Accepted Manuscript
Effects of prenatal exposure to cigarettes on anthropometrics,
energy intake, energy expenditure, and screen time in children
Jameason D. Cameron, Éric Doucet, Kristi B. Adamo, Mark
Walker, Alessandro Tirelli, Joel D. Barnes, Kaamel Hafizi, Marisa
Murray, Gary S. Goldfield
PII:
DOI:
Reference:
S0031-9384(18)30370-6
doi:10.1016/j.physbeh.2018.06.020
PHB 12239
To appear in:
Physiology & Behavior
Received date:
Revised date:
Accepted date:
5 March 2018
7 June 2018
14 June 2018
Please cite this article as: Jameason D. Cameron, Éric Doucet, Kristi B. Adamo, Mark
Walker, Alessandro Tirelli, Joel D. Barnes, Kaamel Hafizi, Marisa Murray, Gary S.
Goldfield , Effects of prenatal exposure to cigarettes on anthropometrics, energy intake,
energy expenditure, and screen time in children. Phb (2018), doi:10.1016/
j.physbeh.2018.06.020
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ACCEPTED MANUSCRIPT
Effects of prenatal exposure to cigarettes on anthropometrics , energy intake, energy expenditure,
and screen time in children.
Jameason D. Cameron1 , Éric Doucet2 , Kristi B. Adamo1,2 , Mark Walker2,4 , Alessandro Tirelli2 , Joel D.
Barnes1 , Kaamel Hafizi2 , Marisa Murray2 and Gary S. Goldfield1,2,3
1
Children’s Hospital of Eastern Ontario Research Institute, Ottawa, Canada
University of Ottawa, Ottawa, Canada
3
Carleton University, Ottawa, Canada
4
Ottawa Hospital Research Institute, Ottawa, Canada
Send correspondence and reprint requests to:
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Jameason Cameron, Ph.D.
Children’s Hospital of Eastern Ontario
Ottawa (ON), Canada, K1H 8L1
Phone: 613-737-7600 ext.: 4103
Fax: 613-738-4800
E-mail: jcameron@cheo.on.ca
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Abstract
Background
Maternal prenatal smoking is associated with downstream childhood obesity. Although animal research
suggests reduced resting energy expenditure (REE), decreased physical activity (PA), and increased
energy intake as mechanisms, these relationships are unclear in humans. The objectives were to
examine the association of prenatal maternal smoking with non-volitional energy expenditure (REE
daily sedentary behavior (SB)), and screen time
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(LPA), daily moderate-to-vigorous PA (MVPA),
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and the thermic effect of feeding [TEF]), child adiposity, energy intake, free-living PA (daily light PA
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(television and computer/video game) in children.
Methods
As part of a longitudinal study, 46 children (n=27 controls and n=19 smoking exposed) with mean age
years were recruited. Body weight and composition (Bioelectrical Impedance), height
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7.6±2
(Stadiometer), waist circumference (cm; tape), BMI (kg/m2 ), REE (kcal/day; indirect calorimetry), PA
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(minutes; Accelerometry), screen time (hours; self-report) and ad libitum energy intake (lunch buffet;
7-day food log) were measured. Effects sizes were evaluated using Cohen’s d.
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Results
Relative to controls, after controlling for age and family income, children who were exposed to
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cigarette smoke in utero exhibited greater waist circumference (p=0.04, Cohen’s d=1.03),
percent
body fat (%BF; p=0.02, Cohen’s d=0.97), and a trend for BMI (p=0.05, Cohen’s d=0.86). Exposed
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children did not differ in REE (trend for lower: p=0.1, Cohen’s d=0.42) or TEF but were shown to have
significantly higher ad libitum energy intake (p=0.02, Cohen’s D=0.70) from the palatable lunch
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buffet, but not from the out of laboratory 7-day energy intake (p=0.8). Examining screen time
behaviors, exposed children spent more time watching television during the week (p=0.03, Cohen’s
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D=0.82), and overall television watching (p=0.02, Cohen’s D=0.80); there were no group differences in
any other screen time behaviors.
Conclusions
Children exposed to cigarette smoke in utero exhibit greater adiposity, and this exposure may have as
contributing factors higher screen time, ad libitum energy intake, and a trend for reduced REE. The
data suggest that lifestyle factors such as diet and screen time represent targets for obesity prevention in
a high-risk population of young children exposed to prenatal cigarette smoke. Findings also highlight
the need for smoking cessation programs to reduce downstream obesity in offspring.
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Keywords: Prenatal Smoking, Resting Energy Expenditure, Energy Intake, Physical Activity, Body
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Composition, Sedentary Behavior
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Introduction
Recent epidemiological data indicate that approximately one-third of North American children
are overweight or obese (Ogden, Carroll et al. 2014). These rates represent a serious public health crisis
given that obesity tracks from childhood into adulthood and obesity increases the risk of morbidity and
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mortality (Tremblay and Willms 2000). Importantly, children with higher range BMIs, as early as 24
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months, are more likely to be overweight at age 12 (Nader, O'Brien et al. 2006), and approximately
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80% of obese adolescents become obese adults (Daniels, Arnett et al. 2005). Since therapeutic
interventions for children and adults living with obesity are costly and frequently ineffective over the
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long-term (Jeffery, Drewnowski et al. 2000; Goldfield, Raynor et al. 2002), identifying modifiable
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determinants is a vital step in advancing the prevention of childhood and adulthood obesity and its
comorbidities. Although increased public awareness of the dangers of prenatal cigarette smoke
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exposure has resulted in a significant decline in the rate of maternal smoking in the United States, with
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estimates suggesting that 8% of pregnant women in the US smoke during their pregnancy (Curtin and
Matthews 2016), cigarette smoking is still the number one cause of preventable disease and death in the
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US (Prevention 2005). In contrast to the widely accepted observation that smoking during pregnancy
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reduces fetal growth (Lassen and Oei 1998; Kramer, Platt et al. 1999), the course of subsequent
postnatal development of children born to smokers is less definitive. A few early studies reported
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persistent diminished growth in the offspring of maternal smokers (Fogelman and Manor 1988;
Rantakallio 1993) but, in the preponderance of recent, well-controlled research, the growth deficits of
the infants born to smokers was not observed beyond the first year (Day, Cornelius et al. 1992; Jones,
Riley et al. 1999; Ong, Preece et al. 2002). In fact, several systematic reviews and meta-analyses have
identified maternal smoking during pregnancy as a risk factor for obesity in the offspring, suggesting
an increase in the odds of child overweight and obesity by up to 50% (Oken, Levitan et al. 2008;
Rayfield and Plugge 2016). This so-called “catch-up” during infancy has been viewed by some as
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compensation for intrauterine growth restraint induced by maternal smoking (Ong et al. 2002), but the
mechanisms that signal this enhanced growth rate are unknown. This theoretical postulation of “catch
up” growth is appealing, and there is biological evidence from animal work suggesting mechanisms,
such as alterations in energy metabolism and appetite signaling. In this way, fetal adaptation to
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maternal smoking may result in long-lasting alterations and subsequent weight gain.
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Animal models of prenatal nicotine exposure that reproduce the plasma levels of nicotine found
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in typical smokers provide compelling evidence for a mechanistic connection between maternal
smoking and metabolic abnormalities contributing to the subsequent risk of obesity in the offspring.
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These mechanisms converge on two factors that play a major role in the regulation of metabolic
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activity, namely the sympathetic nervous system and pancreatic function. In a series of papers
investigating the offspring of pregnant rats who received nicotine, Slotkin and coworkers demonstrated
norepinephrine content and
impairment of tonic noradrenergic activity in peripheral
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reduced
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sympathetic pathways, including renal, lung, hepatic and cardiac inputs (Navarro, Seidler et al. 1989a;
Navarro, Seidler et al. 1989b; Navarro, Mills et al. 1990; Slotkin, Saleh et al. 1997). These effects were
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seen even at exposures comparable to those in light smokers (Navarro et al. 1989b). Further, the
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sympathoadrenal system of the nicotine-exposed offspring failed to be activated by standard stressors
that stimulate important cardiovascular and metabolic homeostatic adjustments (Navarro et al. 1990;
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Slotkin et al. 1997) and instead, parasympathetic pathways, which reduce metabolic activity, were
inappropriately activated (Slotkin, Epps et al. 1999). Importantly, the relationship of these animal
findings to offspring of human smokers was confirmed by studies of impaired sympathoadrenal
activation in newborn babies born to smokers (Slotkin et al. 1997). Adrenergic sympathetic innervation
is critical for the regulation of flux of free fatty acids, namely, lipogenesis and lipolysis. Additionally,
recent reports from rats have noted that fetal and neonatal exposure to nicotine resulted in postnatal
metabolic changes consistent with obesity and type 2 diabetes in humans, including increased adiposity
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and impaired glucose homeostasis (Holloway, Lim et al. 2005). Collectively then, research using
animal models is suggestive of biological and behavioral alterations, as evidenced by attenuated energy
metabolism, as well as changes in feeding-related signaling at the level of the hypothalamus (Grove,
Sekhon et al. 2001). Indeed, in vivo work in the newborn rhesus macaque has shown that chronic
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maternal nicotine exposure alters neuronal systems at the arcuate nucleus of the hypothalamus, and
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similar alterations have been shown to increase energy intake in other animal models (Grove, Brogan et
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al. 2001). Indeed, altered appetite regulation may underlie the long-term impact of maternal smoking
and the direct influences of metabolites of cigarette smoke on the increased weight of offspring.
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This human study was designed to identify differences in energy balance variables that may
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differentiate the offspring of smokers from the offspring of non-smokers during pregnancy. To our
knowledge, there has yet to be a human study whose main objective was to examine the association of
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prenatal maternal smoking with resting energy expenditure (REE), energy intake, anthropometrics, and
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free-living physical activity in young children. Examining these relationships is critical to identifying
early modifiable risk factors for downstream child obesity and may provide preliminary information on
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the degree to which an attenuated energy expenditure or possibly increased energy intake may explain
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the link between prenatal smoking and child adiposity. It was hypothesized that children prenatally
exposed to cigarettes would exhibit greater adiposity, lower REE, and higher total energy intake when
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compared to those not prenatally exposed, and that these differences in energy balance variables would
have a stronger association with adiposity in exposed children compared to controls. Secondary
objectives were to examine the association between in-utero cigarette exposure and postprandial
energy expenditure (i.e. thermic effects of food (TEF)), free-living physical activity ((daily light PA
(LPA), daily moderate-to-vigorous PA (MVPA), or daily sedentary behavior), and screen time
behavior (television and computer/video game). It was hypothesized that children exposed in utero to
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cigarette smoking would exhibit lower TEF as well as higher sedentary time and more television screen
time when compared to unexposed children.
2. Materials and Methods
2.1 Subjects
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As part of a longitudinal study 46 children (n=27 controls and n=19 smoking exposed) with
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mean age 7.6±2 years, body weight 29.3±9.4 kg, REE 1139±302 kcal/day, BMI 17.5±2.6 kg/m2 , and
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body fat 18.4±6.5 % were recruited. Recruitment was performed using women and children who were
recruited into the Ottawa-Kingston Birth Cohort (OaK). This prospective cohort study initiated in 2003
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consisted of mother-baby pairs that were recruited at 12-20 weeks gestation at their prenatal visit at the
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Ottawa Hospital, thus providing a unique opportunity to study the effects of maternal behaviors during
pregnancy on a variety of outcomes in the offspring. Further details about the cohort of pregnant
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women have been published elsewhere (Wen, Chen et al. 2008). Once the OaK contact list was
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exhausted (n=8 exposed and n=17 controls), we extended recruitment out to the greater Ottawa
Community, where we recruited an additional 21 participants (n=11 exposed and n=10 control). Of
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note, there were no significant differences between the OaK and Community groups for gestational
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weight gain, gestational weeks, birthweight of the child, or child anthropometrics; there was a
significant difference in age, where the mean age for OaK was 6.5±1.8 years, whereas the mean age for
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the Community was 8.5±1.8, p=0-01, Cohen’s D=0.2 (results not shown). Given the homogeneity of
sample characteristics, the OAK and community groups were combined to maximize power. Smoking
during pregnancy was defined as those reporting smoking 1 or more cigarettes per day during weeks
12-20 of gestation, distinguishing smokers from non-smokers. This operational definition was used in
several studies that found an association between prenatal cigarette exposure and child overweight and
obesity in our target (6-9 years) population (Toschke, Koletzko et al. 2002; Wideroe, Vik et al. 2003).
The validity of information on self-reported maternal smoking provided by patients in our OaK cohort
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has recently been compared participants’ to maternal and umbilical cotinine levels, and agreement was
high (Perkins, Belcher et al. 1997).
The main inclusion criteria were: pre-pubertal (assessed with Tanner staging) children of both
sexes between the ages of 6-11 years, not currently participating in a weight control program, and
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relatively weight stable (± 3 kg in the three months before enrollment). Children were excluded if they
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had endocrine or thyroid conditions, diabetes, spinal cord injuries, and developmental disabilities, as
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well as those with known genetic disorders (i.e. Prader Willi syndrome) to remove those with
conditions known to influence energy expenditure and energy intake. Children who were taking
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medications (stimulants, anti-depressants, thyroid etc.) or herbal supplements that are known to
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influence body composition, growth or energy metabolism were also excluded from the study. This
study was conducted according to the guidelines laid down in the Declaration of Helsinki and received
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approval from the Research Ethics Boards at the Children’s Hospital of Eastern Ontario (protocol
was obtained from all participants.
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2.2 Procedures
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#13/105X) and the Ottawa Hospital Research Institute (protocol #20130580). Written informed consent
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Initially, all potential participants (mothers of children) were contacted via telephone by an OaK
cohort staff member to determine if they would be interested in being contacted for research. If they
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expressed an interest, they were contacted by the research team to hear more about the study and the
requirements of participation. For the general recruitment of members of the Ottawa Community
(outside of the OaK dataset), participants responded to local poster advertising around community
centers and hospitals. Interested participants (mother and child) meeting basic inclusion criteria over
the phone were invited to visit our laboratory at the CHEO Research Institute to verify eligibility and to
provide informed assent (children) and consent (parent) and participate in a half-day session of testing.
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Participants
were
then
subjected
to
the
following
measurements: 0730-
anthropometric
measurements (height, body weight, waist circumference, and body composition, followed by a 30-min
resting period; 0800- 30-min resting energy expenditure measurement; 0830- children were asked to
rate their appetite on a 5-point Likert rating scale, and continued to rate their appetite sensations
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throughout the morning at 60-minute intervals; 0835- children were served a standardized breakfast
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(~400kcal) consisting of 1 slice of white bread, 2 tables spoons of strawberry jam, and 350ml of orange
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juice; ~0850 to 1100- measurement of the thermic effect of the meal (TEF). Two 15-min TEF sampling
periods continued to be done every hour for the 2-hour measurement interval; 1200- children were
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served an ad libitum lunch consisting of cheese pizza. Following the lab measures, parents were
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instructed on how to strap on an elasticized belt that held the omni-directional accelerometer (Actical,
Respironics, Oregon, USA) on their child’s hip that was worn for seven days to assess free-living
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physical activity. Parents were also instructed on how to complete a 7-day food diary to assess their
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own and their child’s out of lab dietary intake and finally instructed on how to mail back all of the
study materials.
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2.3 Measurements
Demographics were evaluated by self-report,
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a) Demographic variables and smoking behavior:
including family size, maternal and paternal education, maternal weight history (pre-pregnancy
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weight/BMI), maternal medical history, obstetrical data (weeks of gestation at birth), smoking status
(smoker vs non-smoker) and smoking behavior (number of cigarettes smoked per day at 12-20 weeks).
b) Anthropometric measures:
Child weight was assessed using a SECA scale (Seca GmBH & Co.
Hamburg Germany) calibrated to 0.1 kg.
Child height was assessed using a SECA stadiometer (Seca
GmBH & Co. Hamburg Germany), and BMI (kg/m2 ) was calculated, which was adjusted to BMI
percentiles based on WHO growth curves (www.who.int/growthref). Waist circumference was
measured at a level midway between the lowest rib and the top of the iliac crest, as previously
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described (Alberga, Goldfield et al. 2012). Child body composition (% body fat, lean mass, and fat
mass) was assessed using a Tanita bioelectrical impedance scale (Tanita 300A, Tanita Corporation of
America, Inc. Arlington Heights, IL, USA). Previous work in our lab found that measures from this
Tanita scale compared to measures of dual-energy X-ray absorptiometry (gold standard) in young
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children to yield correlations of percent body fat, fat mass, and fat-free mass of 0.85, 0.97, and 0.94,
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respectively (Goldfield, Cloutier et al. 2006).
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c) Resting Energy Expenditure (REE) and Thermic Effect of Food (TEF): In order to effectively
determine REE and TEF in the children, O2 consumption and CO 2 production was measured using an
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indirect calorimetry protocol with breath-by-breath samples collected using an Ultima PF/PFX
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metabolic cart (Medical Graphics Corporation, St. Paul, Minn.). The first and last 5 minutes of
measurement were discarded, and the values of VO 2 and VCO 2 for the middle 20 minutes were
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averaged for the calculation of the rate of REE. The Weir formula was used for the calculation of REE
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(Weir 1949). To ensure standardization, each subject arrived 12-hour overnight fasted and having
refrained from vigorous exercise the previous two days. Children then rested in a semi-reclined
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position, in a thermoneutral environment. A pediatric neoprene mask covering only the mouth and
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nose, which we have found to be more comfortable than the plexiglass hood in children, was properly
fitted on each child and a 30-minute data collection period begun to capture REE. TEF was measured
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in the same way as REE, and both were measured in absolute terms (kilocalories) and also adjusted for
body weight, in relative terms. TEF is the amount of energy expended involved in the mechanical and
metabolic activities related to nutrient ingestion, digestion, absorption, storage and metabolism. REE
and TEF are both considered non-volitional forms of energy expenditure.
d) Energy Intake: in lab meals and out of lab food and drink: After the REE measure and before the
TEF measures, children were required to eat a standardized breakfast meal, which consisted of 1 piece
of white bread, 2 tablespoons of strawberry jam, and 250 ml of orange juice. Children were instructed
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to eat everything within 15 minutes. Ad libitum food intake was measured in-lab as well as out-of-lab.
For the former, an all-you-want-to-eat pizza lunch was offered at 1200. The test food was Selection®
three cheese mini pizza (106g; 260kcal), offered one mini pizza at a time. Children were instructed to
eat as much or as little as they wanted, but that they only had a maximum of 30 minutes. More pizza
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was made available by asking part-way through consumption of each pizza if they would like to have
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another mini pizza. All food was weighed to the nearest 0.1 g before and after ingestion.
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Information on out-of-lab child dietary intake was assessed using 7-day food records. Regarding the
reliability of this measure, in a recent paper examining the magnitude of energy intake misreporting it
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was determined that there was no significant difference between the medians of percentage of
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misreporters when comparing three of the main methods of self-reported food intake: 24-hour recall, 3and 7-day food logs, and weighed food records (underestimation of energy intake was 13.4%, 12.2%,
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and 18.0%, respectively) (Poslusna, Ruprich et al. 2009). Furthermore, looking at the literature we can
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see agreement between 3- and 7-day food logs (12% under-reporting (Taren, Tobar et al. 1999) vs. 17%
under-reporting (Velthuis-te Wierik, Westerterp et al. 1995)), for example, thus while there is indeed a
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large variability not only in day-to-day feeding at the individual level and large variability at the inter-
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individual level as well, we argue that this is within an acceptable tolerance of variability. Under the
age of 8 years, children do not have the cognitive capabilities to self-report food intake (Livingstone,
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Robson et al. 2004) thus parents were asked to complete the food records. During the lab testing visit,
parents were given instructions on how to complete the food records by demonstrating various
measuring devices (food scales, cups, spoons, etc.) to enhance the accuracy of dietary intake
measurement. Parents were instructed to record the quantity of all food and beverages consumed or by
weight and to record methods of food preparation, brand names and ingredients of foods, and recipes of
mixed dishes when possible. Parents were counseled on appropriate portion sizes using food models
supplied with a handout that described in detail how to measure food portions and if they did not have
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access to measuring tools (cups, spoons, etc.), they were advised to following the Canadian Diabetes
Association’s
Handy
Portion
Guide
included
in
the
handout
(www.diabetes.ca/Files/plan%20your%20portions.pdf). Food records were completed over the seven
days following testing, in conjunction with the out-of-lab free-living physical activity measurement via
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accelerometry. The food logs were analyzed with food composition analysis software (The Food
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Processor SQL 2006, ESHA Research, Salem, OR).
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e) Appetite—visual analogue scales: Appetite ratings were measured using a pen and paper on a 150mm visual analogue scale adapted from Hill and Blundell
(1984) as previously described (Hill,
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Magson et al. 1984). Susceptibility to hunger, desire to eat, fullness and prospective food consumption
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were all measured with anchors such as “not hungry at all- as hungry as I have ever felt’ and ‘very
weak- very strong”.
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f) Free-Living Physical Activity: The Actical Accelerometer (Respironics Inc. Bend, OR 97701, USA)
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is a small, waterproof, omnidirectional sensor that measures free-living physical activity by the
occurrence and intensity of motion. The device is worn on a belt around the waist and positioned on the
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right hip. Children wore these motion sensors for seven days and study staff trained parents how to
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properly strap these monitors on the children. Instructions were to remove the Actical at night before
going to bed, and then replace the monitor around the waist every morning when waking up for the 7-
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day period. To account for the possibility that not all children will wear an Actical every day or may
vary the length of time they wear the device, the proportion of physical activity and sedentary behavior
per hour of wear time was computed in a manner consistent with the Canadian Health Measures Survey
(Colley, Garriguet et al. 2011). The Actical has been validated to measure physical activity in young
children in our targeted range (Puyau, Adolph et al. 2004). Physical activity was quantified by the
number of counts obtained with this device, and we also calculated time spent in daily sedentary
behavior, daily light PA (LPA), and daily moderate-vigorous (MVPA) intensity activity using validated
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cut-points in young children in our age category from Puyau et al. (Puyau et al. 2004)
Note that
sedentary behavior is commonly defined as expending ≤1.5 metabolic equivalents of energy during
waking hours, and while in a sitting or reclining position (Tremblay, Aubert et al. 2017). In line with
previous research, only children with 10 or more hours of accelerometer data per day on at least 4 out
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of the seven days (Trost, Pate et al. 2000) were included in the final analysis. Participants were given
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self-addressed pre-stamped envelopes to mail back the accelerometers, food diaries and log sheets after
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the 7-day out-of-lab measuring period.
g) Screen Time Behavior: Screen time was assessed by asking parents of participants to report how
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much time, in hours per day, they spent watching television, playing seated/inactive video games
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(excluding computer games), and using the computer for recreational reasons (excluding school work).
These questions were separated to assess weekday and weekend screen time. Composite scores were
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calculated by adding total television time, total video/computer time, and then a grand total of all screen
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time combined. This questionnaire has been validated (Prevention 2012).
3. Statistical Analysis
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Baseline characteristics were summarized as means with standard deviations for continuous
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data and frequencies with percentages for categorical data. General linear models with a univariate
analysis of variance (ANOVA), with a fixed factor of maternal smoking (smokers vs non-smokers)
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during pregnancy (referred to as the group variable) and household income and child age as covariates
were used to examine main outcome effects of smoking status on REE, energy intake, and
anthropometrics, as well as secondary outcomes on TEF, free-living physical activity, appetite, and
screen time behavior. With each ANOVA, an effect size was calculated based on Cohen’s d, with
ranges of 0.0 to 0.2 reflecting small effects, 0.3 to 0.7 moderate effects, and 0.8 or greater reflecting
large effects (Cohen, 1980). Partial correlations were performed to examine the associations of
adiposity and other anthropometric variables with REE, free-living physical activity, energy intake and
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screen time behavior. Statistical analyses were performed using SPSS version 24 (Chicago, SPSS Inc.).
Significant results presented are at p<0.05.
4. Results
Descriptive characteristics of the sample are presented in Table 1. Results obtained from
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general linear models are summarized in Tables 2-3. After adjusting the model for child age and family
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income, there were statistically significant group differences in anthropometrics, whereby children
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exposed to prenatal smoking presented with higher waist circumference (p=0.04), %BF (p=0.02), and
a trend for higher BMI (p=0.05); there were no group differences in any other anthropometric variable.
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There were no significant group differences in energy metabolism (REE and TEF). Of note,
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however, there was a trend for lower weight-adjusted REE (p=0.1, Cohen’s d=0.42, moderate) in
children of smokers compared to controls (see Table 2).
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Examining the age and family income adjusted models looking at group differences in energy
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intake, it was found that children exposed to smoking in utero (vs. controls) had higher in lab energy
intake from the buffet lunch (476±196 vs 313±217 kcal, p=0.02, Cohen’s D 0.75, moderate to large),
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but no significant difference emerged in overall intake for the out of laboratory
7-day feeding (12
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564±3481 vs 12855±3872 kcal, p=0.8 Cohen’s D -0.075). There were no statistically significant group
differences in any appetite measure.
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Examining the adjusted (age and family income) model for differences in physical activity and
sedentary behavior (Table 3), there were group differences in mean time spent in LPA (mins/day),
where exposed children had higher LPA. Examining the sedentary screen time behaviors, exposed
children spent more time watching television during the week (p=0.03), as well as more time with total
television watching (p=0.02), but there were no group differences in any other screen time behaviors
(Table 3).
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Examining the correlations with child anthropometrics and energy balance measures, in the
exposed children there was a significant negative correlation with %body fat and weight adjusted REE
(r=0.5, p=0.04) and a significant positive correlation with %body fat and daily sedentary behavior
(r=0.64, p=0.01) expressed as mean minutes per day spent in sedentary time. No such significant
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correlations were noted for control children.
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Controls
(n= 27)
Mean (SD)
6.9 (1.9)
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14 (51.9%)
13 (48.1%)
3.4 (0.394)
15.3 (3.6)
39.4 (1.4)
10.5%
84.2%
5.3%
0
7.4%
85.2%
7.4%
0
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12 (63.2%)
7 (36.8%)
3.2 (0.506)
16.4 (7.1)
39.1 (1.3)
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Age Child (years)
Sex Child
Male (%)
Female (%)
Birthweight Child (kg)
Gestational Gain (kg)
Gestation weeks
Maternal Education
High School (%)
College/University (%)
Don’t know (%)
Refuse to answer
Paternal Education
High School (%)
College/University (%)
Don’t know (%)
Refuse to answer
Household Income
$0-24,999
$25,000-49,000
$50,000-79,000
>$80,000
Cigarette Exposed
(n=19)
Mean (SD)
8.6 (1.8)
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Variables
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Table 1. Participant Characteristics by Group
P-Value
0.005
0.450
0.190
0.490
0.500
0.660
0.310
10.5%
68.4%
10.5%
10.5%
14.8%
74.1%
7.4%
3.7%
0.369
0
21.1%
31.6%
47.4%
3.7%
7.4%
25.9%
63.0%
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Table 2. Anthropometric and Indirect Calorimetry Variables by Group Controlling for Child Age and
Family Income.
Controls
(n= 19)
(n= 27)
Body Weight (kg)
Mean (SD)
31.3 (9.5)
Mean (SD)
27.9 (7.7)
0.06
0.43
Waist Circumference (cm)
64.5 (13.4)
57.7 (6.6)
0.04
1.03
Body Mass Index (kg/m2 )
18.5 (3.2)
16.9 (1.8)
0.05
0.86
Fat Free Mass (kg)
26.8 (7.5)
23.0 (6.3)
0.06
0.60
Fat Mass (kg)
7.2 (4.4)
5.3 (3.2)
0.11
.06
% Body Fat
21.2 (7.3)
16.3 (5.0)
0.02
0.97
REE (kcal/day)
1227 (335)
1137 (274)
0.35
0.33
44 (10)
0.09
0.42
40 (15)
IP
T
ES
CR
AN
Relative REE (kcal/kg/day)
P-Value
Cohen’s D
Cigarette Exposed
US
Variables
0.3 (0.01)
0.36 (.01)
0.2
4.40
TEF2 (kcal/kg/min)
0.3 (0.01)
0.37 (.01)
0.11
5.20
TEF3 (kcal/kg/min)
0.3 (0.01)
0.34 (0.01)
0.37
3.75
TEF4 (kcal/kg/min)
0.02 (0.001)
0.03 (0.009)
0.06
5.50
ED
M
TEF1 (kcal/kg/min)
AC
CE
PT
Note: kg=kilogram, cm=centimeter, REE=resting energy expenditure, kcal=kilocalories (1kcal=4.18
Joules), , ES=effect size, TEF=thermic effect of feeding 1-4 occurred over a 2-hour post-prandial
period, expressed in relative terms.
ACCEPTED MANUSCRIPT
Table 3. Physical Activity and Sedentary Behavior by Group Controlling for Wear Time, Child Age,
and Family Income.
(n=19)
(n=27)
Mean (SD)
Mean (SD)
311 (39)
297 (100)
0.55
0.14
Steps Per Day
12185 (3726)
11700
0.73
0.14
0.01
0.77
44 (20)
0.93
0.03
5.0 (5.2)
0.03
0.82
T
Controls
Daily Sedentary Behavior (min)
P-Value
Cohen’s D
Cigarette Exposed
IP
Variables
ES
203 (33)
155 (63)
Daily MVPA (min)
45 (23)
TV Weekday (hrs)
9.3 (6.4)
TV Weekend (hrs)
5.4 (1.9)
4.0 (2.3)
0.06
0.61
Total TV (hrs)
14.7 (6.9)
9.1 (7.0)
0.02
0.80
Video/Computer Screen Weekday
4.3 (4.1)
0.99
0.005
3.5 (2.6)
3.7 (2.5)
0.77
-0.10
7.6 (5.6)
8.1 (6.3)
0.82
-0.07
22.1 (8.9)
17.5 (10.0)
0.20
0.46
US
CR
Daily LPA (min)
AN
(3475)
4.3 (3.5)
M
(hrs)
Video/Computer Screen Weekend
ED
(hrs)
Total Video/Computer (hrs)
PT
All Total Screen Time (hrs)
AC
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Note: All free-living physical activity scores were standardized; LPA=light physical activity,
MVPA=moderate-to-vigorous physical activity; TV=television
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5. Discussion
The objective of this study was to determine if there were measurable differences in energy
balance variables that might differentiate the offspring of prenatal smokers from the offspring of nonsmokers during pregnancy. Relative to controls, after controlling for age and family income, children in
T
our sample who were exposed to cigarette smoke in utero exhibited greater waist circumference,
IP
adiposity, and a trend for greater BMI (p=0.05). Although we did not find statistically significant group
CR
differences for non-volitional energy expenditure, it is worth noting that the trends for an attenuated
REE were in the hypothesized direction with a moderate effect size (see Table 2). On the other side of
US
the energy balance equation, our hypothesis was partially confirmed where exposed children,
AN
confronted with a buffet-style ad libitum feeding situation, demonstrated higher energy intake of a
palatable pizza meal when compared to controls. However, there were no group differences in 7-day
M
out-of-laboratory energy intake. There were significant differences in the hypothesized direction for
ED
our secondary outcomes, whereby children who were exposed to cigarette smoke in utero demonstrated
higher levels of sedentary behavior in the form of significantly higher mean hours spent watching
PT
television per week. Finally, except for the finding that offspring of mothers who smoked while
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pregnant had significantly higher levels of daily LPA, there were no group differences in MVPA, or
time spent in any of the other measures of free-living physical activity.
AC
Growth beyond infancy among the offspring of maternal smokers has been the focus of a
number of recent well-controlled studies including cohorts from the United States (Day et al. 1992;
Day, Richardson et al. 1994), Canada (Fried, James et al. 2001; Dubois and Girard 2006), and the
United Kingdom(Ong et al. 2002). The findings in these studies have been remarkably consistent with
the children of smokers more likely to be i) overweight/obese than the children of non-smokers, ii) of
higher body mass index (BMI [kg/m2 ]), BMI percentile cutoffs for overweight/obesity, and/or ponderal
index (ratio of weight to height). The present study provides evidence confirming that children who are
ACCEPTED MANUSCRIPT
exposed in utero to cigarette smoke have higher objectively measured adiposity than non-exposed
controls. Our findings that exposed children had significantly higher adiposity (see Table 2) are
consistent with the tenets of the ‘fetal origin of adult diseases’ (Barker 1990) proposing that the fetus
undergoes adaptive changes to compensate for reduced nutrition and unhealthy in utero environment,
T
which can lead to “catch up” weight gain in early childhood. The mean age of exposed children in our
IP
sample was 8.6 years old, and several other studies have also shown the “catch up” growth at or around
CR
this same age (Vik, Jacobsen et al. 1996; Fried, Watkinson et al. 1999; von Kries, Koletzko et al.
1999).
US
A main outcome of the current study was to examine if cigarette exposure impacted the non-
AN
volitional component of energy expenditure (REE and TEF). Our hypothesis was based on animal data
showing that in utero exposure to nicotine causes blunting of responsiveness of peripheral/and central
M
sympathetic nervous system activity, and attenuated responsiveness of central norepinephrine signaling
ED
(Levin 2005), but in our sample, we did not find any group differences in absolute or relative REE.
However, we did find a trend for reduced relative (weight-adjusted) REE in children of prenatal
PT
smokers vs controls, which was in the hypothesized direction, and the moderate effect size can be
CE
viewed as clinically meaningful, suggesting that a slightly larger sample size may have yielded
statistically significant differences. In fact, the measured difference of 4kcal/kg/day lower REE in
AC
exposed children (see Table 2), although not statistically significant, amounts to approximately 10%
fewer Calories burned per day from metabolic heat production for the exposed group (approximately
4kcal x 30kg=120kcal). Given that Hill et al. (2003) have argued that affecting energy balance by
merely 100kcal per day could prevent weight gain in much of the US population (Hill, Wyatt et al.
2003), this trend of attenuated REE in exposed children warrants further attention with larger sample
sizes.
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Examining the other side of the energy balance equation, we also aimed to elucidate whether ad
libitum energy intake was different by group, and hypothesized that exposed children would eat more
in an ad libitum feeding environment. Animal data in rats and mice have shown that prenatal tobacco
smoke exposure (relative to non-exposed) causes higher food consumption, as well as higher total body
T
mass and higher fat mass (Santos-Silva, Oliveira et al. 2013; Lisboa, Soares et al. 2017), and appears to
IP
be associated with dysregulated hypothalamic signaling of appetite-related neuropeptides (de Oliveira,
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Moura et al. 2010). In many Western societies food availability is essentially unlimited, and
considering metabolic efficiency (that is, REE and TEF) for any given genotype has adjustment limits
US
approximating 5-10% (Horton, Drougas et al. 1995), the impact of food intake on body weight and
AN
adiposity must be highlighted. Food intake plays a dichotomous part in the equation of energy
balance—not only does it account for energy intake but it also accounts for a portion of energy
M
expenditure. There are three variables to consider when assessing the measurement of food intake, in
ED
what amounts to be the computing of total energy expenditure: 1) TEF, 2) REE, and 3) free-living
physical activity. Note that the first two are non-volitional, whereas, the last is considered the sum of
PT
volitional and non-volitional energy expenditure (Neilson, Robson et al. 2008). Our findings that
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children exposed to cigarette smoke in utero have higher ad libitum feeding in a palatable lunch buffet
meal are novel, and although we did not see group differences in the relationship between adiposity and
AC
lunch buffet meal intake, this does not necessarily indicate that increased energy intake cannot explain
why exposed children are heavier and more obese than controls. In fact, our findings showing higher
in-lab consumption of a palatable lunch meal dovetails with experimental animal data showing that
prenatal tobacco exposure increases the offspring’s consumption of palatable food (Franke, Park et al.
2008), and highlights the need to look at reward-related or hedonic feeding in the development of
increased adiposity and obesity in exposed children. Our finding that there were no significant
differences in out-of-lab feeding by group speaks to the difference in measurement approach. For
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example, for out-of-lab feeding, compliance cannot be assessed with 100% accuracy and in free-living
conditions there are many social, environmental and behavioural factors that have been shown to
greatly impact energy intake (i.e. the day of the week, the number of people present, the relationship of
eating companions to the subject, etc.) (De Castro 1996). To be sure, more laboratory research,
T
preferably longitudinal in design, is needed to look at both sides of the energy balance equation to
IP
examine which pathways are leading to higher energy stored as adipocytes in children whose mothers
CR
smoked while pregnant.
Another factor that can impact body weight and adiposity is sedentary behavior (Katzmarzyk,
US
Barreira et al. 2015). In animal models it is known that tobacco exposed rats have degenerative changes
AN
in the sympathoadrenal system (Sayed 2016), and although the role of adrenal medullary function in
the regulation of energy balance in humans is not clear, there is evidence to show that low activity of
M
the adrenal medulla is associated with body weight gain and central adiposity (Tataranni, Young et al.
ED
1997), which could be due to sedentariness. In our sample, we had two measures of sedentary behavior,
one measured objectively with accelerometry from mean time spent sedentary, and the other measured
PT
via self-reported screen-time behavior, both measured over seven days (see Figure 3). Although there
CE
were no group differences in sedentary time measured from accelerometry, children exposed to
cigarette smoke in utero did spend significantly higher amounts of time watching television, where
AC
exposed children watched an average of 14.7 hours of television/week, whereas the control children
watched 9.1 hours/week. Furthermore, daily sedentary behavior was positively associated with child
adiposity, but only in smoking exposed children. Given that sedentary behavior—particularly watching
television —is associated with an increased risk of obesity and cardiometabolic complications in youth
(Tremblay, LeBlanc et al. 2011b), and the fact that sedentary behavior tracks from childhood through
adolescence (Francis, Stancel et al. 2011) and into adulthood (Busschaert, Cardon et al. 2015), our data
suggest that intervention strategies should target reductions in television screen time, particularly in
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youth who are at a higher risk of obesity due to their exposure to cigarette smoke prenatally. Indeed,
most children in North America exceed the guidelines of 2 hours or less of screen time per day
(Tremblay, Leblanc et al. 2011a) and our sample was no exception.
This study had some limitations. Along with a smaller than desired sample size due to
T
recruitment problems, primarily that many mothers who smoked during pregnancy were hesitant to
IP
participate, the sample itself was composed of individuals who either agreed to be contacted for
CR
research purposes, or alternatively, who contacted us to display interest, which may introduce a selction
bias and the sample is not representative of the general population. For the measure of out of lab energy
US
intake, as with any measure of self-reported feeding, there is the potential of dietary underreporting
AN
(Karelis, Lavoie et al. 2010). However, food logs (involving weighing, or as in the current study,
quantifying with household measures) have been considered the most accurate and feasible method of
M
dietary assessment (Barrett-Connor 1991; Hill and Davies 2001), especially due to the high costs
ED
associated with isolating participants in a controlled environment in order to precisely measure energy
intake. Although we statistically controlled for age and socioeconomic factors, the possibility of
PT
residual confounding of these variables cannot be completely discounted. Although we did have
CE
uranalysis to verify the validity of self-reported smoking status during pregnancy, we cannot account
for previous smoking status of mothers prior to entering into the study and possible epigenetic effects
AC
on the unfertilized egg. This study also had several strengths. The medical chart records were available
for most of the sample and as such we were able to avoid self-reporting where possible (i.e. urinary
cotinine to confirm smoking status, objective measures of child birthweight, weeks gestation, etc.). We
captured
free-living
physical
activity
objectively
using
accelerometry
according
to
methods
standardized with Canadian Health Measures Survey (Colley, Carson et al. 2017) and non-volitional
energy expenditure with indirect calorimetry and not via estimates, which are both strengths of the
design. Finally, we assessed adiposity with bioelectric impedance where most of the epidemiological
ACCEPTED MANUSCRIPT
data looking at maternal smoking and child obesity are based on self-reported measures of BMI or
body weight. Most importantly, we believe this is the first study in humans to examine a more
comprehensive spectrum of energy balance behaviors in attempt to explain the relationship between
prenatal smoking and increased rates of obesity in children, highlighting the novelty of the data.
T
In conclusion, we did not find significant main effects for prenatal cigarette exposure on any
IP
form of metabolism as measured by indirect calorimetry, but the data presented here show trends in the
CR
predicted direction. Thus, the observed trend for attenuated resting energy expenditure warrants further
inquiry as a possible mechanism linking prenatal smoking and child obesity, as shown in animal
Children exposed to cigarette smoke in utero exhibited greater adiposity than the controls,
US
research.
AN
and this relationship may have as contributing factors higher average television watching and higher ad
libitum in lab energy intake, and reduced REE, suggesting that interventions should target these
M
indicators to prevent obesity in exposed children. Future research using larger samples and longitudinal
ED
designs are needed to better understand the behavioral and biological drivers of how prenatal cigarette
smoking increases the risk of child adiposity and obesity.
PT
Conflict of Interest
Funding
CE
None Disclosed
13-0003085
AC
Research relating to this project was funded by the Heart and Stroke Foundation of Canada Grant # G-
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Highlights
 Children exposed to cigarette smoke in utero exhibit greater adiposity vs controls
Exposed children had higher ad libitum energy intake in lab vs controls

Exposed children had a trend for lower resting energy expenditure vs controls

Exposed children engaged in more screen time than controls
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