Medical Tests: the more the better

I have two gripes with the medical profession. First, doctors should take more data not less. Also, medical professionals should recognize that most clinical trials assume that individuals are identical, which we know is untrue. I don’t direct these gripes at my doctors because I believe they are deeper thinkers than average and accommodate my eccentricities.

All tests have uncertainties and test results can also be affected by uncontrollable variables in people’s lives. As an example, physicians are reluctant to give the PSA (Prostate Specific Antigen) test to younger men under the pretense that anomalously high readings lead to unneeded tests (such as biopsies), which can have harmful side effects. But a paucity of data is the true culprit, where one bad reading in vacuum has little predictive value. The need for early testing is to set a baseline. It’s the change in PSA levels that should be of concern, not a single high reading.

The plot of my PSA data clearly shows a rise above the baseline starting in 2014. Such a slow rise could have many causes, such as an enlarged prostate – a common condition. In 2014, the data were too sparse to establish a trend, so my doctor suggested that we wait until the following year to see if the PSA levels had dropped. The level had not dropped the following year but was still below that magic value of 4, so we let it go another year. Once it hit 4, my doctor ordered an additional test that gives the ratio of free to bound PSA, which also came out too high, so he sent me to a specialist who ordered an MRI. Eventually this led to a biopsy, a diagnosis of prostate cancer and its removal in 2020. But this is an old story. 

See: https://unknownphysicist.blogspot.com/2020/06/i-was-relieved-i-had-cancer.html and https://unknownphysicist.blogspot.com/2021/01/can-major-surgery-cure-depression.html 

More recently, I got COVID while at a meeting in Seattle, then passed it along to my wife. My children had noticed that their resting heart rates had climbed when they had COVID last year. Since we all have Fitbits, I plotted the values for my wife and me, which appear in the plot here. The trends are remarkably similar.

During my travel days, presumably when I picked up the virus, my resting heat rate was at the baseline value of 47 beats per minute. The day after I returned, my resting heart rate began to rise and I tested positive for COVID on the second day after my return. Given that Omicron has a two-day incubation period, testing positive on the second day of my return is consistent with me being infected while traveling on trains and planes. My wife tested positive two days after I tested positive, consistent with me infecting her the night I returned. My resting heart rate at its peak was 53 BPS, which is 13% above my baseline, while my wife peaked at 17% above her baseline. Interestingly, the Fitbit records the average resting heart rate over the full day, and mine read 55 at its peak in the middle of the day before dropping to an average for the day of 53, so if I had used 55 as the peak, my increase too would have been 17% above the baseline. Here again, we can see that a simple measure of a common physiological metric provides useful health information, and used in combination with other tests could yield even better predictions of health risks that inform behavior. Having a nurse measuring your pulse once a year will only catch huge variation, which might miss important red flags. So, we need more testing!

Two family friends who I believe are both excellent physicians tell me that my metrics defy the conclusions of medical studies. My guess is that there are two factors involved. First, each human being has its own genetic makeup. There are many commonalities humans, such as infections making us sick and our immune system fighting back. But the details of how we each respond depend on our genetic programming. People like me have no problem with COVID, which I got in 2020 at a time when vaccines were not available, and the death rate was high. It’s genetic variability that gave me the strength to play ice hockey while infected during a time when others were on respirators and died.

Population studies take averages of metrics over people of varied genetic traits. If a certain diet is found to lead to a 12% decrease in mortality, it could be that 2/3 of the population experiences a 21% decrease in mortality while 1/3 of the population experiences a 6% increase in mortality. So, making recommendations based on an average value might help a majority of individuals, but could be harming a large minority of them. To take such effects into account, any large populations studies should include a breakdown by individual to determine if subgroups have adverse outcomes. Unfortunately, many studies do not have a large enough sample size for such fine-grained studies of the sort needed to get at the truth. In such cases, the studies are more than worthless to the unlucky minority.

Cancer treatments are an example where researchers and clinicians are now beginning to tailor treatments to an individual’s DNA, which results in much improved outcomes. One must ask about the ethics of choosing a medical treatment based on a population average when it is known that some treatments may not only lack efficacy but might do harm to the patient.

Another difficulty is ensuring that study participants adhere to the study’s parameters. For example, many studies use questionnaires to query participants about their habits. How many people on a self-proclaimed low carb diets cheat by eating a bit of sugar here and there. It’s only human nature to downplay our transgressions and exaggerate our virtues. While N=1 studies are unreliable for generalization to the population at large, they are useful to the important 1; the individual being tested.

In a past post, I described how eliminating fruit while keeping the rest of my diet fixed greatly reduced my blood glucose. (see: https://unknownphysicist.blogspot.com/2023/01/fruit-is-bad-for-you-at-least-it-is-for.html) Obviously, many people do well with fruit but not me. As an update to that study, I recently noticed that my blood glucose had drifted upwards during my bout with COVID. It never got into triple digits, but on average was elevated. Then I realized that I had been using lozenges to soothe my throat. They contain honey and sugars, so taking about a half dozen of them per day was enough to be noticeable. Within a few days of stopping, my blood glucose dropped.

For these reasons, I am an advocate for taking and analyzing more data. In aggregate, the data contains much more information than a yearly reading. Also, I believe that I’m more disciplined than the average survey taker, so I can trust my data to decide on a healthy lifestyle that suits my genetics. After my recent data with resting heart rate, I am motivated to cull my data in search of other interesting correlations that I can use to design new experiments. Until then, happy self-experimentation!