Hostname: page-component-cd9895bd7-dk4vv Total loading time: 0 Render date: 2024-12-18T19:05:47.121Z Has data issue: false hasContentIssue false
  • English
  • Français

Empirically determined severity levels for binge-eating disorder outperform existing severity classification schemes

Published online by Cambridge University Press:  30 June 2020

Lauren N. Forrest Open the ORCID record for Lauren N. Forrest [Opens in a new window] ,
Ross C. Jacobucci Open the ORCID record for Ross C. Jacobucci [Opens in a new window]  and
Carlos M. Grilo
Lauren N. Forrest*
Affiliation:
Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA Department of Psychology, Miami University, Oxford, OH, USA
Ross C. Jacobucci
Affiliation:
Department of Psychology, University of Notre Dame, Notre Dame, IN, USA
Carlos M. Grilo
Affiliation:
Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
*
Author for correspondence: Lauren N. Forrest, E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

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.

Methods

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.

Results

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.

Conclusions

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.

Keywords

Binge-eating disorderexploratory data miningSEM Treesseverityeating disorders
Type
Original Article
Information
Psychological Medicine , Volume 52 , Issue 4 , March 2022 , pp. 685 - 695
Check for updates
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Altman, D. G., & Royston, P. (2006). The cost of dichotomising continuous variables. British Medical Journal, 332(7549), 1080. doi: 10.1136/bmj.332.7549.1080.CrossRefGoogle ScholarPubMed
American Psychiatric Association (2004). Diagnostic and statistical manual of mental disorders (4th ed., text rev.). Washington, DC: Author.Google Scholar
American Psychiatric Association (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: Author.Google Scholar
Balodis, I. M., Grilo, C. M., & Potenza, M. N. (2015). Neurobiological features of binge eating disorder. CNS Spectrums, 20, 557–565.CrossRefGoogle ScholarPubMed
Beck, A. T., & Steer, R. A. (1987). Manual for the beck depression inventory. New York: Psychological Corporation.Google Scholar
Boker, S. M., Neale, M. C., Maes, H. H., Wilde, M. J., Spiegel, M., Brick, T. R., … Brandmaier, A. (2012). OpenMx 1.2 User Guide.Google Scholar
Boker, S. M., Neale, M. C., Maes, H. H., Wilde, M. J., Spiegel, M., Brick, T. R., … Fox, J. (2011). Openmx: An open source extended structural equation modeling framework. Psychometrika, 76(2), 306–317. doi: 10.1007/s11336-010-9200-6.CrossRefGoogle ScholarPubMed
Borsboom, D., & Cramer, A. O. J. (2013). Network analysis: An integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology, 9(1), 91–121. doi: 10.1146/annurev-clinpsy-050212-185608.CrossRefGoogle Scholar
Brandmaier, A. M., Oertzen, T. V., McArdle, J. J., & Lindenberger, U. (2013). Structural equation model trees. Psychological Methods, 18, 71–86. doi: 10.1037/a0030001.CrossRefGoogle ScholarPubMed
Brandmaier, A. M., Prindle, J. J., McArdle, J. J., & Lindenberger, U. (2016). Theory-guided exploration with structural equation model forests. Psychological Methods, 21(4), 566–582. doi: 10.1037/meth0000090.CrossRefGoogle ScholarPubMed
Coffino, J. A., Udo, T., & Grilo, C. M. (2019). The significance of overvaluation of shape or weight in binge-eating disorder: Results from a national sample of U.S. adults. Obesity, 27(8), 1367–1371. doi: 10.1002/oby.22539.CrossRefGoogle ScholarPubMed
Dakanalis, A., Colmegna, F., Riva, G., & Clerici, M. (2017a). Validity and utility of the DSM-5 severity specifier for binge-eating disorder. International Journal of Eating Disorders, 50(8), 917–923. doi: 10.1002/eat.22696.CrossRefGoogle ScholarPubMed
Dakanalis, A., Riva, G., Serino, S., Colmegna, F., & Clerici, M. (2017b). Classifying adults with binge eating disorder based on severity levels. European Eating Disorders Review, 25(4), 268–274. doi: 10.1002/erv.2518.CrossRefGoogle ScholarPubMed
DuBois, R. H., Rodgers, R. F., Franko, D. L., Eddy, K. T., & Thomas, J. J. (2017). A network analysis investigation of the cognitive-behavioral theory of eating disorders. Behaviour Research and Therapy, 97, 213–221. doi: 10.1016/j.brat.2017.08.004.CrossRefGoogle ScholarPubMed
Fairburn, C. G., & Cooper, Z. (1993). The eating disorder examination. In Fairburn, C. G. & Wilson, G. T. (Eds.), Binge eating: Nature, assessment, and treatment (pp. 317–360). New York: Guilford Press.Google Scholar
Fairburn, C. G., Cooper, Z., & Shafran, R. (2003). Cognitive behaviour therapy for eating disorders: A “transdiagnostic” theory and treatment. Behaviour Research and Therapy, 41, 509–528. doi: 10.1016/S0005-7967(02)00088-8.CrossRefGoogle ScholarPubMed
Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). Thousand Oaks, CA: SAGE Publications, Inc.Google Scholar
First, M. B., Spitzer, R. L., Gibbon, M., & Williams, J. B. W. (1997). Structured clinical interview for DSM-IV axis I disorders – patient edition (SCID-I/P, version 2.0). New York: New York State Psychiatric Institute.Google Scholar
Forrest, L. N., Jones, P., Ortiz, S. N., & Smith, A. R. (2018). Core psychopathology in anorexia nervosa and bulimia nervosa: A network analysis. International Journal of Eating Disorders, 51, 668–679. doi: 10.1002/eat.22871.CrossRefGoogle ScholarPubMed
Giannini, L., Roberto, C. A., Attia, E., Walsh, B. T., Thomas, J. J., Eddy, K. T., … Sysko, R. (2017). Mild, moderate, meaningful? Examining the psychological and functioning correlates of DSM-5 eating disorder severity specifiers. International Journal of Eating Disorders, 50, 906–916. doi: 10.1002/eat.22728.CrossRefGoogle Scholar
Goldschmidt, A. B., Hilbert, A., Manwaring, J. L., Wilfley, D. E., Pike, K. M., Fairburn, C. G., & Striegel-Moore, R. H. (2010). The significance of overvaluation of shape and weight in binge eating disorder. Behaviour Research and Therapy, 48(3), 187–193. doi: 10.1016/j.brat.2009.10.008.CrossRefGoogle ScholarPubMed
Grilo, C. M. (2013). Why no cognitive body image feature such as overvaluation of shape/weight in the binge eating disorder diagnosis? International Journal of Eating Disorders, 46(3), 208–211. doi: 10.1002/eat.22082.CrossRefGoogle ScholarPubMed
Grilo, C. M., Crosby, R. D., Peterson, C. B., Masheb, R. M., White, M. A., Crow, S. J., … Mitchell, J. E. (2010). Factor structure of the eating disorder examination interview in patients with binge-eating disorder. Obesity, 18(5), 977–981. doi: 10.1038/oby.2009.321.CrossRefGoogle ScholarPubMed
Grilo, C. M., Ivezaj, V., & White, M. A. (2015a). Evaluation of the DSM-5 severity indicator for binge eating disorder in a clinical sample. Behaviour Research and Therapy, 71, 110–114. doi: 10.1016/j.brat.2015.05.003.CrossRefGoogle Scholar
Grilo, C. M., Ivezaj, V., & White, M. A. (2015b). Evaluation of the DSM-5 severity indicator for binge eating disorder in a community sample. Behaviour Research and Therapy, 66, 72–76. doi: 10.1016/j.brat.2015.01.004.CrossRefGoogle Scholar
Grilo, C. M., Masheb, R. M., & Crosby, R. D. (2012). Predictors and moderators of response to cognitive behavioral therapy and medication for the treatment of binge eating disorder. Journal of Consulting and Clinical Psychology, 80(5), 897–906.CrossRefGoogle ScholarPubMed
Grilo, C. M., Masheb, R. M., & Wilson, G. T. (2001). Subtyping binge eating disorder. Journal of Consulting and Clinical Psychology, 69(6), 1066–1072.CrossRefGoogle ScholarPubMed
Grilo, C. M., White, M. A., Gueroguieva, R., Wilson, G. T., & Masheb, R. M. (2013). Predictive significance of the overvaluation of shape/weight in obese patients with binge eating disorder: Findings from a randomized controlled trial with 12-month follow-up. Psychological Medicine, 43(6), 1335–1344. doi: 10.1017/S0033291712002097.CrossRefGoogle ScholarPubMed
Haynos, A. F., Wang, S. B., Lipson, S., Peterson, C. B., Mitchell, J. E., Halmi, K. A., … Crow, S. J. (in press). Machine learning enhances prediction of illness course: A longitudinal study in eating disorders. Psychological Medicine.Google Scholar
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. doi: 10.1080/10705519909540118.CrossRefGoogle Scholar
Hudson, J. I., Hiripi, E., Pope, H. G., & Kessler, R. C. (2007). The prevalence and correlates of eating disorders in the National Comorbidity Survey Replication. Biological Psychiatry, 61(3), 348–358. doi: 10.1016/j.biopsych.2006.03.040.CrossRefGoogle ScholarPubMed
Kenny, T. E., & Carter, J. C. (2018). I weigh therefore I am: Implications of using different criteria to define overvaluation of shape and weight in binge-eating disorder. International Journal of Eating Disorders, 51(11), 1244–1251. doi: 10.1002/eat.22956.CrossRefGoogle ScholarPubMed
King, M. W., & Resick, P. A. (2014). Data mining in psychological treatment research: A primer on classification and regression trees. Journal of Counseling and Clinical Psychology, 82(5), 895–905. doi: 10.1037/a0035886.CrossRefGoogle ScholarPubMed
Kober, H., & Boswell, R. G. (2018). Potential psychological and neural mechanisms in binge eating disorder: Implications for treatment. Clinical Psychology Review, 60, 32–44. doi: 10.1016/j.cpr.2017.12.004.CrossRefGoogle ScholarPubMed
Lydecker, J. A., Ivezaj, V., & Grilo, C. M. (2020). Testing the validity and clinical utility of the severity specifiers for binge-eating disorder for predicting treatment outcomes. Journal of Consulting and Clinical Psychology, 88(2), 172–178. doi: 10.1037/ccp0000464.CrossRefGoogle ScholarPubMed
Mehl, A., Rohde, P., Gau, J. M., & Stice, E. (2019). Disaggregating the predictive effects of impaired psychosocial functioning on future DSM-5 eating disorder onset in high-risk female adolescents. International Journal of Eating Disorders, 52(7), 817–824. doi: 10.1002/eat.23082.CrossRefGoogle ScholarPubMed
Nakai, Y., Nin, K., Noma, S., Teramukai, S., Fujikawa, K., & Wonderlich, S. A. (2017). The impact of DSM-5 on the diagnosis and severity indicator of eating disorders in a treatment-seeking sample. International Journal of Eating Disorders, 50(11), 1247–1254. doi: 10.1002/eat.22777.CrossRefGoogle Scholar
R Core Team (2013). R: A language and environment for statistical computing. R Foundation of Statistical Computing, Vienna, Austria. Retrieved from http://www.R-project.org.Google Scholar
Rosseel, Y. (2012). Lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36.CrossRefGoogle Scholar
Smith, K. E., Ellison, J. M., Crosby, R. D., Engel, S. G., Mitchell, J. E., Crow, S. J., … Wonderlich, S. A. (2017). The validity of DSM-5 severity specifiers for anorexia nervosa, bulimia nervosa, and binge-eating disorder. International Journal of Eating Disorders, 50(9), 1109–1113. doi: 10.1002/eat.22739.CrossRefGoogle ScholarPubMed
Stein, D. J., Szatmari, P., Gaebel, W., Berk, M., Vieta, E., Maj, M., … Reed, G. M. (2020). Mental, behavioral and neurodevelopmental disorders in the ICD-11: An international perspective on key changes and controversies. BMC Medicine, 18(1), 21. doi: 10.1186/s12916-020-1495-2.CrossRefGoogle ScholarPubMed
Stice, E., & Desjardins, C. D. (2018). Interactions between risk factors in the prediction of onset of eating disorders: Exploratory hypothesis generating analyses. Behaviour Research and Therapy, 105, 52–62. doi: 10.1016/j.brat.2018.03.005.CrossRefGoogle ScholarPubMed
Udo, T., & Grilo, C. M. (2019). Psychiatric and medical correlates of DSM-5 eating disorders in a nationally representative sample of adults in the United States. International Journal of Eating Disorders, 52(1), 42–50. doi: 10.1002/eat.23004.CrossRefGoogle Scholar
van Buuren, S., & Groothuis-Oudshoorn, K. (2011). Mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3), 1–67.Google Scholar
Wang, S. B., Jones, P. J., Dreier, M., Elliott, H., & Grilo, C. M. (2019). Core psychopathology of treatment-seeking patients with binge-eating disorder: A network analysis investigation. Psychological Medicine, 49(11), 1923–1928. doi: 10.1017/S0033291718002702.CrossRefGoogle ScholarPubMed
Supplementary material: File

Forrest et al. supplementary material

Forrest et al. supplementary material

Download Forrest et al. supplementary material(File)
File 16.4 KB