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Published Online: 1 July 2013

The Dynamics of Subthreshold Psychopathology: Implications for Diagnosis and Treatment

Subthreshold Extended Phenotypes

The article by Zammit et al. (1) in this issue of the Journal confirms that mental disorders appear to be continuous—phenomenologically and longitudinally—with subthreshold states, or extended phenotypes, of psychopathology. There is well-replicated evidence that major depression can be traced to subthreshold depressive states (2), common mental disorders to subthreshold neurotic symptoms (3), bipolar disorder to subthreshold mania (4), autism to subthreshold autistic traits (5), and psychotic disorders to subthreshold psychotic experiences (6). In addition, research indicates that normal variation and the extreme end of the distribution tend to share the same genetic and nongenetic causes (710), indicating at least a degree of etiological continuity in addition to psychometric and predictive continuity.
Subthreshold extended phenotypes in the general population are conceptually quite different from the “ultrahigh risk” or “at-risk mental state” populations in the psychosis literature. In the area of psychosis, ultrahigh risk or at-risk mental states refer to individuals with subthreshold psychosis who seek help at mental health services. Given that individuals with subthreshold psychosis who present to services have more severe psychopathology than those with subthreshold psychosis in the general population who do not seek help (a phenomenon known as frailty or comorbidity bias), transition rates from subthreshold psychosis to clinical disorder will, by definition, be higher in the help-seeking at-risk mental state population than in the general population. For example, meta-analytic research in the area of psychosis indicates that the yearly transition rate from subthreshold psychosis to a psychotic disorder in the general population is less than 1% (11)—0.9% in the Zammit et al. study—but more than 20% in the at-risk mental state population (12).

Do Subthreshold States Specifically Predict the Corresponding Clinical Outcome?

Much of the research on subthreshold extended phenotypes is conducted within noncommunicating silos so that, for example, subthreshold depressive states are presented as specifically predicting depression, subthreshold mania as specifically predicting bipolar disorder, and subthreshold psychosis as specifically predicting psychotic disorders. As a consequence, some DSM-5 working groups considered adding specific risk syndromes for each disorder, the best known example being the attenuated psychosis syndrome. One of the main reasons for not including risk syndromes for each disorder in the main body of the manual, however, was mounting evidence that the earliest expressions of psychopathology are a nonspecific, mixed bag of affective dysregulation, aberrant salience, motivational alterations, anxiety states, and other early symptoms that dynamically affect each other, forming a causal network. Over time, these symptoms gradually and nonlinearly differentiate into more distinct syndromes (13, 14), as summarized in Figure 1.
FIGURE 1. Staging Model of Causal Symptom Circuitsa
a Psychiatric syndromes (for example, anxiety, depression, and psychosis) come about as sets of symptoms (different colors represent different symptoms; different sizes reflect different levels of severity) and become connected in a sequence of causal relations (indicated by arrows between symptoms, depicted in three examples), resulting in the gradual differentiation from nonspecific states of early mental distress to diagnosable syndromes of anxiety, depression, and psychosis. Preventing the sequence of causal impacts in the symptom circuit may prevent differentiation into diagnosable mental syndromes.

From Specific Risk Syndromes to a Dynamic Circuit of Early Psychopathology

It has been suggested that mental disorders are reducible to sets of symptoms that are connected through systems of causal relations (15), and evidence from general population studies suggest that this model applies particularly to the earliest stages of psychopathology. The first, indirect, indication is that transition from a given subthreshold state to a given mental disorder is not symptom specific; for example, subthreshold psychosis predicts both psychotic and (albeit more weakly) nonpsychotic outcomes (11). The second, direct, indication is that the earliest expressions of psychopathology dynamically interact with each other. For example, there is evidence that the transition from subthreshold psychosis to psychotic disorder is contingent on the earlier presence of subtle indices of motivational impairment (early negative symptoms) (16) and that the transition from subthreshold mania to bipolar disorder is contingent on the presence of earlier subthreshold psychosis (17). Insomnia has an impact on paranoia that may be mediated by affective dysregulation (18). Interaction may also occur between symptoms within the same domain, as studies have shown that early hallucinatory states are more likely to result in clinical outcomes when combined with delusional ideation (19). Similarly, subthreshold psychosis is strongly associated with subthreshold depression as well as subthreshold mania in a dose-response fashion, implying causality; the stronger the association between the two domains, the greater the risk of transition to a clinical disorder (20). Finally, many individuals with subthreshold psychosis present with disorders of anxiety and depression, and it has been shown that the presence of subthreshold psychosis has a negative impact on course and outcome of these disorders (21, 22). Therefore, the paradigm of subthreshold psychosis specifically predicting psychotic disorder needs to be complemented by a focus on the impact of subthreshold psychosis on the course and outcome of nonpsychotic disorders (23).

Challenges

The challenge in the years to come is to understand how the earliest expressions of psychopathology form part of a dynamic circuit of symptoms that affect and reinforce each other, gradually differentiating across stages of psychopathology into more specific, but still largely overlapping, clinical syndromes. There is evidence that the manner in which early states of psychopathology affect each other over time is influenced by genetic and environmental factors (24). Similarly, it is attractive to speculate that symptom circuits in the earliest stages of psychopathology reflect the dynamics of neural circuits subserving regulation of affect, assignment of salience, motivational processes, and social cognition. Of particular interest is how changes in the dynamics of early psychopathology contribute to changes in functioning and personal attributions of ill health, resulting in help-seeking behavior and entrance into the at-risk mental state sampling framework. Finally, assessing the early dynamics of symptoms that affect each other over time facilitates person-specific diagnostic formulations and the development of treatments that target the most dominant links in the dynamic circuit of early psychopathology (25).

References

1.
Zammit S, Kounali D, Cannon M, David AS, Gunnell D, Heron J, Jones PB, Lewis S, Sullivan S, Wolke D, Lewis G: Psychotic experiences and psychotic disorders at age 18 in relation to psychotic experiences at age 12 in a longitudinal population-based cohort study. Am J Psychiatry 2013; 170:742–750
2.
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3.
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4.
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11.
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12.
Fusar-Poli P, Bonoldi I, Yung AR, Borgwardt S, Kempton MJ, Valmaggia L, Barale F, Caverzasi E, McGuire P: Predicting psychosis: meta-analysis of transition outcomes in individuals at high clinical risk. Arch Gen Psychiatry 2012; 69:220–229
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20.
van Rossum I, Dominguez MD, Lieb R, Wittchen HU, van Os J: Affective dysregulation and reality distortion: a 10-year prospective study of their association and clinical relevance. Schizophr Bull 2011; 37:561–571
21.
Wigman JT, van Nierop M, Vollebergh WA, Lieb R, Beesdo-Baum K, Wittchen HU, van Os J: Evidence that psychotic symptoms are prevalent in disorders of anxiety and depression, impacting on illness onset, risk, and severity: implications for diagnosis and ultra-high risk research. Schizophr Bull 2012; 38:247–257
22.
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van Os J, Murray RM: Can we identify and treat “schizophrenia light” to prevent true psychotic illness? BMJ 2013; 346:f304
24.
Wigman JT, Collip D, Wichers M, Delespaul P, Derom C, Thiery E, Vollebergh WA, Lataster T, Jacobs N, Myin-Germeys I, van Os J: Altered Transfer of Momentary Mental States (ATOMS) as the basic unit of psychosis liability in interaction with environment and emotions. PLoS ONE 2013; 8:e54653
25.
van Os J, Delespaul P, Wigman J, Myin-Germeys I, Wichers M: Beyond DSM and ICD: introducing “precision diagnosis” for psychiatry using momentary assessment technology. World Psychiatry 2013 (in press)

Information & Authors

Information

Published In

Go to American Journal of Psychiatry
Go to American Journal of Psychiatry
American Journal of Psychiatry
Pages: 695 - 698
PubMed: 23820827

History

Accepted: April 2013
Published online: 1 July 2013
Published in print: July 2013

Authors

Details

Jim van Os, Ph.D.
From Maastricht University Medical Center, South Limburg Mental Health Research and Teaching Network, the European Graduate School of Neuroscience, Maastricht, the Netherlands, and the Department of Psychosis Studies, Institute of Psychiatry, King’s College London.

Notes

Address correspondence to Dr. van Os ([email protected]).

Funding Information

The author reports no financial relationships with commercial interests.Supported by the European Community’s Seventh Framework Program under grant agreement number HEALTH-F2-2009-241909 (Project EU-GEI).

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