This study used fully data-driven approaches to identify homogeneous classes of individuals based on lifetime-rated psychotic symptoms in a sample of 4286 subjects. EFAs suggest symptom variation is best represented by 5 continuous dimensions: positive, negative, manic, disorganized, and depression. Subsequent latent class analyses found that 7 categorically different classes of subjects fitted the data well. The latent classes are shown to have differential associations with (1) diagnosis, (2) intelligence, and (3) clinical and social needs.
Latent class analysis based on the factor scores for each of the 5 dimensions was used to identify 7 groups (classes) of people who have similar symptom profiles and may therefore be more homogenous in their underlying neuropathology. The class to which the majority (85%) of the schizophrenia and schizoaffective patients was assigned shows many similarities with the group described by Kraepelin as suffering from dementia praecox. We therefore refer to this class as Kraepelinian schizophrenia. A smaller proportion (15%) of the schizophrenia and schizoaffective patients was assigned to a class which was characterized by relatively low scores on disorganization and negative symptom dimensions as compared with their positive and affective symptoms. They also showed normal (average) IQ and a relatively good outcome. This class was referred to as the affective psychosis class because of the prominent positive and affective symptoms in the absence of disorganization and negative symptoms. Latent class assignment was more heterogeneous in bipolar disorder which suggests that the group of bipolar patients may explain the reported overlap in genetic risk factors with schizophrenia24
as 41% shows a symptom and outcome profile which is characteristic for schizophrenia patients. It should be mentioned here that the bipolar patients were mainly diagnosed with bipolar I disorder (91%), and it is therefore likely that in a combined sample of bipolar I and bipolar II heterogeneity would be even larger, although we cannot rule out the possibility that homogeneity would increase in a sample of bipolar II patients.
The fact that 85% of the schizophrenia patients is assigned to the same class suggests that schizophrenia is a well delineated clinical construct with the vast majority of the diagnosed patients comprising a homogeneous group of individuals displaying decreased cognitive performance and poor outcome. In contrast, the bipolar construct is much less homogeneous as patients with bipolar I disorder are assigned with equal probabilities to 2 classes showing quite different symptomatic and outcome profiles (ie, the Kraepelinian schizophrenia class and the affective psychosis class). We may therefore conclude that the rather vague boundaries between schizophrenia and bipolar disorder are mainly the result of heterogeneity in patients with bipolar disorder and are to a much smaller extent explained by heterogeneity in schizophrenia. Genetic studies of schizophrenia probably use more homogeneous samples than genetic studies of bipolar disorder.
This is the first study to address the latent structure of psychotic symptoms in a large sample including not only patients but also individuals diagnosed with depression as well as healthy subjects. The 5 symptom dimensions reported in the present study agree with the findings from factor analyses in patient samples.25,26
Although others have reported a 4 instead of a 5-factor solution27–29
, this is mainly due to difficulties in separating the negative and disorganization dimensions.30
Previous studies on subtypes of psychotic disorder have identified more psychotic clusters than we report in our study8,31–33
, although there is agreement with the class solution presented here. Kendler and colleagues34
applied latent class analysis to 21 items assessed in 343 patients diagnosed with schizophrenia or an affective illness. They report the presence of 6 latent classes, described as classic schizophrenia, major depression, schizophreniform disorder, bipolar-schizomania, schizodepression, and hebephrenia. There is close similarity between the classic schizophrenia class and our Kraepelinian schizophrenia class, while the bipolar schizomanic class is similar to the manic-depression class found here. Furthermore, major depression is found in both samples, although we have not found the differentiation into hebephrenia, bipolar—schizodepression and schizophreniform classes. Boks and colleagues32
report on the presence of 6 clusters in a sample of 1056 psychosis patients; their solution largely agreeing with the clusters described by Kendler.34
Applying their latent class solution to the national Child development UK cohort study,35
it was shown that the “classic schizophrenia” cluster better predicted neurodevelopmental risk factors than DSM-IV diagnosis. The sample in the Boks et al32
study partly overlaps with the patients included in the current sample, and the methodology (using a combination of factor analysis and latent class analysis) was similar to the methodology used here.
The fact that we find only 2 classes characterized by high levels of psychosis is probably explained by the presence of relatives and community controls in this study, which allows the symptom variation within psychosis patients to be shown against the reference group of subjects without psychosis. Therefore, we detect only the most relevant differences between patients as relatively small differences appear marginal compared with the marked differences between patients and healthy subjects.
Analyses of symptom variation in a combined sample of patients and controls show that the prominent difference within the patient sample is based on the distinction between low vs high levels of disorganization and negative symptoms and is not based on the level of positive (psychosis) symptoms. We further show that the separation based on disorganization and negative symptoms is associated with cognitive performance and outcome. Our findings therefore reflect the distinction made by Kraepelin5
who separated schizophrenia (dementia praecox) from affective disorder. In Kraepelin’s dichotomy, dementia praecox was characterized by poor outcome and a decrease in cognitive functioning over time while patients with affective disorder showed a much better outcome. It should be emphasized that cognitive functioning in schizophrenia patients is already reduced before the onset of psychosis while the term “dementia praecox” suggests that poor cognitive performance is the end stage of the illness. Nevertheless, we have confirmed the presence of 2 psychosis classes which are separated by the level of disorganization and negative symptoms, cognitive performance, and outcome.
An advantage of our approach is that it allows for a classification of both subjects with and without a DSM-IV diagnosis for schizophrenia based on the presence of psychotic symptoms. Thus, the findings of this study show that empirically derived subtypes are not confined within classical diagnostic boundaries but cut across traditional DSM diagnoses and may more adequately capture the inherent clinical heterogeneity of psychosis than diagnostic groups.
Within (healthy) relatives of patients, we found an association between latent class and IQ which was not present in community controls. A previously published study has shown that 92% of the covariance of intelligence and schizophrenia is explained by genetic factors.16
Therefore, lower IQ in the relatives assigned to the Kraepelinian schizophrenia, affective psychosis, and deficit nonpsychosis classes may reflect an increased genetic liability for psychosis suggesting that our classification is sensitive enough to distinguish between relatives with increased genetic or environmental vulnerability for psychosis and relatives without such vulnerability. Indeed, subjects without a family history of psychosis did not show an association between IQ and latent class membership. This may in part be the result of a decreased statistical power due to the smaller sample size in the subgroup analyses, but it also suggests that the association between intelligence and latent class membership is mediated by family history.
The results of this study should be interpreted in view of the following limitations. First, we used a 2 step approach in our statistical analyses by first performing factor analyses and then as a next step generating latent classes. Ideally, we would incorporate dimensions and classes into the same model but it was not computationally feasible to estimate the parameters of such a model. Second, the latent class model including 7 latent classes did not show the best model fit according to the BIC. However, parameter estimates appeared to be unstable when including more than 7 classes. Furthermore, inspection of the latent class profiles revealed newly added classes mainly represent severity differences within healthy subjects. That is these extra classes represented quantitatively and not qualitatively different classes. A third limitation is that no information on IQ and social and clinical needs was available for the subjects who did not participate in the GROUP study. Therefore, validation of the latent classes was possible for a subset of the sample only. Fourth, the number of subjects diagnosed with bipolar disorder (N = 208) and depression (N = 480) was small compared with the number of schizophrenia patients. The low number of bipolar patients is mainly due to the fact that patients with affective psychosis were not included in the GROUP study. We do believe that the numbers are large enough to result in a stable latent class solution. Fifth, the bipolar sample mainly consisted of subjects diagnosed with bipolar I disorder, and our conclusions are therefore limited to bipolar I and not bipolar II. However, many studies combine bipolar I and bipolar II patients which would further increase heterogeneity, and therefore, our estimate of heterogeneity in bipolar patients may even be an underestimation of the true heterogeneity. Finally, the patients and controls included in this study were not randomly selected from the general population, and it is therefore possible that the factor and latent class solutions reported here would have been different in an epidemiologically selected sample. However, the fact that we included large numbers of patients, relatives of patients, and community controls in our analyses probably allowed us to study the full range of psychosis symptoms.
Our results show that 85% of the patients diagnosed with schizophrenia form a rather homogeneous group which resembles the description of dementia praecox as defined by Kraepelin. The remaining 15% of the schizophrenia and schizoaffective patients is assigned to an affective psychosis class with few disorganization and negative symptoms, normal cognitive functioning, and a relatively good outcome. This group may be etiologically different from the Kraepelinian schizophrenia class, and the separation into Kraepelinian schizophrenia and affective psychosis should therefore be incorporated in future genetic and treatment studies. An additional finding was that heterogeneity in latent class assignment was much more pronounced in the bipolar patients while heterogeneity was also quite large in relatives of patients and in community controls. We have described a deficit nonpsychosis class characterized by high levels of disorganization and negative symptoms and relatively low IQ in the absence of psychosis. Follow-up studies should reveal whether the young adults assigned to this class have an increased chance to develop psychosis.
So far, few gene finding studies have used empirically derived symptom profiles, although the few studies that exist to date look promising and suggest that genetic variants may be associated with specific subgroups of schizophrenia (eg, deficit subgroup)33,36
see Fanous and Kendler37
for a review. Although the approaches used in the previously published studies all reduce phenotypic heterogeneity within patients, our analysis has the major advantage that the results not only reduce clinical heterogeneity in psychosis patients but also in relatives of patients and in community controls. Overall, we have shown that both within psychosis patients and controls we can effectively identify distinct groups which may have the potential to facilitate etiological research.