Self Test - Part II: the effect of ezetimibe on visceral fat

 

This post focuses on the power of smoothing, which is a process that picks out trends from noisy data.  We'll see that using a low-cost bathroom scale that has a visceral fat reading, we can see an observable effect that is correlated with the start of taking Ezetimibe, a cholesterol-reducing medication. 

My Yunmai bathroom scale provides a measure of visceral fat, that greasy stuff that lines our organs.  Higher percentages of it in our body increases risks of all sorts of diseases, so visceral fat is a good quantity to track and try to minimize.  The best way to reduce its percentage is to exercise to turn fat to muscle and to lose weight.

My scale has four electrodes, which are arranged in two pairs.  The left and right pair measure the resistance of the soles of each foot.  The resistance from one foot to the other measures the resistance of your body.  Combined with your weight and height, a formula is used to estimate the visceral fat.

I talked to an engineer at Cal Tech whose research was in the general area of biometrics, and she told me that such resistance measurements are related to the visceral fat, but they are not so accurate.  Furthermore, scales such as mine are biased towards the lower part of the body while those that use electrodes that you grasp in your hands is biased to the upper body.  So, corrections need to be made.

The bottom line is that I do not trust the absolute measurement but changes in the reading has some meaning provided that the scale is sensitive enough to detect the changes.  In my case, my reading ranges from 5 % to 9% percent as whole numbers.  The black points in the figure above show the visceral fat percentage as a function of date.  These points form horizontal lines at the whole numbers and alone give us very little useful information.

However, readings fluctuate between 8% and 9% at early times then between 7% and 8% and so on, implying that the visceral fat is falling with time.  Smoothing is the process of averaging adjacent points.  The red points show 50-point smoothing, where 25 consecutive points to the left of the data point and 25 to the right are averaged and plotted at the middle point of the range.  As a result, we get values between the whole numbers.  Suppose that my visceral fat is at 6.8%.  Then, the scale would read 7.0% more often that 6.0%, so the 50-point average would yield something around 6.8%.  Smoothing also eliminates day to day fluctuations that might hide the long terms trends.

The blue line shows 200-point smoothing, corresponding to over a half-year smoothing window.  This eliminates all but the most long-term trends.  The light blue vertical line in mid-2018 shows the date on which I started to take Ezetimibe, a medication that reduces cholesterol.  In my last post, I described how I used smoothing and a fit to a saturated exponential that found that my weight increased by 3 pounds after I started taking Ezetimibe.  The visceral fat data correlates with the observed weight gain.

To summarize the graph, smoothing shows my visceral fat falling from late 2016 and leveling off in early 2018 while I was on my high-fat diet.  This correlates with my weight loss.  Then, after taking Ezetimibe, my visceral fat started a long-term climb from 6% to about 6.5%.  Smoothing has allowed me to determine this rise to a precision that exceeds the whole numbers provided by the scale.

Finally, there is a dip in the 50-point smoothed curve that falls below 6%.  There is also one reading of 5% as seen by the black point.  The date corresponds to one week after my radical prostatectomy surgery and correlates with my initial weight loss then weight gain after the surgery.  Interestingly, the 200-point smoothed curve shows two plateaus – the first after starting the Ezetimibe and the second one after my prostate surgery.

As I have stressed in my past posts, this is a single experiment with potentially many confounding factors, so we cannot conclude that Ezetimibe increases visceral fat.  However, the result provides a hypothesis that could be tested with a larger number of participants.  Putting it all together, Ezetimibe drastically reduced my serum cholesterol (a huge effect that is consistent with controlled experiments) and is correlated with a small weight gain.  This added data shows that it might add to visceral fat.  This brings up the point that medications have complex effects on the body.  They work as intended to treat one condition, but the side effects might oppose some of the benefits.  In this case, the drop in cholesterol is far greater than the potentially ill effects of a small gain in visceral fat.

If there are others out there like me, pooling our data together could increase the confidence that the correlation is not a mere coincidence.  So give it a try!

Self Testing - Part I

 Those of you who know me will not be surprised that I love data.  Even noisy data can bring a picture into focus if there is enough of it.  I collect data on all sorts of things but find biometrics fun because it’s easy to collect and it can potentially give useful insights.

A couple of previous posts alluded to some obvious trends that I have observed.  To all those people who mocked me decades ago for starting a low carb diet, contrary to their predictions that I would gain weight, I shed 50 pounds by eating mostly fat with a bit of protein and as few carbs as possible.  In the process, my cholesterol also dropped to healthy levels (see http://unknownphysicist.blogspot.com/2011/10/eating-lots-of-fat-to-lose-weight.html).  I kept my weight down for a decade.

After a trip to Belgium and then to Italy, I adopted a Mediterranean diet, which was a gateway to me eating lots of carbs again, which produced an upward spiral in my weight.  Then in 2012, when I had almost regained all the weight that I had lost, I went on a strict low carb diet for a second time.  Again, it worked, but over the last two years, I have struggled with a small weight gain, whose source I have tried to identify using my vast stores of data.

In the interim, PSA (prostate specific antigen) data that I had been tracking took off, as I described in my post describing my ordeal with prostate cancer. (see http://unknownphysicist.blogspot.com/2021/01/can-major-surgery-cure-depression.html and http://unknownphysicist.blogspot.com/2020/06/i-was-relieved-i-had-cancer.html)

The graph below shows my weight as a function of time.  There are a series of drops each followed by a plateau.  Also plotted are my measurements of total cholesterol and LDL cholesterol along with error bars that reflect the accuracy of my instrument.  LDL is the bad cholesterol.  My triglycerides were always very low, so I was not so concerned about my cholesterol while it slowly fell over 4 years (1500 days).  Then, for no apparent reason, my cholesterol shot up even as I was losing more weight.

  

Both my doctor and I got concerned with the rise, so he prescribed the highest dose of atorvastatin – which scrubs serum cholesterol.  My cholesterol remained high for months while taking it, so he sent me to a cardiologist, who suggested I add ezetimibe, which prevents cholesterol from being absorbed through the intestines.  I was reluctant to take ezetimibe because I had had a bad experience with Vytorin, which combines atorvastatin with ezetimibe.  I hadn’t realized how much energy Vytorin was sopping away (and resulting in weight gain) until I accidentally stopped taking it for a week.  I then regained my usual vigor.  That’s when I stopped Vytorin and went back to my strict diet.

My cardiologist suggested that I take an ultralow dose of Ezetimibe, which I did at the time shown by the vertical gray line in the plot.  Indeed, it drastically decreased my serum cholesterol, as you can see in the plots, and I retained my energy levels within my ability to notice.  And my weight appeared to remain stable, as shown within the gray dashed box.

The plot below shows a magnified view of my weight after starting to take Ezetimibe.  The raw data (points) is quite noisy and can vary by as much as 6 pounds over a few days.  That variation is real.  There are days when I exercise vigorously and might also dehydrate, so I could easily lose a few pounds, which I regain when hydrating and remaining sedentary for a few days.

The raw weight data does not appear to show any trends.  However, a plot of 50 (dark curve) and 100 point smoothed data (gray curve) shows a different story.  My weight appears to be increasing and it also fluctuates over time.  The fluctuations might also be real due to seasonal variations in my diet and activity.  The gray vertical dashed line falls on the date that I started taking Ezetimibe and the pink dashed line when I had prostate surgery.  Each seems to have been responsible for some weight gain.

Next I fit the raw data to saturated exponentials starting at the time I began my Ezetimibe regimen (light blue curve) and then after prostate surgery (magenta curve).   The fit shows that the Ezetimibe resulted in almost a 3 pound weight gain (when averaged over a couple months).  This is consistent with the fact that I observed a huge weight increase when I was taking a larger dose.  I gained a little less than two pounds after my radical prostatectomy.  Though minor, I have noticed a little edema around the incision area, so this increase is also explainable.

The skeptic might complain that the pink curve start lower than the flat part of the blue curve.  A closer examination of the data right after my surgery shows that my weight dropped by 5 pounds from the previous baseline for a week right after surgery, so that biases the early parts of the fit.  However, all of the fits and the smoothed data nicely follow each other aside from seasonal variations.

I would not conclude form my data that ezetimibe results in weight gain nor that prostate surgery does so too.  However, the data is suggestive that this might be the case and my data is consistent with secondary observations.  I find it amazing how such small effects can be drawn from the data and gives a small level of confidence that it might be true.

In the meantime, I continue to take and analyze the data to see if any other correlations emerge that might signal an interesting underlying cause.  Apologies for typos and the like, but my Fitbit just informed me that I missed my 250 step goal this hour while writing.  So I have to get up and walk...