucleosis and
persistent cough for more than 1 month during teenage years.

Table 3.1. Questions Inquiring Frequency of Occurrence and Subjective Rankings.

Twins Mothers
Frequency of occurrence
Self report How often were you sick in
infancy?
How often was twin A sick in
infancy?
Proxy report How often was your twin sick
in infancy?
How often was twin B sick in
infancy?
Answer choices

Never
Rarely
Sometimes
Frequently
Don’t know
Never
Rarely
Sometimes
Frequently
Don’t know
Subjective Ranking
Which twin was sick more
often in infancy?
Which twin was sick more often
in infancy?
Answer choices Me
My twin
Same
Don’t know
Twin A
Twin B
Same
Don’t know

Each twin and co-twin was asked to rank themselves with respect to their twin in
magnitude, sequence and frequency for 56 characteristics, and to indicate, for him or
herself and by proxy for his or her twin, the actual level for 51 of the variables
56
(excluding age-specific puberty milestones and rates of growth) (Table 3.1). The mothers
were asked to subjectively rank the twins for 54 items and to estimate the same two
frequencies for each of the 51 items.

3.3.3. Statistical Analysis
The taller twin of the pair was designated as “Twin A” and the shorter as “Twin
B”. Each twin’s relative (subjective) response ranking themselves and their twin was
compared to their own frequency-based ranking. For each twin, a summary ranking for
each exposure was created based on the relative ranking, supplemented by the frequency-
based ranking when the relative ranking was unavailable (e.g. “don’t know”). A response
was deemed “informative” if it provided at least one ranking between the paired twins.
For each exposure, the percentage of informative responses and the ratio of rankings
(twin B more/ twin A more) were compared between the paired twins and between sets of
paired twins and their mother using Spearman correlation. Each coefficient of
determination (R
2
) and each set of P-values for the Spearman correlation coefficients are
reported.
To assess an individual twins’ tendency to consistently respond to questions with
“same”, we examined the two subsets of paired discordant responses: those in which one
twin answered ‘same’ and the other twin “don’t know”, and those in which one answered
“same” and the other twin specified a ranking. Within each set we compared the expected
frequency of a “same” answer if it were to be determined by chance to the actual
observed distribution, summing the responses from the twin in each pair more likely and
57
the twin less likely to respond “same”, and dividing the excess by two to correct for
multiple comparisons. For each set, chi square P-value are reported.

3.4. RESULTS
The majority of the twins were white (87%) and there was an equal distribution of
male-male and female-female pairs (Table 3.2). The mean age of participation was 31
years old.

Table 3.2. Demographic Distribution of Participating Twin Pairs.

Twin pairs
a
Among all CTP pairs
b

N % N %
Total 126 7249
Gender
Male-Male 62 49.2 1803 24.9
Female-Female 64 50.8 3937 54.3
Male-Female 1509 20.8
Ethnicity
White 109 86.5 5659 78.1
Black 4 3.2 198 2.7
Latino 8 6.4 860 11.9
Asian 4 3.1 264 3.6
Other 1 0.7 268 3.7

Twins' age at questionnaire 31 (22-39)
Moms' age at questionnaire 57 (42-71)

Abbreviations: N, Number of pairs; CTP, California Twin Program
a
Twins pairs in which both members and their mothers completed questionnaire.
b
Double responding twins from CTP born between 1968 and 1982

58
Although we comprehensively evaluated the responses to each question, we chose
subsets of relatively diverse exposures representing different developmental periods for
analysis. Both relative and frequency-based rankings were more commonly provided for
recent exposures, puberty milestones, or characteristics, and, to a lesser degree, for
exposures occurring at birth or early thereafter (Table 3.3, Figure 3.1). Rankings were
less commonly provided for exposures occurring in the years between early childhood
and maturity. For each question, more twins were able to provide a relative ranking than
were able to provide a frequency-based ranking (Table 3.3, Figure 3.1).

Table 3.3. From Each Twin, the Relative Ranking of the Pair vs. the Frequency-based
Ranking of the Pair, Selected Events/Characteristics

Twin Frequency Based Ranking
Twin Relative
Ranking
Don’t
Know
Same
A
More
B
More
Total
Informative
Responses

Higher Weight
at Birth
Don’t know 21 3 0 0 24

217 (86.5%)
Same 1 9 0 0 10
A more 24 7 77 0 108
B more 23 5 2 79 109
Total 69 24 79 79 251
Informative
Responses
158 (62.9%)

More Infant
Ear Infection
Don’t know 77 48 0 0 125
47 (18.7%)
Same 4 75 0 0 79
A more 3 6 16 0 25
B more 4 11 0 7 22
Total 88 140 16 7 251
Informative
Responses
23 (9.2%)

59
Table 3.3, Continued

Twin Frequency Based Ranking
Twin Relative
Ranking
Don’t
Know
Same
A
More
B
More
Total
Informative
Responses

More Toddler
Sickness
Don’t know 51 53 0 0 104
33 (13.1%)
Same 3 112 0 0 115
A more 2 8 4 1 15
B more 2 13 0 3 18
Total 58 186 4 4 252
Informative
Responses
8 (3.2%)

More Pre-
adolescent
Tonsillitis
Don’t know 16 74 1 0 91
63 (25.1%)
Same 1 94 1 1 97
A more 1 15 23 0 39
B more 3 9 0 12 24
Total 21 192 25 13 251
Informative
Responses
38 (15.1%)

First Female
Menarche
Don’t know 7 10 0 1 18
86 (67.2%)
Same 0 24 0 0 24
A more 0 33 10 0 43
B more 0 35 1 7 43
Total 7 102 11 8 128
Informative
Responses
19 (14.8%)

Higher Weight
at 18
Don’t know 3 6 0 1 10
204 (81.3%)
Same 3 29 2 3 37
A more 10 29 62 2 103
B more 11 35 0 55 101
Total 27 99 64 61 251
Informative
Responses
125 (49.8%)

60
Figure 3.1. Percentage of Informative Reports Provided From Subjective Ranking and
Frequency-based Ranking.

When rankings could not be provided, twins were more likely to indicate that they
had been the “same” than to indicate “don’t know”, and this was especially true for the
frequency-based rankings.

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Weight at 18
First Menarche
First Shaving
Teenage Cough
Teenage Sickness
Adolescent Missed School
Adolescent Sickness
Pre‐adolescent Stomach "Flu"
Pre‐adolescent Tonsillitis
Elementary School Antibiotics
Elementary School Sickness
Toddler Doctors
Toddler Ear Infection
Toddler Sickness
Infant Ear Infection
Infant Sickness
Birth weight
% Respondents providing informative answers
Relative Ranking Frequency‐based Ranking
61
Figure 3.2. Correlation of the Percent of Question-Specific Informative Responses Within
Identical Twin Pairs.

A strong correlation (R
2
= 0.97, p =<0.0001, Figure 3.2) between matched twins
was found for the percent of question-specific informative responses. The cluster on the
lower end of the trend line represents responses to questions about the occurrence of
childhood illnesses, and the cluster on the higher end of the trend line represents
responses about age-specific body weights and puberty milestones.

R² = 0.9743
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0% 20% 40% 60% 80% 100%
% Informative Answers from Twin B
% Informative Answers from Twin A
62
Figure 3.3. Ratio of Rankings Reported by Identical Twins and their Matched Co-twins.

Not only were matched twins similar in their ability to provide informative
rankings with respect to childhood events and characteristics, but they were in general
agreement (R
2
= 0.67, p =<0.0001) with respect to the strength of the ratio of rankings
(Figure 3.3). Most of the agreed-upon rankings reflect ratios more extreme than the
average rankings representing individual twin estimates, suggesting that rankings were
more often agreed upon when differences were extreme. Only a modest, non-significant
correlation (R
2
= 0.12) between the magnitude of the single twin rankings and those
reflecting agreement was observed (data not shown).
R² = 0.6691
0
1
2
3
0123
Ratio of Ranking Reported by Twin B
Ratio of Ranking Reported by Twin A
63
The tendency of respondents who were unable or unwilling to rank their relative
exposures most commonly responded by choosing “same” as the default choice. When
the frequency of such responses was high, the number of pairs from which one twin
chose “same” and the other “don’t know” was much higher than might have been
expected from the frequency of agreed upon “don’t know” responses. From the set of all
paired responses to the 13 analyzed questions, 402 paired responses had one twin
choosing “same” and one “don’t know”. If each twin was equally likely by chance to
choose “don’t know” over “same”, one would expect that each would choose “don’t
know” 201 times. In fact, within each pair, one or the other of the paired twins, in answer
to a given question, consistently chose “same” rather than “don’t know” 336 times. At a
minimum, after adjustment for multiple comparison, this consistent choice more often as
chance would predict (P <0.0001), indicating that in each pair, one twin was more willing
to presume the twins identical in experience. Similarly, 399 of the paired responses that
consisted of one twin ranking the pair, and the other choosing “same”. By chance, one
twin in 199.5 of these pairs would be expected to choose “same”. Empirically, a single
member of 323 question-pairs chose “same”, more often than expected (P < 0.0001).
These two observations suggest that twins differ by individual choice in their ability or
willingness to make a comparative choice.

64
Table 3.4. Pattern of Paired Twin Summary Rankings Provided by Twins and Their
Mothers, Selected Events and Characteristics

Mother Summary Ranking
Twin Summary
Ranking
Don’t
Know
Same
A
More
B
More
Total
Informative
Responses

Higher Weight
at Birth
Don’t know 0 1 12 8 21
217 (86.5%)
Same 0 7 2 4 13
A more 1 5 137 12 155
B more 1 1 8 52 62
Total 2 14 159 76 251
Informative
Responses
235 (93.6%)

More Infant
Ear Infection
Don’t know 5 57 5 10 77
47 (18.7%)
Same 6 101 9 11 127
A more 0 13 5 0 18
B more 1 8 5 15 29
Total 12 179 24 36 251
Informative
Responses
60 (23.9%)

More Toddler
Sickness
Don’t know 1 33 5 12 51
33 (13.1%)
Same 3 111 11 43 168
A more 0 7 0 3 10
B more 0 9 0 14 23
Total 4 160 16 72 252
Informative
Responses
88 (34.9%)

More Pre-
adolescent
Tonsillitis
Don’t know 4 11 1 0 16
64 (25.4%)
Same 10 125 25 12 172
A more 4 18 8 1 31
B more 0 14 4 15 33
Total 18 168 38 28 252
Informative
Responses
66 (26.2%)

65
Table 3.4, Continued

Mother Summary Ranking

Twin Summary
Ranking
Don’t
Know
Same
A
More
B
More
Total
Informative
Responses

First Female
Menarche
Don’t know 0 4 4 0 8
76 (59.4%)
Same 9 17 6 2 34
A more 9 15 12 0 36
B more 12 16 14 8 40
Total 30 52 36 10 128
Informative
Responses
46 (35.9%)

Higher Weight
at 18
Don’t know 1 1 1 0 3
205 (81.7%)
Same 7 14 15 7 43
A more 12 15 115 14 156
B more 4 7 3 35 49
Total 24 37 134 56 251
Informative
Responses
190 (75.7%)

Table 3.4 describes the correspondence between the rankings provided by the
twins who agreed with those provided by their mothers. For each question about
childhood events or characteristics, mothers more commonly could provide an
informative ranking, especially for events in early childhood (Figure 3.4). The ability of
the mothers to provide an informative ranking was correlated (R
2
= 0.84, p < 0.0001)
with the ability of their twin children to do so (Figure 3.5). Again, the cluster of
responses on the low end of the trend line is to questions about early childhood, while the
cluster of responses on the high end is to questions about puberty milestones. A high
correlation (R
2
=0.72, p < 0.0001) was also found between the maternal and twin relative
matched ratio of rankings (Figure 3.6).
66
Figure 3.4. Percentage of Informative Reports Provided From Subjective Ranking and
Comparative Frequency Ranking

0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Birth
Weight
Infant
Sickness
Toddler
Sickness
Elementary
School
Sickness
Adolescent
Sickness
Teenage
Sickness
Male
Shaving
Menarche Weight at
18
Twin A Twin B Mother
67
Figure 3.5. Correlation of the Percent of Question-Specific Informative Responses From
Mothers with that from their Individual Twins

Figure 3.6. Ratio of Ranking Measured by Rankings Reported From Mothers and Twin
Pairs in Agreement

R² = 0.8448
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
%Informative Answers From Mothers
% Informative Answers From Each Twin
R² = 0.7214
0
1
2
3
4
5
6
012 345 6
Ratio of Ranking Reported by
Mothers
Ratio of Ranking Reported by Twin Pairs in Agreement
68
3.5. DISCUSSION
We have found that a proportion of healthy 22-39 year old identical twins can
informatively recall the relative frequency of illness, priority of puberty milestones, and
magnitude of age-specific body weight from childhood and thus provide intra-pair
rankings. Relative ranking was a more sensitive form of recall than frequency-based
ranking on the comparison of age-specific exposures. Although the sensitivity of
frequency-based rankings would probably depend on the answer choices offered (ie.
broader answer choice categories would be likely to constitute less sensitive probes),
relative rankings were shown to be consistent with frequency-based rankings from the
same individuals. The information provided by paired twins was consistently in
agreement. Information from single respondents appear to be provided on the basis of
individual choice rather than chance, and are likely to reflect more subtle distinctions,
since the co-twins of such respondents were consistently unwilling or unable to agree.
Mothers of the twins provided informative reports on their twins’ relative childhood
biological history more frequently than did the twins themselves. Their ability to do so
correlated with the ability of their twin children to agree on the same differences, and
their specific estimates of the directionality of the rankings were consistent with those of
the twins themselves.
A few studies have examined validity of adult study participants’ recall on
childhood health. Krall et al (Krall et al. 1988) reported that 50 year old subjects’ recall
of childhood infectious diseases corresponded to evidence from health records. But other
studies have found that although participants’ long term recall on body size and major
69
surgery during childhood was reliable, other non-salient illnesses and chronic conditions
were not (Nelson et al. 1994; Burgess et al. 2006; Macgregor et al. 2006).
We have previously shown that some effect estimates obtained from proxy reports
from co-twins compare well with self reports (Hamilton et al. 2000). Another twin study
by Reynolds et al has also reported that older adult twins aged 40 to 77 can reliably report
comparative health status before age 20 (kappa 0.55) and that relative rankings were
more accurate than each twin’s self reported quantitative comparisons (Reynolds et al.
2005). To our knowledge, however, this is the first study to elicit the parental recall of
events in the childhood of any adult study participants. Previous studies of parental recall
were based on recollections of the earlier medical history of children under the age of 15.
Parents of adolescent children have been shown to accurately report events related to
birth such as birth weight (Pless et al. 1995; O'Sullivan et al. 2000; Walton et al. 2000),
although the parents of underweight infants tended to over-report and those of overweight
infants tended to under-report their weight (Walton et al. 2000). Parental long term recall
of prior episodes of ear infection and medication use was not found to be as reliable
(Pless et al. 1995); when children had many ear infections parents tended to over-report
them, whereas when children had few ear infection they were underreported (Daly et al.
1994). Such misreporting may lead to misclassification of exposure discordance.
Because identical twins tend to stay in closer contact and are repeatedly made
more aware of the differences between them than are fraternal twins or singleton siblings,
their proxy reports are likely to be more complete and in better agreement. Reynolds et al
70
also reported that recollections of health status before age 20 between fraternal were not
as comparable as that of identical twins (Reynolds et al. 2005).
Self reported health history is often subject to recall bias due to current or past
disease status or recent reminders from previous interviewing (Burgess et al. 2006). Our
participants were healthy adults unbiased by concurrent disease, and had not been
approached for other studies in the decades since original recruitment.
We could not make use of an independent source, such as a medical record, to
measure the ‘true’ difference between the severity, priority, or magnitude of childhood
illnesses, milestones or physical measurements of the twins. In general, access to such a
‘gold standard’ decades after the pertinent events is unfeasible. Moreover, quantitative
counts of childhood illness from medical records are themselves likely to be inaccurate
(Krall et al. 1988; Hedin et al. 2006), as are those obtained from subjects themselves.
Twins and their mothers are unusually able to provide subjective rankings even about
non- salient characteristics usually ignored by those creating medical records. While the
comparative frequency rankings described here also required arbitrary choices between
‘never, rarely, sometimes, frequently or don’t know’, since the twins had been raised by
the same mother, all three are likely to define these terms on the basis of the same
standard.
Because the twins’ questionnaires were self administered (paper version or
online) and the mothers’ were completed by telephone interview, the observed higher
proportion of informative reports from the mothers may be partly attributable to the
method of data collection. However, it has been reported that for matters of childhood
71
health history, mode of data collection (online or telephone interview) do not make a
difference in completeness of the questionnaire (Smith 2009).
Epidemiologic studies involving early life exposures depend heavily on recall of
events from decades past, and the accuracy of such recollections should always be
carefully considered. Generally long term recall of anthropometric measures or life style
factors (smoking, alcohol) have been shown to be more or less accurately reported, but
less salient items such as dietary intake and physical activity are poorly reported (Dwyer
et al. 1989; Blair et al. 1991; Friedenreich 1994). Long term parental recall of children’s
diet has also been reported to be unreliable (Chavarro et al. 2009). Food frequency recall
of childhood diet by adult subjects is biased by their current diet and even though such
quantitative information is prone to misclassification, it has been shown useful as a
measure of relative consumption, even in studies of singleton adults (Dwyer et al. 1989;
Dwyer et al. 1997). Thus the more accurate comparative recollections from twins should
provide a better quality of information, since, as we have shown, twins are often able to
recall and describe a relatively minor difference between them. It may even be possible to
make quantitative distinctions useful for establishing a dose-response relationship by the
making more detailed distinctions in the degree of discordance (ie. a lot more, a little
more, the same, a little less, a lot less). We have successfully used subjective rankings
reported from twins to examine associations between childhood experiences to various
adult diseases such as breast cancer, multiple sclerosis and Hodgkin’s disease (Hamilton
et al. 2000; Islam et al. 2007; Cozen et al. 2009).
72
However, it is clear that however accurate of the available rankings, the
proportion of twins who can provide such distinctions is limited, especially for events in
the middle childhood years, and it will always be necessary to screen large numbers of
outcome-discordant identical twin pairs in order to identify an appropriately large sample
of exposure-discordant reports.
Adult participants can reliably recall childhood health information, and their
mothers can validate the same information with greater consistency. In studies of
childhood illnesses where pediatric medical records are neither available nor applicable,
mothers’ recall can dependably provide valuable data and for events that occurred at later
childhood years, the adult participants themselves may be able to effectively provide
reliable information. Using twins as study participants can facilitate precise measurement
of childhood experiences information thus further help answer scientific questions about
childhood infections, diet, and other factors that are conventionally difficult to measure.

73
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76

Chapter 4. The Effect of Childhood Infections on Adult Height in
Monozygotic Twins

4.1. ABSTRACT
Adult height is determined by genetics as modified by childhood nutrition, but
deleterious childhood experiences, particularly infections, may also play a role. Identical
twins are matched on genome and thus offer an advantage when examining
environmental determinants. In a case-control study, we compared the relative childhood
experience of twins in 140 healthy monozygotic pairs selected from the California Twin
Program who differed in self-reported adult height by at least 1 inch. Interviews were
conducted with the mothers of the twins in order to validate reported childhood infections
and growth patterns. Conditional logistic regression matched on twin pair was used to
examine the effect of childhood infections on adult height. Measures of childhood
infection were highly correlated (r
2
= 0.34-0.75, P= 0.05 - < 0.0001). More reported
episodes of febrile illness were associated with shorter stature within twin pairs (odds
ratio: 2.0, 95% confidence interval: 1.43, 3.60). The association was strongest for
infections in the toddler years (ages 1 to 5; odds ratio: 3.34, 95 % confidence interval:
1.47, 7.59) and was similar when restricted to twins who were the same birth length.
Childhood infection appears to retard growth, independent of birth length and genome.

4.2. INTRODUCTION
Height is a heritable characteristic of the human phenotype. A recent genome
wide association scan recently found that 80% of the variance in height is heritable
77

(N'Diaye et al. 2011). However, environmental determinants of height include caloric
intake, nutritional balance (i.e. vitamin D) and pubertal development (Berkey et al. 2000;
Metcalfe et al. 2001; Onland-Moret et al. 2005).
Height is a known predictor of overall mortality (Gunnell et al. 2001), is linked to
increased risks of several types of cancer, including Hodgkin’s lymphoma, breast cancer,
and leukemia (Batty et al. 2006; Green et al. 2011), and is inversely associated with
cardiovascular disease, stroke and stomach cancer (Gunnell et al. 2001; Smith et al. 2001;
Barker 2004). Prospective cohort studies and meta-analyses examining height-disease
relationships have found that, even after adjusting for socioeconomic status, body mass
index, menarche and nutrition, a consistent association between adult height and adult
disease is apparent, suggesting that the effect of height may reflect its environmental
determinants (Gunnell et al. 2001; Batty et al. 2006; Green et al. 2011).
In developing countries, children with serious infectious disease and concomitant
malnutrition, economic hardship, and limited access to health care, experience arrested
growth (Mata et al. 1977; Rowland et al. 1977; Rowland et al. 1988). Although the effect
of infections in childhood on adult height has not been extensively examined in
nutritionally and socioeconomically adequate environments, results of a few studies are
suggestive (Wadsworth et al. 2002; Crimmins et al. 2006).
Although the majority of identical twin pairs achieve an identical height, a small
proportion of them are discordant. Because such twins share all genetic factors, we
postulated that differential environmental exposures, especially infections, could play a
role in determining their adult height differences. Identical twins as case-control subjects
78

offer the unique advantages of being matched on genome, age, gender, early
socioeconomic status and environment. Twins and their mothers are also accustomed to
making intra-pair comparisons, and thus can reliably report relative differences between
paired twins that are otherwise difficult to quantify (O'Sullivan et al. 2000; Walton et al.
2000; Jokovic et al. 2004; Reynolds et al. 2005). Accordingly, we conducted a matched
case-control study in monozygotic twins discordant in height by at least one inch, based
on interviews with the twins and their mothers, to examine the effect of childhood
infectious illnesses on relative adult height. Specifically, our hypothesis was that the
member of the twin pair who experienced fewer childhood illnesses and infections would
achieve the greater height, and therefore the twin with more illness would become the
shorter of the pair.

4.3. MATERIALS AND METHODS
This study was approved by the Institutional Review Board of the Keck School of
Medicine of the University of Southern California.

4.3.1. Source of Participants.
Subjects were paired identical twins identified from the California Twin Program
(CTP), together with their mothers. The CTP is a population-based registry of twins born
in California between 1908 and 1982 and is described in detail elsewhere (Cockburn et
al. 2002; Cockburn et al. 2006). Briefly, twins were identified from California birth
records and contact addresses were obtained by linkage to the records of California
79

Department of Motor Vehicles. During 1991-2000, a 16-page screening questionnaire
was sent to members of twin pairs with information on demographic characteristics,
zygosity, growth and development, reproductive history, life style factors, dietary
preferences and medical history. Of 115,733 questionnaires mailed, 51,609 (representing
36,965 pairs) were returned, yielding of a response rate of 44.6%, similar to or better than
those reported among similarly aged persons in other cohort studies (Kolonel et al. 2000;
Bernstein et al. 2002).
A subset of 305 healthy monozygotic (MZ) twin pairs born between 1968 and
1982 were selected. The members of each such pair agreed in reporting a difference in
height between them by at least one inch, and described themselves free of diabetes,
rheumatoid arthritis, cancer, immunodeficiency, inflammatory bowel disease and
neurological diseases. Female twins whose age at menarche occurred more than 6 months
apart were excluded. Twins and their mothers were invited to participate in the study, and
were asked to complete a questionnaire with emphasis on childhood health history,
puberty markers and physical development. Of the 610 individual twins initially
identified, we located and contacted 378 twins, of whom 349, representing 168 pairs,
agreed to participate. When agreed, the mothers of 161 pairs consented to participate. Of
those who consented, a total of 295 twins (133 double respondent and 29 single
respondent pairs) and 157 mothers successfully completed the questionnaires. We
restricted the current analysis to twins from 140 families from which the twins and their
mothers agreed on the twins’ adult height difference. The shorter twin member of the pair
was designated as the ‘case’ twin and the taller, as the ‘control’ twin.
80

4.3.2. Questionnaire & exposure assessment (see Appendix 1 & 2).
The participants were asked to provide information on each twin’s experience
with febrile illnesses, doctor visits, ear infections, antibiotic use, and the extent to which
the twins missed daycare and school and the mothers missed work due to the twins’
illnesses. These questions were repeated for 5 age-specific categories: infancy (<1 year),
toddlerhood (1-5 years), elementary school years (6-10 years), adolescence (11-13 years),
and teenage years (14-18 years). The information was obtained from questions framed
both in absolute terms (how often was twin A sick? how often was twin B sick?) and as
relative rankings (which twin was sick more often?). In order to capture the absolute
differences between the twins’ childhood infectious experience, broad subjective
categories were used (never, rarely, sometimes, and frequently). We previously found
that questions framed as relative rankings are more sensitive measures of a difference in
exposure than absolute questions, although absolute questions can reliably supplement
missing information (unpublished). Hence relative responses from the mothers,
supplemented with their absolute responses, were used to assess the association between
early life health experience and adult height difference.
A scoring system was used to summarize the twins’ childhood illness experience.
The twin who had comparatively greater exposure (ie. had more febrile illness, missed
school more often, had more ear infections, etc.) was given a score of 1 for each
individual exposure, and the twin with a lower exposure level for that particular variable
was given a score of 0. When exposures were reported as similar in both twins, both were
given a score of 0.
81

4.3.3. Statistical analysis.
The Spearman correlation coefficient was used to assess independence of the
exposures. Conditional logistic regression modeling adjusted for birth length and weight
was employed to test the hypothesis that relatively more illness was associated with
shorter stature. A stepwise model selection process was used to determine the exposures
that best predicted shorter stature. The variables with the most robust and significant
findings were selected for subsequent analysis: febrile illness, doctor visits, antibiotic use,
and missing school due to illness, for all ages combined and within each of the 5 age
groups described above (Figure 4.1).
To address the possibility of misclassified height difference, we conducted a
stratified analysis by the degree of difference (1 inch vs. > 1 inches) to assess whether the
effect of early illness varied by adult height difference. We also performed a test for
interaction between the two groups defined by adult height difference and early life
illness, and between gender and early life illness.
In order to examine the association between early life illness and adult height
independent of birth length, we repeated the analysis stratified by birth length (case twin
longer at birth (N= 14 pairs), case twin shorter at birth (N= 58 pairs), case and control
twins same length at birth (N= 68 pairs), and performed tests for interaction between
birth length and early life illness.
We also examined the relationship between birth length and weight and the
exposures of interest. The independent variables for these analyses were the relative birth
length and weight difference between the members of the twin pair and the dependent
82

variable was their relative difference in illness markers at different ages. The likelihood
ratio chi square test was used to test the general association between the dependent and
independent variables and effect was estimated using the odds ratio (OR) calculated when
fitting the conditional logistic regression models. The effects of birth length, birth weight
and growth at ages 6, 10, 13, 18 on adult height were also examined using similar
methods, adjusting for body size at each previous age.
To further evaluate the effect of caloric intake on adult height as a possible
confounder, we used information on individual food preference (how much do you
like...?) and relative intra-pair food consumption comparisons (which twin ate more...?)
available from the original CTP questionnaire, and used the responses as a proxy for food
intake during childhood. The twins’ self-reported food preference (like it a lot, like it,
take it or leave it, dislike it, or dislike it a lot) and relative food consumption (I ate more,
my twin ate more) to protein- and carbohydrate-rich food items (ie. meat, seafood, main
dishes, potatoes, grains and baked goods) were scored and summed. The summed food
preference scores and consumption scores were compared between the case (shorter twin)
and control (taller twin) using a paired t-test.
All ORs are reported with 95% confidence interval (CI). If the number of
exposure-discordant pairs was less than 5, Fisher’s exact test was used to calculate exact
OR and 95% CI for logistic regression analyses. P values are reported for test of
interaction and likelihood ratio chi square tests. Statistical analysis was performed using
SAS version 9.2 (SAS institute, USA).

83

Figure 4.1. Forest Plot Showing Effect of Illnesses in Childhood on Shorter Stature
Within Height-Discordant Monozygotic Twin Pairs, Listing All Childhood Illness
Factors by Age Group.
84

4.4. RESULTS
Slightly more males than females participated, in comparison with the CTP twins
who were eligible for this study, and with the total CTP respondents of the same age
(Table 4.1). The ethnic distribution was primarily white and similar to that of the overall
CTP source population.

Table 4.1. Demographic Distribution of Participating Twin Pairs

Twin pairs
a

Among all
eligible pairs
b

Among all CTP
pairs
c

N % N % N %
Total 140 305 7249
Gender

Male-Male 73 52.1 148 48.5 1803 24.9
Female-Female 67 47.9 157 51.5 3937 54.3
Male-Female
1509 20.8
Ethnicity

White 111 79.3 227 74.4 5659 78.1
Black 5 3.6 15 4.9 198 2.7
Latino 12 8.6 34 11.1 860 11.9
Asian 5 3.6 17 5.6 264 3.6
Other 7 5.0 12 3.9 268 3.7

Participation status

Mother only 5 3.6
Mother and one twin 18 12.9
Mother and both twins 117 83.6

Twins' age at questionnaire 31 (22-39)
Moms' age at questionnaire 58 (42-72)
Abbreviations: N, Number of pairs; CTP, California Twin Program
a
Twins pairs whose mother responses contributed to this study.
b
Double responding MZ twins from CTP born between 1968 and 1982 who were
discordant in height by at least one inch and free of diabetes, rheumatoid arthritis,
cancer, immunodeficiency, and neurological diseases.
c
Double responding twins from CTP born between 1968 and 1982.
85

Table 4.2. Correlation Coefficients
a
for Illness Measures in Toddler Years and All Ages.

Febrile
sickness
Doctor
visit
Antibiotics Missed school
due to sickness
Toddler years
Febrile sickness 1.00 0.75* 0.69* 0.37*
Doctor visit 1.00 0.74* 0.51*
Antibiotics 1.00 0.48*

All ages

Febrile sickness 1.00 0.61* 0.70* 0.51*
Doctor visit 1.00 0.64* 0.61*
Antibiotics 1.00 0.50*
*P < 0.0001
a
Spearman Correlation Coefficients

The exposures used in the analysis were significantly correlated across all ages
(Table 4.2) and within each age category (toddler years shown in Table 4.2, data not
shown for other ages); Spearman correlation coefficients ranged from 0.48 (missed
school during childhood due to illness vs. antibiotics) to 0.75 (relative frequency of
doctor visits during toddler years vs. relative frequency of febrile illness during toddler
years) with all P values > 0.0001.
86

Table 4.3. The Effect of Childhood Illness on Shorter Stature Within Monozygotic Twin Pairs Discordant for Adult Height
a

(N = 140)

All Ages (0-18) Toddler years (1-5)
N
b
OR
unadjusted
95% CI OR
adjusted
c
95% CI N
b
OR
adjusted
c
95% CI
More febrile illnesses 59/26 2.27 1.43, 3.60 2.00 1.18, 3.40 38/11 3.34 1.47, 7.59
More doctor visits 56/27 2.07 1.31, 3.28 1.89 1.10, 3.26 32/13 2.46 1.11, 5.46
More antibiotic use 51/26 1.96 1.22, 3.15 1.80 1.04, 3.13 34/15 1.96 0.95, 4.01
More school missed due to
illnesses
32/20 1.60 0.92, 2.80 1.80 0.91, 3.58 14/4 4.67 1.13, 19.22

Abbreviations: N, Number of twin pairs; OR, odds ratio; CI, confidence interval
a
Discordant for adult height by at least 1 inch.
b
Number of twin pairs in which case (shorter twin) is exposed and control (taller twin) is not exposed / Number of twin pairs in
which case (shorter twin) is unexposed and control (taller twin) is exposed
c
Adjusted for birth weight and birth length

87

The twin with more episodes of maternally reported childhood infection was
twice as likely to be the shorter twin of the pair (Table 4.3). The effect was consistent
across each of the measures of childhood illness and was statistically significant and
persisted after adjusting for birth weight and birth length. The ORs were strongest for
illnesses during the toddler years (1-5 years of age), ranging from 1.96 for more antibiotic
use to 4.67 for more days of missed school due to illness. The twin who had more febrile
illnesses during the toddler years was 3 times as likely to become the shorter twin of the
pair (OR=3.34, 95% CI=1.47-7.59). The results were confirmed using a stepwise model
selection process that showed that febrile illness during toddler years was the strongest
and most significantly predictive factor of all measures tested across all age groups for
adult height difference between the twins (data not shown).
When we restricted the analysis to twins with the same birth length, we found
slightly higher, although less precise, effect estimates (Table 4.4). For example, the twin
with more reported antibiotic use was 3 times as likely to become the shorter twin. We
found no evidence of interaction between birth length and childhood illness measures
(data not shown).
The estimate of the effect of childhood infection during toddler years on shorter
stature was generally of greater magnitude among twin pairs with more than 1 inch
difference in height, (Table 4.5), although the 95% CIs included 1.0. No statistically
significant evidence of an interaction by adult height difference or gender was observed
(data not shown).
88

Table 4.4. The Effect of Childhood Illness on Shorter Stature Within Monozygotic Twin Pairs Discordant for Adult Height
a

but with Similar Birth Length (N = 68).
Abbreviations: N, Number of pairs; OR, odds ratio; CI, confidence interval
a
Discordant for adult height by at least 1 inch.
b
Number of twin pairs in which case (shorter twin) is exposed and control (taller twin) is not exposed / Number of twin pairs in which case (shorter
twin) is unexposed and control (taller twin) is exposed
c
Adjusted for birth weight and birth length

All Ages (0-18) Toddler years (1-5)
N
b
OR
unadjusted
95% CI OR
adjusted
c
95% CI N
b
OR
adjusted
c
95% CI
More febrile illnesses 28/11 2.55 1.27, 5.11 2.95 1.34, 6.48 12/5 3.21 0.86, 15.38
More doctor visits 24/15 1.60 0.84, 3.05 2.18 1.00, 4.74 10/5 3.66 0.90, 19.12
More antibiotic use 22/10 2.20 1.04, 4.65 3.30 1.34, 8.09 11/6 2.45 0.79, 7.63
More school missed due to
illnesses
15/6 2.50 0.97, 6.44 4.45 1.36, 14.54 5/1 45.90 0.91, 1,000
89

Table 4.5. The Effect of Infectious Illnesses during Toddler Years on Shorter Stature Within Monozygotic Twin Pairs
Stratified, by Adult Height Differences (1 inch vs. > 1 inch).

Abbreviations: N, Number of pairs; OR, odds ratio; CI, confidence interval
a
Number of twin pairs in which case (shorter twin) is exposed and control (taller twin) is not exposed / Number of twin pairs
in which case (shorter twin) is unexposed and control (taller twin) is exposed
b
Adjusted for birth weight and birth length
c
Test of interaction

Adult height difference = 1 inch
(N = 100)

Adult height difference > 1 inch
(N = 40)
P Value
c

N
a
OR
adjusted
b
95% CI N
a
OR
adjusted
b
95% CI
More febrile illness 24/9 2.46 1.02, 5.95 14/2 11.01 1.08, 723.43 0.24
More doctor visits 20/10 2.18 0.90, 5.26 12/3 3.75 0.48, 52.99 0.65
More antibiotic use 23/11 1.88 0.84, 4.21 11/4 2.21 0.38, 16.86 0.82
More school missed due to
illnesses
10/1 16.92 1.48, 1,000 4/3 1.15 0.08, 17.91 0.08
90

Birth length was strongly positively associated with measures of childhood
infection after adjusting for birth weight, whereas birth weight was inversely, but not
statistically significantly, associated with such measures after adjusting for birth length
(Table 4.6a-b). As expected, body size differences in early life were robustly associated
with adult height differences (Table 4.7). Shorter height at 6, 10, 13 and 18 years of age
was more strongly associated with shorter adult height than weight at these ages. Shorter
height at 6 years old was the most strongly predictive of shorter adult height (OR= 27.4,
ratio of exposure discordant twin pairs =89/3).
There was no meaningful difference in food preference (P t-test = 0.41) or relative
food consumption (P t-test = 0.99) between the twins.
91

Table 4.6a. The Effect of Smaller Birth Size on Illness in Toddler Years Among Height-Discordant MZ Twin Pairs.

More febrile illnesses

More doctor visits
More antibiotic use More missed school due to
illnesses
N
a

OR

unadj
b

OR
adj
c

P
d

N
a

OR
unadj
b

OR
adj
c

P
d

N
a

OR

unadj
b

OR
adj
c

P
d

N
a

OR
unadj
b

OR
adj
c

P
d

Shorter
birth
length
28/8 3.5* 4.1* 0.05

23/10 2.3* 3.4* 0.14

25/10 2.5* 2.3 0.16

11/5 2.2 2.5 0.05
Lower
birth
weight
28/19 1.5 0.8 0.77

24/19 1.3
0.6

0.70

30/17 1.8 1.1 0.51

10/7 1.4 0.9 0.8

Table 4.6b. The Effect of Smaller Birth Size on Illness in Childhood Among Height-Discordant MZ Twin Pairs.

More febrile illnesses

More doctor visits
More antibiotic use More missed school due to
illnesses
N
a

OR

unadj
b

OR
adj
c

P
d

N
a

OR
unadj
b

OR
adj
c

P
d

N
a

OR

unadj
b

OR
adj
c

P
d

N
a

OR
unadj
b

OR
adj
c

P
d

Shorter
birth
length
41/14 2.9* 3.2* 0.04

39/13 3.0* 4.6* 0.06

37/15 2.5* 3.0*
0.00
4

23/1
4
1.6 3.0* 0.09
Lower
birth
weight
48/32 1.5 0.9 0.33

44/34 1.3 0.6 0.95

42/29 1.5 0.8 0.54

24/2
5
0.9 0.5 0.90
Abbreviations: MZ, Monozygotic; N, Number of pairs; OR, odds ratio; CI, confidence interval *P<0.05
a
Number of twin pairs in which case (shorter twin) is exposed and control (taller twin) is not exposed / Number of twin pairs in which case (shorter
twin) is unexposed and control (taller twin) is exposed
b
Unadjusted odds ratio
c
Odds ratio adjusted for birth weight for ‘shorter birth length exposure’ and adjusted for birth length for ‘smaller birth weight’ exposure
d
2-sided P values for test of association between illness in early years and birth size using likelihood ratio chi square test.

92

Table 4.7. The Effect of Smaller Body Size at Different Ages on Shorter Adult Stature Within Height-Discordant MZ Twin
Pairs.
Exposures N
a
OR
adjusted
b
95% CI Adjustment variables
c

Shorter birth length 66/18 2.68 1.48, 4.85 Birth weight
Shorter height at age 6 89/3 27.36 8.43, 143.26 Birth length
Shorter height at age 10 97/4 6.58 1.66, 35.79 Birth length and relative height at age 6
Shorter height at age 13 98/4 4.11 0.82, 24.11 Birth length and relative height at age 6, 10
Shorter height at age 18 110/4 6.51 1.96, 31.89 Birth length and relative height at age 6, 10, 13
Lower birth weight 92/39 1.60 1.03, 2.48 Birth length
Lower weight at age 6 82/20 3.86 2.00, 7.44 Birth weight
Lower weight at age 10 85/20 2.53 1.16, 5.50 Birth weight and relative weight at age 6
Lower weight at age 13 84/19 2.27 0.81, 6.34 Birth weight and relative weight at age 6, 10
Lower weight at age 18 79/32 0.52 0.22, 1.22 Birth weight and relative weight at age 6, 10, 13
Slower growth rate in infancy 39/23 1.18 0.65, 2.15 Birth weight and length
Slower growth rate in toddler years 28/11 1.28 0.52, 3.11 Birth weight and length, relative growth rate in infancy
Slower growth rate in elementary
school years
38/9 3.48 1.38, 8.78
Birth weight and length, relative growth rate in infancy,
toddler years
Slower growth rate in adolescence 43/4 10.16 3.07, 33.57
Birth weight and length, relative growth rate in infancy,
toddler, elementary school years
Slower growth rate in teenage years 44/9 1.44 0.48, 4.32
Birth weight and length, relative growth rate in infancy,
toddler, elementary school years, adolescence
Abbreviations: N, Number of pairs; OR, odds ratio; CI, confidence interval
a
Number of twin pairs in which case (shorter twin) is exposed and control (taller twin) is not exposed / Number of twin pairs
in which case (shorter twin) is unexposed and control (taller twin) is exposed
b
The effect of the exposure on shorter adult stature within the pair, adjusted for early life body size variables
c
Birth weight and birth length were adjusted as continuous values and all other body size factors were adjusted as relative
scores.
93

4.5. DISCUSSION
We found that the twin with more episodes of childhood illness reported by their
mothers was approximately 2 times as likely to be the shorter twin of the pair. This
association was strongest for infections occurring during toddler years, and was
independent of birth length and weight, and nutrition. Because most of the questions
asked were relevant to infections, (e.g. frequency of antibiotic use and febrile illness),
autoimmune diseases having been excluded, childhood infections probably accounted for
the majority of illnesses reported by the twins’ mothers.
It is widely accepted that children who become infected with Cryptosporidium
parvum (Checkley et al. 1997; Guerrant et al. 1999; Kosek et al. 2001), Helicobacter
pylori (Dale et al. 1998; Choe et al. 2000; Ertem et al. 2002; Bravo et al. 2003; Ozen et
al. 2011) and frequent infections with organisms causing diarrhea (Mata et al. 1977;
Langford et al. 2011), experience a delay in growth. Studies conducted in Brazil, Peru
and Guinea-Bissau found that the growth deficit associated with C. parvum can be
persistent, even with opportunities of recovery and catch-up growth, especially if the
infection occurred at young age (Guerrant et al. 1999; Kosek et al. 2001). Ertem et al
found that H. pylori infection in Turkish children affected childhood growth patterns
independent of socioeconomic status (Ertem et al. 2002). Most studies reporting such
links were conducted in developing countries, where infectious illnesses are accompanied
by malnutrition, economic hardship and limited access to health care. However, a
relationship between infections and height has been observed in countries that are now
economically advanced as well. Crimmins et al. found that the birth cohorts from
94

Sweden, France, England and Switzerland that experienced higher rates of childhood
mortality due to infectious diseases over the last 200 years were shorter in stature than
those with lower infectious disease mortality rates (Crimmins et al. 2006). Using
participants from a British birth cohort born in 1946, Wadsworth et al. demonstrated that
after adjusting for SES, nutrition, and birth size, shorter adult trunk length was associated
with having serious illness before 5 years of age (Wadsworth et al. 2002).
Because height is highly heritable and is heavily influenced by nutrition, and
other early lifestyle factors, adequately adjusting for these variables to isolate the
exclusive effect of illness in childhood is challenging. Comparing the childhood
experience of MZ twins enabled us to examine the effect of illness in early years on adult
growth, independent of genetics, childhood SES and parental behavior. We also showed
that the twins, who share meals as children, had no difference in general measures of
current food preference, likely to reflect differences in childhood nutritional status.
Several possible mechanisms may explain an association between childhood
illness and adult height. First, infections during early childhood are likely to result in
periods of catabolism, diverting calories away from growth (McDade 2005). Immune
cells require energy to maintain routine housekeeping functions and for specific
responses which occur in the presence of infections, requiring lymphocyte expansion and
protein production (cytokines, cell surface proteins and enzymes) (Buttgereit et al. 2000).
Compared to quiescent immune cells, activated immune cells require 40% additional
energy in the form of ATP than quiescent immune cells (Buttgereit et al. 2000). Fever
associated with infections results in a 13% increased metabolic rate (DuBois 1921;
95

Baracos et al. 1987); moreover, respiratory infections and diarrhea reduce food intake by
20% (Martorell et al. 1980; Butte et al. 1989), promoting a negative energy balance
during infections. Early childhood years are immunologically and developmentally
demanding periods during which there is a substantial proliferation of T and B cells, and
simultaneous physical growth occurs at its highest velocity (Buttgereit et al. 2000). Thus,
the balance between energy allocated for immune function and for growth must be
carefully maintained (McDade 2003). Ill children may spend their energy fighting
infection instead of increasing long bone length, resulting in shorter adult height.
Second, inflammatory responses to infections may directly alter production of
growth hormone (GH) and insulin-like growth factor 1 (IGF-1), both of which are
crucially involved in long bone growth. GH and IGF-1 promote linear growth via cell
division and differentiation at the level of the growth plate (Ahmed et al. 2009; Clayton et
al. 2011). During infections, increases in pro-inflammatory cytokines IL-6, TNF- α, and
IL-1 β are thought to mediate decreases in IGF-1 and GH (De Benedetti et al. 1997;
Ahmed et al. 2009). IL-6, TNF- α and IL-1 β also inhibit growth plate chondrocyte
differentiation (Ahmed et al. 2009) hence chronic exposure to inflammation can delay
growth (De Benedetti et al. 1997; Ballinger et al. 2003).
Third, smaller birth length and weight are associated with smaller thymus size
(Varga et al. 2011), which in turn is associated with decreased thymic function (Ngom et
al. 2004) and increased childhood mortality (Aaby et al. 2002; Garly et al. 2008). It is
possible that a smaller twin is born with a smaller thymus and thus a less mature immune
system, resulting in greater susceptibility to childhood infections and ultimately shorter
96

adult stature. Within twin pairs, we found a positive association between shorter birth
length (but not birth weight) and more frequent infections, but no significant interaction
between the effect of birth length and childhood illness on relative adult height. In
addition, the association between childhood infections and adult height remained when
we adjusted for birth weight and length (and presumably the thymus size). Thus, to the
extent that thymus size is correlated with birth length and weight, our study does not
support thymus size as the underlying mechanism.
Limitations of our study include self-reported height not validated by direct
measurement, maternal reports of relative illness, and potential recall bias of height
difference. A study of Australian twins found that adult twins accurately report height,
although shorter individuals tend to slightly overestimate and taller individuals tend to
slightly underestimate their height (Macgregor et al. 2006). If this misclassification
occurred in our study, it would have resulted in underestimation of the average height
differences, suggesting that the measured effects are slightly overestimated for the height
difference. Although exposure assessment was based on relative and subjective measures
of illness from the twins’ mothers and was not confirmed with medical records, highly
reliable and consistent reports from mothers (Pless et al. 1995; O'Sullivan et al. 2000;
Walton et al. 2000; Jokovic et al. 2004) are a credible data source. Medical record
validation is infeasible and in any case physician visits would not be sought for each
illness (Hedin et al. 2006) and documentation would not be sufficiently thorough. Twins
and especially their mothers are used to making comparisons between the twins
throughout their lives. Twins tend to agree about qualitative differences about
97

developmental milestones (Reynolds et al. 2005), and we have found (unpublished data)
these to be validated by maternal observations. The consistently high and significant
correlations between the frequency of physician visits, relative use of antibiotics, and
relative occurrence of febrile illness support the validity of the data.
Recall bias would occur if mothers differentially remembered and reported more
frequent illnesses in the shorter compared to taller twin. However, upon recruitment, we
asked the twins what they believed caused the difference in height. None of the twins
reported that a difference in childhood illnesses would be the cause, and because the
twins would learn of their early comparative experience from their mothers, we believe
that the mothers would think similarly.
In this study, we report that childhood illness likely to represent infection is
significantly associated with growth, independent of genetics, nutrition, SES and parental
behavior. Although the relationship between childhood infections and growth has been
studied extensively in developing countries, our results suggest that there is also an
impact of frequent infections also impact on adult height in a generally healthy
population. The well-established association between height and adult diseases such as
lymphoma, breast cancer, leukemia, stomach cancer and cardiovascular disease (Gunnell
et al. 2001; Smith et al. 2001; Batty et al. 2006; Green et al. 2011) may be partially
explained by the early childhood infection history and its long-term impact on adult
disease susceptibility.

98

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children: a prospective cohort study. J Pediatr Gastroenterol Nutr 37(5): 614-619.
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Nutr Soc 48(2): 303-312.
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fundamental and therapeutic aspects. Immunology Today 21(4): 192-199.
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Epidemiol 145(2): 156-163.
Choe, Y. H., Kim, S. K., et al. (2000). Helicobacter pylori infection with iron deficiency
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99

Clayton, P. E., Banerjee, I., et al. (2011). Growth hormone, the insulin-like growth factor
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Cockburn, M. G., Hamilton, A. S., et al. (2006). Twins as willing research participants:
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Crimmins, E. M., Finch, C. E., et al. (2006). Infection, inflammation, height, and
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Dale, A., Thomas, J. E., et al. (1998). Helicobacter pylori infection, gastric acid secretion,
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101

Ngom, P. T., Collinson, A. C., et al. (2004). Improved thymic function in exclusively
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102
Chapter 5. Conclusion

Early childhood health experiences have a long lasting impact on a person’s
health. It is roughly estimated that 26.3% of all cancers in developing countries and 7.7%
in developed countries can be eliminated if the infections known to be causally associated
with cancers are eliminated (Parkin 2006). Exposure to some infectious agents is
necessary to develop a properly regulated immune system, as suggested by the “hygiene
hypothesis”. The living conditions and environment to which children are exposed to are
globally and rapidly changing, and may contribute significantly to their vulnerability to
chronic conditions as adults. The exact mechanisms of which the childhood illnesses
engrave lasting susceptibility for diseases decades later are not well established.

Table 5.1. Previously Reported Associations Between Various Cancers, Height and
Childhood Infections
Exposure Positive Association Negative Association No association
Taller stature Colorectal Stomach CNS
Prostate Esophageal Testicular
Breast CVD Cervical
Endometrial
Uterine
Hematologic
Melanoma
Lung
Kidney

Childhood
Infection
Non Hodgkin Lymphoma Hodgkin lymphoma
Non-breast cancer
General cancer
Ovarian
Brain
Diabetes
103
d
b
ev
ag
co
F
d
o
m
ap
There
iseases in th
e causally lin
vidence of a
gents (ie. sib
onditions.

igure 5.1. O

In this
elayed expo
f illness duri
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pparent heig
has been gr
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apparent asso
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ers of childho
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). Many stud
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estation of
and that histo
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104
childhood infection experience (Figure 5.1). In order to broaden our understanding in this
area of childhood illnesses and adult diseases, the factors of early childhood health that
can potentially elicit lifetime susceptibility for chronic illness should be accurately
measured and assessed.
Retrospectively assessing early childhood health history is challenging, and
constructing a birth cohort is extremely costly and requires an exceedingly long follow up.
Several ongoing birth cohorts are in place collecting health information (Elliott et al.
2006; Power et al. 2006) but even prospective cohort studies face a difficulty in fully
capturing a comprehensive picture of a child’s health. In this dissertation, I have shown
that twins and their mothers are reliable source of data when studying childhood health.
They may be an ideal solution to the challenging task of data collection in studies of
childhood health.
It is important to recognize the significance of the children’s environment and the
complex nature of how their experience may have a lifelong effect in their health in
adulthood, in order to aid in understanding of etiology, risk factors and implement
preventive measures.

105
CHAPTER 5 REFERENCES
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106
Comprehensive References

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sclerosis: a meta-analysis. Annals of Neurology 59(3): 499-503.
Tishler, P. V. and Carey, V. J. (2007). Can comparison of MZ- and DZ-twin concordance
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712-717.
Uemura, N., Okamoto, S., et al. (2001). Helicobacter pylori infection and the
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119

Appendix A. Questionnaire for Twins

TITLE OF THE PROJECT:
CHILDHOOD EXPERIENCE, GROWTH & DEVELOPMENT IN
TWINS

Thank you for participating in this study of growth and development in twins. The
following questionnaire consists of 4 sections; general information, activity level &
diet, childhood health experience, and growth & development. Some questions deal
with your experience at specific ages. Even if you do not remember the experiences
exactly, please answer to the best of your ability. There are total of 171 questions.
No writing is required. Please fill in the appropriate circle for each question. The
questionnaire will take approximately 30 minutes to complete. Please fill out this
questionnaire independently without consulting your twin or your mother so we can
compare the responses later.
If you have any questions about the questionnaire, call Amie Hwang at 323-865-
0316 or send an email to hwang_a@ccnt.hsc.usc.edu.

I. GENERAL INFORMATION

1. What is your date of birth? Please provide in MM/DD/YYYY format.

_____/_____/_________

2. What is your gender?

o Male
o Female

3. What is your ethnicity?

o White
o African American
o Latino
o Asian
o Native American
o Pacific Islander
o Other
o Don’t know

120

4. Are you identical or fraternal?

o Identical
o Fraternal
o Don’t know

PLEASE ANSWER THE NEXT SET OF QUESTIONS ABOUT ILLNESSES YOU
HAD AT ANY AGE BEFORE YOU WERE 13 (OR FINISHED THE 8
TH
GRADE)

5. Before you were 13, how often did you have episodes of tonsillitis or strep throat with
pus?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

6. Before you were 13, how often did your twin have episodes of tonsillitis or strep throat
with pus?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

7. Before you were 13, which twin had more episodes of tonsillitis or strep throat with pus?

o You
o Your Twin
o Same
o Don’t know

8. Before you were 13, did you have your tonsils removed?

o Yes
o No
o Don’t know

9. Before you were 13, did your twin have his or her tonsils removed?

o Yes
o No
o Don’t know

121

10. If both you and your twin had your tonsils removed before you were 13, which twin had
them removed first?

o You
o Your Twin
o I did not have my tonsils removed
o My twin did not have his or her tonsils removed
o Both my twin and I did not have our tonsils removed
o Don’t know
11. At what age did you have your tonsils removed?

o Did not have my tonsils removed
o Before age 1
o 1-4
o 5-7
o 8-9
o 10-11
o 12-13
o Don’t know

12. At what age did your twin have his or her tonsils removed?

o Did not have his or her tonsils removed
o Before age 1
o 1-4
o 5-7
o 8-9
o 10-11
o 12-13
o Don’t know

13. Before you were 13, did you have your appendix removed?

o Yes
o No
o Don’t know

14. Before you were 13, did your twin have his or her appendix removed?

o Yes
o No
o Don’t know

122

15. If both you and your twin had your appendix removed before you were 13, which twin
had it removed first?

o You
o Your Twin
o I did not have my appendix removed
o My twin did not have his or her appendix removed
o Both my twin and I did not have our appendixes removed
o Don’t know

16. At what age did you have your appendix removed?

o Did not have my appendix removed
o Before age 1
o 1-4
o 5-7
o 8-9
o 10-11
o 12-13
o Don’t know

17. At what age did your twin have his or her appendix removed?

o Did not have his or her appendix removed
o Before age 1
o 1-4
o 5-7
o 8-9
o 10-11
o 12-13
o Don’t know

18. Before you were 13, how often did you have episodes of stomach flu with vomiting and
fever?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

123

19. Before you were 13, how often did your twin have episodes of stomach flu with vomiting
and fever?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

20. Which twin had more episodes of stomach flu with vomiting and fever?

o You
o Your Twin
o Same
o Don’t know

21. Before you were 13, how often did you experience persistent diarrhea?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

22. Before you were 13, how often did your twin experience persistent diarrhea?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

23. Which twin had more episodes of persistent diarrhea?

o You
o Your Twin
o Same
o Don’t know

II. ACTIVITY LEVEL & DIET

PLEASE ANSWER THE NEXT SET OF QUESTIONS ABOUT ACTIVITY LEVEL
AND DIET BEFORE YOU WERE 13 YEARS OLD (OR FINISHED THE 8
TH

GRADE)

124

1. Before age 13, did you have a large appetite (large =always hungry and ate everything on
the plate)?

o Yes
o No
o Don’t know

2. Before age 13, did your twin have a large appetite (large =always hungry and ate
everything on the plate)?

o Yes
o No
o Don’t know

3. Before age 13, were you a vegetarian?

o Yes
o No
o Don’t know

4. Before age 13, was your twin a vegetarian?

o Yes
o No
o Don’t know

5. Before age 13, how often did you participate in organized sports, dancing or other
strenuous physical activity?

o Never
o Occasionally
o Weekly
o Daily
o Don’t know

6. Before age 13, how often did your twin participate in organized sports, dancing or other
strenuous physical activity?

o Never
o Occasionally
o Weekly
o Daily
o Don’t know

125

7. Before age 13, which twin participated in more strenuous physical activity?

o You
o Your Twin
o Same
o Don’t know

III. CHILDHOOD HEALTH EXPERIENCE

PLEASE ANSWER THE NEXT SET OF QUESTIONS ABOUT THE TIME YOU
WERE AN INFANT, LESS THAN 1 YEAR OLD.

1. As an infant, did you attend daycare for at least 6 months?

o Yes
o No
o Don’t know

2. As an infant, did your twin attend daycare for at least 6 months?

o Yes
o No
o Don’t know

3. As an infant, how often were you sick? (Sick = illness with fever)

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

4. As an infant, how often was your twin sick? (Sick = illness with fever)

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

126

5. As an infant, which twin was sick more often?

o You
o Your Twin
o Same
o Don’t know

6. As an infant, how often were you seen by a doctor?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

7. As an infant, how often was your twin by a doctor?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

8. As an infant, which twin was seen by a doctor more often?

o You
o Your Twin
o Same
o Don’t know

9. As an infant, how often did you have ear infections?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

10. As an infant, how often did your twin have ear infections?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

127

11. As an infant, which twin had ear infections more often?

o You
o Your Twin
o Same
o Don’t know

12. As an infant, how often did you take antibiotics?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

13. As an infant, how often did your twin take antibiotics?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

14. As an infant, which twin took antibiotics more often?

o You
o Your Twin
o Same
o Don’t know

15. As an infant, how often did you miss daycare due to sickness (sick = illness with fever)?

o Did not attend daycare for at least 6 months
o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

128

16. As an infant, how often did your twin miss daycare due to sickness (sick = illness with
fever)?

o Did not attend daycare for at least 6 months
o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

17. As an infant, which twin missed more days of daycare due to sickness (sick = illness with
fever)?

o Did not attend daycare for at least 6 months
o You
o Your Twin
o Same
o Don’t know

PLEASE ANSWER THE NEXT SET OF QUESTIONS ABOUT THE TIME YOU
WERE A TODDLER BETWEEN 1 AND 5 YEARS OLD

18. As a toddler, did you attend daycare or nursery school for at least 6 months?

o Yes
o No
o Don’t know

19. As a toddler, did your twin attend daycare or nursery school for at least 6 months?

o Yes
o No
o Don’t know

20. As a toddler, how often were you sick (sick = illness with a fever)?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

129

21. As a toddler, how often was your twin sick (sick = illness with a fever)?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

22. As a toddler, which twin was sick more often (sick = illness with a fever)?

o You
o Your Twin
o Same
o Don’t know

23. As a toddler, how often were you seen by a doctor?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

24. As a toddler, how often was your twin seen by a doctor?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

25. As a toddler, which twin was seen by a doctor more often?

o You
o Your Twin
o Same
o Don’t know

26. As a toddler, how often did you have an ear infection?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

130

27. As a toddler, how often did your twin have an ear infection?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

28. As a toddler, which twin had an ear infection more often?

o You
o Your Twin
o Same
o Don’t know

29. As a toddler, how often did you take antibiotics?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

30. As a toddler, how often did your twin take antibiotics?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

31. As a toddler, which twin took antibiotics more often?

o You
o Your Twin
o Same
o Don’t know

32. As a toddler, how often did you miss daycare or school days due to sickness (sick =
illness with fever)?

o Did not attend
o Never
o Rarely
o Sometimes
o Frequently
o Don’t know
131

33. As a toddler, how often did your twin miss daycare or school days due to sickness (sick =
illness with fever)?

o Did not attend
o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

34. As a toddler, which twin missed more days of daycare or school days due to sickness
(sick = illness with fever)?

o Did not attend
o You
o Your Twin
o Same
o Don’t know

PLEASE ANSWER THE NEXT SET OF QUESTIONS ABOUT THE TIME YOU
WERE A CHILD BETWEEN 6 AND 10 YEARS OLD (DURING THE TIME YOU
WENT TO ELEMENTARY SCHOOL)

35. As a child, how many different elementary schools did you attend?

o One
o Two
o Three
o Four
o More than five
o Don’t know

36. As a child, how many different elementary schools did your twin attend?

o One
o Two
o Three
o Four
o More than five
o Don’t know

132

37. As a child, how often were you sick (sick = illness with a fever)?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

38. As a child, how often was your twin sick (sick = illness with a fever)?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

39. As a child, which twin was sick more often (sick=illness with a fever)?

o You
o Your Twin
o Same
o Don’t know

40. As a child, how often were you seen by a doctor?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

41. As a child, how often was your twin seen by a doctor?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

42. As a child, which twin was seen by a doctor more often?

o You
o Your Twin
o Same
o Don’t know

133

43. As a child, how often did you have an ear infection?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

44. As a child, how often did your twin have an ear infection?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

45. As a child, which twin had an ear infection more often?

o You
o Your Twin
o Same
o Don’t know

46. As a child, how often did you take antibiotics?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

47. As a child, how often did your twin take antibiotics?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

48. As a child, which twin took antibiotics more often?

o You
o Your Twin
o Same
o Don’t know

134

49. As a child, how often did you miss school due to sickness (sick = illness with fever)?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

50. As a child, how often did your twin miss school due to sickness (sick = illness with
fever)?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

51. As a child, which twin missed more school due to sickness (sick = illness with fever)?

o You
o Your Twin
o Same
o Don’t know

PLEASE ANSWER THE NEXT SET OF QUESTIONS ABOUT THE TIME YOU
WERE AN ADOLESCENT BETWEEN 11 AND 13 YEARS OLD (DURING THE
TIME YOU WENT TO JUNIOR HIGH SCHOOL)

52. As an adolescent, how many different junior high schools did you attend?

o One
o Two
o Three
o Four
o More than five
o Don’t know

53. As an adolescent, how many different junior high schools did your twin attend?

o One
o Two
o Three
o Four
o More than five
o Don’t know
135

54. As an adolescent, how often were you sick (sick = illness with a fever)?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

55. As an adolescent, how often was your twin sick (sick = illness with a fever)?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

56. As an adolescent, which twin was sick more often (sick=illness with a fever)?

o You
o Your Twin
o Same
o Don’t know

57. As an adolescent, how often were you seen by a doctor?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

58. As an adolescent, how often was your twin seen by a doctor?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

59. As an adolescent, which twin was seen by a doctor more often?

o You
o Your Twin
o Same
o Don’t know

136

60. As an adolescent, how often did you take antibiotics?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

61. As an adolescent, how often did your twin take antibiotics?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

62. As an adolescent, which twin took antibiotics more often?

o You
o Your Twin
o Same
o Don’t know

63. As an adolescent, how often did you miss school due to sickness (sick = illness with
fever)?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

64. As an adolescent, how often did your twin miss school due to sickness (sick = illness
with fever)?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

137

65. As an adolescent, which twin missed more school due to sickness (sick = illness with
fever)?

o You
o Your Twin
o Same
o Don’t know

PLEASE ANSWER THE NEXT SET OF QUESTIONS ABOUT THE TIME YOU
WERE A TEENAGER BETWEEN 14 TO 18 YEARS OLD (DURING THE TIME
YOU WENT TO HIGH SCHOOL)

66. As a teenager, how many different high schools did you attend?

o One
o Two
o Three
o Four
o More than five
o Don’t know

67. As a teenager, how many different high schools did your twin attend?

o One
o Two
o Three
o Four
o More than five
o Don’t know

68. As a teenager, how often were you sick (sick = illness with a fever)?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

69. As a teenager, how often was your twin sick (sick = illness with a fever)?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

138

70. As a teenager, which twin was sick more often (sick = illness with a fever)?

o You
o Your Twin
o Same
o Don’t know

71. As a teenager, how often were you seen by a doctor?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

72. As a teenager, how often was your twin seen by a doctor?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

73. As a teenager, which twin was seen by a doctor more often?

o You
o Your Twin
o Same
o Don’t know

74. As a teenager, how often did you take antibiotics?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

75. As a teenager, how often did your twin take antibiotics?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

139

76. As a teenager, which twin took antibiotics more often?

o You
o Your Twin
o Same
o Don’t know

77. As a teenager, how often did you miss school due to sickness (sick = illness with fever)?

o Did not attend
o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

78. As a teenager, how often did your twin miss school due to sickness (sick = illness with
fever)?

o Did not attend
o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

79. As a teenager, which twin missed more school due to sickness (sick = illness with fever)?

o Did not attend
o You
o Your Twin
o Same
o Don’t know

80. As a teenager, did you ever have infectious mononucleosis?

o Yes
o No
o Don’t know

81. As a teenager, did your twin ever have infectious mononucleosis?

o Yes
o No
o Don’t know

140

82. As a teenager, did you ever have a cough lasting longer than 1 month?

o Yes
o No
o Don’t know

83. As a teenager, did your twin ever have a cough lasting longer than 1 month?

o Yes
o No
o Don’t know

84. As a teenager, which twin had more episodes of cough lasting longer than 1 month?

o You
o Your Twin
o Same
o Don’t know

85. As a teenager, how often did you have influenza (influenza = high fevers and muscle
aches)?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

86. As a teenager, how often did your twin have influenza (influenza = high fevers and
muscle aches)?

o Never
o Rarely
o Sometimes
o Frequently
o Don’t know

87. As a teenager, which twin had more episodes of influenza (influenza = high fevers and
muscle aches)?

o You
o Your Twin
o Same
o Don’t know

141

IV. GROWTH & DEVELOPMENT

PLEASE ANSWER THE NEXT SET OF QUESTIONS ABOUT YOUR GENERAL
GROWTH AND DEVELOPMENT HISTORY.

(FOR MALES ONLY): IF YOU ARE A FEMALE, PLEASE SKIP TO QUESTIONS 7
ON PAGE 23.

1. At what age did your voice begin to change?

o 8
o 9
o 10
o 11
o 12
o 13
o 14
o 15
o 16
o 17
o 18
o 19 or older
o Don’t know

2. At what age did your twin’s voice begin to change?

o 8
o 9
o 10
o 11
o 12
o 13
o 14
o 15
o 16
o 17
o 18
o 19 or older
o Don’t know

142

3. How much earlier or later did your voice begin to change compared to your twin?

o My voice changed 2 or more years earlier
o My voice changed 1 to 2 years earlier
o My voice changed 7 to 11 months earlier
o My voice changed 6 months or less earlier
o My voice changed at the same time as my twin
o My voice changed 6 months or less later
o My voice changed 7 to 11 months later
o My voice changed 1 to 2 years later
o My voice changed 2 or more years later
o Don’t know

4. At what age did you begin shaving?

o 8
o 9
o 10
o 11
o 12
o 13
o 14
o 15
o 16
o 17
o 18
o 19 or older
o Don’t know

5. At what age did your twin begin shaving?

o 8
o 9
o 10
o 11
o 12
o 13
o 14
o 15
o 16
o 17
o 18
o 19 or older
o Don’t know

143

6. How much earlier or later you begin to shave compared to your twin?

o I began to shave 2 or more years earlier
o I began to shave 1 to 2 years earlier
o I began to shave 7 to 11 months earlier
o I began to shave 6 months or less earlier
o I began to shave at the same time as my twin
o I began to shave 6 months or less later
o I began to shave 7 to 11 months later
o I began to shave 1 to 2 years later
o I began to shave 2 or more years later
o Don’t know

(FOR FEMALES ONLY): IF YOU ARE A MALE, PLEASE SKIP TO QUESTION 16
ON PAGE 27.

7. At what age did you first develop breasts?

o 8
o 9
o 10
o 11
o 12
o 13
o 14
o 15
o 16
o 17
o 18
o 19 or older
o Don’t know

144

8. At what age did your twin first develop breasts?

o 8
o 9
o 10
o 11
o 12
o 13
o 14
o 15
o 16
o 17
o 18
o 19 or older
o Don’t know

9. How much earlier or later did you first develop breasts compared to your twin?

o I developed 2 or more years earlier
o I developed 1 to 2 years earlier
o I developed 7 to 11 months earlier
o I developed 6 months or less earlier
o I developed at the same time as my twin
o I developed 6 months or less later
o I developed 7 to 11 months later
o I developed 1 to 2 years later
o I developed 2 or more years later
o Don’t know

10. At what age did you get your first menstrual period?

o 8
o 9
o 10
o 11
o 12
o 13
o 14
o 15
o 16
o 17
o 18
o 19 or older
o Don’t know

145

11. At what age did your twin get her first menstrual period?

o 8
o 9
o 10
o 11
o 12
o 13
o 14
o 15
o 16
o 17
o 18
o 19 or older
o Don’t know

12. How much earlier or later did you get your first menstrual period compared to your twin?

o I began 2 or more years earlier
o I began 1 to 2 years earlier
o I began 7 to 11 months earlier
o I began 6 months or less earlier
o I began at the same time as my twin
o I began 6 months or less later
o I began 7 to 11 months later
o I began 1 to 2 years later
o I began 2 or more years later
o Don’t know

13. At what age did you first begin having regular (predictable) menstrual periods?

o 8
o 9
o 10
o 11
o 12
o 13
o 14
o 15
o 16
o 17
o 18
o 19
o 20
o 21 or older
o Don’t know
146

14. At what age did your twin first begin having regular (predictable) menstrual periods?

o 8
o 9
o 10
o 11
o 12
o 13
o 14
o 15
o 16
o 17
o 18
o 19
o 20
o 21 or older
o Don’t know

15. When did your menstrual period become regular (predictable) compared to your twin?

o 2 or more years earlier
o 1 to 2 years earlier
o 7 to 11 months earlier
o 6 months or less earlier
o Same time as my twin
o 6 months or less later
o 7 to 11 months later
o 1 to 2 years later
o 2 or more years later
o Don’t know

147

(MALES AND FEMALES) PLEASE ANSWER THE NEXT SET OF QUESTIONS
ABOUT YOUR GENERAL GROWTH AND DEVELOPMENT HISTORY.

16. How much did you weigh at birth?
Pounds Ounces
o Less than 3 o 0
o 3 o 1
o 4 o 2
o 5 o 3
o 6 o 4
o 7 o 5
o 8 o 6
o 9 o 7
o 10 o 8
o 11 o 9
o 12 o 10
o 13 o 11
o More than 14 o 12
o Don’t know weight o 13
o 14
o 15

17. How much did your twin weigh at birth?

Pounds Ounces
o Less than 3 o 0
o 3 o 1
o 4 o 2
o 5 o 3
o 6 o 4
o 7 o 5
o 8 o 6
o 9 o 7
o 10 o 8
o 11 o 9
o 12 o 10
o 13 o 11
o More than 14 o 12
o Don’t know weight o 13
o 14
o 15

148

18. Which twin weighed more at birth?

o You
o Your Twin
o Same
o Don’t know

19. Which twin weighed more at age 6?

o You
o Your Twin
o Same
o Don’t know

20. Which twin weighed more at age 10?

o You
o Your Twin
o Same
o Don’t know

21. Which twin weighed more at age 13?

o You
o Your Twin
o Same
o Don’t know

22. Which twin weighed more at age 18?

o You
o Your Twin
o Same
o Don’t know

23. Which twin weighs more now?

o You
o Your Twin
o Same
o Don’t know

149

24. How long were you at birth? If your birth length falls between the lengths given here,
round up to the next length. For example, if your birth length was 12 ¾ inches, round to
13 inches.
o Less than 10 inches
o 10 inches
o 11 inches
o 12 inches
o 13 inches
o 14 inches
o 15 inches
o 16 inches
o 17 inches
o 18 inches
o 19 inches
o 20 inches
o 21 inches
o 22 inches
o 23 inches
o 24 inches
o 25 inches
o 26 inches or more
o Don’t know

25. How long was your twin at birth? If your twin’s birth length falls between the lengths
given here, round up to the next length. For example, if your twin’s birth length was 12 ¾
inches, round to 13 inches.

o Less than 10 inches
o 10 inches
o 11 inches
o 12 inches
o 13 inches
o 14 inches
o 15 inches
o 16 inches
o 17 inches
o 18 inches
o 19 inches
o 20 inches
o 21 inches
o 22 inches
o 23 inches
o 24 inches
o 25 inches
o 26 inches or more
o Don’t know
150

26. Which twin was longer at birth?

o You
o Your Twin
o Same
o Don’t know

27. Which twin was taller at age 6?

o You
o Your Twin
o Same
o Don’t know

28. Which twin was taller at age 10?

o You
o Your Twin
o Same
o Don’t know

29. Which twin was taller at age 13?

o You
o Your Twin
o Same
o Don’t know

30. Which twin was taller at age 18?

o You
o Your Twin
o Same
o Don’t know

31. Which twin is taller now?

o You
o Your Twin
o Same
o Don’t know

151

32. Which twin seemed to grow fastest during infancy (infancy = less than one year old)?

o You
o Your Twin
o Same
o Don’t know

33. Which twin seemed to grow fastest as a toddler (toddler = between 1 and 5 years old)?

o You
o Your Twin
o Same
o Don’t know

34. Which twin seemed to grow fastest during childhood (childhood = between 6 and 10
years old)?

o You
o Your Twin
o Same
o Don’t know

35. Which twin seemed to grow fastest during adolescence (adolescence = between 11 and
13 years old)?

o You
o Your Twin
o Same
o Don’t know

36. Which twin seemed to grow fastest as a teenager (teenager = between 14 to 18 years
old)?

o You
o Your Twin
o Same
o Don’t know

152

37. How much did you weigh at age 18?

o Less than 80 lbs
o 80-89 lbs
o 90-99 lbs
o 100-109 lbs
o 110-119 lbs
o 120-129 lbs
o 130-139 lbs
o 140-149 lbs
o 150-159 lbs
o 160-169 lbs
o 170-179 lbs
o 180-189 lbs
o 190-199 lbs
o 200-209 lbs
o 210-219 lbs
o 220-239 lbs
o 240-259 lbs
o 260-279 lbs
o More than 280 lbs
o Don’t know

38. How much did your twin weigh at age 18?

o Less than 80 lbs
o 80-89 lbs
o 90-99 lbs
o 100-109 lbs
o 110-119 lbs
o 120-129 lbs
o 130-139 lbs
o 140-149 lbs
o 150-159 lbs
o 160-169 lbs
o 170-179 lbs
o 180-189 lbs
o 190-199 lbs
o 200-209 lbs
o 210-219 lbs
o 220-239 lbs
o 240-259 lbs
o 260-279 lbs
o More than 280 lbs
o Don’t know

153

39. How much do you weigh now?

o Less than 80 lbs
o 80-89 lbs
o 90-99 lbs
o 100-109 lbs
o 110-119 lbs
o 120-129 lbs
o 130-139 lbs
o 140-149 lbs
o 150-159 lbs
o 160-169 lbs
o 170-179 lbs
o 180-189 lbs
o 190-199 lbs
o 200-209 lbs
o 210-219 lbs
o 220-239 lbs
o 240-259 lbs
o 260-279 lbs
o More than 280 lbs
o Don’t know

40. How much does your twin weigh now?

o Less than 80 lbs
o 80-89 lbs
o 90-99 lbs
o 100-109 lbs
o 110-119 lbs
o 120-129 lbs
o 130-139 lbs
o 140-149 lbs
o 150-159 lbs
o 160-169 lbs
o 170-179 lbs
o 180-189 lbs
o 190-199 lbs
o 200-209 lbs
o 210-219 lbs
o 220-239 lbs
o 240-259 lbs
o 260-279 lbs
o More than 280 lbs
o Don’t know

154

41. Which twin weighed more at age of 18?

o You
o Your Twin
o Same (Skip to Question 43)
o Don’t know (Skip to Question 43)

42. (From the Question 41) By how much?

o Less than 3 lbs
o 4 – 5 lbs
o 6 – 10 lbs
o 11 – 15 lbs
o More than 16 lbs

43. Which twin weighs more now?

o You
o Your Twin
o Same (Skip to Question 45)
o Don’t know (Skip to Question 45)

44. (From the Question 43) By how much?

o Less than 3 lbs
o 4 – 5 lbs
o 6 – 10 lbs
o 11 – 15 lbs
o More than 16 lbs

45. How tall were you at age 18? If your height falls between the heights given here, round
up to the next height. For example, if your height was 5 feet 2 ¾ inches, round to 5 feet 3
inches.
Feet Nearest Inch
o Less than 4 o 0 o 6
o 4 o ½ o 6 ½
o 5 o 1 o 7
o 6 o 1 ½ o 7 ½
o More than 7 o 2 o 8
o Don’t know o 2 ½ o 8 ½
o 3 o 9
o 3 ½ o 9½
o 4 o 10
o 4 ½ o 10 ½
o 5 o 11
o 5½ o 11 ½
155

46. How tall was your twin at age 18? If your twin’s height falls between the heights given
here, round up to the next height. For example, if your twin’s height was 5 feet 2 ¾
inches, round to 5 feet 3 inches.

Feet Nearest Inch
o Less than 4 o 0 o 6
o 4 o ½ o 6 ½
o 5 o 1 o 7
o 6 o 1 ½ o 7 ½
o More than 7 o 2 o 8
o Don’t know o 2 ½ o 8 ½
o 3 o 9
o 3 ½ o 9½
o 4 o 10
o 4 ½ o 10 ½
o 5 o 11
o 5½ o 11 ½

47. How tall are you now? If your height falls between the heights given here, round up to
the next height. For example, if your height is 5 feet 2 ¾ inches, round to 5 feet 3 inches.

Feet Nearest Inch
o Less than 4 o 0 o 6
o 4 o ½ o 6 ½
o 5 o 1 o 7
o 6 o 1 ½ o 7 ½
o More than 7 o 2 o 8
o Don’t know o 2 ½ o 8 ½
o 3 o 9
o 3 ½ o 9½
o 4 o 10
o 4 ½ o 10 ½
o 5 o 11
o 5½ o 11 ½

156

48. How tall is your twin now? If your twin’s height falls between the heights given here,
round up to the next height. For example, if your twin’s height is 5 feet 2 ¾ inches, round
to 5 feet 3 inches.

Feet Nearest Inch
o Less than 4 o 0 o 6
o 4 o ½ o 6 ½
o 5 o 1 o 7
o 6 o 1 ½ o 7 ½
o More than 7 o 2 o 8
o Don’t know o 2 ½ o 8 ½
o 3 o 9
o 3 ½ o 9½
o 4 o 10
o 4 ½ o 10 ½
o 5 o 11
o 5½ o 11 ½

49. How much taller or shorter are you compared to your twin?

o I am 4 or more inches taller
o I am 3 inches taller
o I am 2 inches taller
o I am 1 inch taller
o We are the same height
o I am 1 inch shorter
o I am 2 inches shorter
o I am 3 inches shorter
o I am 4 or more inches shorter
o Don’t know

50. If one twin is taller, at what age did the difference first appear?

o Under 6 years old
o 6-10 years old
o 11-12 years old
o 13-14 years old
o 15-16 years old
o 17-18 years old
o Over 18 years old
o Don’t know

157

51. Which twin was slimmer (fit into smaller pants) at age 10?

o You
o Your Twin
o Same
o Don’t know

52. Which twin was slimmer (fit into smaller pants) at age 13?

o You
o Your Twin
o Same
o Don’t know

53. Which twin was slimmer (fit into smaller pants) at age 18?

o You
o Your Twin
o Same
o Don’t know

54. Which twin has longer legs now?

o You
o Your Twin
o Same
o Don’t know

-------------------------------------------END OF QUESTIONNAIRE-------------------------------------

*Thank you for completing the questionnaire. Your participation is greatly
appreciated. If you’d like the results of entire analysis upon completion of the
study, please check the circle below. Please note that no individual results will
be provided. The results are given as aggregate data only.

o Yes, I would like to receive the result.
o No, thanks.
158

Appendix B. Questionnaire for Mothers

TITLE OF THE PROJECT:
CHILDHOOD EXPERIENCE, GROWTH & DEVELOPMENT IN
TWINS

Thank you for participating in this study of growth and development in twins. The
questionnaire consists of 4 sections; general information, activity level & diet,
childhood health experience, and growth & development. Some questions deal with
your children at specific ages. Even if you do not remember the experiences exactly,
please answer to the best of your ability. The questionnaire will take approximately
30 to 45 minutes to complete.

I. GENERAL HISTORY

THE FIRST SET OF QUESTIONS ARE ABOUT ILLNESSES YOUR TWINS HAD AT ANY
AGE BEFORE THEY WERE 13 (OR FINISHED THE 8
TH
GRADE)

1. Before your twins were 13, how often did Twin A (Twin B) have episodes of tonsillitis or strep
throat with pus?

2. Which twin had more episodes of tonsillitis or strep throat with pus?

o Twin A
o Twin B
o Same
o Don’t know

3. Before your twins were 13, did Twin A (Twin B) have their tonsils removed? (If no, skip to #6)

Twin A Twin B
o Yes o Yes
o No o No
o Don’t know o Don’t know

Twin A Twin B
o Never o Never
o Rarely o Rarely
o Sometimes o Sometimes
o Frequently o Frequently
o Don’t know o Don’t know
159

4. If both twins had their tonsils removed, which twin had them removed first?

o Twin A
o Twin B
o Twin A did not have tonsils removed
o Twin B did not have tonsils removed
o Both twins did not have tonsils removed
o Don’t know

5. At what age did Twin A (Twin B) have their tonsils removed?

Twin A Twin B
o Did not have tonsils removed o Did not have tonsils removed
o Before age 1 o Before age 1
o 1-4 o 1-4
o 5-7 o 5-7
o 8-9 o 8-9
o 10-11 o 10-11
o 12-13 o 12-13
o Don’t know o Don’t know

6. Before twins were 13, did Twin A (Twin B) have their appendix removed? (If no, skip to #9)

Twin A Twin B
o Yes o Yes
o No o No
o Don’t know o Don’t know

7. If both twins had their appendix removed, which twin had it removed first?

o Twin A
o Twin B
o Twin A did not have the appendix removed
o Twin B did not have the appendix removed
o Both twins did not have appendix removed
o Don’t know

8. At what age did Twin A (Twin B) have the appendix removed?

Twin A Twin B
o Did not have the appendix
removed
o Did not have the appendix
removed
o Before age 1 o Before age 1
o 1-4 o 1-4
o 5-7 o 5-7
o 8-9 o 8-9
o 10-11 o 10-11
o 12-13 o 12-13
o Don’t know o Don’t know

160

9. Before twins were 13, how often did Twin A (Twin B) have episodes of stomach flu with
vomiting and fever?

Twin A Twin B
o Never o Never
o Rarely o Rarely
o Sometimes o Sometimes
o Frequently o Frequently
o Don’t know o Don’t know

10. Which twin had more episodes of stomach flu with vomiting and fever?

o Twin A
o Twin B
o Same
o Don’t know

11. Before twins were 13, how often did Twin A (Twin B) experience persistent diarrhea?

Twin A Twin B
o Never o Never
o Rarely o Rarely
o Sometimes o Sometimes
o Frequently o Frequently
o Don’t know o Don’t know

12. Which twin had more episodes of persistent diarrhea?

o Twin A
o Twin B
o Same
o Don’t know

II. ACTIVITY LEVEL & DIET

THE NEXT SET OF QUESTIONS IS ABOUT ACTIVITY LEVEL AND DIET BEFORE
THEY WERE 13 (OR FINISHED THE 8
TH
GRADE)

1. Before age 13, did Twin A (Twin B) have a large appetite? (large appetite is defined as always
hungry and ate everything on the plate)

Twin A Twin B
o Yes o Yes
o No o No
o Don’t know o Don’t know

161

2. Before age 13, was Twin A (Twin B) a vegetarian?

Twin A Twin B
o Yes o Yes
o No o No
o Don’t know o Don’t know

3. Before age 13, how often did Twin A (Twin B) participate in organized sports, dancing or other
strenuous physical activity?

Twin A Twin B
o Never o Never
o Occasionally o Occasionally
o Weekly o Weekly
o Daily o Daily
o Don’t know o Don’t know

4. Before age 13, which twin participated in more strenuous physical activity?

o Twin A
o Twin B
o Same
o Don’t know

III. GENERAL HEALTH

THE NEXT SET OF QUESTIONS IS ABOUT THE TIME THE TWINS WERE INFANTS,
LESS THAN 1YEAR OLD

1. As an infant, did Twin A (Twin B) attend day care for at least 6 months?

Twin A Twin B
o Yes o Yes
o No o No
o Don’t know o Don’t know

2. As an infant, how often was Twin A (Twin B) sick? (Sick is defined as having illness with fever)

Twin A Twin B
o Never o Never
o Rarely o Rarely
o Sometimes o Sometimes
o Frequently o Frequently
o Don’t know o Don’t know

162

3. As an infant, which twin was sick more often?

o Twin A
o Twin B
o Same
o Don’t know

4. As an infant, how often was Twin A (Twin B) seen by a doctor?

Twin A Twin B
o Never o Never
o Rarely o Rarely
o Sometimes o Sometimes
o Frequently o Frequently
o Don’t know o Don’t know

5. As an infant, which twin was seen by a doctor more often?

o Twin A
o Twin B
o Same
o Don’t know

6. As an infant, how often did Twin A (Twin B) have ear infections?

Twin A Twin B
o Never o Never
o Rarely o Rarely
o Sometimes o Sometimes
o Frequently o Frequently
o Don’t know o Don’t know

7. As an infant, which twin had ear infections more often?

o Twin A
o Twin B
o Same
o Don’t know

8. As an infant, how often did Twin A (Twin B) take antibiotics?

Twin A Twin B
o Never o Never
o Rarely o Rarely
o Sometimes o Sometimes
o Frequently o Frequently
o Don’t know o Don’t know

163

9. As an infant, which twin took antibiotics more often?

o Twin A
o Twin B
o Same
o Don’t know

10. As an infant, how often did Twin A (Twin B) miss daycare due to sickness (Sick is defined as
having illness with fever)?

Twin A Twin B
o Did not attend for at least 6 mo o Did not attend for at least 6 mo
o Never o Never
o Rarely o Rarely
o Sometimes o Sometimes
o Frequently o Frequently
o Don’t know o Don’t know

11. As an infant which twin missed more daycare due to sickness? (illness with fever)

o Did not attend daycare for at least 6 mo
o Twin A
o Twin B
o Same
o Don’t know

12. How often did you miss work to take care your twins when they were sick?

Twin A Twin B
o Did not work o Did not work
o Never o Never
o Rarely o Rarely
o Sometimes o Sometimes
o Frequently o Frequently
o Don’t know o Don’t know

13. For which twin did you miss more work to take care of them when they were sick?

o Did not work
o Twin A
o Twin B
o Same
o Don’t know

164

THE NEXT SET OF QUESTIONS IS ABOUT THE TIME YOUR TWINS WERE TODDLERS
BETWEEN 1 AND 5 YEARS OLD

14. As a toddler, did Twin A (Twin B) attend day care or nursery school for at least 6 months?

Twin A Twin B
o Yes o Yes
o No o No
o Don’t know o Don’t know

15. As a toddler, how often was Twin A (Twin B) sick? (Sick being defined as having illness with a
fever)?

Twin A Twin B
o Never o Never
o Rarely o Rarely
o Sometimes o Sometimes
o Frequently o Frequently
o Don’t know o Don’t know

16. As a toddler, which twin was sick more often? (Illness with a fever)

o Twin A
o Twin B
o Same
o Don’t know

17. As a toddler, how often were Twin A (Twin B) seen by a doctor?

Twin A Twin B
o Never o Never
o Rarely o Rarely
o Sometimes o Sometimes
o Frequently o Frequently
o Don’t know o Don’t know
18. As a toddler, which twin was seen by a doctor more often?

o Twin A
o Twin B
o Same
o Don’t know

19. As a toddler, how often did Twin A (Twin B) have an ear infection?

Twin A Twin B
o Never o Never
o Rarely o Rarely
o Sometimes o Sometimes
o Frequently o Frequently
o Don’t know o Don’t know

165

20. As a toddler, which twin had an ear infection more often?

o Twin A
o Twin B
o Same
o Don’t know

21. As a toddler, how often did Twin A (Twin B) take antibiotics?

Twin A Twin B
o Never o Never
o Rarely o Rarely
o Sometimes o Sometimes
o Frequently o Frequently
o Don’t know o Don’t know

22. As a toddler, which twin took antibiotics more often?

o Twin A
o Twin B
o Same
o Don’t know

23. As a toddler, how often did Twin A (Twin B) miss daycare or school days due to sickness (illness
with fever)?

Twin A Twin B
o Did not attend o Did not attend
o Never o Never
o Rarely o Rarely
o Sometimes o Sometimes
o Frequently o Frequently
o Don’t know o Don’t know

24. As a toddler, which twin missed more daycare or school days due to sickness (illness with fever)?

o Did not attend daycare
o Twin A
o Twin B
o Same
o Don’t know

25. How often did you miss work to take care of your twins when they were sick?

Twin A Twin B
o Did not work o Did not work
o Never o Never
o Rarely o Rarely
o Sometimes o Sometimes
o Frequently o Frequently
o Don’t know o Don’t know
166

26. For which twin did you miss more work to tend take care of them when they were sick?

o Did not work
o Twin A
o Twin B
o Same
o Don’t know

THE NEXT SET OF QUESTIONS IS ABOUT THE TIME YOUR TWINS WERE CHILDREN
BETWEEN 6 TO 1O YEARS OLD (DURING THE TIME THEY WENT TO ELEMANARY
SCHOOL)

27. As a child, how many different elementary schools did Twin A (Twin B) attend?

Twin A Twin B
o One o One
o Two o Two
o Three o Three
o Four o Four
o More than five o More than five
o Don’t know o Don’t know

28. As a child, how often was Twin A (Twin B) sick? (Sick is defined as having illness with a fever)?

Twin A Twin B
o Never o Never
o Rarely o Rarely
o Sometimes o Sometimes
o Frequently o Frequently
o Don’t know o Don’t know

29. As a child, which twin was sick more often? (illness with a fever)

o Twin A
o Twin B
o Same
o Don’t know

30. As a child, how often were Twin A (Twin B) seen by a doctor?

Twin A Twin B
o Never o Never
o Rarely o Rarely
o Sometimes o Sometimes
o Frequently o Frequently
o Don’t know o Don’t know

167

31. As a child, which twin was seen by a doctor more often?

o Twin A
o Twin B
o Same
o Don’t know

32. As a child, how often did Twin A (Twin B) have an ear infection?

Twin A Twin B
o Never o Never
o Rarely o Rarely
o Sometimes o Sometimes
o Frequently o Frequently
o Don’t know o Don’t know

33. As a child, which twin had an ear infection more often?

o Twin A
o Twin B
o Same
o Don’t know

34. As a child, how often did Twin A (Twin B) take antibiotics?

Twin A Twin B
o Never o Never
o Rarely o Rarely
o Sometimes o Sometimes
o Frequently o Frequently
o Don’t know o Don’t know

35. As a child, which twin took antibiotics more often?

o Twin A
o Twin B
o Same
o Don’t know

36. As a child how often did Twin A (Twin B) miss school due to sickness (illness with fever)?

Twin A Twin B
o Never o Never
o Rarely o Rarely
o Sometimes o Sometimes
o Frequently o Frequently
o Don’t know o Don’t know

168

37. As a child which twin missed more school due to sickness (illness with fever)?

o Twin A
o Twin B
o Same
o Don’t know

38. How often did you miss work to take care of your twins when they were sick?

Twin A Twin B
o Did not work o Did not work
o Never o Never
o Rarely o Rarely
o Sometimes o Sometimes
o Frequently o Frequently
o Don’t know o Don’t know

39. For which twin did you miss work to take care of them when they were sick?

o Did not work
o Twin A
o Twin B
o Same
o Don’t know

THE NEXT SET OF QUESTIONS IS ABOUT THE TIME YOUR TWINS WERE
ADOLESCENTS BETWEEN 11 TO 13 YEARS OLD (DURING THE TIME THEY WENT TO
JUNIOR HIGH SCHOOL)

40. As an adolescent, how many different junior high schools did Twin A (Twin B) attend?

Twin A Twin B
o One o One
o Two o Two
o Three o Three
o Four o Four
o More than five o More than five
o Don’t know o Don’t know

41. As an adolescent, how often was Twin A (Twin B) sick? (Sick is defined as having illness with a
fever)?

Twin A Twin B
o Never o Never
o Rarely o Rarely
o Sometimes o Sometimes
o Frequently o Frequently
o Don’t know o Don’t know

169

42. As an adolescent, which twin was sick more often? (illness with a fever)

o Twin A
o Twin B
o Same
o Don’t know

43. As an adolescent, how often were Twin A (Twin B) seen by a doctor?

Twin A Twin B
o Never o Never
o Rarely o Rarely
o Sometimes o Sometimes
o Frequently o Frequently
o Don’t know o Don’t know

44. As an adolescent, which twin was seen by a doctor more often?

o Twin A
o Twin B
o Same
o Don’t know

45. As an adolescent, how often did Twin A (Twin B) have an ear infection?

Twin A Twin B
o Never o Never
o Rarely o Rarely
o Sometimes o Sometimes
o Frequently o Frequently
o Don’t know o Don’t know

46. As an adolescent, which twin had an ear infection more often?

o Twin A
o Twin B
o Same
o Don’t know

47. As an adolescent, how often did Twin A (Twin B) take antibiotics?

Twin A Twin B
o Never o Never
o Rarely o Rarely
o Sometimes o Sometimes
o Frequently o Frequently
o Don’t know o Don’t know

170

48. As an adolescent, which twin took antibiotics more often?

o Twin A
o Twin B
o Same
o Don’t know

49. As an adolescent, how often did Twin A (Twin B) miss school due to sickness (illness with
fever)?

Twin A Twin B
o Never o Never
o Rarely o Rarely
o Sometimes o Sometimes
o Frequently o Frequently
o Don’t know o Don’t know

50. As an adolescent, which twin missed more school due to sickness (illness with fever)?

o Twin A
o Twin B
o Same
o Don’t know

51. How often did you miss work to take care of your twins when they were sick?

Twin A Twin B
o Did not work o Did not work
o Never o Never
o Rarely o Rarely
o Sometimes o Sometimes
o Frequently o Frequently
o Don’t know o Don’t know

52. For which twin did you miss more work to take care of them when they were sick?

o Did not work
o Twin A
o Twin B
o Same
o Don’t know

171

THE NEXT SET OF QUESTIONS IS ABOUT THE TIME YOUR TWINS WERE
TEENAGERS BETWEEN 13 TO 18 YEARS OLD (DURING THE TIME THE WENT TO
HIGH SCHOOL)

53. As a teenager, how many different high schools did Twin A (Twin B) attend?

Twin A Twin B
o One o One
o Two o Two
o Three o Three
o Four o Four
o More than five o More than five
o Don’t know o Don’t know

54. As a teenager, how often was Twin A (Twin B) sick? (defined as having illness with a fever)?

Twin A Twin B
o Never o Never
o Rarely o Rarely
o Sometimes o Sometimes
o Frequently o Frequently
o Don’t know o Don’t know

55. As a teenager, which twin was sick more often? (illness with a fever)

o Twin A
o Twin B
o Same
o Don’t know

56. As a teenager, how often were Twin A (Twin B) seen by a doctor?

Twin A Twin B
o Never o Never
o Rarely o Rarely
o Sometimes o Sometimes
o Frequently o Frequently
o Don’t know o Don’t know

57. As a teenager, which twin was seen by a doctor more often?

o Twin A
o Twin B
o Same
o Don’t know

172

58. As a teenager, how often did Twin A (Twin B) take antibiotics?

Twin A Twin B
o Never o Never
o Rarely o Rarely
o Sometimes o Sometimes
o Frequently o Frequently
o Don’t know o Don’t know

59. As a teenager, which twin took antibiotics more often?

o Twin A
o Twin B
o Same
o Don’t know

60. As a teenager, how often did Twin A (Twin B) miss school due to sickness (illness with fever)?

Twin A Twin B
o Did not attend o Did not attend
o Never o Never
o Rarely o Rarely
o Sometimes o Sometimes
o Frequently o Frequently
o Don’t know o Don’t know

61. As a teenager, which twin missed more school due to sickness (illness with fever)?

o Did not attend
o Twin A
o Twin B
o Same
o Don’t know

62. How often did you miss work to take care of your twins when they were sick?

Twin A Twin B
o Did not work o Did not work
o Never o Never
o Rarely o Rarely
o Sometimes o Sometimes
o Frequently o Frequently
o Don’t know o Don’t know

63. For which twin did you miss more work to take care of them when they were sick?

o Did not work
o Twin A
o Twin B
o Same
o Don’t know
173

64. As a teenager, did Twin A (Twin B) ever have infectious mononucleosis?

Twin A Twin B
o Yes o Yes
o No o No
o Don’t know o Don’t know

65. As a teenager, did Twin A (Twin B) ever have a cough lasting longer than 1 month?

Twin A Twin B
o Yes o Yes
o No o No
o Don’t know o Don’t know

66. As a teenager, which twin had more episodes of cough lasting longer than 1 month?

o Twin A
o Twin B
o Same
o Don’t know

67. As a teenager, how often did Twin A (Twin B) have influenza (high fevers and muscle aches)?

Twin A Twin B
o Never o Never
o Rarely o Rarely
o Sometimes o Sometimes
o Frequently o Frequently
o Don’t know o Don’t know

68. As a teenager, which twin had more episodes of influenza (high fevers and muscle aches)?

o Twin A
o Twin B
o Same
o Don’t know

IV. GROWTH & DEVELOPMENT

THE NEXT QUESTIONS ARE ABOUT YOUR TWINS GENERAL GROWTH AND
DEVELOPMENT HISTORY.

174

(For males only): If the twins are females, skip to question 7.

1. At what age did Twin A’s (Twin B’s) voice begin to change?

Twin A Twin B
o 8 o 8
o 9 o 9
o 10 o 10
o 11 o 11
o 12 o 12
o 13 o 13
o 14 o 14
o 15 o 15
o 16 o 16
o 17 o 17
o 18 o 18
o 19 or older o 19 or older
o Don’t know o Don’t know

2. How much earlier or later did Twin A’s voice begin to change compared to Twin B?

o Twin A changed 2 or more years earlier
o Twin A changed 1 to 2 years earlier
o Twin A changed 7 to 11 months earlier
o Twin A changed 6 months or less earlier
o Twin A changed at the same time as twin B
o Twin A changed 6 moths or less later
o Twin A changed 7 to 11 months later
o Twin A changed 1 to 2 years later
o Twin A changed 2 or more years later
o Don’t know

3. At what age did Twin A (Twin B) begin shaving?

Twin A Twin B
o 8 o 8
o 9 o 9
o 10 o 10
o 11 o 11
o 12 o 12
o 13 o 13
o 14 o 14
o 15 o 15
o 16 o 16
o 17 o 17
o 18 o 18
o 19 or older o 19 or older
o Don’t know o Don’t know

175

4. How much earlier or later did Twin A begin to shave compared to Twin B?

o Twin A began to shave 2 or more years earlier
o Twin A began to shave 1 to 2 years earlier
o Twin A began to shave 7 to 11 months earlier
o Twin A began to shave 6 months or less earlier
o Twin A began to shave at the same time as twin B
o Twin A began to shave 6 moths or less later
o Twin A began to shave 7 to 11 months later
o Twin A began to shave 1 to 2 years later
o Twin A began to shave 2 or more years later
o Don’t know

(For females only): If the twins are males, skip to question 9.

5. At which age did Twin A’s (Twin B’s) get their first menstrual period?

Twin A Twin B
o 8 o 8
o 9 o 9
o 10 o 10
o 11 o 11
o 12 o 12
o 13 o 13
o 14 o 14
o 15 o 15
o 16 o 16
o 17 o 17
o 18 o 18
o 19 or older o 19 or older
o Don’t know o Don’t know

6. How much earlier or later did Twin A get her first menstrual period compared to Twin B?

o Twin A began 2 or more years earlier
o Twin A began 1 to 2 years earlier
o Twin A began 7 to 11 months earlier
o Twin A began 6 months or less earlier
o Twin A began at the same time as twin B
o Twin A began 6 moths or less later
o Twin A began 7 to 11 months later
o Twin A began 1 to 2 years later
o Twin A began 2 or more years later
o Don’t know

176

(MALES AND FEMALES) PLEASE ANWER THE SET OF QUESTIONS ABOUT YOUR TWIN’S
GENERAL HEALTH AND DEVELOPMENT HISTORY.

7. Which twin was born first?

o Twin A
o Twin B
o Don’t know

8. Were the twins conceived by IVF (In Vitro Fertilization) or other pharmacologically assisted or
induced ovulation?

o Yes
o No
o Don’t know

9. If yes, what type of method did you use?

_____________________________________________

10. Were the twins born prematurely?

o Yes
o No
o Don’t know

11. At what gestational age were the twins born?

o Less than 20 weeks
o 21-25 weeks
o 26-30 weeks
o 31-34 weeks
o 35-37 weeks
o 38-40 weeks
o More than 41 weeks
o Don’t know but premature
o Don’t know

12. Were the twins born by Cesarean section or vaginal delivery?

o Cesarean Section
o Vaginal delivery
o Don’t know

177

13. How much did Twin A (Twin B) weigh at birth?

Twin A Twin B
Pounds Ounces Pounds Ounces
o Less than 3 o 0 o Less than 3 o 0
o 3 o 1 o 3 o 1
o 4 o 2 o 4 o 2
o 5 o 3 o 5 o 3
o 6 o 4 o 6 o 4
o 7 o 5 o 7 o 5
o 8 o 6 o 8 o 6
o 9 o 7 o 9 o 7
o 10 o 8 o 10 o 8
o 11 o 9 o 11 o 9
o 12 o 10 o 12 o 10
o 13 o 11 o 13 o 11
o 14 o 12 o 14 o 12
o Don’t know
weight
o 13 o Don’t know
weight
o 13
o 14 o 14
o 15 o 15

14. Which twin weighed more at birth?

o Twin A
o Twin B
o Same
o Don’t know

15. Which twin weighed more at each of the ages shown in the table below:

Age 6 Age 10 Age 13 Age 18 Now
Twin A o o o o o
Twin B o o o o o
Same o o o o o
Don’t know o o o o o

178

16. How long was Twin A (Twin B) at birth? (in inches)

Twin A Twin B
o Less than 10 o Less than 10
o 11 o 11
o 12 o 12
o 13 o 13
o 14 o 14
o 15 o 15
o 16 o 16
o 17 o 17
o 18 o 18
o 19 o 19
o 20 o 20
o 21 o 21
o 22 o 22
o 23 o 23
o 24 o 24
o 25 o 25
o 26 or more o 26 or more
o Don’t know o Don’t know

17. Which twin was longer at birth?

o Twin A
o Twin B
o Same
o Don’t know

18. Which twin was taller at each of the ages shown in the table below:

Age 6 Age 10 Age 13 Age 18 Now
Twin A o o o o o
Twin B o o o o o
Same o o o o o
Don’t know o o o o o

19. Which twin seemed to grow fastest during the following years:

Infancy
< 1 yr
Toddler
1-5 yrs
Childhood
6-10 yrs
Adolescence
11-13 yrs
Teenage
14-18 yrs
Twin A o o o o o
Twin B o o o o o
Same o o o o o
Don’t know o o o o o

179

20. How much did Twin A (Twin B) weigh at the age of 18? How much does Twin A (Twin B)
weigh now?

Twin A Twin B
Age 18 Now Age 18 Now
Under 80 pounds o o o o
80-89 o o o o
90-99 o o o o
100-109 o o o o
110-119 o o o o
120-129 o o o o
130-139 o o o o
140-149 o o o o
150-159 o o o o
160-169 o o o o
170-179 o o o o
180-189 o o o o
190-199 o o o o
200-219 o o o o
220-239 o o o o
240-259 o o o o
260-279 o o o o
280 or more o o o o
Don’t know o o o o

21. Which twin weighed more at age of 18? Which twin weighs more now?

Age 18 Now
Twin A o o
Twin B o o
Same o o
Don’t know o o

22. How tall was Twin A (Twin B) at the age of 18?

Twin A Twin B
Feet Nearest Inch Feet Nearest Inch
o Less
than 4
o 0 o 6 o Less
than 4
o 0 o 6
o ½ o 6 ½ o ½ o 6 ½
o 4 o 1 o 7 o 4 o 1 o 7
o 5 o 1 ½ o 7 ½ o 5 o 1 ½ o 7 ½
o 6 o 2 o 8 o 6 o 2 o 8
o More
than 7
o 2 ½ o 8 ½ o More
than 7
o 2 ½ o 8 ½
o 3 o 9 o 3 o 9
o Don’t
know
o 3 ½ o 9½ o Don’t
know
o 3 ½ o 9½
o 4 o 10 o 4 o 10
o 4 ½ o 10 ½ o 4 ½ o 10 ½
o 5 o 11 o 5 o 11
o 5½ o 11 ½ o 5½ o 11 ½

180

23. How tall is Twin A (Twin B) NOW?

Twin A Twin B
Feet Nearest Inch Feet Nearest Inch
o Less
than 4
o 0 o 6 o Less
than 4
o 0 o 6
o ½ o 6 ½ o ½ o 6 ½
o 4 o 1 o 7 o 4 o 1 o 7
o 5 o 1 ½ o 7 ½ o 5 o 1 ½ o 7 ½
o 6 o 2 o 8 o 6 o 2 o 8
o More
than 7
o 2 ½ o 8 ½ o More
than 7
o 2 ½ o 8 ½
o 3 o 9 o 3 o 9
o Don’t
know
o 3 ½ o 9½ o Don’t
know
o 3 ½ o 9½
o 4 o 10 o 4 o 10
o 4 ½ o 10 ½ o 4 ½ o 10 ½
o 5 o 11 o 5 o 11
o 5½ o 11 ½ o 5½ o 11 ½

24. How much TALLER or SHORTER is Twin A compared to Twin B?

o Twin A is 4 or more inches taller
o Twin A is 3 inches taller
o Twin A is 2 inches taller
o Twin A is 1 inch taller
o Twin A & B are same height
o Twin A is 1 inch shorter
o Twin A is 2 inches shorter
o Twin A is 3 inches shorter
o Twin A is 4 or more inches shorter
o Don’t know

25. If one twin is taller, at what age did the difference first appear?

o Under 6 years
o 6-10
o 11-12
o 13-14
o 15-16
o 17-18

26. Which twin was slimmer (fit into smaller pants) at each of the ages shown in the table below::

Age 10 Age 13 Age 18
Twin A o o o
Twin B o o o
Same o o o
Don’t know o o o

181

27. Which twin has longer legs NOW?

o Twin A
o Twin B
o Same
o Don’t know

-------------------------------------------END OF QUESTIONNAIRE------------------------------------------

*Thank you for completing the questionnaire. Your participation is greatly
appreciated. If you’d like the results of entire analysis upon completion of the
study, please check the box below. Please note that no individual results will be
provided. The results are given as aggregate data only.

o Yes, I would like to receive the result.
o No, thanks.

182

Abstract (if available)

Abstract Childhood health experience may have a long term effects lasting into adulthood. Childhood infections have an etiologic role in the development of cancers and chronic diseases. For example, Epstein Barr virus (EBV) and Helicobactor pylori are well established causes of EBV-positive Hodgkin’s lymphoma and stomach cancer, respectively. Height has also been commonly associated with many cancers and other chronic conditions. It is usually interpreted as a proxy for childhood socioeconomic status or nutrition. However the effect of height on diseases has been consistently reported independent of the effects of childhood socioeconomic status and diet, suggesting height may represent other underlying biological mechanisms. Studies examining childhood experiences are challenging due to unreliable recall by study participants and the unavailability and incomplete of medical records. ❧ Twins offer unique advantages for studying childhood health experience because they can provide relative differences in exposure, they can validate each other’s answers, and they are either partially or entirely matched on genome. This dissertation consists of several projects examining childhood health in which twins identified from the California Twin Program were the participants. For two of the studies, both young adult twins and their mothers were interviewed, providing consistent and reliable information pertaining to the twins’ childhood illness history. In a descriptive study, comparing responses from mothers and twins about childhood exposures, I found all subjects were able to provide the most information on differences between the twins when questions were framed in a comparative fashion with ordinal answers. Although the number of pairs reporting differences in exposures was small, their answers were generally consistent with their mothers. ❧ Infectious mononucleosis, a disease caused by a delayed infection of EBV, is associated with EBV positive Hodgkin’s lymphoma and multiple sclerosis. I conducted a heritability study in monozygotic and dizygotic twins and found evidence suggesting a genetic component in the development of infectious mononucleosis. ❧ The effect of childhood illnesses on adult height was assessed in healthy identical twins differing in height as adults, to control for genetic factors, childhood socioeconomic status and parental exposures. The twin who reported more frequent episodes of febrile illnesses was twice as likely to become the shorter twin as an adult. This effect was consistent after adjusting for birth weight and birth height and was strongest and most significant during toddler years (1-5 years of age). ❧ In conclusion, these studies suggest that childhood illnesses are a determinant of adult height. In the future, these findings can be applied to elucidate the relationship between childhood infections and adult diseases.

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Asset Metadata

Creator Hwang, Amie Eunah (author)

Core Title Early childhood health experience & adult phenotype in twins

School Keck School of Medicine

Degree Doctor of Philosophy

Degree Program Epidemiology

Publication Date 04/11/2012

Defense Date 02/10/2012

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

Tag Childhood,concordance,Growth,height,infectious disease,Infectious Mononucleosis,long term recall,OAI-PMH Harvest,parental report,pediatric,twin

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Cozen, Wendy (committee chair), Bernstein, Leslie (committee member), Crimmins, Eileen M. (committee member), Gauderman, William James (committee member), Mack, Thomas M. (committee member)

Creator Email amiehwan@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-5262

Unique identifier UC1112461

Identifier usctheses-c3-5262 (legacy record id)

Legacy Identifier etd-HwangAmieE-594.pdf

Dmrecord 5262

Document Type Dissertation

Rights Hwang, Amie Eunah

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

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA