9b
Thanh Le and Alex S. Cohen
The chapter by Kerns (this volume) on “Cluster A” disorders provides an authoritative and thoughtful discussion of schizoid, paranoid, and schizotypal personality disorders as operationalized in the Diagnostic and Statistical Manual – 5th edition (DSM-5; American Psychiatric Association [APA], 2013) and among many researchers. From this chapter, it is clear that these diagnoses offer value to clinical, scientific, and consumer communities.
First, these disorders provide a valuable clinical tool for demarcating a particular type of functional deficit and distress and help identify appropriate evidence-based pharmacological and psychosocial treatments to address them. Second, they provide important prognostic information regarding social, cognitive, and vocational functioning and co-occurring psychopathology (e.g., substance use, depression, anxiety). Finally, they provide a tool for understanding clinical and individual differences associated with the development of other psychosis and schizophrenia-spectrum disorders; collectively one of the most economically costly and devastating illnesses known to humankind (Barbato, 1998).
It is also clear that DSM-5 Cluster A disorders suffer from issues as a scientific construct; issues which dovetail criticisms levied against DSM-5 disorders more broadly (Cuthbert & Insel, 2013; Krueger et al., 2018). For example, there are no diagnostic criteria unique to Cluster A disorders that aren’t common to other disorders (Strauss & Cohen, 2017). Moreover, Cluster A disorders each allow for dramatically heterogeneous phenotypes (Bollini & Walker, 2007). Of particular note, schizotypal personality disorder encompasses nine symptoms reflecting abnormalities in a broad array of behavioral, social, language, perceptual, meta-cognitive, and affective systems; of which only five symptoms are required for diagnosis (thus allowing for many different combinations of symptoms to meet criteria). From a neurodevelopmental perspective, it is not clear that Cluster A disorders are categorically distinct from each other (Chun, Barrantes-Vidal, Sheinbaum, & Kwapil, 2017) or from schizophrenia-spectrum pathology broadly defined (Insel, 2010). Finally, diagnostic reliability via clinical interviews and patient self-report is far from optimal (Chmielewski, Clark, Bagby, & Watson, 2015).
Given these limitations, it should be no surprise that cures for them do not exist, treatment is palliative at best, and no “necessary and sufficient” genetic, epigenetic, neurobiological, or functional mechanisms underlying them have been identified (Cohen, Chan & Debbané, 2018; Kirchner, Roeh, Nolden, & Hasan, 2018). In this commentary, we posit that the imprecision of DSM-5 Cluster A diagnoses constrains our ability to meaningfully measure, treat, and understand individual patients, at least, at a level expected by consumers and clinicians and comparable to that seen in many biomedical and bioengineering fields (Cohen, 2019). To address this, we consider how diagnosis and measurement of Cluster A disorders could conceivably change in the future. In doing so, we address (a) the viability of alternative diagnostic systems for operationalizing and measuring Cluster A disorders, (b) the utility of operationalizing Cluster A disorders within a broader spectrum of schizophrenia-related disorders and conditions, and (c) the viability of objectifying Cluster A disorders using various genotyping and phenotyping technologies.
Viability of Alternative Systems for Operationalizing and Measuring Cluster A Disorders
Alternative systems for operationalizing and measuring personality disorders, mental disorders, and their underlying psychological/physiological strata have been gaining traction in recent years, including: the DSM-5 alternative personality disorder (i.e., Section III of DSM 5 – “emerging measures and models,” APA, 2013), the Five-Factor Model (FFM; McCrae & John, 1992), the Research Domain Criteria (RDoC; Insel et al., 2010), and the Hierarchical Taxonomy of Psychopathology (HiTOP; Krueger et al., 2018) systems. These systems are potentially advantageous for clinical and research applications in that they are each associated with established assessments, hence potentially improving their “inter-rater” reliability. Moreover, they are each embedded within a hierarchy of other mental illness traits and symptoms, thus providing a framework for understanding and documenting phenotypic heterogeneity within Cluster A disorders and with co-occurring disorders. Finally, scientific literatures associated with these alternate systems are quite large, at least in regards to some of their domains. This helps provide clues to their underlying pathophysiological causes and mechanisms. Although these systems can hypothetically accommodate Cluster A disorders, none can yet satisfactorily explain all of their traits. For example, the DSM-5 alternative personality disorder section provides a theoretically-driven classification system that includes five personality domains: negative affect, detachment, psychoticism, antagonism, and disinhibition that roughly correspond to traditional FFM domains. This system can accommodate much of the phenotypic heterogeneity of Cluster A disorders, with suspiciousness, social anxiety, diminished social drive, odd speech/behavior, and other traits being reflected in varying and mutually exclusive levels of neuroticism, extraversion, openness to experience, agreeableness, and conscientiousness. However, both the DSM-5 alternative personality (i.e., psychoticism) and the FFM (i.e., openness to experience) struggle to meaningfully capture core positive schizotypal traits. As noted by Kerns (this volume), psychoticism has been proposed to be a component of openness to experience, though this is far from universally accepted (Chmielewski, Bagby, Markon, Ring, & Ryder, 2014). Similar struggles in integrating positive schizophrenia symptoms into both HiTOP and RDoC have been noted (Ford et al., 2014; Weinberger, Glick, & Klein, 2015; Wittchen & Beesdo-Baum, 2018). While proposed alternate systems offer some benefits for understanding Cluster A disorders, they cannot yet accommodate the full spectrum of Cluster A disorder traits.
Utility of Operationalizing Cluster A Disorders within a Schizophrenia-Spectrum
Alternatively, Cluster A disorders can be operationalized as components within a broader “schizophrenia-spectrum” (Kerns, this volume). Conceptual (Bollini & Walker, 2007) and empirical (Kendler, Neale, & Walsh, 1995) links between Cluster A disorders and schizophrenia are well established, and there is a general consensus that both reflect “schizotypal” traits (Lenzenweger, 2006). Within the schizophrenia research space, there is considerable effort worldwide to identify genetic and objective markers of this risk, and to use these markers in nomothetic risk rubrics for providing individual-level prognostic information. Moreover, the positive, negative, and disorganized traits associated with Cluster A disorders are conceptually and pathophysiologically related to the positive, negative, and disorganized symptoms of schizophrenia (Lenzenweger, 2006). Hence, efforts to understand and treat individual traits/symptoms within the broader schizophrenia-spectrum can hold importance for Cluster A disorders. That being said, our understanding of the mechanisms (e.g., genetic, psychosocial) underlying the diverse phenotypic expression in schizophrenia is poor, and treatments are palliative at best. While integrating Cluster A disorders within a schizophrenia-spectrum helps create continuity between them, there are likely few obvious treatment or assessment applications for schizophrenia that have not been already considered for Cluster A disorders (e.g., Kirchner et al., 2018; Kerns, this volume).
Defining and Measuring Cluster A Disorders Using Objective Technologies
The rise of “digital phenotyping” offers enticing opportunities for re-operationalizing Cluster A disorders. Digital phenotyping refers to the use of various objective biobehavioral technologies (e.g., electrophysiology, language analysis, geolocation) to quantify aspects of psychopathology (Insel, 2017; Wright & Simms, 2016). Psychological constructs that map onto Cluster A traits, such as social drive/behavior, speech coherence, emotional expression, and emotional and perceptual experience, can be quantified using a host of subjective and objective recording technologies. The near omnipresent availability of natural behavior tracked with audio, video, and mobile devices provides unprecedented volumes of data that are “high resolution” with respect to temporal (i.e., changes over time) and spatial (i.e., changes in reaction to environmental events) characteristics (see Cohen, 2019 for elaboration of this point). Importantly, these data are scalable, such that they can be aggregated over user-defined time and space. For example, an individual’s speech can be compared to existing corpuses to quantify level of “coherence,” emotional intensity, and aloofness. It can then be evaluated as a function of setting (e.g., work), type of social interaction (e.g., familial, professional), circadian rhythm, and other epochs of interest.
Digital phenotyping is an increasingly important component of the RDoC effort (Torous, Onnela, & Keshavan, 2017), and can complement the other “alternate” diagnostic systems discussed above. However, operationally defining Cluster A disorders based on high resolution, objective data represents a huge challenge. In large part, this is because the dynamics of Cluster A traits are poorly understood. It is well established that Cluster A traits tend to be fairly stable across family pedigrees (Kendler, McGuire, Gruenberg, & Walsh, 1995) and, within probands, are static over time using self-report questionnaires and interview ratings. However, individuals show incredible variability in phenotypes and corollary functional deficits over their lifespan. Even patients with severely debilitating psychosis recruited from inpatient settings show remitted symptoms, occupational and academic successes, and improved quality of life over large temporal epochs (e.g., Harrow, Jobe, & Faull, 2012). Over brief temporal epochs and across varied contexts, traits and symptoms associated with suspiciousness, delusional content, confusing language, anhedonia and so on, all vary considerably (Ben-Zeev, Ellington, Swendsen, & Granholm, 2011; Chun et al., 2017; Swendsen, Ben-Zeev, & Granholm, 2011). Individuals who are eccentric, aloof, or suspicious in one moment may be very different another moment in a different context.
To complicate matters further, the behaviors underlying Cluster A traits are highly variant over culture, gender, age, socioeconomic status, and cognitive ability (e.g., Fonseca-Pedrero et al., 2018). Hence, defining pathology based solely on “high resolution” data, as is done in many biomedical fields (e.g., diabetes, hypertension), is unfeasible for Cluster A disorders at the present time.
Summary
In sum, there is a pressing need for a more “precise” operationalization of Cluster A disorders. Ideally, this operationalization could account for their phenotypic heterogeneity, provide objective “necessary and sufficient” markers of risk and illness severity, and facilitate individualized assessment/monitoring, treatment and even prevention and cures. Substantial scientific resources are being devoted to develop alternate systems for operationalizing psychopathology, though these systems are inadequate for explaining Cluster A disorders at the present time. To help advance these efforts with respect to schizophrenia-spectrum pathology, the newly-formed International Consortium on Schizotypy Research (ICSR; Cohen et al., 2018; Docherty et al., 2018) is focused on information, technology, and data sharing within an international network of multidisciplinary collaborators. It is hoped that this effort, and efforts like it, can yield large translational data sets from culturally-diverse individuals to provide better resolution for understanding, redefining, and objectifying Cluster A disorders. For the time being, however, DSM-5 offers the most accepted operational definition for Cluster A disorders, and a combination of clinical interviews and self-reports are the most accepted methods of assessing them.
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