summarize: The balance of our nonalcohol diet consists of carbohydrates, protein, and fat,
and it’s largely a job of finding the right mix for you as an individual. In the
days of labeled diets, we would assemble our macronutrients and sort out the
different types of foods, using rules and arbitrary boundaries—you can eat
this, but not that; these, but not those. We would basically be guessing at the
right mix. And then we would wait to see whether it “worked,” typically
defined in terms of whether the person lost weight over a period of weeks or
months. Now we have more sophisticated ways of looking at macronutrients,
beginning with the most abundant: carbohydrates.
Carbs probably create more confusion than any other macro. They are
neither “good” nor “bad”—although some types are better than others.
Overall, it’s more a question of matching dose to tolerance and demand,
which is much less tricky than it used to be. Thanks to advancements in
technology, we no longer need to guess; we now have data.
Carbohydrates are our primary energy source. In digestion, most
carbohydrates are broken down to glucose, which is consumed by all cells to
create energy in the form of ATP. Excess glucose, beyond what we need
immediately, can be stored in the liver or muscles as glycogen for near-term
use or socked away in adipose tissue (or other places) as fat. This decision is
made with the help of the hormone insulin, which surges in response to the
increase in blood glucose.
We already know that it’s not good to consume excessive calories. In the
form of carbohydrates, those extra calories can cause a multitude of
problems, from NAFLD to insulin resistance to type 2 diabetes, as we saw in
chapter 6. We know that elevated blood glucose, over a long enough period of
time, amplifies the risk of all the Horsemen. But there is also evidence
suggesting that repeated blood glucose spikes, and the accompanying rise(s)
in insulin, may have negative consequences in and of themselves.
Each person will respond differently to an influx of glucose. Too much
glucose (or carbohydrate) for one person might be barely enough for another.
An athlete who is training or competing in high-level endurance events might
easily take in—and burn up—six hundred or eight hundred grams of
carbohydrates per day. If I consumed that much now, day to day, it would
probably render me a diabetic within a year. So how much is too much? And
what about quality? Obviously that piece of pie is going to affect an
endurance athlete differently from a sedentary person—and the pie will also
have a different effect than a baked potato or french fries.
Now we have a tool to help us understand our own individual
carbohydrate tolerance and how we respond to specific foods. This is called
continuous glucose monitoring, or CGM, and it has become a very important
part of my armamentarium in recent years.
[*2]
The device consists of a microscopic filament sensor that is implanted in
the upper arm, attached to a fingertip-sized transmitter that sends data[*3] to
the patient’s phone in real time. As its name suggests, CGM gives continuous,
real-time information on blood glucose levels, which is extraordinary: the
patient can see, moment by moment, how their blood sugar levels are
responding to whatever they eat, whether a doughnut, a steak, or a handful of
Raisinets. More importantly, it also keeps track of glucose levels over time,
capturing historical averages and variance, and registering each and every
time that blood glucose spikes upward or crashes downward.
CGM represents a huge improvement over the Medicine 2.0 standard of
one fasting glucose test per year, which in my opinion tells you almost
nothing of value. Think back to my self-driving cars analogy from Part I:
fasting blood glucose, annually, does tell us something, but it’s not too far
from strapping a brick to the gas pedal. With CGM, you start to approximate
the sensors currently found on cars with elaborate driver-assistance tools.
The power of CGM is that it enables us to view a person’s response to
carbohydrate consumption in real time and make changes rapidly to flatten
the curve and lower the average. Real-time blood glucose serves as a decent
proxy for the insulin response, which we also look to minimize. And, last, I
find that it is much more accurate, and more actionable, than HbA1c, the
traditional blood test used to estimate average blood glucose over time.
At the moment, CGM is available only by prescription and is most
commonly worn by patients diagnosed with type 1 or type 2 diabetes, who
need to monitor their glucose levels from moment to moment. For these
people, CGM is an essential tool that can protect them from life-threatening
swings in blood glucose. But I think nearly every adult could benefit from it,
at least for a few weeks, and it will likely be available to consumers without a
prescription in the not-too-distant future.
[*4] It’s currently fairly easy for a
nondiabetic to obtain a CGM from one of several online metabolic health
start-ups.
Yet some experts and evidence-based medicine types have criticized the
growing use of CGM in nondiabetic people. They argue, as these sorts of
people always do, that the “cost” is excessive. CGM costs about $120 a
month, which is not insignificant—but I would argue that even this is still far
cheaper than allowing someone to slide into metabolic dysfunction and
eventually type 2 diabetes. Insulin treatment alone can cost hundreds of
dollars a month. Also, as CGM becomes more common, and more readily
available without a prescription, the cost is sure to come down. Typically, my
healthy patients need to use CGM only for a month or two before they begin
to understand what foods are spiking their glucose (and insulin) and how to
adjust their eating pattern to obtain a more stable glucose curve. Once they
have this knowledge, many of them no longer need CGM. It’s a worthwhile
investment.
The second argument against using CGM in healthy patients is also pretty
typical: There are no randomized clinical trials showing a benefit from the
technology. This is true, strictly speaking, but it is also a weak argument. For
one thing, use of CGM is growing so fast and the technology is advancing so
much that by the time you are reading this there may very well be published
RCTs (assuming a study can be designed to test the metrics that matter most
over a long enough period of time).
I am confident that such studies will show a benefit, if done correctly,
because there are already ample data showing how important it is to keep
blood glucose low and stable. A 2011 study looking at twenty thousand
people, mostly without type 2 diabetes, found that their risk of mortality
increased monotonically with their average blood glucose levels (measured
via HbA1c). The higher their blood glucose, the greater their risk of death—
even in the nondiabetic range of blood glucose. Another study in 2019 looked
at the degree of variation in subjects’ blood glucose levels and found that the
people in the highest quartile of glucose variability had a 2.67 times greater
risk of mortality than those in the lowest (most stable) quartile. From these
studies, it seems quite clear that we want to lower average blood glucose and
reduce the amount of variability from day to day and hour to hour. CGM is a
tool that can help us achieve that. We use it in healthy people in order to help
them stay healthy. That shouldn’t be controversial.
When I’ve put my patients on CGM, I’ve observed that there are two
distinct phases to the process. The first is the insight phase, where you learn
how different foods, exercise, sleep (especially lack thereof), and stress affect
your glucose readings in real time. The benefit of this information can’t be
overstated. Almost always, patients are stunned to see how some of their
favorite foods send their glucose soaring, then crashing back to earth. This
leads to the second phase, which is what I call the behavior phase. Here you
mostly know how your glucose is going to respond to that bag of potato chips,
and that knowledge is what prevents you from mindlessly eating it. I’ve found
that CGM powerfully activates the Hawthorne effect, the long-observed
phenomenon whereby people modify their behavior when they are being
watched. (The Hawthorne effect is also what makes it difficult to study what
people actually eat, for the same reason.)
Typically, the first month or so of using CGM is dominated by insights.
Thereafter, it’s really dominated by behavior modification. But both are quite
powerful, and even after my patients stop using CGM, I find that the
Hawthorne effect persists, because they know what that bag of potato chips
will do to their glucose levels. (Those who need more “training” to break their
snacking habit will typically need to use CGM for longer.) CGM has proved
especially useful in patients with APOE e4, where we often see big glucose
spikes, even in relatively young people. In these patients, the behavior
modification that CGM prompts is an important part of their Alzheimer’s
disease prevention strategy.
The real beauty of CGM is that it allows me to titrate a patient’s diet
while remaining flexible. No longer do we need to try to hit some arbitrary
target for carbohydrate or fat intake and hope for the best. Instead, we can
observe in real time how their body handles the food they are eating. Is their
average blood glucose a little bit high? Are they “spiking” above 160 mg/dL
more often than I would like? Or could they perhaps tolerate a little bit more
carbohydrate in their diet? Not everyone needs to restrict carbohydrates;
some people can handle more than others, and some have a hard time
sticking to severe carbohydrate restriction. Overall, I like to keep average
glucose at or below 100 mg/dL, with a standard deviation of less than 15
mg/dL.
[*5] These are aggressive goals: 100 mg/dL corresponds to an HbA1c
of 5.1 percent, which is quite low. But I believe that the reward, in terms of
lower risk of mortality and disease, is well worth it given the ample evidence
in nondiabetics and diabetics alike.
All of this takes experimentation and iteration; dietary restriction has to
be adaptive, changing with the patient’s lifestyle, age, exercise habits, and so
on. It’s always interesting to see which specific foods cause elevated CGM
readings in some patients but not in others. The SAD sends most people’s
CGM readings through the roof, as all the sugar and processed carbohydrates
dump into the bloodstream at once, provoking a strong insulin response,
which is what we don’t want. But seemingly “healthy” meals, for example
certain kinds of vegetarian tacos, can also send glucose levels soaring in some
people but not others. It also depends on when those carbs are eaten. If you
eat 150 grams of carbohydrates as a serving of rice and beans in one sitting,
that has a different effect than eating the same amount of rice and beans
spread out over the day (and, obviously, much different from ingesting
150 grams of carbs in the form of Frosted MiniWheats). Also, everyone
tends to be more insulin sensitive in the morning than in the evening, so it
makes sense to front-load our carb consumption earlier in the day.
One thing CGM pretty quickly teaches you is that your carbohydrate
tolerance is heavily influenced by other factors, especially your activity level
and sleep. An ultraendurance athlete, someone who is training for long rides
or swims or runs, can eat many more grams of carbs per day because they are
blowing through those carbs every time they train—and they are also vastly
increasing their ability to dispose of glucose via the muscles and their moreefficient mitochondria.
[*6] Also, sleep disruption or reduction dramatically
impairs glucose homeostasis over time. From years of experience with my
own CGM and that of my patients, it still amazes me how much even one
night of horrible sleep cripples our ability to dispose of glucose the next day.
Another surprising thing I’ve learned thanks to CGM is about what
happens to a patient’s glucose levels during the night. If she goes to bed at,
say, 80 mg/dL, but then her glucose ramps up to 110 for most of the night,
that tells me that she is likely dealing with psychological stress. Stress
prompts an elevation in cortisol, which in turn stimulates the liver to drip
more glucose into circulation. This tells me that we need to address her stress
levels and probably also her sleep quality.
This doesn’t need to be an exercise in deprivation: one patient of mine
gleefully confessed that his CGM, which he had only reluctantly agreed to
wear, had given him a “superpower” to cheat. By eating certain “forbidden”
types of carbohydrates only at certain times, either mixed with other foods or
after exercising, he had figured out how he could hit his average glucose goals
while still enjoying all the foods he loved. He was gaming his CGM, but he
had also unwittingly discovered another rule of nutrition, which is that timing
is important: If you scarf a large baked potato before working out, it will
leave much less of a footprint on your daily glucose profile than if you eat it
right before bedtime.
It is important to remember the limitations of CGM—chiefly, that it
measures one variable. This variable happens to be very important, but it is
not the only one. Thus, CGM data alone are not going to help you find the
ideal diet. Eating bacon for breakfast, lunch, and dinner might give you a
great CGM tracing, even though it’s obviously not an optimal diet. Similarly,
a bathroom scale will suggest that smoking is good for you because you lost
weight. This is why I monitor my patients’ other biomarkers closely as well, to
ensure that their CGM-driven choices are not increasing their risk of
something else, such as cardiovascular disease. We also monitor other
variables that are relevant to diet, beginning with weight (obviously) but
continuing with body composition, the ratios of lean mass and fat mass, and
how they change. We can also look at biomarkers such as lipids, uric acid,
insulin, and liver enzymes. All of these taken together start to give us a better
way to evaluate our progress than any one in isolation.