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Predicting progression to type 1 diabetes from ages 3 to 6 in islet autoantibody positive TEDDY children

Jacobsen, Laura M. (författare)
University of Florida
Larsson, Helena E. (författare)
Lund University,Lunds universitet,Pediatrisk endokrinologi,Forskargrupper vid Lunds universitet,Paediatric Endocrinology,Lund University Research Groups,Skåne University Hospital
Tamura, Roy N. (författare)
University of South Florida
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Vehik, Kendra (författare)
University of South Florida
Clasen, Joanna (författare)
University of South Florida
Sosenko, Jay (författare)
University of Miami
Hagopian, William A. (författare)
Pacific Northwest Research Institute
She, Jin Xiong (författare)
Medical College of Georgia
Steck, Andrea K. (författare)
University of Colorado
Rewers, Marian (författare)
University of Colorado
Simell, Olli (författare)
Turku University Hospital
Toppari, Jorma (författare)
University of Turku,Turku University Hospital
Veijola, Riitta (författare)
University of Oulu,Oulu University Hospital
Ziegler, Anette G. (författare)
Helmholtz Zentrum München
Krischer, Jeffrey P. (författare)
University of South Florida
Akolkar, Beena (författare)
National Institute of Diabetes and Digestive and Kidney Diseases
Haller, Michael J. (författare)
University of Florida
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 (creator_code:org_t)
 
2019-01-29
2019
Engelska.
Ingår i: Pediatric Diabetes. - : Hindawi Limited. - 1399-543X .- 1399-5448. ; 20:3, s. 263-270
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
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  • Objective: The capacity to precisely predict progression to type 1 diabetes (T1D) in young children over a short time span is an unmet need. We sought to develop a risk algorithm to predict progression in children with high-risk human leukocyte antigen (HLA) genes followed in The Environmental Determinants of Diabetes in the Young (TEDDY) study. Methods: Logistic regression and 4-fold cross-validation examined 38 candidate predictors of risk from clinical, immunologic, metabolic, and genetic data. TEDDY subjects with at least one persistent, confirmed autoantibody at age 3 were analyzed with progression to T1D by age 6 serving as the primary endpoint. The logistic regression prediction model was compared to two non-statistical predictors, multiple autoantibody status, and presence of insulinoma-associated-2 autoantibodies (IA-2A). Results: A total of 363 subjects had at least one autoantibody at age 3. Twenty-one percent of subjects developed T1D by age 6. Logistic regression modeling identified 5 significant predictors - IA-2A status, hemoglobin A1c, body mass index Z-score, single-nucleotide polymorphism rs12708716_G, and a combination marker of autoantibody number plus fasting insulin level. The logistic model yielded a receiver operating characteristic area under the curve (AUC) of 0.80, higher than the two other predictors; however, the differences in AUC, sensitivity, and specificity were small across models. Conclusions: This study highlights the application of precision medicine techniques to predict progression to diabetes over a 3-year window in TEDDY subjects. This multifaceted model provides preliminary improvement in prediction over simpler prediction tools. Additional tools are needed to maximize the predictive value of these approaches.

Ämnesord

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

Nyckelord

autoantibodies
metabolic
pediatric
prediction
type 1 diabetes

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art (ämneskategori)
ref (ämneskategori)

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