The standard ranges in the software are obtained from Quest Diagnostics and represent the " normal" ranges from the largest blood lab in the USA.
I think an important question to ask ourselves is how are “Normal Values” derived? As you likely know, the majority of the conventional, standard, or “normal” reference ranges are based on the Gaussian distribution of a bell curve, which says that 95% of the population are “normal” and 2.5% of the population is above the “normal” range and 2.5% is below the “normal” range. The “normal” range is based on statistics and not on whether a certain value represents good health or function. “Normal” ranges are designed to identify and diagnose disease states, tissue changes, and pathology. When allopathic physicians review a patient’s blood test results, their only concern is when a particular result is outside the “normal” laboratory reference range because values outside of the normal range are designed to identify and diagnose disease states and pathology.
The “normal” reference values tend to change from year to year depending upon the prevalence of disease in the general population. As our population becomes more dysfunctional and obese and suffers from more cardiovascular disease, the “normal” reference range gets wider and wider. This leaves a larger number of the population testing in a range that is considered “normal”, which means, from our perspective, that a vast majority of these “normal” people actually have some medical problems. The problem is that “normal” reference ranges usually represent “average” populations, rather than the optimal level required to maintain good health. “Normal” does not mean optimal. Clearly, most “normal” reference ranges are too broad to adequately detect health problems before they become a pathology and are not useful for detecting dysfunction.
Lastly, normal lab ranges will vary from place to place and from state to state. This means you could be diagnosed with hypoglycemia in Maryland but be completely normal in California
What you really want is “optimal” health as opposed to “normal” health, which is why I use optimal ranges that don't change based on the prevalence of disease in a population, nor by geography.
How are optimal reference ranges determined?
The best way to determine optimal ranges is to identify levels associated with optimal health or, at the very least, the absence of disease. The research will sometimes, almost as an afterthought, note that certain levels observed during studies were associated with an improvement in health. This valuable information can be gathered and cataloged to help establish a useable database of optimal ranges.
Of course, it is first necessary for clinicians to recognize exactly what a biomarker represents. Indeed, “understanding the relationship between measurable biological processes and clinical outcomes is vital to expanding our arsenal of treatments for all diseases, and for deepening our understanding of normal, healthy physiology.”
Biomarkers may reflect underlying deficient, insufficient/suboptimal, adequate, or excess nutrient status. For example, elevated homocysteine may indicate an insufficiency of vitamins B6, B12, riboflavin, and folate. A suboptimal alkaline phosphatase level may be indicative of suboptimal zinc status. Monitoring trends toward or away from optimal ranges will assist in identifying nutrient insufficiencies that may be corrected before outright deficiency and morbidity occur.
So, in summary, allopathic physicians evaluate blood chemistry in comparison to ranges that determine pathology. If pathology is not present, the patient is considered “healthy.” If your numbers are within the normal range then everything is normal. However, we know that the majority of patients that report to our clinics are by no means optimal and often have disruptions in the physiological function i.e. they are dysfunctional. From my perspective, normal is not the same as optimal and “normal” is a far cry from being functionally optimal.
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 Lamers Y. Approaches to improving micronutrient status assessment at the population level. Proc Nutr Soc. 2019 May;78(2):170-176. doi: 10.1017/S0029665118002781. Epub 2019 Jan 15. PubMed PMID: 30642406. https://www.ncbi.nlm.nih.gov/pubmed/30642406
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