Dynamic Modeling of Non-Pharmacological Interventions for Type 2 Diabetes

The prevalence of type 2 diabetes (T2D) is projected to increase by 100 million within years.

However, while lifestyle changes are recommended by the WHO to counter & mitigate diabetes, comprehensive intervention experiments that consider the interplay of different lifestyle factors are lacking, as are clinical tools to evaluate such interplay.

The present project will address these outstanding issues through two major undertakings:(1) A first long-term dynamic intervention experiment encompassing multiple lifestyle factors (diet, sleep & exercise) will be conducted in 200 subjects with & without T2D.

The participants’ daily multimodal health data (biochemical markers, sleep, questionnaires, etc.) will be recorded, to form a high-resolution dynamic intervention database with unprecedented integrative & temporal resolution, which will be stored securely and made available for fellow scholars and the public to take advantage of.(2) The project will, for the first time, use the concept “Order Parameters” from statistical physics to represent different health states treated as distinct “ordered statuses”.

Thus, a novel multimodal model will be built to characterize the temporal evolution of health variables that the modelling pinpoints as being the most predictive longitudinal parameters.

The project will thus enable personalized predictions for holistic health trajectory, by analyzing the general physical laws followed by the temporal evolution of model parameters, also in relevant T2D & healthy subgroups.The two undertakings provide excellent quality control & risk assessment via six tailored work packages consisting of 32 milestones and 4 deliverables.

This will provide an effective assessment tool for public health monitoring, at both the individual & policy level.

More importantly, it will enable a paradigm shift from traditional group-level descriptive statistics, to precise quantitative assessment for longitudinal evaluation of key clinical health parameters.