anorexia nervosa case study ppt

“I’m Not Hungry:” Bodily Representations and Bodily Experiences in Anorexia Nervosa

• Published: 30 May 2024

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• Mara Floris   ORCID: orcid.org/0000-0003-0956-063X 1 &
• Matteo Panero   ORCID: orcid.org/0000-0002-9385-0332 2  

Anorexia Nervosa (AN) is a psychiatric illness that presents a complex variety of perceptual alterations and somatic sensations. These alterations occur at the level of (1) bodily representations and (2) bodily experiences. The alterations are widespread, and they involve multiple cognitive functions. We reviewed the current literature linking the psychiatric literature on AN with the philosophical debate on the Cognitive Penetrability of Perception (CPP). We describe the alterations in perception, starting from the most widespread and studied, i.e., those concerning distortions in the estimation of the dimensions of one's body (“dysmorphophobia” or “body image disturbance”) to then describe those of more recent analysis involving alterations in bodily experiences. Body image disturbances in AN have been linked to alexithymia, emotional dysregulation, and altered interoceptive awareness, highlighting the complex interaction between emotion, cognition, and perception in AN. We show how the recent debate on CPP can benefit from the empirical investigations in AN, and can, in turn, serve to outline new lines of research.

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Floris, M., Panero, M. “I’m Not Hungry:” Bodily Representations and Bodily Experiences in Anorexia Nervosa. Rev.Phil.Psych. (2024). https://doi.org/10.1007/s13164-024-00735-y

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Anorexia Nervosa: A Case Study

Oct 04, 2014

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Anorexia Nervosa: A Case Study. By: Colleen Shank Sodexo Dietetic Intern April 30, 2014. Presentation of Anorexia Nervosa.

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Anorexia Nervosa: A Case Study By: Colleen Shank Sodexo Dietetic Intern April 30, 2014

Presentation of Anorexia Nervosa • “Up to 24 million people of all ages and genders suffer from an eating disorder (anorexia, bulimia and binge eating disorder) in the U.S (The Renfrew Center Foundation for Eating Disorders)” • “Only 35% of people that receive treatment for eating disorders get treatment at a specialized facility for eating disorders” (Noordenbox, 2002)

Presentation of Anorexia Nervosa • “A review of nearly fifty years of research confirms that anorexia nervosa has the highest mortality rate of any psychiatric disorder” (Arcelus, Mitchell, Wales, & Nielsen, 2011) • “20% of people suffering from anorexia will prematurely die from complications related to their eating disorder, including suicide and heart problems” (The Renfrew Center Foundation for Eating Disorders)

Presentation of Anorexia Nervosa Overview of how one may suffer from AN: Body image distortion Restrictive intake and or binging/purging Excessive exercise Severe weight loss Fear of becoming fat Physiological changes Psychological changes

Presentation of Anorexia Nervosa Two types: • Restricting type • Energy intake is restricted • Binge-eating/purge type • Vomiting • Excessive exercising • Both types suffer from fear of gaining weight

Presentation of Anorexia Nervosa Diagnosis criteria: DSM-5 • Restriction of energy intake relative to requirements leading to a significantly low body weight in the context of age, sex, developmental trajectory, and physical health. • Intense fear of gaining weight or becoming fat, even though underweight. • Disturbance in the way in which one's body weight or shape is experienced, undue influence of body weight or shape on self-evaluation, or denial of the seriousness of the current low body weight The Alliance for Eating Disorders

Presentation of Anorexia Nervosa Types of Questions: • Gender, height, weight • How often one feels, experiences, likes, or avoids certain things • Avoiding foods when hungry, feeling guilty after eating, eat diet foods, etc. • How often one partakes in certain behaviors • Vomiting, binging, and exercising Screening Tools: EDI-3 Eat-26 • Can be given by health Care professionals • Can be accessed online • Can help assess risk • Do not diagnose eating disorders

Presentation of Anorexia Nervosa Physical Signs & Symptoms: • Weight loss • Tiredness • Thinning hair • Hair loss • Dry skin • Swelling of arms/legs • Lanugo • Intolerance to cold

Presentation of Anorexia Nervosa Internal Changes: • Body systems are affected • Examples: cardiovascular, neuroendocrine, renal, and gastrointestinal systems • Slow heart rate • Anemia • Stomach gets smaller • Constipation • Dehydration • Amenorrhea • Osteoporosis • Hypothermia • Hypotension

Presentation of Anorexia Nervosa Psychological Signs & Symptoms: • Not wanting to eat • Fear of weight gain • Extreme exercise • Depression • Preoccupation with food • Lying • Lack of social interaction

Presentation of Anorexia Nervosa Tests/Labs: • CBC • Electrolytes • Total protein • Minerals • H/H • Glucose • B12 • Etc. Tests/Labs: • Height, weight, BMI • Look at • Heart • Liver • Kidneys • Bones • Thyroid • Etc.

Presentation of Anorexia Nervosa Examples of Abnormalities: • Abnormal lipoprotein profile • Low zinc • Low vitamin B-12 • Alkalosis • Low chloride and potassium • Elevated bicarbonate • Hypomagnesmia • Hypophosphatemia • Lymphocytosis • Low resting metabolic rate • Mitral valve prolapse

Presentation of Anorexia Nervosa Treatment: • Requires a team • Physician, Psychologist/Psychiatrist, RD • Not all treatment plans are the same • Everyone needs a treatment plan specific to them • Inpatient, outpatient, both

Presentation of Anorexia Nervosa Treatment: Psychological • Different types of therapy • CBT • IPT • SSCM • Research? Treatment: Psychological • One-on-one • Group • Family • Discover underlying issues

Presentation of Anorexia Nervosa Treatment: Pharmacotherapy • Not to treat AN specifically • Used to treat underlying issues • Antidepressants, antipsychotics • Olanzapine, Fluoxetine, Prozac, Risperidone • Research? • Can drugs help improve weight gain?

Presentation of Anorexia Nervosa MNT: AND Position Paper • “Nutrition intervention, includingnutrition counseling by a registered dietitian, is an essential component of the team treatment of patients with anorexia nervosa, bulimia nervosa, and other eating disorders during assessment and treatment across the continuum of care”

Presentation of Anorexia Nervosa MNT: RDs Role • Assess the patient • Determine nutrition risks • Define nutrition diagnosis • Identify nutrition intervention • Write nutrition prescription • Define nutritional goals

Presentation of Anorexia Nervosa MNT: RD Assessment • What is important to assess? • Of course the RD will assess physical signs and symptoms but there are other things that should be included in their assessment of the patient • Current dietary intake • Present eating patterns • History related to foods • Nutrient deficiencies • Supplement use • Risk of refeeding syndrome

Presentation of Anorexia Nervosa Treatment: Current Guidelines • Intake recommendations • Calculating needs • Kcal • Starting point • Increase by 100-200kcals • Macronutrients • CHO: 50-55% • PRO: 15-20% • Fat: 25-30% • Micronutrients? • Weight gain • Differences between in and out patient settings • Increase in kcal needs

Presentation of Anorexia Nervosa Treatment: Refeeding Syndrome • Refeeding a starved patient • Clinical implications • Low Mg, K, P • Thiamine deficiency • Must be aware of the affects • Must follow protocol to help prevent refeeding • Monitor electrolytes and fluids

Presentation of Anorexia Nervosa Treatment: Nutrition Support • Need for nutrition support depends on needs of the patient • PN should only be used when medically necessary

Presentation of C.H. Basics: • Age: 56 • Sex: Female • Lives at home with her mother and sister • Dates of hospital stay: January 15, 2014-February 14, 2014 • Date transferred to Manor Care: February 14, 2014

Presentation of C.H. Hospital Stay: • Dx: FTT secondary to malnutrition, Pancytopenia, Hypothermia related to malnutrition, Bradycardia related to hypothermia, and Hypotension related to dehydration • PMH: Anorexia, Anemia

Presentation of C.H. Hospital Stay: • Reason for going to ER: inability to ambulate and weakness • Vital 1.5 • 3 day calorie count • Labs: Labs: BG 49, HGB 3.7, Creatinine 0.67, BUN 60 • Per patient: • Reported that weight loss started several months ago • No menstruation anymore • No diarrhea, blood in the stool • Was on iron pill but stopped taking due to negative side effects • Has struggled with weight since age 11

Presentation of C.H. Manor Care: • Admit dx: FTT, (GERD), Refeeding Syndrome, Pancytopenia, and History of intussusception • Her admission note states she was "in an anorexic and malnourished state" • Admit weight 76.6#, Height 62.0”, BMI 14.0 • Stage 3 gluteal wound • Left hip wound

Presentation of C.H. Manor Care: • No smoking, drinking, drug use history • February 18, 2014 • AOA involved • Mother and sister were not allowed to bring in food to patient

Presentation of C.H. Manor Care: Plan • Physical and occupational therapy • Continue current diet, supplements, folic acid, MVI, zinc, labs as scheduled • Follow up with GI at the hospital as scheduled • Wound: local care with santyl, daily dressing change/pressure relief, nutritional support

Presentation of C.H. • Ca: 8.9 • Alb: 3.6 • Total pro: 6.3 • GFR: >60 • WBC: 6.6 • RBC: 3.96 L • HGB: 9.3 L • HCT: 31.3 L • MCV: 79.1 L • MCH: 23.4 L Manor Care: • Labs from February 21, 2014 • Random glucose: 78 • BUN: 12 • Creat: 0.40 • K: 4.2 • NA: 136 • AST: 21 • ALT: 30 • Alkphos: 66 • Total bilirubin: 0.3

Presentation of C.H. Manor Care: Medications • Cholecalciferol 2000 unit po daily • Heparin 5000 units SQ • Folic acid 1mg po daily • MVI po daily • Protonix 40mg po daily • Zinc sulfate 220mg po daily • As needed: Miralax, Colace, Tylenol, MOM, Dulcolax, • Ferrous liquid 220g po daily (added at a later date 3x/week)

Presentation of C.H. Manor Care: • On admission was placed on gluten intolerance diet and enhanced food • Prior to RD assessment • Was later changed to a regular diet • No history of Celiac Disease

Presentation of C.H. Manor Care: RD Assessment • February 19, 2014 • Current weight 77.2#, BMI 14.1 • Interview • Pt prefers “plain foods” • Pt reports allergy to guar gum • Consumption of meals 75-100% • Eats meals slowly (1-1.5hours) • No diarrhea, constipation, steatorrhea, communication, dental/oral, or functional problems noted

Presentation of C.H. Manor Care: RD Assessment • Calculated needs (with IBW 110#: • 35kcal/kg = 1750kcal/day • 1.5g/kg pro= 75g/day • 30mL/kg fluid= 1500mL/day • Diet order: Regular diet, Supplement TID • No longer giving enhanced foods due to pt liking plain foods • Recommendations: weekly CMP, CBC, P, Mg, LFTs, iron supplement

Presentation of C.H. Manor Care: • Weekly weights • 2/14/14 76.6# • 2/18/14 77.2 # • 2/24/14 77.6# • 3/4/14 82 #

Presentation of C.H. Manor Care: Med Options Assessment • Mental health evaluation (2 visits) • Main issue: AN • Patient has difficulty with mood functioning, behavioral functioning, and lack of insight • "I am not an anorexic" • "I do eat- I like food but I have a difficult time keeping the weight on"

Presentation of C.H. Manor Care: My interaction with C.H • Usual intake • 3 meals per day (breakfast, lunch, and dinner) as well as snacks in between meals • UBW: 110-115# • Since she has been sick she reports her weight has been 85-90# • States she does not usually keep track of weight • Reports she could feel she was losing weight when she started getting sick • Reports when she was taking her iron pill that would help her gain weight

Update on C.H. • Was d/c on March 4, 2014 • D/c to home with mother and sister • No further info on AOA • Weight at d/c 82#

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Treatment Basics. NEDA. https://www.nationaleatingdisorders.org. Accessed April 4, 2014. • Eating Disorders. How can a psychologist help someone recover? APA. https://www.apa.org/. Revised October 2011. Accessed April 10, 2014. • Le Grange, D., Lock, J. Family-based Treatment of Adolescent Anorexia Nervosa: The Maudsley Approach. Maudsley Parents. http://www.maudsleyparents.org/whatismaudsley.html. Accessed April 10, 2014. • DeAngelis, T. Promising Treatments for anorexia and bulimia. Monitor on Psychology. March 2002; 33 (3): 38. http://www.library.illinois.edu/learn/research/citation/ama.html. Accessed April 10, 2014. • Schmidt U, Oldershaw A, Jichi F, et al. Out-patient psychological therapies for adults with anorexia nervosa: randomised controlled trial. The British Journal of Psychology. 2012, (201):392-399. DOI: 10.1192/bjp.bp.112.112078. Accessed April 10, 2014. • Carter, F, Jordan, J, McIntosh, V. V.W, et al. The long-term efficacy of three psychotherapies for anorexia nervosa: A randomized, controlled trial. Int. J. Eat. Disord. 2011; (44): 647–654. DOI: 10.1002/eat.20879. Accessed April 10, 2014.

YagerJ, Devlin M, Halmi K, et al. Guideline Watch: Practice Guideline for the Treatment of Patients with Eating Disorders. 3rd ed. APA. 2012. http://psychiatryonline.org/pdfaccess. Accessed April 10, 2014. • Mickley D. Medication for Anorexia Nervosa and Bulimia Nervosa. Eating Disorders Recovery Today. 2004; 2(4). http://www.eatingdisordersrecoverytoday.com. Accessed April 11, 2014. • Attia E, Kaplan A, Walsh B, et al. Olanzapine versus placebo for out-patients with anorexia nervosa [Abstract]. Psychological Medicine. 2011; 41(10): 2177-2182. DOI: http://dx.doi.org/10.1017/S0033291711000390 Accessed April 11, 2014. • Hagman J, Gralla J, Sigel E, et al. A Double-Blind, Placebo-Controlled Study of Risperidone for the Treatment of Adolescents and Young Adults with Anorexia Nervosa: A Pilot Study. JAACAP. 2011; 50(9): 915-924. DOI:10.1016/j.jaac.2011.06.009. • Walsh T, Kaplan A, Attia E, et al. Fluoxetine After Weight Restoration in Anorexia NervosaA Randomized Controlled Trial. JAMA. 2006;295(22):2605-2612. DOI:10.1001/jama.295.22.2605. • Ozier A, Henry B. Position of the American Dietetic Association: Nutrition Intervention in the Treatment of Eating Disorders. JADA. 2011;111:1236-1241. http://www.eatright.org/ Accessed April 11, 2014.

Waterhous T, Jacob M. Practice Paper of the American Dietetic Association: Nutrition Intervention in the Treatment of Eating Disorder. ADA. 2011; 11(8): 1261. http://www.eatright.org/ Accessed April 10, 2014. • Parent Toolkit. NEDA. 47. http://www.nationaleatingdisorders.org/sites/default/files/Toolkits/parenttoolkit/. Accessed April 11, 2014. • Anorexia Nervosa Nutrition Prescription. Academy of Nutrition and Dietetics Nutrition Care Manual. http://www.nutritioncaremanual.org. Accessed April 10, 2014. • Schebendach J. Nutrition in Eating Disorders. In: Mahan LK, Escott-Stump S. Krause’s Food & Nutrition Therapy. St. Louis, MO; Saunders Elsevier; 2008: 563-586.  • Anorexia Nervosa Nutrition Support. Academy of Nutrition and Dietetics Nutrition Care Manual. http://www.nutritioncaremanual.org. Accessed April 11, 2014. • Robb A, Silber T, Orrell- Valente J, Valadez-Meltzer A, et al. Supplemental Nocturnal Nasogastric Refeeding for Better Short-Term Outcome in Hospitalized Adolescent Girls With Anorexia Nervosa. Am J Psychiatry. 2002;159:1347-1353. DOI:10.1176/appi.ajp.159.8.1347. Accessed April 11, 2014.

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Affiliations.

• 1 CEDS-S, FAED, Gaudiani Clinic, Denver, CO, USA. [email protected].
• 2 Department of Psychiatry, University of Colorado School of Medicine, Aurora, CO, USA.
• PMID: 35168671
• PMCID: PMC8845309
• DOI: 10.1186/s40337-022-00548-3

Background: Most individuals with eating disorders will either recover, settle into an unrecovered but self-defined acceptable quality of life, or continue to cycle from crisis to relative stability over time. However, a minority of those with severe and enduring eating disorders recognize after years of trying that recovery remains elusive, and further treatment seems both futile and harmful. No level of harm reduction proves achievable or adequately ameliorates their suffering. In this subgroup, many of those with anorexia nervosa will experience the medical consequences of malnutrition as their future cause of death. Whereas anyone who wishes to keep striving for recovery despite exhaustion and depletion should wholeheartedly be supported in doing so, some patients simply cannot continue to fight. They recognize that death from anorexia nervosa, while perhaps not welcome, will be inevitable. Unfortunately, these patients and their carers often receive minimal support from eating disorders health professionals who are conflicted about terminal care, and who are hampered and limited by the paucity of literature on end-of-life care for those with anorexia nervosa.

Case presentation: Three case studies elucidate this condition. One patient was so passionate about this topic that she asked to be a posthumous co-author of this paper.

Conclusions: Consistent with literature on managing terminal illness, this article proposes clinical characteristics of patients who may be considered to have a terminal eating disorder: diagnosis of anorexia nervosa, older age (e.g. age over 30), previous participation in high quality care, and clear and consistent determination by a patient who possesses decision-making capacity that additional treatment would be futile, knowing their actions will result in death. By proposing the clinical characteristics of terminal anorexia nervosa, we hope to educate, inspire compassion, and help providers properly assess these patients and provide appropriate care. We hope that this proposal stimulates further expert consensus definitions and clinical guidelines for management of this population. In our view, these patients deserve the same attendant care and rights as all other patients with terminal illness, up to and including medical aid in dying in jurisdictions where such care is legal.

Keywords: Anorexia nervosa; Case presentation; Criteria; Definition; Hospice; Medical aid in dying; Obsessive compulsive disorder; Palliative; Severe and enduring anorexia nervosa; Terminal.

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• Study Protocol
• Open access
• Published: 30 May 2024

Characterising illness stages and recovery trajectories of eating disorders in young people via remote measurement technology (STORY): a multi-centre prospective cohort study protocol

• Carina Kuehne 1   na1 ,
• Matthew D. Phillips 1   na1 ,
• Sarah Moody 2 ,
• Callum Bryson 1 ,
• Iain C. Campbell 1 ,
• Pauline Conde 3 ,
• Nicholas Cummins 3 ,
• Sylvane Desrivières 4 ,
• Judith Dineley 3 ,
• Richard Dobson 3 , 5 , 6 ,
• Daire Douglas 1 ,
• Amos Folarin 3 , 5 , 6 ,
• Lucy Gallop 1 ,
• Amelia Hemmings 1 ,
• Başak İnce 1 ,
• Luke Mason 7 ,
• Zulqarnain Rashid 3 ,
• Alice Bromell 8 ,
• Christopher Sims 8 ,
• Karina Allen 1 , 9 ,
• Chantal Bailie 10 ,
• Parveen Bains 11 ,
• Mike Basher 12 ,
• Francesca Battisti 11 ,
• Julian Baudinet 1 , 9 ,
• Katherine Bristow 12 ,
• Nicola Dawson 13 ,
• Lizzie Dodd 14 ,
• Victoria Frater 15 ,
• Robert Freudenthal 16 ,
• Beth Gripton 17 ,
• Carol Kan 18 ,
• Joel W. T. Khor 19 ,
• Nicus Kotze 20 ,
• Stuart Laverack 21 ,
• Lee Martin 17 ,
• Sarah Maxwell 22 ,
• Sarah McDonald 23 ,
• Delysia McKnight 24 ,
• Ruairidh McKay 25 ,
• Jessica Merrin 14 ,
• Mel Nash 26 ,
• Dasha Nicholls 18 , 27 ,
• Shirlie Palmer 28 ,
• Samantha Pearce 10 ,
• Catherine Roberts 29 ,
• Lucy Serpell 30 , 31 ,
• Emilia Severs 30 ,
• Mima Simic 9 ,
• Amelia Staton 23 ,
• Sian Westaway 32 ,
• Helen Sharpe 2   na2 ,
• Ulrike Schmidt 1 , 9   na2 ,
• EDIFY consortium ,
• Heike Bartel ,
• Tara French ,
• Jonathan Kelly ,
• Nadia Micali ,
• Sneha Raman ,
• Janet Treasure ,
• Umairah Malik ,
• Diego Rabelo-da-Ponte ,
• Fiona Stephens ,
• Tine Opitz ,
• Nora Trompeter ,
• Jessica Wilkins ,
• Tamsin Parnell ,
• Ruby Abbas ,
• Alice Bromell ,
• Grace Davis ,
• Cameron Eadie ,
• Lara Gracie ,
• Beck Heslop ,
• Katie McKenzie ,
• Eniola Odubanjo ,
• Chris Sims ,
• Tallulah Street ,
• Andreia Tavares-Semedo ,
• Eleanor Wilkinson &
• Lucy Zocek  

BMC Psychiatry volume  24 , Article number:  409 ( 2024 ) Cite this article

Metrics details

Eating disorders (EDs) are serious, often chronic, conditions associated with pronounced morbidity, mortality, and dysfunction increasingly affecting young people worldwide. Illness progression, stages and recovery trajectories of EDs are still poorly characterised. The STORY study dynamically and longitudinally assesses young people with different EDs (restricting; bingeing/bulimic presentations) and illness durations (earlier; later stages) compared to healthy controls. Remote measurement technology (RMT) with active and passive sensing is used to advance understanding of the heterogeneity of earlier and more progressed clinical presentations and predictors of recovery or relapse.

STORY follows 720 young people aged 16–25 with EDs and 120 healthy controls for 12 months. Online self-report questionnaires regularly assess ED symptoms, psychiatric comorbidities, quality of life, and socioeconomic environment. Additional ongoing monitoring using multi-parametric RMT via smartphones and wearable smart rings (‘Ōura ring’) unobtrusively measures individuals’ daily behaviour and physiology (e.g., Bluetooth connections, sleep, autonomic arousal). A subgroup of participants completes additional in-person cognitive and neuroimaging assessments at study-baseline and after 12 months.

By leveraging these large-scale longitudinal data from participants across ED diagnoses and illness durations, the STORY study seeks to elucidate potential biopsychosocial predictors of outcome, their interplay with developmental and socioemotional changes, and barriers and facilitators of recovery. STORY holds the promise of providing actionable findings that can be translated into clinical practice by informing the development of both early intervention and personalised treatment that is tailored to illness stage and individual circumstances, ultimately disrupting the long-term burden of EDs on individuals and their families.

Eating disorders (EDs) are serious mental health conditions characterised by disturbances in eating behaviours, thoughts, and emotions, with significant physical and psychological consequences [ 1 , 2 ]. Affecting about one in every six young females and one in 20 males, they pose a growing global public health concern, comparable to anxiety and depression [ 3 , 4 ]. However, EDs have historically received little attention in research, leaving significant gaps in understanding their progression, variations in illness durations, and optimal treatment selection.

The peak onset of EDs occurs during the transitional period from adolescence to young adulthood, impacting socio-emotional, cognitive, and educational development [ 5 ]. This vulnerability is compounded by evidence suggesting that EDs are progressive disorders, where longer untreated illness duration is associated with poorer treatment outcomes [ 6 , 7 ], greater symptom interconnectivity [ 8 , 9 ], and neurobiological and behavioural changes that drive progression [ 10 ], altogether underscoring the critical importance of early intervention in ED management [ 11 , 12 ].

Clinical staging models, that define the illness phenotypes along developmental lines with escalating symptom severity, offer a promising framework for understanding and intervening in the progressive nature of EDs [ 13 , 14 ]. This contrasts with traditional approaches that view conditions as static and typically derive diagnostic criteria from advanced presentations, impeding early detection of the conditions in their nascent form. Establishing the underlying biopsychosocial processes at each stage that maintain illness, enhance progression or support recovery may inform stage-specific treatment to prevent further progression. These models, successfully adopted in psychiatry, including psychosis [ 15 ], are of current interest in EDs [ 16 ]. A proposed 4-stage model for Anorexia Nervosa (AN) ranges from an at-risk phase with attenuated symptoms to a chronic phase with severe, enduring symptoms [ 17 ]. However, variability remains in defining ED stages in terms of duration, symptom profiles, and treatment with much research solely focusing on AN.

The implication of staging models for prevention and early intervention proposes the possibility of symptom recovery at each stage, yet EDs exhibit low sustained recovery rates with only half achieving full remission with best available treatments [ 18 ]. This complexity is exacerbated by inconsistent conceptualisations of ED recovery that are predominantly biomedical (e.g., weight restoration, absence of ED behaviours), neglecting psychosocial dimensions and ED cognitions (e.g., subjective well-being, freedom from weight concerns) [ 19 , 20 ]. Relapse risks persist until these underlying factors improve [ 21 , 22 ]. Patients often describe their recovery as a protracted process with multiple ‘ups and downs’ that may take years to stabilise [ 23 ]. This intermediate state of partial improvement without regaining pre-illness health and functioning highlights the need for a more nuanced definition of ED recovery, using physical, behavioural, and psychological indices, and delineating partially and fully recovered groups.

Remote measurement technology (RMT) provides an unobtrusive, cost-efficient means to capture individuals’ daily behaviours and physiology using digital devices, gaining wider application in research across conditions [ 24 , 25 ]. Active RMT enables delivery of smartphone-based assessments for detecting momentary changes. For instance, speech characteristics (incl. pitch, pauses, speaking rate) collected through smartphone microphones in app-based tasks can serve as scalable digital biomarkers of health outcomes, including depression severity, by providing information on cognitive, neuromuscular, and physiological aspects [ 26 ]. Passive RMT continuously gathers background data via smartphone and wearable sensors (e.g., location, heart rate, activity, screentime). The sensor data indicate behavioural markers relevant to clinical states (e.g., circadian rhythm, autonomic arousal, sociability). This range of domains measured by RMT reflects the proposed multidimensional nature of ED recovery, promising to elucidate the recovery process and outcome predictors. Research applying RMT to EDs is significantly lacking [ 27 ].

Through combining a traditional prospective cohort design with continuous remote monitoring, the STORY study (Illness Stages, Progression, and Recovery Trajectories of Eating Disorders in Young People) gathers comprehensive data from a large, deeply and dynamically phenotyped cohort of young people with a range of ED presentations. It will inform conceptual models of illness stages, progression, and recovery across illness durations, diagnoses, and age groups. STORY is part of the UKRI Footnote 1 -funded ‘EDIFY’ consortium which unites a UK-wide, multi-disciplinary team of investigators with the shared aim of improving prevention and early intervention for young people with EDs [ 28 ].

Study objectives

The primary aim of the STORY study is twofold. The first (objectives 1–3) is to identify how biopsychosocial and neurocognitive symptom profiles differ between earlier and more progressed stages of EDs and which variables maintain illness, enhance progression or support recovery. The second (objectives 4–6) is to explore recovery processes and the factors that influence them by obtaining real-world data from participants’ daily lives.

Objective 1: To use a multi-modal assessment protocol to cross-sectionally and longitudinally compare young people with earlier and later illness stages in terms of their biopsychosocial profiles and how these change over time within and across ED diagnostic groups.

Objective 2: To identify baseline biopsychosocial predictors of outcome at 6 and 12 months within and across ED diagnostic and illness duration groups.

Objective 3: To use cognitive tasks with illness-relevant stimuli to compare young people with earlier and later-stage illnesses in terms of their cognitive profiles over time within and across ED diagnostic and illness duration groups.

Objective 4: To use biological and psychological RMT measures to compare young people presenting with an earlier-stage ED with healthy young people.

Objective 5: To assess differences in recovery trajectories within and across ED groups.

Objective 6: To identify early RMT predictors of ED recovery or lack of recovery at 12 months.

Study design

STORY is a multi-centre prospective cohort study, using ongoing remote monitoring for one year. Data will be collected via self-report online assessments at baseline, 6 and 12 months, via smartphones and wearable devices throughout the study period, and via neurocognitive measures completed in person by a subset of participants at baseline and 12 months. A further follow-up at 24 months is planned, recognising that ED recovery can continue over several years. These assessments are distinct from the main STORY study and not detailed in this protocol.

Study sample

The total sample size target is 840 young people aged 16–25 years, capturing the critical period where EDs commonly manifest and progress while ensuring cognitive maturity to provide consent and complete study measures. Participants are divided into three groups based on symptom profiles and illness duration at baseline:

▶ 480 young people with an earlier-stage ED (illness duration ≤ 3 years);

▶ 240 young people with a later-stage ED (illness duration > 3 years);

▶ 120 healthy controls (HCs).

The 3-year cut-off reflects more responsive treatment patterns in first-episode EDs of fewer than three years [ 29 ]. Symptom profiles distinguish between restricting-type presentations that involve severe limitations in food intake (e.g., Anorexia Nervosa [AN], Avoidant restrictive food intake disorder [ARFID]), and bingeing/bulimic-type presentations that involve episodes of binge eating, sometimes followed by compensatory actions, like purging or excessive exercise (e.g., Bulimia Nervosa [BN], Binge Eating Disorder [BED]). Individuals with atypical and subthreshold ED presentations (i.e., those exhibiting clinically significant symptoms without meeting full diagnostic criteria) are included to capture a comprehensive spectrum of ED symptomatology [ 30 ]. HCs have no current or past ED or other major mental disorders.

For the earlier-stage ED group, an estimated 100 recoveries within each of the two diagnostic groups are needed to test the predictive validity of RMTs, if 10 variables are to be entered into the predictive model [ 31 ]. Assuming a recovery rate of 50% at 12 months [ 18 ] and accounting for a 20% dropout rate, 480 participants are required to detect a medium-sized effect with 80% power ( f  = 0.15, α = 0.05; G*Power 3.1), aiming for an equal distribution of the two diagnostic groups.

The sample size for the later-stage ED group considers the total group for the comparisons between longer and shorter illness durations and varies between outcomes due to selective participation in some measures (e.g., neuroimaging). A sample of 480 provides 90% power to detect a small within-between group interaction effect ( f  = 0.08, α  = 0.05), with two groups assessed twice (baseline, 12-months). Therefore, 240 participants with later-stage EDs will be recruited. A subsample of 100 for the additional in-person assessments, provides 95% power for small-medium interaction effects ( f  = 0.18, α  = 0.05) with the two illness duration groups assessed at two time points.

A sample of 120 HCs represents 25% of the condition group for comparison analyses between control, ED and illness progression subgroups. The eligibility criteria are summarised in Table  1 .

Study procedures

Recruitment.

ED participants are recruited from an established network of 50 + FREED early intervention services Footnote 2 and specialist child, adolescent and adult ED services across the UK. Clinicians conduct a preliminary assessment of the inclusion criteria and provide study materials to potential participants to review. Participants are also identified via primary care services, waiting lists of ED services, third-sector organisations (e.g., ED charities), schools and universities, relevant websites, social media, posters in public places, and existing research cohorts (e.g., ESTRA, GLAD and EDGI cohorts [ 32 , 33 , 34 ]). This wide recruitment strategy is hoped to allow for greater diversity in our sample than typically found in the research base, to ensure representation of various demographic groups, including those who do not commonly present to ED services (e.g., males, minoritised ethnic groups, those from the LGBTQ + community, those with higher body weight, those from rural locations) [ 35 ].

Interested individuals scan a QR code on recruitment materials linking to the online screening questionnaire to assess eligibility and inform group allocation (incl. sociodemographics, medical and ED history). Symptoms consistent with a current full or subthreshold diagnosis of an ED, as well as lack thereof for HCs, are confirmed via the Eating Disorder Diagnostic Scale (EDDS) [ 36 ]. ED illness duration is determined using adapted questions from the comprehensive onset interview used in FREED early intervention services [ 29 ]. Eligible participants are contacted by the research team and directed to an electronic consent form, where they can opt into optional study components. Researchers will follow up with participants where necessary, for instance, to confirm diagnoses, comorbidities or willingness to use the study devices.

Study assessments

Following consent, participants self-complete the online baseline assessments via Research Electronic Data Capture software (REDCap), a web application for managing online surveys [ 37 ]. These assessments are repeated at 6 and 12 months (see 2.4.1 for measures). REDCap sends automatic survey invitations and reminders to participants for the duration of the study.

At baseline, participants are sent an Android study smartphone (where not already owned) and Ōura smart ring (where consented) and attend an enrolment session online or in-person (subject to preference) with a researcher for assisted setup of the devices. The remote monitoring starts following the setup of the devices and lasts for 12 months (see 2.4.2 for active and passive measures).

Additional optional in-person cognitive testing and neuroimaging assessments are completed at baseline and 12 months. Optional qualitative interviews are conducted at 6 and 12 months. See Table  2 for the complete schedule of observations and Fig.  1 for participant flow through the study.

Participant flowchart

Remuneration

Participants receive a total of £50 for completing the online assessments (£20 at baseline, £15 for each follow-up), and £25 for completing the app-based assessments at the end of the data collection period. While the Ōura rings are to be returned by participants after the data collection periods, the study smartphone can be kept. In-person cognitive testing and neuroimaging assessments are reimbursed with £25 per assessment visit (adding up to an additional £100), plus travel costs. All monetary reimbursements are made via bank transfer.

Ethical approval and consent to participate

STORY is conducted according to the Declaration of Helsinki and Good Clinical Practice, adhering to principles outlined in the NHS Research Governance Framework for Health and Social Care. Ethical approval was obtained in October 2023 from the London-Bloomsbury Research Ethics Committee (REC reference: 23/PR/0927). All staff working on the study have received training in study conduct, informed consent and risk assessment. All data is pseudonymised and stored securely in a research database per the General Data Protection Regulation.

Emphasis is placed on informed decision-making regarding participation and signed informed consent is obtained from all participants. Participants’ relationships with care teams are not impacted by participation or withdrawal from the study. If necessary, participants are signposted to third-sector organisations for additional support or encouraged to seek help in the NHS for clinical management.

Outcome measures

Core outcome measures are grouped into online assessments measuring psychological, social, and functional outcomes at baseline, 6 and 12 months (see 2.4.1), and continuous active and passive RMT measures over the study period (see 2.4.2). Additional outcome measures include in-person cognitive and neuroimaging assessments at baseline and 12 months (see 2.4.3), and qualitative interviews at 6 and 12 months (see 2.4.4).

Online questionnaires

The primary outcome is the Eating Disorder Examination Questionnaire (EDE-Q) [ 38 ] global score at 12 months which provides data informative to the dual study aims of STORY investigating illness progression (higher scores indicating greater severity) and recovery (global score < 2.8; additional criterion of BMI > 18.5 kg/m 2 for AN [ 39 ]).

Secondary outcomes are:

▶ ED-related attitudes and behaviours (Eating Disorder Scale, ED-15 [ 40 ]; six questions from the Avon Longitudinal Study of Parents and Children [ 41 ]).

▶ Motivation and readiness to change eating difficulties (two visual analogue scales; VAS).

▶ Muscularity-related attitudes (muscularity-oriented body image subscale of the Drive for Muscularity Scale, DMS [ 42 ]).

▶ Mood states and emotions (Profile of Mood States, POMS [ 43 ]; Positive and Negative Affect Scale, PANAS [ 44 ]).

▶ Depression symptoms (Patient Health Questionnaire; PHQ-8 [ 45 ]).

▶ Anxiety symptoms (Generalized Anxiety Disorder Questionnaire, GAD-7 [ 46 ]).

▶ Obsessive–compulsive symptoms (Obsessive Compulsive Inventory-Child Version, OCI-CV [ 47 ].

▶ Autistic traits (Autism Spectrum Quotient, AQ-10 [ 48 ]) at baseline only.

▶ Symptomatic and functional impairment (Psychological Outcome Profiles, PSYCHLOPS [ 49 ]; Work and Social Adjustment Scale–Youth-Version, WSAS-Y [ 50 ]).

▶ Emotion regulation difficulties (Difficulties in Emotion Regulation Scale, DERS-16 [ 51 ]).

▶ Loneliness (UCLA Loneliness Scale – Short form, UCLA-4 [ 52 ]).

▶ Addiction-reinforcing risk personality traits, e.g., impulsivity (Substance Use Risk Profile Scale, SURPS [ 53 ]) at baseline only.

▶ Mobile phone and social media use (13 questions from the Study of Cognition, Adolescents and Mobile Phones study [ 54 ], Motivations for Social Media Use Scale, MSMU [ 55 ]).

▶ Alcohol use (Alcohol Use Disorders Identification Test; AUDIT [ 56 ]) and smoking (two questions from Perman-Howe and colleagues [ 57 ]).

Remote data collection

Remote monitoring consists of active and passive components, following procedures established in previous research programmes [ 58 , 59 ]. The open-source RADAR-base platform used to support the RMT data collection is described elsewhere [ 60 ].

Active RMT (aRMT) app

Participants install a purpose-built app that is part of the RADAR-base and was successfully applied in multiple projects. The app notifies participants to complete assessments according to the study schedule:

▶ ED symptoms and motivation to change eating difficulties every two weeks (ED-15 [ 40 ]; two VAS). Participants are invited to enter their weight monthly.

▶ Anxiety and depressive symptoms every two weeks (GAD-7 [ 46 ]; PHQ-8 [ 45 ]).

▶ Short speech tasks once a month, as used in previous studies [ 26 ]. A first scripted speech task asks participants to record themselves reading aloud excerpts from Aesop’s fable “The North Wind and The Sun” [ 61 ], which is reasonably phonetically balanced while relatively short, taking less than a minute to read aloud [ 62 ]. A second, free-response task asks participants to briefly speak about something they have coming up in the next week and how they feel about it (Appendix A). Participants can rerecord their response up to five times, if they are interrupted, or skip the task. The data is recorded, encrypted and uploaded to a secure server, then processed to extract linguistic and paralinguistic features (acoustic, prosodic, e.g., pitch, speaking rate, intensity) for analysis using similar pipelines to Cummins et al. and Zhang et al. [ 26 , 63 ].

▶ Every 12 weeks, participants are prompted to complete brief in-the-moment assessments known as experience sampling method (ESM). ESM assesses mood changes, social interactions, and physical states in daily life. The schedule is initiated at six semi-random times per day within 90-min blocks between 08.30 and 22.00 for six consecutive days. Each ESM assessment consists of approximately 28 items and takes less than two minutes to complete (Appendix B). This intensity of assessment has demonstrated good acceptability in other clinical populations [ 64 ].

Passive RMT (pRMT) app

Participants install a second purpose-built pRMT app that is part of the RADAR base. This runs in the background and collects ongoing data via smartphone sensors, to test potential digital markers of change in ED symptoms and impairments. These include relative location data, Footnote 3 ambient light and noise, weather conditions, sociability (e.g., via Bluetooth proximity data, length and duration of calls, keystrokes, number of text messages and emails), app use, and battery life. The pRMT app requires the Android operating system; participants who own non-compatible phones will be provided with Android smartphones.

Wearables sensors

Participants are invited to wear an ‘Ōura’ ring for the duration of the study (12 months), which collects ongoing data on sleep, autonomic arousal and physical activity, including heart rate, heart rate variability, step count, electrodermal activity, sleep efficiency, latency and fragmentation, skin temperature and oxygen saturation (SpO 2 ). To access the Ōura app, participants enter deidentified login credentials generated by the research team. The Ōura app interface will not display any measured health data apart from the ring’s battery life and synch status. The pseudonymised data collected by the ring is synchronised with a smartphone app via Bluetooth, transmitted to Ōura Servers via WiFi, and then pulled to secure sFTP storage located in King’s College London.

The Ōura ring was selected due to the range of measurements available, improved accuracy in sleep tracking, competitive pricing, and ability to be safely implemented in an ED population (see 2.7). The Ōura ring has been shown to provide valid physical measurements comparable to gold-standard methods (e.g., polysomnography) in adult and adolescent populations [ 65 , 66 ]. The minimal, aesthetically appealing design is aimed to minimise stigma and burden for the user.

Cognitive tasks and neuroimaging

Reward behaviour , inhibitory control and food-related decision-making are assessed via three cognitive tasks completed in person with a researcher present. These are a face-affective go/no-go task [ 67 ], a Pavlovian to Instrumental Transfer task [ 68 ], and a food choice task, where participants rate 42 food images for perceived healthiness and tastiness compared to a self-chosen ‘neutral’ reference item [ 69 ]. Additionally, participants complete the following five tasks utilising eye-tracking technology (Tobii TX300 eye tracker):

▶ Visual probe task [ 70 ]: Participants view high or low-calorie food items alongside resembling non-food objects, followed by a probe presented randomly over one stimulus which participants must respond to with a keypress. Response latency, time to first fixation and fixation duration are collected to assess attentional biases toward food cues.

▶ Two naturalistic scenes: Participants view a 124-s clip from the 1995-film ‘Welcome to the Dollhouse’ depicting a social situation of a young female attempting to find a table in a school cafeteria [ 71 ], followed by a 40-s clip of people being interviewed in the street [ 72 ]. During both videos, eye-tracking data will be collected to measure social attention and comprehension .

▶ Films Expressions Task [ 73 ]: Participants match a descriptive emotional verb (e.g., “shocked”) to a corresponding face image out of three, each being displayed for 500ms. Reaction times, accuracy and eye-movement data provide insight into participants’ emotion recognition abilities.

▶ Gap-Overlap task [ 74 ]: Participants view a centrally presented stimulus and then shift their attention to a peripheral stimulus presented randomly to either side. This task assesses the speed and accuracy of shifts of low-level overt attention . Attentional disengagement is manipulated via the timing and ordering of stimulus presentation, relative to a baseline condition.

Neuroimaging assessments include task-negative functional Magnetic Resonance Imaging (fMRI) and arterial spin labelling (ASL) to measure regional interactions in a resting state. The Amsterdam Resting-State Questionnaire (ARSQ) [ 75 ] is administered prior to the scan to measure cognitive state and thought wandering . Resting state scans also provide control images for the following tasks:

▶ Monetary Incentive Delay task [ 76 ]: Participants respond to visual stimuli to either win or avoid losing money, capturing neural substrates of different processing stages of reward-based learning and motivation control in the context of temporal discounting.

▶ Stop signal task [ 77 ]: Participants have to respond or withhold their response to a visual stimulus. The task yields an estimate of the participant’s reactive response inhibition serving as a proxy for impulse control .

▶ Movie-watching [ 78 ]: Participants watch a short clip from the movie ‘Despicable Me’ while in the scanner. This allows to measure natural and real functional brain states in response to continuous and immersive sensory stimulation that may not otherwise be detectable in traditional task-based designs.

Qualitative interviews

Participants are invited to online interviews at 6 and 12 months to investigate personal accounts of ED recovery. This information will complement quantitative data by offering a contextual understanding of individuals’ lived experiences and psychosocial dimensions of recovery (e.g., coping strategies). The interviews additionally serve as a process evaluation, exploring participants’ experiences within the study and RMT specifically. Understanding potential challenges and comfort levels with the study apps and devices will help refine and optimise their integration into future studies.

Adverse events and study withdrawal

Due to STORY’s observational nature, it is not anticipated that participation increases significant risks of harm to participants. There may be several reasons for withdrawal from the study:

Participant chooses to no longer participate. Participants are informed of the voluntary nature of participation and their right to withdraw without providing a reason, with no impact on their care.

The research team withdraws the participant in the event of inter-current illness, adverse event, protocol violation, administrative or other reasons.

Participant loses capacity for continued participation.

Should a participant decide to withdraw from the study, efforts will be made to follow up to establish the reason for withdrawal to gather data on the acceptability of the study. Data from withdrawn participants will be included in the final analysis unless otherwise requested. In case of lack of engagement or missing data for more than three days, follow-up efforts will be made with participants via email and text message (if consented) up to three times before they are withdrawn from the study. Similarly, researchers will conduct random checks on the completion of aRMT measures, prompting participants as needed to ensure continued engagement and data quality.

Statistical and analysis plan

The STORY study is exploratory and not using directional hypotheses. Analyses will be pre-registered (e.g., https://osf.io/ ) and any reports will clearly distinguish between a-priori and additional post-hoc/exploratory analyses. Datasets will be prepared, stored and shared in line with open science best practices and FAIR principles ( www.go-fair.org/fair-principles ) to allow replication.

To meet our first aim, various modelling approaches are used to characterise ED symptoms during illness progression and stages and identify outcome predictors. For example, network analysis methods are used that conceptualise factors (e.g., ED symptoms, comorbidities, other traits) as nodes and their associations as edges connecting the nodes to represent the psychopathology of EDs in a network of interconnected symptoms. To gain mechanistic insights and reveal differences that characterise ED subgroups and illness progression, our analyses further include comparisons between (i) controls, anorexic-type and bulimic-type subgroups, (ii) patient groups with different illness duration, and (iii) the initial and follow-up assessments.

To meet our second aim, features obtained from biosensors, questionnaires, tasks, and ESM assessments are used for analyses within and between groups. Initial raw data from smartphones and wearables is aggregated to generate feature sets. Time-independent and dependent probabilistic models are applied to investigate biological and psychological markers of recovery or illness progression, trajectory, and stage classification in EDs and identify predictors of outcome, including Mixture latent Markov (MLM) models. MLM models allow to identify unobserved subgroups (clusters) within the data that share similar symptom trajectories over time. This allows to explore how ED symptom patterns evolve differently across participant groups. Anomaly/novelty detection methods are used to investigate deviations from baseline data and the relationship between these changes and their symptoms.

Qualitative data is analysed using thematic analysis [ 79 ]. The thematic framework initially draws upon qualitative patient and public involvement (PPI) work conducted prior to STORY (see 2.7) and remains subject to development throughout analysis, as codes and themes are identified in the data.

The results of the study will be disseminated as widely as possible into the scientific and broader community, including via publications in peer-reviewed journals, scholarly book chapters, presentations at conferences, and publications in proceedings.

Patient and public involvement

The original proposal of EDIFY was co-developed with eight young people with lived ED experience. The EDIFY project has a youth advisory board of 15 young experts-by-experience, six of whom are directly involved with the STORY study, having provided advice on STORY’s design (e.g., feasibility; attractiveness; questionnaire protocol; recovery definitions), and development of the study materials (e.g., designing documents; helping to avoid jargon; developing the recruitment video [ https://www.youtube.com/watch?v=gRyVHnKYw4Y ]). Youth advisors will continue to provide advice and feedback throughout the study.

Extensive pilot work has informed the acceptability of RMTs within STORY’s target population and its safe integration into the study. The perceived impact of RMT on weight- and food-related behaviours and attitudes was assessed as part of a qualitative interview study with former participants from the RADAR-MDD study who reported an ED diagnosis during their participation [ 58 ]. In an iterative process, the youth advisory board of the wider EDIFY consortium provided further in-depth feedback around the choice and integration of the wearable device. Overall, having access to measured health metrics was perceived to increase preoccupation with activity, weight and diet, thereby adversely impact ED symptomatology. In response to the feedback received, a smart ring was chosen as the wearable in STORY in contrast to other fitness-focused activity trackers used in similar studies (e.g., Fitbit, Garmin [ 31 , 59 ]), and its use has been made optional. Additionally, access to data measured by the ring in the accompanying app can be restricted remotely by the research team allowing complete blinding.

While public and scientific awareness of EDs has grown over the past decades, the factors that perpetuate illness or are associated with sustained recovery remain poorly understood. STORY’s multidimensional data, capturing participants’ experiences in naturalistic everyday settings, will explore both neurobiological and psychosocial correlates of illness progression and recovery. This holds the potential for actionable results, paving the way for a more bespoke approach to treatment, aiming for earlier recovery and reduced chronicity. Integrating a qualitative component to complement the comprehensive quantitative assessments will foster a holistic understanding of recovery to shape interventions that resonate with individuals’ diverse needs.

The use of RMT in ED research is nascent and typically only over short periods [ 80 , 81 , 82 ]; its application to the STORY population and study duration is novel. The continuous monitoring of biopsychosocial factors promises to improve understanding of complex recovery processes and to explore under-researched factors potentially influencing ED progression, such as circadian rhythm and heart-rate variability [ 83 , 84 ]. In the long run, these technologies could revolutionise clinical care. In contrast to existing ED treatment models, typically based on population effects or clinical expertise, personalised devices can monitor multidimensional outcomes and individual treatment responses in real time to inform clinical decisions (e.g., adjusting treatment type or intensity) [ 85 ]. Measurement-based care has proven effective in managing both physical and mental health conditions [ 86 ]. However, implementing RMT in an ED population presents unique challenges, most notably the use of wearable devices that are commonly associated with fitness and diet tracking. Such technology has been shown to trigger, maintain and worsen ED symptomatology in clinical and non-clinical populations [ 87 , 88 ], mirrored in reluctance amongst individuals with an ED history to participate in RMT studies [ 89 ]. To understand how RMT can be safely integrated into ED research and clinical practice, we encourage future research to follow processes similar to those in STORY (e.g., PPI; close consultation with experts-by-experience; process evaluations).

The STORY study prioritises diverse representation by using liberal inclusion criteria and including groups commonly underrepresented in research, such as people of the global majority, individuals with under-researched EDs (e.g., ARFID) and those with persistent symptoms [ 35 ]. Individuals are eligible if they show significant ED symptoms at screening but have not been formally diagnosed yet which will help capture the full spectrum of ED experiences and severities. Recognising frequent psychological or neurodevelopmental comorbidities of EDs (e.g., mood or anxiety disorders, obsessive–compulsive disorder, autism), participants are not necessarily excluded for these unless significantly impaired or at safety risk. STORY further proactively explores diversity-related aspects (e.g., ethnicity, sexuality, gender, socio-economic background), to identify potential disparities in care and improve support for minority and marginalised groups. Finally, by encompassing an age range that straddles common divisions in research, policy, and service provision (i.e., < 18s vs. ≥ 18s), data from STORY allows for a more integrated and inclusive understanding of EDs in youth.

The STORY study is an ambitious project not without its challenges, primarily in participant recruitment and retention due to its longitudinal design, large number of variables measured, and transient study population. Recruitment challenges are likely to be eased by the wide reach of the study and broad inclusion criteria. To reduce attrition, participants are remunerated for individual assessments and allegiance to the study is fostered using purpose-designed study merchandise (e.g., tote bags, travel mugs), newsletters and events as successfully used in previous studies. Retention will be further aided by contact with dedicated research team members who provide technical support as needed, remind participants of the importance of data collection, and motivate them to contribute study data, as evidenced in previous longitudinal RMT studies [ 89 ]. The STORY study prioritises capturing young people’s experiences with EDs, enabling an in-depth exploration of individual factors related to illness progression and recovery. This focus excludes family or caregiver perspectives, known to influence ED development and recovery, and future research including both parties could provide valuable insights. However, focusing on individual experiences allows for a controlled design, avoiding potential biases introduced by family interactions during data collection.

Ultimately, the comprehensive data gathered from the STORY study, together with other initiatives within the EDIFY research programme, aspires to redefine the approach towards understanding and treating EDs. By spreading awareness and learning more about these disorders, we hope to identify them earlier and encourage people to seek help sooner, thereby fostering swifter recovery and diminishing long-term complications. Understanding the data-driven stories of young people with EDs is a crucial first step in rewriting those of young people in the future.

Availability of data and materials

No datasets were generated or analysed during the current study.

UK Research and Innovation (UKRI) is a national funding agency investing in science and research in the UK.

First Episode Rapid Early Intervention for Eating Disorders (FREED).

GPS location data is obfuscated; that is, providing relative location data, not absolute coordinates, preventing identification of an individual’s home address or precise geographical location.

Abbreviations

Adverse event

Anorexia Nervosa

10-item autism spectrum quotient questionnaire

Avoidant Restrictive Food Intake Disorder

Active remote measurement technology

Amsterdam Resting State Questionnaire

Autism spectrum disorder

Arterial spin labelling

Alcohol use disorders identification test

Binge Eating Disorder

Body Mass Index

Bulimia Nervosa

16-item difficulties in emotion regulation scale

Drive for muscularity scale

Diagnostic and statistical manual

Eating disorder

Eating disorder scale

Eating disorder diagnostic scale

Eating disorder examination questionnaire

Eating Disorders Genetics Initiative UK

Eating Disorders: Delineating Illness and Recovery Trajectories to Inform Personalised Prevention and Early Intervention in Young People

Experience sampling method

Earlier detection and stratification of eating disorders and comorbid mental illnesses

Functional magnetic resonance imaging

First episode and Rapid Early intervention for Eating Disorders

7-item generalised anxiety disorder questionnaire

Go/No-Go Task

Genetic Links to Anxiety and Depression

Global positioning system

Monetary incentive delay task

Mixture latent Markov model

Magnetic resonance imaging

Motivations for social media use questionnaire

Obsessive compulsive disorder

Obsessive Compulsive Inventory

Positive and negative affect scale

8-item patient health questionnaire

Pavlovian to instrumental transfer task

Profile of mood states questionnaire

Patient and Public Involvement

passive remote measurement technology

Psychological outcome profiles questionnaire

Remote assessment of disease and relapse-Central nervous system

Remote assessment of disease and relapse-Major depressive disorder

Research Ethics Committee

Research electronic data capture

• Remote measurement technology

Secure File Transfer Protocol

Stop-signal task

Substance use risk profile scale

4-item UCLA-Loneliness scale

Visual analogue scale

Work and social adjustment scale-Youth version

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Acknowledgements

We would like to thank our colleagues within the EDIFY consortium across all involved institutions for their contribution to the development of this protocol. We thank all the members of the EDIFY youth advisory board for their contribution to the device selection procedures, and their invaluable advice throughout the study protocol design. On behalf of the EDIFY consortium: Heike Bartel, Tara French, Jonathan Kelly, Nadia Micali, Sneha Raman, Janet Treasure, Umairah Malik, Diego Rabelo-da-Ponte, Fiona Stephens, Tine Opitz, Nora Trompeter, Jessica Wilkins, Tamsin Parnell, Ruby Abbas, Alice Bromell, Grace Davis, Cameron Eadie, Lara Gracie, Beck Heslop, Katie McKenzie, Eniola Odubanjo, Chris Sims, Tallulah Street, Andreia Tavares-Semedo, Eleanor Wilkinson, Lucy Zocek.

This work is supported by the Medical Research Council/Arts and Humanities Research Council/Economic and Social Research Council Adolescence, Mental Health and the Developing Mind initiative as part of the EDIFY programme (grant number MR/W002418/1). This paper also represents independent research funded by the National Institute of Health Research (NIHR) Maudsley Biomedical Research Centre (BRC) at South London and Maudsley NHS Foundation Trust (SlaM) and King’s College London (KCL). US receives salary support from the NIHR BRC at SLaM and KCL. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. CK and AH are supported by NIHR Maudsley BRC PhD scholarships. KA is supported by the Medical Research Council as part of grant MR/X030539/1 (Eating Disorders Clinical Research Network).

Author information

Carina Kuehne and Matthew D. Phillips equally contributed to this work.

Helen Sharpe and Ulrike Schmidt equally contributed to this work.

Authors and Affiliations

Centre for Research in Eating and Weight Disorders, Institute of Psychiatry, King’s College London, Psychology & Neuroscience London (IoPPN), 103 Denmark Hill, First Floor, London, SE5 8AZ, UK

Carina Kuehne, Matthew D. Phillips, Callum Bryson, Iain C. Campbell, Daire Douglas, Lucy Gallop, Amelia Hemmings, Başak İnce, Karina Allen, Julian Baudinet & Ulrike Schmidt

School of Health in Social Science, The University of Edinburgh, Edinburgh, UK

Sarah Moody & Helen Sharpe

Department of Biostatistics & Health Informatics, IoPPN, King’s College London, London, UK

Pauline Conde, Nicholas Cummins, Judith Dineley, Richard Dobson, Amos Folarin & Zulqarnain Rashid

Social, Genetic & Developmental Psychiatry Centre, IoPPN, King’s College London, London, UK

Sylvane Desrivières

NIHR Maudsley Biomedical Research Centre, London, UK

Richard Dobson & Amos Folarin

University College London, Institute of Health Informatics, London, UK

Department of Forensic and Neurodevelopmental Science, IoPPN, King’s College London, London, UK

EDIFY, London, UK

Alice Bromell & Christopher Sims

South London and Maudsley NHS Foundation Trust, London, UK

Karina Allen, Julian Baudinet, Mima Simic & Ulrike Schmidt

Cornwall Partnership NHS Foundation Trus, Bodmin, Cornwall, UK

Chantal Bailie & Samantha Pearce

Oxford Health NHS Foundation Trust, Oxford, Oxfordshire, UK

Parveen Bains & Francesca Battisti

Cambridgeshire and Peterborough NHS Foundation Trust, Fulbourn, Cambridgeshire, UK

Mike Basher & Katherine Bristow

Bradford District Care NHS Foundation Trust, West Yorkshire, UK

Nicola Dawson

South West Yorkshire Partnership NHS Foundation Trust, Wakefield, UK

Lizzie Dodd & Jessica Merrin

Cumbria Northumberland Tyne and Wear NHS Foundation Trust, Newcastle Upon Tyne, UK

Victoria Frater

Barnet, Enfield and Haringey Mental Health NHS Foundation Trust, London, UK

Robert Freudenthal

Leeds and York Partnership NHS Foundation Trust, Leeds, UK

Beth Gripton & Lee Martin

Central and North West London NHS Foundation Trust, London, UK

Carol Kan & Dasha Nicholls

South West London & St. George’s Mental Health NHS Trust, St George’s Eating Disorders Service, London, UK

Joel W. T. Khor

Dorset Healthcare University NHS Foundation Trust, Poole, Dorset, UK

Nicus Kotze

Derbyshire Healthcare NHS Foundation Trust, Derby, Derbyshire, UK

Stuart Laverack

Norfolk and Suffolk NHS Foundation Trust, Norwich, Norfolk, UK

Sarah Maxwell

Nottinghamshire Healthcare NHS Foundation Trust, Nottingham, UK

Sarah McDonald & Amelia Staton

North Staffordshire Combined Healthcare NHS Trust; Trentham, Staffordshire, UK

Delysia McKnight

NHS Lothian – NHS Scotland, Edinburgh, UK

Ruairidh McKay

Devon Partnership NHS Foundation Trust, Exeter, Devon, UK

Division of Psychiatry, Department of Brain Sciences, Imperial College London, London, UK

Dasha Nicholls

Somerset Partnership NHS Foundation Trust, Taunton, UK

Shirlie Palmer

Solent NHS Foundation Trust, Southampton, UK

Catherine Roberts

North East London NHS Foundation Trust, London, UK

Lucy Serpell & Emilia Severs

Division of Psychology and Language Sciences, University College London, London, UK

Lucy Serpell

Herefordshire and Worcestershire Health and Care NHS Trust, Worcester, UK

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U.S. and H.S. developed the main conceptual ideas for the EDIFY programme. U.S., H.S., C.K., M.P., S.M., B.İ., A.H., L.G., and I.C. were responsible for the conception and design of the STORY study. U.S., H.S., C.K., M.P., and S.M. developed and wrote the protocol for ethical approval. C.K. and M.P. were responsible for the initial drafting of this manuscript. C.B., I.C., D.D., A.H., B.İ., S.M., H.S., and U.S. have contributed to reviewing and revising the manuscript critically for important intellectual content. U.S., H.S., B.İ., C.K., S.M., and C.B. are responsible for the coordination of the study across sites. All local research site PIs are responsible for the coordination of the study in their respective Trust area. P.C., R.D., A.F., and Z.R. developed the RADAR-base system and the apps used for data collection and management, and data protection, security, and storage systems. P.C., R.D., A.F., and Z.R. also developed an analytic method for handling data collected via the RADAR-base system. N.C. and J.D. have contributed to the implementation of the speech tasks. L.M. has contributed to the implementation of the eye-tracking tasks. All authors have been involved in reviewing the manuscript and given approval for it to be published. All authors have agreed to be accountable for all aspects of the work, ensuring that questions relating to the accuracy or the integrity of any part of the work are appropriately investigated and resolved.

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Kuehne, C., Phillips, M.D., Moody, S. et al. Characterising illness stages and recovery trajectories of eating disorders in young people via remote measurement technology (STORY): a multi-centre prospective cohort study protocol. BMC Psychiatry 24 , 409 (2024). https://doi.org/10.1186/s12888-024-05841-w

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The present Research Topic wishes to focus on the four recognized eating disorders by the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V): Anorexia Nervosa, Bulimia Nervosa, Binge Eating Disorder (BED), and Avoidant/Restrictive Food Intake Disorder (ARPID). Given the rising incidence of cases of people affected by eating disorders and some studies even suggesting an “epidemic” of eating disorders, we would like to analyze via an interdisciplinary approach the current situation and disseminate the latest findings. Knowledge in this field is constantly expanding, especially in the last 10 years, with authors providing new and fascinating results. However, there are several areas where research is still lacking. We would like to encourage you to share the latest findings in the field. We welcome the submission of manuscripts (original research, brief research report, focused review, hypothesis and theory, perspective, data report, case report, community case study) related, but not limited to: • Causes and risk factors. • Socio-cultural factors. • Psychological factors. • Family factors through enmeshment and criticism. • Peer influence • The impact of the media, by spreading the ideal of thinness. • Negative affect, low self-esteem, and body dissatisfaction. • Biological and genetic bases. • Brain functioning in eating disorders. • Epidemiology, statistics, and mortality. • Diagnostics. • Physiological consequences of eating disorders. • Psycho-therapeutic intervention. • Progresses and challenges related to eating disorders. • Prevention. • Evolution. • Adverse effects. • Eating disorders as coping mechanisms.

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• Volume 37, Issue 3
• Association between the frontoparietal network, clinical symptoms and treatment response in individuals with untreated anorexia nervosa
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• http://orcid.org/0000-0001-7559-9183 Qianqian He 1 , 2 ,
• http://orcid.org/0000-0001-9735-5657 Hui Zheng 3 ,
• Jialin Zhang 4 ,
• http://orcid.org/0000-0001-6476-0189 Ling Yue 1 ,
• Qing Kang 1 ,
• Cheng Lian 1 ,
• Lei Guo 1 ,
• Yan Chen 1 ,
• Yanran Hu 1 ,
• Yuping Wang 1 ,
• http://orcid.org/0000-0003-2680-9687 Sufang Peng 1 ,
• http://orcid.org/0000-0003-4319-5314 Zhen Wang 1 ,
• Qiang Liu 1 and
• 1 Department of Clinical Psychology , Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine , Shanghai , China
• 2 Department of Clinical Psychology , Shanghai Pudong New Area Mental Health Center, Tongji University School of Medicine , Shanghai , China
• 3 Shanghai Key Laboratory of Psychotic Disorders, Brain Health Institute, National Center for Mental Disorders , Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine , Shanghai , China
• 4 State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research , Beijing Normal University , Beijing , China
• Correspondence to Dr Jue Chen; chenjue2088{at}163.com ; Dr Qiang Liu; 752706355{at}qq.com

Background Anorexia nervosa (AN) has been characterised as a psychiatric disorder associated with increased control. Currently, it remains difficult to predict treatment response in patients with AN. Their cognitive abilities are known to be resistant to treatment. It has been established that the frontoparietal control network (FPCN) is the direct counterpart of the executive control network. Therefore, the resting-state brain activity of the FPCN may serve as a biomarker to predict treatment response in AN.

Aims The study aimed to investigate the association between resting-state functional connectivity (RSFC) of the FPCN, clinical symptoms and treatment response in patients with AN.

Methods In this case-control study, 79 female patients with AN and no prior treatment from the Shanghai Mental Health Center and 40 matched healthy controls (HCs) were recruited from January 2015 to March 2022. All participants completed the Questionnaire Version of the Eating Disorder Examination (version 6.0) to assess the severity of their eating disorder symptoms. Additionally, RSFC data were obtained from all participants at baseline by functional magnetic resonance imaging. Patients with AN underwent routine outpatient treatment at the 4th and 12th week, during which time their clinical symptoms were evaluated using the same measures as at baseline.

Results Among the 79 patients, 40 completed the 4-week follow-up and 35 completed the 12-week follow-up. The RSFC from the right posterior parietal cortex (PPC) and dorsolateral prefrontal cortex (dlPFC) increased in 79 patients with AN vs 40 HCs after controlling for depression and anxiety symptoms. By multiple linear regression, the RSFC of the PPC to the inferior frontal gyrus was found to be a significant factor for self-reported eating disorder symptoms at baseline and the treatment response to cognitive preoccupations about eating and body image, after controlling for age, age of onset and body mass index. The RSFC in the dlPFC to the middle temporal gyrus and the superior frontal gyrus may be significant factors in the treatment response to binge eating and loss of control/overeating in patients with AN.

Conclusions Alterations in RSFC in the FPCN appear to affect self-reported eating disorder symptoms and treatment response in patients with AN. Our findings offer new insight into the pathogenesis of AN and could promote early prevention and treatment.

• Anorexia Nervosa
• Case-Control Studies

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https://doi.org/10.1136/gpsych-2023-101389

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WHAT IS ALREADY KNOWN ON THIS TOPIC

Anorexia nervosa (AN) has been considered a disorder of overcontrol and is associated with elevated resting-state functional connectivity (RSFC) in the frontoparietal control network (FPCN).

WHAT THIS STUDY ADDS

Alterations in the RSFC of the posterior parietal cortex play an important role in self-reported eating disorder symptoms and the treatment response to cognitive preoccupations about eating and body image.

Alterations in the RSFC of the dorsolateral prefrontal cortex seem to influence the treatment response to binge eating behaviours and loss of control/overeating behaviours in patients with AN.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

This study shows the importance of the FPCN in self-reported eating disorder symptoms and treatment response in patients with AN.

Our findings may formulate new ideas for the pathogenesis of AN and potential strategies for early prevention and treatment of the disorder.

Introduction

Anorexia nervosa (AN) is an eating disorder characterised by severe dietary restriction, misperceptions of body shape and weight, and fear of weight gain, combined with some emotional problems. 1 It has the highest and ever-increasing mortality rates (5%–6%) among psychiatric illnesses, and it especially threatens the physical and psychological health of female adolescents. 2 AN is divided into two subtypes with diverse symptoms: the restricting type (AN-R) and the binge/purging type (AN-BP). One study has found that the ability to control impulses and respond to emotional stimuli is closely associated with risk of binge eating and purging episodes in patients with AN-BP, 3 and enhanced cognitive control function is closely related to distorted body image and excessive focus on food intake and body shape in patients with AN-R. 4 The frontoparietal control network (FPCN) has been reported to be involved in humans’ cognitive and impulsive control processes. 5 Alterations in the resting-state functional connectivity (RSFC) of the FPCN, which have been studied widely and implicated in psychiatric disorders, 6 are also associated with the aetiology of AN. 7 Moreover, AN is prone to relapse, and no predictive markers for treatment efficacy have been found. Therefore, further investigation of its pathogenesis and potential predictive markers is necessary. 8

AN has been described as a disorder with excessive cognitive control functions associated with its related altered brain network, which may contribute to its onset and persistence. 4 The FPCN has been linked to humans’ cognitive function and impulsive control processes. 5 As one of its functions, the FPCN regulates eating behaviours; altered brain activation in the FPCN is associated with abnormal eating behaviours. 9 Patients with AN exhibit widespread alterations in executive function and impaired cognitive flexibility, which are seemingly linked to an aberrant FPCN function. 10 Another study suggested that patients with AN had enhanced executive function and greater inhibitory control. 11

The FPCN is generally known as the central executive network or cognitive control network. It encompasses the brain regions of the bilateral posterior parietal cortex (PPC) and the bilateral dorsolateral prefrontal cortex (dlPFC). It is a flexible hub with a high degree of connectivity across the brain. 12 Alterations in brain-wide connectivity in the FPCN have been linked to a wide range of mental illnesses and are known to play a crucial role in the onset and persistence of mental disorders, including AN. 13 A previous study suggested that neural activities in the frontal and parietal brain were associated with enhanced inhibition and cognitive control abilities. 9 Another study indicated that increased RSFC within the FPCN may be linked to excessive cognitive control in AN. 7 Therefore, dysfunction of the FPCN may be an important neuropathological factor in AN. In addition, previous studies investigated the relationship between antidepressant treatments, psychiatric symptoms and functional connectivity between brain regions. One study showed that altered RSFC related to the FPCN could predict antidepressant medication and psychological treatment outcomes in patients with major depressive disorder. 14 In another study, disease remission was achieved by normalising abnormal regional brain connections through treatment, including the regions in the FPCN. 15 However, the predictive role of RSFC within the FPCN indetermining treatment response in AN has not been studied.

Given the above, the FPCN is robustly related to cognitive control, executive function and disordered eating symptoms in AN. However, few previous studies have predicted the neurological markers of clinical outcomes in patients with AN. The present study addresses the urgent need to explore the neural mechanisms of AN pathogenesis and clinical treatment. It holds the following hypotheses: AN is associated with altered RSFC in the FPCN compared with healthy controls (HCs), and the RSFC in the FPCN may be a neural marker that can predict clinical symptoms and treatment response in AN.

Participants

Seventy-nine females with treatment-naive AN were recruited from January 2015 to March 2022 at the Eating Disorder Treatment Center, Shanghai Mental Health Center (SMHC), of whom 47 were of AN-R subtype and 32 were of AN-BP subtype. Two or more senior psychiatrists evaluated the patients according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition ( DSM-V ) criteria. Inclusion criteria for patients with AN were as follows: (1) an initial diagnosis of AN according to the DSM-V criteria, (2) aged 13–25 years, (3) Han Chinese ethnicity, (4) right-handed and (5) a body mass index (BMI) of 13.0–18.5 kg/m 2 . Exclusion criteria for this group were as follows: (1) a past diagnosis of or treatment for eating disorders, (2) evidence of a serious or chronic somatic disease and a history of other psychiatric disorders, (3) any psychiatric or psychological therapy for at least 12 weeks prior to the study, (4) currently pregnant or breast feeding and (5) metal implants in their body that would prevent them from undergoing magnetic resonance imaging (MRI) examination. Forty HCs were recruited among the students and health workers at the SMHC by advertisements and were matched to the patients in terms of age and educational level. The same criteria were applied to HCs, except they had no history of eating disorders and no restrictions on BMI parameters. All participants signed informed consent.

Study design

In this case-control study, all participants were administered a physical examination, a clinical questionnaire and a resting-state functional MRI (fMRI) examination at baseline. Subsequently, patients with AN received routine outpatient treatment, mainly including psychological counselling, nutritional evaluation and pharmacological therapies based on their situations. The pharmacological treatments mainly included selective serotonin reuptake inhibitors (n=32), atypical antipsychotics (n=14) and benzodiazepines (including alprazolam, lorazepam and estazolam; n=6) and zolpidem (n=4). Physical examination and clinical symptoms were evaluated at 4-week and 12-week follow-up.

Outcome assessments

Demographic and clinical characteristics, including age, educational level and BMI (calculated by current weight and height), were collected from all subjects. The severity of eating disorder was measured with the Eating Disorder Examination Questionnaire (EDE-Q 6.0). This widely used scale has 28 items that assess eating disorder symptoms, including thoughts, feelings and behaviours related to eating and body image, over the past 28 days. The scale includes 6 open-ended items—the frequency of overeating, loss of control, binge eating, self-induced vomiting, laxative abuse and compulsive exercise—and 22 attitudinal items, which are combined to create four subscales: restraint, eating, body shape and weight concerns. Purging behaviours are the sum of self-induced vomiting, laxative abuse and compulsive exercise. Higher scores on the four subscales and the total scale indicate more severe symptoms. Our previous study has confirmed good reliability and validity of the scale in mainland China, with Cronbach’s alpha coefficients of 0.95 and test-retest reliability of 0.73. 16

Additional questionnaires

The Beck Depression Inventory-II (BDI-II) and the Beck Anxiety Inventory (BAI) were adopted to evaluate the depressive and anxiety symptoms of all subjects. The Chinese version of BDI-II has demonstrated excellent internal consistency, with a Cronbach’s alpha of 0.94, 17 and the reliability and validity of the Chinese version of BAI were satisfactory. 18

MRI acquisition and preprocessing

All fMRIs were acquired on a 3.0T Siemens Verio MRI scanner (Erlangen, Germany) with a 12-channel head coil at the SMHC. The resting-state scans were acquired using a gradient echo-planar imaging sequence (number of slices =45 170 scans, repetition time (TR) =3000 ms, echo time (TE) =30 ms, flip angle =85, field of view (FOV) =216 mm, matrix =64×64, voxel size =3×3×3 mm 3 , slice thickness =3.0 mm). High-resolution anatomical scans were acquired with a T1-weighted three-dimension magnetization-prepared rapid gradient echo sequence (192 sagittal slices, TR=2300 ms, TE=2.96 ms, flip angle=85, FOV=256 mm, matrix =256×256, slice thickness =1 mm). Before scanning, participants were instructed to stay still and remain awake with their eyes closed. Foam padding and earplugs were provided to control head motion and reduce scanner noise.

Image preprocessing and analysis were conducted using SPM V.12 ( http://www.fil.ion.ucl.ac.uk/spm ) and CONN toolbox V.20.b ( http://www.nitrc.org/projects/conn ) on MATLAB V.R2021b (Mathworks). The fMRI preprocessing steps were as follows: discarding the first 10 volumes, motion correction, realignment, slice-timing correction and outlier scans for scrubbing. Then, functional and structural images were segmented by grey/white/cerebrospinal fluid and normalised into the Montreal Neurological Institute space. Functional images were spatially smoothed with a Gaussian kernel of 6 mm full width at half maxima, and the bandpass filtering was 0.008–0.09 Hertz. Potential confounders such as white matter, cerebrospinal fluid, realignment, scrubbing and effect of rest were regressed out.

Region of interest selection and quality check

FPCN seeds were identified using the Harvard-Oxford cortical and structural atlas, and brain regions were defined using the Brodmann area (BA) atlas, including four brain regions of the bilateral PPC and dlPFC. The PPC seed was explored in one brain region located at BA 48, and the dlPFC seed was explored in three brain regions located at BA 8, BA 25 and BA 48. Seed-based correlation analysis was used to investigate time series connectivity between the seeds in the FPCN and all other voxels in the whole brain, and the connection strength was extracted from all participants. The quality of brain image values for each participant was checked based on the registration quality and head motion. Head motion was assessed using mean framewise displacement, with the maximal value set to 0.5 mm. Those with poor image quality were excluded from further analysis. Images from all participants passed the quality check, leading to a sample size of 79 patients and 40 HCs who passed quality checks of both clinical questionnaires and brain images.

Statistical analyses

The demographic and clinical characteristics were analysed using SPSS V.25.0. Continuous data are presented as mean (SD), and categorical data are presented as numbers or percentages. Group comparisons were conducted using independent-sample t-tests or Mann-Whitney U tests for continuous variables and the χ 2 test for categorical variables. Pearson’s correlation was used to assess the correlation between the EDE-Q 6.0 total score and the score on items 13–18, treatment response and the RSFC in seeds of the FPCN. Treatment response was expressed as the rate of decrease or reduction in the EDE-Q 6.0 total score and the score on EDE-Q 6.0 items 13–18 in the sensitive analysis. Multiple linear regression was adopted in the prediction model, with the EDE-Q 6.0 total score, the scores on items 13–18 at baseline and the treatment response as the dependent variables, respectively. The RSFC in the PPC and dlPFC seeds of the FPCN were the independent variables, and age, age of onset and BMI were the covariates. A mixed linear model was used to analyse the clinical response at follow-up time points. All tests were two-tailed, and p<0.05 was considered statistically significant.

A seed-to-voxel analysis was adopted using the CONN toolbox to explore the RSFC between the AN and the HC groups. Fisher-transformed correlation coefficients were generated between blood oxygen level-dependent time series in seeds within the FPCN and all other voxels in the whole brain to create functional connectivity maps. A general linear model was used to explore the differences in functional connectivity between the AN and the HC groups. The BDI-II and BAI total scores were included as covariates to exclude their influence on the results. Connectivity differences were considered significant between the two groups when a voxel height threshold was p<0.001 and a cluster size threshold was corrected at a p<0.05 false discovery rate.

We employed support vector regression (SVR) to construct the prediction model, given the modest size of our data set. SVR is chosen for its documented stability in handling small sample sizes. Five specific brain activities were used as predictive features for each type of variation. Our data set was divided into three subsets: a training set, a validation set and an independent test set comprising 20% of the total data. Data fitting is predominantly assessed through R 2 values and scatter plots. A higher R 2 value indicates better alignment between the data and the model, although an excessively higher R 2 value (~1) indicates overfitting. Scatter plots serve as visual aids to evaluate prediction accuracy, with closer proximity to the diagonal line of equality (where the x-axis is the observed values and the y-axis is the predicted values) indicating the predictive performance is better. Given the limited number of follow-up data points, the risk of overfitting is heightened.

Demographic and clinical characteristics

A total of 79 patients with AN and 40 HC participants took part in the study, of whom 40 patients completed the 4-week follow-up and 35 completed the 12-week follow-up. The other 39 patients did not attend the follow-up, mainly due to difficulty accessing medical care during the COVID-19 outbreak, reluctance to continue outpatient visits after improvement or additional comorbid mental disorders during the study. A flowchart of the study is shown in figure 1 .

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Flowchart of the study design. AN, anorexia nervosa; BAI, Beck Anxiety Inventory; BDI-II, Beck Depression Inventory; BMI, body mass index; DSM-V , Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition ; EDE-Q 6.0, Questionnaire Version of the Eating Disorder Examination (version 6.0); fMRI, functional MRI; HC, healthy control.

There were no significant differences in age (t=−1.75, p=0.083) and educational level (t=−1.49, p=0.130) between the patients and HCs. The BDI-II scale (t’=5.88, p<0.001), BAI scale (t’=6.78, p<0.001) and EDE-Q 6.0 total score (t’=8.07, p<0.001) were higher for patients with AN than for HCs. As expected, patients with AN had a significantly lower BMI (t=−10.49, p<0.001) than HCs ( table 1 ). There were no significant differences in age, age of onset, BMI, BDI-II and BAI scores, and EDE-Q 6.0 total score between patients who attended the follow-up at the 4th and 12th weeks and those who did not (see online supplemental table 1 ).

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Resting-state functional connectivity

According to seed-to-voxel analyses, RSFC maps in the FPCN seeds showed significant differences between the AN and the HC groups. A significantly increased RSFC (p<0.05, false discovery rate-corrected) was observed in patients with AN compared with HCs between the right PPC seed and the left inferior frontal gyrus (IFG) (x=−42, y=6, z=20, t=5.48, k=117, size p=0.029), the right dlPFC seed and the middle temporal gyrus (MTG) (x=−48, y=−20, z=−6, t=5.16, k=111, size p=0.032), the posterior division left (x=−6, y=38, z=48, t=5.56, k=102, size p=0.032) and the subcallosal cortex (x=−2, y=18, z=−6, t=5.62, k=96, size p=0.032). For the significant clusters in conn, we extracted their specific values and used SPSS to perform an independent samples t-test statistic. The result shows significantly increased RSFC in the FPCN in patients with AN than HCs, and their statistical values are (t=4.04, p<0.001), (t=5.17, p<0.001), (t=4.75, p<0.001), (t=4.82, p<0.001) respectively in the same order as above. was observed in patients with AN compared with HCs (t(degrees of freedom)=, p=, chon'd=). ( figure 2 and online supplemental table 2 ). No significant differences were found in the RSFC maps generated from the seeds of the left PPC and the left dlPFC. Significant results for the dlPFC seed are presented in figure 2 . Furthermore, no significant differences in the RSFC maps of the FPCN seeds were found between the AN-R and AN-BP subtypes.

(A) Voxels showing significant connectivity for the seeds in PPC. (B) Graphs showing significant clusters from PPC seeds between the AN and the HC groups. (C) Voxels showing significant connectivity for the seeds in dlPFC. (D) Graphs showing significant clusters from dlPFC seeds between the AN and the HC groups. Error bars represent SEM. Connectivity strength corresponds to Fisher-transformed correlation coefficient values; ***p<0.001. AN, anorexia nervosa; dlPFC, dorsolateral prefrontal cortex; HC, healthy control; PPC, posterior parietal cortex.

Correlations between the RSFC in different brain regions and clinical variables

Pearson’s correlation analysis was conducted to explore the associations between the Z scores of clusters that showed significant differences in the RSFC of the FPCN seeds and relevant self-reported eating disorder symptoms at baseline in the AN and HC groups. Significant correlations were found between the PPC to IFG connectivity strength and the EDE-Q 6.0 total score (r=−0.335, p=0.003), frequency of overeating behaviours (r=−0.238, p=0.036) and frequency of loss of control (r=−0.270, p=0.017) at baseline in patients with AN. No significant correlations were found between the RSFC in the FPCN and self-reported eating disorder symptoms in the HCs ( figure 3A–C ).

(A) Correlation between the RSFC in PPC–IFG and the EDE-Q 6.0 total score between AN and HC groups at baseline. (B) Correlation between the RSFC in PPC–IFG and overeating behaviours at baseline in AN. (C) Correlation between the RSFC in PPC–IFG and loss of control at baseline in AN. (D) Correlation between the RSFC in PPC–IFG and the decrease of EDE-Q 6.0 total score in the fourth week. (E) Correlation between the RSFC in dlPFC–MTG and the improvement of loss of control. (F) Correlation between the RSFC in dlPFC–MTG and the improvement of binge eating. (G) Correlation between the RSFC in dlPFC–SFG and the improvement of overeating behaviours. (H) Correlation between the RSFC in PPC–IFG and the decrease of EDE-Q 6.0 total score at the 12th week. AN, anorexia nervosa; dlPFC, dorsolateral prefrontal cortex; EDE-Q 6.0, Questionnaire Version of the Eating Disorder Examination (version 6.0); HC, healthy control; IFG, inferior frontal gyrus; MTG, middle temporal gyrus; PPC, posterior parietal cortex; RSFC, resting-state functional connectivity.

Treatment response was expressed as the decrease in the EDE-Q 6.0 total score between baseline and the corresponding treatment node and the decrease rate of the scores on items 13–18 of the EDE-Q 6.0. A mixed linear model was used to analyse the treatment response at the follow-up time points. It showed that the time main effect of the EDE-Q 6.0 total score was not significant over time, and the loss of control and overeating in the model failed to fit due to missing data. The results are shown in online supplemental materials (tables 3–7 and figure 1).

Correlation analysis was performed between treatment response and five different brain regions. It showed that the RSFC of the PPC–IFG had an approximately significant or significant correlation with treatment response both in the 4th week (r=−0.278, p=0.083) and 12th week (r=−0.416, p=0.013) ( figure 3D,H ), and the RSFC of the right dlPFC–MTG had a significant correlation with improvement in the frequency of loss of control (r=0.451, p=0.031) and binge eating (r=0.556, p=0.013) ( figure 3E,F ). Also, the RSFC of the right dlPFC–left superior frontal gyrus (SFG) had a significant correlation with improvement in the frequency of overeating behaviours (r=0.502, p=0.029) ( figure 3G ).

Furthermore, multiple linear regression was conducted with the EDE-Q 6.0 total score ( F =5.16, p=0.008, R 2 =0.12), frequency of overeating ( F =2.21, p=0.076, R 2 =0.11) and loss of control ( F =2.91, p=0.027, R 2 =0.14) at baseline as dependent variables, respectively, with the RSFC in seeds of the FPCN as independent variables, and age, age of onset and BMI as covariates. Multiple linear regression was conducted with the decrease in the EDE-Q 6.0 total score in the 4th week ( F =3.74, p=0.061, R 2 =0.38) and 12th week ( F =3.098, p=0.039, R 2 =0.38), and with the improvement in loss of control ( F =4.08, p=0.032, R 2 =0.29) and binge eating ( F =3.85, p=0.050, R 2 =0.66) as the dependent variables, respectively. The RSFC in seeds of the FPCN were the independent variables, and age, age of onset and BMI were the covariates. As shown in table 2 , the RSFC in the PPC–IFG was a significant factor for both the clinical symptoms at baseline and the treatment response in the 4th and 12th weeks for patients with AN.

Results of linear regression with self-reported eating disorder symptoms or treatment response as the dependent variables and PPC–IFG connectivity strength as the independent variable

We also explored the machine learning prediction model using SVR. Five brain activities were used as predictive features for each type of variation. These results are shown in the support vector machine regression parts of online supplemental tables 8–39 and figures 2–17 .

Main findings

To our knowledge, the present study is the first to investigate the role of significant seeds in the FPCN in self-reported eating disorder symptoms and treatment response in patients with AN. First, after controlling for depression and anxiety symptoms, the RSFC from the PPC and dlPFC of the FPCN increased in patients with AN versus HCs. Second, the RSFC of the PPC to IFG was a significant neural marker of self-reported eating disorder symptoms after controlling for age, and it was a significant neural marker of treatment response to cognitive preoccupations about eating/body image after controlling for age, age of onset and BMI. Likewise, the RSFC of the dlPFC to MTG/SFG may be a significant neural marker of the treatment response to binge eating and loss of control/overeating behaviours in patients with AN. Because no significant differences in the RSFC of the FPCN were found between patients with AN-R or AN-BP, the RSFC of the FPCN may not be a neural marker to differentiate the two subtypes of AN. Thus, these results provide evidence for the important role of the FPCN in patients with AN.

Moreover, the RSFC in the PPC to IFG and the dlPFC to MTG/SMG increased in AN versus HCs in the study, further supporting that AN is a disorder of excessive cognitive control, a finding that agrees with previous studies that found the function in the FPCN increased in patients with AN. 7 19 The FPCN engages in various executive functions by allocating top-down attentional resources to arrange cognitive control processes. 20 The impaired cognitive control ability has been associated with decreased cognitive flexibility and hyperdetailed information processing in patients with AN. 10 One study confirmed that the FPCN functional connectivity contributed to the loss of control in patients with binge drinking, possibly by impairing cognitive function and response inhibition. 21 In addition, the right IFG is involved in inhibiting control and stopping the upcoming impulsive responses, and the impairment of the right IFG is closely related to impaired inhibitory control. 22 One study pointed out that age was an important moderator of overall cognitive performance in AN, including executive function, with younger participants had better performance than the older participants. 19 Considering the increased function in the FPCN at a young onset age, we put forward a viewpoint that the neural circuitry of the FPCN in patients with AN is premature.

Furthermore, our study showed that the aberrant RSFC in the FPCN was related to the abnormal eating disorder symptoms in patients with AN but not in the HCs, supporting the hypothesis that the RSFC in the FPCN may contribute to the regulation of pathological symptoms in patients with AN. The alteration of the FPCN neural function may be an important factor in causing pathological symptoms and psychological characteristics in patients with eating disorders, which is consistent with our findings. 23 The FPCN system is responsible for various cognitive functions and regulates eating behaviours. A lower function connectivity in the FPCN was associated with worse self-control in eating, further contributing to disturbed eating disorders. 24 Additionally, the PPC and dlPFC of the FPCN are involved in regulating eating behaviours through cognitive control when confronted with tempting food. 25 It has been suggested that the food consumption of individuals with AN is closely related to the dlPFC and dorsal striatal connectivity. 23 The above studies have provided the theoretical basis for our results.

The current study also showed that the aberrant RSFC in the PPC to IFG was negatively associated with self-reported eating disorder symptoms, loss of control and overeating, and that the treatment response was negatively associated with cognitive preoccupations about eating/body image. Interestingly, the aberrant RSFC in the dlPFC to MTG/SMG was positively associated with the treatment response to binge eating and loss of control/overeating behaviours of AN. On the other hand, we found a stronger RSFC in the PPC predicted poorer clinical outcomes regarding cognitive preoccupations about eating and body concerns. It may be that a stronger RSFC in the PPC implies more attention to eating, body shape and body weight, which leads to potential excessive attention to eating, body shape and body weight, as well as excessive control over diet. However, the increased RSFC in the dlPFC predicted an improvement in loss of control and binge eating or overeating behaviours, analogous to previous results that showed increased dlPFC activity of the cognitive control circuit predicted dietary improvements. 26

A recent study indicated that the FPCN, as a crucial cognitive control network, and the functions of its different parts were associated with the selection and maintenance of various stimuli and features. Its functional connectivity can also combine external information with internal representations to guide decision-making. 27 It has been argued that treatment response in patients with depressive disorder was associated with altered connectivity within and between networks, including the FPCN. 14 In addition, increased functional connectivity between the frontal cortex and the FPCN may be one of the neurological mechanisms involved in successfully resolving response conflicts. 27 Patients with AN may develop an adaptive neural mechanism to maintain their extreme eating behaviours due to prolonged malnutrition. The above theory may provide the rationale for the seemingly opposing results derived from the study. Further validation of the effect of the PPC and the dlPFC on patients with AN and the clinical outcomes is still needed. It is currently difficult to predict treatment response in patients with AN; however, the resting-state brain activity of the FPCN may serve as a biomarker to predict treatment response in AN. As noted in this study, the dlPFC is an important target for understanding the pathogenesis of AN, 28 and the symptoms of binge eating and loss of control/overeating behaviours. The study also found that the PPC may be a significant target for cognitive preoccupations about eating/body image in patients with AN, suggesting that it may serve as an important target for interventions addressing cognitive preoccupations that could be considered in future treatment protocols. This study did not track the FPCN function after treatment to determine long-term effectiveness, which needs further investigation.

No significant differences in the RSFC of the FPCN between the AN-R and AN-BP subtypes were found in this study. A long-term follow-up study on patients with different subtypes of AN showed that most patients with AN-R have an early age of onset, and most patients with AN started with the AN-R subtype. Approximately 88% of those with AN-R develop bulimic behaviours, and 62% of patients with AN-R eventually develop into the AN-BP subtype. 29 Based on our results, we conjecture that the RSFC of the FPCN is altered in the early onset of AN. Moreover, most patients are adolescents and not yet adults, a stage when the neural activity of the brain is still at a premature stage of development, and the brain is more plastic in structure and function compared to adults, 30 which may explain the short-term improvement of clinical symptoms.

Limitations

There are several limitations to the present study. First, the study was based on a cross-sectional analysis, which could not determine the causation, and it was not clear how the RSFC of the FPCN changed after treatment. Second, the study did not consider the effect of undernutrition on brain networks. Third, more participants than expected were lost to follow-up due to COVID-19 outbreak; thus, their BMI was not tracked. Fourth, treatment response in AN was expressed as a reduction rather than a reduction rate in the EDE-Q 6.0 total score, which may increase the power to predict the outcome. As the study was exploratory, we did not perform multiple tests for corrections to increase the likelihood of obtaining significant results. Since this study is the first to explore the neurobiological mechanisms of treatment response in AN, future research with larger samples and a longitudinal design is needed to validate our findings.

Implications

The current study provides evidence that the RSFC in the FPCN is increased in patients with AN versus HCs after controlling for emotional symptoms, and it yields an initial indication that the RSFC in the PPC to IFG may be a significant neural marker of self-reported eating disorder symptoms and treatment response to cognitive preoccupations about eating/body image. Also, the RSFC of the dlPFC to MTG/SMG may be a significant neural marker for treatment response to binge eating behaviours and loss of control/overeating behaviours of AN. These findings are helpful in further understanding the pathogenesis of AN, as well as opening up potential avenues for relevant target strategies for the prevention and treatment of this pervasive mental disorder.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

This study involves human participants and was approved by the Research Ethics Committee of Shanghai Mental Health Center, Shanghai Jiaotong University, China (ethical approval no: 2018-28; 2021ky-169). Participants gave informed consent to participate in the study before taking part.

Acknowledgments

We would like to express our sincere gratitude to all participants who contributed to the study and all of the editors and reviewers.

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Qianqian He obtained her doctoral degreefrom Shanghai Jiao Tong University School of Medicine in China in June 2023. During her doctoral program, she studied at the Shanghai Mental Health Center. She has worked as an attending physician in the Clinical Psychology Department of the Mental Health Center of Tongji University since August 2023, where she was awarded Outstanding Young Medical Talent of Pudong New Area. Her main research interests include neuromodulation and MRI, EEG, biogenetic pathogenesis, and clinical treatment research related to eating disorders and mood disorders, such as anxiety and adolescent depression.

Hui Zheng is a PhD student in his fourth year at Shanghai Jiao Tong University School in China. He is studying in the Shanghai Key Laboratory of Psychotic Disorders at the Shanghai Mental Health Center in China. He received his master's degree in psychology in 2019. His main research interests include the mechanisms and treatment of addiction by the use of MRI, EEG, TMS and TES. He is also interested in resilience and cognitive flexibility, especially maladaptive learning (eg, goal-directed [model-based] and habitual control [model-free] behaviour) in patients with addictive disorders, including substance use and behavioural addiction.

Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

• Data supplement 1

QH and HZ are joint first authors.

Permission Not applicable.

Contributors QH: methodology, formal analysis, writing—original draft preparation and visualisation. HZ: conceptualisation, writing—reviewing and editing. JZ and LY: writing—reviewing and editing. LY, QK, CL, LG, YC, YH, YW, SP and ZW: data curation and resources. QL: project administration. JC: funding and supervision. JC: responsible for the overall content. All authors read and approved the final manuscript.

Funding The study was supported by grants from Shanghai Jiao Tong University (YG2022ZD026), National Natural Science Foundation of China (81771461, 82071545), Science and Technology Commission of Shanghai Municipality (20Y11906500), Shanghai Clinical Medical Research Center for Psychiatric and Psychological Disorders (19MC1911100), hospital-level research projects of Shanghai Mental Health Center (2020-YJ09, 2020-QH-04) and Youth Project of Shanghai Health Commission (20224Y0267).

Competing interests None declared.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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Clinical Case Discussion: Binge Eating Disorder, Obesity and Tobacco Smoking

Marney a. white.

1 Department of Psychiatry, Yale University School of Medicine

Carlos M. Grilo

2 Department of Psychology, Yale University School of Medicine

Stephanie S. O'Malley

Marc n. potenza.

3 Child Study Center, Yale University School of Medicine

This clinical case involves an obese woman requesting treatment for her binge eating and obesity. The information is presented to expert clinicians who provide their thoughts regarding the case, assessment, treatment formulation, and associated clinical and research issues.

Case Description

A 48-year old African American woman presented for treatment for binge eating and weight loss. She presented for treatment following a recent routine physical examination during which her primary care physician noted concerns about her increasing weight. The physician recommended that she try to lose weight but did not provide any specific or further guidance. In light of her previous “failed” experiences with commercial weight loss programs, she decided to seek treatment at a university-based program. At initial evaluation, she was 64 inches tall and weighed 230 pounds yielding a body mass index (BMI) of 39.5, which reflects obesity. She had moderately elevated blood pressure and high cholesterol but was otherwise in good health. The patient completed college and a master's degree in education and had been employed as a special education teacher in the same job for 11 years. She lived with her husband of 24 years, and one of her two adult children. She reported that her relationships with her husband and family were good, that her job was enjoyable and rewarding, and that she had a good circle of close relationships.

Weight and dieting history

The patient reported an onset of overweight during adulthood. She reported having been involved in sports throughout childhood, and although she viewed herself as ‘big-boned’, she did not have body image concerns nor did she recall feeling dissatisfied with her weight or shape when younger. She denied any significant dieting behaviors until age 29. She reported maintaining a weight of approximately 150 pounds (BMI = 25.7) until age 28, at which age she became pregnant with her second child. She reported that she never fully lost the ‘baby weight’ and subsequently began to gradually gain weight throughout her 30s despite numerous dieting efforts. She reported a rapid weight gain of approximately 25 pounds in the past 6 months.

Binge eating

The patient reported an onset of “eating binges” at approximately age 16. The binge eating began soon after she began babysitting for neighborhood children. She estimated that she would engage in binge eating approximately 1-2 times per month which occurred during times that she babysat at night and had access to assorted snack foods. During those times she would ‘load up on junk food’ that the family had provided. She recalled that she would eat chips, cookies, and brownies “non-stop,” and that these eating episodes often lasted throughout the evening. She recalled feeling a loss of control during these episodes and stated that she would continue to eat despite not feeling physically hungry and that she would not stop until feeling physically ill. She reported that she was very embarrassed and secretive about these eating behaviors. She also recalled feeling embarrassed when worried that it was likely that the missing food was apparent to the family for whom she was babysitting. She denied any history of extreme inappropriate weight control or purging behaviors such as self-inducing vomiting or misusing laxatives.

The patient reported infrequent and sporadic binge eating throughout her late teens and early 20s, estimating a frequency of once per month which tended to correspond with social functions. During her 30s, however, the frequency of her binge eating increased considerably and became more regular except during periods of dieting efforts. The patient reported that she had enrolled in commercial weight loss programs approximately five different times, and had, in addition, tried to follow multiple self-help diets. She reported that when she was following a weight loss plan, she could successfully lose approximately 10 pounds, but that she would ‘hit a wall’ and discontinue after about one month of dieting. She reported that in-between diets, her binge eating would resume at a frequency of 2 to 3 times per week, and persist at that level until the next dieting attempt. The patient reported that she had not engaged any formal dieting in the past 18 months, although she frequently skipped meals in an effort to reduce her weight.

Recent course

The patient noted an increase in binge eating frequency approximately six months ago, corresponding with her mother's hospitalization and rapid physical decline. The patient was the primary caregiver for her mother, and noted that the months preceding her mother's death were extremely stressful. She reported that her binge eating increased in frequency to 3 to 4 times per week during her mother's illness, and increased to 6 to 7 times per week following her mother's death.

The patient described her typical binge episode as starting with an evening meal and extending for several hours. Her daily pattern of eating was to skip breakfast, and to consume a standard school cafeteria lunch at 11:30 a.m. She would then not eat again until preparing the evening meal, at which point she would ‘graze’ while cooking. The patient reported that most nights she would eat a ‘normal’ meal with her family, consisting of 5-6 ounces of meat, 2 or 3 types of vegetables, and bread. However, she would then eat the ‘leftovers’ while cleaning up after the meal, such that overall she would have consumed the equivalent of two full meals. She would then eat various foods throughout the rest of the evening until bedtime. During these episodes, she would alternate between salty and sweet snacks. One example binge episode, occurring approximately 30 minutes after the evening meal and spanning the two hours before bedtime, included: a roll of Ritz crackers with 6 ounces of cheese, 2 doughnuts, 4 handfuls of Chex mix, and ½ of a large (12 oz.) Cadbury candy bar.

Smoking History and Cessation

The patient reported that she had recently quit smoking ‘cold turkey’ and had successfully maintained abstinence for four months. She reported quitting smoking following the death of her mother because she died of cancer. She quit smoking without any professional help and without the use of any nicotine replacements or medications to assist with the smoking cessation.

In terms of her smoking history, the patient reported that she began smoking at age 18, that she had successfully quit smoking upon becoming pregnant at age 24, but resumed when she returned to work 11 years ago. She reported a daily smoking frequency of 15 to 20 cigarettes per day. She reported no serious efforts to stop smoking during the past 11 years prior to this recent period of complete abstinence. The patient reported that since quitting smoking, she has experienced more frequent and intense urges to binge eat, and that in the few weeks prior to intake the urges to smoke had increased in frequency and intensity. She reported urges to smoke primarily in the evenings.

Diagnostic Instrument

In addition to a standard intake history, the patient was administered the Eating Disorder Examination (EDE; Fairburn and Cooper, 1993 ). The EDE is a semi-structured investigator-based interview that evaluates current eating behaviors and eating disorder psychopathology. The EDE focuses on the previous 28 days, except for diagnostic items – such as binge eating behaviors - which are assessed for the duration stipulations for each ED. More specifically, the EDE assesses the frequency of different forms of overeating, including objective bulimic episodes (binge eating defined as unusually large amounts of food with a subjective sense of loss of control) and various inappropriate weight control methods (e.g., purging, laxative abuse, etc). The EDE contains four scales reflecting different aspects of ED psychopathology (dietary restraint, eating concerns, weight concern, and shape concern). The EDE is considered the best-established method for assessing and tracking over time the behavioral and cognitive features of EDs and has psychometric support specifically with BED ( Grilo, Masheb, Lozano-Blanco, & Barry, 2004 ; Grilo, Masheb, & Wilson, 2001 ). The interview was administered before treatment and at treatment conclusion to evaluate treatment gains.

The patient was treated with 12 weekly individual sessions of cognitive behavioral therapy (CBT) for binge eating. Expert opinion ( Wilson, Grilo, & Vitousek, 2007 ) and quantitative meta-analytic reviews ( NICE, 2004 ) conclude CBT is the best-established and treatment-of-choice for BED. CBT, a focal and structured treatment, consists of three overlapping phases conducted in a collaborative and interactive method with patients. The first phase focused on educating the patient about the nature of binge eating. Standard behavioral strategies such as self-monitoring and record keeping were used to help the patient identify better her disordered eating patterns while working towards the central goal of normalizing and achieving a structured regular pattern of eating (i.e., not skipping meals). The second phase integrated cognitive procedures to help the patient identify and challenge maladaptive cognitions regarding her eating, possible triggers for dyscontrol, and associated eating/shape concerns. The final phase focuses on consolidating and maintaining the changes and relapse prevention issues.

During the overview of treatment and the ‘meal pattern prescription,’ the patient became tearful, stating that she is not organized enough to follow a meal pattern consisting of three meals and three snacks. She expressed a fear that eating more frequent meals would result in more weight gain, and stated that she was fearful of failing at another weight loss effort. The patient was encouraged to follow the meal and snack pattern as an ‘experiment’ for the first week of treatment. When the patient's fears were alleviated (i.e., disproved owing to weight maintenance during the first week of treatment), she moved through the treatment steps without difficulty. Although she initially voiced concern about the self-monitoring she eventually regarded it as one of the most essential tools that she gained during the treatment.

Overall, at treatment completion the patient's binge eating had remitted fully. She reported no objective bulimic episodes in the last 4 weeks of treatment. Her weight remained relatively stable, with a post-treatment weight loss of five pounds (final weight = 225; BMI = 38.6). Although the patient was pleased to have stopped binge eating, she reported continued distress over her weight and a persisting desire to lose weight.

Carlos M. Grilo, Ph.D.

This clinical case involves a combination of a behavioral (BED) and a physical medical problem (obesity) that often co-occur. This case is also notable for a positive lifetime history of a pharmacological addiction (nicotine) despite not being “active” at the time of presentation for treatment for the eating/weight concerns might nonetheless have important implications. In several respects, this case is fairly typical of BED in obese persons and serves to illustrate a number of important issues facing clinicians and researchers.

Background: Diagnosis, Distribution, and Clinical Features of BED

BED is a specific example of eating disorder not-otherwise-specified (EDNOS) and was included as a “research category” with provisional research diagnostic criteria in Appendix B of the DSM-IV ( American Psychiatric Association, 1994 ). BED is defined primarily by recurrent episodes of binge eating without the regular use of inappropriate compensatory weight control methods (such as purging) that characterize bulimia nervosa (BN). Binge eating is defined as eating unusually large amounts of food while experiencing a subjective sense of loss of control. The research criteria require marked distress about the binge eating and that the binge eating occurs on at least two days per week over the past six months. Unlike the two “formal” eating disorders (anorexia nervosa and bulimia nervosa), the DSM-IV does not include a cognitive criterion pertaining to disturbed body image (i.e., overvaluation of shape or weight) for the diagnosis of BED although such disturbances are present in many patients with BED ( Grilo, Hrabosky, White, Allison, Stunkard, & Masheb, 2008 ). Research has supported the distinctiveness of BED from both other eating disorders (BN) and from obesity without co-existing binge eating (Grilo, Crosby et al., in press; Grilo, Masheb, & White, in press ). A recent critical review of the literature concluded that there exists sufficient empirical evidence to support the inclusion of BED as a distinct and formal ED diagnosis in the DSM-V ( Striegel-Moore & Franko, 2008 ).

Recent epidemiological research has reported a prevalence rate for BED of roughly 3.5% in adult women, which is greater than anorexia nervosa and bulimia nervosa combined ( Hudson, Hiripi, Pope, & Kessler, 2007 ). The distribution of BED is much broader and more diverse than that of the other eating disorders. BED is evenly distributed throughout adulthood and is common in both men and women as well as across ethnic and racial groups ( Hudson et al., 2007 ; Grilo, Lozano, & Masheb, 2005 ). BED is strongly associated with obesity (which is not a required criterion) ( Hudson et al., 2007 ) and therefore with substantially increased morbidity associated with excess weight (e.g., diabetes, metabolic problems). The excess weight in patients with BED is attributable to a combination of binge eating in the absence of weight compensatory behaviors in addition to a general lack of dietary “restraint” that is salient and characteristic of the other eating disorders ( Grilo, 2010 ). Patients with BED who seek treatment are typically older than patients with other eating disorders despite the fact that many report a longstanding duration of the binge eating often dating back to adolescence ( Reas & Grilo, 2007 ). Moreover, unlike the case for the other eating disorders, which most frequently begin following intensive dieting attempts, nearly half of patients with BED report that the onset of their binge eating preceded their first diet ( Reas & Grilo, 2007 ). Regardless of the exact longitudinal sequence, the binge eating and the associated weight gain over time motivate multiple diet attempts over time many of which are not successful ( Reas & Grilo, 2007 ; Roehrig, Masheb, White, & Grilo, 2009 ).

Observations About the Specific Case

I will offer a number of observations regarding this specific case that are illustrative regarding selected issues of relevance to clinicians and researchers. This case is typical in a number of important respects yet it differs in several important ways that I will note with a view of characterizing the heterogeneity of this behavioral disorder. Evolving research has identified a number of treatments that have efficacy for a majority of such patients although two major challenges remain. First, many patients with BED do not get accurately identified, and few receive empirically-supported treatments ( Wilson, Grilo, & Vitousek, 2007 ).

Treatment-Seeking

Although obese patients with BED have elevated psychiatric and medical problems and greater health care utilization patterns relative to their obese peers who do not binge eat, they infrequently seek specialized psychological or psychiatric care for their binge eating. Obese persons who binge eat, along with many generalist health care providers, frequently see the binge eating problem as merely reflecting their obesity and need for better diet and weight loss. In this case, the patient and her physician discussed the need for weight loss, although her binge eating problem was not specifically addressed. Despite not being able to provide the patient with specific guidance, this interaction nonetheless represents an important first step. Many health care providers are uncomfortable in raising or discussing excess weight issues with their patients. This is likely due to a many reasons including, for example, negative biases or views about obesity, personal discomfort, perceived lack of expertise, and concerns about “harming” the therapeutic relationship ( Puhl & Heuer, 2009 ). The patient-physician interaction in this case seemed positive enough to support and motivate the patient to seek more specialized care. It is critically important for generalist health care providers to be receptive and open when discussing their patients' excess weight and potential treatment avenues.

Clinical Presenting Picture

This patient presented with co-occurring obesity and BED. Although she had moderately elevated blood pressure and high cholesterol, she had not yet developed metabolic syndrome although she was clearly at risk to do so along with other medical problems. Thus, her proactive treatment-seeking is certainly a very positive step. This is noteworthy because some research has suggested that black women who are obese and who binge eat are less likely to seek treatment than their white peers until both problems are substantially worse ( Grilo, Lozano, & Masheb, 2005 ; Pike, Dohm, Striegel-Moore, Wilfley, Fairburn, 2001 ). Her primary concern was her increasingly weight gain that started in her 30s despite numerous dieting attempts. More recently, her weight gain had increased markedly and this seemed related, in part, to her increased binge eating behaviors. Based on her clinical history, she did not seem to suffer from body image dissatisfaction or from body image disturbance that are characteristic of eating disorders. The EDE interview provides specific quantification of different aspects of body image disturbance and would yield detailed information regarding behavioral, affective, and cognitive aspects of body image to inform both treatment interventions and to assess changes over time ( Grilo et al., 2001 ). Although the absence of such body image problems in this specific patient signals a less disturbed variant of BED ( Grilo et al., 2008 ) with a positive prognosis ( Masheb & Grilo, 2008a ), treating the obese patient with BED will still remain challenging relative to treating obesity only ( Grilo et al., 2008 ). She did not appear to have significant psychosocial problems either independent or associated with the obesity and BED. Her psychosocial functioning seemed rather positive and this is not uncharacteristic of many patients with BED. Conversely, since it is not uncommon for many patients to have associated psychosocial problems, clinicians should routinely assess for any on-going difficulties as context for formulating and implementing treatment. Importantly, the patient did report a specific life stressor (her mother's death) which seemed associated with an intensification of her binge eating.

Psychiatrically, no additional lifetime or current problems were reported, although no formal structured diagnostic interview was administered. Patients with BED have elevated lifetime rates of psychiatric disorders, including most notably mood, anxiety, and substance use disorders ( Grilo, White, & Masheb, 2009 ), although roughly 25% have never experienced another psychiatric problem. For comprehensive treatment formulation and planning, the presence of other psychiatric disorders should be carefully ascertained. However, it is noteworthy that psychiatric co-morbidity has not emerged as a significant predictor or moderator of outcomes for BED treatments that have empirical support ( Masheb & Grilo, 2008b ; see Wilson et al., 2007 ).

The positive smoking history is especially noteworthy in this patient. Unfortunately, the significance of smoking in this patient group is still poorly understood and is often overlooked by clinicians and researchers alike. This case suggests some potentially important associations among smoking, eating, and weight domains. First, preliminary research suggests that smoking histories are not uncommon in patients with BED and, if present, signal increased risk for psychiatric problems, most notably anxiety disorders ( White & Grilo, 2006 ). Although this patient was not determined to have anxiety disorder co-morbidity, both binge eating and smoking may serve to regulate affect. The exacerbation of the patient's binge eating immediately following her mother's death and her smoking quit attempt can perhaps be conceptualized in this way (i.e., increased binge eating to cope with increased negative affect). Second, preliminary research also suggests that BED patients with smoking histories are characterized by heightened levels of maladaptive and rigid eating and dieting behaviors as well as heightened food “cravings” that must be addressed along with the binge eating ( White & Grilo, 2007 ). Third, weight gain following smoking cessation is common and may be especially problematic for obese patients with BED. A recent study found that obese patients with BED reported gaining significantly more weight following a smoking quit attempt than their non-binge-eating obese peers ( White, Masheb, & Grilo, in press ). This patient's rapid recent weight gain following her most recent smoking quit attempt is consistent with this finding and represents an important clinical challenge because it potentially represents a challenge to continued abstinence.

This patient's eating behavior and patterns are fairly representative of patients with BED. First, binge eating occurs most frequently during evenings, although many patients report having episodes at varying times throughout the day. The large amount consisting of mixed foods often based on availability and ease is typical. Also typical in this patient group is that the binge eating often follows eating behaviors or episodes that are occurring without a sense loss of control. Unlike bulimia nervosa where the binge eating episodes are very clear episodes following excessive restraint, patients with BED are characterized by a more chaotic and amorphous eating pattern. This patient attempts some dietary restraint (skipping breakfast, not eating for long period following lunch) but her eating is fairly continuous throughout the evening. Rather than eating a clear meal (dinner), she appears to eat continuously and during part of this time also experiences a sense of loss of control. Thus, these patients require assistance in several complex tasks including: normalizing and scheduling their eating (i.e., not skipping meals), lessening certain maladaptive restraint behaviors (i.e., not going long periods without eating), increasing certain adaptive restraint behaviors (i.e., not overeating during meals, not grazing or nibbling at odd times), in addition to eliminating the binge eating episodes (Allison, Grilo, Masheb, & Stunkard, 2006; Masheb & Grilo, 2006 ).

Treatment Options

Critical meta-analytic ( NICE, 2004 ) and qualitative reviews ( Wilson et al., 2007 ) of the treatment literature have concluded that cognitive behavioral therapy is the treatment of choice for BED. Studies of CBT for BED consistently report remission rates of 50% or greater along with broad improvements in associated psychological and psychosocial functioning, although weight loss tends to be minimal ( Wilson et al., 2007 ). Different research groups have documented that CBT is superior to other active treatments, including behavioral weight loss therapy ( Grilo & Masheb, 2005 ; Wilson et al., in press ) and pharmacotherapy with fluoxetine ( Grilo et al., 2005 ; Ricca et al., 2001 ), and that the benefits of CBT for BED are well-maintained through 24-months (Wilfley, Wilson, & Agras, 2008) following treatment. There is also some empirical support for two alternative psychotherapies (interpersonal psychotherapy and dialectical behavior therapy) which also produce substantial reductions in binge eating but, like CBT, fail to reduce weight ( Wilson et al., 2007 ). Finally, there is also empirical support for behavioral weight control therapy (structured manualized treatment delivered by professionals but not necessarily for the widely-available commercial programs or self-help diets) for reducing binge eating although findings regarding weight losses are also surprisingly mixed ( Grilo & Masheb, 2005 ; Wilson et al., 2007 ). Lastly, a critical meta-analysis of pharmacotherapy treatment research concluded that certain medications have a clinically significant advantage over placebo for producing short-term remission from binge eating and for reducing weight, although the weight losses tend to be quite modest and of uncertain clinical significance ( Reas & Grilo, 2008 ). The meta-analysis highlighted the potential efficacy of an anti-obesity agent (sibutramine) and anti-epileptic medications (particularly topiramate) but suggested more limited utility of SSRIs given their smaller effects on binge eating and essentially no effect on weight. Unlike the psychosocial treatments, the longer-term effects of these medications are unknown. The few available data from blinded ( Grilo, Masheb, & Wilson, 2005 ) and open-label ( Ricca et al., 2001 ) trials directly comparing the effectiveness of pharmacotherapy and psychological treatments indicate that CBT is significantly superior to SSRIs. In terms of combining approaches, most studies have found that adding pharmacotherapy to psychological approaches has generally not enhanced outcomes ( Reas & Grilo, 2008 ). Noteworthy exceptions are studies that reported adding orlistat ( Grilo, Masheb, & Salant, 2005 ) or topiramate ( Claudino et al., 2007 ) to CBT significantly enhanced the weight losses.

Treatment Course

Thus, it is fortunate that this patient sought treatment at a university-based program where she was offered an empirically-supported treatment. This patient's response to CBT was fairly typical in that she experienced an early and rapid response to the treatment ( Grilo, Masheb, & Wilson, 2006 ; Masheb & Grilo, 2007 ), stopped binge eating entirely by the end of treatment, but unfortunately did not lose weight. Many obese patients with BED fail to lose clinically meaningful amounts of weight despite the substantial reductions in binge eating achieved via CBT, which is not unlike the case for other psychological ( Wilson et al., 2007 ) and pharmacological treatments ( Reas & Grilo, 2008 ). Although the patient failed to lose significant weight (only five pounds), the CBT and presumably the cessation of binge eating were associated with a stabilization of weight. The patient entered treatment following a period of rapid and marked weight gain so the weight stabilization does represent a potentially important first step. Unfortunately, the failure to produce weight loss does leave this patient at risk for developing medical problems and given her frustration and distress about the weight may put her at heightened risk for relapse in both the binge eating and the smoking domains.

Future Directions

Finding ways to produce or enhance weight loss in obese patients with BED represents a major research priority ( Grilo, 2010 ). Interestingly, research has found that combining treatments, for example combining pharmacotherapy, has generally not enhanced outcomes ( Reas & Grilo, 2008 ). Possible notable exceptions have included findings from controlled trials suggesting that adding orlistat ( Grilo, Masheb, & Salant, 2005 ) or topiramate ( Claudino et al., 2007 ) may enhance weight losses achieved with CBT for BED. It has been suggested that greater attention to non-normative eating behaviors and patterns ( Masheb & Grilo, 2006 ) in addition to the CBT focus on normalization of eating meals and reducing binge eating may facilitate greater weight loss. Future treatment studies should include analyses of mediators of outcomes in order to guide the process of improving further our existing treatments ( Wilson et al., 2007 ).

Stephanie S. O'Malley, Ph.D.

This case history highlights the important interface between smoking and binge eating behavior and suggests how treatment of binge eating may have beneficial effects on maintenance of smoking abstinence.

Co-occurring Conditions and Complicating Factors

While smokers tend to be leaner compared to nonsmokers, a significant proportion of obese individuals smoke, placing them at increased risk of attendant health consequences such as diabetes and cardiovascular disease. Smoking related health consequences, experienced by the smoker or another family member, often motivate a smoker to quit as was the case for this patient. However, women compared to men are less likely to remain abstinent from smoking despite a motivating “health shock” for a variety of reasons, including concerns about weight gain. Smoking cessation can result in weight gain at one year of about 11 pounds on average, due to decreased energy expenditure, increased appetite and greater food intake. The degree of weight gain, however, is variable. Binge eating appears to be an important risk factor. In a retrospective study of overweight individuals who had quit smoking, those with significant binge eating problems gained substantially more weight in the year following smoking cessation (24.6 pounds) compared to those without binge eating (11 pounds) ( White, Masheb & Grilo, in press ).

Consistent with this report, this patient recently experienced rapid weight gain that initially began during the stressful period of her mother's illness and coincided with a four-month period of smoking abstinence. Her weight gain of 25 pounds over the recent six months, four of which followed smoking cessation, suggests that without intervention her binge eating is a major risk factor for continued weight gain.

Her maladaptive eating may also place her at risk of smoking relapse. Indeed, she reports that her urges to smoke had increased in recent weeks and were more intense in the evenings. Her pattern of depriving herself of food during the day and then binge eating in the evening could undermine maintenance of smoking abstinence in several ways. Food deprivation can increase the reinforcing effects of drugs, including nicotine, making any lapses to smoking more likely to promote continued smoking. Her efforts to resist eating may also tax her self-control resources and undermine her ability to resist smoking. The evening binge eating episodes she reports follow restricted eating during the day and may result in abstinence violation effects in which she experiences demoralizing recriminations over her loss of control. The resulting increase in negative affect and decreased self-efficacy could promote smoking urges and place her at risk of resorting to smoking to cope with negative affect, a common risk factor for smoking relapse. Finally, the expectation that smoking can limit binge eating is another risk factor for smoking relapse.

Treatment Considerations

Given this conceptualization, the treatment plan for her binge eating may help her also remain abstinent from smoking. The “meal prescription” of regular meals and several small snacks should prevent periods of food deprivation that could increase smoking urges, and diminished frequency of binge eating should increase feelings of self-efficacy and remove the compensatory need for smoking to limit binge eating. The remission of her binge eating and the resulting stabilization of her weight may remove the motivation to resume smoking in an effort to manage her weight.

Cognitive behavioral therapy for eating disorders, including binge eating, also addresses the development of alternative coping skills for handling negative affective states and other triggers of maladaptive eating patterns. Given that many smokers use smoking to cope with negative affective states, teaching her alternative coping skills for handling negative affect is likely to have benefits that generalize and help her maintain abstinence from smoking. The therapist could make this connection explicit by examining the circumstances that elicit the urge to smoke, noting any parallels with the circumstances that provoke binge eating as a coping strategy and emphasizing that the new coping skills learned as alternatives to maladaptive eating could serve as alternatives to smoking as a coping response. Evidence for coping skills therapy targeted to one maladaptive behavior generalizing to another behavior is evident in a study of cognitive behavioral therapy for alcoholism, in which improvements in eating disturbances occurred in addition to reductions in alcohol intake ( O'Malley et al., 2007 ). Learning new coping skills and introducing a regular pattern of eating during the day could ultimately minimize stress, a major precipitant of binge eating and smoking.

In the smoking literature, a recent meta-analysis concluded that smoking interventions that incorporate a weight control component result in short-term (< 3 months) improvements in smoking abstinence and reduced weight gain compared to smoking cessation interventions alone ( Spring et al., 2009 ). In one study, for example, a cognitive behavioral intervention designed to reduce over-concern with weight gain improved smoking quit rates and reduce weight gain compared to standard care or a weight control intervention ( Perkins et al., 2001 ). Further development of CBT interventions for weight concerned smokers may be well served by incorporating additional elements of CBT for binge eating, such as meal patterning, especially for those with a history of binge eating or other eating disorder that may predispose for the development or worsening of eating problems during a quit attempt. Likewise, the clinician should consider smoking history in the management of obese patients who present for treatment of binge eating disorder. As a group, these individuals have higher overall psychiatric co-morbidity and more severe binge eating pathology than overweight individuals without a history of smoking and may require specialized care ( White & Grilo, 2006 , 2007 ).

Marc N. Potenza, M.D., Ph.D.

Diagnostic considerations.

The current case describes the treatment of an individual who has demonstrated seemingly excessive engagement in two domains – tobacco use and food consumption. In anticipation of DSM-V, there exist discussions about how best to define and categorize disorders seemingly addictive in nature, and whether excessive engagement in non-drug behaviors (e.g., pathological gambling) might be grouped together with substance use disorders as addictions ( Petry, 2006 ; Potenza, 2006 ). The current case raises questions about whether excessive eating behaviors manifesting in BED and/or obesity might similarly be considered within an addiction framework, and, if so, how such a conceptualization might influence studies into the etiology, prevention and treatment of “behavioral” and drug addictions ( Grant et al., 2006 ; Holden, 2001 ).

Historically, the term “addiction” has undergone multiple changes in usage. Derived from the Latin word meaning “bound to” or “enslaved by”, the term was originally used independent of drug use. However, several hundred years ago the term became linked to excessive patterns of alcohol use and more recently drug use such that by the time when DSM-III-R was being generated, expert consensus was that “addiction” referred to compulsive drug-taking ( O'Brien et al., 2006 ). More recently researchers have proposed core elements of addiction (continued engagement despite adverse consequences, a compulsive quality, an appetitive urge typically preceding engagement in the behavior, and diminished self-control over the behavior) ( Potenza, 2006 ; Shaffer, 1999 ). If these features are seen as the defining qualities of addiction, then conditions like BED and obesity might be considered as addictions ( Volkow and O'Brien, 2007 ; Volkow and Wise, 2005 ).

Mechanisms and Treatment

Obesity, like addictions, appears to have multiple environmental and biological factors contributing to the disorder ( Gearhardt, Corbin, & Brownell, 2009 ; Gold et al., 2009 ). For example, food availability and advertising may increase the societal rates of obesity ( Brownell, 2004 ), and individual difference factors (e.g., specific genetic allelic variants) may predispose people to greater risks for obesity ( Paracchini et al., 2005 ; van Deneen et al., 2009 ). Arguably, a historical focus on the biological mechanisms underlying obesity has involved metabolism and imbalanced energy homeostasis (i.e., “energy in” and “energy out”) ( Abizaid et al., 2006 ). However, the application of motivational behavioral models to food consumption, like those that have been applied to drug use ( Chambers et al., 2003 ; Everitt and Robbins, 2005 ), may lead to identification of novel factors involved in the pathophysiology of obesity and BED ( Volkow and Wise, 2005 ; Hoebel et al.., 2009 ). Given that neurocircuity implicated in drug abuse appears similarly implicated in obesity (e.g., relatively diminished dopamine D2-like receptor availability in the striatum ( Wang et al., 2004 ; Wang et al., 2009 )), additional research is warranted to understand more completely the biological similarities and differences between drug addictions and obesity. The more complete and precise identification of these similarities and differences could help advance prevention and treatment strategies across disorders. Such a strategy has proven fruitful for pathological gambling, where proposed mechanisms underlying pathological gambling and substance addictions led to the hypothesis that opioid antagonists such as naltrexone, approved for the treatments of alcohol dependence and opioid dependence, would be efficacious in the treatment of pathological gambling ( Brewer et al., 2008 ; Grant et al., 2008 ; Tamminga and Nestler, 2006 ). Analogously, glutamatergic agents (e.g, N-acetyl cysteine) have demonstrated initial promise with respect to weight loss, tobacco smoking, pathological gambling and cocaine dependence ( Souza et al., 2008 ; Knackstadt et al., 2009; Grant et al., 2007 ; LaRowe et al., 2006 ), and further research is needed to further evaluate their efficacies and tolerabilities, particularly amongst dually diagnoses populations.

Specific aspects of the case also warrant mention as they relate to the relationship between disorders, like drug dependence, typically have been conceptualized as addictions and others, like obesity and BED, that typically have not. For example, it is noteworthy that the patient reports having recently quit smoking prior to entering treatment, as well as having had several periods of time of time when she was smoking regularly and others when she had quit for prolonged durations. This pattern raises questions about the natural history of smoking and eating behaviors, both individually and in conjunction. Addictions have historically been considered chronic relapsing conditions, a conceptualization based in considerable part on clinical samples. Epidemiological data suggest that both “behavioral” and drug addictions might follow less pernicious natural histories than originally thought, with many individuals recovering without formal interventions ( Slutske, 2006 ; Tamminga et al., 2006 ). Nonetheless, many individuals do require formal interventions, often on multiple occasions. Furthermore, how one behavioral domain might influence the other is incompletely understood. The phenomenon of “switching addictions”, as is suggested in other domains (e.g., alcoholism and problem gambling ( Potenza et al., 2005 )), may be reflected here in increased food cravings, food consumption and weight gain following smoking cessation, with multiple possible contributing mechanisms related to motivation, metabolic changes, stress reduction, or coping with uncomfortable or dysphoric states, as Dr. O'Malley indicates.

Life stressors appear to play an important role in the patient's clinical course, both with respect to smoking and eating. As such, therapies like CBT that include instruction in healthy coping strategies might be particularly relevant for the patient. From a biological perspective, the neural mechanisms underlying stress responses overlap with those implicated in impulse control and addiction ( Kalivas and Duffy, 1989 ; Piazza and Le Moal, 1996 ). Consistently, identification of specific intermediary phenotypes or endophenotypes in the domains of stress responsiveness and impulsivity would appear to have important implications across a broad range of disorders, including obesity, BED and nicotine dependence ( Blanco et al., 2009 ). As Dr. Grilo notes, combinations of pharmacological and behavioral therapies might be most helpful for BED, and consideration of pharmacological agents that target important intermediary phenotypes will represent important areas of future development.

Concluding Comments and Future Directions

The changes over time in the patient's smoking and eating behaviors highlight the importance of considering behaviors with addictive potential within a developmental framework, particularly as early problems have important implications for adult functioning ( Chambers et al., 2003 ). Early life interventions aimed at developing healthy eating, exercise, stress-coping skills, emotional regulation and general health behaviors at early ages, and particularly involving youth who might be considered high-risk, will be important in preventing the development of a broad range of addictive disorders including obesity ( Merlo et al., 2009 ). Public health interventions like those that appear effective in reducing youth smoking (e.g., increased taxation of cigarettes) warrant consideration for foods associated with obesity ( Brownell et al., 2009 ). It is likely that only through multiple interdisciplinary approaches will we be able to effectively target the public health concerns of obesity and drug addictions, ones that currently are estimated to cost US society hundreds of billions of dollars annually and impart significant personal and familial suffering ( Surgeon General, 2001 ; Uhl and Grow, 2004 ; Potenza and Taylor, 2009 ).

Acknowledgments

Acknowledgments and Disclosures: This work was supported by the NIH grants RL1 AA017539, UL1 DE19586, K23 KD071646, K24 DK070052, R01 DK49587, RC1 DA028279, P50 AA015632, NIH Roadmap for Medical Research/Common Fund, and the VA VISN1 MIRECC. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of any of the funding agencies. Dr. Potenza has received financial support or compensation for the following: Dr. Potenza consults for and is an advisor to Boehringer Ingelheim; has consulted for and has financial interests in Somaxon; has received research support from the National Institutes of Health, Veteran's Administration, Mohegan Sun Casino, the National Center for Responsible Gaming and its affiliated Institute for Research on Gambling Disorders, and Forest Laboratories, Ortho-McNeil, Oy-Control/Biotie and Glaxo-SmithKline pharmaceuticals; has participated in surveys, mailings or telephone consultations related to drug addiction, impulse control disorders or other health topics; has consulted for law offices and the federal public defender's office in issues related to impulse control disorders; provides clinical care in the Connecticut Department of Mental Health and Addiction Services Problem Gambling Services Program; has performed grant reviews for the National Institutes of Health and other agencies; has given academic lectures in grand rounds, CME events and other clinical or scientific venues; and has generated books or book chapters for publishers of mental health texts. Dr. Grilo has received research support from the National Institutes of Health, medical research foundations (Donaghue Foundation, American Heart Association, Borderline Personality Research Foundation), has delivered lectures and papers at scientific conferences, and has generated books and chapters for academic book publishers. Dr. O'Malley is a member of the ACNP workgroup, the Alcohol Clinical Trial Initiative, sponsored by Eli Lilly, Janssen, Schering Plough, Lundbeck, Glaxo-Smith Kline and Alkermes; a partner in Applied Behavioral Research; a Scientific Panel member, Butler Center for Research at Hazelden. Dr. O'Malley participates in studies in which Nabi Biopharmaceuticals and Sanofi Aventis donated medications, has given academic lectures at professional societies and has received grant support form the National Institutes of Health.

All authors report no conflicts of interest with the current manuscript.

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