Silvia Garavelli1,2, Sara Bruzzaniti2,3, Elena Tagliabue1, Dario Di Silvestre4, Francesco Prattichizzo1, Enza Mozzillo5, Valentina Fattorusso5, Lucia La Sala1, Antonio Ceriello1, Annibale A Puca1,6, Pierluigi Mauri4, Rocky Strollo7, Marco Marigliano8, Claudio Maffeis8, Alessandra Petrelli9, Emanuele Bosi9,10, Adriana Franzese5, Mario Galgani11,12, Giuseppe Matarese13,14, Paola de Candia15. 1. IRCCS MultiMedica, via G. Fantoli 16/15, 20138, Milan, Italy. 2. Institute for Endocrinology and Experimental Oncology 'G. Salvatore', C.N.R, via Pansini 5, 80131, Naples, Italy. 3. Department of Biology, University of Naples 'Federico II', Naples, Italy. 4. Institute of Biomedical Technologies, C. N. R, Segrate, Milan, Italy. 5. Centre of Paediatric Diabetology, Department of Translational Medical Sciences, University of Naples 'Federico II', Naples, Italy. 6. Department of Medicine and Surgery, University of Salerno, Baronissi, Italy. 7. Department of Medicine, Unit of Endocrinology & Diabetes, Università Campus Bio-Medico, Rome, Italy. 8. Paediatric Diabetes and Metabolic Disorders Unit, University of Verona, Verona, Italy. 9. San Raffaele Diabetes Research Institute, IRCCS Ospedale San Raffaele, Milan, Italy. 10. Vita-Salute San Raffaele University, Milan, Italy. 11. Institute for Endocrinology and Experimental Oncology 'G. Salvatore', C.N.R, via Pansini 5, 80131, Naples, Italy. mario.galgani@unina.it. 12. Department of Molecular Medicine and Medical Biotechnology, University of Naples 'Federico II', via Pansini 5, 80131, Naples, Italy. mario.galgani@unina.it. 13. Institute for Endocrinology and Experimental Oncology 'G. Salvatore', C.N.R, via Pansini 5, 80131, Naples, Italy. giuseppe.matarese@unina.it. 14. Department of Molecular Medicine and Medical Biotechnology, University of Naples 'Federico II', via Pansini 5, 80131, Naples, Italy. giuseppe.matarese@unina.it. 15. IRCCS MultiMedica, via G. Fantoli 16/15, 20138, Milan, Italy. paola.decandia@multimedica.it.
Abstract
AIMS/HYPOTHESIS: We aimed to analyse the association between plasma circulating microRNAs (miRNAs) and the immunometabolic profile in children with type 1 diabetes and to identify a composite signature of miRNAs/immunometabolic factors able to predict type 1 diabetes progression. METHODS: Plasma samples were obtained from children at diagnosis of type 1 diabetes (n = 88) and at 12 (n = 32) and 24 (n = 30) months after disease onset and from healthy control children with similar sex and age distribution (n = 47). We quantified 60 robustly expressed plasma circulating miRNAs by quantitative RT-PCR and nine plasma immunometabolic factors with a recognised role at the interface of metabolic and immune alterations in type 1 diabetes. Based on fasting C-peptide loss over time, children with type 1 diabetes were stratified into the following groups: those who had lost >90% of C-peptide compared with diagnosis level; those who had lost <10% of C-peptide; those showing an intermediate C-peptide loss. To evaluate the modulation of plasma circulating miRNAs during the course of type 1 diabetes, logistic regression models were implemented and the correlation between miRNAs and immunometabolic factors was also assessed. Results were then validated in an independent cohort of children with recent-onset type 1 diabetes (n = 18). The prognostic value of the identified plasma signature was tested by a neural network-based model. RESULTS: Plasma circulating miR-23~27~24 clusters (miR-23a-3p, miR-23b-3p, miR-24-3p, miR-27a-3p and miR-27b-3p) were upmodulated upon type 1 diabetes progression, showed positive correlation with osteoprotegerin (OPG) and were negatively correlated with soluble CD40 ligand, resistin, myeloperoxidase and soluble TNF receptor in children with type 1 diabetes but not in healthy children. The combination of plasma circulating miR-23a-3p, miR-23b-3p, miR-24-3p, miR-27b-3p and OPG, quantified at disease onset, showed a significant capability to predict the decline in insulin secretion 12 months after disease diagnosis in two independent cohorts of children with type 1 diabetes. CONCLUSIONS/INTERPRETATIONS: We have pinpointed a novel miR-23a-3p/miR-23b-3p/miR-24-3p/miR-27b-3p/OPG plasma signature that may be developed into a novel blood-based method to better stratify patients with type 1 diabetes and predict C-peptide loss.
AIMS/HYPOTHESIS: We aimed to analyse the association between plasma circulating microRNAs (miRNAs) and the immunometabolic profile in children with type 1 diabetes and to identify a composite signature of miRNAs/immunometabolic factors able to predict type 1 diabetes progression. METHODS: Plasma samples were obtained from children at diagnosis of type 1 diabetes (n = 88) and at 12 (n = 32) and 24 (n = 30) months after disease onset and from healthy control children with similar sex and age distribution (n = 47). We quantified 60 robustly expressed plasma circulating miRNAs by quantitative RT-PCR and nine plasma immunometabolic factors with a recognised role at the interface of metabolic and immune alterations in type 1 diabetes. Based on fasting C-peptide loss over time, children with type 1 diabetes were stratified into the following groups: those who had lost >90% of C-peptide compared with diagnosis level; those who had lost <10% of C-peptide; those showing an intermediate C-peptide loss. To evaluate the modulation of plasma circulating miRNAs during the course of type 1 diabetes, logistic regression models were implemented and the correlation between miRNAs and immunometabolic factors was also assessed. Results were then validated in an independent cohort of children with recent-onset type 1 diabetes (n = 18). The prognostic value of the identified plasma signature was tested by a neural network-based model. RESULTS: Plasma circulating miR-23~27~24 clusters (miR-23a-3p, miR-23b-3p, miR-24-3p, miR-27a-3p and miR-27b-3p) were upmodulated upon type 1 diabetes progression, showed positive correlation with osteoprotegerin (OPG) and were negatively correlated with soluble CD40 ligand, resistin, myeloperoxidase and soluble TNF receptor in children with type 1 diabetes but not in healthy children. The combination of plasma circulating miR-23a-3p, miR-23b-3p, miR-24-3p, miR-27b-3p and OPG, quantified at disease onset, showed a significant capability to predict the decline in insulin secretion 12 months after disease diagnosis in two independent cohorts of children with type 1 diabetes. CONCLUSIONS/INTERPRETATIONS: We have pinpointed a novel miR-23a-3p/miR-23b-3p/miR-24-3p/miR-27b-3p/OPG plasma signature that may be developed into a novel blood-based method to better stratify patients with type 1 diabetes and predict C-peptide loss.
Authors: Lucia La Sala; Maurizio Crestani; Silvia Garavelli; Paola de Candia; Antonio E Pontiroli Journal: Int J Mol Sci Date: 2020-12-25 Impact factor: 5.923