Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2018 Apr 27;122(9):1276-1289.
doi: 10.1161/CIRCRESAHA.117.310999.

Taking Systems Medicine to Heart

Kalliopi Trachana  1 , Rhishikesh Bargaje  1 , Gustavo Glusman  1 , Nathan D Price  1 , Sui Huang  1   2 , Leroy E Hood  1
Affiliations
Review

Taking Systems Medicine to Heart

Kalliopi Trachana et al. Circ Res. .

Abstract

Systems medicine is a holistic approach to deciphering the complexity of human physiology in health and disease. In essence, a living body is constituted of networks of dynamically interacting units (molecules, cells, organs, etc) that underlie its collective functions. Declining resilience because of aging and other chronic environmental exposures drives the system to transition from a health state to a disease state; these transitions, triggered by acute perturbations or chronic disturbance, manifest as qualitative shifts in the interactions and dynamics of the disease-perturbed networks. Understanding health-to-disease transitions poses a high-dimensional nonlinear reconstruction problem that requires deep understanding of biology and innovation in study design, technology, and data analysis. With a focus on the principles of systems medicine, this Review discusses approaches for deciphering this biological complexity from a novel perspective, namely, understanding how disease-perturbed networks function; their study provides insights into fundamental disease mechanisms. The immediate goals for systems medicine are to identify early transitions to cardiovascular (and other chronic) diseases and to accelerate the translation of new preventive, diagnostic, or therapeutic targets into clinical practice, a critical step in the development of personalized, predictive, preventive, and participatory (P4) medicine.

Keywords: cardiovascular disease; chronic disease; environmental exposure; genomics; systems biology.

PubMed Disclaimer

Figures

Figure 1
Figure 1
The Network-of-networks. Our bodies are made up of many networks that are integrated at and communicating on multiple scales.
Figure 2
Figure 2
Health-to-Disease as a Critical State Transition. a) Health (blue line) and Disease (red line) are two alternative stable states of the system (reflected in the value of the vertical axis, the system state variable), as a function of a “parameter” × (ho horizontal axis) that characterizes the regime of behavior of a system (“bifurcation diagram). Dashed lines represent a possible path for the abrupt transition from one stable state (health) to another one (disease) as the parameter × increases. The empty circles mark unstable states that forces the system to undergo the switch-like transition to the alternative stable state and represent the critical thresholds (“tipping points”), where the qualitative behavior of the system state changes abruptly. A snapshot based on the parameter value ×1 cannot distinguish between health or disease state without obtaining more information about the actual system state (other variables in omics dimension, represented by y-axis), e.g., 120 mg/dL of fasting glucose can indicate a pre-diabetic individual (blue line) or a diabetic patient under diabetic medication (red line). c) The healthy state (1): The width and steepness of the potential well (around stable lowest “energy” states) are shaped both by genetic and environmental perturbations. The measured variation over time of any active protein, metabolite or other omics measurements (dark blue nodes in the network) informs on individual’s healthy physiological range. At this state, the system is resilient to perturbations. The destabilized healthy (pre-disease) state (2): The potential well has almost flattened allowing access to the disease state. The measured variation over time for certain proteins, metabolites or other omics measurements (light blue nodes) varies significantly and may corresponds to disease state markers (black box). The system has low resilience and is sensitive to perturbations. The disease state (3): The system has shift to a new, resilient steady state. The measured variation over time of any active protein, metabolite or other omics measurements (red nodes in the network) informs on individual’s disease progression. U: landscape potential, x: any systems parameter (e.g., fasting glucose, see Table 2)
Figure 3
Figure 3
Clinical Research Study Landscape. We can categorize the selected study-designs based on their duration (long-term longitudinal vs. a single measurement/snapshot), the number of participants (single participant vs. thousands/big epidemiological cohorts) and the measures variables -dimensions (a few clinically important variants vs. all-omics-platforms). The P100 study is the closest match to a long-term, high-dimensional longitudinal study for a large population. (iPOP: integrated Personal Omics Profiling, P100 study: Pioneer 100 Wellness study, RCT: Randomized Control Trial)
Figure 4
Figure 4
A new strategy to stratify health prevention and identify early disease signs. Recovery time after exposure to a specific risk factor (stress test) can be a new biomarker to stratify patients. A, C) The individuals exhibit a very similar profile before stress test – although their resilience (width and steepness of the potential well) is different. B, D) After the stress test, omics profiling can identify the most variable proteins/metabolites for each individual (red boxes) enabling personalized recommendations. While the recovery time (t1 vs. t2) can identify the high-risk individuals (longer time, higher risk), who can be further profiled for early disease biomarkers (black boxes). U: landscape potential (Table 2).
Figure 5
Figure 5
The principle of equifinality. A) The health state variability: Individuals show heterogeneity in the concentration of measured physiological dimensions (e.g., metabolites or proteins in the blood) as indicated by the color shade (dark blue corresponds to higher detected concentrations). B) The pre-disease state variability: Exposure to specific perturbations (e.g., nutrition, infection agent etc.) reveals the pre-disease state profiles. Blue nodes correspond to early molecular signs for a health-to-disease transition signifying the future disease-perturbed network (black box), known as leading network hypothesis. Individuals may show “out-of-range” values for different nodes of the “leading network”. C) The disease state variability: Individuals show heterogeneity in the concentration of measured physiological dimensions as indicated by the color shade (dark red corresponds to higher detected concentrations).
See this image and copyright information in PMC

References

    1. Lenfant C. Shattuck lecture–clinical research to clinical practice–lost in translation? N Engl J Med. 2003;349:868–874. - PubMed
    1. Wang JJ, Aboulhosn JA, Hofer IS, Mahajan A, Wang Y, Vondriska TM. Operationalizing Precision Cardiovascular Medicine: Three Innovations. Circ Res. 2016;119:984–987. - PMC - PubMed
    1. Califf RM, Robb MA, Bindman AB, et al. Transforming Evidence Generation to Support Health and Health Care Decisions. N Engl J Med. 2016;375:2395–2400. - PubMed
    1. Benjamin EJ, Blaha MJ, Chiuve SE, et al. Heart Disease and Stroke Statistics-2017 Update: A Report From the American Heart Association. Circulation. 2017;135:e146–e603. - PMC - PubMed
    1. Tricoci P, Allen JM, Kramer JM, Califf RM, Smith SC. Scientific evidence underlying the ACC/AHA clinical practice guidelines. JAMA. 2009;301:831–841. - PubMed

Publication types

MeSH terms