Diabete Claudio Maffeis UOC Pediatria Indirizzo Diabetologico e

Diabete Claudio Maffeis UOC Pediatria Indirizzo Diabetologico e Malattie del Metabolismo Centro Regionale Diabetologia Pediatrica Centro di Riferimento Europeo per la Diabetologia Pediatrica Università e Azienda Ospedaliera Universitaria Integrata - Verona

IPERTENSIONE DISLIPIDEMIA ICTUS DIABETE INFARTO MIOCARDIO OBESITA’ TUMORI

Survey on etiological diagnosis of diabetes in 1244 Italian diabetic children and adolescents: Impact of access to genetic testing 0, 2 4, 9 prevalence (%) 91, 8 T 1 DM T 2 DM Monogenic diabetes Mozzillo E, Maffeis C, et al. Diabetes Res Clin Pract. 2015; 107(3): e 15 -8

Incidenza di DMT 1 Regione Veneto (2008 -2012) Incidenza media di DMT 1 in Veneto 2008 -2012: 16, 5 nuovi casi per 100. 000 soggetti per anno Marigliano M, Maffeis C, et al. Diabetes Res Clin Pract. 2015

Prevalenza di DMT 1 Regione Veneto Prevalenza media di DMT 1 in Veneto 2008 -2013: 1, 26 casi per 1. 000 soggetti di età 0 -18 anni Marigliano M, Maffeis C, et al. Diabetes Res Clin Pract. 2015

Estimated 2002 incidence of T 1 DM (A) and T 2 DM (B) among U. S. individuals aged <20 years by age and race/ethnicity. Imperatore G et al. Diab Care 2012

The Time Is Right for a New Classification System for Diabetes: Rationale and Implications of the β-Cell–Centric Classification Schema Schwartz SS, et al. Diabetes 2016

Schwartz SS, et al. Diabetes 2016

A unified model of the relationship between environmental factors, beta-cell endoplasmic reticulum stress, generation of neoautoantigesns (HIPs), and loss of immune tolerance that triggers islet autoimmunity Rewers & Ludvigsson Lancet 2016

Environmental triggers and protective factors for islet autoimmunity and promoters of progression to type 1 diabetes for which an association has been suggested

What are the fundamental determinants of metabolic control? Insulin regimen Diet Exercise Structure of clinical care teams Motivation Family support Mental health Access to care Goal setting Family function Cameron & Wherrett. Lancet 2015; 385: 2096 -106.

obiettivi di trattamento • Variabilità glicemica • Ipoglicemia • Iperglicemia Malik FS & Taplin CE Pediatr Drugs 2014; 16: 141 -50

• • • Automonitoraggio CGM CSII Pancreas artificiale (CHC) insulina

Evidence of a Strong Association Between Frequency of Self. Monitoring of Blood Glucose and Hemoglobin A 1 c Levels in T 1 D Exchange Clinic Registry Participants 13 – 26 years 1 – 13 years Pumps 13 – 26 years 1 – 13 years Means are adjusted for potential confounders Injections Conclusions There is a strong association between higher SMBG frequency and lower Hb. A 1 c levels. It is important for insurers to consider that reducing restrictions on the number of test strips provided per month may lead to improved glycemic control for some patients with type 1 diabetes. Miller KM, et al. Diabetes Care 2013; 36: 2009 -14

CGM

Hb. A 1 c according to insulin modality/CGM use status. CGM No CGM Injection Pump Foster NC, et al. Diabetes Care 2016; 39

Microinfusore di insulina

Long-term outcome of insulin pump therapy in children with T 1 D assessed in a large population-based case–control study Non pump therapy Pump therapy Johnson SR, et al. Diabetologia 2013; 56: 2392– 2400

Closed-loop system for type 1 diabetes therapy (artificial pancreas) Atkinson MA et al. Lancet 2014

Algoritmo di controllo: prime applicazioni in uso oggi.

Threshold-Based Insulin-Pump Interruption for Reduction of Hypoglycemia Bergenstal RM, et al. NEJM 2013; 369: 224 -32

Efficacia del Pancreas Artificiale durante la notte

Effect of Sensor-Augmented Insulin Pump Therapy and Automated Insulin Suspension vs Standard Insulin Pump Therapy on Hypoglycemia in Patients With T 1 D: a Randomized Clinical Trial. Ly TT, et al. JAMA. 2013; 310: 1240 -1247.

Nocturnal Glucose Control with an Artificial Pancreas at a Diabetes Camp Artificial Pancreas Phillip M, et al NEJM 2013; 368: 824 -33 Control

Systematically In Silico Comparison of Unihormonal and Bihormonal Artificial Pancreas Systems Glucose management results of the standard subject under 4 algorithms, where the whole testing duration is 24 h Gao X, et al. Computat Math Met Med, 2013

Systematically In Silico Comparison of Unihormonal and Bihormonal Artificial Pancreas Systems The blood glucose curves of ten virtual subjects under two proposed therapies P-type therapy PD-type therapy Gao X, et al. Computat Math Met Med, 2013

Pancreas Artificiale: il progetto italiano in età pediatrica (Bardonecchia 2015). Gruppo di Ricerca: Diabetologia Pediatrica di: 1. Verona; 2. Milano; 3. Torino; 4. Roma; 5. Napoli. Università di Padova: Diabetologia dell’adulto e Ingegneria Medica Università di Pavia: Ingegneria Medica. Del Favero S et al. Diab Care 2016

Microinfusore Sensore per la glicemia Di. AS (pancreas artificiale)

32 bambini (età 5 -9 anni) randomizzati in due gruppi A B 3 giorni SAP (microinfusore + sensore) (microinfusore + sensore Di. AS (pancreas artificiale)

Tele. Monitoraggio

Take home message La tecnologia consentirà a breve di disporre di strumenti dotati di intelligenza artificiale che potranno migliorare ulteriormente il controllo metabolico del diabete del bambino e dell’adolescente. Il pediatra insieme al paziente e alla sua famiglia dovrà quindi acquisire nuove competenze per assicurare un livello di cura ottimale del diabete.

The use and efficacy of continuous glucose monitoring in T 1 D treated with insulin pump therapy: a randomised controlled trial Battelino T, et al. Diabetologia 2012; 55: 3155 -62