ANKK1 Taq1A Gene Variance and Executive Functions in Female Youth with Binge Eating Disorder: A Cross-sectional Study

Abstract

Background:

Binge eating disorder (BED) biological and psychological etiology is still not fully understood. This study investigates the relationship between the ANKK1 Taq1A gene variant and BED in female youth with obesity, while also assessing executive functions (EFs).

Methods:

The study included eighty young females with obesity, divided into BED and non-BED groups. EFs were assessed using the Stroop test, Digit Span, and the PEBL battery (including Card Sorting, Iowa Gambling, and Go/No-go tests). The ANKK1 Taq1A gene variance was detected via real-time PCR.

Results:

No significant differences were found in ANKK1 Taq1A genotypes or allele frequencies between the groups. However, the BED group exhibited worse cognitive flexibility, decision-making, and higher depression levels. Specifically, the BED group scored poorly on tests such as Card Sorting, Iowa Gambling, and Digit Span (p < .001, p = .005, p = 0.07, respectively). A logistic regression analysis indicated that worse cognitive flexibility and high depression severity significantly increased the probability of BED (p = .01, OR = 1.42; p = .001, OR = 1.26, respectively).

Conclusions:

No association was found between ANKK1 Taq1A gene variance and BED or EFs performance. The female youth with obesity and BED demonstrated worse cognitive flexibility and decision-making, along with greater depression severity. Worse cognitive flexibility and greater depression severity were found to increase the probability of BED. Thus, evaluating and addressing EF deficits in BED patients, along with managing co-morbid psychiatric conditions, is crucial for improving treatment outcomes.

Keywords

Binge eating disorder executive functions ANKK1 Taq1A gene obesity

Key Messages:

Worse cognitive flexibility and greater depression severity increase the probability of BED.

No association was found between ANKK1 Taq1A gene variance and other EFs and BED.

Assessment of EFs and co-morbid psychiatric conditions in subjects with BED should be an essential component of the BED management plan.

Binge-eating disorder (BED) is considered the most prevalent eating disorder.¹ It was recently included as a distinct psychiatric disorder in the main section of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5).² According to DSM-5 criteria, BED is characterized by recurrent episodes of binge-eating during which the individual consumes large amounts of food and experiences a sense of loss of control over eating. Inappropriate compensatory behaviors do not follow these episodes and occur at least once a week for a period of three months. Literature shows that youth is a critical stage for the onset of eating disorders, including BED. BED develops during adolescence with a prevalence rate of 1.6% among youth in the general population and up to 25% among treatment-seeking adolescents with overweight and obesity.³ Studies have shown that BED negatively impacts health-related quality of life, as adolescents with BED tend to experience more impairment in social/interpersonal functioning as well as in academic and occupational achievements. Moreover, BED in youth predicts a wide range of mental and medical health disorders. Adolescents with BED showed prospective associations with internalizing problems, especially depression, anxiety, and substance use disorder, in addition to obesity and metabolic disorders.^4,5

The etiology of BED remains an understudied topic. Studies suggest that individuals with BED exhibit impaired cognitive functions, altered reward sensitivities, genetic predisposition, and dysregulated activation of brain regions related to impulsivity and compulsivity.⁶ Dopamine is believed to play a significant role in the etiology of BED. It serves as the primary modulator of the brain reward system and significantly regulates food intake. Research suggests that individuals with obesity and BED exhibit hyper-responsiveness to rewarding stimuli, which increases the likelihood of overeating, especially in societies abundant with highly palatable food.⁷ Studies agree that BED has a familial aggregation, with heritability estimates ranging from 38% to 70%.⁸ Taq1A gene variance, a polymorphism related to the dopamine (D2) receptor gene, has been the focus of many genetic studies on BED, but findings have been inconsistent.^9,10 This variance is identified by the exchange of a single nucleotide (cytosine for thymine), which causes a glutamine to lysine substitution in the 11th ankyrin repeat of ANKK1 Taq1A, resulting in two alleles referred to as G (A2) and A (A1). Carriers of the A1 allele typically have 30%–40% fewer striatal D2 receptors, which is associated with increased risk for addiction, overeating, and certain psychiatric disorders, as well as altered reward processing and metabolism.¹¹ This reduction in D2 receptor density is thought to contribute to behavioral traits such as impulsivity and altered learning, and may underlie susceptibility to conditions involving the brain’s reward system.¹² The dopaminergic system influences the activity and functioning of the prefrontal cortex (PFC). Dopamine dysfunction has been evidenced in disorders where executive function (EF) deficits play a crucial role, such as psychosis and ADHD.^13,14 Since EFs depend mainly on PFC neural networks,¹⁵ studies suggest that individuals with BED have EF deficits that predispose them to exhibit abnormal responses in the context of food-related stimuli, which may trigger binge-eating behaviors.⁶

The current study aimed to assess the relationship between ANKK1 Taq1A gene variance, BED, and EFs in female youth with obesity. We hypothesized that the frequency of the G allele of the ANKK1 Taq1A gene would be higher than that of the A allele in young females with obesity and BED compared to young non-BED females with obesity. Furthermore, we hypothesized that carriers of the G allele of the ANKK1 Taq1A gene polymorphism would show worse performance in EFs tests than carriers of the A allele.

Methods

Ethical Considerations

The Institutional Ethics Committee approved the study. Written informed consent was obtained in Arabic. Assent was obtained from those less than 18 years of age, in addition to parental consent.

Participants

This comparative cross-sectional study included 80 female participants divided into two equal groups: A and B. Patients from both groups were recruited consecutively as a convenience sample from the obesity outpatient clinic at the National Nutrition Institute from February 2021 to December 2021. The sample size calculation was performed using G*Power software version 3.1.9.2, with an α error of 0.05 and a power of 0.8, to determine a sample size of 35 participants in each group, as per a previous study. The population was of Northern African ethnicity. Group A included 40 participants with obesity and BED diagnosed according to DSM-5, while Group B included 40 participants with obesity without BED. Inclusion criteria for both groups were: age range “15–24” years (according to the United Nations Definition of Youth),¹⁶ body mass index (BMI) ≥30, clinically average I.Q., and ability to read and write. Exclusion criteria were the presence of other eating disorders, any psychotic disorder, bipolar disorder, substance use disorder, any chronic medical illness, the use of psychotropics, or any drugs that affect appetite, pregnancy, or breastfeeding.

Tools

Eating Disorder Assessment 5 (EDA-5)¹⁷

The EDA-5 is a validated semi-structured interview-based tool used for diagnosing DSM-5 Feeding and Eating Disorders. It applies a hierarchical approach to diagnostic information. Therefore, when criteria for a specific disorder are met, criteria for other superseded disorders are not assessed. Permission for Arabic translation was obtained from the authors. Then, one of the researchers translated it, and the other researchers back-translated and revised it.

The Binge-eating Scale¹⁸

It is a brief measure that assesses the severity of binge-eating behavior in individuals with overweight and obesity. It consists of a 16-item self-report questionnaire that evaluates the behavioral, cognitive, and emotional features of objective binge eating (OBE) in overweight and obese individuals. A score of less than 17 is considered normal; higher scores indicate higher severity of binge-eating. The validated Arabic version was used.¹⁹

Hamilton Depression Rating Scale (HDRS-17)²⁰

This is a clinician-administered depression assessment tool that consists of 17 symptoms of depression experienced over the past week. The scoring method varies depending on the version. For the HDRS-17, a score of 0–7 is considered normal, but depression severity rises with higher scores. The HDRS was used due to the high comorbidity of depression with BED.

EFs Scales

Stroop color-word interference test, Arabic version²¹: Assesses cognitive inhibition. A higher score indicates a higher resistance to interference.

Digit backwards subtest of the Wechsler Intelligence Scale-Revised²²: Assesses working memory. The final score is the longest span of numbers that the subject can recall correctly without error.

Psychology Experiment Building Language (PEBL) version 2²³: A cross-platform system for designing and running computer-based experiments and tests. The following parts of the PEBL battery were used: the Card Sorting Test (CST), which assesses cognitive flexibility, abstract reasoning, and problem-solving. The Go/No-Go test evaluates inhibitory control, while the Iowa Gambling Test assesses decision-making.

Body Mass Index

Body weight was measured using a scale, and height was measured using a measuring tape to calculate the BMI, which is calculated as weight in kilograms divided by height in meters squared. BMI classifications are as follows: Overweight (25–29.9), obese class I (30–34.9), obese class II (35–39.9), obese class III (≥40).

ANKK1 Taq1A Alleles

The ANKK1 gene name is ANKYRIN REPEAT- AND KINASE DOMAIN-CONTAINING PROTEIN 1; ANKK1 mapped to chromosome 11q23.2.²⁴ The studied variant, according to the HGVS nomenclature,²⁵ is located in the following coordinates on the ANKK1 gene (NG_012976.1: g.17316G>A, NM_178510.2: c.2137G>A, NP_848605.1: p. (Glu713Lys) on GRCh37 (hg19).

Variations in ANKK1 Taq1 A alleles were detected using DNA extraction, real-time PCR amplification, and genotyping for rs1800497. Gene JETTM Genomic DNA Purification Kit (#K0721), Fermentas Life Sciences, was used for DNA extraction. Peripheral blood leucocyte DNA was extracted through proteinase K, which breaks down proteins such as histones, releasing the DNA strands. RNA was removed from the sample by treating it with RNase A. The lysate was mixed with ethanol and loaded on the purification column, where DNA binds to the silica membrane. Impurities are effectively removed by washing the column with the prepared wash buffers. Genomic DNA was eluted under low ionic strength conditions with the Elution Buffer. DNA was then stored in –20 °C freezers till analysis.

Genotyping for rs1800497 was performed using a real-time PCR reaction. In this reaction, the TaqMan™ Universal PCR Master Mix (Thermo Fisher Scientific, USA) -which is a ready-to-use solution containing DNA Polymerase, optimized DreamTaq buffer, MgCl2, and dNTPs- is added to the assay probe, TaqMan™ SNP Genotyping Assay, Human (Thermo Fisher Scientific), and the DNA template is added. The probe sequence used was CACAGCCATCCTCAAAGTGCTGGTC[A/G]AGGCAGGCGCCCAGCTGGACGTCCA. DNA is amplified through thermal cycling. As more copies of the target sequence accumulate, the probe will bind to the variant of interest. As the DNA polymerase proceeds to amplify the single-stranded DNA, it cleaves the probe at the 5’ end. It only separates the reporter dye from the quencher dye when the probe combines perfectly with the target DNA. This cleavage results in a real-time PCR detection system monitoring the fluorescent signal. An increase in the fluorescent signal (CT) is proportional to the amount of the specific PCR product. In this reaction, the wild variant A was labeled by the VIC dye (VIC is a proprietary by Thermo Fisher Scientific, the exact structure not publicly disclosed, with an Excitation/Emission wavelengths at: ~538 nm/~554 nm), and the FAM dye labeled the minor allele variant G (FAM is 6-Carboxyfluorescein, a fluorophore with an Excitation/Emission wavelengths of ~495 nm/~520 nm, respectively). The increased intensity of each dye testified that the variant was present in the template DNA, and the appearance of both dyes indicated that the patient was heterozygous.

Procedure

Each participant underwent a 60–75-minute interview, followed by blood sample collection. All scales were administered to all patients by a single researcher, who was blinded to the genotyping results. Five milliliters of blood were withdrawn under aseptic conditions in a violet-top EDTA tube. Tubes were stored at –20 °C till extraction. Genomic DNA was extracted using a column-based extraction method, followed by amplification with a Real-time PCR technique and detection of the rs1800497 single-nucleotide polymorphism (SNP) variant.

Statistical Analysis

Results were evaluated statistically using the Statistical Package for the Social Sciences (SPSS) version 20.²⁶ The Kolmogorov–Smirnov test was used to assess the normality of the data. Data were described using frequency (percent) and mean ± SD Student t-test and Mann–Whitney U test were used for comparisons, while Analysis of Variance (ANOVA) was used to compare more than two groups. Pearson’s correlation test was used for correlations. A Chi-square test was used to compare qualitative variables. Logistic regression was performed. A p value less than .05 was considered statistically significant.

Results

Participants with and without BED were matched for age, weight, and BMI. The genotype frequencies of the ANKK1 Taq1A polymorphism were 61.2% GG (n = 49), 35.0% AG (n = 28), and 3.75% AA (n = 3). Based on the calculated allele frequencies (G = 0.7875, A = 0.2125), the expected genotype frequencies under Hardy-Weinberg equilibrium were 62.0% GG, 33.5% AG, and 4.5% AA. A chi-square test yielded a statistic of 0.17 with a corresponding p value of .682, indicating no significant deviation from Hardy-Weinberg equilibrium in this study.

The GG genotype was the most frequent among both groups (60% in the BED group and 62.5% in the non-BED group). Meanwhile, the AA genotype was the least frequent (5% in the BED group and 2.5% in the non-BED group). The frequency of the G allele was higher than that of the A allele in both groups (60% of the BED group and 62.5% of the non-BED group had the G allele).

There was no significant statistical difference in ANKK1 Taq1A genotypes (p = .84) or G and A allele frequencies (p = .82) between the two groups. However, the two groups differed significantly in the mean scores of Card Sorting preservation errors (p < .001), the Iowa test (p = .005), and the Digit Forward span test (p = .007). Hamilton Depression scores were significantly higher in the BED group (p < .001) (Table 1). There was no significant difference between carriers of either the A allele or the G allele in both groups (BED and non-BED groups) regarding BMI, performance in EF tests, and severity of depression (Table 2).

Table 1.

Comparison Between Both Groups (BED and non-BED) Regarding Age, Weight, Body Mass Index, Executive Functions, Hamilton Depression Rating Scale and ANKK1 Taq1A Genotypes and Alleles.

			Non-BED Group No = 40	BED Group No = 40	p Value
Age		Mean	20.3	21.28	.23
Age		SD	3.33	2.86	.23
Weight		Mean	90.55	90.81	.58
Weight		SD	13.41	18.49	.58
Body mass index		Mean	36.21	36.11	.54
Body mass index		SD	5.09	6.34	.54
Digit backwards		Mean	3.95	3.63	.13
Digit backwards		SD	0.93	0.67	.13
Stroop interference		Mean	8.83	6.19	.15
Stroop interference		SD	9.69	8.22	.15
Card sorting preservation errors		Mean	11.04	14.21	<.001
Card sorting preservation errors		SD	2.35	4.85	<.001
Iowa		Mean	9.3	–3.63	.005
Iowa		SD	21.16	23.09	.005
Go/no go errors		Mean	23.55	23.73	.69
Go/no go errors		SD	16.19	14.43	.69
Digit forwards		Mean	6.35	5.78	.007
Digit forwards		SD	0.83	1.09	.007
Hamilton depression rating scale		Mean	10.18	15.48	<.001
Hamilton depression rating scale		SD	5.41	4.67	<.001
Genotype	GA	Count	14	14	.84
	GA	% Within group	35.0	35.0
	G.G.	Count	25	24
	G.G.	% Within group	62.5	60.0
	A.A.	Count	1	2
	A.A.	% Within group	2.5	5.0
Allele	G	Count	25	24	.82
	G	% Within group	62.5	60.0
	A	Count	15	16
	A	% Within group	37.5	40.0

Mann–Whitney U test and Chi-square test. The odds of carrying the G allele in the No BED group are 1.16 times higher than in the BED group.‎ However, the 95% CI was 0.54–2.48. The odds of having the G.G. genotype in the No BED group are 1.11 times the odds in the BED group, with a 95% CI (0.45–2.73).

p < .05 = significant difference, p < .001 = highly significant difference.

BED: Binge eating disorder.

Values in bold are statistically significant at p < 0.05.

Table 2.

Comparison Between Carriers of G and A Alleles of ANKK1 Taq1A Gene in Both Groups (BED and non-BED) Regarding Body Mass Index, Executive Functions, and Hamilton Depression Rating Scale.

	G Allele (n = 49)	A Allele (n = 31)	p Value
Body mass index	36.27 ± 5.33	36 ± 6.36	.49
Digit forward	6.12 ± 0.99	5.97 ± 1.05	.54
Digit backward	3.78 ± 0.72	3.81 ± 0.98	.72
Stroop interference	7.99 ± 8.49	6.75 ± 9.93	.36
Card sorting preservation errors	12.36 ± 4.11	13.04 ± 4.15	.28
Iowa	5.02 ± 23.63	–0.61 ± 21.75	.54
Go/no go errors	23.88 ± 17.39	23.26 ± 11.26	.57
Hamilton depression rating scale	12.59 ± 5.99	13.19 ± 5.25	.88

Mann–Whitney U test.

BED: Binge eating disorder.

Within the BED group, age showed a significant negative correlation with interference control, as resistance to interference, measured by the Stroop task, decreased with increasing age (p = .000, r = –0.54). In addition, BMI showed a significant negative correlation with decision-making, as measured by the Iowa test, where decision-making ability decreased with increasing BMI (p = .017, r = –0.37). Also, depression severity, measured by the Hamilton ‎Depression Scale, showed a significant negative correlation with short-term memory, measured by Digit Forward span, as depression severity increased, short-term memory worsened (p = .002, r = –0.47). Binge-eating severity did not significantly correlate with age, BMI, or severity of depression (Table 3).

Table 3.

Correlation Between Executive Functions With Age, Body Mass Index, Hamilton Depression Scale, and Binge Eating Scale Scores Within the BED Group.

		Digit Forwards	Digit Backwards	Stroop Interference	Card Sorting Preservation Errors	Iowa	Go/No Go Errors	Binge Eating Scale
Age	r	–0.18	0.02	–0.54	–0.14	0.25	–0.07	–0.05
	p	.26	.93	<.001	.38	.13	.66	.77
	N	40	40	40	40	40	40	40
Body mass index	r	0.18	–0.1	0.17	0.27	–0.37	–0.16	0.2
	p	.27	.53	.3	.09	.017	.33	.21
	N	40	40	40	40	40	40	40
Hamilton depression scale	r	–0.47	–0.17	–0.18	–0.20	–0.01	0.04	0.19
	p	.002	.29	.28	.21	.93	.82	.23
	N	40	40	40	40	40	40	40
Binge eating scale	r	0.11	0.21	–0.07	–0.12	–0.18	–0.31
	p	.49	.19	.65	.45	.26	.06
	N	40	40	40	40	40	40

Spearman correlation test.

p < .05 = significant difference.

BED: Binge eating disorder.

Logistic regression analysis was performed with the Hamilton Depression Scale score, ANKK1 Taq1A genotype, Digit Forward test, Card Sorting preservation errors test, Iowa Gambling task scores as independent variables, and BED as the dependent variable. The results showed that worse cognitive flexibility and higher depression severity significantly increased the probability of BED (p = .005, OR = 1.42; p = .001, OR = 1.26, respectively). However, ANKK1 Taq1A genotype, short-term memory, and decision-making did not significantly affect BED (Table 4).

Table 4.

Association Between Hamilton Depression Rating Scale Score, ANKK1 Taq1A Genotype, Digit Forward Test, Card Sorting Preservation Errors Test and Iowa Test in Predicting the Probability of Binge Eating Disorder.

	p Value	Exp (B)	95% CI for EXP (B)
	p Value	Exp (B)	Lower	Upper
Hamilton depression scale	.001	1.26	1.11	1.43
Digit forward	.12	0.59	0.31	1.15
Card sorting preservation errors	.005	1.42	1.11	1.82
Iowa	.07	0.98	0.95	1
GA	.74	1.68	0.08	36.48
GG	.9	0.83	0.04	17.3
Constant	.21	0.01

Binary logistic regression.

Model summary: Nagelkerke R² = 0‎.54, p < .05 = Significant difference.

Discussion

This study aimed to investigate a possible role of the dopamine-related gene, ANKK1 Taq 1A, in the pathology of EFs and BED. As far as we know, this is the first study in our country investigating the ANKK1 Taq 1A gene variance and EFs in female youth with obesity and BED. Dysfunction in the dopaminergic system is the link between them. The relation of the ANKK1 Taq1A gene to BED is controversial in the literature. As genetic structure can differ between races, studies in different countries yielded conflicting results. Some studies support the hypothesis of increased reward sensitivity in participants with BED, while other studies have opposite views.

We had hypothesized that the G allele of ANKK1 Taq 1A would be more frequent among the BED group than in the non-BED group. However, we did not find a significant statistical difference between the two groups regarding ANNK Taq 1A alleles or genotypes. On the other hand, previous studies^27,28 found that the G allele (Taq1A2) of rs1800497 was more frequent among individuals with BED compared to patients with obesity. In another study, 70% of participants with BED exhibited the G/G genotype of rs1800497, which was a significant statistical difference compared to participants with obesity without BED.⁷ On the other hand, a previous study⁹ did not find a significant association between BED and ANKK1 Taq1A gene variance. The lack of statistical significance in our study might be related to the small sample size, as individual genetic polymorphisms typically account for only a small proportion of phenotypic variance. Therefore, they may fail to achieve statistical significance in studies with relatively small clinical samples. In another study, there was a significant association between obesity and the DRD2 Taq 1A allele among severely obese individuals seeking weight-loss treatment.²⁹ Nevertheless, the frequency of the G allele and GG genotype was higher than that of the A allele and AA genotype in both groups.

Research investigating the EFs in participants with obesity with/without BED yielded conflicting results. The heterogeneity of participants and the use of different EF tests across studies might explain these contradictory results. In the current study, we investigated five domains of EFs. Cognitive flexibility and decision-making differed significantly between the two groups, with worse performance in the BED group. However, there was no statistically significant difference between the two groups in terms of working memory, inhibitory control, and cognitive inhibition. Impairment of EFs in BED was explained in many studies. Neuroimaging studies have shown hypoactivity in the PFC and heightened responsiveness in reward-related regions, such as the nucleus accumbens, leading to impulsive eating behavior and poor self-control.^30,31 These impairments are compounded by difficulties in emotion regulation and increased impulsivity, often triggered by negative affect.³² Collectively, these deficits contribute to the loss of cognitive control during binge episodes.³³

BED is an impulsive-compulsive disorder characterized by compulsive food-seeking behavior. Poor cognitive flexibility in individuals with BED may explain their persistent thoughts about food and difficulty adapting to changing circumstances related to eating behavior. Previous meta-analyses and systematic reviews concur with our study, as they provide evidence of poor cognitive flexibility in individuals with BED compared to controls.^33–35 Additionally, regression analysis revealed a significant effect of cognitive flexibility on BED, indicating that worse cognitive flexibility increases the probability of BED. However, a previous study³⁶ found no significant difference in cognitive flexibility between individuals with BED and controls.

Regarding decision-making and BED, our results showed a significant statistical difference between the two groups, with poorer decision-making ability in the BED group. Such deficits in individuals with BED could explain why they engage in binge-eating episodes despite the distress and negative physical consequences they experience afterward. Similarly, a meta-analysis³⁷ revealed that BED was associated with significantly poorer performance in decision-making tasks relative to controls. On the other hand, a recent meta-analysis did not find significant differences in decision-making between individuals with BED and controls.³⁶

Regarding inhibitory control, our study findings align with multiple meta-analyses and systematic reviews that report no significant deficits in response inhibition tasks among individuals with BED compared to obese or normal-weight controls, even when using food-related stimuli for general inhibitory control.^35,36–38 On the other hand, a study³⁹ found that young adults with BED exhibited response inhibition deficits as measured by the Stop Signal test. Deficits in cognitive interference control may contribute to the difficulty in inhibiting attention to urges or cravings, which can precipitate binge-eating episodes.³⁴ However, our study did not find significant statistical differences regarding cognitive interference control. The current study results align with a previous meta-analysis, which concluded that individuals with BED do not show deficits in interference control.

As for working memory, working memory impairment might make it challenging to keep track of ongoing impulsive acts such as binge-eating behavior. This impairment may contribute to the maintenance of such behavior.⁴⁰ A recent meta-analysis suggested that individuals with BED may show alterations in working memory compared to obese individuals without the disorder.³⁶ Our study did not find statistically significant differences between the two groups in terms of working memory. This finding could be explained by the fluid nature of working memory, which is susceptible to fluctuations due to factors such as sleep problems, medication use, or nutrition. Another possible explanation is the presence of obesity in both groups, as obesity has been associated with working memory deficits that contribute to the development and maintenance of obesity.⁴¹ Thus, poor performance in working memory tasks could be attributed to several factors beyond BED. This finding is comparable to previous reviews that reported the absence of consistent evidence of impairments in working memory in participants with BED compared to obese controls.^35–42

Regarding the relationship between genetic variations and EFs, a study⁴³ found that the ANKK1 Taq1A A1-allele significantly affected almost all EFs variables in participants with obesity. However, in our study, no significant statistical difference existed between carriers of different Taq 1A gene alleles and genotypes regarding EFs in either group. This finding aligns with another study,⁴⁴ where no significant associations could be found between ANKK1 Taq1A gene variance and any domain of EFs. This might be explained by the limited effect of single-gene variance on endophenotypic outcomes, as genome-wide association studies indicate that cognitive abilities are polygenic.⁴⁵ Additionally, this variance affects intermediate phenotypes at a neural level rather than directly impacting EFs. Although our study showed that carriers of the AA genotype of the ANKK1 Taq1A gene had the best short-term memory performance within the BED group, this finding could be biased by the small number of participants carrying the AA genotype within the BED group (2 out of 40 participants). Our results revealed a significant negative correlation between cognitive interference control and age. This finding contradicts the results of a previous study, which found that interference control is better in adults than in younger age groups,⁴⁶ suggesting a possible effect of BED on cognitive interference control.

Furthermore, the BMI correlated negatively with decision-making within the BED group. This might explain why individuals with binge-eating often report engaging in behavior that is incongruent with their goals, leading to unsuccessful weight-loss attempts and progressive weight gain. Although the BED group showed worse performance in cognitive flexibility and decision-making tasks, we did not find a significant correlation between binge-eating severity and other EFs. Contrary to our finding, a meta-analysis⁴⁷ reported that greater binge eating severity was associated with more pronounced EF impairment. A possible explanation for this discrepancy is that most participants in the BED group had moderate binge-eating severity. At the same time, EF deficits may only become evident in participants with severe binge-eating.

In the current study, we did not find a significant association between ANKK1 Taq1A gene variance and EF deficits in female youth with obesity and BED. However, regression analysis revealed other factors that could increase the probability of BED, such as cognitive flexibility deficits and higher depression severity. Moreover, a significant statistical difference was found between the BED group and the non-BED group regarding cognitive flexibility, decision-making, and depression severity, with worse performance in the BED group.

As regards depression, it is a highly prevalent disorder in females with obesity, with or without BED. In the current study, participants with BED exhibited significantly higher depression severity compared to participants without BED. In addition, regression analysis revealed a significant effect of depression severity on BED, indicating that higher depression severity significantly increased the probability of BED. These findings align with previous literature, which highlights the strong association between BED and psychiatric comorbidities. Binge-eating episodes are often triggered by negative affect. Prior research has reported that mood and anxiety disorders are the most frequent comorbidities among these patients.^48,49 Moreover, some studies suggest a bidirectional relationship between BED and depression in adolescent girls.^50,51

In our study, depression severity correlated negatively with short-term memory. This finding is consistent with previous studies indicating short-term memory impairment in patients with depression.⁵² However, this finding may not be solely due to memory storage deficits, but rather secondary to other dysfunctions, such as attentional deficits, negative schemas, lack of motivation, and impaired cognitive initiative. We did not find a significant correlation between depression severity and other EFs in either group. The EF deficits in BED or obesity and depressive symptoms may be too subtle to be detected by traditional neuropsychological tests, which are designed to measure more severe cognitive deficits. Moreover, as most participants in both groups had mild to moderate depression, any EF deficits may not have been pronounced enough to be captured.

Our finding is consistent with a study that did not yield significant differences in EFs between patients with BED with no-to-mild and moderate-to-severe depression and controls.⁵³ On the other hand, other research suggests that, in eating disorders, greater depression severity is linked to impairments in specific EFs, such as cognitive flexibility.⁵⁴ Contrary to a few prior studies, we did not find a significant correlation between depression severity and binge-eating severity measured by the binge-eating scale (BES). In contrast, two previous studies^55,56 reported a positive association between binge-eating severity measured by the BES and depression symptoms.

Limitations

First, the sample size was statistically adequate based on previous research; however, its modest size may limit the generalizability of the findings. Second, the study sample included only female participants, so the results cannot be generalized to both genders. Third, it was a cross-sectional study; a longitudinal study could provide more substantial evidence of a causal relationship. Finally, this study did not exclude the presence of co-morbid psychiatric disorders with BED, such as anxiety, ADHD, and personality disorders. These disorders could be confounding factors as they are associated with EF deficits. However, other psychiatric disorders, such as bipolar disorder, schizophrenia, and substance use disorder, were excluded from our study.

Implications

Based on our findings, assessing EFs in participants with BED should be an essential part of a management plan to identify cognitive deficits that may impact treatment response. In addition, EF training, especially cognitive flexibility, in participants with BED could enhance the existing treatments and reduce the rates of relapse. Moreover, screening and management of co-morbid psychiatric conditions in patients with obesity and BED could improve the outcome of treatment. Also, it is essential to develop training programs for nutritionists about the screening and assessment of eating disorders in participants with obesity, as nutritionists frequently miss them.

Conclusions

No significant association was found between ANKK1 Taq1A variance and BED or EFs. However, female youth with obesity and BED exhibited poorer cognitive flexibility, impaired decision-making, and greater depression severity. Additionally, deficits in cognitive flexibility and higher depression severity were identified as significant predictors of BED.

Supplemental Material

Supplemental material for this article available online.

Footnotes

Data Availability Statement

The data that support the findings of this study are available from the corresponding author (Ola M. Ibrahim, goforurgoal2007@yahoo.com).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Declaration Regarding the Use of Generative AI

None used.

Ethical Approval

The study was approved by the Institutional Ethics Committee.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Informed Consent

The study included only participants who agreed to participate. The study protocol was explained to all participants. Participants were offered written Arabic consent forms and asked to sign them after reading. Assent was obtained from those < 18 years of age in addition to parental consent.

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Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

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ANKK1 Taq1A Gene Variance and Executive Functions in Female Youth with Binge Eating Disorder: A Cross-sectional Study

Abstract

Background:

Methods:

Results:

Conclusions:

Keywords

Key Messages:

Methods

Ethical Considerations

Participants

Tools

Eating Disorder Assessment 5 (EDA-5) 17

The Binge-eating Scale 18

Hamilton Depression Rating Scale (HDRS-17) 20

EFs Scales

Body Mass Index

ANKK1 Taq1A Alleles

Procedure

Statistical Analysis

Results

Discussion

Limitations

Implications

Conclusions

Supplemental Material

Footnotes

Data Availability Statement

Declaration of Conflicting Interests

Declaration Regarding the Use of Generative AI

Ethical Approval

Funding

Informed Consent

References

Supplementary Material

Eating Disorder Assessment 5 (EDA-5)¹⁷

The Binge-eating Scale¹⁸

Hamilton Depression Rating Scale (HDRS-17)²⁰