|Year : 2020 | Volume
| Issue : 3 | Page : 147-152
Correlating cognitive and executive functions to BMI
Nermin M Shaker1, Mohamed H EAbd El Moneam1, Mostafa A El Shahhat1, Samah H Rabei2
1 Department of Neuropsychiatry, Ain Shams University, Egypt
2 Department of Neuropsychiatry, Helwan University, Cairo, Egypt
|Date of Submission||19-Apr-2020|
|Date of Acceptance||30-Apr-2020|
|Date of Web Publication||5-Oct-2020|
MD Samah H Rabei
Department of Neuropsychiatry, Helwan University, MRCPsyc, Omroo Alkays, Nasr City, Cairo 11727
Source of Support: None, Conflict of Interest: None
Objective Cognitive and executive dysfunction is related to BMI. The present study seeks to correlate cognitive functions to BMI.
Participants and methods An observational study included 60 patients who underwent laboratory and clinical evaluation including BMI and a series of psychometric tests, a standardized mini-mental state examination, the Wechsler adult intelligence scale, and the Wisconsin card sorting test.
Results BMI has stronger significant inverse correlation to performance intelligence quotient than the significant inverse correlation to verbal and total intelligence quotient. Also, BMI has significant inverse correlation to executive functions.
Discussion Our results indicate that higher BMI is correlated to lower cognitive and executive functions. Patients with obesity had low scores on general intelligence and executive functions, particularly abstraction ability and the ability to shift cognitive strategies in response to a changing environment. Future research should incorporate brain imaging techniques to further elucidate the effects of obesity on cognition.
Keywords: body mass index, cognitive dysfunction, cognitive profile, executive functions, uncomplicated obesity
|How to cite this article:|
Shaker NM, EAbd El Moneam MH, El Shahhat MA, Rabei SH. Correlating cognitive and executive functions to BMI. Egypt J Psychiatr 2020;41:147-52
| Introduction|| |
The health consequences of high BMI extend to cognitive function. High BMI can disturb planning functions, problem solving, mental flexibility, and inhibitory processes; these findings reflect alterations in the frontal lobe (Fagundo et al., 2012). A recent study by Kittel et al. (2017) studied the executive functions in adolescents with high BMI and binge eating disorders. The authors concluded that adolescents with binge eating disorders and high BMI displayed significantly poorer inhibitory control than normal-weight adolescents.
Possible pathways linking high BMI and cognitive deficit may be metabolic imbalance, clinically silent stroke, atherosclerotic changes, altered distribution of cerebral blood flow, demyelination, or microinfarction in the cerebral white matter (Elias et al., 2001). It is also important to consider that the relationship between high BMI and cognition may have a reverse direction such that specific cognitive attributes increase the risk of obesity (Cortese and Castellanos, 2014).
There is increasing evidence for the role of inflammatory process, an important characteristic of obesity. Recent clinical findings indicate that increased systemic expression of inflammatory markers (e.g. cytokines) in individuals with obesity correlates with neuropsychiatric status, specifically, mood, and cognitive function (Castanon et al., 2014).
Consistent with this last point, inflammatory factors can induce the synthesis of the enzymes indoleamine 2,3-dioxygenase in monocytes/macrophages and dendritic cells. This hinders the biosynthesis of key monoamines (e.g. serotonin, dopamine) known to play a major role in mood regulation and cognitive function (Raison et al., 2010).
In sum, previous studies on cognitive function in individuals with obesity suggest that cognitive dysfunction might be pronounced in such individuals (Gunstad et al., 2006; Fitzpatrick et al., 2013; Gaillard, 2015; Santangeli et al., 2015; Cheke et al., 2016). However, results are often mixed. These mixed findings may be explained by Prickett et al. (2015), who identified five design considerations for studies linking obesity and cognition: control of confounders (e.g. age, education); appropriate study design (e.g. appropriate exclusion criteria and adequate sample size); appropriate control groups; assessment of cognitive functions relevant to the specific research question; and valid, reliable psychometric tools for assessment of cognition. Most of these design features have been incorporated in the current study.
This study uses a series of established neuropsychological tests to investigate the interplay of obesity and cognitive function in Egyptian individuals with obesity. We hypothesized that patients with uncomplicated obesity would demonstrate greater difficulties with cognitive function (general intelligence, concentration, abstract thinking, strategic planning, the ability to use environmental feedback to shift cognitive sets, and visuomotor coordination) than normal-weight controls. Furthermore, we hypothesized that the more severe the uncomplicated obesity, the more intensely the cognitive function would be affected.
| Participants and methods|| |
Design observational design
Ethical approval of the research protocol was obtained by the authority of Ain Shams University Ethics and Research Committee. The researchers explained the details of the research goals to the participants, ensured that the obtained data would be confidential, and informed participants that they could withdraw from the study at any time. Those who refused to participate were excluded. Then, a written informed consent was sought from all participants. The recruitment continued until 30 uncomplicated obese individuals and 30 matched healthy controls were enrolled. Subsequently, participants were asked to complete a series of neuropsychological tasks in a fixed order. The research was performed from April 2015 until the end of July 2016.
Participants and sample size
A total of 60 individuals were calculated using Epi-Info (centers for disease control and prevention, Atlanta Georgia USA SPSS 22, International Business machines corporation IBM, New York, USA) program version 6 assuming: 95% confidence interval, 80% power of the test. Accordingly, the following equation was used:
n=(z/e)2 (p) (1−p)
where n is the sample size, P the expected prevalence, z the critical value of 1.96, and e is the margin of sample error tolerated, which is 0.05.
The expected prevalence according to Yaffe et al. (2009) is 4.1%. Therefore, the sample size was calculated to be 60.
A convenient sample was collected from patients who visited the outpatient eating clinic of the Institute of Internal Medicine at Ain Shams University twice weekly for 4 months and were enrolled in the study. The institute is located in eastern Cairo and serves about a third of greater Cairo. It serves both urban and rural areas, including areas around greater Cairo. Age of the patients was between 30 and 50 years, normal or corrected-to-normal vision, and sufficient Arabic language skills.
Participants who come under the following were excluded:
- Significant medical conditions (e.g. cardiovascular or metabolic diseases, including type 2 diabetes) were excluded; they were identified through a standardized medical screening questionnaire, laboratory investigations (fasting blood glucose and fasting lipid profile), and radiological investigations (duplex on carotid arteries to exclude atherosclerotic changes);
- Neurological or other psychiatric conditions, the current use of medications (e.g. anticholinergic agents, anticonvulsants, antiparkinsonian agents, antihypertensive/cardiac medications, corticosteroids, narcotic analgesics) and/or substances known to alter mood, reaction time, or cognitive capacity, including smoking, alcohol consumption (≥50 g/week), and recreational drug use. Exclusion of psychiatric disorders was done using the Arabic version of GHQ (Thabet and Vostanis, 2005) (Goldberg and Williams, 1978).
- Patients with vision, hearing, or motor coordination problems that could impair the ability to complete the computerized version of cognitive function testing.
Anthropometric measurements were taken. Height was measured to the nearest 0.1 cm with a stadiometer; weight was recorded on a digital platform scale accurate to 0.1 kg [BMI=weight (kg)/height (m2)) (Dorian, 2010). Obesity was diagnosed according to the WHO guidelines (WHO, 2015) (BMI ≥30.0 kg/m−2).
Cognitive dysfunction was diagnosed using the Wechsler adult intelligence scale (WAIS) and the Wisconsin card sorting test (WCST). Cognitive performance was measured by a standardized mini-mental state examination (SMMSE).
Standardized mini-mental state examination (Molloy et al., 1991).
The SMMSE is a clinical assessment tool which is used as a screening test for cognitive impairment. It consists of 12 items or questions which cover a range of cognitive domains. Each item has a maximum score. The test items have limits of allowable score values. The scores are summed for the 12 items and can range from a minimum of 0 to a maximum of 30. Interpretation of results include four possibilities: normal cognition (25–30 points), mild cognitive impairment (19–24 points), moderate cognitive impairment (10–18 points), and severe cognitive impairment (≤9 points).
Wechsler adult intelligence scale (Wechsler, 1981)
The WAIS is the most commonly administered general intelligence test for adults and is also viewed as a broad assessment of cognitive function. It is an individually administered measure of intelligence, intended for adults aged 16–89 years. It is the most widely used intelligence test in clinical practice and is intended to measure human intelligence in both verbal and performance abilities.
Wisconsin card sorting test, computerized version (Heaton et al., 2003)
The WCST was developed to assess abstraction ability and the ability to shift cognitive strategies in response to changing environmental contingencies. It is considered a measure of executive function that requires strategic planning, organized searching, the ability to use environmental feedback to shift cognitive sets, goal-oriented behavior, and the ability to modulate impulsive responding. It provides information on several aspects of problem-solving behavior beyond basic indices of task success or failure, such as the number of perseverative errors, the failure to maintain set, and the number of categories achieved.
Data analysis plan
Data analysis was performed using the Statistical Package for Social Sciences Version-22 (SPSS-22). Student’s t-test was used to determine the significance of an independent variable if there were only two values of this variable (to compare between independent means). Pearson’s χ2 was used for comparison between qualitative variables. The correlation coefficient (r) was used to measures the strength and direction of a linear relationship between two variables on a scatterplot. The P value was used to indicate the level of significance, where P less than or equal to 0.05 is considered significant; P less than or equal to 0.01 is highly significant; P less than or equal to 0.001 is very highly significant.
| Results|| |
Obese individuals were of highly significant older age than the nonobese (P=0.009) and had significant higher rates of marital status (P=0.04). There were no significant differences in sex (P=0.20) between the obese and the nonobese ([Table 1]).
|Table 1 Demographic data, SMMSE, WAIS, and WCST of the obese and nonobese individuals|
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Cognitive and executives domains
- Cognitive performance (SMMSE) showed no significant difference between obese and nonobese individuals, indicating that obese individuals had no impaired general cognitive performance ([Table 1]).
- Cognitive and executive functions (WAIS and WCST) showed highly significant lower scores of the obese compared with the nonobese in all parameters, except for the similarities domain ([Table 1]).
- Significant inverse correlations were found between BMI and all parameters of executive functions and cognitive function except verbal intelligence quotient (IQ) (P>0.05) ([Table 2]).
|Table 2 Correlation of BMI to cognitive function parameters in individuals with obesity|
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- Picture completion showed the strongest inverse correlation with BMI (−0.53), followed by performance IQ (−0.49), total IQ (−0.42), conceptual level responses (−0.41), categories completed (−0.31), preservative responses (−0.38), arithmetic (−0.38), digital span (−0.33), comprehensive (−0.31), coding (−0.30), block design (−0.29), and similarities (−0.25) ([Table 2]).
| Discussion|| |
Obesity is a risk factor for lowered cognitive function in obese individuals without clinically diagnosed stroke and dementia. Obesity is associated with cognitive dysfunction in adults, as shown by an increasingly large number of cross-sectional, case–control, longitudinal, and weight management intervention studies (Jartti et al., 2009; Van den Berg et al., 2009; Siervo et al., 2011; Smith et al., 2011; Fitzpatrick et al., 2013; Prickett et al., 2015).
In keeping with previous limitations identified in studies investigating obesity and cognition (Crichton et al., 2012), inconsistencies related to poor study design, small sample size, limited consideration of confounders, and the array of psychometric tests used make it difficult to compare studies (Cheke et al., 2016).
In using the standardized mini-mental state examination to assess cognitive performance of both groups. No significant difference was found between obese individuals and nonobese controls. This was in accordance with previous studies that did not find any significant differences between groups (normal, overweight, and obese) on the MMSE. The MMSE was designed as a screening tool for dementia and is not sensitive to mild cognitive deficits, particularly attention and executive dysfunction. Accordingly, there appears to be no evidence to support impaired general cognitive performance in obese adults (Nichelli et al., 1993).
In this study, IQ was assessed in the groups using the WAIS as a method of assessing general intelligence. We found a highly significant difference between obese individuals and nonobese controls in all parameters (P<0.001), except for the domain similarities, which had no difference (P>0.05). Intellectual function in obese adults was assessed in seven previous studies. Three studies utilizing data from the Danish draft board between 1956 and 1977 reported a significant relationship between intellectual function and obesity. Four studies, two of which utilized normative comparisons, found no significant differences in general intelligence between obese individuals and normative or control comparisons (Pignatti et al., 2006). Our research is in keeping with the Danish studies.
The WCST is one of the most widely used psychological testing tools to assess executive functioning skills such as problem solving, decision making, inhibitory control, and working memory. Our assessment of study groups using the WCST showed that obese individuals had more perseverative responses and more conceptual level responses, required more trials to complete categories, and had more difficulties in concentration, abstract thinking, and visuomotor coordination. This shows that patients had impairment in nearly all aspects of executive functions. These results were in accordance with the results of Elias et al. (2003), who examined the effects of obesity on cognitive functioning in older adults without a history of clinical stroke, dementia, or cardiovascular disease. There was a significant multivariate effect of obesity for men, namely, after considering all the covariates in the model. Obese men performed more poorly on the Logical Memory Immediate Recall, Visual Reproductions, Digit Span Backward, Word Fluency, and Logical Memory Delayed Recall tests. A cross-sectional longitudinal study of more than 2000 middle-aged workers supported a linear association between BMI and cognitive function. The authors assessed the cognitive function using the word-list learning test, which evaluates verbal learning and memory, and the digit-symbol substitution test, which assesses attention, response speed, and visuomotor coordination. Individuals with obesity recalled fewer words in the word-list learning test and took longer to complete the digit-symbol substitution test relative to normal-weight individuals (Cournot et al., 2006). Across studies, the different cognitive domains analyzed make it difficult to draw absolute comparisons, but impairment of specific cognitive domains such as executive function and short-term memory have been consistently identified in obese individuals when compared with normal-weight counterparts (Cournot et al., 2006; Mond et al., 2007; Lokken et al., 2009).
In an attempt to determine a relationship between the severity of obesity and degree of cognitive impairment in obese individuals using WAIS and WCST, there was a positive correlation between BMI and all parameters of the WAIS except for verbal IQ. BMI positively correlated with perseverative responses, conceptual level responses, and categories completed, indicating that with increasing obesity there is more cognitive impairment. Other studies that assessed severity of obesity and degree of cognitive impairment support our findings in the following domains: intellectual functioning, psychomotor performance and speed, visual construction, concept formation and set shifting, and decision making. Limited evidence was available for the domain of time estimation. Available evidence was equivocal for a relationship between obesity and visual memory, verbal memory, complex attention, delay discounting, and inhibition. There was no evidence of deficits in areas of general cognitive performance, time judgment, working memory, and verbal fluency. While these results support the conclusions from a previous review that found consistent evidence that mid-life obesity was associated with impaired cognitive function, specifically in the area of executive functioning, it also provides evidence for a relationship between obesity and intellectual functioning, psychomotor performance and speed, and visual construction (Smith et al., 2011).
There are a number of novel strengths in this study that support the reliability and validity of our findings. First, this research directly compared specific domains of cognitive function to enable more direct comparisons between studies, and we provide a more detailed reflection of how such methodological issues may influence interpretation of results. Second, since this paper specifically addressed mid-life, strict inclusion criteria were used to minimize variability in findings, which improves the quality of our results.
Our study is limited by its comparatively small sample size, which necessitates careful conclusions. Second, we did not include overweight adults, so we are unable to speculate on the cognitive performance of this group.
Future directions for research should take into consideration that, although the cross-sectional design of our study precludes causal relationships from being determined, our findings of reduced general intelligence and executive function in obese individuals deserve further examination via longitudinal research. Large epidemiological studies across the BMI spectrum and clinical trials examining the effect of acute and longer-term weight loss on the cognitive function of young adults with obesity are also warranted. Further, it would be interesting to speculate on possible mechanisms as to why obesity affects intellectual functioning.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Castanon N, Lasselin J, Capuron L (2014). Neuropsychiatric comorbidity in obesity: role of inflammatory processes. Front Endocrinol 5:74.
Cheke LG, Simons JS, Clayton NS (2016). Higher body mass index is associated with episodic memory deficits in young adults. Quarter J Exp Psychol 69:2305–2316.
Cortese S, Castellanos FX (2014). The relationship between ADHD and obesity: implications for therapy. Expert Rev Neurother 14:473–479.
Cournot MCMJ, Marquie JC, Ansiau D, Martinaud C, Fonds H, Ferrieres J, Ruidavets JB (2006). Relation between body mass index and cognitive function in healthy middle-aged men and women. Neurology 67:1208–1214.
Crichton GE, Elias MF, Buckley JD, Murphy KJ, Bryan J, Frisardi V (2012). Metabolic syndrome, cognitive performance, and dementia. Jo Alzheimer’s Dis 30(s2):S77–S87.
Dorian RS (2010). Anesthesia for the surgical patient. In: Brunicardi FC, Schwartz SI (Editors). Schwartzs Principles of Surgery (9th ed. New York, NY: McGraw-Hill, Medical Publication Division. 3373–3412.
Elias MF, Elias PK, Robbins MA, Wolf PA, D’Agostino RP (2001). Cardiovascular risk factors and cognitive functioning: an epidemiological perspective. In: Waldstein SR, Elias MF (editors). Neuropsychology of Cardiovascular Disease. Mahwah, NJ: Lawrence Erlbaum Associates Publishers 83–104.
Elias MF, Elias PK, Sullivan LM, Wolf PA, D’agostino RB (2003). Lower cognitive function in the presence of obesity and hypertension: the Framingham heart study. Int J Obes 27:260–268.
Fagundo AB, De la Torre R, Jiménez-Murcia S, Agüera Z, Granero R, Tárrega S, Forcano L (2012). Executive functions profile in extreme eating/weight conditions: from anorexia nervosa to obesity. PLoS One 7:e43382.
Fitzpatrick S, Gilbert S, Serpell L (2013). Systematic review: are overweight and obese individuals impaired on behavioural tasks of executive functioning?. Neuropsychol Rev 23:138–156.
Gaillard R (2015). Maternal obesity during pregnancy and cardiovascular development and disease in the offspring. Eur J Epidemiol 30:1141–1152.
Goldberg D, Williams P (1978). General Health Questionnaire. Windsor: NFER Publishing Company.
Gunstad J, Paul RH, Cohen RA, Tate DF, Gordon E (2006). Obesity is associated with memory deficits in young and middle-aged adults. Eat Weight Disord 11:e15–e19.
Heaton RK, Staff PAR, Goldin JN (2003). WCST: CV4 Wisconsin Card Sorting Test: Computer Version 4 Research Edition User’s Manual. Lutz, FL: Psychological Assessment Resources.
Jartti T, Saarikoski L, Jartti L, Lisinen I, Jula A, Huupponen R, Raitakari OT (2009). Obesity, adipokines and asthma. Allergy 64:770–777.
Kittel R, Schmidt R, Hilbert A (2017). Executive functions in adolescents with binge‐eating disorder and obesity. Int J Eat Disord 50:933–941.
Lokken KL, Boeka AG, Austin HM, Gunstad J, Harmon CM (2009). Evidence of executive dysfunction in extremely obese adolescents: a pilot study. Surg Obes Relat Dis 5:547–552.
Molloy DW, Alemayehu E, Roberts R (1991). Reliability of a standardized mini-mental state examination compared with the traditional mini-mental state examination. Am J Psychiatry 148:102–105.
Mond JM, Rodgers B, Hay PJ, Darby A, Owen C, Baune BT, Kennedy RL (2007). Obesity and impairment in psychosocial functioning in women: the mediating role of eating disorder features. Obesity 15:2769–2779.
Nichelli P, Venneri A, Molinari M, Tavani F, Grafman J (1993). Precision and accuracy of subjective time estimation in different memory disorders. Cogn Brain Res 1:87–93.
Pignatti R, Bertella L, Albani G, Mauro A, Molinari E, Semenza C (2006). Decision-making in obesity: a study using the Gambling task. Eat Weight Disord 11:126–132.
Prickett C, Brennan L, Stolwyk R (2015). Examining the relationship between obesity and cognitive function: a systematic literature review. Obes Res Clin Pract 9:93–113.
Raison CL, Dantzer R, Kelley KW, Lawson MA, Woolwine BJ, Vogt G, Miller AH (2010). CSF concentrations of brain tryptophan and kynurenines during immune stimulation with IFN-α: relationship to CNS immune responses and depression. Mol Psychiatry 15:393–403.
Santangeli L, Sattar N, Huda SS (2015). Impact of maternal obesity on perinatal and childhood outcomes. Best Pract Res Clin Obstetr Gynaecol 29:438–448.
Siervo M, Arnold R, Wells JCK, Tagliabue A, Colantuoni A, Albanese E, Stephan BCM (2011). Intentional weight loss in overweight and obese individuals and cognitive function: a systematic review and meta‐analysis. Obes Rev 12:968–983.
Smith E, Hay P, Campbell L, Trollor JN (2011). A review of the association between obesity and cognitive function across the lifespan: implications for novel approaches to prevention and treatment. Obes Rev 12:740–755.
Thabet AA, Vostanis P (2005). Validity of the Arabic version of the General Health Questionnaire in the Gaza Strip. Palest Med J 1:33–36.
Van den Berg E, Kloppenborg RP, Kessels RP, Kappelle LJ, Biessels GJ (2009). Type 2 diabetes mellitus, hypertension, dyslipidemia and obesity: a systematic comparison of their impact on cognition. Biochim Biophys Acta 1792:470–481.
Wechsler D (1981). The psychometric tradition: developing the Wechsler adult intelligence scale. Contemp Educ Psychol 6:82–85.
Yaffe K, Weston A, Blackwell T, Kruger K (2009). The metabolic syndrome and development of cognitive impairment among older women. Arch Neurol 66:324–328.
[Table 1], [Table 2]