개인 맞춤형 영양: 개인별 맞춤형 식단

### 개인 맞춤형 영양: 식생활 방식 혁신
* 식단 관련 질병의 만연에도 불구하고 수년간의 연구에도 불구하고 **인간에게 가장 좋은 식단에 대한 확실한 해답**은 아직 찾지 못했습니다.
* 📊 혈당 수치가 건강과 체중 관리에 미치는 중대한 영향을 고려하여 이 연구는 **개인 맞춤형 식사 포도당 반응**을 측정하는 데 중점을 두었습니다.
* 🍞 탄수화물은 일반적으로 반응을 증가시키고 지방은 감소시키는 등 몇 가지 추세가 관찰되었지만, 중요한 발견은 **음식에 대한 반응이 개인마다 크게 다르다**는 것입니다.
* 식단에 의해 변화될 수 있는 장내 미생물은 **포도당 반응의 변동성에 영향을 미치는 중요한 요인**으로 밝혀졌습니다.
* 📈 고급 머신 러닝 알고리즘을 사용하여 **혈당 수치를 정상화하는 개인 맞춤형 식단**을 성공적으로 예측하고 테스트했습니다.
* 🍦 이 연구에 따르면 인간에게 가장 적합한 식단은 없으며, **개인 맞춤형 식단 조언이 필수적**입니다.
* 🔮 영양학의 미래는 **포도당 반응과 마이크로바이옴 구성**과 같은 개인 데이터를 활용하여 맞춤형 식단을 추천하고 증가하는 식단 관련 질병의 유행에 대처하는 데 있습니다.

인간에게 가장 좋은 식단은 존재하지 않으며, 음식에 대한 반응은 개인마다 다르기 때문에 식단 조언은 개인 맞춤형이어야 합니다.

Translator: Rhonda Jacobs.
Reviewer: Leonardo Silva. This is me ten years ago. I weighed 40 pounds more than today, and like many people,
I wanted to lose weight. Like many people, I wanted to know
what is the best diet for humans. Many of us actually have an opinion
about this question. Some believe that a low-fat,
plant-based diet is the best. Others, that a low-carb diet, rich in protein
and animal fat, is the best. Others have opinions
on how much sugar we should eat, or how much salt, cholesterol,
saturated fat, eggs, or dairy products we should have in our diet. But the question of what the best diet is is a scientific one, so there should be no room
for opinions or beliefs. If Diet A is really better than Diet B, then a study that compares
the two on enough people should show that definitively. No opinions, no beliefs,
just hard data, right?

What is also clear is that if the best diet does exist, then we haven't yet found it, because the incidence of diet-related disease has increased dramatically in the past several decades. Now, you might think it's because people don't listen to what we tell them. But in fact, that's not true; people actually generally do follow dietary guidelines. But according to the Center for Disease Control, if you live in the United States, there's over a 70 percent chance that you're either overweight, diabetic, or have non-alcoholic fatty-liver disease. And there's overwhelming evidence that diet and lifestyle are major drivers of these conditions. So why is it that after so much research, we still don't have an answer to the seemingly simple question of what is the best diet for humans? What I'd like to propose to you today is that the reason we don't have an answer is because we've been asking the wrong question.

And it's the wrong question,
because it assumes that the best diet depends
only on the food and not on the person eating it. But what if differences in our genetics,
lifestyle, our gut bacteria cause us to respond differently to food? What if these differences explain why
some diets work for some people but not for others? What if our nutrition needs to be
personally tailored to our unique make-up? This is exactly the question
we set out to ask in our own research, which I did with my colleague Eran Elinav and several graduate students
from the Weizmann Institute of Science. To take a scientific approach, we first searched for a metric
of healthy nutrition that we should study. Most studies examine weight loss
or risk of heart disease after some diet. But the problem is that these are affected
by many factors unrelated to diet, they take many weeks to change, and in the end, you get
a single measure of success. And if it didn't work, well then
it's very hard to understand why.

And so, instead, we searched for a metric that would still be relevant
for weight management and diet-related disease, but one that we could also easily
and accurately measure across many people. And this led us to focus
on blood glucose levels, and more precisely, changes
in blood glucose levels after a meal. We call this a meal glucose response. Why is it important? Well, because high
glucose levels after a meal promote both hunger and weight gain. After we eat, our body digests the carbohydrates
in the food into simple sugars and releases them into the bloodstream. From there, with the help of insulin, cells throughout our body
remove the glucose from the blood so that they can use it
as a source of energy. But insulin also signals our body
to convert excess sugar into fat and store it, and that's a primary way
by which we gain weight. In addition, fast flow
of glucose into the blood often causes our body
to release too much insulin.

which could lower our glucose levels
to below baseline, making us feel hungry and eat more. Meal glucose responses
are also very relevant for our health because they've been shown
to be risk factors for obesity, diabetes, cardiovascular disease,
and other metabolic disorders. A recent study that followed
2,000 people for over 30 years found that higher meal
glucose levels after meals predict overall higher mortality. Finally, and not least important,
with recent technological advances, we can now follow a person's
glucose levels continuously for an entire week. And since the average person
eats around 50 meals a week, it allows us to measure glucose responses
to 50 meals in just a single week. Meal glucose responses also provide us with a way to directly measure
the effect of every single meal, as opposed to common approaches that only evaluate
the effect of an overall diet. Now, of course, there are many factors
beyond glucose levels that influence a healthy diet.

But this is a very important one, and solving it
can be a major step forward. Luckily for us, we managed to convince
1,000 healthy people of this idea, and we connected them
to one of these small glucose sensors and tracked their glucose levels
continuously for an entire week. And during that week, participants
logged everything that they ate on a mobile app that we developed. And so that allowed us
to measure glucose responses to 50 different meals for each person and around 50,000 different meals
across all 1,000 participants, making our study the largest one that was ever done
on this problem until today. So what did we find? Well, when we looked
at averages, we saw trends. For example, more
carbohydrates in the meal generally increase the response. This is not so surprising. Another, perhaps more surprising, trend is that more fat in the meal
generally decreased the response. But - and this is
the key finding of our study -

for every trend we found, there were many people
who were very different from it. Basically, when the same person
ate the same meal on different days, the response was very similar. But when different people
ate the same meal, the response was very different. For example, white bread
induced almost no effect on the blood sugar levels of some people, but in others, it induced huge spikes. And the same was true
for every single food we tested, including rice, pizza,
sushi, and even chocolate. For every food, there were some people
who had low responses, others who had medium responses, and yet others
that had very high responses. It wasn't just about the food; it was also about the person eating it. So while averages
and trends are informative, for any given individual,
they may not mean much. Now, it wasn't just about
how good the body was at handling sugar; each person had different foods
that spiked his levels.

Some people even had opposite responses. For example, some people
spiked for ice cream but not for rice. But then, others spiked for rice
and not for ice cream. In fact, more people spiked for rice
than for ice cream. Now, my wife is a clinical dietician, so when I showed her
this data, she was shocked because as a practitioner, she of course
relies on general dietary guidelines, and so one of the first things that she tells her many
newly diagnosed pre-diabetics is to stop eating foods such as ice cream and instead eat more
complex carbohydrates such as brown rice. So, as soon as she saw our data, she of course realized
that for most of her patients not only does her dietary advice not help, but in fact, it pushes them faster
to develop the very same disease that her advice was meant to prevent. So these results of ours
on such a large data set convinced us that responses
to food are personal.

and that diets that maintain
normal blood glucose levels must therefore be personally
tailored to the individual. They also show, in our view,
why the current nutritional paradigm that searches for that one best diet
is inherently flawed. The best diet for humans does not exist. Our responses to food are personal, so our dietary advice
must also be personal. And personalized dietary advice
was our next challenge. To tackle it, we measured
many parameters across participants that we thought may explain
people's variability in glucose response to meals. And these included basic metrics
and lifestyle factors like age, weight, height, and physical activity, but also blood tests, medical background,
and food frequency questionnaires, and also DNA sequencing
of both the human genome and the gut bacteria composition
of each person. Now, of these, the gut bacteria was perhaps the most novel
component that we examined. For hundreds of years, we know
that bacteria live within our body. But only with recent advances
in DNA sequencing

could we begin to study them extensively. And when we did,
we found that this vast collection of hundreds of different species,
that we each host, collectively termed our microbiome, has a major impact
on our health and disease. And what makes the microbiome
even more exciting is that unlike our genetics,
we can also change it even by simple means,
such as changing what we eat. Our bacteria help us digest
some of the food that we eat, and in turn, produce molecules
that are taken by our own cells and affect our physiology. For example, in our own research,
we studied artificial sweeteners, which the vast majority of us
consume on a daily basis in various diet soda drinks
and other products. And we found that consumption
of artificial sweeteners alters the composition of the gut bacteria,
such that when transferred into mice, causes the mice
to develop symptoms of diabetes. And so this and several other studies led us to ask whether the microbiome
would also be important.

for explaining people's
glucose variability in response to meals. And so we took this microbiome
and other clinical data that we collected, and we used advanced
machine learning algorithms to automatically search for rules that predict personalized
glucose responses to meals. For example, one such rule could be that if you're over 50,
and you have a certain bacterial species, then your response
to a banana will be high. The overall algorithm
combined tens of thousands of such rules that it automatically
deduced from the data. This approach is actually similar to how websites like Amazon
make book recommendations, except that we applied it
to how people respond to food. And we could show that this algorithm
could then take any person, even people who are not part
of our original study, and predict the response
to arbitrary meals with high accuracy. So as a final step, we asked
whether we can also use this algorithm to design personalized diets
that normalize blood glucose levels.

So we recruited and profiled new participants, and we asked the algorithm to predict two diets for each person; in one diet, which we called the bad diet, we asked the algorithm to predict foods for which that person would have high responses. And in the other - good diet - we asked it to predict foods for which that person would have low responses. And each person then followed each diet for one week. Now, by design, the diets had to be identical in calories. In fact, all breakfasts, lunches, and dinners had the same calories on different days. And it's also important to note that each person received a different personalized diet, and there were even some foods that were given to some people on their good diet but to others on their bad diet. Now, to show you that these diets are not the obvious ones you might think of, here they are for one of our participants. Now, take a moment and see if you can guess for yourself

which one the algorithm predicted to be the good diet, and which to be the bad diet for this particular participant. And as you look at these, notice that each diet contains foods that would not typically appear in standard diets. And now for fun, let's play a quick guessing game, and you all have to participate. So, raise your hand if you think the diet on the right is the good one. Okay. Now raise your hand if you think the diet on the left is the good one. Okay, definitely we see nearly a 50/50 split here, showing you that it's definitely not trivial to guess. And I can tell you that for this participant, the algorithm predicted the diet on the right, the one with the ice cream, to be the good one. And so now the only question is how good did these diets work. And what I'll show you next is in our view perhaps the most striking result that came out of our study.

So here are the continuous glucose levels of this participant
when following the bad diet. And you can clearly see abnormally high
glucose levels after meals, indicating that this participant
has impaired glucose tolerance and is likely pre-diabetic. But on the good diet,
the one with the ice cream and the same amount of calories
as the bad diet, this same pre-diabetic participant
achieved fully normal blood glucose levels without even a single spike
across the entire week. Obviously, we were very happy
to find out these results, and, in fact, we found similar results
for most participants for which we designed personalized diets
using our algorithm. Now, not only that, but the good diet
also induced several consistent changes in the gut bacteria of most participants. And it seemed that
these changes were beneficial because bacteria that in other studies
were associated with good outcomes tended to increase after the good diet, and bacteria associated with disease
tended to decrease.

And this result is, of course,
very intriguing because it suggests that in addition
to normalizing blood glucose levels during the intervention week, the good diet also induced
beneficial effects that may persist even beyond
the intervention week. So what's the take-home message
from all of this? Well, based on the glucose
responses variability that we saw across 1,000 people, our conclusion is that
there is no single best diet for humans because we are all too different. It also means that if
a certain diet hasn't worked for you, then maybe it was the wrong diet for you. Your dietary failures
may not be your fault. Your diet may have failed simply because it did not take information
about you as an individual into account. So what can you do
with this information now? Well, right now, you can actually measure your personal glucose responses
to your favorite meals using simple glucose devices
that you can buy at your local drug store. And I guarantee that you'll be surprised

at which foods personally spike
your glucose levels and which do not. As a more complete solution, we are working hard to make
our algorithms available to everyone so that you'll be able, from your home, to provide basic
clinical information about yourself, send a sample of your microbiome, and in return receive
personalized dietary advice. We are also starting longer-term
dietary intervention studies in both pre-diabetics and diabetics
that will go on for a full year. Because we believe that if the effect
of normalizing blood glucose levels that we were able to obtain in one week
could persist for a longer time period, then we might be able to reverse,
and even cure, these conditions, which constitute one
of the worst epidemics of our times. More broadly, I believe that we are entering
a new era in the study of nutrition, one in which we will move away from asking
what is the best diet for humans, and instead, focus
on the more appropriate question

of what is the best diet for me. Thank you very much. (Applause)

What is the best diet for humans? | Eran Segal | TEDxRuppin

TEDx is an international community that organizes TED-style events anywhere and everywhere -- celebrating locally-driven ideas and elevating them to a global stage. TEDx events are produced independently of TED conferences, each event curates speakers on their own, but based on TED's format and rules.

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TEDx Talks

최고의 다이어트를 위한 탐구: 과학적 관점

최고의 식단을 찾는 것은 단순한 의견이나 신념이 아닌 확실한 데이터가 필요한 과학적인 문제입니다.

식단 관련 질병의 발병률이 증가하는 것은 사람들이 일반적으로 식단 지침을 따르고 있음에도 불구하고 최고의 식단을 아직 찾지 못했음을 시사합니다.

발표자는 최고의 식단을 찾지 못한 이유는 우리가 잘못된 질문을 해왔기 때문이며, 개인차를 고려한 맞춤형 영양 섭취가 핵심이 될 수 있다고 제안합니다.

식사 후 포도당 반응은 체중 관리와 식단 관련 질병의 발병에 중요한 역할을 합니다.

식후 높은 포도당 수치는 배고픔과 체중 증가를 촉진할 뿐만 아니라 비만, 당뇨병, 심혈관 질환의 위험 요인이 될 수 있습니다.

기술의 발전으로 포도당 수치를 지속적으로 추적할 수 있게 되어 모든 식사의 효과를 측정할 수 있게 되었으며, 최근 이 분야에 대한 연구는 지금까지 가장 큰 규모의 연구입니다.

개인 맞춤형 음식 반응과 일반적인 식단 조언의 결함

개인마다 같은 음식에 대해 독특한 혈당 반응을 보여 보편적인 최고의 식단이라는 개념이 무색해졌습니다.

이 데이터는 임상 영양사에게 충격을 주었으며, 일반적인 식단 지침이 당뇨병과 같은 질병을 예방하기보다는 악화시킬 수 있다는 사실을 드러냈습니다.

이 연구는 개인의 특정 포도당 반응과 유전학 및 장내 박테리아 같은 요인에 따라 개인화된 식단 조언이 필요하다는 점을 강조합니다.

마이크로바이옴이 건강과 개인 맞춤형 식단에 미치는 영향

인공 감미료와 포도당 반응에 대한 연구에서 알 수 있듯이 인간의 마이크로바이옴은 건강에 큰 영향을 미치며 식단에 의해 영향을 받을 수 있습니다.

고급 머신 러닝 알고리즘은 개인의 마이크로바이옴과 임상 데이터를 기반으로 식사에 대한 개인 맞춤형 포도당 반응을 예측하는 데 사용되었습니다.

이러한 알고리즘은 혈당 수치를 정상화할 수 있는 개인 맞춤형 식단을 설계하는 데도 사용되었으며, 참가자들은 일주일 동안 이러한 식단을 따르며 그 효과를 입증했습니다.

개인 맞춤형 식단이 혈당 수치에 미치는 영향

알고리즘은 아이스크림이 포함된 식단이 당뇨병 전단계 참가자에게 좋은 식단이라고 예측하여 혈당 수치를 정상으로 유지하도록 했습니다.

또한 이 좋은 식단은 대부분의 참가자에게 장내 세균에 지속적으로 긍정적인 변화를 일으켰습니다.

이 연구의 결론은 인간에게 가장 좋은 단일 식단은 없으며, 개인의 포도당 반응에 따른 맞춤형 영양 섭취의 중요성을 강조합니다.

개인 맞춤형 식단 조언과 영양의 미래

개별 임상 정보와 마이크로바이옴 샘플을 기반으로 개인 맞춤형 식이요법 조언을 제공하는 알고리즘이 개발되고 있습니다.

당뇨병 전단계 환자와 당뇨병 환자를 대상으로 장기적인 식이 중재 연구가 진행되어 이러한 상태를 잠재적으로 개선하고 치료할 수 있습니다.

영양 연구의 초점은 획일적인 접근 방식이 아닌 각 개인에게 가장 적합한 식단을 찾는 쪽으로 옮겨가고 있습니다.