ORIGINAL RESEARCH article

Front. Psychiatry, 13 August 2013

Volume 4 - 2013 | https://doi.org/10.3389/fpsyt.2013.00084

Performance in Neurocognitive Tasks in Obese Patients. Does Somatic Comorbidity Matter?

  • WK

    Wibke Kiunke 1

  • CB

    Christina Brandl 2

  • EG

    Ekaterini Georgiadou 3

  • KG

    Kerstin Gruner-Labitzke 4

  • TH

    Thomas Horbach 5

  • HK

    Hinrich Köhler 4

  • MD

    Martina de Zwaan 3

  • AM

    Astrid Müller 3*

  • 1. Schoen Clinic Bad Bramstedt, Bad Bramstedt, Germany

  • 2. Department of Psychosomatic Medicine and Psychotherapy, Friedrich-Alexander University of Erlangen-Nuremberg, Erlangen, Germany

  • 3. Department of Psychosomatic Medicine and Psychotherapy, Hannover Medical School, Hannover, Germany

  • 4. Department of Surgery, Herzogin Elisabeth Hospital, Braunschweig, Germany

  • 5. Department of Surgery, Municipal Hospital Schwabach, Schwabach, Germany

Abstract

The aim of the present study was to examine if obese individuals with obesity-related somatic comorbidity (i.e., hypertension, diabetes, sleep apnea, dyslipidemia, pain disorder) perform worse in neurocognitive tasks compared to obese individuals without any somatic disorder. Neurocognitive functioning was measured by a computerized test battery that consisted of the following tasks: Corsi Block Tapping Test, Auditory Word Learning Task, Trail Making Test-Part B, Stroop Test, Labyrinth Test, and a four-disk version of the Tower of Hanoi. The total sample consisted of 146 patients, the majority (N = 113) suffered from obesity grade 3, 26 individuals had obesity grade 2, and only 7 individuals obesity grade 1. Ninety-eight participants (67.1%) reported at least one somatic disorder (Soma+-group). Hypertension was present in 75 individuals (51.4%), type 2 diabetes in 34 participants (23.3%), 38 individuals had sleep apnea (26.0%), 16 suffered from dyslipidemia (11.0%), and 14 individuals reported having a chronic pain disorder (9.6%). Participants without a coexisting somatic disorder were younger [MSoma− = 33.7, SD = 9.8 vs. MSoma+ = 42.7, SD = 11.0, F(1, 144) = 23.01, p < 0.001] and more often female [89.6 and 62.2%, χ2(1) = 11.751, p = 0.001] but did not differ with respect to education, regular binge eating, or depressive symptoms from those in the Soma+-group. The Soma-group performed better on cognitive tasks related to memory and mental flexibility. However, the group differences disappeared completely after controlling for age. The findings indicate that in some obese patients increasing age may not only be accompanied by an increase of obesity severity and by more obesity-related somatic disorders but also by poorer cognitive functioning.

Introduction

Research indicates an association between obesity and neurocognitive dysfunction (14). The causal pathways for this relationship, however, remain poorly understood. One potential explanation concerns the association between obesity and comorbid medical conditions that in turn may lead to cognitive impairment. It remains uncertain whether the proposed cognitive decline can be attributed to effects of a specific obesity-related somatic disorder or rather to additive effects of multiple conditions. Cognitive dysfunction seen in patients with obesity might be caused by the presence of several somatic disorders that are prevalent in obese individuals such as diabetes type 2, hypertension, sleep apnea, dyslipidemia, or chronic pain.

Type 2 diabetes that is characterized by reduced insulin sensitivity and relative insulin deficiency is related to microvascular and macrovascular complications that may affect the brain (57). The underlying mechanisms of diabetes-related cognitive impairment, however, are difficult to establish due to the presence of several confounding somatic comorbidities. There exists evidence that cerebrovascular disease contributes substantially to decreased cognitive abilities in patients with diabetes type 2 (6, 7). Similarly, chronic elevation in blood pressure is common in obese persons and linked to an increased risk for alterations in the cerebral artery structure, vascular dementia, and Alzheimer-type neuropathy (8). Hypertension affects cerebral circulation that may adversely influence performance in cognitive tasks, in particular those assessing executive functions (911). Another vascular risk factor for cognitive dysfunction is dyslipidemia that is strongly associated with diabetes type 2 and hypertension in obese individuals (12, 13). Furthermore, sleep apnea has been associated with a higher risk for cognitive impairment (14, 15). Sleep fragmentation and cessation or reduction of breathing during sleep resulting in recurrent hypoxemia can contribute to structural and functional brain abnormalities that are related to cognitive dysfunction (1618). In addition, there exists evidence for the negative influence of chronic pain on cognitive performance (19, 20). Possible mechanisms involved in pain-related cognitive impairment include the overlap in brain morphology, neurotransmitters, and other neural mediators that are involved in both pain processing and cognition, altered neuroplasticity, and dysregulated neurochemistry [for review see (20)].

There are other variables that can affect cognitive functioning. It is well known that morbidly obese individuals suffer from psychiatric disorders [e.g., (2123)]. For example, depressive symptoms are prevalent in obese persons (24) and may account for their cognitive abilities (25). One of the most common psychiatric disorders reported in morbidly obese patients is binge eating disorder (BED) with prevalence rates up to 50% (23, 2629). BED is characterized by experiencing loss of control while eating an unusual large amount of food and is therefore linked to food-related high impulsivity (30). Few studies have examined cognitive functions, in particular food-unrelated decision-making, in obese individuals with BED reporting mixed results. While one study found decision-making deficits in overweight and obese women with BED compared to those without (31) others did not (32, 33). The inconsistency between the studies could be caused by different cognitive tasks and by sampling characteristics [e.g., different body mass index (BMI) ranges].

Furthermore, higher education is often related to better performance in cognitive tasks (32, 34). Last but not least, there have been many reports regarding the inverse association between age and performance in cognitive tasks. The question at which age the cognitive decline starts, however, remains unresolved (34, 35).

The aim of the present study was to examine the relationship between neurocognitive performance and somatic disorders in obese patients taking into account BMI, depressive and eating disorder symptoms, and education. Based on the literature, we expected a reduced neurocognitive performance in obese individuals with any obesity-related somatic disorder compared to those without.

Materials and Methods

Participants and study design

Participation in the study was completely voluntarily. Inclusion criteria were a BMI ≥ 30 kg/m2, age between 18 and 65 years, and sufficient German language skills. Exclusion criteria were any neurological disorder, psychosis, dementia, current substance abuse, developmental or learning disorders, sensory impairments, and intellectual disability.

Between January 2011 and May 2012, 84 patients considering bariatric surgery who were seen for a routine preoperative psychiatric evaluation at the University Hospital Erlangen or the Hannover Medical School and 62 subjects from a psychosomatic inpatient unit in Bad Bramstedt were recruited. The total sample consisted of 146 patients (71.2% women, 28.8% men) with an average age of 39.8 years (SD = 11.4, Range 18–65). The majority (N = 113) suffered from obesity grade 3 (BMI ≥ 40 kg/m2), 26 individuals had obesity grade 2 (BMI: 35–39.9 kg/m2), and only 7 individuals had obesity grade 1 (BMI: 30–34.9 kg/m2). All participants gave written informed consent according to procedures approved by the Institutional Ethics Committees of the three study sites.

Assessments

The participants provided information on education, height, and weight. Information on medication and the following somatic disorders was taken from patients’ charts of the surgical departments or the inpatient unit: hypertension, diabetes, sleep apnea, dyslipidemia, and pain disorder.

To measure eating disorder symptoms, the German version of the Eating Disorder Examination-Questionnaire (EDE-Q) (36) was used. This questionnaire consists of specific items to assess objective binge eating episodes (OBE; i.e., eating an objectively large amount of food with a sense of loss of control). Regular binge eating was defined as eight or more OBEs during the past 28 days.

The German version of the 9-item Patient Health Questionnaire depression scale (PHQ-9) (37) was administered in order to assess depressive symptoms. The PHQ-9 scores each of the nine DSM-IV criteria for depression from 0 (not at all) to 3 (nearly every day).

Neurocognitive functioning was measured by a computerized test battery that consisted of the following neurocognitive tasks.

The Corsi Block Tapping Test (CBT) was administered to assess visual attention and working memory (38). Participants had to reproduce several sequences of block tappings displayed only once by the computer. When the sequence was correctly copied, the number of cubes which had to be touched was increased step-wise. The outcome variable of this task was the total number of correct answers.

To assess verbal memory, we administered an auditory verbal learning task (wordlist) in which a list of 15 not associated words was presented five times. Participants were asked to reproduce as many words as possible after each of the five trials. The number of correctly recalled words per trial was registered and the total of all correct answers across the five trials was used as outcome variable.

The Trail Making Test-Part B (TMT-B) (39) was used to examine mental flexibility and the ability to switch attention. Participants had to tap numbers (1–9) and digits (A–I) in an alternating sequence as quickly and as accurately as possible within three trials. The mean time to completion per trial (in seconds) was used as dependent variable in this task.

The Stroop Test is a well known diagnostic tool for assessing selective attention and response inhibition (40). In this study, the answers were given not verbally but by pressing the corresponding button on the touch screen. The number of correctly identified words in the interference condition was used as dependent variable.

The Labyrinth Test was used to assess spatial memory, planning, and error utilization (41). Participants had to find a hidden path through a spatial maze. The task was completed after the hidden path was found two times without errors. The total number of errors during this task was used as dependent variable.

Finally, a four-disk version of the Tower of Hanoi (ToH) was used to measure planning abilities (42). The ratio between the number of ideal disk moves and the number of actually needed disk moves (effectivity) was the dependent variable.

Data analysis

Analyses were performed using IBM SPSS Statistics v.20. The total sample was divided in a group without any somatic disorder and a group with at least one somatic disorder. Univariate analyses of variance (ANOVA) and χ2-tests were used to compare the groups in terms of sociodemographic variables, BMI and depression, as appropriate. In a second step, the groups were compared with regard to their performance in the neurocognitive tasks. These analyses were controlled for potential confounding variables that were found to be different between the groups in the aforementioned analyses (ANCOVA). The group comparisons were then repeated subsequently for specific somatic disorders (i.e., hypertension yes/no, diabetes yes/no, sleep apnea yes/no).

To investigate the relationship between the number of somatic disorders and the performance in neurocognitive tasks, we conducted Pearson correlations. Finally, to examine the association between neurocognitive performance and the presence of any somatic disorder we performed a set of logistic regression analyses adjusting for variables that differed between the two groups (age, gender, BMI).

The group comparisons regarding cognitive functioning and the regressions were made on the basis of Bonferroni-corrected significance levels.

Results

Somatic comorbidity, BMI, medication, sociodemographic characteristic, eating disorder symptoms, and depression

Based on patients’ charts, 67.1% of the total sample (N = 98) suffered from at least one somatic disorder (Soma+-group). Hypertension was present in 75 individuals (51.4%), type 2 diabetes in 34 participants (23.3%), 38 individuals had sleep apnea (26.0%), 16 suffered from dyslipidemia (11.0%), and 14 individuals reported having a chronic pain disorder (9.6%). The mean number of disorders in the Soma+-group ranged from 1 to 5 (M = 1.81, SD = 0.94). Almost half of that group reported suffering from only one somatic disorder (49%), 26.5% had two, and 20.4% three somatic disorders. The number of somatic disorders in the Soma+-group was positively related to age (r = 0.293, p < 0.01).

The groups did not differ in terms of any psychopharmacological treatment including neuroleptics, mood stabilizer, antidepressants, anxiolytics, stimulants, or anticonvulsive drugs [Soma: 37.5% vs. Soma+: 45.4%, χ2(1) = 0.811, p = 0.368]. Many participants reported taking antidepressant medication [Soma: 33.3% vs. Soma+: 43.3%, χ2(1) = 1.329, p = 0.249]. While only two participants in the Soma-group took non-opiate pain medication, 81.4% of participants with somatic comorbidity had at least one of the following pharmacological treatments: insulin and/or non-insulin anti-diabetic medication, antihypertensive agents, opiate or non-opiate pain medication, or cholesterol lowering drugs.

As shown in Table 1, patients with any somatic disorder were substantially older and had a significantly higher BMI than those without somatic disorders. There were more male patients in the Soma+-group. The groups did not differ significantly from each other with regard to education.

Table 1

Soma (N = 48)Soma+ (N = 98)Group comparison

M (SD)M (SD)ANOVA
Age (years)33.7 (9.8)42.7 (11.0)F(1, 144) = 23.008, p < 0.001
BMI43.9 (8.4)48.4 (8.3)F(1, 144) = 9.483, p = 0.001
PHQ-910.6 (6.6)9.4 (5.8)F(1, 124) = 1.088, p = 0.299
EDE-Q3.13 (1.05)2.90 (1.00)F(1, 130) = 1.538, p = 0.217

N (%)N (%)χ2-test

OBESITY GRADE
Grade I4 (8.3)3 (3.1)χ2(2) = 14.904, p = 0.001
Grade II16 (33.3)10 (10.2)
Grade III28 (58.3)85 (86.7)
Gender, female43 (89.6)61 (62.2)χ2(1) = 11.751, p = 0.001
SCHOOL YEARS
≤99 (19.6)33 (34.4)χ2(3) = 4.029, p = 0.258
1023 (50.0)42 (43.8)
11–139 (19.6)11 (11.5)
>135 (10.9)10 (10.4)

Comparison of sociodemographic characteristics, BMI, eating disorder, and depressive symptoms between individuals without any somatic disorder (Soma) and those with any somatic disorder (Soma+).

PHQ-9, Patient Health Questionnaire, depression scale (data were available from 43 participants without any somatic disorder and 83 participants with at least one somatic disorder); EDE-Q, Eating Disorder Examination-Questionnaire (data were available from 44 participants without any somatic disorder and 88 participants with at least one somatic disorder).

According to the EDE-Q mean total scores which were available from 134 patients, we did not find any significant group differences in terms of eating disorder symptoms including binge eating. The mean number of OBEs during the past 28 days did not differ between the two groups [MSoma+ = 4.27, SD = 6.34 and MSoma− = 4.78, SD = 7.18, F(1, 133) = 0.175, p = 0.676]. Regular binge eating was found in 21.3% of the Soma+ group and 28.9% of the Soma group [χ2(1) = 0.935, p = 0.334]. Information on depressive symptoms was available from 126 patients (86% of the total sample). The PHQ-9 data indicated a lack of significant group differences with respect to depression.

Comparison of performance in neurocognitive tasks between patients without and with somatic disorders

As can be seen in Table 2, the results of the univariate ANOVA suggest differences between the Soma and the Soma+ groups in three of six tasks, particularly in the CBT, the wordlist, and the TMT-B. After controlling for gender, only the difference in the CBT remained significant. After adjusting for age all group differences disappeared completely (Table 2).

Table 2

SomaSoma+Group comparison


M (SD)aM (SD)aANOVAANCOVA controlled for ageANCOVA controlled for genderANCOVA controlled for BMI
Corsi Block Tapping Test7.31 (2.38)5.64 (2.19)F(1, 144) = 17.50**F(1, 143) = 6.89F(1, 142) = 10.39*F(1, 143) = 16.34**
Wordlist58.75 (8.23)52.83 (8.92)F(1, 143) = 14.85**F(1, 142) = 4.69F(1, 141) = 3.24F(1, 142) = 13.35**
Trail Making Test, Part B6792.06 (2216.35)8420.36 (3321.52)F(1, 143) = 9.44*F(1, 142) = 0.95F(1, 141) = 6.58F(1, 142) = 11.49**
Stroop Test19.85 (0.36)19.58 (1.14)F(1, 144) = 2.61F(1, 143) = 0.03F(1, 142) = 0.001F(1, 143) = 4.59
Austin Maze43.00 (35.90)48.97 (30.50)F(1, 136) = 1.04F(1, 135) = 0.79F(1, 134) = 0.08F(1, 135) = 0.72
Tower of Hanoi0.52 (0.22)0.51 (0.23)F(1, 143) = 0.02F(1, 142) = 0.03F(1, 141) = 0.22F(1, 142) = 0.03

Comparison of performances in neurocognitive tasks between individuals without any somatic disorder (Soma, N = 48) and those with any somatic disorder (Soma+, N = 98).

aunadjusted means and standard deviations; *p < 0.008 (0.05/6); **p < 0.001.

Figures 13 present the unadjusted group differences of task performance in the CBT, the wordlist, and the TMT-B by the presence/absence of specific disorders, in particular hypertension, diabetes, and sleep apnea indicating the strongest effect on cognitive performance for hypertension. Similarly to the aforementioned findings, after controlling for age the differences were no longer significant (data not reported here but available upon request).

Figure 1

Figure 2

Figure 3

Due to the relatively low prevalence of dyslipidemia and pain disorder in the present sample we did not conduct group comparisons based on the absence or presence of these conditions.

Relationship between the number of comorbid disorders and performance in neurocognitive tasks

Pearson correlations between the number of somatic disorders and neurocognitive performance yielded significant negative correlations with regard to the CBT (r = −0.273, p < 0.01), the wordlist (r = −0.309, p < 0.001), and the TMT-B (r = −0.315, p < 0.001) but not the other tasks. Since the number of somatic disorders was positively correlated with age (r = 0.452, p < 0.001), we conducted additional partial correlations controlling for age. After this, the correlations mentioned above were no longer significant (CBT: r = −0.111, ns; wordlist: r = −0.126, ns; TMT-B: r = 0.111, ns).

Association between performance in neurocognitive tasks and somatic comorbidity

Table 3 summarizes the results of hierarchical regression analyses with the performance in the specific neurocognitive tasks as dependent variables. Age, gender, and BMI were entered into block 1 of the regressions as control variables. The variable “presence of any somatic disorder” was entered in a second step. Changes in predictive ability (R2) were examined to determine significance. As shown in Table 3, only lower age was associated with more correct answers in the CBT and with a higher number of correctly recalled words in the wordlist task. Furthermore, age was positively related to an increased time to completion in the TMT-B.

Table 3

Corsi Block Tapping Test
Wordlist
Trail Making Test, part B
βR2βR2βR2
Step 1
Age−0.39**0.17**−0.40**0.22**0.48**0.25**
Gender0.050.19*−0.10
BMI−0.07−0.08−0.08
Step 2
Age−0.32**0.20**−0.36**0.23**0.45**0.26**
Gender−0.000.16−0.08
BMI−0.03−0.05−0.10
Somatic comorbidity−0.23−0.110.08

Summary of linear regression models concerning the association between performance in neurocognitive tasks and the presence of any comorbid somatic disorder, controlled for age, gender, and BMI.

*p < 0.017 (0.05/3); **p < 0.001.

Discussion

In the present sample, individuals with any somatic disorder were about 10 years older and reported significantly higher BMIs than those without any somatic disorder. Moreover, age was positively related to the number of obesity-related somatic disorders in the Soma+-group.

At the first glance, our results concerning cognitive performance suggested working and verbal memory deficits (CBT, wordlist) and poorer mental flexibility (TMT-B) in obese individuals with obesity-related somatic comorbidity. While controlling for BMI did not impact the results substantially, the group differences disappeared after adjusting for age and gender. Given the results of the regression analyses with the performance in the specific neurocognitive tasks as dependent variables and age, gender, BMI, and somatic comorbidity as independent variables, the better task performance in the Soma-group seems mainly driven by younger age. The groups did not differ in terms of education, depressive symptoms, or eating pathology. Hence, we may assume that the findings were not confounded by these variables. Taken together, it appears that worse performance in three of the six cognitive tasks were rather a problem of older age than of somatic comorbidity. This is in line with previous research demonstrating age-related cognitive decline (34, 35).

The findings do not support the assumption that somatic comorbidity is related to a higher risk for cognitive impairment in obese individuals. This is surprising given earlier reports on the association between cognitive dysfunction and somatic comorbidity. Though, many of those did not focus on obese individuals [e.g., (10, 15, 19)]. The discrepancy between our and previous findings reporting cognitive decline in overweight or obese individuals with somatic comorbidity might be explained by differences in test selection. Moreover, differences in sampling may account for the discrepant findings. For example, other studies had included either substantially younger [e.g., (17)] or older [e.g., (9)] individuals or those with lower BMIs [e.g., (7)]. It is certainly possible that the lack of group differences in the present investigation was related to a ceiling effect in both the Soma+ and the Soma group given the high BMI range. According to previous reports, obese individuals suffer from impaired cognitive function compared to normal-weight controls [e.g., (3, 4, 43, 44)]. However, the present investigation did not include a normal-weight control group that is a shortcoming.

On the other hand, our results are consistent with other reports of lacking differences in cognitive function between individuals with and without somatic comorbidity. Recently, Singh-Manou et al. (45) examined the association between midlife obesity, metabolic abnormalities (i.e., dyslipidemia, hypertension, hyperglycemia), and cognitive decline in early old age in a large longitudinal study (N = 6401). The sample consisted of 582 obese adults. In this study, the metabolically healthy obese participants did not have a better cognitive profile than individuals with metabolic abnormalities. Also, no significant differences were found between the two groups with regard to cognitive decline over a 10 years period.

There are some other limitations that need to be acknowledged. First, the diagnosis of both depressive symptoms and binge eating based on questionnaires instead of a standardized interview is concerning. Second, medication for somatic disorders could have influenced the task performance. Also, the group with somatic comorbidity is twice as large as the group without any somatic disorder that might have biased the results.

In conclusion, our findings indicate that in some obese patients increasing age may not only be accompanied by an increase of obesity severity and by more obesity-related somatic disorders but also by poorer cognitive functioning. Further studies should include control groups matched for age, gender, and education to examine whether somatic comorbidity may add a negative impact on cognition above the effect of age.

Statements

Acknowledgments

We kindly thank Dr. Laszlo Gaal (Bernried) for his support during the course of the study and the two reviewers for their helpful comments.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

  • 1

    CournotMMarquié JC, AnsiaunDMartinaudCFondsHFerriereJet alRelation between body mass index and cognitive function in healthy middle-aged men and women. Neurology (2006) 67:120814.10.1212/01.wnl.0000238082.13860.50

  • 2

    DahlAKHassingLB. Obesity and cognitive aging. Epidemiol Rev (2012). [Epub ahead of print].10.1093/epirev/mxs002

  • 3

    FagundoABde laTorreRJiménez-MurciaSAgüeraZGraneroRTárregaSet alExecutive functions profile in extreme eating/weight conditions: from anorexia nervosa to obesity. PLoS ONE (2012) 7:e43382.10.1371/journal.pone.0043382

  • 4

    GunstadJPaulRHCohenRATateDFGordonE. Obesity is associated with memory deficits in young and middle-aged adults. Eat Weight Disord (2006) 11:159.

  • 5

    EliasMFEliasPKSullivanLMWolfPAD’AgostinoRB. Obesity, diabetes and cognitive deficit: the Framingham heart study. Neurobiol Aging (2005) 26S:S116.10.1016/j.neurobiolaging.2005.08.019

  • 6

    McCrimmonRJRyanCMFrierBM. Diabetes and cognitive dysfunction. Lancet (2012) 379:22919.10.1016/S0140-6736(12)60360-2

  • 7

    PedersenMPedersenKKBruunsgaardHKrabbeKSThomsenCFærchKet alCognitive functions in middle aged individuals are related to metabolic disturbances and aerobic capacity: a cross-sectional study. PLoS ONE (2012) 7:e51132.10.1371/journal.pone.0051132

  • 8

    EliasMFGoodellALDoreGA. Hypertension and cognitive functioning. A perspective in historical context. Hypertension (2012) 60:2608.10.1161/HYPERTENSIONAHA.111.186429

  • 9

    EliasMFEliasPKSullivanLMWolfPAD’AgostinoRB. Lower cognitive function in the presence of obesity and hypertension: the Framingham heart study. Int J Obes Relat Metab Disord (2003) 27:2608.10.1038/sj.ijo.802225

  • 10

    RazNRodrigueKMAckerD. Hypertension and the brain: vulnerability of the prefrontal regions and executive functions. Behav Neurosci (2003) 117:116980.10.1037/0735-7044.117.6.1169

  • 11

    SaxbyBKHarringtonFMcKeithIGWesnesKFordGA. Effects of hypertension on attention, memory, and executive function in older adults. Health Psychol (2003) 22:58791.10.1037/0278-6133.22.6.587

  • 12

    RinconFWrightCB. Vascular cognitive impairment. Curr Opin Neurol (2013) 26:2936.10.1097/WCO.0b013e32835c4f04

  • 13

    van den BergEKloppenborgRPKesselsRPCKappelleLJBiesselsGJ. Type 2 diabetes mellitus, hypertension, dyslipidemia and obesity: a systematic comparison of their impact on cognition. Biochim Biophys Acta (2009) 1792:47081.10.1016/j.bbadis.2008.09.004

  • 14

    AdamsNStraussMESchluchterMRedlineS. Relation of measures of sleep-disordered breathing to neuropsychological functioning. Am J Respir Crit Care Med (2001) 163:162631.10.1164/ajrccm.163.7.2004014

  • 15

    NaëgeléBLaunoisSHMazzaSFeuersteinCPépinJLLévyP. Which memory processes are affected in patients with obstructive sleep apnea? An evaluation of 3 types of memory. Sleep (2006) 29:53344.

  • 16

    CanessaNCastronovoVCappaSAloiaMMarelliSFaliniAet alObstructive sleep apnea. Brain structural changes and neuro cognitive function before and after treatment. Am J Respir Crit Care Med (2011) 183:141926.10.1164/rccm.201005-0693OC

  • 17

    HannonTSRofeyDLRyanCMClapperDAChakravortySArslanianSA. Relationships among obstructive sleep apnea, anthropometric measures, and neurocognitive functioning in adolescents with severe obesity. J Pediatr (2012) 160:7325.10.1016/j.jpeds.2011.10.029

  • 18

    LalCStrangeCBachmanD. Neurocognitive impairment in obstructive sleep apnea. Chest (2012) 14:160110.10.1378/chest.11-2214

  • 19

    LandrøNIForsEAVåpenstadLLHoltheOStilesTCBorchgrevinkPC. The extent of neurocognitive dysfunction in a multidisciplinary pain centre population. Is there a relation between reported and tested neuropsychological functioning?Pain (2013) 154:9727.10.1016/j.pain.2013.01.013

  • 20

    MoriartyOMcGuireBEFinnDP. The effect of pain on cognitive function: a review of clinical and preclinical research. Prog Neurobiol (2011) 93:385404.10.1016/j.pneurobio.2011.01.002

  • 21

    HerpertzSBurgmerRStangAde ZwaanMWolfAMChen-StuteAet alPrevalence of mental disorders in normal-weight and obese individuals with and without weight loss treatment in a German urban population. J Psychosom Res (2006) 61:95103.10.1016/j.jpsychores.2005.10.003

  • 22

    KalarchianMAMarcusMDLevineMDCourcoulasAPPilkonisPARinghamRMet alPsychiatric disorders among bariatric surgery candidates: relationship to obesity and functional health status. Am J Psychiatry (2007) 164:32834.10.1176/appi.ajp.164.2.328

  • 23

    MühlhansBHorbachTde ZwaanM. Psychiatric disorders in bariatric surgery candidates: a review of the literature and results of a German prebariatric surgery sample. Gen Hosp Psychiatry (2009) 31:41421.10.1016/j.genhosppsych.2009.05.004

  • 24

    de ZwaanMEnderleJWagnerSMühlhansBDitzenBGefellerOet alAnxiety and depression in bariatric surgery patients: a prospective, follow-up study using structured clinical interviews. J Affect Disord (2011) 133:618.10.1016/j.jad.2011.03.025

  • 25

    CserjésiRLuminetOPonceletASLénárdL. Altered executive function in obesity. Exploration of the role of affective states on cognitive abilities. Appetite (2009) 52:5359.10.1016/j.appet.2009.01.003

  • 26

    Alger-MayerSRosatiCPolimeniJMMaloneM. Preoperative binge eating status and gastric bypass surgery:  a long-term outcome study. Obes Surg (2009) 19:13945.10.1007/s11695-008-9540-9

  • 27

    BurgmerRGrigutschKZipfelSWolfAMde ZwaanMHusemannBet alThe influence of eating behavior and eating pathology on weight loss after gastric restriction operations. Obes Surg (2005) 15:68491.10.1381/0960892053923798

  • 28

    HsuLKMullikenBMcDonaghBKrupa DasSRandWFairburnCGet alBinge eating disorder in extreme obesity. Int J Obes Relat Metab Disord (2002) 26:1398403.10.1038/sj.ijo.0802081

  • 29

    Jones-CorneilleLRWaddenTASarwerDBFaulconbridgeLFFabricatoreANStackRM. Axis I psychopathology in bariatric surgery candidates with and without binge eating disorder: results of structured clinical interviews. Obes Surg (2012) 22:38997.10.1007/s11695-010-0322-9

  • 30

    SchagKSchönleberJTeufelMZipfelSGielK. Food-related impulsivity in obesity and binge eating disorder – a systematic review. Obes Rev (2013) 14:47795.10.1111/obr.12017

  • 31

    SvaldiJBrandMTuschen-CaffierB. Decision-making impairments in women with binge eating disorder. Appetite (2010) 54:8492.10.1016/j.appet.2009.09.010

  • 32

    DavisCPatteKCurtisCReidC. Immediate pleasure and future consequences. A neuropsychological study of binge eating and obesity. Appetite (2010) 54:20813.10.1016/j.appet.2009.11.002

  • 33

    DannerUNOuwehandCvan HaastertNLHornsveldHde RidderDT. Decision making impairments in women with binge eating disorder in comparison with obese and normal weight women. Eur Eat Disord Rev (2012) 20:e5662.10.1002/erv.1098

  • 34

    EliasMFEliasPKD’AgostinoRBSilbershatzHWolfPA. Role of age, education, and gender on cognitive performance in the Framingham Heart Study: community-based norms. Exp Aging Res (1997) 23:20135.10.1080/03610739708254281

  • 35

    SalthouseTA. When does age-related cognitive decline begin?Neurobiol Aging (2009) 30:5257.10.1016/j.neurobiolaging.2008.11.012

  • 36

    HilbertATuschen-CaffierB. Eating Disorder Examination-Questionnaire. Deutsche Übersetzung. Münster: Verlag für Psychotherapie (2006).

  • 37

    LöweBSpitzerRLZipfelSHerzogW. PHQ-D: Gesundheitsfragebogen für Patienten: Manual – Komplettversion und Kurzform, from Pfizer. (2012). Available from: http://infektionskrankheiten.klinikum.uni-heidelberg.de/fileadmin/Psychosomatische_Klinik/download/PHQ_Manual1.pdf

  • 38

    KesselsRPCvan ZandvoortMJEPostmaAKappelleLJde HaanEHF. The corsi block-tapping task: standardization and normative data. Appl Neuropsychol (2000) 7:2528.10.1207/S15324826AN0704_8

  • 39

    ReitanRM. Trail Making Test: Manual for Administration and Scoring. Tucson, AZ: Reitan Neuropsychology Laboratory (1992).

  • 40

    GoldenCJ. The Stroop Color and Word Test. Chicago, IL: Stoelting Company (1978).

  • 41

    BowdenSCSmithLC. What does the Austin maze measure?Aust Psychol (1994) 29:347.10.1080/00050069408257317

  • 42

    WelshMCHuizingaM. Tower of Hanoi disk transfer task: influences of strategy knowledge and learning on performance. Learn Individ Differ (2005) 15:28398.10.1016/j.lindif.2005.05.002

  • 43

    GaliotoRSpitznagelMBStrainGDevlinMCohenRPaulRet alCognitive function in morbidly obese individuals with and without binge eating disorder. Compr Psychiatry (2012) 53:4905.10.1016/j.comppsych.2011.09.002

  • 44

    GunstadJPaulRHCohenRATateDFSpitznagelMBGordonE. Elevated body mass index is associated with executive dysfunction in otherwise healthy adults. Compr Psychiatry (2007) 48:5761.10.1016/j.comppsych.2006.05.001

  • 45

    Singh-ManouACzernichowSElbazADugravotASabiaSHagger-JohnsonGet alObesity phenotypes in midlife and cognition in early old age: the Whitehall II cohort study. Neurology (2012) 79:75562.10.1212/WNL.0b013e3182661f63

Summary

Keywords

obesity, neurocognitive functioning, type 2 diabetes, hypertension, sleep apnea

Citation

Kiunke W, Brandl C, Georgiadou E, Gruner-Labitzke K, Horbach T, Köhler H, de Zwaan M and Müller A (2013) Performance in Neurocognitive Tasks in Obese Patients. Does Somatic Comorbidity Matter?. Front. Psychiatry 4:84. doi: 10.3389/fpsyt.2013.00084

Received

27 May 2013

Accepted

26 July 2013

Published

13 August 2013

Volume

4 - 2013

Edited by

Kelly Costello Allison, University of Pennsylvania, USA

Reviewed by

Unna N. Danner, Altrecht Eating Disorders Rintveld, Netherlands; Amanda Schurle Bruce, University of Missouri-Kansas City, USA

Copyright

*Correspondence: Astrid Müller, Department of Psychosomatic Medicine and Psychotherapy, Hannover Medical School, Carl-Neuberg-Str. 1, D-30625 Hannover, Germany e-mail:

This article was submitted to Frontiers in Eating Behavior, a specialty of Frontiers in Psychiatry.

Disclaimer

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Outline

Figures

Cite article

Copy to clipboard


Export citation file


Share article

Article metrics