K.K. Jain, Textbook of Personalized Medicine, DOI 10.1007/978-1-4939-2553-7_18, 565
© Springer Science+Business Media New York 2015
Chapter 18
Personalized Approaches to Miscellaneous
Problems in Healthcare
Personalized Management of Diabetes
Worlwide prevalence of diabetes mellitus is ~347 million. There are two main types:
type 1 diabetes mellitus (T1DM) or insulin-dependent DM affecting 10 % of indi-
viduals and type II diabetes mellitus (T2DM) affecting the rest, i.e., 90 %. Monitoring
of diabetes mellitus is rapidly advancing toward fully automated glucose control
systems such a personalized glucose advisory system (PGASystem) for manage-
ment of DM. Adults with T1DM appear to be enthusiastic about using a PGASystem
system for their diabetes management but also have signifi cant concerns affecting
their overall willingness to follow such a system’s advice because of the following
concerns: (1) how the advice is generated; (2) relinquishing control to automated
technology; and (3) inadequate personalization of the system (Shepard et al. 2012 ).
DM provides an example of chronic disease management with a particular focus
on patient self-management. Despite advances in DM therapy, many affected per-
sons still fail to achieve treatment targets and remain at risk of complications.
Personalizing the management of diabetes according to the patient’s individual pro-
fi le can help in improving therapy adherence and treatment outcomes. A 6-step
cycle for personalized DM (self-) management and collaborative use of structured
blood glucose data has been described (Ceriello et al. 2012 ). E-health solutions can
be used to improve process effi ciencies and enable remote access. Decision support
tools and algorithms can help physicians in making therapeutic decisions based on
individual patient profi les.
There is a need for technology that can accurately assess β cell death in order to
improve diagnosis of DM, allow for disease staging, and provide improved evalua-
tion of the effi cacy of treatment. A PCR-based technology (Islet Sciences) can be
used to identify β cell death before the onset of hyperglycemia and soon after the
onset of T1DM. The method uses a stepwise detection and analysis of β cell and
non-β cell-derived insulin DNA based on the existence of unique DNA methylation
patterns in the β cells that are absent from other cells in the body.