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Food Addiction: Prevalence, Severity and Impact on Vascular Stroke Risk Factors in a French Hospital-Based Sample

A peer-reviewed version of this preprint was published in:
Nutrients 2024, 16(24), 4327. https://doi.org/10.3390/nu16244327

Submitted:

26 November 2024

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27 November 2024

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Abstract

Background: Stroke ranks as the second leading cause of death and the third leading cause of disability in adults worldwide. While unhealthy diet is an independent risk factor for stroke, its association with disordered eating behaviours on stroke remains overlooked. This study aimed to evaluate the prevalence and severity of addictive-like eating behaviours in stroke patients and their association with the main vascular stroke risk factors. Methods: First-ever stroke patients with no severe aphasia or dementia were included. Addictive-like eating was assessed using the Yale Food Addiction Scale 2.0 (YFAS 2.0). The variables of interest were: i) the proportion of patients meeting the diagnosis of Food Addiction (FA); ii) FA symptoms count and severity; iii) addictive-like eating profile severity. Their association with four main vascular risk factors (obesity, hypertension, dyslipidemia and diabetes), were tested using univariate and multivariate analyses. Results: Over a 4-month period, 101 patients (mean (SD), 62.8 (13.7) years; males: 60.4%) were consecutively screened. Overall, 5% of the sample endorsed a FA diagnosis and 38.6% screened positive for at least one of the symptom criteria, with the most frequently endorsed symptom being “Inability to cut down”. Dyslipidemia was the only vascular risk factor associated with the FA diagnosis (p=0.04). However, the severity of the addictive profile was associated with dyslipidemia (p=0.016) and diabetes (p=0.038), but only independently with dyslipidemia (p=0.05; OR=1.25; 95%CI=1.00-1.56). There were significant associations between the number of vascular risk factors and the severity of Time spent, Tolerance, and Use despite adverse consequences FA symptoms, both in univariate and multivariate analyses (all p<0.05). The number of vascular risk factors and total number of FA symptoms were significantly associated in univariate analyses (p=0.007), but not after adjusting for age or sex. Conclusion: This study highlighted the potential importance of addictive-like behaviours in the secondary prevention of stroke. However, larger and longer-term studies investigating addictive-like eating in diverse samples of stroke patients are warranted to achieve precision medicine.

Keywords: 
Food addiction
;  
stroke
;  
hypertension
;  
obesity
;  
diabetes
;  
dyslipidaemia
;  
disordered eating behaviour
Subject: 
Medicine and Pharmacology  -   Neuroscience and Neurology

1. Introduction

According to recent Global Burden of Disease (GBD) estimates, stroke is the second leading cause of death and third leading cause of disability worldwide, with 12.2 million incident cases of stroke, 143 million disability-adjusted life-years (DALYs) lost, and 6.6 million deaths reported in 2019 [1]. Globally, although life expectancy (LE) increased to 73.2 years in 2021, ageing remains a primary risk factor for stroke events alongside a ratio of healthy life expectancy (HLE) to LE of 0.83 [2]. On the current trajectory, at the average age for a first stroke onset at approximately 70 years coupled with a reduction in case fatality after stroke [3,4], the demographic shift is expected to significantly increase the prevalence of stroke by 2030 [5]. Considering the distress caused to patients and families, in addition to the healthcare and social costs [6], the implementation of public health action plans seeking to redress the imbalance of healthcare expenses concentrated towards the last stages of life represents a research priority.
Traditional strategies to curb healthcare costs associated with stroke have predominantly focused on compensatory approaches aiming to alleviate the consequences of impairments instead of tackling their causal factors [7]. Nevertheless, with enhanced understanding of the pathophysiology and aetiology of the disease, there is robust evidence indicating that more than 90% of the stroke burden could be attributable to modifiable risk factors [8,9,10,11]. Importantly, whilst dietary risks accounted for 11 million diet-related deaths in 2017, of which cardiovascular diseases were the leading cause worldwide [12], the extant literature has primarily focused on dietary patterns such as the Mediterranean diet which has been heralded as one of the healthiest patterns exhibiting cardioprotective properties [13,14,15]. However, amidst the coincidental prominence of obesogenic environments promoting the intake of palatable foods rich in fat, salt and sugar with high intrinsic addictive properties, there is emerging evidence to now recognise addictive-like eating behaviours as a potentially major, yet understudied, concern in patients with stroke [16,17].
More recently, the concept of food addiction (FA), defined as an “addiction-like behaviour that develops in association with compulsive and consistent over-consumption of energy-dense foods”, has emerged as a relatively new approach to understanding problematic eating behaviours [18]. Though significantly limited, mounting evidence suggests that foods rich in fat, salt or sugar appear to overstimulate neurobiological reward pathways and increase the release of dopamine, thereby triggering FA [19,20,21]. However, beyond the higher prevalence of FA extensively studied in patients with eating disorders, obesity and/or major depressive disorders (MDD) [22,23,24], the concept of FA in stroke remains overlooked.
Therefore, the aims of this hospital-based study were to explore the prevalence and severity of FA and assess the associations between FA and the critical vascular risk factors for stroke, including hypertension, diabetes, obesity and dyslipidaemia [25].

2. Materials and Methods

2.1. Study population

This study included 101 patients consecutively hospitalised for the first stroke event at the Department of Neurology, Centre Hospitalier Universitaire (CHU) Bordeaux, between March and July 2019 and discharged to home. Individuals were considered eligible with the ability to answer self-report questionnaires and provide valid informed consent. Patients were not eligible if they had a disease affecting the nervous system (i.e. epilepsy, dementia), a severe cognitive impairment or psychiatric diagnosis (i.e. neurodevelopmental, schizophrenia spectrum), severe aphasia or a medical history which could influence study completion. The study was conducted in accordance with the latest version of the Declaration of Helsinki.

2.2. Assessment of Stroke Outcome

To evaluate the baseline neurologic outcome and stroke severity in patients, the National Institutes of Health Stroke Scale (NIHSS) was used. The NIHSS is a valid, reliable and responsive tool assessing domains of 1) consciousness, 2) eye movement, 3) visual fields, 4) facial palsy, 5) motor arm, 6) motor leg, 7) limb ataxia, 8) sensory, 9) language aphasia, 10) speech (dysarthria), and 11) hemi-inattention (neglect). Individual scores from each item are summed, with a minimum and maximum score of 0 and 42, respectively [26].

2.3. Determination of Food Addiction (FA) in Stroke Patients

FA was determined using the Yale Food Addiction Scale 2.0 (YFAS 2.0), as previously assessed by Gearhardt and colleagues [27] and validated for French-speaking individuals [28]. Briefly, the YFAS 2.0 is a self-reported questionnaire that is assumed to assess each of the 11 DSM-5 substance use disorders criteria as applied to the intake of food items high in fat, refined carbohydrates and/or sugar and salt, plus the associated clinically significant impairment or distress [29]. Although the wider body of the literature focuses primarily on the diagnostic scoring option of the scale (i.e., when the participant reports 2 or more symptoms plus clinically significant impairment or distress), this study also assessed the symptom count scoring option (number of FA symptoms endorsed, without the presence of clinically significant impairment or distress), as well as their proportion across the four domains of addiction symptoms: 1) Impaired control; 2) Risky use; 3) Pharmacological criteria and 4) Social impairment. For individuals endorsing a diagnosis of FA, severity thresholds were also specified (mild: 2–3 symptoms plus impairment or distress, moderate: 4–5 symptoms plus impairment or distress, severe: 6 or more symptoms plus impairment or distress). We complemented our approach on the severity of the phenotype by using a Global Addictive-like eating Profile Severity Index (GAPSI) (See statistical analyses, section 2.5.2).

2.4. Key Outcome

This study assessed the association of FA on four of the main vascular risk factors of stroke: obesity, diabetes, hypertension and dyslipidaemia, as independent and cumulative values. Body mass index (BMI) was measured using the standard formula (weight(kg)/(height(m2)) and categorised as underweight (BMI<18.5kg/m2), healthy (18.5-24.9kg/m2), overweight (25–29.9kg/m2), and obese (≥30kg/m2), according to the World Health Organisation’s criteria for Caucasian populations. Diabetes was diagnosed if the level of haemoglobin A1c (HbA1c) exceeded the cut-point of 48mmol/mol (6.5%), or if there was a previous medical history of the disease. Patients were considered as hypertensive if they presented a medical history of hypertension or if blood pressure exceeded 140/90 millimetres of mercury (mm Hg). Dyslipidaemia was defined based on fasting serum lipid profile values according to the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (ATP III) [30].

2.5. Statistical Analyses

2.5.1. Participant Characteristics

Descriptive data were summarised using means (standard deviation (SD)) for non-normally distributed continuous data. Two-group comparisons for each vascular risk factor (coded as binary indicators), demographic and clinical variables were performed using univariate analyses for non-normally distributed variable (Mann-Whitney U test, Pearson chi-square (χ2) or Fisher’s test for independent sample, as appropriate).

2.5.2. Severity of FA

To compute the GAPSI, the items were scored on a four-category ordered scale: 1= from “Never” to “Less than once a month”; 2= from “Once a month” to “Two to three times a month”; 3= from “Once a week” to “Two to three times a week” and 4= from “Four to six times a week” to “Everyday”. As the evaluation of each symptom encompasses several items, the categorisation was based on the one item endorsing the highest frequency score.
A principal component analysis (PCA) was then applied to all the 11 addiction-related items symptoms plus 1 item summarizing the associated clinically significant impact (distress and/or impairment), scored on the four-category ordered scale. The first component was used to build a weighted summary, or global severity index, for the addictive profile (see Supplementary Table 1). PCA scores reflect the combination of the symptoms weighted according to their contribution on the factor. Higher PCA scores reflect higher individual frequency scores on most of the symptom criteria. Restricted cubic splines (three knots) were used to take into account possible departure from linearity in the case of continuous predictors (age and BMI). The number of predictors was deliberately limited to those known from literature review and from univariate testing in order to conform to the 10-15 events per variable rule of thumb (even if the lowest minority class was 20 positive cases out of 98 in the case of diabetes). The parameters of those models were estimated using ridge (L2) penalization, whereby the tuning of model hyper-parameters (penalty) was performed using repeated 10-fold cross-validations (25 repetitions). The final solution chosen was the one that most minimized the cross-validated log-likelihood on the optimal penalty parameter.
Parameter estimates corrected for overfitting were obtained using bootstrap resampling (B=200): for each bootstrap sample, the same regression model was used, and the accuracy measure of each parameter was compared to that found in the original sample, and a final optimism corrected estimate was computed by averaging those apparent biases over all bootstrap iterations. Calibration of each regression model was assessed by comparing predicted probabilities with observed frequencies for the corresponding outcome. The pseudo R-squared value and Somer’s D index were used to assess the goodness of fit of each model.

2.5.3. Associations Between Cumulative Vascular Risk Factors and Individual Food Addiction (FA) Symptom

Each vascular risk factor was scored, and the sum of vascular risk factors was treated as ordered categorical variables, ranging from 0 to 4 (i.e., hypertension (score 1), obesity (score 1), diabetes (score 1), and dyslipidaemia (score 1)). Associations between the number of vascular risk factors, the severity of FA symptoms and symptom counts were then analysed using Spearman’s rank correlation coefficient, which does not assume a normal distribution of the variables. The null hypothesis (H0) was tested, with Spearman’s rank correlation coefficient equal to 0. The alternative hypothesis was that the correlation coefficient was different from 0. The null hypothesis (H0) was rejected at p < 0.05 (α = 0.05). To control for age and sex as potential confounders based on our univariate analyses, partial correlations were also performed. In these analyses, given that multicollinearity between age and sex may obscure the computation because of the overlapping correlation, each confounder was analysed separately.
All statistical tests were performed using R statistical software (version 4.1.0) and by considering a fixed type I error rate of 5%. Multiple comparisons were controlled using a stepwise FDR correction.

3. Results

3.1. Description of the Study Population

Participant characteristics (n=101), captured at baseline, and classified according to the presence of vascular risk factors (diabetes, obesity, hypertension, dyslipidaemia), are presented in Table 1. The study sample was predominantly males (60.4%), on average overweight (BMI: mean (SD), 27.6 (5.77) kg/m2) and had a mean (SD) age of 62.8 (13.7) years. Among all stroke cases, 86.1% were identified as ischemic stroke patients, 5% as haemorrhagic stroke patients and 8.9% were diagnosed with transient ischaemic attack (TIA). The mean (SD) NIHSS score was at 2.31 (3.13) points for the overall sample, representing a minor stroke severity as per the stroke scale [31].

3.2. Prevalence of Food Addiction (FA) Diagnosis and Symptoms

Table 2 shows the prevalence of patients with a diagnosis of FA and the detailed prevalence of addictive-like eating behaviours by symptoms endorsed. In this study, 5% (n=5) of participants, all with ischemic stroke, endorsed a diagnosis of FA. Interestingly, whereas the endorsement of either clinical distress or impairment is necessary to establish a FA diagnosis, these factors were only experienced by 3.0 % and 7.9% of patients respectively, thereby corresponding with the overall low prevalence of FA in this sample. However, 23.8% of patients screened positive for at least two of the symptom criteria and 9.0 % of patients endorsed at least four symptoms. The most prevalent symptoms (i.e., > 10%) were “Inability to cut down” (16.8%), “Use in physically hazardous situations” (15.8%), “Use despite social/interpersonal problems” (12.9%), “Feelings of withdrawal symptoms” (11.9%), and “Tolerance” (10.9%). When combined under specific domains, “Impaired control” was the most prevalent domain, with 25.7% of patients experiencing at least one symptom of the domain.

3.3. Food Addiction (FA) Diagnosis, its Severity and the Association with Vascular Risk Factors

Table 3 shows the characteristics of patients who met the diagnosis of FA. The mean (SD) age of the patients with FA was 57.0 (13.7) years, with three individuals out of five living with obesity and being females. Four out five of the patients experienced severe FA, with only one patient suffering from mild FA but living with multi-morbidities including obesity, hypertension, dyslipidemia and diabetes (Patient 1). The GAPSI score was at 2.7 in the patient with mild FA and ranged from 5.2 to 7.2 in those experiencing severe FA.
As shown in Table 4, dyslipidemia was the only vascular risk factor significantly associated with the FA diagnosis (p=0.04), with four out five of patients with a FA diagnosis having dyslipidemia, whereas diabetes was present in only one patient. Higher GAPSI scores were also associated with dyslipidemia (p=0.016) and diabetes (p=0.038) in univariate comparisons. However, GAPSI scores were independently associated with dyslipidemia only (p=0.05; OR=1.25; 95%CI=1.00-1.56) (See Supplementary Material for the detailed regression models with each vascular risk factor).
The correlation coefficients as well as p-values are presented in Table 5. The relationship between the total number of symptoms endorsed and the number of vascular risk factors was significant only in univariate analyses. When analysing the severity of each specific symptom criteria, there were no significant associations observed with the number of vascular risk factors and most of the FA symptoms, except for (i) Time spent, (ii) Tolerance and (iii) Use despite adverse emotional or physical consequences. These correlations remained significant after controlling for age and sex.

4. Discussion

4.1. Principal Findings

To the best of our knowledge, this was the first study to investigate the prevalence of FA and the association of addictive-like eating patterns in the presence of vascular risk factors in a sample of adults with a first-ever mild stroke event. The diagnostic threshold for FA (i.e., two or more symptoms plus clinically significant impairment or distress) was only met by 5% of participants. From this sub-sample group, dyslipidaemia was the only vascular risk factor which was significantly associated with a diagnosis of FA, and the severity of the addictive profile was independently associated with dyslipidemia only. Concerning the relationship between vascular risk factors and the severity of FA symptoms in the overall sample, significant associations were noted between the number of vascular risk factors and the severity of Time spent, Tolerance, and Use despite adverse consequences symptom criteria, both in univariate and multivariate analyses. However, the number of vascular risk factors and total number of symptoms endorsed were significantly correlated in univariate analyses only.

4.2. Prevalence of Food Addiction (FA) Diagnosis and Comparison with Other Studies

Compared with the average prevalence of FA reported in the general French-speaking population at 8.2% (28), our study reported a slightly lower percentage of participants who met the diagnostic threshold for FA at 5%. One potential explanation could pertain to the low endorsement of both clinical distress and impairment (clinical distress: 3%; clinical impairment: 7.9%), either of which is required to establish a FA diagnosis. Indeed, whereas clinical impairment or distress has more often been reported among young females [32,33] and people living with obesity [34,35], our overall study sample was predominantly males (60.4%), in their later adulthood stages at the mean (SD) age of 62.8 (13.7) years, and with a mean (SD) BMI representing overweight at 27.6 (5.77) kg/m2. In this context, the demographics of our overall study sample may have precluded a diagnosis of FA in many participants. This finding could in effect be substantiated by the fact that our subsample of patients with a FA diagnosis was on average 5 years younger than the overall sample and of whom two were females and with severe FA (Table 3). However, given that 23.8% of participants screened positive for at least two FA symptoms, the use of the YFAS 2.0 questionnaire solely as a diagnostic scoring option appears reductive in the context of stroke considering that the types and number of symptoms with and without the endorsement of clinical distress or impairment criterion could be informative for research and treatment [36], as discussed in Section 4.3.

4.3. Endorsement Rates for Food Addiction (FA) Symptom Criteria in the Overall Sample

Overall, 38.6% of the patients screened positive for at least one of the 11 FA symptom criteria (i.e without clinical impairment or distress). Consistent with other studies [33,37,38], the most frequent symptom endorsed by the current sample was “Inability to cut down”. One potential explanation underpinning this common finding across cohorts may pertain to the acquired benefits from the ubiquity of low-cost, energy-dense, and palatable ultra processed foods (UPFs) and the abundance of external cues recalling triggers of snacking behaviours [39]. Although UPFs form are a highly heterogeneous food category and also include low-calorie products, products "fortified" with beneficial nutrients or frozen meals [40], the majority of UPFs are high in energy density, salts, sugars, trans fats as well as additives [41]. Mechanistically, preclinical and human studies have demonstrated that a repeated intake of UPFs triggers addiction-like biological (e.g., dopaminergic sensitisation) and behavioural responses (e.g., tolerance, withdrawal, use despite consequences) through the pharmacokinetic properties of rewarding ingredients (e.g., fatty-salty and fatty-sugary) that are rapidly absorbed by the system [42]. However, in the absence of a detailed analysis regarding the amount of high-fat or high-sugar foods consumed in this study sample, these explanations, though insightful, are only speculative.

4.4. Association Between Food Addiction (FA) Diagnosis and Vascular Risk Factors

In this case series (n=5 with a FA diagnosis), there were no robust associations between FA diagnosis and diabetes, and/or obesity, and/or hypertension. Nevertheless, the overall sample size could have limited the statistical power. Clinically, given that it has been showed that type 2 diabetic patients with a FA diagnosis are mostly in the age range of 20–29 years [43], it is possible that associations between a FA diagnosis and vascular risk factors may be stronger in younger stroke survivors. Although more needs to be understood about developmental changes in healthy and unhealthy food preferences [44], as several studies have shown the age-related effects on the central regulation [45,46]. Compared to older-aged adults, younger individuals have been shown to be more motivated by food preferences, to experience greater food craving for unhealthy foods, and to have increased striatal sensitivity to food stimuli, all of which corresponded with an imbalanced development of the prefrontal cortex and limbic system [47]. Importantly, besides the potential long-term neurological repercussions in younger individuals [48], these connections between brain health and dietary intake also parallel the premature onset of vascular risk factors for cardiovascular diseases [49,50,51]. To this end, the lack of significant relationship in our study sample could be due to the fact that the patients of this study belonged primarily to Generation X and Boomers II cohorts and therefore, might have been less exposed to calorie-dense environments in their earlier stages of life.
Nevertheless, whilst the possibility of a type I error cannot be negated with regards to the limited sample size, FA and dyslipidaemia were significantly associated in this study sample. Despite not meeting the criteria for a FA diagnosis, similar results were reported by Nelder and colleagues whereby YFAS symptoms were significantly associated with triglyceride levels, and inversely associated with high-density lipoproteins (HDL) levels in a larger study sample of healthy adults [52]. Mechanistically, the associations between FA and dyslipidaemia could be supported by preclinical studies whereby prolonged high-fat intake in rodents has been shown to reduce mesolimbic dopamine (DA) function nucleus accumbens (NAc) DA overflow besides modulating striatal neuroadaptive responses and blunting DA reuptake in the NAc [53,54,55,56]. Importantly, although evidence for causality remains enigmatic, these observations could be potentially problematic considering the co-existence of diabetes, obesity and hypertension with dyslipidaemia [57,58,59]. However, given that our own results indicated no significant correlation between the number of vascular risk factors and symptom counts in controlled analyses, and no significant association between the number of vascular risk factors and most FA symptoms in multivariate analyses (except for Time spent, Tolerance and Use despite adverse consequences), larger studies and longer-term studies are warranted to improve understanding of the impact of such eating behaviours on health, as well as the direction of association between FA and vascular risk factors.

4.6. Strengths and Limitations of this Study

Even with multivariable adjustments, unmeasured, measurement error-related, or residual confounding cannot be excluded in our observational design. We followed a robust inclusion and exclusion criteria, but the final study sample lacked age and sex diversity. Importantly, whereas stroke and FA association may differ by ethnicity, genetic composition and physical activity level [60,61,62], our study did not measure these potential key confounders. Future studies with larger and more diverse samples, and including a detailed analysis regarding the amount of high-fat or high-sugar foods consumed, are needed to conclude for an association between a diagnosis of food addiction and vascular risk factors. Regarding the evaluation of at risk lifestyle behaviours among stroke population, the YFAS 2.0 questionnaire being self-reported, the possibility of recall and social desirability bias cannot be excluded. However, given the high validity of the YFAS 2.0 from previous studies worldwide and in French-speaking populations [28], we believe it could be used to optimise stroke secondary prevention and identify patients with specific unmet needs regarding unhealthy eating patterns in their care pathways. Finally, due to the cross-sectional design of the study, our statistical results inherently possess limitations that preclude causal interpretations.

5. Conclusion

Our study highlighted the potential importance of considering addictive-like eating behaviours as part of the management of stroke patients. From the evaluation of the extant literature, our study findings emphasise a research gap pertaining to such eating patterns in people with stroke and multiple vascular risk factors. Clearly, the growing number of studies establishing associations between FA and younger age, obesity, or eating disorders, point to taking into account such disordered eating behaviors for the improvement of post-stroke primary and secondary prevention. Importantly, even if only 5% of participants endorsed a FA diagnosis in the present study, the prevalence of two or more FA symptoms observed in 23.8% of the study sample calls for the broader use of the YFAS 2.0 tool beyond its diagnostic purposes. However, to better inform policies and achieve precision medicine, future prospective studies should prioritise the investigation of the prevalence and severity of FA and its symptoms in larger and more diverse samples of stroke patients.

Author Contributions

Conceptualization, IS and SB; methodology, IS, SB, and CL; formal analysis, BAS, YR, CL, and SB; investigation, YR and SB; data curation, BAS, YR, SB; writing—original draft preparation, BAS, IS, SB; writing—review and editing, BAS, YR, CL, IS, and SB; supervision, IS and SB; project administration, IS and SB; funding acquisition, Not Applicable. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding. Yolaine Rabat was a PhD fellow funded by the Ministry of Research and Higher Education. Bibi Aliya Seelarbokus is a PhD student funded by the Neurocampus International Graduate Program managed by the Agency Nationale de la Recherche (French National Research Agency) under reference ANR-17-EURE-0028. .

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and received local ethical board approval (CER-BDX-2022-04).

Data Availability Statement

The data presented in this study are available upon reasonable request from the corresponding author. The data are not publicly available due to specifications from the study sponsor.

Acknowledgments

The authors would like to thank all patients and the staff from Bordeaux University Hospital Neurovascular Unit for their kind contribution to this research.

Conflicts of Interest

The authors declare no conflicts of interest. Igor Sibon has served as an advisor for Servier and Boehringer Ingelheim, has received teaching honoraria from Medtronic and Bayer. The French Ministry of Research and Higher Education had no role in the design, execution, interpretation, or writing of the study.

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Table 1. Characteristics of all study participants (n=101) and univariate between-group comparisons.
Table 1. Characteristics of all study participants (n=101) and univariate between-group comparisons.
Hypertension Diabetes Dyslipidaemia Obesity
Characteristics Overall HTN- (n=49) HTN+ (n=52) P-value,
(ES) 		DIA- (n=80) DIA+ (n=21) P-value, (ES) DYS- (n=67) DYS+ (n=34) P-value, (ES) OB- (n=71) OB+ (n=29) P-value, (ES)
Sex, % men (n) 60.40 (61) 61.20 (30) 59.60 (31) 0.870
(0.387)		 56.30 (45) 76.20 (16) 0.096 (0.165) 56.70 (38) 67.60 (23) 0.288 (0.106) 63.40 (45) 51.70 (15) 0.320
(-0.099)
Age, years (mean (SD)) 62.80 (13.7) 56.30 (14.0) 68.90 (10.1) <0.001
(-0.461)		 61.70 (14.7) 66.80 (7.80) 0.215
(-0.124)		 60. 60 (14.6) 67.10 (10.6) 0.032
(-0.214)		 61.40 (14.8) 66.40 (9.87) 0.305
(-0.102)
NIHSS (mean (SD)) 2.31 (3.13) 1.84 (2.94) 2.75 (3.30) 0.014
(0.370)		 2.34 (3.42) 2.19 (1.66) 0.151 (0.253) 2.06 (2.48) 2.79 (4.13) 0.470 (0.403) 2.13 (3.08) 2.83 (3.29) 0.063 (0.381)
BMI, kg/m2 (mean (SD)) (n=100) 27.60 (5.77) 26.00 (5.02) 28.90 (6.22) 0.009
(-0.260)		 27.10 (5.52) 29.00 (6.75) 0.034
(-0.212)		 26.60 (5.07) 29.20 (6.86) <0.001
(0.807) 		24.60 (2.96) 34.50 (5.01) <0.001
(-0.777)
Comorbidities
Obesity, % (n) (n=100) 29.00 (29) 18.40 (9) 39.20 (20) 0.022 (0.629) 26.60 (21) 38.10 (8) 0.310 (0.572) 23.90 (16) 39.40 (13) 0.110 (0.886)
Dyslipidaemia, % (n) 33.70 (34) 14.30 (7) 51.90 (27) <0.001
(0.452)		 26.30 (21) 61.90 (13) 0.002 (0.750) 28.20 (20) 44.80 (13) 0.110 (0.773)
Diabetes, % (n) 20.80 (21) 6.10 (3) 34.60 (18) <0.001
(0.376)		 11.90 (8) 38.20 (13) 0.002 (0.763) 18.30 (13) 27.60 (8) 0.301 (0.763)
Hypertension, % (n) 51.50 (52) 42.50 (34) 85.70 (18) <0.001
(0.654)		 37.30 (25) 79.40 (27) <0.001
(0.950)		 43.70 (31) 69.0 (20) 0.022 (0.747)
HYP,DIA,DYS, OBE - : patients without hypertension, diabetes, dyslipidemia, obestity; HYP,DIA,DYS, OBE + : patients with hypertension, diabetes, dyslipidemia, obesity; ES- effect size; BMI: body mass index; BMI* : n= 100; NIHSS: National Institute of Health Stroke Scale; SD: Standard deviation.
Table 2. Prevalence of Food Addiction (FA) diagnosis and detailed symptom count (n=101).
Table 2. Prevalence of Food Addiction (FA) diagnosis and detailed symptom count (n=101).
Prevalence, % (n)
FA diagnosis
Two or more symptoms plus clinically significant distress or impairment 5.0 (5)
Symptom criteria
(1) Inability to cut down 16.8 (17)
(2) Physically hazardous situations 15.8 (16)
(3) Interpersonal problems 12.9 (13)
(4) Withdrawal 11.9 (12)
(5) Time spent 10.9 (11)
(6) Tolerance 8.9 (9)
(7) Loss of control 6.9 (7)
(8) Adverse consequences 5.9 (6)
(9) Craving 5.9 (6)
(10) Impaired daily functioning 5.0 (5)
(11) Activities given up 4.0 (4)
Clinical significance
Distress 3.0 (3)
Impairment 7.9 (8)
Domains (symptom criteria)
Impaired control (symptoms 1,5,7,9) 25.7 (26)
Risky use (symptoms 2,8) 18.8 (19)
Pharmacological criteria (symptoms 4,6) 16.8 (17)
Social impairment (symptoms 3,10,11) 16.8 (17)
Symptom count
No symptom 61.4 (62)
One symptom endorsed 14.9 (15)
Two symptoms endorsed 5.9 (6)
Three symptoms endorsed 8.9 (9)
Four symptoms endorsed 2.0 (2)
Five symptoms endorsed 2.0 (2)
Six symptoms endorsed 2.0 (2)
Seven symptoms endorsed 2.0 (2)
Table 3. Characteristics of patients with a Food Addiction (FA) diagnosis.
Table 3. Characteristics of patients with a Food Addiction (FA) diagnosis.
Characteristics Patient 1 Patient 2 Patient 3 Patient 4 Patient 5
Sex Male Female Male Female Female
Age, y 63 38 64 48 72
NIHSS 3 7 11 1 2
Weight status Obese Healthy Obese Healthy Obese
BMI, kg/m2 31.6 24.4 40.8 21.3 37.2
Hypertension Yes No No No Yes
Diabetes Yes No No No No
Dyslipidaemia Yes Yes Yes No Yes
FA severity Mild Severe Severe Severe Severe
GAPSI 2.7 7.2 6.7 5.2 11.2
NIHSS: National Institute of Health Stroke Scale; BMI: Body mass index; GAPSI: Global Addictive-like eating Profile Severity Index.
Table 4. Associations between Food Addiction diagnosis by vascular stroke risk factor and the Global Addictive-like eating Profile Severity Index (GAPSI).
Table 4. Associations between Food Addiction diagnosis by vascular stroke risk factor and the Global Addictive-like eating Profile Severity Index (GAPSI).
Vascular risk factors
Hypertension Diabetes Dyslipidaemia Obesity
Characteristics Overall HTN- (n=49) HTN+ (n=52) P-value DIA- (n=80) DIA+ (n=21) P-value DYS- (n=67) DYS+ (n=34) P-value OB- (n=71) OB+ (n=29) P-value
FA diagnosis, % (n) 5 (5) 40 (2) 60 (3) 0.671 80 (4) 20 (1) 1.000 20 (1) 80 (4) 0.043 40 (2) 60 (3) 0.151
GAPSI, Mean (SD) -3.05-15 (2.23) -0.18
(2.25)		 0.17
(2.23)		 0.150 -0.16
(2.28)		 0.60
(1.96)		 0.038 -0.37
(1.77)		 0.72
(2.83)		 0.016 -0.31
(1.81)		 0.70
(2.96)		 0.090
GAPSI: Global Addictive-like eating Profile Severity Index3.4. Associations between the food addiction (FA) symptom criteria and vascular risk factor scores.
Table 5. Correlations between the number of vascular risk factors (0-4) and the symptom count, and the severity of each symptom criteria. Values presented are correlation coefficients, ρ (rho), and p-value.
Table 5. Correlations between the number of vascular risk factors (0-4) and the symptom count, and the severity of each symptom criteria. Values presented are correlation coefficients, ρ (rho), and p-value.
Association with number of vascular risk factors
Unadjusted,
ρ (p-value)		 Adjusted for age,
ρ (p-value)		 Adjusted for sex,
ρ (p-value)
Number of symptoms endorsed 0.265 (0.007) 0.194 (0.055) 0.174 (0.083)
Severity of symptoms criteria
[1] Inability to cut down 0.028 (0.782) -0.009 (0.928) 0.023 (0.817)
(2) Physically hazardous situations -0.042 (0.680) -0.004 (0.968) -0054 (0.597)
(3) Interpersonal problems -0.113 (0.261) -0.057 (0.575) -0.116 (0.249)
(4) Withdrawal 0.087 (0.387) 0.021 (0.833) 0.051 (0.616)
(5) Time spent 0.219 (0.028) 0.212 (0.035) 0.236 (0.018)
(6) Tolerance 0.260 (0.009) 0.343 (<0.001) 0.319 (0.001)
(7) Loss of control 0.132 (0.189) 0.041 (0.684) 0.021 (0.837)
(8) Use despite adverse consequences 0.230 (0.020) 0.251 (0.012) 0.214 (0.033)
(9) Craving 0.072 (0.472) 0.094 (0.357) 0.059 (0.558)
(10) Impaired daily functioning -0.065 (0.518) 0.010 (0.922) -0.024 (0.813)
(11) Activities given up 0.081 (0.419) 0.108 (0.287) 0.103 (0.307)
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