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

Chan, Man K.; Gottschalk, Michael G.; Haenisch, F.; Tomasik, Jakub; Ruland, Tillmann; Rahmoune, Hassan; Guest, Paul C.; Bahn, Sabine

doi:10.1016/j.pneurobio.2014.08.002

Cited by 84 publications

(72 citation statements)

References 520 publications

(507 reference statements)

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“…material, see www.karger.com/doi/10.1159/000441457). Second, we performed targeted MEDLINE searches for 52 emerging biomarkers published in recent comprehensive reviews [3,18] using the specific ‘biomarker name' and ‘meta-analysis' as search terms (see online suppl. table S1 for the full list of biomarkers).…”

Section: Methodsmentioning

confidence: 99%

Bias in Peripheral Depression Biomarkers

Carvalho

Köhler

Brunoni

et al. 2016

Psychother Psychosom

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Background: To aid in the differentiation of individuals with major depressive disorder (MDD) from healthy controls, numerous peripheral biomarkers have been proposed. To date, no comprehensive evaluation of the existence of bias favoring the publication of significant results or inflating effect sizes has been conducted. Methods: Here, we performed a comprehensive review of meta-analyses of peripheral nongenetic biomarkers that could discriminate individuals with MDD from nondepressed controls. PubMed/MEDLINE, EMBASE, and PsycINFO databases were searched through April 10, 2015. Results: From 15 references, we obtained 31 eligible meta-analyses evaluating biomarkers in MDD (21,201 cases and 78,363 controls). Twenty meta-analyses reported statistically significant effect size estimates. Heterogeneity was high (I² ≥50%) in 29 meta-analyses. We plausibly assumed that the true effect size for a meta-analysis would equal the one of its largest study. A significant summary effect size estimate was observed for 20 biomarkers. We observed an excess of statistically significant studies in 21 meta-analyses. The summary effect size of the meta-analysis was higher than the effect of its largest study in 25 meta-analyses, while 11 meta-analyses had evidence of small-study effects. Conclusions: Our findings suggest that there is an excess of studies with statistically significant results in the literature of peripheral biomarkers for MDD. The selective publication of ‘positive studies' and the selective reporting of outcomes are possible mechanisms. Effect size estimates of meta-analyses may be inflated in this literature.

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Section: Methodsmentioning

confidence: 99%

Bias in Peripheral Depression Biomarkers

Carvalho

Köhler

Brunoni

et al. 2016

Psychother Psychosom

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“…The overlap of 22 genes suggests that peripheral biomarkers of either SZ or BD may have some genetic component that could drive differential expression. This leaves open the possibility that a number of serum proteins [81-85] and transcripts will advance as biomarkers.…”

Section: Discussionmentioning

confidence: 99%

Exon Array Biomarkers for the Differential Diagnosis of Schizophrenia and Bipolar Disorder

et al. 2017

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This study developed potential blood-based biomarker tests for diagnosing and differentiating schizophrenia (SZ), bipolar disorder type I (BD), and normal control (NC) subjects using mRNA gene expression signatures. A total of 90 subjects (n = 30 each for the three groups of subjects) provided blood samples at two visits. The Affymetrix exon microarray was used to profile the expression of over 1.4 million probesets. We selected potential biomarker panels using the temporal stability of the probesets and also back-tested them at two different visits for each subject. The 18-gene biomarker panels, using logistic regression modeling, correctly differentiated the three groups of subjects with high accuracy across the two different clinical visits (83–88% accuracy). The results are also consistent with the actual data and the “leave-one-out” analyses, indicating that the models should be predictive when applied to independent data cohorts. Many of the SZ and BD subjects were taking antipsychotic and mood stabilizer medications at the time of blood draw, raising the possibility that these drugs could have affected some of the differential transcription signatures. Using an independent Illumina data set of gene expression data from antipsychotic medication-free SZ subjects, the 18-gene biomarker panels produced a receiver operating characteristic curve accuracy greater than 0.866 in patients that were less than 30 years of age and medication free. We confirmed select transcripts by quantitative PCR and the nCounter® System. The episodic nature of psychiatric disorders might lead to highly variable results depending on when blood is collected in relation to the severity of the disease/symptoms. We have found stable trait gene panel markers for lifelong psychiatric disorders that may have diagnostic utility in younger undiagnosed subjects where there is a critical unmet need. The study requires replication in subjects for ultimate proof of the utility of the differential diagnosis.

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“…Several studies suggest that future biomarkers will be used in panels including several markers of different sensitivity and specifi city [ 48 ]. Whether PACAP will indeed be a biomarker is a question to be answered by future studies, but based on our current knowledge it seems that PACAP is a good candidate for certain diseases and can be included in a panel of biomarkers after mapping the conditions where PACAP alterations are present or absent.…”

Section: Concluding Remarks: Future Perspectives and Limitationsmentioning

confidence: 99%

PACAP as a Potential Biomarker: Alterations of PACAP Levels in Human Physiological and Pathological Conditions

Reglődi

Helyes

Németh

et al. 2016

Current Topics in Neurotoxicity

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Pituitary adenylate cyclase activating polypeptide (PACAP) is a neuropeptide with wide distribution and diverse effects. Its presence has not only been revealed in the nervous system and peripheral organs, but also in the blood and other biological fl uids. Although PACAP is rapidly cleaved in the blood by peptidases, increasing number of evidence has been published in the last 10 years that show alterations of PACAP levels in different pathological conditions. The present review summarizes results obtained in the human blood and other fl uids under normal, physiological and pathological conditions with clinical relevance, such as pregnancy and delivery, as well as in several diseases. Most clinical data have been obtained so far in neurological diseases, such as Alzheimer's disease, migraine, traumatic brain injury, multiple sclerosis, and cerebral hemorrhage, but some other diseases have also been shown to be accompanied by alterations of PACAP levels. These results indicate that the altered levels may have diagnostic and/or prognostic values in several diseases.

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

Cited by 84 publications

References 520 publications

Bias in Peripheral Depression Biomarkers

Bias in Peripheral Depression Biomarkers

Exon Array Biomarkers for the Differential Diagnosis of Schizophrenia and Bipolar Disorder

PACAP as a Potential Biomarker: Alterations of PACAP Levels in Human Physiological and Pathological Conditions

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