Empirical classification of eating disorders

Eating Behaviors 6 (2005) 53 – 62

Empirical classification of eating disorders Denise M. Sloana,*, J. Scott Mizesb, Eva M. Epsteina a

Department of Psychology, Temple University, Weiss Hall, Philadelphia 19122, USA b West Virginia University School of Medicine, USA Received 29 January 2004; accepted 25 June 2004

Abstract Although the eating disorder nosology has become refined over the years, considerable problems remain. The purpose of the present study was to empirically examine eating disorder classification using a sample of treatmentseeking eating-disorder patients. One hundred and fifty-nine patients with diagnoses of anorexia nervosa (AN), bulimia nervosa (BN), and eating disorder, not otherwise specified (EDNOS), were included in a cluster analysis using a variety of eating disorder variables. Findings revealed four clusters, with three clusters resembling AN, restricting type, BN, and binge-eating disorder (BED). The remaining cluster appeared to be a group of patients that were subthresholded in terms of symptom severity. Results also indicated a relatively poor fit between the empirically derived groupings and clinical diagnoses. The implications of these findings for both the current classification system and treatment considerations are discussed. D 2004 Elsevier Ltd. All rights reserved. Keywords: Eating disorders; Classification; Anorexia; Bulimia; Binge-eating disorder; Cluster analysis

1. Introduction Since its introduction in the Diagnostic and Statistical Manual (DSM), specifications of eating disorder diagnoses have become more refined. With this refinement was the hope that greater distinction among the eating disorder diagnoses would result, which would, in turn, lead to improvements in the

* Corresponding author. Tel.: +1 215 2041571; fax: +1 215 2045539. E-mail address: [email protected] (D.M. Sloan). 1471-0153/$ - see front matter D 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.eatbeh.2004.06.002

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assessment and treatment of eating disorders. One example of this refinement in classification is the subtyping of anorexia nervosa (AN) and bulimia nervosa (BN) diagnoses (Hay & Fairburn, 1998; McCAnn, Rossiter, King, & Agras, 1991; Mitchell, 1992; Willmuth, Leitenberg, Rosen, & Cado, 1988). In addition to the greater distinction within eating disorder diagnoses, other eating disorder syndromes are being considered for inclusion in the DSM, such as the provisional diagnosis of binge-eating disorder (BED). While the field has made strides in the distinction of eating disorder categories, considerable problems remain. One issue is that there is concern that the DSM does not accurately distinguish eating disorder diagnoses from one another. An illustration of this problem is the considerable symptom overlap between AN and BN diagnoses, particularly with regard to symptoms of obsessive concern about body weight, restrictive eating, and fear of fatness (Thaw, Williamson, & Martin, 2001). A second concern regarding the current classification system is the distinction, or lack thereof, between individuals who meet full diagnostic criteria compared with those who are subthresholded for the same diagnostic category (Crow, Agras, Halmi, Mitchell, & Kraemer, 2002). The lack of clear distinction has resulted in the eating disorder not otherwise specified (EDNOS) category becoming a catch-all diagnosis that composes approximately one third of all eating disorder patients, and as a diagnostic category, does not inform treatment planning (Williams, Gleaves, & Savin, 1992). The importance of the subthreshold issue was highlighted in a recent study of AN, BN, and BED full and subthreshold patients conducted by Crow et al. (2002). These investigators found that although AN, BN, and BED categories could be distinguished from one another, full and partial AN and BED patients were not distinguishable. In addition to subthreshold AN and BN patients being placed in the EDNOS category, there is increasing evidence that distinct eating disorder syndromes may exist within this diagnostic category, specifically BED (Delvin, Goldfein, & Dobrow, 2003). Mizes and Sloan (1998) also found evidence of a distinct BED syndrome within the EDNOS diagnosis in their empirical examination of the diagnosis. Specifically, using a cluster analysis approach Mizes and Sloan found one distinct subgroup of patients within the EDNOS category. This distinct subgroup was characterized by a high body mass index (BMI), a large weight fluctuation history, and a large number of current bingeing behaviors. Although these individuals appeared to fit most of the criteria for the provisional BED diagnosis, Mizes and Sloan found that the distinct subgroup of patients also reported some inappropriate compensatory behaviors, which is inconsistent with the provisional BED criteria outlined in the DSM-IV (American Phsychiatric Association, 1994). Mizes and Sloan also found another subgroup within EDNOS, although this subgroup appeared to be heterogeneous in terms of eating disorder symptoms. Williams et al. (1992) also used a cluster analysis approach to examine distinct categories within the EDNOS diagnosis. These researchers found three distinct subgroups that they described as subthreshold anorexia (differed from AN in terms of degree of weight loss), subthreshold bulimia, nonpurging type, and a binge-eating group that resembled the current BED provisional category outlined in the DSM-IV. Although the empirical classification approach to studying EDNOS is helpful, examining the entire spectrum of eating disorders is necessary to more fully investigate the question of whether eating disorders represent a nosology or is better represented by a continuum approach (Stice, Killen, Hayward, & Taylor, 1998; Vanderheyden & Boland, 1987). Bulik et al. (2000) addressed this question in a study using a latent class analysis approach with Caucasian female twins selected from a population-based registry. Results indicated a six-class solution, with three classes broadly reflecting the existing DSM-IV classifications of AN, BN, and BED. However, the class that resembled AN did not universally endorse

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the symptom dfeeling fat even when thinT or the amenorrhea criteria. All individuals falling in the class that resembled the BED diagnosis endorsed patterns of binge eating, but not all reported feeling out of control. The other three classes included low-weight individuals without the psychological features of AN, a class of individuals who exhibited preoccupations with their shape and weight but were not of low weight, and a class of individuals who were low weight, reported some bingeing behaviors but evidenced no other symptoms of AN or BN. In addition, the authors found that the classes that reflected the AN, BN, and BED categories were characterized by greater symptom severity than the other three classes were, and the authors speculated that the remaining three classes may represent individuals at risk for development of eating disorders. One limitation of the Bulik et al. (2000) study was that the empirical classification was based on only nine symptoms of eating disorders, and these symptoms were drawn from the AN and BN criteria described in the revised third edition of the DSM. Greater specification may have been obtained using a larger variety of symptoms not exclusively based on DSM eating disorder criteria (e.g., weight fluctuation history). Overall, there does appear to be support for a taxonomic approach to eating disorders (e.g., Williamson et al., 2002). However, the specific categories to be included within the nosology of eating disorders remain unclear. The purpose of this study was to further examine the classification of eating disorders using a cluster analysis approach with a sample of treatment-seeking patients diagnosed with AN, BN, and EDNOS.

2. Method 2.1. Participants Patients were drawn from four eating disorders clinics and one psychology clinic. The patients in the study were 159 individuals who presented to the clinic for treatment of an eating disorder. Thirty (18.9%) patients in the sample received a diagnosis of AN, 77 (48.4%) received a diagnosis of BN, and 52 (32.7%) received a diagnosis of EDNOS. Consistent with the demographic characteristics of eating-disorder patients (Striegel-Moore et al., 2003), the majority of the patients were female (98%), White (94%), single (70%), had at least some college education (80%), and had a mean age of 25.4 years (S.D.=8.4). 2.2. Measures Eating Disorders Inventory (EDI; Gardner, Olmstead, & Polivy, 1983). The EDI is a 64-item selfreport measure that assesses the psychological and behavioral characteristics of eating disorders. The measure has eight subscales and is rated using a Likert-type scale ranging from 1 (strongly agree) to 5 (strongly disagree). Internal consistency for the subscales has been reported as high, and discriminant validity for diagnostic categories of AN and BN has also been demonstrated (Gardner et al., 1983). In addition to the eight EDI subscales, several other variables were included in the study. These variables were weight fluctuation, weight goal, weight dissatisfaction, BMI, lowest and highest adult BMI, and average number of self-reported binges and purges per week. Weight fluctuation was calculated by subtracting the patient’s self-reported highest adult BMI from the self-reported lowest adult BMI. Weight goal was calculated by subtracting the patients’ self-defined ideal weight from the

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normative expected weight. Using this calculation, a larger weight goal value would reflect greater unrealistic desired body weight. Weight dissatisfaction was calculated by subtracting the patients’ selfdefined ideal weight from their current weight, with larger values reflecting greater dissatisfaction (for validity of the weight fluctuation, weight goal variables, and weight dissatisfaction variables, see Mizes, 1992). Current BMI was based on measured height and weight, with weight measured on a balanced beam scale with shoes removed. 2.3. Procedure All patients were assessed by trained clinicians. To ensure consistency across sites, clinicians completed the same eating disorder diagnostic symptom checklist (based on DSM-IV diagnostic criteria). Structured interviews were not conducted due to practical limitations of conducting lengthy assessment procedures at clinical sites. In addition to standard questions regarding eating disorder symptoms, the patients were also questioned regarding their highest and lowest adult weight and their self-defined ideal weight. Clinicians also inquired about demographic characteristics (e.g., racial background, age, and marital status). Following the clinical interview, the patient’s height and weight were taken. The initial assessment concluded with patients completing the EDI questionnaire. All patients provided written informed consent to allow their assessment material to be used for research purposes, and the study was approved by the Institutional Review Board associated with each site.

3. Results To use an empirical classification of ED patients, a cluster analysis was conducted using Ward’s minimum variance method to establish homogeneous subgroups. Ward’s method is based on an algorithm that defines a cluster by minimization of variance within clusters. This type of cluster analysis approach has also been used by other investigators to identify subtypes within the EDNOS diagnosis (Mizes & Sloan, 1998; Williams et al., 1992). The variables used in the cluster analysis were the eight subscales of the EDI, weight dissatisfaction, weight goal, weight fluctuation, current BMI, highest and lowest adult BMI, and average number of self-reported binges and purges per week. There were 16 variables in total included in the cluster analysis. Ward’s method indicated four cluster groupings. Next, to examine which variables produced the cluster groupings, several multivariate analysis of variance (MANOVA) were performed. The 16 dependent variables were grouped into four rationally derived categories to perform the MANOVA tests. This statistical approach was taken to minimize Type 1 error. The four categories included body dissatisfaction (EDI-DT, EDI-BD, weight goal, and weight dissatisfaction), current weight/weight history (current BMI, lowest adult BMI, highest adult BMI, and weight fluctuation), bingeing/purging behaviors (EDI-B and self-reported binges and purges per week), and associated characteristics (EDI-P, EDI-ID, EDI-I, EDI- IA, and EDI-MF). Mean score and standard deviation for each category variable as a function of cluster group are listed in Table 1. A significant MANOVA was found for the body dissatisfaction category [ F(4,155)=17.20, Pb.001], which was further examined using Scheffe post hoc tests. No cluster group differences for the EDI-DT variable were found. However, for the EDI-BD variable, patients in Clusters 2 and 4 had significantly higher mean scores than did patients in Clusters 1 and 3 ( Psb.01). No other significant differences were

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Table 1 Descriptive statistics for variables included in the study as a function of cluster membership Cluster 1 (n=81)

Cluster 2 (n=43)

Cluster 3 (n=17)

Cluster 4 (n=18)

Body dissatisfaction EDI—drive for thinness EDI—body dissatisfaction Weight goal Weight dissatisfaction

13.3 (6.3) 15.2 (8.3) 23.9 (13.7) 2.5 (4.2)

14.8 21.9 11.0 22.2

13.5 (6.4) 13.6 (9.4) 17.4 (2.3) 6.5 (4.8)

14.4 (5.3) 23.5 (5.2) 6.1 (6.0) 78.3 (25.9)

Current weight/weight history Current BMI Lowest adult BMI Highest adult BMI Weight fluctuation

17.8 (2.4) 16.5 (2.3) 22.7 (3.8) 6.2 (4.0)

24.1 (2.3) 19.1 (2.6) 27.3 (5.3) 8.2 (5.0)

20.1 (2.4) 17.4 (2.7) 23.4 (2.8) 6.0 (2.1)

34.5 23.3 38.4 15.0

4.5 (5.5) 2.3 (3.1) 3.2 (4.4)

10.8 (5.5) 5.8 (5.4) 5.8 (5.8)

13.2 (4.7) 18.3 (8.4) 29.1 (14.1)

13.7 (5.8) 13.6 (9.6) 13.1 (8.2)

8.2 (4.8) 6.3 (4.4) 10.2 (7.1) 9.5 (6.6) 4.3 (4.4)

7.7 (5.1) 6.2 (4.4) 12.9 (7.0) 12.5 (7.6) 3.0 (2.9)

8.1 4.3 9.5 11.6 6.5

8.8 (5.0) 7.7 (4.9) 15.2 (7.0) 13.3 (5.9) 5.0 (4.5)

Bingeing/purging behaviors EDI—bulimia Average binges/week Average purges/week Associated characteristics EDI—perfectionism EDI—interpersonal distrust EDI—ineffectiveness EDI—interoceptive awareness EDI—maturity fears

(6.4) (7.0) (10.3) (9.9)

(4.1) (3.7) (7.0) (6.1) (4.4)

(5.2) (3.9) (5.2) (6.0)

EDI-eating disorders inventory; BMI-body mass index.

found. For the weight goal variable, there were also significant cluster group differences, with patients in Cluster 1 reporting a significantly greater unrealistic weight goal than did the patients in Clusters 2 and 4 ( Psb.001). No other cluster group differences were found for weight goal. Lastly, for the weight dissatisfaction variable, patients in Cluster 4 reported significantly greater weight dissatisfaction than did patients in the other three clusters ( Psb.001), and patients in Cluster 2 reported significantly greater weight dissatisfaction than did patients in Clusters 1 and 3 ( Psb.01). A significant MANOVA was also found for the current weight/weight history category [ F(4,155)=19.42, Pb.001]. Post hoc tests indicated that patients in Cluster 4 had significantly higher current BMI than did the patients in the other three clusters ( Psb.001), patients in Cluster 2 had significantly higher current BMI than did the patients in Cluster 3 ( Pb.001). In contrast, patients in Cluster 1 had significantly lower current BMI than patients in the other three clusters ( Psb.01). For lowest adult BMI, post hoc tests indicated that patients in Cluster 4 had significantly higher lowest adult BMI than patients in the other three clusters did ( Psb.001), and patients in Cluster 1 had significantly lower lowest adult BMI than patients in Cluster 2 did ( Pb.001). For highest adult BMI, again, patients in Cluster 4 had significantly higher highest adult BMI than did the patients in the other three clusters ( Psb.001), and patients in Cluster 2 had significantly higher highest adult BMI compared with patients in Clusters 1 and 3 ( Pb.001). For weight fluctuation, the only cluster group difference found was that patients in Cluster 4 had significantly greater weight fluctuation than did the patients in the other three clusters ( Psb.001).

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A significant MANOVA was also found for the bingeing/purging behavior category [ F(3,156)=14.98, Pb.001]. Post hoc tests indicated that for the EDI-B variable, patients in Cluster 1 had a significantly lower average EDI-B score than did the patients in the other three clusters [ F(3,155)=19.42, Psb.001]. For self-reported average binges, patients in Clusters 3 and 4 reported significantly greater number average binges per week compared with patients in Clusters 1 and 2 ( Psb.01). Lastly, patients in Cluster 3 reported significant greater number of purging behaviors than did the patients in the other three clusters ( Psb.001), and patients in Cluster 4 reported significantly greater number purging behaviors than did patients in Cluster 1. No differences in self-reported purging behaviors were found for patients in Clusters 1 and 2. The MANOVA result for the category of associated characteristics was not significant. Therefore, cluster group differences on the variables within this category were not examined. To summarize these findings, patients in Cluster 1 were distinct in terms of their unrealistic weight goal, low current body weight, a history of low body weight, and few self-reported bingeing and purging behaviors. Given these distinguishing symptoms, patients in this cluster resemble the DSM diagnostic category of AN, restricting type. Patients in Cluster 4 were distinct in terms of their high body and weight dissatisfaction, high current body weight, history of high body weight, history of large weight fluctuations, and high number of self-reported bingeing behavior. Thus, this cluster group resembles the provisional BED diagnostic category. Patients in Cluster 3 were distinct in terms of bingeing and purging behaviors. Specifically, patients in this cluster group evidenced moderate dissatisfaction with their body shape, demonstrated realistic weight goals, normal body weight and weight history, but report a high number of binges and purging behaviors (binges=18.3, purging=29.1). Given these symptoms, it would appear that patients in the third cluster would be a group of individuals who would meet DSM criteria for BN diagnosis. Patients in Cluster 2 seemed less distinct than were the patients in the other three clusters. Patients in this cluster were relatively high in body and weight dissatisfaction, had relatively high current body weight (BMI=24.1), and relatively low bingeing and purging behaviors. Therefore, these patients appear to be a group of individuals who are distinct only in terms of their high body weight and weight dissatisfaction. Given this description, these individuals would likely be diagnosed with EDNOS and may be at risk for development of either AN or BN or have a history of either of these disorders. It should be noted that patients in all four of the cluster groupings were similar in their scores on the Drive for Thinness subscale of the EDI. The clinical diagnosis of patients in each cluster group is shown in Table 2. As can be seen in Table 2, BN patients were almost exclusively grouped into Cluster 3. This is not surprising, given the large number of self-reported binges and purges for this cluster. Similarly, all of the AN patients were grouped into Cluster 1, although this cluster also contained an equal percentage of patients diagnosed with BN and EDNOS. Clusters 2 and 4 were comprised of approximately an equal percentage of BN and EDNOS

Table 2 Proportion of patients with eating disorder diagnoses as a function of cluster membership Anorexia nervosa Bulimia nervosa Eating disorder, NOS NOS: not otherwise specified.

Cluster 1 (n=81)

Cluster 2 (n=43)

Cluster 3 (n=17)

Cluster 4 (n=18)

37 30 33

0 63 37

0 94 6

0 56 44

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patients. Thus, with the exception of Cluster 3, which was mostly comprised of BN patients (i.e., 94%), the remaining clusters contained a mix of AN, BN, and EDNOS patients.

4. Discussion The findings of this study indicate that there is distinct clustering of ED patients. Specifically, one subgroup emerged that appeared similar to AN diagnosis, both the binge/purge and restricting subtypes. Another cluster resembled the DSM-IV BN diagnostic category, as they were distinguished by a high frequency of bingeing and purging behaviors. However, these patients reported average binge/purge frequency much higher than the observed in clinical samples (i.e., 10–12 episodes per week, Guertin, 1999). This cluster was also distinguished by a highest adult BMI that was lower than the other cluster that resembled the BN diagnostic category. Moreover, the lowest adult BMI of the high-frequency binge/ purge cluster did not differ from the cluster that resembled the AN diagnosis, with both clusters’ lowest adult BMI consistent with the a BMI of 17.5. Thus, although the AN-like and high-frequency binge/ purge cluster groups differed in terms of their current symptomatology, the two cluster groups were similar in terms of their weight history. These findings raise the possibility that the high-frequency binge/ purge cluster may be a group of individuals who previously met or nearly met DSM-IV criteria for AN. This possibility would also be consistent with the finding that some ED patients shift ED diagnosis over time, with one of the more common diagnostic shifts from AN to BN (Bulik, Sullivan, Fear, & Pickering, 1997). Another cluster group emerged in this study that resemble the DSM-IV BN diagnosis even more closely than the high-frequency binge/purge cluster group did. Specifically, this cluster group evidenced an average binge/purge frequency consistent with clinical BN samples reported in the literature (Guertin, 1999). Additionally, the cluster group was consistent with BN clinical samples reported in the literature in terms of their current weight status (i.e., BMI=24) and their relatively high highest adult weight (i.e., BMI=27). The last cluster group that emerged in this study resembled the provisional BED category outlined in the DSM-IV. In addition to the presence of clinically significant levels of binge eating, this group was also distinguished by high current weight (i.e., BMI=35) and by their history of large weight fluctuation (i.e., 15 lbs.). In fact, the current weight of this cluster was in the obese range, a weight observation that has been reported in the majority of studies examining BED patients (Delvin et al., 2003). Yet, it is noteworthy that current weight and weight history are not included in the DSM-IV provisional diagnosis for BED. Given the findings of this study, as well as the findings reported in other investigations of BED, weight status should be considered for inclusion in the BED diagnosis. Another interesting finding of the cluster that resembled BED was the relatively high frequency of purging behaviors. This finding is intriguing because the provisional BED criteria specifies that a person cannot engage in purging behaviors to meet criteria for BED. It is possible that the high frequency of purging behaviors was the result of the eating-disorder treatment-seeking sample examined in this study. Thus, such purging behaviors may not be observed among BED patients in other settings, such as those who present to weight management clinics. Nevertheless, the presence and frequency of purging behaviors should be further investigated in BED studies. Although examining different samples of individuals, the findings obtained in this study are similar to those reported by Bulik et al. (2000) who used latent class analysis to empirically examine eating

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disorder classes with a community-based sample of women. Similar to the findings of this study, Bulik et al. found three empirically derived groups that broadly reflected the diagnostic categories of AN, BN, and BED. Although Bulik et al. found evidence for three distinct eating disorder groups, all three groups would have been subthresholded for ED diagnoses as defined by the DSM. However, the relatively low severity of ED symptoms is not surprising, given that the sample was entirely drawn from the general community. It should be noted that, although three clusters emerged in our study that resembled DSM-IV AN, BN, and BED diagnoses, the actual diagnoses of the patients that comprised these three clusters did not match the DSM-IV AN, BN, and BED diagnostic categories. That is, while a portion of DSM-IV BN patients comprised nearly an entire cluster, the remaining DSM-IV BN diagnosed patients were distributed in the remaining three clusters (AN, high frequency binge/purge, and BED). In contrast, all of the DSM-IV AN diagnosed patients fell into one cluster. However, this cluster also contained a large percentage of patients (i.e., 63%) who received diagnoses of either DSM-IV BN or EDNOS. The cluster that resembled the BED syndrome contained an approximately equal percentage of patients diagnosed with DSM-IV BN and EDNOS. Finally, it is noteworthy that a cluster of patients similar to the DSM-IV EDNOS bnear missesQ for meeting full criteria for AN or BN did not emerge from the empirical classification approach used here. This is striking, as research indicates that this group comprises 25– 30% of all patients seeking treatment. It is possible that these near misses were the DSM-IV EDNOS patients included in this study’s AN and BN groups. This would suggest that the current DSM-IV criteria may be too restrictive, resulting in an excessive number of EDNOS diagnoses. Overall, conducting an empirical classification of treatment-seeking eating-disorder patients appears informative in terms of the lack of correspondence between the empirical approach and the DSM classification. Indeed, there does appear to be distinct groupings of AN, BN, and BED, but these groupings do not appear to map on well to the AN, BN, and BED diagnoses outlined in our current DSM diagnostic categories. Thus, although the findings of this study support the dimensional approach to eating disorders, the results also indicate the need to closely examine the current DSM criteria for eating disorders. For instance, current weight and weight history appeared important in distinguishing the clusters. The findings also indicated that the frequency of bingeing and purging behaviors may be important in distinguishing groups of ED patients. Based on the results of this study, in combination with the findings of other studies examining empirical classification of ED groups, the utility of alternatives to the current DSM-IV classification scheme in terms of treatment outcome and differential treatments merits further investigation. In the interim, clinicians may want to carefully consider basing treatment approaches solely on DSM diagnoses. A more effective treatment planning strategy may be to take an idiographic approach in which symptoms of the individuals are considered as well as the function of these symptoms (Garfinkel, 2002). Several limitations of this study should be noted. First, only treatment-seeking ED patients were included in this study, which may have resulted in a biased sample of participants with greater severity than would be observed in a community-based sample. It may be useful for future studies to include both treatment-seeking and community-based participants when examining empirically derived ED groups. Another potential limitation of this study relates to the variables used to cluster patients. We elected to use a variety of symptoms derived from both a psychometrically sound measure of eating disorder symptoms and self-report data obtained from a clinical interview. While we attempted to include a variety of symptoms that would best capture eating disorder symptomatology, we may have excluded variables that would have further distinguished the groups.

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In summary, the findings obtained here indicate that there are distinct groupings of eating-disorder patients that resemble the categories outlined in the DSM. However, the specific DSM criteria may need to be reconsidered based on the relatively poor match between the empirical approach used in this study and the DSM diagnoses that the patients were assigned by trained clinicians. While the DSM has made significant advances in eating disorder classification over the years, more attention to the eating disorder nosology is needed. Empirical investigation approach may shed considerable light in this area.

Acknowledgement This study was supported in part by a Temple University Research Study Leave awarded to Denise M. Sloan.

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