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BIRTH SIZE, INFANT GROWTH, AND CHILD BMI AT AGE
5 YEARS IN A MULTIETHNIC POPULATION
A DISSERTATION SUBMITTED TO THE GRADUATE DIVISION OF THE
UNIVERSITY OF HAWAI‘I AT MĀNOA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
IN
EPIDEMIOLOGY
DECEMBER 2012
By
Caryn E.S. Oshiro
Dissertation Committee:
Rachel Novotny, Chairperson Eric Hurwitz John Grove
Thomas Vogt Dian Dooley
Keywords: BMI, child, infant growth, birth weight
ii
ACKNOWLEDGMENTS
I would like to thank my dissertation committee members for their invaluable
mentoring and guidance throughout my years at the University of Hawai‘i in the
Department of Human Nutrition, Food, and Animal Sciences and Office of Public Health
Studies.
I would also like to acknowledge the United States Department of Agriculture
(USDA) National Research Institute Grant No: 2008-55215-18821 (2/15/2008 -
2/14/2012). In addition, I would like to thank the staff and mentors at Kaiser
Permanente Center for Health Research for their ongoing support of my dissertation
work.
Finally, a thank you to my husband, Dan, children, Megan and Sean, and
my extended family for their encouragement throughout my years in graduate school.
iii
ABSTRACT
Child overweight is a public health concern and it is imperative that steps are
taken to examine early factors that may contribute to this unhealthful start to life.
Prenatal and postnatal determinants of overweight (e.g., maternal overweight, birth
weight, and increased weight gain during infancy) have been studied. However, few
studies have examined the effect of other measures of birth size (birth length, indices of
weight/length, gestational age) and infant growth patterns on BMI at age five years in a
multiethnic population.
This is a retrospective, longitudinal study using data from the Kaiser Permanente
Hawai‘i Electronic Medical Record. Singleton children, born in years 2004 and 2005 at
Kaiser Permanente, with birth and linked maternal information were initially included
(n = 894). Subsequently, children with measured weights (n = 597) and lengths (n = 473)
from ages 2 to 4 and 22 to 24 months were included.
A higher birth weight was associated with a higher BMI at age five years after
controlling for gestational age, age, sex, race/ethnicity, and maternal factors (pre-
pregnancy weight, age, education, and smoking). Birth length was not associated with
BMI at age five after adjusting for birth weight and gestational age. A higher pre-
pregnancy maternal weight was also associated with a higher child BMI at age five years.
For every 100 g/month increase in weight and 1 cm increase in length over the
infant period of 20 months, BMI increased by 1 kg/m2 at age five years. However, this
was not true for change in BMI during infancy. The effect of birth weight on BMI at age
five years was not mediated by infant growth and the interaction was not significant.
iv
Birth weight, change in infant weight, and BMI at age five varied by race/
ethnicity, but not by sex. Birth weight and change in infant weight was higher in Whites
and Other Pacific Islanders, with most differences observed after age two years.
Early indicators such as a higher birth weight and change in infant weight and
length, and higher maternal pre-pregnancy weight, are key indicators associated with a
higher child BMI at age five.
v
TABLE OF CONTENTS ACKNOWLEDGMENTS .................................................................................................. ii ABSTRACT ....................................................................................................................... iii LIST OF TABLES ............................................................................................................ vii LIST OF FIGURES ........................................................................................................... ix LIST OF ABBREVIATIONS ............................................................................................ xi CHAPTER 1. INTRODUCTION ........................................................................................1 1.1 Child Overweight ...........................................................................................................1
1.1.2 Determinants of Child Overweight ..................................................................... 4 1.2 Birth Size and Child Overweight ...................................................................................4 1.3 Birth Size and Infant Growth .........................................................................................7
1.3.1 Implications of Birth Size and Subsequent Catch-Up Growth .......................... 7 1.3.2 Birth Length/BMI and Infant Growth ................................................................ 8
1.4 Early Patterns of Growth: Rapid Growth during Infancy ............................................9 1.5 The Role of Birth Size on Accelerated Infant Growth and Child Overweight ...........16 1.6 The Role of Accelerated Growth on Birth Size and Child Overweight .....................17 1.7 Other Factors Influencing Birth Size and Child Overweight ......................................18
1.7.1 Socioeconomic Status (SES) ............................................................................ 18 1.7.2 Race/Ethnicity ................................................................................................. 18 1.7.3 Maternal Factors ............................................................................................. 19 1.7.4 Infant Feeding ................................................................................................. 21
1.8 Literature Summary and Research Gap ......................................................................22 1.9 Significance/Rationale ................................................................................................23 1.10 Study Goal ................................................................................................................24 CHAPTER 2. METHODS .................................................................................................26 2.1 Study Design ...............................................................................................................26 2.2 Study Population and Sampling ..................................................................................26
2.2.1 Inclusion Criteria ............................................................................................. 29 2.3 Description of Birth Measures and Data Cleaning .....................................................29
2.3.1 Birth Size Variables (Birth Weight, Birth Length) and Gestational Age ........ 32 2.3.1.1 Birth Weight ................................................................................................. 32 2.3.1.2 Birth Length ................................................................................................. 33 2.3.1.3 Gestational age ............................................................................................. 34
2.4 Infant Growth Data .....................................................................................................37 2.4.1 Selection of Infant Weight and Length Data ................................................... 37 2.4.2 Cleaning of Infant Weight and Length Data ................................................... 39
2.5 BMI at Age Five .........................................................................................................42 2.6 Covariates and Potential Confounders ........................................................................43
2.6.1 Age and Sex ..................................................................................................... 43 2.6.2 Race/Ethnicity ................................................................................................. 44 2.6.3 Socioeconomic Status ...................................................................................... 45 2.6.4 Infant Feeding ................................................................................................. 45 2.6.5 Maternal Factors ............................................................................................. 46
2.7 Missing Data ...............................................................................................................48 2.8 Final Study Sample .....................................................................................................49
vi
2.10 Human Subjects Approval ........................................................................................51 2.11 Analytic Plan and Statistical Analyses .....................................................................51 2.12 Modeling of Study Aims ...........................................................................................53 CHAPTER 3. RESULTS ..................................................................................................57 3.1 Core Model Results......................................................................................................57
3.1.1 Relative Role of Birth Weight, Birth Length, and Gestational Age in BMI at Age 5 Years ................................................................................................................ 57
3.2 Study Aim 1 ................................................................................................................60 3.2 Study Aim 2a – Change in Infant Weight ...................................................................67 3.3 Study Aims 2b and 2c .................................................................................................74 3.4 Study Aim 3 and 4 – Mediation or Moderation by Infant Growth .............................84 CHAPTER 4. DISCUSSION .............................................................................................87 4.1 Higher Birth Weight, Higher BMI at Age 5 Years .....................................................87 4.2 Higher Change in Weight and Length, Higher BMI at Age 5 Years ..........................87 4.3 Change in BMI in the first 2 years was not associated with BMI at age 5 years .......91 4.4 Effect of Birth Weight on BMI at age 5 years is not mediated by Infant Growth ......91
in the First Two Years ................................................................................................91 4.5 Birth Weight, Change in Infant Weight, and BMI at Age 5 Years vary by ................91
Race/Ethnicity ............................................................................................................91 4.6 Maternal Pre-Pregnancy Weight and Child BMI ........................................................95 4.7 Clinical applications ....................................................................................................96 4.8 Strengths and Limitations ...........................................................................................96 CHAPTER 5. CONCLUSION.........................................................................................101 5.1 Public Health Implications ........................................................................................101 5.2 Future Studies ...........................................................................................................102 LITERATURE CITED ....................................................................................................105
vii
LIST OF TABLES
Chapter 1
1.1 CDC-defined BMI Categories ...................................................................................3
1.2 Change in Infant Weight and Child Overweight, Adjusted for Gestational Age
and Birth Weight ................................................................................................... 12
1.3 Change in Infant Length and Child Overweight ....................................................14
1.4 Change in Infant Weight-for-Length and Child Overweight ................................. 15
1.5 Change in Infant BMI and Child Overweight .........................................................15
1.6 Specific Aims ..........................................................................................................24
Chapter 2
2.1 Race/Ethnic Distribution of KPH population and Hawai‘i State Data by OMB
Categories ................................................................................................................28
2.2 Mean Birth Weight, Birth Length, Gestational Age ...............................................37
2.3 Example of Invalid EMR Recording of Weight for a Child ...................................41
2.4 Data Cleaning of Infant Growth (Weight and Length/Height) ..............................42
2.5 Biologically Implausible Values (BIV) for BMI-for-Age and Sex .........................43
2.6 Low and High BIVBMI by Race/Ethnic Group ......................................................44
Chapter 3
3.1 Birth Weight, Birth Length, Gestational Age and their Relative Contribution
to BMI at Age 5 Years, β (95% CI) n = 1,729 .......................................................59
3.2 Indices of Birth Weight for Birth Length adjusted for Gestational Age with BMI at
Age 5 Years β (95% CI) n = 1,729 .........................................................................59
3.3 Descriptive Statistics of Child Demographics (n = 894) .........................................60
3.4 Descriptive Statistics of Birth Size, Child BMI and Covariates (n = 894) .............61
3.5 Effect of Adding a Covariate on the Regression Coefficient of BMI at Age 5
Years on Birth Weight Core Model (n = 894) .........................................................63
viii
LIST OF TABLES (CONT.)
3.6 Regression of BMI at Age 5 Years on Birth Weight
(Final Model, n = 894) ...........................................................................................65
3.7 Comparison of Demographics and Main Variables of Study Aim 1 Sample
(n = 894) with Cleaned Birth Size Sample (n = 3358) ............................................67
3.8 Descriptive Statistics of Child Demographics for Study Aim 2a (n = 597) ...........68
3.9 Descriptive Statistics of Birth Weight, Gestational Age, Infant Growth and
BMI at age 5 Years (n = 597) ..................................................................................68
3.10 Analysis of Multiple Regression BMI at Age 5 Years on Birth Weight and
Change in Infant Weight (n = 597) .........................................................................70
3.11 Comparison of Demographics and Main Variables of Study Aim 2a Sample
(n = 597) with Sample without Weights and Lengths within Specified Time
Periods (n = 3625) ...................................................................................................74
3.12 Descriptive Statistics of Child Demographics of Children in the Final Sample
(n = 473) ................................................................................................................75
3.13 Descriptive Statistics for Birth Weight, Gestational Age, Change in Length
and BMI and BMI at Age 5 Years (n = 473) ...........................................................76
3.14 Analysis of Multiple Regression of BMI at Age 5 Years on Birth Weight
and Change in Length (n = 473) ............................................................................77
3.15 Analysis of Multiple Regression of BMI at Age Five Years on Birth Weight and
Change in BMI (n = 473) ........................................................................................81
3.16 Comparison of Child Demographics and Main Variables of Study Aim 2b
Sample (n = 473) with Sample without Growth Data within Specified Time
Periods (n = 421) .....................................................................................................84
ix
LIST OF FIGURES
Chapter 1
1.1 Conceptual Framework of Early Life Factors, Child Overweight, and
Subsequent Disease Risk ...........................................................................................5
1.2 Conceptual Framework Present Study ....................................................................25
Chapter 2
2.1 Sampling Framework ..............................................................................................27
2.2 Data Flow: Clinic to EMR to Research ...................................................................30
2.3 Existing KPH EMR Data Tables for Research Dataset ..........................................31
2.4 Number of Children Attending Well Child Visits by Child Age in Months
in the sample (n = 1477) .........................................................................................38
2.5 KPH EMR Sample of Children ...............................................................................50
Chapter 3
3.1 Birth Weight by Race/Ethnicity (n = 894) ..............................................................66
3.2 Birth Weight by Sex (n = 894) ................................................................................66
3.3 Number of Weights and Lengths during Infant Growth Period ..............................69
3.4 Change in Weight during the Infant Period by Race/Ethnicity (n = 597) ...............71
3.5 Change in Weight from Birth to Age 5 Years by Race/Ethnicity ...........................72
3.6 Change in Weight during the Infant Period by Sex (n = 597) .................................73
3.7 Change in Weight from Birth to Age 5 Years by Sex ............................................73
3.8 Change in Length during the Infant Period by Race/Ethnicity (n = 473) ...............78
3.9 Change in Length from Birth to Age 5 Years by Race/Ethnicity ...........................78
3.10 Change in Length during the Infant Period by Sex (n = 473) .................................79
3.11 Change in Length from Birth to 5 Years of Age by Sex (n = 473) .........................79
3.12 Change in BMI during the Infant Period by Race/Ethnicity (n = 473) ...................82
3.13 Change in BMI from Birth to Age 5 Years by Race/Ethnicity (n = 473) ...............82
x
LIST OF FIGURES (CONT.)
3.14 Change in BMI and Differences by Sex (n = 473) ..................................................83
3.15 Change in BMI from Birth to Age 5 Years by Sex (n = 473) .................................83
3.16. Mediation of Change in Infant Weight on Birth Weight and 85 Child BMI at Age 5 Years Relationship .................................................................85
3.17 Mediation of Change in Infant Length on Birth Weight and Child BMI at Age 5 Years Relationship ........................................................................................85
3.18 Mediation of Change in Infant BMI on Birth Weight and Child BMI at Age 5 Years Relationship ........................................................................................86
Chapter 4
4.1 KPH Weight Velocity Plot from 1 to 5 Years of Age .............................................88
4.2 Birth Weight and Change in Weight share a Similar Pattern by
Race/Ethnicity .........................................................................................................92
4.2 Change in Weight varies by Race/Ethnicity and Age in Months ............................92
4.3 Visual plots showing Different Trajectories by Race/Ethnicity ..............................93
xi
LIST OF ABBREVIATIONS
AAP American Academy of Pediatrics
AFRI Agriculture and Food Research Initiative
AGA Appropriate-for Gestational Age
ALSPAC Avon Longitudinal Study of Parents and Children
AC Abdominal Circumference
AR Adiposity Rebound
BIV Biologically Implausible Values
BIVWT Biologically Implausible Values for Weight
BMI Body Mass Index
BPL Biparietal Diameter
CDC Centers for Disease Control
CI Confidence Interval
CVD Cardiovascular Disease
EMR Electronic Medical Records
FITS Feeding Infants and Toddlers Study
FL Femur Length
GDM Gestational Diabetes Mellitus
GWG Gestational Weight Gain
IGF-1 Insulin Like Growth Factor-1
IUGR Intrauterine Growth Retardation
IOM Institute of Medicine
IOTF International Obesity Task Force
KPH Kaiser Permanente Hawai‘i
LGA Large-for-Gestational Age
LMP Last Menstrual Period
HC Head Circumference
HMO Health Maintenance Organization
HMORN Health Maintenance Organization Research Network
xii
MRN Medical Record Number
NCHS National Center for Health Statistics
NHANES National Health and Nutrition Examination Survey
NICU Neonatal Intensive Care Unit
NIFA National Institute of Food and Agriculture
NOS None Other Specified
NRI National Research Initiative
OB Obstetrics
OB/GYN Obstetrics and Gynecology
OMB Office of Management and Budget
OR Odds Ratio
PacDASH Pacific Kids DASH for Health
PCP Primary Care Physician
PI Pacific Islander
ROC Receiver Operating Curve
SD Standard Deviation
SEER Surveillance Epidemiology and End Results
SES Socioeconomic Status
SGA Small-for-Gestational Age
UK United Kingdom
US United States
USDA United States Department of Agriculture
VDW Virtual Data Warehouse
WHO World Health Organization
1
CHAPTER 1. INTRODUCTION
1.1 Child Overweight
Child overweight is a public health concern. The prevalence of childhood over-
weight has more than doubled among younger children and tripled for adolescents in the
contiguous United States (US) since 1980.1 According to the recent National Health and
Nutrition Examination Survey (NHANES) 2007 – 2008, 31.7% and 16.9% of US
children and adolescents ages 2 to 19 years had a Body Mass Index (BMI)-for-age > 85th
and > 95th percentile respectively (percentiles based on 1963 – 1994 data).2, 3 Prevalence
of obesity in 8,550 four year old children born in the US was reported at 18.4% in 2005
among American Indian/Native Alaskan, Hispanic, non-Hispanic Black, non-Hispanic
White, and Asian ethnic groups.4 In the state of Hawai‘i, a study conducted on 10,199
public school students entering kindergarten (ages 4 to 6, completed in 2002 – 2003),
found a prevalence of 28.5% overweight and obese.5
Child overweight varies by ethnicity, starting as early as four years of age4 where
the distribution of obese children within ethnic groups (n = 8,550) was reported as Native
American (31%), Hispanic (22%), African American (21%), White (16%), and Asian
(13%). NHANES reported that African American and Mexican American children (ages
6 to 11) were more likely to be overweight than non-Hispanic, White children.1 Baruffi
et al. reported that, within a cross-sectional study of 21,911 children participating in the
Hawai‘i Special Supplemental Nutrition Program for Women, Infants, and Children
(1997-1998), Samoan children were heaviest (n = 633, 27.0% obese, > 95th percentile)
and Asians the least heavy (n = 630, 12.2% underweight, < 10th percentile) among 2 to 4
year olds.6 Racial/ethnic disparities in early life risk factors for child obesity are present
in the infant and preschool years.7 African American and Hispanic children grew rapidly
in the first six months and were more likely to consume sugar sweetened beverages and
fast foods after two years of age in comparison with White children.
Common co-morbidity disorders in overweight children include elevated blood
pressure, elevated cholesterol, triglyceride, and insulin levels, and psychosocial
problems.8, 9 In addition, child overweight increases later risk for morbidity even if it
may not persist in adulthood.9 Child overweight is also shown to track strongly into
2
adolescence and adulthood.10, 11 In a study with 233 children, excess weight gain in the
primary school-aged child was gained by age five years; and, weight at age five years
predicted weight at age nine years.12 BMI at a younger age was correlated with later
adult BMI.13, 14 Increased BMI z-scores at age 21 years were observed for children who
were normal weight at age five years and at Tanner pubertal stage four or five for breast/
genitalia and pubic hair development at age 14 years.13 In comparison, overweight
children at age five years had an even greater increase in BMI z-scores at age 21 years
regardless of the stage of puberty at age 14 years.
Other literature has demonstrated that child overweight is associated with early
onset of puberty and early age at menarche,15 which are predictive of subsequent risk of
obesity,16 insulin resistance,17 and breast cancer later in life.18 Hormone-dependent
cancers such as breast cancer are associated with early age at menarche due to much
earlier and longer estrogen exposure over time.19-21 A possible mechanism includes the
role of rapid growth during infancy resulting in taller childhood stature, early induction of
growth hormone receptors, and thus high levels of insulin-like growth factor-1(IGF-1).22
Higher levels of IGF-1 are positively associated with breast and prostate cancer.23 Small
size at birth and rapid growth from 0 to 2 years was associated with early puberty.15
More specifically, rapid weight gain was associated with child obesity at ages 5 and 8
years, with evidence for insulin resistance as determined by high fasting insulin. In
addition, earlier onset of adrenarche as measured by early androgen secretion and low
levels of sex hormone-binding globulin decreasing the body’s ability to regulate sex
steroid bioavailabilty22 were also observed.
Early infant weight gain in the first six months was associated with increased fat
mass and central fat distribution in children and adolescents ages 4 to 20 years.24 Weight
gain in the first two years of life was associated with more peripheral fat distribution.25
These findings suggest that prevention should begin in early infancy and childhood to
deter metabolic changes that would result in an altered trajectory towards obesity later in
life.
3
1.1.1 Definitions and Identification of Child Overweight
Within the child overweight literature, definitions of child ‘overweight’ and
‘obesity’ vary across studies, between countries, and over time. Most of these definitions
describe body weight or weight adjusted for height as opposed to fatness.26
The Centers for Disease Control and Prevention (CDC) and American Academy
of Pediatrics (AAP) recommend use of BMI-for-sex and age-specific percentiles for
overweight and obesity screening in children and teens ages, 2 to 19 years. BMI is
plotted to obtain a percentile ranking, which is used as an indicator of size and growth of
individual children, and is compared to children of the same sex and age. Table 1.1
shows the current CDC-defined BMI weight status categories for percentile ranking used
with children and teens.27
It is important to note that according to CDC, BMI is a screening tool, not a
diagnostic tool.27 BMI values are useful for screening and population surveillance;
Table 1.1. CDC-defined BMI Categories
Weight Status Category Percentile Range
Underweight < 5th percentile
Healthy Weight 5th percentile - < 85th percentile
Overweight 85th percentile - < 95th percentile
Obese > 95th percentile
however, they do not identify children who are at risk for excess fat or for future weight
and health related problems on an individual basis. The Childhood Obesity Task Force
of the United States Preventive Services Task Force28 stated that more studies are needed
to determine what might be the best indicator for children at risk for future health out-
comes due to overweight or obesity. For the purposes of this study, ‘child overweight’
will be used as an overall term to reflect the biological status of children in relation to
either overweight or obesity.
4
1.1.2 Determinants of Child Overweight
Multiple factors are related to the development of child obesity. Genetics,
imbalance of energy intake and expenditure, culture, and the ‘obesogenic’ environment
are a few known factors that contribute to the obese state.29 Development of child
obesity has also been discussed as having origins in utero with a continuous influence on
later periods of growth and development. Figure 1.1 illustrates potential risk factors for
child overweight during pregnancy, birth and infancy. Furthermore, subsequent
risk for early expression of disease during adolescence and adulthood is exemplified.
1.2 Birth Size and Child Overweight
Birth size, a marker of conditions during pregnancy, has been found to be
associated with child overweight.30 Most studies investigate birth weight, which is a
measure of mass and therefore a starting point for weight gain and part of the BMI. Birth
length, on the contrary, has not been examined in relation to child overweight, although it
is part of the BMI measure. Measures of birth weight-for-length, often mentioned as
BMI = f(weight/length2) or ponderal index = f(weight/length3), are examined with child
and adolescent overweight.31, 32 Newborn BMI predicted child overweight at age five
years.33 New World Health Organization (WHO) charts now allow for measurement of
newborn BMI, whereas CDC charts only provide BMI from two years of age.34
Early literature on birth size and child obesity centered on the ‘developmental
origins of adult disease’.35 Barker hypothesized that small size at birth is associated with
fetal malnutrition which resulted in fetal adaption and programming to survive in a
postnatal external environment of abundance thus increasing the risk for adult disease.
These findings have been replicated in other studies.35-38 However, birth size has also
been shown to be positively associated with child overweight.39
A most recent systematic review of birth weight and later overweight included 33
studies of 478 citations from five electronic databases.39 Thirteen studies did not provide
enough dichotomous data for birth weight and obesity,11, 32, 40-50 therefore they were not
included in the meta-analysis. Twenty studies were included in the meta-analysis which
reported a positive relationship with birth weight and child overweight.51-71 Children
Figure 1.1. Conceptual Framework of Early Life Factors, Child Overweight, and Subsequent Disease Risk
Maternal Smoking/Alcohol GDM BMI Weight Gain Parity Birth Order Gestational Weeks
Birth Size
Infant Growth
Child Overweight
Insulin Resistance Diabetes
Elevated BP Cardiovascular Disease
Adolescent Overweight
Adult Overweight
Early Maturation Cancer
Increased Body Fat
Prenatal Adulthood Birth/Infancy Childhood Adolescence
5
6
with higher birth weights (> 4,000 g) had a two-fold higher risk (Odds Ratio (OR) = 2.07,
95% Confidence Interval (CI): 1.91 – 2.24) than those < 4,000 g. Initial analysis of low
birth weight (< 2,500 g) children reported a decreased risk of overweight compared to
children > 2,500 g; however, after removal of two studies for selection bias, the
association was not significant. Sensitivity analyses determined that the lack of a low
birth weight and obesity relationship could be explained by differences in study design,
sample size, and quality of studies.39 Other studies suggest that low birth weight results
in later rapid growth and future obesity.72 In the systematic review, the authors also
performed subgroup analysis with different growth and developmental stages of children
and adolescents. High birth weight remained positively associated with increased risk of
obesity. They concluded that birth weight may play a role as a mediator between prenatal
status and later obesity risk. Limitations to the meta-analysis included differences in
study methods such as methodology for capturing birth weight information as
measurements vs. questionnaires and interviews administered at different postnatal ages.
Also, obesity was determined using different definitions, both reported and measured.
Lastly, seventy percent of studies included in the meta-analysis were done in China.
Previous literature examined BMI attained in childhood and adulthood; most
studies cited a positive relationship with birth weight.30 Reported BMI magnitude ranges
were 0.5 to 0.7 kg/m2 for each 1 kg increment in birth weight.30 36, 73-75 76-79 However,
there were a few limitations to these studies, including lack of adequate data on gesta-
tional age, birth length, parental body size, maternal tobacco use, and socioeconomic
status. In addition, some of the studies were published at a time when few premature
babies survived into adulthood compared with current survival rates.75 Adjustment for
gestational age is critical in order to separate the effects of prematurity and impaired fetal
growth. After adjustment for gestational age, ponderal index,48 or birth length,74 few
studies have shown that the positive birth weight and child overweight relationship
remained. Other studies determined that the positive relationship of birth weight with
child overweight may be explained by accelerated growth during infancy.80-84
7
1.3 Birth Size and Infant Growth
In a study of determinants of growth during early infancy, birth weight, sex,
maternal smoking habits, and energy intake at four months were the main determinants of
weight gain velocity (F-value >10% significance, R2 = 0.24). Using stepwise regression,
birth weight was inversely related to weight velocity (r = -0.23, p = 0.002),85 though
neither birth length nor gestational age improved the model (p = 0.10) in the stepwise
process. Gestational age was positively correlated with birth weight (r = 0.33) but not
with weight velocity (r = -0.05).
1.3.1 Implications of Birth Size and Subsequent Catch-Up Growth
Previous literature led to formulation of hypotheses related to early fetal
programming of size at birth, and compensatory accelerated weight velocity resulting in
overweight during childhood. The most referred-to example is the recognized “natural
process” of “catch-up-growth” that occurs in children who were growth-restricted in
utero or small-for-gestational age.72, 86 Birth weight is often viewed as a surrogate for
inade- quate fetal nutrition during pregnancy and is inversely associated with adult
chronic disease risk.87 The risk is increased when considering current body size.88 Low-
birth-weight infants experience the most postnatal catch-up growth in the first 6 to 12
months, as defined by weight or length.72, 89, 90 In a study done in the Philippines, infants
in the lowest tertile for birth weight also experienced larger weight gain increments in the
first six months compared with those in the middle and highest tertiles for birth weight.87
However, the authors do not appear to have adjusted for gestational age.
Birth weight is usually a function of gestational age91 and therefore gestational
age should be taken into consideration due to varying lengths of gestation. Previous
literature has examined the effect of birth size in terms of small- and large-for-gestational
age as defined by birth weight-for-gestational age.72, 92 Shorter gestation was associated
with being in the tertile of fastest growth rate during birth to three months and 3 to 12
months.93 Rapid growth has also been observed for children whose weights are
appropriate-for-gestational-age (AGA). Over a quarter of the AGA children (29%) in a
German longitudinal cohort experienced rapid growth between birth and 24 months.14
Cameron et al. examined the relationship of rapid weight gain, obesity, and skeletal
8
maturity in African American children.94 AGA children who experienced rapid weight
gain were taller and heavier and were subcutaneously fatter through childhood,
independent of advanced skeletal maturity as assessed by bone age at age nine years.94
Cameron et al. emphasized that rapid weight gain in this sample of AGA children is not a
result of regression to the mean, because anthropometric values would be expected to be
closer to the 50th percentile compared to those reported for the usual gain group. Mean
weight, height, and body composition values were closer to the 75th percentile for the
children who experienced rapid weight gain in infancy.94 Maternal height was similar
between normal and rapid weight gain groups, indicating that greater size in AGA
children may not be genetically determined. A hypothesis for why we might see larger
size and greater infant weight gain in AGA children is that they may have normal to
lower birth weights or shorter gestational age.
1.3.2 Birth Length/BMI and Infant Growth
Few studies have looked at the effect of either birth length or BMI on rapid infant
growth. Ong et al. examined influence of birth length on change in weight-for-age z-
score greater than 0.67 Standard Deviation (SD) from birth to24 months. The 0.67 SD is
derived from the difference between centile lines on United Kingdom (UK) infant and
child growth charts.72 A gain of > +0.67 is interpreted as “upward centile crossing” of at
least one centile band (e.g., 2nd to 9th centile or 9th to 25th centile). This upward crossing
of major percentile lines on UK growth infant and child growth charts is demonstrated by
at least 25% of normal infants. Children with lower birth weight, length, and ponderal
index values showed rapid weight-for-age gain (adjusted for gestational age) between 0
and 2 years compared with other children.72 In a longitudinal birth cohort (birth to age 21
years) conducted in Cebu, the Philippines, lower BMI at birth influenced rapid early
infant weight gain, but not length.87 More work is needed to disentangle the relative role
of birth weight, gestational age, and birth length in predicting rapid weight gain and
eventual childhood overweight.
Early growth patterns, such as rapid growth during infancy and early ‘adiposity
rebound’ in childhood,95 are postulated as the link amongst the relationship of fetal
growth, birth size, and adult disease risk.31, 81-84, 87, 96, 97 A recent study by Singhal and
9
Lucas found that accelerated infant growth patterns, independent of birth size, may help
to explain early ‘origins’ of cardiovascular disease.80
1.4 Early Patterns of Growth: Rapid Growth during Infancy
Rapid growth - in particular rapid weight gain - continues to be studied as a
potential risk factor for subsequent obesity. A literature search began in June 2008 to
obtain all available published articles on rapid growth and child overweight. PubMed
was the primary search engine, and Medical Subject Headings (MeSH) of the National
Library of Medicine was used to perform the literature search. MeSH key words
included: “Growth and Development”, “Infant”, and “Obesity”, and limited to categories
of “English”, “Human”, and “Infant: Birth to 23 months”. Articles that were referenced
in citations of these papers were selected based on similar criteria. The literature search
resulted in about 2060 articles between 1970 - 2012. Further restriction to categories of
“gain in weight”, ‘length”, or “weight-for-length” resulted in 509 articles related to infant
weight gain, 70 for infant length gain, and 18 for infant weight-for-length/height gain.
Among these articles were three systematic reviews that summarized literature
examining the relationship of rapid infant weight gain and later obesity risk between
1965 and 2006.98-100 In this systematic review, Monteiro and Victoria reported that 13 of
15 articles on early rapid growth found significant positive associations with later obesity
in childhood and adulthood.98 This association was independent of varying definitions of
rapid growth and obesity risk or age at which rapid growth, overweight, obesity, or
adiposity were measured. However, most rapid growth was assessed prior to two years
of age. Studies defined rapid growth as either continuous (e.g., gain in gram weight) or
dichotomous variables (e.g., > 0.67 SD). Overweight and obesity outcomes were also
defined as either a continuous value (calculated BMI) or dichotomous (BMI percentile >
85th). Age range at final weight or adiposity measurement also varied from 3 to 70 years
of age. Ten studies evaluated the study outcome within the first two decades of life, and
methodological issues with studies included lack of representation of the original
sample,50, 70, 101-104 technological limitations on statistical analysis such as computer
analysis programs,94, 102, 105 not adjusting for confounders, 50, 83, 94, 101, 104, 106 and
inadequately addressing loss to follow-up. 83, 94, 103, 105
10
The second systematic review evaluated 10 studies that examined the relationship
of infant growth and subsequent obesity99 (six overlap with the previous review, plus
four new studies). Seven of the 10 studies reported that rapid weight gain in infancy was
associated with greater risk of obesity at ages ranging from 4.5 – 20.0 years. Six studies
reported the outcome of overweight or obesity in children. In four of these studies the
OR for obesity in children ranged from 1.06 – 5.70, for those who grew more rapidly in
infancy compared with those who grew less rapidly.11, 107-109 Although birth weight was
controlled for in two of the four studies, 11, 107 gestational age was not considered. Six of
seven studies adjusted for important confounders.11, 70, 107, 108, 110, 111 Two studies in
children failed to show an association between infant weight gain and obesity.102, 105
A review of the literature was completed by Ong and Loos (2006) to update the
previous reviews and standardize the results.100 Only infancy weight gain up to two years
and later obesity risk was assessed. Eight of the 21 studies reviewed by Ong and Loos
were not included in the two previous reviews. Standardization of rapid weight gain
measures has been recommended by Monteiro and Victoria who suggested use of 0.67
z-score variation for rapid weight gain.98 Effect sizes were transformed to a standard
exposure of > +0.67 change in weight-for-age z-score to define rapid infancy weight
gain. The summary of 21 studies supports a significant positive association (OR for
obesity per 0.67 SD wt gain = 1.84) between rapid infancy weight gain and increased
subsequent obesity risk. The earliest age of obesity assessment was at age three years;
still, further analysis of datasets to examine the association of rapid weight gain during
infancy and the role of birth size with child obesity is needed, since the onset of obesity
continues to occur at an increasingly earlier age.100
Further limitations were placed on literature collected from these early systematic
reviews and on studies from 2006 to present that examine the relationship of early
growth patterns and the onset of obesity at an earlier age. These limitations include:
1) that the BMI outcome was measured < 10 years of age 2) English language, and
3) measured rapid weight gain or rapid length gain or rapid weight-for-length gain, and
4) adequate consideration for methods to eliminate bias, including statistical adjustment
for confounders (birth weight and gestational age). Two of the 21 studies from the three
systematic reviews met these requirements. In addition, another PubMed search from
11
2006 to 2012 was conducted with the same key words and limits as described earlier.
Three studies after 2006 did not adjust for birth weight and gestational age. Tables 1.2 –
1.5 summarize a total of eight key salient papers that met these criteria.14, 31, 93, 107, 108, 112-
114 Most studies measured weight gain, two from the three systematic reviews107, 108 and
three new studies.14, 93, 112 The main outcome measures were BMI z-score or category
based on BMI (CDC or International Obesity Task Force (IOTF) with ages ranging from
4 to 7 years. Most studies were longitudinal in design, except for one cross-sectional
study.108 Most studies examined were conducted in White populations (6 of 8 studies). 14, 107, 108, 112, 113, 115, 116 Two studies measured change in length,31, 113 one examined
weight-for-length,113 and one change in infant BMI.116
Change in weight was measured either as a continuous variable (gram weight gain
per month or in a year), as a categorical variable, or as a change in weight z-score >+0.67
SD and measured in different time periods during the first two years of life in five
studies. The BMI outcome was measured at different ages and was mostly defined as a
categorical variable using the IOTF117or CDC categories27 for overweight and obesity
cut points. In addition use of z-scores indicates use of a reference population in the
calculation, which varied among studies. The overall direction indicated by these studies
(Table 1.2) is positive, indicative of a higher risk of child overweight with rapid weight
gain after adjustment for potential confounders, such as birth weight and gestational age.
Different periods of rapid weight gain between birth and 24 months have been
found influential on subsequent obesity. In a prospective cohort of 6,075 Chinese
children, a positive association was observed between infant weight gain and child
obesity, with a stronger effect for weight gain in the period of birth to three months
β=0.50 (0.46 – 0.53) compared with weight gain during 3 to 12 months β=0.33 (0.28 –
0.37).93 Botton et al. noted two critical time periods, from birth up to six months and
from two years on, in which rapid weight gain was associated with later adolescent
body composition.118 The relationship of growth during these two time periods with
later obesity is described as being controlled by different mechanisms. In the first
period, growth is finally free from maternal intrauterine constraint, in which case
genetics may be expressed. Weight growth is also consistently associated with later
body composition. The second period is the adiposity rebound period, where fat gain
Table 1.2. Change in Infant Weight and Child Overweight, Adjusted for Gestational Age and Birth Weight
Reference Type of Studya Exposure
Duration:Birth to (mo/y) Outcome Results Covariatesc
Stettler (2002) 108 White M, F n = 5,514
C
Weight gain in the first year of life (kg) (retrospective data, continuous)
1 y BMI at 4.5 y (Categorical, Overwt or obesity, IOTF)
OR = 1.46 (1.27 - 1.67) overwt OR = 1.59 (1.29 - 1.97) obesity
Age, Sex, birth weight, gestational age Grade Level, Maternal BMI, Parent Occupation
Stettler, (2002)107 White, AAb M, F n =19,397
PC
Weight gain in first four months of life (continuous,100g/month)
4 mo
BMI at 7 y (Categorical, BMI >95th percentile)
UnAdj OR = 1.29 (1.25- 1.33) Adj OR = 1.17 (1.11 - 1.24)
Sex, Race, birth weight, gestational age, Weight at age 1 year (100g), First-born status, Maternal BMI, Maternal Education, Age, Study site
Karaolis-Danckert, (2006)14 White M, F n = 206
PC ∆ in weight z-score > +0.67 SD (categorical)
2 y BMI z-score at 7 y (Categorical, Overwt, IOTF)
UnAdj OR = 3.9 (1.8 – 8.3) Adj OR = 6.2 (2.4 - 16.5)
Sex, BMI at birth, gestational age group, Breastfeeding for 4mo, Maternal weight status and Maternal education
Dubois (2006)112 White M, F n = 1,550
PC Ratio of the weights at both ages divided by number of months between them (categorical)
6 mo BMI at 4.5 y (Categorical, BMI >95th percentile)
Quintile 4 = OR 1.8 (1.0 - 3.5) Quintile 5 = OR 3.9 (1.9 - 7.9)
Birth weight, Gestational Age
aPC = Prospective Cohort, C = Cross-Sectional bAA = African American
12
Table 1.2. (Continued) Change in Infant Weight and Child Overweight, Adjusted for Gestational Age and Birth Weight
aPC= Prospective Cohort
Reference Type of Studya Exposure
Duration: Birth to (mo/y) Outcome Results Covariatesc
Hui (2008)93 Asian M, F n = 6,075
PC ∆ in weight z-score > +0.67 SD (Categorical)
3 mo 3 mo to 1y
BMI z-score at 7 y (Continuous)
0 to 3mo β = 0.50 (0.46 - 0.53) 3 to 12 mo β = 0.33 (0.28 - 0.37)
Sex, Birth weight, Gestational age, BF (ever or never), Birth Order (first born or otherwise), Social Deprivation of Household
13
Table 1.3. Change in Infant Length and Child Overweight
aPC = Prospective Cohort
Reference
Type of
Studya Exposure
Duration:Birth to (mo/y) Outcome Results Covariates
Jones-Smith (2007) 31 Mexican M, F n = 163
PC Change in length for age z-score
(Continuous)
1 y BMI z-score at 4 to 6 y (Continuous) (Categorical, Overweight > 85th percentile)
Birth LAZ = -1 to 0 UNADJ: β = 0.54 (0.05 – 1.02) ADJ: β = 0.87 (0.35 – 1.39)
UNADJ OR: 1.26 (0.78 - 2.03) ADJ OR: 1.38 (0.80 - 2.39)
Gestational age, current child age, child sex, current maternal BMI, maternal height, maternal age, family SES
Taveras (2009)113 White M, F n = 559
PC Change in length for age z-score (Continuous)
6 mo BMI z scores at 3 y (Continuous)
No association with BMI z-scores at 3 y
Age, gender, maternal age education, income, parity, child’s race/ethnicity, gestational weight gain, maternal smoking, maternal prepregnancy BMI, and paternal BMI
14
Table 1.4. Change in Infant Weight-for-Length and Child Overweight
Reference
Type of
Studya
Exposure
Duration: Birth to (mo/y) Outcome Results Covariates
Taveras (2009) 113 White, M, F n =559
PC Weight-for- length for age z-score
6 mo BMI z scores at 3 y β = 0.47 (0.40 - 0.53) See Table 1.3
aPC = Prospective Cohort
Table 1.5. Change in Infant BMI and Child Overweight
Reference
Type of
Studya Exposure
Duration: Birth to (mo/y) Outcome Results Covariates
Karaolis-Danckert (2008) 116 White, M, F n = 370
PC BMI z-score 6 mo BMI z-scores at 2 y UNADJ: β= 0.91 ± 0.10 p = 0.0003 ADJ: β =1.07 ± 0.13 p = 0.0004
BMI SD score at birth, gestational age group, time, bottle-feeding
aPC = Prospective Cohort
15
16
can explain the variability in weight gain differences in growth from age 3 to 5 years old.
Postnatal weight gain, especially after two years, was an important contributor to obesity
at age five years compared with birth weight and prenatal factors, such as gestational
weight gain, diabetes, preeclampsia, pre-pregnancy maternal BMI, and smoking during
pregnancy.119
Most rapid growth studies examined weight gain as indicator of a positive
energy balance.120 Change in weight can be further described by an increase in length-
for-age and weight-for-length. Change in length was measured in two studies (Table
1.3).31, 113 Rapid change in length-for-age z-score from birth to age one year was not
associated with increased odds of overweight in children31 and did not modify the
positive relationship of length-for-age z-score at birth with odds of childhood overweight.
In another study, change in length-for-age z-score was not associated with BMI z-score at
age three years.113
Height or length, in addition to weight measures, provides a closer indicator of
proportionality or size as well as adiposity, 121 113 and is thus a more descriptive estimate
of obesity risk. One study described growth, as gain in weight, as a function of length
and examined its relationship with obesity during early childhood (Table 1.4). In a study
by Tavares et al., rapid increases in weight-for-length in first 6 months were positively
associated with obesity at three years of age (n = 559, B = 0.51 (95% CI: 0.43 - 0.59).
BMI at birth, a measure of body mass, can now be compared with international standards.
Thus, it is suggested that BMI should be used to track child BMI from birth to age five
years.122 Larger BMI at birth was protective OR = 0.54 (95% CI: 0.38 - 0.77) in
preventing rapid weight gain (Table 1.5).116
1.5 The Role of Birth Size on Accelerated Infant Growth and Child Overweight
Early systematic reviews98, 100 did not specifically test the effect of birth size
on the association of infant weight gain and child overweight. Only one study 70
tested this relationship, among children presenting with intrauterine growth retardation
(IUGR); however, the interaction between infants born with IUGR and infant weight gain
was not significant for overweight and obesity. Two later studies demonstrated that size
at birth, specifically birth weight and BMI at birth, modified the association between
17
rapid weight gain and child overweight.31, 93 In a birth cohort of 6,075 Chinese children,
lower birth weight and gestational age both contributed to faster infant weight gain from
birth to three months (p<0.001) and from 3 to 12 months in boys and girls than higher
birth weight and gestational age.93 The association of infant weight gain from birth to
three months with BMI at age seven years varied by birth weight, but not with BMI
between 3 and 12 months. A higher birth weight resulted in faster growth and the highest
BMI z-score at age seven years compared with lower birth weights;93 however, the
sample size for this observation was smaller due to fast growth, more commonly noted in
children born with low birth weight. Between 3 and 12 months, differences in growth
were only related to the effect of sex. Low birth weight boys who experienced faster
growth had the same increase in BMI compared with high birth weight boys.
In other studies, birth size did not affect the association of rapid weight gain and
child overweight. Stetter et al. determined that rapid weight gain in the first four months
influenced obesity at age seven years, independently of birth weight.107 However, Hui et
al. commented that an effect may not have been observed due to loss of statistical power
as a result of a dichotomous outcome and large number of groups of birth weight and
growth.93
A potential problem in measuring the effect of birth size on the infant growth to
child overweight relationship is the change in infant growth being related to initial value
or, in this case, birth size.123 This may attenuate the actual effect of birth size since it is
also included in the change-in-infant growth equation. Statistically speaking, the error
term of the regression model will be included on both sides of the equation. Most studies
that examined infant growth, measured change in weight from birth, which justifies the
need to re-examine the effect of birth size removed from the change equation.
1.6 The Role of Accelerated Growth on Birth Size and Child Overweight
Does postnatal growth or magnitude of centile crossing influence health in its own
right? Is it growth itself in the causal pathway or is it a modifying factor based on
thnature of fetal programming? Recent literature has debated whether change in body
size or accelerated infant growth modifies the relationship of birth size and child
obesity.14 Does the effect of growth vary depending on birth size? In a study of low-
18
income, Mexican children with low and normal BMI z-scores at birth and an accelerated
growth pattern during the first year of life, the children experienced increased odds of
overweight at age 4 to 6 years. This study adjusted for gestational age, child age, child
sex, maternal BMI, maternal height, and family socioeconomic status.31 Children who
were born small (BMI z-score at birth) or of normal size and who had experienced a
positive BMI z-score change of + 1 z-score unit in the first year of life had higher odds of
being overweight (OR = 3.58, 95% CI: 1.68 – 7.44) and obese (OR = 2.23, 95% CI: 1.12
– 4.46) compared to children who were born small or normal size and who did not
experience a positive change in BMI z-score. This relationship did not hold true for
infants with a larger BMI at birth.31 Length-for-age z-score at birth and change in length-
for-age z-score were positively associated with BMI z-score age. However, the
relationship of length-for-age z-score and BMI z-score at age 4 to 6 years was not
moderated by change in length-for-age score from birth to one year of age. A limitation
of this study is the small sample size (n = 163).
1.7 Other Factors Influencing Birth Size and Child Overweight
1.7.1 Socioeconomic Status (SES)
In NHANES, 2005 – 2008,124 children and adolescents from lower income
families were more likely to be obese compared with children from higher income
families. However, these relationships were not consistent across race and ethnic groups.
In addition, as education levels increased in the household, child obesity decreased for
boys and for girls of non-Hispanic White and non-Hispanic Black ethnicity.
1.7.2 Race/Ethnicity
Racial/ethnic group representation in most of these studies was White with one
study done in Asians,87, 93 and one in Mexican American children.31 Birth weights have
been found to vary by race/ethnicity.125 Birth weight from birth certificates (1968 –
1972) for Oahu, Hawai‘i, reviewed by Crowell et al.125 ranked race/ethnic groups, based
on the mean birth weights, from heaviest to least heavy. Whites ranked the highest
followed by Native Hawaiian, Japanese, and then Filipino. In a later study by Crowell et
19
al., Samoans had the largest mean birth weight whether based on single or mixed
race/ethnic percentages, among other race/ethnic groups (Whites, Chinese, Filipino,
Native Hawaiian, and Japanese).126 Tam et al.127 indicated a possible genetically-based
race/ethnic difference with regards to age at reaching peak BMI. Chinese/Filipino
children reached their peak BMI at six months in comparison with Swedish children 128
who reached their peak BMI between 12 – 15 months. Further research is needed to
clarify the relationship of race/ ethnicity to birth size, rapid infant growth, and child
obesity in a multiethnic population.
1.7.3 Maternal Factors
Birth size is determined by prenatal factors such as maternal BMI, pregnancy
weight gain, Gestational Diabetes Mellitus (GDM), smoking during pregnancy, and age
at first pregnancy.129-131 Genetic factors partially control human growth patterns, which
may suggest reasonable relationships between parental BMI and child obesity. In the
Avon Longitudinal Study of Parents and Children (ALSPAC) cohort study, the observed
risk for obesity at age seven years, was three to four times higher if one parent was obese
and 10 times higher if both were obese.11, 132
Maternal adiposity133, 134 and BMI135 are directly associated with offspring
birth weight, with stronger association for the mother compared to the father.133, 134
However, a recent study by Freeman et al. determined that the odds for child obesity
increased in children who had an overweight father (OR = 4.18, 95% CI: 1.01 – 17.33) or
obese father (OR = 14.88, 95% CI: 2.61 – 84.77) and healthy mother. This study
suggests that the father could also be a potential and novel point of intervention.136 A
limitation to this study is the use of self-reported weight and height which are not always
valid in comparison with measured weights and heights. Semmler et al. reported that
parental leanness is protective against the development of overweight in children,
independently of family SES.137 Parental obesity is a risk factor for child overweight,
especially in lower SES families,137 which may be attributable to food insecurity and a
relatively energy dense quality of the diet.
20
Mothers who begin pregnancy overweight or obese have a higher chance of
having children born with a higher birth size or large-for-gestational age30 and their
offspring are at increased risk for obesity during childhood, adolescence, and adult-
hood.138-141 High levels of glucose and insulin due to high maternal pre-pregnancy BMI
result in increased newborn weight.142
Excessive weight gain during pregnancy is also closely related to maternal pre-
pregnancy BMI, higher birth weight143 and risk for child overweight.131 Excessive
weight gain during pregnancy is associated with increased risk of overweight at age three
years (OR 4.35, 95% CI: 1.69 – 11.24).144, 145 Weight gain, especially during the first
trimester, was associated with child BMI at age five years.146
GDM is associated with infant birth weights greater than 4000 g or large for
gestational age ( > 90th percentile for gestational age).147, 148 Studies by Pettitt et al.149, 150
report the presence of obesity at ages 5 to 19 years in Pima Indian infants born to mothers
who have GDM.129 However, in a recent systematic review, 12 studies (years 1998 –
2010) were included and reported crude ORs ranging from 0.7 – 6.3; eight of these
studies did not show a significant relationship between maternal GDM and infant birth
weight. Only two studies adjusted for pre-pregnancy obesity, which resulted in
attenuation of the estimates and no statistical significance.151 However, Andegiorgish et
al. published a new study since the systematic review and reported an increased odds for
child overweight (OR = 2.76, 95% CI: 1.37–4.50) in 3,140 Chinese students (age 7 to 18
years) who were born to women positive for gestational diabetes.152
Maternal smoking during pregnancy has been associated with an increased risk
for child overweight,144, 145, 153-155 independently of its relationship with reduced birth
weight.44, 156 Several mechanisms are postulated: 1) direct influence of nicotine on
hypothalamic function related to appetite of the fetus and/or infant, 2) weight gain that is
associated with nicotine withdrawal which is also seen in adults who stop smoking, 3)
and decreased placental and fetal hormones, such as growth hormone, insulin-like growth
factor, and leptin.157 Tobacco compounds, such as nicotine, are also readily available to
the infant through breast milk from the mother who smokes.158 In particular, mothers
who smoke during pregnancy have increased risk for low birth weight infants159-161 who
21
also experience later catch-up growth.72 Rapid catch-up growth has been found to be
related to child overweight and obesity.72, 108
Alcohol consumption during early pregnancy is associated with fetal growth
restriction resulting in low birth weight 162 and in later pregnancy can affect prenatal and
postnatal growth. This is due to the decreased cell proliferation in early fetal life and
inadequate nutrient uptake causing malnutrition in the latter part of pregnancy.163
Preterm deliveries, due to multiple births164 and maternal parity159 are also related
to low birth weight. Infants who were born of primiparous mothers had lower birth
weight, higher birth lengths, and smaller head circumferences than infants born of
multiparous mothers.159 First-born status is associated with overweight at seven years.107
In addition, rapid growers were more likely to be first-born than less rapid growers.14
1.7.4 Infant Feeding
Greater growth velocity has been noted in infants who have been formula-fed
in comparison to breast-fed infants.165-167 Breastfeeding has been associated with lower
weight gain in infancy and with less obesity in childhood and adolescence than formula
feeding;168 and, the weight gain pattern varies somewhat, with earlier weight gain among
breastfeeding infants.169 This may be due to greater protein content in formula than in
breast milk165, 170 and behavioral factors, such as greater infant regulation of the feeding
during breastfeeding.
Increased dietary protein in infant formula may promote excess fat gain by
inducing insulin secretion.171 In the Early Nutrition Programming project, infants
consuming a low protein formula (similar to breastfeeding) had slower growth rates and
lower BMI at two years of age compared to infants on a high protein formula.172 In
addition, shorter duration of breastfeeding has been associated with increased risk of
childhood obesity.173-176 On the contrary, Dennison et al.177 reported that the association
between rapid infancy weight gain and obesity development was the same between
formula-fed and breast-fed babies. Another study reported that breastfeeding is
associated with faster weight gain in the first six months compared to formula feeding,
and only later in infancy did breast-fed infants have lower weights.178 Among 124
22
infants fully breast-fed for > four months, no significant differences were found between
rapid growers and normal growers, although the direction of association was positive
(protective) for risk of overweight at age seven years, similar to what has been found in
other cohorts.83, 179
1.8 Literature Summary and Research Gap
The state of research on the relationship between birth size and rapid infant
growth with child overweight provides supportive evidence for a positive association of
both variables with child overweight. However few studies have adequately examined
components of birth size (birth weight, length) or proportionality (e.g., weight-for-
length), or adjusted for gestational age. Nor have studies included many non-White
ethnic groups, or examined obesity outcome at age five years, a critical age in the
lifespan as a child transitions to school.
Contributions of prenatal (maternal factors) or postnatal (birth size and accelera-
ted growth) factors on the risk of child overweight require further study. These
differences can be further teased apart by examining whether birth size or accelerated
growth modifies, or rate of infant growth mediates, the positive association of birth size
and child overweight. Examining these relationships will provide direction for develop-
ment of future interventions. It is essential to look at these sensitive periods of growth,
where effects of certain exposures are limited to one particular stage of growth. Health
services data that provide longitudinal information related to mother, infant, and child
overweight status provide opportunity to look at these relationships.
Therefore, further research is needed, specifically, to examine: 1) the association
of birth size and child overweight adjusted for gestational age and maternal factors, 2) the
association of infant growth (as measured by gain in weight, length, and weight-for-
length gain) and child overweight with adjustment for birth size (birth weight, birth
length and birth weight-for-length) and gestational age in a multiethnic population, 3) the
role of infant growth (mediation or moderation) on the association of birth size and child
overweight.
The format of the infant growth variable for analysis (continuous or categorical)
also varied among previous studies. Categorization of growth and obesity is often based
23
on cut points which are arbitrary and may not be applicable to all populations. Growth is
a biological process which should initially be examined as a continuous measure.
Separating the effects of birth size from the change-in-infant growth equation should also
be tested. Finally, further exploration of sex and ethnic differences in birth size and its
influence on infant growth patterns may explain differences in overweight prevalence.
Longitudinal data provided through the Kaiser Permanente Hawai‘i (KPH)
Electronic Medical Record (EMR) system provide retrospective, measures to study
growth and overweight in early childhood. In addition, the data structure provides the
ability to adjust for maternal and postnatal confounders which were not available in
previous longitudinal birth cohorts.
1.9 Significance/Rationale
Growth during childhood has been proposed as a possible linkage between fetal
growth and risk for adult diseases.50, 87, 97 Child overweight is associated with early
maturation, adolescent and adult overweight and subsequent risk for type 2 diabetes,
cardiovascular disease (CVD), musculoskeletal disorders, certain cancers, and overall
mortality. The importance of drawing links between factors associated with child
overweight and subsequent health risks in adulthood has become increasingly evident.
Studies of recent, multiethnic cohorts are needed to understand further race/ethnic
differences in birth size, growth patterns, and risk for overweight and health disparity
related to these factors.
Growth is a basic developmental process for children, and it is a period of life that
is strongly influenced by many factors. Recent literature suggests a need for exploring
these factors, including weight, length, and BMI gain. Furthermore, identification of
early nutrition and growth patterns as markers for intervention is key to program planning
for prevention and better treatment of child overweight. Obesity is occurring in
increasingly earlier childhood; and, prevention, prior to the onset of puberty, will permit
continued growth that will support optimal health. The present study will examine the
relationship of birth size to rate of infant growth and the relationship of both variables to
risk of childhood overweight in the multiethnic KPH population.
24
1.10 Study Goal
To elucidate further the relationship of birth size and rate of infant growth to child BMI at
age five years in a multiethnic population. The specific aims for this study are described
in Table 1.6 and conceptualized in Figure 1.2.
Table 1.6. Specific Aims
AIM 1 Is birth size associated with child BMI at age 5 years?
H0 = Birth size is not associated with child BMI at age 5 years.
Based on Literature:
Higher birth size is associated with higher BMI at age 5 years.
AIM 2 Is change in infant growth associated with child BMI at age 5 years?
H0 = Change in infant growth is not associated with child BMI at
age 5 years.
Based on Literature:
Greater change in infant growth is associated with higher BMI at age 5
years.
AIM 3
Is the effect of birth size on BMI at age 5 years mediated by change in infant
growth?
H0 = The effect of birth size on BMI at age 5 years is not mediated by
change in infant growth.
AIM 4 Is the effect of birth size on BMI at age 5 years modified by change in infant
growth?
H0 = The effect of birth size on BMI at age 5 years is not modified by levels
of infant growth
Based on Literature:
Small and average birth weight is associated with faster rate of infant weight
gain and higher BMI at age 5 years.
25
Figure 1.2. Conceptual Framework of Present Study
Birth Size
Infant Growth
BMI at Age 5
AIM 3, 4
AIM 2
AIM 1
26
CHAPTER 2. METHODS
2.1 Study Design
The study design is a retrospective, longitudinal study using the KPH EMR. The
dataset for this study is part of a larger dataset provided for the Pacific Kids DASH for
Health (PacDASH) study.
The PacDASH study is a four-year, United States Department of Agriculture
(USDA) funded study, (Rachel Novotny, PI, : 2008-55215-18821 (2/15/2008 –
2/14/2012) under the Cooperative State Research, Education, and Extension Services,
National Research Initiative (NRI) Competitive Grants Program Award [(now National
Institute of Food and Agriculture (NIFA) and Agriculture and Food Research Initiative
(AFRI)]. PacDASH is comprised of two main objectives: 1) to develop a community-
based participatory intervention that links food, physical activity, and health which
targets children (> 50th – 99th BMI-for-age and sex percentile, ages 5 – 8 years) in
Hawai‘i with a goal of preventing further weight gain, and 2) to describe environmental,
social, economic, and cultural factors associated with child overweight in the KPH
population by using secondary data from the KPH EMR.
Objective #2 also aims to understand the ecological framework of child obesity
and factors that are known or hypothesized to influence the development of child obesity
in Hawai‘i’s multicultural population. Objective #2 data are being examined in two
ways: a) an expansion of the PacDASH intervention sample to include other children and
information on early life factors that may influence later overweight for cross-sectional
and later prospective analysis, and b) estimation of the relationship between birth size,
infant growth and child overweight risk at age five years (see Figure 2.1).
2.2 Study Population and Sampling
KPH is a non-profit integrated health care system that provided health care in
2011 to about 19% of the employed population of Hawai‘i (~220,000 members).180 The
KPH membership covers a wide range of socioeconomic backgrounds, from professional
to blue-collar worker, and 10% of the membership receives coverage from Medicare/
Medicaid or Quest, the state insurance program.180 KPH provides medical
27
Figure 2.1. Sampling Framework
care in 18 outpatient clinics on Oahu, Hawai‘i, and Maui, and in a 235-bed hospital
located on the island of Oahu. KPH membership is ethnically similar to the general
Hawai‘i population. Table 2.1 shows a comparison of Hawai‘i state data on sex and
racial/ethnic distribution181 with KPH data estimated through an EMR sample in 2011.
KPH coordinates and maintains patient care documentation in the EMR and in
online communication systems, with a comprehensive EMR interface referred to as
HealthConnect®. KPH HealthConnect® coordinates patient care which includes
information on all visits, procedures, diagnoses, hospitalizations, membership types, and
demographics of plan members.182 This innovative tool further prevents regular
occurrence of incomplete, missing, and unreadable charts. The EMR system began in
KPH in 2004, and became fully operational in 2005.
Objective 2a PacDASH EMR All KPH children Ages 5 to 8 years
Born in 2002 - 2005 n = ~8,000
Objective 2b OSHIRO
Dissertation Children
Ages 4 to 6 years Born in 2004 -
2005
Objective 1 PacDASH
Intervention Study Children
Ages 5 to 8 years Born in 2002 - 2005
n = ~100
28
Table 2.1. Race/Ethnic Distribution of KPH population and Hawai‘i State Data by OMB Categoriesa
aFormatted based on Office of Management and Budget, bKaiser Permanente Hawai‘i
EMRs provide benefits to both patients and providers in health care delivery
systems. They also provide observational data from clinical practice that can be useful
for researchers interested in investigating practice patterns and in evaluating quality
indicators, disease rates, and longitudinal trends.183 EMR data have also become a
useful tool for conducting health services and epidemiologic research. Understanding
ways to analyze such data, with consideration for internal and external validity, continues
to be a part of the research. Since 1998, intensive efforts have been made to standardize
medical terminology, clinical data, and units of measures in EMR data.184 However
researchers still need to identify and account for measurement error.
KPH is a member of the Health Maintenance Organization Research Network
(HMORN) which consists of a membership of 19 HMOs across the United States.185 The
Virtual Data Warehouse (VDW) is an existing resource of data developed by the
HMORN which provides standardized coding terms across health systems in order to
allow comparisons of data among 15 of the 19 sites.184 These data are derived from
data tables that exist under the HealthConnect® Interface of the KPH EMR. Figure 2.2
depicts the flow of information gathering from members to research data tables.
KPHb 225,104
(%)
Hawai‘i State Data181 1,360,301
(%) Sex
Females 51.0 50.0 Males 49.0 50.0
Ethnic Category Hispanic or Latino 5.1 8.9
Racial Categories American Indian/Alaska
Native1.0 0.3
Asian 38.0 38.6 Native Hawaiian and Other
Pacific Islander33.0 10.0
Black or African American 1.0 1.6 White 27.0 24.7
29
Variables exist in different HealthConnect® tables which are identifiable for
research in the VDW. Existing tables used for this study include: Patient (demographics,
geocoding), Vitals (repeated measures or observations), Baby Birth, and Maternal/
Pregnancy data tables (Figure 2.3).
2.2.1 Inclusion Criteria
To estimate the association of birth size, infant growth, with child overweight,
data requirements include the following inclusion criteria for this retrospective study:
all children who are current members of the KPH HMO as of January 2010,
and who were born in 2004 – 2005 at KPH
birth information (birth weight, birth length, gestational age)
weight and height measures at three time points (2 to 4 months, 22 – 24
months, 4 to 6 years)
singleton births
2.3 Description of Birth Measures and Data Cleaning
Since EMR data are not collected initially for research purposes, in a controlled
research environment, it is especially important to consider sources of error before data
analysis. In addition, weights and heights are measured by health care professionals in
different clinics which likely increases error, and results in decreased power to detect
associations. This study applies a systematic data cleaning process that is relatively new,
given the short era of electronic medical record systems.
KPH data were examined for biologically implausible values of birth size and
infant weight and length/height information which included univariate and bivariate
analysis (e.g., plotting of gestational age and birth weight) for identification of outliers.
Assessment of linear regression assumptions including multivariate normality were also
assessed by checking the variance and distribution of the residuals of study models.
CLINIC EMR RESEARCH
Figure 2.2. Data Flow: Clinic to EMR to Research
Business/Clinical Workflows
Inpatient Outpatient Check-in/Check-out Back office/Admin
KP Health Connect (EMR) Data Tables (CLARITY)
VIRTUAL DATA WAREHOUSE
30
CLARITY TABLES DATASETS*
Encounter
Patient
Virtual Data Warehouse
(VDW)
Ob History
Vitals
Demographics/Geocode
Baby Birth
Maternal Pregnancy**
- - - - - - - - - In Progress *Datasets are linked by a unique ID – Child Medical Record Number **Child needs to be linked with mom for maternal/pregnancy information Figure 2.3. Existing KPH EMR Data Tables for Research Dataset
31
32
2.3.1 Birth Size Variables (Birth Weight, Birth Length) and Gestational Age
Steps for cleaning birth size variables (birth weight, birth length) and gestational age
1) Check biological plausibility of values (Univariate analysis)
a) Examine distribution of birth weight, birth length, and gestational age
b) Determine biologically plausible ranges for birth size categories based on
plausible values described in literature and clinical practice
c) Examine values above and below biologically plausible values
d) Examine values with unique birth weight, birth length, and gestational
age
e) Remove unique medical record numbers (MRN) with outlying values
2) Check for invalid proportionality (Bivariate analysis)
a) Perform visual inspection by plotting birth weight by birth length, birth
weight by gestational age, and birth length by gestational age
b) Compare invalid proportions with those identified through reference
proportion categories for birth weight and gestational age186
c) Remove unique MRNs with outlying values
3) Check for degree of deviation from the regression line (Multivariate normality)
a) Fit regression model to obtain residual information
(e.g., BMI = birth weight, gestational age, birth length)
b) Run univariate analysis on residuals
c) Remove unique MRN for those with SD > 4.0
2.3.1.1 Birth Weight
Birth weight is a function of length and of pregnancy duration, or gestational age
(e.g., fetal growth)91 and thus the two variables are correlated. However, these factors
are not interchangeable due to differences related to risk, etiology, and consequences.
Birth weight is defined as the “weight of fetus or infant at delivery”91 and is recorded in
grams, or pounds and ounces. It is also viewed, conceptually, as a measure of the extent
of maturity and physical development of the fetus and may be influenced by genetic
predisposition or prenatal environmental exposures.91 Birth weight is an indicator of
33
newborn infant survival or risk for early morbidity. Live births in 2001 – 2002 to US
resident mothers identified median birth weight for singleton, full-term live birth babies
as 3,487 g and the mean birth weight was 3,303 g.91 Low birth weight refers to the
weight of an infant at delivery regardless of gestational age or length and is defined as
weighing less than 2,500 g.91, 187 The following further classifications of low birth weight
have been established to identify high risk infants: Moderately Low Birth Weight (1,500
– 2,499 g), Very low birth weight (< 1,500 g), Extremely Low Birth Weight (<1,000 g).
High birth weight, or macrosomia, is defined as > 4,000 g.91, 188
In KPH, birth weights are measured and entered as grams at birth and then are
electronically converted to ounces in HealthConnect®. Birth weights were electronically
converted back to grams for analysis. A total of 5,074 children born at KPH from 2004
to 2005 had birth weight information. To adjust for gestational age, the sample was
limited to include those who had both a recorded birth weight and a gestational age (n =
4,271).
In Step #1, lower and upper limits of birth weight were estimated and frequencies
were run, starting with a lower limit of 454 grams (1 lb). First year survival has been
estimated at 51% for children born < 454 grams (1 lb).189 The upper limit was initially
set at 4,536 grams (10 lbs) to examine the upper range of values.
Values were continuous and close to each other until about 5,443 g (12 lbs);
birth weight values then rose to numbers that were implausible (> 9,752 g, 21.5 lbs).
Therefore, based on plausible values as described, the birth weight range was set at > 454
g and < 5,443 g. Two children were removed from the sample (values of 9,752 and
35,380 g) at Step #1 resulting in a sample of n = 4,269 with plausible birth weights.
2.3.1.2 Birth Length
At KPH, birth lengths are measured and entered into HealthConnect® as inches at
birth. Birth length was electronically converted to centimeters. Of the 5,074 children
born at KPH, 3,707 had a birth length value, and 1,367 were missing birth length
information.
Univariate analysis indicated a large jump among the lowest extreme observa-
tions from 2.04 to 18.79 cm. Two children were removed with values less than 18.79 cm
34
(0.46 cm and 2.05 cm). Upper end estimates indicate continuous values from 18.79 to
63.5 cm. Twenty children were removed with values ranging from 99.1 to 254 cm. A
total of 23 children were removed at Step #1 resulting in a sample of n = 4,246.
2.3.1.3 Gestational age
Gestational age is typically defined as the length of time in weeks from the first
date of a women’s last menstrual period (LMP) to the date of the infant’s birth.91 This
interval has served as the gold standard for determination of gestational age and has been
used in validation studies with alternative methods for measuring gestational age.91, 190
Limitations to using LMP for estimating gestational age include overestimation
of the duration of pregnancy by two weeks, inaccuracies of poor recall of LMP, or early
menstrual spotting in pregnancy.191 Other methods include ultrasound dating and
developmental assessment. Ultrasound dating commonly uses reporting software that
calculates a composite gestational age using measurements of biparietal diameter (BPL),
Head Circumference (HC), Abdominal Circumference (AC), and Femur Length (FL)
conducted before 22 weeks of pregnancy. However, ultrasound predictions of gesta-
tional age becomes less accurate as pregnancy progresses due to individual variations in
fetal growth and environmental exposures.192 In addition, women facing barriers to
prenatal care, may be less likely to have ultrasound measures, especially in developing
countries; and, the quality of ultrasound equipment and level of training of ultrasound
technicians may vary across care. Thus, the use of a growth estimate of gestational age
during pregnancy while studying an outcome of growth also introduces error. A post-
natal method (Ballard method) for determining gestational age includes a developmental
assessment and scoring system of physical and neurological characteristics of the
newborn (infant weight, length, head circumference, condition of skin and hair, reflexes,
muscle tone, posture and vital signs) devised by Dubowitz and coworkers193 and later
modified by Ballard et al.194 Concerns for utilizing this method are the accuracy for use
in preterm and very preterm infants and application to different race/ethnic groups.
Current epidemiological studies use the LMP or with the clinical estimate of LMP as
reported on birth certificates.91
35
Data cleaning criteria for gestational age include the age at fetal viability or age at
which the fetus is able to survive outside of the womb.195, 196 Babies born at 23 weeks
may survive with access to a state-of-the-art Neonatal Intensive Care Unit (NICU), but
the odds of survival are low. A baby born at 24 weeks would generally require exten-
sive intervention, potentially including mechanical ventilation and other invasive
treatments followed by a lengthy stay in a NICU. Twenty-four weeks is the lowest age
cutoff point for which physicians will use intensive medical intervention to attempt to
save the life of a baby born prematurely.189
In KPH, gestational age at delivery is measured in weeks. Gestational age is
based on the last menstrual period and is updated with ultrasound measures and time of
actual delivery. Of the 5,074 children born at KPH, 4,271 had a gestational age value and
803 had missing gestational age information.
Of the current sample, one child had a value less than 24 weeks (23.5); this child
was included in the sample since the age was close to the lowest age of fetal viability (23
weeks). Most deliveries occur prior to 42 weeks or the pregnancy is induced to decrease
risk for complications that can occur due to circumstances, such as decreased amniotic
fluid and decreased function of the placenta. Two children were delivered at 44 weeks
gestation and were included in the sample. It is plausible that a child be delivered after
42 weeks, which is considered a post-term pregnancy, if the mother did not select elective
delivery at 42 weeks. 197 Two children had an erroneous value of three weeks and 99
weeks and therefore were not included in the sample. The selected gestational age range
for this sample was 23 to 44 weeks, which will be examined as a continuous variable.
Two children were removed at Step #1 of data cleaning resulting in a sample of 4,244.
Clinically, gestational age has been categorized into Preterm (< 37 weeks), Term
(37 – 41 weeks) and Post term (> 42 weeks) for the purpose of assessing health risks
associated with gestational age and fetal growth.91 Further sub-categories have been
developed to assess risk of early or late gestational age and fetal growth and are used on a
population basis to determine need for services: small-for-gestational age term (37 – 40
weeks gestation < 10th percentile of birth weight for gestational age), average-for-
gestational age (10th – 90th percentile of birth weight for gestational age), and large-for-
gestational age (> 90th percentile of birth weight for gestational age). Clinically, these
36
classifications are useful guidelines for assessment of risk. However for research
purposes, arbitrary cut points assume “one-size fits all” and may not fit for populations
different from those used to derive the clinical cut point. For the purposes of adequately
describing the birth size distribution of the KPH population, all levels of birth weight and
gestational age were retained. Future work will include describing the population in
these categories as they apply to clinical practice.
Step #2 in birth size cleaning included visual inspection of bivariate plots. This
was done by plotting birth weight and birth length by gestational age and also by
checking outliers identified by comparison with a referenced method for inclusion of
plausible birth weight and gestational age data.186 Initial bivariate plotting and visual
inspection of birth weight by gestational age identified five cases that had an implausi-
bly high birth weight (2,900 – 4,000 g) for gestational age (< 31 weeks). These five cases
were verified using the reference method which resulted in exclusion of the same cases.
Three cases had a low birth weight (< 1,800 g) for gestational age (> 38 weeks), two
cases had a high birth weight (> 3,000 g) for birth length ( < 24 cm); nine cases had a low
birth weight ( < 2,800 g) for birth length (29 – 40 cm). A total of 19 cases were removed
based on bivariate plotting resulting in a sample of n = 4,227.
With step #3, seven cases differed from their expected value by more than 4.0 SD
and were removed from the study sample. After cleaning step #1, #2, and #3, a total of
53 EMR (27 in step #1, 19 in Step #2, 7 in step #3) had been removed as possible errors
in birth measures, resulting in a total of n = 2,946 children with all three clean measures
of birth weight, birth length, and gestational age. Mean birth size and gestational age
measures before and after cleaning are described in Table 2.2.
37
Table 2.2. Mean Birth Weight, Birth Length, Gestational Age
Before Cleaning N Mean ± SD Minimum Maximum
Birth Weight (g)a 5074 3292 ± 745 546 35380
Birth Length (cm)b 3707 49.8 ± 7.3 0.5 254
Gestational Age (weeks)
4271 38.6 ± 2.4 3 99
After Cleaning N Mean Min Max
Birth Weight (g) 4221 3264 ± 597 546 5103
Birth Length (cm) 2946 49.2 ± 3.2 30.5 58.4
Gestational Age (weeks)
4221 38.6 ± 2.1 24 44
agrams, bcentimeters
2.4 Infant Growth Data
2.4.1 Selection of Infant Weight and Length Data
The first two years of life were selected as the time period for measuring rate of
growth since this is a critical time period related to child and later overweight.159
Catch-up growth begins within the first three postnatal months and is completed by about
12 – 18 months whereas catch-down or “compensatory deceleration”198 of normal growth
starts a little later and may not be complete until 18 – 24 months.199, 200 It is important to
consider any crossing of growth percentiles during this time period for early recognition
of, and efforts to, prevent obesity. Thereafter pre-pubertal growth is stable on the
trajectory established during the previous infant period.200
To assess infant growth, weight and length measures from scheduled well child
visits were utilized. KPH has a schedule of well child visits that aligns with suggested
immunizations and supports monitoring of normal growth and development. These well
child visits are scheduled at 1 week after birth, 2, 4, 6, 9, 12, 18, 24, 36, 48, 60, 72
months, and every two years thereafter. Calendar dates of well child visits vary by child,
however many well child visits do occur during the recommended months for well child
visits (Figure 2.4). The much higher attendance at the 60 month visit is related to
immunization requirements for school admission.
38
Rate of infant growth is defined as change in infant weight and length per unit
time. To measure change, data were sampled in two age ranges: early infancy (2 to 4
months) and at the end of infancy (22 to 24 months). A starting point of two months was
selected as distant enough from the birth period, in order to separate its effect from the
influence of birth size on infant growth. A range of months was created to capture all
possible weights and lengths. Children scheduled for the two month well child visit
might come in anytime from the start of the 2nd months (62 days) through the 4th months
(124 days), end of the 22nd months (682 days) through the 24th months (744 days), and
from the 4th year (1,461 days) up to the 6th year (2,155 days). If several measures were
taken within each age range per individual child, data cleaning processes were conducted
and the last measure by MRN, per time period, was used for consistency. To account for
differences in duration of time between visits, change in weight or length was divided by
change in age in months (infant period = 2 to 24 months)
* From cleaned sample of birth weight and gestational age n = 4,106 (4,252 total, 146 missing). Not all months are featured, mainly well child visits.
Figure 2.4. Number of Children Attending Well Child Visits by Child Age in Months in the sample (n = 1,477*)
39
2.4.2 Cleaning of Infant Weight and Length Data
Standard clinical practice guidelines for measuring infant length are to measure
supine length in children up to 24 months and standing height or length or both from 24
to 26 months.201 Standing height is less than recumbent length with mean differences
ranging from 0.4 to 2.3 cm between 18 and 36 months of age.201 Although measuring
supine length is a recommended201 practice guideline, this may not be consistently
followed if the child is able to stand before 24 months.202 This is a limitation that is
acknowledged; however, only weights/lengths up through 24 months were examined,
which would decrease the number of children measured using standing height.
Electronic medical records are used in routine medical care. This type of
monitoring is not without risk of data entry error. A study investigated error rates of
electronic weight and height data of children aged 0 – 18 years. receiving care at Kaiser
Permanente Southern California and found low error rates [children < 2 years (0.4%); 2 –
5 years (0.7%); 6 – 9 years (1.0%); 10 – 13 years (1.0%); 14 – 18 years (0.7%) after
excluding implausible values].203
To decrease error, weight and length data for this study were obtained only from
well child care visits, an encounter code in data tables. This assures that weights and
lengths were obtained from the pediatric or family practice departments, which are more
practiced with performing these measures, and not from other departments (e.g.,
Emergency room visits).
The first step in infant weight and length data cleaning was to identify
biologically implausible values (BIV). A SAS program for CDC growth charts was used
to identify outlier observations.204 This program is recommended for use by the HMO
Obesity Research Network Subgroup.205 The program uses z-scores to determine out-of
range values in a population. It is very common for these implausible values to be based
on data entry or measurement error rather than extreme growth values.204 The WHO
provides the recommendations on outlier cut-offs based on the 1977 National Center for
Health Statistics (NCHS)/WHO growth charts which have been used worldwide for
anthropometric measurement. Two methods are recommended: 1) the flexible exclusion
40
range: 4 z-score units from the observed mean z-score, with a maximum height-for-age z-
score of + 3.0 and 2) the fixed exclusion range:
weight-for-age, < -5.0 and > +5.0 height-for-age, < -5.0 and > +3.0 weight-for-height < -5.0 and > +5.0
Flags are created on a linear scale so that their performance at extreme values can
be mathematically calculated. The SD is calculated above and below the median. This is
calculated by taking half the difference between 2 z-scores and 0-score points. A flag is
then calculated using the “fixed” SD. For example, if a flag < - 5.0 SD for weight-for-
age then Biologically Implausible Values for Weight (BIVWT) = 1 (too low) and if a flag
> 5 then BIVWT = 2 (too high). A BIVWT of 0 = normal range. The fixed exclusion
range for weight-for-height z score equaled < - 4.0 and > 5.0 and this range can also be
applied to BMI-for-age.
The second step in infant weight and length cleaning included checking for
implausible values within the biologically acceptable range of values for individual
observations.
This was determined by:
1) Calculating a variable that measures the difference between each measure
(excluding the first observation).
2) Calculating days between each measure and units per week (e.g., g/gained/
week).
3) Comparing with guidelines for average weight or length gains. (Limit the
sample to the difference greater than or less than average expected gain for
this age range).
4) Examining data for gross differences in weight or length.
For example, an infant might be expected to gain about 140 to 200 g (0.14 to 0.20 kg per
week) in the first six months and to double his/her birth weight by about five months.206
The most weight gain expected on average is a 1.1 – 1.6 kg gain in two months. Limits
were set on any difference in weight greater than 2 kg gain and less than 1 kg loss.
41
Negligible weight losses are expected due to recording errors203 or possibly due to a
child being sick. However since children are growing, observation of a large loss in
length is not expected. If a difference in weight or length was questionable, average
weight gain or loss/week was examined to determine the plausibility of the finding.
Further visual inspection of the sequence of weight or length/height measures within
individuals was completed to gain a better understanding of the reason for large
differences. In most cases, large differences ( > 3 kg) were due to gross error between
measures. Table 2.5 illustrates an example of what is commonly seen.
Table 2.3. Example of Invalid EMR Recording of Weight for a Child
Measure Date
Age in Months
Weight (kg)
Difference in
Weight (kg)
Difference in
Days Weight(kg)/week March 19 3.02 6.84 - - -
March 22 3.12 7.08 0.16 3 0.0228
March 26 3.25 3.09 -3.99 4 - 0.57
March 29 3.35 7.14 4.05 3 0.58
April 28 4.30 7.65 3.6 30 0.12
In the case where the erroneous value was the last observation that would be selected for
that period, the value was set to missing and the previous value was selected. These
values were also rechecked to assure plausible weight or height selection. The same
method was completed for weights and lengths/ heights for two time periods: 22 – 24
months and the 4 to 6 years.
Decision rules for visual inspection are:
1) If the previous value of the last measure of the child was implausible, the
last measure was still valid and therefore the child was left in.
2) If the previous value within age range was a plausible measure then the
invalid measure was set to missing and selection was based on previous
valid measure.
42
3) Measures were rechecked to assure plausible weight or height selection.
4) If the last value of the child was an implausible measure and there were no
previous plausible values within the age range, the child was excluded.
Most erroneous values were removed through the CDC SAS program. Most
visual inspection cases fell within decision rules 1 and 2 (14 cases). Only one unique
observation was deleted because it met decision rule #3. Total records removed from
cleaning steps for infant growth data are summarized in Table 2.6.
Table 2.4. Data Cleaning of Infant Growth (Weight and Length/Height)
Period of Growth
Step 1 CDC
Step 2 Visual
Inspection
Total removed
Total missing
Weight (kg) 2 to 4 mo 10 0 10 81
22 to 24 mo 11 1 12 61
4 to 6 y 22 0 22 1
Length/Height (cm) 2 to 4 mo 33 0 13 871
22 to 24 mo 15 0 15 673
4 to 6 y 37 0 37 6
2.5 BMI at Age Five
The outcome variable, BMI at age five years, was calculated with a weight and
height taken at a well-child visit between the ages of 4 to 6 years of age. A range of
ages was selected since visits usually do not occur exactly at the specific “well child visit
age”. Also, since multiple visits could be taken between four to six years of age, the
sample was further limited to observations or well child visits that had both a weight and
height and utilized the last visit available that had both a weight and a height measure-
ment. BIVBMI values are shown in Table 2.7.
Low BIVBMI values were considered implausible if the BMI was < 13.5 (< 1st
percentile; n = 7, BMI range 2.45 – 11.8, Table 2.7). A high BIV value is indicative of a
BMI which is > 18 ( > 95th percentile; n = 41, BMI range = 23.2 – 64.6, Table 2.7).
43
High and low BIV values were also examined by ethnic group to see if there was
a race/ethnic group relationship with outlying values (Table 2.8). The SAS program for
CDC growth charts uses the 2000 CDC growth charts reference.204 The reference
population which has a lower percentage of Asian and Pacific Islander representation
than the KPH population. Although the weight-for-height z-score range of inclusion is
quite wide, we expect that some race/ethnic groups (e.g., Pacific Islanders, Native
Hawaiians) may have a higher BMI than White non-Hispanic children.207 This is an
important consideration to note for future study; nonetheless, the CDC SAS program was
used as the reference for removing outlying values due to the absence of an alternative.
Table 2.5. Biologically Implausible Values (BIV) for BMI-for-Age and Sex
BIVBMI* n Percent
0 2490 98.11 1 7 0.28 2 41 1.62
*1 = High BIVBMI, 2 = Low BIVBMI
2.6 Covariates and Potential Confounders
Variables were selected to improve the precision of the model or as potential
confounders based on the current literature. Improvement in the precision of the model
(e.g., adjusting for age) assists in removing variability in the estimate of the exposure on
the outcome contributed by other known predictors. Thus, precision of the effect estimate
is demonstrated by of narrowing the CI, as a result of controlling for covariates and
potential confounders.
2.6.1 Age and Sex
Age in years is calculated as a continuous variable. Female gender was
significantly associated with higher child body mass index at age six years.173 Sex is a
categorical variable: Male and Female. Both variables were added to the model as
precision variables.
44
Table 2.6. Low and High BIVBMI by Race/Ethnic Group
Race/Ethnic n Mean + SD Minimum Maximum LOW BIVBMI White 2 8.52 ± 3.1 6.3 10.7 Asian 2 10.77 ± 1.5 9.7 11.8 Native Hawaiian 1 11.4 11.4 11.4 Other PIa 1 5.2 5.2 5.2 Unknown 1 2.5 2.5 2.5 HIGH BIVBMI White 1 29.9 29.9 29.9 Asian 7 27.9 ± 6.1 23.6 39.9 Native Hawaiian 17 28.5 ± 9.5 23.7 64.6 Other PI 12 25.7 ± 2.4 23.2 31.0 Unknown 3 27.9 ± 1.0 26.2 27.9 Other 1 25.6 25.6 25.6
a Other Pacific Islanders
2.6.2 Race/Ethnicity
Child overweight and birth size vary by race/ethnicity.4, 125, 126 Therefore, since
race/ethnicity is associated with both the exposure and outcome, race/ethnicity is a
potential confounder. KPH race/ethnic information is gathered from one of three
sources: 1) upon inpatient admission via interview, 2) by personal history sheet
completed by the parent for the child at all clinics, 3) and as a part of the tumor registry,
which uses the above methods, plus physician notes, to assign race/ethnicity. The race
fields within the user interface for Health Connect® are now at least 80% populated from
these sources, providing the race/ethnicity information for the KPH VDW. The KPH
personal history sheet, provides opportunity to fill in more than one race/ethnic category,
which is coded as a subsequent race/ethnic variable per individual (race/ethnic variable 1
– 5). Race coding for the VDW race/ethnic variable is based on the methodology used by
Surveillance Epidemiology and End Results (SEER)208 The first race/ethnic variable
indicates the first race/ethnic group indicated by member. This first race/ethnic group
variable (Race 1) will be used as an initial step to describe race/ethnicity.
45
For each race/ethnic variable, there are 27 race categories, which were collapsed to
six categories for these analyses: Asian, White, Native Hawaiian, Other Pacific Islanders
(Other PI), Other, and Unknown. “Asian” included Chinese, Japanese, Filipino, Korean,
Asian Indian, Vietnamese, Laotian, Hmong, Kampuchean, Thai, and Other Asian.
“Native Hawaiian” race/ethnic group is coded similarly as the Hawai‘i Health Survey
algorithm where any persons who are recorded as a combination of Native Hawaiian and
any other race are coded as Native Hawaiian.209 “Other PI” included Micronesian -none
other specified (NOS), Chamorran, Guamanian-NOS, Polynesian-NOS, Tahitian,
Samoan, Tongan, Melanesian-NOS, Fiji Islander, New Guinean, Pacific Islander-NOS.
“Other” included Black, American Indian/Aleutian/Eskimo, and Other. “Unknown”
indicated that race/ethnicity information was missing.
2.6.3 Socioeconomic Status
Children from lower income families are more likely to be obese compared with
children from higher income families.124 Maternal education was used as a proxy for
socioeconomic status210, 211 and is available through child birth certificate information. It
is included as a precision variable and described as the total number of years of
education.
2.6.4 Infant Feeding
Breastfeeding has been associated with lower weight gain in infancy and with less
obesity in childhood and adolescence than formula feeding.166 Thus, this variable was
included as a potential confounder in Study Aim 2 models. A coded field for breast-
feeding is currently not available. Common text/phrases related to breastfeeding
available in Smart Sets212 (a template tool used by clinicians for documentation, coding
diagnoses, and ordering tests and procedures) were identified and used for searching the
text fields of notes that document the physician’s interaction with the mother. Text
search words included “breast”, “breastfed”, “breastfeeding”, “BF”, “nursing”, and
“pumping”. These words were searched in well child visit Smart Sets from birth to 24
months. A categorical variable called “breastfeeding status” was created (Breastfed = Y,
Not Breastfed = N). Breastfeeding duration was determined by using the last report of
46
breastfeeding at any encounter minus date of birth and is reported in months. Although
this variable does not take into account the fact that women may have breastfed past this
last encounter, it provides an indicator of the average minimum duration of breastfeeding.
2.6.5 Maternal Factors
The KPH EMR provides the opportunity to link the child with existing maternal
data. Each child was linked by medical record with mother’s medical record which is via
parent membership (mother or father) information at Kaiser Permanente. In addition,
maternal information gathered on the birth certificate through the Vital Statistics
Department at KPH provides supplemental maternal information.
Multiple births such as twins and triplets are associated with different factors and
complications during pregnancy.213 This may be related to birth size and later infant
growth. For the purpose of this analysis, only singleton births were included (98% of
births).
Mothers who are overweight or obese have a higher chance of having children
born with a large birth size30 and their offspring are at increased risk for obesity during
childhood, adolescence, and adulthood.138-141 Maternal BMI is both a variable of interest
and potential confounder. To calculate maternal BMI, pre-pregnancy weights taken one
year before estimated conception date were used as long as the women were not pregnant
or did not deliver during that year. In a 2004 Kaiser Women’s Health Survey, 84% of
women of child-bearing age reported attending a health care visit in the previous year.214
However, the available number of pre-pregnancy weights was greater than first
pregnancy weights; this is due to the source of maternal weight (paper charts vs.
availability of electronic data during the 2003 - 2004 years). Pre-pregnancy weights were
mostly measured at their Primary Care Physician (PCP) visit and first pregnancy weights
at the Obstetrics (OB) visit. During the birth years of this sample population, (2004 –
2005), the majority of the maternal information for the corresponding pregnancy (2003 –
2004) gathered up to that point was done via paper and was not converted to electronic
data. Electronic entry of this information was conducted over time from 2005 in
conjunction meeting overall KPH HealthConnect® requirements and Obstetrics-
Gynecology (OB-GYN) departmental needs. In these data, there were more pre-
47
pregnancy weights available (n = 1,120, <365 days) than first pregnancy weights (n =
89, first trimester) and 298 with both. In women with both (n = 298) a pre-pregnancy
weight one year prior to estimated conception date and first pregnancy weight, mean
difference was very small (-1.4 lbs, p < - 0.02). Pre-pregnancy weights are less
influenced by pregnancy, e.g., hormones, fluid, weight gain, compared with first
pregnancy weights. For these reasons and to maintain consistency in interpretation, pre-
pregnancy weights up to one year prior to estimated conception date were used. A
problem with use of weights one year before estimated conception date is the possibility
that these women were pregnant and had given birth during the during this one year
period. Thus, women were flagged if they had a pregnancy diagnoses or had delivered (n
= 213) within the last year prior to estimated conception date and therefore they were
excluded from pre-pregnancy weights. Estimated conception dates were calculated based
on gestational age at delivery. The last measured height at a time point in adulthood was
used for calculation of BMI. Pre-pregnancy BMI was calculated using pre-pregnancy
weights as defined above and the last measured height available.
Pre-pregnancy weights and maternal heights were examined for extreme outliers.
Detection of outliers was completed through observation of univariate distributions and
descriptive analyses (mean, median, SD).
The National Research Council and Institute of Medicine (IOM) defines
gestational weight gain (GWG) as “the amount of weight a pregnant woman gains
between the time of conception and the onset of labor”.215 The recommended weight
gain ranges based on pre-pregnancy BMI are: underweight (28 – 40 lbs), normal weight
(25 – 35 lbs), overweight (15 – 25), obese (11 – 20 lbs). Often, there is lack of
consistency in the time at which the last pregnancy weight is taken before the onset of
labor. In addition, first and last pregnancy weights were mostly recorded in the paper
charts during the selected years and were not adequately recorded electronically (36% of
first pregnancy weights and 28% last pregnancy weights of the total number of linked
records). This is probably due to the transition from paper charts to electronic medical
records at KPH. In addition, the period of time during pregnancy at which the mother
attended first and last pregnancy visits varied, in terms of trimester recorded. For
example, there are ‘last’ pregnancy weights recorded in the second trimester which do not
48
provide a good indicator of actual GWG. Gestational weight gain information was also
available from birth certificate information that was abstracted from KPH electronic/
paper records. This was determined by the most recent weight during the pregnancy (to
the date of recording) minus the starting weight from the first prenatal visit. There was
more information provided through this variable than from the coded values in the EMR
because of availability of chart-abstracted information supplementing the EMR.
Mother’s age at current pregnancy was described in years and determined at time
of delivery. This variable was included as a precision variable. Mothers less than 16
years of age were excluded (2%) since peak attained height is usually reached by age 16
years by the average female as shown by the plateau in the CDC growth charts.216
During these earlier adolescent years, mothers are still growing and therefore calculated
pre-pregnancy BMI would not be comparable with the remaining mothers.
Children born to women positive for gestational diabetes are born with a larger
birth size147, 217 and have an increased odds for child overweight.152 Diagnosis of GDM
was determined by using ICD-9 coding for a diagnoses of GDM at delivery and was
included in the model as a potential confounder. Birth order of the child was determined
by parity (i.e., number of live births).
Maternal smoking during pregnancy is associated with a risk for a lower birth
weight44, 156 and also later child overweight.144, 145, 153-155 This is plausible through the
mechanism of a lower birth weight and subsequent “catch-up” or rapid infant growth.
Maternal smoking was included as a potential confounder. Maternal tobacco and alcohol
use during pregnancy was coded as yes (vs. no) if they had smoked or drank alcohol at
any point in pregnancy. A question from birth certificates asked “Tobacco use during
pregnancy: yes or no”.
2.7 Missing Data
Intermittent missing data are common methodological problems in longitudinal
studies. Well child visits are scheduled at specific ages during two years of life; however
KPH members may not attend each visit or may not attend at the expected age. In
addition, members join the health care system at different points in their lifetime.
However, BMI data at age five years is expected to be relatively complete since children
49
registering for Kindergarten in the State of Hawai‘i usually require a physical
examination by a physician. Comparisons of groups of subjects with and without birth
size and growth data were completed to investigate selection bias and to determine
whether missing data are “informative” or “ignorable”218 - specifically, comparisons of
important predictor variables that are related to the outcome of interest (BMI at age five
years), such as birth size, gestational age, age, sex, and ethnicity.
2.8 Final Study Sample
Figure 2.5 outlines the strategy used for determining the available study sample.
After consideration of all main variables for analysis, a complete data set was available
for a final sample of 894 children for Study Aim 1, 597 for Study Aim 2a, and 473 for
Study Aim 2b, and 2c. (see Box A)
50
Figure 2.5. KPH EMR Sample of Children
Clean Birth Size Step 1 – Remove
Outliers - 4 GA - 2 BW – 2
(4,267 unique 108,272 obs)
Clean Birth Size Step 2 –Bivariate Plot , Remove - 8
(4,259 unique 108,069 obs)
Clean Birth Size Step 3 –Remove
Residuals - 7
(4,252 unique 107,920 obs)
Infant Growth periods and BMI at age 5
Infants out of study time
periods (4,222 unique 76,062 obs)
BMI at age ~5 (4 to 6 yrs)
(5,120 weight and height ≠ missing
13,734 obs)
Infant period 22 to 24 mos (2,303 unique
4,351obs)
Infant period 2 to 4 mos
(3,114 unique 7,144 obs)
CDC SAS program BMI cleaning
34 removed 2 low BIV, 32 high BIV
(2,480 unique)
KP Maternal Data
Unique population (4,807)
Maternal/Birth/BMI at age 5yrs
(2,490 unique)
STUDY AIM 1 n MATERNAL Pre-preg wt < =1 yr (1,120) Height >= 51 in (1,080) Age >16 yr (1,058) Preg/delivery flag in yr before EDC (912)
Duplicates Remove 7
(2,514 unique, 5,113 obs
Visual Inspection Remove 5 for
invalid heights for last measure
(2,480 unique)
BOX A
Limit to those with Birth
Weights (5,074) and Gest. Age
(4,271) (4,271 unique 108,272 obs)
Born at KPH (2004 – 2005), Age < 6 yrs (5,074 unique MRNs/128,232 Observations (obs))
+
Infant Weights (2 to 4 mos) 3072 unique
STUDY AIMS 2, 3, 4 = Infant growth Models 1) Change in weight (n = 597) 2) Change in length (n = 473) 3) Change in weight/length (n = 473)
Infant Weights (22 to 24 mos) 2286 unique
Infant Lengths (2 to 4 mos) 2850 unique
Infant Lengths (22 to 24 mos) 1,892 unique
51
2.10 Human Subjects Approval
This study was approved by the Kaiser Permanente Hawai‘i Institutional
Review Board and the University of Hawai‘i Committee on Human Subjects.
2.11 Analytic Plan and Statistical Analyses
Descriptive statistics for dependent and independent variables were computed for
continuous (mean, medians ± SD) and categorical (frequencies) variables. The main
variables were BMI at age five years (dependent), birth weight (independent), gestational
age, child’s age, sex, and ethnicity (covariates). Univariate and bivariate distributions of
main variables were examined to identify outliers. Multiple regression model parameters
were estimated to assess the association of a dependent variable with independent
variables of interest, adjusting for covariates and potential confounders. The model
building strategy included adding covariates and potential confounders to the core model
that was derived from initial birth size analysis. The main core model included BMI at
age five years as the dependent variable and birth weight adjusted for gestational age as
an independent variable. For study aim 1, the covariates, child age, sex, and ethnicity,
were added to the core model since they improve the precision of the model.
CORE MODEL: [Mean BMI at age 5 years = birth weight, gestational age, child
age, sex, ethnicity]
After fitting the core model, the following covariates were screened by adding each
variable one at a time: maternal height (in), pre-pregnancy weight (kg), BMI (kg/m2), age
(years), education (years), smoking and alcohol during pregnancy (Y/N), GDM status
(Y/N), parity (nulliparous, parous), and weight gain during pregnancy (lbs), and breast
feeding (Y/N), and breastfeeding duration (months). Variables that modified the
coefficient of birth weight by 10% 116, 219 in the basic models and/or a p-value less than
0.2 220, 221 were included in subsequent multiple regression models. Statistical testing of
linearity assumptions and tests for adequacy of the model were conducted by adding
additional squared terms and cross products and testing if they were significant.
Multivariate tests for differences in means between included and excluded
52
subpopulations were tested using logistic regression models. Demographics, main
outcome and independent variables and explanatory variables were compared between
the included and excluded samples to quantify the differences between the samples and
their effect on study conclusions. Associations were considered non-significant if p >
0.05.
Change in infant growth as measured separately by weight, length, and weight
-for-length was examined as a continuous variable and by the categorical variables of sex
and race/ethnicity. Changes in weight and length were calculated as weight (kg) and
length (cm) at age 22 to 24 months minus weight and length at age 2 to 4 months divided
by difference in age in months. Weight/length is the ratio of relative weight in kg divided
by length in cm. Change was calculated using the same age period as described for Study
Aim 2. Infant weight (g/month), height (cm/month), and BMI (kg/m2) change variables
were added to the final core model from Study Aim1 to examine the effect of infant
growth on birth weight and BMI at age five years.
BMI is a moderate indicator of fatness in children 222 and, therefore, is more
informative to study BMI as a continuous variable as opposed to using arbitrary intervals,
assuming health risks increase with increasing body fatness. Linearity of BMI with
age was assessed using its squared terms. BMI was examined as a continuous variable in
statistical models.
Statistical analyses were conducted using the SAS Software program, Enterprise
Guide 4.3.223 A summary of the analysis steps for each aim is provided below.
53
2.12 Modeling of Study Aims
AIM 1: Is Birth Size associated with Child BMI at age 5 years?
Test the hypotheses:
H0: Birth weight is not associated with Child BMI at age 5 years.
Dependent variable: BMI (kg/m2) at age 5 years Independent variable: Birth Weight Statistical Methods: Simple and Multiple Linear Regression
Example model building:
Dependent variable = Independent variable(s) + Precision Variables + Potential Confounders
1a) Mean BMI at age 5 years = birth weight (g) Mean BMI at age 5 years = birth weight (g) + gestational age (weeks) Mean BMI at age 5 years = birth weight (g) + gestational age (weeks) + child age (years) + sex + ethnicity Mean BMI at age 5 years = birth weight (g) + gestational age (weeks) + child age (years) + sex + ethnicity + maternal variables
Cont = continuous, Cat = categorical
Study Aim 1 SubAims
1b) Describe Birth Weight by Ethnicity and Sex
COVARIATES
Precision variables Potential Confounders
Child variables Sex (cat) Age (years, cont)
Maternal variables Gestational age (weeks, cont) Maternal Age (years, cont) Maternal Education (years, cont) Birth Order (0/ > 1, cat) Breastfeeding Duration (months, cont)
Ethnicity (dummy, cat) Maternal BMI ( kg/m2, cont) (Maternal Pre-pregnancy weight (kg, cont) Maternal Height (in, cont) Maternal Smoking (Y/N, cat) GDM diagnoses during pregnancy (Y/N, cat) Breastfeeding ( Y/N, cat)
MODEL: Calculated BMI (kg/m2) at age 5 years (continuous) = α0 + α1(birth weight, continuous)
54
AIM 2: Is change in infant growth associated with Child BMI at age 5 years?
Test the hypothesis:
H0: Change in weight during infancy is not associated with child BMI at 5years
H0: Change in length during infancy is not associated with child BMI at 5 years
H0: Change in weight/length2 during infancy is not associated with child BMI at age 5 years
Dependent variable: BMI (kg/m2) at age 5 years Independent variable(s): Change in weight, length, weight/length2 during infancy Statistical Methods: Simple and Multiple Linear Regression
Example Model Building:
Dependent variable = Independent variable(s) + Precision variables + Potential Confounders
2a) Mean BMI at age 5 years = birth weight (g) + gestational age (weeks) + child age + sex + ethnicity + maternal variables (dependent on results of Aim1) + change in weight (100 g/month)
Other models to be tested: 2b) Mean BMI at age 5 years (cont) = change in length (cm/month) 2c) Mean BMI at age 5 years (cont) = change in weight/length2 gain (kg/m2/month)
COVARIATES (Dependent on Aim 1)
Precision variables Potential Confounders
Child variables Sex (cat) Age (years, cont)
Maternal variables Gestational age (weeks, cont) Maternal Age (years, cont) Maternal Education (years, cont) Birth Order (0/ > 1, cat) Breastfeeding Duration (months, cont)
Ethnicity (dummy, cat) Maternal BMI ( kg/m2, cont) (Maternal Pre-pregnancy weight (kg,
cont) Maternal Height (in, cont) Maternal Smoking (Y/N, cat) GDM diagnoses during pregnancy (Y/N, cat) Breastfeeding ( Y/N, cat)
Cont = continuous, Cat = categorical
MODELS: Calculated BMI(kg/m2) at age 5 years (continuous) = β0 + β1(infant growth, continuous)
Infant growth (continuous) = θ0 + θ1(birth size, continuous)
55
SubAims: 2d) Examine infant growth variables by sex and ethnicity 2e) Examine model 2a – c with binary outcome “overweight/not overweight” using
logistic regression
AIM 3: Does change in infant growth mediate the relationship between birth weight and BMI at age 5 years?
Test the hypotheses: H0: The effect of birth weight on child BMI is not mediated through
infant growth
Dependent variable: BMI (kg/m2) at age 5 years Independent variable(s): Change in weight, length, weight/length2 during infancy Statistical Tests: Simple and Multiple Linear Regression
Statistical Tests: Simple and Multiple Linear Regression
MODEL: Calculated BMI(kg/m2) at age 5 years (continuous) = β0 + β1(infant Growth, continuous) + β2 (Birth weight)
Calculated BMI(kg/m2) at age 5 years (continuous) = β0 + β1 (Birth weight) (model without infant growth)
Use core model and covariates used in Study Aim 1 and 2
56
AIM 4: Is the effect of Birth size on BMI at age 5 years modified
by change in infant growth? Test the hypothesis:
H0: There is no interaction of infant growth and birth size and child BMI
Interaction Terms: Birth weight and change in weight Birth length and change in length Birth weight/length2 and change in weight/length2
MODEL: Calculated BMI (kg/m2) at age 5y (continuous) = β0 + β1(infant Growth, continuous) + β2 (Birth weight) + β3 (Birth weight*infant
growth)
Use core model and covariates used in Study Aim 1 and 2
57
CHAPTER 3. RESULTS
Figure 2.5 illustrates the sampling strategy for the study population. A total of
5,074 children met initial inclusion criteria, which included children born at KPH during
the years 2004 – 2005 and current health plan members of KPH.
3.1 Core Model Results
3.1.1 Relative Role of Birth Weight, Birth Length, and Gestational Age in BMI at Age 5 Years
In preparation for Study Aim 1, birth size modeling was used to examine the
association between BMI at age five years with birth weight, birth length, and gesta-
tional age. Birth size is described by weight in grams, length in centimeters, and
weight/lengthP (p = power of 0,1,2,3) and gestational age in weeks. To understand
interrelationships further, correlation matrices were produced with birth size variables
which provided preliminary information for model building. Various powers of birth
size weight/length = f(weight/lengthP) were tested as different functions of the ratio of
weight/ lengthP to understand the relationship of weight to length at birth in relation to
BMI at age five years. The best function or predictor of BMI in the model was used for
future modeling.
Functions of Weight/LengthP Tested:
Mean BMI at age 5y = β0 + β1 (birth weight/lengthP) P = 0,1,2,3 Mean BMI at age 5y = β0 + β1 (birth weight) + β2(birth length) Mean BMI at age 5y = β0 + β1 (birth weight) + β2(birth length) + β3(birth weight/length) Mean BMI at age 5y = β0 + β1 (birth weight) + β2(birth length) + β3(birth weight/length)
+β4(birth weight/lengthP)
Gestational age is correlated with birth weight. Therefore a test was done to
explore if adjusting for gestational age affected the regression of BMI (kg/m2) at age
five years on birth size. Models were adjusted for gestational age as appropriate.
58
Models Tested:
Mean BMI at age 5y = β0 + β1(birth weight) Mean BMI at age 5y = β0 + β1(birth weight) + β2(gestational age).
The overall sample was limited to those individuals who had clean data for birth
weight, birth length, and gestational age and BMI at age five years (n = 1,729). In Table
3.1, bivariate analysis shows that both birth weight and birth length were positively
associated with BMI at age five years. Addition of gestational age to the birth weight
model strengthened the relationship of birth weight with BMI (β = 0.690 to 0.981) and
was independently and inversely associated with BMI at age five years (β = -0.118, 95%
CI: -0.176 – - 0.060, p = <0.0001). In model 4, birth weight adjusted for gestational age
remains highly significant; therefore, gestational age needs to be included in the model.
The test for interaction between birth weight and gestational age was not statistically
significant. After adding birth length to the model with birth weight and gestational age,
birth length was no longer a predictor of BMI and the interaction with birth weight and
birth length was not statistically significant. Birth length was not included in subsequent
models that examined the relationship of birth weight, gestational age, and BMI. In
addition to absolute weight, functions of relative weight for length (weight for length,
length2 and length3), adjusted for gestational age, were tested. Coefficients of
determination actually decreased when birth weight was adjusted for length in three
different ways (Table 3.2). Thus, birth weight is an independent and stronger predictor
of BMI at age five years than birth length or gestational age. A test for linearity was
was performed by adding squared terms to the model which were not significant. Thus,
the birth size and gestational age modeling step resulted in the following core model:
Mean BMI at age 5y = β0 + β1 (birth weight) + β2 (gestational age)
Birth Size BMI at age
5y
Gestational Age
Table 3.1. Birth Weight, Birth Length, Gestational Age and their Relative Contribution to BMI at Age 5 Years, β (95% CI) n = 1,729
*p = <0.001, aR2 = 0.051, Interactions between birth weight with gestational age (p = 0.3318) and birth weight and birth length (p= 0.7762)
Table 3.2. Indices of Birth Weight for Birth Length adjusted for Gestational Age with BMI at Age 5 Years, β (95% CI) n = 1,729
*p<0.001
Model 1 Model 2 Model 3 Model 4 Model 5a
Birth Weight(kg)
0.690(0.539 – 0.841)*
0.981 (0.774 – 1.19)*
0.993 (0.745 – 1.25)*
Birth Length(cm)
0.08 (0.056 –0.112)*
-0.004 (-0.047 – 0.040)
Gestational Age(weeks)
0.070 (0.027 – 0.113)*
-0.118 (-0.176 – -0.060)*
-0.117 (-0.177 – -0.056)*
β ± SE R2
Birth Weight 0.690 ± 0.077* 0.051
Weight/Length 0.534 ± 0.061* 0.047 Weight/Length2 0.198 ±0.030* 0.029 Weight/Length3 0.048 ± 0.012* 0.013
59
60
Model building for Study Aim 1 was completed by adding other demographic and
maternal factors that added precision to the model or were considered confounders of this
relationship. Further analysis was completed on the cleaned data sample with the larger
sample of cleaned birth weight and gestational age (n = 4,252) and not limited by
available birth length (n = 2,946).
3.2 Study Aim 1
As depicted in Figure 2.5, children with cleaned birth weight and gestational age
(n = 4,252) and a BMI measurement at age five years (n = 1,729) resulted in a sample of
1,729 children. Further addition of required covariates, including maternal variables
resulted in a sample of 894 children. Descriptive data for this sample are provided in
Tables 3.3 and 3.4. There were more male (53.6%) than female children. Most children
were born close to term (38.7 weeks), on average. Mothers had a mean of 14 years of
education and mean pre-pregnancy maternal BMI (26.5) was considered overweight by
CDC BMI cut off points for adult criteria.224 The number of mothers who smoked
(5.6%) or drank alcohol (1.6%) during pregnancy was low according to birth certificate
information. Most children were breastfed for at least 18 months and were first born.
Table 3.3. Descriptive Statistics of Child Demographics (n = 894)
Variable N (%) Gender Female 415 (46.4) Race/Ethnicity White 70 (7.8) Asiana 264 (29.5) Native Hawaiianb 330 (36.9) Other Pacific Islanderc 181 (20.3) Otherd 23 (2.6) Unknowne 26 (2.9)
a Chinese (15.5%), Japanese (27.7%), Filipino (47.3), Korean (1.9%), Asian Indian (0.8%), Vietnamese (1.5%), Thai (0.4%), Asian-NOS (4.9%) b Persons race is recorded as combination of Native Hawaiian and any other race208c Guamanian (1.1%),
Tahitian (1.1%), Samoan (14.4%), Tongan (1.1%), Pacific Islander – none other specified (82.3%) d American Indian/Aleutian/Eskimo (17%), African American (26%), Other (57%) e Race/Ethnicity information is missing
61
Table 3.4. Descriptive Statistics of Birth Size, Child BMI and Covariates (n = 894)
Variables Mean ± SD Minimum Maximum Birth Size Birth Weight (kg) 3.3 ± 0.6 0.6 5.0 Gestational Age (weeks) 38.7 ± 1.9 23.5 42.0 Child at Age 5 Years Age (years) 4.9 ± 0.5 4.0 5.9 BMI (kg/m2) 16.1 ± 1.8 11.7 25.0 Maternal Variables and Infant Feeding
Pre-pregnancy Weight (kg) 68.8 ± 18.7 33.1 161.9 Maternal Age (years) 29.5 ± 6.1 17.0 44.0 Maternal BMI (kg/m2) 26.5 ± 6.7 14.3 63.9 Maternal Height (cm) 161.0 ± 7.1 131.6 190.5 Maternal Education (years) 14.0 ± 2.1 8.0 17.0 Breastfeeding Duration (months)a 18.0 ± 3.2 0.5 24.0 Maternal Weight Gain during
Pregnancy (kg)b 14.1 ± 5.7 4.1 29.0
N (%) Breast-fed (Y/N) 888 (99.3) Maternal Smoking (Y/N) 50 (5.6) Maternal Alcohol (Y/N) 14 (1.6) Birth Order (>1 live birth) 246 (27.5) Maternal GDMc (Yes, with
diagnosis code) 100 (11.2)
a n = 888 b n = 461 c Gestational Diabetes Mellitus
62
Additional multiple regression models including maternal factors related to birth
weight and later child BMI were estimated (Table 3.5) to explore the effect of these
variables on the relationship between birth weight and BMI. Models were compared with
the smaller sample size with breastfeeding duration information (n = 888). Due to
minimal differences in regression coefficients (0.01 to 0.03 difference) the overall sample
of n = 894 was retained for subsequent analysis.
Maternal pre-pregnancy weight, maternal BMI, maternal height, maternal age,
maternal education, and maternal smoking met the criterion of p < 0.2 for inclusion into
the core model. Maternal pre-pregnancy weight was positively associated (β = 0.028,
95% CI: 0.022 – 0.034) with BMI at age five years and decreased the effect of birth
weight on BMI (β = 0.926 to 0.665). Maternal pre-pregnancy weight had a greater effect
on infant birth weight than maternal BMI or height when included into the model,
although maternal BMI still explained a higher proportion of the variability of BMI at age
five years than other variables. Lower maternal education was associated with increased
BMI at age five years (β = -0.083, p = 0.005). The effect of having been breastfed,
breastfeeding duration, maternal alcohol consumption, maternal GDM status, maternal
weight gain during pregnancy, and birth order on birth weight and BMI at age five were
not significant (p >0.2) and were not included in subsequent models. These models
explained only 7 – 8% of the variance. A sub-analysis was conducted on maternal weight
gain since there was a much smaller sample that had this information (n = 439). An
initial plausible range of weight gain of 11 to 65 lbs (5th to 95th percentile) was selected
in our population based on distribution and after consulting current guidelines for
GWG.215 However, it minimally affected the relationship of birth weight and BMI at age
five years (β = 0.926 to β = 0.838). A sub-analysis of the effect of the larger range of
weight gained (7 to 82 lbs, n = 461) was attempted; however, addition of another 18
values did not improve the model.
Table 3.5. Effect of Adding a Covariate on the Regression Coefficient of BMI at Age 5 Years on Birth Weight Core Model (n = 894)
Covariates in the Model used to
Adjust Birth Weight Birth Weight (g)
Model
Β 95% CI Adjusted R2 Core Modela 0.926 0.662 – 1.192 0.08 Maternal Pre- pregnancy Wt (kg)b * 0.665 0.405 – 0.924 0.15 Maternal BMI (kg/m2)* 0.734 0.476 – 0.992 0.14 Maternal Weight Gain During Pregnancy (kg)c
0.838 0.461 – 1.216 0.08
Maternal Height (cm)* 0.868 0.599 – 1.136 0.08 Maternal GDM (Y/N) 0.917 0.651 – 1.184 0.07 Breast Fed (Y/N) 0.926 0.661 – 1.190 0.07 Breastfeeding Duration (months) 0.928 0.662 – 1.193 0.07 Birth Order (> 1 live birth) 0.928 0.662 – 1.194 0.07 Maternal Alcohol during Pregnancy (Y/N)
0.928 0.663 – 1.192 0.07
Maternal Smoking during Pregnancy (Y/N)*
0.936 0.671 – 1.200 0.08
Maternal Education (years)* 0.939 0.675 – 1.202 0.08 Maternal Age at Pregnancy (years)* 0.957 0.689 – 1.224 0.08 * Variables included in subsequent modeling with p<0.02 aCore model = Birth weight, Gestational age, Child Age, Sex, Race/Ethnicity (Reference group = White), p<0.0001
bkilogram c lbs = pounds, n = 439
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As explained in Chapter 2 (p.53), variables with significance levels < 0.2 were
retained in the final model (Table 3.6). Pre-pregnancy weight, maternal age, maternal
smoking, and maternal education were added one by one to the core model which
improved the adjusted R2 from 0.08 to 15.6%. Maternal BMI and maternal height with
pre-pregnancy weight were also tested; however, pre-pregnancy weight (t = 8.83) was an
overall better predictor than maternal BMI (t = 8.24) or maternal height (t = - 0.16).
Therefore pre-pregnancy weight was used in this model and had the most impact on the
model R2 (an increase from 0.08 to 0.15). Maternal education was no longer statistically
significant after adding maternal age. The inclusion of all of these variables decreased the
effect of birth weight on BMI (β = 0.926 to 0.707), justifying the need to adjust for these
variables. Therefore this model (Table 3.6) became the final model for Study Aim 1.
Higher birth weight and pre-pregnancy weight were both associated with higher BMI at
age five years. Compared to Whites, Asian and Other Pacific Islander children had a
higher BMI at age five years.
Mean birth weight was lowest among Asians (3.21 kg) followed by Other (3.20 kg),
Native Hawaiian (3.32 kg), Unknown (3.34 kg), Other Pacific Islander (PI) (3.37 kg), and
White (3.41 kg) (Figure 3.1). Asian children had a significantly lower birth weight
compared with the Other PI children (p = 0.033). No other race/ethnic groups were
significantly different. There was no statistical difference in birth weight between males
(3.33 kg) and females (3.27 kg, p = 0.060, Figure 3.2). Study Aim 1 sample (n = 894)
and the remaining sample (n = 3,358) were not statistically different (Wald X2 = p <
0.054, Table 3.5). However, a few observed differences in race/ethnic groups were
noted. Asian, Native Hawaiian, and Unknown race/ethnic groups had a >5% difference
between samples. There were slightly more Asian and Native Hawaiian children in
Study Aim 1 sample and more Unknown in the remaining sample.
Table 3.6. Regression of BMI at Age 5 Years on Birth Weight (FINAL MODEL*, n = 894)
Variables β 95% CI t p
Birth Weight (kg)a 0.707 0.446 – 0.969 5.31 <0.0001 Gestational Age (weeks) -0.074 -0.150 – 0.001 -1.94 0.053 Age (years) 0.020 0.002 – 0.039 2.14 0.033 Female -0.100 -0.320 – 0.120 -0.89 0.374 Asian 0.538 0.100 – 0.976 2.41 0.016 Native Hawaiian 0.381 -0.059 – 0.821 1.70 0.090 Other Pacific Islander 0.623 0.153 – 1.094 2.60 0.010 Other Race/Ethnicity 0.290 -0.492 – 1.071 0.73 0.467 Unknown Race/Ethnicity 0.191 -0.568 – 0.949 0.49 0.622 Pre-pregnancy Weight (kg) 0.028 0.022 – 0.034 8.83 <.0001 Maternal Age at Pregnancy (years) -0.014 -0.035 – 0.006 -1.36 0.175 Maternal Education (years) -0.026 -0.087 – 0.035 -0.84 0.403 Maternal Smoking (Y/N) 0.390 -0.091 – 0.872 1.59 0.112
*Adjusted R2 = 0.16, Reference Group = White akilogram
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ANOVA: F = 2.93, p<0.008, Adjusted for Sex *p <0.05
Figure 3.1. Birth Weight by Race/Ethnicity (n = 894)
ANOVA, F = 3.55, p= 0.06 Figure 3.2. Birth Weight by Sex (n = 894)
*
*
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Table 3.7. Comparison of Demographics and Main Variables of Study Aim 1 Sample (n =894) with remaining Cleaned Birth Size Sample (n = 3358)
Study Aim 1 N = 894 N = 3358 Birth Characteristics Mean ± SD Birth weight (kg) 3.3 ± 573 3.3 ± 601 Gestational age (weeks) 38.7 ± 1.9 38.6 ± 2.1 Child Demographics N (%) Males 479 (53.6) 1671 (51.3) Females 415 (46.4) 1589 (48.7) Asian 264 (29.5) 744 (22.8)** White 70 (7.8) 326 (10.0)** Native Hawaiian 330 (36.9) 829 (25.4)** Other Pacific Islander 181 (20.3) 763 (23.4)** Other Race/Ethnicity 23 (2.6) 100 (3.1)** Unknown Race/Ethnicity 25 (2.8) 350 (10.7)** Child BMI Mean ± SD BMI at age 5 years (kg/m2) 16.2 ± 2.0 (n = 520) 16.3 ± 2.4 (n = 1079)
Overall Wald Chi Square, p < 0.054 * Total cleaned birth size sample, n = 4,252 ** 98 missing, n = 3,260, total cleaned birth size sample, n = 4,154
3.2 Study Aim 2a – Change in Infant Weight
Among the 894 children included in Study Aim 1 who had both birth weight and
gestational age measures, maternal information, and BMI calculated at age five years,
67% (n = 597) had a weight measured at three time points (2 to 4 months, 22 – 24
months, 4 to 6 years). In the Study Aim 2 sample, there were more males than females
(53%) and a larger proportion of Asian, Native Hawaiian, and Other PI children than
other race/ethnic groups (Table 3.6).
Mean birth weight, gestational age, child age, BMI at age five years, and maternal
variables were similar to the means of the Study Aim 1 sample (Table 3.7). The mean
infant weight gain was about 280 grams per month per child (114 – 498 g) for the infant
growth period of 2.0 – 4.1 months to 22.4 – 24.4 months. Mean difference in months
between measures was 20.4 (18.4 – 22.4 months). There were more weights and lengths
measured at 2 and 24 months of the infant growth period being examined (Figure 3.3)
which corresponds to the scheduled well child visits.
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Table 3.8. Descriptive Statistics of Child Demographics for Study Aim 2a (n = 597)
Variables N (%) Gender Female 283 (47.4) Race/Ethnicity White 47 (7.9) Asiana 199 (33.3) Native Hawaiianb 216 (36.2) Other Pacific Islanderc 110 (18.4) Otherd 13 (2.2) Unknowne 12 (2.0) a Chinese (14.6%), Japanese (27.1%), Filipino (50.3%), Korean (1.5%), Asian Indian (1.0%), Vietnamese (1.5%), Asian-NOS (4.0 %) b Persons race is recorded as combination of Native Hawaiian and any other race208c Guamanian(1.8%),
Tahitian(1.8%), Samoan(16.4%), Tongan(1.8%), Pacific Islander – none other specified (78.2%) d American Indian/Aleutian/Eskimo (7.7%), African American (38.5%), Other (53.8%) e Race/Ethnicity information is missing
Table 3.9. Descriptive Statistics of Birth Weight, Gestational Age, Infant Growth and BMI at Age 5 Years (n = 597)
Variables Mean ± SD Minimum Maximum Birth Size Birth Weight (kg)a 3.3 ± 0.6 0.6 4.7 Gestational Age (weeks) 38.6 ± 2.1 23.5 41.6 Child at Age 5 Years Age (years) 4.4 ± 0.5 3.6 5.5 BMI (kg/m2) 16.1 ± 1.8 11.7 23.5 Infant Growth Change in Weightb
(g/month) 285.4 ± 60.0 114.0 498.0
Difference in Agec (months) 20.5 ± 1.0 18.4 22.4 Maternal Variables Pre-pregnancy Weight (kg) 67.7 ± 18.2 36.3 161.9 Maternal Age (years) 29.5 ± 6.0 17.0 42.0 Maternal Education (years) 14.0 ± 2.1 8.0 17.0 N (%) Maternal Smoking (Y) 40 (6.7) a kilograms b Change in weight (100g/month) = Change in weight/Age at 22 to 24 month – Age at 2 to 4 months c Age at 22 to 24 month – Age at 2 to 4 months
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Figure 3.3. Number of Weights and Heights during Infant Growth Period
In the regression analysis of BMI on change in infant weight (Table 3.10), birth weight
remained positively associated with BMI at age five years, after adjusting for the main
independent variable of infant weight and covariates (age, child’s age, child ethnicity,
child sex, maternal pre-pregnancy weight, maternal age and maternal education, and
maternal smoking) in the model. A higher change in weight was also associated with a
higher BMI at age five years, meaning that for every 100 g/month increase in weight,
BMI increased by 1.0 kg/m2 at age five years. Addition of the change in weight variable
added 12% to the model variance (Adjusted R2 = 28%).
Asian (β = 0.782, 95% CI: 0.292 – 1.273), Other Pacific Islander (β = 0.934, 95%
CI: 0.400 – 1.468), and Native Hawaiian (β = 0.504, 95% CI: 0.002 – 1.007) children had
a higher BMI at age five years compared with White children adjusting for age, sex, pre-
pregnancy weight, maternal education, maternal smoking, and rate of weight gain. Other
and Unknown children were not significantly different from Whites. BMI at age five
years was not significantly different among males and females. Change in weight was
significantly different between race/ethnic groups (Figure 3.4); (ANOVA, F = 2.68, p =
0.021). White children had a significantly greater change in weight (313 g/month)
Table 3.10. Analysis of Multiple Regression of BMI at Age 5 Years on Birth Weight and Change in Infant Weighta (n = 597) Modela 1 Model 2 Model 3
Variables Β 95% CI β 95% CI β 95% CI
Birth Weight (kg) 0.688 0.374 – 1.002*** 0.636 0.344 – 0.927*** -0.334 -1.368 – 0.700
Change in Infant Weightb during Infant Periodc (100g/mo)
1.05 0.841 – 1.263*** -0.046 -1.190 – 1.097
Interaction term: Birth Weight X Rate of Weight Gain 0.333 -0.008 – 0.673
Adjusted R2 0.16 0.28 0.28
aModels adjusted for gestational age, child age, child ethnicity, child sex, maternal pre-pregnancy weight, maternal age, maternal education, and maternal smoking
b Change in weight (100g/month) = Change in weight/Age at 22 to 24 month – Age at 2 to 4 months cInfant period = 2 to 4 months to 22 to 24 months *** p<0.001
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aChange in Weight = Between 2 to 24 months ANOVA, F = 2.68, p= 0.02 *p<0.05 Figure 3.4. Change in Weighta during the Infant Period by Ethnicity (n = 597)
than Asian (279 g/month, p = 0.006) and Native Hawaiian (283 g/month, p = 0.029)
children. Change in weight was not significantly different among Other PI, Other, and
Unknown race/ethnic groups compared with the other groups. However, sample sizes for
the Other (n = 13) and Unknown groups (n = 12) were small and it is possible that there
was not enough power to detect a moderate difference.
Also, after plotting weight over time, Pacific Islanders had a different trajectory
after the first two years of life compared with other groups (Figure 3.5). Change in
weight during the infant period was not statistically different between males (286
g/month) and females (284 g/month). Weight gain from birth to age five years was
similar for males and females with males at a slightly higher trajectory from 2 to 4
months to 22 to 24 months than females.
Comparisons were made among the Study Aim 2a sample (n = 597) with children
who did not have weight and length measures within the specified time period (n = 3625,
Table 3.9). The overall Wald Χ2 was not significant, indicating that statistical differences
were not detectable between study samples. There were minimal differences
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in birth weight, gestational age, sex, and BMI at age five years between samples. The
Study Aim 2a sample had more Asian and Native Hawaiian children, and fewer Other
Pacific Islander children and Unknown race/ethnicity group which may imply that these
groups may attend fewer well child visits.
aChange in Weight = Between 2 to 24 months Other PI = Other Pacific Islanders Figure 3.5. Change in Weighta from Birth to Age Five Years by Race/Ethnicity (n = 597)
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aChange in Weight = Between 2 to 24 months ANOVA, F = 0.13, p = 0.720
Figure 3.6. Change in Weighta during the Infant Period by Sex (n = 597)
Figure 3.7. Change in Weighta from Birth to Age 5 Years by Sex (n = 597)
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Table 3.11. Comparison of Demographics and Main Variables of Study Aim 2a
Sample (n = 597) with Sample without Weights and Lengths within Specified Time Periods (n = 3625)
Variables N = 597 N = 3,625 Birth Characteristics Birth weight (g) 3.3 ± 0.6 3.3 ± 0.6 Gestational Age (weeks) 38.6 ± 2.1 38.6 ± 2.1 Child Demographics Males 314 (52.6) 1828 (51.8)* Females 283 (47.4) 1699 (48.2)* Asian 199 (33.3) 803 (22.8)* White 47 (7.9) 348 (9.9)* Native Hawaiian 216 (36.2) 940 (26.7)* Other Pacific Islander 110 (18.4) 825 (23.4)* Other Race/Ethnicity 13 (2.2) 110 (3.1)* Unknown Race/Ethnicity 11 (1.8) 359 (10.2)* BMI (kg/m2) 16.2 ± 2.1 (n = 200) 16.2 ± 2.0 (n = 882)
Overall Wald Chi Square, p<0.104 * Total cleaned birth size sample, n = 4,222 ** 98 missing, n = 3,527, total cleaned birth size sample, n = 4,124 3.3 Study Aims 2b and 2c
The final sample to examine the association of change in length between 2 and 24
months of age and BMI consisted of 473 children. Length measures were less available
than weight measures, therefore limiting the sample to those with both measures.
Descriptive statistics for this sample are reported in Tables 3.12 and 3.13.
There were similar proportions of males and females, and numbers for the Other
and Unknown ethnic groups were small. Birth size, child age and BMI, and maternal
values were similar with this final sample of n = 473. Average rate of length gain was
1.2 cm/month and BMI gain was -0.04 kg/m2/month over a 20 month duration.
Change in length was positively associated with BMI at age five years (Table
3.14). A 1 cm/month increase in length was associated with a 1.6 kg/m2 increase in BMI
at age five years. This change in length did not vary by race/ethnicity (Figure 3.8, F =
1.25, p = 0.284) and initial plots of length gain (Figure 3.9) demonstrates that a similar
pattern was followed by all race/ethnic groups. However a visible separation was noted
after 24 months with the Other and Other PI children at the top of all other race/ethnic
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plots. Change in length and pattern of length/height growth was not different between
males (1.22 cm/month) and females (1.25 cm/month, p = 0.105) (Figures 3.10 and 3.11).
Table 3.12. Descriptive Statistics of Child Demographics of Children in the Final Sample (n = 473)
Variables N (%) Gender Female 233 (49.3) Race/Ethnicity White 40 (8.5) Asian 172 (36.4) Native Hawaiian 152 (32.1) Other Pacific Islander 89 (18.8) Other 8 (1.7) Unknown 12 (2.5)
a Chinese (15.1%), Japanese (28.5%), Filipino (48.3), Korean (1.7%), Asian Indian (1.2%), Vietnamese (1.2%), Asian-NOS (4.1%) b Persons race is recorded as combination of Native Hawaiian and any other race208 c Guamanian(1.1%), Tahitian(1.1%), Samoan(16.9%), Tongan(1.1%), Pacific Islander – none other specified (79.8%) d American Indian/Aleutian/Eskimo (12.5%), African American (37.5%), Other (50%) e Race/Ethnicity information is missing
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Table 3.13. Descriptive Statistics for Birth Weight, Gestational Age, Change in Lengtha and BMIb and BMI at Age 5 Years (n = 473)
Variables Mean ± SD Minimum Maximum Birth Size Birth weight (kg) 3.3 ± 0.6 1.0 4.7 Gestational Age (weeks) 38.7 ± 2.0 27.0 41.6 Child at Age 5 years Age (years) 4.4 ± 0.5 4.0 5.5 BMI (kg/m2) 16.1 ± 1.8 11.7 25.0 Infant Growth Change in Length (cm/month)a 1.2 ± 0.2 0.7 1.7 Change in BMI (kg/m2/month)b -0.04 ± 0.1 -0.3 0.2 Difference in Age (month)c 20.7 ± 1.0 18.4 22.4 Maternal Variables Pre-pregnancy weight (kg) 67.6 ±17.9 38.6 161.9 Maternal Age (years) 30.1 ± 5.9 17.0 42.0 Maternal Education (years) 14.0 ± 2.1 8.0 17.0 N (%) Maternal Smoking (Y/N) 31 (6.6)
aChange in Length (cm/month) = Change in length / Age at 22to24 months – Age at 2 to 4 months bChange in BMI (kg/m2/month) = Change in BMI/Age at 22 to 24 months – Age at 2 to 4 months cAge at 22 to 24 months – Age at 2 to 4 months
Table 3.14. Analysis of Multiple Regression of BMI at Age 5 Years on Birth Weight and Change in Length (n = 473)
Modela 1 Model 2 Model 3
Variables
β 95% CI β 95% CI β 95% CI
Birth weight (kg) 0.816 0.459 – 1.173*** 0.840 0.487 – 1.193*** 0.186 -1.788 – 2.160
Change in lengthb during infant periodc (cm/month)
1.583 0.668 – 2.498*** -0.082 -5.113 – 4.950
Interaction Term: Birth weight X Change in length 0.511 -1.007 – 2.029
Adjusted R2 0.15 0.17 0.17 aModels adjusted for gestational age, child age, child ethnicity, child sex, maternal pre-pregnancy weight, maternal age, maternal education, maternal smoking bChange in Length (cm/month) = Change in length / Age at 22to24 months – Age at 2 to 4 months cInfant period = 2 to 4 months to 22 to 24 months *** p<0.001
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aChange in Length = Between 2 to 24 months ANOVA, F = 1.72, p = 0.129 Figure 3.8. Change in Lengtha during the Infant Period by Race/Ethnicity
(n = 473)
aChange in Length = Between 2 to 24 months Other PI = Other Pacific Islander Figure 3.9. Change in Lengtha from Birth to 5 Years of Age by
Race/Ethnicity (n = 473)
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aChange in Length = Between 2 to 24 months ANOVA, F = 2.95, p = 0.087 Figure 3.10. Change in Lengtha during the Infant Period by Sex (n = 473)
Figure 3.11. Change in Lengtha from Birth to 5 Years of Age by Sex (n = 473)
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Change in BMI between 2 and 24 months was not associated with BMI at age five
years (Table 3.15) and did not vary by race/ethnicity (range = 0.02 – 0.05 kg/m2, Figure
3.12) or by sex (p = 0.8078, Figure 3.11). BMI plotted over time also shows initial
difference from birth to about one year of age and where there is a decline; and, at two
years of age, race/ethnic difference of patterns of growth visually emerge (Figure 3.13).
There were no statistical differences in BMI between males and females (Figure
3.14). BMI plotted over time by sex varied slightly at the 2 to 4 month BMI and became
equal at age five years (Figure 3.15). Comparisons were made between the Study Aim
2b and 2c samples (n = 473) with children who did not have length measures in Study
Aim 2a (n = 413, Table 3.16). The overall Wald Χ2 was significant (p<0.000), however
differences were between race/ethnic groups but not the main independent variables
(birth weight and gestational age) and BMI at age five years. Study Aim 2b and 2c
samples had more Asian (36.4% vs. 21.9%) and White (8.5% vs. 7.1%) and fewer Native
Hawaiian (32.1% vs 42.3), Other Pacific Islander (18.8% vs. 21.9%, Other (1.7% vs.
3.6%) and Unknown (2.3% vs. 3.3%) children compared with the remaining sample.
Children with early disease conditions and congenital anomalies present at birth
may influence birth size, infant growth, and later BMI. Logistic regression was used to
compare differences in the main variables between those with and without congenital
anomalies for Study Aim 1 (n = 87 of 894). Gestational age (Wald Χ2, 9.29, p = 0.002)
was slightly lower (38.0 ± 3.4 weeks) in the group with congenital anomalies compared
to those without (38.7 ± 1.7 weeks).
In the KPH sample, 10% (Study Aim 1, 89 of 894) met the preterm birth
definition. Sub-analysis using logistic regression was conducted to test if preterm births
influenced the results and no differences were noted. Since there were no major
differences in the results of comparisons with and without congenital anomaly and
preterm subgroups, these children were retained for the purposes of this study and in the
interest of describing the overall KPH child population five years of age. For further
understanding of these relationships, later sub-analyses will be considered.
Table 3.15. Analysis of Multiple Regression of BMI at Age Five Years on Birth Weight and Change in BMI (n = 473)
Model 1a Model 2 Model 3
Variables β 95% CI β 95% CI β 95% CI
Birth BMI (kg/m2) 0.817 0.461 – 1.173*** 0.821 0.463 – 1.178*** 0.850 0.465 – 1.235***
Change in BMIb during infant period (kg/m2/month)
0.400 -1.293 – 2.093 -1.759 -12.336 –8.819
Interaction Term: Birth Weight X Change in BMI 0.669 -2.569 – 3.908
Adjusted R2 0.15 0.15 0.15 aModels adjusted for gestational age, child age, child ethnicity, child sex, maternal pre-pregnancy weight, maternal age, maternal education, maternal smoking
bChange in BMI (kg/m2/month) = Change in BMI/Age at 22 to 24 months – Age at 2 to 4 months cInfant period = 2 to 4 months to 22 to 24 months *** p<0.001
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ANOVA, F = 1.03, p = 0.401 Figure 3.12. Change in BMI during the Infant Period by Race/Ethnicity
(N = 473)
Other PI = Other Pacific Islander Figure 3.13. Change in BMI from Birth to Age 5 Years by Race/Ethnicity (n = 473)
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ANOVA, F = 0.06, p = 0.808 Figure 3.14. Change in BMI Differences by Sex (n = 473)
Figure 3.15. Change in BMI from Birth to Age 5 Years by Sex (n = 473)
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Table 3.16. Comparison of Child Demographics and Main Variables of Study Aim 2b Sample (n = 473) with Sample* without Growth Data within
Specified Time Periods (n = 421)
Variables n = 473 n = 421 Birth Characteristics Birth weight (g) 3.3 ± 0.6 3.3 ± 0.6 Gestational age (weeks) 38.7 ± 1.8 38.6 ± 2.1 Child Demographics Males 240 (50.7) 239 (56.8) Females 233 (49.3) 182 (43.2) Asian 172 (36.4) 92 (21.9) White 40 (8.5) 30 (7.1) Native Hawaiian 152 (32.1) 178(42.3) Other Pacific Islander 89 (18.8) 92 (21.9) Other Race/Ethnicity 8 (1.7) 15 (3.6) Unknown Race/Ethnicity 11 (2.3) 14 (3.3) BMI (kg/m2) 16.0 ± 1.8 16.1 ± 1.8
Overall Wald Chi Square, p<0.000 * Total Sample from Aim 1, n =894
3.4 Study Aim 3 and 4 – Mediation or Moderation by Infant Growth
For Study Aim 3, change in infant weight was not significantly associated with
birth weight (p = 0.385) and including it in the regression model changed the regression
coefficient for birth weight by only 7.6%. Similarly, change in infant length and change
in infant BMI changed the regression coefficient of birth weight by 2.9% and 0.5%
respectively. There was no evidence of mediation of the relationship of birth weight and
BMI at age five years by infant growth. For Study Aim 4, tests of significance for the
interaction between birth weight and change in weight (p = 0.055), change in length (p =
0.509), and change in BMI (p = 0.685) were not statistically significant.
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Adjusted for gestational age, child age, child ethnicity, child sex, maternal pre-pregnancy weight, maternal age, maternal education, maternal smoking *** p<0.001
Figure 3.16. Mediation of Change in Infant Weight on Birth Weight and Child BMI at Age 5 Years Relationship
* p< 0.05, ** p<0.01, *** p<0.001 Adjusted for gestational age, child age, child ethnicity, child sex, maternal pre-pregnancy weight, maternal age, maternal education, maternal smoking *** p<0.001
Figure 3.17. Mediation of Change in Infant Length on Birth Weight and Child BMI at Age 5 Years Relationship
Birth Weight
Change in Infant Weight (100g/mo, 2 to 24 mos)
Child BMI at Age Five Years
Change in Infant Length (cm/mo, 2 to 24 mos)
Birth Weight Child BMI at Age Five Years
0.050 ± 0.057 1.05 ± 0.107***
0.636 ± 0.148***
-0.015 ± 0.018 1.58 ± 0.466***
0.816 ±0.182***
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Adjusted for gestational age, child age, child ethnicity, child sex, maternal pre-pregnancy weight, maternal age, maternal education, maternal smoking *** p<0.001
Figure 3.18. Mediation of Change in Infant BMI on Birth Weight and Child BMI at Age 5 Years Relationship
Birth BMI
Change in Infant BMI (kg/m2/mo, 2 to 24 mo)
Child BMI at Age Five Years
-0.012 ± 0.010 0.400 ± 0.862
0.816 ± 0.182***
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CHAPTER 4. DISCUSSION
This study demonstrates that a higher birth weight and greater change in weight
and length during infancy, but not change in BMI during infancy, are associated with a
higher BMI at age five years. Furthermore, the role of underlying factors that influence
these relationships such as maternal factors, infant feeding, timing and amount of gain
and variation by race/ethnic group during the first five years are discussed.
4.1 Higher Birth Weight, Higher BMI at Age 5 Years
Birth weight is an independent and stronger predictor of BMI at age five years
than birth length or gestational age. In addition to absolute weight, functions of relative
weight for length (weight for length, length2 and length3), adjusted for gestational age,
did not account for additional variance as compared to previous birth weight models.
A higher birth weight was associated with a higher BMI at age approximately five
years, adjusting for gestational age, sex, child’s age, race/ethnicity, maternal pre-preg-
nancy weight, maternal age at pregnancy, maternal smoking, and maternal education.
This study validates the results of previous studies,39, 225 in a multiethnic population,
adjusting for gestational age, and other maternal factors previously not considered or
available. Other factors that were in the causal pathway of birth weight and BMI at age
five years, such as infant growth and feeding, were further explored.
4.2 Higher Change in Weight and Length, Higher BMI at Age 5 Years
A 100 g/month increase in weight during the first two years (gain at the 50th
percentile from 2 to 24 months) was associated with a 1 kg/m2 increase in BMI. The
average duration of time measured for the infant growth period (2.04 – 4.07 months to
22.4 – 24.4 months) ranged from 18.4 – 22.4 total months. According to the Nelson’s
Textbook of Pediatrics, children are expected to gain weight; and, approximate daily
weight gain varies by the age of the child reference. Figure 4.1 provides a visual
depiction of expected weight gain velocity (kg/year) that occurs from birth to adolescence
illustrated by Tanner and Whitehouse.226 Most infant weight gain occurs in the first year
KPH High
Growth Ref
KPH Avg
KPH Low
Figure 4.1 KPH Weight Velocity Plot226 from 1 to 5 Years of Age
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89
year (e.g., 30 g/day from 0 to 3 months, 12 g/day from 9 to 12 months) then drops to 8
g/day in years 1 - 3 and 6 g/day from years 4 – 6.227 If extrapolated over a 24 month
period, growth reference children would gain about 410 g/month (9.8 kg/year), average
rate of weight gain in the KPH sample would be 388 g/month (9.3 kg/year), high rate of
weight gain would be 571 g/month (13.7 kg/year), and low rate of weight gain would be
237 g/month (5.7 kg/year). However, comparability of the KPH rate of weight gain with
the growth reference is questionable since the reference calculated values of daily and
monthly growth 227 are based on data from the Fels Longitudinal Study of primarily
White middle-class children who were predominantly formula fed. Nonetheless, actual
weight gain per month in KPH White children was 313 g/month, similar to the Fels
growth reference of 370 g/month. For the actual present study sample period of 21
months, the mean weight gain was about 280 g/month.
Very few studies have examined rate of infant weight gain as a continuous
variable. However, a continuous variable would be representative of the biology of
growth and can provide a basis for clinical application. Stettler et al. examined infant
weight gain in g/month and reported that for every 100 g/month increase between birth
and four months, risk for child obesity at seven years, or later, increased by 17% after
adjustment for weight at one year.110 In comparison with the current study time period (2
to 24 months), a sustained increase of 100 g/month weight may increase risk for later
overweight, since this is over a longer period of time.
Another study completed with pediatric health maintenance records (n = 129)
examined growth data as a continuous variable.228 Using receiver operating
characteristic (ROC) curve analysis, Gungor et al. determined a category of “risky”
infant weight gain, which was selected after examining sensitivity and specificity in a
period where child overweight risk was best predicted. Infants defined as “at-risk”
gained 8.2 and up to 10.2 kg between the ages of 0 to 24 months. Interestingly, 31.4%
of at-risk children became overweight; and the rest were defined as “resilient” (68.6%).
The child overweight prevalence in the cohort was 24.8% and increased to 31.4% in at-
risk infants. Characteristics of resilient children included lower weight gain during this
period, parents who were more educated, being exclusively breastfed for six months or
longer and introduction of solid foods at a later time. This study highlights the
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importance of other behavioral factors that predict rate of growth during this time period
that are crucial to later development (after infancy). The authors state that 8.2 kg is
certainly not considered rapid in two years since there are infants who would still track
on a centile line at this weight. However, they still find it appropriate to define this
threshold as “risky” growth. In addition, they justify that the high prevalence of
overweight in their study indicates that growth does not have to be rapid to be risky and
another recent study supports this notion that “normal infant growth is not without
risk”.229 This study was limited in that it had a relatively small sample (n = 129).
Using similar methods, Toshke et al. assigned risk for child overweight using the
best combined sensitivity and specificity which was at 9.8 kg (higher than the 8.2 kg
threshold).109 One difference of both of these studies compared to the present study is
whether the ROC method would provide the same threshold amount of weight gain and
optimal screening interval during childhood for a multiethnic population. This study’s
population had a mean total of about 5.9 kg gain (280 g/month x average of 21 months)
in the first 21 months. Over an estimated equivalent period of 24 months, KPH children
on the high end of the range grew approximately about 13.7 kg or 3.9 to 5.5 kg more than
the above 8.2 kg or 9.8 kg thresholds.109, 228 In comparison with a sample of French
children (n = 1,582), KPH children grew at a faster rate of 388 g/month compared with
285 g/month over the first two years of life.230 Risk for overweight at ages 7 to 9 years
was associated with 246g/month weight gain in boys (OR for 100 g/mo = 2.06, 95% CI:
1.22 - 3.48) and weight at one year (9.4 kg) in girls (OR for 1 kg = 2.24, 95% CI: 1.37 -
3.66) average variation in monthly weight gain (-13.8 g in boys and – 11.8 g in girls) or
change in velocity slope (OR for 1 g = 1.13, 95% CI: 1.04 - 1.22).230 These studies
provide reference for comparison and understanding infant growth in terms of weight
gain in grams per month, race/ethnicity, and sex differences in study populations.
In the current study, a higher length gain during the infant period was associated
with a higher BMI at age five years. A similar finding was noted from birth up to 1 year
of age in a study of Mexican children where change in length-for-age z score was
positively associated with BMI z-score at age 4 to 6 years (p < 0.01).31 Length is a part
of the BMI measure and children continue to gain in length based on their genetic
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potential during these first two years, even though weight and length gain may decelerate
during the 2nd year.
4.3 Change in BMI in the first 2 years was not associated with BMI at age 5 years
The BMI measure consists of weight adjusted for length. This measure illustrates
the natural deceleration or ‘slowing down’ of growth that occurs during the second year
of life; however, if observed as separate measures of infant weight and length gain, the
gradual increase indicates continuation of normal growth.
4.4 Effect of Birth Weight on BMI at age 5 years is not mediated by Infant Growth in the First Two Years
Change in infant weight, length, or BMI did not play a role as mediator for the
effect of birth weight and BMI at age five years. It has also been hypothesized that birth
weight is a mediator itself in the pathway of maternal factors and later child overweight.
In previous studies, levels of birth weight [e.g., small-for-gestational- age (SGA)
or appropriate-for-gestational-age (AGA)] and infant weight gain modified the effect of
birth weight on BMI at age five years.14 In this study, the interaction terms that included
birth weight and change in infant weight gain, length, and BMI were tested; however, for
three separate tests for interaction, a Bonferroni adjustment would require a p-value of
0.05/3 = 0.0167. The smallest of the three p-values was 0.058, which is not significant.
It is also possible that an effect of different levels of birth weight or infant growth
on BMI at age five years may not be detectable due to a canceling out of effects due to
differences by race/ethnicity. Whites and Other PI children were born with larger birth
weights than other groups; however, Whites grew much faster during the infant period,
whereas the Other PI group seems to pick up their growth rate after two years.
4.5 Birth Weight, Change in Infant Weight, and BMI at Age 5 Years vary by
Race/Ethnicity The effect of biological differences that exist between race/ethnic groups and the
burden of clinical measurement are evident. A recent study reported that South Asian
infants in Canada were being misclassified as underweight at birth or small for
gestational age, which then facilitated further tests and possibly unnecessary stress for the
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infant and parents.231 The authors have since developed birth weight curves for the
maternal world region.232 Another study done at KPH included more screen positives in
genetic sonograms due to shorter long bone measures in non-white ethnic groups.233
Birth weight varied by race/ethnicity between the Asian and Other Pacific
Islanders, 3.21 vs. 3.37 kg, p = 0.03. Change in infant weight reflected a similar pattern
of birth weight differences in race/ethnic groups (Figure 4.2). White children had a
higher mean birth weight and change in infant weight compared to Asian and Other PI
children.
Other PI and White children had higher birth weights than other race/ethnic
groups. However, change in infant weight was higher in White children than Other PI. It
appears that Other PI children experience a slower change in infant weight in the first two
years and have more weight gain in the following three years after the infant period,
compared with other race/ethnic groups (Figure 4.3). In another study, Rush and
colleagues reported that Pacific Islander children were born heavy, had faster weight gain
over four years, and faster height gain between two and four years.234
Figure 4.2. Birth Weight and Change in Weight shared a similar pattern by Race/Ethnicity
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Figure 4.3. Change in Weight varied by Race/Ethnicity and Age in Months
Exploration of BMI plotted over time provides a visual presentation of race/ethnic
differences and timing of these differences (Figure 4.4). Other PI and White children
appear to experience a slower BMI deceleration with age compared with Asian, Native
Hawaiian, Other, Unknown, which corresponds with the reported change in BMI values
Figure 4.4. Visual Plots showing Different Trajectories by Race/Ethnicity
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noted in Figure 3.12 (Other PI, -0.02 kg/m2 ,White, -0.03 kg/m2, Asian, Other,
Unknown -0.04 kg/m2, Native Hawaiian -0.05 kg/m2). From two to four months of age
there is a notable difference in deceleration patterns of BMI between race/ethnic groups.
White children decrease at a slower rate until about two years of age and then decrease to
a lower BMI compared with all race/ethnic groups at age five, whereas the Other PI
children markedly increase in BMI from 2 to 5 years.
Asian, Native Hawaiian, and Unknown children also decreased or maintained
their BMI from 2 to 5 years. The Other race/ethnic group mimics the pattern of Other PI
but ends at a lower BMI at age five years. Other Pacific Islanders had a significantly
higher BMI at age five years compared with Asian and White children (16.5 vs. 15.9
kg/m2, p = 0.005 and 15.5 kg/m2, p = 0.002). Native Hawaiian children were also
significantly higher in BMI than White children (16.2 and 15.5 kg/m2, p = 0.044).
The “adiposity rebound” period, is a term coined by Rolland-Cachera,235
describes the period when there is a decrease in BMI until it reaches its nadir and then
starts to increase again. Studies have described the association of an early adiposity
rebound with later overweight due to children gaining more weight over a longer period
After about one year of age, BMI decreases through the preschool years until it reaches
its nadir, which usually occurs between 5 to 7 years of age.235 It is interesting to note that
the “adiposity rebound” period appears to be occurring earlier for the Other PI and Other
groups as designated by the circle around age two years. The remaining race/ethnic
groups seem to experience their adiposity rebound at the usual period of ages 4 up to 7
years.
It has also been demonstrated that the earlier adiposity rebound results in a gain in
fat mass versus lean mass in girls.236 Type of body mass (lean and fat mass) that is
gained by sex and race/ethnic group may also vary dependent on timing of adiposity
rebound. The adiposity rebound period was not an aim of this study due to the need to
have more growth points past five years of age to identify the age adequately at which
this occurs. The longitudinal plot of BMI provides a visual explanation for differences
between race/ethnic groups that require further study.
The interplay of biology and environmental or lifestyle factors after birth may
influence future growth trajectories. The growth trajectory of the first two years may be
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influenced by prenatal experience and enhanced by exposure to first foods (e.g.,
breastmilk, formula) and transition to solids/juices/dairy milk and development of food
preferences that assist in reaching the genetic potential. The growth trajectory after two
years of age is then coupled with continued exposure to new foods (e.g., introduction to
table foods) and new environments, such as preschool, that introduce an added layer of
factors that influence behavior.
In the Feeding Infants and Toddlers Study (FITS) a large percentage of calories
from table foods were consumed in the form of desserts / daily candy and French fries,
which was the most reported vegetable at age 18 to 24 months. Family structure (older
siblings, single parent) and lifestyle “cast a mold” for these exposures.237 For example,
busy lifestyles that include after school activities call for more fast food consumption on
school nights, thus increasing exposure to easy, soft finger foods such as french fries.
Cultural factors may influence food introduction and selection, which would
impact food preferences in the next three years, up to age five years. Mothers’ food
selection and preferences are also influential at this point. In particular, mothers who are
already overweight may have established unhealthy eating and physical activity habits
that are passed to her child.
4.6 Maternal Pre-Pregnancy Weight and Child BMI
A higher pre-pregnancy weight was associated with a higher BMI at age five
years and was a confounder of the relationship of birth weight and BMI. Pre-pregnancy
weight was a better predictor of child BMI compared with maternal height and BMI.
Younger maternal age was associated with higher BMI at age five years. However, when
adjusted for maternal education the effect was attenuated. Maternal nutrition has also
been related to maternal obesity during pregnancy238 which is intuitive because there
could be a connection between maternal feeding habits and formation of infant to child
feeding habits. Indicators of maternal lifestyle factors, starting with breastfeeding
exclusively, financial situation, work environment, and education, are also key to
formation of healthy habits of mother and infant.
Adjusted for gestational age, absolute mass of the infant at birth (birth weight)
was more highly associated with child’s BMI at age five years, than BMI at birth or other
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measures of relative weight (weight/length, weight/length2, weight/length3). In addition,
maternal pre-pregnancy weight (r = 0.29) had a higher correlation with child’s BMI at
age five years than maternal BMI (r = 0.27).
Maternal weight gain would intuitively be a possible mediator in the relationship
of pre-pregnancy weight and BMI at age five years which is also indicative of the
importance of maternal weight. However, maternal weight gain was not a predictor of
child BMI at age five years in this study. It is also plausible that the association of
maternal pre-pregnancy weight and child BMI at age five years is an indicator of the
influences of a shared environment and dietary habits of the mother that supports
development of later overweight in children of school-age.239
4.7 Clinical applications
Previous studies have defined growth as rapid when it reaches > 0.67 SD, which
is a measure of the SD between the percentiles of the United Kingdom (UK) growth
chart. It can be argued that the reference UK growth chart is different from the current
CDC/WHO growth chart, and therefore the > 0.67 SD may not be a good marker for
crossing percentiles in the US. Clinicians typically use upward crossing of two or more
percentiles for weight-for-length or BMI as an indicator of risk for later child obesity. In
the interest of clinical application, a longitudinal study conducted at another multi-site
clinical practice examined the growth of children who crossed upwards of two or more
weight-for-length percentiles in the first 24 months. These children had two times the risk
of developing obesity at age five years (OR = 2.08, 95% CI: 1.84 – 2.34).240 Obesity
prevalence at five years was highest (32.9%) in children who crossed percentiles in the
first six months compared with 6 and 12 months (29.7%), 12 to 18 months (32.0%) and
18 to 24 months (31.8%). The assessment of crossing percentiles was not a part of the
present study aims. However, this is a future step for describing the KPH population for
local clinicians.
4.8 Strengths and Limitations
This sample provides a longitudinal look at early factors related to later child
BMI, which is a strength of these data. These data were not collected for research
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purposes but rather for the health care of members. Thus data measurement and entry
error is expected. Methods were developed for data cleaning, inspecting growth data, and
processing data for use in this research study. However there are certain issues that are
beyond our control and should be acknowledged. For example, when measuring lengths
of infants, they should be lying supine length up through age 24 months. It is possible
that if the child was able to stand, clinical staff might measure the child’s standing height,
which could affect child measures by 0.4 to 2.3 cm from 18 – 36 months 201, 202 This is a
limitation that is acknowledged; however only weights/lengths up through 24 months
were examined, which would decrease the number of children measured using standing
height.
Ethnic data are self-reported and gathered from several sources for the EMR to
assure completeness of the data. Coming from different sources may introduce bias
although most of the data now come from the personal history sheet and are coded by a
standard method for the VDW. Another issue is the difficulty in separating mixed ethnic
groups, such as the use of Native Hawaiian even if persons are part-Hawaiian combined
with any other race.
A strength of this study is the access to actual measures versus reported measures
for birth information and for maternal weights and heights. The KPH EMR allows for
capture of diagnostic and medical information that is comprehensive and contains
standardized language. This allows for control of factors such as GDM unavailable in
other data sets. Self-reported data are common among other studies and are associated
with subject over- and under-reporting. Underestimated maternal pre-pregnancy weights
have been shown to overestimate associations of maternal BMI and birth outcomes.146 A
suggestion is to be able to link pregnancy data with measured pre-conception weights
from medical records or to obtain weights. The limitation, however, is the dependency
on regular attendance at visits and the need for medical care to be captured in the KPH
EMR. For example, maternal information is often not available before women know that
they are pregnant, unless they had a recent pregnancy or came in for sick care.
Children with congenital anomalies present at birth or born preterm may influence
birth size, infant growth, and later BMI. Use of EMRs provides the ability to look at
these factors. Only one other study reviewed was able to exclude children with
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conditions that may influence infant growth.116 That study conducted a subanalysis
excluding children (n = 87 of 888, 9.8%) with similar conditions: systemic disease
(5.38%), chromosomal or congenital abnormalities (3.84%), or abnormalities of heart
(0.11%), gastrointestinal tract (0.46%) or urogenital tract (0%). Preterm births (defined
as < 37 weeks gestational age) are also associated with altered growth trajectories.241 In
the United States, preterm births decreased to 12.2% in 2009 from 12.8% in 2006.242 In
this sample no major differences were noted between those with congenital anomalies
and preterm births and for those without these conditions.
The present study did not include birth weight as part of the estimation of change
in infant growth. This was done in order to examine the effect of birth weight, birth
length and birth BMI on infant growth adequately. Most studies have measured change
in infant growth by including the birth measure, which introduces error if birth weight is
an independent variable and is part of the intermediate variable, in this case, infant
growth. However, it could be a limitation that the first large gains during infancy in the
first couple of months of growth were not included.
Infant time periods vary in terms of growth with most weight gain occurring in
the first year. This study did not examine infant time periods of growth and relation to
BMI at age five years. Previous studies have determined weight gain during certain
time periods during infancy were associated with later child overweight. Other studies
have found no difference between 0 – 6, 6 – 12, 12 - 18 months.230
It is difficult to quantify a meaningful value of BMI at age five years and
associated later health risk since there is not enough research to inform clinical use of
BMI as a diagnostic tool for later disease risk in children. However, we can classify
BMI into categories of overweight/obese, which are known risk factors for early
maturation, development of insulin resistance, and other child co-morbidities. Use of
BMI category as a screening tool for individual risk is “poor”.243 The link of BMI
during childhood with metabolic disturbances, such as insulin resistance, triglycerides in
later adulthood, is not clear.243 However, BMI is a proxy for adiposity which is a
determinant of insulin resistance. 243, 244 Therefore it is suggested that we move in the
direction of adiposity standards by gathering direct measures of body fat.243
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A major limitation is the timing of data pulled from the KPH EMR in 2010. This
was done to obtain a retrospective sample of children born in 2004 and 2005. The EMR
was not complete until 2005 and Obstetrics/Gynecology department electronic entry was
done later than this. Information was collected mostly on paper and entered at a later
time. This limited the sample numbers, based on availability of data for specific maternal
variables. It is possible that women are seeing their PCP before pregnancy; then, once
pregnant, they will see an OB/GYN (pre-pregnancy vs 1st pregnancy). Therefore we find
fewer first pregnancy weights, due to incompleteness of OB/GYN electronic notes in the
years at which data are pulled. However, the first pregnancy weights available were not
always in the first trimester; therefore, pre-pregnancy weights were used to calculate
BMI. This also made calculation of GWG a challenge since the actual duration of weight
gain varied largely between participants who had a first and last weight. For this reason,
maternal weight gain provided on child birth certificates was used in a subanalysis.
The information gathered on infant feeding is limited and accessible only via the
text as Smart Set phrases in the doctor’s electronic notes. The contribution of exclusive
breastfeeding to child BMI cannot be demonstrated in this study and requires further
study. This is due to the limits of defining breastfeeding through text information.
Further work in the EMR needs to be done to be able to separate out breastfeeding and/or
formula feeding, which is also noted to be important to differentiate because formula fed
babies grow faster than breast-fed babies.166, 245 Also the protein content in formula is
higher than in breast milk,245 which may have deleterious effects, including increased
insulin growth factor 1 secretion, which is associated with faster weight gain and higher
adiposity.245 Overfeeding is also a known concern for excessive caloric intake via bottle
or through added calorie sources such as infant cereal.108, 246, 247
Early introduction of solid foods post-weaning is also associated with later child
overweight.248-250 Capturing this information in the EMR is a challenge because it also
falls within Smart Set text indicated by physicians. This is also complicated by lack of a
standardized way of capturing this information. Mothers will feed early based on their
perception of infant hunger or large size of infant,250, 251 which may be more harmful than
helpful in terms of overfeeding. The foundations for flavors and textures are set early, as
children transition from a liquid diet in early infancy. Children should experience a
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variety of foods and foods should be nutrient-rich. They should also be provided with
repeated opportunities to try different foods so infants can learn to like the taste of a new
food.108, 252, 253
Infant sleep patterns and excess television viewing have also been associated with
child overweight.107, 254, 255 This is information that was also unavailable through the
EMR. In addition, diet and physical activity patterns during childhood could influence
BMI at age four to six years. Unhealthful eating habits may be highly influenced by what
is provided in the first two years of life.
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CHAPTER 5. CONCLUSION
This retrospective, longitudinal study examined the role of birth size and infant
growth with child BMI at age five years. Birth weight adjusted for gestational age was
an independent predictor of BMI at age five years but not birth length or functions of
relative weight-for-length. Maternal pre-pregnancy weight, birth weight, and change in
infant weight and length, predicted child BMI at age five years. The relationship of a
higher birth weight and higher child BMI at age five years was not explained by change
in infant weight, length, or BMI. It is plausible that birth weight may serve as a mediator
for the prenatal and maternal factors and later child BMI period.
Tests for linearity showed no evidence of a U-shape relationship after including
squared terms of birth weight in the model. This justifies accepting the linear
relationship between birth weight and child BMI at age five years. Absolute pre-
pregnancy weight of the mother and the child’s birth weight as opposed to maternal BMI
and child birth length were better predictors of child BMI at age five years.
Race/ethnic differences in birth weight and change in infant weight were observed
between Other PIs and White and Asian race/ethnic groups. Exploratory plots of BMI
trajectories among race/ethnic groups suggest differences after age one year. This may
be an indicator of other unmeasured factors (e.g., transition to solid/table foods) that
that differ by race/ethnic groups that should be explored.
5.1 Public Health Implications
The significance of this observational study is identification and validation of key
markers for early prevention of child overweight before age five years. The health care
system can be used to identify and target these key areas for intervention. Application of
these results would be beneficial to clinicians for improving their assessment and
identification of those at risk for child overweight. The known presence of race/ethnic
differences in birth size and growth patterns emphasizes the importance of reconsidering
how standards apply to these groups and whether there should be closer examination
based on these differences.
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The infant time period of birth up to age 24 months is heavily influenced by the
immediate family environment which then transitions to the external environment during
preschool and school age years. This may be an influential time to modify and instill
dietary and lifestyle changes in the family to set the child on the right path for health.
Population-wide benefit of prevention of chronic disease can begin with the
exploration of the improvement of the health of women of child-bearing age prior to
pregnancy. A healthy lifespan perpetuates future health in children who become healthy
men and women. Healthy mothers have healthy babies. Preparing young woman for a
healthful lifestyle can result in a ‘trickle-down’ effect of health through her own future,
her future children and other family members. Attention to the presence of these key
factors may provide targets for early intervention.
5.2 Future Studies
Use of the KPH EMR to assess risk for child overweight with consideration for
maternal pre-pregnancy weight, birth size, infant growth, and child overweight is needed.
Further assessment of the predictive ability of these factors would be beneficial for
clinical application and guidance for KPH physicians in improving prevention of child
overweight. In the identification of child overweight, further research on race/ethnic-
specific clinical assessment would be helpful. Validation of the use of standard methods
for assessment of growth using the new WHO growth charts in the KPH population could
provide further information on race/ethnic differences. Marked differences in body size
and growth trajectories in Pacific Island children were compared with WHO growth
standards of 2006,207 indicating race/ethnic differences in growth. Examination of
differences in adiposity or fat mass gain in race/ethnic groups would be additive to BMI
screening and could assist in predicting the onset of early pubertal development. In
addition, this expanded screening would prevent future deleterious metabolic risk factors
in early adolescence. Exploration of non-invasive anthropometric measures (e.g.,
skinfolds) to indicate fatness at an early age and reference standards for the KPH
population should also be considered.
Patterns of good health start early and are likely to be dependent on previous
exposure and experience. A healthful lifestyle during the child-bearing age can be
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influential on the next pregnancy period, and later infant/toddler/family health. Early
intervention should begin with an understanding of the needs during this time period.
Since maternal pre-pregnancy weight was a primary indicator of child BMI at age five
years, assessment of the feasibility of a ‘well-woman’ or preconception care early
prevention program is needed. Preconception care promotes the health of women of
reproductive age with the goal of improving pregnancy related health outcomes.256 The
goal is to provide risk screening, health promotion, and interventions as a part of routine
health care. A needs assessment would be helpful in understanding current practices at
KPH that support preconception care and exploring the feasibility of developing a
program further. An example of this would be to include designing an early intervention
for changing health behaviors in preparation for pregnancy in overweight women.
Furthermore, determining an innovative and effective way to deliver this program should
be explored by engaging women to be a part of the planning, which would also assure
successful implementation and sustainability. Attitudes and perceptions of health, as well
as motivators and barriers to obtain good health, should be assessed. In these modern
times, women multi-task and have busy lifestyles, which calls for a different approach to
supporting healthy behaviors in order to achieve good health. Assessment of different
media or health technology efforts that are tied to the health care system that would
facilitate preconception care should be assessed in order to create a sustainable program.
Race/ethnic differences in seeking health care and socioeconomic factors should also be
explored. Providing guidance and rationale for formation of these early health habits are
key during this stage in life and will support the maintenance of these skills and modeling
for their children.
Optimal maternal health during pregnancy is conducive to a healthy pregnancy
and baby. Future studies should focus on interventions that target maintenance of
healthful weight gain based on current IOM guidelines.215 Maternal nutrition is related to
maternal obesity. Creation of new methods for teaching or supporting healthful food
choices and prescribed activity should be explored. During the transitional periods of
infant feeding (from breast to introduction of solid foods, in late infancy and from the
toddler to preschool age) introduction to new tastes and environments are additional key
areas to target. During this time, children are still largely influenced by their parents and
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what their parents eat. Evaluation of the depth of parents’ knowledge upon leaving the
health care system of what to feed or how to feed their child should be assessed. For
example, repetitive feedings of fruit or vegetables for a period of time is important to
increase exposure and acceptance of these foods.253 Future studies should also assess the
adequacy and availability of learning tools that are essential to mothers and/or fathers in
order to begin healthful feedings that are compatible with their culture, family structure
(e.g., single parent, siblings), and lifestyle.
Furthermore, consideration for future exploration of infant feeding information
gathered by the KPH EMR and areas for improvement could be beneficial to improving
health care. A future secondary analysis could involve a deeper understanding of race/
ethnic, infant feeding, and maternal information in the improved KPH EMR. The most
recent years will have more complete information and can provide a validation of gaps in
capturing the information which might be indicative of the need for intervention.
It is imperative that children have healthy beginnings that will foster and enable
their development into healthy adults. Early identification of determinants of child over-
weight is part of primary prevention to deter unfavorable consequences of subsequent
obesity. Early intervention can instill perpetuation of healthy habits in mothers who can
model and support a healthy start to life for their child and for future generations.
105
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