Profiling intra-patient type I diabetes behaviors

Highlights

• The methodology helps to identify the most common situations affecting blood glucose control for every single patient.

• Adjusting insulin therapy to the identified situations leads to more accurate blood glucose control.

• The insights provided by the method allow doctors and patients to understand and act properly in the daily life situations.

• The methodology can be easily integrated in the existing commercial platforms based on CGM-CSII systems.

Abstract

Background

The large intra-patient variability in type 1 diabetic patients dramatically reduces the ability to achieve adequate blood glucose control. A novel methodology to identify different blood glucose dynamics profiles will allow therapies to be more accurate and tailored according to patient's conditions and to the situations faced by patients (exercise, week-ends, holidays, menstruation, etc).

Materials and methods

A clustering methodology based on the normalized compression distance is applied to identify different profiles for diabetic patients. First, the methodology is validated using “in silico” data from 10 patients in 3 different scenarios: days without exercise, poor controlled exercise days and days with well-controlled exercise. Second, we perform a series of in vivo experiments using data from 10 patients assessing the ability of the proposed methodology in real scenarios.

Results

In silico experiments show that the methodology is able to identify poor and well-controlled days in theoretical scenarios. In vivo experiments present meaningful profiles for working days, bank days and other situations, where different insulin requirements were detected.

Conclusions

A tool for profiling blood glucose dynamics of patients can be implemented in a short term to enhance existing analysis platforms using combined CGM-CSII systems. Besides coping with the information overload, the tool will assist physicians to adjust and improve insulin therapy and patients in the self-management of the disease.

Introduction

Integrated systems using continuous glucose monitors (CGM) and continuous subcutaneous insulin infusion (CSII) have had a significant impact in type 1 diabetes (T1D) management. The use of CSII-CGM systems was shown to reduce HbA1c without increasing the time spent in hypoglycemia [1]. Subsequently, CSII-CGM systems with automatic features such as automatic suspension of insulin delivery during hypoglycemia and predictive low glucose suspend have shown to reduce nocturnal hypoglycemia without increasing mean glucose substantially [2], [3]. Furthermore, the recent advances in CGM have led to more robust and portable devices which have demonstrated their value in improving the glycemic control working with closed-loop algorithms [4], [5]. The integration of data originating from sensor-based systems and electronic health records combined with smart data analytics methods and powerful user centered approaches enables the shift toward preventive, predictive, personalized, and participatory diabetes care [6]. However, the limited capacity of current solutions to process the data extracted from glucose monitors limits the development of enhanced diabetes management solutions.

Nowadays, companies commercializing CSII-CGM systems offer software platforms that provide tools to upload data from sensors, to share information with physicians and give patient support to diabetes management. These platforms are currently integrating methodologies to support treatment management by analyzing the data generated by the CGM. A notable example is the Medtronic software CareLink Pro/Personal® which provides functionalities to analyze BG values depending on the events occurring at specific periods, assisting patients and physicians with recommendations, which can lead to an improved insulin therapy. However, these features are still unable to detect the majority of scenarios with high impact in the blood glucose variability such as exercise, menstrual period, seasons, diet disturbances, or habits among other factors. Thus, the complexity of generating accurate treatments is accentuated when we have to deal with disturbances that can arise in the same patient. This intra-patient variability has different effects on BG levels altering the patterns usually generated by patients with T1D. The ability to identify such patterns would allow generating temporal profiles, which in turn can assist in identifying the causes of poor glycemic control and aid in the therapy adjustment. With the objective of analyzing similarities among time series we use a hierarchical clustering methodology based on an innovative similarity measure.

In the medical field, clustering methodologies are used in problems such as identifying effective treatments, recognition of diseases and detection of best practices. Broadly speaking, clustering methodologies are used to discover and study the macroscopic structure and relations between objects. The hierarchical clustering family of methods is defined by the method used to compute distances and a linkage criterion. A well-known distance metric is the normalized compression distance (NCD). The NCD is a compression-based similarity distance based on the Kolmogorov complexity [7]. This determines the similarity in terms of information distance between pairs of objects according to the most dominant common features. Previous studies have demonstrated that the NCD is a reliable tool for classification on a number of domains (see Ref. [8]). Furthermore, NCD has been applied successfully in many areas such as classification of genomes [9], protein structure comparison [10], genotyping [11], tumor subclassifications [12] or virus detection [13] among many others. In this study, we are presenting a new methodology that may provide a novel tool capable of extracting information on glucose profile of patients using CGM. This tool can help physicians and patients to detect patterns of poor glycemic control easily, providing useful information for the therapy adjustment.