Glucose dysregulation in antipsychotic-naive first-episode psychosis: in silico exploration of gene expression signatures

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作者
Jiwon Lee
Xiangning Xue
Emily Au
William B. McIntyre
Roshanak Asgariroozbehani
Kristoffer Panganiban
George C. Tseng
Maria Papoulias
Emily Smith
Jonathan Monteiro
Divia Shah
Kateryna Maksyutynska
Samantha Cavalier
Emril Radoncic
Femin Prasad
Sri Mahavir Agarwal
Robert Mccullumsmith
Zachary Freyberg
Ryan W. Logan
Margaret K. Hahn
机构
[1] University of Toronto,Institute of Medical Science
[2] Centre for Addiction and Mental Health,Department of Biostatistics, Graduate School of Public Health
[3] University of Pittsburgh,Department of Pharmacology and Toxicology
[4] University of Toronto,Department of Psychiatry
[5] University of Pittsburgh,Department of Neurobiology
[6] University of Massachusetts Chan Medical School,Department of Neurosciences
[7] University of Toledo,Department of Cell Biology
[8] ProMedica,Department of Psychiatry
[9] University of Pittsburgh,Department of Pharmacology, Physiology & Biophysics
[10] University of Massachusetts Chan Medical School,Department of Psychiatry
[11] Boston University School of Medicine,undefined
[12] University of Toronto,undefined
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Antipsychotic (AP)-naive first-episode psychosis (FEP) patients display early dysglycemia, including insulin resistance and prediabetes. Metabolic dysregulation may therefore be intrinsic to psychosis spectrum disorders (PSDs), independent of the metabolic effects of APs. However, the potential biological pathways that overlap between PSDs and dysglycemic states remain to be identified. Using meta-analytic approaches of transcriptomic datasets, we investigated whether AP-naive FEP patients share overlapping gene expression signatures with non-psychiatrically ill early dysglycemia individuals. We meta-analyzed peripheral transcriptomic datasets of AP-naive FEP patients and non-psychiatrically ill early dysglycemia subjects to identify common gene expression signatures. Common signatures underwent pathway enrichment analysis and were then used to identify potential new pharmacological compounds via Integrative Library of Integrated Network-Based Cellular Signatures (iLINCS). Our search results yielded 5 AP-naive FEP studies and 4 early dysglycemia studies which met inclusion criteria. We discovered that AP-naive FEP and non-psychiatrically ill subjects exhibiting early dysglycemia shared 221 common signatures, which were enriched for pathways related to endoplasmic reticulum stress and abnormal brain energetics. Nine FDA-approved drugs were identified as potential drug treatments, of which the antidiabetic metformin, the first-line treatment for type 2 diabetes, has evidence to attenuate metabolic dysfunction in PSDs. Taken together, our findings support shared gene expression changes and biological pathways associating PSDs with dysglycemic disorders. These data suggest that the pathobiology of PSDs overlaps and potentially contributes to dysglycemia. Finally, we find that metformin may be a potential treatment for early metabolic dysfunction intrinsic to PSDs.
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