Integrative Systems Biology

While we have always relied on usage of data from clinical studies when developing our digital twins, since a few years back, we are also doing our own clinical studies. These clinical studies are of one of two types: i) to collect that can be used to develop, test, and refine our digital twin models, ii) to test the benefits of using our digital twins in practice.

In figure 1 below, we can see a schematic overview of the iterative process of developing a model. The starting mechanistic explanation and experimental data are inputs to the process.

As a first step, a model is developed to describe the behavior in the data. If an explanation is unable to describe the data, this hypothesis is rejected, and the mechanistic explanation needs to be revisited and reformulated. This loop continues until one or more mechanistic explanations can describe the data, and then we proceed to the core prediction analysis. Here, the value of big data comes to show, as we want to test our model against new data (validation data) that is unseen to the model.

The idea is that if the model formulation is close to the true system, then the model hypothesis should be able to explain unseen data from different experiments (on the same system). Here, again, the hypothesis can be rejected as data might not be described and the iterative process continues. Once a model formulation passes this validation data, this is a core prediction. However, new data and hypotheses can always be added to further develop the model formulation and to come closer to a model formulation that can describe the true system.

The first type of clinical studies are used to collect data that are specifically meant for our digital twin modelling. A key feature of such studies is that they are multi-timescale, i.e. combining detailed short-term timeseries with more long-term timeseries measurements. An example of such a study is the MeChamp study (see Figure 1, above). MeChamp stands for “Mechanisms of Changes in measured blood Pressure”, and it is driven by Kajsa Tunedal, who is a joint Ph.D. student between ISBgroup and the group of Tino Ebbers. In this study, we examine patients who have just been diagnosed with hypertension, i.e. high blood pressure, and who are about to start take a blood pressure medication. Before they start taking this medication, we take some baseline measurements, which involves not only analysis of blood pressure, plasma and urine, but also detailed MRI-protocol. This MRI-protocol involves something called 4D-flow MRI, which is a protocol which allows you to measure the 3D flow around the heart, over time during a single heartbeat. We have used such data to develop our heart models (see references below). In the MeChamp study, we collect such 4D-flow data both at rest and during a mild exercise. This gives as data on a short time-scale (ms to minutes). We then ask the patient to regularly measure home blood pressure, and to come back for at least one more such detailed examination, around 4-6 weeks later. This gives as data also on a longer timescale, measuring the impact of the blood pressure medication.

The second type of clinical study includes those that are meant to examine the effects of using our new eHealth apps. Two such studies are done within the big EU project STRATIF-AI. The first of those studies is illustrated in Figure 2, above. That study starts in a primary healthcare setting, with a version of something called a Health Dialogue (HD). In this HD, a health curve is presented for the individual. This health curve illustrates the current health status of the patient, in 13 different categories (e.g. exercise habits, food habits, blood pressure, etc), and range between green and red, i.e. between good and bad status. The hypothesis that we want to test is whether this health curve is explained in a better way using the digital twin, i.e. whether it leads to better understanding and motivation. The digital twin app is also used to help set and store the goals. These goals are then carried over to the next phase, which is done by another actor in the new integrated healthcare eco-system: personal health coaches. In this study, we use a coaching program called “Livshanteringsprogrammet” (the life handling program), which has been developed by the founder of Friskis and Svettis: Johan Holmsäter. In this program, you have various tasks each week, which allows you to work with your original goals, and also to learn more about health in general. In this clinical study, we will compare 400 test subjects who use the app, with corresponding healthy controls, and hope to see not only improvements in motivation and understanding, but also in behavior, risk factors, and ultimately even in the risk of a stroke.