Biomarker Symptom Profiles for Schizophrenia and Schizoaffective Psychosis


Background: Neuroscience can assist clinical understanding and therapy by finding neurobiological markers for mental illness symptoms. Objectives: To quantify biomarkers for schizophrenia and schizoaffective disorder and relate these to discrete symptoms of psychosis. Methods: Within a case-control design with multiple exclusion criteria to exclude organic causes and confounding variables, 67 DSM IV-R diagnosed and 67 control participants from a defined hospital, clinic and community catchment area were investigated for candidate markers. Participants underwent protocol-based diagnostic-checking and symptom rating via Brief Psychiatric Rating Scale and Positive and Negative Syndrome Scale, functional-rating scales, biological sample-collection and sensory-processing assessment. Blood and urine samples were analysed for monoamine neurotransmitters, their metabolites, vitamin cofactors and intermediate-substances related to oxidative stress and metabolism of monoamines. Neurocognitive assessment of visual and auditory processing was conducted at both peripheral and central levels. Biomarkers were defined by Receiver Operating Curve (ROC) analysis. Spearman’s analysis explored correlations between discrete symptoms and the biomarkers. Results: Receiver Operating Curve (ROC) analysis identified twenty-one biomarkers: elevated urinary dopamine, noradrenaline, adrenaline and hydroxy pyrroline-2-one as a marker of oxidative stress. Other biomarkers were deficits in vitamins D, B6 and folate, elevation of serum B12 and free serum copper to zinc ratio, along with deficits in dichotic listening, distance vision, visual and auditory speed of processing, visual and auditory working memory and six middle ear acoustic reflex parameters. Discrete symptoms such as delusions, hostility, suicidality and auditory hallucinations were biomarker-defined and symptom biomarker correlations assumed an understandable pattern in terms of the catecholamines and their relationship to biochemistry, brain function and disconnectivity. Conclusions: In the absence of a full diagnosis, biomarker-symptom-signatures inform psychiatry about the structure of psychosis and provide an understandable pattern that points in the direction of a new neurobiological system of symptom-formation and treatment.

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Fryar-Williams, S. and Strobel, J. (2015) Biomarker Symptom Profiles for Schizophrenia and Schizoaffective Psychosis. Open Journal of Psychiatry, 5, 78-112. doi: 10.4236/ojpsych.2015.51011.

1. Introduction

The architecture of schizophrenia is still largely unknown and conventional categories for diagnosing schizophrenia and schizoaffective states are still based on descriptions of symptoms and behaviours [1] . While progress has been made regarding the underlying molecular biology and neuropathology of schizophrenia, characterization of discrete symptoms does not reflect underlying neurobiological mechanisms [2] . Schizophrenia and related psychotic conditions are increasingly viewed as complex, polygenic diseases involving overlap of hundreds of forms of functional pathology. Although the search for peripheral and central markers for schizophrenia has been underway for many years investigating monoamine, neuro-immune, inflammatory, neuroendocrine and neuroplasticity markers and others [3] [4] , only marginal understanding of symptom-formation in psychosis has been gained and no integrated, causal framework for therapy has yet emerged [5] . At the same time, clinicians have recognized changing diagnoses across time and added comorbidity within their patients’ conditions, to the extent that there has been fading faith in fuzzy categorical boundaries within descriptive classification systems [6] .

In the clinical setting, presenting symptoms that fail to join with others to meet sufficient criteria for any firm classified diagnosis cause clinicians considerable stress. Clinicians walk a fine line between knowing what a single symptom such as hallucinations might mean in the longer term and the knowledge that the earlier schizophrenia is confirmed and treatment is able to begin, then the better is the outcome [7] . Such symptoms may sometimes subside and unnecessary cost, distress and stigma may accompany an inaccurate diagnosis [8] . For these reasons, biomarkers for discrete symptoms of serious mental illness are urgently required, to complement phenomenology and inform clinical management [9] .

Schizophrenia and schizoaffective disorder are allied conditions within the clinical setting, with the DSM- IV-R classification system describing schizoaffective disorder in terms of a major mood disorder episode occurring concurrent with symptoms that meet characteristic symptoms for schizophrenia [10] . The Mental Health Biomarker Project (Fryar-Williams 2010-2014) sought to discover, investigate and quantify biochemical and neuro-physiological markers of schizophrenia and schizoaffective disorder across a number of domains. These included neurotransmitter synthesis and metabolism, oxidative stress, nutrition-related vitamin and mineral co-factors, visual and auditory information processing and middle ear acoustic reflexes. The selection of vitamin and mineral cofactors in the biochemical domain related to their theoretical background in remote and proximal biochemical pathways related to monoamine synthesis and their purported relationship with monoamine metabolism through cofactor deficiencies in folate and methylation cycles. Also, their proximity to trans-sulfuration pathways relates to protection against oxidative stress. The theoretical background for selection of biological markers in this study is presented in Figure 1. The theory behind these relationships has been described in the literature [11] -[15] and research initiatives in this area have been well summarised by Frankenburg F.R. (2007), [16] . However, vitamin and mineral cofactors for enzymes within these biochemical pathways have not been quantified as biomarkers for mental illness. Indole-catecholamines have been extensively investigated in many body fluids, in relationship to psychosis and schizophrenia and there have been contrasting findings [17] . Urinary hydroxyhemopyrroline-2-one (HPL), a metabolite reported in schizophrenia is considered an indicator of disturbed porphyrin synthesis and oxidative stress [18] . Vitamins and mineral cofactors and intermediate substances related to folate and methionine (one-carbon) cycles have theoretical potential to exert subtle, cumulative effects on neurotransmitter synthesis and metabolism [19] -[21] . These allied pathways are affected by vitamin B6, B12, red cell folate and plasma homocysteine levels [22] -[25] , serum copper [26] , serum ceruloplasmin [27] , red cell zinc [28] , serum histamine [29] and serum methyltetrahydrofolate reductase (MTHFR 677 C- > T) gene polymorphism [30] , whilst vitamin D has a proven epidemiological link with schizophrenia [31] .

Figure 1. Theoretical background for selection of biological markers. MTHFR: Methylenetetrahydrofolate reductase, MAT: Methionine adenosyltransferase, SAMe: S-Adenosylmethionine, MT: Methyltransferase, SAH: S-Adenosylhomocysteine, SAHH: S-Adenosylhomocysteine-hydrolase, CBS: Cystathione Beta Synthetase, BHMT: Betainehomocysteine methyltrasfe- rase, DMG: Dimethylglycine, TMG: Trimethylglycine, MSR: Methionine sulphoxide reductase, MS: Methioninesynthase, IAA: 5-hydroxyondolacetic acid, HVA: homovanillic acid, MAO: monoamineoxidase. MHMA: 3-methoxy-4-hydroxy- mandelic acid; VMA: Vanillylmandelic acid, FAD: Flavine adenine dinucleotide. MHPG: 4-hydroxy-3-methoxyphenylgly- col, COMT: catec-hol-o-methyl-transferase. DOPAL: 3,4-dihydroxyphenylacetaldehyde, DOPAC: 3,4-dihydroxyphenylace- tic acid, DOPEGAL: 3, 4-dihydroxyphenylglycolaldeyde, DOMA: 3,4-dihydroxymandelic acid.

2. Methods

2.1. Study Design

A case-control study design was used to clearly differentiate candidate markers between cases and controls. Multiple exclusion criteria (Section 2.3) were applied to case selection in order to strip psychosis in schizophrenia and schizoaffective disorder down to a functional core uninfluenced by organic causes such as substance abuse, head injury, sensory disability or medication variables that could unduly influence outcome measures.

2.2. Participants

This study was conducted between May 2010 and December 2014. Participants were enrolled between May 2010 and December 2013 at the Queen Elizabeth Hospital, Woodville, South Australia and at its satellite mental health clinics in the Western Adelaide community catchment area. All participants were informed of the goals, assessment procedures and funding of this study and provided written consent. Ethics permission for the study was obtained from the hospital ethics committee. Participants were from multi-ethnic backgrounds and the age-range was 18 to 60 years. A highly-characterized group of patients diagnosed with schizophrenia or schizoaffective disorder were compared with an age and gender matched group of controls, who had subclinical symptoms only. Similar psychotic symptoms occur in both schizophrenia and schizoaffective conditions [10] and these diagnoses occurred at a 1.2:1 ratio respectively in the ward and clinic population. Recruitment of patients with both of these allied conditions allowed sufficient numbers of patients with psychosis to be obtained within the confines of the multiple exclusion criteria described in Section 2.3. It also gave scope for biomarker analysis of depressed mood that may co-occur in schizophrenia and depressed and manic symptoms that may co-occur in schizoaffective disorder.

In order to minimise setting variables participant assessments took place within a 4 day window. After casenote review, a standard interview protocol collected demographic information and information related to development, organic causes or sensory disability. Also recorded were height, weight and absence or presence of developmental difficulty or learning delay, medical co-morbidity, head-injury, family history of mental illness, use of glasses or hearing aids, in order to identify participants with peripheral sensory issues. Other information collected was time of onset of illness and duration of illness, social attachment durability and vision and hearing history.

All participants were rated using standard rating scales [32] [33] . These scales included the Brief Psychiatric Rating Scale (BPRS) and the Positive and Negative Syndrome Scale for schizophrenia (PANSS), which were amalgamated in the interest of reducing assessment time. Using this rating tool, each symptom was rated 1 to 7 intensity to give an overall symptom intensity rating (SIR), which was taken as a measure of symptom clinical severity. Hospital and clinic ratings were made by psychiatric trained registrars, who were blind to index laboratory and sensory processing test results, but not to patient status. Patient diagnostic checklist and symptom ratings for control participants were made by one psychiatric trained assessor who was not blind to the diagnostic status of the participant, since in the real-world clinic setting, many patients are unable to mask their condition due to residual symptoms of psychosis.

2.3. Patient Recruitment and Sample Selection

Non-detained ward patients in partial remission but with residual symptoms of psychosis, were recruited and assessed in the expected last week of their admission, by which time they were sufficiently recovered to give informed consent. Other patients were evaluated in satellite psychiatric outpatient clinics and the nearby research institute setting. A total of 82 symptomatic participants (cases) were recruited and completed assessment. Early statistical analysis of confounders required that 15 participants on SSRI or SNRI antidepressant medication be excluded from the study, due to the masking effect of SSRI and SNRI medication on catecholamine levels, sensory-processing and middle-ear outcome measurements. Therefore the number of patients in the final statistical analysis was 67.

Patients were included in the study if they received a diagnosis of schizophrenia or schizoaffective disorder made by a consultant psychiatrist in the ward or satellite psychiatric outpatient department, according to the DSM-IV-R classification criteria [10] . Diagnoses were checked against a DSM-IV-R symptom checklist, at the recruitment stage, to confirm that a correct diagnosis had been made. Ward patients were recruited within a week of their expected discharge back into the community so that severity bias between patients and community based controls was minimized. Ward patients were recruited and assessed in the ward setting and nearby research institute and community patients were recruited directly and by phone, from the satellite clinics they attended.

The multiple exclusion criteria applied to patient selection are now described: clinical appraisal of a patient’s judgment capacity and orientation in time, place and person was undertaken during recruitment, in order to confirm capacity to consent and exclude delirium. Informal examination to exclude ocular muscle dysfunction, hand, forearm and shoulder dysfunction was also conducted at this stage, in order to ensure that a patient was free to proceed to neuro-physiological tests in the absence of rigidity, dyskinesia, tremor or postural instability as a result of extra-pyramidal side-effects from antipsychotic medication [34] . Persons medicated with Clozapine, Olanzapine, anti-histamines, or vitamin therapy were excluded, since these variables had potential to confound biochemical results for histamine. Participants taking antipsychotic-agents with lesser effect on histamine receptors (Zuclopenthixol, Modecate, Amisulpride (Solian) and Risperidone) were however included. The only exception was seven patients on Quetiapine. Mood stabilisers were allowed and antipsychotic medication remained stable during the assessment period. Persons with active or unremitted use of alcohol or other substance abuse were excluded, since this can confound neurotransmitter results. Persons with upper respiratory tract infections, intellectual, visual or auditory disability or clinically documented or descriptive history of hospitalized head injury with unconsciousness, were also excluded in order to ensure that middle ear congestion, known sensory disability or organic cerebral damage did not confound results. It was not possible to control for smoking to have any chance of patient recruitment.

2.4. Control Recruitment and Sample Selection

A total of 72 control participants were recruited with the assistance of the Population Research and Outcomes Studies (PROS) Unit of the University of Adelaide. These participants were volunteers from the same catchment area as patients affiliated with the Queen Elizabeth Hospital and the North West Adelaide Health Study (NWAHS). Using the same exclusion protocol as for patients, these participants were age-stratified and randomly recruited by phone over the same assessment period as patients. None of the controls reached symptom threshold for schizophrenia or any DSM-diagnosable mental illness, but were rated for reported and observed subclinical symptoms by a psychiatric trained assessor who was not blind to their asymptomatic status, but was blind to biological test results. Two younger controls required for younger age matching, were recruited from a local surf life-saving club within the same NWAHS catchment area. Five control participants were excluded due to their failure to meet exclusion criteria on assessment. The control sample used for the final analysis was drawn from the same catchment area as the patients and final sample consisted 67 controls.

2.5. Biochemical Assays and Specimen Collection

Laboratory tests used to assay biochemical markers in this study were commercially-available blood and urine samples, collected from all participants. Collection methods are documented in Table 1.

All biochemical testing was conducted by independent laboratories that were blind to participants’ case or control status and all participant symptom raters and neurophysiological assessors were blind to laboratory results. Baseline fasting spot-urine samples were collected for biochemical assays of dopamine, noradrenaline and adrenaline as well as their metabolites homovanillic acid (HVA) and methoxy-hydroxymandelic acid (MHMA) and the serotonin metabolite 5-hydroxyindoleacetic acid (5-HIAA). As part of this analysis, urinary creatinine was determined as a urine concentration standard and this standard was also used for urinary hydroxyhemopyrroline-2-one (HPL). In order to ensure that urine neurotransmitter levels were not affected by hypothalamic pituitary axis (HPA) activation, due to the procedure of blood-taking, urine collection preceded blood-taking, by a minimum of two hours. Fasting blood was also collected for vitamins and mineral cofactors.

2.6. Sensory Processing Assessments

Reverse digit span working memory and spatial working memory deficits have a known association with schizophrenia [35] [36] . After determining visual and hearing acuity all participants were assessed for selected sensory and cognitive variables related to auditory and visual processing. Assessments were conducted in auditory and visual domains, at a time separated from both blood and urine collection and within 4 days of such biological sample-collection. Apart from the prerequisite audiogram assessment, equipment used is compact, carried in an attache case and the assessment time is forty five minutes (Image 1 and Image 2).

Image 1. Sensory processing briefcase equipment (used with permission).

Image 2. Sensory processing speed assessment.

Where applicable, visual assessment was conducted using the participant’s usual glasses and alternate-cover- test was conducted prior to visual testing, to exclude visual fixation disparity (phoria or tropia) as a potentially confounding variable [37] . Visual assessments used patients own glasses where appropriate and included near and distance visual acuity, visual attention span, speed and accuracy of visual performance as outlined in Table 2.

Auditory assessments were conducted in a quiet room (ambient noise level 20 dB) and preceded by examination of the external auditory meatus to exclude obvious pathology or sebum obstruction. Audiometric examination was conducted using the MAICO Audiogram MA 40 [38] , at 250 Hz to 4000 Hz to determine hearing deficits (defined as air-bone conduction gaps > 10 Hz and/or sensory threshold abnormalities > 500 - 1000 Hz) and laterality differences. Auditory processing outcome measures were of acuity, attention and threshold speed and accuracy of auditory processing (Table 3).

In an effort to investigate pre-attentive deficits relating to acoustic reflexes and their relationship to central auditory-processing deficits or delays, specific measures were obtained for ear canal volume at threshold auditory response, peak middle ear pressure at threshold auditory response and gradient of the middle ear pressure elevation, using the Tymp GSI 38 by VIASYS Healthcare [39] . These measures were undertaken to quantify the tympanic muscle activating reflex response to sound entering the ear, along with the strength and duration of the sound dampening stapes muscle, which were surmised to be related to brain stimulus activation and inhibition, respectively. These two sequentially activated muscles regulate middle ear sound-conduction, with sound conductance by the tympanic and stapes muscle, followed by a sound dampening protective response as the handle of the stapes bone is finally applied to the oval window of the cochlea. In a novel approach, the strength and timing of the tympanic and stapes reflex responses were directly measured from the graph read-out of the machine, in order to ascertain whether any alacrity or delay of these sound-conducting and gating mechanisms might be associated with stimulus distortion in psychosis. Where repeated testing was necessary, an interval of 30 seconds or more was allowed between trials, in order to prevent error from muscle fatigue. Middle ear acoustic reflex assessment methods are documented in Table 4.

3. Data Analysis and Results

Data analysis on a sample consisting of 67 cases and 67 controls, within which there were 30 female cases with a mean age of 42 years and 37 males with a mean age of 39. In the control sample there were 34 females with a mean age of 45 and 33 males with a mean age of 46. For the total participant sample females totalled 64 and males totalled 70. The mean age for cases was 41 years and the mean age for controls was 46 years, matched on average to ±3 years. Variable distributions were mapped and quantile functions calculated using EasyFit software [40] . The sample was initially analysed as total cases versus total controls and no separate analysis by age and sex was conducted due to sample size. Basic demographic data relating to the participants and the

Table 1. Summary of biochemistry tests and methods with literature citations.

Table 2. Summary of visual assessment methods with literature citations.

Table 3. Auditory assessment methods with literature citations.

Table 4. Summary of middle-ear assessment methods with website links.

biomarker variables is provided in Table 5 and Table S2, respectively. Statistical analysis was conducted using XLSTAT [41] for Receiver Operating Curve (ROC) analysis of variables, at a 95 per cent level of confidence.

ROC analysis plots the sensitivity and specificity of the test result against each outcome-measurement, to give an indication of a variable ability to split between cases and controls, which is a test of screening capacity for that variable and thus, its biomarker status [42] [43] . Criteria used for candidate markers to achieve biomarker status in this project were: 1) a ROC area under the curve (AUC) lower boundary of ≥0.65 at a 95% level of significance and P ≥ 0.05,with a good test having an area of 0.7 to 0.9; 2) A risk of rejection of the null hypothesis (RR-Ho), for which the ideal value is <10% (Table S1); 3) For this data set, a minimum Spearman’ Rank Correlation coefficient of rho > 0.195, giving a P value of >0.05 and an ideal rho of >0.35 or more, giving a P value of 0.000 to <0.0001.

Though we essentially limited biomarkers identification to candidate markers that met the above criteria 1)-3), the ideal odds ratio of association with diagnosis of schizophrenia or schizoaffective disorder (Table S1), was an odds ratio that when divided by 3 (to compensate for a case-control study design), was either singly or in combination within a domain, able to provide an odds ratio of >2. Other ideal criteria include high percentage values for both sensitivity and specificity, (ideally > 90 percent). However in reality, there is often trade-off between sensitivity and specificity with lower values for one relative to higher values of the other.

Having identified our biomarkers using the above criteria, we then used Spearman’s correlation matrix to explore and rank the relationship of the participants’ symptom-ratings to their ROC-discovered biomarkers.

3.1. ROC Analysis Results

All candidate biochemical variables tested, (with the exception of MTHFR C667T polymorphism, and plasma homocysteine), produced a high area under the curve (AUC > 0.6). High serum B12 ROC AUC was marginal at

Table 5. Basic demographic data of case and control group participants (n 67).

0.565 and high histamine produced a poor quality ROC curve. In the field of sensory processing assessment, all candidate variables tested reached ROC identified biomarker status, with the exception of outcome measures for gap-detection and auditory-figure ground tests within the SCAN test for auditory processing disorder and outcomes for near vision using the Sussex near vision test. Catecholamine metabolites homovanillic acid (HVA) and methoxy-hydroxymandelic acid (MHMA), along with the serotonin metabolite, 5-hydroxyindoleacetic acid (HIAA), also failed to reach significance on ROC analysis.

On ROC analysis, twenty-one outcome measures achieved the required area under the curve (AUC > 0.65), risk of rejection (RR < 10%) and sufficient correlation coefficient (usually with sufficient odds ratio and/or specificity), to achieve status as a biomarker (Table 6, Table S1). These biomarkers were elevated dopamine, noradrenaline and adrenaline, along with elevated urinary hydroxyhemopyrroline-2-one (HPL/creatinine), low red cell folate, low activated B6 (pyridoxal-5’-phosphate coenzyme form), elevated ratio of percent free copper to red cell zinc and low serum vitamin D (25-OH) and elevated vitamin B12 (AUC 0.565) contributed positively to the overall nutrition-biochemical domain biomarkers. Visual processing variables were visual (symbol) span, threshold visual speed of processing performance as a percentage of age (expresses visual processing speed in terms of the visual processing system’s relative age) and distance-vision on right (asymmetric binocular distance-vision acuity). Variables eligible for biomarker status within auditory processing assessment, were reverse digit span (measures verbal, auditory working memory), competing-words performance for age as a percentage of age (measures intra-cerebral dichotic processing of auditory information) and threshold speed of auditory processing as a percentage of age (expresses auditory processing speed in terms of the auditory processing systems relative to age). Six biomarkers were discovered on middle ear impedance testing. These were: threshold percentage length of the base of the stapes reflex divided by the total duration of the reflex (a measure of the strength of the stapes reflex during its maximal period of contraction), threshold stapes amplitude projected (alternative measure of stapes contraction strength), threshold time-to-off-set of the stapes reflex contraction divided by the base length (gives a measure of acoustic reflex decay), threshold ear canal volume, threshold peak middle ear pressure and threshold gradient middle ear pressure, as outlined in Table 6.

3.2. Symptom-Profile Analysis

In order to draw meaning from the correlation between symptoms and biomarkers, we segregated symptoms in two main ways. Firstly according to their lead (strongest) correlative biomarker and secondly according to the strength of their correlation with the three principal catecholamine components, noradrenaline, adrenaline and dopamine. In the following presentation the P values for Spearman’s correlates are not presented, however significance levels correspond to the following general rules: for rho > 0.80 to 0.40, P < 0.0001, for rho 0.4 to 0.350, P = 0.000, for rho 0.350 to 0.307, P = 0.001, for rho 0.260, P = 0.007, for rho 0.255, P = 0.008, for rho 0.250, P = 0.009, for rho 0. 247, P = 0.010, for rho 0.231, P = 0.016 and for rho 0.195, P = 0.043.

Table 6. Twenty one outcome variables achieving ROC status as biomarkers for schizophrenia and schizoaffective disorder.

ROC: Receuver Operating Curve, AUC, No Obs, Number of observations, Area Under Receiver Operating Curve (ideally > 0.6), SENS: Sensitivity, SPEC. Specificity, ROC P value. Receiver Operating Curve variavle P value at 95% Confidence Interval, % RR-Ho. Percentage Risk Ratio for Null Hypothesis (ideally < 0.10), HPL: Urinary hydroxyhemopyrroline-2-one.

3.3. Symptom-Correlates in Relationship to Leading Biomarkers

When schizophrenia and schizoaffective disorder were segregated and ranked according to their lead (strongest) correlative biomarker, they fell into five main groups, outlined in Tables 7-11.

1) Low visual span Tables 7(a)-(f).

2) Low visual speed of processing Tables 8(a)-(c).

3) Low auditory speed of processing (ASOP) biomarker Tables 9(a)-(d).

4) Elevated noradrenaline Table 10(a) and Table 10(b).

5) Elevated adrenaline Table 11(a) and Table 11(b).

(a) (b) (c) (d) (e) (f)

Table 7. (a)-(f) Symptom correlates where the lead biomarker is low visual span.

(a) (b) (c)

Table 8. (a) (b) Symptoms where lead biomarker is low visual speed of processing.

(a) (b) (c) (d)

Table 9. (a)-(d) Symptoms where lead biomarker is low auditory speed of processing.

(a) (b)

Table 10. (a) (b) Symptoms where lead biomarker is elevated noradrenaline.

(a) (b)

Table 11. (a) (b) Symptoms where lead biomarker is elevated adrenaline.

3.4. Symptom-Correlates in Relationship to Catecholamine Biomarkers

When symptoms were segregated according to significant positive and negative correlative strengths for the three principal ROC catecholamines noradrenaline (NA), adrenaline (AD) and dopamine (DA), symptom formation for schizophrenia and schizoaffective disorder fell into seven finely tuned graduated levels of catecholamines.

1) Symptoms with significant correlates for both elevated NA, AD and a lower correlate for DA.

(Severity (SIR) (NA 0.575, AD 0.521) > DA 0.304).

2) Symptoms with significant positive correlates for NA and AD, but low DA correlates.

3) Symptoms with elevated NA (n 133, rho 0.5, P < 0.0001), compared with weaker AD and DA.

4) Symptoms with strongest correlation for adrenaline (AD) in a setting of a weak dopamine correlate.

5) Decreasing correlative strengths for dopamine (DA) n 133, (together with strong NA and AD correlates) and symptoms demonstrating no significant correlates for the dopamine biomarker.

6) Symptoms demonstrating an absence of significant correlates for the dopamine biomarker.

7) Symptoms with significant positive correlates for elevated dopamine biomarker.

8) Symptoms with generalised low or absent correlates for all catecholamines.

These graduated levels of catecholamines are presented in relationship to their biomarker correlates in Table 12.

3.5. Symptom-Correlates for the Oxidative Stress Marker, Elevated Haeme-Hydroxy- Pyrroline-2-One (HPL)

Elevated HPL/creatinine levels correlated with high DA ROC (n 131, rho 0.270, P 0.002) and high HIAA ROC (AUC 0.677 at 70% level of significance, n 131, rho 0.200, P 0.022) and high NA ROC (n 131, rho 0.232, P 0.008) and high AD ROC (n 131, rho 0.241, P 0.006). Significant positive correlates were obtained for HPL with clinical severity (SIR index) and a number of negative symptoms: Abstract thinking impairment 0.395, poor attention 0.350, clinical severity (SIR) rho 0.342, cognitive disorganisation 0.332, unusual thought content 0.325, disturbed volition 0.327, lack of spontaneous conversation 0.325, self-neglect 0.321, judgment and insight impairment 0.316, social avoidance 0.314, poor rapport 0.305, emotional withdrawal 0.294. Most notably, HPL held a significant correlation with the symptom of anxiety (n 133, rho 0.245, P 0.004) and marginally-significant correlations with reverse digit span (working memory) deficit (n 127, rho 0.204, P 0.022) and competing words (dichotic listening) deficit (n 123, rho 0.183, P 0.043). Additional marginal correlates were found for high free copper:zinc ratio (n 129, rho 0.171, P 0.053).

3.6. Symptom-Correlates for Peripheral Sensory Dysfunction

Impaired long distance vision (on right) was significantly related to all symptoms with the exception of negative

Table 12. Symptoms segregated according to catecholamine biomarker levels.

symptoms of depressed mood, disturbed volition, suicide and dissociative symptoms. In contrast, low, significant correlates were largely found for positive symptoms with delusions 0.410, thought preoccupation 0.400, judgment and insight impairment 0.397, suspiciousness 0.390, motor-hyperactivity 0.371, symptom intensity rating (SIR) 0.368, grandiosity 0.368, unusual thought 0.363, excitement 0.357, hallucinations 0.355, cognitive disorganisation 0.341, judgment and insight impairment 0.313, bizzare behaviour 0.311 and poor impulse control 0.304.

Middle ear dysfunction biomarkers demonstrated correlates with schizophrenia and schizoaffective disorderthat included symptoms of disturbed communication:

1) Low attainment of threshold peak middle ear pressure (sensitivity 70%, SIR n 124, rho 0.184, P 0.04), correlated with symptoms of suspiciousness 0.284, hostility 0.262, unusual thought content 0.240, delusions 0.221, thought pre-occupation 0.219, judgment and insight impairment 0.213. This variable also correlated significantly with pre-aged, delayed auditory speed of processing (n 117, rho 0.243, P 0.008) and with elevated noradrenaline levels (n 123, rho 0.220, P 0.014).

2) High external canal volume (ECV) held a biomarker specificity of 82% and for 123 observations, correlated with hallucinations 0.250, emotional withdrawal 0.227, SIR 0.226, depressed mood 0.225, social avoidance 0.244, suicidality 0.222. This variable also correlated specifically with delayed auditory speed of process- ing (n 116, rho 0.284, P 0.02).

3) High threshold gradient middle ear pressure, may be compensative to 1. This biomarker held a ROC specificity of 89% and was significantly correlated with SIR (n 124, 0.282, P 0.005) and with symptoms of poor rapport 0.411, lack of spontaneous conversation 0.282, suspiciousness 0.284, hostility 0.262 and unusual thought 0.240.

4) There was a tendency for increased strength and duration of stapes sound-dampening, acoustic reflex (possibly compensative to 1 and 2), to be also related to negative symptoms:

a) Long % base-length compared with total duration of the maximal contraction of the stapes muscle at threshold is indicative of a strong, long-duration stapes contraction. This biomarker held a sensitivity of 77% for 122 observations and correlated positively with poor attention 0.384, delusions 0.325, suspiciousness 0.303, poor impulse control 0.342, anxiety 0.341, abstract thinking impairment 0.367, SIR 0.365, distractibility 0.331, lack of spontaneous conversation 0.339, stereotypic thinking 0.336, judgment and insight impairment 0.327, poor impulse control 0.303, disturbed volition 0.301, social avoidance 0.301. This variable representing increased duration of stapes contraction also significantly correlated with elevated levels of both noradrenaline (n 121, rho 0.212, P 0.020) and adrenaline (n 121, rho 0.264, P 0.003), respectively.

b) High stapes projected amplitude at threshold contraction response relates to increased strength of stapes contraction as it dampens sound by landing on the oval window of the cochlea. This biomarker was also significantly correlated with SIR (n 123, rho 0.242, P 0.009, and with symptoms of emotional withdrawal (n 123, rho 0.317, P 0.001) and stereotypic thinking (n 123, rho 0.290, P 0.009).

c) Low threshold time to offset/base length (n 122) indicates a protracted stapes contraction, that also correlated significantly emotional withdrawal 0.277 and with similar symptoms to a), such as distractibility 0.268, poor impulse control 0.264, poor rapport 0.259, poor attention 0.257, disturbed volition 0.251, anxiety 0.243, social avoidance 0.241, abstract thinking impairment 0.236 and SIR 0.239. This variable also came close to significant correlation with delayed-for-age auditory processing speed (n 115, rho 0.161, P 0.071).

3.7. Symptom-Correlates for Vitamin and Mineral Co-Factor Dysfunction

Nutritional-Biochemical variables significantly correlated with neurotransmitter ROC variables for high dopamine (n 124, rho 0.301, P 0.001), high noradrenaline (n 124, rho 0.381, P < 0.0001 and high adrenaline (n 124 rho 0.302, P < 0.0001). In this setting, low, significant positive correlations for symptoms in relationship to biochemical and nutritional biomarkers were found at 95% confidence level. These included:

1) Elevated vitamin B12 ROC n 134, which correlated with symptoms of cognitive disorganisation 0.283, self-neglect 0.265, abstract thinking impairment 0.219, bizarre behaviour 0.251, unusual thought content 0.249, but had a low correlate with severity (SIR 0.194).

2) Low vitamin D ROC (n 132), which correlated with symptoms of poor rapport 0.349, uncooperativeness 0.315, poor impulse control 0.305, social avoidance 0.300, hostility 0.291 and SIR 0.254.

3) Low red cell folate (n 133), which correlated with symptoms of delusions 0.318, excitement 0.291, uncooperativeness 0.290, SIR 0.278.

4) Low activated B6 (n 126), which correlated with poor impulse control 0.278, hostility 0.270, excitement 0. 268, suspiciousness 0.228, uncooperativeness 0. 242, grandiosity 0.206 and SIR 0.187.

5) High percent free copper: zinc ratio biomarker (n 133), which marginally correlated with symptoms of suspiciousness (rho 0.181, P 0.038), abstract thinking impairment (rho 0.191, P 0.028), anxiety (rho 0.228, P 0.008), unusual thought (rho 0.192, P 0.027), judgment and insight impairment (rho 0.193, P 0.026), thought preoccupation (rho 0.188, P 0.030), distractibility (rho 0.192, P 0.027) and SIR (rho 0.179, P 0.040).

4. Discussion

This research brings together ROC-defined biomarkers for schizophrenia and schizoaffective psychosis and investigates their correlation profiles in relation to discrete symptoms of these conditions.

The study indicates that finely-tuned relative-strengths between noradrenaline, adrenaline and dopamine (NA, AD and DA) levels are critical in differentiating symptom-formation in schizophrenia and schizoaffective dis- order. The graduated difference between the elevated NA biomarker and the level of DA appears to turn symp- tom correlates from manic (with high DA) to disorganized and distracted (high NA and somewhat lower AD and DA), to depressive and negative (low DA ± high NA, or low DA alone), to dissociative symptoms such as ex- periencing “blank periods” that are characterised by generally low or absent catecholamine correlates. DA gives high frequency conditioning to glutaminergic synapses in order to potentiate goal-directed sensory information flow around the cortex, whilst high NA raises arousal and vigilance and AD promotes flight [44] . In moderate amounts NA also promotes sensory attention, however disproportionately excessive amounts of NA are reported to suppresses and disrupt DA-facilitated information flow around the cortex [45] -[53] . In this project the elevated NA biomarker correlated strongly with symptom severity as measured by the Symptom Intensity Rating (SIR) index. Elevated NA also correlated significantly with symptoms representing sensory flow disruption and symptoms indicative of sensory-disconnectivity within the cortex, such as impaired attention and cognitive disorganisation. We believe that noradrenergic disrupted internal sensory processing strain then activates the hypothalamic pituitary adrenal axis (HPA) [54] -[56] , stimulating further catecholamine synthesis and setting in motion a vicious cycle of raised catecholamine output related to symptom formation in antipsychotic-free and antipsychotic treated persons, alike [57] . In this setting of increased catecholamine requirement, any deficiency of vitamin B6 due to poor nutrition [58] will be exacerbated by its increased requirement as a cofactor by the DA- synthesis-enzyme tyrosine hydroxylase and also for NA-synthesis by the enzyme dopa-decarboxylase [59] [60] , (Figure 1). Vitamin B6 is also necessary to combat oxidative stress by the brain anti-oxidant glutathione, which utilises B6 in its synthesis via cystathione beta synthase (CBS) and cystathionase [61] , (Figure 1). Low folate precursor can theoretically reduce synthesis of upstream S-adenosylmethionine (SAMe) [62] and if SAMe is depleted, then the catechol-o-methyl transferase (COMT) enzyme is inhibited, since SAMe acts as a necessary cofactor for this enzyme which resides at the end-stage of catecholamine metabolism [63] . Therefore, in the absence of its cofactor, catecholamines will be elevated. The predominance of noradrenaline over dopamine and adrenaline in this study may then be further explained by the role of free copper in promoting the synthesis of noradrenaline from dopamine (Figure 1). In this setting, low folate (which serves as a remote precursor of SAMe), may also contribute to SAMe unavailability and by lessening conversion of adrenaline from noradrenaline, further contribute to noradrenaline excess (Figure 1, Table 12). Taken together, these two dynamics explain why SAMe unavailability allows prominence of noradrenaline (NA) in this project’s findings, with relatively lesser levels of dopamine (DA) and adrenaline (AD) in the presence of low folate and high free copper. These are interesting integrated findings which may provide an alternative explanation for the role of the COMT enzyme which has been already implicated in schizophrenia [64] [65] .

Elevated noradrenaline’s ability to disrupt DA’s promotion of the sensory signal around the cortex from frontal to temporal-hippocampal areas, fits well with the findings of this study where high NA correlates with symp- toms of poor attention, disorganisation, self-neglect and delusions, provide residual evidence of front-hippo- campal disconnectivity. At the same time noradrenergic fronto-parietal sensory signal disruption [45] -[53] isolates the frontal cortex and also the default network [66] [67] , resulting in poor frontal surveillance symptoms such judgement impairment, mixed with hostility, symptomatic of frontally unrestrained amygdala function. Our findings have also demonstrated that such disconnectivity may also be induced by oxidative stress (with elevated HPL), with vitamin deficits and elevated free copper also known to cause neuronal damage in sensory- processing circuits [68] [69] .

Urinary HPL is thought to arise under conditions of extreme oxidative stress where it is excreted as a by- product of haeme-related porphyrin synthesis [70] -[74] . In this study, HPL established its value as a biomarker that is notably linked with clinical severity, with elevated neurotransmitter levels, with impaired auditory working memory, with dichotic deficit, with delayed auditory processing systems and also with significant negative symptoms and anxiety. Taken together, this gives the impression that oxidative stress aligns with and potentiates the action of elevated catecholamines in disrupting sensory processing circuits in the cortex, leading to anxiety and rebound HPA activation, with potential for a vicious cycle of elevated catecholamine synthesis. In literature, elevated HPL is reported to relate to vitamin B6 and zinc deficiency, since HPL is a breakdown product from porphyrin synthesis and such synthesis is impaired in a setting of vitamin B6 and zinc unavailability [75] . Despite such reports, our study only found marginal correlates for elevated HPL with high free copper: zinc ratio (Figure 2).

In keeping with their various influences on catecholamine biochemistry (Figure 1), biomarkers for nutritional deficits such as vitamin deficiency (D, B6, folate) and low zinc, correlated significantly with elevated catecholamine levels and appear to impact a diffuse range of predominantly positive symptoms. Folate has been reported to be low in schizophrenia [76] and low zinc has been found related to noradrenaline excess [77] . Low vitamin D is an established finding in schizophrenia [78] and in this study, its biomarker correlated highly with auditory processing biomarkers and moderately with middle ear and elevated catecholamine biomarkers. A proposed mechanism linking elevated catecholamines and low vitamin D levels occurs via parathyroid hormone, the secretion of which is primed by catecholamines, which function to encourage parathyroid hormone action with release of calcium (Ca2+) ions from neuronal cells-an effect that relates to neuronal excitotoxicity, but which is directly opposed by the action of vitamin D [79] -[81] . Thus catecholamine-induced draw-down on body reserves of this over-utilised vitamin together with the indoor lifestyle and lack of sun exposure that may accompany the schizophrenia condition, may work together to potentiate these effects.

Evidence of significant correlative relationships between elevated catecholamines and auditory and visual processing speed and working memory deficits are postulated to occur via a vicious cycle of catecholamine excess, whereby NA is progressively elevated until fronto-temporal disconnectivity occurs [82] . Auditory and visual working memory deficits (low reverse digit span and visual span), dichotic listening deficits and pre-aged visual and auditory processing speed deficits not attributable to anti-psychotic medication may occur in this manner and have been reported in schizophrenia [83] -[91] . In this study, we have established that these deficits

Figure 2. Vicious cycle of NA and catecholamines, in relationship to vitamin and mineral cofactor biomarkers.

qualify as biomarkers, which inter-correlate with each other, with elevated catecholamines, with delay in both auditory and visual speed of processing and also with clinical symptom severity within the research patient population.

In keeping with this noradrenergic fronto-temporal disconnection hypothesis, low visual span may represent ramped-up noradrenaline-induced disconnectivity, reducing the action of the higher visual cortical magnocellular pathway and forcing reliance on the lower parvocellular visual pathway within the default brain network [92] . In this study, the range of symptoms correlating with reduced visual span, appear to reflect absence of the magnocellular pathway since this symptom repeatedly combines with symptoms of parietal inattention, disorganization and delayed visual speed of processing. At the same time, hippocampal isolation arising from hippocampal disconnection from frontal rationalization processes, allows internal semantic confabulation, resulting in delusional thought-formation. At the sub-cortical level the amygdala is similarly isolated from frontal cortical inhibition, with resultant hostility.

Middle ear ROC reflex findings of elevated tympanic contraction gradient and increased volume of the external auditory canal―yet collateral attainment of low middle ear peak pressure―indicate that middle ear patency problems associated with tympanic membrane pathology or Eustachian tube dysfunction are an integral part of schizophrenia and schizoaffective disorder [39] . Significant correlations for some of these variables with elevated catecholamine biomarkers may further explain catecholamine-related acoustic-hypersensitivity findings reported by Adler et al. in 1990 [93] . Our findings imply that in the setting of elevated catecholamines, tympanic muscle contraction facilitates hearing by undergoing compensatory over-contraction, which is then transmitted across the middle ear to influence stronger, delayed stapes muscle contraction at the other end of the ossicle chain. Such stronger stapes contraction then over-dampens and delays the passage of sound as it enters the cochlear. This may be the peripheral cause of the postulated pre-attentive auditory processing deficit in schizophrenia [94] [95] . Our findings also demonstrated significant ROC correlations between high external ear canal volume, low attainment of middle ear pressure and delayed offset of stapes contraction in relationship to delayed auditory speed of processing, confirming our understanding that the overall outcome of exacerbated tympanic muscle contraction transmission to the stapes muscle is increased delay of the auditory signal as it enters the cochlear and that this significantly relates to delayed auditory speed of processing. It is therefore of further interest that our study found prominent correlation of these middle ear auditory dampening and delay biomarkers with elevated NA and AD and negative symptoms of emotional withdrawal and lack of verbal spontaneity. In contrast, other peripheral biomarkers, such as long-distance vision abnormality correlate with clinical symptom severity and activated symptoms of motor-hyperactivity, grandiosity, excitement, bizzare-behaviour and hallucinations. If the brain does not get signals from its senses it constructs them internally. These peripheral findings represent unmet needs within the patient participants in our study and, imply that broader clinical assessment and targeted remediation of these conditions may assist symptom reduction in schizophrenia and schizoaffective disorder.

5. Limitations

In order to exclude confounding factors and isolate the functional core of schizophrenia, many exclusion criteria were applied to sample selection. This reduced available participants resulting in a small discovery data-set. Small sample size together with non-normal data distribution for many variables precluded use of principal component, multivariate and cluster analysis.

Though case-control studies are suitable for discovery projects for low prevalence disorders such as schizophrenia, their cross-sectional design has inherent susceptibility to prevalence and selection bias [96] [97] . Though differences in selection processes in this study were also inherent to the design, there was occasional cross-over in recruitment methodology and recruitment bias may have been offset by the random nature of recruitment success, as there was a refusal to consent to consent ratio of 4 to 1, for approached patients and controls alike.

Although most ROC variables identified in our study achieved a robust odds ratio of much over 2, some statisticians claim that odds of association results may be inflated by up to three times with a case-control design. [98] . For this reason all biomarker odds ratios were submitted to a “division by three” test, as described in the Data analysis and results Section 3, page 86.

The application of this design to only one bracket of psychotic disorders (schizophrenia and schizoaffective disorder) in this project, limits understanding of disease specificity across other mental illness states. A large cohort study with fully-blinded investigators or a prospective, multi-site-clinic trial on a single series consecutive patients with an emphasis on collecting symptomatic data from ultra-high risk medication naïve subjects, will therefore be required to validate these findings.

Though elevated urinary monoamine biomarkers identified in this study related to psychiatric symptoms in understandable ways, fully-synthesized monoamines do not cross the blood-brain barrier even though their precursor substance, L-Dopa, is capable of this transition [99] . Though spot-urine collection for peripheral monoamine analysis achieved good face validity with respect to symptom correlates in this study, this method of urine collection has attracted criticism [100] . The spot urine collection method is however practical in a psychiatric and pediatric settings [101] and is gaining ground as a useful analytic method relating to body biochemistry [102] . Although the oxidative-stress related molecule urinary HPL earned its place as a biomarker in this study, an understanding of this molecule’s synthesis is incomplete and additional biomarkers for oxidative stress would assist to quantifying the role of oxidative stress in schizophrenia and schizoaffective disorder.

Although rigorous efforts were made to exclude substance-related diagnoses, it is known that alcohol misuse is often under-reported by patients [103] , and has a known association with low folate, high B12, elevated catecholamine levels and reduced methylation [104] [105] . It was not possible to control for participant smoking in this project and smoking also has a reported effect on monoamine oxidase levels that can theoretically influence monoamine levels [106] . Though fasting biological samples were collected for this study, there was no longer term control or assessment of dietary intake, which in principle, could affect reserves of vitamins, minerals and monoamines [107] .

This research occurred with antipsychotic medicated patients who met strict criteria for such medication.

Nevertheless, the effect of medication on catecholamine results cannot be fully excluded as a confounding factor. In support of the fact that medications have not influenced these catecholamine results is the fact that there is widespread research evidence that antipsychotic medications increase catecholamine metabolic turnover and reduce dopamine and noradrenaline levels [108] -[110] . Also, the elevated urinary catecholamine biomarkers identified in this study strongly correlated with Symptom Intensity Rating (SIR), as a measure of clinical severity and they also demonstrated face validity by relating to the different psychiatric symptoms in a clinically meaningful manner. The fact that elevated catecholamines reached biomarkers status in this project and demonstrated their relationship to symptom-severity and sensory disconnectivity, means that many current antipsychotic medications may be failing to counteract high levels of catecholamines and reduce the impact of sensory disconnection on psychosis. In this context of potential for residual symptom-formation and treatment-resistance, it is therefore understandable that there is increasing pharmacological interest in noradrenergic targets for treatment of cognitive deficits and other aspects of schizophrenia condition [111] .

6. Conclusions

Though it is expected that hundreds of biomarkers for schizophrenia will eventually be discovered, this project provides an advanced understanding of psychosis by correlating the mood, perceptual and behavioural symptoms of schizophrenia and schizoaffective disorder with twenty-one quantified biomarkers.

Examining symptoms in relationship to biomarkers reveals that schizophrenic and schizoaffective disorder symptoms consist of a conglomerate of biochemical and neurophysiological dysfunctions related to dysregulation of visual and auditory sensory processing and neurochemistry relating to the biosynthesis and metabolism of catecholamines. Through the remote and direct action of cofactors associated with catecholamine synthesis and metabolism, noradrenaline levels are generally elevated which delays and disrupts cortical visual and auditory sensory processing pathways. This in turn activates the hypothalamic pituitary adrenal axis to produce further catecholamines. In this process, elevated free copper facilitates noradrenaline synthesis at the expense of dopamine and also promotes oxidative stress, whilst vitamin B6 over-utilisation reflects HPA pressure for dopamine synthesis and such lack also accentuates oxidative stress. Low folate remotely influences the ability of catecholamines to be metabolized by COMT via its cofactor, SAMe. Under the variable influence of these cofactors, altering ratios between noradrenaline, adrenaline and dopamine correlate meaningfully with mood, attention and behavioural effects of these catecholamines. In this setting, a predominance of very high-noradrena- line causes fronto-temporal disconnectivity resulting in symptoms of delayed, disrupted and disorganised visual and auditory attention, mixed with frontal disconnection and disinhibition symptoms of poor judgement, poor executive function, poor working memory and poor impulse control. Isolated temporal regions are then left to function alone with impaired insight and delusions, whilst the isolated amygdala within the unmasked default network manifests as symptoms of agitation, aggression and hostility.

This research provides a new understanding of the substructure of many symptoms within the context of psychosis. In particular, biomarkers correlate and combine to underlie symptoms and behaviours of key relevance within the clinical setting, such as suicidality, hostility and auditory hallucinations.

In this context, suicidality is seen to relate to high adrenaline levels representing the flight impulse, together with low-dopamine-related symptom of emotional withdrawal. In addition high noradrenaline levels convey the capacity to act-out on the basis of these impulses and symptoms, in the setting of a psychotic break in sensory processing with dichotic listening deficit, visual and auditory processing deficits, middle ear pathology and deficiency of all three nutrients―D, B6 and folate.

In the context of predicting and preventing aggression in psychosis, the biomarker profile of hostile, aggressive behaviour is of particular interest. Hostility is seen to relate to high noradrenaline levels and has a strong correlate with the symptom of blunted affect. Hostility also relates to impaired visual span and auditory processing difficulty and has underlying pathology relating to vitamin D deficiency and middle ear pathology. Such findings may be helpful for prediction of hostility in a clinical or forensic context.

Auditory hallucinations are seen to relate to high levels of both noradrenaline and adrenaline with delays in both auditory and visual processing speed, together with reduced visual span. In addition, dichotic listening deficit, middle ear pathology, low folate, low vitamin D and B6 contribute to the constellation of disorders and dysfunctions that subtly accumulate to produce this distressing symptom.

Anxiety is also seen to relate to elevated noradrenaline and adrenaline levels, with impairment of visual span and visual processing speeds to a greater extent than delayed auditory processing. In addition, auditory working memory and dichotic listening deficits impair sensory processing. Underlying this dysfunction resides peripheral abnormalities of long distance vision and the middle ear function, in addition to oxidative stress and low vitamin D and folate.

Biomarkers of acoustic reflexes relating to middle ear pathology, distance vision deficits, dichotic listening disorder and biochemical-nutritional deficits generally represent unmet needs within the clinical population assessed. This implies that there is scope for broader clinical assessment and remediation of persons with schizophrenia and schizoaffective disorder.

Biomarkers that correlate with discrete symptoms or sets of symptoms have therapeutic potential for targeted correction towards cure. They inform about the structure of psychosis, symptom-formation and the patterns in which symptoms present, allowing clinicians greater confidence in diagnosis and management of discrete, resistant, difficult or dangerous symptoms. Such multi-domain understandings provide a template for new biological system of symptom-interpretation in serious mental illness states. In the wider social context, the demonstration of clear biological underpinnings for schizophrenia and schizoaffective psychosis will reduce social stigma associated with these conditions and improve outcome expectations for patients and their families.


The authors acknowledge the additional support of Ms. Natasha J. Radcliffe (MHumNutr, GradDipSocSci, GradCertDisStud, BA (Sociology)) who undertook control stratification and enrolment with analysis of variable distributions, proxy standard errors, ROC analysis, odds ratio analysis and Spearman’s correlation analysis. Statistician Graeme Tucker (B.Sc.) also conducted final review statistical analysis. Queen Elizabeth Hospital academic and secretarial staff, nursing staff, Registrars and Consultants. Dr Geoffrey Schrader MBBS, M Clin Sc, PhD, FRANZCP and Dr Helen Tingay FRANZCP. Western Mental Health Service, Janet Grant (Population Research and Outcome Studies (PROS) and North West Adelaide Health Study assistance with participant selection. Laboratory support from Dr. Michael Metz BS, MD, FAAP, MAACB, FRCPA at Clinpath Laboratories and Dianne Zercher (TQEH Biochem Laboratory) and Dr Malcolm Whiting at SA Pathology. General support from Professor Robert Adams Head of Department of Medicine TQEH, Professor Richard Ruffin AM, Judith Snowden, Helen Goldsack, Dr Marilyn Dyson MBBS, MHP, DRCOG, Dr Sinclair Bode MB BS FACNEM, Veronica Steer B AppSc (OT), Jan Pollard (technical audiology support), Dr N. Williams (Ophthalmologist FRANZCO). Graphic support Adlab SA.

A provisional patent application was filed by the author at the conclusion of data-collection and initial analysis, in December 2013.

Authors Contributions

As Chief Researcher, Dr Stephanie Fryar-Williams MB BS. BSc (Biochem/Pharmacol) FRANZCP is an Honorary Research Fellow of the University of Adelaide. Dr Fryar-Williams conceived the project, selected the candidate markers and designed the research protocols, oriented staff and raters in ward and clinic settings, managed and supervised the recruitment of case and control participants, prepared the data-set for analysis by data-transformation, supervised ROC and other statistical analysis, directed and interpreted data analysis within biochemical, neurological and psychiatric theory and wrote the paper.

As a previous Clinical Director of Psychiatry at the Queen Elizabeth Hospital, Woodville SA., Dr Jorg Strobel MD (Bonn). Spec Psych, Psychotherapy FRANZCP facilitated the laboratory, ward and community organisation and coordination components of the project and revised past and final papers for publication.

Supplementary Tables

Table S1. ROC results for schizophrenia and schizoaffective disorder, with odds ratios.

Table S2. Distribution summary for biomarker variables.

Conflicts of Interest

The authors declare no conflicts of interest.


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