Use of Latent Profile Analysis to Identify Eating Disorder Phenotypes in an Adult Australian Twin Cohort

ArticleinArchives of General Psychiatry 63(12):1377-84 · January 2007with13 Reads
DOI: 10.1001/archpsyc.63.12.1377 · Source: PubMed
The relationships among the different eating disorders that exist in the community are poorly understood, especially for residual disorders in which bingeing or purging occurs in the absence of other behaviors. To examine a community sample for the number of mutually exclusive weight and eating profiles. Data regarding lifetime eating disorder symptoms and weight range were submitted to a latent profile analysis. Profiles were compared regarding personality, current eating and weight, retrospectively reported life events, and lifetime depressive psychopathology. Longitudinal study among female twins from the Australian Twin Registry in whom eating was assessed by a telephone interview. A community sample of 1002 twins (individuals) who had participated in earlier waves of data collection. Number and clinical character of latent profiles. The best fit was a 5-profile solution with women who were (1) of normal weight with few lifetime eating disorders (4.3%), (2) overweight (10.6% had a lifetime eating disorder), (3) underweight and generally had no eating disorders except for 5.3% who had restricting anorexia nervosa, (4) of low to normal weight (89.0% had a lifetime eating disorder), and (5) obese (37.0% had a lifetime eating disorder). Each profile contained more than 1 type of lifetime eating disorder except for the third profile. Women in the first and third profiles had the best functioning, with women in the fourth and fifth profiles having similarly poorer functioning. The women in the fourth group had a symptom profile distinctive from the other 4 groups in terms of severity; they were also more likely to have had lifetime major depression and suicidality. Lifetime weight ranges and the severity of eating disorder symptoms affected clustering more than the type of eating disorder symptom.
    • "Empirical classification studies have also been inconsistent in their support of the validity of the AN-R/AN-BP subtypes. Taxometric studies have yielded inconsistent findings about whether AN-R and AN-BP (and bulimic behavior more generally) are distinct entities or exist on a continuum [25,26], with some evidence from taxometric and latent structure analyses suggesting that AN-BP is more similar to bulimia nervosa than to AN-R272829. In addition, considerable data suggest that subtypes remain inconsistent over time, with individuals with AN-R often crossing over into the AN-BP classification [7,18,30]. "
    [Show abstract] [Hide abstract] ABSTRACT: The purpose of this investigation was to examine whether narrowing the criteria of anorexia nervosa (AN) subtypes among adults based on further delineations of current binge eating and purging (i.e., binge eating only, purging only, binge eating and purging, and restricting only) improves the potential clinical utility of the current DSM-5 system that specifies two types (i.e., current binge eating and/or purging and restricting, specified as the absence of current binge eating and/or purging).
    Full-text · Article · Feb 2016 · Journal of drug issues
    • "Numerous studies have supported the distinction between bulimia nervosa and BED [69][70][71][72][73][74][75][76]. Diagnostically, bulimia nervosa differs from BED by its requirement of recurrent inappropriate compensatory behaviors in order to prevent weight gain, such as self-induced vomiting; misuse of laxatives, diuretics, or other medications; fasting ; or excessive exercise [3]. "
    [Show abstract] [Hide abstract] ABSTRACT: Objective: To describe the epidemiology, clinical features, clinical course, medical complications, and treatment of binge-eating disorder (BED). Methods: Review of the literature. Results: BED, the most common eating disorder, is a distinct pattern of binge eating accompanied by a sense of loss of control over eating without inappropriate compensatory behaviors. Because people with BED more commonly seek treatment for the psychological and medical factors that are associated with the disorder, patients' first point of contact with the medical profession is likely to be the primary care physician (PCP). The PCP's role includes making efforts to screen for BED symptoms, employing motivational interviewing strategies to enhance likelihood of following through with treatment, providing psychoeducational information about eating and weight control, monitoring eating, weight, and related medical problems at follow-up visits, and making referrals to behavioral health specialists who can deliver empirically supported treatments for BED. Conclusion: Proper screening and referral in the primary care setting can optimize the likelihood that patients obtain empirically supported treatment. Copyright 2015 by Turner White Communications Inc., Wayne, PA. All rights reserved.
    Full-text · Article · Nov 2015
    • "To avoid local maxima, starting values for optimization were fixed at 500 random starts and 50 iterations at initial stage (Geiser, 2010). The class solution was evaluated by its empirical fit regarding parsimony and quality of classification: A minimization of the adjusted Bayesian information criteria (aBIC) and of the cross classification probabilities and a maximization of entropy and the log-likelihood value were desired (Gollwitzer, 2008;Nylund, Asparouhov, & Muthén, 2007;Wade, Crosby, & Martin, 2006). An average latent class probability of at least .80 "
    [Show abstract] [Hide abstract] ABSTRACT: This study’s purpose is to describe European adolescents’ alcohol use patterns by grouping adolescents regarding their current alcohol use by cluster analysis (CA). Discriminant and latent profile analyses (LPA) evaluate and validate the solution that will be described further by ANOVAs. From 25 European countries, 57,771 students (49.4% male, 13.87 years) are grouped using hierarchical and k-means clustering. Alcohol use is measured by frequency of drinking occasions during the previous month and number of beverages consumed on the last drinking occasion. CA suggests four drinking patterns: mild (73.6%), episodic (20.0%), frequent (3.8%), and heavy episodic use (2.5%). Discriminant analysis attests a classification reliability of 94%, and confirmatory LPA replicates the cluster solution with a satisfying model fit. Three of the found patterns fulfill criteria for heavy drinking and underline the importance of individualized indicated prevention by promoting responsible use.
    Full-text · Article · Jun 2015
Show more