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Kesava Asam,1 Kimberly A Lewis,2 Kord Kober,2,3 Xingyue Gong,2 Alka M Kanaya,4,5 Bradley E Aouizerat,1,6 Elena Flowers2,7 1Bluestone Center for Clinical Research, New York University, New York City, NY, USA; 2Department of Physiological Nursing, University of California, San Francisco, CA, USA; 3Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA; 4Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA; 5Department of Medicine, University of California, San Francisco, CA, USA; 6Department of Oral and Maxillofacial Surgery, New York University, New York City, NY, USA; 7Institute for Human Genetics, University of California, San Francisco, CA, USACorrespondence: Elena Flowers, University of California, San Francisco, Department of Physiological Nursing, 2 Koret Way, #605L, San Francisco, CA, 94143-0610, USA, Tel + 1 415-476-0983, Email elena.flowers@ucsf.eduIntroduction: Integrated transcriptome and microRNA differential gene expression (DEG) analyses may help to explain type 2 diabetes (T2D) pathogenesis in at-risk populations. The purpose of this study was to characterize DEG in banked biospecimens from underactive adult participants who responded to a randomized clinical trial measuring the effects of lifestyle interventions on T2D risk factors. DEGs were further examined within the context of annotated biological pathways.Methods: Participants (n = 52) in a previously completed clinical trial that assessed a 12-week behavioural intervention for T2D risk reduction were included. Participants who showed > 6mg/dL decrease in fasting blood glucose were identified as responders. Gene expression was measured by RNASeq, and overrepresentation analysis within KEGG pathways and weighted gene correlation network analysis (WGCNA) were performed.Results: No genes remained significantly differentially expressed after correction for multiple comparisons. One module derived by WGCNA related to body mass index was identified, which contained genes located in KEGG pathways related to known mechanisms underlying risk for T2D as well as pathways related to neurodegeneration and protein misfolding. A network analysis showed indirect connections between genes in this module and islet amyloid polypeptide (IAPP), which has previously been hypothesized as a mechanism for T2D.Discussion: We validated prior studies that showed pathways related to metabolism, inflammation/immunity, and endocrine/hormone function are related to risk for T2D. We identified evidence for new potential mechanisms that include protein misfolding. Additional studies are needed to determine whether these are potential therapeutic targets to decrease risk for T2D.Keywords: transcriptome, diabetes, fasting blood glucose, biomarkers, pathway analysis |
