Remember the hassle and pitfalls of driving before GPS? There’s no way to predict traffic jams or detours, no way to know if you’re on the best route. Now our satellite app guides us, warns of dangers and finds the fastest way to our destination.
Imagine a similar guidance system for guiding patient care. A system that can warn you of impending disease even before symptoms appear and guide you in making the best treatment decisions. Instead of trying to find the most effective treatment for an existing disease, we proactively predict and prevent it. This is a radical, but belated and necessary shift.
I believe we have developed such a system. The Clinician’s “Physiological Health Landscape” (PFL) model is a revolutionary framework for delivering ideal healthcare. Its broad application can transform clinical practice from an imprecise and imperfect, post-hoc, treatment-based approach to utilizing applied bioinformatics to create patient-specific, highly individualized, precise guidelines for maintaining good health Methods.
fitness landscape is a well-known conceptual model within and outside the field of evolutionary biology. We have applied the main principles of species-level fitness modeling to a physical scaffold and leveraged the predictive power of computer algorithms to propose a data-driven approach to bioinformatics clinical practice that could revolutionize healthcare.
Energy metabolism and health are intrinsically linked. Mitochondrial energy production and metabolic efficiency begin to decline around age 30. But its usually slow decline, and the consequent susceptibility to disease, is accelerated and exacerbated by chronic physical and emotional stress.
In the PFL model, like the model on which it is based, fitness is visualized as existing in a three-dimensional landscape of hills and valleys. Starting from the plateau at the bottom of the valley, which represents baseline homeostasis and physical fitness, we are constantly being pushed higher by internal and external stressors. What matters is how quickly and easily we can get back to the baseline.
Poor diet, alcoholism, physical inactivity, microbiome disturbance, smoking, work and family stress. They all help propel us to a perilous peak we might jump off without warning with the slightest additional provocation. For the young and robust, the stressors of life are the gentle hills in the PFL model. But for the elderly and the unhealthy, any additional stress can be the last step toward diabetes, heart attack, stroke, Alzheimer’s disease, cancer or premature death.
The toxic effects of stress are cumulative. Without stress tolerance, each new stressor pushes us into a new baseline—a higher plateau, well above the valley safety zone. This unstable zone is where inflammation and metabolic syndrome originate. From there, uncontrolled stressors inevitably take us to the next plateau until, eventually, we reach a critical threshold. Here, on the top of a mountain, we are easily pushed to the threshold of reversibility – dropping us from the top into a state of poor health where any intervention may not help.
We recommend regular use of the integrative metabolome to establish a healthy baseline for each patient. The accumulated data will allow us to easily identify small, asymptomatic changes in an individual’s metabolic efficiency and transform our field by anonymously collecting and combining this powerful bioinformatics to create algorithm-based standards of preventive healthcare.
The PFL model can serve as a conceptual and clinical tool for clinicians to develop therapeutic interventions aimed at changing the well-established trajectories of aging and chronic disease. By applying its principles to the care of individual patients, healthcare providers will be able to harness the power of data. Rather than treating an existing disease, they will be able to see warning signs of impending disease and stop it before it begins.
Photo: metamorworks, Getty Images
