A Blueprint for Optimum Health Utilizing a Revolutionary New Model of Health & Disease: The Metabolic Fitness Landscape

For decades the state of health of US citizens has been in continuous decline. Much of the blame has been linked to the poor nutritional value of available food products. As an experienced physician with 30+ years of practice in endocrinology and metabolism, and an intimate familiarity with the US healthcare system, we can state with confidence that there are also major systemic issues resulting in suboptimal outcomes. One critical problem being inadequate integration of patient care, including patient data analysis, and a lack of holistic approaches aimed at maintaining and improving overall health. 

In our books on metabolism [1-3], we point to an urgent need for implementing what we here refer to as a so-called “quintet of quintets”, or a “5x5 Rubik's cube” of lifestyle medicine recommendations. The first quintet of vitalizing stress behaviors comprise the following: 1-optimal diet, 2-adaptive (stress resistance programs of) sleep, 3-inspiring mental activity, 4-animating physical exercise and, 5-supportive social networks (Figure 1). Each of the five categories of behaviors need to satisfy five optimal conditions: 1-quantity, 2-quality, 3-time: circadian cycles, 4-time: duration and 5-time: frequency. Most self-help “gurus” focus on only one or two of these recommendations, not all twenty-five that comprise the quintet of quintets.

 Figure 1: The key vitalizing behaviors affecting overall health. 

In a recently published popular book [3], we lift the hood of our biological engine, i.e. healthy metabolism as a condition for maximally decelerated aging, and delve into the details of each element and the integration of this Rubik’s cube for optimum health. Its foundational priority is underscored by how it minimizes the reliance on pharmacotherapy and other medical care, due to the associated side effects, and boosts efficacy and tolerability of medical therapeutics, which is both beneficial for patients and produces enormous healthcare savings. 

Beyond these general “recipes” for health and longevity, we have designed a new and groundbreaking methodology for personalized medicine, which we call the “Metabolic Fitness Landscape” (MFL). The MFL blueprint was inspired by an ingenious approach first developed in physics where it earned a Nobel Prize to one of its creators, Lev Landau, in the context of the systems undergoing phase transitions. It was then applied to other fields, such as genetics and evolution, but unfortunately, it has not yet been applied to medicine where it would have maximum impact on human life. Its applicability to human physiology is made manifest by the fact that a shift from health to disease in the human organism can be viewed as a phase transition as has been abundantly pointed out in the scientific literature [4-7].

It is a mathematical precision-oriented and personalized algorithm offering a dynamic scale of medicine for maximum human health and lifespan. Imagine a metaphorical mountainous terrain of connecting peaks and valleys that tracks the patient’s state of health and disease over time in a multi-parameter space of physiological data. The MFL is utilized as a blueprint for optimizing algorithms of AI and bioinformatics using a standardized profile of high volume and multidimensional biomarkers. Figure 2 schematically illustrates the metabolic fitness function as a quantitative representation of the states of health and disease.

Figure 2: Schematic Illustration of the Metabolic Fitness Function for a single stressor present (panels a-c) and for two stressors A and B present (panel d) 

In the case of aging, the metabolic fitness landscape of an individual changes over time (in years) and its “altitude” decreases in accordance with the loss of physiological fitness due to both external and internal factors (which we call control parameters). This is schematically summarized in figure 3.

 Figure 3: The key aspects of the pace of aging and an associated loss of physiological fitness. 

The driving force for accelerated aging is stress. However, its consequences on the person’s physiology as well as mental health are numerous and lead to a variety of symptoms, many of which are intertwined in feedback loops as shown in figure 4.

Figure 4: An illustration of the multitude of effects of stress on accelerated aging involving both physiological and psychological deterioration of the person’s health.

• The MFL methodology provides a transformative evolution to the current standards including: 

1. Preventive approaches that change the metabolic trajectory away from disease moving it toward the direction of health
2. A precision scale predictability of chronic disease
3. A precision scale predictability of responses to medical and lifestyle interventions of all forms
4. A reduction in healthcare expenditure by ? to ? equating to roughly $7.1 trillion to $21.3 trillion over 1-2 generations 

• The MFL model remedies the core 4 shortcomings of current medicine: 

1. Over-reliance on population studies, which are not personalized, precision scaled or dynamic and may include statistical biases such as inadequate sampling and lack of stratification according to sex, age, race, ethnicity and even lifestyle and patient history factors. 
2. Over-reliance on empirical observation - a more buttressed scientific foundation that factors biological chaos and complexity, classical and even quantum electromagnetic effects and pleiotropic systems biology is needed
3. Compartmentalization band aid medicine that doesn’t address root causes and misses integration of factors which are interlinked across subfields of human physiology and hence medical specializations.
4. The nature of a hypothesis in medicine, which is to at all costs prove itself correct (a so-called confirmation bias), with half of the holy grail standards of care being incorrect and not holding up to replication studies, whereas the goal of a hypothesis in physics and other areas of exact science is embodied by the formulation of the centuries-old scientific method, is to find flaws by studying it and to make incremental progress and asymptotically evolve the hypothesis to bring it closer to an absolute truth

Beta testing would address the following: 

• Prediction of metabolic disease as a healthcare navigation roadmap. Simplifying the complexity of biological beings using a model of a standardized profile of thousands to hundreds of thousands of biomarkers. This would ultimately require many millions of people studied worldwide to find the critical patterns of every conceivable disease subtype. The power of the model would increase exponentially as the number of biomarkers and individuals tested escalates. The number of biomarkers and subjects needed would be determined by statistical expertise, but preferably the maximum number of both within the limits of practicality for maximum predictability. A political initiative would therefore be ideal in order to achieve such an ambitious goal. Every disease would require individually beta testing across the relevant population base, i.e. cardiovascular diseases, diabetes, kidney disease, Alzheimer's disease, chronic respiratory diseases, cancers, mental illnesses, etc. as well as testing of secondary, tertiary and other diseases that might follow. 

• Prediction of patient responses to therapeutic applications using a model of external control parameters (potential stressors that can be applied to the system; stressors here are meant in general sense, not just psychological, but as any external factor that affects human physiology) and internal order parameters (a variable that the human physiological system changes in response to the control parameter and whose value can be used as a a measure or a biomarker of health or disease). Testing would occur first with a low and safe dose to generate at least 3, but ideally 5 or more data points in the multi-dimensional space of physiological parameters, in order to generate adequate functional cause-effect (also sometimes called dose-response) relationship, which is almost always nonlinear, which requires careful curve fitting. Various therapeutic agents or lifestyle changes targeting each component of our 5x5 Rubik’s cube of lifestyle medicine would be tested, e.g. pharmaceuticals such as statins and GLP-1 agonists, mind-body coordination strategies such as meditation, yoga, tai chi, and other lifestyle interventions including diet, exercise, sleep, etc.

Bioinformatic data (glucose, insulin and other data) would be obtained via a continuously wearable activated chip in addition to other order and control parameter data inputs including proteins, lipids, genetic markers, microbiota profiles, and other products of metabolism obtained from blood, urine, sputum and fecal samples. This huge real-time data flow would be integrated into algorithms using AI juxtaposed to millions of personalized profiles from individuals worldwide with various states of health and every conceivable disease state to plot the trajectory of a new personalized metabolic fitness terrain on the background of population generated statistical averages that would provide a confidence interval on the basis of which the physician could provide informed patient-specific recommendations. It also integrates therapeutic and preventive interventions that have proved successful for individuals similar to the patient’s profile. A healthcare practitioner would use a balanced and intuitive understanding of the information provided and of the patient, the optimal alternative intervention that best matches the patient's fears, expectations, belief systems, etc. to begin navigating a guided empirical management.

A 58-year-old obese man (BMI 32) with an 6-year history of type 2 diabetes with underlying insulin resistance, dyslipidemia (elevated triglycerides and low HDL cholesterol), but a normal LDL cholesterol, hypertension and high endogenous blood insulin level and a HbA1c of 9.5 (average blood sugar 225 mg/dL) presents to the office referred by his primary care physician. He is currently treated with insulin and metformin for his blood sugar as well as low dose Atorvastatin and Prinivil. He has no retinopathy or kidney disease, but does have mild hypersthenic peripheral neuropathy in his feet with no history of chronic foot ulcers or infections. The physician obtains the standardized profile of metabolic biomarkers from blood, urine, sputum and fecal samples integrated into an AI algorithm juxtaposed to millions of personalized metabolic profiles from individuals worldwide stored in the computer software. 

The juxtaposed data plots his MFL terrain illustrating a trajectory showing an 82% risk of developing adenocarcinoma of the pancreas within the next 5 years, a 69% likelihood of an acute myocardial infarction (heart attack) over the next 3 years, an 8% likelihood of a fatal arrhythmia over the next 3 years, a 46% likelihood of colorectal carcinoma over the next 10 years, a 95% likelihood of accelerated cognitive decline of aging over the next 3 years and an 18% likelihood of Alzheimer's disease (AD) over the next 10 years. 

Additionally, it computes some unexpected predictions that a higher dose of an alternative statin drug, Rosuvastatin 40 mg, reduces the chances of pancreatic and colorectal carcinoma in half (41% and 23%, respectively, in the said number of years) with no change in risk of myocardial infarction, while maximum dose Atorvastatin 80 mg actually increases the risk of myocardial infarction to 92% over the next 3 years, and robustly increases the chances of AD to 65% over the next 5 years. Bariatric surgery with gastric sleeve reduces the risk of all the listed baseline morbidities ranging from ? - ? while GLP-1 agonists (Ozempic and Mounjaro) reduce the same risks in the range of 25%-33%, although a slight increase likelihood of a fatal arrhythmia to 12% over the next 3 years. The patient has long held fears of having surgical complications and opted to use Ozempic with lifestyle modifications rather than a gastric sleeve. Adjustments are made in accordance to these findings and the patient is scheduled to have follow-ups on a 3-month basis for MFL terrain updated reanalysis. 

Moreover, it was advised to reduce insulin dosing by ? and add low dose therapies with Pioglitazone and Jardiance. The universal antioxidant alpha-lipoic acid, supplemental vitamin D and lifestyle management with an organic and Mediterranean diet with limited sugary desserts, and a personal trainer for moderate (for his degree of fitness) resistive and aerobic training were also agreed upon. The impacts of these changes are expected to have a significant favorable influence on all metabolic parameters of health, and will be quantitatively re-assessed every 3 months. Nonlinear responses to new interventions will be able to be assessed within 9 months. Shorter intervals between MFL assessments may be indicated to assess patient response to interventions per provider discretion. Careful empirical observation by the practitioner is not abdicated by the more robust science of AI healthcare and the MFL.

Our intention is to show why we need such a blueprint, which is what we feel is the epochal missing link capable of invoking AI (quantum or classical) and bioinformatics/Big Data Analytics that advances medicine and wellness in a maximal efficiency and unbiased form. We believe this requires a mathematical precision and dynamic scale that is personalized, using a high-volume profile of standardized metabolic data, or biomarkers, to predict and prevent disease as well as favorable and adverse interventions of all forms, including pharmacotherapy, vaccines, lifestyle changes, mind-body coordination strategies and spiritual practices. 

For practical purposes budgetary requests would have to be limited initially to beta testing of approximately ~1,000 biomarkers in ~3,000 subjects at a possible cost of $1-2 million. Ultimately our vision is to go much bigger, using tens to hundreds of thousands of biomarkers standardized over millions of individuals to allow the algorithms to find patterns, separating the plotted metabolic trajectory of health and disease from “noise”, which is always part and parcel of dynamical systems such as the human body. 

For context to healthcare savings the CDC has shown data that healthcare expenditure during the last 2 years of life for a centenarian is ? less than for someone who dies at the age of 70, with an estimated healthcare savings of $7.1 trillion over 50 years. We feel the MFL model ultimately has the transformative potential for the overwhelming majority of the world’s population to live well beyond 100 years approximating the same lifespan (the age before a major chronic disease ensues). Now imagine the additional vitalizing work and productivity of people aged 100 with a lifespan of 115, who feel energetically, mentally and physically similar to a current day 65-year-old with a lifespan of 80. Such a wide swath of the population would enjoy a life of continued productivity and feeling of intrinsic personal value not needing to live off of government support. What would be the total estimated savings for the US government? Might it be $15 or $30 trillion per 25-year generation?

It allows integration of the totality of bioinformatic data into a multidimensional MFL. 

It is our conviction that, if and when implemented within our healthcare system, it would not only lead to a marked improvement in the state of health of our nation, but also lead to cost savings, better individual outcomes and satisfaction as well as a brighter future for generations to come. 

Physics, over the centuries, has uncovered universal laws of Nature. Modern medicine, with its immense quantity of data and great successes in eradicating many diseases leading to an unprecedented improvement of the quality of life, still doesn’t have an overarching conceptual framework for our understanding of the states of health and disease. This collaboration involving two physicians and a physicist, has made strides toward uncovering an organizing framework for understanding the dynamics of the human body. It provides a visual “map” toward optimum health with the ability to have easy and timely feedback that empowers patients to take control of their own health. 

With the mandate of MAHA supported by the current US administration, it is our hope to contribute to this noble mission by sharing with the decision-makers our insights and implementing the findings briefly described above.

1. Fertig B (2022) Metabolism and Medicine: The Physics of Biological Engines. CRC Press, USA.
2. Fertig B (2022) Metabolism and Medicine: The Metabolic Landscape of Health and Disease. CRC Press, USA.
3. Tuszynski JA (2018) Molecular and cellular biophysics. CRC Press, USA.
4. Nedelsky NB, Taylor JP (2019) Bridging biophysics and neurology: aberrant phase transitions in neurodegenerative disease. Nature Reviews Neurology 15: 272-286.
5. Ding M, Xu W, Pei G, Li P (2024) Long way up: rethink diseases in light of phase separation and phase transition. Protein & Cell 15: 475-492.
6. Verdile V, De Paola E, Paronetto MP (2019) Aberrant phase transitions: side effects and novel therapeutic strategies in human disease. Frontiers in genetics 10: 173.
7. Davies PC, Demetrius L, Tuszynski JA (2011) Cancer as a dynamical phase transition. Theoretical Biology and Medical Modelling 8: 1-16.