Dataset Information

Identification of Distinct Psychosis Biotypes Using Brain-Based Biomarkers.

ABSTRACT: Clinical phenomenology remains the primary means for classifying psychoses despite considerable evidence that this method incompletely captures biologically meaningful differentiations. Rather than relying on clinical diagnoses as the gold standard, this project drew on neurobiological heterogeneity among psychosis cases to delineate subgroups independent of their phenomenological manifestations.A large biomarker panel (neuropsychological, stop signal, saccadic control, and auditory stimulation paradigms) characterizing diverse aspects of brain function was collected on individuals with schizophrenia, schizoaffective disorder, and bipolar disorder with psychosis (N=711), their first-degree relatives (N=883), and demographically comparable healthy subjects (N=278). Biomarker variance across paradigms was exploited to create nine integrated variables that were used to capture neurobiological variance among the psychosis cases. Data on external validating measures (social functioning, structural magnetic resonance imaging, family biomarkers, and clinical information) were collected.Multivariate taxometric analyses identified three neurobiologically distinct psychosis biotypes that did not respect clinical diagnosis boundaries. The same analysis procedure using clinical DSM diagnoses as the criteria was best described by a single severity continuum (schizophrenia worse than schizoaffective disorder worse than bipolar psychosis); this was not the case for biotypes. The external validating measures supported the distinctiveness of these subgroups compared with clinical diagnosis, highlighting a possible advantage of neurobiological versus clinical categorization schemes for differentiating psychotic disorders.These data illustrate how multiple pathways may lead to clinically similar psychosis manifestations, and they provide explanations for the marked heterogeneity observed across laboratories on the same biomarker variables when DSM diagnoses are used as the gold standard.

SUBMITTER: Clementz BA

PROVIDER: S-EPMC5314432 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

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Identification of Distinct Psychosis Biotypes Using Brain-Based Biomarkers.

Clementz Brett A BA Sweeney John A JA Hamm Jordan P JP Ivleva Elena I EI Ethridge Lauren E LE Pearlson Godfrey D GD Keshavan Matcheri S MS Tamminga Carol A CA

The American journal of psychiatry 20151207 4

<h4>Objective</h4>Clinical phenomenology remains the primary means for classifying psychoses despite considerable evidence that this method incompletely captures biologically meaningful differentiations. Rather than relying on clinical diagnoses as the gold standard, this project drew on neurobiological heterogeneity among psychosis cases to delineate subgroups independent of their phenomenological manifestations.<h4>Method</h4>A large biomarker panel (neuropsychological, stop signal, saccadic c ...[more]

PMID: 26651391

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Project description:BackgroundThe current definitions of psychotic illness lack biological validity, motivating alternative biomarker-driven disease entities. Building on experimental constructs-Biotypes-that were previously developed from cognitive and neurophysiologic measures, we contrast brain anatomy characteristics across Biotypes alongside conventional diagnoses, examining gray matter density (GMD) as an independent validator for the Biotypes.MethodsWhole brain GMD measures were examined in probands, their relatives, and healthy subjects organized by Biotype and then by DSM-IV-TR diagnosis (n = 1409) using voxel-based morphometry with subsequent subject-level regional characterization and distribution analyses.ResultsProbands grouped by Biotype versus healthy controls showed a stepwise pattern of GMD reductions as follows: Biotype1, extensive and diffusely distributed GMD loss, with the largest effects in frontal, anterior/middle cingulate cortex, and temporal regions; Biotype2, intermediate and more localized reductions, with the largest effects in insula and frontotemporal regions; and Biotype3, small reductions localized to anterior limbic regions. Relatives showed regionally distinct GMD reductions versus healthy controls, with primarily anterior (frontotemporal) effects in Biotype1; posterior (temporo-parieto-cerebellar) in Biotype2; and normal GMD in Biotype3. Schizophrenia and schizoaffective probands versus healthy controls showed overlapping GMD reductions, with the largest effects in frontotemporal and parietal regions; psychotic bipolar probands had small reductions, primarily in frontal regions. GMD changes in relatives followed regional patterns observed in probands, albeit less extensive. Biotypes showed stronger between-group separation based on GMD than the conventional diagnoses and were the strongest predictor of GMD change.ConclusionsGMD biomarkers depicted unique brain structure characteristics within Biotypes, consistent with their cognitive and sensorimotor profiles, and provided stronger discrimination for biologically driven biotypes than symptom-based diagnoses.

Project description:To better understand the relationship between peripheral blood- and brain-based epigenetic activity, we conducted a pilot study on captive baboons (Papio hamadryas) to investigate correlations between miRNA expression in peripheral blood mononuclear cells (PBMCs) and 14 different cortical and subcortical brain regions, represented by two study groups comprised of 4 and 6 animals. Using next-generation sequencing, we identified 362 miRNAs expressed at ≥10 read counts in 80% or more of the brain samples analyzed. Nominally significant pairwise correlations (one-sided P<0.05) between peripheral blood and mean brain expression levels of individual miRNAs were observed for 39 and 44 miRNAs in each group. When miRNA expression levels were averaged across animals within the groups, pairwise correlations between PBMCs and individual brain regions are all highly significant (P<2.2x10^-16), although correlations between brain regions are markedly stronger (rs=0.86-0.99) than those between blood and brain (rs=0.47-0.57). Principal component analysis revealed differentiation in miRNA expression between peripheral blood and the brain regions for the first component (accounting for ~75% of variance). Linear mixed effects modeling attributed most of the variance in expression to differences between miRNAs (>70%), with non-significant 7.5% and 13.1% assigned to differences between blood and brain-based samples in the two study groups. Hierarchical UPGMA clustering revealed two major co-expression branches that are evident in both groups, with one comprised of miRNAs hyper-expressed in blood relative to the brain samples, exhibiting an enrichment of miRNAs expressed in immune cells (CD14+, CD15+, CD19+, CD3+, and CD56+ leukocytes) among the top blood-brain correlates. In the other major branch, hypo-expression in the blood samples was observed, with enrichment for miRNAs associated with Alzheimer’s Disease among its top correlates. Although some differentiation was observed between tissue types, these preliminary findings reveal wider correlated patterns between blood- and brain-expressed miRNAs, suggesting the utility of blood-based miRNA profiling for investigating by proxy the epigenetic underpinnings of neurological and neuroinflammatory processes in the brain.

Dataset Information

Identification of Distinct Psychosis Biotypes Using Brain-Based Biomarkers.

Publications

Identification of Distinct Psychosis Biotypes Using Brain-Based Biomarkers.

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