Why You and Your Body Add Up to More Than Just a Diagnosis or “Normal” Lab Range Value







A  Different Treatment Plan for Every Unique Person Vs. One Pill for Every Common Ill

This weekend, I was able to share on my new book, BreakFree Medicine with a wonderful audience in Saratoga, NY.  The BreakFree Medicine model is healthcare based on patient empowerment that combines the science and art of medicine through the philosophies of naturopathic and functional medicine. It is one that acknowledges the current pitfalls and problems in our current model of sick-care and focuses on true health.

In order to treat someone holistically, we need a model that is based on all of the following:

  1. honoring the therapeutic partnership between the healthcare practitioner and the patient or client
  2. choosing from the best in conventional and natural treatments based on a person’s unique biochemical needs
  3. treating the whole person in a systems-based and integrated model
  4. addressing the cause to move beyond symptom control and the delusion of the “miracle pill cure.”


The Problem with Numbers

During my talk, I discussed an article I mentioned in my series that questioned the sanity of our current medical model. Specifically, I highlighted the findings from the Journal Sentinel and the Chicago Tribune that reported on concerns for basing diagnosis (up to 60-70%) on lab tests that haven’t been proven to be accurate and may vary from one lab company to the next. Furthermore, the Journal Sentinel alluded to the fact that the accrediting process for laboratories was also lacking. This was due to the failure to address potentially harmful consequences from inadequate lab policies and procedures. Furthermore, lab ranges are based on wide fluctuations of a random sample of people who are not displaying symptomatology of a selected disease. This is a far cry from functional measures of a population that is truly healthy and evaluating for clinically relevant markers on how to maintain vitality.

Recently, Science Daily reported on the findings from the Icahn School of Medicine at Mount Sinai, that other factors, such as “testing service, subject, blood collection time, and “other”, i.e., residuals, unexplained by other covariates”, were also a factor in lab accuracy. This is in spite of the fact that the researchers accounted for food intake (fasting), exercise, fluid, weight, and substance abuse of the subjects being assessed.

Furthermore, the study found differences between a test that used finger pricking versus blood draw when reporting on the same measures of lipids and complete blood count. Science Daily summarizes the findings based on 60 individuals below:

Scientists from the Icahn School of Medicine at Mount Sinai performed an in-depth comparison of basic blood tests run by commercial laboratories to assess comparability of the tests among the different laboratories, finding that testing service and time of collection significantly influenced results. Given that lab tests are used to help decide everything from disease diagnosis to whether a patient needs medicine or whether that medication is working, this study highlights the importance of knowing the accuracy and variability of test results.

The IRB-approved research study, which was first designed in early 2015 with data collected last July, analyzed results from comparable blood tests on healthy adults conducted at LabCorp, Quest Diagnostics, and Theranos.

The authors concluded in their results in the Journal of Clinical Investigation (bold emphasis mine):

Given the large amount of variability, a single measurement can be misleading. Additionally, nonequivalence between testing services raises concerns about what might be biological changes that are clinically meaningful versus methodological differences that haven’t been standardized. For example, the large intra- and interservice variability in specific lab tests (e.g., platelets) may have clinical implications for individual treatment decisions (Figure 7).


Bottom Line on Labs:

A clinician should base suggestions not simply on lab markers finding a disease, but on function of the body, the current symptom picture of the patient, and individual variations in biochemical needs (nutrition, diet, stress, genetic variations in enzyme function, etc.) Furthermore, doctors should work with patients in a therapeutic partnership that is truly based on finding the root cause for illness, not just manipulating numbers that may be inaccurate or based on false assumptions. (See this website for more information on harms verses risks for medication interventions.)








The Pitfalls of Diagnosis

One of the biggest “preventative tests” that is the most controversial is mammography. Many clinicians are confused with the studies that report on when to screen and for which patient populations. This is due to studies reporting on risk of over-diagnosis weighed against the concern for each life. In 2015, the American Cancer Society stated in their article in JAMA that screening should begin at 45 years of age, “While the 5-year absolute risk of breast cancer increases steadily over this age span, the 5-year risk among women aged 45 to 49 years (0.9%) and women aged 50 to 54 years (1.1%) is similar, and greater than that for women aged 40 to 44 years (0.6%).”

The U.S. Preventive Services Task Force recommends biennial screening for women over 50 years of age and determination if screening is necessary prior to age 50 by assessing individual risks. The American Congress of Obstetricians and Gynecologists (ACOG) believes women should start at 40 years of age.  The World Health Organization’s conclusions from who can benefit from mammography are a little more complex and based on the resource setting and age group. The WHO’s 78-page paper includes summaries of trials and the outcomes, risk for biases, and harms caused by over-diagnosis, among other factors. Interestingly, the GDG (Guideline Development Group) did not consider all-cause mortality data for recommendations due to lack of sufficient data. Furthermore, the concern for bias in study design, population selection, and follow-up was stated:

General considerations

There is evidence across all age groups that organized population-based mammography screening programmes can reduce breast cancer mortality by around 20% in the group invited to participate in screening versus the uninvited group. In general, there appears to be a narrow balance of benefits compared with harms, particularly in younger and older women.

There is uncertainty about the magnitude of the harms – particularly overdiagnosis and overtreatment. In addition, the best trade-off seems to be provided by screening every two years.

All-cause mortality was rated as an important outcome for decision- making. However, in view of the limitations of the available data the GDG did not consider these data to be sufficiently accurate or reliable to influence the recommendations.

Recently, a study was released that stated that about half of women at age 40 have risk factors enough to warrant mammograms. This provided scrutiny from the above recommendations to begin at 45 or 50 years of age.  What is a woman (and her doctor) to do?

The biggest issue is one in which overdiagnosis causes undue emotional, economical, and physical harm on a woman. An article in the New England Journal of Medicine stated the following in 2014:

What about the risk of overdiagnosis — being diagnosed with and treated for a tumor that would never have become clinically significant? The potential toxic effects of treatments, ranging from chemotherapy and radiation to lumpectomy and mastectomy, make overdiagnosis the greatest potential harm of mammography screening. Though overdiagnosis has been notoriously difficult to quantify, a recent analysis of data on mammography screening over the past 30 years suggests that of all breast cancers diagnosed, 22 to 31% are overdiagnosed.6 Nevertheless, there are few risks of this magnitude that are more “off-screen” than overdiagnosis.

The first challenge in conveying this risk to women is that many are simply unaware that overdiagnosis occurs. One survey showed that only 7% of women believed that there could be tumors that grow so slowly that an affected woman would need no treatment; another study showed that women found the concept confusing even after a brief educational intervention. After being educated, women thought the information should be considered in decision making, but most believed it would not affect their own intent to be screened.3,7

This disconnect between awareness and intent speaks to the fundamental challenge of conveying the potential harms of mammography screening. That is: we do not think risk; we feel it.

Many people may be unaware of overdiagnosis in general. A recent UK study reported in the BMJ:

Data from 390 participants were analysed. Almost a third (30.0%) of participants reported having previously encountered the term. However, their responses often indicated that they had no knowledge of its meaning. The most prevalent theme consisted of responses related to the diagnosis itself. Subthemes indicated common misconceptions, including an ‘overly negative or complicated diagnosis’, ‘false-positive diagnosis’ or ‘misdiagnosis’. Other recurring themes consisted of responses related to testing (ie, ‘too many tests’), treatment (eg, ‘overtreatment’) and patient psychology (eg, ‘overthinking’). Responses categorised as consistent with ‘overdiagnosis’ (defined as detection of a disease that would not cause symptoms or death) were notably rare (n=10; 2.6%).

Could overdiagnosis be partially explained by treating too aggressively too early, rather than the screening itself? It was suggested in the past that since mammography, the incidence of ductal carcinoma in situ (DCIS) has risen. This may be a factor with overdiagnosis. The following excerpt from the Journal of National Cancer Institute stated:

Corresponding to the increased use of mammography, the incidence of ductal carcinoma in situ (DCIS) has risen dramatically in the past 30 years. Despite its growing incidence, the treatment of DCIS remains highly variable and controversial. Although DCIS itself does not metastasize and is never lethal, it may be a precursor of invasive breast cancer and is a marker of increased breast cancer risk. Confusing a precursor lesion with cancer, many clinicians apply an invasive breast cancer treatment paradigm to DCIS patients, offering adjuvant radiation therapy and tamoxifen after diagnosis. In this commentary, we outline the issues associated with DCIS management—is DCIS a cancer, a precursor of cancer, or a marker of invasive carcinoma risk? Specifically, we argue that consideration be given to removing the term “carcinoma” from DCIS, using cancer “occurrence” to mean the diagnosis of invasive cancer after DCIS instead of “recurrence,” and make the argument that a prophylactic paradigm of treatment after excision may be more appropriate.

Interestingly, there was just a recent article on the overdiagnosis of a type of thyroid cancer and reclassifying encapsulated follicular variant of papillary thyroid carcinoma (EFVPTC) to “noninvasive follicular thyroid neoplasm with papillary-like nuclear features” (NIFTP). The authors conclude (bold emphasis mine):

Thyroid tumors currently diagnosed as noninvasive EFVPTC have a very low risk of adverse outcome and should be termed NIFTP. This reclassification will affect a large population of patients worldwide and result in a significant reduction in psychological and clinical consequences associated with the diagnosis of cancer.


Bottom Line:

More diagnosis and findings of diseases doesn’t necessarily mean better outcomes. Recently, it was found that expansion of Medicare increased in the poor, but reports for overall health outcomes due to these health services being more accessible were inconclusive. Health Day reports:

Using data from a large, national survey of more than 40,000 low-income adults, researchers at the University of California, Los Angeles, and the University of Michigan examined what they said were a broader array of outcomes than were included in previous Medicaid expansion studies.

The findings were reported online April 18 in the Annals of Internal Medicine.

Among Medicaid expansion states, the study revealed a sharp, 6.6-percentage-point increase in the proportion of low-income adults who reported seeing a physician in the previous 12 months compared with states that did not expand eligibility under the Affordable Care Act (ACA).

Diagnosis rates of diabetes and high cholesterol also increased significantly in the expansion states versus non-expansion states, the study found.

However, researchers were unable to show that low-income people were any healthier as a result of the Medicaid expansions. They found no improvement in self-reported health among low-income adults in the expansion states.

It’s important for us to be focusing on true preventative measures such as lifestyle, diet, and exercise for quality of life, not just length of lifespan. It was just reported that Americans live longer, but not healthier. Diagnosis and finding cancer is of utmost importance, but clinicians should be weighing the individual risks for the patient in front of them, not basing decisions for screening only on one factor, such as age.


This team are making a name for themselves







The Future of Diagnostics and The Patients Needs

Thankfully, new functional assessments and genomics are creating more integrative diagnostics based on individual risks and how to support body processes. One new test is matching genomics with FDA treatments. Still, wouldn’t it be great to expand this to lifestyle interventions and diet as well? This should be considered a standard of care, in my opinion. Furthermore, even genetic mutations may not translate to disease. A breakthrough study in Nature reported on how genes are not destiny after studying over a half a million individuals:

The finding, published on 11 April in Nature Biotechnology1, demonstrates an important proof of concept: by sharing massive genomic data sets, scientists can find healthy individuals who harbour mutations that normally cause disease. Studying these people can help scientists to identify factors that protect against sickness, says Stephen Friend, a co-author of the study and director of Sage Bionetworks in Seattle, Washington….

Researchers have known for years that patients with mutations that should cause death or disabling conditions can go on to lead extended healthy lives. For example, women with a mutation in BRCA1 have a high risk of developing breast cancer. Yet many women who carry that mutation do not develop the disease.

Bottom Line:

So, genes may predispose us to certain diseases or even mood states, but they aren’t our destiny.

Several more studies this month pointed out the interplay of epigenetics. These gene-environment interactions can affect health outcomes in a variety of ways. A worm study displayed the potential mechanism of how stressors that can turn off and on certain genes can be passed down from generations. Furthermore, our environment can shape how our body processes and extracts nutrients from foods. In fact, a study in Molecular Biology and Evolution reported that genetic variation helped certain populations derive an increased amount of healthier polyunsaturated fatty acids from plant foods in times of meat scarcity. This provided an adaptive advantage.

Using rodent studies, which have inherent flaws, may be helpful in determining disease mechanisms,  but should not be used as a basis for treatments. This is why we need clinical trials. However, these trial have their own issues with reproducibility in the real world as well. It’s time to use more than generalities in populations and combine these studies with individualized and personalized medicine.

We also need to consider the role of the mind and social connections more. Click here to read about this.



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