Titre
A composite immune signature parallels disease progression across T1D subjects.
Type
article
Institution
Externe
Périodique
Auteur(s)
Speake, C.
Auteure/Auteur
Skinner, S.O.
Auteure/Auteur
Berel, D.
Auteure/Auteur
Whalen, E.
Auteure/Auteur
Dufort, M.J.
Auteure/Auteur
Young, W.C.
Auteure/Auteur
Odegard, J.M.
Auteure/Auteur
Pesenacker, A.M.
Auteure/Auteur
Gorus, F.K.
Auteure/Auteur
James, E.A.
Auteure/Auteur
Levings, M.K.
Auteure/Auteur
Linsley, P.S.
Auteure/Auteur
Akirav, E.M.
Auteure/Auteur
Pugliese, A.
Auteure/Auteur
Hessner, M.J.
Auteure/Auteur
Nepom, G.T.
Auteure/Auteur
Gottardo, R.
Auteure/Auteur
Long, S.A.
Auteure/Auteur
Liens vers les personnes
ISSN
2379-3708
Statut éditorial
Publié
Date de publication
2019-12-05
Volume
4
Numéro
23
Peer-reviewed
Oui
Langue
anglais
Notes
Publication types: Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
Publication Status: epublish
Publication Status: epublish
Résumé
At diagnosis, most people with type 1 diabetes (T1D) produce measurable levels of endogenous insulin, but the rate at which insulin secretion declines is heterogeneous. To explain this heterogeneity, we sought to identify a composite signature predictive of insulin secretion, using a collaborative assay evaluation and analysis pipeline that incorporated multiple cellular and serum measures reflecting β cell health and immune system activity. The ability to predict decline in insulin secretion would be useful for patient stratification for clinical trial enrollment or therapeutic selection. Analytes from 12 qualified assays were measured in shared samples from subjects newly diagnosed with T1D. We developed a computational tool (DIFAcTO, Data Integration Flexible to Account for different Types of data and Outcomes) to identify a composite panel associated with decline in insulin secretion over 2 years following diagnosis. DIFAcTO uses multiple filtering steps to reduce data dimensionality, incorporates error estimation techniques including cross-validation and sensitivity analysis, and is flexible to assay type, clinical outcome, and disease setting. Using this novel analytical tool, we identified a panel of immune markers that, in combination, are highly associated with loss of insulin secretion. The methods used here represent a potentially novel process for identifying combined immune signatures that predict outcomes relevant for complex and heterogeneous diseases like T1D.
PID Serval
serval:BIB_76BC38830B74
PMID
Open Access
Oui
Date de création
2022-02-28T10:45:29.348Z
Date de création dans IRIS
2025-05-21T03:14:33Z