Content uploaded by Patrice Brassard
Author content
All content in this area was uploaded by Patrice Brassard
Content may be subject to copyright.
© 2010 Tremblay et al, publisher and licensee Dove Medical Press Ltd. This is an Open Access article
which permits unrestricted noncommercial use, provided the original work is properly cited.
Risk Management and Healthcare Policy 2010:3 27–32
Risk Management and Healthcare Policy Dovepress
submit your manuscript | www.dovepress.com
Dovepress 27
REVIEW
open access to scientific and medical research
Open Access Full Text Article
7986
Impact of “noncaloric” activity-related factors
on the predisposition to obesity in children
Angelo Tremblay
Émilie Pérusse-Lachance
Patrice Brassard
Division de Kinésiologie, PEPS,
Université Laval and Centre de
Recherche de l’Institut Universitaire
en Cardiologie et Pneumologie de
Québec, Québec, Canada
Correspondence: Angelo Tremblay
Division de Kinésiologie, PEPS, Bureau
0234, Université Laval, Québec,
Canada G1V 0A6
Tel +1 418-656-7294
Fax +1 418-656-3044
Email angelo.tremblay@kin.msp.ulaval.ca
Abstract: The research related to childhood obesity generally emphasizes the impact of
unhealthy eating and sedentary behavior as the main determinants of the predisposition to the
positive energy balance that underlies excess body fat accumulation. Recent investigations
have, however, demonstrated that “noncaloric” activity-related factors can induce a significant
imbalance between spontaneous energy intake and energy expenditure. This is the case for
short sleep duration that favors hormonal changes that increase hunger and energy intake. This
agrees with our research experience demonstrating that short sleeping predicts the risk of obesity
in children to a greater extent than sedentary behavior. Recent research by our team has also
showed that demanding mental work promotes a substantial increase in energy intake without
altering energy expenditure. In addition, our preliminary data suggest that the regular practice
of school-related cognitive efforts is predictive of an increase in abdominal fat accumulation.
As discussed in this paper, individual variations in brain oxygenation and its related cerebral
aerobic fitness might play a role in the relationship between mental work, energy intake, and
the risk of excess body weight.
Keywords: sleep duration, mental work, brain oxygenation, energy intake, energy
expenditure
Introduction
A successful preventive strategy of a health problem generally begins with the iden-
tification and the characterization of factors promoting the problem. With respect to
obesity, it is known that the problem is the ultimate consequence of long term excess
energy intake over expenditure. However, this principle, which reflects the implications
of the first law of thermodynamics in the field of obesity, does not document the key
issues pertaining to its determinants in a specific population. In this regard, the main
question of interest refers to the factors that underlie a positive energy balance under
free-living conditions and the habitual answer presented by health professionals is that
unhealthy eating and sedentary behavior are explanatory factors.
As described in this paper, our recent research and that of others emphasize the
relevance in considering activity-related factors which do not have a significant direct
impact on energy expenditure, be it because of their low energy cost or their inability
to increase energy expenditure when performed even at a high intensity. These factors
include short sleeping and demanding mental work which can be viewed as “noncaloric”
factors having the potential to induce a large caloric imbalance.
Number of times this article has been viewed
This article was published in the following Dove Press journal:
Risk Management and Healthcare Policy
2 July 2010
Risk Management and Healthcare Policy 2010:3
submit your manuscript | www.dovepress.com
Dovepress
Dovepress
28
Tremblay et al
Short sleeping and obesity
in children
A careful review of the literature reveals that the apparently
newly documented sleep-obesity relationship is not so new.
For instance, at the beginning of the 1990s, a significant
association was reported between sleep duration and body
weight variations.1 However, this issue of investigation
gained much credibility when the team of Van Cauter showed
that short term experimentally controlled sleep deprivation
induced a decrease in leptinemia and an increase in plasma
ghrelin level.2 Accordingly, an increase in hunger sensation
was observed in the sleep-restricted subjects. These observa-
tions as well as population data indicating a decrease in sleep
duration over the last decades also led Van Cauter to propose
an association between the progressive decrease in sleep
duration and the development of an obesity epidemic.3
This research prompted us to incorporate sleep duration
in the list of variables to be considered in analyses aimed at
a better understanding of variations in body weight. In the
Quebec en Forme Project, we examined the relationship
between anthropometric indicators and various lifestyle
markers known to predict the risk of obesity in children.
As expected, we found that low education level and familial
income, parental obesity, long television viewing, and sed-
entary behavior were all statistically significant predictors
of an increased risk of obesity.4 However, we were surprised
to note that short sleeping was by far the best predictor of
obesity in this cohort. Further analyses also demonstrated
that the excess body weight related to short sleep duration is
preferentially deposited in the abdominal area in children.5
Considering that excess abdominal fat increases the risk to
develop a metabolic syndrome in the long term,6 optimal
sleep duration should be included in the indicators of lifestyle
that are associated to optimal metabolic health.
We also took advantage of the Quebec Family Study to
give extension to our analyses in an adult population. As in
children, short sleepers were found to be heavier than normal
sleepers reporting 7–8 hr sleep per day.7 In addition, this
study showed that long sleepers were also displaying excess
weight gain over time.8
The Quebec Family Study also includes some metabolic
indicators which permit the investigation of some mechanistic
explanations of the sleep-body fat relationship. We observed
that short sleepers are characterized by a lower plasma leptin
concentration than the level predicted by their body fat mass.7
We also noted that both short and long sleepers display an
increased plasma glucose area below basal level during an
oral glucose tolerance test.9 Since this hypoglycemic profile
has been shown to predict long term weight gain,10 this may
represent an explanation of the increased risk of obesity
related to variations in sleep duration. Finally, it is relevant
to emphasize that this increased proneness to mild hypogly-
cemia also represents an independent risk factor of glucose
intolerance and diabetes.11
These observations suggest that sleep, which is the activ-
ity with the theoretically lowest energy cost, does not mainly
influence body weight via its impact on energy expenditure.
Its effects on body weight and fat seem to be mediated by
metabolic effects that affect the control of appetite. This is
concordant with the recent demonstration that sleep depriva-
tion favors an increase in energy intake at snack time.12
Mental work, energy intake,
and body weight stability
An environment that is increasingly globalized and relies
on computerization to permit competitiveness inevitably
requires additional cognitive effort. This is the case of workers
in many fields of expertise and of children for whom learn-
ing activities become more demanding. In our opinion, this
issue is not dissociated from the literature that documents
the potential link between intellectual capacities and the
proneness to obesity. As early as the 1960s, Canning and
Mayer13 reported that the proportion of nonobese students in
the upper quartile of academic success was greater than that
of obese subjects. This issue was subsequently examined by
Kreze et al14 who found a decreased obesity prevalence in
women characterized by a high intellectual quotient (IQ). This
trend was also observed in men but to a lesser extent than
in women. This topic was further investigated by the group
of Sorensen et al15 who found lower results for intelligence
tests in subjects characterized by a high body mass index
(BMI). Accordingly, this team observed increased odd ratios
of school difficulties in obese children.16 More recently, the
same team concluded that intelligence tests and education
level are negatively related with subsequent changes in BMI
and the risk of obesity.17 This appears concordant with the
results of Lawlor et al18 who also found a negative association
between IQ at childhood and BMI at adulthood.
These studies and others have led to the very recent
publication of a literature review pertaining to the theme of
“intelligence and obesity”19 which summarizes a gradient of
opinion that, although not reflecting a consensus, might lead to
the perception that IQ measurement would be a relevant tool of
prediction of the proneness to obesity. We have reacted to the
publication of this paper by proposing that demanding mental
work can act as an intermediate factor in this relationship.20
Risk Management and Healthcare Policy 2010:3 submit your manuscript | www.dovepress.com
Dovepress
Dovepress
29
Risk of obesity in children
The first study performed in our laboratory to investigate
the metabolic effects of mental work was a case study in
which one of us (AT) was subjected to either a 60-min
cognitive effort to prepare a grant application or a relaxing
session of similar duration.21 Unexpectedly, performing
mental work promoted glycemic and insulinemic instability
that was related to an increase in hunger and desire to eat.
Moreover, concomitant experiments revealed that demand-
ing knowledge-based work (KBW) increased spontaneous
energy intake in graduate students.21
These preliminary studies were followed by investiga-
tions involving Laval University students who were randomly
assigned to a reading-writing task or a relaxing session of
45 min each. In the first study,22 the use of indirect calorimetry
showed that mental work exerts a trivial effect on energy
expenditure since the total energy cost of the KBW session
exceeded by only 3 kcal the value measured during the control
relaxing session. Conversely, the study showed that a large
increase in ad libitum energy intake was observed after the
cognitive effort (Study 1 in Table 1) and this was confirmed
by the results of a subsequent study performed in another
group of female university students (Study 2).23 Specifically,
mental work induced a mean increase of 229 kcal in energy
intake during a buffet-type meal. In the second study,23 the
reading-writing task promoted a comparable increase in ad
libitum caloric intake. Furthermore, this spontaneous increase
reached 250 kcal when subjects of Study 2 performed a third
session of computer tasks (Table 1).
The second study of Laval University students also gave
us the opportunity to evaluate the effects of the intensity
of mental work on energy intake and related variables.
For that purpose, we measured the reaction time to a second
task (vocal reaction by saying a short word in response
to a randomly emitted noise) during the cognitive effort.
As previously demonstrated, this method provides an indica-
tion of the intensity of mental effort which increases when
reaction time increases and vice versa.24,25 In our hands, this
technique also had a satisfactory discriminative potential
since the greater was reaction time, ie, the greater the inten-
sity of mental work, the greater was the increase in energy
intake.26
The consideration of the effects of activity-related factors
such as short sleep and mental work also led us to reconsider
the notion of sedentary behavior which should no more be
viewed as only reflecting the insufficiency of physical activ-
ity practice. We thus tested the predictive value of the risk
of obesity in children based on the four following markers
of sedentary activity: movement counts (accelerometry),
television viewing, sleep duration, and time allocated to
school homework. As expected, the suboptimal practice
regarding each factor accentuated the risk to be overweight.27
Specifically, 64% of children were overweight/obese when
their practice was suboptimal for each indicator of sedentary
activity. Therefore, sedentary behavior appears as a cluster of
activities which could influence energy balance via different
mechanisms and according to different temporal modalities.
For instance, a low movement count is related to a greater
risk of obesity whereas a low duration of sleep predicts the
opposite effect on body weight.
Towards the characterization
of cerebral aerobic tness
The brain needs uninterrupted oxygen provision and delivery
of oxygen to cerebral tissue is mainly provided by its bind-
ing to hemoglobin. The evaluation of cerebral hemoglobin
oxygenation represents the balance between changes in blood
flow through the tissue and cerebral oxygen consumption.28
A high energy demand is mainly met through oxidative phos-
phorylation in the brain mitochondria,29,30 which is respon-
sible at rest for the consumption of most glucose used by the
brain. This process is closely coupled to oxygen utilization
of the brain and is responsible for about 20% of total oxygen
consumption in the human body.31 Mental work is associ-
ated with changes in cerebral oxygenation which has been
shown by the use of near-infrared spectroscopy (NIRS). For
instance, frontal lobe oxygenation increases with cognitive
processing using a measure of verbal fluency,32 lateral frontal
lobe oxygenation elevates with translation of short sentences
and language switching in bilingual student volunteers,33
and oxygenation of the inferior and superior parietal areas
increase with processing and solving arithmetic problems.34
Specifically, the NIRS-derived frontal lobe concentration
of oxygenated hemoglobin increases and the concentration
of deoxygenated hemoglobin decreases when performing
cognitive tasks.35 This may be associated with an increase
or the absence of a change in total hemoglobin concentra-
tion.36 Such changes in cerebral oxygenation measured by
positron emission tomography are considered to represent
Table 1 Spontaneous energy intake (kJ) following rest and mental
work
Rest Mental work
Reading/writing Computer games
Study 1 3,923 4,882
Study 2 3,595 4,443 4,652
Notes: See Chaput et al22,23 for Study 1 and 2, respectively.
Risk Management and Healthcare Policy 2010:3
submit your manuscript | www.dovepress.com
Dovepress
Dovepress
30
Tremblay et al
cerebral activation,37 and a good agreement exists between
measurements of cerebral oxygenation derived from NIRS
and functional magnetic resonance in young and elderly
subjects during brain activation.38 Importantly, the difficulty
level of a mental task, such as an algorithm task, increases
NIRS-derived cerebral oxygenation.34–36 The discussion of
these issues is also relevant for the recovery of a mental task
during which brain activation remains elevated.39 Although
a fraction of this elevated brain activation may be related to
mental processing, it could also be attributed to metabolic
and other neuronal functions. Since frontal lobes are involved
in functions such as planning, organization, problem solv-
ing, memory, and judgment, it is important to examine the
hypothesis according to which brain oxygenation represents
a determinant of the intensity of mental work and its related
metabolic changes.
The more pronounced increase in NIRS-derived frontal
lobe oxygenation potentially characterizing individuals who
perceive a mental task as difficult has a certain number of
nutritional implications. As discussed above, the effect on
which our research team has mostly focused on up to now is
a change in spontaneous energy intake that was repeatedly
found to be increased by 200 to 250 kcal following demand-
ing mental work.21,22,40 This observation is concordant with
the results of Wallis et al41 who reported that chocolate intake
was increased by 15% after a stressful cognitive task (Stroop
Test) compared to a control session.
There are also indications that the increased energy
intake resulting from mental work is related to variations
in cortisolemia. Indeed, we have demonstrated that female
university students perceiving a standardized mental task as
more demanding also displayed a more pronounced increase
in energy intake and cortisolemia.42 Accordingly, the study
of Epel et al43 showed that stress-induced cortisol reactivity
was associated with an increased energy intake after the first
stress session. Since cortisol is well known for its orexigenic
properties, it seems logical to hypothesize that the greater the
increase in cortisolemia induced by mental work, the greater
its hyperphagic effect. We also believe that spontaneous
energy intake will be increased in these subjects.
According to the glucostatic theory of appetite control,44
the hyperphagia induced by mental work can be reasonably
associated with variations in the availability of glucose.
Indeed, since neurons essentially rely on glucose for their
metabolism under normal feeding conditions and that cere-
bral carbohydrate stores are low, it is likely that the increased
brain energy metabolism imposed by a cognitive effort
implies an equivalent increase in glucose oxidation. In this
regard, the data recently reported by Volkow et al45 are
particularly informative. They indeed showed that the amount
of glucose utilized by the brain is increased when performing
a cognitive task. They also demonstrated that methylpheni-
date, which is known for its properties to stimulate brain
functioning and thus reducing attention deficit, attenuated the
increase in brain glucose oxidation when performing the cog-
nitive task. Such an effect was not observed when there was
no cognitive stimulation. In our opinion, these observations
are quite compatible with the main concept presented here,
ie, decreased difficulty of a cognitive task reflects a metaboli-
cally efficient process with a reduced metabolic demand. We
also perceive these results as being in accordance with the
lower instability of plasma glucose concentrations that we
observed in female university students for whom the esti-
mated intensity of mental work was reduced.26 This is also
in agreement with a recent case study that we performed by
using methylphenidate to reduce the stress of the cognitive
effort. Indeed, blood pressure was then decreased during the
mental work that was followed by a diminished spontane-
ous caloric intake (Pérusse-Lachance et al in preparation).
Taken together, these observations suggest once again that
a greater mental effort is metabolically disturbing, probably
via modifications in glucose metabolism.
Another category of nutritional variables that is worth
considering in the study of the mental work-brain oxygen-
ation-obesity relationship is hematological profile. To our
knowledge, Wenzel et al46 were the first investigators to
note a relationship between obesity and anemia. In 1962,
they reported that obese adolescents had a lower serum iron
level than adolescents of normal weight. Concordant results
were further reported by Seltzer and Mayer.47 More recently,
it has been possible to confirm and quantify the association
between body weight and anemia since three cross-sectional
studies documented the prevalence of iron deficiency in
children and adolescents of different body weights. In each
study, it appears that the iron deficit was greater in overweight
or obese children and adolescents than in normal weight
individuals.48–50 Obesity seems to have a similar effect on
iron status in adults. Indeed, Micozzi et al51 found that an
increase in body weight in 25 to 74 year-old women was
associated with a decrease in serum iron concentrations. In
obese post-menopausal women, similar results were reported,
as reflected by a positive correlation between BMI and soluble
transferrin receptor levels.52 With respect to the specific link
between the hematological profile and the cognitive function,
the most relevant data were reported by Krestch et al53 who
observed a reducing effect of weight loss on hemoglobin,
Risk Management and Healthcare Policy 2010:3 submit your manuscript | www.dovepress.com
Dovepress
Dovepress
31
Risk of obesity in children
hematocrit, and red blood count. Interestingly, they also
noted a significant association between this change in the
hematological profile and the decrease in the cognitive func-
tion of their subjects. In summary, these findings emphasize
the hematological vulnerability of obesity-prone individuals
and the possibility that such a profile be part of the etiology
of the association between mental work and obesity that is
discussed in this paper.
The global integration of the above discussed literature
tends to show that a good “cerebral aerobic fitness” would
permit performance of a given mental task with a reduced
brain activation reflected by a lower NIRS-derived frontal
lobe oxygenation. As discussed above, this greater efficiency
is likely associated with a better metabolic fitness, be it
reflected by a more stable glycemia, a reduced cortisolemia,
a facilitated appetite control, and a lower blood pressure.
Conclusion
This paper summarizes our research experience regarding the
effects of short sleeping and demanding mental work as activ-
ity modalities which can influence body composition. Since
their energy cost is very low, it is not surprising to observe
that their potential effects on energy balance are mediated by
a significant impact on energy intake and related metabolic
variables. Thus, the consideration of these “noncaloric”
factors as determinants of the risk of obesity impose on us
to review the perception of sedentary behavior which is not
simply an insufficient physical activity practice. Furthermore,
this paper reminds us that we have entered a new era where
cerebral fitness will be an issue, be for its link with cognitive
performance and productivity or its side effects such as the
hyperphagia induced by mental work.
Disclosure
The authors report no conflicts of interest in this work.
References
1. Locard E, Mamelle N, Billette A, Miginiac M, Munoz F, Rey S.
Risk factors of obesity in a five year old population. Parental versus
environmental factors. Int J Obes Relat Metab Disord. 1992;16(10):
721–729.
2. Spiegel K, Tasali E, Penev P, Van Cauter E. Brief communication: sleep
curtailment in healthy young men is associated with decreased leptin
levels, elevated ghrelin levels, and increased hunger and appetite. Ann
Intern Med. 2004;141(11):846–850.
3. Van Cauter E, Spiegel K, Tasali E, Leproult R. Metabolic consequences
of sleep and sleep loss. Sleep Med. 2008;9 Suppl 1:S23–S28.
4. Chaput JP, Brunet M, Tremblay A. Relationship between short sleeping
hours and childhood overweight/obesity: results from the ‘Quebec en
Forme’ Project. Int J Obes (Lond). 2006;30(7):1080–1085.
5. Chaput JP, Tremblay A. Does short sleep duration favor abdominal
adiposity in children? Int J Pediatr Obes. 2007;2(3):188–191.
6. Despres JP, Lemieux I, Prud’homme D. Treatment of obesity:
need to focus on high risk abdominally obese patients. BMJ.
2001;322(7288):716–720.
7. Chaput JP, Despres JP, Bouchard C, Tremblay A. Short sleep duration is
associated with reduced leptin levels and increased adiposity: results from
the Quebec family study. Obesity (Silver Spring). 2007;15(1):253–261.
8. Chaput JP, Despres JP, Bouchard C, Tremblay A. The association
between sleep duration and weight gain in adults: a 6-year prospective
study from the Quebec Family Study. Sleep. 2008;31(4):517–523.
9. Chaput JP, Despres JP, Bouchard C, Tremblay A. Association of
sleep duration with type 2 diabetes and impaired glucose tolerance.
Diabetologia. 2007;50(11):2298–2304.
10. Boule NG, Chaput JP, Doucet E, et al. Glucose homeostasis predicts
weight gain: prospective and clinical evidence. Diabetes Metab Res
Rev. 2008;24(2):123–129.
11. Chaput JP, Despres JP, Bouchard C, Astrup A, Tremblay A. Sleep
duration as a risk factor for the development of type 2 diabetes or
impaired glucose tolerance: analyses of the Quebec Family Study. Sleep
Med. 2009;10(8):919–924.
12. Nedeltcheva AV, Kilkus JM, Imperial J, Kasza K, Schoeller DA,
Penev PD. Sleep curtailment is accompanied by increased intake of
calories from snacks. Am J Clin Nutr. 2009;89(1):126–133.
13. Canning H, Mayer J. Obesity: an influence on high school performance?
Am J Clin Nutr. 1967;20(4):352–354.
14. Kreze A, Zelina M, Juhas J, Garbara M. Relationship between intelligence
and relative prevalence of obesity. Hum Biol. 1974;46(1):109–113.
15. Sorensen TI, Sonne-Holm S, Christensen U, Kreiner S. Reduced
intellectual performance in extreme overweight. Hum Biol.
1982;54(4):765–775.
16. Lissau I, Sorensen TI. School difficulties in childhood and risk of over-
weight and obesity in young adulthood: a ten year prospective popula-
tion study. Int J Obes Relat Metab Disord. 1993;17(3):169–175.
17. Halkjaer J, Holst C, Sorensen TI. Intelligence test score and edu-
cational level in relation to BMI changes and obesity. Obes Res.
2003;11(10):1238–1245.
18. Lawlor DA, Clark H, Davey Smith G, Leon DA. Childhood intelligence,
educational attainment, and adult body mass index: findings from a
prospective cohort and within sibling-pairs analysis. Int J Obes (Lond).
2006;30(12):1758–1765.
19. Yu ZB, Han SP, Cao XG, Guo XR. Intelligence in relation to obesity:
a systematic review and meta-analysis. Obes Rev. 2009 Sep 23. [Epub
ahead of print]
20. Chaput JP, Tremblay A. Intelligence and obesity: does the intensity of
mental workload matter? Obes Rev. 2010 Feb 8. [Epub ahead of print]
21. Tremblay A, Therrien F. Physical activity and body functionality: impli-
cations for obesity prevention and treatment. Can J Physiol Pharmacol.
2006;84(2):149–156.
22. Chaput JP, Tremblay A. Acute effects of knowledge-based work on feed-
ing behavior and energy intake. Physiol Behav. 2007;90(1):66–72.
23. Chaput JP, Drapeau V, Poirier P, Teasdale N, Tremblay A. Glycemic
instability and spontaneous energy intake: association with knowledge-
based work. Psychosom Med. 2008;70(7):797–804.
24. Humphrey DG, Kramer AF. Toward a psychophysiological assess-
ment of dynamic changes in mental workload. Hum Factors.
1994;36(1):3–26.
25. Ullsperger P, Freude G, Erdmann U. Auditory probe sensitivity to
mental workload changes – an event-related potential study. Int J
Psychophysiol. 2001;40(3):201–209.
26. Chaput JP, Tremblay A. The glucostatic theory of appetite control and the
risk of obesity and diabetes. Int J Obes (Lond). 2009;33(1):46–53.
27. Mathieu ME, Chaput JP, O’Loughlin J, Lambert M, Tremblay A.
Current guidelines may protect children against overweight and
abdominal obesity. Obes Facts. 2009;2:72.
28. Teng Y, Ding H, Gong Q. Non-invasive monitoring of human cerebral
oxygen saturation by near infrared spectroscopy: instrumentation, cali-
bration, and application in cardiopulmonary bypass. Conf Proc IEEE
Eng Med Biol Soc. 2005;1:670–673.
Risk Management and Healthcare Policy
Publish your work in this journal
Submit your manuscript here: http://www.dovepress.com/risk-management-and-healthcare-policy-journal
Risk Management and Healthcare Policy is an international, peer-
reviewed, open access journal focusing on all aspects of public health,
policy, and preventative measures to promote good health and improve
morbidity and mortality in the population. The journal welcomes submit-
ted papers covering original research, basic science, clinical & epidemio-
logical studies, reviews and evaluations, guidelines, expert opinion and
commentary, case reports and extended reports. The manuscript manage-
ment system is completely online and includes a very quick and fair peer-
review system, which is all easy to use. Visit http://www.dovepress.com/
testimonials.php to read real quotes from published authors.
Risk Management and Healthcare Policy 2010:3
submit your manuscript | www.dovepress.com
Dovepress
Dovepress
Dovepress
32
Tremblay et al
29. Attwell D, Laughlin SB. An energy budget for signaling in the grey matter
of the brain. J Cereb Blood Flow Metab. 2001;21(10):1133–1145.
30. Du F, Zhu XH, Zhang Y, et al. Tightly coupled brain activity and
cerebral ATP metabolic rate. Proc Natl Acad Sci U S A. 2008;105(17):
6409–6414.
31. Zhu XH, Zhang N, Zhang Y, Ugurbil K, Chen W. New insights into
central roles of cerebral oxygen metabolism in the resting and stimulus-
evoked brain. J Cereb Blood Flow Metab. 2009;29(1):10–18.
32. Jayakar A, Dunoyer C, Rey G, Yaylali I, Jayakar P. Near-infrared
spectroscopy to define cognitive frontal lobe functions. J Clin Neuro-
physiol. 2005;22(6):415–417.
33. Quaresima V, Ferrari M, van der Sluijs MC, Menssen J, Colier WN.
Lateral frontal cortex oxygenation changes during translation and
language switching revealed by non-invasive near-infrared multi-point
measurements. Brain Res Bull. 2002;59(3):235–243.
34. Richter MM, Zierhut KC, Dresler T, et al. Changes in cortical blood
oxygenation during arithmetical tasks measured by near-infrared spec-
troscopy. J Neural Transm. 2009;116(3):267–273.
35. Villringer A, Planck J, Hock C, Schleinkofer L, Dirnagl U. Near infra-
red spectroscopy (NIRS): a new tool to study hemodynamic changes
during activation of brain function in human adults. Neurosci Lett.
1993;154(1–2):101–104.
36. Hoshi Y, Tamura M. Detection of dynamic changes in cerebral oxygen-
ation coupled to neuronal function during mental work in man. Neurosci
Lett. 1993;150(1):5–8.
37. Fox PT, Raichle ME. Focal physiological uncoupling of cerebral blood
flow and oxidative metabolism during somatosensory stimulation in
human subjects. Proc Natl Acad Sci U S A. 1986;83(4):1140–1144.
38. Mehagnoul-Schipper DJ, van der Kallen BF, Colier WN, et al. Simul-
taneous measurements of cerebral oxygenation changes during brain
activation by near-infrared spectroscopy and functional magnetic
resonance imaging in healthy young and elderly subjects. Hum Brain
Mapp. 2002;16(1):14–23.
39. Lichty W, Sakatania K, Lin F, Sun P, Ding H, Wang F. Near-infrared
spectroscopic investigations of oxygenation changes related to brain
activation. Proc SPIE. 1999;3863:197–201.
40. Chaput JP, Arguin H, Gagnon C, Tremblay A. Increase in depression
symptoms with weight loss: association with glucose homeostasis and
thyroid function. Appl Physiol Nutr Metab. 2008;33(1):86–92.
41. Wallis DJ, Hetherington MM. Stress and eating: the effects of ego-threat
and cognitive demand on food intake in restrained and emotional eaters.
Appetite. 2004;43(1):39–46.
42. Chaput JP, Tremblay A. Obesity and physical inactivity: the rel-
evance of reconsidering the notion of sedentariness. Obes Facts.
2009;2(4):249–254.
43. Epel E, Lapidus R, McEwen B, Brownell K. Stress may add bite to
appetite in women: a laboratory study of stress-induced cortisol and
eating behavior. Psychoneuroendocrinology. 2001;26(1):37–49.
44. Mayer J. Glucostatic mechanism of regulation of food intake. N Engl
J Med. 1953;249(1):13–16.
45. Volkow ND, Fowler JS, Wang GJ, et al. Methylphenidate decreased
the amount of glucose needed by the brain to perform a cognitive task.
PLoS One. 2008;3(4):e2017.
46. Wenzel BJ, Stults HB, Mayer J. Hypoferremia in obese adolescents.
Lancet. 1962;2(7251):327–328.
47. Seltzer CC, Mayer J. Serum iron and iron-binding capacity in adoles-
cents. II. Comparison of obese and non-obese subjects. Am J Clin Nutr.
1963;13:354–361.
48. Nead KG, Halterman JS, Kaczorowski JM, Auinger P, Weitzman M.
Overweight children and adolescents: a risk group for iron deficiency.
Pediatrics. 2004;114(1):104–108.
49. Pinhas-Hamiel O, Newfield RS, Koren I, Agmon A, Lilos P, Phillip M.
Greater prevalence of iron deficiency in overweight and obese children
and adolescents. Int J Obes Relat Metab Disord. 2003;27(3):
416–418.
50. Tussing-Humphreys LM, Liang H, Nemeth E, Freels S, Braunschweig CA.
Excess adiposity, inflammation, and iron-deficiency in female adoles-
cents. J Am Diet Assoc. 2009;109(2):297–302.
51. Micozzi MS, Albanes D, Stevens RG. Relation of body size and com-
position to clinical biochemical and hematologic indices in US men
and women. Am J Clin Nutr. 1989;50(6):1276–1281.
52. Lecube A, Car rera A, Losada E, Hernandez C, Simo R, Mesa J. Iron
deficiency in obese postmenopausal women. Obesity (Silver Spring).
2006;14(10):1724–1730.
53. Kretsch MJ, Green MW, Fong AK, Elliman NA, Johnson HL. Cognitive
effects of a long-term weight reducing diet. Int J Obes Relat Metab
Disord. 1997;21(1):14–21.