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Nakayasu, E.S.* ; Bramer, L.M.* ; Ansong, C.* ; Schepmoes, A.A.* ; Fillmore, T.L.* ; Gritsenko, M.A.* ; Clauss, T.R.* ; Gao, Y.* ; Piehowski, P.D.* ; Stanfill, B.A.* ; Engel, D.W.* ; Orton, D.J.* ; Moore, R.J.* ; Qian, W.J.* ; Sechi, S.* ; Frohnert, B.I.* ; Toppari, J.* ; Ziegler, A.-G. ; Lernmark, A.* ; Hagopian, W.* ; Akolkar, B.* ; Smith, R.D.* ; Rewers, M.J.* ; Webb-Robertson, B.J.M.* ; Metz, T.O.*

Plasma protein biomarkers predict the development of persistent autoantibodies and type 1 diabetes 6 months prior to the onset of autoimmunity.

Cell Rep. Med. 4:101093 (2023)
Publ. Version/Full Text DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Type 1 diabetes (T1D) results from autoimmune destruction of β cells. Insufficient availability of biomarkers represents a significant gap in understanding the disease cause and progression. We conduct blinded, two-phase case-control plasma proteomics on the TEDDY study to identify biomarkers predictive of T1D development. Untargeted proteomics of 2,252 samples from 184 individuals identify 376 regulated proteins, showing alteration of complement, inflammatory signaling, and metabolic proteins even prior to autoimmunity onset. Extracellular matrix and antigen presentation proteins are differentially regulated in individuals who progress to T1D vs. those that remain in autoimmunity. Targeted proteomics measurements of 167 proteins in 6,426 samples from 990 individuals validate 83 biomarkers. A machine learning analysis predicts if individuals would remain in autoimmunity or develop T1D 6 months before autoantibody appearance, with areas under receiver operating characteristic curves of 0.871 and 0.918, respectively. Our study identifies and validates biomarkers, highlighting pathways affected during T1D development.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Autoimmune Response ; Biomarkers ; Plasma Proteomics ; Type 1 Diabetes; Software Tool; Discovery; Quantification; Children; Cohort
Language english
Publication Year 2023
HGF-reported in Year 2023
ISSN (print) / ISBN 2666-3791
e-ISSN 2666-3791
Quellenangaben Volume: 4, Issue: 7, Pages: , Article Number: 101093 Supplement: ,
Publisher Cell Press
Publishing Place 50 Hampshire St, Floor 5, Cambridge, Ma 02139 Usa
Reviewing status Peer reviewed
POF-Topic(s) 30201 - Metabolic Health
Research field(s) Helmholtz Diabetes Center
PSP Element(s) G-502100-001
Grants NLM NIH HHS
NCATS NIH HHS
NIGMS NIH HHS
NIDDK NIH HHS
Scopus ID 85165290518
PubMed ID 37390828
Erfassungsdatum 2023-10-18