THE SCIENCE BEHIND “SCIENTIFIC WELLNESS”

Seattle, April 30, 2019 — Last week, Arivale, the direct-to-consumer “scientific wellness” start-up closed its doors. But the end of Arivale is not the end of “scientific wellness”. The attribute “scientific” is more than for marketing, more than expressing a sentiment. However, to see its substance we have to reach beyond “data science” — which is not science but is what Arivale has relied upon. Deep sequencing and deep learning will not do it, we also need deep thinking. If theory without data is useless, so is data without theory. An epistemic consideration for big data medicine.

In view of the rapid cycles of booms and busts in the arena of big data medicine, the sudden end of the health-tech startup Arivale should not have been worth big news. Yet it sent shock waves into the land of a promising new genre of health service. Because Arivale has spearheaded the selling of “scientific wellness” as a novel type of service, its demise carries the weight of an indicator for the future of an entire field. But beyond all the usual Monday-morning quarterbacking about absence of a market or failure of marketing or of management, etc. there is a much deeper issue that the closing of Arivale shall entice us to reflect upon: What actually is “scientific wellness”, which Arivale has trade-marked, but is now used ubiquitously? I have found not a single onomasiologically solid definition on the internet. A broader epistemological consideration shall hence be in order.

DATA SCIENCE IS NOT SCIENCE

The brute-force collecting, correlating and categorizing of data and then interrogating medical knowledge bases for “actionables” that would improve wellness is all but a scientific method. Such an approach to big data is not the recipe for a home-run in medicine; it is the desperation behind a Hail Mary pass. Yes, data is necessary in science. But data alone is of little use without science, and may even stifle scientific progress. The blind algorithmic sieving of vast amounts of multi-omic, longitudinal data with the hope of finding the gold nuggets that may be monetized, using ever more sophisticated statistical tools, is not science.

PLAUSIBLE CAUSAL MECHANISMS ARE NEAR USELESS IN MEDICINE

The kind or highly plausible, mechanistic explanations as exemplified above just don’t work in medicine (most of the time). Few researchers without long exposure to medical research, including the data scientists and bioinformaticians who now enter biomedicine in droves because of their indispensable skills, appreciate this fact. Clever “engineering hacks”, logically deduced from however solid causal mechanism, no matter whether identified by human reasoning or sophisticated statistical inference algorithms, are not suited for informing care decisions in medicine. The most extreme modern form of such a primitive mode of thought is epitomized by “precision medicine” that naively treats the human body as a complicated Rube-Goldberg machine of sequential molecular mechanisms amenable to linear thinking about causation. This is not how diseases work.

The Self-Operating Napkin by Rube Goldberg (Originally published in Collier’s Weekly, September 26 1931). http://www.tcj.com/rube-goldberg-butts-in/

BEYOND EVIDENCED-BASED MEDICINE AND LINEAR MECHANISTIC REASONING

Enters “scientific wellness”. I propose that the scientific principle warranting the attribute ‘scientific’ is the application of burgeoning formal concepts from systems biology, which is what had originally fostered the idea of “scientific wellness”. The term, coined by Dr. Leroy Hood, is far from a tacit suggestion that current medicine is “non-scientific” –of course modern medicine is based on science. But in Hood’s view, the ‘scientific wellness’ implies an emphasis on optimization of wellness, informed by scientific principles, without explicit reference to ‘disease’. There is a subtle but fundamental, clear-cut difference between “optimizing wellness” and “preventing disease” — beyond the sentimental half-full/half-empty glass analogy. The focus on optimizing wellness is motivated by insights into the very nature of transitions from the regime of ‘being well’ into that of ‘being sick’, if we imagine, following a formalism from the theory of dynamical systems, that these two regimes of health are complementary domains in the space of all possible health states (the green and red zones in the figure below).

SYSTEMS DYNAMICS OF WELLNESS-to-DISEASE TRANSITION (AND HOW TO DELAY IT)

Why is a systems approach different from figuring out individual causal mechanisms in understanding loss of wellness? This gets very technical, but very briefly:

A SYSTEMS DYNAMICS VIEW — an intuitive permissive simplification of the theory. The blue ball and its position represents the heath state of an individual in the high-dimensional space of all possible health states — projected here onto one dimension, the horizontal axis. This state space is divided into two regimes, WELLNESS (green) and ILLNESS (magenta). The “quasi-potential landscape” depicts the stability of a state. As the individual ages, the stable “wellness attractor” becomes destabilized — it flattens, thus slowing down homeostatic recovery (return to the bottom of the potential well). At some point the ball crosses the well-ill regime boundary and “spills” into a new attractor in the red regime, in a self-propelled way (descent). “Optimizing wellness” means to keep the wellness attractor deep for as long as possible.

OUTLOOK: LESSONS (TO BE) LEARNED

Sadly, in current big data medicine, the data scientists do not ask profound scientific questions, they do not formulate hypotheses, let alone frame theories. In a culture of infatuation with big data without theory, to which Arivale may have succumbed over the past few years, we have not only neglected systems theory but also tossed out the classical medical disciplines that embrace theoretical principles and scholarly pursuit, such as biochemistry and pathophysiology, that underlie the path to disease. It is all too tempting to skip all the steps of scholarly discipline, rigorous theory and science in the era of big data. Deep sequencing and deep learning are replacing deep thinking.

Institute for Systems Biology

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