Meta-Immunological Profiling of Children With Type 1 Diabetes Identifies New Biomarkers to Monitor Disease Progression

  1. Giuseppe Matarese1,2
  1. 1Laboratorio di Immunologia, Istituto di Endocrinologia e Oncologia Sperimentale, Consiglio Nazionale delle Ricerche (IEOS-CNR), Napoli, Italy
  2. 2Dipartimento di Medicina e Chirurgia, Facoltà di Medicina, Università di Salerno, Salerno, Italy
  3. 3Dipartimento di Scienze Mediche Preventive, Università di Napoli ‘‘Federico II,” Napoli, Italy
  4. 4Dipartimento di Medicina Clinica e Sperimentale, Università di Napoli “Federico II,” Napoli, Italy
  5. 5Unità di Neuroimmunologia, Fondazione Santa Lucia, Roma, Italy
  6. 6Dipartimento di Pediatria, Università di Napoli ‘‘Federico II,” Napoli, Italy
  7. 7Department of Clinical Sciences, Lund University, Skåne University Hospital SUS, Malmö, Sweden
  8. 8Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
  1. Corresponding author: Giuseppe Matarese, gmatarese{at}unisa.it

Abstract

Type 1 diabetes is characterized by autoimmune destruction of pancreatic β-cells in genetically susceptible individuals. Triggers of islet autoimmunity, time course, and the precise mechanisms responsible for the progressive β-cell failure are not completely understood. The recent escalation of obesity in affluent countries has been suggested to contribute to the increased incidence of type 1 diabetes. Understanding the link between metabolism and immune tolerance could lead to the identification of new markers for the monitoring of disease onset and progression. We studied several immune cell subsets and factors with high metabolic impact as markers associated with disease progression in high-risk subjects and type 1 diabetic patients at onset and at 12 and 24 months after diagnosis. A multiple correlation matrix among different parameters was evaluated statistically and assessed visually on two-dimensional graphs. Markers to predict residual β-cell function up to 1 year after diagnosis were identified in multivariate logistic regression models. The meta-immunological profile changed significantly over time in patients, and a specific signature that was associated with worsening disease was identified. A multivariate logistic regression model measuring age, BMI, fasting C-peptide, number of circulating CD3+CD16+CD56+ cells, and the percentage of CD1c+CD19CD14CD303 type 1 myeloid dendritic cells at disease onset had a significant predictive value. The identification of a specific meta-immunological profile associated with disease status may contribute to our understanding of the basis of diabetes progression.

Footnotes

  • Received September 14, 2012.
  • Accepted February 3, 2013.

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  1. Diabetes vol. 62 no. 7 2481-2491
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