ArticlePDF AvailableLiterature Review

Obesity affects brain structure and function- rescue by bariatric surgery?



Obesity has a major impact on metabolic health thereby negatively affecting brain function and structure, however mechanisms involved are not entirely understood. The increasing prevalence of obesity is accompanied by a growing number of bariatric surgeries (BS). Weight loss after BS appears to improve cognitive function in patients. Therefore, unraveling mechanisms how BS influences brain function may be helpful to develop novel treatments or treatments in combination with BS preventing/inhibiting neurodegenerative disorders like Alzheimer's disease. This review shows the relation between obesity and impaired circulation to and in the brain, brain atrophy, and decreased cognitive functioning. Weight loss seems to recover some of these brain abnormalities as greater white matter and gray matter integrity, functional brain changes and increased cognitive functioning is seen after BS. This relation of body weight and the brain is partly mediated by changes in adipokines, gut hormones and gut microbiota. However, the exact underlying mechanisms remain unknown and further research should be performed.
Contents lists available at ScienceDirect
Neuroscience and Biobehavioral Reviews
journal homepage:
Obesity affects brain structure and function- rescue by bariatric surgery?
Minke H.C. Nota
, Debby Vreeken
, Maximilian Wiesmann
, Edo O. Aarts
Eric J. Hazebroek
, Amanda J Kiliaan
Brain Center Rudolf Magnus, Department of Translational Neuroscience, University Medical Center Utrecht, Utrecht, the Netherlands
Donders Institute for Brain, Cognition and Behaviour, Department of Anatomy, Radboud University Medical Center, Nijmegen, the Netherlands
Department of Surgery, Rijnstate Hospital/Vitalys Clinics, Arnhem, the Netherlands
Division of Human Nutrition and Health, Wageningen University & Research, Wageningen, the Netherlands
Bariatric surgery
Obesity has a major impact on metabolic health thereby negatively affecting brain function and structure,
however mechanisms involved are not entirely understood. The increasing prevalence of obesity is accompanied
by a growing number of bariatric surgeries (BS). Weight loss after BS appears to improve cognitive function in
patients. Therefore, unraveling mechanisms how BS influences brain function may be helpful to develop novel
treatments or treatments in combination with BS preventing/inhibiting neurodegenerative disorders like
Alzheimer’s disease.
This review shows the relation between obesity and impaired circulation to and in the brain, brain atrophy,
and decreased cognitive functioning. Weight loss seems to recover some of these brain abnormalities as greater
white matter and gray matter integrity, functional brain changes and increased cognitive functioning is seen
after BS. This relation of body weight and the brain is partly mediated by changes in adipokines, gut hormones
and gut microbiota. However, the exact underlying mechanisms remain unknown and further research should be
1. Introduction
It is undeniable that obesity continues to increase around the world.
Having tripled over the last 40 years, 39% of adults worldwide are
overweight (body mass index (BMI) ≥ 25 kg/m
) and 13% are obese
(BMI ≥ 30 kg/m
) (WHO, 2018). It is well known that obesity leads to
an increased risk of developing metabolic disorders (WHO, 2018;
Mitchell et al., 2011) and there is accumulating evidence that obesity
negatively affects brain structure and function (Cherbuin et al., 2015;
Gunstad et al., 2007;Raji et al., 2010;Gunstad et al., 2010). For ex-
ample, it has been shown that obese individuals have a decreased re-
gional cerebral blood flow (CBF) in prefrontal brain regions involved in
attention, reasoning, and executive function (Willeumier et al., 2011).
Moreover, obesity is associated with a lowered gray matter (GM) vo-
lume, as well as impaired white matter (WM) microstructure indicating
a loss of WM integrity either via demyelination or due to inflammation
(Debette et al., 2014;Kullmann et al., 2016). Moreover, obesity during
midlife has been associated with accelerated aging of the brain and risk
of developing dementia (Ronan et al., 2016).
Body Mass Index (BMI) is the most commonly used, yet indirect
measure for obesity. However, in determining the risk of individuals to
develop obesity-related comorbidities, such as cardiovascular disease
and type 2 diabetes mellitus, it is important to acknowledge the dis-
tribution of white adipose tissue (WAT) within the body. Excess body
fat mainly accumulates in the abdominal, gluteal and femoral regions in
subcutaneous WAT, but can also be stored around the internal organs in
visceral WAT (Lee et al., 2013). The latter becomes dysregulated and
detrimental in central obesity, while gluteofemoral obesity is associated
with a lower risk for metabolic disorders (Lee et al., 2013;Veit et al.,
2014a). Therefore, waist circumference or waist-to-hip ratio may be a
more informative measure than BMI only, when regarding obesity-re-
lated diseases (Lee et al., 2013).
WAT is known to produce adipokines, such as the hormones leptin
and adiponectin and proinflammatory cytokines. The production of
these adipokines is deregulated in obesity. Especially visceral WAT
produces more proinflammatory cytokines in obesity, which increases
the risk of developing metabolic complications (Lee et al., 2013). This
imbalance of adipokines can also lead to changes in brain function, such
Received 30 July 2019; Received in revised form 28 October 2019; Accepted 29 November 2019
Corresponding author at: Donders Institute for Brain, Cognition and Behaviour, Radboud university medical center, Department of Anatomy (109), Geert
Grooteplein 21N, 6525 EZ Nijmegen, the Netherlands.
E-mail address: (A.J. Kiliaan).
These authors contributed equally to this work.
Neuroscience and Biobehavioral Reviews 108 (2020) 646–657
Available online 30 November 2019
0149-7634/ © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license
as impairment of the cerebral blood flow (CBF) and subsequent neu-
rodegeneration (Arnoldussen et al., 2014). Furthermore, adipokines can
have a widespread effect on body and brain while they are associated
with inflammatory processes, energy balance and hypertension (Kiliaan
et al., 2014).
Despite the various ways to visualize changes in the brain, it is not
entirely clear via which mechanisms obesity affects brain structure and
function. Therefore, it is extremely important that more research is
conducted to not only treat obesity, but also to prevent a consequent
negative impact on the brain. Undoubtedly, given the compelling evi-
dence that obesity poses a risk for a multitude of comorbidities in-
cluding those of the brain, it is essential that improved treatment op-
tions are available.
Conservative treatment options such as dietary restriction and
physical activity, often show disappointing long-term effects, especially
in patients with morbid obesity (BMI ≥ 40 kg/m
) (Gloy et al., 2013).
Contrarily, bariatric surgery (BS) is known to rapidly and sustainably
decrease body mass and lead to a remission of type 2 diabetes mellitus
and metabolic syndrome. One of the most commonly used procedures is
the Roux-en-Y gastric bypass (RYGB), which results in a reduced sto-
mach volume allowing less food intake, and a shorter small intestine
leading to hormonal changes and reduction of nutrient absorption
(Berthoud et al., 2011;Shin et al., 2013). Although this procedure fa-
cilitates rapid weight loss, there are some important issues associated
with the procedure, such as vitamin and mineral deficiencies due to
bypassing the first part of the small intestine that absorbs these nu-
trients (Bal et al., 2012;Dogan et al., 2017). Another commonly per-
formed type of surgery is laparoscopic sleeve gastrectomy (LSG). During
this procedure, the greater curvature side of the stomach is removed
leaving a tube-shaped remnant. LSG is a technically easier and faster
procedure compared to a RYGB. Long-term effects regarding weight loss
seem promising, although more studies evaluating the long-term effi-
cacy of LSG are necessary (Peterli et al., 2018).
Recent studies have indicated a possible association between BS and
reduction of neurological problems such as cognitive impairment, yet un-
derlying mechanisms are still far from understood (O’Brien et al., 2017;
Keshava et al., 2017;Thiara et al., 2017). In order to shed light on the
impact of obesity and BS on the brain, this review will provide an overview
of the current knowledge on the topic. More specifically, known impact on
brain structure as well as cognitive functioning will be discussed. With re-
gard to brain function and structure, this review will focus on CBF and brain
atrophy, specifically GM and WM volume reduction as well as a decrease in
structural and functional connectivity. We will also focus on cognitive do-
mains such as stimulus processing, reward and memory. Additionally, the
potential ability of rapid weight loss after BS to counteract the negative
impact of obesity on the brain will be explored and hypotheses about the
underlying mechanisms will be shared. This review mainly focuses on
human literature, however further research in humans and animals should
be exerted to disentangle the mechanisms responsible for (partially) re-
storing brain structure and cognitive functioning after weight loss. Un-
raveling underlying mechanisms may be of help to develop novel treat-
ments or treatments in combination with BS. Relevant literature was
gathered from Scopus and PubMed published between 1997 and May 2019,
using the following search terms in different combinations: ‘obesity’, ‘brain’,
‘structure’, function’, ‘cerebral blood flow’, ‘bariatric surgery’, ‘neuroima-
ging’, ‘RYGB’ and ‘sleeve gastrectomy’. Articles written in English were
screened for relevance. Besides, we included relevant additional publica-
tions identified from bibliographies from retrieved literature.
2. Influence of obesity on brain structure and function
2.1. Cerebral hemodynamics
Accurate blood flow is crucial for survival and functioning of any
organ, however the brain is fully dependent on blood flow for oxygen
and glucose, and tissue damage may already occur after a very brief
disruption in blood (Cipolla, 2009). Therefore, it is important to un-
derstand how obesity influences blood flow to and in the brain. Studies
show significant negative correlations between BMI and CBF velocity
(CBFV) in the common and internal carotid arteries (Zhang et al.,
2006). Lowered CBFV in obesity is associated with reduced cognitive
performance independent of comorbid medical conditions. More im-
portantly, the effect of BMI on CBFV seems to be independent of other
factors such as hypertension and type 2 diabetes mellitus (Zhang et al.,
2006;Selim et al., 2008).
Recently, it was found that obesity (measured as BMI and waist
circumference) was negatively associated with resting GM CBF. This is
an important finding, as GM CBF is generally correlated with cognitive
functioning, implying that obesity may directly affect cognition via
changes in CBF (Rusinek et al., 2015). Abdominal obesity is a major risk
factor contributing to the metabolic syndrome (MetS). In a late middle-
aged MetS group mean GM CBF was decreased compared to the control
group (excluding medial and inferior parts of the occipital and temporal
lobes). Interestingly, the MetS group also had lower immediate memory
function (Birdsill et al., 2013).
Altogether, this implies that obesity may pose a risk for impaired
blood flow to and in the brain. Contrarily, studies using positron-
emission tomography have shown hypermetabolism in the brain in
obesity, which might lead to an imbalance in reward systems and
cognitive control (Iozzo et al., 2012). Furthermore, CBF and oxygen
metabolism in feeding-related brain regions is higher in obese in-
dividuals than in normal-weight persons (Karhunen et al., 1997). Pos-
sibly, increased activation in the right parietal cortex may relate to
decreased feeding control, which could contribute to development and
maintenance of the obese state (Karhunen et al., 1997).
2.2. Brain volume and integrity
It is well-established that obesity affects GM and WM integrity,
probably caused by impaired CBF leading to ischemic stress and con-
comitant neuronal damage within the brain (Bobb et al., 2014).
2.2.1. Grey matter
Although there is increasing awareness that obesity is a risk factor
for neurodegenerative diseases and cognitive decline, it is not yet clear
how overweight relates to brain structural and functional changes. A
large-scale population neuroimaging study showed a negative associa-
tion between BMI (kg/m2), waist-to-hip ratio and fat index (total fat
mass (kg)/height (m)) with overall GM volume (Hamer and Batty,
2019). Another study reported that obese individuals showed decreased
GM density in different brain areas, notably those involved in taste,
reward and feeding/goal-directed behavior. Contrarily, greater GM
density was also seen in obese subjects when compared to lean coun-
terparts (Pannacciulli et al., 2006).
Subsequent studies on GM atrophy have examined volume and
cortical thickness rather than density and found that obesity/BMI/waist
circumference is as expected, inversely related to GM volume.
However, some have focused more on pin-pointing the underlying
cause of these GM changes, by distinguishing between different aspects
of obesity. For example, one study focused on the underlying cause of
these changes, by distinguishing between fat mass and fat-free mass in
overweight/obese individuals (Weise et al., 2013). Interestingly, this
study indicated that there is an association between excess fat/adiposity
and GM atrophy, which is more attributed to the increased fat-free mass
in obese individuals than increased body fat mass. Nevertheless, it was
found that obesity is negatively related with GM volume, especially in
the medial prefrontal cortex (mPFC) and the anterior cingulate cortex
(ACC). These structures are involved in decision making and inhibitory
control (Weise et al., 2013). On the other hand, Janowitz et al. have
linked waist circumference as a measure for abdominal obesity to GM
volume changes, rather than simply BMI. With 2344 subjects, this large
study has indicated that many brain regions are affected by abdominal
M.H.C. Nota, et al. Neuroscience and Biobehavioral Reviews 108 (2020) 646–657
obesity (Janowitz et al., 2015).
Rather than investigating GM density or volume, Shaw et al. focused
on cortical thickness as a measure of GM integrity. According to this
study, an inverse relationship between BMI and cortical thickness was
found (Shaw et al., 2018). However more importantly, an association
was discovered between cortical thinning and increased visceral WAT,
when adjusted for BMI score (Veit et al., 2014b). This relates well to the
study by Janowitz et al. on effects of abdominal obesity, which in-
dicates that these GM changes are possibly caused by inflammatory
responses due to adipokine release by central WAT (Janowitz et al.,
2015). Similar to other studies, Veit et al. showed an association be-
tween increased BMI and increased visceral WAT with reduced GM
thickness in several brain areas (Veit et al., 2014a).
In conclusion, there is increasing evidence for obesity measures
being associated with GM volumes, although the exact associations and
mechanisms are still under debate. Furthermore, many studies are
performed cross-sectional, and therefore it is not possible to definitely
state the direction of the associations.
2.2.2. White matter
It has become clear that obesity not only influences integrity of GM
in the brain but WM and structural connectivity is affected by adiposity
as well (Verstynen et al., 2013). Conclusions from adiposity and WM
integrity association studies resemble those about GM integrity. Indeed,
several studies using diffusion weighted imaging have shown negative
correlations between obesity measures and fiber connectivity
(Kullmann et al., 2016;Verstynen et al., 2013;Xu et al., 2013;
Bolzenius et al., 2015;van Bloemendaal et al., 2016;Alarcon et al.,
2016). Interestingly, not all studies have implicated the same regions,
although there is some overlap in results. Affected WM structures
comprise for example the corpus callosum (genu, trunk and splenium),
cerebellar peduncle, corona radiata (Verstynen et al., 2013;Xu et al.,
2013), fornix (Xu et al., 2013), and the uncinate fasciculus in older
adults (Bolzenius et al., 2015). One of these studies has indicated a
decrease in WM volume using voxel based morphometry analysis (van
Bloemendaal et al., 2016). Kullmann et al. revealed regionally specific
changes in mean diffusivity and a strong decrease in axial diffusivity in
obese young adults in the corticospinal tract, anterior thalamic radia-
tion and superior longitudinal fasciculus indicating an increased risk for
cognitive decline in obese individuals (Kullmann et al., 2016).
It is important to note that Hamer et al. did not find any association
between obesity measures and WM and others even observed a positive
interaction between BMI and WM integrity and volume (Hamer and
Batty, 2019;Koivukangas et al., 2016;Haltia et al., 2007). In short,
there is less conclusive evidence about the associations between obesity
measures and WM compared to GM. An increase in WM volume might
be due to an abnormal lipid metabolism and therefore fat accumulation
in myelin throughout the brain (Haltia et al., 2007). More importantly,
it would be interesting to see whether these WM changes affect cog-
nitive impairment.
2.3. Resting state activity
Lastly, several neuroimaging studies demonstrated a marked dif-
ference in resting-state functional connectivity (RSFC) between obese
and normal-weight individuals. This is indicative of lower functional
connectivity in the middle frontal gyrus (Garcia-Garcia et al., 2015). On
the other hand, increased activity synchronicity was found in the left
putamen of obese men before consumption of a meal. Furthermore, the
same study also indicated lower functionality of the (pre)frontal cortex
before food intake, possibly leading to decreased inhibition (Zhang
et al., 2015).
Table 1 gives an overview of the studies discussed, focusing on the
influence of obesity on blood flow, brain structure and resting state
3. Influence of obesity on cognitive functioning
3.1. Food-related stimulus processing
Areas concerning feeding behavior, such as the frontal operculum,
post-central gyrus, dorsal striatum, prefrontal cortex and hippocampus,
often show a decreased volume in obesity (Pannacciulli et al., 2006;
Janowitz et al., 2015). Concurrent with this, obese people do in fact
show different responses to visual food cues. When observing high-
calorie foods obese women show higher blood oxygen level dependent
(BOLD) activation in the dorsal striatum, a brain area that has been
implicated in habit learning and addictive behavior (Rothemund et al.,
2007). Moreover, obese children, adolescents and adults show higher
activation of several brain regions, including the nucleus accumbens
and caudate nucleus, compared to normal-weight controls when tasting
sweet, bitter and high-calorie substances (Feldstein Ewing et al., 2017;
Boutelle et al., 2015;Szalay et al., 2012). In general, it is noteworthy
that increased BMI/waist circumference is associated with altered
gustatory perception, although it should be investigated whether this is
a cause or consequence of obesity.
In addition, obesity has been shown to be associated with aberrant
reward responsivity. Several studies have indicated that connectivity in
reward-related networks is less strong in obese individuals in compar-
ison to normal-weight counterparts (Garcia-Garcia et al., 2013;
Wijngaarden et al., 2015). However, there appears to be a stronger
activation of reward-processing areas during tasks such as monetary
reward paradigms (Opel et al., 2015). This suggests that disinhibition
takes place due to decreased connectivity. Additionally, BMI is posi-
tively associated with serotonin availability in areas such as the nucleus
accumbens and ventral pallidum, which are involved in reward pro-
cessing (Haahr et al., 2012). This indicates that obese individuals have a
stronger sense of reward after ingestion of palatable foods. Increased
serotonin levels have also been found in hippocampus and the orbito-
frontal cortex in obese subjects, which are both involved in (food) re-
ward learning and processing (Haahr et al., 2012).
Moreover, obesity is associated with changes in activity of brain
regions that are related to feeding behavior and stronger reward ac-
tivity (Rothemund et al., 2007;Szalay et al., 2012). This suggests that
these alterations cause obesity, rather than obesity causes changes in
brain activity (Janowitz et al., 2015).
3.2. Cognitive function and control
Obesity has been associated with decreased memory performance
and learning ability, as shown through various parameters. For ex-
ample, it has been found that working memory is decreased in obese
individuals when compared to normal-weight counterparts (Stingl
et al., 2012). Interestingly, this was associated with an increase in
neural activity, rather than a decrease, during the early phase after
stimulus presentation. This possibly indicates disinhibition, which has
indeed been observed in obesity and can lead to insufficient suppression
of unwanted responses, thereby decreasing accuracy and reaction speed
(Stingl et al., 2012). Additionally, recent evidence suggests that obese
individuals exhibit inadequate implicit learning, for example by failing
to apply negative prediction error in tasks requiring adaptation of be-
havior. This is possibly due to inadequate dopamine signaling (Mathar
et al., 2017). It has further been shown that compared to normal-weight
individuals, obese participants exhibit decreased activity in regions
associated to memory and learning, such as the hippocampus, angular
gyrus, precuneus and the parahippocampal gyrus and parts of the
prefrontal cortex. Areas such as these have been implicated to be af-
fected by obesity, making this decreased activity consistent with find-
ings of volume and density loss mentioned earlier (Cheke et al., 2017).
Lastly, there are reports on increased impulsivity/lack of inhibitory
control in obese individuals, which is in accordance with structural
alterations observed in regions associated with cognitive control (Weise
M.H.C. Nota, et al. Neuroscience and Biobehavioral Reviews 108 (2020) 646–657
Table 1
Summary of studies based on the influence of obesity on blood flow, brain structure and resting state activity. *mean ± SD. (f)MRI: functional magnetic resonance imaging; DTI: diffusion tensor imaging; BMI: body mass
index; FA: fractional anisotropy; SLF: superior longitudinal fasciculus; ILF: inferior longitudinal fasciculus; CBF: cerebral blood flow; GM: gray matter; MetS: metabolic syndrome; VBM: voxel-based morphometry; WM:
white matter; WHR: waist-hip ratio; SPECT: single photon emission computed tomography; rCBF: regional CBF; RD: radial diffusivity; CC: corpus callosum; rCBF: regional cerebral blood flow; DBP: diastolic blood
pressure; TCD: transcranial Doppler; BFV: blood flow velocities; SBP: systolic blood pressure; CVR: cerebrovascular resistance; T2DM: type 2 diabetes mellitus; VAT: visceral adipose tissue; FFMI: fat-free mass index; FMI:
fat mass index; GMV: gray matter volume; vmPFC: ventromedial prefrontal cortex; OFC: orbital frontal cortex; λ1: axial eigenvalue; CR: corona radiata; λ
: radial eigenvalue; CBFV: cerebral blood flow velocity; CCA:
common carotid artery; CA: carotid artery; ICA: internal carotid artery; mPFC: medial prefrontal cortex.
Mean Age of Participants Additional Information Tests/methodology Observations
Alarcon et al., 2016 152 (85:67) 14.1 ± 1.3 3 study groups: normal-weight
(n=88), overweight (n=46), obese
(f)MRI, DTI BMI correlated to FA ↓ (L SLF/ILF)
Birdsill et al., 2013 69 (26:43) 60.4 ± 6.1 2 study groups: control (n=40),
metabolic syndrome (n=29)
Arterial spin labelling for CBF, MRI,
neuropsychological assessment
GM CBF and immediate memory function ↓ in MetS compared to
control. Abdominal obesity and triglyceride ↑ correlate with CBF
Bolzenius et al., 2015 62 (20:42) 62.4 ± 8.44 MRI, DTI, neuropsychological assessment BMI associated with FA (uncinate fasciculus)
Garcia-Garcia et al., 2015 41 (15:26) 31.3 ± 6.0 (lean), 33.6 ± 5.6 (obese) 2 study groups: lean (n=21), obese
Task-related and resting state fMRI ↓ Functional connectivity of the middle frontal gyrus and the
lateral occipital cortex in obese compared to lean.
Haltia et al., 2007 46 (20:26) 37 ± 21 (lean), 37 ± 12 (obese) 2 study groups: lean (n=16), obese
MRI for VBM WM volumes ↑ in obese compared to lean (temporal gyri,
fusiform and parahippocampal gyri, brainstem and cerebellum).
WHR and serum free fatty acid concentration correlate with WM
volume ↑ in obese group. Dieting/weight loss correlates with
WM volume ↓
Hamer and Batty, 2019 9652
55.4 ± 7.5 Medical examinations, MRI GM volume associated with BMI, WHR and fat mass. No
associations between obesity and white matter.
Janowitz et al., 2015 2344
49.8 ± 9.3 (SHIP-2); 46.3 ± 11.3
2 study populations: SHIP-2
(n=758), SHIP-TREND (n=1586)
Questionnaire, medical examination, MRI
for VBM
Waist circumference associated with GM ↓ (various parts of
cerebral cortex, striatum, limbic system)
Karhunen et al., 1997 23 (0:23) 39.8 ± 9.7 (lean); 45.0 ± 10.0 (obese) 2 study groups: lean (n=12), obese
SPECT for rCBF No differences in total CBF obese vs. lean; temporal/parietal
rCBF ↓ in obese subjects in control situation, L parietal rCBF ↓ in
food-exposed situation; rCBF R side > L side of parietal cortex/
thalamus of obese women during food-exposure, compared to
normal-weight. R temporal and parietal rCBF ↑ between control
and food-exposed situation in the obese group compared to the
lean. ↑ R parietal rCBF was associated with ↑ feeling of hunger in
the obese group.
Koivukangas et al., 2016 88 (29:59) 22.3 ± 0.7 (risk); 22.2 ± 0.7 (control) 2 study groups: familial risk for
psychosis (n=42), control (n=46)
MRI, DTI In risk group, BMI correlated with FA ↓ (R parietal and
periventricular areas) and RD ↑; In control group, BMI associated
with FA ↑ (L hemisphere)
Kullmann et al., 2016 48 (25:23) subcohort: 26.68 ± 3.68 (lean); 26.12
± 1.95 (overweight); 26.88 ± 4.45
3 study groups: lean (n=24),
overweight (n=12), obese (n=12)
MRI for voxel-based quantification and DTI BMI associated with differences in DTI parameters indication
myelin (parts of L SLF, thalamic radiation, internal capsule, CC),
water ↑ (R SLF), iron content alteration (thalamic radiation, CC,
cingulum), FA ↓ (cerebellar peduncle, corticospinal tract,
thalamic radiation)
Pannacciulli et al., 2006 60 (36:24) 33 ±9 (lean); 32 ± 8 (obese) 2 study groups: lean (n=36), obese
PET for rCBF, MRI for voxel-based
GM density ↓ (cerebellum, L postcentral gyrus, R frontal
operculum, putamen, middle frontal gyrus), in obese vs. normal-
weight. Higher GM density of the L calcarine cortex, L middle
occipital and inferior frontal gyri, and the R cuneus, and higher
WM density around striatum, in obesity vs. normal-weight
Rusinek et al., 2015 87 (37:50) 51.8 ± 3.8 (healthy); 50.9 ± 4.5
(insulin resistance); 54.2 ± 5.2 (T2DM)
3 study groups: control (n=37),
insulin resistant (n=27), diabetes
Medical evaluation, neuropsychological
assessment, arterial spin labelling to
measure CBF
CBF associated with sex, waist circumference, DBP, end tidal
CO2, verbal fluency score and BMI, and was significantly
different between the groups.
Selim et al., 2008 197 (90:107) 56.8 ± 13.2 (healthy); 61.3 ± 7.2
(T2DM); 52.9 ± 11.3 (hypertension);
58.2 ± 9.8 (stroke)
4 study groups: control (n=90),
diabetes (n=30), hypertension
(n=45), stroke (n=32)
Medical examination, TCD analysis, MR
angiography, MRI
Age/BMI associated with mean BFV ↓; SBP associated with BFV
in hypertension group; mean BFV ↓ in men vs. women in stroke
group; BMI associated with CVR ↑ and atherogenic index
Shaw et al., 2018 792 44–49 (midlife); 60–66 (late-life) 2 study groups: midlife (n=405),
late-life (n=387)
Medical examination, MRI BMI associated with ↑ cortical thinning at midlife (posterior
cingulate). In late-life, increasing BMI is associated with ↓
cortical thinning (R supramarginal cortex, frontal regions).
(continued on next page)
M.H.C. Nota, et al. Neuroscience and Biobehavioral Reviews 108 (2020) 646–657
et al., 2013;Skoranski et al., 2013). However, it is difficult to assess
whether obesity causes increased impulsivity or vice versa, as it seems
more plausible that impulsive individuals have a higher disposition to
develop obesity. Nevertheless, this association should be investigated
Table 2 gives an overview of the studies discussed based on the
influence of obesity on cognitive functioning.
3.3. Underlying mechanisms between obesity and brain structure and
As mentioned earlier, it has been suggested that especially visceral
and abdominal WAT becomes inflamed and dysregulated in obesity,
producing adipokines, such as inflammatory cytokines that can cause
inflammation (Verstynen et al., 2013). Examples are monocyte che-
moattractant protein-1 (MCP-1), tumor necrosis factor-α (TNF-α) and
interleukins (IL) such as IL-6 and IL-β (Kiliaan et al., 2014;Jaganathan
et al., 2018). This increased secretion of inflammatory factors has been
associated with damage to food-intake regulating circuits of the brain
(Cazettes et al., 2011). Moreover, the pro-inflammatory IL-6 may
especially affects hippocampal volume and function (Kiliaan et al.,
2014). These and other adipokines, such as angiotensinogen, serum
amyloid A (SAA) and plasminogen activator inhibitor-1 (PAI-1) have
adverse effects on the cardiovascular system, such as hypertension,
thrombosis, atherosclerosis and endothelial dysfunction which may
contribute to the changes observed in the blood circulation and con-
sequently CBF (Arnoldussen et al., 2014;Kiliaan et al., 2014;Verstynen
et al., 2013). Additionally, altered CBF itself has a negative effect on
cognitive function (Rusinek et al., 2015). Leptin is one of the more well-
known adipokines that may contribute to the negative effects of obesity
on the brain. Leptin can have various roles in the brain such as energy
intake regulation in the hypothalamus, memory, neurogenesis and
brain structure (Arnoldussen et al., 2014). It has been shown that
concentrations of leptin in the blood are negatively correlated with GM
volume (Pannacciulli et al., 2007).
Changes in WM in obesity seem to be related to vascular and in-
flammatory factors as well (Bettcher et al., 2013). For example, in-
flammatory cytokines can lead to a cellular response of microglia
leading to more water in brain tissue causing loss of WM integrity
(Rosano et al., 2012;Kullmann et al., 2015).
Sex differences should be considered when looking at WAT in the
relation between the obese phenotype and brain function and structure
(Horstmann et al., 2011). There are significant discrepancies in fat
distribution between men and women which might lead to different
effects on the brain. Most importantly, men typically store more fat in
the abdomen, whereas in women, fat is mostly stored in gluteofemoral
WAT (White and Tchoukalova, 2014). This is also associated with sex-
related differences in adipokine levels (Kiliaan et al., 2014). Therefore,
it is important to have equal representation of males and females in
Next to WAT, the gut, gut hormones and its microbiome have large
effects on the brain, such as regulating eating behavior (Torres-Fuentes
et al., 2017). It has been found that microbiota of obese individuals are
different and less diverse compared to lean individuals, with a lower
proportion of the bacteria group Bacteroidetes and higher proportion of
Firmicutes (Ley et al., 2006). Additionally, gut microbiota can generate
short chain fatty acids (SCFAs) via fermentation of dietary fibers. In
obesity, increased SCFA concentrations are observed and these SCFAs
can influence the production of neurotransmitters and their precursors
(van de Wouw et al., 2017;Schwiertz et al., 2010). Furthermore, gut
microbiota can affect gastrointestinal barrier permeability. As shown in
diet-induced obese mice, obesity is associated with increased gut per-
meability (Lam et al., 2012).
Therefore, it is plausible that the obese phenotype is related to
changes in the gut as well as changes in WAT, which influence brain
function and structure (see Fig. 1a).
Table 1 (continued)
Mean Age of Participants Additional Information Tests/methodology Observations
van Bloemendaal et al.,
46 (24:22) 57.3 ± 1.9 (lean); 57.7 ± 2.2 (obese);
61.4 ± 1.5 (T2DM)
3 study groups: lean (n=15), obese
(n=15), obese T2DM (n=16)
MRI for VBM and DTI WM volume/integrity ↓ in T2DM vs. lean (R corticospinal/
fronto-occipital tracts, R SLF/forceps major), BMI associated
with WM volume/integrity ↓ (L external capsule, R inferior
parietal lobe)
Veit et al., 2014a,2014b 72 (42:30) 29.7 ± 8.2 Medical examination, MRI BMI/VAT associated with cortical thickness ↓ (parts of R frontal/
temporal lobe, L temporal/parietal/occipital cortex)
Verstynen et al., 2013 155 (78:77) 40.7 ± 6.2 Medical examination, DTI Adiposity correlated with FA (CC, peduncle, corona radiata),
mediated by BP, dyslipidemia, inflammation and glucose
Weise et al., 2013 76 (52:24) 32.1 ± 8.8 Body composition assessment, MRI for
FFMI, and for some parts also FMI, correlated with GMV ↓ of
temporal lobes, vmPFC, caudolateral OFC and L mid-posterior
insula. FMI correlated with L cerebellar GMV ↓
Xu et al., 2013 51 (30:21) 29.6 ± 10.0 2 study groups: BMI < 25 (n=22),
BMI ≥ 25(n=29)
DTI BMI correlated with FA ↓ of CC, λ1 ↑ of R CR/SLF, and λ
↑ of
Zhang et al., 2006 1323 (474:849) 56.41 ± 8.19 Questionnaire, medical examination, TCD
ultra-sound examination
CBFV ↓ associated with age (CCA in men, all CA in women), BMI
(ICA in men, CCA and ICA in women), and SBP (CCA)
Zhang et al., 2015 20 (20:0) 24 ± 4 (lean); 24 ± 4 (obese) 2 study groups: lean (n = 20), obese
Resting state fMRI in hunger and satiety
states, blood samples.
Before food intake: ↑ synchronicity of activity in l putamen, ↓
synchronicity of activity in OFC and mPFC in obese subjects. ↑
ratings of hunger.
After food intake: no differences between obese and lean. In all
participants ↑ synchronicity of activity in OFC.
M.H.C. Nota, et al. Neuroscience and Biobehavioral Reviews 108 (2020) 646–657
Table 2
Summary of studies based on the influence of obesity on cognitive functioning. *mean ± SD. (f)MRI: functional magnetic resonance imaging; IR: insulin resistance; BMI: body mass index; PFC: prefrontal cortex; PET:
positron emission tomography; 5-HT4R: 5-hydroxytryptamine receptor 4; NAcc: nucleus accumbens; OFC: orbital frontal cortex; PE: prediction error; vlPFC: ventrolateral prefrontal cortex; SMA: supplementary motor
area; EEG: electroencephalography; ERP: event-related potential; ERN: error-related negativity; MEG: magnetoencephalography; RMS: root-mean-square; dlPFC: dorsolateral prefrontal cortex; dACC: dorsal anterior
cingulate cortex.
Participants (M:F) Mean Age of Participants Additional Information Tests/methodology Observations
Boutelle et al., 2015 23 10.4 ± 0.3 2 study groups: obese (n = 10),
healthy (n = 13)
fMRI ↑ response to sucrose in obese compared to healthy weight, and to water
in healthy weight compared to obese (paracingulate, medial/middle
frontal and lingual gyri, R amygdala, L posterior middle temporal gyrus)
Cheke et al., 2017 32 (18:14) 27.3 ± 5.9 (lean); 27.7 ± 5.7 (obese) 2 study groups: lean (n = 16),
obese (n = 16)
Treasure-Hunt task, fMRI IR but not BMI is associated with task performance (error rate). Activity ↑
in angular and parahippocampal gyrus, hippocampus, L anterior PFC, L
precuneus of lean vs. obese. Similar for low vs. high IR. Leptin associated
with R parahippocampal/angular gyrus and L precuneus activity.
Feldstein Ewing et al.,
24 (20:4) 16.46 ± 1.4 1 study group, all overweight. fMRI gustatory cue exposure task ↑ response in various regions for high vs low calorie beverages. BMI
associated to ↑ response for high vs low calorie beverages.
Garcia-Garcia et al.,
37 (13:24) 34.78 ± 4.45 (obese); 32.00 ± 5.87
2 study groups: obese (n = 18),
normal-weight (n = 19)
fMRI ↓ connectivity in visual, frontal and default mode networks during
rewarding stimuli, and occipital, frontal and default mode networks
during neutral stimuli, in obese vs. normal-weight.
Haahr et al., 2012 28 (15:13) 41.3 ± 15.4 (normal-weight); 41.0 ± 19.9
2 study groups: normal-weight
(n = 16), overweight/obese (n
= 12)
MRI, PET for 5-HT4R binding
BMI associated with 5-HT4R density in NAcc, ventral pallidum, L OFC
and L hippocampus.
Mathar et al., 2017 58 (28:30) 26.6 ± 3.6 (lean women); 28.3 ± 4.7
(obese women); 26.0 ± 3.2 (lean men);
27.2 ± 5.3 (obese men)
2 study groups: lean (n = 30),
obese (n = 28)
Weather prediction task (positive/
negative prediction error learning),
Learning performance ↓ in obese vs. lean. Learning strategy more
random-like in obese vs. lean. Response consistency and negative PE use
↓ in obese vs. lean. Prediction-related BOLD activation ↓ in L superior
frontal gyrus, L vlPFC, precuneus and R premotor cortex of obese vs.
lean. Complexity-related BOLD activation ↑ in L putamen, premotor
cortex, SMA, R thalamus, precuneus, lingual/parahippocampal gyrus, L
inferior parietal lobe of lean vs. obese. PE-related functional
connectivity/coupling ↓ between ventral striatum and SMA/motor cortex
in obese vs. lean.
Opel et al., 2015 56 (30:26) 42.04 ± 10.17 (normal-weight); 43.79 ±
8.86 (obese)
2 study groups: normal-weight
(n = 28), obese (n = 28)
fMRI BOLD response ↑ for reward vs. control in insula, OFC, putamen, PFC,
anterior cingulate cortex, temporal/occipital lobe and cerebellum of
obese, compared to normal-weight.
Rothemund et al.,
26 (0:26) 29 ± 5.6 (normal-weight); 31 ± 9.4
2 study groups: normal-weight
(n = 13), obese (n = 13)
Psychometric evaluation, fMRI Activation ↑ in parts of striatum, L insula, L hippocampus and L parietal
lobe under high-calorie condition, in several frontal, occipital and
temporal gyri under low-calorie condition, and in L middle frontal gyrus,
L cuneus and R inferior parietal lobe under utensil condition, in obese vs.
control. ↑ activity in R caudate body under utensil condition in control
vs. obese. BMI associated with BOLD signal change in various regions
across the brain under high-calorie condition.
Skoranski et al., 2013 60 (23:37) 12.8 ± 2.4 (obese); 12.8 ± 2.5 (control) 2 study groups: obese (n = 22),
control (n = 32)
Arrow task, EEG/ERP ERN amplitude ↓ after errors in obese vs. control. Pe component ↓ in
obese vs. control. Error rates ↑ in obese vs. control.
Stingl et al., 2012 68 (20:48) 36.5 ± 9.5 (lean); 38.4 ± 11 (obese) 2 study groups: lean (n = 34),
obese (n = 34)
MEG Weight associated with response accuracy ↓ and reaction time ↑. RMS ↓
in obese vs. lean. BMI associated with neural activity ↓ in occipital area
at 250−350 ms. BMI correlated with neural activity in R dlPFC at
100−350 ms.
Szalay et al., 2012 24 (6:18) 38.3 ± 4.2 (obese); 37.1 ± 3.8 (healthy) 2 study groups: obese (n = 12),
healthy (n = 12)
Taste stimulation on fMRI, MRI Pleasantness rating ↑ for sucrose and vanilla, and ↓ for quinine (bitter) in
obese vs. control. Taste induced brain activity ↑ in operculum, insula,
middle frontal gyrus, OFC, amygdala, striatum and thalamus of obese vs.
control. BMI and subjective hedonic sucrose/vanilla ratings associated
with activation ↑ for these areas under taste condition. Quinine ratings
associated with activation ↓.
Wijngaarden et al.,
24 (4:20) 31 ± 3 (obese); 28 ± 3 (lean) 2 study groups: obese (n = 13),
lean (n = 11)
fMRI After 48 h fast, connectivity ↓ between hypothalamus and L insula/
superior temporal gyrus, and ↑ between amygdala and L caudate nucleus
in obese but not lean. Hypothalamus connectivity to dACC ↓ in obese but
↑ in lean.
M.H.C. Nota, et al. Neuroscience and Biobehavioral Reviews 108 (2020) 646–657
Fig. 1. The potential mechanism between obesity (a) and the potential effect of bariatric surgery induced weight loss (b) on brain function and structure based on
changes in adipose tissue and the gut. WM: white matter; GM: grey matter; SAA: serum amyloid A; PAI-1: plasmogen activator inhibitor-1; MCP-1: monocyte
chemotactic protein-1; TNF-α: tumor necrosis factor-alpha; IL-β: interleukin-beta; IL-6: interleukin-6; PYY: peptide YY; GLP-1: glucagon-like peptide-1.
M.H.C. Nota, et al. Neuroscience and Biobehavioral Reviews 108 (2020) 646–657
4. Effects of bariatric surgery
Given the fact that BS is concomitantly increasing with the number
of morbidly obese individuals, it would be interesting to determine
whether drastic weight loss measures can reverse the obese-related
effects previously explained.
4.1. Structural alteration
Some recent studies investigated the possibly reversing effects of BS
on structural alterations in the brain. It has been confirmed that morbid
obese individuals showed marked changes in both GM and WM density
of various brain regions prior to surgery, in comparison to healthy
controls (Tuulari et al., 2016;Zhang et al., 2016). Surprisingly, six
months after BS alterations in GM density as well as WM density were
shown. Both GM and WM densities recovered after weight loss, being
most apparent in WM (Tuulari et al., 2016). Moreover, a study on the
effects of LSG showed improvement of WM integrity and connectivity,
already one month after surgery (Zhang et al., 2016). The effect on GM
is different, as only modest GM recovery is visible six months after
surgery. Nevertheless, follow-up in the aforementioned studies is rela-
tively short and long-term follow-up of BS may reveal significant im-
provement of GM (Tuulari et al., 2016).
4.2. Cognitive improvement
Compared to brain structure, more research has been performed on
cognitive changes after BS. It was assumed that surgery itself could pose
a risk of (further) cognitive dysfunction due to nutritional deficiencies
that can occur after BS. However the study by Gunstad et al. indicated
that patients’ cognitive functioning in several domains, such as
memory, attention, executive function and language had in fact im-
proved after surgery, from below average to (greater than) average
(Gunstad et al., 2011). On the other hand, obese controls who had not
been subjected to surgery, did not show such improvement, but rather a
decline of cognitive performance (Gunstad et al., 2011).
A similar study with three to four year follow-up of patients who
underwent BS, also showed that cognitive improvement was main-
tained for attention, executive function and memory: significantly
fewer patients showed cognitive impairment three years after surgery
than before, and cognitive functioning improved from low average to
average. Moreover, four years after BS, executive function even im-
proved to a high average score. Interestingly, it was shown that when
BMI in patients increased again, which was seen mostly between the 2
and 3
year after surgery, attention scores were decreased (Alosco
et al., 2014). However, this was not observed in all measures of cog-
nitive functioning.
Although, surgically induced weight loss is associated with im-
proved cognitive function, a recent study showed that when compared
to healthy controls who had never been obese, ex-obese individuals
who had undergone BS still do not perform at the same cognitive level
(Tarantino et al., 2017). This implies that some alterations in the brain
due to excess weight cannot be restored in the short term. However, the
healthy control group still had a significant lower BMI compared to the
ex-obese individuals, as BS mostly only reduces the severity of over-
weight. This might also explain the difference seen in cognition be-
tween healthy controls who had never been obese and the ex-obese
individuals. Longer follow-up is required to confirm these findings.
4.3. Functional changes
Observed neural activity in patients before and after BS demon-
strates changes in brain function. For example, several studies indicate
that people show less craving of high calorie (HC) food (Li et al., 2019)
and less response to visual HC food cues, especially within the meso-
limbic pathway already one month after surgery (Ochner et al., 2012).
More importantly, activity within the mesolimbic pathway before sur-
gery seems to potentially predict weight loss 12 months after LSG
(Holsen et al., 2018), which may indicate neural activity as a useful
biomarker for BS eligibility. Furthermore, differences in activity are
seen between LSG and RYGB; BOLD signal in the ventral tegmental area
(important for reward processing) for HC food cues declined more after
RYGB compared to LSG (Faulconbridge et al., 2016).
As the obese state is associated with altered RSFC, it is interesting to
see whether weight loss after BS can reverse these changes. Decreased
RSFC within reward processing and cognitive control areas as seen in
obesity, has been shown to recover after BS (Li et al., 2018a). Wie-
merslage et al. have indeed found changes in RSFC following BS, in-
cluding the insula and putamen dependent on prandial state
(Wiemerslage et al., 2017). These areas affect self-referential processing
and learning, therefore changes within these regions might alter control
of eating behavior. This is in line with more recent literature showing
reduced RSFC within regions affecting self-referential processing (Li
et al., 2018b).
The reviewed studies focusing on impact of BS on the brain are
summarized in Table 3.
4.4. Underlying mechanisms
Despite the positive influence of BS on brain function, it is not yet
entirely clear whether these changes are brought about solely by weight
loss. Therefore it will be worthwhile to determine whether ‘traditional
weight loss’ programs which involve dietary modification and exercise
show the same positive influence as BS on the brain. However, it has
been indicated that especially RYGB may influence gut-brain commu-
nication as well as adipokine secretion, which may provide additional
benefits to brain function recovery (Berthoud et al., 2011;Ballsmider
et al., 2015).
Additionally, after BS altered adipokine secretion is found, with
among others reduced angiotensinogen (Ghanim et al., 2018) and PAI-1
levels (Tschoner et al., 2012). These changes may lead to remission of
hypertension and reduction of atherosclerosis which has a positive in-
fluence on the vascular wall health (Tschoner et al., 2013;Wilhelm
et al., 2014). This might contribute to a better blood circulation and
therewith higher CBF.
Furthermore, changes in gut hormone levels after BS are related to
weight loss (Alamuddin et al., 2017) and functional brain changes (Li
et al., 2019;Zhang et al., 2019). In summary, changes in fasting ghrelin
levels and postprandial higher levels of peptide YY (PYY) and gluca-
gon‑like peptide‑1 (GLP-1) are seen after BS (le Roux et al., 2006;Field
et al., 2010;Kalinowski et al., 2017). These gut hormones play a role in
the regulation of energy homeostasis in the brain. Especially the de-
crease of ghrelin after LSG due to the removal of the gastric fundus in
which ghrelin is mainly produced, has shown to directly influence the
brain. For example, the study by Li et al. showed that the reduction in
ghrelin was associated with less cravings to HC food and reduction in
dorsolateral prefrontal cortex activation to food cues along with
strengthened connectivity between regions important for self-control
and executive functions (Li et al., 2019). Others also demonstrated that
ghrelin directly affects the hippocampus via modulating its connectivity
with the insula (Zhang et al., 2019). This implies that ghrelin underlies
changes in brain reactivity and eating behavior.
Furthermore, recovery of obesity-related brain volume might be due
to a reduction of inflammatory cytokines and less metabolic stress
(Tuulari et al., 2016). Reduced levels of the adipokines IL-6, TNF-α and
MCP-1 (Kelly et al., 2016) are found after BS, and lead to lower WAT
inflammation and systemic inflammation (Sams et al., 2016). For ex-
ample, since an association exists between IL-6 plasma levels and lower
hippocampal GM volume, reduction of IL-6 levels possibly mediate
memory improvement (Marsland et al., 2008).
Recovery of WM might be due to remyelination (Bhatt et al., 2014).
The fact that WM is able to recover due to weight reduction, also
M.H.C. Nota, et al. Neuroscience and Biobehavioral Reviews 108 (2020) 646–657
Table 3
Summary of studies based on the effects of bariatric surgery on brain function and structure. *mean ± SD. Wk: week; Mo: month; RYGB: Roux-en-Y gastric bypass; LSG: laparoscopic sleeve gastrectomy; (f)MRI: functional
magnetic resonance imaging; HC: high calorie; VTA: ventral tegmental area; NAcc: nucleus accumbens; DLPFC: dorsolateral prefrontal cortex; DMPFC: dorsomedial prefrontal cortex; %TWL: percentage total weight loss;
lFCD: local functional connectivity density; VMPFC: ventromedial prefrontal cortex; PCC: posterior cingulate cortex; dACC: dorsal anterior cingulate cortex; BMI: body mass index; RS: resting state; OFC: orbital frontal
cortex; MFG: middle frontal gyrus; SFG: superior frontal gyrus; GR: gyrus rectus; FC: functional connectivity; BS: bariatric surgery; LC: low calorie; PFC: prefrontal cortex; EEG: electroencephalography; ERP: event-related
potential; RT; reaction time; VBM: voxel-based morphometry; WMV; white matter volume; GMV: gray matter volume; fALFF: fractional amplitude of low-frequency fluctuations; DTI: diffusion tensor imaging; FA:
fractional anisotropy; CC: corpus callosum; MD: mean diffusivity; SLF: superior longitudinal fasciculus.
Participants (M:F) Mean Age of Participants Additional Information Tests/methodology Observations
Alosco et al., 2014 50 (4:46) 44.08 ± 10.76 Bariatric surgery patients Neuropsychological assessment (memory,
executive function, language)
Cognitive impairment prevalence ↓ at 36mo post-surgery. Attention ↑
from baseline to 12wk, 12 and 24mo post-surgery, but ↓ between 24-
36mo. Memory/executive function ↑ from baseline to 12wk, 12, 24, 36
and 48mo. Weight regain between 24-36mo associated with attention ↓
Faulconbridge et al.,
59 (0:59) 37.2 ± 9.3 (RYGB); 40.3 ± 8.9
(LSG); 36.4 ± 8.2 (obese
3 study groups: RYGB (n=22), LSG
(n=18), obese control (n=19)
fMRI, fasting blood samples After RYGB and LSG ↓ liking ratings for HC food. ↓ activity in the VTA
to HC in RYGB compared to control. Changes in ghrelin (+) correlated
with changes in VTA activity in RYGB and LSG, but not in control
Gunstad et al., 2011 150 (25:125) 44.66 ± 11.03 (surgery); 40.42
± 11.48 (obese control)
2 study groups: bariatric surgery
patients (n=109), obese control
Neuropsychological assessment(memory,
attention, executive function, language)
Memory performance ↑ at 12wk post-surgery vs. Baseline, largely
irrespective of weight change or medical conditions.
Holsen et al., 2018 18 (2:16) 38.4± 10.1 LSG patients Fasting blood samples, questionnaires, fMRI After LSG: reduction in ghrelin, leptin, glucose and insulin, improved
maladaptive eating behaviors, ↓ activity in NAcc, caudate, pallidum,
amygdala and ↑ DLPFC and DMPFC during desire for HC food
enhancement vs. regulation.
Baseline activity in NAcc and hypothalamus during HC food
enhancement predict %TWL at 12m.
Li et al., 2018a, 41 (21:20) 26.64 ± 1.83 (LSG); 28.63 ±
2.06 (control)
2 study groups: LSG (n=22), obese
control (n=19)
Fasting blood samples, fMRI After LSG: ↓ lFCD in VMPFC, PCC/precuneus, dACC/DMPFC. ↓ lFCD in
VMPFC and PCC/precuneus was (-) correlated with change in BMI. ↑
connectivity between VMPFC and L DLPFC and between PCC/
precuneus and R caudate and L DLPFC. ↓ connectivity between VMPFC
and hippocampus.
,2018b 68 (37:31) 27.8 ± 6.9 (surgery); 26.7 ±
6.8 (control)
2 study groups: bariatric surgery
patients (n=34), normal-weight
fMRI ↓ RS activity in regions as OFC, MFG, SFG, GR in preop. subjects
compared to controls and ↑ FC in these regions. BMI was associated
with these changes. BS recovered this dysfunction.
Li et al., 2019 41 (21:20) 26.64 ± 1.83 (surgery); 28.63
± 2.06 (obese control)
2 study groups: bariatric surgery
patients (n=22), obese control
Fasting blood samples, questionnaires, fMRI
food cue-reactivity task
After LSG: ↓ fasting plasma of ghrelin, leptin and insulin, ↓ cravings for
HC food, ↓ brain activation in the RDLPFC in response to HC vs LC food
cues. ↓ brain activation in the R DLPFC is (+) correlated to reduction in
ghrelin and cravings. R DLPFC had ↑ connectivity with the vACC.
Changes in BMI were (-) correlated with changes in connectivity
between the R DLPFC and vACC in the LSG group only.
Ochner et al., 2012 5 (0:5) 36 ± 13 RYGB candidates fMRI Neural responsivity to HC vs. LC food cues ↓ in insula, PFC and motor/
sensory cortices after surgery. Preoperative differences in neural
responsivity fasted vs. fed state disappeared postoperatively.
Tarantino et al., 2017 43 (12:31) 40.57 ± 11.05 (patient); 40.18
± 12.18 (control)
2 study groups: ex-obese bariatric
surgery patients (n=21), normal-
weight (n=22)
EEG/ERP, Stroop/Switching tasks, Sustained
attention to response test (SART)
No differences in Stroop accuracy, spatial RT Stroop effect, RT mixing
cost, accuracy mixing and switch cost, and accuracy % and RT NoGo
trials, between groups. Verbal RT Stroop effect and RT switch cost ↑ in
patients vs. controls. Cue-evoked ERPs (switch trials), and target-
evoked ERPs, different between patients and controls: positivity ↓ in
patients. Early negative peak (N1) ↑ in patient vs. control. Negative N2
peak ↑ on NoGo stimuli in patients vs. controls. Positive P3 peak ↓ on
Go trials in patient vs. control.
Tuulari et al., 2016 76 (11:65) 45.9 ± 11.8 (normal-weight);
44.9 ± 9.0 (morbid obese)
2 study groups: normal-weight
(n=29), morbidly obese (n=47)
MRI for VBM, pre- and postoperative WMV ↑ throughout the brain, slight GMV ↑ in occipital and temporal
lobe, 6 months post-surgery. Presurgery regional GM/WM density
associated with postsurgical weight loss.
(continued on next page)
M.H.C. Nota, et al. Neuroscience and Biobehavioral Reviews 108 (2020) 646–657
indicates that initial decrease of WM in obesity is likely an effect of the
obese state, rather than a cause for weight gain.
In fact, these changes in the gut, gut hormones, blood circulation
and inflammation affect brain structure and function (Fig. 1b). Un-
fortunately, these changes have not been studied extensively.
With regard to improving brain structure and function, it might also
be valuable to consider other confounding health benefits associated
with BS. For example, it has been shown that cardiorespiratory fitness is
inceased after BS (Tettero et al., 2018), but also directly associated with
GM volume increase (Esteban-Cornejo et al., 2017). Moreover, it has
been proposed that improved quality of sleep due to alleviation of sleep
apnea as well as altered gut hormone levels may aid in improvement of
brain structure and function (Tuulari et al., 2016). Therefore, to study
the underlying mechanisms it is important to take into account exercise
and quality of sleep before and after BS, as this may increase benefits
associated with the surgical procedure.
5. Discussion
In this review, a summary on the impact of obesity and surgically
induced weight loss on different aspects of neurological health was
presented. Multiple studies have shown that obesity, as measured by
high BMI, adiposity and/or waist circumference, affects function and
structure of the brain, independent from obesity related comorbidities,
such as hypertension and type 2 diabetes mellitus. It is clear that in-
dividuals with high BMI are more likely to have poorer circulation to
and in the brain (Willeumier et al., 2011). There is also a large body of
evidence suggesting that obesity is directly linked to brain atrophy. In
fact, obese individuals have lower GM and WM volumes and WM in-
tegrity, although there is much debate about which areas are pre-
dominantly affected (Kullmann et al., 2016;Pannacciulli et al., 2006;
Janowitz et al., 2015;Xu et al., 2013;van Bloemendaal et al., 2016).
Still, it is clear that obesity is independently and negatively correlated
with brain volume, which in turn is highly associated with cognitive
functioning (Walther et al., 2010). A multitude of cognitive measures
have been found to be altered in the obese brain, such as stimulus and
reward processing, as well as memory, learning and cognitive control
(Garcia-Garcia et al., 2013;Wijngaarden et al., 2015;Stingl et al., 2012;
Cheke et al., 2017;Skoranski et al., 2013). Furthermore, changes in
resting-state activity are seen in obese individuals (Garcia-Garcia et al.,
2015;Zhang et al., 2015).
As all of the aforementioned effects were associated independently
with obesity parameters, such as BMI or waist circumference, it would
be interesting to investigate whether these effects are reversible, for
example by rapid weight loss after BS. Indeed, recent studies have been
able to show a positive effect of procedures such as RYGB and LSG on
brain volume and function (Tuulari et al., 2016;Zhang et al., 2016;
Gunstad et al., 2011;Alosco et al., 2014;Li et al., 2019;Ochner et al.,
2012;Holsen et al., 2018;Faulconbridge et al., 2016;Li et al., 2018a;
Wiemerslage et al., 2017;Li et al., 2018b;Alamuddin et al., 2017).
Interestingly, these effects occurred relatively quickly after surgery and,
more importantly, changes in cognitive functioning were long-lasting.
However, the potential of BS to rescue negative impact of obesity on
the brain, especially in the long term, should be investigated further. It
would be particularly interesting to follow patients for a longer time
period after surgery, as BS is known to have disadvantages as well, such
as vitamin deficiencies (Bal et al., 2012;Dogan et al., 2017). Besides, it
would be of interest to include gut-microbiota and metabolic para-
meters in these studies to identify underlying mechanisms. As men-
tioned earlier, more health related benefits associated with BS, such as
improved quality of sleep, should be considered in future studies.
In addition, many studies only use BMI as a measure of obesity,
although it has been shown that the location of adiposity is an im-
portant factor in the risk of brain impairment. For example central
obesity is assumed to have a greater risk of developing metabolic dis-
orders and brain impairment. This difference has not yet been
Table 3 (continued)
Participants (M:F) Mean Age of Participants Additional Information Tests/methodology Observations
Wiemerslage et al.,
11 (0:11) 42 ± 10 RYGB candidates fMRI Resting-state activity (fALFF) in claustrum, precentral/superior
temporal/inferior and middle frontal/supramarginal gyri, thalamus,
putamen, cingulate cortex and insula, after surgery. fALFF ↑ in
cerebellum, thalamus and superior frontal gyrus, after surgery. Fasted
state fALFF ↓ in pre-/postcentral/middle frontal gyrus and insula post
vs. presurgery. Sated fALFF ↓ in precentral gyrus and ↑ in superior
parietal lobule after surgery.
Zhang et al., 2016 33 (11:22) 25.8 ± 2.2 (obese); 27.0 ±1.9
2 study groups: obese (n=15),
normal-weight (n=18)
MRI for VBM/DTI, pre- and postoperative FA ↑ in parts of corona radiata, CC, fornix, stria terminalis, sagittal
stratum and fasciculus of obese post vs. pre surgery. MD ↓ in parts of
corona radiata, internal/external capsule, SLF and sagittal stratum of
obese post vs. pre surgery. GM density ↑ in parts of frontal gyrus,
anterior cingulate, PFC, temporal lobe and fusiform/postcentral gyrus
of obese post vs. pre surgery. WM density ↑ in caudate, thalamus,
frontal/postcentral gyrus, cingulate cortex and precuneus of obese post
vs. pre surgery. BMI and food addiction score correlation with MD and
GM density disappeared, some negative correlations of BMI with FA
and WM density remain.
M.H.C. Nota, et al. Neuroscience and Biobehavioral Reviews 108 (2020) 646–657
investigated thoroughly and therefore including several obesity mea-
sures and comparing the outcome of these measures is advisable for
further research. This will not only support to unravel underlying me-
chanisms, but will more accurately investigate and represent obesity-
related effects.
6. Conclusion
Overall, obesity is associated with functional and structural altera-
tions in the brain. There are indications that rapid weight loss after BS
can rescue these various pathological effects. This includes changes in
cognitive functions, functional brain changes and GM and WM volume
and integrity, which may be related to improvement of blood vessel
quality, lower blood pressure and less atherosclerosis (Tschoner et al.,
2013;Wilhelm et al., 2014). Furthermore, changes in gut microbiota,
gut hormones and less systemic inflammation might counteract some of
the pathological effects of obesity on the brain (Berthoud et al., 2011;
Ballsmider et al., 2015;Sams et al., 2016). Further knowledge on the
underlying mechanisms via which these processes influence the brain
could be helpful in the development of treatment and prevention of
obesity. In the future, longitudinal studies combining neuroimaging,
cognition and biological markers (e.g. gut hormones, adipokines, gut-
microbiota) are necessary to provide more insight on the effects of
weight loss on brain structure and functioning.
Declaration of Competing Interest
The authors declare no competing financial interests.
This work was supported by a grant of the Rijnstate-Radboudumc
promotion fund.
WHO, 2018. Obesity and Overweight; Fact Sheet.
Mitchell, N.S., et al., 2011. Obesity: overview of an epidemic. Psychiatr. Clin. North Am.
34 (4), 717–732.
Cherbuin, N., et al., 2015. Being overweight is associated with hippocampal atrophy: the
PATH Through Life Study. Int. J. Obes. (Lond) 39 (10), 1509–1514.
Gunstad, J., et al., 2007. Elevated body mass index is associated with executive dys-
function in otherwise healthy adults. Compr. Psychiatry 48 (1), 57–61. https://doi.
Raji, C.A., et al., 2010. Brain structure and obesity. Hum. Brain Mapp. 31 (3), 353–364.
Gunstad, J., et al., 2010. Longitudinal examination of obesity and cognitive function:
results from the Baltimore longitudinal study of aging. Neuroepidemiology 34 (4),
Willeumier, K.C., Taylor, D.V., Amen, D.G., 2011. Elevated BMI is associated with de-
creased blood flow in the prefrontal cortex using SPECT imaging in healthy adults.
Obesity (Silver Spring) 19 (5), 1095–1097.
Debette, S., et al., 2014. Abdominal obesity and lower gray matter volume: a Mendelian
randomization study. Neurobiol. Aging 35 (2), 378–386.
Kullmann, S., et al., 2016. Specific white matter tissue microstructure changes associated
with obesity. Neuroimage 125, 36–44.
Ronan, L., et al., 2016. Obesity associated with increased brain age from midlife.
Neurobiol. Aging 47, 63–70.
Lee, M.J., Wu, Y., Fried, S.K., 2013. Adipose tissue heterogeneity: implication of depot
differences in adipose tissue for obesity complications. Mol. Aspects Med. 34 (1),
Veit, R., et al., 2014a. Reduced cortical thickness associated with visceral fat and BMI.
NeuroImage 6, 307–311.
Arnoldussen, I.A., Kiliaan, A.J., Gustafson, D.R., 2014. Obesity and dementia: adipokines
interact with the brain. Eur. Neuropsychopharmacol. 24 (12), 1982–1999. https://doi.
Kiliaan, A.J., Arnoldussen, I.A.C., Gustafson, D.R., 2014. Adipokines: a link between
obesity and dementia? Lancet Neurol. 13 (9), 913–923.
Gloy, V.L., et al., 2013. Bariatric surgery versus non-surgical treatment for obesity: a
systematic review and meta-analysis of randomised controlled trials. BMJ 347, f5934.
Berthoud, H.R., Shin, A.C., Zheng, H., 2011. Obesity surgery and gut-brain communica-
tion. Physiol. Behav. 105 (1), 106–119.
Shin, A.C., et al., 2013. Longitudinal assessment of food intake, fecal energy loss, and
energy expenditure after Roux-en-Y gastric bypass surgery in high-fat-fed obese rats.
Obes. Surg. 23 (4), 531–540.
Bal, B.S., et al., 2012. Nutritional deficiencies after bariatric surgery. Nat. Rev.
Endocrinol. 8 (9), 544–556.
Dogan, K., et al., 2017. Long-term nutritional status in patients following Roux-en-Y
gastric bypass surgery. Clin. Nutr. 37 (2), 612–617.
Peterli, R., et al., 2018. Effect of laparoscopic sleeve gastrectomy vs laparoscopic Roux-en-
Y gastric bypass on weight loss in patients with morbid obesity: the SM-BOSS ran-
domized clinical trial. JAMA 319 (3), 255–265.
O’Brien, P.D., et al., 2017. Neurological consequences of obesity. Lancet Neurol. 16 (6),
Keshava, H.B., et al., 2017. Bariatric surgery may reduce the risk of Alzheimer’s diseases
through GLP-1 mediated neuroprotective effects. Med. Hypotheses 104, 4–9. https://
Thiara, G., et al., 2017. Evidence for neurocognitive improvement after bariatric surgery:
a systematic review. Psychosomatics 58 (3), 217–227.
Cipolla, M.J., 2009. Chapter 5: control of cerebral blood flow. The Cerebral Circulation.
San Rafael (CA), pp. 29–36.
Zhang, P., et al., 2006. Gender and risk factor dependence of cerebral blood flow velocity
in Chinese adults. Brain Res. Bull. 69 (3), 282–287.
Selim, M.J.R., Novak, P., Zhao, P., Novak, V., 2008. The effects of body mass index on
cerebral blood flow velocity. Clin. Auton. Res. 18 (6), 331–338.
Rusinek, H., et al., 2015. Cerebral perfusion in insulin resistance and type 2 diabetes. J.
Cereb. Blood Flow Metab. 35 (1), 95–102.
Birdsill, A.C., et al., 2013. Low cerebral blood flow is associated with lower memory
function in metabolic syndrome. Obesity (Silver Spring) 21 (7), 1313–1320. https://
Iozzo, P., et al., 2012. Brain PET imaging in obesity and food addiction: current evidence
and hypothesis. Obes. Facts 5 (2), 155–164.
Karhunen, L.J., et al., 1997. Regional cerebral blood flow during food exposure in obese
and normal-weight women. Brain: a journal of neurology 120 (9), 1675–1684.
Bobb, J.F., et al., 2014. Cross-sectional and longitudinal association of body mass index
and brain volume. Hum. Brain Mapp. 35 (1), 75–88.
Hamer, M., Batty, G.D., 2019. Association of body mass index and waist-to-hip ratio with
brain structure: UK Biobank study. Neurology 92 (6), e594–e600.
Pannacciulli, N., et al., 2006. Brain abnormalities in human obesity: a voxel-based mor-
phometric study. Neuroimage 31 (4), 1419–1425.
Weise, C.M., et al., 2013. Fat-free body mass but not fat mass is associated with reduced
gray matter volume of cortical brain regions implicated in autonomic and homeo-
static regulation. Neuroimage 64, 712–721.
Janowitz, D., et al., 2015. Association between waist circumference and gray matter
volume in 2344 individuals from two adult community-based samples. Neuroimage
122, 149–157.
Shaw, M.E., et al., 2018. Body mass index is associated with cortical thinning with dif-
ferent patterns in mid- and late-life. Int. J. Obes. (Lond) 42 (3), 455–461. https://doi.
Veit, R., et al., 2014b. Reduced cortical thickness associated with visceral fat and BMI.
Neuroimage Clin. 6, 307–311.
Verstynen, T.D., et al., 2013. Competing physiological pathways link individual differ-
ences in weight and abdominal adiposity to white matter microstructure. Neuroimage
79, 129–137.
Xu, J., et al., 2013. Body mass index correlates negatively with white matter integrity in
the fornix and corpus callosum: a diffusion tensor imaging study. Hum. Brain Mapp.
34 (5), 1044–1052.
Bolzenius, J.D., et al., 2015. Brain structure and cognitive correlates of body mass index
in healthy older adults. Behav. Brain Res. 278, 342–347.
van Bloemendaal, L., et al., 2016. Alterations in white matter volume and integrity in
obesity and type 2 diabetes. Metab. Brain Dis. 31 (3), 621–629.
Alarcon, G., Ray, S., Nagel, B.J., 2016. Lower working memory performance in over-
weight and obese adolescents is mediated by white matter microstructure. J. Int.
Neuropsychol. Soc. 22 (3), 281–292.
Koivukangas, J., et al., 2016. Body mass index and brain white matter structure in young
adults at risk for psychosis - the Oulu Brain and Mind Study. Psychiatry Res. 254,
Haltia, L.T., et al., 2007. Brain white matter expansion in human obesity and the re-
covering effect of dieting. J. Clin. Endocrinol. Metab. 92 (8), 3278–3284. https://doi.
Garcia-Garcia, I., et al., 2015. Functional network centrality in obesity: a resting-state and
task fMRI study. Psychiatry Res. 233 (3), 331–338.
M.H.C. Nota, et al. Neuroscience and Biobehavioral Reviews 108 (2020) 646–657
Zhang, B., et al., 2015. Altered baseline brain activities before food intake in obese men: a
resting state fMRI study. Neurosci. Lett. 584, 156–161.
Rothemund, Y., et al., 2007. Differential activation of the dorsal striatum by high-calorie
visual food stimuli in obese individuals. Neuroimage 37 (2), 410–421. https://doi.
Feldstein Ewing, S.W., et al., 2017. Overweight adolescents’ brain response to sweetened
beverages mirrors addiction pathways. Brain Imaging Behav. 11 (4), 925–935.
Boutelle, K.N., et al., 2015. Increased brain response to appetitive tastes in the insula and
amygdala in obese compared with healthy weight children when sated. Int. J. Obes.
(Lond) 39 (4), 620–628.
Szalay, C., et al., 2012. Gustatory perception alterations in obesity: an fMRI study. Brain
Res. 1473, 131–140.
Garcia-Garcia, I., et al., 2013. Functional connectivity in obesity during reward proces-
sing. Neuroimage 66, 232–239.
Wijngaarden, M.A., et al., 2015. Obesity is marked by distinct functional connectivity in
brain networks involved in food reward and salience. Behav. Brain Res. 287,
Opel, N., et al., 2015. Enhanced neural responsiveness to reward associated with obesity
in the absence of food-related stimuli. Hum. Brain Mapp. 36 (6).
1002/hbm.22773. 2330-2237.
Haahr, M.E., et al., 2012. Obesity is associated with high serotonin 4 receptor availability
in the brain reward circuitry. Neuroimage 61 (4), 884–888.
Stingl, K.T., et al., 2012. Neuronal correlates of reduced memory performance in over-
weight subjects. Neuroimage 60 (1), 362–369.
Mathar, D., et al., 2017. Failing to learn from negative prediction errors: obesity is as-
sociated with alterations in a fundamental neural learning mechanism. Cortex 95,
Cheke, L.G., et al., 2017. Obesity and insulin resistance are associated with reduced ac-
tivity in core memory regions of the brain. Neuropsychologia 96, 137–149. https://
Skoranski, A.M., et al., 2013. Response monitoring and cognitive control in childhood
obesity. Biol. Psychol. 92 (2), 199–204.
Jaganathan, R., Ravindran, R., Dhanasekaran, S., 2018. Emerging role of adipocytokines
in type 2 diabetes as mediators of insulin resistance and cardiovascular disease. Can.
J. Diabetes 42 (4), 446–456.
Cazettes, F., et al., 2011. Obesity-mediated inflammation may damage the brain circuit
that regulates food intake. Brain Res. 1373, 101–109.
Pannacciulli, N., et al., 2007. Relationships between plasma leptin concentrations and
human brain structure: a voxel-based morphometric study. Neurosci. Lett. 412 (3),
Bettcher, B.M., et al., 2013. Body mass and white matter integrity: the influence of vas-
cular and inflammatory markers. PLoS One 8 (10), e77741.
Rosano, C., Marsland, A.L., Gianaros, P.J., 2012. Maintaining brain health by monitoring
inflammatory processes: a mechanism to promote successful aging. Aging Dis. 3 (1),
Kullmann, S., et al., 2015. Compromised white matter integrity in obesity. Obes. Rev. 16
(4), 273–281.
Horstmann, A., et al., 2011. Obesity-related differences between women and men in brain
structure and goal-directed behavior. Front. Hum. Neurosci. 5, 58–67. https://doi.
White, U.A., Tchoukalova, Y.D., 2014. Sex dimorphism and depot differences in adipose
tissue function. Biochim. Biophys. Acta 1842 (3), 377–392.
Torres-Fuentes, C., et al., 2017. The microbiota-gut-brain axis in obesity. Lancet
Gastroenterol. Hepatol. 2 (10), 747–756.
Ley, R.E., et al., 2006. Microbial ecology: human gut microbes associated with obesity.
Nature 444 (7122), 1022–1023.
van de Wouw, M., et al., 2017. Microbiota-gut-Brain Axis: modulator of host metabolism
and appetite. J. Nutr. 147 (5), 727–745.
Schwiertz, A., et al., 2010. Microbiota and SCFA in lean and overweight healthy subjects.
Obesity (Silver Spring) 18 (1), 190–205.
Lam, Y.Y., et al., 2012. Increased gut permeability and microbiota change associate with
mesenteric fat inflammation and metabolic dysfunction in diet-induced obese mice.
PLoS One 7 (3), e34233.
Tuulari, J.J., et al., 2016. Bariatric surgery induces white and grey matter density re-
covery in the morbidly obese: a voxel-based morphometric study. Hum. Brain Mapp.
37 (11), 3745–3756.
Zhang, Y., et al., 2016. Recovery of brain structural abnormalities in morbidly obese
patients after bariatric surgery. Int. J. Obes. (Lond) 40 (10), 1558–1565. https://doi.
Gunstad, J., et al., 2011. Improved memory function 12 weeks after bariatric surgery.
Surg. Obes. Relat. Dis. 7 (4), 465–472.
Alosco, M.L., et al., 2014. Cognitive function after bariatric surgery: evidence for
improvement 3 years after surgery. Am. J. Surg. 207 (6), 870–876.
Tarantino, V., et al., 2017. Behavioral and electrophysiological correlates of cognitive
control in ex-obese adults. Biol. Psychol. 127, 198–208.
Li, G., et al., 2019. Reduced plasma ghrelin concentrations are associated with decreased
brain reactivity to food cues after laparoscopic sleeve gastrectomy.
Psychoneuroendocrinology 100, 229–236.
Ochner, C.N., et al., 2012. Neural responsivity to food cues in fasted and fed states pre and
post gastric bypass surgery. Neurosci. Res. 74 (2), 138–143.
Holsen, L.M., et al., 2018. Neural predictors of 12-month weight loss outcomes following
bariatric surgery. Int. J. Obes. (Lond) 42 (4), 785–793.
Faulconbridge, L.F., et al., 2016. Changes in neural responsivity to highly palatable foods
following roux-en-Y gastric bypass, sleeve gastrectomy, or weight stability: an fMRI
study. Obesity (Silver Spring) 24 (5), 1054–1060.
Li, P., et al., 2018a. Sleeve gastrectomy recovering disordered brain function in subjects
with obesity: a longitudinal fMRI study. Obes. Surg. 28 (8), 2421–2428. https://doi.
Wiemerslage, L., et al., 2017. A resting-state fMRI study of obese females between pre-
and postprandial states before and after bariatric surgery. Eur. J. Neurosci. 45 (3),
Li, G., et al., 2018b. Bariatric surgery in obese patients reduced resting connectivity of
brain regions involved with self-referential processing. Hum. Brain Mapp. 39 (12),
Ballsmider, L.A.V.A.C., David, M., Hajnal, A., Di Lorenzo, P.M., Czaja, K., 2015. Sleeve
gastrectomy and Roux-en-Y gastric bypass alter the gut-brain communication. Neural
Plast. 2015, 1396–1402.
Ghanim, H., et al., 2018. Decreases in neprilysin and vasoconstrictors and increases in
vasodilators following bariatric surgery. Diabetes Obes. Metab. 20 (8), 2029–2033.
Tschoner, A., et al., 2012. Plasminogen activator inhibitor 1 and visceral obesity during
pronounced weight loss after bariatric surgery. Nutr. Metab. Cardiovasc. Dis. 22 (4),
Tschoner, A., et al., 2013. Long-term effects of weight loss after bariatric surgery on
functional and structural markers of atherosclerosis. Obesity (Silver Spring) 21 (10),
Wilhelm, S.M., Young, J., Kale-Pradhan, P.B., 2014. Effect of bariatric surgery on hy-
pertension: a meta-analysis. Ann. Pharmacother. 48 (6), 674–682.
Alamuddin, N., et al., 2017. Changes in fasting and prandial gut and adiposity hormones
following vertical sleeve gastrectomy or Roux-en-Y-Gastric bypass: an 18-Month
prospective study. Obes. Surg. 27 (6), 1563–1572.
Zhang, Y., et al., 2019. Ghrelin reductions following bariatric surgery were associated
with decreased resting state activity in the hippocampus. Int. J. Obes. (Lond) 43 (4),
le Roux, C.W., et al., 2006. Gut hormone profiles following bariatric surgery favor an
anorectic state, facilitate weight loss, and improve metabolic parameters. Ann. Surg.
243 (1), 108–114.
Field, B.C., Chaudhri, O.B., Bloom, S.R., 2010. Bowels control brain: gut hormones and
obesity. Nat. Rev. Endocrinol. 6 (8), 444–453.
Kalinowski, P., et al., 2017. Ghrelin, leptin, and glycemic control after sleeve gastrectomy
versus Roux-en-Y gastric bypass-results of a randomized clinical trial. Surg. Obes.
Relat. Dis. 13 (2), 181–188.
Kelly, A.S., et al., 2016. Changes in inflammation, oxidative stress and adipokines fol-
lowing bariatric surgery among adolescents with severe obesity. Int. J. Obes. (Lond)
40 (2), 275–280.
Sams, V.G., et al., 2016. Effect of bariatric surgery on systemic and adipose tissue in-
flammation. Surg. Endosc. 30 (8), 3499–3504.
Marsland, A.L., et al., 2008. Interleukin-6 covaries inversely with hippocampal grey
matter volume in middle-aged adults. Biol. Psychiatry 64 (6), 484–490. https://doi.
Bhatt, A., Fan, L.W., Pang, Y., 2014. Strategies for myelin regeneration: lessons learned
from development. Neural Regen. Res. 9 (14), 1347–1350.
Tettero, O.M., et al., 2018. Increase in physical activity after bariatric surgery demon-
strates improvement in weight loss and cardiorespiratory fitness. Obes. Surg. 28 (12),
Esteban-Cornejo, I., et al., 2017. A whole brain volumetric approach in overweight/obese
children: examining the association with different physical fitness components and
academic performance. The ActiveBrains project. Neuroimage 159, 346–354. https://
Walther, K., et al., 2010. Structural brain differences and cognitive functioning related to
body mass index in older females. Hum. Brain Mapp. 31 (7), 1052–1064. https://doi.
M.H.C. Nota, et al. Neuroscience and Biobehavioral Reviews 108 (2020) 646–657
... Mild cognitive impairment is a predictive factor for Alzheimer's disease (AD) and cognitive dysfunction. These factors were found to be more common in individuals with obesity [7,8] and in patients undergoing BS due to their elevated BMI, with one report showing more than 50% of patients undergoing BS being diagnosed with mild cognitive impairment [9,10]. Underlying mechanisms including a rise in tau protein and neurofibrillary tangles, the build-up of amyloid plaques and neuroinflammatory processes, brain atrophy, as well as premature synaptotoxicity may put elderly people in danger of AD [9]. ...
... Underlying mechanisms including a rise in tau protein and neurofibrillary tangles, the build-up of amyloid plaques and neuroinflammatory processes, brain atrophy, as well as premature synaptotoxicity may put elderly people in danger of AD [9]. To shed light on the sequelae of obesity on cognitive function, researchers evaluated key factors and found that the distribution of white adipose tissue (WAT) plays an important role [7,10]. Specifically, the abdominal WAT leads to an increase in inflammation and AD-related adipokines such as monocyte chemoattractant protein-1 (MCP-1), tumor necrosis factor-α (TNF-α), interleukin-β (IL-β), and IL-6 [11]. ...
... Beside the effects of IL-6 on hippocampal volume/function, the inflammation and enhanced cytokine levels may cause 1) cardiovascular diseases such as hypertension thrombosis, atherosclerosis, and endothelial dysfunction, 2) poor blood circulation leading to a reduction in Cerebral Blood Flow (CBF) resulting in decreased cognitive function [12], 3) damages to food-intake regulating circuits of the brain [13] and 4) affects other adipokines expression i.e. angiotensinogen, serum amyloid A (SAA), and plasminogen activator inhibitor-1 (PAI-1) [5]. In terms of brain structure and function, higher levels of leptin released by fat cells are closely related to a reduction in grey matter (GM) volume in people with obesity ( Fig. 1) [10]. These elevated levels of leptin also affect the energy intake regulation in hypothalamus neurogenesis, memory, and brain structure [14,15]. ...
Full-text available
Background There is a correlation between gut microbiota and cognitive function. The mechanisms and pathways explain why the incidence of Alzheimer's disease in subjects undergoing bariatric surgery is lower than in other people with obesity. Methods In this review article, we aim to discuss the association of obesity, cognitive impairment, and physiological changes after bariatric surgery. Results Bariatric surgery has a series of physiological benefits which may lead to an improvement in cognitive functions in individuals who are prone to later developing Alzheimer's disease. Also, taxonomical change in the gut microbiome profile provides a healthy condition for living with better levels of cognition without neuropathological damages in older ages. Conclusion It can be concluded that there is a possible correlation between cognitive dysfunction and increased risk of cognitive dysfunction in people with a BMI higher than 40 kg/m². Bariatric surgery may increase neurotransmitters and improve the gut bacteria, leading to a significant reduction in the risk of Alzheimer's disease.
... Bariatric surgery is the most effective mean of obtaining durable weight loss in individuals affected by obesity, with benefits extending to many obesity-related comorbid conditions [13]. Weight loss after bariatric surgery has been shown to reduce brain glucose hypermetabolism [14], recover brain tissue integrity [15,16] and improve cognitive function [17][18][19][20][21][22][23][24][25]. However, the underlying mechanisms are still poorly understood although changes in the hormonal setting are likely to be implicated. ...
... Six months after RYGB, CMRg was markedly diminished (Fig. 1) in several brain regions of interest (i.e., orbitofrontal cortex, temporal lobe, parietal lobe, primary visual cortex, hippocampus, caudate and putamen nucleus; all p = 0.01 or less; Fig. 2). (19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) to 28 (range 24-30), p = 0.002] with the largest improvement occurring in visuospatial/executive skills and in the domain of abstraction (from 3.6 ± 0.8 to 4.4 ± 0.6, p = 0.04 and from 1.3 ± 0.8 to 1.8 ± 0.3, p < 0.05, respectively). At follow-up, the time for completion of the TMT-A improved slightly (from 46.3 ± 26.5 to 37.0 ± 12.7 s), approaching the statistical significance (p = 0.07). ...
Full-text available
Background/Objectives The link between obesity and brain function is a fascinating but still an enigmatic topic. We evaluated the effect of Roux-en-Y gastric bypass (RYGB) on peripheral glucose metabolism, insulin sensitivity, brain glucose utilization and cognitive abilities in people with obesity. Subjects/Methods Thirteen subjects with obesity (F/M 11/2; age 44.4 ± 9.8 years; BMI 46.1 ± 4.9 kg/m²) underwent 75-g OGTT during a [18F]FDG dynamic brain PET/CT study at baseline and 6 months after RYGB. At the same timepoints, cognitive performance was tested with Mini Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Trail making test (TMT) and Token test (TT). Glucose, insulin, C-peptide, GLP-1, GIP, and VIP levels were measured during OGTT. Leptin and BDNF levels were measured before glucose ingestion. Results RYGB resulted in significant weight loss (from 46.1 ± 4.9 to 35.3 ± 5.0 kg/m²; p < 0.01 vs baseline). Insulin sensitivity improved (disposition index: from 1.1 ± 0.2 to 2.9 ± 1.1; p = 0.02) and cerebral glucose metabolic rate (CMRg) declined in various brain areas (all p ≤ 0.01). MMSE and MoCA score significantly improved (p = 0.001 and p = 0.002, respectively). TMT and TT scores showed a slight improvement. A positive correlation was found between CMRg change and HOMA-IR change in the caudate nucleus (ρ = 0.65, p = 0.01). Fasting leptin decreased (from 80.4 ± 13.0 to 16.1 ± 2.4 ng/dl; p = 0.001) and correlated with CMRg change in the hippocampus (ρ = 0.50; p = 0.008). CMRg change was correlated with cognitive scores changes on the TMT and TT (all p = 0.04 or less). Conclusions Bariatric surgery improves CMRg directly related to a better cognitive testing result. This study highlights the potential pleiotropic effects of bariatric surgery. Trial registry number NCT03414333.
... Additionally, pregnancy results in systemic physiological and psychological changes which increase the risk of depression or anxiety during pregnancy [19,34]. Furthermore, MBS has been shown to alter brain function and structure through changes in gut hormones and adipokines [35,36]. The combination of changes in weight, physical function, physiological, and psychological changes from pregnancy combined with physiological and psychological changes from MBS may interact and increase the risk of depression/anxiety for women who have previously underwent MBS. ...
Full-text available
Purpose Obesity is a well-known risk factor for depression and mental illnesses. Metabolic and bariatric surgery (MBS) is a common treatment for individuals with severe obesity. Studies suggest that MBS is associated with increased risk of depression. However, little is known if pregnant women following MBS have greater incidence of depression/anxiety than non-surgical pregnant women with severe obesity. Materials and Methods Utah Bariatric Surgery Registry (UBSR) was used to identify subjects who underwent bariatric surgery between 1996 and 2016 and were matched (1:2 matching) to subjects with severe obesity from the Utah Population Database (UPDB). Depression and anxiety diagnoses during pregnancy were identified from birth records and electronic medical records (EMRs) during 10 months before birth. A multivariate logistic regression with clustering due to same subjects with multiple births was used. Results Patients included 1427 MBS women (associated 2492 births) and 2854 non-surgical women (associated 4984 births). In the surgical group, 24.4% of the pregnancies had diagnosed depression/anxiety, while 14.3% of the pregnancies in the control group had depression/anxiety (p < 0.01). The surgery group had 1.51 times higher odds of depression and/or anxiety during pregnancy than the control group after controlling for covariates (OR = 1.51, p < 0.01). Conclusion The present study provides evidence that women who previously underwent MBS have higher odds of depression/anxiety during pregnancy than women with obesity who did not undergo MBS.
... The moderating effect of AN subtype may also be related to this, since patients with a restrictive subtype are often characterized by more rapid and extensive weight loss (29)(30)(31). Interestingly, abnormally high body weight has also been also associated with lower grey matter and bariatric surgery seems to reverse some of these effects (32). Underlining the importance of state effects such as weight loss and subsequent weight gain, our three-group comparison showed that partial weight recovery was associated with an attenuated reduction in all three grey matter metrics seen in acutely underweight AN (36-52% smaller differences compared to patients that were at the very beginning of treatment). ...
The pattern of structural brain abnormalities in anorexia nervosa (AN) is still not well understood. While several studies report substantial deficits in grey matter volume and cortical thickness in acutely underweight patients, others find no differences, or even increases in patients compared with healthy controls. Recent weight regain before scanning may explain some of this heterogeneity across studies. To clarify the extent, magnitude, and dependencies of grey matter changes in AN, we conducted a prospective, coordinated meta-analysis of multicenter neuroimaging data. We analyzed T1-weighted structural MRI scans assessed with standardized methods from 685 female AN patients and 963 female healthy controls across 22 sites worldwide. In addition to a case-control comparison, we conducted a three-group analysis comparing healthy controls to acutely underweight AN patients (n = 466), and to those in treatment and partially weight-restored (n = 251). In AN, reductions in cortical thickness, subcortical volumes, and, to a lesser extent, cortical surface area, were sizable (Cohen’s d up to 0.95), widespread and co-localized with hub regions. Highlighting the effects of undernutrition, these deficits associated with lower BMI in the AN sample and were less pronounced in partially weight-restored patients. Notably, the effect sizes observed for cortical thickness deficits in acute AN are the largest of any psychiatric disorder investigated in the ENIGMA consortium to date. These results confirm the importance of considering weight loss and renutrition in biomedical research on AN and underscore the importance of treatment engagement to prevent potentially long-lasting structural brain changes in this population.Competing Interest StatementThe authors have declared no competing interest.Funding StatementThis work was supported by the Carus Promotionskolleg (KB), the Ministerio de Igualdad, Spain (grant number 234/09) and by the Generalitat de Catalunya (2009 SGR 1119) (SA), NIH R21MH86017, NIH R01MH113588 (ABG), Biomedical Research Centre (BRC) UK (ICC), the Alicia Koplowitz Foundation (FAK) (DN040546) and by the Generalitat de Catalunya 2017SGR4881 (JCF), German Ministry for Education and Research (grants 01GV0602 and 01GV0623) (BD), NIMH R01MH105662, NIMH R01MH093535 (JDF), NIMH K23MH080135, R01MH096777 (GKWF), NIH RC1MH088678 (JLG), German Ministry for Education and Research (grants 01GV0602 and 01GV0623) (BHD), the CAMH AFP Innovation Fund (ASK), Swiss Anorexia Nervosa Foundation (project no. 19-12), the Palatin Foundation, and the Gottfried and Julia Bangerter-Rhyner-Foundation (LKK), NIH R01MH042984- 17A1, Price Foundation, NIH R01MH113588 (WHK), NIMH K23MH112949 (SSK), NIH RC1MH088678 (KSL), the Carlos III Research Institute of the Spanish Ministry of Health, FIS PI040829 and by the Generalitat de Catalunya (2009 SGR 1119) (LL), the CAMH AFP Innovation Fund (AEM), Swiss Anorexia Nervosa Foundation (project no. 19-12), the Palatin Foundation, and the Gottfried and Julia Bangerter-Rhyner-Foundation (GFM), Biomedical Research Centre (BRC) UK (OOD), Biomedical Research Centre (BRC) UK (UHS), German Ministry for Education and Research (grants 01GV0602 and 01GV0623) (JS), NIMH K23MH080135, R01MH096777 (MES), DFG: SI 2087/2-1, BR 4852/1-1, Swiss Anorexia Nervosa Foundation: 57-16 (JJS), Research Council of Norway (#288083, #223273); South-Eastern Norway Regional Health Authority (#2019069, #2021070, #500189) (CKT), the CAMH AFP Innovation Fund (ANV), German Ministry for Education and Research (grants 01GV0602 and 01GV0623) (GGvP), NIH R21MH86017, R01MH113588 (CEW), NIH RC1MH088678 (NLZ), NIH RC1MH088678 (JAK), a National Institute of Health Research (NIHR) Senior Investigator Award (US), the NIHR Mental Health Biomedical Research Centre at the South London and Maudsley NHS Foundation Trust and Kings College London (ICC, US and OOD), K23MH118418; NARSAD Young Investigator Grant from the Brain & Behavior Research Foundation (LAB), and SFB 940, DFG: EH 367/5-1, EH 367/7-1 and the Swiss Anorexia Nervosa Foundation (SE). This work is further supported by the European Unions Horizon 2020 research and innovation programme (EarlyCause, grant number 848158, to EW). The ENIGMA Working Group acknowledges the NIH Big Data to Knowledge (BD2K) award for foundational support and consortium development (U54 EB020403 to Paul M. Thompson).Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:All participating sites obtained approval from local institutional review boards and ethics committees, and all study participants provided written informed consent (SM section 1.1). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesAll data produced in the present study are available upon reasonable request to the authors.
Background The pattern of structural brain abnormalities in anorexia nervosa (AN) is still not well understood. While several studies report substantial deficits in grey matter volume and cortical thickness in acutely underweight patients, others find no differences, or even increases in patients compared with healthy controls. Recent weight regain before scanning may explain some of this heterogeneity. To clarify the extent, magnitude, and dependencies of grey matter changes in AN, we conducted a prospective, coordinated meta-analysis of multicenter neuroimaging data. Methods We analyzed T1-weighted structural MRI scans assessed with standardized methods from 685 female AN patients and 963 female healthy controls across 22 sites worldwide. In addition to a case-control comparison, we conducted a three-group analysis comparing healthy controls to acutely underweight AN patients (n = 466), and to those in treatment and partially weight-restored (n = 251). Results In AN, reductions in cortical thickness, subcortical volumes, and, to a lesser extent, cortical surface area, were sizable (Cohen’s d up to 0.95), widespread and co-localized with hub regions. Highlighting the effects of undernutrition, these deficits associated with lower BMI in the AN sample and were less pronounced in partially weight-restored patients. Conclusion The effect sizes observed for cortical thickness deficits in acute AN are the largest of any psychiatric disorder investigated in the ENIGMA consortium to date. These results confirm the importance of considering weight loss and renutrition in biomedical research on AN and underscore the importance of treatment engagement to prevent potentially long-lasting structural brain changes in this population.
Attentional bias (AB) to food after bariatric surgery might be a cognitive marker for weight regain. The visual probe task (VPT) is commonly used to capture AB at automatic, pre-conscious, and conscious orientation of attention. The aim of this study was to investigate how the preoperative BMI of patients submitted to Roux-en-Y gastric bypass (RYGB) impacts AB to food. We assessed patients who had preoperative BMI>50 (n = 28) or preoperative BMI<50 (n = 31) months after the RYGB procedure. Participants underwent clinical, psychological, and VPT evaluations. In VPT, pairs of food and matching non-food images were shown for 100 ms, 500 ms or 2000 ms and AB for food was assessed for each exposure time. A significant AB to food was observed at 2000 ms for all patients in this study, suggesting that the overall sample were consciously orienting their attention toward food cues after surgery, a finding that might be relevant for understanding weight control. When groups with preoperative BMI higher and lower than 50 Kg/m² were compared, a significant difference on AB to food stimuli at 500 ms was observed, controlling for excess weight lost since surgery and postoperative time. Subjects with preoperative BMI>50 had a positive and reliable AB to food while subjects with preoperative BMI<50 had a negative AB. This suggests that food stimuli have a higher incentive salience even after surgery for those with BMI>50, which might explain why subjects with higher preoperative weight have higher risks for weight regain. These results may indicate that RYGB can impact incentive salience for food cues in a differential manner, increasing conscious AB in all patients and decreasing pre-conscious AB only in those with BMI<50 Kg/m².
The objective of this review is to present the impact of nutritional education, physical activity and support network interventions on the nutritional status, cognitive and academic achievement of students under 18 years of age. According to the literature, multicomponent interventions that address diet, physical activity, and involve parents concluded to be more effective in combating obesity and enhancing academic achievement in young people. Therefore, the implementation of public policies that commit to intervene in a timely manner in the first stages of the life cycle, would have a considerably beneficial impact on health.
Background Plausible phenotype mechanisms following bariatric surgery include changes in neural and gastrointestinal physiology. This pilot study aims to investigate individual and combined neurologic, gut microbiome, and plasma hormone changes pre- versus post-vertical sleeve gastrectomy (VSG), Roux-en-Y gastric bypass (RYGB), and medical weight loss (MWL). We hypothesized post-weight loss phenotype would be associated with changes in central reward system brain connectivity, differences in postprandial gut hormone responses, and increased gut microbiome diversity. Methods Subjects included participants undergoing VSG, n = 7; RYGB, n = 9; and MWL, n = 6. Ghrelin, glucagon-like peptide-1, peptide-YY, gut microbiome, and resting state functional magnetic resonance imaging (rsfMRI; using fractional amplitude of low-frequency fluctuations [fALFF]) were measured pre- and post-intervention in fasting and fed states. We explored phenotype characterization using clustering on gut hormone, microbiome, and rsfMRI datasets and a combined analysis. Results We observed more widespread fALFF differences post-bariatric surgery versus post-MWL. Decreased post-prandial fALFF was seen in food reward regions post-RYGB. The highest number of microbial taxa that increased post-intervention occurred in the RYGB group, followed by VSG and MWL. The combined hormone, microbiome, and MRI dataset most accurately clustered samples into pre- versus post-VSG phenotypes followed by RYGB subjects. Conclusion The data suggest surgical weight loss (VSG and RYGB) has a bigger impact on brain and gut function versus MWL and leads to lesser post-prandial activation of food-related neural circuits. VSG subjects had the greatest phenotype differences in interactions of microbiome, rsfMRI, and gut hormone features, followed by RYGB and MWL. These results will inform future prospective research studying gut-brain changes post-bariatric surgery.
Type 2 diabetes (T2D) and its target organ injuries cause distressing impacts on personal health and put an enormous burden on the healthcare system, and increasing attention has been paid to T2D-associated cognitive dysfunction (TDACD). TDACD is characterized by cognitive dysfunction, delayed executive ability, and impeded information-processing speed. Brain imaging data suggest that extensive brain regions are affected in patients with T2D. Based on current findings, a wide spectrum of non-specific neurodegenerative mechanisms that partially overlap with the mechanisms of neurodegenerative diseases is hypothesized to be associated with TDACD. However, it remains unclear whether TDACD is a consequence of T2D or a complication that co-occurs with T2D. Theoretically, anti-diabetes methods are promising neuromodulatory approaches to reduce brain injury in patients with T2D. In this review, we summarize potential mechanisms underlying TDACD and promising neurotropic effects of anti-diabetes methods and some neuroprotective natural compounds. Constructing screening or diagnostic tools and developing targeted treatment and preventive strategies would be expected to reduce the burden of TDACD.
The hedonic regulation of eating behavior has been shown to be altered in case of obesity, notably the chemo-sensory functions and the reward system. Although bariatric surgery is the most effective treatment for obesity, patients do not all respond the same way to this treatment and some of them might regain weight after a certain time. It is essential to promote an adapted balanced diet for the long-term weight maintenance of bariatric surgery patients. A better understanding of the postoperative modifications of food choices and preferences will enable clinicians to give personalized nutritional advice in the context of a precision bariatric medicine. This doctoral work aims to advance this knowledge, by responding to four objectives. The first objective was to synthesize the evidence for the link between bariatric surgery in relation to changes in food preferences by a systematic and meta-analytical approach. With an original approach considering various methods to assess food preferences our systemic analysis of the evidence showed a change in food preferences in patients with obesity who undergo bariatric surgery at specific times during their weight loss trajectory. The second objective was to study the links between food preferences, taste, smell and the weight loss success of bariatric surgery. To this aim, we used an online questionnaire in a cohort of bariatric surgery patients. We found that food preferences were different between patients with and without sensory alterations. For those who experienced sensory alterations, there was a decreased preference for unhealthy foods. We also found that food preferences were different between patients in a weight loss success and failure. Of importance, a higher appreciation for green vegetables was associated with a weight loss success. The third objective of this thesis was to adapt and use a behavioral computerized task in a clinical setting, to compare food reward (i.e., ‘liking’ and ‘wanting’) between patients with unoperated obesity, a sleeve gastrectomy and a Roux-en-Y gastric bypass. Our results showed that the Leeds Food Preference Questionnaire could be clinically relevant to identify post-operative alterations in food reward and to guide caregivers to give personalized advice in patients. Especially, we found that ‘liking’ for a large range of food categories was lower among post-operative patients compared to non-operative patients with obesity while ‘wanting’ was lower among post-operative patients for certain food categories only, including highly palatable foods. The fourth objective of this thesis was to develop a protocol to study food preferences after bariatric surgery in a more realistic environment. We designed a study using a buffet meal in an experimental restaurant, which will be used to study differences in terms of diet quality, food intake and microstructure of the meal between patients with obesity, with and without a bariatric surgery. This doctoral work is original as it used a multidisciplinary approach and a diversity of methods to move forward knowledge about the issue of modifications of food preferences in the context of bariatric surgery. It also highlighted the importance of a personalized nutritional strategy for the bariatric surgery patients.
Full-text available
Background Low cardiorespiratory fitness is strongly associated with cardiovascular diseases and mortality. Although increased physical activity can improve cardiorespiratory fitness, this relationship has not been examined in a large bariatric population undergoing perioperative care focusing on long-term lifestyle change. Objectives To evaluate changes in physical activity, weight loss, and cardiorespiratory fitness up to 24 months after bariatric surgery, and to evaluate the relationships of change in physical activity with weight loss and change in cardiorespiratory fitness. Materials and Methods Four thousand seven hundred eighty-five patients who underwent primary Roux-en-Y gastric bypass or sleeve gastrectomy between January 2012 and December 2014 were included. Physical activity was assessed by the Baecke questionnaire (work, leisure, and sport activity) and cardiorespiratory fitness, defined as VO2max relative to fat-free mass (VO2max/FFM), was assessed by the Åstrand test. Results Twenty-four months postoperative, significant improvements were seen in sport and leisure activity assessments (n = 3548, P < 0.001), weight loss (n = 3695, P < 0.001), and VO2max/FFM (n = 1852, P < 0.001). Furthermore, regression analysis showed that change in leisure activity was positively associated with weight loss (n = 3535, ß = 1.352, P < 0.001) and change in sport activity was positively associated with change in VO2max/FFM (n = 1743, ß = 1.730, P < 0.001). Conclusion Bariatric surgery complemented by a comprehensive bariatric care program can lead to improvement in physical activity, as well as weight loss and improvement in cardiorespiratory fitness. The positive associations of change in leisure activity with weight loss and change in sport activity with cardiorespiratory fitness suggest that bariatric care programs can enhance postoperative outcomes by improving the patient’s physical activity.
Full-text available
Background/Objective: Laparoscopic sleeve gastrectomy (LSG) is an effective bariatric surgery to treat obesity, and involves removal of the gastric fundus where ghrelin is mainly produced. Ghrelin stimulates appetite and regulates food-intake through its effect on the hypothalamus and hippocampus (HIPP). While ghrelin’s role on the hypothalamus has been explored, little is known about its role on HIPP. We tested the hypothesis that LSG-induced reductions in ghrelin levels would be associated with changes in HIPP activity. Subjects/Methods: Brain activity was measured with amplitude of low-frequency fluctuations (ALFF) captured with resting-state functional magnetic resonance imaging (fMRI) in thirty obese participants, both before and after one-month of LSG, and in 26 obese controls without surgery that were studied at baseline and one-month later. A two-way analysis of variance (ANOVA) was performed to model the group and time effects on ALFF and resting-state functional connectivity. Results: One-month post-LSG there were significant decreases in appetite, body mass index (BMI), fasting plasma ghrelin and leptin levels, anxiety, and ALFF in HIPP and ALFF increases in posterior cingulate cortex (PCC, PFWE < 0.05). Decreases in HIPP ALFF correlated positively with decreases in fasting ghrelin and anxiety, and increases in PCC ALFF correlated positively with decreases in anxiety. Seed-voxel correlation analysis showed stronger connectivity between HIPP and insula, and between PCC and dorsolateral prefrontal cortex (DLPFC) post-LSG. Conclusions: These findings suggest that ghrelin effects in HIPP modulate connectivity with the insula, which processes interoception and might be relevant to LSG-induced reductions in appetite/anxiety. Role of LSG in PCC and its enhanced connectivity with DLPFC in improving self-regulation following LSG requires further investigation.
Full-text available
Objective: Bariatric surgery could recover regional dysfunction of cerebral cortex. However, it is unknown whether bariatric surgery could recover the global-level dysfunction in subjects with obesity. The aim of this study was to investigate the effect of bariatric surgery on global-level dysfunction in subjects with obesity by resting-state functional magnetic resonance imaging (fMRI). Methods: Resting-state fMRI was used to investigate dysfunction of whole-brain in 34 subjects with obesity and 34 age-and gender-matched normal-weight subjects, in which 17 subjects with obesity received sleeve gastrectomy. Fractional amplitude of low-frequency fluctuation (fALFF) and functional connectivity (FC) among the whole brain were used to estimate the brain functional differences among the preoperative subjects, postoperative subjects, and the controls. Results: The preoperative subjects compared to controls had decreased resting-state activities in reward processing and cognitive control regions such as orbitofrontal cortex, middle frontal gyrus, superior frontal gyrus, and gyrus rectus. It was important that increased FC was also found in these regions. Correlation analysis showed that body mass index (BMI) was associated with these decreased activity and increased FC. More importantly, the dysfunction in these regions was recovered by the bariatric surgery. Conclusions: These results suggest that bariatric surgery-induced weight loss could reverse the global-level dysfunction in subjects with obesity. The dysfunction in these regions might play a key role in the development of obesity, which might serve as a biomarker in the treatment of obesity.
Full-text available
Importance Sleeve gastrectomy is increasingly used in the treatment of morbid obesity, but its long-term outcome vs the standard Roux-en-Y gastric bypass procedure is unknown. Objective To determine whether there are differences between sleeve gastrectomy and Roux-en-Y gastric bypass in terms of weight loss, changes in comorbidities, increase in quality of life, and adverse events. Design, Setting, and Participants The Swiss Multicenter Bypass or Sleeve Study (SM-BOSS), a 2-group randomized trial, was conducted from January 2007 until November 2011 (last follow-up in March 2017). Of 3971 morbidly obese patients evaluated for bariatric surgery at 4 Swiss bariatric centers, 217 patients were enrolled and randomly assigned to sleeve gastrectomy or Roux-en-Y gastric bypass with a 5-year follow-up period. Interventions Patients were randomly assigned to undergo laparoscopic sleeve gastrectomy (n = 107) or laparoscopic Roux-en-Y gastric bypass (n = 110). Main Outcomes and Measures The primary end point was weight loss, expressed as percentage excess body mass index (BMI) loss. Exploratory end points were changes in comorbidities and adverse events. Results Among the 217 patients (mean age, 45.5 years; 72% women; mean BMI, 43.9) 205 (94.5%) completed the trial. Excess BMI loss was not significantly different at 5 years: for sleeve gastrectomy, 61.1%, vs Roux-en-Y gastric bypass, 68.3% (absolute difference, −7.18%; 95% CI, −14.30% to −0.06%; P = .22 after adjustment for multiple comparisons). Gastric reflux remission was observed more frequently after Roux-en-Y gastric bypass (60.4%) than after sleeve gastrectomy (25.0%). Gastric reflux worsened (more symptoms or increase in therapy) more often after sleeve gastrectomy (31.8%) than after Roux-en-Y gastric bypass (6.3%). The number of patients with reoperations or interventions was 16/101 (15.8%) after sleeve gastrectomy and 23/104 (22.1%) after Roux-en-Y gastric bypass. Conclusions and Relevance Among patients with morbid obesity, there was no significant difference in excess BMI loss between laparoscopic sleeve gastrectomy and laparoscopic Roux-en-Y gastric bypass at 5 years of follow-up after surgery. Trial Registration Identifier: NCT00356213
Full-text available
Adipose tissue is an enormously active endocrine organ, secreting various hormones, such as adiponectin, leptin, resistin and visfatin, together with classical cytokines, such as tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6). All these adipocytokines plays significant roles in the regulation of energy metabolism, glucose and lipid metabolism, reproduction, cardiovascular function and immunity. Adipocytokines are significantly regulated by nutritional status and can directly influence other organ systems, including brain, liver and skeletal muscle. Adiponectin plays a key role as an anti-inflammatory hormone. Upregulated expression of resistin, vaspin, apelin and TNF-α plays a significant role in induction of insulin resistance linked with obesity and type 2 diabetes. Ghrelin, the circulating peptide, has been found to stimulate appetite and regulate energy balance. Thus, it can be considered 1 of the candidate genes for obesity and type 2 diabetes. Omentin is a novel adipokine produced by visceral adipose tissue. Circulating levels of omentin are decreased in insulin-resistant states, for example, in obesity and diabetes. IL-6 plays a vital role in regulating the accumulation of lipids intramyocardially. Based on the biologic relevance of these adipocytokines, they can no longer be considered as energy storage sites alone but must also be considered in metabolic control. Hence, the present review summarizes the regulatory roles of adipocytokines in diabetes linked with obesity.
Full-text available
Objective: High BMI at midlife is associated with increased risk of dementia as well as faster decline in cognitive function. In late-life, however, high BMI has been found to be associated with both increased and decreased dementia risk. The objective of this study was to investigate the neural substrates of this age-related change in BMI risk. Methods: We measured longitudinal cortical thinning over the whole brain, based on MRI scans for 910 individuals aged 44 to 66 years at baseline. Subjects were sampled from a large population study (PATH). After attrition and exclusions, the final analysis was based on 792 individuals, including 370 individuals aged 60-66 years and 399 individuals aged 44-49 years. A mixed-effects model was used to test the association between cortical thinning and baseline BMI, as well as percentage change in BMI. Results: Increasing BMI was associated with increased cortical thinning in posterior cingulate at midlife (0.014 mm/kg/m(2), CI=0.005, 0.023, P<0.05 FDR corrected). In late-life, increasing BMI was associated with reduced cortical thickness, most prominently in the right supramarginal cortex (0.010 mm/kg/m(2), CI=0.005-0.016, P<0.05 FDR corrected), as well as frontal regions. In late-life, decreasing BMI was also associated with increased cortical thinning, including right caudal middle frontal cortex (0.014 mm/kg/m(2) (CI=0.006-0.023, P<0.05 FDR corrected). Conclusions: The pattern of cortical thinning -- in association with increasing BMI at both midlife and late-life-is consistent with known obesity-related dementia risk. Increased cortical thinning in association with decreasing BMI at late-life may help explain the 'obesity paradox', where high BMI in midlife appears to be a risk factor for dementia, but high BMI in late-life appears, at times, to be protective.International Journal of Obesity accepted article preview online, 10 October 2017. doi:10.1038/ijo.2017.254.
Objective: To examine the association of body mass index (BMI) and waist-to-hip ratio (WHR) with brain volume. Methods: We used cross-sectional data from the UK Biobank study (n = 9,652, age 55.4 ± 7.5 years, 47.9% men). Measures included BMI, WHR, and total fat mass as ascertained from bioimpedance. Brain images were produced with structural MRI. Results: After adjustment for a range of covariates, higher levels of all obesity measures were related to lower gray matter volume: BMI per 1 SD (β coefficient -4,113, 95% confidence interval [CI] -4,862 to -3,364), WHR (β coefficient -4,272, 95% CI -5,280 to -3,264), and fat mass (β coefficient -4,590, 95% CI -5,386 to -3,793). The combination of overall obesity (BMI ≥30 kg/m2) and central obesity (WHR >0.85 for women, >0.90 for men) was associated with the lowest gray matter compared with that in lean adults. In hypothesis-free testing with a Bonferroni correction, obesity was also related to various regional brain volumes, including caudate, putamen, pallidum, and nucleus accumbens. No associations between obesity and white matter were apparent. Conclusion: The combination of heightened BMI and WHR may be an important risk factor for gray matter atrophy.
The “hunger” hormone ghrelin regulates food-intake and preference for high-calorie (HC) food through modulation of the mesocortico-limbic dopaminergic pathway. Laparoscopic sleeve gastrectomy (LSG) is an effective bariatric surgery to treat morbid obesity. We tested the hypothesis that LSG-induced reductions in appetite and total ghrelin levels in blood are associated with reduced prefrontal brain reactivity to food cues. A functional magnetic resonance imaging (fMRI) cue-reactivity task with HC and low-calorie (LC) food pictures was used to investigate brain reactivity in 22 obese participants tested before and one month after bariatric surgery (BS). Nineteen obese controls (Ctr) without surgery were also tested at baseline and one-month later. LSG significantly decreased (1) fasting plasma concentrations of total ghrelin, leptin and insulin, (2) craving for HC food, and (3) brain activation in the right dorsolateral prefrontal cortex (DLPFC) in response to HC vs. LC food cues (PFWE<0.05). LSG-induced reduction in DLPFC activation to food cues were positively correlated with reduction in ghrelin levels and reduction in craving ratings for food. Psychophysiological interaction (PPI) connectivity analyses showed that the right DLPFC had stronger connectivity with the ventral anterior cingulate cortex (vACC) after LSG, and changes in BMI were negatively correlated with changes in connectivity between the right DLPFC and vACC in the LSG group only. These findings suggest that LSG-induced weight-loss may be related to reductions in ghrelin, possibly leading to decreased food craving and hypothetically reducing DLPFC response to the HC food cues.
Obese individuals exhibit brain alterations of resting-state functional connectivity (RSFC) integrity of resting-state networks (RSNs) related to food intake. Bariatric surgery is currently the most effective treatment for combating morbid obesity. How bariatric surgery influences neurocircuitry is mostly unknown. Functional connectivity density (FCD) mapping was employed to calculate local (lFCD)/global (gFCD) voxelwise connectivity metrics in 22 obese participants who underwent functional magnetic resonance imaging before and one month after sleeve gastrectomy (SG), and in 19 obese controls (Ctr) without surgery but tested twice (baseline and one-month later). Two factor (group, time) repeated measures ANOVA was used to assess main and interaction effects in lFCD/gFCD; regions of interest were identified for subsequent seed to voxel connectivity analyses to assess resting-state functional connectivity and to examine association with weight loss. Bariatric surgery significantly decreased lFCD in VMPFC, posterior cingulate cortex (PCC)/precuneus, and dorsal anterior cingulate cortex (dACC)/dorsomedial prefrontal cortex (DMPFC) and decreased gFCD in VMPFC, right dorsolateral prefrontal cortex (DLPFC) and right insula (PFWE < 0.05). lFCD decreased in VMPFC and PCC/precuneus correlated with reduction in BMI after surgery. Seed to voxel connectivity analyses showed the VMPFC had stronger connectivity with left DLPFC and weaker connectivity with hippocampus/parahippocampus, and PCC/precuneus had stronger connectivity with right caudate and left DLPFC after surgery. Bariatric surgery significantly decreased FCD in regions involved in self-referential processing (VMPFC, DMPFC, dACC, precuneus), and interoception (insula), and changes in VMPFC/precuneus were associated with reduction in BMI suggesting a role in improving control of eating behaviors following surgery.
Since morbid obesity is associated with congestive cardiac failure and hypertension, and gastric bypass surgery is followed by a reduction in blood pressure and in the risk of congestive cardiac failure, we hypothesized that weight loss following Roux‐en‐Y gastric bypass (RYGB) surgery in morbidly obese patients is associated with a decrease in plasma concentrations of neprilysin, mediators of renin angiotensin system (RAS), catecholamines and endothelin‐1 with an increase in the concentrations of vasodilators. Fasting blood samples were obtained from 15 patients with morbid obesity and diabetes prior to and 6 months after RYGB. Circulating levels of neprilysin; vasoconstrictors, vasodilators and the mRNA expression of related genes in circulating mononuclear cells (MNC) were measured. Six months after RYGB, concentrations of neprilysin, angiotensinogen, angiotensin II, renin and endothelin‐1 fell significantly by 27±16%, 22±10% 22±8%, 35±13% and 17±6% (p<0.05 for all), respectively, while ANP concentrations increased significantly by 24±13%. There was no significant change in aldosterone, BNP, cAMP or cGMP concentrations or angiotensin converting enzyme (ACE) expression. These changes may contribute to the reduction congestive cardiac failure and blood pressure risk after RYGB.