Personalized Therapy Algorithms for Type 2 Diabetes: A Phenotypization-based Approach

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1 Personalized Therapy Algorithms for Type 2 Diabetes: A Phenotypization-based Approach Riccardo Candido on behalf of the Personalized Therapy AMD Study Group Diabetes Canter A.S.S. 1 Triestina, Italy

2 BACKGROUND Type 2 diabetes is a progressive disease with a complex and multifactorial pathophysiology. Patients with type 2 diabetes show a variety of clinical features, including different phenotypes of hyperglycemia (eg, fasting/preprandial or postprandial). The best treatment choice is sometimes difficult to make, and treatment initiation or optimization is postponed. This situation may explain why, despite the existing complex therapeutic armamentarium and guidelines for the treatment of type 2 diabetes, a significant proportion of patients do not have good metabolic control and are at risk of developing the late complications of diabetes. UK Prospective Diabetes Study Group. Diabetes 1995;44: DeFronzo RA. Diabetes. 2009;58:

3 Diabetes Care. 2012, 35:

4 Diabetes Care. 2012, 35:

5 Need for a personalized treatment algorithm for type 2 diabetes Outline the available therapeutic options for T2D, along with their mechanism of action, advantages, and disadvantages/side effects, as well as their possible combinations, presenting practitioners with multiple choices to make. Set a general HbA1c target, even if some acknowledge that this target should be individualized. With some exceptions, indicate that the HbA1c level at entry does not influence treatment choices.

6 Need for a personalized treatment algorithm for type 2 diabetes Recommend lifestyle modification and metformin as initial steps; after metformin failure, not all possible combinations of recommended drugs are evidence-based. Put cost as a priority in selection of medication, prioritizing sulfonylureas as a second-line therapy Do not recommend routine self-monitoring of blood glucose for T2D not treated with insulin

7 AIM The Italian Association of Medical Diabetologists (AMD) considered appropriate to develop a Therapy Algorithm for Type 2 Diabetes that addresses two aspects of the problem: 1) the personalisation of therapy 2) therapeutic pathways aimed at rendering this approach useable (customized treatment pathways).

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9 Parameters for the characterization of patients with type 2 diabetes

10 Patient phenotype Patients are phenotyped on the basis of type and prevalence of blood glucose levels during the day using fasting/preprandial glucose levels and levels measured 2 hours after main meals by SMBG. Consequently, three types of automonitoring schemes are proposed: 1) intensive, ie, seven glycemic measurements per day for 3 consecutive days (before and 2 hours after main meals and at bed time). 2) staggered, ie, monitoring blood glucose before and 2 hours after breakfast on the first day, before and 2 hours after lunch, and before and 2 hours after the evening meal on the third day; this cycle is repeated until the end of the week. 3) self-monitoring of blood glucose five times daily over 3 consecutive days (monitoring blood glucose before breakfast and the evening meal, and 2 hours after the three main meals).

11 Fasting* and pre-prandial glycaemia: mg/dl (3,9-6,4 mmol/l) (if target HbA1c 7; personalised, if target >7). *Hyperglycaemia that is primarily fasting: where a proportion of hyperglycaemic values >60% of the total number of measurements taken while fasting or before a meal (for example, 3 values out of 5 are above the target). Post-prandial glycaemia**: 160 mg/dl (8,9 mmol/l) (if target HbA1c 7; personalised, if target >7). **Hyperglycaemia that is primarily post-prandial: where a proportion of hyperglycaemic values >60% of the total number of measurements taken 1-2 h after a meal (for example, 3 values out of 5 are above the target).

12 Choose the main feature of your T2DM patients The Italian algorithm takes into account some individual variables and characteristics as follows: an initial HbA1c value, ie, a cut-off point of 9%, has been chosen, leading to subcategories and consequently to different therapeutic options BMI < 30 kg/m 2 and 30 kg/m 2 occupations associated with a risk of hypoglycemia, such as workers at high altitude, pilots, drivers, crane operators, and platform workers presence of chronic renal failure frail elderly status

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15 Essential notes for correct use of the algorithm Clickable boxes allow the transition to the next step if the glycemic target is not reached. The frequency of blood glucose testing should be determined by the physician on an individual basis, taking into account the treatment regimen, the degree of glycemic control, as well as clinical and educational needs, according chiefly to their appropriateness. For self-monitoring schemes, one should refer to the IDF guideline on self-monitoring of blood glucose in non-insulin treated type 2 diabetes (available at:

16 The following specifications apply in all interventional flowcharts 1. Each flowchart refers to newly diagnosed patients and/or those who are not receiving anti-diabetic treatment. For other cases, enter the algorithm at the level nearest to the patient s characteristics and therapy. 2. Proposed HbA1c target values are intended as goals that must be pursed safely, to minimize the risk of hypoglycaemia. 3. In the presence of a tendency towards hypoglycaemia, sulphonylureas or glinides must not be considered as options. 4. At any stage, it is always possible to start insulin therapy, even temporarily.

17 The following specifications apply in all interventional flowcharts 5. At every level, pursuing the target body weight of the patient is recommended; in case of excess body weight, reducing initial body weight by 5-10% is indicated, or at least its stabilisation. 6. If an assessment indicates that a treatment can be reduced/simplified to meet a change in clinical needs, you can follow the algorithm backwards. 7. Suggestions about the use of various drug combinations are intended to be used in accordance with their respective summary of product characteristics, which are available from the European Medicines Agency.

18 Conclusions Given that T2D patients are heterogeneous in their clinical features and that T2D is a progressive disease, there is a clinical need for a personalized algorithm that covers these issues. The Italian algorithm for the treatment of T2D is an innovative, accessible, and easy to use dedicated online tool for doctors who treat patients with the disease. It is innovative in that patients can be easily fitted into a clinical category and phenotyped, and the gradual treatment is easy to follow as diabetes progresses.

19 Conclusions Another novelty lies in the fact it uses self-monitoring of blood glucose levels as a guide for selecting which therapies to prescribe. Patients are assigned a phenotype, according to the type and prevalence of their various daily blood sugar levels (fasting, pre-, and post-prandial), and this is used as a determining factor in guiding the choice of the most appropriate intervention. Whilst we are fully aware that the algorithm presented here neither covers all of the possible combinations encountered in daily clinical practice, nor meets all the unsatisfied management needs of patients with type 2 diabetes, we believe that it may counteract clinical inertia, and by getting more patients to their fixed targets and reducing long-term complications, may lessen the burden of T2D and its related costs.

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21 Acknowledgments Marco Gallo Antonio Ceriello Alberto De Micheli Katia Esposito Sandro Gentile Gerardo Medea (for the GPs)