RESEARCH ARTICLE Neuropsychiatric Genetics
The Interaction Effect Between BDNF val66met Polymorphism and Obesity on Executive Functions and Frontal Structure Idoia Marque´s-Iturria,1,2 Maite Garolera,3,4 Roser Pueyo,1,2,3 Ba`rbara Segura,1,3 Imma Hernan,5 Isabel Garcı´a-Garcı´a,1,2 Consuelo Sa´nchez-Garre,6 Marı´a Vernet-Vernet,7 Marı´a Jose´ SenderPalacios,7 Ana Narberhaus,1 Mar Ariza,1 Carme Junque´,1,3,8 and Marı´a ´Angeles Jurado1,2,3* 1
Department of Psychiatry and Clinical Psychobiology, University of Barcelona, Barcelona, Spain
2
Institute for Brain, Cognition and Behaviour (IR3C), University of Barcelona, Barcelona, Spain
3
Grup de Recerca Consolidat en Neuropsicologia (SGR0941), University of Barcelona, Barcelona, Spain Neuropsychology Unit, Hospital de Terrassa, Consorci Sanitari de Terrassa, Terrassa, Spain
4 5
Molecular Genetics Unit, Hospital de Terrassa, Consorci Sanitari de Terrassa, Terrassa, Spain
6
Pediatric Endocrinology Unit, Department of Pediatrics, Hospital de Terrassa, Consorci Sanitari de Terrassa, Terrassa, Spain CAP Terrassa Nord, Consorci Sanitari de Terrassa, Terrassa, Spain 8 Institut d’Investigacions Biome`diques August Pi i Sunyer (IDIBAPS), Barcelona, Spain 7
Manuscript Received: 3 September 2013; Manuscript Accepted: 19 February 2014
The prevalence of obesity is increasing worldwide. Previous research has shown a relationship between obesity and both executive functioning alterations and frontal cortex volume reductions. The Brain Derived Neurotrophic Factor val66met polymorphism, involved in eating behavior, has also been associated with executive functions and prefrontal cortex volume, but to date it has not been studied in relation to obesity. Our aim is to elucidate whether the interaction between the Brain Derived Neurotrophic Factor val66met polymorphism and obesity status influences executive performance and frontal-subcortical brain structure. Sixty-one volunteers, 34 obese and 27 controls, age range 12–40, participated in the study. Participants were assigned to one of two genotype groups (met allele carriers, n ¼ 16, or noncarriers, n ¼ 45). Neuropsychological assessment comprised the Trail Making Test, the Stroop Test and the Wisconsin Card Sorting Test, all tasks that require response inhibition and cognitive flexibility. Subjects underwent magnetic resonance imaging in a Siemens TIM TRIO 3T scanner and images were analyzed using the FreeSurfer software. Analyses of covariance controlling for age and intelligence showed an effect of the obesity-by-genotype interaction on perseverative responses on the Wisconsin Card Sorting Test as well as on precentral and caudal middle frontal cortical thickness: obese met allele carriers showed more perseverations on the Wisconsin Card Sorting Test and lower frontal thickness than obese non-carriers and controls. In conclusion, the Brain Derived Neurotrophic Factor may play an important role in executive functioning and frontal brain structure in obesity. Ó 2014 Wiley Periodicals, Inc.
Key words: cortical thickness; BMI; WCST Ó 2014 Wiley Periodicals, Inc.
How to Cite this Article: Marque´s-Iturria I, Garolera M, Pueyo R, Segura B, Hernan I, Garcı´a-Garcı´a I, Sa´nchez-Garre C, Vernet-Vernet M, Sender-Palacios MJ, Narberhaus A, Ariza M, Junque´ C, Jurado MA´. 2014. The Interaction Effect Between BDNF val66met Polymorphism and Obesity on Executive Functions and Frontal Structure. Am J Med Genet Part B 165B:245–253.
INTRODUCTION Obesity is a multifactorial disease whose prevalence is increasing worldwide [World Health Organization, 2012a]. Neuropsychological studies have shown an obesity-related cognitive profile in which Grant sponsor: Ministerio de ciencia e Innovacio´ n (MICIIN); Grant number: PSI2008-05803-C02-00/PSIC; Grant sponsor: University of Barcelona; Grant number: ADR2011-2012; Grant sponsor: Government of Catalonia; Grant number: 2009 SGR0941. Correspondence to: Marı´a A´ngeles Jurado, Ph.D., Department of Psychiatry and Clinical Psychobiology, University of Barcelona, Passeig de la Vall d’Hebron, 171, 08035 Barcelona, Spain. E-mail: [email protected] Article first published online in Wiley Online Library (wileyonlinelibrary.com): 12 March 2014 DOI 10.1002/ajmg.b.32229
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246 executive function measures are consistently altered in obese children, adolescents, and adults [Smith et al., 2011], and suggest a frontal-subcortical pattern of cognitive dysfunction in obesity [Stanek et al., 2013]. Studies in obesity have shown reductions in response inhibition and cognitive flexibility [Waldstein and Katzel, 2006; Verdejo-Garcia et al., 2010; Cohen et al., 2011; Fagundo et al., 2012], functions that are both involved in cognitive control processing. Cognitive control related to food intake behavior involves the prefrontal cortex [Cohen et al., 2011]. Magnetic Resonance studies have shown frontal cortex volume reductions [Pannacciulli et al., 2006; Raji et al., 2010; Maayan et al., 2011; Brooks et al., 2013] and cortical thinning [Hassenstab et al., 2012; Marque´sIturria et al., 2013] in obesity. The Brain Derived Neurotrophic Factor (BDNF) is a member of the neurotrophin family of growth factors [Leibrock et al., 1989]. This neurotrophin regulates energy balance and homeostasis, food intake, and feeding behavior [Rosas-Vargas et al., 2011]. The most frequent BDNF genetic variation is the single nucleotide polymorphism 196G/ A (dbSNP reference number rs6265), which causes a change from valine to methionine at codon 66 (val66met) in the 50 pro-BDNF sequence. This polymorphism is related to eating behavior and eating disorders [Gunstad et al., 2006; Monteleone et al., 2006]. A metaanalysis performed with 39 studies revealed a 33% increase in the risk of eating disorders in carriers of the met allele [Gratacos et al., 2007]. This allele has been associated with anorexia nervosa, bulimia nervosa and binge-eating disorder [Ribases et al., 2004] as well as to body mass index (BMI) increases [Skledar et al., 2012] and it is involved in the pathogenesis of obesity [Beckers et al., 2008]. Research has shown that the BDNF val66met polymorphism influences hippocampal function and memory [Egan et al., 2003; Hariri et al., 2003] and various components of the executive function [Rybakowski et al., 2003]. The met allele is associated with weaker performance [Savitz et al., 2006]. The involvement of the BDNF val66met polymorphism in executive functions has been described in bipolar disorder [Rybakowski et al., 2003, 2006], schizophrenia [Lu et al., 2012] and obsessive compulsive disorder [Da Rocha et al., 2011; Tukel et al., 2012]. However, this involvement has not yet been described in eating disorders or obesity. BDNF is expressed throughout the brain, with abundant expression in the hippocampus and prefrontal cortex [Pezawas et al., 2004]. The BDNF met allele has been associated with volume and cortical thickness reductions in temporal and frontal lobes in healthy subjects [Pezawas et al., 2004; Schofield et al., 2009; Voineskos et al., 2011; Yang et al., 2012] and also in schizophrenia [Ho et al., 2007; Takahashi et al., 2008], although other studies found no association in psychiatric patients [Agartz et al., 2006; Varnas et al., 2008]. In view of the involvement of the BDNF val66met polymorphism in eating behavior, our aim was to assess whether its interaction with obesity status influences executive function performance and frontal-subcortical brain structures.
METHODS Subjects Sixty-one healthy volunteers aged 12–40 years were randomly recruited from the population living within the catchment area of three public medical centers belonging to the Consorci Sanitari de
AMERICAN JOURNAL OF MEDICAL GENETICS PART B Terrassa. The current study shares part of the adult sample previously described elsewhere [Ariza et al., 2012], specifically the participants who underwent imaging acquisition, and extended the sample by including adolescent subjects. According to their BMI, 34 participants were included in the obesity group and 27 in the control group [Cole et al., 2000; World Health Organization, 2012b]. Exclusion criteria included any neurological or psychiatric disorder, anxiety or depression assessed by the Hospital Anxiety and Depression Scale [Zigmond, 1983], pathological use of drugs evaluated with the Structured Clinical Interview for DSM-IV [First et al., 1999], any obesity-related disorder such as thyroid dysfunctions, presence of metabolic syndrome, and presence of general intellectual disability assessed by the Vocabulary subtest of the Wechsler Adult Intelligence Scale 3rd version and the Wechsler Intelligence Scale for Children 4th version. The study was approved by the institutional ethics committee (Comissio´ de Bioe`tica de la Universitat de Barcelona); Institutional Review Board IRB 00003099 Assurance number: FWA00004225 (http://www.ub.edu/recerca/ comissiobioetica.htm) and the research was conducted in accordance with the Helsinki Declaration.
Genotyping Genomic DNA from all subjects was extracted automatically using the MagNaPure Compact Instrument (Roche, Barcelona, Spain) according to the manufacturer’s protocol. A LightCycler real-time PCR assay combined with fluorescence resonance energy transfer (FRET) hybridization probe melting curve analysis was used to analyze BDNF val66met genotype. The primers BDNF-F (50 -ACTCTGGAGAGCGTGAATGG-30 ) and BDNF-R (50 -CCAAAGGCACTTGACTACTGA30 ) were used to amplify the 184 bp fragment containing the G > A transition responsible for val66met change. Specific fluorescence labeled probes (BDNF_FLU 50 -AAGAGGCTTGACATCATTGGCTGACACT-Fluorescein-30 and BDNF_Met 50 -LC640-CGAACACA TGATAGAAGAGCTGTT-30 ) were designed (TIB MOLBIOL, Berlin, Germany). PCR mixture contained 12.6 ml PCR grade water, 1.6 ml 25 mM MgCl2, 0.2 ml BDNF-F (10 mM), 1 ml BDNF-R (10 mM), 0.3 ml of each probe (10 mM), 2 ml Light Cycler DNA Master Hybridization Probe Mix (Roche) and 2 ml sample DNA. The amplification setting consisted of an initial denaturation at 95˚C for 10 min followed by 45 cycles of denaturation at 95˚C for 10 sec, annealing at 55˚C for 10 sec (a single acquisition step) and extension at 72˚C for 12 sec. After amplification, a melting curve analysis was performed by heating the real-time PCR products at 95˚C for 20 sec, cooling at 40˚C for 20 sec and then increasing the temperature to 85˚C while continuously monitoring the fluorescence (temperature transition rate of 0.2˚C/sec). Data were analyzed with LightCycler software, version 4, according to the manufacturer’s instructions. Genotype frequencies were tested for goodness-of-fit to the Hardy– Weinberg equilibrium applying Chi-square analysis. The distributions did not differ from Hardy–Weinberg equilibrium expectations for either the obesity or the control group. The genotype was recorded as carrier versus non-carrier of the allele of interest (met).
Neuropsychological Assessment Participants underwent a neuropsychological assessment of executive functions. The assessment included the Trail Making Test
MARQUE´S-ITURRIA ET AL. (TMT) [Reitan, 1958], Stroop Test [Golden, 1995] and the Wisconsin Card Sorting Test (WCST) [Heaton, 1999]. For the purposes of the study, the difference between the A and B parts of the TMT, the interference score on the Stroop Test and the percentage of perseverative responses on the WCST were computed. These scores are commonly used for assessing response inhibition and cognitive flexibility. Raw scores of these measures were used in all the analyses.
Imaging All participants underwent Magnetic Resonance Imaging (MRI) on a 3T Siemens TIM TRIO 3T scanner (Siemens, Erlangen, Germany) (parameters: repetition time 2,300 msec; echo time 2.98 msec; inversion time 900 msec; field of view 256 mm 256 mm, 1 mm isotropic voxel) in order to obtain high resolution T1-weighted MPRAGE 3D scans, performed at the Hospital Clı´nic de Barcelona.
Data Processing FreeSurfer v5.1.0 (http://surfer.nmr.mgh.harvard.edu) was used to process MRI data. The procedure includes motion correction and averaging of volumetric T1 weighted images, removal of non-brain tissue, Talairach transformation, segmentation of volumetric structures, intensity normalization, tessellation of the gray matter white matter boundary, topology correction, and surface deformation following intensity gradients. Representations of cortical thickness are calculated as the closest distance from the gray/white boundary to the gray/CSF boundary at each vertex on the surface [Fischl and Dale, 2000]. FreeSurfer also obtains subcortical volume measures via a whole brain segmentation procedure [Fischl et al., 2002]. The post-processing outputs for each subject were examined visually to ensure processing accuracy and image quality. Manual edits were not required.
Statistical Analyses Since our sample covered a wide age range, from adolescence to adulthood, statistical analyses were performed using age and intelligence (estimated with scalar scores on Vocabulary) as covariates, in order to eliminate possible effects of developmental and cognitive level on neuropsychological performance and brain anatomy. With PWAS 18.0 software, analysis of covariance (ANCOVA) was used to determine the effects of obesity (obesity or control) and genotype (met carrier or non-carrier) and their interaction on executive function, controlling for age and intelligence. The general linear modeling implemented in FreeSurfer (Qdec tool) was used to analyze the main effects of obesity and genotype and their interaction on cortical thickness analyses, including age and intelligence as nuisance factors. Data were smoothed on the surface with a kernel of 15 mm full-width half-maximum. The mean thickness of the clusters that remained significant after MonteCarlo correction (10,000 repetitions; P < 0.01) was extracted for each participant, and then analysis of variance (ANOVA) was performed with PWAS 18.0. Subcortical volumes were obtained from automatic subcortical segmentation of brain volume, based on the existence of an atlas containing probabilistic information on the location of structures,
247 which is fully described in Fischl et al. [2002]. The relative volume was then calculated dividing each structure by intracranial volume. Group, genotype and interaction effect on subcortical structure relative volumes were analyzed by ANCOVA, controlling for age and intelligence. Finally, in order to link together the results obtained in the previous analyses, we examined whether the interaction between obesity and BDNF genotype had an effect on the relationship between neuropsychological variables and brain structure, using the Qdec tool in FreeSurfer (15 mm FWHM, MonteCarlo 10,000 repetitions; P < 0.01). Effect sizes were calculated with Partial Eta Squared (h2p ) interpreted as 0.010 small, 0.060 medium and 0.138 large effects [Cohen, 1988].
RESULTS The demographic characteristics of the sample are shown in Table I. Obesity and genotype ANOVA on the sample characteristic variables showed an effect of obesity, as expected, on BMI (F(3,57) ¼ 83.002, P ¼ 0.001) and waist circumference (F(3,57) ¼ 51.743, P < 0.001), and a genotype effect on intelligence (F(3,57) ¼ 7.423, P ¼ 0.009). No group genotype interaction effect was observed. No other effect was shown on these variables. Regarding the neuropsychological scores (Table S1), the results showed an effect of obesity on the WCST percentage of perseverative responses (F(3,55) ¼ 10.846, P ¼ 0.002, h2p ¼ 0:165), with a higher number of perseverations in the obesity group than in controls. Genotype effects were also evident in this variable of the WCST (F(3,55) ¼ 9.538, P ¼ 0.003, h2p ¼ 0:148), with met allele carriers performing worse than non-carriers. Interestingly, in this cognitive variable there was also an obesity genotype interaction effect (F(3,55) ¼ 4.202, P ¼ 0.045, h2p ¼ 0:071). Figure 1a represents this latter effect, with the met obese group showing the greatest percentage of perseverative responses and the control non-carrier group the lowest. Effect sizes were large for main obesity and genotype effects and medium for the interaction effect (h2p ¼ 0:071 0:165). Cortical thickness analyses revealed a main effect of obesity on six clusters: left precentral cortex extending to the caudal middle frontal, superior frontal, postcentral, superior parietal, and paracentral cortex (5,873 mm2; Talairach peak: 33.8, 12.0, 61.2), right precentral cortex (950 mm2, Talairach peak: 37.7, 14.9, 57.4), right precentral cortex extending to the caudal middle frontal cortex (1,887 mm2, Talairach peak: 55.7, 0.1, 33.1), right paracentral cortex extending to the superior frontal and posterior cingulate cortex (1,050 mm2, Talairach peak: 5.5, 13.3, 56.8), right fusiform cortex extending to the inferior temporal, middle temporal and temporal pole (594 mm2, Talairach peak: 34.8, 1.5, 35.8) and right medial orbitofrontal cortex extending to the pars orbitalis, frontal pole and rostral middle frontal cortex (911 mm2, Talairach peak: 10.9, 53.1, 7.6) (Fig. S1). Genotype main effect was evident on pericalcarine cortex extending to the cuneus and lateral occipital cortex (1,009 mm2, Talairach peak: 15.9, 81.3, 8.6) (Fig. S2). Finally, we observed obesity by genotype interaction effect on two frontal clusters (Fig. 2); one on left precentral cortex extending to the paracentral, superior frontal and postcentral cortex (1,676 mm2; Talairach peak: 10.4, 26.1, 61.9) and another one on right caudal middle frontal cortex extending to the
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TABLE I. Characteristics of the Sample Per Group Obesity group Age (years) Intelligence (vocabulary, WAIS-III/WISC-IV, SS)a BMI (kg/m2)b WC (cm)b Gender Mean thickness (mm)
Control group
Non-carrier (n ¼ 28) 25.57 (12–39) 11.04 (7–16)
Carrier (n ¼ 6) 21.17 (12–39) 9.67 (8–11)
Non-carrier (n ¼ 17) 29.06 (14–40) 12.35 (9–15)
Carrier (n ¼ 10) 25.70 (14–39) 10.50 (8–14)
34.22 (27.47–49.69) 102.61 (83–137) 8 m, 20 f 2.67 (2.39–2.95)
31.74 (27.60–36.58) 97 (85–119) 3 m, 3 f 2.69 (2.56–2.86)
22.07 (19.53–24.97) 79.52 (68–92) 8 m, 9 f 2.66 (2.48–2.76)
21.65 (19.89–24.87) 73.50 (66–88) 2 m, 8 f 2.78 (2.62–2.97)
Values showed as mean (range), except for gender; m, male; f, female. BMI, body mass index; SS, scalar score; WAIS, Wechsler Adult Intelligence Scale; WC, waist circumference; WISC, Wechsler Intelligence Scale for Children. a Genotype effect on ANOVA analysis. b Group effect on ANOVA analysis.
precentral and rostral middle frontal cortex (1,405 mm2; Talairach peak: 39.0, 20.9, 42.4). Interaction effect of the two clusters (F(3,57) ¼ 26.855, P < 0.001, h2p ¼ 0:320; F(3,57) ¼ 14.241, P < 0.001, h2p ¼ 0:200) can be observed in Figure 1b,c, indicating that met carriers in the obesity group showed the thinnest cortex areas and control met carriers the thickest. Locations are shown according to Desikan atlas [Desikan et al., 2006]. Subcortical structure volume analyses showed a group effect on the bilateral thalamus (F(3,55) ¼ 7.373, P ¼ 0.009, h2p ¼ 0:118; F(3,55) ¼ 7.493, P ¼ 0.008, h2p ¼ 0:120), brain stem (F(3,55) ¼ 10.733, P ¼ 0.002, h2p ¼ 0:163) and bilateral ventral diencephalon
(F(3,55) ¼ 11.677, P ¼ 0.001, h2p ¼ 0:175; F(3,55) ¼ 9.218, P ¼ 0.004, h2p ¼ 0:144), with the obese group showing lower volumes than controls; a genotype effect on bilateral caudate (F(3,55) ¼ 5.335, P ¼ 0.025, h2p ¼ 0:088; F(3,55) ¼ 4.119, P ¼ 0.047, h2p ¼ 0:070) where met carriers showed lower volumes than non-carriers; and no group by genotype interaction on any subcortical structure. Further imaging analyses relating neuropsychological and cortical thickness results confirmed a group by genotype interaction in the correlation between the percentage of perseverative responses of the WCST and cortical thickness. We found two significant clusters: one involving the left precentral cortex and the caudal
FIG. 1. Group by genotype interaction effect on perseverative responses performance (a) and frontal cortical thickness (b,c). The measure of the perseverative responses was obtained by regressing age and intelligence on the dependent variable and then saving the standardized residual from the model. Cortical thickness values are shown in mm.
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DISCUSSION
FIG. 2. Maps showing group by genotype interaction effect on cortical thickness. In both clusters, obese met carriers had the thinnest cortex and control met carriers the thickest.
middle frontal cortex (1,367 mm2; Talairach peak: 30.1, 10.1, 50.9; Z-value ¼ 5.898; CWP < 0.001) and the other involving the right caudal middle frontal cortex and the rostral middle frontal, pars opercularis, and precentral cortex (1,879 mm2; Talairach peak: 36.5, 26.3, 40.5; Z-value ¼ 3.45; CWP < 0.001; Fig. 3).
FIG. 3. Maps showing the group by genotype interaction effect on the correlation between WCST perseverative responses and cortical thickness.
Our study has three main results. First, obesity has an impact on executive function and frontal structure, leading to worse performance and reduced regional thickness and subcortical volume compared to controls. Second, the BDNF val66met polymorphism has an impact on executive function, since met allele carriers performed worse than non-carriers. Third, the interaction of the BDNF val66met polymorphism and obesity influences both executive function performance and frontal cortical thickness. Our results suggest a modulatory effect between obesity and the BDNF val66met polymorphism in frontal regions, which is evidenced at cognitive level and also at brain structural level, both independently and merged. Previous studies have demonstrated executive performance alterations related to obesity in response inhibition and cognitive flexibility [Fagundo et al., 2012]. Alterations with executive function have also been reported in youth at risk for obesity [Riggs et al., 2012]. Our results corroborate these findings by demonstrating poorer executive function performance in obese participants than in controls. Specifically, obese participants performed more perseverative responses than controls on the WCST. WCST perseverative responses also revealed a genotype effect, with met carriers showing a higher number of perseverations than non-carriers. These results are in line with those of previous studies of genetic influences on cognition which have associated the met allele of the BDNF val66met polymorphism with worse performance on executive function tasks [Savitz et al., 2006]. Interestingly, obese met carriers committed more perseverations than non-carriers, while the number was similar between control met carriers and non-carriers. The WCST is a well-established complex measure of executive function, widely used in neurocognitive outcome studies related to eating behavior and considered as the most sensitive executive measure related to BMI [Vainik et al., 2013]. The successful completion of the WCST requires strategic planning, organized searching, the ability to use environmental feedback to shift cognitive set, goal-oriented behavior, and the ability to modulate impulsive responding [Strauss et al., 2006]. Together, the results indicate that obesity, genotype and their interaction have clear effects on complex executive function processing such as that required on the WCST. However, Stroop Test and the TMT tasks did not show the same obesity by genotype interaction effects. Although the WCST shares a common cognitive flexibility core with the Stroop Test and the TMT tasks, all these tests involve task-specific executive function components that account for the complex executive function construct [Strauss et al., 2006]. Specifically, perseveration on the WCST is considered as the inappropriate maintenance of a current category or framework [Possin et al., 2005], a function that has classically been related to frontal, dorsal and lateral regions [Stuss and Knight, 2002]. Overall, our results demonstrate that being obese and a met carrier is a disadvantage in terms of perseverative executive performance in comparison to the other conditions, and point to an important role for the BDNF val66met genotype in complex executive performance in obesity. Our results agree with previous reports of an influence of BDNF val66met on executive performance in different psychiatric conditions. In bipolar disorder, it has been shown that met allele carriers
250 perform worse on WCST than non-carriers [Rybakowski et al., 2003; Rybakowski et al., 2006]. In schizophrenia, patients with the met allele have a greater percentage of perseverative errors than those without it [Lu et al., 2012]. In obsessive-compulsive disorder, the met allele has been associated with poorer set-shifting and perseverance abilities [Tukel et al., 2012], and with impairments of cognitive functions related to the orbitofrontal cortex, such as the Iowa Gambling Task [Da Rocha et al., 2011]. Although research on psychiatric samples has implicated the met allele in executive function disturbances, a meta-analysis including both healthy and mental health patients did not detect a consistent significant genetic association between the BDNF val66met polymorphism and executive functioning or any of the other cognitive domains analyzed (general cognitive ability, memory, visual processing skills, and cognitive fluency) [Mandelman and Grigorenko, 2012]. The results of studies with healthy samples are mixed. The met allele enhances executive functioning in healthy participants [Beste et al., 2010; Gajewski et al., 2012]. Furthermore, Alfimova et al. [2012] showed that healthy participants carrying the met allele performed better on working memory but presented no advantages in interference processing such as set shifting; they concluded that the BDNF val66met polymorphism has a greater impact on performance speed in prefrontal tasks than on the executive function mechanisms. Taken together, it seems that in terms of executive functioning the met allele confers a benefit or null effect on healthy subjects, but disadvantages psychiatric patients with pathologies in which the fronto-striatal circuit functioning is altered [Del Casale et al., 2011; Diwadkar et al., 2011]. The results found in our sample point to an executive functioning pattern mediated by the BDNF val66met polymorphism similar to that shown in patients with fronto-striatal dysfunctions. Indeed, fronto-striatal circuitry has recently been implicated in food perception, intake, feeding and satiety, suggesting that imbalances in this circuitry may mediate obesity [Michaelides et al., 2012]. Therefore, it is reasonable to suppose that the executive pattern associated with obesity and mediated by the BDNF val66met polymorphism points in the same direction: that is, carrying the met allele means worse executive performance. As far as neuroimaging data are concerned, several frontal regions (precentral, paracentral, medial orbitofrontal) and subcortical structures (thalamus, brain stem, ventral diencephalon) showed a main effect of obesity on cortical thickness and on subcortical structure volume. The obesity group presented lower cortical thickness and decreased subcortical structure volume than controls, in agreement with studies that show frontal [Reinert et al., 2013] and also subcortical reductions in obesity [Marque´sIturria et al., 2013]. More interestingly, although there was no group by genotype interaction effect on any subcortical structure, we did find an interaction effect on cortical thickness of the precentral and middle frontal regions: met allele carriers in the obesity group showed the lowest cortical thickness in the model, and control met carriers the highest. This indicates that the relationship between frontal brain structure and BDNF val66met polymorphism in obesity is the exact opposite of that found in controls. Along the same lines, it has been shown that met allele carriers have greater reductions in frontal gray matter volume than noncarriers in schizophrenia [Ho et al., 2007]. However, results in pathological samples are mixed. Some studies did not report
AMERICAN JOURNAL OF MEDICAL GENETICS PART B significant relations between the BDNF val66met polymorphism and frontal brain structure in schizophrenia [Agartz et al., 2006; Varnas et al., 2008], and furthermore there is evidence of an interaction effect between genotype and autism spectrum disorder condition in which the patient group carrying the met allele showed the greatest frontal regional volumes. Therefore, it seems that the influences of the BDNF val66met polymorphism on frontal brain volume vary notably across samples, probably due in part to the intrinsic brain structural alterations present in each pathological condition. Here, we present evidence that brain regions that play important roles in the frontal neural networks involved in eating behavior [Inui et al., 2002; Pannacciulli et al., 2006; Walther et al., 2010; Maayan et al., 2011; Kurth et al., 2012] are related to the interaction effect between obesity and BDNF genotype in adolescent and young adults. Finally, we observed a link between neuropsychological and imaging results, showing an effect of the interaction between obesity and genotype on the correlation between the WCST perseverative performance and the cortical thickness of areas including two frontal regions. These results provide further evidence for the combined influence of obesity and BDNF genotype on executive function and brain frontal structure. All in all, considering that perseverations have been related not only to frontal areas but also to caudate nuclei [Lombardi et al., 1999], the frontal-executive neuroanatomical substrate involved in obesity and BDNF may include frontal-striatal circuits. However, our analyses did not show interaction effects on subcortical areas. Future studies using connectivity approaches may shed light on the involvement of frontal-subcortical circuits in obesity and the BDNF val66met polymorphism. The most important limitation of this study is the small sample size. Given the aim of the study, it was necessary to divide the whole sample into carriers and non-carriers of the allele of interest, which inevitably reduced the size of each subgroup and compromised the statistical power of the analyses. However, the results of the study are significant, and the medium to large effect sizes may be replicated in future studies with more extensive samples. In addition, a larger sample would allow creating subgroups on the basis of age ranges in order to perform a more detailed analysis on each developmental stage, as it would be interesting to assess possible developmental variations on the interaction effect between obesity and BDNF val66met polymorphism.
CONCLUSION In conclusion, our data show that obesity status interacts with the BDNF val66met polymorphism and affects executive function performance and frontal cortical thickness, suggesting that the presence of the met allele in obesity may lead to frontal dysfunctions. Genetic variations of the BDNF val66met polymorphism are of great interest in the clinical context of obesity, as they may contribute to the design of effective intervention programs focused on cognitive training in addition to the promotion of healthy behavior.
ACKNOWLEDGEMENTS The authors thank all the participants who made this study possible. They also thank Encarnacio´ Tor for her invaluable help in perform-
MARQUE´S-ITURRIA ET AL. ing blood analyses. This study was supported by grant PSI200805803-C02-00/PSIC from MICIIN to Maria Angeles Jurado, grant ADR2011-2012 from the University of Barcelona to Idoia Marque´sIturria, and grant 2009 SGR0941 from the Government of Catalonia.
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SUPPORTING INFORMATION Additional supporting information may be found in the online version of this article at the publisher’s web-site.
The Interaction Effect Between BDNF val66met Polymorphism and Obesity on Executive Functions and Frontal Structure Idoia Marque´s-Iturria,1,2 Maite Garolera,3,4 Roser Pueyo,1,2,3 Ba`rbara Segura,1,3 Imma Hernan,5 Isabel Garcı´a-Garcı´a,1,2 Consuelo Sa´nchez-Garre,6 Marı´a Vernet-Vernet,7 Marı´a Jose´ SenderPalacios,7 Ana Narberhaus,1 Mar Ariza,1 Carme Junque´,1,3,8 and Marı´a ´Angeles Jurado1,2,3* 1
Department of Psychiatry and Clinical Psychobiology, University of Barcelona, Barcelona, Spain
2
Institute for Brain, Cognition and Behaviour (IR3C), University of Barcelona, Barcelona, Spain
3
Grup de Recerca Consolidat en Neuropsicologia (SGR0941), University of Barcelona, Barcelona, Spain Neuropsychology Unit, Hospital de Terrassa, Consorci Sanitari de Terrassa, Terrassa, Spain
4 5
Molecular Genetics Unit, Hospital de Terrassa, Consorci Sanitari de Terrassa, Terrassa, Spain
6
Pediatric Endocrinology Unit, Department of Pediatrics, Hospital de Terrassa, Consorci Sanitari de Terrassa, Terrassa, Spain CAP Terrassa Nord, Consorci Sanitari de Terrassa, Terrassa, Spain 8 Institut d’Investigacions Biome`diques August Pi i Sunyer (IDIBAPS), Barcelona, Spain 7
Manuscript Received: 3 September 2013; Manuscript Accepted: 19 February 2014
The prevalence of obesity is increasing worldwide. Previous research has shown a relationship between obesity and both executive functioning alterations and frontal cortex volume reductions. The Brain Derived Neurotrophic Factor val66met polymorphism, involved in eating behavior, has also been associated with executive functions and prefrontal cortex volume, but to date it has not been studied in relation to obesity. Our aim is to elucidate whether the interaction between the Brain Derived Neurotrophic Factor val66met polymorphism and obesity status influences executive performance and frontal-subcortical brain structure. Sixty-one volunteers, 34 obese and 27 controls, age range 12–40, participated in the study. Participants were assigned to one of two genotype groups (met allele carriers, n ¼ 16, or noncarriers, n ¼ 45). Neuropsychological assessment comprised the Trail Making Test, the Stroop Test and the Wisconsin Card Sorting Test, all tasks that require response inhibition and cognitive flexibility. Subjects underwent magnetic resonance imaging in a Siemens TIM TRIO 3T scanner and images were analyzed using the FreeSurfer software. Analyses of covariance controlling for age and intelligence showed an effect of the obesity-by-genotype interaction on perseverative responses on the Wisconsin Card Sorting Test as well as on precentral and caudal middle frontal cortical thickness: obese met allele carriers showed more perseverations on the Wisconsin Card Sorting Test and lower frontal thickness than obese non-carriers and controls. In conclusion, the Brain Derived Neurotrophic Factor may play an important role in executive functioning and frontal brain structure in obesity. Ó 2014 Wiley Periodicals, Inc.
Key words: cortical thickness; BMI; WCST Ó 2014 Wiley Periodicals, Inc.
How to Cite this Article: Marque´s-Iturria I, Garolera M, Pueyo R, Segura B, Hernan I, Garcı´a-Garcı´a I, Sa´nchez-Garre C, Vernet-Vernet M, Sender-Palacios MJ, Narberhaus A, Ariza M, Junque´ C, Jurado MA´. 2014. The Interaction Effect Between BDNF val66met Polymorphism and Obesity on Executive Functions and Frontal Structure. Am J Med Genet Part B 165B:245–253.
INTRODUCTION Obesity is a multifactorial disease whose prevalence is increasing worldwide [World Health Organization, 2012a]. Neuropsychological studies have shown an obesity-related cognitive profile in which Grant sponsor: Ministerio de ciencia e Innovacio´ n (MICIIN); Grant number: PSI2008-05803-C02-00/PSIC; Grant sponsor: University of Barcelona; Grant number: ADR2011-2012; Grant sponsor: Government of Catalonia; Grant number: 2009 SGR0941. Correspondence to: Marı´a A´ngeles Jurado, Ph.D., Department of Psychiatry and Clinical Psychobiology, University of Barcelona, Passeig de la Vall d’Hebron, 171, 08035 Barcelona, Spain. E-mail: [email protected] Article first published online in Wiley Online Library (wileyonlinelibrary.com): 12 March 2014 DOI 10.1002/ajmg.b.32229
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246 executive function measures are consistently altered in obese children, adolescents, and adults [Smith et al., 2011], and suggest a frontal-subcortical pattern of cognitive dysfunction in obesity [Stanek et al., 2013]. Studies in obesity have shown reductions in response inhibition and cognitive flexibility [Waldstein and Katzel, 2006; Verdejo-Garcia et al., 2010; Cohen et al., 2011; Fagundo et al., 2012], functions that are both involved in cognitive control processing. Cognitive control related to food intake behavior involves the prefrontal cortex [Cohen et al., 2011]. Magnetic Resonance studies have shown frontal cortex volume reductions [Pannacciulli et al., 2006; Raji et al., 2010; Maayan et al., 2011; Brooks et al., 2013] and cortical thinning [Hassenstab et al., 2012; Marque´sIturria et al., 2013] in obesity. The Brain Derived Neurotrophic Factor (BDNF) is a member of the neurotrophin family of growth factors [Leibrock et al., 1989]. This neurotrophin regulates energy balance and homeostasis, food intake, and feeding behavior [Rosas-Vargas et al., 2011]. The most frequent BDNF genetic variation is the single nucleotide polymorphism 196G/ A (dbSNP reference number rs6265), which causes a change from valine to methionine at codon 66 (val66met) in the 50 pro-BDNF sequence. This polymorphism is related to eating behavior and eating disorders [Gunstad et al., 2006; Monteleone et al., 2006]. A metaanalysis performed with 39 studies revealed a 33% increase in the risk of eating disorders in carriers of the met allele [Gratacos et al., 2007]. This allele has been associated with anorexia nervosa, bulimia nervosa and binge-eating disorder [Ribases et al., 2004] as well as to body mass index (BMI) increases [Skledar et al., 2012] and it is involved in the pathogenesis of obesity [Beckers et al., 2008]. Research has shown that the BDNF val66met polymorphism influences hippocampal function and memory [Egan et al., 2003; Hariri et al., 2003] and various components of the executive function [Rybakowski et al., 2003]. The met allele is associated with weaker performance [Savitz et al., 2006]. The involvement of the BDNF val66met polymorphism in executive functions has been described in bipolar disorder [Rybakowski et al., 2003, 2006], schizophrenia [Lu et al., 2012] and obsessive compulsive disorder [Da Rocha et al., 2011; Tukel et al., 2012]. However, this involvement has not yet been described in eating disorders or obesity. BDNF is expressed throughout the brain, with abundant expression in the hippocampus and prefrontal cortex [Pezawas et al., 2004]. The BDNF met allele has been associated with volume and cortical thickness reductions in temporal and frontal lobes in healthy subjects [Pezawas et al., 2004; Schofield et al., 2009; Voineskos et al., 2011; Yang et al., 2012] and also in schizophrenia [Ho et al., 2007; Takahashi et al., 2008], although other studies found no association in psychiatric patients [Agartz et al., 2006; Varnas et al., 2008]. In view of the involvement of the BDNF val66met polymorphism in eating behavior, our aim was to assess whether its interaction with obesity status influences executive function performance and frontal-subcortical brain structures.
METHODS Subjects Sixty-one healthy volunteers aged 12–40 years were randomly recruited from the population living within the catchment area of three public medical centers belonging to the Consorci Sanitari de
AMERICAN JOURNAL OF MEDICAL GENETICS PART B Terrassa. The current study shares part of the adult sample previously described elsewhere [Ariza et al., 2012], specifically the participants who underwent imaging acquisition, and extended the sample by including adolescent subjects. According to their BMI, 34 participants were included in the obesity group and 27 in the control group [Cole et al., 2000; World Health Organization, 2012b]. Exclusion criteria included any neurological or psychiatric disorder, anxiety or depression assessed by the Hospital Anxiety and Depression Scale [Zigmond, 1983], pathological use of drugs evaluated with the Structured Clinical Interview for DSM-IV [First et al., 1999], any obesity-related disorder such as thyroid dysfunctions, presence of metabolic syndrome, and presence of general intellectual disability assessed by the Vocabulary subtest of the Wechsler Adult Intelligence Scale 3rd version and the Wechsler Intelligence Scale for Children 4th version. The study was approved by the institutional ethics committee (Comissio´ de Bioe`tica de la Universitat de Barcelona); Institutional Review Board IRB 00003099 Assurance number: FWA00004225 (http://www.ub.edu/recerca/ comissiobioetica.htm) and the research was conducted in accordance with the Helsinki Declaration.
Genotyping Genomic DNA from all subjects was extracted automatically using the MagNaPure Compact Instrument (Roche, Barcelona, Spain) according to the manufacturer’s protocol. A LightCycler real-time PCR assay combined with fluorescence resonance energy transfer (FRET) hybridization probe melting curve analysis was used to analyze BDNF val66met genotype. The primers BDNF-F (50 -ACTCTGGAGAGCGTGAATGG-30 ) and BDNF-R (50 -CCAAAGGCACTTGACTACTGA30 ) were used to amplify the 184 bp fragment containing the G > A transition responsible for val66met change. Specific fluorescence labeled probes (BDNF_FLU 50 -AAGAGGCTTGACATCATTGGCTGACACT-Fluorescein-30 and BDNF_Met 50 -LC640-CGAACACA TGATAGAAGAGCTGTT-30 ) were designed (TIB MOLBIOL, Berlin, Germany). PCR mixture contained 12.6 ml PCR grade water, 1.6 ml 25 mM MgCl2, 0.2 ml BDNF-F (10 mM), 1 ml BDNF-R (10 mM), 0.3 ml of each probe (10 mM), 2 ml Light Cycler DNA Master Hybridization Probe Mix (Roche) and 2 ml sample DNA. The amplification setting consisted of an initial denaturation at 95˚C for 10 min followed by 45 cycles of denaturation at 95˚C for 10 sec, annealing at 55˚C for 10 sec (a single acquisition step) and extension at 72˚C for 12 sec. After amplification, a melting curve analysis was performed by heating the real-time PCR products at 95˚C for 20 sec, cooling at 40˚C for 20 sec and then increasing the temperature to 85˚C while continuously monitoring the fluorescence (temperature transition rate of 0.2˚C/sec). Data were analyzed with LightCycler software, version 4, according to the manufacturer’s instructions. Genotype frequencies were tested for goodness-of-fit to the Hardy– Weinberg equilibrium applying Chi-square analysis. The distributions did not differ from Hardy–Weinberg equilibrium expectations for either the obesity or the control group. The genotype was recorded as carrier versus non-carrier of the allele of interest (met).
Neuropsychological Assessment Participants underwent a neuropsychological assessment of executive functions. The assessment included the Trail Making Test
MARQUE´S-ITURRIA ET AL. (TMT) [Reitan, 1958], Stroop Test [Golden, 1995] and the Wisconsin Card Sorting Test (WCST) [Heaton, 1999]. For the purposes of the study, the difference between the A and B parts of the TMT, the interference score on the Stroop Test and the percentage of perseverative responses on the WCST were computed. These scores are commonly used for assessing response inhibition and cognitive flexibility. Raw scores of these measures were used in all the analyses.
Imaging All participants underwent Magnetic Resonance Imaging (MRI) on a 3T Siemens TIM TRIO 3T scanner (Siemens, Erlangen, Germany) (parameters: repetition time 2,300 msec; echo time 2.98 msec; inversion time 900 msec; field of view 256 mm 256 mm, 1 mm isotropic voxel) in order to obtain high resolution T1-weighted MPRAGE 3D scans, performed at the Hospital Clı´nic de Barcelona.
Data Processing FreeSurfer v5.1.0 (http://surfer.nmr.mgh.harvard.edu) was used to process MRI data. The procedure includes motion correction and averaging of volumetric T1 weighted images, removal of non-brain tissue, Talairach transformation, segmentation of volumetric structures, intensity normalization, tessellation of the gray matter white matter boundary, topology correction, and surface deformation following intensity gradients. Representations of cortical thickness are calculated as the closest distance from the gray/white boundary to the gray/CSF boundary at each vertex on the surface [Fischl and Dale, 2000]. FreeSurfer also obtains subcortical volume measures via a whole brain segmentation procedure [Fischl et al., 2002]. The post-processing outputs for each subject were examined visually to ensure processing accuracy and image quality. Manual edits were not required.
Statistical Analyses Since our sample covered a wide age range, from adolescence to adulthood, statistical analyses were performed using age and intelligence (estimated with scalar scores on Vocabulary) as covariates, in order to eliminate possible effects of developmental and cognitive level on neuropsychological performance and brain anatomy. With PWAS 18.0 software, analysis of covariance (ANCOVA) was used to determine the effects of obesity (obesity or control) and genotype (met carrier or non-carrier) and their interaction on executive function, controlling for age and intelligence. The general linear modeling implemented in FreeSurfer (Qdec tool) was used to analyze the main effects of obesity and genotype and their interaction on cortical thickness analyses, including age and intelligence as nuisance factors. Data were smoothed on the surface with a kernel of 15 mm full-width half-maximum. The mean thickness of the clusters that remained significant after MonteCarlo correction (10,000 repetitions; P < 0.01) was extracted for each participant, and then analysis of variance (ANOVA) was performed with PWAS 18.0. Subcortical volumes were obtained from automatic subcortical segmentation of brain volume, based on the existence of an atlas containing probabilistic information on the location of structures,
247 which is fully described in Fischl et al. [2002]. The relative volume was then calculated dividing each structure by intracranial volume. Group, genotype and interaction effect on subcortical structure relative volumes were analyzed by ANCOVA, controlling for age and intelligence. Finally, in order to link together the results obtained in the previous analyses, we examined whether the interaction between obesity and BDNF genotype had an effect on the relationship between neuropsychological variables and brain structure, using the Qdec tool in FreeSurfer (15 mm FWHM, MonteCarlo 10,000 repetitions; P < 0.01). Effect sizes were calculated with Partial Eta Squared (h2p ) interpreted as 0.010 small, 0.060 medium and 0.138 large effects [Cohen, 1988].
RESULTS The demographic characteristics of the sample are shown in Table I. Obesity and genotype ANOVA on the sample characteristic variables showed an effect of obesity, as expected, on BMI (F(3,57) ¼ 83.002, P ¼ 0.001) and waist circumference (F(3,57) ¼ 51.743, P < 0.001), and a genotype effect on intelligence (F(3,57) ¼ 7.423, P ¼ 0.009). No group genotype interaction effect was observed. No other effect was shown on these variables. Regarding the neuropsychological scores (Table S1), the results showed an effect of obesity on the WCST percentage of perseverative responses (F(3,55) ¼ 10.846, P ¼ 0.002, h2p ¼ 0:165), with a higher number of perseverations in the obesity group than in controls. Genotype effects were also evident in this variable of the WCST (F(3,55) ¼ 9.538, P ¼ 0.003, h2p ¼ 0:148), with met allele carriers performing worse than non-carriers. Interestingly, in this cognitive variable there was also an obesity genotype interaction effect (F(3,55) ¼ 4.202, P ¼ 0.045, h2p ¼ 0:071). Figure 1a represents this latter effect, with the met obese group showing the greatest percentage of perseverative responses and the control non-carrier group the lowest. Effect sizes were large for main obesity and genotype effects and medium for the interaction effect (h2p ¼ 0:071 0:165). Cortical thickness analyses revealed a main effect of obesity on six clusters: left precentral cortex extending to the caudal middle frontal, superior frontal, postcentral, superior parietal, and paracentral cortex (5,873 mm2; Talairach peak: 33.8, 12.0, 61.2), right precentral cortex (950 mm2, Talairach peak: 37.7, 14.9, 57.4), right precentral cortex extending to the caudal middle frontal cortex (1,887 mm2, Talairach peak: 55.7, 0.1, 33.1), right paracentral cortex extending to the superior frontal and posterior cingulate cortex (1,050 mm2, Talairach peak: 5.5, 13.3, 56.8), right fusiform cortex extending to the inferior temporal, middle temporal and temporal pole (594 mm2, Talairach peak: 34.8, 1.5, 35.8) and right medial orbitofrontal cortex extending to the pars orbitalis, frontal pole and rostral middle frontal cortex (911 mm2, Talairach peak: 10.9, 53.1, 7.6) (Fig. S1). Genotype main effect was evident on pericalcarine cortex extending to the cuneus and lateral occipital cortex (1,009 mm2, Talairach peak: 15.9, 81.3, 8.6) (Fig. S2). Finally, we observed obesity by genotype interaction effect on two frontal clusters (Fig. 2); one on left precentral cortex extending to the paracentral, superior frontal and postcentral cortex (1,676 mm2; Talairach peak: 10.4, 26.1, 61.9) and another one on right caudal middle frontal cortex extending to the
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TABLE I. Characteristics of the Sample Per Group Obesity group Age (years) Intelligence (vocabulary, WAIS-III/WISC-IV, SS)a BMI (kg/m2)b WC (cm)b Gender Mean thickness (mm)
Control group
Non-carrier (n ¼ 28) 25.57 (12–39) 11.04 (7–16)
Carrier (n ¼ 6) 21.17 (12–39) 9.67 (8–11)
Non-carrier (n ¼ 17) 29.06 (14–40) 12.35 (9–15)
Carrier (n ¼ 10) 25.70 (14–39) 10.50 (8–14)
34.22 (27.47–49.69) 102.61 (83–137) 8 m, 20 f 2.67 (2.39–2.95)
31.74 (27.60–36.58) 97 (85–119) 3 m, 3 f 2.69 (2.56–2.86)
22.07 (19.53–24.97) 79.52 (68–92) 8 m, 9 f 2.66 (2.48–2.76)
21.65 (19.89–24.87) 73.50 (66–88) 2 m, 8 f 2.78 (2.62–2.97)
Values showed as mean (range), except for gender; m, male; f, female. BMI, body mass index; SS, scalar score; WAIS, Wechsler Adult Intelligence Scale; WC, waist circumference; WISC, Wechsler Intelligence Scale for Children. a Genotype effect on ANOVA analysis. b Group effect on ANOVA analysis.
precentral and rostral middle frontal cortex (1,405 mm2; Talairach peak: 39.0, 20.9, 42.4). Interaction effect of the two clusters (F(3,57) ¼ 26.855, P < 0.001, h2p ¼ 0:320; F(3,57) ¼ 14.241, P < 0.001, h2p ¼ 0:200) can be observed in Figure 1b,c, indicating that met carriers in the obesity group showed the thinnest cortex areas and control met carriers the thickest. Locations are shown according to Desikan atlas [Desikan et al., 2006]. Subcortical structure volume analyses showed a group effect on the bilateral thalamus (F(3,55) ¼ 7.373, P ¼ 0.009, h2p ¼ 0:118; F(3,55) ¼ 7.493, P ¼ 0.008, h2p ¼ 0:120), brain stem (F(3,55) ¼ 10.733, P ¼ 0.002, h2p ¼ 0:163) and bilateral ventral diencephalon
(F(3,55) ¼ 11.677, P ¼ 0.001, h2p ¼ 0:175; F(3,55) ¼ 9.218, P ¼ 0.004, h2p ¼ 0:144), with the obese group showing lower volumes than controls; a genotype effect on bilateral caudate (F(3,55) ¼ 5.335, P ¼ 0.025, h2p ¼ 0:088; F(3,55) ¼ 4.119, P ¼ 0.047, h2p ¼ 0:070) where met carriers showed lower volumes than non-carriers; and no group by genotype interaction on any subcortical structure. Further imaging analyses relating neuropsychological and cortical thickness results confirmed a group by genotype interaction in the correlation between the percentage of perseverative responses of the WCST and cortical thickness. We found two significant clusters: one involving the left precentral cortex and the caudal
FIG. 1. Group by genotype interaction effect on perseverative responses performance (a) and frontal cortical thickness (b,c). The measure of the perseverative responses was obtained by regressing age and intelligence on the dependent variable and then saving the standardized residual from the model. Cortical thickness values are shown in mm.
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DISCUSSION
FIG. 2. Maps showing group by genotype interaction effect on cortical thickness. In both clusters, obese met carriers had the thinnest cortex and control met carriers the thickest.
middle frontal cortex (1,367 mm2; Talairach peak: 30.1, 10.1, 50.9; Z-value ¼ 5.898; CWP < 0.001) and the other involving the right caudal middle frontal cortex and the rostral middle frontal, pars opercularis, and precentral cortex (1,879 mm2; Talairach peak: 36.5, 26.3, 40.5; Z-value ¼ 3.45; CWP < 0.001; Fig. 3).
FIG. 3. Maps showing the group by genotype interaction effect on the correlation between WCST perseverative responses and cortical thickness.
Our study has three main results. First, obesity has an impact on executive function and frontal structure, leading to worse performance and reduced regional thickness and subcortical volume compared to controls. Second, the BDNF val66met polymorphism has an impact on executive function, since met allele carriers performed worse than non-carriers. Third, the interaction of the BDNF val66met polymorphism and obesity influences both executive function performance and frontal cortical thickness. Our results suggest a modulatory effect between obesity and the BDNF val66met polymorphism in frontal regions, which is evidenced at cognitive level and also at brain structural level, both independently and merged. Previous studies have demonstrated executive performance alterations related to obesity in response inhibition and cognitive flexibility [Fagundo et al., 2012]. Alterations with executive function have also been reported in youth at risk for obesity [Riggs et al., 2012]. Our results corroborate these findings by demonstrating poorer executive function performance in obese participants than in controls. Specifically, obese participants performed more perseverative responses than controls on the WCST. WCST perseverative responses also revealed a genotype effect, with met carriers showing a higher number of perseverations than non-carriers. These results are in line with those of previous studies of genetic influences on cognition which have associated the met allele of the BDNF val66met polymorphism with worse performance on executive function tasks [Savitz et al., 2006]. Interestingly, obese met carriers committed more perseverations than non-carriers, while the number was similar between control met carriers and non-carriers. The WCST is a well-established complex measure of executive function, widely used in neurocognitive outcome studies related to eating behavior and considered as the most sensitive executive measure related to BMI [Vainik et al., 2013]. The successful completion of the WCST requires strategic planning, organized searching, the ability to use environmental feedback to shift cognitive set, goal-oriented behavior, and the ability to modulate impulsive responding [Strauss et al., 2006]. Together, the results indicate that obesity, genotype and their interaction have clear effects on complex executive function processing such as that required on the WCST. However, Stroop Test and the TMT tasks did not show the same obesity by genotype interaction effects. Although the WCST shares a common cognitive flexibility core with the Stroop Test and the TMT tasks, all these tests involve task-specific executive function components that account for the complex executive function construct [Strauss et al., 2006]. Specifically, perseveration on the WCST is considered as the inappropriate maintenance of a current category or framework [Possin et al., 2005], a function that has classically been related to frontal, dorsal and lateral regions [Stuss and Knight, 2002]. Overall, our results demonstrate that being obese and a met carrier is a disadvantage in terms of perseverative executive performance in comparison to the other conditions, and point to an important role for the BDNF val66met genotype in complex executive performance in obesity. Our results agree with previous reports of an influence of BDNF val66met on executive performance in different psychiatric conditions. In bipolar disorder, it has been shown that met allele carriers
250 perform worse on WCST than non-carriers [Rybakowski et al., 2003; Rybakowski et al., 2006]. In schizophrenia, patients with the met allele have a greater percentage of perseverative errors than those without it [Lu et al., 2012]. In obsessive-compulsive disorder, the met allele has been associated with poorer set-shifting and perseverance abilities [Tukel et al., 2012], and with impairments of cognitive functions related to the orbitofrontal cortex, such as the Iowa Gambling Task [Da Rocha et al., 2011]. Although research on psychiatric samples has implicated the met allele in executive function disturbances, a meta-analysis including both healthy and mental health patients did not detect a consistent significant genetic association between the BDNF val66met polymorphism and executive functioning or any of the other cognitive domains analyzed (general cognitive ability, memory, visual processing skills, and cognitive fluency) [Mandelman and Grigorenko, 2012]. The results of studies with healthy samples are mixed. The met allele enhances executive functioning in healthy participants [Beste et al., 2010; Gajewski et al., 2012]. Furthermore, Alfimova et al. [2012] showed that healthy participants carrying the met allele performed better on working memory but presented no advantages in interference processing such as set shifting; they concluded that the BDNF val66met polymorphism has a greater impact on performance speed in prefrontal tasks than on the executive function mechanisms. Taken together, it seems that in terms of executive functioning the met allele confers a benefit or null effect on healthy subjects, but disadvantages psychiatric patients with pathologies in which the fronto-striatal circuit functioning is altered [Del Casale et al., 2011; Diwadkar et al., 2011]. The results found in our sample point to an executive functioning pattern mediated by the BDNF val66met polymorphism similar to that shown in patients with fronto-striatal dysfunctions. Indeed, fronto-striatal circuitry has recently been implicated in food perception, intake, feeding and satiety, suggesting that imbalances in this circuitry may mediate obesity [Michaelides et al., 2012]. Therefore, it is reasonable to suppose that the executive pattern associated with obesity and mediated by the BDNF val66met polymorphism points in the same direction: that is, carrying the met allele means worse executive performance. As far as neuroimaging data are concerned, several frontal regions (precentral, paracentral, medial orbitofrontal) and subcortical structures (thalamus, brain stem, ventral diencephalon) showed a main effect of obesity on cortical thickness and on subcortical structure volume. The obesity group presented lower cortical thickness and decreased subcortical structure volume than controls, in agreement with studies that show frontal [Reinert et al., 2013] and also subcortical reductions in obesity [Marque´sIturria et al., 2013]. More interestingly, although there was no group by genotype interaction effect on any subcortical structure, we did find an interaction effect on cortical thickness of the precentral and middle frontal regions: met allele carriers in the obesity group showed the lowest cortical thickness in the model, and control met carriers the highest. This indicates that the relationship between frontal brain structure and BDNF val66met polymorphism in obesity is the exact opposite of that found in controls. Along the same lines, it has been shown that met allele carriers have greater reductions in frontal gray matter volume than noncarriers in schizophrenia [Ho et al., 2007]. However, results in pathological samples are mixed. Some studies did not report
AMERICAN JOURNAL OF MEDICAL GENETICS PART B significant relations between the BDNF val66met polymorphism and frontal brain structure in schizophrenia [Agartz et al., 2006; Varnas et al., 2008], and furthermore there is evidence of an interaction effect between genotype and autism spectrum disorder condition in which the patient group carrying the met allele showed the greatest frontal regional volumes. Therefore, it seems that the influences of the BDNF val66met polymorphism on frontal brain volume vary notably across samples, probably due in part to the intrinsic brain structural alterations present in each pathological condition. Here, we present evidence that brain regions that play important roles in the frontal neural networks involved in eating behavior [Inui et al., 2002; Pannacciulli et al., 2006; Walther et al., 2010; Maayan et al., 2011; Kurth et al., 2012] are related to the interaction effect between obesity and BDNF genotype in adolescent and young adults. Finally, we observed a link between neuropsychological and imaging results, showing an effect of the interaction between obesity and genotype on the correlation between the WCST perseverative performance and the cortical thickness of areas including two frontal regions. These results provide further evidence for the combined influence of obesity and BDNF genotype on executive function and brain frontal structure. All in all, considering that perseverations have been related not only to frontal areas but also to caudate nuclei [Lombardi et al., 1999], the frontal-executive neuroanatomical substrate involved in obesity and BDNF may include frontal-striatal circuits. However, our analyses did not show interaction effects on subcortical areas. Future studies using connectivity approaches may shed light on the involvement of frontal-subcortical circuits in obesity and the BDNF val66met polymorphism. The most important limitation of this study is the small sample size. Given the aim of the study, it was necessary to divide the whole sample into carriers and non-carriers of the allele of interest, which inevitably reduced the size of each subgroup and compromised the statistical power of the analyses. However, the results of the study are significant, and the medium to large effect sizes may be replicated in future studies with more extensive samples. In addition, a larger sample would allow creating subgroups on the basis of age ranges in order to perform a more detailed analysis on each developmental stage, as it would be interesting to assess possible developmental variations on the interaction effect between obesity and BDNF val66met polymorphism.
CONCLUSION In conclusion, our data show that obesity status interacts with the BDNF val66met polymorphism and affects executive function performance and frontal cortical thickness, suggesting that the presence of the met allele in obesity may lead to frontal dysfunctions. Genetic variations of the BDNF val66met polymorphism are of great interest in the clinical context of obesity, as they may contribute to the design of effective intervention programs focused on cognitive training in addition to the promotion of healthy behavior.
ACKNOWLEDGEMENTS The authors thank all the participants who made this study possible. They also thank Encarnacio´ Tor for her invaluable help in perform-
MARQUE´S-ITURRIA ET AL. ing blood analyses. This study was supported by grant PSI200805803-C02-00/PSIC from MICIIN to Maria Angeles Jurado, grant ADR2011-2012 from the University of Barcelona to Idoia Marque´sIturria, and grant 2009 SGR0941 from the Government of Catalonia.
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