Evaluating new severity dimensions in the DSM‐5 for bulimic syndromes using mixture modeling

The Diagnostic and Statistical Manual for Mental Disorders Fifth Edition (DSM-5) will include dimensional ratings of severity for eating disorder diagnoses to account for differences in treatment response and course observed within diagnostic categories and to inform clinicians regarding the level of intervention that might be warranted for a given clinical presentation.1 Frequency of inappropriate compensatory behaviors has been proposed for describing severity of bulimia nervosa (BN). Frequency of binge-eating episodes has been proposed for the new diagnosis of binge eating disorder (BED), and body mass index (BMI) has been proposed for anorexia nervosa (AN). However, these same features are used to demarcate one eating disorder from another. For example, BMI would be a key variable that would distinguish between the diagnosis of AN binge-purge subtype and BN, and inappropriate compensatory behaviors would be a key feature for distinguishing between BN and BED. Thus, proposed categories and dimensional ratings for these categories in the DSM-5 appear to view certain eating disorder features as representing both severity dimensions and as demarcating categories. Although this approach fits distinctions that can be made between diagnoses with minimal symptom overlap (e.g., the restricting subtype of AN and BED), it creates ambiguity for understanding whether disorders with significant symptom overlap (e.g., AN binge-purge subtype, BN, and BED) should be thought of as being categorically distinct as proposed for the DSM-5 or as residing on continuums of severity within a broader category of bulimic syndromes (see Figure 1). Figure 1 Proposed Model of Bulimic Syndromes in the DSM-5 vs. an Alternative Dimensional Model of Bulimic Syndromes. One method for addressing this ambiguity is to examine the latent structure of bulimic syndromes from a statistical perspective. Statistical approaches can reveal whether there are naturally occurring cut-points between syndromes, and where these cut-points reside, or whether distinctions between diagnoses appear to be artificially imposed by diagnostic criteria sets. Although a recent review2 found strong support for the predictive validity of distinguishing between AN binge-purge subtype, BN, and BED in terms of treatment response, course and outcome, limited support has emerged for the etiological validity of these distinctions. For example, heritability estimates for bulimic syndromes range from 54% to 83% in female twin samples3–5, with no significant difference in heritability between narrowly (a2=.62) and broadly defined (a2=.61) BN3. In addition, the correlation between additive genetic factors for broadly defined BN and AN has been estimated at ra=.793, supporting the possibility that broad phenotypes may lead to greater understanding of the genetic underpinnings of bulimic syndromes. If statistical analyses indicate that differences in BMI, binge eating, and purging reflect variations in severity rather than type, then future studies might examine whether evidence of predictive validity might also support a dimensional approach, consistent with recommendations for the NIMH Research Domain Criteria (RDoC) initiative.6 Unfortunately, prior latent structure studies of eating disorders have employed methods that assume either latent dimensions (e.g., factor analysis) or latent categories (e.g., latent class analysis) which cannot address this ambiguity. Although taxometric analyses are ideally suited to distinguish categories from continua, they rely on the presence of multiple indicators that discriminate between potential categories – a requirement not met by some bulimic syndrome diagnoses that are typically demarcated from one another by a single feature.7 For example, BN and BED are distinguished by the presence versus absence of inappropriate compensatory behaviors, limiting the utility of taxometric methods for examining whether inappropriate compensatory behaviors represent a severity dimension for bulimic syndromes with BED at one end of the spectrum and BN with high frequency purging at the other or whether presence versus absence of inappropriate compensatory behaviors represents a cut-point between BN and BED as defined within the DSM-5. Recently, factor mixture analyses (FMA) have been developed to allow for the simultaneous modeling of latent categories and latent dimensions8, 9 and seem ideally suited to resolve discrepancies in our understanding of the latent structure of bulimic syndromes. This approach may help resolve questions regarding the relationship between BN and forms of eating disorder not otherwise specified (EDNOS) that may be viewed as separate eating disorders (e.g., BED and purging disorder; PD10) or as variants of BN that differ on the latent dimensions of frequency of inappropriate compensatory behaviors or frequency of binge eating. If certain eating disorder features delineate distinct categories, then adding new diagnostic categories to the DSM-5, such as BED, could reduce reliance on EDNOS without risking loss of information for existing diagnoses.11 In contrast, if certain eating disorder features represent an underlying dimension of severity, then a broad category for bulimic syndromes might subsume related EDNOS (see Figure 1).12 The latter approach also might facilitate identification of underlying dimensions of function related to biological processes, as proposed by the recent Research Domain Criteria (RDoC) initiative.6 Application of FMA to bulimic syndromes appears particularly warranted for examining proposed revisions for the DSM-5. FMA model latent categories that demonstrate within-class covariation of item probabilities due to illness severity and may be viewed as extensions of latent class analyses (LCA) and factor analyses (FA).8, 9 An advantage over LCA is that FMA model one or more latent dimensions of severity that may contribute to significant covariation among indicators within classes that would lead to the identification of a spurious class in LCA. An advantage over FA is the ability to identify homogeneous latent groups which may be represented as diagnostic categories within nosological systems such as the DSM. Studies that have employed FMA have found that a hybrid model that combines categories and dimensions provides a superior fit for the structure of attention deficit hyperactivity disorder,13 substance use dependence,8 and appearance enhancing drug use.14 To our knowledge, no study has employed FMA to examine the latent structure of bulimic syndromes. The current study used mixture modeling, specifically a sequence of LCA, FA, and FMA, to examine the latent structure of bulimic syndromes. We focused on bulimic syndromes, including AN binge-purge subtype, BN, BED, PD, and related EDNOS, because these represent disorders in which a single feature may demarcate different diagnoses or may reflect a latent severity dimension within a single broader category (see Figure 1). A central question of this work is whether bulimic syndromes are best represented as distinct diagnostic groups (and, if so, what are the defining features of these groups), a single group with one or more dimensions of severity, or a combination of distinct groups and dimensions. An advance over previous latent structure studies was the inclusion of large numbers of individuals who met DSM-IV criteria for AN binge-purge subtype, BN, both purging and nonpurging subtypes, BED, proposed research criteria for PD,15 and individuals who reported an array of disordered eating features as well as those reporting no disordered eating. This composition ensured that low base rates, often encountered in population-based samples, would not limit statistical power to detect the presence of distinct groups16 while also providing ample variation (from normality to eating disorders) to ensure that analyses did not simply reify groups selected for participation.2 Based on results from FMA studies of other psychiatric illnesses8, 9, 14, we predicted that a model including both latent categories and latent dimensions would provide the best fit to data. Such a model would fall between the DSM-5 based model and fully dimensional model depicted in Figure 1. However, given that this is the first application of FMA to eating disorders, we did not have a priori hypotheses regarding the number or characteristics of latent groups or dimensions.