1. Diabetes control: a complexity perspective Dr Tim Holt Clinical Lecturer Centre for Primary Health Care Studies Warwick Medical School, UK tim.holt@warwick.ac.uk

2. 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 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 Understanding diabetes from a dynamical viewpoint Using such a model to assist in glycaemic control Using such a model to assist in glycaemic control

3. Missing components in type 1 DM The insulin The insulin The regulatory mechanisms through which variation is controlled The regulatory mechanisms through which variation is controlled Both need to be replaced for tight control Both need to be replaced for tight control

4. The standard approach Replaces the missing insulin Replaces the missing insulin Aims for constant blood glucose levels Aims for constant blood glucose levels Relies on retrospective examination of blood glucose measurements over a period of time to guide future decision making 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 Tends to assess control using average blood glucose levels, as there is usually insufficient information to build up an adequate picture of dynamical patterns

5. Linear versus nonlinear models The linear model The nonlinear model Ignores interactions Ignores interactions Assumes a baseline equilibrium state Assumes a baseline equilibrium state Blood glucose levels are the result of a summation of positive and negative influences Blood glucose levels are the result of a summation of positive and negative influences Dynamics are unimportant Dynamics are unimportant Unpredictability may arise intrinsically through interactions between BG determinants Unpredictability may arise intrinsically through interactions between BG determinants Timing of positive and negative influences on BG levels affect outcomes 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 Dynamics become essential to an adequate description of the system and to control of the system

6. Tampering Control may be worsened through well meaning but misguided attempts at correction 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 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? So how do we enable control to be improved rather than worsened through self monitoring?

7. Phase space Blood glucose level Insulin level Exercise

8. Phase space Blood glucose level Insulin level Exercise. Current state of the system

9. Phase space Blood glucose level Insulin level Exercise.

10. Attractors and patterns in phase space. Point attractor (stasis, equilibrium) Periodicity Chaos

11. Glycaemic phase space The space of possible values for the determinants of blood glucose The space of possible values for the determinants of blood glucose The individuals ‘system’ is continuously moving as a trajectory through it. The individuals ‘system’ is continuously moving as a trajectory through it. Dynamics, as well as ‘average’ values, determine the ‘healthy state’ 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? How can this dynamical state be defined, and how does it relate to physiological dynamics in the non-diabetic state?

12. Order underlying apparent randomness

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

14. **

18. To sum up………… Study of non-linear dynamics may illuminate the dynamical variation experienced by people with diabetes 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 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 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 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 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

19. Thank you for listening