Multiplex agglutination-PCR (ADAP) autoantibody assays compared to radiobinding autoantibodies in type 1 diabetes and celiac disease.

Agglutination-PCR Celiac disease Glutamic acid decarboxylase Insulin Islet antigen-2 Islet autoantibodies Radiobinding assay Tissue transglutaminase Type 1 diabetes Zinc transporter 8

Journal

Journal of immunological methods
ISSN: 1872-7905
Titre abrégé: J Immunol Methods
Pays: Netherlands
ID NLM: 1305440

Informations de publication

Date de publication:
07 2022
Historique:
received: 22 12 2021
revised: 23 03 2022
accepted: 24 03 2022
pubmed: 1 4 2022
medline: 15 6 2022
entrez: 31 3 2022
Statut: ppublish

Résumé

Multiplex Antibody-Detection by Agglutination-PCR (ADAP) assay was compared to singleplex standard radiobinding assays (RBA) to detect autoantibodies against insulin (IAA), GAD65 (GADA), islet antigen-2 (IA-2A), ZnT8 (ZnT8A) and tissue transglutaminase (TGA). Serum samples from 273 (114F/158M), 15-73 years of age healthy controls and 227 (109F/118M) newly diagnosed type 1 diabetes children, 1-11 years of age, were analyzed in both assay systems.The original WHO standard 97/550 and in-house reference standards for RBA were compared to ADAP. The ADAP and RBA generated parallel reference standards in all assays except TGA. Lower detection limits were observed in the ADAP assay for GADA,IAA and ZnT8A, markedly for TGA, but not for IA-2A. The Receiver Operating Characteristics (ROC) curve AUC analyses for pairwise comparison of ADAP with RBA showed no difference for GADA (n.s.), ADAP greater AUC for IAA (p = 0.005), RBA greater AUC for IA-2A (p = 0.0004) and ZnT8A (p < 0.0001) while ADAP TGA had a greater AUC compared to both RBA TGA-IgG (p < 0.0001) and TGA-IgA (p < 0.0001). These data suggest that the ADAP and RBA assays are comparable with equal performance for GADA, better ADAP performance for IAA while the RBA showed better performance in both IA-2A and ZnT8A associated with greater heterogeneity in autoantibody levels. The simultaneous analysis of 5 different autoantibodies by ADAP in sample volume reduced to only 4 μL and at an increased lower detection limit in all assays except IA-2A makes the ADAP automated autoantibody assay a distinct advantage for high throughput screening.

Identifiants

pubmed: 35358496
pii: S0022-1759(22)00052-7
doi: 10.1016/j.jim.2022.113265
pii:
doi:

Substances chimiques

Autoantibodies 0
Glutamate Decarboxylase EC 4.1.1.15

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

113265

Informations de copyright

Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.

Auteurs

Alexander Lind (A)

Enable Biosciences Inc., South San Francisco, CA, USA.

Felipe de Jesus Cortez (F)

Department of Clinical Sciences, Lund University CRC, Skåne University Hospital, Malmö, Sweden.

Anita Ramelius (A)

Enable Biosciences Inc., South San Francisco, CA, USA.

Rasmus Bennet (R)

Department of Clinical Sciences, Lund University CRC, Skåne University Hospital, Malmö, Sweden.

Peter V Robinson (PV)

Enable Biosciences Inc., South San Francisco, CA, USA.

David Seftel (D)

Enable Biosciences Inc., South San Francisco, CA, USA.

David Gebhart (D)

Enable Biosciences Inc., South San Francisco, CA, USA.

Devangkumar Tandel (D)

Enable Biosciences Inc., South San Francisco, CA, USA.

Marlena Maziarz (M)

Department of Clinical Sciences, Lund University CRC, Skåne University Hospital, Malmö, Sweden.

Daniel Agardh (D)

Department of Clinical Sciences, Lund University CRC, Skåne University Hospital, Malmö, Sweden.

Helena Elding Larsson (HE)

Department of Clinical Sciences, Lund University CRC, Skåne University Hospital, Malmö, Sweden.

Markus Lundgren (M)

Department of Clinical Sciences, Lund University CRC, Skåne University Hospital, Malmö, Sweden.

Cheng-Ting Tsai (CT)

Department of Clinical Sciences, Lund University CRC, Skåne University Hospital, Malmö, Sweden. Electronic address: jasontsai@enablebiosciences.com.

Åke Lernmark (Å)

Enable Biosciences Inc., South San Francisco, CA, USA. Electronic address: ake.lernmark@med.lu.se.

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Classifications MeSH