Profiling intra-patient type I diabetes behaviors.
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).


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.
Institut d'Informática i Aplicacions, Universitat de Girona, Campus de Montilivi, Girona, Spain. Electronic address: ivancontrerasfd@gmail.com.
Iván
I
Contreras
Diabetes Unit, Endocrinology and Nutrition Department, Hospital Clínic i Universitari, Barcelona, Spain.
Carmen
C
Quirós
Diabetes Unit, Endocrinology and Nutrition Department, Hospital Clínic i Universitari, Barcelona, Spain.
Marga
M
Giménez
Diabetes Unit, Endocrinology and Nutrition Department, Hospital Clínic i Universitari, Barcelona, Spain.
Ignacio
I
Conget
Institut d'Informática i Aplicacions, Universitat de Girona, Campus de Montilivi, Girona, Spain.
Josep
J
Vehi
D015331:Cohort Studies; D003922:Diabetes Mellitus, Type 1 / Q000188:drug therapy / Q000503:physiopathology / Q000523:psychology*; D015444:Exercise; D006801:Humans; D007328:Insulin / Q000627:therapeutic use
D007328:Insulin
Clustering; Continuous glucose monitoring; Insulin pumps; Time series prediction; Type 1 diabetes