Identifying data-driven subtypes of internalizing and functional disorder symptoms: a factor mixture modeling approach
Creators: name: Van Loo, Hanna
name: Aggen, Steven
name: Rosmalen, Judith
name: Oldehinkel, Albertine
name: Saini, Urvi
Publisher: name: OSF Registries
Year: 2024
Subjects: subject: Mental Disorders
subject: Psychiatry and Psychology
subject: Public Health
subject: Medicine and Health Sciences
subject: Epidemiology
subject: Social and Behavioral Sciences
subject: Psychology
subject: FOS: Psychology
subject: Factor mixture models
subject: Fibromyalgia
subject: Functional disorders
subject: Generalized anxiety disorder
subject: Irritable bowel syndrome
subject: Major depreeive disorder
subject: chronic fatigue syndrome
subject: exploratory factor analysis
subject: latent class analysis

description: Internalizing disorders (IDs) are mental health conditions defined as disordered emotions or mood and include, among other disorders, major depressive disorder (MDD) and generalized anxiety disorder (GAD; Kovacs &amp; Devlin, 1998). Functional disorders (FDs) are characterized by physical symptoms persisting for several months, which cannot be attributed to known objectifiable pathological abnormalities (Buffington, 2009). The major three FDs are myalgic encephalomyeltitis/chronic fatigue syndrome (ME/CFS), irritable bowel syndrome (IBS), and fibromyalgia (FM). Individuals with FDs have a higher prevalence of mood and anxiety disorders than those without FDs (Janssens et al., 2015). It is widely recognized that both IDs and FDs encompass heterogeneous groups of individuals in terms of symptoms and etiology (Nandi et al., 2009; Williams et al., 2017; Martínez et al., 2021). Given the high comorbidity between IDs and FDs, along with overlapping symptoms (Hudson et al., 2004; Kato et al., 2009), one can also assume that individuals may present with diverse combinations of symptoms. 

Comorbidities within IDs and FDs as well as between these two groups could be attributed to the symptom overlap found in their consensus-based criteria. Within IDs, the diagnostic criteria of MDD and GAD share four symptoms (APA, 2013). Within FDs, the distinction between specific disorders reflects the tendency of specialists to focus only on symptoms relevant to their specialty (Wessely et al., 1999). The so-called splitters in the lumper-splitter debate support the need to discriminate between disorders as separate diagnostic categories (Lacourt et al., 2013). Conversely, lumpers have raised the question of whether all FD diagnoses are different names for the same problem (Wessely et al., 1999). However, a prospective population-based study investigating risk factors for new onset of ME/CFS, FM and IBS showed that most identified predictors were specific to one FD and not shared (Monden et al., 2022). Individuals with FDs, particularly those with ME/CFS, have a higher prevalence of mood and anxiety disorders than those without FDs (odds ratios = 3.9-12.6; Thomas et al., 2024). Moreover, individuals with multiple FDs have a higher risk of comorbid psychiatric disorders than those with a single FD (Janssens et al., 2015). Despite the associations found between symptoms of IDs and FDs, no study has yet examined these disorders simultaneously to identify the extent of overlap and how well the established diagnostic criteria for these disorders hold. It is important to study this because understanding the true nature of these comorbidities could better inform diagnostic criteria, leading to more accurate diagnoses which may ultimately contribute to improved patient outcomes.

Attempts have been made to identify underlying structures in the symptoms of IDs and FDs through data-driven subtyping. This approach uses statistical techniques to discern patterns in the reported symptoms of a heterogenous group of individuals. Two commonly used techniques used to recognize these patterns are latent factor models and latent class models (van Loo et al., 2012). Latent factor models, such as exploratory factor analysis (EFA), are used to uncover the underlying dimensional structure of a set of variables by reducing them to fewer latent factors while taking measurement error variance into account. In comparison, latent class models, such as latent class analysis (LCA), group individuals rather than variables into relatively homogenous subgroups. Current research has primarily utilized FA and LCA techniques to investigate within-disorder subtypes (Kessler et al., 1998; van Loo et al., 2012; Huber et al., 2018), highlighting both their contributions and inherent limitations in understanding ID and FD symptoms. Several factor analysis (FA) studies have analyzed the dimensional structure of symptoms of IDs and FDs (Gillespie et al., 1999; van Loo et al., 2022) within specific disorders or groups (i.e., clinical samples or adults younger than 28 years old). An LCA study on CFS, FM, and IBS symptoms yielded no classes corresponding to pure CFS, FM, or IBS but rather eight symptom profiles characterized by specific combinations of symptoms from different disorders (Eliasen et al., 2018). Profiles with multiple symptoms showed worse health status and were strongly associated with factors, such as comorbidity and poor self-perceived health, suggesting a need to consider these mixed symptom profiles in the understanding and management of FDs.
The few studies that have looked at between-disorder heterogeneity have largely focused on disorders within the ID or FD domain (Eliasen et al., 2018, Wanders et al., 2016). To study the etiology of IDs and FDs, it is important to have empirically based subtypes of these disorders. Researchers are more likely to find genetic or environmental risk factors in a group of patients that are homogenous in symptom patterns. Furthermore, although EFA and LCA studies have resulted in more knowledge about ID and FD symptom patterns (Eliasen et al., 2018; Collin et al., 2016; Milaneschi et al., 2016; Huber et al., 2018; Black et al., 2021; van Loo et al., 2022), there are still limitations to these techniques. EFA treats data as though it comes from a single population without distinct subgroups. As such, it does not account for potential differences between subgroups within the data, limiting our insight into the heterogeneity of the disorders. Although LCA can subtype individuals based on their observed symptoms, these subtypes are categorical and do not account for the range in severity of symptoms within and across the subgroups. 
One way to address the drawbacks of performing EFA and LCA separately is by combining them within a factor mixture model (FMM). FMM enables the identification of classes that vary in both qualitative and quantitative aspects (Miettunen et al., 2016). An advantage of FMM is that it captures the latent classes that represent distinct subgroups within a population while also being able to identify latent dimensional factor structures within the classes. This integration allows for a more comprehensive understanding of the heterogeneity present in ID and FD symptoms compared to using FA and LCA alone. Moreover, FMMs offer flexibility in the LCA assumption of local independence by incorporating latent factors into the model, which permits the modeling of within-class correlations among variables. It also addresses the issue of LCA identifying classes that predominately reflect severity differences rather than qualitative differences (van Loo et al., 2012). Previous FMM studies that have tackled symptom heterogeneity in IDs and FDs showed promising results in specifying subgroups with distinct symptom patterns with varying severity levels (Sunderland et al., 2013; Wanders et al., 2016). However, no study has yet investigated symptoms of IDs and FDs concurrently using the FMM approach. 
Incorporating disability as a covariate in studying subtypes of IDs and FDs may enhance our understanding of symptom and severity differences, revealing diverse presentations and informing more effective diagnostic and treatment strategies. A previous FMM study looking at heterogeneity in mood and anxiety disorders found disability to be a significant indicator for identifying clinically relevant subtypes as well (Wanders et al., 2016). Yet, most studies have focused on symptoms without considering their impact on an individual’s ability to function and their perceived well-being in physical, mental, and social domains of life. .Furthermore, adding covariates in FMM has been shown to improve class enumeration and class assignment, as the addition of covariates can increase class separation (Wang et al., 2021a; Wang et al., 2021b). Therefore, incorporating disability as a covariate could lead to more accurate and clinically meaningful subtyping, ultimately enhancing the utility of FMM in understanding and addressing IDs and FDs.
The main objectives of this study are to identify data-driven cross-symptom subtype heterogeneity for the IDs and FDs and to examine how these consensus-based clinical disorder criteria are best modeled in data collected in a large population sample. We will use symptoms and disability measures to identify homogeneous classes of individuals in terms of symptoms and disability. Additionally, we aim to assess the clinical significance of these latent classes by analyzing their association with established risk factors, including sex, sociodemographic factors, stress, and physical health status (Blanco et al., 2014; Dagnino et al., 2020; Monden et al., 2020) .