As many of you know, I am writing a book titled, "Health Freedom Lost" that is a 1000+ year look back at medical policy that has not been in the best interest of health.
A buzz word we are hearing way too often - and never in proper context - is "Safe and Effective." Why do I say "never in proper context?" Read on...
I am working on the final chapter. It is a solutions-based chapter but also raises the question,
has modern medicine contributed to improving health - or just the opposite?
The sad truth, based on a critical review of the key elements of modern medicine, the net contribution to health is negative. That is not to say many doctors are not doing right by their patients. It is the system that is holding them back. This concept is discussed in detail in my book.
Here is the link to the webinar video on Safe & Effective - EXPOSED
And now for a deep dive into safe and effective.
What follows is what I have included in the final book chapter this morning. As a scientist I always try to work back to what are called "first principles." The definition of this concept is to break down complicated problems into basic elements and then reassemble them from the ground up. In medicine, this would be to look at disease from a root-cause. It is a process of questioning everything - every single assumption that you automatically apply to a belief.
An easy way to practice first principles is to ask yourself a simple question about everything and anything you believe or are told....
"where did I learn that?"
Asking this question will take you on some very interesting journeys.
Here is my journey into safe and effective. I use LDL lowering drugs as a gateway into this discussion.
Statin Drugs:
The key one is that statin drugs work by lowering LDL. All doctors who prescribe these drugs believe this is the case including one of my best friends. However, lowering LDL increases mortality. So why is there some meager benefit from statins drugs in selected groups? Because the drugs are also antibiotics. The insubstantial antibiotic activity of statins, in some instances, offsets the negative LDL lowering action. Would you take an antibiotic for life? If you are taking a statin drug, then you are doing just that. The drug companies know these drugs are antibiotic as documented in the Chapter on cholesterol.
The new class of LDL-lowering drugs are classified as "biologics." One type is called PCSK9 inhibitors. Unlike statins, these drugs just lower LDL, and do so much more effectively compared to statins drugs. Credible data on these drugs show a clear increase in mortality risk compared to taking statin drugs because they lack the antibiotic action. Lowering LDL is not an immediate death sentence, however. This is the crux of "safe and effective." If they do not kill you immediately, how long does it take to see an effect? Is it 5 years for every drug? That is the timeframe used to measure harm in a best-case scenario.
There is no reasonable doubt that these drugs cause an increase in mortality, Figure 1.
Clinical trials are supposed to run for 5 years. In some cases, these trials are cut short. For example, the study on PCSK9 Inhibitors presented in Figure 1 ran for 3.2 years. In other cases, like in COVID-19, safety and efficacy studies were not run at all. The purpose of the trials is for safety, more so than efficacy. Efficacy can be shown quite early on and by studying mechanisms. However, safety is another matter.
Five years for a safety study is a completely arbitrary time (where did you learn that?). It is designed based on the presumed need to make the drug available timely while properly investigating safety and efficacy. Many drugs, like statins and the other LDL lowering drugs may be taken for 40 years. Let us examine how nature collides with the 5-year testing window, Figure 2.
To put the Figure above into context, consider a couple of examples.
Example 1, running a 100-meter dash: Most of us can walk the distance in 60 seconds. With some training, jogging that distance in 40 seconds is realistic. With much more training, maybe in a couple of months, most of us could travel that distance in 20 seconds. However, how many of us could ever do this in 12 seconds or less? Think about the training effort required to get to that point. Which curve better fits the effort required to run a 12 second 100-meter dash, the red or green curve?
Example 2, growing pumpkins: As a child I wanted to grow a 100-pound pumpkin. The closest I got was 84 pounds. Not bad. Every Spring it was the same process: plant the seed; wait what seemed to be an interminably long time for the seed to germinate; go to the garden daily to observe the growth of the vines; notice that suddenly vine growth was explosive; then the fruit would start forming - slowly at first - and then progressively faster. Each phase followed the green curve, not the red curve.
Example 3, heart disease: The person has some mild risk factors. Over time, biomarkers start edging up slowly. Stress increases in the person's life and the biomarkers start going up more dramatically. The person develops mild symptoms which are largely ignored. Suddenly the person experiences a tragic adverse health event. Hopefully the person lives. This sudden event is much more accurately explained by the rapid upswing of the green curve.
What does this mean in terms of 5-year (or less) safety and efficacy trials? If you are taking the drug beyond this period, you could be at great risk of an adverse event. In the example presented in Figure 2 above, the likelihood of an adverse event at year 14 is 50 times more likely than at year 5. Fifty times is a 5,000 percent increase in risk.
Here is a look at the PCSK9 Inhibitor data from the 3-year trial shown in Figure 1.
• Study time = 2.3 years
• Number in study = 28,000
• Number of deaths in control = 426
• Number of deaths in the PCSK9 group = 444
• Total number of deaths caused by the PCSK9 treatment: 18
• Relative increase in deaths = 4%
• Absolute increase in deaths = 0.06%
• Potential increase in deaths over 14 years based on Figure X above
o Relative increase in deaths = 200%
o Absolute increase in deaths = 3.2%
o Total increase in deaths = 900!
There is no fuzzy math being used here. This just demonstrates that when drugs are used long-term, the short-term safety data may be completely irrelevant.
I will be presenting this concept and looking forward to your comments in an upcoming webinar.
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