A Meta-analysis of Immune Parameters, Variability, and Assessment of Modal Distribution in Psychosis and Test of the Immune Subgroup Hypothesis

Abstract Immune parameters are elevated in psychosis, but it is unclear whether alterations are homogenous across patients or heterogeneity exists, consistent with the hypothesis that immune alterations are specific to a subgroup of patients. To address this, we examine whether antipsychotic-naïve first-episode psychosis patients exhibit greater variability in blood cytokines, C-reactive protein, and white cell counts compared with controls, and if group mean differences persist after adjusting for skewed data and potential confounds. Databases were searched for studies reporting levels of peripheral immune parameters. Means and variances were extracted and analyzed using multivariate meta-analysis of mean and variability of differences. Outcomes were (1) variability in patients relative to controls, indexed by variability ratio (VR) and coefficient of variation ratio (CVR); (2) mean differences indexed by Hedges g; (3) Modal distribution of raw immune parameter data using Hartigan’s unimodality dip test. Thirty-five studies reporting on 1263 patients and 1470 controls were included. Variability of interleukin-6 (IL6) (VR = 0.19), tumor necrosis factor-α (TNFα) (VR = 0.36), interleukin-1β (VR = 0.35), interleukin-4 (VR = 0.55), and interleukin-8 (VR = 0.28) was reduced in patients. Results persisted for IL6 and IL8 after mean-scaling. Ninety-four percent and one hundred percent of raw data were unimodally distributed in psychosis and controls, respectively. Mean levels of IL6 (g = 0.62), TNFα (g = 0.56), interferon-γ (IFNγ) (g = 0.32), transforming growth factor-β (g = 0.53), and interleukin-17 (IL17) (g = 0.48) were elevated in psychosis. Sensitivity analyses indicated this is unlikely explained by confounders for IL6, IFNγ, and IL17. These findings show elevated cytokines in psychosis after accounting for confounds, and that the hypothesis of an immune subgroup is not supported by the variability or modal distribution.


Introduction
Schizophrenia and related psychotic disorders have a worldwide lifetime prevalence of approximately 1%. 1 They are leading contributors to global disease burden, partly because of inadequate response to antipsychotic treatment in many patients. 2 A greater understanding of illness pathophysiology is required to identify novel therapeutic targets, and develop biologically informed diagnoses. 3 Converging lines of evidence, including genetic, postmortem, and preclinical data, suggest immune dysregulation may play a role in psychosis pathogenesis. [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] Supporting this, meta-analyses have shown raised levels of soluble interleukin-2 receptor (sIL2R), interleukin-6 (IL6), interleukin-8 (IL8), interleukin-10 (IL10), interferon-γ (IFNγ), transforming growth factor-β (TGFβ), tumor necrosis factor-α (TNFα), C-reactive protein (CRP) and white cell counts in both antipsychotic-naïve first episode psychosis (FEP) and chronic medicated schizophrenia compared with controls, with large effect sizes. [16][17][18][19][20][21][22] It has been hypothesized that there are biological subtypes of psychosis, 3,[23][24][25] with immune alterations seen only in a proportion of patients and potentially linked to poor response to treatment. 26,27 In addition to inflating effect sizes for mean differences between patients and controls, the presence of an immune subgroup would be expected to lead to increased immune parameter variability in patients. We therefore set out to assess this by performing a meta-analysis of variability, as previously employed to examine regional brain structural variability in FEP. 28 If immune alterations are seen only in a subgroup of patients then greater immune measure variability in patients relative to controls would be predicted, reflecting heterogeneity in immune dysregulation. Conversely, if immune alterations are a core component of the pathophysiology of psychosis, reduced immune variability in patients compared with controls would be predicted, reflecting homogeneity in immune dysregulation. This approach could however fail to identify subgroups of data contained within the overall data distribution (eg, a bimodal distribution). To address this, we also set out to examine distribution of raw immune parameter data in patients to assess for a multimodal distribution. The presence of latent immune subgroups within the healthy control group 27,29 could also influence variability analyses, thus we also set out to assess for multimodality in controls.
Mean differences in immune parameters between patients and controls reported in previous meta-analyses [16][17][18][19][20][21][22] could be influenced by physiological and environmental confounders, eg, body mass index (BMI), 30,31 smoking, 32 age, 33 gender, 34 hypothalamic-pituitary-adrenal (HPA) axis activity, 35 and ethnicity. 36 Moreover, mean meta-analytic differences may be influenced by data-skew. Many immune parameters are physiologically present at low concentrations, and poor assay sensitivity at these levels can result in a floor effect. 37 This results in positive skew, potentially inflating group differences. 38,39 Indeed, over half the data sets included in previous meta-analyses examining immune parameters in psychosis show evidence of significant skew, 26, and the Cochrane Collaboration recommends that meta-analyses based on means are appropriate only for data that are at least approximately normally distributed. 61 Thus, we also set out to assess if mean differences in immune parameters between patients and controls were robust to sensitivity analyses focusing on studies that matched for physiological/environmental confounds, and with skewed data removed.
To our knowledge, this article represents the first variability meta-analysis of immune parameters, the first study to examine distribution of multiple raw immune parameter data sets, and the first meta-analysis of mean differences to comprehensively examine the role of confounders and data-skew in individuals with psychosis compared with controls.

Selection Procedures
A systematic review was performed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 62 and Meta-Analysis of Observational Studies in Epidemiology (MOOSE) 63 guidelines (supplementary eAppendix 1), following an a priori protocol (supplementary eAppendix 2). Two reviewers (TP and EO) independently searched the Pubmed, EMBASE, and PsycINFO databases from inception to November (week 2) 2017 using the following keywords: (lymphocytes or T-lymphocytes or B-lymphocytes or monocytes or macrophages or inflammat* or IL-* or cytokine or CRP or C-reactive protein or hs-CRP or hsCRP or interleukin* or tumour necrosis factor or transforming growth factor or interferon) and (schizophren* or psycho*) and (first episode or early or antipsychotic* or drug* or neuroleptic*). Only English-written studies were considered. Abstracts were screened, and full texts of studies retrieved. Where texts were unavailable, authors were contacted and manuscripts requested. We also requested raw data sets from authors, and where appropriate, clarification as to whether patients were antipsychotic naïve. TP and EO selected final studies for meta-analysis.

Selection Criteria
Inclusion criteria were (1) patients with FEP, defined either as first treatment contact/patients recruited from FEP services in line with previous studies 64 ; (2) antipsychotic-naïve; (3) a healthy control group; (4) studies assessing blood cytokines/cytokine receptors, C-reactive protein (CRP) (plasma/serum samples), and white cell counts. The rationale behind focusing on antipsychoticnaïve FEP was to minimize confounding effects of medication and lifestyle habits (eg, diet/exercise) associated with chronic psychotic illness 65,66 that may directly (eg, antipsychotics) 67 or indirectly (eg, diet/exercise) 30,31 alter immune parameters. Exclusion criteria were (1) genetic studies (incomplete translation and post-translational modification means mRNA levels cannot be assumed to reflect protein levels) 68 ; (2) in vitro studies; (3) studies examining stimulated cytokine levels; (4) substance/ medication-induced psychosis; (5) absence of data allowing mean and/or standard deviation calculation.

Recorded Variables
Data were extracted as follows: author, publication year, matching criteria, and mean (with standard deviation) measure of immune parameter (table 1 and supplementary  eTable 1). See supplementary eAppendix 3 for further details.

Statistical Analysis
As many studies reported on several parameters, multivariate meta-analysis was used, enabling simultaneous estimation of summary effect sizes across all immune parameters, reducing risk of false positives due to multiple comparisons. 83 For all meta-analyses, an omnibus test evaluated significance of model coefficients across immune parameters. Where the omnibus test was significant, we tested the effect separately for each parameter.

Meta-analysis of Variability
To measure variability, the natural log of the ratio of estimates of the population standard deviations for each group was calculated to give the log variability ratio (VR), as previously described (supplementary eAppendix 3). 28,86 In biological systems, variance often scales with mean. 87 Thus, between group differences in relative variability may, at least partially, be a function of between-group differences in mean. Therefore, a meta-analysis of relative variability of patient compared with control immune parameters scaled to group means was performed: the log coefficient of variation ratio (CVR) (the natural logarithm of the ratio of estimates of population coefficients of variation). 28,86 Where the mean is greater in patients than controls, the CVR is a more conservative estimate of variability. To aid interpretation, summary effect sizes for lnVR and lnCVR were transformed back to a linear scale, as previously described. 28 Thus, a VR (or CVR) of 1 indicates equal variability in patient and control groups, a VR (or CVR) greater than 1 indicates greater relative variability in patient groups, and a VR (or CVR) less than 1 indicates lower relative variability in patient groups.

Meta-analysis of Mean Differences
A meta-analysis of between group differences in immune parameters was performed, indexed using Hedges g.
A random effects model was used owing to expectation of heterogeneity across studies. Data were logtransformed before meta-analysis, since the Cochrane Collaboration recommends log transformation for normalization of positive skew. 58 Log-transformed data were either extracted directly from manuscripts, or following provision of raw study-level data by authors which we subsequently log-transformed. Where log-transformed study level/summary data were unavailable, log-transformation estimates were calculated as described by Higgins and colleagues. 61

Sensitivity Analyses
To determine if findings were influenced by confounding, we aimed to perform sensitivity analyses to determine if findings remained in studies that matched patients and controls for BMI, smoking, age, gender, ethnicity, and HPA axis activity. We also performed sensitivity analyses excluding poor quality studies.
To determine if findings were influenced by data-skew, we conducted sensitivity analyses after excluding data sets that met Cochrane criteria for skew despite log transformation. 61,88 The skew ratio of each immune parameter for patients and controls was determined using the following calculation: lowest possible value for each parameter subtracted from observed mean, divided by standard deviation. A ratio of <1 provides strong evidence of skew, 61,88 and, consequently, studies with a ratio <1 were removed in sensitivity analyses.
To determine if there was a difference in proportion of skewed data between patients and controls (which could influence variability analyses), for each immune parameter, the proportion of data sets with severe skew in

Consideration of Publication Bias, Study Inconsistency, and Study Quality
Publication bias was assessed for mean differences in all parameters by visual inspection of funnel plots of standard errors against immune residuals. We did not assess for publication bias related to variability, as a selective publication bias is extremely unlikely to exist for such measures. Study quality was assessed using the Newcastle-Ottawa Scale (NOS) (supplementary eAppendix 4). 61,90 The thresholds for converting NOS scores into "good," "fair," and "poor" quality followed criteria previously described by systematic reviews 91

Consideration of Raw Data Distribution
For patients and controls, distribution of raw data sets (with >10 data points) for each immune parameter was visually examined using kernel density plots. Studies providing data for the same immune parameter were examined on the same kernel density plot, with values first normalized (mean-scaled). Hartigan's Dip Test of Unimodality 93 was employed to assess the probability of immune parameter data following a unimodal distribution.

Study Selection
Of 3905 citations retrieved, 3751 were excluded after title/ abstract review (supplementary eFigure 1). Following manuscript review, 115 studies were excluded based on failure to meet inclusion criteria. All studies included were cross-sectional. IL2 and IL10 data for patients and controls from 2 data sets [54][55][56][57] were excluded owing to insufficient data to allow mean/standard deviation calculation.

Variability Ratio
We found a significant overall effect of group on log variability ratio across all immune parameters (χ 2 = 150.33, P < .0001). Figure 1 shows that the variability of FEP was significantly reduced compared with controls for the following parameters:

Coefficient of Variation Ratio
We found a significant overall effect of group on log variability ratio across all immune parameters (χ 2 = 68.62, P < .0001). Figure 2 shows that significant variability differences found with VR remained present using CVR for IL6 (CVR = 0.

Mean Differences in Immune Measures
We found a significant overall effect of group on mean levels across all immune parameters (χ 2 = 114.49, P < .0001).

Sensitivity Analysis of the Influence of Confounders
Restricting analysis of mean differences to studies that matched for age, gender, BMI, and smoking created a data set of 8 studies, covering data on 44 immune measures (supplementary eFigure 2). Analysis showed elevated IL6 (g = 0.83; 95% CI = 0.21-1.45; P = .01), IL17 (g = 0.68; 95% CI = 0.09-1.28; P = .02), and IFNγ (g = 0.54; 95% CI = 0.27-0.80; P < .001) in patients, and no significant difference in TNFα, IL10, or IL1β levels between groups. These findings are consistent with the primary meta-analysis, apart from the TNFα outcome, which was no longer significant, suggesting it could be influenced by confounding. There were insufficient data to analyze IL2, sIL2R, IL4, IL8, TGFβ, CRP, and lymphocyte levels. Sensitivity analyses matching for ethnicity and stress were not possible owing to insufficient studies. We combined CRP and hsCRP data a priori (supplementary eAppendix 3). Poor sensitivity of the CRP assay could conceivably mask hsCRP outcomes. Re-analysis of hsCRP data alone suggests this is not the case, with no difference in hsCRP levels between patients and controls (g = 0.87; 95% CI = 0.17-1.91; P = .10).

Publication Bias and Study Inconsistency
The funnel plot for publication bias did not show evidence of asymmetry (supplementary eFigure 4). 26 Higgins' I 2 inconsistency values (supplementary eTable 4) demonstrated a medium-large degree of inconsistency for all parameters, apart from low levels of inconsistency for IFNγ.

Summary of Findings
Our first main finding is that that there is a significant reduction in variability of IL1β, IL4, IL6, IL8, and TNFα in FEP patients compared with controls, which is not explained by mean scaling for IL6 and IL8. After adjusting for mean scaling, there was increased heterogeneity of TGFβ in patients compared with controls. As there is no significant difference in proportion of studies with strong evidence of skewed data in patients compared with controls for all parameters, these variability outcomes do not appear to be the result of reduced data skew in patients, and thus may reflect intrinsic differences in immune variability. An examination of raw data did not provide strong evidence for multimodal data distribution of immune parameters in either patients or controls.
We found elevated IL6, TNFα, IFNγ, TGFβ, and IL17 levels in patients compared with controls with smallmedium effect sizes (range: g = 0.32-0.62). IL6, IFNγ, and IL17 outcomes were robust to sensitivity analyses, indicating these alterations are unlikely to be driven by key potential confounders and data skew.
The absence of variability elevations of most immune parameters in patients compared to controls and the absence of multimodal distribution of most data is evidence against the existence of an immune subgroup of psychosis. Lower variability of IL6 in patients, coupled with a robust difference in mean concentration, could instead be interpreted as this parameter representing a core (or at least more uniformly present) component in the immunobiology of psychosis.

Strengths and Limitations
By focusing on antipsychotic-naïve FEP, we limited duration of secondary illness-related factors known to influence immune parameters, eg, antipsychotics, 67 poor diet, and reduced exercise levels. 30,31 Furthermore, sensitivity analyses focusing on studies with strict environmental and physiological matching provides greater confidence that FEP is associated with elevated immune parameters. Relative to previous meta-analyses in the field, logtransformation to reduce influence of skew on summary effect sizes followed by sensitivity analysis excluding data with persistent evidence of skew provides us with robust evidence that immune alterations are present even when influence of data skew is reduced. Moreover, use of a multivariate meta-analytic approach that models the covariance of immune parameters and allows omnibus testing of results thereby reducing multiplicity concerns, is a strength. Gaining access to raw data to examine distribution of multiple data sets is a further strength, complementing findings of the variability meta-analysis. Finally, since in biological systems variance often scales to mean, 87 performing a mean-scaled (CVR) variability meta-analysis to complement the primary meta-analysis of variability (VR) provides a conservative approach to assess if primary outcomes are influenced by mean group differences, and provides greater confidence regarding the outcome of increased homogeneity of IL6 and IL8 in patients compared with controls.
It should be noted that there is diagnostic heterogeneity in first-episode samples. In general, about two-thirds of FEP patients have a diagnosis of schizophrenia, while the remainder are diagnosed with other psychotic disorders. 94 Of the 32 samples, 18 (56%) included patients with schizophrenia alone (table 1). The remaining samples potentially also included affective psychosis. The proportion of individuals with nonaffective psychosis was not defined, precluding us conducting sensitivity analyses of the effect of diagnosis. Therefore, our findings should not be taken as specific to schizophrenia, but representative of FEP in general. Future studies should provide greater clarity as to whether patients included in analyses presented with an affective or nonaffective psychosis. The combination of affective and nonaffective psychosis in variability analyses should increase heterogeneity in patients and might be expected to influence the modal distribution of patient immune data. However, there was reduced variability of several immune parameters in patients, and no strong evidence for multimodal distribution of patient immune data. Indeed, inclusion of affective psychosis within patient samples may have resulted in under-estimation of increased homogeneity of immune variability in this group, compared with controls.
Inconsistency between studies was moderate to high. This could reflect methodological factors, eg, differences in assay sensitivity, and use of serum/plasma sampling. However, the random effects model used is robust to inconsistency, and would not explain our variability findings, because these reflect within-study variation (where methodologic factors are the same in both patient and control groups in any given study). Moreover, although we were stringent in selecting antipsychotic naïve patients, confirmation of naivety for all psychiatric medications was not universally stated. Thus, use of treatments beyond antipsychotics may have confounded results contributing to inconsistency. Future prospective studies are required that control stringently for medication thereby addressing this potential confound.
Although sample sizes are larger than previous metaanalyses, relatively small sample sizes persist for measures of sIL2R (n = 156), TGFβ (n = 234), and lymphocytes (n = 236). Thus, conclusions regarding these parameters are less secure, and further studies required. Furthermore, only 8 out of 32 (25%) studies matched simultaneously for age, gender, smoking, and BMI, and 2 sensitivity analyses, matching for ethnicity and stress, were not possible owing to insufficient studies. Other factors that could be different between the groups and influence immune measures include recreational substance exposure 95 and subclinical physical comorbidity. [96][97][98] Information on these was not included in most studies, precluding sensitivity analysis. Future studies should aim to match patients appropriately to reduce impact of these potential confounds. Moreover, controls may be unusually healthy compared with the general population because they are screened for illnesses, potentially inflating effect sizes for mean differences. 99 However, inclusion of super-healthy controls would reduce variability of control immune parameters. In fact, we observed the opposite, and the inclusion of "super controls" could even have led to an under-estimate of reduced variability in immune measures in patients. There is the potential for variability analyses to have been influenced by the presence of latent immune subgroups within the control population. We are however reassured by our findings that 100% of raw control data examined met criteria for unimodal distribution.
Although all studies included in analyses used wellvalidated quantification techniques (supplementary eTable 6), insufficient assay sensitivity may have limited ability to detect subtle differences in immune parameters between groups, particularly for titers beneath the limit of assay detection. However, where we could assess impact of poor assay sensitivity, examining hsCRP levels separate from CRP levels, results were unaltered. Future developments in immunoassay technology which bring greater assay sensitivity 100 will potentially provide greater clarity as to the nature of immune alterations in psychosis.
Positive data skew can inflate standard deviation owing to the presence of outliers within the "tail" of the data. 101 We observed evidence of data skew in patients and/or controls for all immune parameters except IL8, TGFβ, and lymphocyte count (supplementary eTable 2). For remaining immune parameters, residual data skew may have influenced variability analyses. However, we demonstrated no significant difference in proportion of skewed data between groups, suggesting that differences in skew in controls compared with patients do not explain variability differences. Moreover, skew does not always inflate standard deviations, since skew describes the shape of data distribution, not scale of spread. 94

Comparison With Previous Meta-analyses
A summary of results from previous meta-analyses examining mean differences in immune parameters in FEP [16][17][18][19][20][21][22] is provided in supplementary eTable 7. There are 2 key differences when compared with previous meta-analyses. First, we failed to observe any differences between patients and controls for mean levels of IL1β, sIL2R, IL4, IL8, IL10, total lymphocyte count, and CRP. However, IL1β, sIL2R, and IL8 levels were elevated in sensitivity analyses excluding data with evidence of skew. These sensitivity analyses are limited by reduced sample size, but suggest the need for future research to determine whether differences in these immune parameters exist between FEP and controls. Second, where differences were observed, effect size estimates were generally smaller than those in previous meta-analyses. For example, Upthegrove et al 19 previously reported effect sizes of 2.21 and 0.94 for elevated IL6 and TNFα, respectively in antipsychotic-naïve FEP compared with controls, whereas we observed effect sizes of 0.62 and 0.56, respectively. These differences may be a consequence of several factors, including increased sample size (participant numbers increasing by up to 7-fold), use of log transformed data, and a multivariate metaanalytic approach that models covariance of immune parameters. Given the additional sample size, the focus on antipsychotic naïve patients, the statistical approach employed, and steps we have taken to assess the influence of data-skew and physiological confounds, we suggest that our updated results are likely the most reliable estimates of peripheral immune alterations in FEP to date.

Interpretation and Implications
Our findings suggest that an immune subtype of psychosis, if present, cannot currently be identified through examination of peripheral immune parameter distribution.
Indeed, our results could instead be interpreted as supporting the hypothesis that alterations in the immune system are a general feature of psychosis. 102 Our results cannot however exclude the possibility that there are alterations in other aspects of the immune system specific to a subgroup of patients. An immune subgroup could manifest itself via various peripheral patterns. Firstly, an immune subgroup might result in wider spread of data points (increased heterogeneity) in patients compared with healthy controls, although, with the potential exception of TGFβ, this is not observed in the current meta-analysis. Secondly, subgroups of patients defined by step increases in immune parameter concentrations could result in multimodal distribution of immune parameter data. Modal analyses in this article suggest a unimodal distribution for most immune parameters, although there was some evidence, albeit weak, for a multimodal distribution of IL6 and IFNγ. Thirdly, a proportion of patients may be more vulnerable to the impact of immune activation, inducing inflammatory and thence psychopathological sequelae in that group (even if proinflammatory titers are of a similar magnitude compared with controls). The observation of increased variability of TGFβ levels in FEP compared with controls following mean scaling could reflect alterations in immune regulatory pathways in a subgroup of individuals with FEP, supporting this model. TGFβ can (although not always) 103 perform an anti-inflammatory role, including inhibition of cytokine production from macrophages 104 and inhibition of B-lymphocyte proliferation. 105 Thus, patients with an impaired TGFβ-mediated immune response could potentially be vulnerable to proinflammatory effects that characterize psychosis (ie, homogenous increases in IL6). Moreover, genetic variants in the TGFβ gene TGFB1 influence susceptibility for schizophrenia. 106 Fourthly, immune susceptibility may arise through a unique network-effect of multiple cytokines to bring about inflammatory sequelae and thence psychopathology. Indeed, Weickert and colleagues have employed cluster analysis to divide patients with schizophreniaspectrum disorders into subgroups based on elevation of multiple cytokines in unison. 27,29 They have observed that, compared with controls, a greater proportion of patients clustered within an "elevated cytokine subgroup." Jeffries and colleagues 107 have used graph theory to examine network connectivity of blood proteins related to neuroimmunology across the psychosis spectrum, observing that protein correlation networks can successfully differentiate between controls, and prodromal individuals who transition/do not transition to psychosis. Based on the current meta-analysis, we are unable to comment on whether there is a FEP immune subgroup characterized by an abnormal immune parameter network.
The findings of elevated IL6, IL17, and IFNγ in psychosis appear robust, as elevations are also seen in sensitivity analyses, indicating they are unlikely due to confounding or other nonspecific factors. Elevations of these 3 cytokines could point toward activation of both innate and adaptive immune responses. 108,109 Moreover, of these 3 cytokines, IL6 shows reduced variability in patients (including after mean scaling), suggesting this could be a core component of the pathophysiology of psychosis. IL6 is a multifunctional cytokine playing a role in inflammation and the acute phase response, 110 immune response, [111][112][113] hematopoiesis, 114 glucose and lipid metabolism, 115 and bone-turnover. 116 It crosses the blood-brain barrier (BBB), 117 and there is evidence of increased cerebrospinal fluid (CSF) IL6 levels in psychosis, 13 indicating that peripheral IL6 could influence brain function. In the CNS, IL6 induces microglial proliferation 118 and activation. 119 Microglia play a key role in synaptic remodeling, among other functions. 120 Thus, raised peripheral IL6 levels could activate microglia and influence secondary brain changes. 4 Supporting this, postmortem studies have reported increased microglial density and morphological changes indicative of microglial activation in schizophrenia, [8][9][10] and some, although not all, positron emission tomography in vivo studies have reported altered binding to a marker expressed on activated microglia in schizophrenia and people at risk of psychosis. 11,12 However, it is important to recognize that there are other potential models explaining the association between immune alterations and psychosis, 98 and recent studies examining therapeutic potential of anti-IL6 immunotherapy in schizophrenia have been disappointing (although this potentially relates to the inability of the monoclonal antibody to cross the BBB). 121,122 Immune alterations could be a consequence of psychosis (eg, psychosocial stress activating an inflammatory response 123 ) or simply an epiphenomenon. Thus, whether peripheral alterations are a cause or a consequence of psychosis remain to be determined. 4 Longitudinal studies examining networks of immune parameters in both CSF and blood following individuals in the prodrome through transition to FEP and experimental medicine studies modulating specific aspects of immune function are needed to identify the mechanism underlying the immune alterations we report, and determine the potential for targeting them therapeutically.

Conclusions
Antipsychotic naïve FEP is associated with elevated levels of IL6, IL17, and IFNγ with small-medium effect sizes after accounting for skew and physiological confounds. There is reduced variability in the levels of immune parameters in FEP, and there does not appear to be a clear multimodal distribution of immune parameters in patients. These findings suggest that an immune subgroup of psychosis cannot currently be defined by examination of peripheral immune data spread, and are consistent with elevated immune markers being typical of psychosis. -naïve patients with first-episode  psychosis show elevated IL6, TNFα, IFNγ, IL17,  and TGFβ levels compared with healthy controls. 2. Patients do not show differences in CRP, total lymphocyte counts, IL1β, IL2, sIL2R, IL4, IL8, and IL10 levels compared with healthy controls.

Antipsychotic
3. Elevated levels of IL6, IL17, and IFNγ are robust to sensitivity analyses which account for data skew, study quality, and physiological/environmental matching.
4. There is reduced variability of immune parameters in patients. Moreover, examination of raw data does not show a multi-modal distribution of immune parameter data in patients.
5. These findings suggest that an immune subgroup of psychosis cannot currently be defined by examination of peripheral immune data spread, and are consistent with elevated immune markers being typical of psychosis.