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Delivering Optimal Health: A Roadmap for the Future of Clinical Care

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Do you remember the hassles and pitfalls of driving before GPS? No way to predict a traffic jam or a detour, no way to know if you were on the best route. Now our satellite applications guide us, warn of dangers and find the fastest way to our destination.

Imagine a similar guidance system for navigating patient care. A system to warn you of impending illness, even before symptoms appear, and guide you in making optimal treatment decisions. Rather than striving to find the most effective treatment for an existing disease, we will proactively anticipate and prevent it. This is a radical change, but expected and necessary.

I believe we have developed such a system. The Physiological Fitness Landscape (PFL) model for clinicians is a revolutionary framework for delivering ideal health care. Its large-scale application could shift clinical practice from an imprecise and imperfect, post-hoc, treatment-based approach to one that leverages applied bioinformatics to create highly individualized, patient-specific precision guidelines for maintaining good health.

Fitness landscapes are well-known conceptual models in and beyond the field of evolutionary biology. We have applied the main principles of species-level fitness modeling to a scaffolding of physics and, using the predictive power of computational algorithms, propose a data-driven bioinformatics approach to clinical practice that can revolutionize Health care.

Energy metabolism and well-being are intrinsically linked. Mitochondrial energy production and metabolic efficiency begin to decline around age 30. But its normally slow decline and accompanying susceptibility to disease are accelerated and exacerbated by chronic physical and emotional stress.

In the PFL model, as in those on which it is based, physical form is visualized as existing in a three-dimensional landscape of hills and valleys. From the valley bottom zone of stability, representing basic homeostasis and good health, we are continually pushed higher by internal and external stressors. What matters is how quickly and easily we can get back to the baseline.

Poor diet, excess alcohol, lack of exercise, disturbed microbiota, smoking, work and family pressures. They all serve to send us to a precarious summit from which we could plunge, without warning, at the slightest additional provocation. For the young and fit, life stressors correspond to gentle slopes in the PFL model. But for the elderly and unhealthy, any added stress could be the final push to diabetes, heart attacks, strokes, Alzheimer’s disease, cancer or untimely death.

The toxic effects of stress are cumulative. Without stress resilience, each new stressor can land us on a new baseline – a higher plateau, far above the valley safe zone. This zone of instability is where inflammatory and metabolic syndromes take hold. From there, uncontrolled stressors inevitably lead us to the next plateau until we finally reach the threshold of criticality. Here at the top of the mountain, we can easily be pushed to the threshold of reversibility – knocking us off the top of the mountain into a state of unhealthiness where no intervention is likely to help.

We propose to use comprehensive metabolic panels at regular intervals to establish a baseline of well-being for each patient. The accumulated data will allow us to easily discern even minute, asymptomatic changes in metabolic efficiency in the individual and, by anonymously collecting and combining this powerful bioinformatics, create algorithm-based preventive healthcare standards that will transform our field. .

The PFL model can serve clinicians as a conceptual and clinical tool for the development of therapeutic interventions aimed at altering the accepted trajectory 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. And rather than treating existing illnesses, they will be able to see the warning signs of impending illness and stop it before it starts.

Photo: Metamorworks, Getty Images