Functional somatic disorders (FSD) are characterized by persistent physical symptoms that cannot be fully explained by structural disease. Whether FSD represent distinct clinical syndromes or a single unified spectrum remains debated. While evidence supports shared epidemiological, psychological, and neurobiological features across syndromes, significant clinical heterogeneity challenges this unified view. This study aimed to identify patient subgroups within an FSD population using data-driven clustering approaches applied to validated self-report measures.

Adult patients referred to a specialized FSD consultation at Lille University Hospital (France) between January 2022 and June 2025 were included. Clinical data were collected using validated self-report questionnaires. A Principal Component Analysis (PCA) was performed as an exploratory step to assess data structure. Then, two clustering algorithms were applied directly to scale scores: k-means analysis on continuous data, and Latent Class Analysis (LCA) on dichotomized data.

A total of 301 patients were included (66.4% female, mean age 46.7 years). The PCA revealed two main groups of variables, with Childhood Trauma Questionnaire subscales forming a dimension independent of the other scales. Both k-means and LCA identified two groups distinguished by overall symptom severity rather than organ-specific symptom profiles, despite the inclusion of multi-system somatic assessments (Patient Health Questionnaire-15). This suggests that while FSDs present with clinically distinct organ-based symptoms, their underlying psychopathological and somatic burden operates primarily along a severity continuum. Childhood trauma scores were elevated across both clusters, independently of symptom burden.

These findings support the hypothesis of a unified FSD spectrum rather than discrete syndromes. Childhood adversity appears to represent a transdiagnostic vulnerability factor independent of clinical severity. Future research should incorporate objective biological markers, longitudinal designs, and broader clinical settings to better capture these clusters and identify predictors of clinical trajectory.