Utility of Biomarkers to Improve the Diagnosis and Treatment of Schizophrenia

Mental illnesses represent a growing human, medical, social and economic tragedy worldwide. Psychotic disorders, which include schizophrenia, bipolar affective disorder and major depression, are arguably amongst the most devastating diseases affecting humankind. Unfortunately, psychotic disorders are also extremely common diseases, with approximately 1% of the world population being affected by schizophrenia alone, increasing to 7% when bipolar affective disorder and major depression are included. Psychotic disorders account for 15% of the burden of disease in developed economies. Schizophrenia costs between US$40 and 60 billion per annum in the USA and between $10 and 52 billion in Europe. Furthermore, schizophrenia accounts for more than 25% of mental health costs and a third of the hospital bed occupancy [1]. In the UK, schizophrenia was responsible for 7.8% of the NHS expenditure on inpatient beds in 1992/1993, totalling a sum of GB£947 million [2].

Although acute psychosis represents a clinical emergency, the diagnosis and treatment remain unsatisfactory or indeed poor. In fact, the current clinical management reduces the burden of schizophrenia by only 13% [3] and the prognosis of a patient presenting with first-episode psychosis is good in only 40% of patients, with an intermediate outcome in 33% and poor prognosis in the remaining 27% of patients [4]. This means that roughly a third of patients will suffer a severe, irreversible disability and another third will have a poor level of functioning.

There are several reasons why the outcome of psychosis, even when treated, is unsatisfactory. One problem is that since the first scientific classification of mental diseases, by the great German psychiatrist Emil Kraepelin (more than 100 years ago), who divided psychotic disorders into dementia praecox (later renamed by Emil Bleuler as schizophrenia) and manisch–depressive Erkrankung (manic depression, a definition that includes bipolar affective disorder and psychotic depression), few advances have been made with regards to diagnosing psychotic disorders. We are still using a classification system that is solely based on the assessment of a patient‘s clinical presentation, in particular, signs, symptoms and history. No laboratory test or quantitative instruments are available to help achieve a correct diagnosis. The currently used classification systems – International Classification of Disease (ICD-10) and Diagnostic and Statistical Manual of Mental Disorders IV text revision (DSM-IV TR) – are largely based on Kurt Schneider‘s first-rank symptoms, which assess alterations in thinking, affect, perception and social functioning of a patient in the form of an interview, for example, the clinician will ask whether the patient is hearing voices or believes the ‘people are out to get him‘. Not surprisingly, this approach is not particularly reliable in distinguishing schizophrenia from other psychotic illnesses [5–6]. Furthermore, during a first onset of psychosis, if the length of the episode does not fulfil the diagnostic DSM or ICD criteria for schizophrenia (which require 6 months or 1 month of continuous psychosis, respectively), a diagnosis cannot be established. Such a presentation may be an isolated episode or may be part of a prodromal presentation of schizophrenia. The accurate identification of a schizophrenia prodrome is of great importance in light of increasing evidence that early intervention can improve patient outcome [7–8]. In particular, the length of duration of untreated psychosis (DUP) is associated with outcome and prognosis; where a prolonged DUP is associated with poorer outcome [9]. Another point to consider is that in many cases the onset of schizophrenia is insidious, where patients may present with mild abnormalities for several years before frank symptoms of psychosis emerge. Many of these patients present with prominent negative symptoms that are frequently misdiagnosed (e.g., as depression or lack of motivation) or ignored and viewed as difficult adolescent behavior. In such patients, a diagnosis is often made too late, when the life quality of the patient has already been almost devastated. It is, therefore, of crucial importance to develop better approaches to diagnose the onset of schizophrenia as early as possible.

One way to achieve more accurate and early diagnosis is through the use of biomarkers, molecular readouts that correlate with the presence and, ideally, severity of a given disease. Equivalent approaches have already been implemented in other clinical fields, especially in the diagnosis of certain cancers, helping to achieve significant improvements in disease outcome [10].

Based on current healthcare spending, it has been predicted that earlier and more accurate diagnosis and intervention in schizophrenia would reduce premature death, hospitalization and hospital admissions. In turn, the relapse rate could also be reduced and, in the longer term, poor outcome would be reduced [11–15].

To improve the diagnosis and treatment of schizophrenia, it is also necessary to gain a better understanding of the disease etiology. Studies conducted on monozygotic twins demonstrated a concordance rate well below 50% [16], underlining the importance of nongenetic, environmental factors in the etiology of the disease. Many risk factors have been associated with an increased risk to develop schizophrenia, including infectious and obstetric insults, maternal malnutrition and many more. It is likely, therefore, that what we currently classify as schizophrenia is in fact a group of different diseases with different etiologies and outcomes. This would also explain the low response rate to antipsychotic treatment, which lies at approximately 30–40% [17].

A major challenge in the identification of reliable early disease markers is to minimize the effects of confounding factors such as medication and lifestyle (smoking, cannabis use etc.), which may be more prevalent in the disease population. For this reason, in our laboratory, we decided to study first-onset drug-naive patients and rigorously select well-matched controls. In collaboration with our clinical co-workers, we collect extensive demographic information about all aspects of lifestyle and perform extensive psychopathological testing on all patient and control subjects. Samples are collected along strict standard operating procedures.

Our laboratory employs a multiomics profiling approach, which integrates RNA, protein and metabolite investigations on postmortem brain tissue as well as samples obtained from living patients (cerebrospinal fluid, serum, blood cells etc.) [18]. We were able to show that profiling changes found in postmortem brain tissue from schizophrenics can also be observed in peripheral tissues of living patients. Of these tissues, serum and plasma are particularly suited for the development of a minimally-invasive diagnostic test [19–20].

The use of biomarkers will also help the treatment of schizophrenia to identify novel drug targets, making it possible to improve and monitor treatment efficacy.

Preliminary analyses of data from our laboratory suggest that certain biomarkers correlate with specific psychopathological symptoms [Unpublished Data]. Others have previously reported correlations between certain disease biomarkers; for example, plasma C-reactive protein levels and Positive And Negative Syndrome Scale (PANSS) scores [21]. Another study investigating a small patient cohort (15 patients) showed positive correlation between a number of acute-phase proteins (C3, C4 and ceruloplasmin) and the score of the negative subscale of the PANSS [22]. S100B has also been reported to positively correlate with the PANSS negative symptoms subscale score [23]. In patients with chronic schizophrenia, high S100B levels were associated with poor verbal memory performance (Auditory Verbal Learning Test) [24]. However, our recent results imply that panels containing multiple biomarkers, rather than a single readout, will correlate more specifically with distinct patient symptoms, for example, cognitive impairment.

A major advance in the management of schizophrenia would be if ‘at-risk‘ patients could be identified before any symptoms become evident. A recent study conducted in our laboratory involving monozygotic twins discordant for schizophrenia and control twins demonstrated that affected and unaffected twins have significantly elevated α-defensin levels in plasma, compared with healthy control twin pairs. Considering that the unaffected twins share the same genotype and usually environmental upbringing, α-defensin and similar markers may represent an important early indicator of the risk to develop schizophrenia [25].

There is no doubt that the identification of disease markers in the laboratory is academically satisfying, yet the translation of such findings into the clinic is the real challenge and requires the investigation of much larger sample cohorts, ideally collected in different clinical centers. Recently, we undertook a large-scale study, where we investigated over 1100 serum samples collected from patients with diverse CNS disorders, including schizophrenia, bipolar affective disorder, major depression, multiple sclerosis, Asperger‘s syndrome and dementias. Disease and control samples were collected in three different clinical centers. Our preliminary results are extremely encouraging, in that a schizophrenia biomarker panel identified in samples from one clinical center is able to identify schizophrenia patients with very high specificity and sensitivity in samples from the other centers, and shows a very strong separation from other neuropsychiatric disorders, including affective disorders [Unpublished Data].

The next stage towards clinical translation, is to conduct clinical trials where samples from diverse psychiatric patients are collected in a prospective study to determine the clinical power and utility of a putative diagnostic test for the early diagnosis of schizophrenia.

Financial & competing interests disclosure

Sabine Bahn is the founder and director of Psynova Neurotech. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript.

Additional information

Funding

Sabine Bahn is the founder and director of Psynova Neurotech. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed