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Diabetes control: a complexity perspective. Dr Tim Holt Clinical Lecturer Centre for Primary Health Care Studies Warwick Medical School, UK [email protected] Complexity and diabetes.

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Diabetes control a complexity perspective l.jpg

Diabetes control: a complexity perspective

Dr Tim Holt

Clinical Lecturer

Centre for Primary Health Care Studies

Warwick Medical School, UK

[email protected]


Complexity and diabetes l.jpg
Complexity and diabetes

  • Development of a non-linear model for understanding the dynamics of blood glucose variation both in diabetes and in the physiological state

  • Understanding diabetes from a dynamical viewpoint

  • Using such a model to assist in glycaemic control


Missing components in type 1 dm l.jpg
Missing components in type 1 DM

  • The insulin

  • The regulatory mechanisms through which variation is controlled

  • Both need to be replaced for tight control


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The standard approach

  • Replaces the missing insulin

  • Aims for constant blood glucose levels

  • Relies on retrospective examination of blood glucose measurements over a period of time to guide future decision making

  • Tends to assess control using average blood glucose levels, as there is usually insufficient information to build up an adequate picture of dynamical patterns


Linear versus nonlinear models the linear model the nonlinear model l.jpg

Ignores interactions

Assumes a baseline equilibrium state

Blood glucose levels are the result of a summation of positive and negative influences

Dynamics are unimportant

Unpredictability may arise intrinsically through interactions between BG determinants

Timing of positive and negative influences on BG levels affect outcomes

Dynamics become essential to an adequate description of the system and to control of the system

Linear versus nonlinear modelsThe linear modelThe nonlinear model


Tampering l.jpg
Tampering

  • Control may be worsened through well meaning but misguided attempts at correction

  • Self-monitoring influences outcomes through feedback between awareness of blood glucose level and behaviour

  • So how do we enable control to be improved rather than worsened through self monitoring?


Phase space l.jpg
Phase space

Blood

glucose

level

Exercise

Insulin level


Phase space8 l.jpg
Phase space

Blood

glucose

level

Current state of the system

.

Exercise

Insulin level


Phase space9 l.jpg
Phase space

Blood

glucose

level

.

Exercise

Insulin level


Attractors and patterns in phase space l.jpg
Attractors and patterns in phase space

Point attractor (stasis,

equilibrium)

.

Chaos

Periodicity


Glycaemic phase space l.jpg
Glycaemic phase space

  • The space of possible values for the determinants of blood glucose

  • The individuals ‘system’ is continuously moving as a trajectory through it.

  • Dynamics, as well as ‘average’ values, determine the ‘healthy state’

  • How can this dynamical state be defined, and how does it relate to physiological dynamics in the non-diabetic state?



Order underlying apparent randomness13 l.jpg
Order underlying apparent randomness

http://www.sat.t.u-tokyo.ac.jp/~hideyuki/java/Attract.html





Slide17 l.jpg

**

**


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To sum up…………

  • Study of non-linear dynamics may illuminate the dynamical variation experienced by people with diabetes

  • Such variation may be an important lever to assist in tight glycaemic control, particularly in type 1 diabetes

  • Unpredictability readily arises in nonlinear systems, even when the number of components is small

  • Conversely, apparently random behaviour may in fact reflect orderly underlying processes

  • The benefits of self monitoring might be assessed through study of dynamical patterns in addition to traditional linear measures such as average blood glucose levels



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