Random C-Peptide and Islet Antibodies at Onset Predict β Cell Function Trajectory and Insulin Dependence in Pediatric Diabetes.

Aβ classification C-peptide islet autoimmunity pediatric diabetes type 1 diabetes type 2 diabetes

Journal

Endocrine practice : official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists
ISSN: 1530-891X
Titre abrégé: Endocr Pract
Pays: United States
ID NLM: 9607439

Informations de publication

Date de publication:
02 Oct 2024
Historique:
received: 23 08 2024
revised: 23 09 2024
accepted: 27 09 2024
medline: 5 10 2024
pubmed: 5 10 2024
entrez: 4 10 2024
Statut: aheadofprint

Résumé

Identification of prognostic biomarkers in pediatric diabetes is important for precision medicine. We assessed whether C-peptide and islet autoantibodies are useful to predict the natural history of children with new-onset diabetes. We prospectively studied 72 children with new-onset diabetes (median follow-up: 8 months) by applying the Aβ classification system ("A+": islet autoantibody positive, "β+": random serum C-peptide≥1.3 ng/mL at diagnosis). Beta-cell function was assessed longitudinally with 2h post-prandial/stimulated urinary C-peptide-to-creatinine ratio (UCPCR) 3-12 weeks (V1) and 6-12 months after diagnosis (V2). We obtained a type 1 diabetes genetic risk score (T1D GRS2) for each participant, and compared characteristics at baseline, and clinical outcomes at V2. The cohort was 50% male. Racial distribution was 76.4% White, 20.8% Black and 2.8% Asian or other races. A total of 46.5% participants were Hispanic. Median age (Q1-Q3) was 12.4 (8.3-14.5) years. The Aβ subgroup frequencies were 46 A+β-(63.9%), 1 A-β-(1.4%), 4 A+β+(5.6%), and 21 A-β+(29.2%). Baseline serum C-peptide correlated with UCPCR at both V1 (r=0.36, p=0.002) and V2 (r=0.47, p<0.001). There were significant subgroup differences in age, race, frequency of diabetic ketoacidosis and T1D GRS2 (p<0.01). At V2, the two β- subgroups had lower UCPCR and higher HbA1c compared with the two β+ subgroups (p<0.001 and p=0.02, respectively). Thirty-eight percent of A-β+ but none of the other subgroups were insulin-independent at V2 (p<0.001). C-peptide and islet autoimmunity at diagnosis define distinct phenotypes and predict beta-cell function and insulin dependence 6-12 months later in racially/ethnically diverse children with new-onset diabetes.

Identifiants

pubmed: 39366507
pii: S1530-891X(24)00791-2
doi: 10.1016/j.eprac.2024.09.116
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2024. Published by Elsevier Inc.

Auteurs

Mustafa Tosur (M)

Department of Pediatrics, Division of Diabetes and Endocrinology, Baylor College of Medicine, Texas Children's Hospital, Houston, TX, USA; Children's Nutrition Research Center, USDA/ARS, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA. Electronic address: mustafa.tosur@bcm.edu.

Saima Deen (S)

Department of Pediatrics, Research Resources Office, Texas Children's Hospital, Baylor College of Medicine, Houston, TX, USA.

Xiaofan Huang (X)

Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA.

Serife Uysal (S)

Department of Pediatrics, Division of Diabetes and Endocrinology, Baylor College of Medicine, Texas Children's Hospital, Houston, TX, USA.

Marcela Astudillo (M)

Department of Pediatrics, Division of Diabetes and Endocrinology, Baylor College of Medicine, Texas Children's Hospital, Houston, TX, USA.

Richard A Oram (RA)

Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK.

Maria J Redondo (MJ)

Department of Pediatrics, Division of Diabetes and Endocrinology, Baylor College of Medicine, Texas Children's Hospital, Houston, TX, USA.

Farook Jahoor (F)

Children's Nutrition Research Center, USDA/ARS, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA.

Ashok Balasubramanyam (A)

Division of Diabetes, Endocrinology and Metabolism, Baylor College of Medicine, Houston, TX, USA.

Classifications MeSH