The Alpina Non-Fiction Publishing House publishes the book Counterclockwise by scientific journalist Polina Loseva. TASS publishes an excerpt on how scientists look for signs of aging in the body.
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In 2001, the first “fragility index” appeared, one of the simplest markers of aging. The researchers tested a large sample of people for five signs of fragility: unintentional weight loss, poor grip strength, slow walking, feeling tired, and being inactive. Those who fit at least three criteria, the authors of the criterion considered fragile. They did have a higher risk of ill health, hospitalization and death. Those who corresponded to only one or two characteristics were assigned an intermediate fragility status. This first-of-its-kind criterion did not yet allow assessing the exact risk for each of the subjects, but with its help it was already possible to measure the risks for the population as a whole and its individual groups.
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Later, based on this idea, a whole forest of fragility indices grew. Since then, the number of features has become significantly larger and can reach hundreds, but the principle remains the same. Each feature is a parameter that:
a) is a defect in health (and not a property of a lifestyle, such as smoking);
b) occurs more often with age;
c) occurs at least 1% of people in the population.
The sum of the signs should cover different areas of the body's work, that is, not only the physical condition, but also the mental and psychological health of a person. In fact, these indices measure the number of deficiencies / injuries in the human body, which is why they are sometimes called defect indices. Each of dozens or hundreds of traits is scored on a scale from 0 to 1, and the subject receives a fragility score that grows as the body ages.
The fragility index is the quintessence of the medical approach to aging, which considers old age as a set of age-related diseases. Therefore, such indices are often used in medical work, and they are good at predicting, for example, the need of an elderly person for intensive care. In rare cases, they work for young people as well, since every defect, every age-related disease seriously impairs their survival rate. However, with their help it is difficult to predict anything other than the risk of death, so for relatively healthy people they are of little use. In addition, fragility indices tell us nothing about the cause of aging and only measure its consequences.
Nevertheless, the principle itself - to use not one parameter, but the sum of markers - is certainly true, because it takes into account the heterogeneity of the population. And now researchers are trying to build multivariate models to estimate biological age.
For example, in the American study CALERIE, which is devoted to calorie restriction, scientists track 18 different signs: from the amount of cholesterol and hemoglobin to the health of the mucous membranes. For each of them, they built a change curve from 26 to 38 years old and built a model that predicts biological age based on the sum of changes in all parameters, multiplied by certain coefficients. Attempts to estimate the biological age of each individual participant have shown that the population of even young people is highly heterogeneous. According to the experimenters, the biological age of the subjects, who according to the passport is 38 years old, can be from 30 to 50. In this study, it is especially important that scientists work with young healthy people in whom the risk of death or development of diseases is almost impossible to estimate. Probably,in the near future, such a complex age marker will appear. The only question is what specific parameters will be included in it.
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What do 5, 10 or even 100 parameters mean compared to the complexity of the organization of the human body? In order not to worry about choosing the most accurate biomarkers, a number of scientists use a fundamentally different approach to calculating biological age - artificial intelligence. Lately, there have been many works in which doctors teach neural networks to diagnose a wide variety of diseases, so why not apply them to aging?
In the USA, this is being done by a group of researchers led by scientist Alex Zhavoronkov. They train artificial intelligence on a wide variety of signs of aging. For example, in 2018, they taught him how to measure a person's age from a photograph of a face. By recognizing the eye and the surrounding skin, the program determined the age with an accuracy of two to five years. At the same time, the most significant feature turned out to be wrinkles in the corner of the eye: as soon as they were closed in photographs, for the neural network, the elderly began to look like small children.
In 2019, Zhavoronkov's group took up blood tests. The parameters they measured resemble a standard biochemical test: the amount of different blood cells, the concentration of proteins, fats, glucose and metabolic products - urea, creatinine (a metabolic product in the muscles, which is usually excreted by the kidneys), bilirubin (waste hemoglobin). And again, artificial intelligence determined the age of the subjects with an accuracy of six years.
Along the way, it turned out that for different genders and ethnic groups it is necessary to take into account a different set of markers. For example, sodium concentration played an important role in calculating the age of South Koreans, but did not significantly depend on the age of Eastern Europeans. And this is another feature that needs to be borne in mind when we are dealing with biological age: it is worth checking each time on the basis of which sample the method for determining it was developed. What makes the old Chinese old does not necessarily work for the Indian.
Next in line are microbes. Despite the fact that we are still not sure how exactly different representatives of the intestinal microflora affect human health, artificial intelligence has already counted them. By comparing the relative numbers of different types of bacteria in the intestines of humans, the neural network has learned to determine age with an accuracy of about four years.
It is interesting that the relationship of certain microbes with the determination of age did not depend on whether they were beneficial to health or, on the contrary, harmful. In this sense, the "aging-friendly" bacteria seem especially curious. These are probably the same newly acquired intestinal dwellers that we talked about in the chapter "Microbes" and which retain the necessary diversity within the aging organism and maintain inflammation at the desired level. But another explanation is also possible: these microbes may be a reflection not so much of old age as of the lifestyle of the generation that has now entered old age: low physical activity, high consumption of sugars and processed foods. And if this is true, then in the future, scientists will have to adjust the method for determining age, not only depending on gender or race,but also from the generation and its way of life.
Artificial intelligence work will certainly broaden the field of view and reveal what classical methods miss. At the same time, not all the parameters that are measured meet the criteria for biomarkers. Therefore, a neural network capable of determining a person's age turns out to be very useful functionally, but raises many questions from a biological point of view.
Which of the parameters that AI takes into account are really important? Which are related to the causes of aging, and which only reflect lifestyle? Now artificial intelligence is guided by an algorithm that is incomprehensible to us and produces unsupported predictions, like the Greek diviner. To get reason to believe his predictions, we have yet to isolate and test the main markers on which he relies.
Conversion difficulties
The list of potential markers of biological age does not end there. Debris of DNA circulating in the blood, the amount of sugar residues on extracellular proteins, and even the features of the brain on MRI scans are proposed as candidates. One recent study calculated brain age by the amount of oxygen consumed per unit of glucose. The breakdown of glucose without the participation of oxygen was considered a "childish" sign, and full oxygen respiration was considered an "adult". This method predicted age with an accuracy of only 8.5 years, but the brains of women were, on average, four years younger than those of men. There are many such examples, and the number of biomarker candidates continues to grow.
The problem is that their predictions do not agree well with each other. And if within each group the markers can still be brought to a common denominator - for example, all types of epigenetic clocks can be calibrated equally - then the differences between the groups remain profound. In different studies, they behave differently: somewhere, Hannam's (but not Croat's) watches predict the risk of reduced mental abilities and motor skills, in other work, only Croat's watches are associated with the risk of cardiovascular diseases and obesity. In the third study, the fragility index is much more accurate in determining biological age than methylation hours, and in the fourth, none of the markers were able to predict any of the age signs accurately enough.
Perhaps the point is in the narrow "specialization" of most biological markers, which are indicative only in "their" area. Lipid concentrations are closely associated with obesity, MRI of the brain - with intelligence, telomere length - with regeneration, and so on. But, if this is so, can we judge the risks to the body as a whole by the age of one organ or organ system?
Strictly speaking, we are not sure that all parts of the human body age at the same rate, and in order to talk about this, we need to have a common aging parameter for all. For example, epigenetically, most tissues (though not all) are about the same age, but the number of senescent cells in them is different.
In this sense, it is interesting to observe patients who have undergone a blood transfusion or organ transplant - after that, cells of different biological ages are found in their bodies. Measurements show that being in the same body does not smooth out the age difference. And if the donor was younger than the recipient, then his cells continue to live in their own time, remaining younger than the surrounding tissues - at least according to the epigenetic clock.
One way or another, a single measure for all organs does not yet exist, just as there is no single biomarker suitable for all experiments. Each parameter used solves its specific problem; in some studies, researchers are specifically looking for separate markers for different areas of the body's life. And this has its own logic: the more specific parameter we measure, the better we understand how it is formed and under the influence of which it can change.
When we try to find one marker for the whole organism, the question immediately arises: what exactly are we actually measuring? Telomeres indicate whether cells are ready to divide, the epigenetic clock indicates how well a cell is repairing its DNA or how well genes are kept untwisted. These two markers almost never coincide with each other in predictions. Perhaps this suggests that each marker measures the thickness of its own pillar of aging - and then it makes no sense to try to link them together.
This is probably the problem of biohackers, who are trying to measure many parameters of their body and adjust them to "optimal" values. There are an incredible number of markers, and each of them individually may mean nothing (just as each individual region of methylated DNA is practically not associated with biological age) or give results that do not coincide with other predictions. Therefore, it is unlikely that we will one day be able to find one specific parameter that will answer all our questions.
We find ourselves at a dead end: it is impossible to find one marker, the same for all, and many smaller markers are still poorly consistent with each other and do not provide biological explanations. This is the same problem that confronts fighters against aging: it seems that we are no longer destined to open one magic pill, and it is hardly possible to fix everything in parts, as Aubrey de Gray suggests. The list of changes that we have compiled on the pages of this part leaves no hope for an easy repair. In the next part we will try to find a middle ground and talk about how the search for the causes of aging helps to find out what can be done with it and which recipe for the "elixir of youth" seems most plausible today.
