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Sökning: WFRF:(Greenbaum Carla J.) > (2021) > Advances in Type 1 ...

Advances in Type 1 Diabetes Prediction Using Islet Autoantibodies : Beyond a Simple Count

So, Michelle (författare)
Benaroya Research Institute
Speake, Cate (författare)
Benaroya Research Institute
Steck, Andrea K. (författare)
University of Colorado School of Medicine
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Lundgren, Markus (författare)
Lund University,Lunds universitet,Pediatrisk endokrinologi,Forskargrupper vid Lunds universitet,Paediatric Endocrinology,Lund University Research Groups
Colman, Peter G. (författare)
Royal Melbourne Hospital
Palmer, Jerry P. (författare)
Washington University School of Medicine
Herold, Kevan C. (författare)
Yale University
Greenbaum, Carla J. (författare)
Benaroya Research Institute
visa färre...
 (creator_code:org_t)
2021-04-21
2021
Engelska 21 s.
Ingår i: Endocrine Reviews. - : The Endocrine Society. - 0163-769X .- 1945-7189. ; 42:5, s. 584-604
  • Forskningsöversikt (refereegranskat)
Abstract Ämnesord
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  • Islet autoantibodies are key markers for the diagnosis of type 1 diabetes. Since their discovery, they have also been recognized for their potential to identify at-risk individuals prior to symptoms. To date, risk prediction using autoantibodies has been based on autoantibody number; it has been robustly shown that nearly all multiple-autoantibody-positive individuals will progress to clinical disease. However, longitudinal studies have demonstrated that the rate of progression among multiple-autoantibody-positive individuals is highly heterogenous. Accurate prediction of the most rapidly progressing individuals is crucial for efficient and informative clinical trials and for identification of candidates most likely to benefit from disease modification. This is increasingly relevant with the recent success in delaying clinical disease in presymptomatic subjects using immunotherapy, and as the field moves toward population-based screening. There have been many studies investigating islet autoantibody characteristics for their predictive potential, beyond a simple categorical count. Predictive features that have emerged include molecular specifics, such as epitope targets and affinity; longitudinal patterns, such as changes in titer and autoantibody reversion; and sequence-dependent risk profiles specific to the autoantibody and the subject's age. These insights are the outworking of decades of prospective cohort studies and international assay standardization efforts and will contribute to the granularity needed for more sensitive and specific preclinical staging. The aim of this review is to identify the dynamic and nuanced manifestations of autoantibodies in type 1 diabetes, and to highlight how these autoantibody features have the potential to improve study design of trials aiming to predict and prevent disease.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Endokrinologi och diabetes (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Endocrinology and Diabetes (hsv//eng)

Nyckelord

autoantibody
autoimmunity
preclinical
prediction
stages
type 1 diabetes

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