Scientific Wellness (quantitative health), what is that?


Conventional medicine, at least in the Western world is based on the notion that medicine developed against a particular ailment or disease will benefit basically everybody with that disease. This implicit believe is also the basis of the common design of clinical studies evaluating new drugs. Patients are selected solely according to their diseased status. 


Although it was long known that different people do not respond the same way to the same drugs, this was either ignored or accepted as an inevitable and incomprehensible consequence of human diversity. The general perception was that such differences could not possibly be taken into account since the basis of these differences was unknown. It was taken care of by by trial-and-error schemes and prior knowledge derived from earlier treatments.


More and more physical parameters yielded themselves to measuring in the clinical laboratory or more recently in research laboratories. Scientist and clinicians were the first to systematically correlate particular measurable physical parameters from then analysis of body fluids (such as blood or urine, or saliva) or tissue samples (e.g. from biopsies or surgeries) not only with particular diseases but also with the outcome of treatments by drugs. This allowed the creation of catalogs of metabolic or genetic constellations for groups of patients or even individuals favorable or prohibitive with regard to selected drugs. This process in general is now summarized under the term “stratification” in personalized Medicine. 


However, taking quantitative measurements of parameters has ist own perils. It is very difficult to define thresholds for individual parameters indicating a “healthy” or “diseased” status for an individual. All measurable parameters naturally occur in a wide range. If one or even several parameters are measured outside the “normal” range this by no means always indicates a health problem. 


Fig. 1 Many parameters define health 


As illustrated in Fig. 1, parameters in all patients are spread out over a range that may span from healthy to diseased. Only a wider collection of parameters (a parameter cloud) and their overall location makes the difference between health and disease. 


The whole picture is highly dynamic and can shift between the two extremes health and disease depending of changing circumstances. Prevention is meant to prolong the time the parameter cloud hovers in the predominantly “healthy area”. Any treatment (therapy), if successful, is supposed to shift the equilibrium of this parameter cloud back towards healthy from diseased. 


Therefore, despite the individual quantitative measurements we can obtain, parameters form a continuum with a shifting gravity center, rather than providing clear-cut answers on their own. Unfortunately, this is also true to some extent for the best of the biomarkers (indicators of specific conditions), which is why individual biomarkers are inherently suboptimal predictors.  


How is all that connected to scientific wellness? Let’s just look at the parameter-cloud in the center of Fig. 1. This person will be most likely rated as healthy. Maybe there is a slight inconvenience noticed occasionally, but by an large this is supposed to be a health person. However, a decade earlier this person might have presented the parameter-cloud on the left side. It might be possible that we are looking at someone sliding out of health without even noticing. A regular health check might not reveal this and as a consequence the person might end up later on as a patient - but maybe only another decade down the road. 


If the complete or near-complete parameter-cloud would be recorded early on in best heath (at least as felt by the person) it would be possible to judge wellness/health in more objective and reproducible way. This would also allow to develop early on recommendations for actions the person should take preventing any sliding towards less wellness or even disease. This is the predominant aim of scientific wellness.


There are basically two types of predictive parameters that can be looked at: Genetic dispositions, that persist for life-time. Therefore, they need to be check only once unless some neoplastic problems arise later on (tumors). The larger group of parameters is dynamic, i.e. changes frequently over time. The parameters need to be evaluated regularly. The upside is that such parameters are suitable to monitor the shift between wellness, health and disease and to control the success of any intervention taken. 


Of course, such extended checkups come at price, especially if this is extrapolated to a larger portion of the population. This is often used as a argument that such an approach would not be feasible (meaning financially). However, this totally ignores the billions of $ spent year after year on the treatment of often preventable diseases such as obesity, diabetes, or lifestyle induced cardiovascular complications. This is often countered by the assumption that people will not follow recommendations and thus there will be no money saved later on as they become ill anyway. In my mind there is mounting evidence that this in lo longer true. People invest into all kinds of fitness gadgets, flood fitness studios and try everything to produce measurable success, either by loosing weight, lowering their pulse rate, being able to run faster and longer, etc. 


Scientific wellness will extend this motivation in two ways: First, a number of so far “invisible” parameters become measurable. Second, individuals can be alerted to personal problems that are independent of general fitness but were they can take actions to prevent such problems to develop into diseases. 


What’s coming up next?


Next week I want to illustrate several aspects of this general description at the example of a cooperation of two institutions (Providence Health & Services and Institute for Systems Biology).

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