Systems Medicine Research Articles

Autonomic, immunological and endocrine influences on adipose tissue as an organ

White Adipose Tissue (WAT) is typically regarded as a passive storage deposit of excess fat. However, recent research suggests that WAT behaves like an organ system that interacts with the autonomous nervous, endocrinological and immunological systems. Therefore, it is possible that WAT acts as a regulatory organ that keeps the body in homeostasis. This cross-sectional study uses physiological data from 30 patients at the Pinewood Natural Health Centre in Toronto, Canada to derive a description of the role of WAT in the mediation of homeostasis. Statistical methods derive a formula describing the dynamic congruence that contributes to a systems medicine (SM) understanding of the organism. Multiple variables including body parameters, composition, and metabolism, heart rate variability and the immune, autonomous, neural, and endocrinologicial systems were measured and correlated using multiple regression analysis. The null hypothesis was that no variables would correlate; the alternative hypothesis was that at least two variables that would correlate with each other to demonstrate congruence and order. This analysis found strong correlations with parameters of the immune system and metabolism and few correlations with the autonomous nervous system. This suggests that despite the body’s complexity, not all systems may contribute equally strongly to overall homeostasis

Deep learning in systems medicine.

Systems medicine (SM) has emerged as a powerful tool for studying the human body at the systems level with the aim of improving our understanding, prevention and treatment of complex diseases. Being able to automatically extract relevant features needed for a given task from high-dimensional, heterogeneous data, deep learning (DL) holds great promise in this endeavour. This review paper addresses the main developments of DL algorithms and a set of general topics where DL is decisive, namely, within the SM landscape. It discusses how DL can be applied to SM with an emphasis on the applications to predictive, preventive and precision medicine. Several key challenges have been highlighted including delivering clinical impact and improving interpretability. We used some prototypical examples to highlight the relevance and significance of the adoption of DL in SM, one of them is involving the creation of a model for personalized Parkinson’s disease. The review offers valuable insights and informs the research in DL and SM.

A Systems‐Based Review of Adipose Tissue as an Organ: A Model of Autonomic, Immunological, and Endocrine Influences

White Adipose Tissue (WAT) has long been regarded as a passive storage deposit of excess fat. However, recent research suggests that this tissue acts more like an organ system that interacts with other biological systems in the body, especially with the autonomous nervous system, the endocrinological and immunological system, i.e. the Neuro-endocrine-immunological (NEI) system, which is itself supported by further dynamic physiological systems. Therefore, it is possible that WAT acts as a regulatory organ that keeps the body in homeostasis. The purpose of this cross sectional study was to use physiological data from 30 patients at the Pinewood Natural Health Centre in Toronto, Canada to derive a framework that describes the role of WAT in mediating homeostasis and use statistical methods to derive a formula to describe the dynamic congruence of the body and contribute to a systems medicine (SM) understanding of the organism. Multiple variables, describing basic body parameters, body composition, metabolism, heart rate variability, immune system, autonomous neural system and endocrinological system were measured in the study and found to correlate with each other. To narrow down the number of correlations so that a formula could be derived, eight of these variables that describe body composition and metabolism, nervous system and immune system were correlated to one another using multiple regression analysis. The null hypothesis was that none of these variables would correlate; the alternative hypothesis was that there were at least two variables that would correlate with each other and represent congruence and demonstrate order. This analysis found strong correlations between parameters of the immune system and metabolism. Surprisingly, contributions of the autonomous nervous system did not contribute significantly. This suggests that despite the body's complexity, not all systems may contribute equally strongly to overall homeostasis or may contribute in varying degrees. It also points towards possibilities to study the interactions in more detail in future studies. Support or Funding Information Privately funded: Pinewood Institute for the Advancement of Medicine Poster Presentation This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Systems Medicine: Hype or Revolution?

Research and innovation in healthcare can change existing practices aiming at constant improvement of diagnosis, treatment and prevention. As a new holistic approach Systems Medicine (SM) may revolutionize the healthcare system. This paper analyzes ethical and economic obstacles of SMs development from a niche innovation to a standard solution. We adapt a model of innovation theory to structure the barriers of adopting SM to become standard in the medical system. SM has the potential to change the medical system if barriers to this innovation can be overcome. The article discusses the potential of SM in becoming the future health paradigm considering these barriers and provides an overview of the current situation.

Novel therapeutic strategy in the management of COPD: a systems medicine approach.

Respiratory diseases including chronic-obstructive-pulmonary-disease (COPD) are globally increasing, with COPD predicted to become the third leading cause of global mortality by 2020. COPD is a heterogeneous disease with COPD-patients displaying different phenotypes as a result of a complex interaction between various genetic, environmental and life-style factors. In recent years, several investigations have been performed to better define such interactions, but the identification of the resulting phenotypes is still somewhat difficult, and may lead to inadequate assessment and management of COPD (usually based solely on the severity of airflow limitation parameter FEV1). In this new scenario, the management of COPD has been driven towards an integrative and holistic approach. The degree of complexity requires analyses based on large datasets (also including advanced functional genomic assays) and novel computational biology approaches (essential to extract information relevant for the clinical decision process and for the development of new drugs). Therefore, according to the emerging "systems/network medicine", COPD should be re.-evaluated considering multiple network(s) perturbations such as genetic and environmental changes. Systems Medicine (SM) platforms, in which patients are extensively characterized, offer a basis for a more targeted clinical approach, which is predictive, preventive, personalized and participatory ("P4-medicine"). It clearly emerges that in the next future, new opportunities will become available for clinical research on rare COPD patterns and for the identification of new biomarkers of comorbidity, severity, and progression. Herein, we overview the literature discussing the opportunity coming from the adoption of SMapproaches in COPD management, focusing on proteomics and metabolomics, and emphasizing the identification of disease sub-clusters, to improve the development of more effective therapies.

Systems medicine of inflammaging.

Systems Medicine (SM) can be defined as an extension of Systems Biology (SB) to Clinical-Epidemiological disciplines through a shifting paradigm, starting from a cellular, toward a patient centered framework. According to this vision, the three pillars of SM are Biomedical hypotheses, experimental data, mainly achieved by Omics technologies and tailored computational, statistical and modeling tools. The three SM pillars are highly interconnected, and their balancing is crucial. Despite the great technological progresses producing huge amount of data (Big Data) and impressive computational facilities, the Bio-Medical hypotheses are still of primary importance. A paradigmatic example of unifying Bio-Medical theory is the concept of Inflammaging. This complex phenotype is involved in a large number of pathologies and patho-physiological processes such as aging, age-related diseases and cancer, all sharing a common inflammatory pathogenesis. This Biomedical hypothesis can be mapped into an ecological perspective capable to describe by quantitative and predictive models some experimentally observed features, such as microenvironment, niche partitioning and phenotype propagation. In this article we show how this idea can be supported by computational methods useful to successfully integrate, analyze and model large data sets, combining cross-sectional and longitudinal information on clinical, environmental and omics data of healthy subjects and patients to provide new multidimensional biomarkers capable of distinguishing between different pathological conditions, e.g. healthy versus unhealthy state, physiological versus pathological aging.

A Systems Medicine Clinical Platform for Understanding and Managing Non- Communicable Diseases

Non-Communicable Diseases (NCDs) are among the most pressing global health problems of the twenty-first century. Their rising incidence and prevalence is linked to severe morbidity and mortality, and they are putting economic and managerial pressure on healthcare systems around the world. Moreover, NCDs are impeding healthy aging by negatively affecting the quality of life of a growing number of the global population. NCDs result from the interaction of various genetic, environmental and habitual factors, and cluster in complex ways, making the complex identification of resulting phenotypes not only difficult, but also a top research priority. The degree of complexity required to interpret large patient datasets generated by advanced high-throughput functional genomics assays has now increased to the point that novel computational biology approaches are essential to extract information that is relevant to the clinical decision-making process. Consequently, system-level models that interpret the interactions between extensive tissues, cellular and molecular measurements and clinical features are also being created to identify new disease phenotypes, so that disease definition and treatment are optimized, and novel therapeutic targets discovered. Likewise, Systems Medicine (SM) platforms applied to extensively-characterized patients provide a basis for more targeted clinical trials, and represent a promising tool to achieve better prevention and patient care, thereby promoting healthy aging globally. The present paper: (1) reviews the novel systems approaches to NCDs; (2) discusses how to move efficiently from Systems Biology to Systems Medicine; and (3) presents the scientific and clinical background of the San Raffaele Systems Medicine Platform.