An Update on Promising Biomarkers in Schizophrenia

This article discusses some of the most promising and potentially clinically relevant biomarkers in schizophrenia, with a focus on markers of inflammation, neuroimaging, brain-derived neurotrophic factor, genetic and epigenetic markers, and speech analysis.

Abstract

Given the heterogeneity of symptoms in patients with schizophrenia and current treatment limitations, biomarkers may play an important role in diagnosis, subtype stratification, and the assessment of treatment response. Though many potential biomarkers have been studied, we have chosen to focus on some of the most promising and potentially clinically relevant biomarkers to review herein. These include markers of inflammation, neuroimaging biomarkers, brain-derived neurotrophic factor, genetic/epigenetic markers, and speech analysis. This will provide a broad overview of putative biomarkers that could become clinically relevant in the future, though none currently appear ready to assist the clinician in identifying cases of schizophrenia, subtypes of the disorder, treatment choice, or response. Nonetheless, some biomarkers, such as C-reactive protein (CRP), may be useful at identifying individuals who may be more highly inflamed, which could drive treatment choice. Though checking CRP is not a standard of practice, this is one example of how biomarkers may drive treatment decisions in the future, supporting precision medicine. Similarly, technological advances may one day allow clinicians to detect changes in speech patterns, which could represent a noninvasive, clinically useful tool in the future. We conclude the review by highlighting two important potential clinical uses for biomarkers in schizophrenia: the identification of individuals who may convert from clinical high risk and the stratification of patients via different biomarkers that may supersede clinical diagnosis. Given the enormous burden of illness of schizophrenia, the search for clinically relevant biomarkers is of great importance to improve the lives of patients with the disorder.

Schizophrenia is a heterogeneous disorder whose core features include positive, negative, and cognitive symptoms in addition to social and occupational dysfunction (1). Schizophrenia causes great suffering to those individuals and their loved ones who are affected, and one estimate puts the cost of the illness to the U.S. health care system at $158 billion per year (2). The current gold standard for the diagnosis of schizophrenia is the clinical interview (3), and its core diagnostic features have largely remained unchanged for the past 100 years (4). Symptoms of schizophrenia are variable among individuals, and the disease has a heterogeneous long-term course (5). Subtypes based on clinical phenotypes such as being paranoid, disorganized, and undifferentiated have poorly explained the heterogeneity of schizophrenia (6) and were subsequently eliminated from the DSM-5 (7). Semistructured diagnostic interviews can help to improve diagnostic reliability (8) but are infrequently used in clinical practice. Biomarkers could play an important role in making the diagnosis of schizophrenia more objective, especially for less seasoned clinicians or practitioners outside of psychiatry (9). Biomarkers may be state and/or trait specific, which could lead to useful tools for the clinician to assess treatment response. Given the heterogeneity of symptoms within the disorder, biomarkers may assist in stratifying patients based on subtype, which ultimately may have significant treatment implications. Furthermore, the use of biomarkers could potentially improve patient buy-in for an illness with symptoms that often include anosognosia (10) and could potentially reduce stigma, putting schizophrenia on par with other medical diagnoses that are diagnosable by laboratory testing, such as diabetes and hypertension.

Considerable effort and research has been put forth to identify biological markers of the illness, which could better help researchers to understand its elusive pathogenesis and trajectory. Efforts to identify biomarkers in individuals with schizophrenia have dated back to the mid-1800s (11) and only have increased over time. A PubMed search using the Medical Subject Heading (MeSH) terms “biomarker” and “schizophrenia” yielded over 2,300 results from 1965 to 2017, and 272 articles resulted in the year 2016 alone. The 2001 Biomarkers Definitions Working Group (12) defined a biomarker as “a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention.” There are three types of biomarkers: diagnostic, prognostic, and theranostic (9). Diagnostic biomarkers help to classify whether a person has a specific disease or not and can ideally help differentiate one condition from another (e.g., bipolar disorder versus schizophrenia). Prognostic biomarkers help to determine whether one will develop a disease. Theranostic biomarkers predict whether an individual will respond to a particular therapy. Much of the research in schizophrenia has been focused on endophenotypes, which have a narrower definition than biomarkers. An endophenotype is associated with the illness, heritable, state independent, cosegregates within families, and is found in unaffected family members at a higher rate than expected in the general population (13). Thus, endophenotypes could be considered to be biomarkers, but not all biomarkers are endophenotypes. Rather than provide a comprehensive review of the literature on biomarkers, we discuss new and promising approaches in identifying biomarkers in schizophrenia, focusing on markers of inflammation, neuroimaging, brain-derived neurotrophic factor (BDNF), genetic and epigenetic markers, and speech analysis.

Markers of Inflammation

The role of the immune system in the pathogenesis of schizophrenia has been an area of active interest, with evidence from epidemiology; preclinical studies of maternal immune activation models; genetic studies; and studies of inflammation, oxidative stress, and the complement system converging to support a putative “immunophenotype” of schizophrenia (14). In terms of potential biomarkers related to the immune system, there have been a number of areas of investigation that warrant consideration, including acute-phase proteins (e.g., C-reactive protein; CRP), inflammatory cytokines, and markers of oxidative stress.

CRP

CRP is an acute-phase protein that is synthesized in the liver in response to cytokine induction, particularly interleukin (IL)-6. CRP is an attractive putative biomarker, as it can be readily assayed in most conventional laboratories in clinical settings. Much work has been done to investigate the role of CRP as a biomarker for individuals with chronic schizophrenia as well as for individuals at clinical high risk for psychosis. One study of CRP with high-risk subjects did not find any significant differences in CRP concentrations relative to healthy controls in either those subjects who did convert or those who did not convert to psychosis (15). In fact, CRP was lower (albeit in a nonsignificant manner) in those subjects who did convert. This suggests the possibility that CRP may be a disease-specific marker that does not reflect at-risk states.

A meta-analysis of 26 studies of CRP concentrations in patients with schizophrenia showed significantly higher CRP concentrations (effect size=0.60) in patients with schizophrenia relative to controls (16). A subset of studies in the meta-analysis of patients with first-episode psychosis (FEP) showed similar results and effect sizes. CRP concentrations did not change as patients progressed from FEP to more chronic schizophrenia, nor did they change with antipsychotic use, suggesting that CRP may be a trait-related biomarker of illness. Of note, three studies have shown a transient increase in CRP in patients with treatment-resistant schizophrenia after starting clozapine (17–19). Interestingly, the meta-analysis found that CRP also appeared to be associated with higher positive symptoms, but not negative symptoms, as well as elevated body mass index (BMI) and younger age. This association with clinical markers suggests that CRP may be a biomarker of severity (at least with positive symptoms), though clinical characteristics such as BMI may confound its use, especially given the increased cardiometabolic burden from atypical antipsychotics.

Inflammatory Markers

Cytokines are inflammatory signaling molecules that help coordinate the function of both the innate and the adaptive immune systems and are involved with a host of physiological processes throughout the body. Peripheral inflammatory cytokines can be measured in both serum and plasma and can access the brain via a number of mechanisms, including “leaky” regions of the blood-brain barrier; binding to specific transporter molecules expressed on brain endothelium (“humoral pathway”); activation of vagal afferent fibers (“neural pathway”); and a “cellular pathway,” in which monocytes access the brain vasculature and parenchyma via cellular trafficking. In terms of their use as biomarkers, inflammatory cytokines are not disease specific, as elevations in the concentrations of inflammatory markers have been demonstrated in major psychiatric illnesses, including schizophrenia, major depressive disorder (MDD), and bipolar disorder (20).

In a meta-analysis of 40 studies (21), patients with FEP and inpatients who were experiencing an acute relapse of their psychosis had similar effect sizes for elevations in the following cytokine concentrations: IL-1beta, IL-6, and transforming growth factor (TGF) beta. This suggests that these cytokines may be considered as state markers, as they also normalized with antipsychotic treatment. The cytokines IL-12, interferon (IFN)-gamma, tumor necrosis factor (TNF), and soluble IL-2 receptor (sIL-2R) were elevated in acute exacerbation and remained elevated after treatment, which suggests that they may be trait illness markers.

In studies of at-risk and ultra high-risk (UHR) subjects, IL-6 was found to be elevated compared with controls in two studies (22, 23). In the North American Prodrome Longitudinal Study (NAPLS) Project, two inflammatory cytokines—IL-1beta and IL-17—were part of a larger panel of peripheral blood markers that predicted conversion from clinical high risk to full psychosis (24). In a meta-analysis of studies investigating antipsychotic-naïve FEP subjects (23 studies), significant elevations in peripheral cytokine concentrations were found for IL-1beta, IL-6, sIL-2R, and TNF (though not for IFN-gamma, as a previous meta-analysis found; 25). This suggests that these peripheral cytokines may be considered as biomarkers that are present at illness onset and are unrelated to antipsychotic treatment.

As mentioned earlier, elevations in peripheral cytokines are not specific to schizophrenia, and perhaps cytokine alterations in major psychiatric illness may share common underlying pathways for immune dysfunction. In a meta-analysis of acutely ill patients (68 studies) and chronically ill patients (40 studies) with schizophrenia, MDD, and bipolar disorder (20), there were significant elevations of IL-6, TNF, sIL-2R, and IL-1 receptor antagonist (IL-1RA), compared with controls in acutely ill patients with these three disorders. Furthermore, levels of these cytokines significantly decreased following treatment of the acute illness. Concentrations of IL-6 were significantly increased in chronically ill patients with schizophrenia, euthymic (but not depressed) patients with bipolar disorder, and patients with MDD compared with controls. Concentrations of IL-1beta and sIL-2R were elevated in chronic schizophrenia and euthymic bipolar disorder but not in MDD.

The findings from these meta-analyses should be interpreted with caution because of the heterogeneity of studies included. Many questions specific to the measurement of inflammatory cytokines as potential biomarkers in schizophrenia remain to be answered. Though these meta-analyses provide some direction for possible state versus trait markers, large prospective longitudinal studies are needed to investigate how these inflammatory markers change over the disease course. Moreover, other clinical variables that affect the immune system (e.g., age, BMI, and smoking) must be carefully considered, in addition to measures of psychopathology, medical comorbidities, and antipsychotic treatment. Which specific cytokines or sets of cytokines would be most useful to measure has yet to be determined. Relationships between peripheral cytokine concentrations and brain imaging, for example, may provide more meaningful results in the future. Importantly, the field has also yet to agree upon a standard assay methodology (i.e., a multiplex system or an ELISA-based system) that could significantly affect the results of these assays. These are just some of the outstanding questions and issues that limit the clinical utility of inflammatory markers as biomarkers of schizophrenia. Nevertheless, adjunctive treatment with anti-inflammatory medications has been a burgeoning area of investigation (26, 27), with small effect sizes in favor of treatment with anti-inflammatory medications. One possible reason for these small effect sizes may be a lack of appropriate targeting of the subset of patients who are highly inflamed, for whom anti-inflammatory drugs would be expected to have an effect. As such, a biomarker that can be readily assayed in clinic, such as CRP, may assist in stratifying which patients may benefit from anti-inflammatory therapies (28).

Markers of Oxidative Stress

Markers of oxidative stress have also been explored as potential biomarkers in schizophrenia. Oxidative stress refers to an imbalance of free radicals, such as reactive oxygen and nitrogen species. When the body’s antioxidant defenses cannot limit the production of these free radicals, cell membrane damage occurs, leading to altered neurotransmission that may affect symptoms in some patients with schizophrenia (29). In a large meta-analysis of 44 studies of markers of oxidative stress in patients with schizophrenia relative to controls (30), total antioxidant status, defined as the combination of all antioxidant molecules in the body (e.g., albumin, uric acid, and ascorbic acid), was significantly decreased in cross-sectional studies of first-episode patients and significantly increased in longitudinal studies of treatment after acute episodes of psychosis. Similar results were found for red blood cell (RBC) catalase (which converts hydrogen peroxide to water and oxygen) and plasma nitrite (used as a marker of nitric oxide activity), which suggests that these may be state-related biomarkers. RBC catalase and plasma nitrite were also significantly increased in stable outpatients. Conversely, RBC superoxide dismutase (catalyzes the conversion of superoxide radicals to hydrogen peroxide) was found to be significantly decreased relative to healthy controls both at first episode and after treatment studies of acute psychosis and in chronic outpatients.

Though this meta-analysis found significant differences between first-episode patients and healthy controls, a meta-analysis of early-onset cases of schizophrenia (defined as a first episode occurring before the age of 18) found no differences between early-onset patients and healthy controls on any marker of oxidative stress (31). These differences may reflect the heterogeneity of populations of patients diagnosed as having schizophrenia as well as the heterogeneity of studies included in meta-analyses. Nonetheless, there is a growing literature for treatment trials with antioxidant adjunctive treatments (e.g., N-acetylcysteine, vitamin C, vitamin E, omega-3 polyunsaturated fatty acids) with small but significant effect sizes, which suggests that perhaps these medications may be useful for patients with more aberrant levels of oxidative stress in the body, and as such, these may reflect important biomarkers that warrant more future investigation (32, 33).

Neuroimaging Biomarkers

Since the initial Johnstone study demonstrating increased ventricular size on computerized tomography (CT) for individuals with schizophrenia when compared with controls (34), findings have generated significant interest in neuroimaging as a method to identify biomarkers and endophenotypes for schizophrenia. A variety of neuroimaging techniques, including CT, magnetic resonance imaging (MRI), functional MRI (fMRI), diffusion tensor imaging (DTI), positron emission tomography (PET), single-photon emission CT (SPECT), and magnetic resonance spectroscopy, have helped to contribute to our understanding of schizophrenia. However, common limitations in neuroimaging studies include small sample sizes, a clinically heterogeneous population, and challenges accounting for comorbidities (35). To address these issues, collaborative efforts with shared protocols across large groups are becoming increasingly common (36, 37).

Structural MRI is one of the most widely studied brain endophenotypes in psychiatry (38). For clinical high-risk individuals who ultimately develop psychosis, rates of gray matter (GM) loss over time have been of interest. The NAPLS group found that UHR individuals who converted to psychosis demonstrated a greater rate of GM loss in the right superior frontal, middle frontal, and medial orbitofrontral cortices, as well as a greater rate of expansion of the third ventricle, than those who did not convert (39). Increased rates of cortical GM loss in individuals who convert to psychosis have been replicated in other groups (40, 41). In individuals with chronic schizophrenia, Shenton et al. (42) conducted a review of 193 studies from 1988 to 2000 demonstrating ventricular enlargement, decreased volume in medial temporal lobe structures (hippocampus, parahippocampal gyrus, and amygdala), decreased volume in the superior temporal gyrus (STG), and subcortical involvement (including the cerebellum, basal ganglia, thalamus, and corpus callosum). Including data from 1998 to 2012, in a volumetric meta-analysis, Haijma et al. (43) found that individuals with schizophrenia who were taking medication had a small but significant reduction in overall intracranial (Cohen’s d=−0.17) and total brain (d=−0.30) volume, as well as decreased volume for total GM (d=−0.49), frontal lobe GM (d=−0.49), hippocampus (d=−0.52), STG GM (d=−0.58), and fusiform gyrus and an increased volume of the third and lateral ventricles. When comparing antipsychotic-naïve individuals with schizophrenia to control individuals, they found similar, though smaller, effect sizes for brain volumes, concluding that volume loss in schizophrenia was part of both a neurodevelopmental process and an illness progression (43). Then, using standardized protocols across 15 international centers, the ENIGMA consortium conducted a study of 2,028 individuals with schizophrenia and 2,540 healthy controls and measured differences in subcortical brain structures (44). For individuals with schizophrenia, they also found a slight reduction in intracranial volume (d=−0.12) and found reduced volumes in the hippocampus (d=−0.46), amydgala (d=−0.31), thalamus (d=−0.31), and accumbens (d=−0.25), while volumes were increased in the pallidum (d=0.21) and lateral ventricles (d=0.37) (44).

Schizophrenia has been characterized as a disorder of connectivity between brain regions (45), and the imaging modalities most commonly used to assess this are resting state fMRI and DTI (46). DTI, first described in the early 1990s (47, 48), is a type of diffusion-weighted MRI that enables researchers to study white matter (WM) tractography in vivo as well as neural connectivity (46). For individuals with schizophrenia, studies have shown a theme of decreased fractional anisotropy (FA) in the superior longitudinal fasiculus, cingulum bundle, unicate fasiculus, inferior longitudinal fasiculus, and arcuate fasiculus (49–51), although findings have been inconsistent across studies (46, 52, 53). The corpus callosum, the largest WM tract in the brain, is responsible for communication between brain hemispheres (54) and is also thought to be impaired in schizophrenia. Two recent meta-analyses have compared healthy controls to individuals with schizophrenia, with Zhuo et al. (55) finding a decrease in the FA in the both genu and splenium regions of the corpus callosum and Shahab et al. (56) finding a reduction in FA in only the genu.

DTI has also been used for illness classification purposes. With a combination of FA and mean diffusivity (MD), individuals with schizophrenia could be differentiated from controls with 96% sensitivity and 96% specificity (57), though the sample size was small and included a mixture of individuals with chronic and first-episode schizophrenia. Considering heterogeneity of the illness as a possible reason for inconsistent results, researchers have begun to divide individuals with different WM patterns into different subtypes of schizophrenia. This approach has been used to differentiate “non-deficit” and “deficit” subtypes (58), to differentiate between groups of individuals with schizophrenia not taking medication (59), and to identify biotypes of schizophrenia that correspond to clinical symptoms (60). Arnedo et al. (60) used a generalized factorization model to identify four groups of schizophrenia in which areas of low FA in certain regions corresponded to different clinical phenotypes (e.g. low FA in the genu corresponded to bizarre behavior).

PET and SPECT imaging allow investigators to better understand schizophrenia on a molecular/neurotransmitter system level. For example, PET/SPECT studies have helped to further elucidate the dopamine hypothesis in schizophrenia, one of the most enduring ideas in psychiatry (61). In a meta-analysis conducted by Howes et al. (62), individuals with schizophrenia, in comparison with controls, had increased presynaptic dopaminergic function (d=0.79) and a small elevation in dopamine subtype 2/3 (D_2/3) receptor availability (d=0.26) but no change in dopamine transporter activity. In a meta-analysis of DOPA PET studies using [¹⁸F] and [¹¹C] radiotracers, individuals with schizophrenia had a 14% increase in striatal dopamine synthesis capacity when compared with controls (63). Taken together, these findings suggest that drug development in schizophrenia should target presynaptic dopamine targets. PET studies could also be used to guide dose ranges for new treatment options (64) or help to detect diagnostic biomarkers for schizophrenia. For example, increased reactivity to amphetamine challenge is present in clinical high-risk individuals, prodromal individuals, and individuals with chronic schizophrenia (65–68). Findings from PET could be used to help determine which at-risk individuals ultimately convert to psychosis, but currently, these studies are underpowered for clinical use (69).

As mentioned earlier, various neuroimaging techniques have been used to differentiate individuals with schizophrenia from those without it. Conventional pattern classification approaches have typically used voxel-based morphometry (VBM) with general linear models to identify discriminating factors in localized regions of the brain (70). However, there is often considerable overlap between cases and noncases at the group level (71). Additionally, combining the intertwined nature of structural and functional abnormalities in schizophrenia has been a challenge, and to overcome methodological issues with traditional univariate data analyses, multivariate analyses have been used (72). In a meta-analysis using multivariate pattern analysis (MVPA) involving 1,602 first-episode and individuals with chronic schizophrenia, patients could be differentiated from normal controls (N=1,637) with a sensitivity and specificity of 80% (73). MVPA has also been used to differentiate women with schizophrenia from women without schizophrenia in a DTI study with an accuracy of 72%–88% (74). Through machine learning models, studies have also differentiated between bipolar disorder and schizophrenia using structural MRI (75, 76) and fMRI (77), with rates of accuracy around 80% or greater. Machine learning models have also been used to predict individuals at UHR of converting to psychosis, as well, with reasonable accuracy (78). Although these results are intriguing, classification experiments should be externally and independently validated (79) and include sample sizes of over 130 individuals (80). Currently, the performance for these classification systems is too low to be used in clinical practice (81). Various modalities of neuroimaging, as well as multimodal approaches, remain an active area of research to identify biomarkers and endophenotypes in schizophrenia.

BDNF

BDNF is the most widely expressed neurotrophin in the human brain and is involved in a number of crucial neurodevelopmental mechanisms, including neurogenesis, neuronal differentiation, and neuronal survival, as well as synapse formation and maturation (82–84). As schizophrenia is recognized as a neurodevelopmental disorder whose pathogenesis involves alterations in neuroplasticity and synaptogenesis (85, 86), it is of no surprise that BDNF would be considered a putative biomarker. Indeed, BDNF has been found to be decreased in patients with schizophrenia (87), though this finding has either been inconsistent (88) or been found to be related to other factors, such as substance use (89). There is also some evidence to suggest that BDNF levels may increase with antipsychotic treatment in some (90), but not all (91), studies. Furthermore, postmortem studies have found decreased BDNF mRNA expression in the hippocampus (92) and prefrontal cortex (93). However, BDNF cannot be considered a disease-specific biomarker, as it has been found to be reduced in both MDD and bipolar disorder as well (94, 95).

A recent meta-analysis has provided some clarity and direction to the heterogeneity of BDNF findings in the literature (96): Fernandes et al. included 41 studies, with over 7,000 participants, that measured peripheral levels of BDNF. Importantly for its consideration as a putative biomarker, the authors note that BDNF crosses the blood-brain barrier (97) and that both serum and plasma levels correlate highly with BDNF concentrations in the cerebrospinal fluid (98–100). The results of the meta-analysis showed an overall moderate decrease in serum and plasma BDNF levels in patients with schizophrenia compared with healthy controls. This finding was confirmed in both first-episode and chronic patients with schizophrenia. The decrease in BDNF levels was found to be more pronounced with length of illness (though not associated with age), suggesting the possibility that the relative decrease in BDNF is involved in neuroprogression, as is seen in both MDD and bipolar disorder (101, 102). Despite this, there was no relationship between BDNF levels and either positive or negative symptoms, though other studies have found relationships between BDNF levels and cognition (103, 104). It therefore remains to be seen whether BDNF could be considered a useful biomarker for disease severity.

The meta-analysis also demonstrated that, in longitudinal treatment studies, BDNF concentrations show a small increase after antipsychotic treatment in both patients who responded to medications (defined as a 40% reduction in Positive and Negative Syndrome Scale [PANSS] scores) and in those who did not respond. This suggests that BDNF may not be a useful biomarker to assess treatment response. (Of note, this treatment effect was only found in plasma, not in serum). The meta-analysis found no dose-related relationships, though individual studies have demonstrated such an effect, such as with clozapine dose, but not with typical antipsychotics (105). As there was a high degree of heterogeneity in the studies included in the meta-analysis, these results should be interpreted with some caution, though they suggest that BDNF may predict the presence of disease. It remains unclear whether BDNF may be predictive of illness phase or treatment response. Its relationship with severity of illness remains questionable, though further work will be necessary to understand whether it may be predictive of improvement in cognition.

Genetic Biomarkers

Developments in statistical methodologies and computational technologies, robust epidemiological studies, and genome projects such as the Schizophrenia Psychiatric Genome-Wide Association Study Consortium (PGC-SCZ), have allowed for significant advances in the ability to detect genetic and epigenetic markers of schizophrenia. These advances have also dramatically increased our knowledge and understanding of the complex epidemiology of schizophrenia and the factors that contribute to its neurophysiological, cognitive, and behavioral phenotypes and have allowed for the formulation of multiple, developmentally driven conceptual models of schizophrenia as well as suggested new possibilities for development of pharmacological interventions. Schizophrenia is a highly heritable disorder and has been investigated through numerous twin and familial studies (106–108). Characteristic alterations of gene expressions in schizophrenia lead to abnormal phenotypic markers (109).

Vulnerability to neurodevelopmental abnormalities associated with schizophrenia are linked to an array of genetic markers found on as many as 108 chromosomal sites; thus, schizophrenia is currently thought to be a polygenic psychiatric condition (107). Presence of the 3q29 microdeletion, for example, is considered the largest genetic risk factor for schizophrenia (110). RELN and GAD1 genes related to GABAergic neuronal function; glutamate receptor and transporter genes; serotonergic receptor gene HTR2A; COMT enzyme gene; and BDNF, important to cognitive function, are among those genes more extensively discussed (108). Other genes linked to schizophrenia include ARC, NMDAR, and VGCC, all critical neurobiological pathway genes that influence neuronal excitability, long-term potentiation, and cognitive processing (110). Extensive lists of top genetic markers identified from genomewide studies are included in Ayalew et al. (111), Flint and Munafó (112), and Rodrigues-Amorim et al. (109), and they illustrate the challenges and complexities involved in understanding genetic risk factors. Genetic markers of schizophrenia are numerous, interrelated, likely interdependent, and, as a result, form a complex genomic profile.

Epigenetic Biomarkers

Epigenetics describes modifications to gene function and resulting phenotypic changes not explained by DNA sequencing (112). DNA methylation, translation of mRNA, and histone modification are among the most studied epigenetic mechanisms, and several key events in genomic development have been associated with disruptions in these mechanisms (106, 108, 109, 113–115). Methylation, the addition of a methyl (CH3) group to DNA, results in the modification of DNA function and appears to be a critical epigenetic mechanism for controlling gene expression (106, 109). Several studies following schizophrenia phenotypic development have investigated the role of the methylation process and have suggested that disruptions in the methylation of genes linked to schizophrenia may lead to neurobiological abnormalities such as dysregulation of the dopamine, NMDA, and GABA signaling pathways (108, 115, 116). Increased methylation of reelin (i.e., RELN), for example, has been implicated in prefrontal cortex dysfunction (109, 117). It has also been suggested that abnormalities in DNA methylation may also be heritable, as well as a dynamic process that can continue throughout the lifespan, making it vulnerable to environmental influence (108).

Current developmental models of schizophrenia supported through human and animal studies, as well as models such as the dopamine hypothesis and the water-shed hypothesis, describe multifactorial relationships that consider the contributions of and complexities among several genetic and environmental factors (107, 108, 113, 118). The conclusions of recent investigations of the relationships among genetic, epigenetic, and phenotypical markers of schizophrenia appear to agree that multiple genetic risk factors—combined with adverse environmental stimuli, including a range of gestational conditions, caregiver and familial experience, exposure to and abuse of substances, and traumatic events—can lead to a cascade of epigenetic abnormalities that may then result in phenotypic abnormalities associated with schizophrenia, as well as potentially other comorbid conditions (107, 111, 115, 119). The implications of these collective findings for risk detection, early monitoring, and therapeutic interventions for schizophrenia are that the most effective course may involve participation of multiple specialties and a longitudinal, systemic approach that is customized to the individual.

Biomarkers of Speech

Much of the recent research has focused on investigating deficits in the cognitive processing of speech sounds and language production in those diagnosed as having schizophrenia; primarily, studies have centered on the abilities to comprehend and produce specific types of verbal utterances and language mechanics. Differences have been found, for example, in the ability to integrate audio-visual information (120). Other research has suggested that auditory processing is altered in a manner that impairs the further processing of speech stimuli, including preattentive processing of speech sounds (121) and vowel phonemes (122), as well as the assignment of meaning to sounds (123). Imaging technologies such as fMRI and electroencephalography (EEG) have supported findings indicating neural correlates of lower sensory-cognitive performance as being measured through traditional cognitive assessment and experimental devices, such as the “cocktail party” condition (124). In general, much of this research suggests that neural mechanisms related to schizophrenia are also related to detectable limitations in sensory-cognitive processing ability and that initial auditory processing of sounds, including verbal stimuli, may be implicated.

Language mechanics has been shown to be abnormal in schizophrenia, suggesting impairment in semantic processing (125, 126). The content of autobiographical narratives in those diagnosed as having schizophrenia also appears to be associated with lower expressivity and complexity but greater self-referencing and repetition of words (127). Differences in frequencies of words categorized as representing emotional content have been found; for example, in a study by Minor et al. (128), use of words lexically associated with anger predicted greater positive and negative symptoms. Innovative technologies, such as social media and electronic messaging platforms, as well as voice recognition software capabilities, present a unique opportunity to further explore and define language-based detectors of psychiatric symptoms using existing knowledge of associated verbal markers.

Recent research has suggested that machine learning models are capable of analyzing text content from Twitter feeds to detect mood-related content associated with schizophrenia with a high degree of accuracy (129). The use of machine learning algorithms to analyze large data sets—including social media activity and neuroimaging data for the detection of mental health markers, including suicide risk and depression, in users (130–132)—is also a topic of emerging research. Studies such as these are currently being conducted through funding from such organizations as the U.S. Department of Defense (DoD) and have been recently publicized in online news articles (133–136). However, these studies represent the comparably sparse body of research available throughout the technology-centric open literature applying emerging technologies to the diagnosis and treatment of psychiatric conditions, and schizophrenia in particular. As a result, this continues to be a potential growth area for clinical investigation and interdisciplinary collaboration.

Conclusions

As reviewed herein, blood-based, neuroimaging, genetic/epigenetic, and speech patterns make up some of the more attractive biomarkers to aid in the prediction of disease state and response to treatment. Currently, none of these putative biomarkers appear ready to assist the clinician in identifying cases of schizophrenia, subtypes of the disorder, treatment choice, or treatment response. Biomarkers, such as CRP, may be most useful at this point in identifying those individuals who may be more highly inflamed, which could drive treatment choice, as may be the case in depression (137, 138). Similarly, detecting differences in speech patterns may be a future clinically useful, noninvasive, tool.

Instead of focusing on individual biomarkers, one area of increasing interest has been on the prognostic utility of combining biomarkers to determine which UHR individuals may convert to psychosis. In a meta-analysis of 2,502 subjects in 27 studies, Fusar-Poli et al. (139) found 29% of individuals meeting UHR criteria converted to psychosis at 2 years, but these studies do not take into account that these cases vary greatly in risk (140). As such, it is of great importance to develop tools that help predict conversion. For example, NAPLS used a multiplex blood assay using plasma analytes of inflammation, oxidative stress, hormones, and metabolism to predict conversion to psychosis (24). This approach of using multiple predictive biomarkers alongside clinical, demographic, and cognitive variables, as NAPLS has done using an externally validated individualized risk prediction tool (assigning an individual a probability of conversion in two years), may yield even stronger predictive value (141, 142). Prognostic biomarkers could play an important role in aiding individuals at higher risk for the disease in initiating interventions that may help to reduce conversion to psychosis (143, 144). If an individual does convert, connection with the mental health services and knowledge of risk prediction could help to reduce the duration of untreated psychosis, a variable that may help to improve long-term outcomes in schizophrenia (145, 146). Another important issue regarding the use of biomarkers in the prediction of conversion to psychosis is that in most CHR research, the diagnostic outcome is dichotomous—conversion or no conversion. As such, both schizophrenia spectrum disorders and affective psychosis are grouped as meeting criteria for conversion, and therefore studies are measuring a nonspecific psychosis outcome. Just as other studies have attempted to use biomarkers to differentiate schizophrenia from affective psychoses, diagnostic specificity may be an important consideration in identifying biomarkers for psychosis conversion.

Besides having prognostic value, the approach of combining various biomarkers may aid in the subtyping of patients with schizophrenia, which could have important future treatment implications. For example, the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) consortium is a group working to further understand biological, rather than clinical, phenomenological differences between schizophrenia, schizoaffective disorder, and bipolar disorder with psychotic features (147). B-SNIP used several endophenotypes, including neuropsychological testing, stop signal, saccadic control, and auditory stimulation paradigms to identify three distinct psychosis biotypes that superseded clinical diagnoses (148). Importantly, identifying these “biotypes” may allow for further elucidation of biomarkers that are specific to each subgroup. This may help explain the heterogeneity in results presented earlier, as certain biomarkers may only be relevant for a specific subgroup of the disorder.

There is much promise in the identification of biomarkers in psychiatry, especially for a disorder as complex and heterogeneous as schizophrenia. Just as biomarkers help predict treatment choices and disease progression in other areas of medicine such as cancer and cardiology, the hope is that we can be more neurobiologically specific in our understanding of disease state and in our prediction of risk and treatment response, all supporting precision medicine. Given the enormous burden of illness of schizophrenia, the search for relevant and useful biomarkers is of great importance for improving the lives of patients with the disorder.