A two-volume series of books entitled Metabolism & Medicine was published recently that details a scientific approach for not only predicting disease, but for preventing it as well.
Written from the perspective of a full-time clinician in collaboration with a world-renowned biophysicist, the author of this tome is Endocrine Society member Brian Fertig, MD, founder and president of the Diabetes & Osteoporosis Center in Piscataway, N.J.; academic associate professor at at Hackensack Meridian Health Medical School; and chair, Department of Diabetes and Endocrinology at Hackensack Meridian Health, JFK University Medical Center, and who hopes his new book will provide a model to change the future of medicine and medical training.
Sparked by the sudden death of a dear friend and colleague, Fertig leveraged his decades of clinical experience in diabetes, endocrinology, and metabolism, and over a decade of subject-matter research, to conceptualize and contextualize a new model of patient care. Metabolism & Medicine introduces a novel understanding of metabolism within the human body using a framework of physics, integrated through the lens of physiology and medicine.
To learn more about this unique approach to patient care, Endocrine News reached out to Fertig to learn more about his inspiration for the book, its target audience, and how his unique patient care model could potentially impact the future of medical care and medical training.
- What inspired you to undertake writing Metabolism & Medicine, and who is the target audience?
The target audience is biomedical researchers, clinicians, students, and interested lay people with a background in the basic sciences.
Essentially, I strongly believe that we need to do better than the current standards of care. Medicine needs to be more integrated and, importantly, the term “alternative” medicine should be removed from the lexicon because it implies one or the other. While functional or integrative medicine and traditional establishment allopathic medicine are two different tracks of care, they converge in practice. The body is an integrated soul, and we need to assimilate the different fields of medicine and healthcare to reflect that. The Physiological Fitness Landscape is a general framework for integrating insights from all disciplines of science and medicine. It’s integrative, not alternative.
We’re closing the fragmented knowledge base disparities across physician, nurse practitioner, and physician assistant providers. Moreover, not only does no single professional have all the information or insights, but the scientifically integrated team produces an outcome which is far greater than the sum of its parts.
As healthcare providers we often feel ill equipped to save patient lives because elements of care are over-compartmentalized. We’re not listening to insights from other disciplines, such as physics, to create models that will help us live longer, animated healthier lives vitalized by a sense of collective belonging with social bonding and networking, as well as the fulfillment of goal-oriented accomplishments and pride.
Medicine is often a dangerous game we play. Danger is inherent in the art of empirical observation that calibrates risk benefit profiles, even carefully followed over time. This stems from treating the symptoms and signs of disease, and not the critically important root cause.
Medicine is an art that’s built on the science of reductionism. Reductionism is the legacy of the 20th century and the crown jewel of analytical science and scientific discovery that taught us linear systems such as gene coding of proteins and molecular pathways of cell biology. However, we have reached the limits of linear predictability and reductionism for advancing medicine because the integrative matrix of bio-physiology is characterized by dynamic balances of metabolic interactions, which are inherently nonlinear and thus nonpredictable. Aristotle described this as emergence, which he called the opposite of reductionism.
The need to predict emergent behaviorled us to introduce the model of the Physiological Fitness Landscape, or PFL. The PFL is a personalized, precision, and dynamic scale of medicine, a general framework for integrating all disciplines of science and medicine, and a blueprint for algorithms of AI (artificial intelligence), that together bolster the science of medicine, capable of predicting emergent behavior of an individual to ultimately prevent disease as well as predict patient responses to therapeutic interventions. It invokes the evolving fields of bioinformatics and healthcare AI that integrates an enormous amount of biological data obtained from patients dynamically at intervals over time. The microbiota, for example, is the most complex of all and it will be critical to understand how symptoms of disease correlate with changes in the microbiota.
Multi-disciplinary teams including all subspecialties of medicine and the life sciences, physics, biophysics, computer science, and so on, will support the healthcare practitioner with whom the patient will share a chip. With each visit, the practitioner will obtain a current set of blood, urine, sputum and fecal samples, and possibly targeted tissue biopsies. From a baseline of optimal health, the chip will be continually updated every three, six, or 12 months and juxtaposed to people with similar profiles to determine a trajectory of disease.
2. Discuss your patient care model and how it could impact the future of medicine as well as medical training.
The PFL Model is a scientifically based general framework for integrating all areas of medicine and science. This includes the integration of sub-elements. It requires high-dimensional bioinformatics, or big data, including a blueprint model of AI.
The AI blueprint is unique because it employs not only a personalized scale of medicine, but also a mathematical precision scale of medicine rooted in physics, and it’s dynamic, which means it is updated over time. The blueprint was inspired by a hugely successful Fitness Landscape model that predicts physical-chemical phase transitions, such as between ice and water, and has already been applied to physiology in the context of evolution by predicting animal survival in changing environments. However, sadly, even tragically, it has never been applied to medicine where it would have maximum impact on human life.
Medicine is often a dangerous game we play. Danger is inherent in the art of empirical observation that calibrates risk benefit profiles, even carefully followed over time. This stems from treating the symptoms and signs of disease, and not the critically important root cause.
Importantly, it does not replace doctors or healthcare providers because the relationship of the practitioner is irreplaceable and distinctly personal. Physicians are relied upon to understand patient fears, expectations, support systems and more, capable not only of quelling sometimes intense anxiety and anguish, but to effectively manage them medically. The physician becomes a support system and friend. It’s a warm empowering, trusted, and necessary relationship incomparable to a computer.
However, there are limits to the knowledge physicians and other practitioners possess. While practitioners need to remain the central interface of patient care, they are a part of a broader team of AI, bioinformatic, and other interdisciplinary experts.
Medicine is an art built on science. The art employs empirical observations while reductionism provides the foundation of science. Predictability comes forth by observing and analyzing data over time and calibrating risk and benefit based on experience. However, such empirical observation may have surprising and dangerous adverse outcomes. The PFL model uses bioinformatics and AI to promote a more bolstered science capable of forecasting emergent physiological behavior, and thus allowing the prevention of pathology and disease onset, as well as the prediction of patient responses to therapeutic applications.
For solutions that are non-pharmacological, we need to follow up response in the same way by securing quantitative and qualitative information from the patient and juxtaposing that to the actual objective. With this approach, every person on the planet will have their own PFL which will be updated over time, in close relationship with the physicians and other practitioners.
It means there will be a full team of people who are working in conjunction together, while not disrupting the relationship between patient and clinician.
We’re closing the fragmented knowledge base disparities across physician, nurse practitioner, and physician assistant providers. Moreover, not only does no single professional have all the information or insights, but the scientifically integrated team produces an outcome which is far greater than the sum of its parts. What I’m pursuing is a robust objective to ensure we achieve the ideal level of knowledge and scope of knowledge across each discipline of science and medicine and integrating it with technological potential. Ultimately, that will lead to substantially better care.
3. What is the next step for physicians and other caregivers in delivering the most effective patient care?
It’s all about addressing the differences that exist in knowledge and insights. The AI we’re putting forth is going to address this important need. The PFL is an integrative cultural shift that will fix this. We’ll have personalized hundreds of thousands of data points that will define the trajectory of a patient’s health at any time and its projection into the future. We also need to strengthen trust bonds between clinicians and patients and deliver on the commitment and compassion that drives healthcare providers. We’ll have the data and approach to make that happen. Right now, I believe we’ve reached the limits of what reductionism can do in the medical field. It’s time for a new paradigm to move us forward and deliver quality, personalized care.
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