To what extent is the new position statement of the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD) 'personalised'?

Q-Score: development of a new metric for continuous glucose monitoring that enables stratification of antihyperglycaemic therapies

BACKGROUND

Continuous glucose monitoring (CGM) has revolutionised diabetes management. CGM enables complete visualisation of the glucose profile, and the uncovering of metabolic 'weak points'. A standardised procedure to evaluate the complex data acquired by CGM, and to create patient-tailored recommendations has not yet been developed. We aimed to develop a new patient-tailored approach for the routine clinical evaluation of CGM profiles. We developed a metric allowing screening for profiles that require therapeutic action and a method to identify the individual CGM parameters with improvement potential.

METHODS

Fifteen parameters frequently used to assess CGM profiles were calculated for 1,562 historic CGM profiles from subjects with type 1 or type 2 diabetes. Factor analysis and varimax rotation was performed to identify factors that accounted for the quality of the profiles.

RESULTS

We identified five primary factors that determined CGM profiles (central tendency, hyperglycaemia, hypoglycaemia, intra- and inter-daily variations). One parameter from each factor was selected for constructing the formula for the screening metric, (the 'Q-Score'). To derive Q-Score classifications, three diabetes specialists independently categorised 766 CGM profiles into groups of 'very good', 'good', 'satisfactory', 'fair', and 'poor' metabolic control. The Q-Score was then calculated for all profiles, and limits were defined based on the categorised groups (<4.0, very good; 4.0-5.9, good; 6.0-8.4, satisfactory; 8.5-11.9, fair; and ≥12.0, poor). Q-Scores increased significantly (P <0.01) with increasing antihyperglycaemic therapy complexity. Accordingly, the percentage of fair and poor profiles was higher in insulin-treated compared with diet-treated subjects (58.4% vs. 9.3%). In total, 90% of profiles categorised as fair or poor had at least three parameters that could potentially be optimised. The improvement potential of those parameters can be categorised as 'low', 'moderate' and 'high'.

CONCLUSIONS

The Q-Score is a new metric suitable to screen for CGM profiles that require therapeutic action. Moreover, because single components of the Q-Score formula respond to individual weak points in glycaemic control, parameters with improvement potential can be identified and used as targets for optimising patient-tailored therapies.

Algorithms for personalized therapy of type 2 diabetes: results of a web-based international survey

OBJECTIVE

In recent years increasing interest in the issue of treatment personalization for type 2 diabetes (T2DM) has emerged. This international web-based survey aimed to evaluate opinions of physicians about tailored therapeutic algorithms developed by the Italian Association of Diabetologists (AMD) and available online, and to get suggestions for future developments. Another aim of this initiative was to assess whether the online advertising and the survey would have increased the global visibility of the AMD algorithms.

RESEARCH DESIGN AND METHODS

The web-based survey, which comprised five questions, has been available from the homepage of the web-version of the journal Diabetes Care throughout the month of December 2013, and on the AMD website between December 2013 and September 2014. Participation was totally free and responders were anonymous.

RESULTS

Overall, 452 physicians (M=58.4%) participated in the survey. Diabetologists accounted for 76.8% of responders. The results of the survey show wide agreement (>90%) by participants on the utility of the algorithms proposed, even if they do not cover all possible needs of patients with T2DM for a personalized therapeutic approach. In the online survey period and in the months after its conclusion, a relevant and durable increase in the number of unique users who visited the websites was registered, compared to the period preceding the survey.

CONCLUSIONS

Patients with T2DM are heterogeneous, and there is interest toward accessible and easy to use personalized therapeutic algorithms. Responders opinions probably reflect the peculiar organization of diabetes care in each country.

Personalized therapy algorithms for type 2 diabetes: a phenotype-based approach

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). Thus, 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 at risk of developing the late complications of diabetes. The Italian Association of Medical Diabetologists has developed an innovative personalized algorithm for the treatment of type 2 diabetes, which is available online. According to the main features shown by the patient, six algorithms are proposed, according to glycated hemoglobin (HbA_1c, ≥9% or ≤9%), body mass index (≤30 kg/m² or ≥30 kg/m²), occupational risk potentially related to hypoglycemia, chronic renal failure, and frail elderly status. Through self-monitoring of blood glucose, patients are phenotyped according to the occurrence of fasting/preprandial or postprandial hyperglycemia. In each of these six algorithms, the gradual choice of treatment is related to the identified phenotype. With one exception, these algorithms contain a stepwise approach for patients with type 2 diabetes who are metformin-intolerant. The glycemic targets (HbA_1c, fasting/preprandial and postprandial glycemia) are also personalized. This accessible and easy to use algorithm may help physicians to choose a personalized treatment plan for each patient and to optimize it in a timely manner, thereby lessening clinical inertia.