Anno: 
2018
Nome e qualifica del proponente del progetto: 
sb_p_963100
Abstract: 

Despite the availability of an array of insulin formulations with different time action profiles and several treatment algorithms, the majority of patients with Type 2 Diabetes Mellitus (T2DM) on insulin therapy fails to achieve targeted glycosylated hemoglobin (HbA1c) levels.

Primary care providers, that in most country treat the majority of T2DM subjects, and patients are often reluctant in making the necessary transition from oral agents to insulin and once started to insulin dosage uptritation to achieve glycemic targets. These factors lead subjects at risk for diabetes complications.

Different clinical trials and metanalysis investigating basal insulin therapy together with the use of different treatment algorithms, either directed by the clinic/physician or by the patients themselves have consistently shown substantial improvements in glycemic control as indicated by reductions in HbA1c values together with a low number of hypoglycemic episodes.

Thus there appears to be an apparent gap between international guideline recommendations, the results of clinical trials, and real-life clinical practice as far as basal insulin initiation and treatment optimization in T2DM¿including titration algorithms¿is concerned.

In the era of personalized medicine, Continuous Glucose Monitoring (CGM) devices are showing that even in the context of T2DM, and particularly for those on insulin therapy, we face different glycemic profiles.

An option in this context is to develop a set of computational models (Virtual Patients, VPs) for T2DM subjects on basal bolus insulin therapy, clinically validate such models using CGM data and then use VPs (in lieu of real patients) within an In-Silico Clinical Trial (ISCT) aiming at assessing safety and efficacy of personalized insulin dose adjustment and frequency of adaptation (e.g., in order to achieve agreed therapeutic target) strategies.

ERC: 
SH1_11
SH3_14
PE6_12
Innovatività: 

The main advancement brought about by the present project are the followings.
First, a cohort of clinically validated VP for type 2 diabetics patients under insulin therapy is developed. This complementes the one from [DRC07] that instead focuses on non-diabetic and type 2 diabetic subjects without insulin treatment. Our cohort of VP enables ISCTs aiming at assessing insulin titration strategies.
Second, a set of insulin titration strategies validated through our cohort of VPs.
At present different international guidelines suggest several algorithms for both basal and prandial insulin dosage modulation [HR07, RMC03]. Each algorithm essentially involves the addition of a small dose increase, e.g., 1¿2 U (for those patients already on higher doses, increments of 5¿10 %), to the daily dose of basal insulin once or twice weekly if the fasting blood glucose (FBG) levels are above the different pre-agreed target [DM05], and down-titration is recommended in case of occurrence of any hypoglycemia. Dose adjustments should be more modest and less frequent as the target comes close (frequency of self-monitoring of FBG also to be reviewed). Unfortunately until now clinical trials have not validated and established the frequency of increase and the more modest increase in this specific context. Moreover during self-titration, frequent contact with the physician may be necessary, but this would involve more intensive contact with the patient than typically available in routine medical care. In the era of personalized medicine "virtually" testing different insulin therapy algorithm in terms of frequency and accordingly number of units adjstuments have the potential to be an effective tool for both clinicians and patients to improve self confidence and acceptance of insulin therapy tritation thus improving clincal inertia.

References A-Q

[Ber81] Bergman, R.N. et al. Physiologic evaluation of factors controlling glucose tolerance in man: measurement of insulin sensitivity and b-cell glucose sensitivity from the response to intravenous glucose. J. Clin. Invest. 68 (1981).
[CP14] P. Colmegna et al. Analysis of three T1DM simulation models for evaluating robust closed-loop controllers. Comp. Meth. and Prog. in Biomedicine, 113 (2014). Elsevier.
[DM05] M. Davies et al. ATLANTUS Study Group. Improvement of glycemic control in subjects with poorly controlled T2DM: comparison of two treatment algorithms using insulin glargine. Diabetes Care. 2005; 28(6).
[DRC07] C. Dalla-Man et al. Meal simulation model of the glucose-insulin system, IEEE Transactions on Biomedical Engineering 54 (10) (2007) 1740-1749.
[Fab06] P. G. Fabietti et al. Control oriented model of insulin and glucose dynamics in type 1 diabetics. Med. Biol. Eng. Comput. 44, (2006).
[FAZ09] F. AZ et al. Impact of fear of insulin or fear of injection on treatment outcomes of patients with diabetes. Curr Med Res Opin. 2009;25(6):1413-20.
[GD11] D. Giugliano et al. Efficacy of insulin analogs in achieving the hemoglobin A1c target of [HIB05] Hirsch IB et al. A real-world approach to insulin therapy in primary care practice. Clin Diabetes. 2005;23(2):78-86.
[HR07] Thorne KI, Farmer AJ, Davies MJ, Keenan JF, Paul S, Levy JC; 4-T Study Group. Addition of biphasic, prandial, or basal insulin to oral therapy in T2DM. N Engl J Med. 2007;357(17):1716-30.
[KBM09] B. Kovatchev et al. In silico preclinical trials: a proof of concept in closed-loop control of type 1 diabetes, Journal of Diabetes Science and Technology 3 (1) (2009).
[KBB11] L. Kovács et al. Induced L 2 -norm minimization of glucose-insulin system for type I diabetic patients, Computer Methods and Programs in Biomedicine 102 (2) (2011).
[K01], Khaw KT et al. Glycated haemoglobin, diabetes, and mortality in men in Norfolk cohort of european prospective investigation of cancer and nutrition (EPIC-Norfolk). BMJ 2001;322:15-8.
[KCE04], Koro CE et al. Glycemic control from 1988 to 2000 among U.S. adults diagnosed with type 2diabetes: a preliminary report. Diabetes Care 27:17-20, 2004
[KWV09] S. S. Kanderian et al. Identification of Intraday Metabolic Profiles during Closed-Loop Glucose Control in Individuals with Type 1 Diabetes. J. of Diabetes Science and Technology Vol. 3, Issue 5, Sept. 2009.
[IDF11] International Diabetes Federation. IDF Diabetes Atlas. 5th ed. Brussels: International Diabetes Federation; 2011.
[LDS09] Lasserson DS et al. Optimal insulin regimens in T2DM mellitus: systematic review and meta-analyses. Diabetologia. 2009;52(10):1990-2000.
[MMB2014] C. D. Man, et al. The UVA/PADOVA Type 1 Diabetes Simulator: New Features. Journal of Diabetes Science and Technology 2014, Vol. 8(1).
[MTS15] T. Mancini et al. Computing Biological Model Parameters by Parallel Statistical Model Checking. Int. Work Conf. on Bioinformatics and Biomedical Engineering. LNCS 9044 (2015). Springer.

Codice Bando: 
963100

© Università degli Studi di Roma "La Sapienza" - Piazzale Aldo Moro 5, 00185 Roma