An Attempted Integration of Information Relevant to Schizophrenic Subtypes

Abstract

The usefulness and validity of traditional subtypes are questionable. The subtypes described in earlier years no longer emerge with the clarity previously described. The four classical subtypes cannot be reliably distinguished and have not been shown to have predictive validity. Subtypes classified along course or prognostic lines may be more clinically useful. Attempts to subdivide schizophrenia along biologic and genetic lines offer promise. Recent efforts to describe new subdivisions of schizophrenia are readily justified, but new descriptive subtypes are likely to prove useful only when validated by biological, genetic, treatment response, and outcome data.

Definition and description of schizophrenia in the DSM-5

“…More studies since 1994 have called into question the continued utilization of the classic subtypes of schizophrenia. Similar to findings of prior studies Strauss et al, 1973;Carpenter and Stephens, 1979), cluster analytic and other approaches to identify taxonic schizophrenia subtypes consistently fail to identify the DSM-IV subtypes (Lykouras et al, 2001;Helmes and Landmark, 2003;Peralta and Cuesta, 2003;Picardi et al, 2012). A review of 24 publications describing 38 analyses of 28 participant cohorts found no support for classic schizophrenia subtypes (Linscott et al, 2010).…”

Characterization of the Deficit Syndrome in Drug-Naive Schizophrenia Patients: The Role of Spontaneous Movement Disorders and Neurological Soft Signs

“…1 While most subtyping systems have reached limited success in this regard, the deficit/nondeficit categorization 2 remains one of the most valid attempts to subclassify schizophrenia. Deficit schizophrenia is characterized by primary and enduring negative symptoms, which is believed to have unique etiology, biological substrate, and clinical features distinguishable from the nondeficit form.…”

Individual Differences: Schizophrenia

“…11,[24][25][26] ) by using a very much larger sample of patients (2567) than in any previous investigation; by investigating the symptoms in the same patients in both the unmedicated and medicated states; by comparing the symptoms in FEP and MEP and by including a more powerful machine learning, community detection, approach. This investigation differs from the analysis of a partly overlapping FEP patient population by Lu et al (in preparation) 16 in that we used here the same participants unmedicated and medicated to understand the effects of the medication, which may produce clearer results than comparing two different groups when unmedicated or not; in the use of more classical clustering approaches to define how the symptoms cluster and how the members of the population cluster; and in the emphasis on the continuous unimodal distribution of the negative symptoms which differs between individuals, instead of focusing on communities that are thought to be distinct because they have been categorised separately by a community detection algorithm which looks for distinct communities.…”