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Journal ArticleDOI

An obesity-associated risk allele within the FTO gene affects human brain activity for areas important for emotion, impulse control and reward in response to food images.

21 Feb 2016-European Journal of Neuroscience (Wiley)-Vol. 43, Iss: 9, pp 1173-1180

TL;DR: The results suggest that the two genotypes are associated with differential neural processing of food images, which may influence weight status through diminished impulse control and reward processing.

AbstractUnderstanding how genetics influences obesity, brain activity and eating behaviour will add important insight for developing strategies for weight-loss treatment, as obesity may stem from different causes and as individual feeding behaviour may depend on genetic differences. To this end, we examined how an obesity risk allele for the FTO gene affects brain activity in response to food images of different caloric content via functional magnetic resonance imaging (fMRI). Thirty participants homozygous for the rs9939609 single nucleotide polymorphism were shown images of low- or high-calorie food while brain activity was measured via fMRI. In a whole-brain analysis, we found that people with the FTO risk allele genotype (AA) had increased activity compared with the non-risk (TT) genotype in the posterior cingulate, cuneus, precuneus and putamen. Moreover, higher body mass index in the AA genotype was associated with reduced activity to food images in areas important for emotion (cingulate cortex), but also in areas important for impulse control (frontal gyri and lentiform nucleus). Lastly, we corroborate our findings with behavioural scales for the behavioural inhibition and activation systems. Our results suggest that the two genotypes are associated with differential neural processing of food images, which may influence weight status through diminished impulse control and reward processing.

Topics: FTO gene (57%), Posterior cingulate (53%), Cingulate cortex (51%), Precuneus (51%), Functional magnetic resonance imaging (50%)

Summary (4 min read)

INTRODUCTION

  • Several different FTO single nucleotide polymorphisms (SNPs) are associated with a higher body mass index (BMI) (Sällman Almén et al., 2013; Scuteri et al., 2007a) , and higher energy intake (Speakman, 2013) .
  • Moreover, experiments in rodents show that changes in FTO expression levels in the hypothalamus affect feeding behavior (Frederiksen, Skakkebaek, & Andersson, 2007; Olszewski et al., 2009; Tung et al., 2010) .
  • Personality scales for the Behavioral Inhibition System (BIS) and Behavioral Activation System (BAS), which measure punishment and reward sensitivity respectively, are two such tools which correlate with inactivity and poor diet (Carver & White, 1994; Dietrich, Federbusch, Grellmann, Villringer, & Horstmann, 2014; Meule, 2013; Voigt et al., 2009) .

FTO Associated Brain Activity 5

  • To date, however, few fMRI studies have examined how genetic profile is associated with brain responses to food in obesity.
  • A recent study found that people with the FTO risk allele for rs8050136 had reduced activity in the right prefrontal cortex while viewing food images in a postprandial state, but not while fasting (Heni et al., 2014) .
  • Notably, their cohort of participants had a normal BMI with no obese participants.
  • Against this background, the authors explore for the first time the association between FTO genotype, BMI, and neural responses to food images of either low-or high-calorie content.

Participants

  • Prior to any experimental procedures, all participants gave written informed consent to the study which conformed to the Declaration of Helsinki and approved by the local ethics committee.
  • Genotyping of the FTO single nucleotide polymorphism (SNP) rs9939609 was performed with a pre-designed Taqman single-nucleotide polymorphism genotyping assay (Applied Biosystems, Foster City, USA) and an ABI7900 genetic analyzer with SDS 2.2 software at the Uppsala Genome Center (http://www.genpat.uu.se/node462).
  • Only homozygous participants were included in the study.
  • Hunger ratings were also assessed on a 1-10 scale with higher numbers indicating greater feelings of hunger.

Behavioral Questionnaires

  • Clinical measures for punishment sensitivity and reward-seeking behavior were acquired using the Behavioral Inhibition System (BIS) and Behavioral Activation System (BAS) questionnaires (Carver & White, 1994) .
  • Each item was represented by a statement, where the participant indicated how much s/he agreed or FTO Associated Brain Activity 7 disagreed on a four-point scale.
  • The BIS included only one scale, evaluating the reactions to the anticipation of punishment and anxiety, while the BAS included three subscales: Drive, which represents the pursuit of desired goals; Fun Seeking, which evaluates the desire for new rewards and impulsivity; and Reward Responsiveness, which focuses on positive reactions anticipating rewards.

Preprocessing of fMRI data

  • All preprocessing steps were performed using software package Statistical Parametric Mapping (SPM, version 8, http://www.fil.ion.ucl.ac.uk/spm/), implemented in MATLAB (version R2014a, 11 FEB 2014 .
  • The images were realigned and estimated to remove movement artefacts in the data.
  • EPI images were further matched with the structural image using coregistration.
  • The anatomical image was segmented to strip away unnecessary tissue in the images.
  • Tissue probability maps were introduced in the segmentation step to differentiate between gray matter, white matter and cerebrospinal fluid in each individual.

Statistical Analysis

  • All fMRI statistical analysis was performed using the same versions of SPM and MATLAB listed in preprocessing steps.
  • For all whole-brain results, a family wise error FTO Associated Brain Activity 9 (FWE) corrected significance level was set at p < 0.05 to correct for multiple testing.
  • This contrast was then tested using a between-groups t-test followed by directional post-hoc comparisons as well as with a multiple regression analysis testing for interactions between genotype and BMI, BIS, or BAS individually.
  • Bilateral masks of such areas were produced using the Wake Forest University Pickatlas toolbox (Maldjian, Laurienti, Kraft, & Burdette, 2003) within SPM.
  • Results for the PCA were considered significant if the percentage of inertia summing from the two largest eigenvalues exceeded values listed in a significance table based on 10,000 analyses with similar numbers of individuals and independent variables (Lê et al., 2008) .

RESULTS

  • The obesity-associated FTO SNP rs9939609, is associated with increased activity in response to food images.
  • BOLD signals were measured as participants were shown images of low-calorie (LC) food, high-calorie (HC) food, or control images in a block design format.
  • Areas included the posterior cingulate cortex (PCC), cingulate gyrus, cuneus, and precuneus (Table 2 ).
  • A multiple regression analysis found an interaction between genotype and BMI, post-hoc comparisons found significant clusters for the AA genotype while BMI was decreasing in the PCC, cingulate gyrus, middle occipital gyrus, and precuneus (Supplementary Table 1 ).
  • Within the t-test comparison between genotypes, a significant cluster showing greater activity in the AA genotype was FTO Associated Brain Activity 11 found in the putamen after performing a small-volumes correction using a 6 mmradius sphere over the lowest FWE-corrected p-value in the cluster (Table 2 ).

Differential patterns of behavior for each FTO genotype depending on body-mass index.

  • The authors next tested if behavioral questionaires corrobarated the findings from the imaging experiments.
  • The authors then performed a principle component analysis within each genotype using the BIS and the three BAS subscales (Drive, Fun Seeking, and Reward Responsiveness) as variables of interest with BMI as a quantitative supplementary variable.
  • For both analyses, all the variables of interest were well projected and the first two dimensions accounted for ≈80% of the variablity (considered significant based on critera listed in methods under statistical analysis subheading, 81.4 > 80.0 for the AA group and 79.2 > 76.5 in the TT group).
  • Moreover, the variables of interest projected to the same quandrants except for the Drive and Fun Seeking subscales, which were switched between the two different genotypes.
  • Furthermore, the authors followed up the association between the BIS and BMI using a multiple regression analysis testing if BIS scores could be predicted by genotype, BMI, or their interaction.

DISCUSSION

  • The authors examined whether an obesity-associated genotype affects the neural processing of food images with different caloric content and to what extent body-mass index (BMI) is an important factor.
  • The authors found the AA genotype had increased brain activity compared to the TT genotype when viewing food images with different caloric contents, specifically in areas important for emotion (cingulate gyrus), memory, and self-image (cuneus and precuneus) and reward .
  • Thus, discrimination between HC and LC foods may be handled differently for each genotype depending on BMI.
  • Next, the authors corroborate their findings in the imaging study with personality questionnaires examining behavioral characteristics related to impulsivity and rewardprocessing: namely the Behavioral Inhibition System (BIS) and Behavioral Activation System (BAS) scales.
  • The authors found that the BIS as well as subscales of the BAS correlated with BMI oppositely in each genotype.

FTO Associated Brain Activity 14

  • In between-groups comparisons, as well as multiple regression analysis, the authors found significant clusters of brain activity when testing a contrast for caloric discrimination (HC food images opposed to LC food images).
  • Specifically, the authors found increased neural activation in the AA genotype compared to the TT genotype within the posterior cingulate cortex (PCC), cingulate gyrus, cuneus and precuneus.
  • The PCC is a well-connected and multifunctional brain area associated with emotional processing, and a central node in the default mode network (DMN): involved in arousal/awareness, balancing external and internal thought, and emotion (Leech & Sharp, 2014) .
  • Thus, the AA genotype in their cohort confirms previous reports equating impulsivity with obesity/overeating (Meule, 2013) specifically in one study which also found a negative correlation between BIS and BMI in males (Dietrich et al., 2014) .
  • In conclusion, their findings offer insight into the relationship between FTO, obesity, and brain activity; and suggest that overweight/obese populations have different attitudes and functional processing for food images depending on genetic background.

B)

  • A region of interest analysis found a significant cluster within the putamen after a smallvolumes correction.
  • The BIS evaluates inhibitory behavior in the anticipation of punishment and anxiety, while the BAS included three subscales: Drive, which represents the pursuit of desired goals; Fun Seeking, which evaluates the desire for new rewards and impulsivity; and Reward Responsiveness, which focuses on positive reactions anticipating rewards.
  • The behavioral variables were all well projected in each group.
  • A) variables factor map for the AA genotype.
  • BMI was positively correlated with BIS and negatively correlated with the BAS Fun Seeking subscale.

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Wiemerslage, L, Nilsson, EK, Solstrand Dahlberg, L, Ence-Eriksson, F, Castillo,
S, Larsen, AL, Bylund, SBA, Hogenkamp, PS, Olivo, G, Bandstein, M, Titova,
OE, Larsson, E-M, Benedict, C, Brooks, SJ and Schiöth, HB
An obesity-associated risk allele within the FTO gene affects human brain
activity for areas important for emotion, impulse control and reward in
response to food images.
http://researchonline.ljmu.ac.uk/id/eprint/9287/
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Citation (please note it is advisable to refer to the publisher’s version if you
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Wiemerslage, L, Nilsson, EK, Solstrand Dahlberg, L, Ence-Eriksson, F,
Castillo, S, Larsen, AL, Bylund, SBA, Hogenkamp, PS, Olivo, G, Bandstein,
M, Titova, OE, Larsson, E-M, Benedict, C, Brooks, SJ and Schiöth, HB (2016)
An obesity-associated risk allele within the FTO gene affects human brain
LJMU Research Online

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FTO Associated Brain Activity 1
Title 1
An obesity-associated risk allele within the FTO gene affects brain activity for areas 2
important for emotion, impulse control, and reward in response to food images. 3
4
Running Title 5
FTO Associated Brain Activity 6
7
Author names and affiliation 8
Lyle Wiemerslage*
§
, Emil K Nilsson
§
, Linda Solstrand Dahlberg
§
, Fia Ence-Eriksson
§
, 9
Sandra Castillo
§
, Anna L Larsen
§
, Simon BA Bylund
§
, Pleunie S Hogenkamp
§
, Gaia 10
Olivo
§
, Marcus Bandstein
§
, Olga E Titova
§
, Elna-Marie Larsson
, Christian Benedict
§
, 11
Samantha J Brooks
, Helgi B Schiöth
§
12
13
Uppsala University § 14
Department of Neuroscience, Functional Pharmacology 15
Biomedicinska Centrum (BMC) 16
Husargatan 3, Box 593 17
751 24 Uppsala, Sweden 18
19
Section of Neuroradiology 20
Department of Radiology, Uppsala University 21
Akademiska Sjukhuset 22
751 85 Uppsala, Sweden 23
24
University of Cape Town 25
Department of Psychiatry 26
Old Groote Schuur Hospital 27
J2 Building 28
Anzio Road 29
Observatory, Cape Town, South Africa. 30
31
Corresponding author 32
Uppsala University * 33
Department of Neuroscience, Functional Pharmacology 34
Biomedicinska Centrum (BMC) 35

FTO Associated Brain Activity 2
Husargatan 3, Box 593 36
751 24 Uppsala, Sweden 37
lyle.wiemerslage@neuro.uu.se 38
39
Number of: 40
Figures = 3 41
Tables = 1 42
Words: 43
o Abstract = 197 44
o Introduction = 565 45
o Entire Manuscript (excluding references and figure legends) = 3,972 46
47
Keywords: 48
FTO, fMRI, SNP, obesity, food 49
50
Conflict of Interest: 51
The authors declare no conflicts of interest. 52

FTO Associated Brain Activity 3
ABSTRACT 53
Understanding how genetics influences obesity, brain activity, and eating behavior will 54
add important insight for developing strategies for weight-loss treatment, as obesity may stem 55
from different causes and as individual feeding behavior may depend on genetic differences. 56
To this end, we examined how an obesity risk-allele for the FTO gene affects brain activity in 57
response to food images of different caloric content via fMRI. 30 participants homozygous 58
for the rs9939609 single nucleotide polymorphism were shown images of low- or high-calorie 59
food while brain activity was measured via fMRI. In a whole-brain analysis, we found that 60
people with the FTO risk-allele genotype (AA) had increased activity than the non-risk (TT) 61
genotype in the posterior cingulate, cuneus, precuneus, and putamen. Moreover, higher BMI 62
in the AA genotype was associated with reduced activity to food images in areas important for 63
emotion (cingulate cortex), but also in areas important for impulse control (frontal gyri and 64
lentiform nucleus). Lastly, we corroborate our findings with behavioral scales for the 65
behavioral inhibition and activation systems (BIS/BAS). Our results suggest that the two 66
genotypes are associated with differential neural processing of food images, which may 67
influence weight status through diminished impulse control and reward processing. 68
69
Keywords: 70
fMRI, FTO, SNP, BMI, food images, obesity 71

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Abstract: J. A. Gray (1981, 1982) holds that 2 general motivational systems underlie behavior and affect: a behavioral inhibition system (BIS) and a behavioral activation system (BAS). Self-report scales to assess dispositional BIS and BAS sensitivities were created. Scale development (Study 1) and convergent and discriminant validity in the form of correlations with alternative measures are reported (Study 2). In Study 3, a situation in which Ss anticipated a punishment was created. Controlling for initial nervousness, Ss high in BIS sensitivity (assessed earlier) were more nervous than those low in BIS sensitivity. In Study 4, a situation in which Ss anticipated a reward was created. Controlling for initial happiness, Ss high in BAS sensitivity (Reward Responsiveness and Drive scales) were happier than those low in BAS sensitivity. In each case the new scales predicted better than an alternative measure. Discussion is focused on conceptual implications.

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Abstract: Analysis and interpretation of functional MRI (fMRI) data have traditionally been based on identifying areas of significance on a thresholded statistical map of the entire imaged brain volume. This form of analysis can be likened to a “fishing expedition.” As we become more knowledgeable about the structure–function relationships of different brain regions, tools for a priori hypothesis testing are needed. These tools must be able to generate region of interest masks for a priori hypothesis testing consistently and with minimal effort. Current tools that generate region of interest masks required for a priori hypothesis testing can be time-consuming and are often laboratory specific. In this paper we demonstrate a method of hypothesis-driven data analysis using an automated atlas-based masking technique. We provide a powerful method of probing fMRI data using automatically generated masks based on lobar anatomy, cortical and subcortical anatomy, and Brodmann areas. Hemisphere, lobar, anatomic label, tissue type, and Brodmann area atlases were generated in MNI space based on the Talairach Daemon. Additionally, we interfaced these multivolume atlases to a widely used fMRI software package, SPM99, and demonstrate the use of the atlas tool with representative fMRI data. This tool represents a necessary evolution in fMRI data analysis for testing of more spatially complex hypotheses.

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"An obesity-associated risk allele w..." refers methods in this paper

  • ...Bilateral masks of such areas were produced using the Wake Forest University Pickatlas toolbox (Maldjian et al., 2003) within SPM. Small volume corrections were performed on clusters trending towards significance (surpassing an uncorrected P 0.001 threshold but failing FWE-correction) only for…...

    [...]

  • ...produced using the Wake Forest University Pickatlas toolbox (Maldjian et al., 2003) within SPM....

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TL;DR: FactoMineR an R package dedicated to multivariate data analysis with the possibility to take into account different types of variables (quantitative or categorical), different kinds of structure on the data, and finally supplementary information (supplementary individuals and variables).
Abstract: In this article, we present FactoMineR an R package dedicated to multivariate data analysis. The main features of this package is the possibility to take into account different types of variables (quantitative or categorical), different types of structure on the data (a partition on the variables, a hierarchy on the variables, a partition on the individuals) and finally supplementary information (supplementary individuals and variables). Moreover, the dimensions issued from the different exploratory data analyses can be automatically described by quantitative and/or categorical variables. Numerous graphics are also available with various options. Finally, a graphical user interface is implemented within the Rcmdr environment in order to propose an user friendly package.

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11 May 2007-Science
TL;DR: A genome-wide search for type 2 diabetes–susceptibility genes identified a common variant in the FTO (fat mass and obesity associated) gene that predisposes to diabetes through an effect on body mass index (BMI).
Abstract: Obesity is a serious international health problem that increases the risk of several common diseases. The genetic factors predisposing to obesity are poorly understood. A genome-wide search for type 2 diabetes-susceptibility genes identified a common variant in the FTO (fat mass and obesity associated) gene that predisposes to diabetes through an effect on body mass index (BMI). An additive association of the variant with BMI was replicated in 13 cohorts with 38,759 participants. The 16% of adults who are homozygous for the risk allele weighed about 3 kilograms more and had 1.67-fold increased odds of obesity when compared with those not inheriting a risk allele. This association was observed from age 7 years upward and reflects a specific increase in fat mass.

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"An obesity-associated risk allele w..." refers background in this paper

  • ...of blood oxygen level-dependent (BOLD) responses to food images, we test whether the genotype homozygous for the at-risk allele for rs9939609 (which is A) (Dina et al., 2007; Frayling et al., 2007; Scuteri et al., 2007) affects brain activity differently from the homozygous genotype with the non-risk allele (T)....

    [...]

  • ...…level-dependent (BOLD) responses to food images, we test whether the genotype homozygous for the at-risk allele for rs9939609 (which is A) (Dina et al., 2007; Frayling et al., 2007; Scuteri et al., 2007) affects brain activity differently from the homozygous genotype with the non-risk allele (T)....

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Journal ArticleDOI
Abstract: The obesity epidemic is responsible for a substantial economic burden in developed countries and is a major risk factor for type 2 diabetes and cardiovascular disease. The disease is the result not only of several environmental risk factors, but also of genetic predisposition. To take advantage of recent advances in gene-mapping technology, we executed a genome-wide association scan to identify genetic variants associated with obesity-related quantitative traits in the genetically isolated population of Sardinia. Initial analysis suggested that several SNPs in the FTO and PFKP genes were associated with increased BMI, hip circumference, and weight. Within the FTO gene, rs9930506 showed the strongest association with BMI (p ¼ 8.6 310 � 7 ), hip circumference (p ¼ 3.4 3 10 � 8 ), and weight (p ¼ 9.1 3 10 � 7 ). In Sardinia, homozygotes for the rare ‘‘G’’ allele of this SNP (minor allele frequency ¼ 0.46) were 1.3 BMI units heavier than homozygotes for the common ‘‘A’’ allele. Within the PFKP gene, rs6602024 showed very strong association with BMI (p ¼4.9 310 � 6 ). Homozygotes for the rare ‘‘A’’ allele of this SNP (minor allele frequency ¼0.12) were 1.8 BMI units heavier than homozygotes for the common ‘‘G’’ allele. To replicate our findings, we genotyped these two SNPs in the GenNet study. In European Americans (N ¼ 1,496) and in Hispanic Americans (N ¼ 839), we replicated significant association between rs9930506 in the FTO gene and BMI (p-value for meta-analysis of European American and Hispanic American follow-up samples, p ¼0.001), weight (p ¼0.001), and hip circumference (p ¼0.0005). We did not replicate association between rs6602024 and obesity-related traits in the GenNet sample, although we found that in European Americans, Hispanic Americans, and African Americans, homozygotes for the rare ‘‘A’’ allele were, on average, 1.0–3.0 BMI units heavier than homozygotes for the more common ‘‘G’’ allele. In summary, we have completed a whole genome– association scan for three obesity-related quantitative traits and report that common genetic variants in the FTO gene are associated with substantial changes in BMI, hip circumference, and body weight. These changes could have a significant impact on the risk of obesity-related morbidity in the general population.

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56 To this end, the authors examined how an obesity risk-allele for the FTO gene affects brain activity in 57 response to food images of different caloric content via fMRI. Their results suggest that the two 66 genotypes are associated with differential neural processing of food images, which may 67 influence weight status through diminished impulse control and reward processing.