Latent profiles of eating disorder symptoms among transgender, gender diverse, and cisgender people with lived experience of eating disorders: associations with neurodivergent traits and gender dysphoria
Abstract
Abstract Eating disorder (ED) research has historically overlooked trans and gender diverse (TGD) people, despite evidence of elevated ED symptoms in this group. TGD people often report co-occurring autistic and ADHD traits that may intersect with their ED. We examined whether distinct profiles of ED symptomatology differ by gender identity, and the extent to which gender dysphoria and neurodivergent traits are associated with profile assignment. We applied latent profile analysis to ED symptom data from 487 individuals with experience of an ED (303 TGD people; 18–62 years). A four-profile solution provided the best fit: (1) Avoidant eating without ED cognitions, 18.7%; (2) Low ED cognitions and behaviours, 23.8%; (3) Avoidant eating with ED cognitions, 28.1%; and (4) Binge eating with ED cognitions, 29.4%. The Avoidant eating without ED cognitions profile was most relevant to gender dysphoria (OR = 1.50, p = .010) and autistic (OR = 1.57, p = .006) and ADHD traits (OR = 1.58, p = .006). Autistic traits increased the odds of membership in profiles characterised by ED cognitions. These findings demonstrate heterogeneity in ED presentations and highlight the value of a transdiagnostic, data-driven approach. They highlight the need to consider dual diagnoses of ARFID and anorexia nervosa, particularly among TGD and neurodivergent groups.
Read the paper
Related studies
- The Matrisome: In Silico Definition and In Vivo Characterization by Proteomics of Normal and Tumor Extracellular Matrices
- The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities
- In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling
- In silico prediction of splice-altering single nucleotide variants in the human genome
- In silico clinical trials: how computer simulation will transform the biomedical industry
- In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.