Diabetes control: a Diabetes control: a complexity perspectivecomplexity perspective

Dr Tim HoltDr Tim HoltClinical LecturerClinical Lecturer

Centre for Primary Health Care StudiesCentre for Primary Health Care StudiesWarwick Medical School, UKWarwick Medical School, UK

tim.holt@warwick.ac.uktim.holt@warwick.ac.uk

Complexity and diabetesComplexity and diabetes

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

• Understanding diabetes from a Understanding diabetes from a dynamical viewpointdynamical viewpoint

• Using such a model to assist in Using such a model to assist in glycaemic controlglycaemic control

Missing components in type 1 Missing components in type 1 DMDM

• The insulinThe insulin

• The regulatory mechanisms through The regulatory mechanisms through which variation is controlled which variation is controlled

• Both need to be replaced for tight Both need to be replaced for tight controlcontrol

The standard approachThe standard approach

• Replaces the missing insulinReplaces the missing insulin• Aims for constant blood glucose levelsAims for constant blood glucose levels• Relies on retrospective examination of Relies on retrospective examination of

blood glucose measurements over a blood glucose measurements over a period of time to guide future decision period of time to guide future decision makingmaking

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

Linear versus nonlinear modelsLinear versus nonlinear models

The linear modelThe linear model The nonlinear The nonlinear modelmodel

• Ignores interactionsIgnores interactions• Assumes a baseline Assumes a baseline

equilibrium stateequilibrium state• Blood glucose levels Blood glucose levels

are the result of a are the result of a summation of positive summation of positive and negative and negative influencesinfluences

• Dynamics are Dynamics are unimportantunimportant

• Unpredictability may Unpredictability may arise intrinsically through arise intrinsically through interactions between BG interactions between BG determinantsdeterminants

• Timing of positive and Timing of positive and negative influences on negative influences on BG levels affect BG levels affect outcomesoutcomes

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

TamperingTampering

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

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

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

Phase spacePhase space

Bloodglucoselevel

Insulin level

Exercise

Phase spacePhase space

Bloodglucoselevel

Insulin level

Exercise

.Current state of the system

Phase spacePhase space

Bloodglucoselevel

Insulin level

Exercise

.

Attractors and patterns in phase spaceAttractors and patterns in phase space

.Point attractor (stasis, equilibrium)

Periodicity

Chaos

Glycaemic phase spaceGlycaemic phase space

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

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

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

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

Order underlying apparent Order underlying apparent randomnessrandomness

Order underlying apparent Order underlying apparent randomnessrandomness

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

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To sum up…………To sum up…………

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

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

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

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

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

Thank you for listeningThank you for listening