Trait Biomarkers Research Articles

First onset depression and the hypothalamic-pituitary-adrenal axis: an investigation of state and trait biomarkers

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Discovery and validation of blood biomarkers for suicidality

Suicides are a leading cause of death in psychiatric patients, and in society at large. Developing more quantitative and objective ways (biomarkers) for predicting and tracking suicidal states would have immediate practical applications and positive societal implications. We undertook such an endeavor. First, building on our previous blood biomarker work in mood disorders and psychosis, we decided to identify blood gene expression biomarkers for suicidality, looking at differential expression of genes in the blood of subjects with a major mood disorder (bipolar disorder), a high-risk population prone to suicidality. We compared no suicidal ideation (SI) states and high SI states using a powerful intrasubject design, as well as an intersubject case–case design, to generate a list of differentially expressed genes. Second, we used a comprehensive Convergent Functional Genomics (CFG) approach to identify and prioritize from the list of differentially expressed gene biomarkers of relevance to suicidality. CFG integrates multiple independent lines of evidence—genetic and functional genomic data—as a Bayesian strategy for identifying and prioritizing findings, reducing the false-positives and false-negatives inherent in each individual approach. Third, we examined whether expression levels of the blood biomarkers identified by us in the live bipolar subject cohort are actually altered in the blood in an age-matched cohort of suicide completers collected from the coroner's office, and report that 13 out of the 41 top CFG scoring biomarkers (32%) show step-wise significant change from no SI to high SI states, and then to the suicide completers group. Six out of them (15%) remained significant after strict Bonferroni correction for multiple comparisons. Fourth, we show that the blood levels of SAT1 (spermidine/spermine N1–acetyltransferase 1), the top biomarker identified by us, at the time of testing for this study, differentiated future as well as past hospitalizations with suicidality, in a live cohort of bipolar disorder subjects, and exhibited a similar but weaker pattern in a live cohort of psychosis (schizophrenia/schizoaffective disorder) subjects. Three other (phosphatase and tensin homolog (PTEN), myristoylated alanine-rich protein kinase C substrate (MARCKS), and mitogen-activated protein kinase kinase kinase 3 (MAP3K3)) of the six biomarkers that survived Bonferroni correction showed similar but weaker effects. Taken together, the prospective and retrospective hospitalization data suggests SAT1, PTEN, MARCKS and MAP3K3 might be not only state biomarkers but trait biomarkers as well. Fifth, we show how a multi-dimensional approach using SAT1 blood expression levels and two simple visual-analog scales for anxiety and mood enhances predictions of future hospitalizations for suicidality in the bipolar cohort (receiver-operating characteristic curve with area under the curve of 0.813). Of note, this simple approach does not directly ask about SI, which some individuals may deny or choose not to share with clinicians. Lastly, we conducted bioinformatic analyses to identify biological pathways, mechanisms and medication targets. Overall, suicidality may be underlined, at least in part, by biological mechanisms related to stress, inflammation and apoptosis.

Plasma amyloid beta measurements - a desired but elusive Alzheimer's disease biomarker

Cerebrospinal fluid and positron emission tomography biomarkers accurately predict an underlying Alzheimer's disease (AD) pathology; however, they represent either invasive or expensive diagnostic tools. Therefore, a blood-based biomarker like plasma amyloid beta (Aβ) that could correlate with the underlying AD pathology and serve as a prognostic biomarker or an AD screening strategy is urgently needed as a cost-effective and non-invasive diagnostic tool. In this paper we review the demographic, biologic, genetic and technical aspects that affect plasma Aβ levels. Findings of cross-sectional and longitudinal studies of plasma Aβ, including autosomal dominant AD cases, sporadic AD cases, Down syndrome cases and population studies, are also discussed. Finally, we review the association between cerebrovascular disease and Aβ plasma levels and the responses observed in clinical trials. Based on our review of the current literature on plasma Aβ, we conclude that further clinical research and assay development are needed before measures of plasma Aβ can be interpreted so they can be applied as trait, risk or state biomarkers for AD.

Resting-state quantitative electroencephalography reveals increased neurophysiologic connectivity in depression.

Symptoms of Major Depressive Disorder (MDD) are hypothesized to arise from dysfunction in brain networks linking the limbic system and cortical regions. Alterations in brain functional cortical connectivity in resting-state networks have been detected with functional imaging techniques, but neurophysiologic connectivity measures have not been systematically examined. We used weighted network analysis to examine resting state functional connectivity as measured by quantitative electroencephalographic (qEEG) coherence in 121 unmedicated subjects with MDD and 37 healthy controls. Subjects with MDD had significantly higher overall coherence as compared to controls in the delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), and beta (12–20 Hz) frequency bands. The frontopolar region contained the greatest number of “hub nodes” (surface recording locations) with high connectivity. MDD subjects expressed higher theta and alpha coherence primarily in longer distance connections between frontopolar and temporal or parietooccipital regions, and higher beta coherence primarily in connections within and between electrodes overlying the dorsolateral prefrontal cortical (DLPFC) or temporal regions. Nearest centroid analysis indicated that MDD subjects were best characterized by six alpha band connections primarily involving the prefrontal region. The present findings indicate a loss of selectivity in resting functional connectivity in MDD. The overall greater coherence observed in depressed subjects establishes a new context for the interpretation of previous studies showing differences in frontal alpha power and synchrony between subjects with MDD and normal controls. These results can inform the development of qEEG state and trait biomarkers for MDD.

Evaluation of state and trait biomarkers in healthy volunteers for the development of novel drug treatments in schizophrenia

Antipsychotic drugs are the mainstay of treatment for schizophrenia but they have little effect on core negative symptoms or cognitive impairment. To meet the deficiencies of current treatments, novel potential compounds are emerging from preclinical research but translation to clinical success has been poor. This article evaluates the possibility that cognitive and physiological abnormalities in schizophrenia can be used as central nervous system biomarkers to predict, in healthy volunteers, the likely efficacy of entirely new pharmacological approaches to treatment. Early detection of efficacy would focus resource on rapidly developing, effective drugs. We review the relevance of selected cognitive and physiological abnormalities as biomarkers in schizophrenia and three of its surrogate populations: (i) healthy volunteers with high trait schizotypy; (ii) unaffected relatives of patients; and (iii) healthy volunteers in a state of cortical glutamate disinhibition induced by low-dose ketamine. Several biomarkers are abnormal in these groups and in some instances there has been exploratory work to determine their sensitivity to drug action. They are generally insensitive to current antipsychotics and therefore their predictive validity cannot be established until novel, therapeutically useful drugs are discovered. Until then such biomarker studies can provide evidence of drugs engaging with the mechanism of interest and encouragement of the concept.

New visual information processing abnormality biomarkers for the diagnosis of schizophrenia

INTRODUCTION: Schizophrenia is currently diagnosed on the basis of patient reports and clinical observations. A diagnosis based on aetiology is inherently more reliable due to being closer to the disease process than the overt clinical manifestations. Accordingly, recent research in schizophrenia has focused on the development of biomarkers in a bit to improve the reliability and neurobiological relevance of the diagnosis. Visual information processing is one of these promising fields of recent biomarker research. AREAS COVERED: This article provides an overview of the available literature regarding deficits in schizophrenia detectable through psychophysical (contrast and motion sensitivity, visual backward-masking), ERP (P1 and N1 visual evoked potentials) and oscillatory (signal power and phase-locking factor of evoked oscilations) measures and their validity as trait or state biomarkers of the disease. The methodology included a search on articles related to visual information processing in schizophrenia on the PubMed database. EXPERT OPINION: Biomarker research in schizophrenia is a rapidly expanding area. Evidence exists to suggest that both psychotic and manic symptoms are associated with visual processing abnormalities. A specific impairment confined to the magnocellular component of the visual system might be a trait biomarker of schizophrenia.

Therapeutic directions for Parkinson's disease

The focus on disease-modifying treatments and cures for Parkinson's disease (PD) has raised expectations for quantum leaps and overshadowed incremental gains that have been slowly achieved. Large multi-center clinical trials such as DATATOP and PRECEPT keep on generating new knowledge that is relevant to clinical care as well as experimental therapeutics. The largely unforeseen relationship between circulating uric acid and the occurrence and progression of PD was developed and confirmed in these clinical trials. Systematic follow-up of clinical trial cohorts after conclusion of the interventional phase provides added value that continues to inform about natural history, state and trait biomarkers, and genotype-phenotype relationships. These efforts are enhanced by data mining, public reporting, and timely sharing of data and biological samples.

Connecting the Brain and New Drug Targets for Schizophrenia

One thing we know for certain after decades of functional imaging in schizophrenia is that it is not a disorder that can simply be attributed to circumscribed lesions in the brain. It is, in other words, a disorder of the connectivity of the brain. In this overview, we will consider the power of connectivity analyses of functional MRI (and PET) data as tools for translational neuroscience. We describe the patterns of functional and effective disconnectivity seen in schizophrenia and particular psychotic symptoms, those that appear to be attributable to genetic and/or environmental risk factors for psychosis, the potential of these disconnectivities as trait and state biomarkers, and their sensitivity to drug effects. We conclude that substantial work needs to be done on standardising connectivity analyses across laboratories and that disconnectivity studies should be an integral part of drug discovery programmes.

Quantifying the risk for alcohol‐use and alcohol‐attributable health disorders: Present findings and future research needs

The aim of the present review was to: (i) highlight epidemiological and other studies that have generated important data on the harmful patterns of drinking that increase the risk for chronic diseases, including alcohol dependence, and on the mechanisms by which alcohol produces and, in some instances, may protect against damage; and (ii) discuss a conceptual basis for quantifying risk criteria for alcohol-induced chronic disease based on the quantity, frequency, and pattern of drinking. The relationship between heavy drinking and risk for adverse health conditions such as alcoholic liver disease (ALD), dementia, and alcohol dependence is well known. However, not everyone who drinks chronically develops ALD or dementia, and the major risk factors for disease development and the mechanisms by which this occurs have remained unclear. Large-scale, general population-based studies have provided the evidence by which quantifying the frequency of a pattern of high-risk drinking can be related directly to risk and the severity of alcohol dependence. Cellular and molecular biology studies have identified the major pathways of alcohol metabolism and how genetics and the environment can interact in some individuals to further increase the risk of organ damage. Extant databases should allow scientists and clinicians jointly to develop the framework for quantifying the drinking patterns that increase the risk of alcohol-induced organ pathologies, to develop clinical practice guidelines, such as those used to diagnose other common complex diseases (e.g. diabetes and hypertension), and to propose future studies for refining such guidelines. Attention must be paid to comorbid conditions such as hepatitis B and C infections, HIV, obesity, and environmental exposures other than alcohol. Developing trait and state biomarkers is critical to the process of discovery and to fulfilling the promise of personalized medicine.