This is a question repeated over and over again in the conventional medical consultation. The lab report is scanned for any results flagged as abnormal by both the patient and the healthcare provider. If none are found, then the assumption is that everything is fine. When looking to diagnose a disease to fit the patient’s symptoms, this is approach is appropriate. But when patients come into an age management or wellness practice seeking guidance to achieve optimal health and performance as they age, this approach falls short in several ways.
To understand the shortcomings of practicing what I like to call “normal range medicine” when trying to optimize health, i.e., to deliver literally healthcare not care of the sick, one must understand how the normal ranges for lab and diagnostic tests are determined. The reference range for a lab test is defined as the set of values within which 95% of a healthy population fall. But here’s the catch. What is meant by “healthy” and how is it determined?
The “healthy” population used by the lab to determine the normal range can come from the published literature, the lab’s own database, or the test manufacturer. However, these sources can only screen their population by diagnosed disease, age, and gender. The latter two are sometimes reported with different ranges if there is compelling data to show a significant difference when analyzed by age and gender. But the labs cannot screen their databases for lifestyle and preclinical disease. These two factors can considerably widen the reference range. This results in the inclusion within the normal range of values that are associated with “healthy” people (if defined solely as not having disease) who have poor lifestyles that worsen the functioning of the organ system under evaluation or who are in the early stages of disease but have not yet been diagnosed. If the patient and the provider simply ignore all the results that fall within the normal range, then they miss opportunities to target the effects of lifestyle choices or to pick up early disease.
The PhysioAge Analytics Optimal Health platform avoids the shortcomings of practicing normal range medicine by fundamentally changing the conversation paradigm of lab interpretation away from “Is everything alright?” to “How can I optimize my health?” It does this by discarding the idea that lab results are binary, either normal or abnormal, for the more useful perspective that sees most lab variables as being continuous. This allows it to interpret lab data in a graded manner even within the normal range. “Normal” and “abnormal”/high and low are replaced with more useful categories that span the entire range of results: Optimal, Healthy, Borderline, Diseased, and Critical.
For example, the commonly ordered liver function test alanine aminotransferase (ALT) has a normal range from 9 to 46 IU/L. But many publications demonstrate that the risk of cardiovascular disease increases linearly within the normal range, from the very low end of less than 20 to the high end at 40, even before they are flagged as being abnormal. These studies show that there is an optimal range for ALT (the lower the better) and looking for behaviors or other factors that are keeping the ALT above this optimal level could benefit your patient’s health. In the case of ALT, it could be moderating further what for many is considered moderate alcohol intake or working on weight loss to reduce visceral fat and fatty liver. The opportunity to engage the patient in more healthy activities or even to prescribe supplements and medications to lower a high normal ALT is missed by the normal range medicine mentality fostered by conventional lab test reporting.
Practicing normal range medicine also results in what I call the “arbitrary edge effect.” By focusing on determining what a “cutoff” value for diagnosing a disease with a physiological test, normal range medicine flies in the face of the fact that most biological variables are continuous. For example, if a man has a testosterone level of 399 ng/dL, then he is considered to have a normal testosterone level because it is above the somewhat arbitrary cutoff of 250 ng/dL and doesn’t make the diagnosis of hypogonadism. But he is more likely than not to have the same symptoms of low testosterone as another man whose testosterone level is 230 ng/dL. In my mind, he has been grouped inappropriately with a man whose testosterone is 800 ng/dL because he could very well benefit from a trial of testosterone therapy to see if he responds well. As you can see from the figure below, 399 ng/dL is in the borderline range while 800 is in the optimal range. Of course, there are still edges, but there is a full range of interpretations that are actionable. In the case of testosterone, age is the major factor that causes the decline in testosterone and the need for more finely parsed interpretations within the normal range. Some labs provide age-adjusted labs, but then a testosterone could be flagged as high and “abnormal” if a patient is on testosterone replacement therapy that is giving him youthful levels.
These are just two examples of how PhysioAge Analytics helps patients and providers move away from the dated and disease-focused “normal range medicine” toward an optimal health paradigm. How does the software do this? My team continually review the medical literature to find well-validated studies that have looked at the effect of age, diet, exercise, lifestyle, and other factors that impact how to interpret laboratory data even when it is in the normal range. Once the evidence is sufficient, we add a graded interpretation to the software to produce the spectrums like those depicted above. If your patients are anything like mine, they are not satisfied with being normal–they want to know what can be done to optimize their health!
(In my next blog post, I will show you how the PhysioAge graded interpretations are a fantastic tool for pinpointing which aspects of a patient’s health may be contributing to a higher or lower overall physiological age as measured by the new kid on the block: DNA methylation age.)