Isaac Cano

Systems Medicine: from molecular features and models to the clinic in COPD

Background and hypothesisChronic Obstructive Pulmonary Disease (COPD) patients are characterized by heterogeneous clinical manifestations and patterns of disease progression. Two major factors that can be used to identify COPD subtypes are muscle dysfunction/wasting and co-morbidity patterns. We hypothesized that COPD heterogeneity is in part the result of complex interactions between several genes and pathways. We explored the possibility of using a Systems Medicine approach to identify such pathways, as well as to generate predictive computational models that may be used in clinic practice.Objective and methodOur overarching goal is to generate clinically applicable predictive models that characterize COPD heterogeneity through a Systems Medicine approach. To this end we have developed a general framework, consisting of three steps/objectives: (1) feature identification, (2) model generation and statistical validation, and (3) application and validation of the predictive models in the clinical scenario. We used muscle dysfunction and co-morbidity as test cases for this framework.ResultsIn the study of muscle wasting we identified relevant features (genes) by a network analysis and generated predictive models that integrate mechanistic and probabilistic models. This allowed us to characterize muscle wasting as a general de-regulation of pathway interactions. In the co-morbidity analysis we identified relevant features (genes/pathways) by the integration of gene-disease and disease-disease associations. We further present a detailed characterization of co-morbidities in COPD patients that was implemented into a predictive model. In both use cases we were able to achieve predictive modeling but we also identified several key challenges, the most pressing being the validation and implementation into actual clinical practice.ConclusionsThe results confirm the potential of the Systems Medicine approach to study complex diseases and generate clinically relevant predictive models. Our study also highlights important obstacles and bottlenecks for such approaches (e.g. data availability and normalization of frameworks among others) and suggests specific proposals to overcome them.

Importance of mitochondrial PO2 in maximal O2 transport and utilization: A theoretical analysis

In previous calculations of how the O2 transport system limits .VO2(max), it was reasonably assumed that mitochondrial P(O2) (Pm(O2)) could be neglected (set to zero). However, in reality, Pm(O2) must exceed zero and the red cell to mitochondrion diffusion gradient may therefore be reduced, impairing diffusive transport of O2 and .VO2(max). Accordingly, we investigated the influence of Pm(O2) on these calculations by coupling previously used equations for O2 transport to one for mitochondrial respiration relating mitochondrial .VO2 to P(O2). This hyperbolic function, characterized by its P50 and V˙MAX, allowed Pm(O2) to become a model output (rather than set to zero as previously). Simulations using data from exercising normal subjects showed that at .VO2(max), Pm(O2) was usually <1mmHg, and that the effects on .VO2(max) were minimal. However, when O2 transport capacity exceeded mitochondrial V˙MAX, or if P50 were elevated,Pm(O2) often reached double digit values, thereby reducing the diffusion gradient and significantly decreasing .VO2(max).

The COPD Knowledge Base: enabling data analysis and computational simulation in translational COPD research

BackgroundPreviously we generated a chronic obstructive pulmonary disease (COPD) specific knowledge base (http://www.copdknowledgebase.eu) from clinical and experimental data, text-mining results and public databases. This knowledge base allowed the retrieval of specific molecular networks together with integrated clinical and experimental data.ResultsThe COPDKB has now been extended to integrate over 40 public data sources on functional interaction (e.g. signal transduction, transcriptional regulation, protein-protein interaction, gene-disease association). In addition we integrated COPD-specific expression and co-morbidity networks connecting over 6 000 genes/proteins with physiological parameters and disease states. Three mathematical models describing different aspects of systemic effects of COPD were connected to clinical and experimental data. We have completely redesigned the technical architecture of the user interface and now provide html and web browser-based access and form-based searches. A network search enables the use of interconnecting information and the generation of disease-specific sub-networks from general knowledge. Integration with the Synergy-COPD Simulation Environment enables multi-scale integrated simulation of individual computational models while integration with a Clinical Decision Support System allows delivery into clinical practice.ConclusionsThe COPD Knowledge Base is the only publicly available knowledge resource dedicated to COPD and combining genetic information with molecular, physiological and clinical data as well as mathematical modelling. Its integrated analysis functions provide overviews about clinical trends and connections while its semantically mapped content enables complex analysis approaches. We plan to further extend the COPDKB by offering it as a repository to publish and semantically integrate data from relevant clinical trials. The COPDKB is freely available after registration at http://www.copdknowledgebase.eu.

An adaptive case management system to support integrated care services

BACKGROUND Extensive deployment and sustainability of integrated care services (ICS) constitute an unmet need to reduce the burden of chronic conditions. The European Union project NEXES (2008-2013) assessed the deployment of four ICS encompassing the spectrum of severity of chronic patients. OBJECTIVE The current study aims to (i) describe the open source Adaptive Case Management (ACM) system (Linkcare®) developed to support the deployment of ICS at the level of healthcare district; (ii) to evaluate its performance; and, (iii) to identify key challenges for regional deployment of ICS. METHODS We first defined a conceptual model for ICS management and execution composed of five main stages. We then specified an associated logical model considering the dynamic runtime of ACM. Finally, we implemented the four ICS as a physical model with an ICS editor to allow professionals (case managers) to play active roles in adapting the system to their needs. Instances of ICS were then run in Linkcare®. Four ICS provided a framework for evaluating the system: Wellness and Rehabilitation (W&R) (number of patients enrolled in the study (n)=173); Enhanced Care (EC) in frail chronic patients to prevent hospital admissions, (n=848); Home Hospitalization and Early Discharge (HH/ED) (n=2314); and, Support to remote diagnosis (Support) (n=7793). The method for assessment of telemedicine applications (MAST) was used for iterative evaluation. RESULTS Linkcare® supports ACM with shared-care plans across healthcare tiers and offers integration with provider-specific electronic health records. Linkcare® successfully contributed to the deployment of the four ICS: W&R facilitated long-term sustainability of training effects (p<0.01) and active life style (p<0.03); EC showed significant positive outcomes (p<0.05); HH/ED reduced on average 5 in-hospital days per patient with a 30-d re-admission rate of 10%; and, Support, enhanced community-based quality forced spirometry testing (p<0.01). Key challenges for regional deployment of personalized care were identified. CONCLUSIONS Linkcare® provided the required functionalities to support integrated care adopting an ACM model, and it showed adaptive potential for its implementation in different health scenarios. The research generated strategies that contributed to face the challenges of the transition toward personalized medicine for chronic patients.

Biomedical research in a Digital Health Framework

This article describes a Digital Health Framework (DHF), benefitting from the lessons learnt during the three-year life span of the FP7 Synergy-COPD project. The DHF aims to embrace the emerging requirements - data and tools - of applying systems medicine into healthcare with a three-tier strategy articulating formal healthcare, informal care and biomedical research. Accordingly, it has been constructed based on three key building blocks, namely, novel integrated care services with the support of information and communication technologies, a personal health folder (PHF) and a biomedical research environment (DHF-research). Details on the functional requirements and necessary components of the DHF-research are extensively presented. Finally, the specifics of the building blocks strategy for deployment of the DHF, as well as the steps toward adoption are analyzed. The proposed architectural solutions and implementation steps constitute a pivotal strategy to foster and enable 4P medicine (Predictive, Preventive, Personalized and Participatory) in practice and should provide a head start to any community and institution currently considering to implement a biomedical research platform.

Workforce preparation: the Biohealth computing model for Master and PhD students

The article addresses the strategic role of workforce preparation in the process of adoption of Systems Medicine as a driver of biomedical research in the new health paradigm. It reports on relevant initiatives, like CASyM, fostering Systems Medicine at EU level. The chapter focuses on the BioHealth Computing Program as a reference for multidisciplinary training of future systems-oriented researchers describing the productive interactions with the Synergy-COPD project.

Proposals for enhanced health risk assessment and stratification in an integrated care scenario.

Objectives Population-based health risk assessment and stratification are considered highly relevant for large-scale implementation of integrated care by facilitating services design and case identification. The principal objective of the study was to analyse five health-risk assessment strategies and health indicators used in the five regions participating in the Advancing Care Coordination and Telehealth Deployment (ACT) programme (http://www.act-programme.eu). The second purpose was to elaborate on strategies toward enhanced health risk predictive modelling in the clinical scenario. Settings The five ACT regions: Scotland (UK), Basque Country (ES), Catalonia (ES), Lombardy (I) and Groningen (NL). Participants Responsible teams for regional data management in the five ACT regions. Primary and secondary outcome measures We characterised and compared risk assessment strategies among ACT regions by analysing operational health risk predictive modelling tools for population-based stratification, as well as available health indicators at regional level. The analysis of the risk assessment tool deployed in Catalonia in 2015 (GMAs, Adjusted Morbidity Groups) was used as a basis to propose how population-based analytics could contribute to clinical risk prediction. Results There was consensus on the need for a population health approach to generate health risk predictive modelling. However, this strategy was fully in place only in two ACT regions: Basque Country and Catalonia. We found marked differences among regions in health risk predictive modelling tools and health indicators, and identified key factors constraining their comparability. The research proposes means to overcome current limitations and the use of population-based health risk prediction for enhanced clinical risk assessment. Conclusions The results indicate the need for further efforts to improve both comparability and flexibility of current population-based health risk predictive modelling approaches. Applicability and impact of the proposals for enhanced clinical risk assessment require prospective evaluation.

From comorbidities of chronic obstructive pulmonary disease to identification of shared molecular mechanisms by data integration

BackgroundDeep mining of healthcare data has provided maps of comorbidity relationships between diseases. In parallel, integrative multi-omics investigations have generated high-resolution molecular maps of putative relevance for understanding disease initiation and progression. Yet, it is unclear how to advance an observation of comorbidity relations (one disease to others) to a molecular understanding of the driver processes and associated biomarkers.ResultsSince Chronic Obstructive Pulmonary disease (COPD) has emerged as a central hub in temporal comorbidity networks, we developed a systematic integrative data-driven framework to identify shared disease-associated genes and pathways, as a proxy for the underlying generative mechanisms inducing comorbidity. We integrated records from approximately 13 M patients from the Medicare database with disease-gene maps that we derived from several resources including a semantic-derived knowledge-base. Using rank-based statistics we not only recovered known comorbidities but also discovered a novel association between COPD and digestive diseases. Furthermore, our analysis provides the first set of COPD co-morbidity candidate biomarkers, including IL15, TNF and JUP, and characterizes their association to aging and life-style conditions, such as smoking and physical activity.ConclusionsThe developed framework provides novel insights in COPD and especially COPD co-morbidity associated mechanisms. The methodology could be used to discover and decipher the molecular underpinning of other comorbidity relationships and furthermore, allow the identification of candidate co-morbidity biomarkers.

Effects of lung ventilation–perfusion and muscle metabolism–perfusion heterogeneities on maximal O2 transport and utilization

We expanded a prior model of whole‐body O2 transport and utilization based on diffusive O2 exchange in the lungs and tissues to additionally allow for both lung ventilation–perfusion and tissue metabolism–perfusion heterogeneities, in order to estimate V̇O2 and mitochondrial PO2 ( PmO2 ) during maximal exercise. Simulations were performed using data from (a) healthy fit subjects exercising at sea level and at altitudes up to the equivalent of Mount Everest and (b) patients with mild and severe chronic obstructive pulmonary disease (COPD) exercising at sea level. Heterogeneity in skeletal muscle may affect maximal O2 availability more than heterogeneity in lung, especially if mitochondrial metabolic capacity ( V̇ MAX ) is only slightly higher than the potential to deliver O2, but when V̇ MAX is substantially higher than O2 delivery, the effect of muscle heterogeneity is comparable to that of lung heterogeneity. Skeletal muscle heterogeneity may result in a wide range of potential mitochondrial PO2 values, a range that becomes narrower as V̇ MAX increases; in regions with a low ratio of metabolic capacity to blood flow, PmO2 can exceed that of mixed muscle venous blood. The combined effects of lung and peripheral heterogeneities on the resistance to O2 flow in health decreases with altitude.

From Systems Understanding to Personalized Medicine: Lessons and Recommendations Based on a Multidisciplinary and Translational Analysis of COPD.

Systems medicine, using and adapting methods and approaches as developed within systems biology, promises to be essential in ongoing efforts of realizing and implementing personalized medicine in clinical practice and research. Here we review and critically assess these opportunities and challenges using our work on COPD as a case study. We find that there are significant unresolved biomedical challenges in how to unravel complex multifactorial components in disease initiation and progression producing different clinical phenotypes. Yet, while such a systems understanding of COPD is necessary, there are other auxiliary challenges that need to be addressed in concert with a systems analysis of COPD. These include information and communication technology (ICT)-related issues such as data harmonization, systematic handling of knowledge, computational modeling, and importantly their translation and support of clinical practice. For example, clinical decision-support systems need a seamless integration with new models and knowledge as systems analysis of COPD continues to develop. Our experience with clinical implementation of systems medicine targeting COPD highlights the need for a change of management including design of appropriate business models and adoption of ICT providing and supporting organizational interoperability among professional teams across healthcare tiers, working around the patient. In conclusion, in our hands the scope and efforts of systems medicine need to concurrently consider these aspects of clinical implementation, which inherently drives the selection of the most relevant and urgent issues and methods that need further development in a systems analysis of disease.