Epidemiological data offer conflicting views of the natural course of binge-eating disorder (BED), with large retrospective studies suggesting a protracted course and small prospective studies suggesting a briefer duration. We thus examined changes in BED diagnostic status in a prospective, community-based study that was larger and more representative with respect to sex, age of onset, and body mass index (BMI) than prior multi-year prospective studies.

Probands and relatives with current DSM-IV BED (n = 156) from a family study of BED (‘baseline’) were selected for follow-up at 2.5 and 5 years. Probands were required to have BMI > 25 (women) or >27 (men). Diagnostic interviews and questionnaires were administered at all timepoints.

Of participants with follow-up data (n = 137), 78.1% were female, and 11.7% and 88.3% reported identifying as Black and White, respectively. At baseline, their mean age was 47.2 years, and mean BMI was 36.1. At 2.5 (and 5) years, 61.3% (45.7%), 23.4% (32.6%), and 15.3% (21.7%) of assessed participants exhibited full, sub-threshold, and no BED, respectively. No participants displayed anorexia or bulimia nervosa at follow-up timepoints. Median time to remission (i.e. no BED) exceeded 60 months, and median time to relapse (i.e. sub-threshold or full BED) after remission was 30 months. Two classes of machine learning methods did not consistently outperform random guessing at predicting time to remission from baseline demographic and clinical variables.

Among community-based adults with higher BMI, BED improves with time, but full remission often takes many years, and relapse is common.

Loss of control eating is more likely to occur in the evening and is uniquely associated with distress. No studies have examined the effect of treatment on within-day timing of loss of control eating severity. We examined whether time of day differentially predicted loss of control eating severity at baseline (i.e. pretreatment), end-of-treatment, and 6-month follow-up for individuals with binge-eating disorder (BED), hypothesizing that loss of control eating severity would increase throughout the day pretreatment and that this pattern would be less pronounced following treatment. We explored differential treatment effects of cognitive-behavioral guided self-help (CBTgsh) and Integrative Cognitive-Affective Therapy (ICAT).

Individuals with BED (N = 112) were randomized to receive CBTgsh or ICAT and completed a 1-week ecological momentary assessment protocol at baseline, end-of-treatment, and 6-month follow-up to assess loss of control eating severity. We used multilevel models to assess within-day slope trajectories of loss of control eating severity across assessment periods and treatment type.

Within-day increases in loss of control eating severity were reduced at end-of-treatment and 6-month follow-up relative to baseline. Evening acceleration of loss of control eating severity was greater at 6-month follow-up relative to end-of-treatment. Within-day increases in loss of control severity did not differ between treatments at end-of-treatment; however, evening loss of control severity intensified for individuals who received CBTgsh relative to those who received ICAT at 6-month follow-up.

Findings suggest that treatment reduces evening-shifted loss of control eating severity, and that this effect may be more durable following ICAT relative to CBTgsh.

Binge-eating disorder (BED) co-occurs with neurobehavioral alterations in the processing of disorder-relevant content such as visual food stimuli. Whether neurofeedback (NF) directly targeting them is suited for treatment remains unclear. This study sought to determine feasibility and estimate effects of individualized, functional near-infrared spectroscopy-based real-time NF (rtfNIRS-NF) and high-beta electroencephalography-based NF (EEG-NF), assuming superiority over waitlist (WL).

Single-center, assessor-blinded feasibility study with randomization to rtfNIRS-NF, EEG-NF, or WL and assessments at baseline (t0), postassessment (t1), and 6-month follow-up (t2). NF comprised 12 60-min food-specific rtfNIRS-NF or EEG-NF sessions over 8 weeks. Primary outcome was the binge-eating frequency at t1 assessed interview-based. Secondary outcomes included feasibility, eating disorder symptoms, mental and physical health, weight management-related behavior, executive functions, and brain activity at t1 and t2.

In 72 patients (intent-to-treat), the results showed feasibility of NF regarding recruitment, attrition, adherence, compliance, acceptance, and assessment completion. Binge eating improved at t1 by −8.0 episodes, without superiority of NF v. WL (−0.8 episodes, 95% CI −2.4 to 4.0), but with improved estimates in NF at t2 relative to t1. NF was better than WL for food craving, anxiety symptoms, and body mass index, but overall effects were mostly small. Brain activity changes were near zero.

The results show feasibility of food-specific rtfNIRS-NF and EEG-NF in BED, and no posttreatment differences v. WL, but possible continued improvement of binge eating. Confirmatory and mechanistic evidence is warranted in a double-blind randomized design with long-term follow-up, considering dose–response relationships and modes of delivery.

While effective treatments exist for binge-eating disorder (BED), prediction of treatment outcomes has proven difficult, and few reliable predictors have been identified. Machine learning is a promising method for improving the accuracy of difficult-to-predict outcomes. We compared the accuracy of traditional and machine-learning approaches for predicting BED treatment outcomes.

Participants were 191 adults with BED in a randomized controlled trial testing 6-month behavioral and stepped-care treatments. Outcomes, determined by independent assessors, were binge-eating (% reduction, abstinence), eating-disorder psychopathology, and weight loss (% loss, ⩾5% loss). Predictors included treatment condition, demographic information, and baseline clinical characteristics. Traditional models were logistic/linear regressions. Machine-learning models were elastic net regressions and random forests. Predictive accuracy was indicated by the area under receiver operator characteristic curve (AUC), root mean square error (RMSE), and R2. Confidence intervals were used to compare accuracy across models.

Across outcomes, AUC ranged from very poor to fair (0.49–0.73) for logistic regressions, elastic nets, and random forests, with few significant differences across model types. RMSE was significantly lower for elastic nets and random forests v. linear regressions but R2 values were low (0.01–0.23).

Different analytic approaches revealed some predictors of key treatment outcomes, but accuracy was limited. Machine-learning models with unbiased resampling methods provided a minimal advantage over traditional models in predictive accuracy for treatment outcomes.

Impulsivity may be a process underlying binge-eating disorder (BED) psychopathology and its treatment. This study examined change in impulsivity during cognitive-behavioral therapy (CBT) and/or pharmacological treatment for BED and associations with treatment outcomes.

In total, 108 patients with BED (NFEMALE = 84) in a randomized placebo-controlled clinical trial evaluating the efficacy of CBT and/or fluoxetine were assessed before treatment, monthly throughout treatment, at post-treatment (16 weeks), and at 12-month follow-up after completing treatment. Patients completed established measures of impulsivity, eating-disorder psychopathology, and depression, and were measured for height and weight [to calculate body mass index (BMI)] during repeated assessments by trained/monitored doctoral research-clinicians. Mixed-effects models using all available data examined changes in impulsivity and the association of rapid and overall changes in impulsivity on treatment outcomes. Exploratory analyses examined whether baseline impulsivity predicted/moderated outcomes.

Impulsivity declined significantly throughout treatment and follow-up across treatment groups. Rapid change in impulsivity and overall change in impulsivity during treatment were significantly associated with reductions in eating-disorder psychopathology, depression scores, and BMI during treatment and at post-treatment. Overall change in impulsivity during treatment was associated with subsequent reductions in depression scores at 12-month follow-up. Baseline impulsivity did not moderate/predict eating-disorder outcomes or BMI but did predict change in depression scores.

Rapid and overall reductions in impulsivity during treatment were associated with improvements in specific eating-disorder psychopathology and associated general outcomes. These effects were found for both CBT and pharmacological treatment for BED. Change in impulsivity may be an important process prospectively related to treatment outcome.

Binge-eating disorder (BED) is a difficult-to-manage clinical entity, that may associate both organic (e.g., obesity, metabolic syndrome) and psychiatric (e.g., anxiety, mood, or substance use disorders) co-morbidity. Psychotherapeutic and pharmacological approaches are usually combined in order to reach the best outcome for these patients, but the disorder seems to have prolonged evolution even under appropriate therapeutic managment.

To evaluate the most evidence-based therapies focused on BED.

A literature review was conducted through main electronic databases, and papers published between January 2000 and August 2020 were included in the analysis.

Cognitive-behavioral therapy (CBT) is supported by multiple trials, and it led to decreased number of binge episodes and increased rates of remission. Behavioral activation may improve certain symptoms of BED (depressive mood, anxiety), but not the binge episodes frequency. Interpersonal group therapy (IPT) may be helpful for BED patients with an external locus of control and significant interpersonal dificulties. Dialectical behavior therapy (DBT) has been applied in BED patients, but the results have been inconclusive. Lisdexamfetamine dimesylate is the only FDA approved drug for this indication, as dasotraline was rejected by FDA and its research discontinued by the manufacturer. Fluoxetine, sertraline, escitalopram, duloxetine, bupropion, atomoxetine, reboxetine, armodafinil, disulfiram, baclofen, zonisamide, lamotrigine, topiramate, samidorphan, liraglutid, and orlistat need more trials in order to validate their efficacy, especially on long term.

There is only one drug currently FDA approved for this indication, lisdexamfetamine, and a number of psychotherapies, with CBT and IPT being the most supported by evidence.

1. (1) What is necessary for brief CBT to be effective for binge-eating disorder (BED)?

2. (2) Is CBT for BED effective in the absence of purging behaviours?

3. (3) What is the most appropriate way to measure remission in CBT for BED?

Psychiatric comorbidity is common in binge-eating disorder (BED) but effects on treatment outcomes are unknown. The current study aimed to determine whether psychiatric comorbidity predicted or moderated BED treatment outcomes.

In total, 636 adults with BED in randomized-controlled trials (RCTs) were assessed prior, throughout, and posttreatment by doctoral research-clinicians using reliably-administered semi-structured interviews, self-report measures, and measured weight. Data were aggregated from RCTs testing cognitive-behavioral therapy, behavioral weight loss, multi-modal (combined pharmacological plus cognitive-behavioral/behavioral), and/or control conditions. Intent-to-treat analyses (all available data) tested comorbidity (mood, anxiety, ‘any disorder’ separately) as predictors and moderators of outcomes. Mixed-effects models tested comorbidity effects on binge-eating frequency, global eating-disorder psychopathology, and weight. Generalized estimating equation models tested binge-eating remission (zero binge-eating episodes during the past month; missing data imputed as failure).

Overall, 41% of patients had current psychiatric comorbidity; 22% had mood and 23% had anxiety disorders. Psychiatric comorbidity did not significantly moderate the outcomes of specific treatments. Psychiatric comorbidity predicted worse eating-disorder psychopathology and higher binge-eating frequency across all treatments and timepoints. Patients with mood comorbidity were significantly less likely to remit than those without mood disorders (30% v. 41%). Psychiatric comorbidity neither predicted nor moderated weight loss.

Psychiatric comorbidity was associated with more severe BED psychopathology throughout treatment but did not moderate outcomes. Findings highlight the need to improve treatments for BED with psychiatric comorbidities but challenge perspectives that combining existing psychological and pharmacological interventions is warranted. Treatment research must identify more effective interventions for BED overall and for patients with comorbidities.

The nature and significance of impulse-control difficulties in binge-eating disorder (BED) are uncertain. Most emerging research has focused on food-specific rather than general impulsivity. The current study examines the clinical presentation of patients with BED categorized with and without clinical levels of general impulsivity.

A total of 343 consecutive treatment-seeking patients with BED were categorized as having BED with general impulsivity (GI+; N = 73) or BED without general impulsivity (GI−: N = 270) based on structured diagnostic and clinical interviews. The groups were compared on demographic, developmental, and psychological features, and on rates of psychiatric and personality comorbidity.

Individuals with BED and general impulsivity (GI+) reported greater severity of eating-disorder psychopathology, greater depressive symptoms, and greater rates of comorbidity than those without general impulsivity (GI−).

A subtype of individuals with BED and general impulsivity may signal a more severe presentation of BED characterized by heightened and broader psychopathology. Future work should investigate whether these impulse-control difficulties relate to treatment outcomes.

Eating-disorder severity indicators should theoretically index symptom intensity, impairment, and level of needed treatment. Two severity indicators for binge-eating disorder (BED) have been proposed (categories of binge-eating frequency and shape/weight overvaluation) but have mixed empirical support including modest clinical utility. This project uses structural equation model (SEM) trees – a form of exploratory data mining – to empirically determine the precise levels of binge-eating frequency and/or shape/weight overvaluation that most significantly differentiate BED severities.

Participants were 788 adults with BED enrolled in BED treatment studies. Participants completed interviews and self-report measures assessing eating-disorder and comorbid symptoms. SEM Tree analyses were performed by specifying an outcome model of BED severity and then recursively partitioning the outcome model into subgroups. Subgroups were split based on empirically determined values of binge-eating frequency and/or shape/weight overvaluation. SEM Forests also quantified which variable contributed more improvement in model fit.

SEM Tree analyses yielded five subgroups, presented in ascending order of severity: overvaluation <1.25, overvaluation = 1.25–2.74, overvaluation = 2.75–4.24, overvaluation ⩾4.25 with weekly binge-eating frequency <4.875, and overvaluation ⩾4.25 with weekly binge-eating frequency ⩾4.875. SEM Forest analyses revealed that splits that occurred on shape/weight overvaluation resulted in much more improvement in model fit than splits that occurred on binge-eating frequency.

Shape/weight overvaluation differentiated BED severity more strongly than binge-eating frequency. Findings indicate a nuanced potential BED severity indicator scheme, based on a combination of cognitive and behavioral eating-disorder symptoms. These results inform BED classification and may allow for the provision of more specific and need-matched treatment formulations.

The aim of this fixed-dose study was to evaluate the efficacy and safety of dasotraline in the treatment of patients with binge-eating disorder (BED).

Patients meeting Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria for BED were randomized to 12 weeks of double-blind treatment with fixed doses of dasotraline (4 and 6 mg/d), or placebo. The primary efficacy endpoint was change in number of binge-eating (BE) days per week at week 12. Secondary efficacy endpoints included week 12 change on the BE CGI-Severity Scale (BE-CGI-S) and the Yale-Brown Obsessive–Compulsive Scale Modified for BE (YBOCS-BE).

At week 12, treatment with dasotraline was associated with significant improvement in number of BE days per week on the dose of 6 mg/d (N = 162) vs placebo (N = 162; −3.47 vs −2.92; P = .0045), but not 4 mg/d (N = 161; −3.21). Improvement vs placebo was observed for dasotraline 6 and 4 mg/d, respectively, on the BE-CGI-S (effect size [ES]: 0.37 and 0.27) and on the YBOCS-BE total score (ES: 0.43 and 0.29). The most common adverse events on dasotraline were insomnia, dry mouth, headache, decreased appetite, nausea, and anxiety. Changes in blood pressure and pulse were minimal.

Treatment with dasotraline 6 mg/d (but not 4 mg/d) was associated with significantly greater reduction in BE days per week. Both doses of dasotraline were generally safe and well-tolerated and resulted in global improvement on the BE-CGI-S, as well as improvement in BE related obsessional thoughts and compulsive behaviors on the YBOCS-BE. These results confirm the findings of a previous flexible dose study.

1. (1) To appreciate that CBT-E is suitable for remote delivery, and to consider the main challenges and potential advantages of this way of working.

2. (2) To identify and discuss the additional eating disorder-related problems that may arise as a result of COVID-19, as well as potential opportunities for adapting some aspects of CBT-E to address them.

3. (3) To learn how to adapt CBT-E for remote delivery to address the consequences of COVID-19. Specifically, to consider adaptations to the assessment and preparation phase, the four stages of treatment and its use with underweight patients and adolescents.

Psychiatric disorders, including eating disorders (EDs), have clinical outcomes that range widely in severity and chronicity. The ability to predict such outcomes is extremely limited. Machine-learning (ML) approaches that model complexity may optimize the prediction of multifaceted psychiatric behaviors. However, the investigations of many psychiatric concerns have not capitalized on ML to improve prognosis. This study conducted the first comparison of an ML approach (elastic net regularized logistic regression) to traditional regression to longitudinally predict ED outcomes.

Females with heterogeneous ED diagnoses completed demographic and psychiatric assessments at baseline (n = 415) and Year 1 (n = 320) and 2 (n = 277) follow-ups. Elastic net and traditional logistic regression models comprising the same baseline variables were compared in ability to longitudinally predict ED diagnosis, binge eating, compensatory behavior, and underweight BMI at Years 1 and 2.

Elastic net models had higher accuracy for all outcomes at Years 1 and 2 [average Area Under the Receiving Operating Characteristics Curve (AUC) = 0.78] compared to logistic regression (average AUC = 0.67). Model performance did not deteriorate when the most important predictor was removed or an alternative ML algorithm (random forests) was applied. Baseline ED (e.g. diagnosis), psychiatric (e.g. hospitalization), and demographic (e.g. ethnicity) characteristics emerged as important predictors in exploratory predictor importance analyses.

ML algorithms can enhance the prediction of ED symptoms for 2 years and may identify important risk markers. The superior accuracy of ML for predicting complex outcomes suggests that these approaches may ultimately aid in advancing precision medicine for serious psychiatric disorders.

Mental disorders may emerge as the result of interactions between observable symptoms. Such interactions can be analyzed using network analysis. Several recent studies have used network analysis to examine eating disorders, indicating a core role of overvaluation of weight and shape. However, no studies to date have applied network models to binge-eating disorder (BED), the most prevalent eating disorder.

We constructed a cross-sectional graphical LASSO network in a sample of 788 individuals with BED. Symptoms were assessed using the Eating Disorders Examination Interview. We identified core symptoms of BED using expected influence centrality.

Overvaluation of shape emerged as the symptom with the highest centrality. Dissatisfaction with weight and overvaluation of weight also emerged as highly central symptoms. On the other hand, behavioral symptoms such as binge eating, eating in secret, and dietary restraint/restriction were less central. The network was stable, allowing for reliable interpretations (centrality stability coefficient = 0.74).

Overvaluation of shape and weight emerged as core symptoms of BED. This trend is consistent with past network analyses of eating disorders more broadly, as well as literature that suggests a primary role of shape and weight concerns in BED. Although DSM-5 diagnostic criteria for BED does not currently include a cognitive criterion related to body image or shape/weight overvaluation, our results provide support for including shape/weight overvaluation as a diagnostic specifier.

Transition across eating disorder diagnoses is common, reflecting instability of specific eating disorder presentations. Previous studies have examined temporal stability of diagnoses in adult treatment-seeking samples but have not uniformly captured initial presentation for treatment. The current study examines transitions across eating disorder diagnostic categories in a large, treatment-seeking sample of individuals born in Sweden and compares these transitions across two birth cohorts and from initial diagnosis.

Data from Swedish eating disorders quality registers were extracted in 2013, including 9622 individuals who were seen at least twice from 1999 to 2013. Patterns of remission were examined in the entire sample and subsequently compared across initial diagnoses. An older (born prior to 1990) and younger birth cohort were also identified, and analyses compared these cohorts on patterns of diagnostic transition.

Although diagnostic instability was common, transition between threshold eating disorder diagnoses was infrequent. For all diagnoses, transition to remission was likely to occur following a diagnosis state that matched initial diagnosis, or through a subthreshold diagnostic state. Individuals in the younger cohort were more likely to transition to a state of remission than those in the older cohort.

Results indicate more temporal continuity in eating disorder presentations than suggested by previous research and highlight the importance of early detection and intervention in achieving remission.

A stepped care approach involves patients first receiving low-intensity treatment followed by higher intensity treatment. This two-step randomized controlled trial investigated the efficacy of a sequential stepped care approach for the psychological treatment of binge-eating disorder (BED).

In the first step, all participants with BED (n = 135) received unguided self-help (USH) based on a cognitive-behavioral therapy model. In the second step, participants who remained in the trial were randomized either to 16 weeks of group psychodynamic-interpersonal psychotherapy (GPIP) (n = 39) or to a no-treatment control condition (n = 46). Outcomes were assessed for USH in step 1, and then for step 2 up to 6-months post-treatment using multilevel regression slope discontinuity models.

In the first step, USH resulted in large and statistically significant reductions in the frequency of binge eating. Statistically significant moderate to large reductions in eating disorder cognitions were also noted. In the second step, there was no difference in change in frequency of binge eating between GPIP and the control condition. Compared with controls, GPIP resulted in significant and large improvement in attachment avoidance and interpersonal problems.

The findings indicated that a second step of a stepped care approach did not significantly reduce binge-eating symptoms beyond the effects of USH alone. The study provided some evidence for the second step potentially to reduce factors known to maintain binge eating in the long run, such as attachment avoidance and interpersonal problems.