Biomarker identification in psychiatric disorders: from neuroscience to clinical practice.

Biomarker Identification in Psychiatric Disorders: From Neuroscience to Clinical Practice

Patients with psychiatric disorders exhibit several neurobehavioral and neuropsychological alterations compared to healthy controls. However, signature endpoints of these behavioral manifestations have not yet been translated into clinical tests for diagnosis and follow-up measures. Recently, neuroproteomic approaches have been utilized to identify unique signature markers indicative of these disorders. Development of reliable biomarkers has the potential to revolutionize the diagnosis, classification, and monitoring of clinical responses in psychiatric diseases. However, the lack of biological gold standards, the evolving nosology of psychiatric disorders, and the complexity of the nervous system are among the major challenges that have hindered efforts to develop reliable biomarkers in the field of neuropsychiatry and drug abuse. While biomarkers currently have a limited role in the area of neuropsychiatry, several promising biomarkers have been proposed in conditions such as dementia, schizophrenia, depression, suicide, and addiction. One of the primary objectives of this review is to discuss the role of proteomics in the development of biomarkers specific to neuropsychiatry. We discuss and evaluate currently available biomarkers as well as those that are under research for clinical use in the future. (Journal of Psychiatric Practice 2015;21:37–48) KEY WORDS: biomarker, proteomic, mass spectrometry, psychiatry, clinical practice

MAHDI RAZAFSHA, MD AUNALI KHAKU, MD HASSAN AZARI, PhD ALI ALAWIEH HURA BEHFORUZI, MD BILAL FADLALLAH, PhD FIRAS H. KOBEISSY, PhD KEVIN K. WANG, PhD MARK S. GOLD, MD

pharmacologic responses to a therapeutic intervention.”1 Biomarkers represent important diagnostic tools for the sensitive identification and selection of patients with a particular disease. They act as monitors of disease progression, prognosis, and classification. They provide information to help guide decisions about dosing of medications and strategies to minimize interindividual variation. They also enhance the evaluation of the benefits and risks of specific therapeutic interventions, are helpful in predicting and monitoring clinical response to interventions in the follow-up of patients with a specific disease, constitute a basis for the design of clinical trials, and serve as starting points for drug discovery and development. Biomarkers have revolutionized medical research, and several biomarkers are currently widely used in clinical medicine. Examples of such diagnostic biomarkers include prostate specific antigen (PSA) for prostate cancer, troponin and creatine phosphokinase MB (CPK-MB) for myocardial infarction, aspartate aminotransferase (AST)/alanine aminotransferase (ALT) for liver pathologies, and the 14-33 protein for Creutzfeldt-Jakob disease (CJD).2 However, efforts to establish and develop sensitive and specific biomarkers in the field of psychiatry RAZAFSHA, KOBEISSY, WANG, BEHFORUZI, and GOLD: McKnight Brain Institute, University of Florida, Gainesville; KHAKU: University of Central Florida, School of Medicine, Orlando; AZARI: Department of Anatomical Sciences, Shiraz University of Medical Sciences, Shiraz, Iran; ALAWIEH: Medical University of South Carolina, Charleston; FADLALLAH: Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta GA. Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved.

A biomarker broadly refers to any measurable biological characteristic that reflects the presence, absence, and/or severity of a disease. In specific terms, a biomarker is a “characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or

Journal of Psychiatric Practice Vol. 21, No. 1

Please send correspondence to: Mahdi Razafsha, MD, Department of Psychiatry, McKnight Brain Institute, University of Florida, 1149 South Newell Drive, Gainesville, FL 32611. [email protected] The authors declare no conflicts of interest. DOI: 10.1097/01.pra.0000460620.87557.02

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Copyright © Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited.

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BIOMARKER IDENTIFICATION IN PSYCHIATRIC DISORDERS

have not been as successful as in other areas of medicine. The great variability in phenotypic clinical presentations, the complicated inheritance patterns of psychiatric illness, the heterogeneous and complex nature of the brain, and the presence of the blood brain barrier may be among the major obstacles hindering these efforts.3,4 Other areas that contribute to the complexity of developing biomarkers in psychiatry include the clinical heterogeneity of mental disorders and the reliance on subjective selfreport in the diagnoses of psychiatric disorders. Many of the biomarkers that have been proposed for different psychiatric conditions lack sufficient specificity, since different psychiatric illnesses with seemingly different phenomenology can produce the same biomarker. This could be due to the overlapping nature of psychiatric symptoms in different psychiatric syndromes. One example that illustrates this problem is brain derived neurotrophic factor (BDNF). Different conditions including schizophrenia,5 depression,6 and bipolar disorder7 have been shown to be associated with elevated levels of BDNF, which poses a major threat to the validity of BDNF as a biomarker of each condition. Another obstacle in developing reliable and valid biomarkers in psychiatry is that most diagnostic models involving biomarkers are based on organ dysfunction and measure a compound that is unique to that organ. However, no clear neuropathologic changes have been identified for most psychiatric disorders, even at autopsy. Furthermore, the brain is an organ that is not easily biopsied and the study of human brain tissue is often limited to postmortem specimens. These factors impose substantial limitations on studies of brain tissue and consequently the discovery of biomarkers in this area.8 Currently, clinical impressions, along with clinical rating scales, are the mainstay of diagnosis and follow-up in the field of psychiatry. While clinical scales and questionnaires are useful tools that increase the validity of psychiatric diagnoses, their utility seems to be similar to that of the electrocardiogram (EKG) or exercise stress test as indicators of cardiac adverse events compared with use of the specific biomarkers troponin or CPK-MB. The absence of sensitive and specific neuropsychiatric biomarkers makes diagnoses vulnerable to clinician biases and subjectivity. Such variability in diagnosis is compounded during the follow-up process, since clinical signs and symptoms may not necessarily change with the actu-

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al course of psychiatric disorders.9 In this regard, biomarkers have the potential to revolutionize the evaluation and treatment of neuropsychiatric diseases and enhance our understanding of the organic basis of psychiatric diseases. However, the field of neuropsychiatric biomarkers is still in its infancy. It was only recently that researchers appreciated the potential utility of high-throughput tools in the identification of biomarkers.10 High-throughput tools (genomics and proteomics) allow researchers to quickly conduct millions of chemical, genetic, or pharmacological tests to increase our understanding of neuropsychiatric disorders.

CHALLENGING NATURE OF PSYCHIATRIC NOSOLOGY AND BIOMARKER DISCOVERY The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), the classification and diagnostic tool of the American Psychiatric Association, was published in 2013, updating the DSM-IV-TR which was published in 2000. DSM-5 provides a common language for describing the psychopathology of psychiatric illnesses. After its publication, several critical questions were raised regarding the validity of the diagnostic criteria. The DSM-5 was criticized for being symptom based rather than providing objective criteria based upon genetics and neuroscience. Thomas Insel, the director of the National Institute of Mental Health (NIMH), criticized the idea of considering a constellation of clinical symptoms as the “gold standard” of diagnosis. “In the rest of medicine, this would be equivalent to creating diagnostic systems based on the nature of chest pain or the quality of fever.”11 Insel’s statement that “Patients with mental disorders deserve better” perhaps prompted David Kupfer, chairman of the DSM-5 Task Force to reply, “We’ve been telling patients for several decades that we are waiting for biomarkers. We’re still waiting.”12 Although this debate was settled by a joint statement of the APA and NIMH,13 the need to identify biomarkers that could transform a phenotype-based definition of disease into a finite number of treatment-relevant subgroups14 remains unmet. The striking improvements in the treatment of breast cancer perhaps best illustrate how “personalized medicine”15 or “stratified medicine”14 can lead to an improvement in the treatment and eventually the prognosis of a specific disease. This improve-




ment was initially triggered by the identification of breast cancer susceptibility genes that could guide the clinical decision-making process. The discovery of human epidermal growth factor subtype 2 (HER2) in breast cancer tissue and recognition of the fact that overexpression of HER2 is associated with a poor prognosis16 paved the way to improve treatment strategies focusing on this receptor. The further development of monoclonal antibody therapies like trastuzumab, which inhibits the HER2 receptor, improved outcomes and long-term survival in breast cancer patients. Assimilating this model to psychiatry is a promising pathway that has the potential to revolutionize psychiatric practice.14 However, this requires a huge leap in our understanding of mental illness and a substantial modification of the current psychiatric diagnostic system (DSM and the International Classification of Diseases). Classification systems based on symptomatology and consensus currently serve a vital role in providing a common language for providers that ultimately leads to improved clinical care.4 While reliability (i.e., different observers reaching the same diagnosis) has been the strength of this type of classification, validity (the extent to which a measurement corresponds accurately to the real world) has been a weakness.17 Using genomics, proteomics, and other novel developments in the technology of neuroscience to develop signature biomarkers will likely be an important part of the solution in improving the validity of psychiatric diagnosis.

PROCESS OF BIOMARKER DISCOVERY The majority of biomarkers that have been identified to date are protein biomarkers, due to their stability in body fluids (cerebrospinal fluid [CSF] and bloodserum). The quest to discover biomarkers often yields a large pipeline of potential candidate markers. The subsequent elimination process depends on many factors, including the biomarker’s dynamic range, its sensitivity and specificity, and translatability into clinical practice, as discussed below. Dynamic range refers to the concentration of the substance in body fluid under normal and pathological circumstances. If the concentration of the biomarker is so low in serum or CSF that it is practically impossible to detect it, it would be difficult to justify the development of that marker. In


addition, if the concentration does not vary widely enough between the pathologic and normal states, it would also not be a suitable biomarker. Sensitivity and specificity. Sensitivity refers to a test’s true positive rate (i.e., the proportion of actual positives that are correctly identified), while specificity measures the proportion of negatives that are correctly identified. If the marker is present in normal subjects at a significant level, or if other disease conditions release the same marker, the sensitivity and/or specificity of the biomarker will be reduced. Translatability into clinical practice. Translating laboratory findings into clinical results is possibly the single most challenging hurdle in the development of biomarkers for neuropsychiatry. Certain biomarkers may look promising in vitro or in animal models but then they do not pan out in the clinical setting in humans. This step, however, is essential in confirming and validating the clinical utility of these putative markers.

DEVELOPMENT OF PROTEOMICS In recent years, proteomic approaches have been used to identify unique protein biomarkers. Proteomics refers to the identification and analysis of proteins that are expressed by a cell, tissue, or organ. It is basically a high-throughput, multidisciplinary, and technology-driven tool that provides the whole protein profile of a cell.4,18 Identifying the interactions of proteins with other proteins and molecules (functional proteomics) is another application that can ultimately lead to a better understanding of the pathophysiological processes going on in a cell. Clinical proteomics refers to the category of proteomics that deals with the development of biomarkers applicable to clinical medicine. The term “psycho-proteomics” refers to proteomic studies in the field of psychiatry. The completion of the Human Genome Project, which provided detailed data about the structures of genes (genomics), had a major impact on the development of the field of proteomics.18 However, it is estimated that proteomics is 100-fold more complex than genomics.4 This complexity is due in part to the fact that proteins are subject to post-translational alterations and modifications. In addition, certain proteins have different levels of expression, which vary over

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time and under different conditions, making the identification of low-expressed proteins difficult. Moreover, human brain samples are not easily accessible, and methods for amplifying proteins, in contrast to methods for amplifying DNA, are not yet available. The field of proteomics has been developing rapidly, not just in tandem with the acquisition of genomic data but also with the development of mass-spectrometry (MS) and bioinformatics/systems biology. MS is an analytic technique that identifies unknown compounds by fragmenting and measuring the massto-charge ratio of each fragment.10,18 Systems biology also incorporates lipidomic approaches, which involve the study of structures, functions, interactions, and dynamics of cellular lipids.

PROPOSED BIOMARKERS FOR PSYCHIATRIC DISORDERS A number of promising biomarkers are currently under research for clinical use in the near future. Alzheimer’s Disease Any disease in which changes in protein concentrations or structures occur can potentially be a candidate for biomarker study using proteomic approaches. One such disease is Alzheimer’s. It is well known that extracellular ␤-amyloid (A␤) protein plaques and intracellular neurofibrillary tangles consisting of tau protein are pathological hallmarks of Alzheimer’s disease (AD). However, measuring these proteins has been clinically challenging. Another area that presents a challenge in clinical practice is the diagnosis of mild cognitive impairment (MCI) before it evolves to AD. Detection of a sub-population of patients with MCI who may progress to AD has been an area of interest for researchers and clinicians, because it may shed light on possible early preventive measures. The use of neuroimaging to detect, and thus predict, the hallmarks of the transition from MCI to AD has been extensively studied. Among subjects with MCI, hippocampal atrophy has been shown to predict a shorter time-to-progression.19 Although hippocampal volumetry has been widely studied in AD, no universally accepted standardized criteria currently exist for measures of hippocampal atrophy.20 The measurement of glucose metabolism by [18F]fluorodeoxyglucose uptake (FDG-PET) is a functional

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imaging technique that has also been used to predict cognitive decline or conversion from MCI to AD. In a multicenter study conducted by the Alzheimer’s Disease Neuroimaging Initiative, subjects with MCI who showed a pattern of hypometabolism in the temporal and parietal regions on FDG-PET were significantly more likely to convert to AD.21 Analysis of markers in CSF has also been investigated as a tool to assess the risk of progression from MCI to AD. CSF tau and ␤-amyloid1-42 (A␤1-42) have been proposed as potential biomarkers of MCI. In a multicenter study using proteomic approaches, Mattsson et al. analyzed the CSF of patients with MCI (n = 750), AD (n = 529), and healthy controls (n = 304). They concluded that total tau (T-tau) protein levels, phosphorylated tau (P-tau), and A␤1-42 levels can accurately predict the transition from MCI to AD.22 The clinical challenge in applying these findings, however, is that CSF is not as easily available as serum, particularly in the population affected by cognitive disorders. In another study, Hampel et al. measured CSF A␤142 and tau levels in 93 patients with probable AD, 52 patients with MCI, and 10 healthy controls.23 CSF samples were taken by lumbar puncture and levels of CSF markers were compared. The mean score on the Mini Mental Status Examination (MMSE) was 22.4 in the patients with AD, 28.9 in those with MCI, and 29.5 in the healthy controls. CSF A␤1-42 predicted conversion from MCI to AD with a sensitivity of 59% and a specificity of 100%. CSF tau yielded a sensitivity of 83% and a specificity of 90%. These findings suggest a high positive predictive power, particularly for CSF A␤1-42, in the group with MCI.23 Blood-based biomarkers have always been an area of interest for investigators, because blood is readily available through less invasive methods compared with CSF. In a 5-year prospective study that used lipidomic approaches, Mapstone et al. recently discovered and validated a set of ten lipids from peripheral blood that predicted conversion of healthy elderly individuals to amnestic MCI or AD with over 90% accuracy.24 The average time for conversion was 2.1 years (range 1–5 years). The cost of this test is significantly lower than those for other biomarkers; however, the results of this study still need to be verified in a larger population.24 Although research has produced advances in the field of biomarkers of dementia, further studies are needed.25 Many researchers are considering the pos-




sible use of a combination of different sets of biomarkers of AD because this would increase the likelihood of accurate diagnosis. Such endeavors will require close collaboration between academia and industry because they will involve the use of large sample sets and sophisticated high-throughput methods such as proteomics and genomics.26 Schizophrenia Over the past decade, schizophrenia has been an area of growing interest for proteomic research and biomarker discovery. The diagnosis of schizophrenia, according to the criteria in the DSM-III-R, published in 1987, required that no organic factors contributed to the initiation and maintenance of the disturbance.27 However, given findings from neurobiological studies, this criterion was dropped in the DSM-IV, published in 1994, opening a new window for biomarker studies.28 Recent advances in genetics, genomics, and proteomics provide ample opportunities to identify biomarkers of schizophrenia. Given the complexity of the disease, a variety of biomarkers, including biological markers, endophenotypes, and neuroimaging-based markers, are under investigation. Among biological markers, BDNF is one the most studied neurotrophic factors in the pathophysiology of schizophrenia. Neurotrophic factors are generally important molecular regulators of neuronal development and plasticity.29 BDNF has been shown to mediate long-term hippocampal potentiation associated with learning and memory.30 Decreased BDNF levels are found in the prefrontal cortex of patients with schizophrenia.5,31 It has been proposed that signaling mediated by BDNF and its receptor tyrosine kinase (TrkB) is important in the dysfunction of the prefrontal cortex gamma-aminobutyric acid (GABA) neurons in schizophrenia, likely contributing to cognitive dysfunction and deficits in working memory.32 In addition, BDNF has been the subject of recent investigation in the clinical entity known as the “first episode of psychosis.”33 The effect of medications on BDNF levels has been another area of tremendous interest. A meta-analysis of data from 7 studies provided evidence of a significant reduction in BDNF serum/plasma levels in drug-naïve patients with schizophrenia compared to healthy controls. The meta-analysis, however, showed no significant increase in BDNF levels after patients were started on antipsychotic medication,34


indicating that BDNF cannot serve as a reliable predictor of response. This meta-analysis also showed greater reduction in BDNF levels in patients with schizophrenia with increasing age.34 Finally, the main finding of reduced levels of BDNF in schizophrenia must be considered alongside findings of altered BDNF levels in several other psychiatric disorders,34 including affective disorders6,35,36 and neurodegenerative disease.37 Shimizu et al. found that serum BDNF levels were reduced in antidepressant-naive patients with depression. Their findings also suggested that antidepressant treatment might increase BDNF levels in depressed patients.6 Notably, levels of BDNF have been shown to be decreased in postmortem brain regions in patients with Alzheimer’s disease.38–40 This poses a significant specificity challenge in considering reduced BDNF as a biomarker of schizophrenia. Therefore, although BDNF may hold considerable promise as a candidate biomarker for schizophrenia and for further novel therapeutic strategies, many questions remain to be answered about the validity of measuring peripheral BDNF.41 Besides BDNF, other signature molecules such as apolipoproteins42 have been suggested as potential biomarkers of schizophrenia. Nevertheless, the diagnosis of schizophrenia currently remains clinical, since the proposed biomarkers suffer from a less than robust effect size and lack of specificity for utility in clinical practice.28 Depression Depression is another area of focus for proteomics research. Although our understanding of the pathophysiology of major depressive disorder (MDD) has advanced significantly, MDD has not yet been amenable to biomarker-assisted clinical care. Clinical approaches, especially when the first antidepressant trial fails, are mainly based on trial and error, with insufficient scientific data available on which to base further clinical decision-making. In the 1970s and 1980s, the dexamethasone suppression test (DST) appeared to have very promising implications for the diagnosis of MDD and clinical prediction of drug response and clinical relapse.14 The idea was that dexamethasone does not suppress plasma cortisol levels in depressed patients compared with normal subjects. However, further research, most notably based on data from the American Psychiatric

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Association Task Force on Laboratory Tests in Psychiatry,43 concluded that the test had limited clinical utility due to low sensitivity. The specificity of the DST is also challenging, since several medications (anticonvulsants and others), and some medical conditions, including weight loss and alcohol use, can produce false positive results. Moreover, a positive initial DST status in MDD does not predict the likelihood of antidepressant response.43 An increasing line of evidence suggests that inflammation may have an important role in the pathophysiology of MDD. Patients with major depression have been found to have elevated serum levels of inflammatory markers.44 Interleukin-6 (IL6) and tumor necrosis factor-␣ (TNF-␣) are among several inflammatory markers that have been found to be elevated in patients with MDD.45 Interestingly, antidepressant treatment has been shown to decrease levels of these markers.46 These findings indicate that TNF-␣ and IL-6 may be potential biomarkers for depression and treatment response. However, as would seem obvious, other inflammatory conditions can also produce elevations in these markers. Uher et al tested C-reactive protein (CRP), a commonly available marker of inflammation, to predict differential response to escitalopram (a serotonin reuptake inhibitor) and nortriptyline (a norepinephrine reuptake inhibitor).47 Their findings indicated that CRP may help in the selection of an antidepressant. Patients with low levels of CRP (< 1 mg/L) showed greater improvement with escitalopram than with nortriptyline, as evidenced by 3 point higher mean scores on the Montgomery-Åsberg Depression Rating Scale (MADRS). On the other hand, patients with higher CRP levels showed greater improvement with nortriptyline than with escitalopram, as evidenced by 3 point higher mean scores on the MADRS. The authors concluded that individuals with high levels of inflammation may benefit more from nortriptyline than from a selective serotonin reuptake inhibitor.47 BDNF6 and insulin-like growth factor-148,49 have also been proposed as biomarkers of MDD; however, their use is also limited by a lack of sensitivity and specificity.50 Suicide The neurobiology of suicide and the molecular changes that occur in the transition between suicidal

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ideation and suicidal attempt are also areas where incorporating biomarkers into clinical settings may help clinicians better screen for those at risk for suicide. Initial studies of biomarkers of suicide have focused on the levels of neurotransmitters and their metabolites in patients with suicidal behavior, with serotonin (5-HT) and its metabolite 5-hydroxyindoleacetic acid (5-HIAA) among the most studied. Several studies have observed lower plasma levels of 5-HT and 5-HIAA in patients who have made a suicide attempt compared with controls.51,52 SpreuxVaroquaux found a lower plasma 5-HIAA concentration was inversely associated with the degree of impulsivity in suicide attempters.51 Several studies have found low 5-HIAA levels in the CSF of suicidal patients.53–55 A meta-analysis by Lester found significantly lower levels of CSF 5HIAA in subjects who had made prior suicide attempts (especially those who used violent methods) compared to psychiatric controls.56 5-HT2A receptors in the platelets have also been studied in patients with suicidal behavior.57 Generally, the current evidence shows an upregulation in 5-HT2A receptors in the platelets of suicidal patients compared with non suicidal controls.52,58,59 While platelet 5-HT2A receptors and CSF 5-HIAA appear to have great potential utility as biomarkers of suicidal behavior, many other studies have yielded inconsistent results.58 The DST, in addition to its implications in depression, has been proposed as a biomarker for suicide. Several studies of the hypothalamic-pituitary-adrenal (HPA) axis have found that individuals who are non-suppressors on the DST are more likely to commit suicide.60,61 In a meta-analysis by Mann et al published in 2006, the odds of suicide completion were estimated to be 4.5-fold greater among non-suppressors on the DST compared with suppressors.62 In a historical cohort of depressed inpatients who were followed for nearly 20 years, Jokinen et al found that DST non-suppression predicted suicide in the suicide attempter group.63 The study failed to distinguish those at risk because there was no statistically significant difference in suicide risk between the suppressors and non-suppressors in the sample as a whole. In a recent review of a large range of neurobiological systems implicated in suicide, the authors concluded that multilevel HPA dysfunction, along with abnormalities in the serotonergic system, represents a top candidate as a biomarker.64




A family-based study found that first-degree relatives of suicide decedents exhibited blunted cortisol reactivity.65 To identify underlying genetic and epigenetic factors, genome-wide screening techniques were employed to identify novel epigenetic associations in the postmortem brain tissue of suicide decedents. The study found that SKA2 gene expression was significantly lower in suicide decedents. The authors proposed that SKA2 may be etiologically relevant to the HPA axis, as SKA2 plays a role in chaperoning the glucocorticoid receptor from the cytoplasm to the nucleus.66 BDNF is another potential biomarker that has been studied in suicide. Several lines of evidence show decreased levels of BDNF in the blood cells of suicidal patients as well as in patients who have committed suicide.67 Patients with MDD who had attempted suicide had significantly lower plasma levels of BDNF than either patients with MDD without suicide attempts or normal controls.68 Postmortem brain studies69 in suicidal patients with and without depression as well as genetic and epigentic70 studies have confirmed the association of suicidal behavior with decreases in BDNF levels.71 Overall, BDNF is a promising biomarker for identifying suicidal patients, but further research is needed before BDNF can be applied in clinical practice.72,73 Attention-Deficit/Hyperactivity Disorder Attention-deficit/hyperactivity disorder (ADHD) is another disease for which there is a growing interest in biomarker discovery. Reduced olfactory function is proposed as putative biomarker in ADHD74,75 along with other candidates including cortisol76 and platelet monoamine oxidase activity.76,77 The task force on biological markers of the World Federation of Societies of Biological Psychiatry (WFSBP) and the World Federation of ADHD recently developed a consensus report on potential biomarkers of ADHD. They concluded that, despite promising candidates, no reliable ADHD biomarker has been developed to date.75

CLINICALLY AVAILABLE BIOMARKERS IN PSYCHIATRY The following section reviews psychiatric biomarkers that have moved from research and are currently available for clinical purposes.


Biomarkers of Alcohol Use Two different set of biomarkers exist for alcohol drinking. We distinguish between biomarkers of longer term alcohol use, such as ␥-glutamyl transferase (GGT) and carbohydrate-deficient transferrin (CDT), which require several weeks or months of sustained alcohol drinking to be significantly elevated, and biomarkers of short-term alcohol use (ethyl sulfate, ethyl glucuronide), which are detectable for a few days following alcohol consumption and may provide important data from a clinical and forensic standpoint. Biomarkers of chronic alcohol use. The major biomarkers of alcohol use currently available are mean corpuscular volume (MCV), alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyltransferase (GGT), and carbohydrate-deficient transferrin (CDT). The majority of research on alcohol use biomarkers has recently focused on GGT and CDT because of their higher sensitivity and specificity. GGT is most often used as a marker of biliary stasis along with alkaline phosphatase (ALP). In addition to the biliary tract, GGT is also found in the spleen, kidneys, pancreas, heart, and brain. Chronic ethanol exposure causes cell inflammation and increases serum GGT.78 GGT measures the cumulative effect of alcohol consumption of four or more drinks a day for 4 to 8 weeks. In addition to heavy drinking, biliary disease, direct hepatocyte injury, obesity, and certain medications including anticonvulsants can increase GGT levels.78,79 Carbohydrate-deficient transferrin (CDT), another biomarker of alcohol use approved by the U.S. Food and Drug Administration (FDA) in 2001, shows greater specificity than GGT.79 Regular consumption of 60–80 g of ethanol/day (approximately 7–10 drink/day) for 1–2 weeks significantly raises the CDT.80 After abstinence, the serum CDT level usually normalizes within approximately 2–4 weeks.81 In addition to excessive alcohol drinking, end-stage liver disease, biliary cirrhosis, and a rare genetic variability will also elevate CDT.79 CDT is also suitable for monitoring alcohol cessation.81 Among the biomarkers of alcohol drinking discussed above, the CDT and GGT tests show the highest sensitivities, ranging from 65% to 73%. AST, ALT, and MCV have significantly lower sensitivities of

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50%, 35%, and 52%, respectively. CDT shows the greatest specificity at 92% compared with GGT, which has a lower specificity of 75%.79 Biomarkers of recent alcohol consumption. The biomarkers of alcohol consumption described in the preceding section are mainly used to detect heavy alcohol use over an extended period of time as they show end organ damage. To confirm recent alcohol consumption, the conventional way is to directly measure alcohol levels in the urine or serum. Laboratory tests that directly detect alcohol levels are important from both a clinical and forensic standpoint. Clinically, detection of alcohol as a proof of relapse may discharge a patient from treatment, and forensically, high risk behavior (such as reckless driving) under the influence of alcohol can have serious legal consequences.82 Direct measurement of alcohol only measures very recent (6–15 hours) alcohol consumption.83 Ethyl glucuronide (EtG) and Ethyl sulphate (EtS) are newly available alcohol biomarkers that can be used to confirm or rule out recent drinking with a longer detection window. EtG and EtS are conjugated ethanol metabolites formed after alcohol consumption. They are excreted in urine and remain detectable for several days (between 40 to 130 hours) after drinking.84 It should be noted that even a very small amount of alcohol, such as that found in mouth wash85 or hand sanitizers used topically86 can result in positive EtG levels in urine. Phosphatidylethanol (PEth), which is an abnormal phospholipid formed only in the presence of ethanol in the blood, is a promising new biomarker for ethanol abuse. Its high specificity and prolonged detectability suggest PEth in blood may be a useful biomarker for recent alcohol abuse.87 Research has found a high sensitivity and specificity of 94.5% and 100%, respectively, for PEth.88 In one study, a mean daily intake of 50 g of ethanol for 3 weeks resulted in detectable levels of PEth.89 The extended time window (up to 2 weeks) in which PEth may be detected after the last consumption of alcohol makes it a clinically and forensically important marker of alcohol abuse.87 Psychogenic Non-Epileptic Seizure Psychogenic non-epileptic seizure (PNES) is a complex diagnosis of exclusion that poses a marked challenge in clinical settings. Diagnosis of PNES requires

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an extensive work-up, including long-term video electroencephalogram. It has been suggested that prolactin may be a valuable biomarker to differentiate PNES from epileptic seizures.90,91 The first observation of increased prolactin levels after a seizure was reported by Ohman et al. in 1976 in patients receiving electroconvulsive therapy (ECT).92 It was widely known that ECT works by iatrogenically inducing a seizure, albeit under controlled conditions, in patients with refractory depression. Further research has shown that postictal elevation of prolactin is one of the most consistent findings after ECT, with the prolactin response to ECT diminishing over time. It is speculated that this elevation in prolactin could be related to a hypodopaminergic state caused by ECT.93 This finding prompted researchers to investigate prolactin as a marker of effective response to ECT; however, further data failed to show this association.94 Since it had been established that levels of prolactin increased after a seizure, it is not surprising that researchers began to examine the potential utility of prolacin in identifying PNES. Trimble reported that PNES does not increase the level of prolactin level.95 A pooled data analysis from the Therapeutics and Technology Assessment Subcommittee of the American Academy of Neurology in 2005 showed that elevated serum prolactin was highly predictive of either generalized tonic–clonic or complex partial seizures.96 The main drawback to using prolactin as a biomarker for seizures and to identify PNES is that other conditions that cause a loss of consciousness, in particular syncope, also elevate the level of prolactin. Thus, while a negative prolactin level reliably excludes a seizure, a positive result does not necessarily confirm that a seizure has occurred.96,97 Pooled sensitivity was 60.0% for generalized tonic–clonic seizures and 46.1% for complex partial seizures, while the pooled specificity was similar for both.96 The authors of the Subcommittee’s report suggested that elevated serum prolactin, when measured 10–20 minutes after a suspected event, is a useful biomarker for the differentiation of generalized tonic–clonic or complex partial seizure from PNES in adults and older children.96 In a meta-analysis of studies assessing the value of serum prolactin in the diagnosis of generalized tonicclonic seizures in patients presenting after a single episode of syncope, researchers found that a serum prolactin concentration greater than three times




baseline, if taken within 1 hour of syncope, was highly predictive of a generalized tonic-clonic seizure, but that a negative result did not necessarily exclude the possibility of a generalized tonic-clonic seizure.98

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CONCLUSION

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Patients with psychiatric disorders exhibit several neurobehavioral differences when compared to healthy people. However, these changes have not yet been translated into clinical tests for diagnosis and follow-up measures. This gap exists for several reasons, including the lack of biological gold standards, the evolving nature of the nosology of psychiatric disorders, the clinical overlap among these disorders, and the complexity of the nervous system. Although numerous studies have identified a number of promising candidates, the field of biomarkers for psychiatric diseases is still in its infancy and further research is needed. Nevertheless, tremendous progress has been made in proteomics and genomics, and opportunities for identifying novel biomarkers in neuropsychiatry are promising.14 Given the general trend toward molecular psychiatry and recent growing interest in proteomic and genomic methods, it is likely that biomarkers will become an integral part of psychiatric practice in the future.

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Pediatric anxiety disorders: from neuroscience to evidence-based clinical practice.

Literature, neurology, and neuroscience: neurological and psychiatric disorders. Preface.

Linking binocular vision neuroscience with clinical practice.

Behavioural treatment for psychiatric disorders in general practice: preliminary communication.

Genomic insights into the overlap between psychiatric disorders: implications for research and clinical practice.

Modern Microglia: Novel Targets in Psychiatric Neuroscience.

The identification of psychiatric disorders in children who fail to attend school: a cluster analysis of a non-clinical population.

Computational psychiatry as a bridge from neuroscience to clinical applications.

From clinical practice to academia.

Modern pain neuroscience in clinical practice: applied to post-cancer, paediatric and sports-related pain.

Psychiatric disorders: diagnosis to therapy.

Psychiatric disorders: A REDD line from stress to depression.

Modeling psychiatric disorders: from genomic findings to cellular phenotypes.

Acute postdisaster psychiatric disorders: identification of persons at risk.

Advances in the clinical practice of psychiatric nursing.

Social versus clinical prediction in minor psychiatric disorders.

Platelets in psychiatric disorders.

Applications of blood-based protein biomarker strategies in the study of psychiatric disorders.

Role of quetiapine beyond its clinical efficacy in bipolar disorder: From neuroprotection to the treatment of psychiatric disorders (Review).

'Indirect' challenges from science to clinical practice.

Parkinson's disease: from genetics to clinical practice.

Drugs of abuse and psychiatric disorders: neurobiological and clinical aspects.

Attaching community psychiatric nurses to general practice.

Psychiatric disorders: Linking genetic risk to pruning.