Introduction
Schizophrenia is characterized by various symptom domains the two most important being positive symptoms, including delusions, hallucinations, excitation, hostility, disorganized thinking, and negative symptoms, including affective flattening, avolition, alogia, anhedonia.Reference Mellor, Marneros, Andreasen and Tsuang 1 –Reference Peralta and Cuesta 3 Positive symptoms are considered to be new and maladaptive mental processes and behaviors that were not present before the onset of schizophrenia and that have emerged as signs of the disorder.Reference Burton 4 Negative symptoms, on the other hand, are conceptualized as emotions (hedonia), thought processes (logic thinking) and behaviors (social interactions) that the patient has lost as a consequence of the disorder.Reference Burton 4
Based on this distinction between positive and negative symptoms of patients with schizophrenia were subdivided according to a two-syndrome concept into those with mainly positive symptoms, named type I schizophrenia, and those with mainly negative symptoms, named type II schizophrenia.Reference Crow 5 When present during acute psychotic exacerbations and the inter-episode, more stable phases of illness the negative symptom cluster is referred to as deficit schizophrenia.Reference Ahmed, Strauss, Buchanan, Kirkpatrick and Carpenter 6 , Reference Kirkpatrick, Buchanan and McKenney 7 Previously, Bleuler described schizophrenia as a psycho-organic illness comprising two syndrome clusters, namely a primary cluster characterized by loosening of associations and withdrawal (negative symptoms) and accessory symptoms including some of the positive symptoms.Reference Bleuler and Zinkin 8 , Reference Jablensky 9 Kraepelin described schizophrenia as “dementia praecox” or an early type of “dementia” characterized by deterioration in neurocognitive functions and goal-directed behaviors, which are negative symptoms.Reference Jablensky 9 Nevertheless, it is debated whether negative symptoms increase in severity along a continuum from the healthy state to schizophrenia with a “fully developed syndrome” (dimensional theory) or whether type II or deficit schizophrenia is a separate nosological class (categorical theory).Reference Takahashi 10 , Reference Kanchanatawan, Sriswasdi and Thika 11
Nevertheless, using supervised and unsupervised machine learning techniques, we showed that within a study sample of patients with stable phase schizophrenia, there are two distinct classes of patients, namely those with deficit and nondeficit schizophrenia.Reference Kanchanatawan, Sriswasdi and Thika 11 , Reference Kanchanatawan, Sriswasdi and Thika 12 Both neurocognitive deficits and neuro-immune aberrations, as well as negative symptoms, define deficit schizophrenia as a distinct diagnostic class which is qualitatively different from nondeficit schizophrenia and controls. Moreover, unsupervised learning generated a class of patients, named major neuro-cognitive psychosis (MNP), that largely overlapped with deficit schizophrenia although the diagnostic criteria (based on negative symptoms) were more restrictive.Reference Kanchanatawan, Sriswasdi and Thika 12 The nondeficit group named simple neuro-cognitive psychosis (SNP) shows a quantitatively distinct profile than MNP with less pronounced neuro-cognitive disorders and negative and positive symptoms, although there were qualitative distinctions with regard to neuroimmune pathways.Reference Kanchanatawan, Sriswasdi and Thika 12 , Reference Maes, Sirivichayakul, Kanchanatawan and Carvalho 13 As such, we have delineated two homogeneous phenotypes of schizophrenia which allow more precise identification of clinical, neurocognitive, and neuroimmune features.
Another major finding of our laboratory is that different symptom domains such as psychotic symptoms (hallucinations, delusions, and suspiciousness), hostility (and poor impulse control and uncooperativeness), excitation (and grandiosity), mannerism (and posturing), and negative symptoms are highly significantly intercorrelated.Reference Kanchanatawan, Sriswasdi and Thika 11 –Reference Maes, Sirivichayakul, Kanchanatawan and Carvalho 13 These findings suggest that the differentiation of negative symptoms vs positive symptoms (including psychosis, hostility, and excitation) is an artificial one because both domains appear to be strongly related.
Furthermore, we delineated formal thought disorders (FTD) and psychomotor retardation (PMR) as two other major clinical domains that shape the phenemenology of schizophrenia and especially MNP.Reference Maes, Sirivichayakul, Kanchanatawan and Carvalho 13 , Reference Sirivichayakul, Kanchanatawan, Thika, Carvalho and Maes 14 First, FTD is characterized by aberrations in abstract and concrete thinking, including disorganized, illogical, and inadequate thought processes coupled with intrusions, fluid thinking and loosened associations.Reference Bleuler and Zinkin 8 , Reference Simpson and Davis 15 –Reference Kay, Fiszbein and Opler 19 We detected that FTD is, in fact, a clinical symptom of the memory deficit syndrome in schizophrenia and especially MNP and that FTD together with memory disorders explain a large part of the variance (around 92%) in negative and psychosis symptoms.Reference Sirivichayakul, Kanchanatawan, Thika, Carvalho and Maes 14 Second, PMR is another symptom domain characterized by impairments in gross and fine motor performance, slow motor responses and slow movements that define schizophrenia and especially MNP.Reference Maes, Sirivichayakul, Kanchanatawan and Carvalho 13 In addition, PMR is strongly associated with other symptom domains including psychosis, hostility, excitation, mannerism, and negative (PHEMN) symptoms.Reference Maes, Sirivichayakul, Kanchanatawan and Carvalho 13 Nevertheless, no research has examined whether the PHEMN symptom domains and FTD and PMR are intercorrelated in subjects with MNP, a restricted subsample of the schizophrenia population, and whether these symptoms may perhaps belong to one and the same underlying construct reflecting the severity of overall psychopathology.
Hence, this study was performed to examine whether these different symptom domains are interrelated phenomena in schizophrenia and whether those domains belong to an underlying latent vector reflecting general psychopathology.
Subjects and Methods
Participants
In this study, we included 120 patients with deficit schizophrenia or MNP and 54 healthy subjects. Patients with schizophrenia and healthy individuals were recruited from the same catchment area, that is Baghdad city, Iraq. Patients were recruited at the Ibn-Rushd Training Hospital for Psychiatric Medicine, Baghdad, Iraq (December 2018 until February 2019). Controls were staff members or their family members or friends. All the patients with schizophrenia were in a stabilized phase of illness for at least 12 weeks. Patients were diagnosed according to DSM-IVTR criteria as “schizophrenia” and according to the schedule of deficit schizophrenia (SDS) criteria as “deficit schizophrenia.”Reference Kirkpatrick, Buchanan and McKenney 7 Moreover, all the patients with schizophrenia also complied with the diagnostic criteria of MNP as published by Kanchanatawan et al.Reference Kanchanatawan, Sriswasdi and Thika 12 Since the MNP diagnostic criteria are somewhat more restrictive than those of deficit schizophrenia, it is more appropriate to use the label MNP although all patients also suffer from deficit schizophrenia. Therefore, we will employ the label MNP all over the text.
Exclusion criteria for patients and controls were: (a) lifetime use of medications that interfere with immune functions including immunosuppressive drugs and glucocorticoids; (b) use of supplements with ω3-polyunsaturated fatty acids or antioxidants the month prior to the study; (c) neurodegenerative and neuroinflammatory disorders including Parkinson’s disease, stroke, multiple sclerosis, and Alzheimer’s disease; (d) (auto)immune illnesses including rheumatoid arthritis, psoriasis, inflammatory bowel disease, COPD, and diabetes mellitus (type 1). Controls were excluded when they presented a current or lifetime diagnosis of DSM-IV-TR axis I diagnosis and additionally when they showed a family history of schizophrenia or psychosis. Patients with schizophrenia were excluded when they suffered psychotic episodes the year prior to the study or axis-1 DSM-IV-TR disorders other than schizophrenia, including bipolar disorder, major depression, schizo-affective disorder, obsessive-compulsive disorder, psycho-organic disorders, and substance use disorders. All subjects had C-reactive protein values <6 mg/L indicating that no overt inflammation was present.
The study was conducted according to Iraq and International ethics and privacy laws. Written informed consent was obtained from all participants as well as the first-degree relatives of schizophrenia participants (the legally authorized representatives are father, mother, spouse, son, or brother) prior to participation in this study. Approval for the study was obtained from the ethics committee (IRB) of the College of Science, University of Kufa, Iraq (347/2019), which is in compliance with the International Guideline for Human Research protection as required by the Declaration of Helsinki.
Clinical assessments
A senior psychiatrist specialized in schizophrenia used a semi-structured interview to assess socio-demographic and clinical data in patients and controls. He made the diagnosis of schizophrenia employing the DSM-IV-TR diagnostic criteria using the Mini-International Neuropsychiatric Interview (M.I.N.I.), in a validated Arabic translation (Iraqi dialect). The same psychiatrist also assessed the SDS,Reference Kirkpatrick, Buchanan and McKenney 7 the Positive and Negative Syndrome Scale (PANSS),Reference Kay, Fiszbein and Opler 19 the Scale for the Assessments of Negative Symptoms (SANS),Reference Andreasen 20 the Brief Psychiatric Rating Scale (BPRS),Reference Overall and Gorham 21 and the Hamilton Depression (HAM-D) and Anxiety (HAM-A) rating scales.Reference Hamilton 22 , Reference Hamilton 23 The same day a research psychologist assessed the Mini-Mental State Examination (MMSE) in a validated Arabic translation.Reference Folstein, Folstein and McHugh 24 We also assessed the drug state of the patients, 68 were treated with fluphenazine, 108 with risperidone, and 11 with olanzapine. The diagnosis of tobacco use disorder was made using the DSM-IV-TR criteria. Body mass index (BMI) was assessed the same day as the clinical interview and was scored as body weight (kg)/length (m2). We constructed different z-unit weighted composite scores based on items of the BPRS, HDRS, PANSS, SANS, and HAM-D as published previously.Reference Maes, Sirivichayakul, Kanchanatawan and Carvalho 13 , Reference Sirivichayakul, Kanchanatawan, Thika, Carvalho and Maes 14 , Reference Sirivichayakul, Kanchanatawan, Thika, Carvalho and Maes 25 Table 1 shows the different z-unit-weighted composite scores used in the current study to assess the severity of symptom domains including PHEMN symptoms, and FTD and PMR.
Table 1. Indices of the Different Symptom Domains and Biomarker Composite Scores Used in the Current Study.
Abbreviations: BPRS, Brief Psychiatric Rating Scale (BPRS); HDRS, Hamilton Depression Rating Scale; PANNS, Positive and Negative Syndrome Scale; SANS, Scale for the Assessment of Negative Symptoms.
Statistics
One-way analysis of variance was used to assess differences in scale variables between groups, and analysis of contingency tables (χ 2 tests) was used to assess associations between categorical variables. Correlations between scale variables were assessed using Pearson’s product moment correlation or Spearman’s rank order correlation coefficients or partial correlation coefficients (while adjusting for extraneous variables). We used multivariate GLM analysis to examine the effects of explanatory variables (age, sex, education, and drug state) on the eight symptom domains, while tests for between-subject effects were used to examine the effects of significant explanatory variables on each of the symptom domains. These multiple tests were checked for false discovery rate (FDR) using the Benjamini-Hochberg procedure.Reference Yoav and Hochberg 26
Multiple regression analysis was used to examine the significant biomarkers that predict the symptom domains using an automatic stepwise method (P-to-entry of .05 and P-to-remove .06) while checking the R 2 change. In addition, the analysis was checked for collinearity (using VIF and tolerance) and homoscedasticity (using the White and Breusch-Pagan tests). When the latter was rejected, we used heteroscedasticy-consistent standard error (SE) (HCSE) or robust SE estimates using the HC3 method. Moreover, analyses were bootstrapped (n = 2000) and the bootstrapped results are reported when there are differences between both approaches. Supplementary Material S1 describes the machine learning techniques used in the current study.
Results
Socio-demographic data
Table 2 shows the socio-demographic and clinical data of the patients with MNP and controls. There were no significant differences in age, sex, marital status, rural/urban living ratio, BMI, and nicotine dependence between the groups. Education was significantly lower in patients than in controls. All rating scale scores, as well as composite scores (PHEM, FTD, and PMR) were significantly higher in patients than in controls. Multivariate GLM analysis did not show any significant effect of smoking (F = 0.94, df = 8/107), BMI (F = 1.97, df = 8/107, P = .652) on the eight symptom domains. Tests for between-subjects effects did not show any effects of sex and education, while age was significantly and negatively related with PANSSnegative (t = −5.59, P < .001), SANS (t = −3.49, P = .001), psychosis (t = −2.64, P = .009), hostility (t = −2.51, P = .013), mannerism (t = −2.40, P = 0.018), and FTD (t = −2.47, P = .015). These effects of age remained significant after FDR P-correction. We used multivariate GLM analysis to examine the effects of the drug state on the symptom domains. Nevertheless, we could not find any significant effects of risperidone (F = 1.72, df = 8/111, P = 0.102), olanzapine (F = 1.71, df = 8/111, P = .103), or fluphenazine (F = 1.76, df = 8/111, P = .092) on the symptom domains.
Table 2. Demographic and Clinical Data in Normal Controls and Patients with Deficit Schizophrenia.
All results are shown as mean (SD).
Abbreviations: BMI, body mass index; BPRS, Brief Psychiatric Rating Scale; FTD, formal thought disorders; HAMA/HAMD, Hamilton Depression Anxiety and Depression Rating Scale; MWU, Results of Mann-Whitney U test; PANSS, the Positive and Negative Syndrome Scale; PMR, psychomotor retardation; SANS, Scale for the Assessment of Negative Symptoms.
Associations between negative and other symptoms
Table 3 shows the results of correlation analyses (partial correlations after adjusting for sex, age, and education) between negative symptoms (SANS and PANSSnegative) and PHEM symptoms, FTD and PMR. In the combined study group as well as in MNP, there were significant associations (all at the P < .001 level after P correction for FDR) between SANS/PANSSnegative and all PHEM symptoms and FTD and PMR. We have also examined whether the drug state of the patients had any significant effects on these associations using partial correlations adjusted for use of olanzapine, risperidone, or fluphenazine. However, we found that the correlation coefficients reported in Table 3 did not change after adjusting for the drug state.
Table 3. Partial Correlation Coefficients Between Negative Symptoms and Positive Symptoms.
The correlation coefficients were adjusted for age, sex, and education.
Abbreviations: FTD, formal thought disorders; PANSS, the Positive and Negative Syndrome Scale; PMR, psychomotor retardation; SANS, Scale for the Assessment of Negative Symptoms.
All significant at P < .001
Based on these results we examined the association between negative symptoms (here we show only the results obtained with PANSSnegative values) and PHEM symptoms, FTD and PMR while allowing for the intervening effects of extraneous variables (age, sex, education, and drug state of the patients). Table 4, regression #1 shows that, in all subjects combined, 90.8% of the variance in PANSSnegative scores was explained by the regression on psychosis, hostility, education, and female sex. Psychosis had by far the greatest impact and other symptoms domains were not significant. Figure S1 in Supplementary Material S2 shows the partial regression plot between PANSSnegative and psychosis in all subjects combined (thus independent from education, sex, and hostility). Table 4, regression #2 shows that, in MNP, 71.0% of the variance in PANSSnegative scores was explained by the regression on psychosis, hostility, and female sex. Psychosis had again the greatest impact, while other symptoms domains and education were not significant. Figure S2 in Supplementary Material S2 shows the partial regression plot between PANSSnegative and psychosis in MNP. We have also examined (in MNP) the association between PANSSpositive subscore and PHEM, FTD, and PMR symptoms allowing for the effects of age, sex, and education. Table 4, regression #3 shows that 94.0% of the variance in positive symptoms was explained by the regression on psychosis, excitation, hostility, FTD, and age (all positively associated). Psychosis and excitation had the most impact on the PANSSpositive score.
Table 4. Results of Multiple Regression Analyses with Negative or Positive Symptoms as Dependent Variables.
Shown are heteroscedascticity-consistent or robust standard errors (SE) estimated using the HC3 method
Abbreviations: FTD, formal thought disorders; HC, healthy controls; MNP, major neuro-cognitive psychosis; PANSS+, positive subscale of the Positive and Negative Syndrome Scale; PANSS−, negative subscales of the Positive and Negative Syndrome Scale.
PCA and exploratory factor analysis
In order to visualize the distribution of the subjects in a 2D space we performed PCA on both controls and patients with MNP and extracted PCs from the data set comprising SANS, PANSSnegative, PHEM symptoms, FTD and PMR. Figure S3 in Supplementary Material S3 shows a PC score plot, namely PC1 (explaining 87% of the variance) vs PC2 (explaining 4%), which displays the distribution of patients with MNP (red dots) and controls (blue squares) in the 2D space made by both PCs. Patients with MNP cluster at the right-hand side of the PC plot, whereas healthy controls cluster at the left-hand side and there is no overlap between the two classes with a large boundary (street) between both classes. Figure S4 in Supplementary Material S3 shows the correlation loadings of the eight symptom domains on PC1 vs PC2. All variables are located between both ellipses and additionally group close together suggesting that they all contribute to the separation of both classes and are significantly and positively intercorrelated. Table 5 shows the results of factor analysis performed on the eight symptom domains. The Keiser-Meier-Olkin (KMO) statistic of sampling adequacy was 0.899 and the significance of Bartlett’s test (X 2 = 876.1, df = 28, P < .00001) indicated that the factorability of the correlation matrix was adequate and, thus, that exploratory factor analysis (EFA) could be applied to our dataset. Only one real-data eigenvalue was greater than 1.0, namely 5.62, while the next eigenvalue was 0.749, while the first factor explained as much as 70% of the variance. The Hull test, parallel analysis based on minimum rank factor analysis and the Bayesian interpretation criterion (BIC) test showed that the advised number of factors was one. Table 5 shows that all eight variables loaded highly on this first factor with six variables having loadings >0.707 and two with loadings of 0.660 (excitation) and 0.682 (PMR). In addition, the unidimensional congruence (UNICO) (>0.95), explained common variance (ECV) (>0.85), and mean of item residual absolute loadings (MIREAL) (<0.3) values indicated that the data should be treated as essentially unidimensional. The model fit indices (Goodness of Fit Index [GFI] and Adjusted GFI [AGFI]) showed an adequate fit of the model and the distribution of residuals as assessed with root mean square of residuals (RMSR) performed well while also weighted root mean square residual (WRMR) (<1.0) showed a good fit. Moreover, the high values of the Generalized H index showed good construct replicability and good performance across studies. The FDI values found here (>0.80) indicate the effectiveness and quality of the factor scores estimates. Table 5 shows also the results of EFA in the combined groups and shows that the data should be regarded as essentially unidimensional and that all parameters (factor scores, explained variance, model fit indices, H-index, and FDI) were even more adequate as compared with the factor model in patients with MNP. As such, EFA showed that the data structure of the eight clinical domains is essentially unidimensional. In order to exclude potential common method bias, we have used the correlation matrix procedure.Reference Tehseen, Ramayah and Sajilan 27 The association matrix between the different latent constructs showed no large associations (all r < 0.90), indicating lack of common method bias.
Table 5. Results of Exploratory Factor Analysis (EFA).
We performed two EFAs one patient with major neuro-cognitive psychosis (MNP) and one in all subjects combined, that is MNP and healthy controls (HC). Significant loadings (>.5) are shown in bold.
Abbreviation: CI, confidence intervals.
Table 6 describes the results of PLS analysis while Supplementary Material S3 describes the results of PLS and SIMCA analysis.
Table 6. Results of Partial Least Squares (PLS) Analysis
We performed two PLS analyses: one in patients with major neuro-cognitive psychosis (MNP) and one in all subjects combined, that is MNP and healthy controls (HC). Significant loadings (>.707) are shown in bold.
Abbreviation: SD, standard deviation.
Discussion
The first major finding of this study is that a single latent trait, which is essentially unidimensional, underlies the key symptom domains of schizophrenia, namely SANS and PANSS negative, PHEM symptoms, FTD and PMR. These findings extend those of a previous report showing that in a study sample of Thai patients with schizophrenia and controls the same symptom domains may be conceptualized under an overall single trait.Reference Maes, Sirivichayakul, Kanchanatawan and Carvalho 13 , Reference Sirivichayakul, Kanchanatawan, Thika, Carvalho and Maes 14 Nevertheless, in the current study, performed on patients from Iraq, we used a restricted study sample of patients with MNP or deficit schizophrenia, indicating that even in a restricted study sample the same latent trait could be established. In fact, restricted sample variance artificially weakens existing correlations and generalizability, and therefore, the correlation coefficients obtained in an unrestricted sample should be corrected for range restriction.Reference Wiberg and Sundstrom 28 , Reference Lakes 29 An unrestricted sample should comprise patients with MNP and SNP as well as normal controls to estimate their actual inter-correlations. Therefore, we have also computed the associations and factor loadings in the combined group of controls and patients with MNP and found, as expected, quite similar albeit somewhat higher correlation coefficients and factor loadings.
The second major finding of this study is that the latent construct extracted from the eight domains showed excellent psychometric properties. First, the obtained AVE value (0.682) showed that the model converged to an adequate result and, therefore, has good convergent validity. Second, the high Cronbach alpha and rho values (both >0.9) indicate good internal consistency reliability or composite reliability. Third, other statistics showed an adequate construct cross-validated communality indicating good predictive relevance and construct replicability. Fourth, the latent construct has also good concurrent validity as established by a highly significant association with a more general index of psychopathology. As such, this single trait underpinning the eight domains represents a reliable and replicable score that indicates overall severity of schizophrenia (OSOS).
Our findings that one OSOS factor represents all eight domains contrasts with previous theories which consistently used a two-dimensional approach of schizophrenia phenomenology. Bleuler’s concept of “schizophrenia” conceptualized that a distinction between basic (or negative) symptoms, and additional (positive) symptoms is the hallmark of schizophrenia.Reference Bleuler and Zinkin 8 , Reference Jablensky 9 Crow also made a quite similar two-dimensional concept that distinguishes between positive and negative symptoms.Reference Crow 5 The NHS and NINH classify schizophrenia symptoms as positive and negative. 30 , 31 Roy and DevriendtReference Roy and DeVriendt 32 summarized that the positive and negative concepts show some validity because negative symptoms are correlated with cognitive deficits and both dimensions may have different substrates. Nevertheless, not only negative, but also positive symptoms are strongly predicted by neurocognitive impairments, including in semantic and episodic memory, attention, and executive functions,Reference Sirivichayakul, Kanchanatawan, Thika, Carvalho and Maes 14 , Reference Sirivichayakul, Kanchanatawan, Thika, Carvalho and Maes 25 while the eight domains included in the current study coupled with neurocognitive tests are in fact manifestations of a single trait in the combined group of patients and controls.Reference Maes, Sirivichayakul, Kanchanatawan and Carvalho 13 Roy and DevriendtReference Roy and DeVriendt 32 also discussed that not all data supported Crow’s modelReference Crow 5 including the existence of other symptom dimensions. In this respect, the current study established that FTD and PMR are other manifestations of the OSOS latent trait. Previously, we found that PMR, as a key symptom of schizophrenia and especially MNP, is significantly associated with the negative and PHEM domains of schizophrenia.Reference Maes, Sirivichayakul, Kanchanatawan and Carvalho 13 Moreover, we reported that FTD, as another hallmark of (deficit) schizophrenia, was significantly associated with memory impairments while in the current study, FTD is strongly associated with PHEM and negative symptoms.Reference Sirivichayakul, Kanchanatawan, Thika, Carvalho and Maes 14 , Reference Sirivichayakul, Kanchanatawan, Thika, Carvalho and Maes 25 In addition, a strong association among the negative domain and either depression or physio-somatic symptom domains was established.Reference Kanchanatawan, Sirivichayakul and Thika 33 , Reference Kanchanatawan, Thika, Sirivichayakul, Carvalho, Geffard and Maes 34
The results of the present study showed that the latent phenomenon OSOS is reflectively measured through its eight effect indicators. As a consequence, this reflective construct is the common cause of the manifestations (eight domains) and the latter is to a large extent modulated by the OSOS index. In addition, we examined second-order constructs (Hierarchical Component Models) with the repeated indicator method and observed that the lack of discriminant validity between PHEM and negative domains did not allow to build a well-fitted Hierarchical Component Model. In the current study and in the study of Sirivichayakul et al,Reference Sirivichayakul, Kanchanatawan, Thika, Carvalho and Maes 25 we found that the negative domain indicators could reliably be added to the positive or PHEM latent traits. Moreover, here we detected that the psychosis, PHEM and “positive” domains could reliably be added to the negative latent vector. Moreover, there are some issues with the commonly applied practice to assess “positive” symptoms using rating scales. In this respect, the current study showed that a large part of the variance in the PANSS positive subscale score could be explained by the combined effects of three “positive” areas (psychosis, hostility, excitation) and FTD, suggesting that “positive symptoms” should be dissected into those key areas to obtain adequate manifestations of the reflective PHEM (but not positive symptom) construct. Moreover, neuroimmune biomarkers often predict the PHEM symptoms but not the positive PANSS subdomain score,Reference Maes, Sirivichayakul, Kanchanatawan and Vodjani 35 further indicating that the latter is not a valid construct.
There is evidence that schizophrenia is a neuro-immune disorderReference Smith and Maes 36 –Reference Davis, Eyre and Jacka 39 and that most neuroimmune biomarkers are significantly associated with both negative and PHEM symptoms, including indices of immune activation, increased levels of CCL11 (eotaxin), breakdown of the paracellular gut pathway, and bacterial translocation.Reference Maes, Sirivichayakul, Kanchanatawan and Carvalho 13 , Reference Maes, Sirivichayakul, Kanchanatawan and Vodjani 35 , Reference Al-Hakeim, Almulla and Maes 40 , Reference Maes, Sirivichayakul, Kanchanatawan and Vodjani 41 Nevertheless, we also observed that neuroimmune biomarkers may be differently associated with both areas. For example, immunoglobulin M (IgM)-mediated autoimmune responses to oxidative specific epitopes (OSEs) including malondialdehyde (MDA) and azelaic acid, and to tryptophan catabolites (TRYCATs) are more associated with negative symptoms, whereas IgA responses to TRYCATs are more associated with “positive” symptoms.Reference Maes, Sirivichayakul, Kanchanatawan and Carvalho 13 , Reference Kanchanatawan, Sirivichayakul and Ruxrungtham 42 , Reference Kanchanatawan, Sirivichayakul and Ruxrungtham 43 The results that some biomarkers may be preferentially associated with one of the clinical areas may, at first sight, be difficult to reconcile with the existence of a single reflective OSOS measurement underpinning all effect indicators. Nevertheless, such findings may be explained by a combination of factors. First, neuroimmune pathways do not act alone but work in networks.Reference Maes, Nowak and Caso 44 For example, lowered IgM responses to OSEs are preferentially associated with negative symptoms but are also associated with immune and TRYCAT pathway activation, which, in turn, are associated with PHEM symptoms.Reference Sirivichayakul, Kanchanatawan, Thika, Carvalho and Maes 25 Second, neuroimmune pathways may cause neuroprogressive processes in different neuronal circuits,Reference Anderson and Maes 37 –Reference Davis, Eyre and Jacka 39 which, in turn, determine symptom domains. Moreover, the neurotoxic effects of the immune networks on the neuronal circuitry are additionally mediated by effects on semantic and episodic memory, attention and executive functions, which all together determine to a large extent the OSOS index.Reference Maes, Sirivichayakul, Kanchanatawan and Carvalho 13 , Reference Sirivichayakul, Kanchanatawan, Thika, Carvalho and Maes 14 , Reference Sirivichayakul, Kanchanatawan, Thika, Carvalho and Maes 25
The third major finding of this study is that MNP or deficit schizophrenia is, as a diagnostic category, better modeled (predicted) by PHEM symptoms, FTD and PMR than by negative symptoms. A combination of all eight domains provided an accuracy of 100% while the top-5 discriminatory predictors were in descending order: hostility, PMR, excitation, and mannerism followed at a distance by the negative SANS symptoms. Previously, we detected, in another study sample, that both negative and PHEM symptoms discriminate MNP or deficit schizophrenia from SNP or nondeficit schizophrenia with great accuracy.Reference Kanchanatawan, Sriswasdi and Thika 11 , Reference Kanchanatawan, Sriswasdi and Thika 12 These findings are at odds with Crow’s theoryReference Crow 5 and with the conclusion of Roy and DevriendtReference Roy and DeVriendt 32 that “it appears to be more productive to conceive negative symptoms as distinct dimensions rather than distinct diseases.” First, in the current study, we have shown that (a) negative symptoms are not a distinct dimension and (b) MNP or deficit schizophrenia is a distinct nosological entity,Reference Kanchanatawan, Sriswasdi and Thika 11 , Reference Kanchanatawan, Sriswasdi and Thika 12 which is better modeled by PHEM, PMR, and FTD symptom areas than by negative symptoms.
At first sight, it may be difficult to reconcile our findings that MNP (deficit schizophrenia) is a distinct nosological entity (categorical distinction) based on negative and PHEM domains and that the dimensional OSOS index (a continuum based on the same symptoms) underpins schizophrenia phenomenology. Nevertheless, not only symptom domains but also neuroimmune and cognitive features model and discriminate SNP from MNP and controls.Reference Kanchanatawan, Sriswasdi and Thika 11 , Reference Kanchanatawan, Sriswasdi and Thika 12 As such, stable-phase schizophrenia comprises two qualitatively distinct nosological classes, namely SNP (a less-well-developed phenotype) and MNP (the full-blown phenotype), which are both modeled by the eight symptom areas, which increase in severity along a continuum thereby shaping two qualitatively distinct classes.
The current study should be interpreted with regard to its possible limitations. First, this study was performed in patients with stable phase schizophrenia and, therefore, cannot be generalized to acute episodes of the illness. Future research should examine the associations among different symptom areas in acute episodes of schizophrenia. Second, this is a case–control study and thus no causal inferences can be made. Future research should examine the time-relationships between the different symptom domains from the premorbid stage to later stages. Thirdly, studies examining the association among clinical variables are prone to common method bias (CMB) although using the correlation matrix procedure no evidence for any CMB could be detected.
In this paper we used a new approach to re-validate schizophrenia symptom dimensions and nosological classifications using supervised and unsupervised machine learning techniques. Therefore, we now discuss the merits of these methods and their contributions to the field.
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• We employed SIMCA as a supervised learning technique to validate deficit schizophrenia and to examine whether this class is a qualitatively distinct category with regard to PHEM symptoms, PMR and FTD, and biomarkers as well.Reference Kanchanatawan, Sriswasdi and Thika 11 , Reference Maes, Schotte, Maes and Cosyns 45 , Reference Al-Hakeim, Almulla, Al-Dujaili and Maes 46 Using this method, we were able to validate deficit schizophrenia as a qualitatively distinct category with regard to PHEM symptoms, FTD and PMR indicating that those symptom domains are together with negative symptoms key features of deficit schizophrenia. Thus, the results of this supervised machine learning technique revealed that the two-syndrome conceptualization of schizophrenia into positive and negative symptom domains cannot be validated,Reference Crow 5 , 30 , 31 and, by inference, that there is no evidence supporting the revised dopamine theory, which posits that positive and negative symptoms may result from hyperactive vs hypoactive mesocortical dopaminergic projections, respectively.Reference Davis, Kahn, Ko and Davidson 47 , Reference Weinberger 48 Moreover, using SIMCA we were able to validate deficit schizophrenia as a NIBCA (neuroimmune and brain-circuit axis) pathway-class, indicating that the phenome of deficit schizophrenia (namely the presence of negative and PHEM symptoms as well as FTD and PMR) is largely predicted by NIBCA pathways.Reference Maes, Sirivichayakul, Kanchanatawan and Carvalho 13 , Reference Maes, Sirivichayakul, Kanchanatawan and Vodjani 35 , Reference Al-Hakeim, Almulla and Maes 40 , Reference Al-Hakeim, Almulla, Al-Dujaili and Maes 46 The latter encompass effects of neuroimmune markers on brain areas including “prefrontal system and frontal lobes, primary and supplementary motor area (SMA) and preSMA cortices, and prefrontal-limbic and cortico-striatal loops that connect the prefrontal cortex with the basal ganglia, thalamus, hypothalamus, hippocampus and amygdala.”Reference Maes, Sirivichayakul, Kanchanatawan and Carvalho 13
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• In the current paper, we also used EFA and PLS path analysis to uncover the latent construct and associations between the different symptom domains underlying deficit schizophrenia. 49-51 Most importantly, we used EFA in conjunction with the BIC dimensionality test, parallel analysis and the Hull test (procedures to determine the number of factors) and unidimensional congruence, explained common variance, and mean of item residual absolute loadings (to ascertain unidimensionality of a common factor).Reference Ferrando and Lorenzo-Seva 49 , Reference Lorenzo-Seva and Ferrando 50 Using those methods, we were able to detect that in patients from Iraq and Thailand, schizophrenia symptom domains belong to one and the same dimension, which is unidimensional and, in addition, has adequate validity and reliability.
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• We employed the Fornell-Larcker Criterion and the Heterotrait-Monotrait Ratio of Correlations (HTMT) to assess discriminant validityReference Ringle, Wende and Becker 51 of positive (and psychotic or PHEM) vs negative symptom constructs. Using these methods, we discovered that in our Iraq and Thai study samples, these constructs lack discriminant validity and that they, additionally, cannot be discriminated from a general psychopathology index. Therefore, our machine learning-derived findings indicate that the “positive symptom” concept may not be valid and additionally that PHEM and negative symptoms as well as FTD and PMR belong to the same latent construct, which reflects OSOS.
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• Finally, we used Confirmatory Tetrad Analysis to assess the correct indicant directionality of the OSOS contructReference Ringle, Wende and Becker 51 in order to examine whether the latter construct should be treated as reflective rather than formative. Using this method, we found that all symptom domains are reflective manifestations of OSOS, which is, therefore, the common cause for the symptom dimensions measured here. These findings suggest that commonly used rating scales to assess severity of schizophrenia may not be adequate as they only measure one subdomain (eg, negative symptoms using the SANS) or add symptom scores to make sum-scores (eg, the BPRS and PANSS), which are thought to reflect OSOS but were not tested for the requirements that the underlying construct should be reflective and unidimensional.Reference Fried, van Borkulo, Epskamp, Schoevers, Tuerlinckx and Borsboom 52 Future research should employ the symptom domains proposed in our studies to construct new rating scales that assess OSOS more adequately.
Conclusions
Negative symptoms (SANS and PANSS negative subscale score), psychosis, hostility, excitation, mannerism, FTD and PMR should be treated as essentially unidimensional. The latent vector extracted from those eight symptoms areas showed excellent convergent validity, internal consistency reliability, composite reliability, predictive relevance, construct replicability, and concurrent validity. The latent trait underpinning the eight domains is reflectively measured through these eight symptom areas and represents a reliable and replicable index of OSOS. The concept “positive symptoms” cannot be validated and positive symptoms should be dissected into relevant domains, namely psychosis, hostility, and excitation, while also other areas are important including PMR, FTD, and mannerism. The bi-dimensional concepts of positive and negative symptoms and type I and II (and deficit) schizophrenia should be revised.
Acknowledgments
We acknowledge the staff of Ibn-Rushd hospital for their help in the collection of samples, especially Dr. Mokhlad Swadi Abed, and the high-skilled staff members of Asia Clinical Laboratory, Najaf city, for their help in the ELISA measurements and Asia Lab in the estimation of biomarkers levels.
Funding
There was no specific funding for this specific study.
Disclosures
Abbas Almulla, Hussein Kadhem Al-Hakeim, and Michael Maes have no conflict of interest with any commercial or other association in connection with the submitted article.
Supplementary Materials
To view supplementary material for this article, please visit http://dx.doi.org/10.1017/S1092852920001182.
