There is a search for diagnostic tests for schizophrenia (SCZ) and its subtypes while the concept of SCZ as a distinct disease entity from other major psychiatric disorders is shifting.
No biologically-based medical test, such as a blood test or the use of brain imaging for diagnosing SCZ exists, but there is much research that aims to come up with reliable tests using these technologies. For the moment, they are mainly intended for use in research that ultimately aims to characterize individual responses to treatment. The goal is to develop diagnostic testing including theranostics, which is aform of diagnostic testing employed for selecting targeted therapy. At the same time, the diagnosis and treatment of aspects of SCZ that antipsychotics do not improve, such as cognitive impairment and negative symptoms, are being explored (1).
At present, it is only once symptoms appear that a diagnosis of SCZ is made. Diagnosis is based on subjective observations that rely on the Diagnostic and Statistical Manual of Mental Disorders (DSM), or the International Classification of Mental Disorders (ICD-10) Clinical descriptions and diagnostic guidelines,that divide major psychiatric disorders such as major depression, bipolar disorder and SCZ, into distinct categories. Some investigators call the distinct categories for these disorders into question, and some evidence is pointing to their existence on a continuum with shared features and causative factors (2).
The root cause of schizophrenia is unknown, and SCZ is thought to be the result of an interplay of both genes and the environment. Many different gene variants and copy number variants (repeats of units of DNA), each with a small effect, may add up to the genetic component of SCZ and appear to contribute to its cause. Many gene variants associated with SCZ are also associated with bipolar disorder (BD), major depression, and autism, diminishing their value as potential diagnostic tools. However, the genetic overlap among these disorders supports the view that the major psychiatric disorders are related to each other, but in ways that are not yet fully understood(3).
As in genomic studies, some of the biochemical and immunological abnormalities seen in SCZ are also seen in other major psychiatric disorders. In the case of immune-mediators associated with SCZ, abnormalities are also shared with several autoimmune disorders (1). The upshot is that the search for a test for a single marker of SCZ has virtually been abandoned. The focus is now on the simultaneous analysis of many markers and/or gene variants that are analyzed using artificial intelligence (AI). Algorithms combined with sophisticated machine learning capable of processing the large and complex sets of data are being studied to come up with “signature” patterns of the levels of different markers associated with disorders on the schizophrenia spectrum. Brain imaging analyzed using AI is also a subject of intense investigation.
One example of AI applied to research into potential diagnostic analyses is the study of biomarkers in the blood of 20 patients with SCZ, 20 with BD, and 20 healthy controls. The levels of the following proteins were measured in the blood of the participants: BDNF, IL-6, IL-10, eotaxin-1, glutathione S-transferase, and glutathione peroxidase (4). Accuracy was 72.5% for distinguishing participants with SCZ from healthy controls, 77.5% for people with BD versus healthy controls, but only 49% for distinguishing BD and SCZ. A value of 49% accuracy indicates that the test does not perform better than random chance in discriminating between the two conditions. The authors propose that additional biomarkers to distinguish the two disorders and to increase accuracy could be explored in future studies.
The following studies report high accuracies for detecting SCZ in small samples of patients using different technologies, all involving complex data sets that were analyzed using AI and machine learning. They are all small, preliminary studies that require confirmation in larger studies. The ability of the diagnostic tests to distinguish between SCZ and BD is considered to be desirable, but this aspect was not addressed, since no controls using subjects with BD were included. However, as understanding of the relationship between the two conditions increases, these tests may eventually serve a slightly different purpose than the diagnosis of SCZ, that is to characterize the features of a particular patient’s condition, for instance, rather than establish a diagnosis falling into an existing disease category.
A study of macro and trace elements (minerals required in large, such as calcium, or small, such as zinc, quantities by the body) in the serum of 130 participants with schizophrenia and 130 healthy controls reported that the analysis of 2 elements together, Boron and Titanium, using inductively coupled plasma-mass spectrometry (ICP-MS), were up to 94.96% accurate in detecting cases of SCZ. The analysis of Titanium alone reached an accuracy of 94.04%. When 39 different macro and trace elements were analyzed simultaneously, an accuracy of 99.21% was achieved (5).
Another study, using functional magnetic resonance imaging (fMRI) reported 95.56% accuracy in detecting SCZ in a group of 24 participants with SCZ and 21 healthy controls(6). A study of electroencephalogram (EEG) data from 34 persons with SCZ and 34 healthy controls yielded an accuracy of 88.24% in the detection of SCZ (7). While these results may show promise, subjects with BD must be included in future analyses to assess the utility of these tests.
The National Institute of Mental Health’s Research Domain Criteria (RDoC) initiative (2)is developing research tools to study psychiatric disorders on the basis of both psychological and neurobiological observations, including the analysis of neural circuitry in the brain. There are also attempts to correlate abnormalities in brain circuitry to genomic findings. One of the RDoC intiative’s goals is to characterize the aspects of psychiatric disorders that overlap among major depression, BD, and SCZ, and to arrive at an understanding that overcomes the strict diagnostic categories based on subjective observations presently in use in research and clinical diagnosis.
The emergence of novel, if costly, approaches to diagnosing SCZ holds promise, even if they are only feasible for use in research. It is expected that they will eventually make it possible to identify subgroups of patients likely to respond to specific treatments. They are deepening understanding of the relationships among major psychiatric disorders. However, Dean raises the question of how progress in research will be translated into improved care, as funding for mental healthcare itself has dropped in the U.S. (1), in spite of advances in research.
- Dean CE. Social inequality, scientific inequality, and the future of mental illness. Philos Ethics Humanit Med PEHM [Internet]. 2017 Dec 19 [cited 2018 Apr 4];12. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5738232/
- Kozak MJ, Cuthbert BN. The NIMH Research Domain Criteria Initiative: Background, Issues, and Pragmatics: NIMH Research Domain Criteria initiative. Psychophysiology. 2016 Mar;53(3):286–97.
- Dean CE. The Death of Specificity in Psychiatry: cheers or tears? Perspect Biol Med. 2012;55(3):443–60.
- Peripheral biomarker signatures of bipolar disorder and schizophrenia: A machine learning approach. Schizophr Res. 2017 Oct 1;188:182–4.
- Lin T, Liu T, Lin Y, Yan L, Chen Z, Wang J. Comparative study on serum levels of macro and trace elements in schizophrenia based on supervised learning methods. J Trace Elem Med Biol. 2017 Sep;43:202–8.
- Zhu Q, Huang J, Xu X. Non-negative discriminative brain functional connectivity for identifying schizophrenia on resting-state fMRI. Biomed Eng OnLine [Internet]. 2018 Mar 13 [cited 2018 Apr 4];17. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5851331/
- Shim M, Hwang H-J, Kim D-W, Lee S-H, Im C-H. Machine-learning-based diagnosis of schizophrenia using combined sensor-level and source-level EEG features. Schizophr Res. 2016 Oct;176(2-3):314–9.
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