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Special Issue "Biomarkers Guided Diagnosis and Therapy: Toward Precision Psychiatry"

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Neurobiology".

Deadline for manuscript submissions: closed (31 August 2020).

Special Issue Editor

Prof. Dr. Yong-Ku Kim
Website
Guest Editor

Special Issue Information

Dear Colleagues,

Biomarkers are an objective measurement of a normal biological process that can be used to predict or indicate the presence of a disease. The key biological substrates that are altered in psychiatric disorders (hormones, neurotransmitters, neurotrophins, cytokines, metabolic, genetic and neuroimaging markers) would be the possible biomarkers; however, there are no biological tests that are able to objectively predict the diagnosis of psychiatric disorders, and there are no validated biomarkers with high specificity and sensitivity that might facilitate early diagnosis and prevent misdiagnosis. Since psychiatric disorders is so heterogeneous and associated with different clinical presentation and different clinical symptoms, the development of a single biomarker is highly unlikely. Therefore, there is an unmet need to search for a validated set of different biomarkers or the complex multimarker panels that might be used to reliably and reproducibly predict and confirm the diagnosis for psychiatric disorders. Precision psychiatry’s ultimate goal is to identify the biomarkers that can be used in clinical practice and can guide psychiatrists in improving treatment outcomes and reducing side effects. The Special Issue presents an overview of the biological measures that may potentially serve as biomarkers in psychiatric disorders and discuss key challenges in their use and application in psychiatry.

Prof. Yong-Ku Kim
Guest Editor

Manuscript Submission Information

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Keywords

  • biomarker
  • pathophysiology
  • cause
  • diagnosis
  • treatment
  • biological marker
  • precision psychiatry

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Published Papers (10 papers)

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Research

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Open AccessArticle
Development and Technical Validation of an Immunoassay for the Detection of APP669–711 (Aβ−3–40) in Biological Samples
Int. J. Mol. Sci. 2020, 21(18), 6564; https://doi.org/10.3390/ijms21186564 - 08 Sep 2020
Abstract
The ratio of amyloid precursor protein (APP)669–711 (Aβ−3–40)/Aβ1–42 in blood plasma was reported to represent a novel Alzheimer’s disease biomarker. Here, we describe the characterization of two antibodies against the N-terminus of Aβ−3–x and the development and “fit-for-purpose” [...] Read more.
The ratio of amyloid precursor protein (APP)669–711 (Aβ−3–40)/Aβ1–42 in blood plasma was reported to represent a novel Alzheimer’s disease biomarker. Here, we describe the characterization of two antibodies against the N-terminus of Aβ−3–x and the development and “fit-for-purpose” technical validation of a sandwich immunoassay for the measurement of Aβ−3–40. Antibody selectivity was assessed by capillary isoelectric focusing immunoassay, Western blot analysis, and immunohistochemistry. The analytical validation addressed assay range, repeatability, specificity, between-run variability, impact of pre-analytical sample handling procedures, assay interference, and analytical spike recoveries. Blood plasma was analyzed after Aβ immunoprecipitation by a two-step immunoassay procedure. Both monoclonal antibodies detected Aβ−3–40 with no appreciable cross reactivity with Aβ1–40 or N-terminally truncated Aβ variants. However, the amyloid precursor protein was also recognized. The immunoassay showed high selectivity for Aβ−3–40 with a quantitative assay range of 22 pg/mL–7.5 ng/mL. Acceptable intermediate imprecision of the complete two-step immunoassay was reached after normalization. In a small clinical sample, the measured Aβ42/Aβ−3–40 and Aβ42/Aβ40 ratios were lower in patients with dementia of the Alzheimer’s type than in other dementias. In summary, the methodological groundwork for further optimization and future studies addressing the Aβ42/Aβ−3–40 ratio as a novel biomarker candidate for Alzheimer’s disease has been set. Full article
(This article belongs to the Special Issue Biomarkers Guided Diagnosis and Therapy: Toward Precision Psychiatry)
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Open AccessArticle
Genetic Markers for Later Remission in Response to Early Improvement of Antidepressants
Int. J. Mol. Sci. 2020, 21(14), 4884; https://doi.org/10.3390/ijms21144884 - 10 Jul 2020
Abstract
Planning subsequent treatment strategies based on early responses rather than waiting for delayed antidepressant action can be helpful. We identified genetic markers for later non-remission in patients exhibiting poor early improvement using whole-exome sequencing data of depressive patients treated in a naturalistic manner. [...] Read more.
Planning subsequent treatment strategies based on early responses rather than waiting for delayed antidepressant action can be helpful. We identified genetic markers for later non-remission in patients exhibiting poor early improvement using whole-exome sequencing data of depressive patients treated in a naturalistic manner. Among 1000 patients, early improvement at 2 weeks (reduction in Hamilton Depression Rating Scale [HAM-D] score ≥ 20%) and remission at 12 weeks (HAM-D score ≤ 7) were evaluated. Gene- and variant-level analyses were conducted to compare patients who did not exhibit early improvement and did not eventually achieve remission (n = 126) with those who exhibited early improvement and achieved remission (n = 385). Genes predicting final non-remission in patients who exhibited poor early improvement (COMT, PRNP, BRPF3, SLC25A40, and CGREF1 in males; PPFIBPI, LZTS3, MEPCE, MAP1A, and PFAS in females; ST3GAL5 in the total population) were determined. Among the significant genes, variants in the PRNP (rs1800014), COMT (rs6267), BRPF3 (rs200565609), and SLC25A40 genes (rs3213633) were identified. However, interpretations should be made cautiously, as complex pharmacotherapy involves various genes and pathways. Early detection of poor early improvement and final non-remission based on genetic risk would be helpful for decision-making in a clinical setting. Full article
(This article belongs to the Special Issue Biomarkers Guided Diagnosis and Therapy: Toward Precision Psychiatry)
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Open AccessArticle
Gender Differences in Developing Biomarker-Based Major Depressive Disorder Diagnostics
Int. J. Mol. Sci. 2020, 21(9), 3039; https://doi.org/10.3390/ijms21093039 - 25 Apr 2020
Abstract
The identification of biomarkers associated with major depressive disorder (MDD) holds great promise to develop an objective laboratory test. However, current biomarkers lack discriminative power due to the complex biological background, and not much is known about the influence of potential modifiers such [...] Read more.
The identification of biomarkers associated with major depressive disorder (MDD) holds great promise to develop an objective laboratory test. However, current biomarkers lack discriminative power due to the complex biological background, and not much is known about the influence of potential modifiers such as gender. We first performed a cross-sectional study on the discriminative power of biomarkers for MDD by investigating gender differences in biomarker levels. Out of 28 biomarkers, 21 biomarkers were significantly different between genders. Second, a novel statistical approach was applied to investigate the effect of gender on MDD disease classification using a panel of biomarkers. Eleven biomarkers were identified in men and eight in women, three of which were active in both genders. Gender stratification caused a (non-significant) increase of Area Under Curve (AUC) for men (AUC = 0.806) and women (AUC = 0.807) compared to non-stratification (AUC = 0.739). In conclusion, we have shown that there are differences in biomarker levels between men and women which may impact accurate disease classification of MDD when gender is not taken into account. Full article
(This article belongs to the Special Issue Biomarkers Guided Diagnosis and Therapy: Toward Precision Psychiatry)
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Open AccessArticle
Structural Changes in Hippocampal Subfields in Patients with Continuous Remission of Drug-Naive Major Depressive Disorder
Int. J. Mol. Sci. 2020, 21(9), 3032; https://doi.org/10.3390/ijms21093032 - 25 Apr 2020
Cited by 1
Abstract
Objective: Hippocampal volume is reduced in patients with major depressive disorder (MDD) compared with healthy controls. The hippocampus is a limbic structure that has a critical role in MDD. The aim of the present study was to investigate the changes in the volume [...] Read more.
Objective: Hippocampal volume is reduced in patients with major depressive disorder (MDD) compared with healthy controls. The hippocampus is a limbic structure that has a critical role in MDD. The aim of the present study was to investigate the changes in the volume of the hippocampus and its subfields in MDD patients who responded to antidepressants and subsequently were in continuous remission. Subjects and Methods: Eighteen patients who met the following criteria were enrolled in the present study: the DSM-IV-TR criteria for MDD, drug-naïve at least 8 weeks or more, scores on the 17-items of Hamilton Rating Scale for Depression (HAMD) of 14 points or more, and antidepressant treatment response within 8 weeks and continuous remission for at least 6 months. All participants underwent T1-weighted structural MRI and were treated with antidepressants for more than 8 weeks. We compared the volumes of the hippocampus, including its subfields, in responders at baseline to the volumes at 6 months. The volumes of the whole hippocampus and the hippocampal subfields were measured using FreeSurfer v6.0. Results: The volumes of the left cornu Ammonis (CA) 3 (p = 0.016) and the granule cell layer of the dentate gyrus (GC-DG) region (p = 0.021) were significantly increased after 6 months of treatment compared with those at baseline. Conclusions: Increases in volume was observed in MDD patients who were in remission for at least 6 months. Full article
(This article belongs to the Special Issue Biomarkers Guided Diagnosis and Therapy: Toward Precision Psychiatry)
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Review

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Open AccessReview
Digital Phenotyping in Bipolar Disorder: Which Integration with Clinical Endophenotypes and Biomarkers?
Int. J. Mol. Sci. 2020, 21(20), 7684; https://doi.org/10.3390/ijms21207684 - 16 Oct 2020
Abstract
Bipolar disorder (BD) is a complex neurobiological disorder characterized by a pathologic mood swing. Digital phenotyping, defined as the ‘moment-by-moment quantification of the individual-level human phenotype in its own environment’, represents a new approach aimed at measuring the human behavior and may theoretically [...] Read more.
Bipolar disorder (BD) is a complex neurobiological disorder characterized by a pathologic mood swing. Digital phenotyping, defined as the ‘moment-by-moment quantification of the individual-level human phenotype in its own environment’, represents a new approach aimed at measuring the human behavior and may theoretically enhance clinicians’ capability in early identification, diagnosis, and management of any mental health conditions, including BD. Moreover, a digital phenotyping approach may easily introduce and allow clinicians to perform a more personalized and patient-tailored diagnostic and therapeutic approach, in line with the framework of precision psychiatry. The aim of the present paper is to investigate the role of digital phenotyping in BD. Despite scarce literature published so far, extremely heterogeneous methodological strategies, and limitations, digital phenotyping may represent a grounding research and clinical field in BD, by owning the potentialities to quickly identify, diagnose, longitudinally monitor, and evaluating clinical response and remission to psychotropic drugs. Finally, digital phenotyping might potentially constitute a possible predictive marker for mood disorders. Full article
(This article belongs to the Special Issue Biomarkers Guided Diagnosis and Therapy: Toward Precision Psychiatry)
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Open AccessReview
Metabolomics in Sleep, Insomnia and Sleep Apnea
Int. J. Mol. Sci. 2020, 21(19), 7244; https://doi.org/10.3390/ijms21197244 - 30 Sep 2020
Abstract
Sleep-wake disorders are highly prevalent disorders, which can lead to negative effects on cognitive, emotional and interpersonal functioning, and can cause maladaptive metabolic changes. Recent studies support the notion that metabolic processes correlate with sleep. The study of metabolite biomarkers (metabolomics) in a [...] Read more.
Sleep-wake disorders are highly prevalent disorders, which can lead to negative effects on cognitive, emotional and interpersonal functioning, and can cause maladaptive metabolic changes. Recent studies support the notion that metabolic processes correlate with sleep. The study of metabolite biomarkers (metabolomics) in a large-scale manner offers unique opportunities to provide insights into the pathology of diseases by revealing alterations in metabolic pathways. This review aims to summarize the status of metabolomic analyses-based knowledge on sleep disorders and to present knowledge in understanding the metabolic role of sleep in psychiatric disorders. Overall, findings suggest that sleep-wake disorders lead to pronounced alterations in specific metabolic pathways, which might contribute to the association of sleep disorders with other psychiatric disorders and medical conditions. These alterations are mainly related to changes in the metabolism of branched-chain amino acids, as well as glucose and lipid metabolism. In insomnia, alterations in branched-chain amino acid and glucose metabolism were shown among studies. In obstructive sleep apnea, biomarkers related to lipid metabolism seem to be of special importance. Future studies are needed to examine severity, subtypes and treatment of sleep-wake disorders in the context of metabolite levels. Full article
(This article belongs to the Special Issue Biomarkers Guided Diagnosis and Therapy: Toward Precision Psychiatry)
Open AccessReview
Neuroinflammation-Associated Alterations of the Brain as Potential Neural Biomarkers in Anxiety Disorders
Int. J. Mol. Sci. 2020, 21(18), 6546; https://doi.org/10.3390/ijms21186546 - 07 Sep 2020
Abstract
Stress-induced changes in the immune system, which lead to neuroinflammation and consequent brain alterations, have been suggested as possible neurobiological substrates of anxiety disorders, with previous literature predominantly focusing on panic disorder, agoraphobia, and generalized anxiety disorder, among the anxiety disorders. Anxiety disorders [...] Read more.
Stress-induced changes in the immune system, which lead to neuroinflammation and consequent brain alterations, have been suggested as possible neurobiological substrates of anxiety disorders, with previous literature predominantly focusing on panic disorder, agoraphobia, and generalized anxiety disorder, among the anxiety disorders. Anxiety disorders have frequently been associated with chronic stress, with chronically stressful situations being reported to precipitate the onset of anxiety disorders. Also, chronic stress has been reported to lead to hypothalamic–pituitary–adrenal axis and autonomic nervous system disruption, which may in turn induce systemic proinflammatory conditions. Preliminary evidence suggests anxiety disorders are also associated with increased inflammation. Systemic inflammation can access the brain, and enhance pro-inflammatory cytokine levels that have been shown to precipitate direct and indirect neurotoxic effects. Prefrontal and limbic structures are widely reported to be influenced by neuroinflammatory conditions. In concordance with these findings, various imaging studies on panic disorder, agoraphobia, and generalized anxiety disorder have reported alterations in structure, function, and connectivity of prefrontal and limbic structures. Further research is needed on the use of inflammatory markers and brain imaging in the early diagnosis of anxiety disorders, along with the possible efficacy of anti-inflammatory interventions on the prevention and treatment of anxiety disorders. Full article
(This article belongs to the Special Issue Biomarkers Guided Diagnosis and Therapy: Toward Precision Psychiatry)
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Open AccessReview
Metabolomic Biomarkers in Anxiety Disorders
Int. J. Mol. Sci. 2020, 21(13), 4784; https://doi.org/10.3390/ijms21134784 - 06 Jul 2020
Abstract
Anxiety disorders range among the most prevalent psychiatric disorders and belong to the leading disorders in the study of the total global burden of disease. Anxiety disorders are complex conditions, with not fully understood etiological mechanisms. Numerous factors, including psychological, genetic, biological, and [...] Read more.
Anxiety disorders range among the most prevalent psychiatric disorders and belong to the leading disorders in the study of the total global burden of disease. Anxiety disorders are complex conditions, with not fully understood etiological mechanisms. Numerous factors, including psychological, genetic, biological, and chemical factors, are thought to be involved in their etiology. Although the diagnosis of anxiety disorders is constantly evolving, diagnostic manuals rely on symptom lists, not on objective biomarkers and treatment effects are small to moderate. The underlying biological factors that drive anxiety disorders may be better suited to serve as biomarkers for guiding personalized medicine, as they are objective and can be measured externally. Therefore, the incorporation of novel biomarkers into current clinical methods might help to generate a classification system for anxiety disorders that can be linked to the underlying dysfunctional pathways. The study of metabolites (metabolomics) in a large-scale manner shows potential for disease diagnosis, for stratification of patients in a heterogeneous patient population, for monitoring therapeutic efficacy and disease progression, and for defining therapeutic targets. All of these are important properties for anxiety disorders, which is a multifactorial condition not involving a single-gene mutation. This review summarizes recent investigations on metabolomics studies in anxiety disorders. Full article
(This article belongs to the Special Issue Biomarkers Guided Diagnosis and Therapy: Toward Precision Psychiatry)
Open AccessReview
d-glutamate and Gut Microbiota in Alzheimer’s Disease
Int. J. Mol. Sci. 2020, 21(8), 2676; https://doi.org/10.3390/ijms21082676 - 11 Apr 2020
Cited by 3
Abstract
Background: An increasing number of studies have shown that the brain–gut–microbiota axis may significantly contribute to Alzheimer’s disease (AD) pathogenesis. Moreover, impaired memory and learning involve the dysfunction neurotransmission of glutamate, the agonist of the N-methyl-d-aspartate receptor and a major [...] Read more.
Background: An increasing number of studies have shown that the brain–gut–microbiota axis may significantly contribute to Alzheimer’s disease (AD) pathogenesis. Moreover, impaired memory and learning involve the dysfunction neurotransmission of glutamate, the agonist of the N-methyl-d-aspartate receptor and a major excitatory neurotransmitter in the brain. This systematic review aimed to summarize the current cutting-edge research on the gut microbiota and glutamate alterations associated with dementia. Methods: PubMed, the Cochrane Collaboration Central Register of Controlled Clinical Trials, and Cochrane Systematic Reviews were reviewed for all studies on glutamate and gut microbiota in dementia published up until Feb 2020. Results: Several pilot studies have reported alterations of gut microbiota and metabolites in AD patients and other forms of dementia. Gut microbiota including Bacteroides vulgatus and Campylobacter jejuni affect glutamate metabolism and decrease the glutamate metabolite 2-keto-glutaramic acid. Meanwhile, gut bacteria with glutamate racemase including Corynebacterium glutamicum, Brevibacterium lactofermentum, and Brevibacterium avium can convert l-glutamate to d-glutamate. N-methyl-d-aspartate glutamate receptor (NMDAR)-enhancing agents have been found to potentially improve cognition in AD or Parkinson’s disease patients. These findings suggest that d-glutamate (d-form glutamate) metabolized by the gut bacteria may influence the glutamate NMDAR and cognitive function in dementia patients. Conclusions: Gut microbiota and glutamate are potential novel interventions to be developed for dementia. Exploring comprehensive cognitive functions in animal and human trials with glutamate-related NMDAR enhancers are warranted to examine d-glutamate signaling efficacy in gut microbiota in patients with AD and other neurodegenerative dementias. Full article
(This article belongs to the Special Issue Biomarkers Guided Diagnosis and Therapy: Toward Precision Psychiatry)
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Open AccessReview
Neuroimaging Biomarkers for Predicting Treatment Response and Recurrence of Major Depressive Disorder
Int. J. Mol. Sci. 2020, 21(6), 2148; https://doi.org/10.3390/ijms21062148 - 20 Mar 2020
Abstract
The acute treatment duration for major depressive disorder (MDD) is 8 weeks or more. Treatment of patients with MDD without predictors of treatment response and future recurrence presents challenges and clinical problems to patients and physicians. Recently, many neuroimaging studies have been published [...] Read more.
The acute treatment duration for major depressive disorder (MDD) is 8 weeks or more. Treatment of patients with MDD without predictors of treatment response and future recurrence presents challenges and clinical problems to patients and physicians. Recently, many neuroimaging studies have been published on biomarkers for treatment response and recurrence of MDD using various methods such as brain volumetric magnetic resonance imaging (MRI), functional MRI (resting-state and affective tasks), diffusion tensor imaging, magnetic resonance spectroscopy, near-infrared spectroscopy, and molecular imaging (i.e., positron emission tomography and single photon emission computed tomography). The results have been inconsistent, and we hypothesize that this could be due to small sample size; different study design, including eligibility criteria; and differences in the imaging and analysis techniques. In the future, we suggest a more sophisticated research design, larger sample size, and a more comprehensive integration including genetics to establish biomarkers for the prediction of treatment response and recurrence of MDD. Full article
(This article belongs to the Special Issue Biomarkers Guided Diagnosis and Therapy: Toward Precision Psychiatry)
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