MINI GRANT PROJECT SUMMARY

Transcription

1 MINI GRANT PROJECT SUMMARY Please complete the project summary and return the completed form to Alyssa Johnson, Administrative Assistant at the Institute on the Environment, at Paper copies will not be accepted. Please also attach any photos, publications, brochures, event agendas or other materials that were a result of the mini grant summary. Date of Report Submission: 04/06/2016 Project PI & Dept/School Matteo Convertino (SPH) Project Title: Diabetes Epigenetics: Detection of Environmental Drivers and Optimal Controls Grant Amount $: $3000 Project Context & Purpose Please include the original project purpose statement and revise for any changes that occurred in the project after the start date with a short explanation of the changes. Ninety percent of all diseases are believed to generate by factors in the environment leading to biological changes in individuals. The ''environment'' (anything outside the skin that also includes relationships with people and habits) is responsible for epigenetic changes that can lead to changes into how genes are expressed, thus in the occurrence and severity of illnesses like Type 1 and 2 diabetes. The ''environment'' includes also what we do as a society to control diabetes T1, yet any prevention and intervention alternative. Public health and medicine have generated hypotheses about T1/T2 diabetes risk factors but causality has not been assessed for these and other potential factors. Additionally, it is also important to quantify how different factors interact with each other, universality and scale dependency (in time and space) of causal factors, for optimizing prevention and intervention controls including individual counseling. Many unknown biological mechanisms need to be understood; for instance, in the area of epigenetics there are many unknown dynamics related to chemical-receptorendpoint relationships and that understanding can lead to a better assessment and management of diabetes, comorbidities (e.g., obesity) and related psychosocial implications. The causal factors for diabetes are at very different scales, from biological to social ones, and they likely require some persistence time to trigger epigenetic changes (Fig. 1). In terms of the nexus between diabetes T2 and obesity the majority of scientific knowledge recognizes only the directional causal pathway obesity -> diabetes T2, however the reverse mechanism is also believed to occur. The project is focused on the detection of the effective environmental factors leading to epigenetic generation of T1/T2 diabetes in developed and developing countries. The work is aligned to the research of the PI at IonE to find the environmental causes of syndemics for designing the environment via biological-based engineering methods in order to maximize population health. A comprehensive multiscale analysis of T1/T2 diabetes is proposed using complexity science models to infer universality, scale dependency, and interaction of diabetes causal factors.

2 Work Completed Please provide a summary of the work that was completed for the mini grant project. We propose to discuss how complex systems models originally developed in biology, and recently introduced to epidemiology can complement traditional epidemiologic statistical models in inferring causality in epidemiology. As a case study we consider diabetes T2 and obesity incidence in USA over space and time at the county scale. We use the obesity case study to compare and contrast the uses, power, assumptions, and limitations of complex system models and traditional statistical models. In particular we focus on the (i) determination of causal factors; (ii) hindcasting and forecasting of obesity incidence over space and time; (iii) definition of optimal management strategies to reduce incidence; and (iv) verification of the directional causal pathway diabetes T2 -> obesity. We highlight common misconceptions about model types, uncertainty and sensitivity assessment, risk versus outcome, stochasticity, calibration and validation, objective-dependent complexity, and design by analogy. The complex system models presented are based on statistical mechanics principles that attempt to reproduce population wide dynamics using macroscopic laws of physics in analogy to other systems. In particular we will present information theoretical models related to reaction-diffusion systems as a general approach for combining multiscale data and providing counterfactuals/predictions by valuing uncertainty, detecting invariance regimes of disease determinants, assessing non-linear interactions, and predicting population health trajectories in space and time. The case study will be related to infectious diseases, but the aim is for general conclusions and theory independent of the dynamics of diseases. We will emphasize how the nature of diseases as for any natural patterns is simpler than expected and how complexity science can formally identify that simplicity. We made a search in ISI web of science and went back till 1850 to search for anything with ''diabetes'' as a keyword. Narrowing down the research even just with few keywords makes the sample really small. The network inference was done in VosViewer and the visualization was made in Gephi where it is also possible to make some statistics. The following steps have been performed: 1) Data mining with exclusion of some outliers that is generated with the search (this can be done a posteriori using some network metrics -> outliers are typically associated to either very low and very high degree, where degree is the number of connections of a node (word)). See Fig. 2. 2) Network coarse graining: establish the level of details of the ''network'' (i.e. the network of causal factors we want to look at but even this one can be determined numerically for a fixed outcome we are interested in, e.g. diabetes incidence). See Fig. 3. 3) Multiscale analysis for different spatial and temporal scales of the same network (reflecting the dynamics of diabetes hopefully) 4) Global sensitivity and uncertainty analyses (GSUA): this analysis goes back to point 2 (GSUA determines numerically the stability of importance and interaction of factors for a given outcome node; the static assessment is already provided by the pairwise interaction of nodes and their connectivity). See Figs. 4 and 5. 5) Detection of tipping points: when an outcome is shifting completely to a different ''state'' (this is typically happening for a topological shift in the network); this can be done via global sensitivity and uncertainty analyses that perturbs the network considering the full variability of input factors. See Figs. 6 and 7. 6) Fine scale data mining: identify datasets (information) for the nodes in the searched literature to fill numerically the nodes rather than just using network metrics (characterizing the same nodes/links) 7) Information theoretic predictions: based on 6 or on network metrics it is possible to make future predictions given potential scenarios to explore (e.g. given variations in exposures -> observing variation in diabetes). This will require validation on real diabetes trajectories before it is possible to make any reliable prediction.

3 Figure 1. Multiscale view of diabetes T2 functional networks. Networks are shown from the metabolic to the population scale. Epigenetic mechanics are typically generated at the population scale and affecting the metabolic / genetic network.

4 Figure 2. Knowledge Mapping about diabetes. The network shows the preliminary mapping of the network derived from all publications in ISI Web of Science related to diabetes T2. Figure 3. Functional network of diabetes T2 obtained after coarse groaning of the network in Fig 2 and analysis of data associated to the studies identified by all nodes.

5 Figure 4. Functional network of diabetes T2 and identification of the major nodes and pathways. The major nodes and pathways are found after applying an information theoretic global sensitivity and uncertainty analyses. The magnitude of a connection (link width) is based on the transfer entropy that is the amount of directional information a variable can explain about the other connected variable. The size of the node is proportional to the relative importance of each node (in this case it is determined by the frequency of occurrence of the term in the ISI web of science literature). The network is obtained after coarse groaning of the network in Fig 2 and analysis of data associated to the studies identified by all nodes.

6 Figure 5. Global sensitivity and uncertainty analysis of the main drivers of diabetes T2. The magnitude of a connection (link width) is based on the transfer entropy that is the amount of directional information a variable can explain about the other connected variable. The sum of all transfer entropies of variables connected to obesity as population outcome constitutes the mutual information that is the total interdependence of each node with all others for determining obesity (σ). The size of the node is proportional to the relative importance of each node for obesity (µ). The higher (µ,σ) the higher the importance of a variable. The knowledge of (µ,σ) about obesity allows one to infer diabetes T2 outcome in a population and to manage both diseases simultaneously.

7 Figure 6. Predictions of obesity rate as a function of protein intake. Predictions are made on the top of the network inferred in Fig. 5 considering the full variablility of all predictive factors. The tipping is related to the sudden shift that may occur from one obesity rate to another when there is a shift in protein intake. Figure 7. Predictions of obesity rate as a function of protein intake and physical inactivity for different temporal scales. Predictions are made on the top of the network inferred in Fig. 5 considering the full variablility of all predictive factors. A higher obesity rate is observed for low protein diet than high protein for the same range of physical inactivity, that implies the high importance of diet versus physical activity.

8 Partnerships & Collaborations Please provide a summary of the project personnel, partnerships and collaborations that worked directly on the project or were started as a direct result of the mini grant project. The project emphasized the collaboration between the Jamia Millia University, Department of Social Work in New Delhi and the HumNat group. Additionally the HumNat group extended the collaboration to Prof. W. Toscano at the School of Public Health and initiated a collaboration about the topic with Allina Health. Project Outcomes & Impacts Please provide a summary of the outcomes and /or impacts of the mini grant project including future plans for the project. Convertino M, Vassilopoulos Y, Murdock M, Toscano W, (2015), Dynamic Biomaker Network for Untangling Diabetes-Obesity Complexity, in preparation Convertino M, Puri Srishti, Panneer Sigamani, Shradha Mathur, Gurumurthy Ramachandran, (2015), Designing Optimal Occupational Health Infrastructure for Syndemic Control: a Probabilistic Portfolio Decision Model, submitted We believe that in order to move forward with the discipline of epidemiology clarity is needed about what new models are, when statistical and complex systems models are better suited for the questions posed, what are their limitations, and what are the lexical commonalities and differences among different approaches developed in different disciplines. We believe in the practical utility of models presented in a consequentialist perspective for epidemiology. This is particularly important when physical based models incorporate decision science models focused on actionable strategies weighted by stakeholders preferences. Yet, the project enhanced the ability to develop further the computational infrastructure for population diseases as depicted in Fig. 8.

9 Figure 8: Computational cyberinfrastructure envisioned after the project. The cyberinfrastructure is composed by the dynamic interplay of models and people in a global system science participatory computing effort that uses public health and socio-environmental data to perform predictions for sociobehavioral control strategy and system design.

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