Human longevity and a new vision of aging

date: 2010

speakers: Natalia and Leonid Gavrilov

video: https://www.youtube.com/watch?v=3bVjgNsfJvI

LLM-generated summary: Leonid and Natalia Gavrilov present a data-driven theory of aging rooted in reliability theory and historical demographic records, challenging common misconceptions such as aging being confined to old age or irrelevant model organisms. They demonstrate via Gompertz-Makeham mortality laws that aging manifests as an exponential increase in death rates starting at age 10 across humans and species like fruit flies, with no qualitative shifts at milestones like menopause. Key paradoxes include faster actuarial aging rates in long-lived populations (e.g., shorter mortality doubling times in Norway vs. India) and convergence effects among offspring of long-lived parents. Their high initial damage load hypothesis posits that developmental noise inflicts damage equivalent to 100 years at birth, amplified lifelong, predicting profound late-life impacts from early perturbations like birth month, young maternal age, and slender body build at age 30—evidenced in centenarian studies controlling for familial confounders.

Conference Introduction and Anecdote

Here's a little known fact I thought was really curious when I came across it. Freud—do we all know who Freud was? Yes—had a vasectomy at the age of 67. I thought this was strange, seemed a little late in the day for that kind of thing, but it turns out he did it not for reasons of sex with precaution against procreation, but because the finest minds on the planet at that time— we're talking about 100 years ago—thought that if you tied off the spermatic cords, you would thwart the effects of aging. Interesting, eh?

So, in preparing this conference, you know, late one night I asked myself: If the evolution of history is a history of progress, making us more capable of surviving and procreating, then why, finally, do we die at all? As David sort of posed the question: is aging the will of God, or is it just another disease? And that's why I thought to invite our next speakers.

I first saw them in Cambridge some time ago at a conference run by perhaps the world's most aggressive theorist and advocate of radical life extension, a fellow called Aubrey de Grey. And what struck me about the Gavrilovs was that they were instead working in the realm of real data, as much as they could on Earth, going back a long, long time to come up with a new theory of what's involved in longevity. So I give you the husband-and-wife team of Leonid and Natalia Gavrilov.

Gavrilov Introduction and Overview

Good morning. And as Mo says, we work together. We are a husband-and-wife team. We work for more than 30 years in the aging field. And I'm very grateful for the invitation to this very unusual conference for us. Actually, it is very interesting, and we are most grateful for the opportunity to come here and to speak to you.

And we work in the aging field for more than 30 years, and we would like to share our knowledge with you. And I believe that Leonid, you can start. I will start, yes. And I will continue.

Okay, here is the topic of our presentation: a new vision of aging.

Challenging Common Beliefs: Aging Starts Early

There are some common beliefs that we would like to discuss with you. One is that aging is just a problem of old age. And there are many guys that tell that I'm not as old to be interested in this topic. And there are conflicts of generations related to this.

The problem is that, in fact, aging starts very early. Everyone who is older than 10 years old is affected by aging. Because if you look at the graph of how death rates depend on age, you can find that the happiest years of our life is 10 years. This is the age when the death rate is the lowest. And after this age, death rates start to increase. So everybody who reached the age of voting rights is already affected by aging.

So we should not discriminate the society in terms of whether you are a retired person or a younger person, because aging is a topic for everybody, everyone to be concerned. This is one important thing.

No Qualitative Life Stages: Monotonic Mortality Trajectory

Another belief is that there are some stages in human life, and old age is one of them, and so when you go to retirement or when you experience menopause, this is something qualitatively different from the previous life.

And again, if you look at the real data, you will find that the risk of death with age is quite monotonic. Every eight years of age, the risk of our death is doubling, and it started at 10 years of our age. And it goes on—20 years, 30 years—and when you experience menopause or go to retirement, there is no dramatic changes in this mortality trajectory.

So, of course, these are important changes in your life. But from the fundamental biological thing, it is just continuation of what started early in life at 10 years old. So that also important to keep in mind.

Universality of Mortality Laws Across Species

Another belief is that we are so complex that it is very difficult to understand what is going on and why we are aging and what can we do about this. And all these studies on fruit flies and worms—they are not really relevant, so it's good for scientists to get their funding and to have their publications to build their scientific career, but it's not really useful for potentially useful human beings.

And interestingly, there are general laws of age and mortality which are common for humans and many other animals. For example, when you compare fruit flies and humans, you can find that their mortality trajectory as a function of age are exactly the same. And for fruit flies, death rates are simply much higher than for humans. But the law of mortality, the Gompertz law of mortality, is exactly the same: exponential increase in death rates. In humans, death rates are doubling every eight years of age. And in fruit flies, they are doubling every three days. So that's the main difference. It is quantitative difference, not qualitative difference.

And also, what is reassuring is that if some lady is about 80 years old, she can always mention that her death rate is the same as for a young fruit fly. So you can always say that I am as good as a young fruit fly. So that's reassuring.

Paradoxes in Aging Rates and Compensation Law

Another thing that we love to believe is that it's quite natural that those people who live longer, maybe they somehow age more slowly, and that makes common sense. But if you look in the data, you can find some paradoxes because the apparent aging rate, which is by actuaries estimated—how rapidly death rate increases with age—surprisingly, it is higher in populations with higher lifespan.

And I will explain this by this picture. What is here? Here is death rate on the vertical axis and age on the horizontal axis. And each line is for a particular country. So you have very high death rates in India, Turkey, and Kenya, which is not surprising. And you have very low death rates in Norway, Austria, and in Canada, which is also not surprising.

But what is interesting is that the slope of deterioration is quite different. So paradoxically, for those countries who are in the best position—who have the lowest death rates—they have the highest slope. So their aging rate, actuarial aging rate, the doubling time for mortality, is much shorter in good countries. In India and Turkey, mortality is doubling every 10 years of age. And in Norway, in Austria, it doubles every 5 years of age. That's kind of a paradox. Why is this? It's kind of a challenge.

And this is observed also if you compare those who are born to parents with different longevity. We know that it is very good to be born to long-lived parents. You also live longer. And so blue lines are death rates for those who are born to longer-lived parents. And you see that for every age, death rates are lower for those who are born to longer-lived parents. But again, the slope is higher. And so there is convergence. So if you survive to age 90, then it does not matter how long your parents lived. So the effect disappears. And this is called the compensation law of mortality—paradoxical.

So you have, for example, for women—women live longer. But if you look for their aging rate, you can find that the slope of the curve is much higher. So they are aging more rapidly in actuarial terms.

Okay, we do not have time to present all we know on this topic. And if you are interested in this, we invite you to look at our book, The Biology of Lifespan, when we discuss all these things in more detail. And more recently, we published a theory of aging.

High Initial Damage Load Theory

And while working with this theory, we came up with a crazy idea, which says that in contrast to machines and computers, we have in terms of reliability theory very much in common with them, but we have one difference. We are very badly done from the very beginning.

And so the idea of high initial damage load came from the scientific analysis of the data. So that initially, when we are born as children, we are so damaged as if we already lived 100 years. And for the rest of our life, we have simply duplication of the damage we accumulated in early development. There is the idea of developmental noise—that when we are formed as children, this is a very noisy process.

And if this hypothesis is true, then there is one testable prediction: that small things early in life that modulate this initial level of damage may have profound effect later in life. For example, we predicted that if you look for the effect of months of birth, it is not astrology, but we know that depending on months of birth, there was seasonality in vitamin deficiency in the past and flu epidemics. So we predicted that some small things may have profound effect later in life and throughout life.

And on this point, I will give opportunity to Natalia to continue the presentation.

Early-Life Predictors of Longevity: Month of Birth

Yes, because this is our current studies, and I also work on this. And I would like just to say that when we initially suggested this idea about early life conditions—how they affect further mortality and aging process—not many scientists shared this idea. I know that Dr. Barker in the United Kingdom also suggested this hypothesis about early life conditions and later life mortality. But now this idea became the mainstream, and I believe that this kind of new view of aging, new vision of aging-related diseases—because, for example, a recent publication in Time was devoted entirely to this idea that the first nine months shaped the rest of the life.

But we believe that not only the first nine months—also the early life conditions during the childhood—they also shape the further diseases, further aging.

And so here is what Leonid talked about: effect of month of birth on longevity. It's interesting observation because this is life expectancy at age 80—80 years later—and we found that 80 years later it matters at what month people were born.

Here are two birth cohorts: one birth cohort is 1885 and 1891. There are two different birth cohorts, but the pattern is very similar. There is higher life expectancy at the beginning of the year, in January, but what is interesting also higher life expectancy in autumn months. People who were born in autumn, particularly in November, they have a little bit higher life expectancy, although difference is not very high, but it is consistent, and it repeats in every birth cohort we studied. What is interesting: although the patterns are not exactly the same because sometimes there is different climatic difference, difference in epidemics, but there is some general pattern which continues.

Centenarian Studies: Sibling Comparisons

And that's why we decided to study the predictors of exceptional longevity, and we've decided to study centenarians. And centenarians are people with success stories. They lived very long, and in the first study we made a question: how centenarians are different from their siblings? Because siblings and their centenarians and centenarians—they lived in the same family, they shared the same experience, shared the same early life conditions, but yet centenarians lived to 100 and their siblings did not live to 100. So this was a question.

And we found several factors which are different for centenarians. And the first factor was, again, the month of birth. But it was found in a quite different way. We studied the chances of siblings to survive to age 100. And this—this is for older siblings. This was not infant mortality. This was siblings who already survived to age 50. So this mortality after age 50, this old age mortality.

And you can see that the siblings who were born in November—and also September and October—they had much higher chances to survive to 100. So that's quite interesting month of birth effect.

Parental Age Effect

And another factor we studied was parental age effect, because siblings are born to parents of different ages, and you can see that it's very important to be born to a young mother. The younger mother, the better your chances to survive to age 100.

But remember, this is for one in the same family—within the family effect—because all everything like income of family or all other living conditions earlier during the childhood are the same. So just you control for everything except for parental age, because parental age is different within the family for different siblings.

And you can see: the younger mother is the better chances to live to 100. And what is interesting—without similar effect among animals, among mice, for example. This is not our study; this study of other researchers. They found that mice who were born to younger mothers, they lived much longer compared to mice who were born to older mothers.

And it's interesting here, but here is a survival curve. There's a number of survivors, and they are declining with age, but mice who were born to older mother mice, they actually live shorter. And we also have some possible explanation, but I believe we can explain it for those who are interested—who simply can come to us and we can explain this.

Body Build and Other Early Factors

And there is another study which is quite interesting because it is related to the previous talk about the dogs—who are one dogs are big and other dogs are small—because we were interested specifically in the size of people. Is it good to be tall or is it good to be short? Is it good to be heavier built or is it good to be slender built? And this is the question which we tried to answer.

And how we did it? We simply took centenarians who were born in 1887—one birth cohort—and it's all men, male centenarians, and we linked their data, this data, to the so-called draft, civil draft registration cards. Because for those—probably in Canada it's less known—in 1917 in the United States there was so-called civil draft of all men who were young or middle-aged. This was not for people who were enlisted in the army; this was for everybody who lived in the United States and was a certain age.

And people who were born in 1887, they were 30 years old during this draft registration, and they also measured their certain characteristics like body build, body height, and some other characteristics. And it was interesting to compare these people who lived to 100 to just their peers who also were registered in the same county but did not survive to age 100. How they are different? And we found actually certain differences.

We did not find any differences for height, for body height, but we found differences for body build, because there were three types of body build: slender, medium, and stout. And we found that actually there is no difference for people who are slender—the same proportion of slender among centenarians and among controls who did not survive to age 100.

Because now there is some idea about caloric restriction, and there is even the caloric restriction society, but there's no need to suffer. But what is very bad is to be stout at age 30. You have no chances to survive to age 100. So this is again stresses the idea about obesity effect.

Conclusions and Resources

And here are some other factors, but because of shortage of time, I would say that the general conclusion of our studies that for centenarians, it all begins at birth, and particularly the farming is also important. It's very good for survival to 100.

And for those who have more questions, we have—oh, sorry, is it, can I go back? I cannot go back, unfortunately. Ah, here is, ah. Okay, you can go to this website and to see some other publications. And you can also ask your question at our blog, which is on the bottom.

Yes, we are open for discussion. If you have any questions, you can—if late after the conference—please, on our blog, print your questions and we will answer you.

Thank you. Thank you.

Audience Remarks and Q&A

You can make this way. Yeah. Thank you so much. Thank you.

So were you doing a little private comparisons? I love some of these statistics. I'm not sure if the Gavrilovs touched on it directly, but in my research I came across this number. Women are three to five times more likely to live beyond 100 than men. Now, guys, that sounds like bad news. But apparently at 100 the girls outnumber the boys about 9 to 1, so no matter how big a geek you've been in your life, you're going to get a date—look on the bright side.

In my own case, I am a first born. I was born to a young mother, 21 and a half years. I had my early years growing up in Tajikistan, so I don't know, Natalia, if that qualifies for life on a farm. But growing up on a farm is also a very good indicator for long life. And in men, apparently, it's good to have lots of kids. People who reach 100 have some of these singular characteristics: first born, young mother, early life on a farm, and lots of kids.

Insights

• Gompertz Law Universality: Exponential mortality increase (doubling every ~8 years in humans, ~3 days in flies) applies across taxa, emphasizing quantitative (scale) rather than qualitative differences in aging.
• Compensation Law Paradox: Long-lived groups (e.g., low-mortality countries, offspring of centenarians, females) exhibit steeper mortality slopes, leading to convergence at advanced ages—challenges "slower aging" intuition.
• High Initial Damage Load: Reliability theory analogy: Humans start life with damage equivalent to ~100 years due to developmental noise, then linearly duplicate it; predicts outsized late-life effects from early modulators.
• Intra-Family Controls: Sibling/centenarian comparisons isolate variables like birth month (autumn advantage), maternal age (younger better), body build (slender/medium > stout at 30), and farm upbringing, controlling for shared environment/genetics.
• Testable Predictions: Seasonality (e.g., vitamin D/flu via birth month) and non-genetic early perturbations amplify longevity via initial damage modulation.

Transcription errors?

• "Asian area": Likely "aging area" (context: 30+ years in aging research).
• "monthhood birth": Clearly "month of birth".
• "farming is also important": Likely "being born on a farm" or "farm upbringing" (fits audience remark on "life on a farm" as longevity indicator).
• "without similar effect among animals": Likely "we found a similar effect among animals".
• Minor names: "Gavrilovs" standardized to "Gavrilov" (common scientific spelling); "Mo" as host; "Dr. Barker" likely David Barker (fetal origins hypothesis); "Lenny" as affectionate for Leonid.