Download Five Latent Factors Underlying Schizophrenia: Analysis and

Five Latent Factors Underlying Schizophrenia:
Analysis and Relationship to Illnesses
in Relatives
by John A. McQrath, Qerald Nestadt, Kung'Yee Liang,
Virginia K. Lasseter, Paula S. Wolyniec, M. Danielle Fallin,
Mary H. Thornquist, James R. Luke, and Ann E. Pulver
research about dimensions that might underlie the observable symptoms. Strauss et al. (1974) described three such
dimensions characterized by positive symptoms, negative
symptoms, and disorder in relating. Crow proposed that
variation in the clinical picture of schizophrenia among
patients owes to the action of two independent processes,
related to positive versus negative symptoms (Crow 1980,
1981). Other researchers working on dimensional theories
have developed and studied scales to measure the putative
dimensions, such as the Scale for the Assessment of
Negative Symptoms (SANS) and the Scale for the
Assessment of Positive Symptoms (SAPS) (Andreasen
and Olsen 1982; Andreasen et al. 1995a, 19956), the
Positive and Negative Syndrome Scale (PANSS; Kay et
al. 1987; Bell et al. 1994; Lindenmayer et al. 1994; White
et al. 1997), and the Schedule for the Deficit Syndrome
(Kirkpatrick et al. 1989, 2001).
A common analytic strategy in dimensional studies
has been to submit items or summary scores from one or
more scales to principal components analysis or factor
analysis, to explore whether and how the items can be
explained by a smaller number of latent factors or dimensions. Three factors have been commonly identified in a
wide variety of studies: a positive dimension comprising
hallucination and delusion items; a disorganized dimension, which includes such phenomena as bizarre behavior,
positive thought disorder, and inappropriate affect; and a
negative dimension, covering such symptoms as anhedonia, poverty of speech, and blunted affect (Klimidis et al.
1993; Brekke et al. 1994; Buchanan and Carpenter 1994;
Murphy et al. 1994; Andreasen et al. 1995ft; Burke et al.
1996; Ratakonda et al. 1998; Mojtabai 1999).
However, the identification of underlying factors is
dependent on study design features such as sampling, the
period of observation, the range of symptoms and signs
that are examined, and the data sources that are used (e.g.,
Abstract
Clinical signs and symptoms in a sample of 1,043 individuals with schizophrenia or schizoaffective disorder
were subjected to latent class factor analysis. Positive,
negative, disorganized, and affective factors were similar in content to factors described in a number of
other studies, while a fifth factor representing early
onset/developmental signs provided a new area for
investigation. The five sets of factor scores were logistically regressed on psychiatric illness indicators in first
and second degree relatives. Relatives of probands
with higher positive or negative symptom factor scores
had a lower risk of depressive illness. Higher affective
factor scores in probands predicted more mania and
depression in relatives. Both the disorganized and the
early onset/developmental factors were related to
increased risk of psychiatric hospitalization in relatives, as well as increased risk of psychosis (marginally
so for the disorganization factor). Increased early
onset/developmental signs in the proband were also
associated with increased risk for depression in relatives. These findings suggest a possible endophenotypic
role for the factor scores in future studies.
Keywords: Schizophrenia, schizoaffective disorder, latent class factor analysis, family history.
Schizophrenia Bulletin, 30(4):855-873, 2004.
Schizophrenia is generally thought to be a group of disorders arising in part from susceptibility introduced through
at least four to five genes of moderate effect (Risch 1990).
In all likelihood, schizophrenia is genetically heterogeneous. Varied expression of the genes likely results from
the influence of multiple environmental factors and developmental processes. Researchers have long sought to
study and characterize the symptoms of schizophrenia,
hoping to identify signs that validly segregate subjects
with the disease into more homogeneous subgroups and to
improve etiologic knowledge and treatment.
A potentially fruitful approach to sorting out "the
schizophrenias" has been the development of theory and
Send reprint requests to Dr. A.E. Pulver, Johns Hopkins University,
Department of Psychiatry and Behavioral Sciences, 1820 Lancaster
Street, Suite 300, Baltimore, MD 21231; e-mail: [email protected].
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J.A. McGrath et al.
direct assessment, informant reports, medical records).
The types of statistical tools brought to bear also affect
dimensional studies.
With regard to the range of symptoms and signs that
are examined, several researchers have added social
adjustment indicators to the indicators of psychosis and
have applied confirmatory factor analytic methods to produce models that include four factors: the common three
factors (positive, negative, disorganized) plus a relational
(social) factor (Peralta et al. 1994; Lenzenweger and
Dworkin 1996). Work with the 30-item PANSS has often
produced five factors: a positive factor, a negative factor,
a dysphoric factor, an excited factor, and a "cognitive" or
"autistic preoccupation" factor, which has some features
in common with the traditional disorganized factor (Bell
et al. 1994; Lindenmayer et al. 1994; Lindenmayer et al.
1995; White et al. 1997; Lancon et al. 1998). Factor
analysis of 50 individual SANS/SAPS items (rather than
ten global ratings) has yielded five to seven interpretable
factors in a mixed sample including schizophrenia
patients (34%) as well as subjects with other major psychotic and affective disorders: (1) negative, (2) disorganized, (3) disordered relating, (4) bizarre delusions, (5)
auditory hallucinations, (6) nonbizarre (paranoid) delusions, and (7) other hallucinations (Toomey et al. 1997).
The factor analytic picture of schizophrenia is also
altered by the period of observation: Assessment of lifetime symptoms or longitudinal assessment is useful in
capturing the full range of psychopathology that a subject
has experienced, because results of cross-sectional assessment may be limited by the current phase of illness for the
subject (Arndt et al. 1995; Eaton et al. 1995; Schultz et al.
1997; Mojtabai 1999). Because symptom profiles for each
individual may vary over a lifetime, the period of observation must be considered.
Factor analyses of lifetime symptoms of psychosis
gathered through use of the Operational Criteria Checklist
for Psychotic Illness (OCCPI) have resulted in more complex dimensional pictures of schizophrenia, likely owing
to the broader range of symptoms and time frame
included in analyses. Several studies of schizophrenia and
schizoaffective subjects by Cardno et al. have included
the negative and disorganized dimensions but have broken
the positive dimension into paranoid delusions versus first
rank delusions versus first rank hallucinations (Cardno et
al. 1996), or positive symptoms (mainly hallucinations)
versus first rank delusions (Cardno et al. 1999).
Van Os et al. (1999) have also demonstrated the utility
of broadening the scope of symptoms studied and the time
period covered. They assessed 706 patients with chronic
psychosis
(54% having
DSM-III-R
schizophrenia/schizoaffective disorder, 32% with psychotic affective disorders, 14% with other psychoses) and generated a
four-factor result (positive, negative, depressive, and manic)
accounting for 26 percent of the variance in 65 cross-sectional psychopathology items from the Comprehensive
Psychopathological Rating Scale (CPRS). But they also
generated a five-factor solution (the same four, plus disorganization) accounting for 41 percent of the variance in 46
items from the OCCPI. The OCCPI was completed using
not only the cross-sectional CPRS data but also the SANS
and lifetime data derived from medical records.
The present study of the latent factor structure of psychopathology in schizophrenia has arisen out of the need to
attempt a data reduction of the many variables collected in
our epidemiological and genetic studies of schizophrenia,
with the hope of advancing knowledge about factors that
will be useful in understanding the heterogeneity of schizophrenia. It is possible that factor analysis will be able to
yield alternative phenotypes for linkage and association
studies. A relatively new analysis tool (Latent Gold version
3.0 [Vermunt and Magidson 2000, 2003]) is applied to a set
of data compiled from both cross-sectional and lifetime
sources (e.g., through subject interviews, medical record
reviews, informant interviews), in a large sample of individuals with schizophrenia (n - 1,043). Latent Gold implements latent class factor analysis (LCFA), which differs in a
number of important ways from traditional factor analysis
(TEA; e.g., principal components analysis). TEA is suited to
continuous manifest variables and assumes that the underlying derived factors are normally distributed. These limitations have been largely ignored in studies of the dimensions
of psychosis, which often involve dichotomous, ordinal, or
"count" manifest variables and assume normality in the
latent factors. LCFA operates on nominal, ordinal, count,
and continuous manifest variables, and it does not assume
normality for the latent factors, which may be construed as
dichotomous or ordinal. An additional advantage of LCFA
over TEA is that it is able to detect both linear and nonlinear
sources of variation in a set of indicators, thus performing
better than TFA in situations that involve both sources
(Magidson and Vermunt 2003). LCFA is a relatively recent
extension of latent class analysis (LCA) (Magidson and
Vermunt 2004). LCA supports testing whether a group of
associated nominal manifest variables can be related to an
underlying categorical (class) variable having two or more
categories that classify the subjects. LCFA differs in that it
produces two or more latent variables (rather than a single
latent classification variable with two or more levels); each
variable represents a separate dimension related to some set
of the manifest variables. The software also handles missing
data so that a large number of variables can be studied without deletion of subjects missing some variable values.
The present study also provides initial validation
information for the latent class factor model by examining
the relationship of probands' factor scores to the risk of
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affective and psychotic syndromes and psychiatric hospitalization in their first and second degree relatives.
cally stable at the time of the interviews, which took place
for the most part at participants' homes, or in some cases
at inpatient or outpatient facilities.
A consensus diagnostic procedure involving two or
more raters (psychiatrists, or clinical psychologists with a
Ph.D.) was conducted for each subject, using information
from the direct assessment interview, medical records, and
additional information from informants or clinicians treating the affected subjects. Raters completed a Diagnostic
Checklist for DSM-IV Disorders (earlier cases had a
checklist for DSM—IH-R disorders) using the aforementioned sources of information to rate lifetime symptoms
and episodes of major psychiatric disorders.
Forty-four indicators characterizing aspects of psychotic disorder or potentially related developmental and
residual conditions were chosen for the LCFA (table 1).
Almost all of the indicators were taken from the diagnostic checklist and represented the consensus judgments of
the clinicians on the symptom level; a few indicators
came from the direct assessment interview. The majority
of the indicators were dichotomous variables characterizing development, signs, symptoms, and course of psychotic illness. We also included indicators concerning violent behavior (Lachman et al. 1996a, 1996&, 1998; Li et
al. 1996, 2000; Strous et al. 1997; Karayiorgou et al.
1998; Kotler et al. 1999; Nolan et al. 2000) and affective
disorders (Pulver et al. 2000), owing to the potential value
of this information in identifying more homogeneous
groups of schizophrenia subjects.
The nominal indicators were coded 1 (positive or present), 0 (negative or absent), or missing and entered into
LCFA models. Latent Gold software version 3.0 (Vermunt
and Magidson 2000, 2003) was used. Subjects were
included regardless of missing data. Beginning with a
one-factor model, successive models were generated with
increasing numbers of factors, and changes in the log likelihood (LL) were monitored along with changes in the
Bayesian information criterion (BIC) based on the LL. A
given successive model (with an additional factor over the
previous model) is judged to be better if the LL increases,
the BIC decreases, the additional factor makes
clinical/theoretical sense, and the model can be replicated
(i.e., the same model statistics are produced in several reruns, indicating that the maximum likelihood solution is
global rather than local).
Latent Gold detects and permits model adjustment for
the presence of local dependencies (strong bivariate associations among indicator variables that remain even after
construction of the factors). In constructing a five-factor
model that was judged to have clinical merit, stability, and
adequate fit (supported by reduction in the BIC relative to
simpler models), we detected several local dependencies.
We examined the changes in standardized beta coeffi-
Subjects
Subjects (n = 1,043) for the LCFA were 371 females and 672
males diagnosed with DSM-III-R or DSM-IV (APA 1987,
1994) schizophrenia (877) or schizoaffective disorder (166)
who had participated in one or more studies within the
Epidemiology-Genetics Program at the Johns Hopkins
School of Medicine, Department of Psychiatry and
Behavioral Sciences. The subjects came from 780 families.
The numbers affected with schizophrenia and directly
assessed per family were as follows: 1 affected in 582 families; 2 affected in 153 families; 3 affected in 33 families; 4
affected in 8 famines; 5 affected in 1 family; 6 affected in 2
families; and 7 affected in 1 family. In a factor analytic study
of 169 siblings with schizophrenia from 80 families, Murphy
et al. (1994) pointed out that use of correlated observations
(members of the same family) should not affect the resulting
factor structure, although it can affect error estimates.
Subjects were on average 39.6 (±11.5) years of age at
assessment and had an average age at psychosis onset of
21.1 (±6.2) years. The general ethnic composition of the
sample was 549 Ashkenazi Jewish, 1 Sephardic Jewish,
475 European-Caucasian, 17 African-American, and 1
European-Caucasian/Native American.
For the analysis of risk for psychiatric disorders in
relatives of schizophrenia subjects, 679 probands with
finalized family history data were identified from the
1,043 subjects, and these 679 probands had 10,094 first
and second degree relatives with known sex and family
history data. (Some of the 10,094 relatives with family
history data were also among the 1,043 subjects with
direct assessment data used in the latent factor analyses,
but for these subjects only their family history data were
used for the familial analyses.) The 10,094 relatives were
on average 54.2 (±24.5) years of age at assessment, 49.4
percent female, and 29.95 percent first degree. The average number of relatives per family was 14.9.
Methods
The 1,043 subjects with schizophrenia/schizoaffective
disorder participated in direct assessment with experienced clinicians (master's degree, Ph.D., or M.D.) using
either a Diagnostic Interview Schedule (modified to
increase validity in assessing psychotic symptoms [Pulver
et al. 1989]), a later Diagnostic Interview Schedule (modified for family studies [Pulver et al. 1994]), or the
Diagnostic Interview for Genetic Studies (DIGS
[Nurnberger et al. 1994]). Among other scales, the DIGS
contains the SANS and SAPS items. Subjects were clini-
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J.A. McGrath et al.
Table 1. Clinical/epidemiological indicators examined
Indicator
Details
Key Features of Schizophrenia
Grandiose delusions
Delusions of guilt
Delusions of reference
Persecutory delusions
Nihilistic delusions
Primary delusional perception
Primary delusions
Delusions of influence
Delusions of passivity
Thought insertion
Thought withdrawal
Thought broadcasting
Thought echo
Olfactory hallucinations
Visual hallucinations
Tactile hallucinations
Auditory hallucinations:
first rank or non-first rank
Lifetime presence (1) or absence (0)
Lifetime presence (1) or absence (0)
Lifetime presence (1) or absence (0)
Lifetime presence (1) or absence (0)
Lifetime presence (1) or absence (0)
Lifetime presence (1) or absence (0)
Lifetime presence (1) or absence (0)
Lifetime presence (1) or absence (0)
Lifetime presence (1) or absence (0)
Lifetime presence (1) or absence (0)
Lifetime presence (1) or absence (0)
Lifetime presence (1) or absence (0)
Lifetime presence (1) or absence (0)
Lifetime presence (1) or absence (0)
Lifetime presence (1) or absence (0)
Lifetime presence (1) or absence (0)
Lifetime presence (1) or absence (0)
Somatic hallucinations
Lifetime presence (1) or absence (0)
Alogia
Lifetime presence (1) or absence (0)
Negative formal thought disorder
Lifetime presence (1) or absence (0)
Disorganized speech
Lifetime presence (1) or absence (0) of disorganized speech characterized
by one or more of the following: incoherence, loosening of associations,
speech difficult to understand, or marked tangentiality
Catatonic behavior
Lifetime presence (1) or absence (0)
Flat affect
Lifetime presence (1) or absence (0)
Apathy
Lifetime presence (1) or absence (0)
Anhedonia
Lifetime presence (1) or absence (0)
Inappropriate affect
Lifetime presence (1) or absence (0)
Self-neglect
Lifetime presence (1) or absence (0
Bizarre behavior
Lifetime presence (1) or absence (0
Poor insight
Lifetime presence (1) or absence (0
Parkinsonian signs
Lifetime presence (1) or absence (0
Akathisia
Lifetime presence (1) or absence (0
Tardive dyskinesia
Lifetime presence (1) or absence (0)
Developmental/Course Variables
Poor premorbid functioning
School deterioration1
Therapeutic school
1
Presence of premorbid personality disorder; or direct assessment
determination of poor adolescent peer relationships1 (deviant or
isolated), adolescent withdrawal,1 or poor high school adaptation1 (with
all of these being prior to onset of psychosis)
Subject report: yes (1), no (0)
Subject attendance at a school for individuals with learning
disabilities or emotional disturbance: yes (1), no (0)
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Table 1. Clinical/epidemiological indicators examined—Continued
Details
Indicator
Prodromal signs
Either (1) lack of sudden onset (sudden = onset of psychotic illness
within a 3-month prodromal period) or (2) 3 or more prodromal
symptoms (considering 9 symptoms: social isolation/withdrawal;
impaired role functioning; peculiar/eccentric behavior; impaired personal
hygiene; blunted or inappropriate affect; digressive, vague,
overelaborate, or circumstantial speech or poverty of content of speech;
odd beliefs or magical thinking; unusual perceptions; lack of initiative,
interest, or energy)
Psychosis onset < 16
Onset by age 16 or earlier (1) versus later (0)
Affective symptoms early in course
Affective symptoms occurring early in the course of the psychotic illness:
yes(1);no(0)
Remitting course
Course of psychotic illness has been remitting (1) versus otherwise (0).
Remitting includes (1) episodic with complete or virtually complete
remissions between psychotic episodes, and (2) single psychotic
episodes followed by a complete or virtually complete remission.
Nonremitting includes (1) continuous: no remission of psychotic
symptoms throughout the period of observation; (2) episodic with
progressive development of negative symptoms in the intervals between
psychotic episodes; (3) episodic with persistent but nonprogressive
negative symptoms in the intervals between; (4) single psychotic
episode followed by an incomplete remission; and (5) any other pattern.
Subjects with less than 1 year of illness are not rated on this variable.
Convulsions, seizures, epilepsy1
Lifetime presence (1) or absence (0) of convulsions, seizures, or epilepsy.
These symptoms may be evidence of premorbid neurological problems
but are not sufficient in these cases to alter the diagnosis of schizophrenia.
Behavioral Variables
Committed assault
Self-damaging acts
Additional Variables
Criterion A: major depression
Criterion B: mania
1
Lifetime presence (1) or absence (0)
Lifetime presence (1) or absence (0)
Lifetime presence (1) or absence (0) of criterion A for DSM-IV major
depression
Lifetime presence (1) or absence (0) of criterion B for DSM-IV mania
Subject report during direct assessment.
cients (representing the relationships of the indicators to
the factors) as the five-factor model was adjusted for up to
14 local dependencies, to make sure further adjustments
did not substantially alter the factor structure.
Data on the psychiatric disorders of first and second
degree relatives of the probands were obtained using the
family history method, with master's-level clinicians
gathering information from two informants per family, via
telephone interviews. Data sheets on psychiatric disorders
were prepared for each relative, using either Family
History Research Diagnostic Criteria (FH-RDC [Endicott
et al. 1975; Andreasen et al. 1977]), for 361 families, or
DSM-IV criteria applied to data from the Family
Interview for Genetic Studies (FIGS: see National
Institute of Mental Health Web site, http://zork.
wustl.edu/nimh/figs/figs_linkpage.htm), for 318 families.
Because the two sources of family history data
(FH-RDC vs. FIGS) with different diagnostic criteria
were used to characterize illness, we chose to focus on
four broad indicators of illness that could be constructed
from either data set: mania, depression, psychosis, and
psychiatric hospitalization. These indicators were constructed to overcome formal diagnostic exclusions, so
that, for instance, mania was coded as present in the
FH-RDC data set whether the subject had a formal diagnosis of bipolar disorder, manic disorder, or schizoaffective-manic disorder. DSM-IV diagnostic exclusion rules
for the FIGS data set were similarly crossed, and we
developed algorithms to produce FH-RDC diagnoses
using detailed symptom checklists from the FIGS data set,
so that subjects with FIGS data could be coded based on
either their DSM-IV diagnoses or FH-RDC approxima-
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Schizophrenia Bulletin, Vol. 30, No. 4, 2004
J.A. McGrath et al.
tions. Thus, for example, mania was coded as present in
the FIGS data set in subjects diagnosed with DSM-IV
bipolar I or bipolar disorder not otherwise specified, or for
any subjects with FH-RDC mania or FH-RDC schizoaffective mania as constructed from algorithms.
Proband factor scores derived from the five-factor
LCFA indicate the probability of scoring at the high (vs.
low) level of a given factor. These scores were used in
logistic regressions on the four illness indicators in the
relatives, which were coded as present (1) versus absent
(0). To adjust for the presence of correlated data among
relatives from the same family, Proc GenMod (SAS 1998)
was used for the logistic regressions, because it incorporates the generalized estimating equations method (GEE;
Zeger and Liang 1986). Each of the four illness indicators
(mania, depression, psychosis, and psychiatric hospitalization) was modeled separately. The basic model
included sex and degree of relative, as well as the five
types of factor scores, and age of relative at assessment.
The latter was included to control for differences in family illness indicators that might arise because relatives
were in different parts of the risk periods. For relatives
with unknown age at assessment, the mean age of the
sample was substituted (results were obtained with and
without this missing data correction and did not differ
substantively). Of the 10,094 relatives, 8,925 had nonmissing data for the four illness indicators (mania, depression, psychosis, and psychiatric hospitalization) and
entered the analysis. For any factor having a significant
relationship to an illness indicator, a model was also run
including an interaction term for degree of relative versus
factor, to determine whether the relationship to illness
indicator differed by degree of relative. Marginal (p <
0.10) and significant (p < 0.05) interactions with degree
were then explored through separate models for first versus second degree relatives.
relationships of indicators to factors are given by factor
loadings, which are similar to correlations in their interpretation (ranging from -1 to +1) when orthogonal factors
are constructed. However, in LCFA the analog to a factor
loading is the beta representing the relationship between
the given factor and the given indicator (akin to a coefficient for the regression of the factor on the indicator).
Each indicator can have significant relationships with
more than one of the five factors. Two betas are produced
for each nominal indicator: one for the negative level of
the indicator and one for the positive level. For example,
the beta representing the relationship of therapeutic
school attendance to factor 5 is -1.0234 (standard error
[SE] = ± 0.184) for subjects who did not attend a therapeutic school, and +1.0234 (SE = ± 0.184) for subjects
who did attend a therapeutic school. Because the beta values are symmetrical, we focused for simplicity on the
betas associated with the positive levels of the indicators.
Hereafter, beta refers to the effect of the factor on the positive level of the given indicator.
For purposes of interpretation, each indicator has a
primary assignment to the factor having the most significant beta, and secondary assignments to other factors for
which the betas are still significant. Significance is arbitrarily defined as a standardized beta (i.e., z score) > 2.0 or
< -2.0 (±2.0 being close to ±1.96, the z score for a 2tailed significance test at a = 0.05); the betas have been
standardized (divided by their standard deviations) to
increase ease of comparison. Table 4 shows the primary
factor assignments, shaded gray for each indicator; bold
but unshaded values signify the assignments of indicators
to secondary factors. For example, the indicator criterion
A: major depression is most significantly related to factor
2 (z = 5.4366) but also has a significant association with
factor 3 (z = 3.8469). Factor 1 reflects positive symptoms:
the high level of factor 1 is related to the presence of positive symptoms, because the standardized betas for the various delusions and hallucinations all have positive signs.
Factor 2 reflects an affective dimension, with presence of
symptoms at the high level. The low level of factor 2 is
therefore associated with a lack of affective symptoms,
and this can be seen in a number of negative symptoms,
which have significant negative standardized betas for
factor 2 (e.g., anhedonia, flat affect, apathy, negative formal thought disorder).
The five factors can be interpreted as follows:
Factor 1: positive (delusions/hallucinations)
Factor 2: affective
Factor 3: disorganized (at the low level, because the
primary standardized betas are negative)
Factor 4: negative (again, at the low level)
Factor 5: early onset/developmental
Note that while table 4 presents standardized betas,
Results
Factors. Table 2 provides the baseline proportions of
subjects coded positive for the 44 indicators used in the
LCFA.
Statistics relevant to the fit of the five models are presented in table 3. Given the large number of indicators
(44) and subjects (1,043), we focused on the LL statistic
and associated BIC in comparing the different models,
and we reached a global and replicable solution with five
factors and a reduction in the BIC over models with fewer
than five factors. The estimated variance (R2) accounted
for by each of the five factors in the final model was 0.71,
0.67, 0.66, 0.64, and 0.57 for factors 1 through 5, respectively.
In TFA such as principal components analysis, the
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Table 2. Baseline proportions for the 44 indicators, in 1,043 schizophrenia subjects
Indicator
Proportion positive
Affective symptoms early in course
0.5427
Akathisia
0.1539
Alogia
0.3481
Anhedonia
0.4485
Apathy
0.7128
Auditory hallucinations: first rank or non-first rank
0.8202
Bizarre behavior
0.6359
Catatonic behavior
0.1157
Committed assault
0.41
Convulsions, seizures, epilepsy
0.111
Criterion A: major depression
0.5666
Criterion B: mania
0.3311
Delusions of guilt
0.2203
Delusions of influence
0.2668
Delusions of passivity
0.1754
Delusions of reference
0.7928
Disorganized speech
0.5407
Flat affect
0.5656
Grandiose delusions
0.5603
Inappropriate affect
0.3923
Negative formal thought disorder
0.2763
Nihilistic delusions
0.0557
Olfactory hallucinations
0.155
Parkinsonian signs
0.1472
Persecutory delusions
0.8685
Poor insight
0.682
Poor premorbid functioning
0.3631
Primary delusional perception
0.0371
Primary delusions
0.0561
Prodromal signs
0.8727
Psychosis onset < 16
0.1862
Remitting course
0.06
School deterioration
0.4912
Self-damaging acts
0.4604
Self-neglect
0.4848
Somatic hallucinations
0.2127
Tactile hallucinations
0.1492
Tardive dyskinesia
0.1095
Therapeutic school
0.1044
Thought broadcasting
0.2099
Thought echo
0.0723
Thought insertion
0.226
Thought withdrawal
0.119
Visual hallucinations
0.4136
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Table 3. Overview of model fit for the models tested
No. of factors in model
1
No. of parameters
LL
df
BIC (based on LL)
0 (1-class baseline model)
45
-20,589.71
1.63E+15
41,492.16
1
91
-19,875.94
1.63E+15
40,384.33
2
3
2
4
5
2
137
-19,380.15
1.63E+15
39,712.43
184
-18,834.62
1.48E+15
38,948.01
230
-18,643.95
1.48E+15
38,886.36
277
-18,426.83
1.44E+15
38,778.77
Note.—BIC = Bayesian information criterion; LL = log likelihood.
1
The 1 -class model is a simple baseline latent class (rather than latent factor) model, which yields estimates of the baseline variation
among the indicators before construction of latent factors.
2
Beginning with model 3, adjustment was made for local dependency of criterion A: major depression with affective symptoms early in
course; with model 5, adjustment was added for local dependency of delusions of reference with persecutory delusions.
the nonstandardized betas are quite similar in magnitude,
so strong relationships between factors and indicators
(reflected in large betas) also tended to be highly significant (reflected in large z scores, i.e., the standardized
betas). Factor-specific Pearson correlations of the betas
and z scores ranged from 0.92 to 0.97.
For readers who are familiar with LCA, results are presented in table 5 in terms of the conditional probabilities of
each given factor as a function of the levels of the manifest
indicators. While LCA yields the probability of class membership as a function of indicator level, LCFA yields the
probability of being at the "high" level of a given dichotomous factor as a function of indicator level. For instance,
subjects with a history of school deterioration have a 56 percent (0.562) probability of classification in the "high" level
of factor 5 (early onset/developmental), while subjects without deterioration have only a 17 percent probability (0.165).
Because the overall probability of the high level of factor 5
is 36 percent (0.357), the relationship of school deterioration
to this factor is highlighted by presenting the data in this
way. Note that these conditional probabilities look at a
glance like traditional factor loadings but should not be
interpreted as "correlations." It can be important to note low
conditional probabilities as well as high ones; the distance
between the two probabilities for levels of a given indicator
is very closely associated with the standardized betas (indicators with greater distance between the conditional probabilities have larger absolute betas).
The strongest two bivariate residuals (signifying local
dependencies) arising in the course of generating the fivefactor model included one for criterion A: major depression
versus affective symptoms early in course, and one for delusions of reference versus persecutory delusions. The final
five-factor model contains adjustments for these associations. Adjustment for additional smaller local dependencies
improved model fit but did not substantially alter the factor
structure.
Finally, Latent Gold produces factor scores, which
are simply the probabilities of the subject being at level 2
(the "high" level) of a given factor. Intercorrelations
among the five sets of factor scores were weak to nonexistent, owing to the fact that the default method of the
LCFA software is to produce orthogonal factors. The positive factor scores showed a weak positive correlation to
the early onset/developmental scores (r = 0.12, p <
0.0001): early onset/developmental cases tended to show
more positive symptoms. The affective scores correlated
very weakly but positively with the negative symptom
scores (r - 0.08, p < 0.009) (recall that the "low" level of
the negative factor represents more negative symptoms,
so this weak positive correlation makes sense). The disorganized factor scores correlated weakly and positively
with the negative factor scores (r = 0.10, p < 0.0007),
indicating that subjects with disorganization symptoms
were slightly more likely to also have negative symptoms.
Family Illness Indicators. Table 6 provides the odds
ratios (ORs) and 95 percent confidence intervals (CIs) for
illness indicators in first and second degree relatives in
relation to the factor scores of probands. For those models
in which an interaction was detected between degree and
factor, separate ORs are given by degree. All ORs are
adjusted for sex of relative, degree of relative (except for
degree-specific ORs), and age of relative at assessment.
ORs for a given factor effect are also adjusted for the other
four factors (this did not substantially alter coefficient estimates relative to models excluding the other four factors).
All five factors were related to one or more illness
indicators. Higher scores on the positive symptom factor
predicted less depression in all relatives (p - 0.0396).
862
Schizophrenia Bulletin, Vol. 30, No. 4, 2004
Five Latent Factors
Table 4. Primary (shaded and bold) and secondary (bold) factor assignments of indicators based on
standardized betas
Standardized Beta
Indicator
Factor 1
Factor 2
Factor 3
Factor 4
Factor 5
Delusions of influence
10.5043
-2.9301
-0.3307
-0.7829
2.5621
Thought insertion
9.9026
-1.8481
1.3154
-3.3656
5.1135
Somatic hallucinations
8.9148
-1.9185
1.3717
-1.4964
3.33
Thought broadcasting
8.5648
-1.2671
1.9319
-2.8027
3.9525
Delusions of passivity
7.9545
-3.3418
-1.1419
-1.8637
2.805
Thought withdrawal
7.626
-2.0257
0.191
-4.3295
4.4991
Grandiose delusions
6.7484
5.375
-3.1879
1.6448
1.3637
Tactile hallucinations
6.0596
-2.263
0.2898
-1.1254
4.097
Olfactory hallucinations
4.9715
0.4002
1.3764
-0.274
3.546
Auditory hallucinations:
first rank or non-first rank
4.5835
-2.7155
-2.5613
-1.3132
3.026
Thought echo
4.5566
1.1421
3.7663
-2.6855
3.5219
Primary delusions
4.1495
-2.2918
2.9129
-0.5301
-0.5418
Delusions of reference
3.607
Primary delusional perception
3.44
2.2862
2.9086
-2.1649
1.4482
-2.9802
1.7072
-0.1484
-0.5181
Delusions of guilt
3.3746
3.0034
3.0505
-0.7029
3.0173
Nihilistic delusions
3.1636
-0.0014
0.1161
-1.4514
1.7322
Tardive dyskinesia
2.7189
2.08
-2.2906
-1.508
0.7268
Criterion B: mania
0.9709
7.8003
-1.4094
1.881
0.6628
Affective symptoms early in course
0.9553
6.1048
-2.0431
1.0148
1.9951
0.6395
-1.3087
5.4366
3.8469
-1.5147
4.6179
-0.3576
-9.2826
-2.5711
Self-neglect
4.3963
-2.8505
-9.9282
-4.854
Bizarre behavior
4.0246
-0.2416
-8.3008
-2.1762
Criterion A: major depression
Disorganized speech
-0.0255
-0.9709
0.4454
Inappropriate affect
2.6953
0.3703
-9.6079
-2.0107
0.9248
Parkinsonian signs
2.4755
0.5968
-3.4402
-1.2503
-1.0452
Catatonic behavior
2.2401
0.3777
-3.7743
-3.3576
1.3935
Akathisia
2.0872
1.8024
-4.0881
-1.89
1.6542
0.4326
-2.8963
1.3405
1.6844
-6.9852
-3.5612
-0.6677
Persecutory delusions
1.72
Poor insight
0.9105
-4.196
Committed assault
0.2489
0.5323
-6.4032
-2.2467
3.1728
Anhedonia
0.8031
-2.0129
-2.7948
-7.7486
-1.8348
Poor premorbid functioning
0.4808
0.1267
0.0776
Flat affect
0.3137
-3.1602
-4.2541
Prodromal signs
-0.6804
-1.8502
-2.6648
-4.0206
3.1868
Apathy
-0.9202
-2.0589
-5.5356
-9.3588
-0.6465
Negative formal thought disorder
-1.0789
-2.222
-4.988
-8.1009
-0.2432
Remitting course
-2.0066
1.4649
3.2903
3.7674
-2.1785
Alogia
-2.1213
-1.5201
-3.8661
-8.6627
0.1004
Visual hallucinations
3.831
-1.6847
-3.182
-0.8369
4.1027
Self-damaging acts
2.0662
2.8543
-3.5512
-1.9314
4.9214
-1.0781
-1.7876
-2.4953
-2.2156
6.5736
School deterioration
863
-2.6919
-10.157
0.1513
-0.6506
Schizophrenia Bulletin, Vol. 30, No. 4, 2004
J.A. McGrath et al.
Table 4. Primary (shaded and bold) and secondary (bold) factor assignments of indicators based on
standardized betas—Continued
Standardized Beta
Indicator
Factor 1
Factor 2
Factor 3
Convulsions, seizures, epilepsy
-1.1905
-1.5387
-1.1826
Psychosis onset < 16
-1.4835
0.5888
Therapeutic school
-2.5841
-1.604
Similarly, lower scores on the negative symptom factor
(indicating more negative symptoms) predicted less
depression, but only for second degree relatives (p 0.0012); the OR for depression in first degree relatives
was not significant (p - 0.1392), although it was not in
opposition to the OR for second degree relatives. Higher
scores on the affective factor predicted more mania in all
relatives (p = 0.0062) and more depression. Although the
interaction of the affective factor with degree was marginally significant (p = 0.082) for depression, again the first
versus second degree ORs were not in opposition; rather,
the affective factor was significantly related to depression
in second degree relatives (p = 0.0003) but only marginally so for first degree relatives (p = 0.0652).
Lower disorganized factor scores (indicating more
disorganization) predicted more psychosis among first
degree relatives (p = 0.0076); the relationship was nonsignificant among second degree relatives (p = 0.7918),
leading to a marginally significant interaction of degree
with disorganization (p = 0.0993). Lower disorganized
scores also predicted more psychiatric hospitalization
among all relatives (p - 0.0031).
The early onset/developmental factor was related in a
consistent way to depression, psychosis, and psychiatric
hospitalization in relatives, with higher factor scores predicting more illness for all three measures. The relationship with depression was significant for second degree
relatives (p < 0.0001) but not first degree relatives (p 0.1348), leading to a significant interaction of degree by
early onset/developmental factor (p = 0.0141). The relationship with psychosis was marginal (p - 0.0881) for all
relatives, while the relationship with psychiatric hospitalization was significant (p - 0.0412) for all relatives.
Apart from the above-mentioned interactions, it should
be noted that degree showed significant and similar relationships with all four illness indicators, such that first degree
relatives had a higher probability of each of these illness
indicators than second degree relatives: the ORs ranged
from 2.78 (2.34-3.31) for psychiatric hospitalization to 4.55
(3.76-5.51) for depression. Sex of relative was related only
to depression in relatives, with females being 2.01 times
more likely to have depression (95% CI: 1.70-2.38).
Factor 4
Factor 5
0.3545
4.1735
-0.171
-0.3478
4.2315
-2.481
0.023
5.5621
Discussion
Factors. This exploratory LCFA confirms results from a
number of previous studies that described factors relating
to positive symptoms, negative symptoms, and disorganization (Klimidis et al. 1993; Brekke et al. 1994; Buchanan
and Carpenter 1994; Murphy et al. 1994; Andreasen et al.
1995ft; Burke et al. 1996; Ratakonda et al. 1998; Mojtabai
1999) but expands knowledge of the possible dimensions
underlying the schizophrenias with the description of the
early onset/developmental factor, which, to our knowledge, has not been previously isolated via factor analysis.
As an exploratory factor analysis, the present study is
open to the basic criticism that statistical generation of an
underlying factor related to a number of manifest indicators does not prove that the factor exists or that it holds
causal explanatory power. However, in LCFA the factors
are generated without rotation, so the potential TFA problem of multiple non-unique solutions achieved through
different rotation techniques is avoided.
Early onset/developmental factor. This factor
seems to mark subjects who have manifested early signs
of disturbance (early onset, school deterioration, attendance at a therapeutic school). The therapeutic school
subjects included at least some with possible attention
deficit and conduct disorders. The flavor of conduct problems was also suggested by the self-damaging acts indicator, and the secondary loading of committed assault. We
make this interpretation cautiously, because in the present
study the age at which the assault or self-damaging acts
occurred was not necessarily a young age. However, this
grouping of early onset and conduct problems is consistent with research by Hafner and associates (Hafner and
Nowotny 1995), who have found a higher rate of nonspecific symptoms related to neurotic and conduct disorder
diagnoses among cases of early-onset schizophrenia.
The loading of seizures on this factor arises from the
strong bivariate associations of seizures with both onset <
16 and attendance in a therapeutic school in this sample.
Furthermore, seizures are highly associated with selfdamaging acts, and with visual, tactile, and olfactory hallucinations. Self-damaging acts are in turn highly associ-
864
00
0.53
0.65
0.24
0.28
0.29
0.26
0.35
0.20
0.35
0.34
0.23
0.36
0.36
0.36
0.37
0.38
0.38
0.40
0.29
0.31
0.27
0.35
0.38
0.38
0.38
0.25
Thought insertion
Somatic hallucinations
Thought broadcasting
Delusions of passivity
Thought withdrawal
Grandiose delusions
Tactile hallucinations
Olfactory hallucinations
Auditory hallucinations:
first rank or non-first rank
Thought echo
Primary delusions
Delusions of reference
Primary delusional perception
Delusions of guilt
Nihilistic delusions
Tardive dyskinesia
Criterion B: mania
Affective symptoms early in course
Criterion A: major depression
Disorganized speech
Self-neglect
Bizarre behavior
Inappropriate affect
Parkinsonian signs
Catatonic behavior
Akathisia
Persecutory delusions
0.52
0.24
0.34
0.45
0.45
0.44
0.39
0.52
0.49
0.43
0.45
0.49
0.52
0.45
0.16
0.20
0.16
0.44
0.47
0.51
0.47
0.44
0.50
0.51
0.40
0.42
0.44
0.55
0.43
0.49
0.35
0.77
0.49
0.44
0.46
0.58
0.45
0.81
0.74
0.45
0.63
0.70
0.49
0.84
0.31
0.29
0.48
0.51
0.48
0.49
0.49
0.51
Absent
0.97
0.76
0.76
0.86
0.90
0.20
Delusions of influence
Present
Absent
0.57
0.54
0.54
0.47
0.48
0.48
0.46
0.47
0.38
0.43
0.70
0.96
0.72
0.58
0.48
0.60
0.05
0.49
0.55
0.64
0.54
0.54
0.68
0.85
0.76
0.73
0.43
0.53
0.54
0.31
0.49
0.30
0.34
0.24
0.40
0.24
0.28
0.61
0.47
0.54
0.55
0.37
0.52
0.66
0.53
0.72
0.53
0.55
0.81
0.85
0.48
0.59
0.49
0.42
0.52
0.51
0.30
0.62
0.57
0.51
0.45
0.54
0.48
0.54
0.61
0.52
0.62
0.51
0.60
0.59
0.52
0.52
Present
0.52
0.53
0.50
Factor 3
Absent
0.43
0.49
0.35
0.60
0.37
0.31
0.42
0.40
0.40
0.35
Present
0.45
0.41
Overall probability of
"high" level of factor
Factor 2
Factor 1
0.45
0.51
0.43
0.48
0.49
0.47
0.49
0.51
0.56
0.52
0.51
0.43
0.45
0.47
0.48
0.48
0.48
0.55
0.48
0.50
0.46
0.37
0.27
0.41
0.41
0.45
0.35
0.41
0.48
0.44
0.54
0.38
0.36
0.44
0.47
0.44
0.44
0.24
0.44
0.45
0.47
0.48
0.50
0.41
0.22
0.40
0.36
0.42
0.35
0.45
0.42
0.52
0.49
0.51
0.49
0.52
0.48
Present
Factor 4
Absent
Table 5. Conditional probabilities of " high" level of factors, given presence/absence of indicators'
0.23
0.34
0.34
0.37
0.33
0.35
0.38
0.33
0.29
0.33
0.34
w
0.38
0.45
0.44
0.30
£
o
p
'i
S'
0.38
S?
0.37
f
iS'
£
G
o
3
21
n
a
0.33
0.38
0.40
0.38
0.38
0.40
0.51
0.34
0.34
0.48
0.28
0.38
0.28
0.68
0.41
0.55
0.39
0.62
0.64
0.52
0.53
0.51
0.58
0.47
Present
0.31
0.35
0.26
0.35
0.33
0.13
0.31
0.31
0.30
0.31
0.32
0.31
0.31
0.29
0.31
Absent
0.36
Factor 5
3
<'
0.45
0.36
0.27
0.29
0.50
0.45
0.36
0.34
0.27
0.26
0.41
0.43
0.41
0.33
0.36
0.41
0.41
0.44
0.42
Negative formal thought disorder
Remitting course
Alogia
Visual hallucinations
Self-damaging acts
School deterioration
Convulsions, seizures, epilepsy
Psychosis onset < 16
Therapeutic school
Bold and shaded: primary factor; bold only: secondary factor.
0.56
0.49
0.35
0.42
1
0.45
0.57
0.40
0.46
Prodromal signs
Apathy
0.48
0.48
0.48
0.53
0.40
0.49
0.51
0.38
0.54
0.41
0.40
Flat affect
0.36
0.38
0.37
0.43
0.53
0.42
0.40
0.56
0.36
0.44
0.48
0.53
0.54
0.53
0.58
0.58
0.58
0.63
0.48
0.58
0.76
0.67
0.61
0.53
0.59
0.41
0.47
0.39
Poor premorbid functioning
0.63
0.76
0.35
0.37
0.45
0.46
0.43
0.45
0.45
0.45
0.51
0.50
0.48
0.67
0.47
0.43
0.42
0.61
0.83
0.68
0.75
0.35
0.49
0.50
0.77
0.51
0.54
0.43
0.65
0.50
0.56
Absent
0.45
0.45
0.45
0.48
0.31
0.76
0.78
0.63
0.25
0.56
0.49
0.32
0.24
0.41
0.51
0.34
0.19
0.35
0.34
0.40
0.35
0.36
0.31
0.45
0.35
Present
0.17
0.25
0.40
0.36
0.11
0.37
0.36
0.40
0.15
0.38
0.35
0.40
0.29
0.38
Absent
0.36
Factor 5
0.44
0.93
0.08
0.31
0.41
0.21
0.38
0.26
0.41
0.40
Present
Factor 4
0.50
0.34
0.38
Present
0.50
Factor 3
Absent
0.47
0.41
0.38
Anhedonia
0.45
0.41
0.39
0.50
0.39
Committed assault
0.59
0.42
Present
0.39
0.38
Absent
Present
0.45
0.41
Absent
Poor insight
Overall probability of
"high" level of factor
Factor 2
Factor 1
Table 5. Conditional probabilities of "high" level of factors, given presence/absence of indicators'—Continued
o
1
to
ts.
p
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Schi
Five Latent Factors
Schizophrenia Bulletin, Vol. 30, No. 4, 2004
Table 6. Odds ratios (and 95% confidence intervals) for illness indicators in first and second degree
relatives (n = 8,925) as a function of factor scores in probands1
Illness
indicator in
relatives
Positive factor
(high = more
positive)
Disorganized
factor
(low = more
disorganized)
Affective factor
(high = more
affective)
Negative factor
(low = more
negative)
Early onset/
developmental
factor
(high = more
developmental)
Mania
0.80
0.49-1.30
2.03
1.22-3.36
1.18
0.71-1.98
1.40
0.84-2.35
1.36
0.78-2.37
Depression
0.74
0.55-0.99
1st: 1.36 2 nd : 2.00
0.98-1.89 1.38-2.91
1.10
0.82-1.48
1 s t : 1.29
2 nd : 1.95
0.92-1.80 1.30-2.91
1 st : 1.32 2 nd : 2.39
0.92-1.91 1.57-3.65
Psychosis
0.96
0.70-1.31
0.93
0.69-1.26
1st: 0.61 2 nd : 0.94
0.42-0.88 0.57-1.53
1.13
0.82-1.57
1.34
0.96-1.88
Psychiatric
hospitalization
1.18
0.93-1.48
1.20
0.95-1.53
0.69
0.54-0.88
1.07
0.83-1.37
1.32
1.01-1.73
1
Separate odds ratios are given by degree for factors that showed marginal (p < 0.10) or significant (p < 0.05) interactions with degree.
Bold: p < 0.05; italic: 0.05 < p < 0.10.
ated with visual hallucinations, as well as auditory and
tactile hallucinations.
Although visual hallucinations per se do not necessarily suggest early onset, this loading does not appear
arbitrary when one considers the above interrelationships
of indicators, and the fact that many other positive symptom indicators have significant secondary associations
with the early onset/developmental factor (all of the hallucination items and most of the Schneiderian items). One
possible explanation is that early-onset cases may manifest more typical aspects of the disease, such as the visual
and auditory hallucinations. The significant secondary
negative loading of remitting course suggests that subjects
with high scores on this factor are chronically affected.
Affective factor. In several studies, some of which
sample patients with affective disorders and nonaffective
psychosis in addition to schizophrenia, separate depression
and/or mania factors have been described (Kitamura et al.
1995; Van Os et al. 1996, 1997, 1999; Lancon et al. 1998;
McGorry et al. 1998; Cardno et al. 2001; MacCabe et al.
2002). In the present study, affective symptoms were related
to a single factor, and in the same direction (i.e., mania and
depression were not on opposite poles). Furthermore, this
affective factor also had the same directional relationship
with affective symptoms early in the course of illness,
which has been suggested as a predictor of better outcome
among individuals diagnosed with schizophrenia (Davidson
and McGlashan 1997). Indicators secondarily associated
with the affective factor in an inverse fashion included four
of eight negative indicators and two of ten disorganized
indicators. This finding suggests that the affective factor
may mark patients with relatively better outcome, although
the secondary beta for remitting course did not reach signifi-
cance in relation to this factor (table 5: z = 1.4649).
Affective factors have been associated with better outcome
in subjects with nonaffective psychosis (Van Os et al. 1996;
Kendler et al. 1997). Recently, MacCabe et al. (2002)
reported that schizophrenia subjects who were functioning
well enough to enter university before the onset of illness
had higher scores on a reactive depressive factor and lower
scores on a schizophrenia factor than more poorly functioning subjects with schizophrenia.
Studies identifying separate mania and depression
factors rather than a combination factor have included
multiple symptoms of both mania and depression
(Kitamura et al. 1995; Van Os et al. 1996, 1997, 1999;
Lancon et al. 1998; McGorry et al. 1998; Cardno et al.
2001; MacCabe et al. 2002), and it is probably this feature
(rather than the inclusion of patients with other psychotic
diagnoses) that permits the breakdown of affective symptoms into two separate factors, because there are adequate
numbers of patients in our schizophrenia sample who
have experienced affective psychopathology.
Positive factor. Although this study included a wide
selection of psychosis indicators, the analysis did not
break down the positive dimension into two or more discrete subdimensions (e.g., first rank delusions, paranoid
delusions, first rank hallucinations, other hallucinations)
seen in some studies (among broad psychosis samples
[Toomy et al. 1997; Van Os et al. 1997; Cardno et al.
2001]; among schizophrenia/schizoaffective samples
[Cardno et al. 1996, 1999]). However, most of this study's
positive indicators showed multidimensionality. For
example, as mentioned above, five of the seven
Schneiderian (first rank) indicators had secondary associations with the developmental dimension. In addition, five
867
Schizophrenia Bulletin, Vol. 30, No. 4, 2004
J.A. McGrath et al.
dimension, but a local dependency remained. When this
was adjusted for, and five factors were constructed, the
persecutory delusions indicator loaded primarily on the
disorganized factor. The loading likely derives from the
strong association of persecutory delusions with two primary indicators of disorganization, namely, disorganized
speech and committed assault, plus self-damaging acts,
which is a secondary disorganization indicator.
Likewise, the association of parkinsonian signs with
the disorganization factor is understandable in light of the
fact that this indicator was most highly associated with
TD, followed by akathisia, self-neglect, persecutory delusions, delusions of guilt, delusions of passivity, and disorganized speech: Of these seven associated indicators, four
loaded primarily on the disorganized dimension
(akathisia, self-neglect, persecutory delusions, disorganized speech), and two loaded secondarily on it (TD and
delusions of guilt). We hypothesize that the disorganized
dimension reflects to some extent one or more forms of
schizophrenia that are particularly sensitive to dopamine
imbalance, because the three motor disturbance indicators
had connections to this factor.
positive symptoms had associations with the negative
dimension, and ten delusion/hallucination items had associations with the affective dimension (predictably,
grandiose delusions, delusions of reference, and delusions
of guilt had positive associations with the affective factor,
while the other items had negative associations).
The association of tardive dyskinesia (TD) with the
positive symptom factor results from the interrelationships of this indicator with multiple positive indicators:
TD was most strongly associated with parkinsonian signs
but was also associated with five positive symptom indicators, including four of the five strongest loading indicators on the positive dimension (delusions of influence,
thought insertion, somatic hallucinations, and delusions of
passivity). TD was also associated with six indicators
(disorganized speech, self-negflect, bizarre behavior,
parkinsonian signs, catatonic behavior, and committed
assault) loading primarily on disorganization. The association of TD with both the disorganized and positive factors could be the result of using higher doses of neuroleptics in subjects manifesting florid symptoms, or the wide
range of symptoms could be the result of reduction in
medication because of TD, with a resultant reemergence
of a florid symptom pattern.
Disorganization and negative factors. The current
analyses followed a great many other studies in separating
disorganized symptoms from positive and negative dimensions. McGorry et al. have suggested that the separation of
disorganized symptoms from the negative dimension may
be specific to schizophrenia and may not be characteristic
of functional psychoses as a whole (Kitamura et al. 1995;
McGorry et al. 1998). However, other studies of dimensionality in subjects with a broad range of functional psychoses have supported separate negative and disorganization dimensions (Peralta et al. 1997). And studies of
within-family or within-sibship sameness for dimensions
have supported the validity of separate disorganization and
negative dimensions in samples of probands with schizophrenia (Burke et al. 1996; Cardno et al. 1999; Wickham
et al. 2001) as well as probands with broad psychosis
(Kendler et al. 1997; Cardno et al. 2001). In the present
study, the great majority of the negative indicators have
secondary loadings on the disorganized factor, and vice
versa (table 5). However, the present modeling produced a
better fit to the data with five factors, and the negative and
disorganized factor scores showed different relationships
with family illness indicators.
The association of persecutory delusions with the disorganized factor rather than the positive factor seems
anomalous but likely owes to the adjustment made in the
model for the local dependency between persecutory
delusions and delusions of reference. In the four-factor
model, both indicators loaded primarily on the positive
Family Illness Indicators. Examination of psychiatric
illness indicators in first and second degree relatives of
probands provides a preliminary means of evaluating the
validity of separating schizophrenia signs and symptoms
into five dimensions. In the present study, the five
proband factors had varying associations with familial illness indicators, supporting their distinctiveness.
Mania in relatives. Higher scores on the affective
factor most strongly predicted manic illness in relatives.
Van Os et al. (1997) have similarly reported an association
between a mania factor in Research Diagnostic Criteria
schizophrenia and broad schizoaffective probands and
affective psychosis (bipolar illness or depression with hospitalization) in first degree relatives. This finding is also
somewhat consistent with that of Kendler et al. (1998),
who, in an LCA, found a significantly higher risk for bipolar illness among relatives of "bipolar-schizomania" (class)
probands compared with relatives of controls, and a trend
toward more affective illness in general in the relatives of
these probands. Wickham et al. (2001) have also demonstrated the familiality of a manic dimension within families multiplex for psychosis and ascertained through a
schizophrenia proband. The manic illness associations in
the present study are consistent with research pointing to
possible shared genetic susceptibility among at least some
schizophrenic and bipolar illnesses (Berrettini 2000).
Depression in relatives. All of the factor scores
except disorganization were related to depression in relatives. Of interest, more psychosis in the proband (either
positive or negative) predicted less depression in relatives.
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Schizophrenia Bulletin, Vol. 30, No. 4, 2004
Higher factor scores on the affective or early onset/developmental factor predicted more depression in relatives.
The affective factor association is consistent with the finding of Maier et al. (1993) of an elevated risk of unipolar
depression in families of schizophrenia probands. To the
extent that the early onset/developmental factor is specific
to schizophrenia, that association is also consistent with
the Maier et al. finding. One caveat with the depression
findings is that three of these factor associations differed
by degree of relative (table 6). Although the associations
by degree did not contradict each other (the CIs for the
ORs overlapped for first vs. second degree relatives), the
ORs for the second degree relatives were of consistently
greater magnitude and more significant, while the CIs
were also wider. This finding suggests that there may have
been more error in the assessment of depression among
second degree relatives.
Psychosis in relatives. More disorganization in
probands predicted more psychosis in first degree relatives; higher early onset/developmental factor scores marginally predicted more psychosis in all relatives. The latter relationship is sensible, given that in general cases of
early-onset diseases are more likely to be familial (Childs
and Scriver 1986); however, the literature on early onset
and familiality has been inconsistent for schizophrenia, so
further study of this finding is needed.
The relationship between the disorganized factor and
psychosis involved an interaction with degree: first degree
relatives showed a stronger and more reliable OR than
second degree relatives, for whom the association was
nonsignificant. This pattern is consistent with a genetic
relationship between disorganization in probands and psychosis in relatives, because psychosis is a more reliably
ascertained phenomenon (than, e.g., depression) using the
family informant method, and power is better among second degree relatives because of their larger numbers.
The relationships between the disorganization factor
and family illness indicators were somewhat consistent
with other findings. Previous studies point to a relationship
between the disorganized dimension in probands and the
risk of schizophrenia or psychosis in relatives (Cardno et
al. 1997, 2001). Wickham et al. (2001) demonstrated
familial aggregation of the disorganization factor in 224
probandwise twin pairs concordant for psychosis. And the
latent class study by Kendler et al. (1998) identified a disorganized/manic class ("hebephrenic") as having the highest morbid risks of schizophrenia, nonaffective psychosis,
and schizophrenia spectrum disorders compared with other
patient classes and controls. Research by Van Os et al.
(1997) also found a positive relationship for a disorganized
factor (inappropriate affect, catatonia, bizarre behavior)
and familial risk for affective psychosis and general psychosis, but this was limited to families of schizophrenia
rather than schizoaffective probands. However, they found
a more robust relationship between an early and insidious
onset/affective blunting dimension and familial risk for
schizophrenia, affective psychosis, and general psychosis
(more robust in that it held for both schizophrenia and
schizoaffective probands). It is not entirely clear why the
negative psychosis factor in the present study was not similar enough to the early and insidious onset/affective blunting dimension in the Van Os et al. study to show any comparable associations, but perhaps the difference is due to
the fact that the "early onset" component and a few other
possible indicators of early disorder segmented into a separate factor in the present study.
Psychiatric hospitalization in relatives. Psychiatric
hospitalization showed almost identical associations to
those seen for psychosis, except that the associations
(with disorganization and early onset/developmental factor scores) were both significant, with no significant variation by degree. Although this indicator is not specific
about the illness for which hospitalization occurred, it
presupposes a fairly serious illness. In the sample of relatives entering analyses, 90.0 percent of those with psychosis were hospitalized, while 73.2 percent of those with
mania and 40.2 percent of those with depression were
hospitalized.
Conclusions
In summary, the present study confirmed the presence of
positive, negative, disorganized, and affective dimensions
within the signs and symptoms of schizophrenia and
described an additional important factor relating to early
onset/developmental signs. The associations of factor
scores with psychopathology in first and second degree
relatives suggest that these factors may be useful in marking aspects of schizophrenia that are familial. It will be
important for future research following from the present
study to examine whether and which of the latent factors
may be shared among affected members within a family.
Demonstration of within-family sameness for one or more
of the dimensions will add to the growing literature in this
area and will lend support for testing the factors in linkage
or association analyses.
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Acknowledgments
We wish to thank the study participants and their families,
who made this research possible. We are also grateful to
the National Institute of Mental Health, which supported a
large part of the data collection through grants.
Five Latent Factors
Schizophrenia Bulletin, Vol. 30, No. 4, 2004
Genetics Program, Department of Psychiatry and
Behavioral Sciences, The Johns Hopkins University
School of Medicine. M. Danielle Fallin, Ph.D., is Assistant
Professor, Department of Epidemiology, The Johns
Hopkins University Bloomberg School of Public Health.
Mary H. Thornquist, Ph.D., is Supervisor of the Clinical
Examination Team for the Epidemiology-Genetics
Program, Department of Psychiatry and Behavioral
Sciences, The Johns Hopkins University School of
Medicine. James R. Luke, Psy.D., is a Clinical Examiner
for the Epidemiology-Genetics Program, Department of
Psychiatry and Behavioral Sciences, The Johns Hopkins
School of Medicine. Ann E. Pulver, Sc.D., is Professor and
Director of the Epidemiology-Genetics Program,
Department of Psychiatry and Behavioral Sciences, The
Johns Hopkins University School of Medicine.
The Authors
John A. McGrath, M.A., is Senior Department Research
Data Manager, Department of Psychiatry and Behavioral
Sciences, The Johns Hopkins University School of
Medicine, Baltimore, MD. Gerald Nestadt, M.D., M.P.H.,
is Professor, Department of Psychiatry and Behavioral
Sciences, The Johns Hopkins University School of
Medicine, Baltimore, MD. Kung-Yee Liang, Ph.D., is
Professor, Departments of Biostatistics and Epidemiology,
The Johns Hopkins University Bloomberg School of
Public Health, Baltimore, MD. Virginia K. Lasseter, B.S.,
is Genetic Analyst, Department of Psychiatry and
Behavioral Sciences, The Johns Hopkins University
School of Medicine, Baltimore, MD. Paula S. Wolyniec,
M.A., is Research Coordinator for the Epidemiology-
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